COVID-19 pandemic has disrupted the economies around the world and the economic fallout from preventive measures such as lockdown is enormous. It has massive repercussions for the sharing economy as well. This research paper empirically analyses the impact of lockdown restrictions on five major sectors of the sharing economy such as ride-hailing, accommodation, freelance work, entertainment and delivery services. To evaluate this impact, we employed the difference-in-difference estimation technique using the Google trends data for selected countries. Daily search data of 2019 and 2020 is reindexed to examine the change in search patterns that occurred after lockdown. The empirical results show that transportation and accommodation sectors are negatively impacted by COVID-19 related lockdown while the other sectors of the sharing economy such as freelance work, streaming services and online deliveries are seeing a surge in searches. The findings of this study provide vital insights into the economic disruptions caused by COVID-19 related lockdown. We have highlighted the sectors that are booming during pandemic thus the sharing economy platforms and government have opportunities to invest in these sectors to jump-start the economy. The recently unemployed gig workers can also be employed in these sectors to address the problem of unemployment.

This research is the earliest attempt to understand the impact of inflation and the interest rate on output growth in the context of Pakistan using the wavelet transformation approach. For this study, we used monthly data on inflation, the interest rate, and industrial production from January 1991 to May 2020. The COVID-19 pandemic has affected economies around the world, especially in view of the measures taken by governmental authorities regarding enforced lockdowns and social distancing. Traditional studies empirically explored the relationship between these important macroeconomic variables only for the short run and long run. Firstly, we employed the autoregressive distributed lag (ARDL) cointegration test and two causality tests (Granger causality and Toda–Yamamoto) to check the cointegration properties and causal relationship among these variables, respectively. After confirming the long-run causality from the ARDL bound test, we decomposed the time series of growth, inflation, and the interest rate into different time scales using wavelet analysis which allows us to study the relationship among variables for the very short run, medium run, long run, and very long run. The continuous wavelet transform (CWT), the cross-wavelet transform (XWT), cross-wavelet coherence (WTC), and multi-scale Granger causality tests were used to investigate the co-movement and nature of the causality between inflation and growth and the interest rate and growth. The results of the wavelet and multi-scale Granger causality tests show that the causal relationship between these variables is not the same across all time horizons; rather, it is unidirectional in the short-run and medium-run but bi-directional in the long-run. Therefore, this study suggests that the central bank should try to maintain inflation and the interest rate at a low level in the short run and medium run instead of putting too much pressure on these variables in the long-run.

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The study aims to empirically explore the link between trade openness and emission level in Pakistan by using Johansson co integration technique during the period from1972 to 2019. The empirical strategy of the study progressively incorporates models from zero interaction terms to complete interaction terms for analyzing the relationship between trade openness and emission level in the presence of scale, composition and technique effect. The variable of the trade openness appeared in all the models with negative sign except in scale effect model.  It means technique effect outweighs the composition and scale effect in Pakistan which ultimately makes decreasing impact of trade on pollution. The scale effect results in higher emissions in Pakistan.  Similarly, composition effect also bears positive sign showing that higher capital labor ratio in Pakistan will increase pollution because capital goods are said to be pollution intensive goods.