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  1. “Methodologies for the estimation of stocks of irregular migrants" Presentation at the Joint UNECE/Eurostat/UNFPA/MEDSTAT II Work Session on Migration Statistics, Geneva, 3-5 March 2008 Michael Jandl

  2. Outline of presentation • Research and data problem • A classification of methods for estimating irregular migrant stocks and flows • Selected methods for estimating irregular migrant stocks • Conclusions Michael Jandl, Geneva, 4.3.2008

  3. Research and data problem Illegal (unauthorized/undocumented) Migration • is deliberately hidden from view (sanctioned) • is statistically not directly quantifyable • can be researched with qualitative social science methods, data trend analysis and quantitative estimation methods • Data and methods should be subject to close scrutiny • Minimum condition: indications on methods, assumptions and datasources of quantitative estimates should be given • Otherwise we should not speak of an „estimate“ Michael Jandl, Geneva, 4.3.2008

  4. A new classification scheme • Differentiation: • Stock data (illegal residence, illegal work) • Flow data (illegal entry) • Subdivisions into: • Direct approaches vs. indirect approaches (+ combined approaches) • Data sources (already existing, specifically generated) • Methodic grouping • Estimation technique Michael Jandl, Geneva, 4.3.2008

  5. Approach Data sources Method Estimation technique Direct approaches Based on immigration enforcement data Multiplier methods Simple Multiplier Capture-recapture Repeated capture Matching of registers Statistical methods Random effect mixed modelling approach Based on administrative statistics Methods of self-identification Evidence based on regularisation data Using data on status adjustments over time Based on surveys Snowball sampling Direct survey methods Single stage link-trace sampling A classification of methods for estimating irregular migrant stocks I Michael Jandl, Geneva, 4.3.2008

  6. Approach Data sources Method Estimation technique Indirect approaches Based on census/registers Residual methods Differences census results – legal immigration data Simple comparison of various registers Demographic methods Use of birth/death rates Based on surveys of “key informants” Subjective Estimations/ Indicators Methods Expert surveys Delphi surveys Based on non-demographic data Econometric methods on shadow economy Inference from estimates on illegal work Based on census/registers/ demographic data Expected population methods Comparison of census/emigration data and immigration statistics Based on admin. statistics Flow-stock methods Calculating the stock through flow figures Based on complem. data sources Indirect inferences Registered school children, household surveys, etc. A classification of methods for estimating irregular migrant stocks II Michael Jandl, Geneva, 4.3.2008

  7. Approach Data sources Method Estimation technique Combined approaches Based on small scale surveys Window/Postal code method Small scale study / use of regression analysis Based on expert opinions Localized Delphi Delphi method / use of regression analysis Adjustment to surveys/ census data Non-threatening survey design Randomized response (3 cards method) / residual method A classification of methods for estimating irregular migrant stocks III Michael Jandl, Geneva, 4.3.2008

  8. Approach Data sources Method Model Direct approaches Based on border apprehension data Multiplier methods Simple multiplier Indirect approaches Based on stock estimates Differential methods Net differences in stocks Based on entry-exit statistics Residual method Double entry card system A classification of methods for estimating irregular migrant flows Michael Jandl, Geneva, 4.3.2008

  9. Methods for estimating irregular migrant stocks I Multiplier Method • Based on the projection of available indicators, using an appropriately defined/derived multiplier .. on stocks/flows • Flow data (illegal entries): e.g. On apprehension data of illegal migrants at the border • Stock data (illegal residence, illegal work) Example: Burgers 1995 Based on the number of apprehended criminal foreigners In-depth interviews with a sample of illegal migrants in Rotterdam to determine share of illegal migrants involved in criminal activities  multiplier  estimate of total Michael Jandl, Geneva, 4.3.2008

  10. Methods for estimating irregular migrant stocks II Repeated Capture method in the NL Van der Leun et al. (1998), Engbersen et al. (2002), Van der Heijden (2006) • Analysis of data from police enforcement records from 25 police districts • Establishing who has been caught 1, 2, 3, … times • Fitting the data to a Poisson distribution and calculate N (y=0) Michael Jandl, Geneva, 4.3.2008

