Social Insurance Under New Employment Patterns Dissertation – Description
As a postgraduate student in Social and Public Policy, I am required to write a dissertation on “Social Insurance for Freelancers in China’s New Employment Model”
Research methods & ethical issues:
Describe what research methods (e.g. interviews, media analysis, secondary data sources) you will use to answer these research questions and, if applicable, who your research participants will be and how you will access / recruit them. This should include (where applicable):
1. Identifying and accessing research participants (including how you will manage the use of any gatekeepers and/or pre-existing relationships between yourself and your participants)
2. Identifying and accessing data sources (e.g. social media platforms, discussion forums, newspapers etc)
3. Participant recruitment (if recruiting online, please include details of which platforms you will use). Please refer to the guidance on “Recruiting respondents from their peers’ before completing this section.
4. If your research involves surveys / questionnaires, confirm which survey tool you will be using. (Online Surveys is the University’s recommended tool).
5. Ensuring informed consent / anonymity
This paper applies a case study approach, focusing on two Chinese cities, Shanghai and Guangzhou, in order to investigate social insurance policies for freelancers in China and examine possibilities for policy improvement to better serve the needs of flexibly employed individuals. The selected time frame covers the period of 2015-2019, marked by labor panic across China, as well as the impact on flexibly employed individuals before and after the outbreak of the covid epidemic (2019-2022). Furthermore, this study conducts a comparative analysis of security policies in Japan and Canada, two Western countries with well-established unemployment protection systems, to extract their mature theoretical approaches, which can be adapted for China’s benefit. Secondary analysis of qualitative data and publicly available information will be utilized, alongside content analysis and policy review techniques, to compare and contrast policies and demographic and labor force statistics across different regions. Finally, prediction analysis will be conducted based on existing Chinese population and labor force databases, forecasting the development of flexibly employed people over the next 5-10 years, and recommending policy improvements accordingly.
If you have answered yes to any of the responses to questions 5 or 6 on the ethics review form, please provide further details here about the ethical issues that might arise and how you will address them.
Due to the nature of my research, which involves examining the effects of the Covid-19 pandemic on flexibly employed individuals, it is crucial to take measures to avoid ethical issues. To this end, I will adopt a number of approaches to ensure the ethical soundness of my research.
Firstly, it is important to note that my research will solely rely on publicly available research data and policy content from the internet. Thus, I will ensure that the data I use is both publicly available and anonymous, so as to avoid any disclosure of the identity or sensitive information of the respondents. Moreover, I will adhere to ethical standards of data protection to protect the privacy of individuals whose data will be used.
Secondly, I will evaluate the reliability and accuracy of the sources from which I obtain my data. As part of this process, I will carefully assess the credibility and trustworthiness of the data sources, and avoid using data from unreliable or unverified sources. Furthermore, I will comply with all relevant laws and regulations governing the use and sharing of data to ensure that my research is conducted ethically.
Finally, when analysing and interpreting data, I will adhere to the principles of academic integrity. I will ensure that I handle the data with care and objectivity, without distorting or manipulating it to suit my research aims. Furthermore, I will be transparent about the methods used to analyse the data, so as to enable readers to understand how the findings were derived.
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