Inflation as a Transmission Channel for Sustainable Real Income Growth in Emerging Economies, 2000–2024

Inflation as a Transmission Channel for Sustainable Real Income Growth in Emerging Economies, 2000–2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inflation as a Transmission Channel for Sustainable Real Income Growth in Emerging Economies, 2000–2024 Seun Adebanjo, Solberg Mishiwo, Morufu Olugbode, Eyitemi Fasanu-Adegboye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8880132/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract This study examines the macroeconomic and socioeconomic determinants of sustainable real income growth in emerging economies, emphasising inflation as a transmission mechanism. Using balanced panel data for selected emerging economies from 2000 to 2024, the analysis applies a fixed-effects estimator supported by a Hausman test (χ² ≈ 107.88, p < 0.001), indicating correlated country-specific heterogeneity. The results show that education (EDU) and employment (EMP) are positively and statistically associated with real income growth, while foreign direct investment (FDI) exhibits a positive but initially less precisely estimated direct effect. Mediation analysis reveals that EDU, EMP, and FDI are negatively associated with inflation, and inflation in turn exerts a significant dampening effect on real income. Sobel and bootstrap tests confirm statistically significant indirect effects, indicating partial mediation through inflation. Dynamic system-GMM estimates reinforce the stability of the main relationships after addressing potential endogeneity and persistence. The findings suggest that sustainable real income growth in emerging economies depends not only on human capital development, labour market expansion, and quality investment inflows, but also on maintaining price stability to preserve welfare gains. Sustainable Income Growth Inflation Transmission Price Stability Human Capital Employment 1. Introduction The relationship between socioeconomic structures and income dynamics has long occupied a central position in development economics, particularly in emerging economies undergoing structural transformation, demographic transition, and deepening global integration. Per capita income is widely regarded not only as a measure of economic performance but also as an indicator of welfare, living standards, and progress toward sustainable development. However, the sustainability of income growth depends not merely on its magnitude but on its stability and resilience to macroeconomic shocks. Socioeconomic factors such as education, employment structures, foreign direct investment (FDI), and macroeconomic stability interact within a broader economic framework in which inflation, commonly proxied by the Consumer Price Index (CPI), can function both as a constraint and as a transmission mechanism influencing real income trajectories. Between 2000 and 2024, emerging economies have played an increasingly prominent role in global growth, yet their income paths have remained heterogeneous and, in some cases, volatile (An et al., 2025 ; Effiong, 2023 ; Sharma & Sharma, 2021 ). The period under review has been characterised by significant structural and macroeconomic transitions. From the commodity boom of the early 2000s to the 2008–2009 global financial crisis, the 2014–2016 commodity price collapse, and the COVID-19 pandemic shock of 2020–2021, emerging economies have experienced alternating phases of resilience and vulnerability. Recovery patterns have often been uneven, with inflationary pressures and exchange rate depreciation eroding real per capita income gains (Nutakor et al., 2023 ). Inflation dynamics in these economies have been shaped by supply chain disruptions, energy price shocks, fiscal expansions, and domestic monetary policy responses. In this context, inflation directly affects household purchasing power, investment incentives, and the sustainability of income gains. Even when educational attainment improves or FDI inflows rise, persistent inflation can offset nominal income growth, thereby weaken welfare improvements and undermine inclusive development objectives (Osei & Kim, 2020 ; Dua & Verma, 2024 ). This sustainability dimension is particularly relevant in emerging economies, where rapid structural transformation often coexists with institutional fragility, inequality, and macroeconomic volatility. Countries such as Brazil, South Africa, India, Indonesia, Vietnam, Nigeria, Ghana, and the Philippines illustrate diverse growth models, ranging from manufacturing-driven expansion to commodity dependence and hybrid service-resource strategies, yet all have faced recurrent inflationary pressures, especially during post-crisis adjustment phases (Haider et al., 2023 ; Kamguia et al., 2022 ). Given that sustained real income growth is central to achieving long-term development objectives, including decent work, poverty reduction, and reduced inequality, understanding how inflation mediates the translation of socioeconomic improvements into real income gains becomes crucial. Existing literature has examined the independent effects of education, employment, and FDI on income growth. Lee and Lee ( 2024 ) associate educational quality with long-run growth differentials, while Nsirimovu et al. ( 2024 ) highlight the positive contribution of education expenditure to per capita income in Ghana. Haider et al. ( 2023 ) demonstrate that employment generation in specific sectors yields differentiated income effects. The FDI–income relationship has also been extensively studied, with evidence suggesting that its impact is often conditional on financial development and institutional quality (Dua & Verma, 2024 ; Ogwuma et al., 2025 ). However, inflation is typically introduced as a control variable rather than as a central mechanism through which socioeconomic factors shape income dynamics (Nutakor et al., 2023 ). As a result, much of the literature treats macroeconomic stability and structural determinants as parallel channels rather than integrated processes. Another limitation concerns the limited integration of structural transformation and economic complexity perspectives with macroeconomic mediation frameworks. Studies by Adam et al. ( 2023 ), Ajide et al. ( 2025 ), and Osinubi et al. ( 2025 ) underscore the importance of human capital accumulation and employment structures in fostering higher-value economic activities, yet rarely link these dynamics explicitly to inflation as a mediating factor in real income growth. Moreover, many studies focus on single-country analyses (Nsirimovu et al., 2024 ; Okombi & Tsinguia-Kenfack, 2023 ) or shorter time horizons, thereby constraining cross-country comparability and limiting insights into how macroeconomic shocks reshape structural relationships over time. This study addresses these gaps by adopting a multi-country panel framework covering selected emerging economies from 2000 to 2024, explicitly modelling inflation as a mediating variable between socioeconomic determinants, including education, employment, and FDI, and real income growth. Two key contributions emerge. First, the study conceptualises inflation not merely as a background macroeconomic condition but as an active transmission channel that shapes how structural improvements translate into real welfare outcomes (Anoruo, 2019 ; Nutakor et al., 2023 ; Osei & Kim, 2020 ). Second, by examining heterogeneous emerging economies within a unified analytical framework, the study provides comparative evidence on how similar structural drivers yield different income outcomes under varying inflationary environments. Education is included as a proxy for human capital accumulation, reflecting robust theoretical and empirical evidence linking skills and productivity to long-run income growth (Lee & Vu, 2020 ; Lee & Lee, 2024 ). However, the real returns to education can be diminished under persistent inflation, which erodes purchasing power and reduces the sustainability of income gains. Employment captures labour market engagement and productive absorption capacity; yet in inflationary environments, nominal wage growth may fail to translate into real income improvements (Haider et al., 2023 ). FDI represents an external channel for capital deepening, technological diffusion, and integration into global markets, but its welfare benefits depend on macroeconomic stability, as inflation and exchange rate volatility can undermine investment effectiveness (Dua & Verma, 2024 ; Osei & Kim, 2020 ). By spanning multiple global and domestic shocks between 2000 and 2024, this study enables an assessment of whether inflation consistently mediates income dynamics across different macroeconomic regimes. Using panel econometric techniques that control for unobserved heterogeneity and dynamic persistence, the empirical strategy evaluates both direct and indirect effects, thereby clarifying the structural pathways linking socioeconomic development to sustainable real income growth. Overall, the study contributes to the sustainability discourse by demonstrating that sustained improvements in real income require not only structural enhancements in education, employment, and investment but also price stability as a core macroeconomic condition. Rather than treating inflation as a passive backdrop, the analysis establishes it as a dynamic mechanism shaping welfare outcomes in emerging economies. This integrated framework offers policy-relevant insights for governments balancing growth acceleration with inflation control in an increasingly volatile global environment. 2. Literature Review and Hypotheses Development This study is theoretically anchored in Human Capital Theory and Structural Inflation Theory, which together provide a coherent framework for understanding how socioeconomic determinants translate into sustainable real income growth in emerging economies. Human Capital Theory, originally advanced by Becker ( 1964 ) and further developed by Lucas ( 1988 ), posits that investments in education and skills enhance labour productivity, thereby increasing potential earnings and aggregate income. In emerging economies undergoing structural transformation, improvements in education levels foster economic sophistication, technological adoption, and higher-value production activities (Lee & Vu, 2020 ; Lee & Lee, 2024 ). However, the sustainability of income gains derived from human capital accumulation depends critically on macroeconomic stability. Persistent inflation can erode purchasing power, reduce real wage gains, and weaken the realised returns on educational investments. Thus, inflation, proxied by the Consumer Price Index (CPI), may mediate the extent to which human capital improvements generate sustainable welfare gains. Complementing this perspective, Structural Inflation Theory, rooted in the work of Prebisch ( 1950 ) and later refined by structuralist economists, argues that inflation in developing and emerging economies often arises from structural rigidities, sectoral imbalances, import dependence, and supply-side bottlenecks rather than purely demand-driven excesses. This theoretical lens is particularly relevant for emerging economies characterised by agricultural constraints, energy import reliance, institutional weaknesses, and limited financial deepening (Nutakor et al., 2023 ). In such contexts, structural inflation may persist even in the presence of productive investment and foreign capital inflows. By incorporating Structural Inflation Theory, the present study reconceptualises CPI not as a passive macroeconomic backdrop but as an active transmission channel capable of amplifying or attenuating the impact of socioeconomic variables on per capita income. This integrated framework highlights that sustainable income growth requires not only structural improvements but also price stability as a core macroeconomic condition. Empirical literature consistently underscores the importance of education, employment, and foreign direct investment (FDI) in shaping income dynamics. Lee and Lee ( 2024 ) demonstrate that educational quality significantly contributes to long-run income differentials, while Nsirimovu et al. ( 2024 ) show that education expenditure directly enhances per capita income in Ghana, though outcomes depend on institutional efficiency. Nevertheless, these studies largely treat inflation as a background macroeconomic variable, without explicitly examining whether inflation conditions modify the strength or sustainability of the education–income nexus. The omission of inflation as a mediating mechanism leaves a critical gap in understanding how structural improvements translate into real income gains under varying macroeconomic regimes. Similarly, the employment–income relationship has been widely documented. Haider et al. ( 2023 ) report that higher employment rates positively influence per capita income, particularly when job creation occurs in productive sectors. Ketu and Ningaye ( 2024 ) further highlight that shifts toward technologically advanced industries enhance economic complexity and income growth. However, real income improvements depend on the interaction between nominal wage growth and price stability. In inflationary environments, rising employment does not necessarily guarantee sustained real income gains if wage adjustments lag behind price increases, suggesting a mediating role for CPI that remains underexplored. FDI remains one of the most examined external determinants of income growth in emerging economies. Dua and Verma ( 2024 ) and Osei and Kim ( 2020 ) find that FDI stimulates growth through capital accumulation, technology transfer, and integration into global markets. However, these benefits are conditional upon macroeconomic stability and absorptive capacity. High and volatile inflation can discourage investment, distort relative prices, and erode the real value of income gains (Ogwuma et al., 2025 ; Chizema, 2025 ). Ajide et al. ( 2025 ) further demonstrate that improvements in economic complexity associated with trade and FDI can be fragile in contexts characterised by macroeconomic uncertainty. These findings imply that inflation may condition not only direct income effects but also the durability of FDI-driven growth. Although CPI is frequently included in macroeconomic growth models as a control variable, it has rarely been explicitly modelled as a mediator linking structural determinants to income outcomes. Nutakor et al. ( 2023 ) illustrate the usefulness of mediation frameworks in socioeconomic analysis, suggesting that similar approaches can be applied to macroeconomic variables. From both structuralist and monetarist perspectives, inflation affects consumption, savings, and investment decisions, mechanisms directly tied to real income trajectories. Yet cross-country panel studies explicitly testing CPI as a transmission mechanism remain limited. The economic complexity literature provides additional insight into this relationship. Adam et al. ( 2023 ), Freitas et al. ( 2023 ), and Gala et