Unlocking the Power of Zip Code-Level Data to Improve Prediction Models and Control for Location-Based Influences
Zip code-level data offers a rich and granular lens through which we can better understand economic, social, and environmental patterns. This data, often used in research and policy analysis, can reveal powerful insights about disparities and opportunities across different geographic regions. However, its potential remains underutilized in many prediction models. This post explores why controlling for location is vital and how selection effects—people’s choices in where they live—can shape the results of our analysis.
The Importance of Controlling for Location in Prediction Models
Location is a critical determinant in economic, social, and environmental outcomes. At a granular level, zip code data captures a wide range of geographic characteristics that can influence variables such as income, housing prices, crime rates, health outcomes, and educational attainment. Controlling for location in prediction models ensures that the analysis accounts for localized factors such as access to infrastructure, proximity to economic centers, and environmental quality.
For example, two individuals with similar income and educational levels may have drastically different financial opportunities purely based on where they live. A high-income zip code might offer better schools, safer streets, and higher property values—creating a cycle of advantages not easily captured by individual-level data alone. Ignoring these geographic differences introduces bias and can lead to misleading conclusions.
In economic and policy modeling, the failure to control for location-specific factors often results in overgeneralized predictions that overlook key inequalities. Incorporating zip code data into models helps sharpen predictions, making them more accurate and actionable. By controlling for location, we adjust for structural and geographic disparities that otherwise skew results, allowing for more precise intervention strategies and insights.
Selection and Self-Sorting: How People Shape Zip Code Data
One of the key challenges with zip code-level data is understanding how selection effects influence outcomes. People don’t randomly live in certain areas; they choose their locations based on preferences, resources, and constraints. This process of self-selection means that individuals within a zip code often share similar characteristics, such as income levels, educational backgrounds, or even lifestyle preferences.
Recent research has shown that relocation decisions can even be influenced by gender norms, as evidenced by a study on household decision-making across Germany and Sweden. This study found that couples tend to relocate in ways that prioritize men’s careers over women’s, often leading to higher earnings for men post-relocation. Such gendered patterns of mobility illustrate how self-sorting at the household level can shape economic outcomes at the zip code level, further complicating predictive models (Jayachandran et al., 2024).1
When we analyze data at the zip code level, it’s crucial to account for this self-sorting behavior. Failure to do so may result in models that attribute outcomes solely to location, without recognizing that the people who choose to live in these areas bring with them a set of characteristics that drive those outcomes.
