Working at the Intersection of Data Science and Public Policy

As the world marks one year since COVID-19 upended everyday life, governments around the world have often touted that they are “guided by data” or are “following the science” as they close or open different sectors of the economy. In practice, however, many of these decisions, from how schools have reopened to travel quarantine policies, have looked very different, highlighting the challenges of using and interpreting data to make decisions that impact entire cities or regions.

In his new book, Public Policy Analytics: Code & Context for Data Science in Government, Ken Steif, associate professor of practice in the Department of City and Regional Planning and the program director of Weitzman’s Master of Urban Spatial Analytics (MUSA) program, sheds light on this complex topic. Designed for policymakers without a technical background as well as for budding data scientists hoping to learn how to build data-driven tools, the book is a first-of-its-kind guide for technical topics and highlights the challenges of using data to address broader issues of equity and bureaucracy.

Penn Today spoke with Steif about the book, the challenges of incorporating data science into government decision-making, and how the pandemic has impacted the field of public policy analytics.

What was the impetus for writing this book?

For 10 years, I have been teaching students in Penn’s Master of Urban Spatial Analytics how to develop data-driven decision-making tools for government. Before coming to MUSA, many of our students are discouraged by their high school and college professors from taking STEM classes. Told to stick to the humanities, these students develop anxiety around coding and statistics.

The impetus for this book and for my teaching is to dispel with these anxieties, empowering students to write code and learn analytics by leveraging their interest in solving complex public policy problems. The book helps by providing a set of code examples and use cases they can use to copy and paste their way to meaningful solutions in domains ranging from housing, criminal justice, health and human services, environment, transportation, and more. I worked hard to provide my students with this resource; it only seems reasonable to share it more broadly.

Are there challenges specific to the use of data science in government and policymaking compared to other fields, such as scientific research or business analytics?

Data science is about data-driven decision-making. It is about service delivery. In business, if a new algorithm increases revenue, that becomes the new standard for decision-making. In government, there are economic interests, but most bottom lines are far more nuanced, like fairness, equity, politics, and bureaucracy.

An engineer can optimize for revenue, but it takes a social scientist to optimize for these other bottom lines. In this way, I argue that data science is akin to planning. For an algorithm to effectively deliver a government resource, it must be developed with great empathy. How has a government service traditionally been delivered? Was that strategy effective? Were some groups given more access than others? Have we been transparent about these issues?

This book also includes open access datasets and code that accompany each chapter. What is the importance in having both a conceptual framework as well as practical examples and exercises?

Over the last 15 years, one of the most transformational movements in government has been open data—the release of free and open-source government administrative data in a machine-readable format.

When leveraged by committed civic technologists, open data has helped scale government innovation at an unprecedented pace. I hope this book fills one related shortcoming—what I call ‘open analytics’. All governments collect the same administrative data and share the same service delivery use cases, like homelessness prevention, drug treatment, child welfare, public health, etc. This common set of use cases means that one agency can develop analytics and supporting materials, share the code, and enable other governments to replicate the solution in their own community. I hope providing code and context in this book helps jump start the open analytics movement.

Public Policy Analytics: Code & Context for Data Science in Government is currently available for preorder from CRC Press and is also available online. Open access datasets and code can also be found on GitHub.

Read the full interview on Penn Today.