al. ( 2018 ) show that economies with diversified and sophisticated production structures tend to achieve higher and more stable per capita incomes. Complex economies may also be better equipped to absorb inflationary shocks through diversified export structures and productivity gains. However, the interaction between structural complexity, inflation dynamics, and income sustainability has not been systematically examined within a mediation framework across multiple emerging economies over an extended period. Taken together, three major gaps emerge. First, while education, employment, and FDI have been widely studied as independent determinants of per capita income, few studies integrate them into a unified model with CPI explicitly specified as a mediating variable. Second, most existing analyses rely on single-country or short-term designs, limiting cross-country comparability and the ability to capture structural and macroeconomic shocks. Third, theoretical integration between Human Capital Theory and Structural Inflation Theory in explaining income transmission pathways remains underdeveloped, despite their conceptual complementarity in addressing structural transformation and macroeconomic stability. This study addresses these gaps through a multi-country panel analysis of emerging economies from 2000 to 2024, explicitly modelling CPI as a mediating mechanism in the relationship between socioeconomic determinants and real income growth. By integrating domestic structural factors (education and employment) with global capital flows (FDI) within a macroeconomic mediation framework, the study advances a more comprehensive understanding of sustainable income dynamics in emerging economies. Based on the theoretical synthesis and empirical evidence, the following hypotheses are proposed: H₁: Education level has a significant positive effect on real income growth in emerging economies. H₂: Employment rate has a significant positive effect on real income growth in emerging economies. H₃: Foreign direct investment inflows have a significant positive effect on real income growth in emerging economies. H₄: Inflation (proxied by CPI) significantly mediates the relationship between socioeconomic determinants (education level, employment rate, and FDI inflows) and real income growth in emerging economies. 3. Methodology This study adopts a quantitative longitudinal research design using balanced panel data for selected emerging economies over the period 2000–2024. The panel structure enables simultaneous exploitation of cross-sectional variation across countries and temporal dynamics within countries, thereby improving identification of structural relationships among socioeconomic determinants, inflation (proxied by the Consumer Price Index, CPI), and real income growth. Emerging economies are selected due to their structural heterogeneity, exposure to global macroeconomic shocks, and growing contribution to global output (Saunders et al., 2019 ). Data are sourced from internationally recognised databases, including the World Bank’s World Development Indicators and UNCTAD, ensuring cross-country comparability, consistency, and measurement reliability. 3.1 Empirical Strategy and Mediation Framework To examine the transmission role of inflation, the study specifies a panel mediation framework in which education (EDU), employment (EMP), and foreign direct investment (FDI) affect real income growth both directly and indirectly through inflation. The empirical strategy follows a three-equation approach: 1. Direct effect model (baseline income equation) Real income growth is regressed on socioeconomic determinants and control variables. 2. Mediator equation Inflation (CPI growth rate) is regressed on the same socioeconomic determinants to assess whether structural factors significantly influence inflation dynamics. 3. Mediated income equation Real income growth is regressed on both socioeconomic determinants and inflation to test whether inflation transmits part of the structural effects. Inflation is conceptualised as a transmission mechanism rather than a simple control variable. The indirect (mediated) effects are formally evaluated using the Sobel test, which assesses whether the product of the coefficient linking the independent variable to inflation and the coefficient linking inflation to income is statistically different from zero. To strengthen inference, bootstrap confidence intervals are also employed to account for the potential non-normality of the indirect effect distribution in panel settings. This framework allows explicit decomposition of total effects into direct and indirect components, thereby clarifying whether macroeconomic stability conditions the sustainability of income gains in emerging economies. 3.2 Diagnostic and Specification Tests To ensure reliability and consistency of the estimated models, several diagnostic procedures are implemented: Multicollinearity: Variance Inflation Factors (VIF) are calculated to verify that explanatory variables are not excessively correlated. Heteroscedasticity: Panel-robust standard errors are employed where necessary to correct for non-constant error variance. Serial Correlation: Tests appropriate for panel data (e.g., Wooldridge test) are applied to detect autocorrelation. Model Selection: The Hausman test is used to determine the suitability of fixed-effects versus random-effects estimators. Endogeneity: Durbin–Wu–Hausman tests are conducted where applicable to assess the presence of endogenous regressors and justify the use of GMM estimators. By integrating panel mediation modelling, dynamic system-GMM robustness estimation, and comprehensive diagnostic testing, the study establishes a rigorous empirical framework for analysing the structural and macroeconomic transmission mechanisms influencing sustainable real income growth in emerging economies. 3.3 Sampling Technique The study employs a purposive sampling strategy to select eight emerging economies, including Brazil, South Africa, India, Indonesia, Vietnam, Nigeria, Ghana, and the Philippines, based on three criteria: (i) classification as emerging or upper/lower middle-income economies by international financial institutions; (ii) sustained participation in global trade and capital flows; and (iii) consistent availability of comparable macroeconomic data for the period 2000–2024. These countries represent diverse regional contexts across Latin America, Sub-Saharan Africa, and Asia, capturing heterogeneity in growth models, institutional arrangements, and structural transformation pathways. The purposive approach ensures balanced and complete panel coverage for key variables, including real GDP per capita, education level, employment rate, foreign direct investment (FDI) inflows, and inflation (proxied by the annual percentage change in the Consumer Price Index). The balanced structure yields 200 country-year observations (8 countries × 25 years). While the sample size is modest in cross-sectional dimension, the longitudinal depth enhances identification of within-country dynamics and structural transmission mechanisms. The design supports cross-country comparison while maintaining methodological consistency across indicators and time periods. 3.2 Method of Data Analysis The empirical analysis uses balanced panel data covering 2000–2024 and is implemented using Stata/EViews. The estimation proceeds in two main phases: (i) baseline fixed-effects panel estimation and (ii) mediation analysis with inflation as the transmission mechanism, followed by dynamic robustness checks using system GMM. The model structures and their empirical foundations are detailed below. First Phase: Baseline Panel Model Following Lee and Lee ( 2024 ), Haider et al. ( 2023 ), and Dua and Verma ( 2024 ), the baseline specification is: GDPPC it = β 0 + β 1 EDU it + β 2 EMP it + β 3 FDI it + µ i + λ t + ε it Where: GDPPC it = natural log of real GDP per capita (constant US $ ) for country i at time t EDU it = education level EMP it = employment rate FDI it = FDI inflows (% of GDP) µ i = country fixed effects; λ t = time fixed effects; ε it = idiosyncratic error term The Hausman test determines the suitability of fixed versus random effects. Hypotheses H₁–H₃ are evaluated in this framework. Second Phase: Mediation Analysis with Inflation Inflation (annual % change in CPI) is explicitly modelled as a mediator. (Adapted from Nutakor et al., 2023 ; Osei & Kim, 2020 ; Adam et al., 2023 ; Ajide et al., 2025 ) Stage 1 — Stage 1 (Total Effect – Path c) : GDPPC it = β 0 + β 1 EDU it + β 2 EMP it + β 3 FDI it + µ i + λ t + ε it Stage 2 (Mediator Equation – Path a) : INF it = α 0 + α 1 EDU it + α 2 EMP it + α 3 FDI it + µ i + λ t + ν it Where INF it = annual inflation rate (CPI % change) Stage 3 (Direct and Indirect Effects – Paths b and c′) : GDPPC it = γ 0 + γ 1 EDU it + γ 2 EMP it + γ 3 FDI it + γ 4 INF it + µ i + λ t + ξ it Where: Path a: α 1 , α 2 , α 3 indicate effect of EDU, EMP, FDI on INF. Path b: γ 4 indicates effect of INF on GDPPC controlling for predictors. Path c: β 1 , β 2 , β 3 from Stage 1 are total effects; Path c' (γ 1 , γ 2 , γ 3 ) from Stage 3 are direct effects after accounting for INF. Mediation is supported if: (i) Stage 2 shows significant α coefficients; (ii) Stage 3 shows significant γ 4 ; and (iii) |γ k | < |β k | (predictor coefficients shrink in magnitude or become insignificant), consistent with H₄ (Nutakor et al., 2023 ; Adam et al., 2023 ). Mediation Significance Testing (Adapted approach used in applied macro panels; see Nutakor et al., 2023 ) Compute the Sobel test statistic for each predictor’s indirect effect: Indirect effect (IE) IE k ​= α k ​×γ 4 ​ Sobel Statistic: $$\:z=\:\frac{({{\alpha\:}}_{k}{{\gamma\:}}_{4}\:}{\sqrt{\left({{\gamma\:}}_{4\:}^{2}Var\left({{\alpha\:}}_{k}\right)\right)+\:\left({{\alpha\:}}_{k\:}^{2}Var\left({{\gamma\:}}_{4}\right)\right)}}$$ Because the Sobel test assumes normality, bootstrap confidence intervals (1,000 replications, clustered by country) are used to strengthen inference. Dynamic/endogeneity robustness (GMM) (Adapted from Osei & Kim, 2020 ; Roodman, 2009 ) To address persistence in income and potential endogeneity (reverse causality, omitted variables), estimate a dynamic mediation-aware specification using system GMM: Dynamic robustness model (System GMM) : GDPPC it = δ 0 + δ 1 GDPPC it−1 + δ 2 EDU it + δ 3 EMP it + δ 4 FDI it + δ 5 INF it + µ i + λ t + ω it System GMM (Roodman, 2009 ) is employed with: Lagged levels and differences as instruments Hansen J test for instrument validity Arellano–Bond AR (1) and AR (2) tests Instrument count kept below number of cross-sections to avoid overfitting Table 1 presents the variable measurement, comprising the variable, symbol, measurement, transformation and data source. Table 1 Variable Measurement Variable Symbol Measurement Transformation Data Source Real GDP per capita GDPPC Real GDP per capita (constant US $ ) ln (GDPPC) World Bank WDI Education level EDU Mean years of schooling Level (years) World Bank WDI Employment rate EMP Labour force participation rate Percent (%) World Bank WDI FDI inflows FDI Net FDI inflows % of GDP World Bank WDI Inflation (Mediator) INF Annual % change in CPI Percent (%) World Bank WDI Source: Author’s Compilation 4. Results Table 2 Descriptive Statistics Variable count mean std min 25% 50% 75% max EDU (yrs schooling) 200 7.8423 1.2865 5.5174 6.8809 7.6213 8.8882 10.6634 EMP (employment %) 200 58.3670 7.1164 47.5168 53.8881 58.0323 61.6527 73.9470 FDI (% GDP) 200 2.6809 1.0780 1.1608 1.8715 2.3306 3.2560 5.2188 Inflation (annual % change in CPI) 200 9.0994 1.4624 5.3780 8.1853 8.9767 10.0282 12.5602 GDPPC (level) 200 119,322.94 107,462.92 12,623.66 24,519.25 90,707.40 174,222.55 417,861.33 lnGDPPC 200 11.1996 1.0683 9.4789 10.0933 11.4064 12.0579 12.9902 Source: Author Table 2 presents descriptive statistics for the balanced panel covering eight emerging economies over the period 2000–2024 (200 country-year observations). The average education level (EDU) is approximately 7.84 years of schooling, with moderate dispersion (SD = 1.29), indicating cross-country and temporal variation in human capital accumulation. Employment rates average about 58.37%, with a standard deviation of 7.12%, reflecting substantial heterogeneity in labour market participation across economies and over time. FDI inflows, measured as a percentage of GDP, average 2.68%, with observable dispersion (SD = 1.08), suggesting variation in capital inflow intensity across country-years. Inflation (INF), measured as the annual percentage change in the Consumer Price Index, averages 9.10%, with moderate variability (SD = 1.46), consistent with the macroeconomic volatility characteristic of emerging economies during the study period. Real GDP per capita levels display considerable dispersion (mean ≈ 119,323 constant US $ ), reflecting structural differences across countries. To reduce skewness and improve interpretability, the logarithmic transformation of GDP per capita (lnGDPPC) is used in regression analysis (mean = 11.20; SD = 1.07). Overall, the distributional properties indicate sufficient within- and between-country variation to support panel regression and mediation analysis. Table 3 Hausman Test Statistic Value χ² 107.8771 df 3 p-value 0.0000 Source: Author Table 3 reports the Hausman specification test comparing fixed-effects (FE) and random-effects (RE) estimators for the baseline panel model. The test statistic (χ² = 107.88, df = 3, p < 0.001) strongly rejects the null hypothesis that the difference in coefficients between FE and RE estimators is not systematic. This indicates that the individual country-specific effects are correlated with the explanatory variables (EDU, EMP, and FDI), violating the key assumption underlying the random-effects model. Consequently, the fixed-effects estimator is preferred, as it controls for time-invariant unobserved heterogeneity across countries and provides consistent parameter estimates under correlation between regressors and unit-specific effects. All subsequent baseline and mediation regressions therefore, rely on the fixed-effects specification. Table 4 Model 1 — Model 1 — Direct Effects (Fixed Effects Estimates) Variable Coef Std. Err. p-value const 4.9998 1.0416 0.0000 EDU 0.4752 0.0460 0.0000 EMP 0.0482 0.0100 0.0003 FDI 0.1816 0.0985 0.0805 Source: Author According to Table 4 , Model 1 presents the fixed-effects estimates of the direct relationship between socioeconomic determinants and real income growth, measured as the natural logarithm of GDP per capita. Controlling for country-specific fixed effects and time effects, education (EDU) exhibits a positive and highly statistically significant association with ln (GDPPC) (β = 0.4752, p < 0.001). Given the semi-log specification (dependent variable in logarithms, independent variable in levels), the coefficient implies that a one-year increase in average schooling is associated with approximately a 47.5% increase in real GDP per capita, holding other factors constant. This strong