Uncovering the Truth about Chinese Urban Unemployment Rates： 2005-2012

Using China Urban Household Survey data from 2005 to 2012, this paper examines the changing pattern of China＇s urban unemployment rates. The paper shows that the annual urban unemployment rates during 2005-2012 averaged approximately 8.5 percent, as opposed to the official figure of approximately 4.1 percent, and despite the significant slowdown of GDP growth since 2008, the urban unemployment rates still exhibit a downward trend. This paper finds that continuous job creation in both the tertiary and the non-state sectors helps explain the decreasing trend in unemployment rates. Meanwhile, the downward trend of the unemployment rates could also be explained by the fact that both the secondary industry and the state-owned sector have destructed fewer jobs because of the execution of macroeconomic stimulus policies since 2008.

LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

Monetary Policy and Unemployment:The Case of Romania

The paper aims to analyze the monetary transmission model between the monetary policy and the labor market variable of unemployment.The results of the data show that,the external shocks have an important impact especially on the Romanian interest rates but also on the domestic production;however,the impact is not significant on unemployment,which proves the resilience of the domestic labor market.The central bank policy rate has a stabilizing effect on the unemployment rate in case of an increase in the euro area policy rate.

China’s Investment Contributes to Stability in Host Countries

As intra-national conflicts replace international wars to be the dominant form of collective political violence,the international dimension of domestic conflict has prompted reflections on the effects of globalization and multinational corporations represented by international investment.Theoretically,international investment may trigger or defuse conflicts.Although China is the world’s second largest source of outward foreign direct investment(OFDI),there has been limited empirical literature on how China’s OFDI has influenced domestic conflict in host countries.Based on the OFDI data of 115 developing countries from 2004 to 2016,this paper offers an empirical study on the effects of China’s OFDI on the eruption of domestic conflict in host countries and the underlying mechanisms.Results suggest that China’s OFDI in developing countries has made domestic conflict significantly less likely to erupt in those countries primarily by reducing the unemployment rate.These findings reflect the contribution of China’s investment to the internal stability of host countries.However,problems in the overseas operations of Chinese companies cannot be overlooked.