Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality

In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order t...

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Main Authors: Giuseppe Arbia, Giuseppe Espa, Diego Giuliani
Format: Article
Language:English
Published: MDPI AG 2015-10-01
Series:Econometrics
Subjects:
Online Access:http://www.mdpi.com/2225-1146/3/4/709
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spelling doaj-00c1fe39039a427caf3df42e056c0f382020-11-24T22:18:44ZengMDPI AGEconometrics2225-11462015-10-013470971810.3390/econometrics3040709econometrics3040709Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for ConfidentialityGiuseppe Arbia0Giuseppe Espa1Diego Giuliani2Department of Statistical Science, Catholic University of the Sacred Heart, Rome 00168, ItalyDepartment of Economics and Management, University of Trento, Trento 38122, ItalyDepartment of Economics and Management, University of Trento, Trento 38122, ItalyIn many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners.http://www.mdpi.com/2225-1146/3/4/709spatial econometricsspatial microeconometricsconsistency of estimatesgeo-maskingconfidentialitydistance evaluation
collection DOAJ
language English
format Article
sources DOAJ
author Giuseppe Arbia
Giuseppe Espa
Diego Giuliani
spellingShingle Giuseppe Arbia
Giuseppe Espa
Diego Giuliani
Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
Econometrics
spatial econometrics
spatial microeconometrics
consistency of estimates
geo-masking
confidentiality
distance evaluation
author_facet Giuseppe Arbia
Giuseppe Espa
Diego Giuliani
author_sort Giuseppe Arbia
title Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
title_short Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
title_full Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
title_fullStr Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
title_full_unstemmed Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
title_sort measurement errors arising when using distances in microeconometric modelling and the individuals’ position is geo-masked for confidentiality
publisher MDPI AG
series Econometrics
issn 2225-1146
publishDate 2015-10-01
description In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners.
topic spatial econometrics
spatial microeconometrics
consistency of estimates
geo-masking
confidentiality
distance evaluation
url http://www.mdpi.com/2225-1146/3/4/709
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