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.

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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.

The purpose of this chapter is to examine the economic impacts of the COVID-19 pandemic on the tourism global value chain (GVC). Theoretical analysis revealed that being triggered by health factors, the coronavirus pandemic exerted an unprecedented shock on both the supply and demand sides of the tourism sector and the global economy. This resulted in implications that are more severe and less predictable than earlier crises that the world had witnessed. Analysis of the economic impact on various components of the tourism GVC revealed that measures adopted by world governments to protect their citizens resulted in massive damage to tourism related industries and to the global economy. The chapter concludes by predicting that the consequences of the current pandemic will inevitably give rise to new, more innovate tourism models, responding to the changing global economic and tourism landscape and to the change in consumers' characteristics and expectations.

Egypt's current 'youth bulge' constitutes the majority of its population, which implies that growth in the working-age population is faster than the overall population growth rate. With a fast-growing young population in Egypt, it is important to understand whether this demographic composition has an impact on the current inequality measures, since an individual's stage in life deems detrimental for understanding his or her wealth holdings. In this light, this paper departs from the basic life-cycle impact on wealth inequality measures, and alternatively proposes an empirical method to adjust for age-effects in cross-sectional inequality for Egypt. The resulting Ageadjusted Gini coefficient (AG), eliminates wealth inequality that is attributed to age, yet it perpetuates inequality arising from other wealth-generating factors. By using wealth equalising measures, results of the Age-adjusted Gini coefficient show that age and household characteristics have no impact on wealth accumulation in Egypt, however, wealth is greatly influenced by increasing levels of education, making way for possible movement up the mobility scale.