Estimation of the supply function in the ride-sourcing market: The case study of Tabriz city

Document Type : applicative

Authors

1 PhD student of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran

2 Professor in Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran

3 Faculty of Economics and Management, University of Tabriz, Tabriz, Iran

10.30473/jier.2025.74771.1497

Abstract

With the expansion of information and communication technologies, the traditional patterns of many markets have been transformed, and the phenomenon known as the internet economy has profoundly influenced the economic and social structures of societies. One prominent manifestation of this transformation is the emergence of internet-based markets in urban transportation, commonly referred to as ride-sourcing services. Given the importance of examining the ride-sourcing market in today’s digitalized economies, this study investigates the behavior of drivers in the ride-sourcing market by estimating the supply function in the case of Tabriz. In this study, the Heckman two-stage model is used to eliminate the sample selection bias caused by the existence of a truncated dependent variable in the supply function estimate. The results obtained show that the variables of age and experience have a positive effect, and the variables of education level and air temperature have a negative effect on the probability of participating in the ride-sourcing market . Also, the net income of drivers has a positive effect on the supply level, which indicates a high sensitivity of supply to net income. In this study, the inverse mills ratio variable has also been used in the model estimation. According to the results, including this explanatory variable in the supply function has corrected the bias error and estimated the supply function more accurately.

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Journal of Industrial Economics researches
Volume 9, Issue 31
September 2025

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History

  • Receive Date: 01 June 2025
  • Revise Date: 09 July 2025
  • Accept Date: 01 September 2025

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