  11. Methods for estimating irregular migrant stocks III A random effect mixed modelling approach This is currently tested for Norway (UDI project, Li-Chun Zhang (2007) N= known/registered population n= apprehended by the police but among N M= unknown/unregistered population to be estimated m= those apprehended among M N(i), n(i), M(i), m(i) are subgroups by age, citizenship,... 1) M(i)/N(i) is a random deviation from a global proportionality coefficient: log(Mi/Ni) =  + vi 2) (mi)/M(i) is a function of n(i)/N(i): log(mi/Mi) = log(ni/Ni) + ei  Mi can be estimated by OLS and M= Mi for all i Michael Jandl, Geneva, 4.3.2008

  12. Methods for estimating irregular migrant stocks IV Evidence from regularizations (examples) Italy’s 2002/2003 regularization: • 700,000 applications - 640,000 regularizations • Top 3 countries were: Romania, Ukraine, Albania Spain’s 2005 regularization: • 690,000 applications – 573,000 positive (end 05) • Top 3 countries were: Ecuador, Romania, Morocco 2004/2007 EU enlargement = regularization?: Problems: • Number of applications is not the same as number of persons • Wide differences in implementation (Italy – easier; Greece – difficult) • Not all illegal residents apply; additional non-residents apply • Persons regularized can fall back into irregularity Michael Jandl, Geneva, 4.3.2008

  13. Methods for estimating irregular migrant stocks V Single-stage link-tracing sampling Frank and Snijders (1994), Li-Chun Zhang (2007) • A specific form of snowball sampling,.. • Taking a random sample s (1,...,i,...n) of persons in a target population U (1,...N) and letting each person (i) nominate m(i) other persons among U, then r(i) denotes the number of persons referring back to sample s. Based on this, under certain assumptions an estimator can be obtained, so that N (est)= n + (n-1)x(m-r)/r (that is the higher the number of persons already nominated by i, the smaller the total population N) Michael Jandl, Geneva, 4.3.2008

  14. Methods for estimating irregular migrant stocks VI „Residual methods“ Based on the differences between • Census results (total population adjusted for undercounting) and • Immigration data, aliens registers, residence permit registers, etc. • Example USA: 2005 estimate: 10.3 m (Passel 2005), Jan 2006 estimate: 11.6m (Hoefer et al); UK: 2001 estimate: 310k-430k-570k Michael Jandl, Geneva, 4.3.2008

  15. Register 1998 1999 2000 2001 2002 2003 2004 2005 Municipal Register 637.085 748.954 923.879 1.370.657 1.977.946 2.664.168 3.034.326 3.730.610 Foreigners with Resident Permit 719.647 801.332 895.720 1.109.060 1.324.001 1.647.011 1.977.291 2.738.932 Difference (MR – Foreigners with Resident Permit - 82.562 -52.378 28.159 261.597 653.945 1.017.157 1.057.035 991.678 Methods for estimating irregular migrant stocks VII „Simple Comparison of Registers“ Indirect estimation based on a comparison of two or more registers with data on the same target population (where irregular migrants may at least partially be included) Example Spain: Legal residents should be registered in database on residents permits while illegal residents have incentives to register in the municipal register Michael Jandl, Geneva, 4.3.2008

  16. Methods for estimating irregular migrant stocks VIII „Comparison of Immigration and Emigration Statistics“ Based on the differences between (estimated) emigration and (recorded) immigration data, the number of migrants without legal resident status is estimated Examples: Mexico-US migration (in: Lederer 1994); Delaunay and Tapinos (1998a): Morocco – Europe (not Tunisia), Fargues (2007): estimates of MENA emigration; Malynovska 2004: estimates on Ukrainians abroad Problems: • Migrants may disperse over a large number of countries not just to one region • Both emigration estimates and immigration data are prone to large margins of error • However, data from countries of origin/consulates could be more exploited Michael Jandl, Geneva, 4.3.2008

  17. Conclusions I • There are a number of feasible estimation methods available • A lot can be learned from existing examples in migration research and from the study of other hidden populations • Not all methods are suitable and applicable in all circumstances and at all times • Some methods need the production of new data, some can use existing data (if available) • In addition to data and methods, a good knowledge of irregular migration processes is necessary ( also qualitiative research!) • A solid estimate usually needs substantial time and resources Michael Jandl, Geneva, 4.3.2008

  18. Conclusions II • No one estimate can be perfect, each method has its strengths and weaknesses • An external validation of the accuracy of results is difficult • A triangulation of several independent estimates can improve confidence in the outcome • The use of multiple, complementary methods is recommendable • New methods and data sources can and should be developed • Technological and administrative innovations can give rise to new methods (e.g. Biometric visa and travel documents for entry-exit method or electronic databases with fingerprints for repeated capture method) Michael Jandl, Geneva, 4.3.2008