Compară metode

	Network Scale-Up Method	Migrant Stock Estimation
Domeniu	Migration Studies	Migration Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	1998	1983
Autorul original	Peter Killworth, Christopher McCarty, H. Russell Bernard, and colleagues	United Nations Population Division (standard measurement conventions)
Tip	Indirect network-based size-estimation pipeline for hidden populations	Cross-source pipeline for counting the resident migrant population
Sursa seminală	Bernard, H. R., Hallett, T., Iovita, A., Johnsen, E. C., Lyerla, R., McCarty, C., Mahy, M., Salganik, M. J., & Stroup, S. (2010). Counting Hard-to-Count Populations: The Network Scale-Up Method for Public Health. Sexually Transmitted Infections, 86(Suppl 2), ii11-ii15.	United Nations (1983). Manual on Methods of Estimating Internal Migration (Manual VI). Population Studies No. 47. New York: United Nations.
Denumiri alternative	NSUM, Scale-Up Method, Aggregate Relational Data Method, Known-Population Network Estimation	Foreign-Born Stock Estimation, International Migrant Stock, Migrant Population Counting, Stock-Based Migration Measurement
Înrudite	3	3
Rezumat	The network scale-up method (NSUM) estimates the size of a hidden population — such as undocumented migrants or members of a stigmatized group — by asking ordinary people in a general survey how many members of that population they personally know. Developed by Killworth, McCarty, Bernard, and colleagues and formalized in their 1998 Evaluation Review paper, it rests on a simple bookkeeping idea: if you know roughly how many people each respondent knows in total, and you observe how many of those acquaintances belong to the hidden group, you can scale that fraction up to the whole society. The trick to recovering the total acquaintance count is to ask about several groups whose sizes are already known — people named Michael, nurses, women who gave birth last year — and use the responses to calibrate each respondent's personal-network size. Bernard and colleagues' 2010 review brought the method into mainstream public-health surveillance and emphasized two crucial corrections: transmission bias, because people often do not know which of their acquaintances belong to a hidden group, and barrier effects, because the hidden group may be socially clustered away from typical respondents. For migration research NSUM is attractive precisely because it never requires contacting migrants directly; it infers their numbers from the social fabric of the wider population.	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.