Educational Research - the Ethics and Aesthetics of Statistics

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98 E. Hemelsoet (IPPR) tries to look at things in a broader perspective. This organisation defines itself as the UK’s leading progressive think tank. Along with other tasks, it tries to engage the media and the public in an informed and evidence-based debate on current policy issues. In the 2006 report (described as a ‘FactFile’) ‘Irregular migration in the UK’, researchers are at a standstill regarding key questions considering this topic. Recognising the usefulness of the estimate in the Home Office report, this ‘FactFile’ attempts to do more than estimate numbers and deal with some of the broader policy issues that irregular migration raises. The following questions are at stake: – Who are irregular migrants? How do they differ from illegal, undocumented and unauthorised migrants? – Why do irregular migrants come to the UK? – How many irregular migrants are there? – How do irregular migrants enter the UK? – Where do irregular migrants come from? – Where do irregular migrants work? – What is the economic impact of irregular migration? – How long do irregular migrants stay? – What rights do irregular migrants have? – What are the policy options? The report deals with a whole range of different themes and gives a descriptive overview of ‘evidence-based facts’ that are for the most part of a quantitative nature. After mapping different definitions related to this group of people, push and pull factors for migration are defined. This study goes beyond a mere estimate of the amount of irregular migrants in the country. Nevertheless, all the other questions refer to an evidence-based paradigm. Answers are given in terms of numbers and quantities that are taken as a starting point to get policy making going. Similar tendencies are apparent at the European level. Clandestino is ‘an interdisciplinary project’ and is ‘a response to the need for supporting policy makers in designing and implementing appropriate policies regarding undocumented migration’ (Picum, 2009, p. 2). Twelve EU countries participate in this project that aims at – providing an inventory of data and estimates on undocumented migration (stocks and flows) in the selected EU countries; – analysing these data comparatively; – discussing the ethical and methodological issues involved in the collection of data, the elaboration of estimates and their use; – proposing a new method for evaluating and classifying data/estimates on undocumented migration in the EU. Again, the number of undocumented migrants in the different countries is at the heart of the research. The fact that there are large methodological problems
7 To Frame the Unframable: Quantifying Irregular Migrants’ Presence 99 related to estimating irregular migrants has not prevented policy makers from focusing on this area. Paying lip service to unreliable figures is having far-reaching consequences. Before approaching this issue, I will consider, what is said in these reports and the conclusions that have been drawn from the findings. 7.2 Techniques for Estimating Irregular Migration Numbers A lot of creativity is put into action to estimate numbers of irregular migrants. The list of different techniques that can be used is long and varied. Without going too deeply into the particularities of different techniques and the problems each presents us with, an overview of methods is outlined here. Then attention is given to some examples of the most widespread methods and how they are applied. In general, there is a consensus on the classification of various techniques. Recent reports of the Clandestino Project (Kraler & Vogel, 2008; Vogel & Kovacheva, 2008; Picum, 2009) and the Home Office (Woodbridge, 2005; Pinkerton et al., 2004) all start from the distinction between ‘stocks’ and ‘flows’, in the line of earlier research (among others Delauney & Tapinos, 1998; Pinkerton, McLaughlan, & Salt, 2004; Jandl, 2004). This fundamental distinction is made in analogy with data on legal migration and refers to different statistical concepts: stocks (e.g. of irregular residents or migrant workers) refer to a number of people that are present in a country at a particular point in time, while flows (e.g. of illegal entrants or migrants ‘overstaying’) refer to movements during a certain period. Flow estimates thus refer to the number of migrants crossing the border during a period in time. Estimations are mainly based on border apprehension data or entry-exit statistics. Given the volatile nature of migration flows, the scarcity of reliable indicators and the dearth of appropriate methods for estimating flows, most efforts have so far concentrated on estimating stocks rather than flows. Methods for estimating stocks of irregular migrants can be divided into direct and indirect approaches. Direct measurement is based on data that ‘captures’ the subject of research directly. Numbers of irregular migrants taken from administrative statistics based on refusals, infractions or regularisations are multiplied to estimate the total number in the population. Indirect estimates do not rely on such data. They mainly start from general data on the population to estimate irregular migrant numbers (e.g. by expecting similar proportions of irregular migrants or calculating residues in official registers). Direct estimation approaches can be further classified into multiplier methods (among which simple multiplier models, capture-recapture models and models using a comparison of administrative registers and random effect mixed modelling), methods of self-identification and snowball sampling methods. Among the indirect approaches, residual methods, demographic methods, subjective estimations or indicator methods, econometric methods on the size and structure of shadow economies, comparisons of immigration and emigration statistics, flow-stock methods and methods based on indirect inferences can be distinguished. It is not the aim here to provide an extensive discussion of all the methods mentioned above (for
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Educational Research: The Ethics an
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Paul Smeyers · Marc Depaepe Editor
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Earlier Volumes in this Series Educ
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viii Contents 10 The Good, the Beau
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6 P. Smeyers and M. Depaepe The cha
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Chapter 2 The Lure of Statistics fo
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2 The Lure of Statistics for Educat
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28 M. Depaepe of the Leuven Catholi
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30 M. Depaepe Tan’s final verdict
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32 M. Depaepe of methodological bot
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34 M. Depaepe tapping other sources
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36 M. Depaepe In the context of dem
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38 M. Depaepe combination. Moreover
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40 M. Depaepe 4. See Pelleriaux (19
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42 M. Depaepe Schneider, J. (2010).
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44 J.J.H. Dekker (see Section 4.4).
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46 J.J.H. Dekker from their own par
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152 J.P. Van Bendegem et al. A good
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156 J.P. Van Bendegem et al. statis
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158 J.P. Van Bendegem et al. 10.4 C
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160 J.P. Van Bendegem et al. Van Be
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162 P. Smeyers to be told. Moreover
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164 P. Smeyers us’. It goes witho
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166 P. Smeyers at the basis of the
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168 P. Smeyers indeterminism, as we
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170 P. Smeyers are steps (a plan as
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172 P. Smeyers to the simple fact t
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174 P. Smeyers 6. The role of the a
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176 P. Smeyers conditions. It is in
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178 I. Munday etc. on a PowerPoint
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180 I. Munday To show how Derrida p
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182 I. Munday might talk of 20 bags
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184 I. Munday is unclear whether th
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186 I. Munday in numerals. This mig
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188 I. Munday We might also note th
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190 R. Smith ‘Statisticism’, on
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192 R. Smith identify, and twice in
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194 R. Smith of the final sentence
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196 R. Smith between admitting that
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198 R. Smith 7 www.equalitytrust.or
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200 R. Smith It is an interesting q
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202 R. Smith self-esteem, happiness
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204 R. Smith Reeves, R. (2009, Apri
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206 P. Standish Some students’ no
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210 P. Standish Rice $1.73 1 / 2 Mo
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212 P. Standish research none of us
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214 P. Standish of the products but
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216 About the Authors co-editor-in-
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218 About the Authors voor Wetensch
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220 Author Index Elkins, G. R., 190
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222 Author Index Silverman, J. G.,
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224 Subject Index I Inequality, 29-
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