Fancy seeing you here…again: Uncovering individual‐level panel data in repeated cross‐sectional surveys

• Published date: 01 November 2023
• Author: Benny Geys
• Date: 01 November 2023
• DOI: http://doi.org/10.1111/puar.13693

RESEARCH ARTICLE

Fancy seeing you here…again: Uncovering individual-level

panel data in repeated cross-sectional surveys

Benny Geys

Department of Economics, BI Norwegian

Business School, Bergen, Norway

Correspondence

Benny Geys, Department of Economics, BI

Norwegian Business School, Kong Christian

Frederiks plass 5, 5006 Bergen, Norway.

Email: benny.geys@bi.no

Abstract

Many theories in Public Administration and Public Management explicitly relate to

changes over time in the attitudes, values, perceptions, and/or motivations of

public-sector employees. Examining such theories using (repeated) cross-sectional

datasets may lead to biased inferences and an inability to expose credible causal

relationships. As developing individual-level panel datasets is costly and time-

consuming, this article presents a method to make better use of existing surveys

fielded repeatedly among the same respondent pool without individual identifiers.

Specifically, it sets out an approach to create a system of unique identifiers using

information about respondents’background characteristics available within the

original data. The result is a panel dataset that allows tracking (a subset of) individ-

ual respondents across time. The article discusses issues of feasibility, credibility as

well as ethical considerations. The methodology has further practical value by

highlighting data characteristics that can help minimize identifiability of respon-

dents while creating public-release datasets.

Evidence for Practice

•Individual background characteristics offer possibilities to create individual-level

panel data from repeated survey waves fielded among the same respon-

dent pool.

•The resulting panel datasets allow improvements in the use and value of exist-

ing survey data for public sector management and practice.

•The presented methodology also offers a tool to help minimize identifiability of

respondents when creating Public Release datasets.

•Ethical concerns regarding anonymity, identifiability, and purpose limitation can

be handled with proper care and sensitivity by following well-designed

procedures.

INTRODUCTION

Cross-sectional surveys involve the collection of informa-

tion from a (possibly large) number of respondents at one

single point in time. Examples of such data collection

efforts in Public Administration scholarship include one-

off surveys on job satisfaction or work–life balance among

civil servants (Feeney & Stritch, 2019; Steijn & Van der

Voet, 2019), on Organizational Citizenship Behavior in the

public sector (de Geus et al., 2020; Vigoda-Gadot &

Beeri, 2011), or on red tape perceptions among public

employees (Hattke et al., 2018; Scott & Pandey, 2005).

Cross-sectional analysis of such datasets has been critical

to the development of many concepts and theories

in Public Administration and Public Management

(Stritch, 2017), and continues to be “a staple in [the] pub-

lic administration methodological toolkit”(Pandey, 2017,

p.135; see also Vigoda-Gadot & Vashdi, 2020). Stritch

(2017), for instance, highlights that only about 10% of all

1041 studies published in top public administration jour-

nals between 2011 and 2015 were longitudinal in nature.

Barely 10% of these longitudinal studies (i.e. 11 articles)

Received: 11 January 2023 Revised: 8 June 2023 Accepted: 15 June 2023

DOI: 10.1111/puar.13693

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribu tion and reproduction in any medium, provided the

original work is properly cited.

© 2023 The Author. Public Administration Review published by Wiley Periodicals LLC on behalf of American Society for Public Administration.

Public Admin Rev. 2023;83:1761–1771. wileyonlinelibrary.com/journal/puar 1761