This paper investigates economic impacts of COVID-19 on households based on differences in the socio-economic status (SES). We determine the household-level effects of the COVID-19 shock using income sources, types of industries, communities’ resilience, household susceptibility, and relevant policy measures. For this purpose, we used primary data of 555 households collected through snowball sampling technique using an online survey questionnaire from different villages mostly located in Sichuan Province, China. Using step-wise binary logistic regression analysis, we estimated and validated the model. Results suggest the use of SES as a better measure for understanding the impacts of COVID-19 on different households. We find that households with low SES tend to depend more on farmland income and transfer payments from the government. Contrarily, high SES households focus more on business and local employment as sources of income generation. Poor households were less resilient and more likely to fall back into poverty due to COVID-19, while the opposite stands true for non-poor households with high SES. Based on the estimations, policies encouraging employment and businesses complemented with loans on lower interest rates are recommended, which may increase the SES, thus minimizing vulnerability and enhancing the households’ resilience towards poverty alleviation and economic shocks.
We empirically determine the role of different forms of infrastructure on a country’s trade. We use an augmented gravity model that incorporates infrastructure in the estimation of merchandise trade flows. We take panel data, including China and 21 selected Asian economies, from 1999 to 2018. We find that the panel ordinary least squares (OLS) and poisson pseudo maximum likelihood (PPML) model estimations prove to be significant. Proxies for Transport Infrastructure including roads, railways, and sea transport, and Proxies for information and communication technology (ICT) infrastructure consisting of mobile, electricity, and internet connections show a strong and positive impact on trade while air transport and landline phone connection have an unexpected negative effect on trade. The positive estimates for quality of infrastructure signify that high standards of Transport and ICT infrastructures lead to increased trade flows of the exporting and importing countries. Results also show that cultural similarity leads to increased trade flows between China and its trading partners in Asia.
ObjectiveRecently, China has experienced a considerable influence of the COVID-19 pandemic on the local people’s health and economy. Hence, the current research aims to investigate the psychological and socioeconomic impact of COVID-19 on rural communities in the Sichuan Province of China.MethodsA total of 499 participants (village representatives of Sichuan Province) were approached to partake in a cross-sectional online survey and share their experience regarding the ongoing pandemic. The descriptive statistics and ordinary least squares (OLS) regression were used to analyse the data.ResultsOur analysis revealed that the pandemic has significantly affected local people psychologically, leading to socioeconomic vulnerability. Notably, we find that local households are worried about their income losses regardless of their socioeconomic status (40%–43%), level of income (37%–43%) and industry involvement (38%–43%). However, as income increases, the level of stress decreases. The results further show that government transfer payment is a significant factor in reducing stress due to its reliable and uninterrupted income flow. Contrary to our proposition, the pandemic stress was less observed, which might be because of people’s trust in government and effective antiepidemic countermeasures to contain the disease.ConclusionThis study finds that COVID-19 has a significant impact on local people’s health, psychology and income. This study is one of the first to provide empirical evidence regarding the early health and socioeconomic effects of COVID-19 at the household level in rural communities, which are very important to devise policies to ease the outbreak and prevent further losses at the local community level.
We investigate the impact of roads and highways within the provinces on the regional trade of China using the augmented Gravity Model and theory of modeling trade. We take a panel data covering 31 provinces of China over 20 years period (1998-2017) for the estimations. We apply ARMA-OLS model, fixed and random effects, and robust findings by Hausman test. The results imply that road and highway lengths within the provinces have a significantly positive impact on the value of the province-wise exports. The positive impact is due to the fact the increased coverage of roads and highways increase accessibility to resources and mobility of goods and services within the regions. Moreover, employment in the transportation sector, per capita GDP and population of the provinces also illustrate positive and significant influence on regional exports and trade. The impact of China's WTO accession on regional exports has been positive, while the financial crisis has had a negative impact. The year dummies show that, in the years following the financial crisis, China was able to regress from the external shock as trade within the provinces increased. The increase in exports after financial crisis is mainly due to the government policies and support to every province.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.