Data is the lifeblood of research, providing the foundation upon which analyses and conclusions are built. In the study at hand, data from the China Labour Dynamics Survey (CLDS) for the years 2014, 2016, and 2018 serves as the primary source of information. Conducted biennially by the Centre for Social Science Survey at Sun Yat-sen University, the CLDS covers urban and rural areas across 29 provinces, municipalities, and autonomous regions in China. This dataset is meticulously collected using a multi-stage, stratified, probability sampling technique to ensure a nationally representative sample.
One of the key aspects of the CLDS dataset that makes it invaluable for this study is its detailed recording of compensation methods. This level of granularity allows researchers to identify and categorize gig economy workers based on self-identified occupation responses and compensation types such as piecework, hourly rates, or daily rates. By focusing on individuals aged between 16 and 60, the study aims to shed light on the gig economy workforce in China and understand the dynamics of their compensation structures.
In addition to individual-level data, the analysis incorporates provincial-level macro data from sources such as the China Statistical Yearbook, the China Science and Technology Statistical Yearbook, and the Peking University Digital Financial Inclusion Index of China. These sources provide a broader context for assessing the development of the digital economy and its impact on wages.
The study’s variable setting includes the explained variable, which is the logarithm of monthly wages (lnW), and the core explanatory variable, which is the interaction between the Digital Economy Index (DEI) and gender. By examining how the impact of digital economy development on wages differs by gender, the study aims to uncover any disparities that may exist in the gig economy workforce.
Control variables such as social insurance coverage, industry upgrade index, and labour dispute success rate are also included in the analysis to account for individual attributes and regional indicators that may influence wage outcomes. Descriptive statistics presented in tables and figures provide a snapshot of the demographic and economic landscape under study, offering insights into wage differentials, gender disparities, and digital economy trends across provinces.
Empirical methods employed in the study include wage functions, Bourguignon, Fournier and Gurgand models, Neumark decomposition, and Generalised Propensity Score Matching. These methods allow researchers to delve deeper into the data, uncovering patterns, relationships, and causal mechanisms that drive wage differentials and gender disparities in the gig economy.
By leveraging a combination of rigorous data collection, sophisticated analytical techniques, and robust empirical methods, the study aims to contribute valuable insights to the field of labour economics and shed light on the complex interplay between the digital economy, gender dynamics, and wage outcomes in China’s evolving gig economy landscape.
