In a paper published in the journal Scientific Reports, researchers explored the relationship between ridesourcing platforms and socioeconomic inequality. Through an agent-based simulation model, they investigated how these platforms benefit from disparities, attributing it to cheap labor availability and a clientele willing to pay premium prices for time-saving services.
Distribution of (A) value of time of travellers opting for ridesourcing, and (B) reservation wage and earnings of job seekers participating in the ridesourcing market, for different values of Gini coefficient g. Mean value of time and reservation wage are provided for the entire population as reference. The highlighted area in each graph corresponds to observed values of the Gini coefficient in cities around the world. https://www.nature.com/articles/s41598-024-57540-x
Their findings revealed a strong correlation between socioeconomic inequality and ridesourcing market dominance, with minimal driver earnings observed in urban areas with significant inequality despite fierce competition for passengers and high platform commissions.
Related Work
Previous research extensively explores the gig economy, emphasizing gig workers' autonomy and challenges, including fluctuating demand and limited access to social security. Recent legal cases underscore the precariousness of gig work, particularly on platforms like Uber and Deliveroo.
Despite potentially comparable hourly earnings, gig workers need more essential benefits like a guaranteed minimum wage, exacerbating income uncertainty. Ridesourcing platforms like Uber, life in Your Hands, Inc. (Lyft), and Didi Chuxing (DiDi) thrive on socioeconomic disparities, catering to affluent users while relying on cheap labor. However, the intricate dynamics of ridesourcing markets and the impact of pricing strategies on travelers and workers require further investigation.
Ridesourcing Market Dynamics
The study's methodology comprised data selection and modeling techniques to explore the dynamics of ridesourcing markets. Initial data sourcing involved utilizing a Dutch activity-based model to capture travel demand, focusing on trips within a specific area, meeting distance and time criteria. The value of travelers' time was determined from a log-normal distribution, incorporating mean and standard deviation values corresponding to the Gini coefficient.
Researchers developed a modeling framework to analyze daily participation decisions within the ridesourcing market, integrating neoclassical labor supply theory principles. This framework allowed for a nuanced understanding of how various factors influence individuals' decisions to participate in the ridesourcing economy.
In modeling traveler behavior, mode choice considerations encompassed multiple attributes, including in-vehicle time, access and egress time, waiting time, transfers, and travel cost. Researchers incorporated attributes into a random utility model to simulate travelers' mode selection, providing insights into their preferences and decision-making processes.
Furthermore, researchers utilized an agent-based model, specifically a multi-agent activity-based simulation of mobility systems (MaaSSim), to simulate the operational aspects of ride-hailing services and assess passengers' waiting times and drivers' incomes under varying supply and demand conditions. By incorporating these elements, the study aimed to capture the intricate dynamics of the ridesourcing market and its impact on travelers and drivers.
Learning from the experience was an essential aspect of the modeling approach, represented using a Markov model. It allowed agents to assign weights to their previous experiences when making participation decisions, contributing to a more realistic representation of decision-making processes within the ridesourcing market.
Additionally, the study accounted for registration dynamics, modeling the spread of information and the associated costs and considerations involved in registering for ridesourcing platforms. These dynamics were crucial in understanding how individuals enter and exit the ridesourcing market over time, shaping its overall dynamics and equilibrium states. The researchers implemented the methodology in Python and conducted multiple replications to ensure the statistical robustness and reliability of the findings.
Ridesourcing Market Dynamics Analysis
The study applied a double-sided ridesourcing market simulation model to a case study resembling Amsterdam, Netherlands, allowing travelers to choose between private cars, bikes, public transport, and ridesourcing. Each simulated traveler and job seeker represented ten individuals in reality based on established transportation studies.
Trip attributes and behavioral preferences were determined from past research findings, ensuring a realistic representation of travel and employment choices. The simulation model generated various outputs: platform profit, request satisfaction rates, traveler waiting times, and driver incomes. The study explored the impact of socioeconomic inequality by adjusting parameters related to travelers' value of time and job seekers' reservation wage, covering a spectrum from perfect equality to extreme inequality.
The results revealed significant macroscopic effects, indicating a positive correlation between socioeconomic inequality and ridesourcing market participation. Higher inequality levels led to increased demand and supply-side involvement, resulting in shorter waiting times for travelers but lower earnings for drivers.
Moreover, the study identified societal implications, showing that ridesourcing was more prevalent among travelers with above-average value of time in unequal societies. Additionally, extreme levels of inequality either limited ridesourcing demand due to cost insensitivity or led to an undersupplied market with reduced driver earnings.
The study also examined the impact of platform pricing strategies on profit and social welfare indicators. It found that higher inequality allowed higher fares and commission rates, maximizing platform profit. However, such strategies often came at the expense of drivers, particularly in highly unequal societies.
Conversely, profit-maximizing pricing strategies were less detrimental to travelers and drivers in relatively equal societies. The findings underscored the complex interplay between socioeconomic inequality, platform profit, and societal welfare in ridesourcing markets.
Conclusion
In summary, analyzing the ridesourcing market dynamics in the simulated case study resembling Amsterdam, Netherlands, elucidated the intricate relationship between socioeconomic inequality, platform profit, and societal welfare. The findings underscored the nuanced effects of varying inequality levels on market participation, traveler behavior, driver earnings, and platform pricing strategies, emphasizing the importance of considering these factors in shaping the future of ridesourcing economies.
Article Revisions
- Jun 25 2024 - Fixed broken journal paper links.