Compare methods

	Discrete-Time Hazard of Migration	Circular Migration Measurement
Field	Migration Studies	Migration Studies
Family	Survival analysis	Survival analysis
Year of origin	1982	2011
Originator	Paul D. Allison	Amelie F. Constant & Klaus F. Zimmermann
Type	Discrete-time hazard model of migration timing	Count-data and repeat-transition measurement of migratory circularity
Seminal source	Allison, P. D. (1982). Discrete-Time Methods for the Analysis of Event Histories. Sociological Methodology, 13, 61-98.	Constant, A. F., & Zimmermann, K. F. (2011). Circular and Repeat Migration: Counts of Exits and Years Away from the Host Country. Population Research and Policy Review, 30(4), 495-515.
Aliases	Person-Period Logit Migration Model, Allison Discrete-Time Event-History Model, Annual-Data Hazard of Moving, Complementary Log-Log Migration Model	Repeat Migration Counts, Circularity Index of Migration, Exits-and-Years-Away Measurement, Markov Repeat-Migration Model
Related	3	3
Summary	The discrete-time hazard model analyzes the timing of migration when the data arrive in chunks of time — usually person-years — rather than as exact dates. Paul Allison's 1982 formulation showed that an event history measured in discrete periods can be analyzed by a remarkably simple device: expand each person into one record per period they are at risk, mark whether the move happened in that period, and fit an ordinary binary regression (logit or complementary log-log) for the conditional probability of moving. The baseline period enters as a set of terms capturing duration dependence — how the risk of moving rises or falls with time elapsed — and covariates can change from period to period. Because annual migration data are the norm in panels and registers, this person-period approach has become the standard event-history tool in migration research, sitting alongside the continuous-time Cox model and extending naturally to competing destinations and repeat moves. Its great practical virtue is that the entire apparatus reduces to a logistic regression any analyst can run.	Circular migration measurement provides a quantitative grammar for distinguishing migrants who move back and forth across a border from those who settle permanently or return for good. Constant and Zimmermann's 2011 study proposed measuring circularity through two simple but powerful quantities: the number of exits a migrant makes from the host country and the cumulative years they spend away. With these counts in hand, the analysis models them statistically — using Poisson or negative-binomial regression for the count of exits and related models for years away — and represents the back-and-forth itself as transitions between being in the host country and being away, in the spirit of a Markov repeat-migration process. The framework turns the fuzzy notion of 'circular' or 'repeat' migration into measurable outcomes that can be explained by individual and contextual covariates and used to classify migrants into permanent stayers, circular movers, and permanent returners. Its contribution is to make circularity countable rather than merely descriptive.