Center Projects

Ridesharing to Help the Disadvantaged Get Moving

Uber, Lyft and other ride-sharing apps

In many American cities and towns, citizens with lower levels of education and skill confront challenges when seeking employment. The jobs best suited for their skills may be located in a different part of the metro area from their homes, and the existing public transportation system may not provide them with an easy way of commuting to those jobs.

Across the nation, transportation agencies are examining potential modes of cooperation between governments, regulatory authorities, and ride-sharing companies to make ridesharing systems available to disadvantaged citizens with commuting challenges.

To assess citizen response to a public ridesharing policy, the CFW is currently in the nascent stages of conducting a well-organized field experiment, with randomized assignment of citizens possessing special transpiration needs to groups that are provided with free or heavily-discounted rideshare coupons, and groups that are not. To keep the scope of our experiment within reasonable financial limits, we will undertake our field study in Pittsburgh and adjacent areas around Allegheny County, but the lessons we take from this experiment will be applicable across the United States and beyond. This semester, a team of Heinz College students is conducting the preliminary analysis and beginning the task of designing the experiment for its capstone project.

 

Forecasting Employment Disruption from the Emergence of Automated Vehicles

Many research economists fear that automated, self-driving vehicles will have a negative impact on those whose livelihoods are deeply tied to traditional transportation business models and practices.

Hybrid truck and blue electric car on wireless charging lane.

There are 1.7 million professional truck drivers in the United States and an additional 1.7 million operators of other commercial land vehicles. How could policymakers prepare for the possible elimination of many of these jobs?

This CFW project will address the challenge by bringing economic expertise together with some of the world’s leaders in autonomous vehicle technology to forecast where and when these individuals might be displaced from their current jobs. With this data in hand, we can begin to design policies that could ameliorate the dislocation of these workers, and have these policies in place before the disruptions emerge. A Heinz College student team is currently collaborating with the New America Foundation to create a first draft of a map in space and time that forecasts the potentially significant job losses associated with the commercial deployment of these technologies.

 

Wage Insurance as a Policy Response to Automated Vehicles and Associated Job Loss

For decades, leading policy analysts have promoted wage insurance as a cost-effective way of helping workers dislocated by new technology, or globalization (Litan, 2015). The idea is simple: workers pay into a fund that dislocated workers can draw upon. Unlike unemployment insurance, however, wage insurance provides dislocated workers income supplements after they receive a new job that pays less than their old one.

Wage Insurance

This project will use estimates of job loss due to the emergence of automated vehicles. Based on estimates of job displacement and income loss, we can create a range of estimates regarding the cost of a relatively generous wage insurance program that insures professional driver-operators up to 50 percent of their income loss for three to five years.

A group of Heinz College students is currently collaborating with the Petersen Institute for International Economics to conduct a preliminary investigation of this wage insurance program.

 

Technology and Skill Formation in the 21st-Century

The model of schooling that Americans have carried into the 21st-century often fails to recognize that students learn in very different ways and at different rates. Intelligent cognitive software “tutors,” which have been developed and successfully tested for Algebra I, analyze student errors, figure out what the student does not understand, and give the student personalized practice problems and instructions to remedy the lack of understanding. These “tutors” offer a cost-effective way of partly individualizing instruction to reflect the individual needs and challenges of every student.

Could intelligent tutors focused on other subjects achieve the same degree of learning acceleration that has been documented in Algebra I? If so, this could transform the skill levels of the American workforce.

This project will seek to answer that question by bringing together experts in the technology of the cognitive tutors, quantitative social scientists who can explain the evidence of their effectiveness, and behavioral economists who understand the ways in which technology adoption decisions can fail to be fully rational. Working in partnership with the Commonwealth of Pennsylvania, we will develop cost-effective ways for the Commonwealth to incentivize school districts to experiment with and adopt these new technologies. The policies and interventions developed in Pennsylvania could potentially be deployed around the nation and across the globe.