平均年龄约39.8岁，748万网约车司机收入如何

Core Viewpoint - The ride-hailing industry has become a significant channel for absorbing labor in new employment forms, providing a large and flexible employment pool. The report reveals the multi-dimensional characteristics of ride-hailing drivers, highlighting their employment and income situations [3][4]. Group 1: Demographics and Employment Characteristics - As of October 2024, there are 7.483 million ride-hailing driver licenses issued in China, with an average driver age of approximately 40 years, indicating a "middle-aged" characteristic [3][5]. - The average monthly income of ride-hailing drivers is 7,623 yuan, ranking second among six types of blue-collar occupations. In first-tier cities, drivers working an average of 8 hours or more daily earn an average of 11,557.1 yuan [9][10]. - About 62.8% of drivers are the sole earners in their households, indicating significant economic responsibility. Over 80% of these households experience financial pressure, leading drivers to prefer jobs with immediate settlement and flexible hours [6][7]. Group 2: Employment Patterns and Job Satisfaction - The average daily online working hours for ride-hailing drivers is 6.41 hours, with a peak around 10 hours. However, only about 30% of drivers are classified as "highly active," indicating reliance on a core group of drivers for platform capacity [6][10]. - Many drivers transitioned from manufacturing or construction jobs, citing reasons such as transparent income, flexible working hours, and enhanced respect due to the dual evaluation system on platforms [7][11]. Group 3: Income Dynamics and Industry Challenges - The average commission rate for sampled drivers is 18.9%, with most drivers' commission rates concentrated between 18% and 20%. The industry has seen a 159% increase in licensed drivers since 2020, while monthly order volume has grown by approximately 38.3%, intensifying competition and price pressure [11][12]. - The report suggests the need for improved industry governance, advocating for fairness and transparency in algorithms and exploring diversified income growth models amidst market saturation [11][12].