Informal employment is a global phenomenon, representing a significant portion of the workforce and a major source of employment growth. It's increasingly recognized as a persistent feature of the global labor market, rather than a temporary condition. Factors contributing to the expansion of informal work include marketization, export-oriented industrialization, and urbanization. In China, the post-1980s reforms and opening-up policies significantly impacted employment, resulting in a reduction in formal employment and a surge in informal work, particularly among rural migrants lacking urban hukou (residential registration). Globally, migrant workers are disproportionately represented in informal employment, drawn to its flexibility and ease of entry. Despite its contribution to poverty alleviation, informal employment often leads to poor working conditions and increased vulnerability. The decent work agenda emphasizes the need to address this, recognizing the heterogeneity within the informal economy. Existing literature often contrasts formal and informal employment, overlooking internal inequalities among informal workers. The Women in Informal Employment: Globalizing and Organizing (WIEGO) model proposes a hierarchical structure, classifying informal workers based on employment status, income, poverty risk, and gender. This research aims to contribute to a better understanding of the heterogeneous nature of informal employment in China by testing the applicability of the WIEGO model in the Chinese context and examining the working conditions of migrant informal workers. The study will also analyze the disparities in income, poverty risk, and gender among different informal employment tiers and provide a critical evaluation of the WIEGO model.

Existing research on working conditions in informal employment generally focuses on the contrast between formal and informal sectors. Studies show that informal workers often lack basic legal protections, face lower wages, and experience higher poverty risks compared to formal workers. This disparity is linked to the rise of flexible capital accumulation and neoliberal policies, which have led to the erosion of job quality and the growth of precarious labor. Migrant workers and women are particularly vulnerable within the informal economy. However, the literature is increasingly recognizing the internal heterogeneity within informal employment. Early research highlighted a duality, with a distinction between those who voluntarily choose informal work and those who enter it due to limited options. Other studies have differentiated informal work based on motivation (needs vs. desires) or categorized informal workers based on structural and individual factors (survivalist, over-exploited, developmentalist). The WIEGO model offers a framework for understanding this heterogeneity by classifying informal workers into tiers based on employment status, income, poverty risk, and gender ratio. While widely used, the model's empirical validity needs further testing beyond the regions it was originally based on.

This study utilizes data from the 2017 China Migrants Dynamic Survey (CMDS), a large-scale nationwide survey of migrants. After removing samples with missing income or work hours data, the analysis focuses on 107,020 migrant informal workers. Informal employment is defined based on ILO criteria and operationalized using information on employment status, contracts, and social security contributions. Informal workers are stratified into six employment types following the WIEGO approach: employers, informal regular workers, own-account operators, outsourced workers, home-based workers, and contributing/unpaid family workers (the last category is excluded due to data limitations). Working conditions are measured using a composite index comprising economic (monthly income), social (employment contract, social security, life difficulties), and occupational health (work intensity, union support, community support) dimensions. Factor analysis is employed to assign weights to the indicators, resulting in a composite working conditions score. The study tests the WIEGO model by examining the relationships between employment tiers and income, poverty risk (defined as the proportion of workers from poor households), and gender ratio. Linear regression and multivariate general linear models are used to analyze these relationships, controlling for gender, education, and hukou factors.

The analysis reveals significant heterogeneity in the working conditions of migrant informal workers in China. Informal regular workers consistently show better working conditions compared to other tiers. While the WIEGO model predicts a positive correlation between employment tier and income, the findings show a more complex pattern. Employers have the highest average income, followed by informal regular workers, while own-account operators have the lowest, despite working longer hours. Regarding poverty risk, the study shows that informal regular workers have the lowest risk, while employers and own-account operators show surprisingly high risks, contradicting the WIEGO model’s prediction. The gender ratio also presents a mixed picture; while employers are predominantly male as the WIEGO model suggests, the proportion of females in outsourced/home-based work is lower than expected. The results further reveal that men consistently have better working conditions than women in terms of income, work intensity, contracts, and social support. Education levels are positively correlated with higher incomes and greater participation in union and community activities. Urban hukou holders generally have higher incomes and receive more union support but are less likely to have contracts compared to rural hukou holders. Notably, rural hukou holders have better social security due to national policies.

The findings partially support and partially challenge the WIEGO model. The model's applicability is context-dependent, as shown by the deviations in the Chinese context. The high poverty risk among employers highlights the economic challenges faced by small businesses and the large income disparities within this group. The lower-than-expected proportion of women in outsourced/home-based work reflects the changing dynamics of China's labor market, where female participation in various sectors, particularly manufacturing, is significant. The study highlights the complexity and plurality of heterogeneities within informal employment and the need for context-specific analyses. The differences in working conditions are not simply hierarchical but also influenced by factors like gender, education, and hukou status. The study suggests that policies aimed at improving working conditions in the informal sector should be disaggregated, taking into account the diverse needs of different employment tiers and gender groups.

This research provides valuable insights into the heterogeneity of working conditions among migrant informal workers in China, revealing significant inequalities both between formal and informal work and within the informal sector itself. The findings demonstrate the limitations of applying a universal model like WIEGO without considering local contexts. The study underscores the need for policy interventions that are tailored to the specific circumstances of different informal employment groups. Future research could explore the regional variations in informal employment across China and other countries, further refining the understanding of the inequality-informality nexus.

The study relies on cross-sectional data, limiting the ability to establish causal relationships. The self-reported nature of some data might introduce biases. While the CMDS dataset is large, it might not capture the full diversity of informal employment practices. Further research with longitudinal data and qualitative methods would strengthen the findings and explore the dynamics of informal work in greater depth.