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Estimating Criminal Populations from Administrative Registers

Authors: Antonella Baldassarini; Valentina Chiariello; Tiziana Tuoto;

Estimating Criminal Populations from Administrative Registers

Abstract

This study proposes a methodology for estimating the hidden criminal population working in markets of drug trafficking, prostitution exploitation and smuggling in Italy during the period 2006–2014. These estimates represent the first step of a wide procedure that has the final objective of measuring the economic flows of illegal transactions in national accounts. We exploit administrative registers coming from the Ministry of Justice, and consider these registers as lists of potential criminals. Unique codes for denounced criminals are not available, limiting so far its exploitation at micro level. This drawback has been overcome in this work by proposing an adjustment of the Zelterman estimator that accounts for the potential linkage errors caused by the lack of exact unique identifiers in the dataset. We obtain yearly estimates of the population size of criminals including also the unknown population, for the crimes of drug trafficking, prostitution exploitation and smuggling during the period 2006–2014.

Country
Italy
Keywords

Illegal economy; population size estimation; Capture-recapture; Zelterman estimator; linkage errors, linkage errors, population size estimation, Statistics, Illegal economy, Capture-recapture, Zelterman estimator, HA1-4737

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Published in a Diamond OA journal