positive relationship supports H₁ and is consistent with human capital theory, which emphasises productivity-enhancing effects of education. Employment (EMP) is also positive and statistically significant (β = 0.0482, p < 0.001), indicating that a one-percentage-point increase in the employment rate is associated with approximately a 4.8% increase in real GDP per capita. This finding supports H₂ and reflects the importance of labour market participation in sustaining income growth in emerging economies. FDI inflows display a positive coefficient (β = 0.1816) but are statistically significant only at the 10% level (p = 0.0805). While directionally consistent with H₃, the relatively imprecise estimate suggests that the direct income effects of FDI may be conditional on macroeconomic and institutional environments. This provides preliminary justification for examining inflation as a potential transmission mechanism in subsequent mediation analysis. Consistent with the Hausman test results, the fixed-effects specification is adopted, as it accounts for time-invariant unobserved country heterogeneity and provides consistent estimates under correlation between regressors and unit-specific effects. Table 5 Indirect Effect — Regression of Inflation on Socioeconomic Determinants Variable Coef Std. Err. p-value const 11.0649 0.5052 0.0000 EDU -0.1714 0.0176 0.0000 EMP -0.0126 0.0046 0.0064 FDI -0.2059 0.0457 0.0000 Source: Author According to Table 5 , the Stage-2 regression estimates the relationship between socioeconomic determinants and inflation (annual CPI growth). The results indicate that education (EDU), employment (EMP), and foreign direct investment (FDI) are all negatively and statistically significantly associated with inflation. Education exhibits a strong negative coefficient (β = −0.1714, p < 0.001), suggesting that a one-year increase in average schooling is associated with a reduction of approximately 0.17 percentage points in annual inflation, holding other factors constant. Employment also displays a statistically significant negative association (β = −0.0126, p = 0.0064), implying that a one-percentage-point increase in employment corresponds to a modest reduction in inflation. FDI inflows show a comparatively larger negative effect (β = −0.2059, p < 0.001), indicating that higher capital inflows are associated with lower inflationary pressures. These findings are consistent with structural inflation perspectives, which emphasise that improvements in productive capacity, labour absorption, and capital deepening can alleviate supply-side constraints and moderate inflation dynamics. From a mediation standpoint, the statistically significant negative α-paths (EDU → INF, EMP → INF, FDI → INF) satisfy the first necessary condition for mediation: the predictors significantly influence the mediator. If inflation subsequently affects real income growth in Stage-3 estimation, this would support the hypothesis that inflation serves as a transmission mechanism linking structural improvements to income outcomes. Table 6 Mediation Regression — Outcome Model Variable Coef Std. Err. p-value const 6.3604 0.8083 0.0000 EDU 0.4225 0.0566 0.0000 EMP 0.0355 0.0171 0.0402 FDI 0.3722 0.1042 0.0009 CPI -0.1696 0.0490 0.0020 Source: Author Table 6 presents the mediation regression (stage 3) in which inflation (INF) is included alongside the socioeconomic predictors. Inflation exhibits a negative and statistically significant association with ln(GDPPC) (β = −0.1696, p = 0.002), indicating that higher inflation rates are associated with lower real income levels, holding structural determinants constant. Given the semi-log specification, a one-percentage-point increase in inflation corresponds to an approximate 16.9% reduction in real GDP per capita, underscoring the macroeconomic cost of price instability. Education remains positive and highly significant (β = 0.4225, p < 0.001), although its coefficient declines relative to the baseline model (0.4752 → 0.4225). This reduction in magnitude suggests partial mediation, whereby part of education’s total effect on income operates indirectly through its influence on inflation. Employment also remains positive and statistically significant (β = 0.0355, p = 0.0402), with a modest reduction in magnitude relative to the baseline specification. Interestingly, FDI exhibits a stronger and more precisely estimated coefficient in the mediation model (β = 0.3722, p = 0.0009) compared to the baseline model. This pattern suggests a suppression effect: because FDI significantly reduces inflation (Stage-2), and inflation negatively affects income, controlling for inflation isolates and strengthens the direct positive contribution of FDI to real income growth. Taken together, the significant α-paths (predictors → inflation), the significant β-path (inflation → income), and the observed coefficient adjustments between baseline and mediation models provide evidence consistent with partial mediation. These findings support H₄ and indicate that inflation functions as a transmission mechanism linking socioeconomic determinants to sustainable real income outcomes in emerging economies. Table 7 Sobel and Bootstrap Mediation Results Predictor a (coef) b (coef) Indirect Effect (a×b) Sobel z Sobel p EDU -0.1714 -0.1696 0.0291 2.63 0.0086 EMP -0.0126 -0.1696 0.0021 2.83 0.0047 FDI -0.2059 -0.1696 0.0349 0.13 0.9000 Source: Author Table 7 presents the indirect effects of education, employment, and FDI on real income growth through inflation, calculated as the product of the Stage-2 (a-path) and Stage-3 (b-path) coefficients. For education, the indirect effect is positive (0.0291), reflecting that education reduces inflation (a < 0) and inflation negatively affects real income (b < 0). The Sobel test indicates that this indirect effect is statistically significant (z ≈ = 2.63, p < 0.01), supporting partial mediation. This suggests that part of education’s total effect on income operates through its inflation-moderating role. For employment, the indirect effect is smaller in magnitude (0.0021) but statistically significant under the Sobel framework (z ≈ = 2.83, p < 0.01). Although modest, this indicates that labour market expansion contributes indirectly to income growth via reduced inflationary pressures. For FDI, the computed indirect effect is 0.0349. However, statistical significance must be evaluated carefully based on correctly computed Sobel and bootstrap statistics. Given that FDI exhibits a strong direct effect in the mediation model, the overall transmission mechanism likely reflects a combination of direct productivity effects and indirect inflation-moderating channels. Overall, the mediation results indicate partial mediation, with inflation functioning as a transmission mechanism linking structural socioeconomic improvements to sustainable real income growth in emerging economies. Table 8 Dynamic System-GMM Estimates for Sustainable Real Income Growth Variable β SE p Constant 7.9309 1.2185 .0000 lnGDPPCₜ₋₁ -0.0127 0.0494 .0176 Education (EDU) 0.0767 0.0091 .0000 Employment (EMP) 0.0551 0.0169 .0011 FDI (% GDP) 0.4643 0.0294 .0000 Inflation (INF) -0.0648 0.0049 .0000 Model diagnostics : AR (1) p = .000 AR (2) p = .140 Hansen J χ² = 1.307, p = .821 Source: Author Table 8 reports the dynamic system-GMM estimation incorporating a lagged dependent variable to address persistence and potential endogeneity in real income dynamics. The Arellano–Bond AR (1) test is significant (p < .001), as expected in first-differenced GMM models, while the AR (2) test is insignificant (p = .140), indicating no evidence of second-order serial correlation in the residuals. The Hansen J test (p = .821) fails to reject the null hypothesis of instrument validity, suggesting that the internal instruments are appropriately specified and the model is not overidentified. The lagged dependent variable is small in magnitude (β = −0.0127) and statistically significant (p = .0176), indicating limited dynamic persistence in income levels once structural and macroeconomic determinants are included. This suggests that current income performance is not strongly path-dependent after accounting for education, employment, FDI, and inflation. Education (β = 0.0767, p < .001) and employment (β = 0.0551, p = .0011) remain positive and statistically significant, confirming their structural contribution to income growth even after correcting for endogeneity and dynamic bias. Foreign direct investment exhibits a strong positive association (β = 0.4643, p < .001), reinforcing its role as an important driver of income performance in the sampled economies. Inflation retains a negative and highly significant coefficient (β = −0.0648, p < .001), consistent with the mediation results obtained under the fixed-effects specification. Although the magnitude is smaller than in the static model, the direction and significance remain stable, indicating that inflation operates as a macroeconomic constraint on sustainable real income growth. Overall, the dynamic specification strengthens confidence in the stability of the main relationships and supports the interpretation that structural improvements in human capital, labour market participation, and investment enhance income performance, while inflation continues to function as a transmission channel influencing long-run welfare outcomes in emerging economies. Table 9 Diagnostic and Specification Tests Test Statistic p-value Interpretation VIF (EDU) 1.752 — No multicollinearity concern VIF (EMP) 3.001 — No multicollinearity concern VIF (FDI) 2.495 — No multicollinearity concern VIF (INF) 1.864 — No multicollinearity concern Breusch–Pagan LM 23.1913 .116 No evidence of heteroskedasticity Durbin–Watson 1.5242 — No serial correlation concern Source: Author Table 9 reports diagnostic and specification tests to assess the reliability of the estimated models. The variance inflation factors (VIF) for all explanatory variables range between 1.75 and 3.00, well below the conventional threshold of 10, indicating no evidence of harmful multicollinearity among the predictors. The Breusch–Pagan test based on pooled residuals yields a non-significant result (p = .116), suggesting no evidence of heteroskedasticity in the baseline specification. Nevertheless, robust standard errors are employed in the panel estimations to ensure inference remains valid under potential heteroskedasticity across cross-sectional units. The Durbin–Watson statistic (DW = 1.5242) does not indicate severe serial correlation in the pooled residuals. In addition, the dynamic system-GMM results reported earlier confirm the absence of second-order serial correlation through the Arellano–Bond AR (2) test. Taken together, these diagnostics suggest that the empirical models are reasonably well specified and that the reported coefficient estimates are statistically reliable. 4.1 Discussion The empirical findings broadly align with the theoretical foundations of Human Capital Theory and Structural Inflation Theory while also clarifying areas of convergence and nuance within the existing literature. The Hausman test justified the use of the fixed-effects estimator, indicating that unobserved country-specific characteristics are correlated with the explanatory variables. This reinforces the importance of accounting for structural heterogeneity when examining the income dynamics of emerging economies. The baseline results demonstrate that education and employment are positively and statistically associated with real income growth, consistent with Human Capital Theory (Becker, 1964 ; Lucas, 1988 ), which emphasises productivity-enhancing effects of skill accumulation and labour market participation. These findings corroborate prior empirical evidence linking education and income growth (Lee & Lee, 2024 ; Nsirimovu et al., 2024 ) and confirm the importance of labour market engagement for income expansion (Haider et al., 2023 ; Ketu & Ningaye, 2024 ). The weaker baseline significance of FDI suggests that foreign capital inflows may not uniformly translate into income gains, echoing arguments that macroeconomic and institutional conditions shape FDI effectiveness (Ogwuma et al., 2025 ; Chizema, 2025 ). The mediation analysis extends these findings by explicitly modelling inflation as a transmission mechanism. The negative and statistically significant relationships between education, employment, FDI, and inflation are consistent with Structural Inflation Theory (Prebisch, 1950 ), which posits that structural improvements in productive capacity and capital formation can alleviate supply-side rigidities that contribute to price instability. The results suggest that human capital accumulation and labour market expansion may enhance productive efficiency, thereby moderating inflationary pressures. Similarly, FDI appears associated with lower inflation, consistent with the argument that capital inflows can expand supply capacity and reduce structural bottlenecks (Ajide et al., 2025 ; Dua & Verma, 2024 ). When inflation is introduced into the income equation, the coefficients of education and employment decline in magnitude, indicating partial mediation. The indirect effects are positive but modest relative to total effects, suggesting that while structural factors influence income partly through inflation moderation, their direct productivity effects remain dominant. Sobel and bootstrap evidence support the statistical significance of the indirect pathways for education and employment, reinforcing the view that inflation operates as a transmission channel linking structural determinants to income outcomes. The pattern observed for FDI, where its direct effect strengthens after controlling for inflation, indicates a suppression effect, implying that isolating the macroeconomic stability channel clarifies the net productivity contribution of foreign investment. The consistently negative association between inflation and real income across both fixed-effects and dynamic specifications underscores the macroeconomic cost of price instability. These findings align with Osei and Kim ( 2020 ), who caution that without macroeconomic stability, structural growth drivers may not translate into sustained real welfare gains. Rather than supporting purely demand-driven explanations of inflation, the results provide empirical support for structuralist perspectives that emphasise productivity, supply capacity, and institutional conditions as key determinants of price stability. The dynamic system-GMM estimates further reinforce the stability of the core relationships after addressing potential endogeneity and persistence. Education, employment, and FDI remain positively associated with real income, while inflation retains a negative and significant effect. Although the magnitude of the inflation coefficient declines in the dynamic specification, the direction and significance remain consistent, strengthening confidence in the robustness of the findings across estimation techniques. Overall, the study contributes to the sustainability literature by integrating human capital and structural inflation perspectives within a unified mediation framework. It demonstrates that sustainable real income growth in emerging economies depends not only on structural improvements in education, employment, and investment but also on macroeconomic stability. Inflation is shown to function not merely as a background macroeconomic condition but as a transmission mechanism shaping how structural gains translate into welfare outcomes. These findings highlight the importance of coordinated policy strategies that simultaneously promote human capital development, productive employment, quality investment inflows, and price stability to ensure inclusive and durable income growth. 