Abstract
Saudi Arabian women traditionally have been dependent on male relatives, hired drivers, or private transportation to get to work as they were not permitted to drive until June 2018. Some believe this has created a barrier for those women who wanted to enter the workforce. This research was conducted to determine whether accessibility (cost and time) for different types of transport has a relationship with women's opportunity to work. The unemployment rate for Saudi women in 2016 was nearly six times that of Saudi men. Qualitative evidence suggests the high cost of private transportation is a limiting factor for women working in Riyadh (Bashraheel, 2009; Jiffry, 2012). However, studies have yet to quantify the relationship between the location of employment, the job participation rate, and commute costs. By using a commuter accessibility model based on the financial cost of commuting for four female employment sectors—manufacturing, retail, healthcare, and education—this research sets out to test the relationship between commute costs and employment for Riyadh women. The study, which provides the first comparative commute cost maps for Riyadh, looks at commute costs for driving alone, private drivers, street-hailed and app-based taxi services, and the new Metro system. The results show that when commute costs increase, employment among women decreases. This means that reducing commute costs, perhaps by allowing women to drive themselves to work, increases the opportunity for women to work. The research also showed that manufacturing is the least accessible sector for women and would benefit from new forms of transit such as car-pooling.
•First comparative commute cost maps for Saudi females in Riyadh•Quantify the relationship between commute cost, accessibility to jobs, and job participation rate for Saudi female.•Utilize Google Transit API to measure financial cost of commute for Saudi females in Riyadh.•Estimate potential financial cost of new transit system and ridesharing for Saudi females in Riyadh.•Uses financial algorithms developed by popular ride hailing services.