Analysing the heterogeneity in working conditions of migrant informal workers in China: a test of the WIEGO model of informal employment

The study addresses how working conditions vary within the large and expanding population of migrant informal workers in China, challenging the common formal–informal dichotomy. Informal employment globally engages around 60% of the workforce and is especially prevalent among migrants. In China, marketization, export-oriented industrialization, and massive rural-to-urban migration under the hukou system have expanded informal employment and concentrated migrants in precarious jobs. The paper seeks to: (1) examine the heterogeneous working conditions (economic, social, and occupational health) of migrant informal workers by employment status; and (2) empirically test WIEGO’s pyramid model that posits systematic relationships between employment tiers and income, poverty, and gender. It argues that heterogeneity within informality is substantial and context-dependent, with implications for understanding inequality and designing decent work policies.

Research commonly contrasts informal with formal employment, depicting informality as low-paid, insecure, and excluded from social protection and social dialogue. Such views, rooted in broader shifts toward flexible accumulation and deregulation, overlook heterogeneity within informality. Prior work has identified dualities and varied motivations (e.g., voluntary vs. survivalist entry; constrained voluntary informalisation) and highlighted the roles of migration and gender in shaping vulnerability. WIEGO’s model stratifies informal workers by employment status (employers, own-account, employees, contributing family workers, cooperative members), linking higher tiers to higher income and male predominance, and lower tiers to higher poverty risk and female predominance. While influential, the model was built from limited regional data and focuses mainly on income, poverty, and gender, leaving broader working-condition dimensions underexplored. This study extends the framework by testing the model in China and incorporating a multidimensional working-conditions index.

Data source: 2017 China Migrants Dynamic Survey (CMDS), a nationwide PPS stratified multi-stage survey covering 32 mainland provincial-level units and 298 cities. Respondents are aged 15+ who migrated for work/livelihood and stayed at least one month. Of 169,988 surveyed, 139,837 had valid income and work-hours data. Defining informal employment: Following ILO (2018) operating criteria and adapting to CMDS items: (i) contributing family workers (not available in CMDS; excluded from tiers analysis), (ii) employers/own-account in informal units (individual businesses/unincorporated/other non-registered), (iii) employees lacking formal protections (no written contract and/or not covered by employer-contributed medical insurance). Employees with written contracts and employer-paid medical insurance were classified as formal; all others as informal. Final analytic sample: 107,020 migrant informal workers. Stratification (WIEGO-based tiers used in this study; five tiers due to data limits): employers; own-account operators; informal “regular” workers (employees with regular employers and written/fixed/flexible-term or probation contracts but otherwise informal); informal casual workers (employees with no written contract or one-off tasks; high turnover); outsourced/home-based workers (without regular employers, subcontracted/one-off tasks, home-based). Contributing/unpaid family workers were not available in CMDS and thus excluded. Poverty risk: Share of workers within each employment tier belonging to poor households, defined by China’s 2017 national poverty line: annual per capita household income < RMB 2952. Working-conditions measurement: A composite index covering economic (monthly income), social (written contract; type of medical insurance; life difficulties), and occupational health/resources (weekly working hours; participation in union activities; participation in community affairs). Indicators were normalized (0–1 for worst–best) and weighted via factor analysis. KMO=0.691; Bartlett’s test p<0.001. Three principal components retained; cumulative variance explained 79.616%. Indicator weights: monthly income (0.098), employment contract (0.142), medical insurance (0.221), life difficulties (0.121), workweek (0.136), union participation (0.132), community participation (0.150). Analytical strategy: Descriptive profiling; linear regression of overall working-conditions scores on employment tiers (controls for socio-demographics and migration characteristics; informal regular workers as reference). Multivariate general linear models for each component (economic income, contract, social security, life situation, work intensity, union support, community support), controlling for gender, education, and hukou. Additional regressions: (Model 1) income by tiers (employers ref.); (Model 2) binary logit of poverty risk by tiers (employers ref.); (Model 3) binary logit of gender (female) by tiers (employers ref.).

