Residential Mobility and Kinship Network Size (Person-Level, Anonymized)

About Dataset Full methodology & context Residential Mobility and Kinship Network Size (Person-Level, Anonymized) A person-level, de-identified dataset for studying how residential mobility relates to the size of a person's recorded kinship network, across age groups and U.S. Census regions.Each row is one person, described only by coarse, generalized attributes. ~750,000 rows. How this differs from the state-migration dataset. This dataset is not aboutorigin→destination flows. It carries no state pair — geography is generalized to the fourCensus regions, and the focus is on mobility behaviour (did the person ever move states,how many moves, how large is their kinship network), not on which states they moved between.Use the state-level migration dataset for directional flows; use this one for themobility–kinship relationship and life-course mobility. What each row represents Column Meaning person_id Random surrogate id — not derived from any real identifier, not reversible, and not shared with any other dataset (cannot be joined). Row key only. age_group Age band from birth year: <25, 25-39, 40-54, 55-69, 70+. region Current U.S. Census region: Northeast, Midwest, South, West. moved_state Whether the person has ever lived in more than one state (1 = moved, 0 = stayer). moves_bin Number of distinct-location moves in the residential history, binned: 0, 1, 2, 3+ (consecutive duplicate locations collapsed first, so a repeated address is not counted as a move). rel_bin Recorded-relatives bucket: none, 1, 2, 3-4, 5+. The headline relationship (and how to read it) Within every age group, the more relatives a person has recorded, the less likely they are to have moved states. Because the pattern holds inside each age band — with age thereforeheld fixed — it is not merely an age artefact. Share who ever moved states, by recordedrelatives, controlling for age (reference figures): age_group 1 2 3-4 5+ <25 15.4% 9.0% 4.3% 5.2% 25-39 29.0% 25.9% 20.9% 24.5% 40-54 35.6% 33.8% 28.5% 27.0% 55-69 35.1% 31.3% 27.3% 26.2% 70+ 33.6% 29.4% 27.7% 27.1% The gradient is consistent and directionally intuitive (larger recorded networks ↔ lowerinterstate mobility), which aligns with migration theory in which social ties anchor peoplein place. The dataset lets researchers reproduce and extend this with their own controls. Important caveats (read before drawing conclusions) rel_bin is recorded relatives, not family size. It counts relatives found inpublic-records data (max 20 slots at the source). none means "none found" — a thinprofile — not "no family". The count is partly a measure of profile completeness. Note,however, that completeness bias would not by itself produce the observed pattern (there isno reason fuller profiles would move less), so it more likely adds noise than creates thesignal — but treat the count as a proxy, not a headcount. Association, not causation. This shows a relationship; it does not establish thatkinship ties cause lower mobility. Reverse direction (staying accumulates local recordedkin over time) or common causes are equally consistent with the data. The gradient is not perfectly monotonic. In some age bands 3-4 dips below 5+. Usethe full table rather than assuming a strict "more relatives → less movement" step. Region, not state. Geography is generalized to four Census regions; sub-regional andstate-level patterns are not recoverable here (that is the other dataset's job). Endpoint-based mobility. moved_state compares first and current state; moves_bincounts location changes in the recorded history, which may be incomplete. What you can do with it Reproduce the mobility × kinship-network relationship with your own age (and region)controls; test its robustness. Study life-course mobility: how moved_state and moves_bin vary across age bands andregions. Use it as a large, person-level companion to survey data, which typically has far smallersamples for this kind of cross-tabulation. How it was built Endpoints & moves derived from each residential history, with consecutive duplicatelocations collapsed and military postal codes (AA/AE/AP) removed. Generalization: state → Census region; exact move count → bins; birth year → age band. Kinship count without links: number of filled relative slots only — relative names andids were never carried forward, so no relationship graph is produced. Sampling: a uniform random sample of 750,000 was drawn from records with complete generalized attributes, so its distributions estimate that population. k-anonymity (k=5) over {age_group, region, moved_state, moves_bin, rel_bin}; profilesin cells smaller than 5 were suppressed (78 rows, <0.01%). Because person_id is a fresh surrogate not shared with other releases, this dataset cannotbe linked to them at the individual level. Provenance & terms 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 / License / Contact migration_mobility_700k.csv — person_id, age_group, region, moved_state, moves_bin, rel_bin. License cc-by-4.0 · [e-mail]