Access to public transit infrastructure changes the patterns of where people live and work. Economists also know those changes are necessarily all improvements, nor do they affect everyone. Bisma Khan, a PhD Candidate with the Department of Economics, studied the creation and implementation of Lahore’s Rapid Transit Bus System (BRT) to learn exactly what changes public transit in developing countries can deliver and for whom. She examined how this introduction of public transit affected residential sorting, and labour supply.
For her job market paper, Public Transit, Residential Sorting and Labor Supply: Evidence and Theory from Lahore’s Bus Rapid Transit System, Khan compared areas within 2 km of completed BRT stops with areas within 2 km of planned, but not yet completed bus lines. Using this quasi-experimental design, her paper highlights the differing consequences of transit infrastructure in developing cities for different population segments.
Among her discoveries? Increased labor force participation that arises from reliable public transportation can be limited to one gender and one socio-economic bracket. As a result, male, less-educated earners benefit more from access to public transportation.
“In terms of labour supply, I found that the BRT positively affected male labour supply but had no significant effect on female labour supply,” Khan said. “In a conservative city like Lahore, social norms heavily influence women’s participation in the labour force. Simply improving mobility isn’t enough to change those patterns.”
While lower-skilled men, and their families, moved closer to the bus stops to access better jobs more easily, the share of people with some college education declined among populations living near the new bus stops. This had implications for the real estate market along the bus corridor.
“Interestingly, land prices around BRT stations did not rise even though population density increased,” Khan explained. “College-educated households prefer high-amenity areas and avoided relocating near the bus corridor. In my paper, I argue that this is because the influx along the corridor was primarily of low–willingness-to-pay households, while the share of higher-income, college-educated residents fell. So, although more people moved to live along the bus-corridor, the overall demand from higher-paying residents declined, leading to a muted response on land prices.”
To interpret her findings, she built a spatial model that incorporates gendered constraints, age-based mobility, endogenous amenities, and commuting costs to explain how households choose where to live and work.
Khan’s work provides valuable insights into how public transportation can best support populations in developing cities.
“Bisma is studying how the introduction of Lahore’s Bus Rapid Transit (BRT) system has reshaped residential sorting and the labor supply of both men and women,” said Professor Gustavo Bobonis, Khan’s thesis co-supervisor. “This is an important issue for populations in large cities across the developing world. Public transit can transform where and how people live and work given the prevalence of low-quality transit alternatives, yet it is deemed to have important distributional consequences.”
While conducting her research, Khan learned that the skills she was using are also widely applicable in understanding the industry sectors that are transforming developed cities.
“Economists are trained in causal inference, and that’s a skill set I’ve developed,” she said. “From what I’ve read, companies like Instacart and Uber are increasingly interested in causal inference, not just prediction and forecasting.”
She used GIS tools like ArcGIS and QGIS extensively in her research, especially in the sections on spatial analysis. These are highly relevant for tech companies, which rely on geospatial data. She also applied machine learning techniques while working at a policy think tank where she used kernel clustering to identify crime hotspots. The experience motivated her to add another qualification to her already extensive CV.
“I completed a certificate from the Data Science Institute this year,” Khan said. “It covered machine learning, Python, SQL, Shell and GitHub, tools that are widely used in the tech industry. During my PhD, I mostly worked with Stata, which is great for academic research and consulting, but less common in tech, so I made a point of learning industry-relevant tools.”
Her emphasis on breadth of knowledge and its relevance across the discipline and industries is also evident in her approach to teaching. She immediately recognized that her students needed to be able to apply what they were learning to a range of possible future pathways.
“One of the most formative experiences was being a teaching assistant for ECO105, Economics for Non-Specialists,” she explained. “That course is designed to teach economic concepts and methodologies in an intuitive way, rather than through heavy theory or math. It helped me learn how to communicate complex ideas clearly and accessibly. We framed the concepts and methodologies in a way that connected to real-world issues. It wasn’t just about theory, it was about helping students understand how economic tools apply to current events and everyday decisions.”
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