Learning by Doing in the Age of Big Data

Funding Source:
MOE Social Science Research Thematic Grant.

Principal Investigator:
Associate Professor Shih-Fen Cheng (SMU)

Co-Principal Investigators:
Professor Sumit Agarwal (NUS)
Assistant Professor Ben Charoenwong (NUS)
Associate Professor Jussi Keppo (NUS)

Synopsis:
"Learning by doing" (LBD) is the phenomenon where a worker's productivity rises with cumulative production experience. As LBD requires no additional investment in hiring or equipment investment, it is viewed by many as an important channel for firms to achieve productivity growth. Unfortunately, although conceptually simple and intuitive, the sources and enablers of LBD remain a mystery; as a result, even when a firm intends to facilitate LBD among its employees, it is not clear how to effectively achieve it. This challenge originates from the difficulty in quantifying and isolating the effects of LBD, and even in a few instances where the measurement of LBD effects (in terms of productivity) is made possible by natural events, these measurements are typically only at the aggregate level.

In this project, the team aims to build a novel Big Data framework to measure the LBD effects for workers in the transport gig economy in Singapore. Our ambition is to measure LBD effects at not just the productivity level, which is easily tainted by other factors, but also at the skill level. We plan to achieve this by mining drivers' microscopic movement traces and trip fulfillment (including both taxi and ride-hailing drivers), and quantify drivers' skills in anticipating demands and competition from other drivers. Our research will provide a rare view into how big data can revamp the understanding of labor productivity and LBD effects at the individual level, and it will help policy makers and platform operators to come up with policies that are more effective in helping workers cope with competitions and sudden changes such as disruptions brought about by the COVID-19 pandemic.