Purpose This paper aims to examine the effect of both inflation rate and interest rate on stock prices using quarterly data on non-financial firms listed in DJIA30 and NASDAQ100 for the period 1999-2016. The stock duration model is used to measure the sensitivity in variations in inflation rates and interest rates on stock prices. Design/methodology/approach The authors use standard statistical tools that include Johansen cointegration test, linearity, normality tests, cointegration regression, Granger causality and vector error correction model. Findings The results of panel Johansen cointegration analysis show that cointegration exists between the stock prices, the changes in stock prices due to inflation rates and the changes in stock prices due to real interest rates. The results of cointegration regression show that inflation rates are negatively associated with stock prices, the real interest rates and stock prices are positively associated, changes in real interest rates and inflation rates Granger cause significant changes in stock prices, significant speed of adjustment to long run equilibrium between observed stock prices and real interest rates and significant speed of adjustment to long run equilibrium between changes in stock prices due to real interest rates and changes in inflation rates. Originality/value This paper contributes to the empirical literature in three ways. The paper examines the effects of inflation and interest rates on stock prices differently from other related studies by separating inflation from real interest rates. The paper examines the causality between stock prices, interest and inflation rates. This paper offers significant updated validity to extended literature that a negative association exists between stock prices and inflation rates. This validity can be considered as an existence a theory of stock prices, inflation rates and interest rates.

Purpose Financial inclusion is an approach for mobilizing saving and facilitating investments that help promote economic development and pave the way for sustainable development. This paper aims to examine the impact of world governance indicators (WGIs) on the improvement of financial inclusion across world economies. Design/methodology/approach This paper uses the global database of financial inclusion indicators (global findex) for the years 2011, 2014 and 2017. The WGIs are used as proxies for the effects of governmental institutional arrangements. Using panel data analysis, a fixed generalized linear model is estimated for four common financial indicators; namely, borrowed from a financial institution, saved at a financial institution, credit card and debit card ownership. Findings The empirical results reveal that control of corruption, government effectiveness, political stability and voice and accountability are the significant WGIs that influence financial inclusion significantly. Originality/value This paper contributes to the literature in two ways. First, this paper offers validating the results previously reported in related studies. Second, this paper offers robust estimates of the effects of the institutional WGIs on the promotion of financial inclusion.

Purpose This paper aims to examine the impact of the daily growth rate of COVID-19 cases in the USA (COVIDg), the Federal Fund Rate (FFR) and the trade-weighted US dollar index (USDX) on S&P500 index daily returns and its 11 constituent sectors’ indices for the time period between January 22, 2020, until June 30, 2020. Design/methodology/approach The study uses the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model to gauge the impacts over the whole period of study, as well as over two sub-periods; first, January 22, 2020, until March 30, 2020, reflecting uncertainty in the US markets and second, from April 1, 2020, until June 30, 2020, reflecting the lockdown. Findings Results of the MGARCH model reveal a negative and significant relation between COVIDg and S&P500 index daily returns over the first sub-period and the whole study period in the following sectors, namely, communications, consumer discretionary, consumer staples, health, technology and materials. Yet, COVIDg showed a positive and significant relation with S&P500 index daily returns during the second time period in the following sectors, namely, communication, consumer discretionary, financial, industrial, information technology (IT) and utilities. Besides, USDX showed a negative significant effect on S&P500 index daily returns and on the daily return on each of its 11 constituent sectors over the second sub-period and the whole period. Further, FFR showed a significant effect only in the second sub-period, specifically, a negative effect on the daily return of the financial sector and a positive effect on the daily return of the technology sector index. Nevertheless, FFR had a positive significant effect on the daily return of the utilities sector index for the whole period under study. Research limitations/implications The impact of the crisis on the S&P500 index can be assessed only with some limitations owing to available global data and the limited time frame of the lock-down. Practical implications The study proposes supporting a smooth, functioning and resilient financial system; increasing fiscal measures by the US Government to increase liquidity on constraints; measures by The Federal Reserve to alleviate US dollar funding shortages; support market integrity; ensure continuous transparency and sharing of information; support the health sector, as well as consumer-based sectors that faced demand shocks and facilitate investments in the technology sector. Originality/value The originality of this paper lies in the examination of the impact of the novel COVID-19 pandemic on each of the 11 sectors constituting the S&P500 index separately, reflecting how the main economic sectors formulating the US economy reacted to the shock during the peak time of the pandemic to observe a full picture of the economic consequences amid the pandemic.

This paper employs structural growth perspective to the analysis of income inequality in 43 countries over the period 2003-2017.The study utilizes two different panel estimation techniques. First, the panel least squares regression examines the relevance of Kuznets effect of the different economic sectors; agriculture, manufacturing and services on income inequality. Second, the pooled mean group (PMG) estimation of dynamic heterogeneous panels gauges the long run impact of the change in sectoral value added as a proxy for structural change on inequality. PMG presents short run adjustments to be country-specific due to the widely different impacts of macroeconomic conditions and vulnerability of each country to income inequality. Empirical findings show that across all countries, sector growth had no to negligible impact on inequality indicating that no signs are evident of Kuznets effect. However, both inflation and unemployment have mixed impacts on inequality in Lower and Middle-Income countries. Results further reveal that unemployment has a relatively stronger influence on inequality than inflation for Upper-middle income countries, unlike in Lower-middle income countries, where unemployment shows a weaker correlation with inequality than inflation. Results for High-income countries show that the influence between inflation and unemployment are not as big as in Upper middle-income countries.

No abstract