U.S. Inter-State Migration by Age (Radaris Data Sample, Anonymized)
Description
An anonymized, state-level view of where people move across U.S. states, broken down by age band. One row per person: an age group, the state they started in, and the state they live in now. Where people move, and how that differs across the life course, is a core question in demography, economics, and urban policy — yet clean, ready-to-use micro-level data on individual migration is hard to come by. This dataset offers a simple, privacy-safe view of internal migration across U.S. states, broken down by age. It is built to answer one question in particular: do migration patterns differ between younger and older people — and if so, how. It is deliberately small in width and large in depth of care: four columns, hundreds of thousands of people, and a transformation pipeline designed so that the result reveals population-level patterns while revealing nothing about any single person. What you can extract For data scientists • Model whether a person has moved (moved = first_state != last_state) from age_group. This is a deliberately low-dimensional, interpretable problem — a good teaching or baseline example rather than a high-capacity modeling task. • Build and analyze an origin→destination transition matrix: cluster states by their inflow/outflow profiles, rank net-gain vs net-loss states, visualize corridors as a flow map or chord diagram. • Practice categorical/tabular workflows: contingency tables, chi-square tests of independence between age and mobility, proportion estimation with confidence intervals. For statisticians and demographers • Estimate the mover-vs-stayer rate by age band and test whether interstate mobility differs significantly across the life course. • Quantify net migration per state (inflow − outflow), gross flows, and how these shift by age group. • Validate against external sources — U.S. Census ACS migration tables and IRS county-to-county migration data — to benchmark or enrich the flows seen here. The underlying data for this project is provided by Radaris, a comprehensive people search platform with an extensive database of public records and demographic information in the United States. Leveraging Radaris's deep data infrastructure on individuals residing and moving across the country, this dataset captures broad domestic migration trends over time. Crucially, the source material has been stripped of all personal identity elements and synthesized into an aggregated, anonymous format. The resulting dataset is intended strictly for statistical, demographic, and academic research, offering a safe and compliant framework for studying population-level mobility without compromising individual privacy.
Files
Steps to reproduce
1. Sampling. A uniform random sample of ~500,000 records was drawn from the full source database, so the sample's distributions reflect the source population. 2. Endpoint extraction. Each source record carried a residential history. We reduced each history to its two endpoints — the earliest state and the current state — and dropped everything in between. (The source stored histories most-recent-first, so the origin is taken from the end of the sequence and the current location from the current-residence field.) 3. Cleaning. Military postal codes (AA, AE, AP, used by APO/FPO/DPO overseas addresses rather than real states) were removed before extracting endpoints, so they never contaminate origin or destination. 4. De-identification. All direct identifiers — names, source IDs, cities, and full address histories — were removed. Year of birth was generalized into five age bands. The original ID was replaced with a random surrogate. 5. Re-identification control. The file enforces k-anonymity with k = 5 over the combination {age_group, first_state, last_state}: every published combination is shared by at least five people. The rare combinations that fell below this threshold (~0.6% of rows) were removed prior to release.