Confronta i metodi

	Time-Location Sampling	Migrant Stock Estimation
Campo	Migration Studies	Migration Studies
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2001	1983
Ideatore	CDC Community Intervention Trial for Youth study team (Muhib, Stueve, and colleagues)	United Nations Population Division (standard measurement conventions)
Tipo	Probability-sampling pipeline for hard-to-reach mobile populations	Cross-source pipeline for counting the resident migrant population
Fonte seminale	Muhib, F. B., Lin, L. S., Stueve, A., Miller, R. L., Ford, W. L., Johnson, W. D., & Smith, P. J. (2001). A Venue-Based Method for Sampling Hard-to-Reach Populations. Public Health Reports, 116(Suppl 1), 216-222.	United Nations (1983). Manual on Methods of Estimating Internal Migration (Manual VI). Population Studies No. 47. New York: United Nations.
Alias	Venue-Based Sampling, Time-Space Sampling, Venue-Day-Time Sampling, VDTS	Foreign-Born Stock Estimation, International Migrant Stock, Migrant Population Counting, Stock-Based Migration Measurement
Correlati	3	3
Sintesi	Time-location sampling, also called venue-based or venue-day-time sampling, is a probability-sampling method for reaching populations that lack any list frame but reliably congregate at identifiable places and times. Developed and codified by Muhib, Stueve, and colleagues in a 2001 Public Health Reports article for a CDC youth study, it replaces the impossible task of enumerating a hidden population with the tractable task of enumerating the venues, days, and time slots where that population gathers. The analyst first builds an ethnographic frame of venue-day-time (VDT) units, then draws units at random, intercepts and enrolls eligible attendees on site, and finally weights respondents by how often they attend so that frequent venue-goers do not dominate the estimate. Because selection probabilities are known at each stage, the design yields defensible, variance-estimable population quantities rather than a convenience sample. For migration research it is especially valuable: day laborers at hiring corners, migrants at remittance shops, consulates, places of worship, markets, and transit hubs are mobile and unlisted, but they are observable in space and time. The method thus converts the visibility of a mobile migrant population into a genuine sampling frame.	Migrant stock estimation answers a deceptively basic question: how many migrants are living in a place at a given moment? Unlike migration flows, which count moves over an interval, a stock is a cross-sectional count of people whose origin differs from their place of residence — most commonly the foreign-born, but sometimes the foreign-national or those who have lived abroad. The United Nations measurement conventions, set out in its migration manuals, fix the core definitions (place of birth versus citizenship, duration thresholds, usual residence) and the at-risk concepts that make stocks comparable. In practice the analyst rarely has one clean source: censuses give place-of-birth tables but miss recent or irregular arrivals, population registers give continuous citizenship-based counts but vary in how they handle departures, and surveys give detail but suffer sampling error. Migrant stock estimation is therefore a pipeline that compiles these sources, harmonizes their differing definitions and geographies, and adjusts for undercount, overstay, and double counting, drawing on the same comparability concerns Bell and colleagues raised for internal migration. The output — a coherent count of migrants by origin, age, and sex — underpins integration policy, flow estimation, and the denominators of countless migration indicators.