[Only English version is available]

Students from the Data Science and Policy Studies (DSPS) programme, School of Governance and Policy Science (SGPS) have delivered an exceptional performance at The Guangdong–Hong Kong–Macau Public Administration Data Analytics Contest 2025–2026 (University Division), with two teams securing 1st Runner-up and 2nd Runner-up honours respectively. This remarkable double achievement reflects not only the technical sophistication and analytical rigour of our students, but also the programme’s commitment to training future professionals who can bridge the gap between data science and public policy. For a programme that emphasises interdisciplinary thinking and real world problem solving, seeing our students recognised on this prestigious regional stage is a proud moment and a testament to their hard work and creativity.

Background of the Contest

The Guangdong–Hong Kong–Macau Public Administration Data Analytics Contest is a flagship competition designed to cultivate the next generation of data literate public affairs professionals across the Greater Bay Area. Organised by the Department of Public and International Affairs at City University of Hong Kong, the contest brings together government bodies, universities, and industry partners from across the region to promote innovative approaches to public governance.

This year’s theme challenged participants to apply spatial data analytics and data visualisation techniques to support the health and wellbeing of elderly residents, vulnerable populations, and young people in Hong Kong and the Greater Bay Area. The competition provides a valuable platform for students to demonstrate how applied data skills can inform evidence based policymaking and address pressing social challenges facing the region.

Overview of the Award Winning Projects

1st Runner-up Project: Elderly Employment in Hong Kong

The 1st Runner-up team turned their attention to a critical demographic challenge: elderly employment in Hong Kong. Their project evaluated the effectiveness of the Employment Programme for the Elderly and Middle-aged by drawing on a rich combination of publicly available Census data and web scraped job postings. Through a blend of statistical analysis, spatial analysis, and GIS mapping, the team uncovered significant mismatches between where jobs are located and where elderly residents actually live. They also assessed whether available positions align with the skills and salary expectations of older workers, identifying concrete gaps that policymakers could address. The project stood out for its thoughtful integration of quantitative methods with careful policy interpretation, demonstrating how data can illuminate practical pathways for improving employment support programmes.

2nd Runner-up Project: Fraud, Economic Shocks, and Youth Dynamics

The 2nd Runner-up team tackled a timely and complex question: why did fraud rates in Hong Kong surge so dramatically after 2020? To investigate this, they combined district level police records with census data, constructing a panel dataset that allowed them to trace how economic shocks and shifting youth demographics jointly influence fraud incidence. Using fixed effects models and interpreting their findings through a political economy lens, the team found that economic downturns significantly increase fraud growth, with this effect amplified in areas experiencing rapid changes in youth populations. Their work offers valuable, data driven insights that can inform anti-fraud policy and preventive strategies.

Student Name List

1st Runner-up Team: NGUYEN Lam Nhi (Team Leader), JIANG Haowen, JIANG Shuming

2nd Runner-up Team: HE Yunkun (Team Leader), WANG Yitian, SHU Ran, KIM Terry

Student Reflections on the Learning Experience

Members of both teams shared their thoughts on what participating in the contest meant for their development as aspiring policy professionals.

From the 1st Runner-up Team:

“Our project examined elderly employment trends in Hong Kong and evaluated the effectiveness of the Employment Programme for the Elderly and Middle-aged (EPEM). The data we used included both publicly available Census data as well as web-scraped job postings. Through statistical and spatial analysis, we highlighted mismatches between job locations and elderly residential patterns, assessed salary and skill alignment, and identified gaps for policy improvements. Throughout the competition, we applied skills learned from Data Science and Policy Studies, including web-scraping, data cleaning, data visualisation, GIS mapping, and policy interpretation. This experience gave us valuable insights into how data analytics and policy reasoning, when used together, can drive real policy improvements, and we are honoured that our work was recognised with the 1st Runner-up award.”

From the 2nd Runner-up Team:

“Our project, Economic Shocks, Youth Dynamics, and Fraud: Evidence from Hong Kong, examined why fraud surged dramatically after 2020. We combined district-level police records with census data to analyze how income shocks and youth demographic changes jointly shape fraud incidence. Our findings show that economic downturns significantly increase fraud growth, and this effect is amplified when youth populations shift rapidly. Applying Data Science and Policy Studies skills, we built a panel dataset, implemented fixed-effects models, and interpreted results through a political economy lens. This interdisciplinary approach helped us win the award and demonstrated how data-driven insights can inform anti-fraud policy.”