This study examines the relationship between political stability and absence of violence (PSAV), control of corruption (COC), gross domestic product per capita, current account balance (CAB), general government gross debt (GGGD), unemployment, and the KOF Globalization Index within the South Asian Association for Regional Cooperation (SAARC) countries from 2000 to 2022. Through panel vector autoregression (PVAR) analysis, the study calculates the long-term correlation between variables using a vector error correction model (VECM). The PVAR approach addresses the endogeneity issue by including endogenous interactions between the variables in the model. Additionally, the impulse response functions and the influence of various variables on certain lags are evaluated. Cointegration between variables was assessed using dynamic ordinary least squares and fully modified ordinary least squares panel analysis to investigate the long-term link further. The results demonstrate a robust cointegration relationship, revealing a shared equilibrium across the SAARC countries’ economic variables. The VECM estimates results for a model with three lags. The coefficients of the cointegrating vector indicate the presence of at least one cointegrated vector between the series. Three of the velocity fit parameters show significance at least at the 5% level, specifically COC, CAB, and GGGD. These non-zero coefficients suggest a causal relationship among the variables in the long-run Granger causality. On the other hand, the matrix β denotes the cointegrate parameter matrix. It is observed that COC, CAB, and GGGD are significant at the 5% level. The equation reveals that COC, CAB, and GGGD variables have a positive, increasing effect on PSAV in the long-term cointegrated parameter values. This suggests that while these variables have a positive relationship with PSAV, their impact is not conclusively significant.

In the pursuit of sustainable development, it is crucial to examine whether technological innovation (TI) creates equitable economic distribution or widens income inequality (II). The study aims to investigate TI’s impact on II in BRICS-T and BRICS PLUS-T countries within the context of the technological Kuznets curve (TKC). Along with TI, financial development is a driving force for economic development, and the research explores FD’s influence on II. To analyze these relationships while accounting for globalization (G), the method of moments quantile regression (MMQR) is used. The study’s findings validate the U-shaped TKC, suggesting that TI, in the initial stage, reduces II. However, beyond a certain threshold, TI² significantly widens II in both country groups. We found that the impact of TI on II was more pronounced in BRICS-T countries than in BRICS-PLUS-T countries, and the inclusion of newly added countries did not significantly affect the U-shaped relationship. Additionally, by enhancing financial inclusion, FD had a substantial and negative effect on II. In contrast, G, which concentrates wealth, was associated with higher II in both groups. This study offers a significant contribution by introducing the TKC in the context of BRICS-T and BRICS PLUS-T countries. Furthermore, the research contributes to the literature by evaluating the influence of FD and G within the context of TKC and applying novel MMQR. The study highlights a pathway to achieving the Sustainable Development Goals (SDGs 1, 7, and 10) and provides a policy framework for strategically allocating funds to technological advancements to reduce II.