Research On The Labor Supply Of Chinese Taxi (online Car-hailing) Drivers Based On Reference Dependence Preference Theor

This research explores the issue of wage elasticity in labor supply from both theoretical and empirical perspectives.Using a new dataset of Chinese taxi(ride-hailing)drivers,the labor supply behavior is examined under the framework of neoclassical theory and reference dependence preference theory.Based on the research findings,policy implications and recommendations are provided.Drawing on the research conducted by scholars on wage elasticity in labor supply,this study thoroughly reviews the conclusions from previous literature regarding the neoclassical theory and reference dependence preference theory.Furthermore,the labor supply theory is further enriched and refined,promoting a more comprehensive analysis framework by integrating theoretical insights with empirical evidence.Building upon the reference dependence preference theory in labor supply,the study categorizes the models into three types: income-based reference dependence models,working time-based reference dependence models,and models that consider income and working time simultaneously as objectives.While the neoclassical model predicts a positive wage elasticity in labor supply,the conclusions of income-based and working time-based reference dependence models do not support the predictions of the neoclassical model.Therefore,the research direction shifts towards the more flexible reference dependence models that consider both income and working time as objectives.The empirical research models in labor supply decisions are then elaborated,focusing on the estimation of wage elasticity in labor supply using the OLS method,discrete choice stopping models,and asymmetric estimation based on citation-dependent preferences,providing a theoretical basis for subsequent empirical research.The subsequent sections define the data concepts,explain the relevant data,and provide detailed explanations of the variables.A distinctive feature of this study is the utilization of a new dataset from the taxi(ride-hailing)industry in Chengdu,China.The dataset is collected automatically through devices installed in taxis(ride-hailing vehicles),which record all GPS location information and fare details.Unlike the taxi(ride-hailing)industry in New York,which relies on handwritten receipts,China has a well-established invoice system,ensuring better data quality,completeness,and accuracy.Additionally,tipping is not a common practice among passengers in China,thus the fare accurately reflects the driver’s income.This study employs OLS linear regression to examine the hourly wage and working time of taxi(ride-hailing)drivers,estimates the labor supply decisions of drivers using a dynamic discrete choice model,and tests the income-based reference dependence model,working time-based reference dependence model,and the reference dependence model that considers both income and working time as objectives.This study conducts OLS estimation in three aspects: wage elasticity in labor supply,discrete choice stopping models,and asymmetric estimation based on reference-dependent preferences.The dataset is refined,and the robustness of sample selection is examined.Furthermore,the study investigates cross-sectional variations among different categories of taxi(ride-hailing)drivers,such as those with low and high incomes,as well as short-term and long-term working hours.In terms of time-series variations,differences between sunny and rainy days,as well as weekends and weekdays,are examined.The examination of cross-sectional and time-series variations not only provides further robustness tests for the main analyses using the entire sample of taxi(ride-hailing)drivers but also offers additional insights into driver efficiency in labor supply decision-making.Innovations:1.Addressing the limitations of reference dependence preference theory.In practice,reference dependence preference theory faces challenges in terms of subjectivity and operationalization.Determining the reference point is a subjective process that can vary among individuals and may even change over time and in different contexts.This complexity complicates the application and empirical research of the theory,requiring more nuanced analysis.The theory does not provide clear guidance on selecting the optimal reference point,which significantly affects the evaluation of decision outcomes and behavioral choices.This study introduces additional factors in setting the reference point,such as driver preferences,time preferences,and weather variations,enhancing the value of the research.2.Expanding the analysis framework of labor supply theory and enriching the labor supply theory for new employment groups.The existing labor supply theory in China primarily focuses on the macro-level and employs qualitative analysis methods.The labor supply theory comprises neoclassical theory and reference dependence theory.Through a combination of theoretical and empirical approaches,this study aims to investigate the labor supply theory from a micro-level perspective,expanding the analysis framework of labor supply theory and further advancing the development of economic theory by exploring the practical applications of reference dependence preference theory and neoclassical theory.3.Utilizing GPS big data,rigorous data cleaning,and employing multiple models for empirical research are the innovative highlights of this study.GPS data is relatively more objective,has a larger sample size,and offers higher efficiency compared to survey-based data.However,effectively utilizing GPS data poses a challenge,requiring carefully designed research methods.One-time empirical studies may lack effectiveness,and thus,this study incorporates extensive validation research,conducting multiple studies on the same issue.This approach provides robustness tests not only for the main analysis using the entire sample of taxi(ride-hailing)drivers but also offers more insights into driver efficiency in labor supply decision-making through cross-sectional and time-series variations.