In my summer research with Professor Pinar Yildirim, we analyzed Uber driver demographics and possible impact that automation has on employment pattern. We gathered and generated demographics and automation level data by region. Our research has confirmed previous observations on the demographic characteristics of Uber drivers: young people who are educated yet have relatively low-income, usually taking ride-sharing job in addition to their full-time employment. The finding points to a positive correlation between automation level and share of driver. We also identified specific industries of which the labor market is more directly correlated with participation in ride-sharing economy. The correlation with automation is obvious when we examine the effect of automation interaction when regressing Uber participation on income and female employee industry distribution. We confirmed that the effect of income on driver number depends on automation level. We also observed that model with interaction effects of automation displays strong evidence: automation and female employment in food services together has a significant positive influence in driver participation. Our research will hopefully provide insights into the rise of gig economy and encourage further discussion on shifting job market. Throughout the research, I worked with R extensively and learned basic econometrics methods. As my first formal research project, my summer research experience has been extremely rewarding. We started from a basic direction and continued to expand on and modify our methods and included database to achieve the final result. I am continuing with more research projects with Professor Yildirim on online behaviors and fake news, and I am really excited for more real-world analysis and a more intense research experience ahead.
In my summer research with Professor Pinar Yildirim, we analyzed Uber driver demographics and possible impact that automation has on employment pattern. We gathered and generated demographics and automation level data by region. Our research has confirmed previous observations on the demographic characteristics of Uber drivers: young people who are educated yet have relatively low-income, usually taking ride-sharing job in addition to their full-time employment. The finding points to a positive correlation between automation level and share of driver. We also identified specific industries of which the labor market is more directly correlated with participation in ride-sharing economy. The correlation with automation is obvious when we examine the effect of automation interaction when regressing Uber participation on income and female employee industry distribution. We confirmed that the effect of income on driver number depends on automation level. We also observed that model with interaction effects of automation displays strong evidence: automation and female employment in food services together has a significant positive influence in driver participation. Our research will hopefully provide insights into the rise of gig economy and encourage further discussion on shifting job market. Throughout the research, I worked with R extensively and learned basic econometrics methods. As my first formal research project, my summer research experience has been extremely rewarding. We started from a basic direction and continued to expand on and modify our methods and included database to achieve the final result. I am continuing with more research projects with Professor Yildirim on online behaviors and fake news, and I am really excited for more real-world analysis and a more intense research experience ahead.