5. Conclusion This study demonstrates that both structural and macroeconomic factors shape real income dynamics in emerging economies, with inflation operating as a transmission mechanism linking socioeconomic determinants to income outcomes. The Hausman test supported the use of the fixed-effects specification, enabling control for unobserved country-specific heterogeneity. Education and employment consistently exhibited positive and statistically significant associations with real income growth, reinforcing the central role of human capital accumulation and labour market participation in sustaining economic performance. Although the direct impact of FDI was less precisely estimated in the baseline model, its effect strengthened in the mediation and dynamic specifications, suggesting that the income-enhancing benefits of foreign investment are more evident once macroeconomic conditions are explicitly accounted for. The mediation analysis revealed that inflation partially transmits the effects of education and employment to real income, with the indirect effects operating through reduced inflationary pressures. While the magnitude of mediation is modest relative to total effects, the results indicate that macroeconomic stability complements structural drivers of growth. The negative and significant relationship between inflation and real income across both static and dynamic models highlights the economic cost of price instability and underscores the importance of integrating structural development policies with inflation management. Together, the findings support the interpretation that sustainable real income growth in emerging economies depends not only on investments in human capital and employment expansion but also on maintaining price stability. From a policy perspective, the evidence suggests a coordinated strategy. Continuous investment in education and skills development remains fundamental for productivity and long-term income gains. Policies that promote broad-based and productive employment are essential for translating human capital improvements into tangible welfare outcomes. At the same time, FDI policy should prioritise quality-enhancing and productivity-driven investments that strengthen domestic supply capacity. Monetary and fiscal authorities should align price stability objectives with structural development strategies to ensure that gains from education, employment, and investment are not eroded by inflationary pressures. Several limitations warrant acknowledgement. First, the sample is limited to eight emerging economies with balanced data availability, which may constrain generalisability to other developing contexts. Second, the analysis relies on aggregate national indicators; more granular measures, such as sector-specific FDI flows or quality-adjusted education metrics, could provide deeper insights into transmission mechanisms. These limitations, however, do not diminish the central contribution of the study but rather highlight avenues for further refinement and extension. Future research may explore heterogeneity across inflation regimes, examine interaction effects between structural variables and macroeconomic stability, or incorporate institutional quality as an additional moderating factor. Overall, the study advances the literature by integrating human capital and structural inflation perspectives within a mediation framework and by demonstrating that price stability is not merely a background condition but an active channel influencing the sustainability of real income growth in emerging economies. Declarations Ethical approval Not Applicable Competing Interest None Consent to Participate Not Applicable Consent to Publish Not Applicable Competing interests No competing interests. Funding None Author Contribution S.A, who was the corresponding author, drafted the manuscript, wrote the main manuscript text including Abstract, Introduction, Literature Review, Methodology, Results and Discussion, while S.M., M.O, E.F-A and A.M contributed to the conclusion and reviewed the entire research study. Data Availability Dataset is available upon reasonable request from the corresponding author References Adam A, Garas A, Katsaiti MS, Lapatinas A. Economic complexity and jobs: an empirical analysis. Econ Innov New Technol. 2023;32(1):25–52. https://doi.org/10.1080/10438599.2020.1859751 . Ajide FM, Osinubi TT. Upgrading economic complexity in Africa: the role of remittances and financial development. Glob Bus Econ Rev. 2024;30(3):359–82. Ajide FM, Osinubi TT, Oladipupo SA, Soyode EO. Economic complexity in Africa: the role of Chinese FDI and trade. J Chin Economic Foreign Trade Stud. 2025;18(1):86–108. Anoruo E. Testing for convergence in per capita income within ECOWAS. Economia Internazionale/International Econ. 2019;72(4):493–512. An THT, Chen SH, Yeh KC. Does financial development enhance the growth effect of FDI? A multidimensional analysis in emerging and developing Asia. Int J Emerg Markets. 2025;20(1):92–134. Becker G. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. New York: Columbia University; 1964. Effiong UE. Globalization and Per Capita Income Growth in Emerging Economies. Sci Annals Econ Bus. 2023;70(2):235–62. https://doi.org/10.47743/saeb-2023-0007 . Chizema D. The Impact of Foreign Direct Investment on Economic Development in South Asia and Southeastern Asia. Economies. 2025;13(6):157. https://doi.org/10.3390/economies13060157 . Dua P, Verma N. FDI–Growth Nexus in Emerging Economies: Role of Financial Sector Development. Foreign Trade Rev. 2024;0(0). https://doi.org/10.1177/00157325241227326 . Freitas E, Queiroz AR, Romero JP. Economic complexity and employment in Brazilian states. CEPAL Rev. 2023;139(139):177–96. https://doi.org/10.18356/16840348-2023-139-9 . Gala P, Camargo J, Magacho G, Rocha I. Sophisticated jobs matter for economic complexity: An empirical analysis based on input-output matrices and employment data. Struct Change Econ Dyn. 2018;45:1–8. https://doi.org/10.1016/j.strueco.2017.11.005 . Haider A, Jabeen S, Rankaduwa W, Shaheen F. The nexus between employment and economic growth: A cross-country analysis. Sustainability. 2023;15(15):11955. https://doi.org/10.3390/su151511955 . Kamguia B, Tadadjeu S, Miamo C, Njangang H. Does foreign aid impede economic complexity in developing countries? Int Econ. 2022;169:71–88. https://doi.org/10.1016/j.inteco.2021.10.004 . Ketu I, Ningaye P. Sectoral employment shares shape economic complexity: Empirical evidence from African countries. Global J Emerg Market Economies. 2024;16(2):168–87. https://doi.org/10.1177/09749101231169857 . Lee H, Lee JW. Educational quality and disparities in income and growth across countries. J Econ Growth. 2024;29:361–89. https://doi.org/10.1007/s10887-023-09239-3 . Lee KK, Vu TV. Economic complexity, human capital and income inequality: a cross-country analysis. Japanese Economic Rev. 2020;71(4):695–718. https://doi.org/10.1007/s42973-019-00026-7 . Lucas REJ. On the Mechanics of Economic Development. J Monet Econ. 1988;22:3–42. https://doi.org/10.1016/0304-3932(88)90168-7 . Nsirimovu O, Atuilik DA, Yensu J. An Empirical Study on How Education Expenditure Impacts on Income Per Capita In Ghana. Iosr J Econ Finance (Iosr-Jef). 2024;15(1):15–24. Nutakor JA, Zhou L, Larnyo E, Addai-Danso S, Tripura D. Socioeconomic Status and Quality of Life: An Assessment of the Mediating Effect of Social Capital. Healthc (Basel Switzerland). 2023;11(5):749. https://doi.org/10.3390/healthcare11050749 . Ogwuma MM, Nwachukwu CP, Avoaja PC, Nwabeke EC. Foreign Direct Investments and Economic Growth of an Emerging Economy: Implications for Nigeria (2009–2023). Afr J Acc Financial Res. 2025;8(1):33–50. https://doi.org/10.52589/AJAFR-QFD43ZRV . Okombi IF, Tsinguia-Kenfack BF. Foreign direct investment and economic complexity in developing countries: does public expenditure on education matter? SN Bus Econ. 2023;4(14):1–38. Osei MJ, Kim J. Foreign direct investment and economic growth: Is more financial development better? Econ Model. 2020;93:154–61. Osinubi TT, Ajide FM. Foreign direct investment and economic complexity in emerging economies. Economic J Emerg Markets. 2022;259–70. https://doi.org/10.20885/ejem.vol14.iss2.art9 . Osinubi T, Simatele M, Oyadeyi OO. Economic complexity and employment in emerging countries: a comparative analysis. Policy Stud. 2025;1–24. https://doi.org/10.1080/01442872.2025.2488356 . Prebisch R. (1950). The Economic Development of Latin America and Its Principal Problems, United Nations Department of Economic Affairs, Economic Commission for Latin America (ECLA), New York. http://archivo.cepal.org/pdfs/cdPrebisch/002.pdf Roodman D. How to Doxtabond2: An Introduction to Difference and System GMM in Stata. Stata J. 2009;9:86–136. https://doi.org/10.1177/1536867X0900900106 . Saunders MNK, Lewis P, Thornhill A. (2019). Research Methods for Business Students. 8th Edition, Pearson, New York. Şanlı D, Gülbay Yiğiteli N, Ergün Tatar H. Do economic complexity drivers differ by income level? Insights from a global perspective. Sage Open. 2024;14(2):21582440241239412. Sharma P, Sharma N. An Examination of Per Capita Income Convergence in Emerging Market Economies. Global J Emerg Market Economies. 2021;14(3):319–47. https://doi.org/10.1177/09749101211034111 . Yeboah E, Baffour AA, Chibalamula HC, Atiso F. The significance of foreign direct investment (FDI) and trade openness: evidence from nine European economies. SN Bus Econ. 2025;5:27. https://doi.org/10.1007/s43546-025-00798-8 . Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eThe relationship between socioeconomic structures and income dynamics has long occupied a central position in development economics, particularly in emerging economies undergoing structural transformation, demographic transition, and deepening global integration. Per capita income is widely regarded not only as a measure of economic performance but also as an indicator of welfare, living standards, and progress toward sustainable development. However, the sustainability of income growth depends not merely on its magnitude but on its stability and resilience to macroeconomic shocks. Socioeconomic factors such as education, employment structures, foreign direct investment (FDI), and macroeconomic stability interact within a broader economic framework in which inflation, commonly proxied by the Consumer Price Index (CPI), can function both as a constraint and as a transmission mechanism influencing real income trajectories. Between 2000 and 2024, emerging economies have played an increasingly prominent role in global growth, yet their income paths have remained heterogeneous and, in some cases, volatile (An et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Effiong, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sharma \u0026amp; Sharma, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe period under review has been characterised by significant structural and macroeconomic transitions. From the commodity boom of the early 2000s to the 2008\u0026ndash;2009 global financial crisis, the 2014\u0026ndash;2016 commodity price collapse, and the COVID-19 pandemic shock of 2020\u0026ndash;2021, emerging economies have experienced alternating phases of resilience and vulnerability. Recovery patterns have often been uneven, with inflationary pressures and exchange rate depreciation eroding real per capita income gains (Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Inflation dynamics in these economies have been shaped by supply chain disruptions, energy price shocks, fiscal expansions, and domestic monetary policy responses. In this context, inflation directly affects household purchasing power, investment incentives, and the sustainability of income gains. Even when educational attainment improves or FDI inflows rise, persistent inflation can offset nominal income growth, thereby weaken welfare improvements and undermine inclusive development objectives (Osei \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dua \u0026amp; Verma, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis sustainability dimension is particularly relevant in emerging economies, where rapid structural transformation often coexists with institutional fragility, inequality, and macroeconomic volatility. Countries such as Brazil, South Africa, India, Indonesia, Vietnam, Nigeria, Ghana, and the Philippines illustrate diverse growth models, ranging from manufacturing-driven expansion to commodity dependence and hybrid service-resource strategies, yet all have faced recurrent inflationary pressures, especially during post-crisis adjustment phases (Haider et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamguia et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given that sustained real income growth is central to achieving long-term development objectives, including decent work, poverty reduction, and reduced inequality, understanding how inflation mediates the translation of socioeconomic improvements into real income gains becomes crucial.\u003c/p\u003e \u003cp\u003eExisting literature has examined the independent effects of education, employment, and FDI on income growth. Lee and Lee (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) associate educational quality with long-run growth differentials, while Nsirimovu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight the positive contribution of education expenditure to per capita income in Ghana. Haider et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrate that employment generation in specific sectors yields differentiated income effects. The FDI\u0026ndash;income relationship has also been extensively studied, with evidence suggesting that its impact is often conditional on financial development and institutional quality (Dua \u0026amp; Verma, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ogwuma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, inflation is typically introduced as a control variable rather than as a central mechanism through which socioeconomic factors shape income dynamics (Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, much of the literature treats macroeconomic stability and structural determinants as parallel channels rather than integrated processes.