A contribution of foreign direct investment to economic growth is possibly one of the widely examined topics in academic research in the last five decades. However, few studies have examined both the short run and long run impacts of this effect concurrently for developing and emerging markets, in particular during the period of economic turmoil that includes the global financial crisis. As such, this paper examines and provides additional and relevant quantitative evidence on the impact of foreign direct investment (FDI) on economic growth, both in the short run and the long run in developing countries of the lower-middle-income group in 2000–2014. Various econometric methods are employed such as the panel-based unit root test, Johansen cointegration test, Vector Error Correction Model (VECM), and Fully Modified OLS (FMOLS) to ensure the robustness of the findings. The results of this study show that FDI helps stimulate economic growth in the long run, although it has a negative impact in the short run for the countries in this study. Other macroeconomic factors also play an important role in explaining economic growth in these countries. Money supply has a positive effect on growth in the short run while total credit for private sector has a negative effect. In addition, long-run economic growth is driven by money supply, human capital, total domestic investment, and domestic credit for the private sector. Based on these results, recommendations for the governments of these countries have been developed.

Income inequality in many middle-income countries has increased at an alarming level. While the time series relationship between income inequality and economic growth has been extensively investigated, the causal and dynamic link between them, particularly for the middle-income countries, has been largely ignored in the current literature. This study was conducted to fill in this gap on two different samples for the period from 1960 to 2014: (i) a full sample of 158 countries; and (ii) a sample of 86 middle-income countries. The Granger causality test and a system generalized method of moments (GMM) are utilized in this study. The findings from this study indicate that causality is found from economic growth to income inequality and vice versa in both samples of countries. In addition, this study also finds that income inequality contributes negatively to the economic growth in the middle-income countries in the research period.

Financial development has been considered an efficient and effective mechanism for the sustainable economic growth and development of emerging markets in past decades. However, various concerns have emerged in relation to the influences of financial sector development on income inequality. It is the claim of this paper that findings from the current literature are incomplete. This is because various proxies have been utilized inconsistently for both financial development and income inequality in previous empirical studies. This study extends the current literature on this important finance–inequality nexus by examining a sample of 21 emerging countries for the period of 1961–2017. Various estimation techniques were employed with the aim of ensuring robust findings. Findings from this paper confirm the existence of an inverted U-curve relationship between financial development and income inequality, implying that income inequality may rise at the early stage of financial development and fall after a certain level is achieved. Policy implications have emerged from the findings of this study.

Previous empirical studies have generally considered biofuel as a main factor in changes in the relationship between oil and agricultural prices because these changes happened after U.S. biofuel policies were implemented. However, it has been argued that other economic factors can trigger the correlation of these two markets. This study was conducted to examine the transmission mechanisms that influence the relationship between oil and agricultural prices. This paper used the interacted panel vector autoregressive framework, which allowed us to investigate the effect of biofuel production under different regimes of exchange rates and global economic activities. The responses of agricultural prices to oil prices at different levels of biofuel production, global economic activity, and exchange rates were examined in this paper. Data on prices for 10 agricultural commodities—barley, beans, corn, cotton, oats, rice, sorghum, soybean, sunflower, and wheat—from January 2000 to May 2019, were used in this study. Our findings indicate that oil prices can affect agricultural prices through biofuel and exchange rates. Moreover, the effect of biofuel depends on the level of global economic activity and exchange rates. We offer some policy implications on the basis of our findings in this study.