Jahrbücher für Nationalökonomie und Statistik

Open Access Published by De Gruyter Oldenbourg

Volume 225 Issue 5

October 2005

Issue of Jahrbücher für Nationalökonomie und Statistik

Contents
Journal Overview

Publicly Available September 20, 2016

Titelei

Page range: 511-512

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September 20, 2016

Abhandlungen / Original Papers

September 20, 2016

Guest Editorial “Econometrics of Anonymized Micro Data”

Winfried Pohlmeier, Gerd Ronning, Joachim Wagner

Page range: 515-516

September 20, 2016

Data Masking by Noise Addition and the Estimation of Nonparametric Regression Models

Sandra Lechner, Winfried Pohlmeier

Page range: 517-528

Abstract

Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the data and limits its use for empirical research. A popular masking procedure is noise addition, which leads to inconsistent estimates if the additional measurement errors are ignored. This paper investigates to what extent appropriate econometric techniques can obtain consistent estimates of the true data generating process for parametric and nonparametric models when data is masked by noise addition. We show how the reduction of the data quality can be minimized using the local polynomial Simulation-Extrapolation (SIMEX) estimator. Evidence is provided by a Monte-Carlo study and by an application to firm-level data, where we analyze the impact of innovative activity on employment.

September 20, 2016

The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

Matthias Schmid, Hans Schneeweiss

Page range: 529-543

Abstract

Microaggregation is a set of procedures that distort empirical data in order to guarantee their factual anonymity. At the same time the information content of data sets should not be reduced too much and should still be useful for scientific research. This paper investigates the effect of microaggregation on the estimation of a linear regression by ordinary least squares. It studies, by way of an extensive simulation experiment, the bias of the slope parameter estimator induced by various microaggregation techniques. Some microaggregation procedures lead to consistent estimates while others imply an asymptotic bias for the estimator.

September 20, 2016

Post-Randomization Under Test: Estimation of the Probit Model

Gerd Ronning, Martin Rosemann, Harald Strotmann

Page range: 544-566

Abstract

The paper analyzes effects of randomized response with respect to some binary dependent variable on the estimation of the probit model. This approach is used in interviews when asking sensitive questions or if a respondent erroneously chooses the wrong category in an interview leading to ‘misdassification’. Alternatively, randomization can be used for statistical disclosure control and then is called ‘post randomization method’ (PRAM). We consider two variants which are termed ‘ordinary’ and ‘invariant’ PRAM the latter being of importance mainly in descriptive analysis. Maximum likelihood estimation of the corrected likelihood results in consistent estimates although variances increase considerably for ‘strong’ randomization. Moreover a finite sample bias has been observed in the simulation study, but it is much less pronounced than the bias implied from use of the ‘naive’ probit estimator when the binary dependent variable has been randomized. Effects of randomization on the probit estimates are also illustrated by an empirical study using cross-section data from the German ‘ΑΒ establishment panel’ (IAB Betriebspanel). The decision of firms to accept a collective bargaining agreement (‘Tarifvertrag’) is analyzed in a binary probit model using both original data and data masked by ordinary and invariant PRAM. Here, too, a remarkable bias is observed in case of ‘strong’ randomization.

September 20, 2016

Microdata Disclosure Control by Resampling - Effects on Regression Results

Sandra Gottschalk

Page range: 567-583

Abstract

Nonparametric resampling is a method for generating synthetic microdata and is introduced as a procedure for microdata disclosure limitation. Theoretically, re-identification of individuals or firms is not possible with synthetic data. The resampling procedure creates datasets - the resample - which nearly have the same empirical cumulative distribution functions as the original survey data and thus permit econometricians to calculate meaningful regression results. The idea of nonparametric resampling, especially, is to draw from univariate or multivariate empirical distribution functions without having to estimate these explicitly. Until now, the resampling procedure shown here has only been applicable to variables with continuous distribution functions. Monte Carlo simulations and applications with data from the Mannheim Innovation Panel show that results of linear and nonlinear regression analyses can be reproduced quite precisely by nonparametric resamples. A univariate and a multivariate resampling version are examined. The univariate version as well as the multivariate version which is using the correlation structure of the original data as a scaling instrument turn out to be able to retain the coefficients of model estimations. Furthermore, multivariate resampling best reproduces regression results if all variables are anonymised.

September 20, 2016

Anonymized Firm Data under Test: Evidence from a Replication Study

Joachim Wagner

Page range: 584-591

Abstract

This paper contributes to the literature on the use of anonymized firm level data by reporting results from a replication study. To test for the practical usefulness of anonymized data I selected two of my published papers based on different cross sections of firm data. The data used there were anonymized by micro aggregation. I replicated the analyses reported in the papers with the anonymized data, and then compared the results to those produced with the original data. Frequently, the reported levels of statistical significance differ. Furthermore, statistically significant coefficients sometimes differ by order of magnitude. Therefore, at least for the moderate sample sizes used here micro-aggregated firm data should not be considered as a tool for empirical research.

September 20, 2016

Buchbesprechung / Book Review

Karl Heinrich Oppenländer

Page range: 592-598

About this journal

The Journal of Economics and Statistics (Jahrbücher für Nationalökonomie und Statistik) is a scientific journal published in Germany since 1863. The journal publishes papers in all fields of economics and applied statistics. A specific focus is on papers combining theory with empirical analyses. Papers providing conclusions for economic policy in Europe are particularly welcome. Nevertheless, distinguished papers dealing exlusively with economic theory, empirical models, or economic history, will not be excluded from consideration.

The editors invite replication studies focusing on the robustness of empirical findings. The section Data Observer presents articles on data sets available for empirical research and institutions providing research data. The journal also publishes special issues, short comments, and book reviews. All freely accessible contributions, including the Data Observer articles, can be found via the website's article search functionality.

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Call for Papers

Special Issue on "Sports Economics"

Guest Editors: Bernd Frick, Paderborn University; Thomas Peeters, Erasmus University Rotterdam; Rob Simmons, Lancaster University

Timeline: Submission of full papers November 30, 2023

Further details

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Special Issue on "Advancing Agent-Based Economics"

Guest Editors: Michael Neugart, Technical University of Darmstadt) and Frank Westerhoff, University of Bamberg

Timeline: Submission of full papers December 31, 2023

Further details

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Special Issue on "Central Bank Digital Currency"

Guest Editors: Katrin Assenmacher, European Central Bank and Michael Frenkel, WHU

Timeline: Submission of full papers January 08, 2024

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Volume 225 Issue 5

Issue of Jahrbücher für Nationalökonomie und Statistik

Titelei

Inhalt / Contents

Guest Editorial “Econometrics of Anonymized Micro Data”

Data Masking by Noise Addition and the Estimation of Nonparametric Regression Models

Abstract

The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

Abstract

Post-Randomization Under Test: Estimation of the Probit Model

Abstract

Microdata Disclosure Control by Resampling - Effects on Regression Results

Abstract

Anonymized Firm Data under Test: Evidence from a Replication Study

Abstract

Buchbesprechung / Book Review