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Socioeconomic Status (SES), an overall measure of a person's economic and social status relative to others combining factors such as economics and sociology, has received a lot of attention from researchers, as its assessment can help relevant orga-nizations to make various policies and decisions (governmental formulation of social policies, advertising personalized services, etc).In addition, with the development of big data technology and machine learning in recent years, assessing people's socioeconomic attributes (SEAs) and further obtaining the corresponding socioeconomic status with a data-driven approach can address the issue of extremely high cost of traditional methods.Therefore, this paper summarizes the research progresses of applying big data techniques to socioeconomic status analysis in recent years.It first introduces the basic concept of socioeconomic status and discusses the challenges posed by big data methods compared to traditional methods.After that, it systematically summarizes and classifies the state-of-the-art related methods based on the information in the learning process, and present them in detail, discusses the pros and cons of each type of method.Finally, it discusses the challenges and problems of inferring people's socioeconomic status and provides an outlook on future research directions.

We examine how government venture capital (GVC)—a specific type of political connection—affected initial public offering (IPO) valuation. Contrary to the well-recognized benefits of political connections in channeling access to financial resources in China, our analysis of 959 IPOs between 2008 and 2014 suggests that GVC backing lowers IPO valuation. This baseline effect is moderated by other sources of political connection (e.g. government ownership, state sector experience of top management team members, private sector partner status, and institutional environments). We argue that it is the negative signaling mechanism revolving around political connections that accounts for this observable pattern. This research enriches the signaling theory by uncovering signal emergence and analyzing the interactions between several signaling sources of political connection. Specifically, it contributes to a better understanding of political connections by specifying an undesirable consequence of state-led financialization, which has timely practical relevance as China's capital market is steering toward a rule-based system. [ABSTRACT FROM AUTHOR]

Byline: Jar-Der Luo There is a kind of knowledge, possessed by groups, that is more than the aggregation of individual knowledge. Nahapiet and Ghoshal call it intellectual capital. How does social structure, in a project team, influence collective knowing capability? In this study, several conclusions are made. First, a project team with a median centralisation of power has a good evaluation. Second, a team structure with fully connected cliques has a significant impact on group performance. It is also found that only consultative ties are related to knowledge exchange and combination, while friendship ties have nothing to do with the team's performance.