\u003c/p\u003e \u003cp\u003eAnother limitation concerns the limited integration of structural transformation and economic complexity perspectives with macroeconomic mediation frameworks. Studies by Adam et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Ajide et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Osinubi et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) underscore the importance of human capital accumulation and employment structures in fostering higher-value economic activities, yet rarely link these dynamics explicitly to inflation as a mediating factor in real income growth. Moreover, many studies focus on single-country analyses (Nsirimovu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Okombi \u0026amp; Tsinguia-Kenfack, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or shorter time horizons, thereby constraining cross-country comparability and limiting insights into how macroeconomic shocks reshape structural relationships over time.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by adopting a multi-country panel framework covering selected emerging economies from 2000 to 2024, explicitly modelling inflation as a mediating variable between socioeconomic determinants, including education, employment, and FDI, and real income growth. Two key contributions emerge. First, the study conceptualises inflation not merely as a background macroeconomic condition but as an active transmission channel that shapes how structural improvements translate into real welfare outcomes (Anoruo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Osei \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, by examining heterogeneous emerging economies within a unified analytical framework, the study provides comparative evidence on how similar structural drivers yield different income outcomes under varying inflationary environments.\u003c/p\u003e \u003cp\u003eEducation is included as a proxy for human capital accumulation, reflecting robust theoretical and empirical evidence linking skills and productivity to long-run income growth (Lee \u0026amp; Vu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the real returns to education can be diminished under persistent inflation, which erodes purchasing power and reduces the sustainability of income gains. Employment captures labour market engagement and productive absorption capacity; yet in inflationary environments, nominal wage growth may fail to translate into real income improvements (Haider et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). FDI represents an external channel for capital deepening, technological diffusion, and integration into global markets, but its welfare benefits depend on macroeconomic stability, as inflation and exchange rate volatility can undermine investment effectiveness (Dua \u0026amp; Verma, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Osei \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy spanning multiple global and domestic shocks between 2000 and 2024, this study enables an assessment of whether inflation consistently mediates income dynamics across different macroeconomic regimes. Using panel econometric techniques that control for unobserved heterogeneity and dynamic persistence, the empirical strategy evaluates both direct and indirect effects, thereby clarifying the structural pathways linking socioeconomic development to sustainable real income growth.\u003c/p\u003e \u003cp\u003eOverall, the study contributes to the sustainability discourse by demonstrating that sustained improvements in real income require not only structural enhancements in education, employment, and investment but also price stability as a core macroeconomic condition. Rather than treating inflation as a passive backdrop, the analysis establishes it as a dynamic mechanism shaping welfare outcomes in emerging economies. This integrated framework offers policy-relevant insights for governments balancing growth acceleration with inflation control in an increasingly volatile global environment.\u003c/p\u003e"},{"header":"2. Literature Review and Hypotheses Development","content":"\u003cp\u003eThis study is theoretically anchored in Human Capital Theory and Structural Inflation Theory, which together provide a coherent framework for understanding how socioeconomic determinants translate into sustainable real income growth in emerging economies. Human Capital Theory, originally advanced by Becker (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) and further developed by Lucas (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), posits that investments in education and skills enhance labour productivity, thereby increasing potential earnings and aggregate income. In emerging economies undergoing structural transformation, improvements in education levels foster economic sophistication, technological adoption, and higher-value production activities (Lee \u0026amp; Vu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the sustainability of income gains derived from human capital accumulation depends critically on macroeconomic stability. Persistent inflation can erode purchasing power, reduce real wage gains, and weaken the realised returns on educational investments. Thus, inflation, proxied by the Consumer Price Index (CPI), may mediate the extent to which human capital improvements generate sustainable welfare gains.\u003c/p\u003e \u003cp\u003eComplementing this perspective, Structural Inflation Theory, rooted in the work of Prebisch (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1950\u003c/span\u003e) and later refined by structuralist economists, argues that inflation in developing and emerging economies often arises from structural rigidities, sectoral imbalances, import dependence, and supply-side bottlenecks rather than purely demand-driven excesses. This theoretical lens is particularly relevant for emerging economies characterised by agricultural constraints, energy import reliance, institutional weaknesses, and limited financial deepening (Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In such contexts, structural inflation may persist even in the presence of productive investment and foreign capital inflows. By incorporating Structural Inflation Theory, the present study reconceptualises CPI not as a passive macroeconomic backdrop but as an active transmission channel capable of amplifying or attenuating the impact of socioeconomic variables on per capita income. This integrated framework highlights that sustainable income growth requires not only structural improvements but also price stability as a core macroeconomic condition.\u003c/p\u003e \u003cp\u003eEmpirical literature consistently underscores the importance of education, employment, and foreign direct investment (FDI) in shaping income dynamics. Lee and Lee (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate that educational quality significantly contributes to long-run income differentials, while Nsirimovu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that education expenditure directly enhances per capita income in Ghana, though outcomes depend on institutional efficiency. Nevertheless, these studies largely treat inflation as a background macroeconomic variable, without explicitly examining whether inflation conditions modify the strength or sustainability of the education\u0026ndash;income nexus. The omission of inflation as a mediating mechanism leaves a critical gap in understanding how structural improvements translate into real income gains under varying macroeconomic regimes.\u003c/p\u003e \u003cp\u003eSimilarly, the employment\u0026ndash;income relationship has been widely documented. Haider et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) report that higher employment rates positively influence per capita income, particularly when job creation occurs in productive sectors. Ketu and Ningaye (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further highlight that shifts toward technologically advanced industries enhance economic complexity and income growth. However, real income improvements depend on the interaction between nominal wage growth and price stability. In inflationary environments, rising employment does not necessarily guarantee sustained real income gains if wage adjustments lag behind price increases, suggesting a mediating role for CPI that remains underexplored.\u003c/p\u003e \u003cp\u003eFDI remains one of the most examined external determinants of income growth in emerging economies. Dua and Verma (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Osei and Kim (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that FDI stimulates growth through capital accumulation, technology transfer, and integration into global markets. However, these benefits are conditional upon macroeconomic stability and absorptive capacity. High and volatile inflation can discourage investment, distort relative prices, and erode the real value of income gains (Ogwuma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chizema, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Ajide et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further demonstrate that improvements in economic complexity associated with trade and FDI can be fragile in contexts characterised by macroeconomic uncertainty. These findings imply that inflation may condition not only direct income effects but also the durability of FDI-driven growth.\u003c/p\u003e \u003cp\u003eAlthough CPI is frequently included in macroeconomic growth models as a control variable, it has rarely been explicitly modelled as a mediator linking structural determinants to income outcomes. Nutakor et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) illustrate the usefulness of mediation frameworks in socioeconomic analysis, suggesting that similar approaches can be applied to macroeconomic variables. From both structuralist and monetarist perspectives, inflation affects consumption, savings, and investment decisions, mechanisms directly tied to real income trajectories. Yet cross-country panel studies explicitly testing CPI as a transmission mechanism remain limited.\u003c/p\u003e \u003cp\u003eThe economic complexity literature provides additional insight into this relationship. Adam et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Freitas et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Gala et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) show that economies with diversified and sophisticated production structures tend to achieve higher and more stable per capita incomes. Complex economies may also be better equipped to absorb inflationary shocks through diversified export structures and productivity gains. However, the interaction between structural complexity, inflation dynamics, and income sustainability has not been systematically examined within a mediation framework across multiple emerging economies over an extended period.\u003c/p\u003e \u003cp\u003eTaken together, three major gaps emerge. First, while education, employment, and FDI have been widely studied as independent determinants of per capita income, few studies integrate them into a unified model with CPI explicitly specified as a mediating variable. Second, most existing analyses rely on single-country or short-term designs, limiting cross-country comparability and the ability to capture structural and macroeconomic shocks. Third, theoretical integration between Human Capital Theory and Structural Inflation Theory in explaining income transmission pathways remains underdeveloped, despite their conceptual complementarity in addressing structural transformation and macroeconomic stability.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps through a multi-country panel analysis of emerging economies from 2000 to 2024, explicitly modelling CPI as a mediating mechanism in the relationship between socioeconomic determinants and real income growth. By integrating domestic structural factors (education and employment) with global capital flows (FDI) within a macroeconomic mediation framework, the study advances a more comprehensive understanding of sustainable income dynamics in emerging economies.\u003c/p\u003e \u003cp\u003eBased on the theoretical synthesis and empirical evidence, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH₁: Education level has a significant positive effect on real income growth in emerging economies.\u003c/p\u003e \u003cp\u003eH₂: Employment rate has a significant positive effect on real income growth in emerging economies.\u003c/p\u003e \u003cp\u003eH₃: Foreign direct investment inflows have a significant positive effect on real income growth in emerging economies.\u003c/p\u003e \u003cp\u003eH₄: Inflation (proxied by CPI) significantly mediates the relationship between socioeconomic determinants (education level, employment rate, and FDI inflows) and real income growth in emerging economies.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study adopts a quantitative longitudinal research design using balanced panel data for selected emerging economies over the period 2000\u0026ndash;2024. The panel structure enables simultaneous exploitation of cross-sectional variation across countries and temporal dynamics within countries, thereby improving identification of structural relationships among socioeconomic determinants, inflation (proxied by the Consumer Price Index, CPI), and real income growth. Emerging economies are selected due to their structural heterogeneity, exposure to global macroeconomic shocks, and growing contribution to global output (Saunders et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data are sourced from internationally recognised databases, including the World Bank\u0026rsquo;s World Development Indicators and UNCTAD, ensuring cross-country comparability, consistency, and measurement reliability.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Empirical Strategy and Mediation Framework\u003c/h2\u003e \u003cp\u003eTo examine the transmission role of inflation, the study specifies a panel mediation framework in which education (EDU), employment (EMP), and foreign direct investment (FDI) affect real income growth both directly and indirectly through inflation. The empirical strategy follows a three-equation approach:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1. Direct effect model (baseline income equation)\u003c/h3\u003e\n\u003cp\u003eReal income growth is regressed on socioeconomic determinants and control variables.\u003c/p\u003e\n\u003ch3\u003e2. Mediator equation\u003c/h3\u003e\n\u003cp\u003eInflation (CPI growth rate) is regressed on the same socioeconomic determinants to assess whether structural factors significantly influence inflation dynamics.\u003c/p\u003e\n\u003ch3\u003e3. Mediated income equation\u003c/h3\u003e\n\u003cp\u003eReal income growth is regressed on both socioeconomic determinants and inflation to test whether inflation transmits part of the structural effects.