- Scale and composition: Roughly 80% of migrants are in informal employment; 65.3% in tertiary, 31.9% in secondary sectors. Informal workers are 57.2% male; 81.9% have rural hukou; 49.5% are inter-provincial migrants; education levels are modest (only ~10% junior college or above). - Working-conditions index: Informal workers’ average score = 0.34, below the overall migrant average (0.41) and well below formal workers (0.64). - Tier distribution: Own-account operators (~42%) are the largest group, followed by informal casual (27%), informal regular (21%), employers (6.3%), and outsourced/home-based (1.5%). - Overall working conditions by tier: Regression shows informal regular workers have the best conditions; other tiers fare worse in descending order: informal casual, outsourced/home-based, employers, own-account operators (worst). Pseudo R2=0.416; all differences highly significant. - Economic income by tier: Employers have the highest earnings. Regression indicates own-account operators have the lowest income statistically, below outsourced/home-based and informal casual. However, average monthly incomes show: employers RMB 7070.82; own-account RMB 4171.28; informal regular RMB 3821.06; outsourced/home-based RMB 3728.98; informal casual RMB 3398.97. The discrepancy reflects high variance and instability among own-account operators (including 2.4% with negative monthly income). - Poverty risk by tier (share from poor households): Informal regular workers lowest (0.11), followed by outsourced/home-based (0.24) and informal casual (0.22). Employers (0.47) and own-account operators (0.46) have the highest poverty risks, diverging from the WIEGO expectation of lower risk at higher tiers. - Gender segmentation by tier (proportion female): Informal regular workers highest (45.73%), then informal casual (43.41%), own-account (42.57%), employers (38.05%), and outsourced/home-based (23.15%). Employers are predominantly men, but outsourced/home-based are not predominantly women in this context. - Social protections and contracts: Informal regular workers are much more likely to have written contracts than casual or outsourced/home-based workers; employers and own-account operators typically have none. Employers are more likely to hold urban staff medical insurance; informal regular workers more often lack insurance. - Life difficulties: Most severe for outsourced/home-based workers, followed by own-account operators; informal casual and employers similar; informal regular least severe. - Work intensity: Highest to lowest—informal regular, outsourced/home-based, informal casual, employers, own-account operators. - Collective and community support: Informal regular workers engage more in union activities; others are similar and lower. Community support is higher among informal regular workers, employers, and own-account operators than among casual and outsourced/home-based workers. - Covariates: Men have higher incomes, lower work intensity, and greater likelihood of contracts and union/community support than women. Higher education associates with higher income, lower intensity, and greater union/community participation. Urban–rural hukou differences are pronounced: urban hukou links to higher income and union support but lower likelihood of labour contracts; rural hukou associates with better coverage via rural medical schemes.

Findings demonstrate pronounced heterogeneity within migrant informal employment in China. Contrary to a simple hierarchy where conditions improve monotonically with tier elevation, informal regular workers enjoy the best overall conditions, while own-account operators and employers can face poor conditions and high poverty risks. The divergence from the WIEGO model’s predicted gradients is attributable to China’s specific institutional and labour market context: hukou-based welfare exclusion, sectoral structures, subcontracting arrangements, business volatility among micro-employers, and the availability of factory wage jobs for women that reduce their concentration in home-based outsourcing. These results refine understanding of the informality–inequality nexus by showing that within-informality inequalities can be as large as formal–informal divides and that tier–income–poverty–gender relationships are contingent on local socio-economic dynamics. The study underscores the need for nuanced, status-specific policy responses and context-aware applications of global models of informality.

The paper advances research on informal employment by constructing a multidimensional index of working conditions and providing the first empirical test of the WIEGO model in China using 107,020 migrant informal workers from the CMDS. It shows: (1) substantial internal heterogeneity of working conditions across employment tiers; (2) partial alignment with WIEGO on employers’ male predominance and higher earnings; and (3) key divergences—particularly high poverty risks among employers/own-account workers and a low female share in outsourced/home-based work. Policy implications include: designing tier-differentiated support to address distinct needs; expanding access to education and training to improve earnings and reduce work intensity; and strengthening associational power and organizational innovations for informal workers, especially those lacking contracts. The authors call for comparative research on regional variations in informality’s heterogeneity to assess the broader applicability of the WIEGO model and to inform localized decent work strategies.

- Data constraints excluded contributing/unpaid family workers from the tier analysis due to absence of corresponding CMDS items. - CMDS lacks questions on paid leave and pension insurance; formal/informal classification and social protection measures relied on available proxies (written contracts and medical insurance types). - Cross-sectional 2017 data limit causal inference and may capture period-specific business conditions (e.g., volatility among micro-employers).