The gig economy refers to a flexible, short-term work pattern distinct from traditional "9-to-5" employment. It leverages internet and mobile technologies to rapidly match supply and demand, serving as a vital component of the sharing economy and representing a novel approach to human resource allocation. This economic sector consists of freelancers handling small workloads, facilitated by digital platforms that efficiently connect service providers and consumers. It encompasses two primary forms: crowd-based work and app-mediated on-demand work.
China's Gig Economy Online Recruitment Data is a comprehensive thematic dataset constructed from full-coverage online recruitment data. Using keywords including takeaway delivery, couriers, ride-hailing, designated drivers, part-time work, flexible employment, and platform economy as search criteria, we systematically extract gig economy-related job postings from multi-source recruitment information. The dataset covers core fields such as company name, work location, position title, job description, salary, education requirements(学历要求), work experience(工作经验), and publication date. Spanning from 2014 to the present, it provides a systematic micro-level empirical foundation for studying the gig economy's scale evolution, occupational structure, geographical distribution, and employment characteristics.
Key Features:
- Multi-Source Coverage for Holistic Market Representation: Data encompasses five online recruitment sources, capturing diverse labor market segments — from white-collar and technical positions on comprehensive platforms to service-oriented and entry-level roles on specialized platforms. Gig economy positions exhibit significantly higher representation in entry-level platforms. Multi-source coverage ensures comprehensive capture of market scale and structure, mitigating sample bias inherent in single-platform limitations.
- Broad Extraction + Tagging System Empowers Researcher Flexibility: For BCE sources, all position-related text fields (position title, description, tags, functions, etc.) are included in matching. Matching source tags accompany each record. Researchers retain full flexibility to preserve all matched records or prioritize highly relevant matches based on their operational definition of "gig economy positions" — not limited by predetermined extraction standards.
- Longitudinal Coverage Enables Dynamic Trend Tracking: Temporal coverage spans from 2014 to the present, capturing China's gig economy evolution through embryonic development, rapid expansion, and regulatory maturation phases. Longitudinal data empowers researchers to track annual fluctuations in gig position demand, platform evolution, and structural transformations — providing essential temporal foundations for causal inference and trend analyses.
Research Applications:
- Systematic Measurement of Scale and Structural Evolution: Leveraging longitudinal, multi-source recruitment announcements, researchers can systematically quantify changes in gig positions' labor market share, demand trends across gig segments (e.g., food delivery, ride-hailing, online part-time work), and expansion/contraction patterns of the platform economy. These metrics offer policymakers quantitative evidence on real-time flexible labor market dynamics.
- Causal Identification of Policy Shocks and Institutional Change: China's gig economy has experienced intensive regulatory reforms — including platform worker rights protection, social insurance coverage, and minimum wage applications. The longitudinal, multi-regional panel data creates natural quasi-experimental settings for Difference-in-Differences, Event Study and other causal inference methods, enabling precise evaluation of policies' causal effects on gig position quantity, wage levels, and employment conditions.
- Corporate and Industry-Level Gig Employment Pattern Analysis: By linking company names to business registries, researchers can analyze: firm typologies favoring gig models, gig adoption's relationship with firm size/ownership/industry, and corporate gig workforce adjustment strategies during economic cycles. This dataset supports systematic research into how traditional enterprises increasingly adopt flexible staffing beyond platform companies.
- Skill Demand and Human Capital Research: Job descriptions offer rich text for analyzing gig position skill requirements. Through text mining, researchers can identify hard and soft skill combinations, track skill evolution trends, and compare skill profiles across gig segments — providing micro-level evidence for optimizing vocational training and labor transition studies.
Characterized by multi-source coverage, longitudinal accumulation, and flexible tagging systems, China's Gig Economy Online Recruitment Data delivers a systematic, reproducible micro-level foundation for academic and industry research on the gig economy's scale evolution, regulatory impacts, regional heterogeneity, and employment features. Preserving raw textual job information and full extraction rule disclosure enables both structured econometric analysis and unstructured deep text mining.
Database Application Guide
CnOpenData Application Logic for China's Gig Economy Online Recruitment Data: https://mp.weixin.qq.com/s/EjHtR9ms0nuPwgLjuQFRsQ
Data Scale
Temporal Coverage
- Source B, C, E: May 2014–2025
- Source D: May 2014–July 2021
- Source E: May 2014–October 2021
Field Specifications
Sample Data
中国零工经济线上招聘数据-B来源
中国零工经济线上招聘数据-C来源
中国零工经济线上招聘数据-D来源
中国零工经济线上招聘数据-E来源
中国零工经济线上招聘数据-F来源
参考文献
- 莫怡青、李力行，2022：《零工经济对创业的影响——以外卖平台的兴起为例》《管理世界》第2期。
- Barrios，J. M.，Hochberg，Y. V. and Yi，H. Y.，2020，“Launching with a Parachute：The Gig Economy and New Business Formation”,Journal of Financial Economics.
数据更新频率
年度更新