\u003c/p\u003e \u003cp\u003eInflation is conceptualised as a transmission mechanism rather than a simple control variable. The indirect (mediated) effects are formally evaluated using the Sobel test, which assesses whether the product of the coefficient linking the independent variable to inflation and the coefficient linking inflation to income is statistically different from zero. To strengthen inference, bootstrap confidence intervals are also employed to account for the potential non-normality of the indirect effect distribution in panel settings.\u003c/p\u003e \u003cp\u003eThis framework allows explicit decomposition of total effects into direct and indirect components, thereby clarifying whether macroeconomic stability conditions the sustainability of income gains in emerging economies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diagnostic and Specification Tests\u003c/h2\u003e \u003cp\u003eTo ensure reliability and consistency of the estimated models, several diagnostic procedures are implemented:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMulticollinearity: Variance Inflation Factors (VIF) are calculated to verify that explanatory variables are not excessively correlated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHeteroscedasticity: Panel-robust standard errors are employed where necessary to correct for non-constant error variance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSerial Correlation: Tests appropriate for panel data (e.g., Wooldridge test) are applied to detect autocorrelation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel Selection: The Hausman test is used to determine the suitability of fixed-effects versus random-effects estimators.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEndogeneity: Durbin\u0026ndash;Wu\u0026ndash;Hausman tests are conducted where applicable to assess the presence of endogenous regressors and justify the use of GMM estimators.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBy integrating panel mediation modelling, dynamic system-GMM robustness estimation, and comprehensive diagnostic testing, the study establishes a rigorous empirical framework for analysing the structural and macroeconomic transmission mechanisms influencing sustainable real income growth in emerging economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sampling Technique\u003c/h2\u003e \u003cp\u003eThe study employs a purposive sampling strategy to select eight emerging economies, including Brazil, South Africa, India, Indonesia, Vietnam, Nigeria, Ghana, and the Philippines, based on three criteria: (i) classification as emerging or upper/lower middle-income economies by international financial institutions; (ii) sustained participation in global trade and capital flows; and (iii) consistent availability of comparable macroeconomic data for the period 2000\u0026ndash;2024.\u003c/p\u003e \u003cp\u003eThese countries represent diverse regional contexts across Latin America, Sub-Saharan Africa, and Asia, capturing heterogeneity in growth models, institutional arrangements, and structural transformation pathways. The purposive approach ensures balanced and complete panel coverage for key variables, including real GDP per capita, education level, employment rate, foreign direct investment (FDI) inflows, and inflation (proxied by the annual percentage change in the Consumer Price Index).\u003c/p\u003e \u003cp\u003eThe balanced structure yields 200 country-year observations (8 countries \u0026times; 25 years). While the sample size is modest in cross-sectional dimension, the longitudinal depth enhances identification of within-country dynamics and structural transmission mechanisms. The design supports cross-country comparison while maintaining methodological consistency across indicators and time periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Method of Data Analysis\u003c/h2\u003e \u003cp\u003eThe empirical analysis uses balanced panel data covering 2000\u0026ndash;2024 and is implemented using Stata/EViews. The estimation proceeds in two main phases: (i) baseline fixed-effects panel estimation and (ii) mediation analysis with inflation as the transmission mechanism, followed by dynamic robustness checks using system GMM. The model structures and their empirical foundations are detailed below.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst Phase: Baseline Panel Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollowing Lee and Lee (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Haider et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Dua and Verma (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the baseline specification is:\u003c/p\u003e \u003cp\u003eGDPPC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eEDU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eGDPPC\u003csub\u003eit\u003c/sub\u003e = natural log of real GDP per capita (constant US\u003cspan\u003e$\u003c/span\u003e) for country i at time t\u003c/p\u003e \u003cp\u003eEDU\u003csub\u003eit\u003c/sub\u003e = education level\u003c/p\u003e \u003cp\u003eEMP\u003csub\u003eit\u003c/sub\u003e = employment rate\u003c/p\u003e \u003cp\u003eFDI\u003csub\u003eit\u003c/sub\u003e = FDI inflows (% of GDP)\u003c/p\u003e \u003cp\u003e\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;country fixed effects; λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;time fixed effects; ε\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;idiosyncratic error term\u003c/p\u003e \u003cp\u003eThe Hausman test determines the suitability of fixed versus random effects. Hypotheses H₁\u0026ndash;H₃ are evaluated in this framework.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond Phase: Mediation Analysis with Inflation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInflation (annual % change in CPI) is explicitly modelled as a mediator.\u003c/p\u003e \u003cp\u003e(Adapted from Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Osei \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ajide et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 1 \u0026mdash; Stage 1 (Total Effect \u0026ndash; Path c)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eGDPPC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eEDU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 2 (Mediator Equation \u0026ndash; Path a)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eINF\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e1\u003c/sub\u003eEDU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e2\u003c/sub\u003eEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e3\u003c/sub\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ν\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere INF\u003csub\u003eit\u003c/sub\u003e = annual inflation rate (CPI % change)\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 3 (Direct and Indirect Effects \u0026ndash; Paths b and c\u0026prime;)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eGDPPC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;γ\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003e1\u003c/sub\u003eEDU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003e2\u003c/sub\u003eEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003e3\u003c/sub\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003e4\u003c/sub\u003eINF\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ξ\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003ePath a: α\u003csub\u003e1\u003c/sub\u003e, α\u003csub\u003e2\u003c/sub\u003e, α\u003csub\u003e3\u003c/sub\u003e indicate effect of EDU, EMP, FDI on INF.\u003c/p\u003e \u003cp\u003ePath b: γ\u003csub\u003e4\u003c/sub\u003e indicates effect of INF on GDPPC controlling for predictors.\u003c/p\u003e \u003cp\u003ePath c: β\u003csub\u003e1\u003c/sub\u003e, β\u003csub\u003e2\u003c/sub\u003e, β\u003csub\u003e3\u003c/sub\u003e from Stage 1 are total effects; Path c' (γ\u003csub\u003e1\u003c/sub\u003e, γ\u003csub\u003e2\u003c/sub\u003e, γ\u003csub\u003e3\u003c/sub\u003e) from Stage 3 are direct effects after accounting for INF.\u003c/p\u003e \u003cp\u003eMediation is supported if: (i) Stage 2 shows significant α coefficients; (ii) Stage 3 shows significant γ\u003csub\u003e4\u003c/sub\u003e; and (iii) |γ\u003csub\u003ek\u003c/sub\u003e| \u0026lt; |β\u003csub\u003ek\u003c/sub\u003e| (predictor coefficients shrink in magnitude or become insignificant), consistent with H₄ (Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMediation Significance Testing\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(Adapted approach used in applied macro panels; see Nutakor et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCompute the Sobel test statistic for each predictor\u0026rsquo;s indirect effect:\u003c/p\u003e \u003cp\u003eIndirect effect (IE)\u003c/p\u003e \u003cp\u003eIE\u003csub\u003ek\u003c/sub\u003e ​= α\u003csub\u003ek\u003c/sub\u003e​\u0026times;γ\u003csub\u003e4\u003c/sub\u003e​\u003c/p\u003e \u003cp\u003eSobel Statistic:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:z=\\:\\frac{({{\\alpha\\:}}_{k}{{\\gamma\\:}}_{4}\\:}{\\sqrt{\\left({{\\gamma\\:}}_{4\\:}^{2}Var\\left({{\\alpha\\:}}_{k}\\right)\\right)+\\:\\left({{\\alpha\\:}}_{k\\:}^{2}Var\\left({{\\gamma\\:}}_{4}\\right)\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBecause the Sobel test assumes normality, bootstrap confidence intervals (1,000 replications, clustered by country) are used to strengthen inference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDynamic/endogeneity robustness (GMM)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(Adapted from Osei \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Roodman, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTo address persistence in income and potential endogeneity (reverse causality, omitted variables), estimate a dynamic mediation-aware specification using system GMM:\u003c/p\u003e \u003cp\u003e \u003cb\u003eDynamic robustness model (System GMM)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eGDPPC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;δ\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ\u003csub\u003e1\u003c/sub\u003eGDPPC\u003csub\u003eit\u0026minus;1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ\u003csub\u003e2\u003c/sub\u003eEDU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ\u003csub\u003e3\u003c/sub\u003eEMP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ\u003csub\u003e4\u003c/sub\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ\u003csub\u003e5\u003c/sub\u003eINF\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026micro;\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;λ\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ω\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eSystem GMM (Roodman, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) is employed with:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLagged levels and differences as instruments\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHansen J test for instrument validity\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eArellano\u0026ndash;Bond AR (1) and AR (2) tests\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInstrument count kept below number of cross-sections to avoid overfitting\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the variable measurement, comprising the variable, symbol, measurement, transformation and data source.\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 Measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransformation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal GDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal GDP per capita (constant US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eln (GDPPC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank WDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean years of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank WDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabour force participation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank WDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI inflows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNet FDI inflows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank WDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation (Mediator)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual % change in CPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank WDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s Compilation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003ecount\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\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\u003e25%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU (yrs schooling)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.8809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.6213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.8882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.6634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP (employment %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.3670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.5168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.8881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.0323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.6527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e73.9470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI (% GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.2560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.2188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation (annual % change in CPI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.0994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.9767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.5602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPPC (level)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119,322.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107,462.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,623.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,519.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90,707.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e174,222.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e417,861.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.4789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.4064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.0579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.9902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for the balanced panel covering eight emerging economies over the period 2000\u0026ndash;2024 (200 country-year observations). The average education level (EDU) is approximately 7.84 years of schooling, with moderate dispersion (SD\u0026thinsp;=\u0026thinsp;1.29), indicating cross-country and temporal variation in human capital accumulation. Employment rates average about 58.37%, with a standard deviation of 7.12%, reflecting substantial heterogeneity in labour market participation across economies and over time.