Global Economy Journal

In this study, in order to investigate the different forms of capital flow between developing and developed countries in the steady state, a two-country dynamic stochastic general equilibrium (DSGE) model, under asymmetric information, is developed. For simulating countries, the parameters of previous studies are used. The results showed that international risk-sharing can explain the Lucas paradox. In both symmetric and asymmetric information structures, net foreign assets in the forms of stocks in steady state are negative for developing countries. In other words, in the steady state, capital exits from the developing country in the form of bonds and enters these countries in the form of stocks. Besides, net capital inflows in the form of stocks and net outflows of capital in the form of bonds in the present model under asymmetric information are larger than that under symmetric information structure. Furthermore, impulse function results show that the two countries are highly correlated. So, the occurrence of a shock in one country changes the production and consumption of another country. But the impact of the shock on macro variables in the country itself is greater than in another.

This paper explores the empirical relationship between informality and several indicators of financial development (FD) and financial inclusion (FI). We exploit a panel of 152 countries with annual information between 1991 and 2017. Using panel cointegration techniques, we find evidence of a negative long-run relationship between informality and FD/FI for different groups of countries. Moreover, exogeneity tests indicate that some FD/FI indicators cause less informality. Specifically, we find that in developing countries FD reduces informality when measured as “financial credit” and “bank credit”, whereas FI reduces informality when measured as “number of bank accounts”. These results suggest that higher credit and more bank accounts have contributed to reducing informality in developing countries in the long run. Additionally, we find evidence of double causality between informality and other FD/FI indicators in developing and Latin American countries.

The effect of economic policy uncertainty (EPU) on other macroeconomic and financial variables has been the subject of prior research investigations. However, a dearth of work explicitly examines the connection between EPU exchange rate changes. By utilizing both nonlinear ARDL (NARDL) and multiple threshold NARDL (MTNARDL) models, we add to the body of literature by analyzing the nonlinear impact of EPU on exchange rates. The MTNARDL model, which distinguishes between the impacts of very small changes in the EPU from very large changes in the EPU on the exchange rate, is an expanded version of the NARDL model. The MTNARDL results confirm an asymmetric effect of EPU on exchange rates in the long run for all sample countries, contrary to the NARDL estimates, which show that EPU has an asymmetric effect in Brazil, Turkey, and China only. Similarly, only one country is supported by NARDL estimates for the short-run asymmetric effect, but MTNARDL estimates support the effect in five countries. Therefore, in our study, the MTNARDL model aids the prior literature in examining the more comprehensive impact of EPU on the exchange rates.

This research explores how new financial technologies are able to resolve economic stagnation by considering two dimensions of financial technology’s spread: the spread to poor people and that to rural area people. Based on a sample of 109 countries, our findings indicate that the spread of new financial technologies to poor people not only benefits economic growth directly, but also generates an indirect positive influence on economic growth via the channel of financial institutions’ development. By comparison, the effect of spreading financial technologies to rural area people on stimulating economic growth is insignificant. Lastly, we offer some policy implications associated with the results.