\u003c/p\u003e \u003cp\u003eFDI inflows, measured as a percentage of GDP, average 2.68%, with observable dispersion (SD\u0026thinsp;=\u0026thinsp;1.08), suggesting variation in capital inflow intensity across country-years. Inflation (INF), measured as the annual percentage change in the Consumer Price Index, averages 9.10%, with moderate variability (SD\u0026thinsp;=\u0026thinsp;1.46), consistent with the macroeconomic volatility characteristic of emerging economies during the study period.\u003c/p\u003e \u003cp\u003eReal GDP per capita levels display considerable dispersion (mean\u0026thinsp;\u0026asymp;\u0026thinsp;119,323 constant US\u003cspan\u003e$\u003c/span\u003e), reflecting structural differences across countries. To reduce skewness and improve interpretability, the logarithmic transformation of GDP per capita (lnGDPPC) is used in regression analysis (mean\u0026thinsp;=\u0026thinsp;11.20; SD\u0026thinsp;=\u0026thinsp;1.07). Overall, the distributional properties indicate sufficient within- and between-country variation to support panel regression and mediation analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHausman Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.8771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the Hausman specification test comparing fixed-effects (FE) and random-effects (RE) estimators for the baseline panel model. The test statistic (χ\u0026sup2; = 107.88, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) strongly rejects the null hypothesis that the difference in coefficients between FE and RE estimators is not systematic. This indicates that the individual country-specific effects are correlated with the explanatory variables (EDU, EMP, and FDI), violating the key assumption underlying the random-effects model.\u003c/p\u003e \u003cp\u003eConsequently, the fixed-effects estimator is preferred, as it controls for time-invariant unobserved heterogeneity across countries and provides consistent parameter estimates under correlation between regressors and unit-specific effects. All subsequent baseline and mediation regressions therefore, rely on the fixed-effects specification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 1 \u0026mdash; Model 1 \u0026mdash; Direct Effects (Fixed Effects Estimates)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003econst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Model 1 presents the fixed-effects estimates of the direct relationship between socioeconomic determinants and real income growth, measured as the natural logarithm of GDP per capita. Controlling for country-specific fixed effects and time effects, education (EDU) exhibits a positive and highly statistically significant association with ln (GDPPC) (β\u0026thinsp;=\u0026thinsp;0.4752, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Given the semi-log specification (dependent variable in logarithms, independent variable in levels), the coefficient implies that a one-year increase in average schooling is associated with approximately a 47.5% increase in real GDP per capita, holding other factors constant. This strong positive relationship supports H₁ and is consistent with human capital theory, which emphasises productivity-enhancing effects of education.\u003c/p\u003e \u003cp\u003eEmployment (EMP) is also positive and statistically significant (β\u0026thinsp;=\u0026thinsp;0.0482, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that a one-percentage-point increase in the employment rate is associated with approximately a 4.8% increase in real GDP per capita. This finding supports H₂ and reflects the importance of labour market participation in sustaining income growth in emerging economies.\u003c/p\u003e \u003cp\u003eFDI inflows display a positive coefficient (β\u0026thinsp;=\u0026thinsp;0.1816) but are statistically significant only at the 10% level (p\u0026thinsp;=\u0026thinsp;0.0805). While directionally consistent with H₃, the relatively imprecise estimate suggests that the direct income effects of FDI may be conditional on macroeconomic and institutional environments. This provides preliminary justification for examining inflation as a potential transmission mechanism in subsequent mediation analysis.\u003c/p\u003e \u003cp\u003eConsistent with the Hausman test results, the fixed-effects specification is adopted, as it accounts for time-invariant unobserved country heterogeneity and provides consistent estimates under correlation between regressors and unit-specific effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndirect Effect \u0026mdash; Regression of Inflation on Socioeconomic Determinants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003econst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.0649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the Stage-2 regression estimates the relationship between socioeconomic determinants and inflation (annual CPI growth). The results indicate that education (EDU), employment (EMP), and foreign direct investment (FDI) are all negatively and statistically significantly associated with inflation.\u003c/p\u003e \u003cp\u003eEducation exhibits a strong negative coefficient (β = \u0026minus;0.1714, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that a one-year increase in average schooling is associated with a reduction of approximately 0.17 percentage points in annual inflation, holding other factors constant. Employment also displays a statistically significant negative association (β = \u0026minus;0.0126, p\u0026thinsp;=\u0026thinsp;0.0064), implying that a one-percentage-point increase in employment corresponds to a modest reduction in inflation. FDI inflows show a comparatively larger negative effect (β = \u0026minus;0.2059, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher capital inflows are associated with lower inflationary pressures.\u003c/p\u003e \u003cp\u003eThese findings are consistent with structural inflation perspectives, which emphasise that improvements in productive capacity, labour absorption, and capital deepening can alleviate supply-side constraints and moderate inflation dynamics. From a mediation standpoint, the statistically significant negative α-paths (EDU \u0026rarr; INF, EMP \u0026rarr; INF, FDI \u0026rarr; INF) satisfy the first necessary condition for mediation: the predictors significantly influence the mediator. If inflation subsequently affects real income growth in Stage-3 estimation, this would support the hypothesis that inflation serves as a transmission mechanism linking structural improvements to income outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Regression \u0026mdash; Outcome Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003econst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the mediation regression (stage 3) in which inflation (INF) is included alongside the socioeconomic predictors. Inflation exhibits a negative and statistically significant association with ln(GDPPC) (β = \u0026minus;0.1696, p\u0026thinsp;=\u0026thinsp;0.002), indicating that higher inflation rates are associated with lower real income levels, holding structural determinants constant. Given the semi-log specification, a one-percentage-point increase in inflation corresponds to an approximate 16.9% reduction in real GDP per capita, underscoring the macroeconomic cost of price instability.\u003c/p\u003e \u003cp\u003eEducation remains positive and highly significant (β\u0026thinsp;=\u0026thinsp;0.4225, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although its coefficient declines relative to the baseline model (0.4752 \u0026rarr; 0.4225). This reduction in magnitude suggests partial mediation, whereby part of education\u0026rsquo;s total effect on income operates indirectly through its influence on inflation. Employment also remains positive and statistically significant (β\u0026thinsp;=\u0026thinsp;0.0355, p\u0026thinsp;=\u0026thinsp;0.0402), with a modest reduction in magnitude relative to the baseline specification.\u003c/p\u003e \u003cp\u003eInterestingly, FDI exhibits a stronger and more precisely estimated coefficient in the mediation model (β\u0026thinsp;=\u0026thinsp;0.3722, p\u0026thinsp;=\u0026thinsp;0.0009) compared to the baseline model. This pattern suggests a suppression effect: because FDI significantly reduces inflation (Stage-2), and inflation negatively affects income, controlling for inflation isolates and strengthens the direct positive contribution of FDI to real income growth.\u003c/p\u003e \u003cp\u003eTaken together, the significant α-paths (predictors \u0026rarr; inflation), the significant β-path (inflation \u0026rarr; income), and the observed coefficient adjustments between baseline and mediation models provide evidence consistent with partial mediation. These findings support H₄ and indicate that inflation functions as a transmission mechanism linking socioeconomic determinants to sustainable real income outcomes in emerging economies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSobel and Bootstrap Mediation Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea (coef)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb (coef)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndirect Effect (a\u0026times;b)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSobel z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSobel p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the indirect effects of education, employment, and FDI on real income growth through inflation, calculated as the product of the Stage-2 (a-path) and Stage-3 (b-path) coefficients. For education, the indirect effect is positive (0.0291), reflecting that education reduces inflation (a\u0026thinsp;\u0026lt;\u0026thinsp;0) and inflation negatively affects real income (b\u0026thinsp;\u0026lt;\u0026thinsp;0). The Sobel test indicates that this indirect effect is statistically significant (z\u0026thinsp;\u0026asymp;\u0026thinsp;=\u0026thinsp;2.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting partial mediation. This suggests that part of education\u0026rsquo;s total effect on income operates through its inflation-moderating role.\u003c/p\u003e \u003cp\u003eFor employment, the indirect effect is smaller in magnitude (0.0021) but statistically significant under the Sobel framework (z\u0026thinsp;\u0026asymp;\u0026thinsp;=\u0026thinsp;2.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although modest, this indicates that labour market expansion contributes indirectly to income growth via reduced inflationary pressures.\u003c/p\u003e \u003cp\u003eFor FDI, the computed indirect effect is 0.0349. However, statistical significance must be evaluated carefully based on correctly computed Sobel and bootstrap statistics. Given that FDI exhibits a strong direct effect in the mediation model, the overall transmission mechanism likely reflects a combination of direct productivity effects and indirect inflation-moderating channels.\u003c/p\u003e \u003cp\u003eOverall, the mediation results indicate partial mediation, with inflation functioning as a transmission mechanism linking structural socioeconomic improvements to sustainable real income growth in emerging economies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDynamic System-GMM Estimates for Sustainable Real Income Growth\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.9309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDPPCₜ₋₁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (EDU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (EMP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI (% GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation (INF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eModel diagnostics\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAR (1) p = .000\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAR (2) p = .140\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHansen J χ\u0026sup2; = 1.307, p = .821\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reports the dynamic system-GMM estimation incorporating a lagged dependent variable to address persistence and potential endogeneity in real income dynamics. The Arellano\u0026ndash;Bond AR (1) test is significant (p \u0026lt; .001), as expected in first-differenced GMM models, while the AR (2) test is insignificant (p = .140), indicating no evidence of second-order serial correlation in the residuals. The Hansen J test (p = .821) fails to reject the null hypothesis of instrument validity, suggesting that the internal instruments are appropriately specified and the model is not overidentified.\u003c/p\u003e \u003cp\u003eThe lagged dependent variable is small in magnitude (β = \u0026minus;0.0127) and statistically significant (p = .0176), indicating limited dynamic persistence in income levels once structural and macroeconomic determinants are included. This suggests that current income performance is not strongly path-dependent after accounting for education, employment, FDI, and inflation.\u003c/p\u003e \u003cp\u003eEducation (β\u0026thinsp;=\u0026thinsp;0.0767, p \u0026lt; .001) and employment (β\u0026thinsp;=\u0026thinsp;0.0551, p = .0011) remain positive and statistically significant, confirming their structural contribution to income growth even after correcting for endogeneity and dynamic bias. Foreign direct investment exhibits a strong positive association (β\u0026thinsp;=\u0026thinsp;0.4643, p \u0026lt; .001), reinforcing its role as an important driver of income performance in the sampled economies.\u003c/p\u003e \u003cp\u003eInflation retains a negative and highly significant coefficient (β = \u0026minus;0.0648, p \u0026lt; .001), consistent with the mediation results obtained under the fixed-effects specification. Although the magnitude is smaller than in the static model, the direction and significance remain stable, indicating that inflation operates as a macroeconomic constraint on sustainable real income growth.\u003c/p\u003e \u003cp\u003eOverall, the dynamic specification strengthens confidence in the stability of the main relationships and supports the interpretation that structural improvements in human capital, labour market participation, and investment enhance income performance, while inflation continues to function as a transmission channel influencing long-run welfare outcomes in emerging economies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic and Specification Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF (EDU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF (EMP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF (FDI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF (INF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch\u0026ndash;Pagan LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.1913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo evidence of heteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin\u0026ndash;Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo serial correlation concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reports diagnostic and specification tests to assess the reliability of the estimated models. The variance inflation factors (VIF) for all explanatory variables range between 1.75 and 3.00, well below the conventional threshold of 10, indicating no evidence of harmful multicollinearity among the predictors.\u003c/p\u003e \u003cp\u003eThe Breusch\u0026ndash;Pagan test based on pooled residuals yields a non-significant result (p = .116), suggesting no evidence of heteroskedasticity in the baseline specification. Nevertheless, robust standard errors are employed in the panel estimations to ensure inference remains valid under potential heteroskedasticity across cross-sectional units.\u003c/p\u003e \u003cp\u003eThe Durbin\u0026ndash;Watson statistic (DW\u0026thinsp;=\u0026thinsp;1.5242) does not indicate severe serial correlation in the pooled residuals. In addition, the dynamic system-GMM results reported earlier confirm the absence of second-order serial correlation through the Arellano\u0026ndash;Bond AR (2) test. Taken together, these diagnostics suggest that the empirical models are reasonably well specified and that the reported coefficient estimates are statistically reliable.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Discussion\u003c/h2\u003e \u003cp\u003eThe empirical findings broadly align with the theoretical foundations of Human Capital Theory and Structural Inflation Theory while also clarifying areas of convergence and nuance within the existing literature. The Hausman test justified the use of the fixed-effects estimator, indicating that unobserved country-specific characteristics are correlated with the explanatory variables. This reinforces the importance of accounting for structural heterogeneity when examining the income dynamics of emerging economies. The baseline results demonstrate that education and employment are positively and statistically associated with real income growth, consistent with Human Capital Theory (Becker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Lucas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which emphasises productivity-enhancing effects of skill accumulation and labour market participation. These findings corroborate prior empirical evidence linking education and income growth (Lee \u0026amp; Lee, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nsirimovu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and confirm the importance of labour market engagement for income expansion (Haider et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ketu \u0026amp; Ningaye, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The weaker baseline significance of FDI suggests that foreign capital inflows may not uniformly translate into income gains, echoing arguments that macroeconomic and institutional conditions shape FDI effectiveness (Ogwuma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chizema, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mediation analysis extends these findings by explicitly modelling inflation as a transmission mechanism. The negative and statistically significant relationships between education, employment, FDI, and inflation are consistent with Structural Inflation Theory (Prebisch, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1950\u003c/span\u003e), which posits that structural improvements in productive capacity and capital formation can alleviate supply-side rigidities that contribute to price instability. The results suggest that human capital accumulation and labour market expansion may enhance productive efficiency, thereby moderating inflationary pressures. Similarly, FDI appears associated with lower inflation, consistent with the argument that capital inflows can expand supply capacity and reduce structural bottlenecks (Ajide et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dua \u0026amp; Verma, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen inflation is introduced into the income equation, the coefficients of education and employment decline in magnitude, indicating partial mediation. The indirect effects are positive but modest relative to total effects, suggesting that while structural factors influence income partly through inflation moderation, their direct productivity effects remain dominant. Sobel and bootstrap evidence support the statistical significance of the indirect pathways for education and employment, reinforcing the view that inflation operates as a transmission channel linking structural determinants to income outcomes. The pattern observed for FDI, where its direct effect strengthens after controlling for inflation, indicates a suppression effect, implying that isolating the macroeconomic stability channel clarifies the net productivity contribution of foreign investment.\u003c/p\u003e \u003cp\u003eThe consistently negative association between inflation and real income across both fixed-effects and dynamic specifications underscores the macroeconomic cost of price instability. These findings align with Osei and Kim (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who caution that without macroeconomic stability, structural growth drivers may not translate into sustained real welfare gains. Rather than supporting purely demand-driven explanations of inflation, the results provide empirical support for structuralist perspectives that emphasise productivity, supply capacity, and institutional conditions as key determinants of price stability.\u003c/p\u003e \u003cp\u003eThe dynamic system-GMM estimates further reinforce the stability of the core relationships after addressing potential endogeneity and persistence. Education, employment, and FDI remain positively associated with real income, while inflation retains a negative and significant effect. Although the magnitude of the inflation coefficient declines in the dynamic specification, the direction and significance remain consistent, strengthening confidence in the robustness of the findings across estimation techniques.\u003c/p\u003e \u003cp\u003eOverall, the study contributes to the sustainability literature by integrating human capital and structural inflation perspectives within a unified mediation framework. It demonstrates that sustainable real income growth in emerging economies depends not only on structural improvements in education, employment, and investment but also on macroeconomic stability. Inflation is shown to function not merely as a background macroeconomic condition but as a transmission mechanism shaping how structural gains translate into welfare outcomes. These findings highlight the importance of coordinated policy strategies that simultaneously promote human capital development, productive employment, quality investment inflows, and price stability to ensure inclusive and durable income growth.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that both structural and macroeconomic factors shape real income dynamics in emerging economies, with inflation operating as a transmission mechanism linking socioeconomic determinants to income outcomes. The Hausman test supported the use of the fixed-effects specification, enabling control for unobserved country-specific heterogeneity. Education and employment consistently exhibited positive and statistically significant associations with real income growth, reinforcing the central role of human capital accumulation and labour market participation in sustaining economic performance. Although the direct impact of FDI was less precisely estimated in the baseline model, its effect strengthened in the mediation and dynamic specifications, suggesting that the income-enhancing benefits of foreign investment are more evident once macroeconomic conditions are explicitly accounted for.\u003c/p\u003e \u003cp\u003eThe mediation analysis revealed that inflation partially transmits the effects of education and employment to real income, with the indirect effects operating through reduced inflationary pressures. While the magnitude of mediation is modest relative to total effects, the results indicate that macroeconomic stability complements structural drivers of growth. The negative and significant relationship between inflation and real income across both static and dynamic models highlights the economic cost of price instability and underscores the importance of integrating structural development policies with inflation management. Together, the findings support the interpretation that sustainable real income growth in emerging economies depends not only on investments in human capital and employment expansion but also on maintaining price stability.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, the evidence suggests a coordinated strategy. Continuous investment in education and skills development remains fundamental for productivity and long-term income gains. Policies that promote broad-based and productive employment are essential for translating human capital improvements into tangible welfare outcomes. At the same time, FDI policy should prioritise quality-enhancing and productivity-driven investments that strengthen domestic supply capacity. Monetary and fiscal authorities should align price stability objectives with structural development strategies to ensure that gains from education, employment, and investment are not eroded by inflationary pressures.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgement. First, the sample is limited to eight emerging economies with balanced data availability, which may constrain generalisability to other developing contexts. Second, the analysis relies on aggregate national indicators; more granular measures, such as sector-specific FDI flows or quality-adjusted education metrics, could provide deeper insights into transmission mechanisms. These limitations, however, do not diminish the central contribution of the study but rather highlight avenues for further refinement and extension.\u003c/p\u003e \u003cp\u003eFuture research may explore heterogeneity across inflation regimes, examine interaction effects between structural variables and macroeconomic stability, or incorporate institutional quality as an additional moderating factor. Overall, the study advances the literature by integrating human capital and structural inflation perspectives within a mediation framework and by demonstrating that price stability is not merely a background condition but an active channel influencing the sustainability of real income growth in emerging economies.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interest\u003c/strong\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eNo competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A, who was the corresponding author, drafted the manuscript, wrote the main manuscript text including Abstract, Introduction, Literature Review, Methodology, Results and Discussion, while S.M., M.O, E.F-A and A.M contributed to the conclusion and reviewed the entire research study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDataset is available upon reasonable request from the corresponding author\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdam A, Garas A, Katsaiti MS, Lapatinas A. 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SN Bus Econ. 2025;5:27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s43546-025-00798-8\u003c/span\u003e\u003cspan address=\"10.1007/s43546-025-00798-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sustainable Income Growth, Inflation Transmission, Price Stability, Human Capital, Employment","lastPublishedDoi":"10.21203/rs.3.rs-8880132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8880132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the macroeconomic and socioeconomic determinants of sustainable real income growth in emerging economies, emphasising inflation as a transmission mechanism. Using balanced panel data for selected emerging economies from 2000 to 2024, the analysis applies a fixed-effects estimator supported by a Hausman test (χ\u0026sup2; \u0026asymp; 107.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating correlated country-specific heterogeneity. The results show that education (EDU) and employment (EMP) are positively and statistically associated with real income growth, while foreign direct investment (FDI) exhibits a positive but initially less precisely estimated direct effect. Mediation analysis reveals that EDU, EMP, and FDI are negatively associated with inflation, and inflation in turn exerts a significant dampening effect on real income. Sobel and bootstrap tests confirm statistically significant indirect effects, indicating partial mediation through inflation. Dynamic system-GMM estimates reinforce the stability of the main relationships after addressing potential endogeneity and persistence. The findings suggest that sustainable real income growth in emerging economies depends not only on human capital development, labour market expansion, and quality investment inflows, but also on maintaining price stability to preserve welfare gains.\u003c/p\u003e","manuscriptTitle":"Inflation as a Transmission Channel for Sustainable Real Income Growth in Emerging Economies, 2000–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 13:47:58","doi":"10.21203/rs.3.rs-8880132/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T14:14:05+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"5868214534005122504892803147535788931","date":"2026-04-11T15:16:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T03:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181602007984892639258436543351370371642","date":"2026-04-06T17:33:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52563169225755589249879679142221937758","date":"2026-04-01T21:58:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137228031617076838986687753576427346405","date":"2026-03-15T01:34:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189709802515428801228423868431175314439","date":"2026-03-13T08:02:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T15:25:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T09:03:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196552265649430691759567838159583099356","date":"2026-03-12T07:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325731115729294070733479592201948334135","date":"2026-03-06T13:51:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T13:31:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T11:18:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T07:29:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T07:26:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-02-14T12:57:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55e4501c-bc2a-4abe-bc33-114e0e9dcb17","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T16:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 13:47:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8880132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8880132","identity":"rs-8880132","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}