The research data center PIAAC at GESIS

Periodical
Journal of Economics and Statistics
Volume
236
Year
2016
Issue number
5
Page range
581-593
Relates to study/studies
PIAAC Cycle 1

The research data center PIAAC at GESIS

Abstract

Introduction

With the Programme for the International Assessment of Adult Competencies (PIAAC) researchers can shed light on how competencies are acquired, how its use helps us maintain and further develop skills, and whether adults are prepared for the challenges of modern knowledge societies (OECD 2013a). The Organisation for Economic Co-operation and Development (OECD) initiated PIAAC in more than 30 countries to assess competencies of the adult population. Similar to the Programme for International Student Assessment (PISA), PIAAC is planned to be repeated in regular intervals. Therefore, the next cycle of PIAAC is planned for 2022.

The OECD published the PIAAC international public use file of the first cycle of PIAAC (OECD 2015) in 2013. Due to German confidentiality rules, GESIS published a scientific use file (Rammstedt et al. 2015) that includes information that could not be released in the public use file. Further national data and para data for PIAAC can shed light on further research questions as well as methodological aspects of PIAAC. This data is currently and will be made available in the Research Data Center PIAAC (RDC PIAAC) at GESIS. In addition to this, various add-on studies were and are currently being conducted in Germany, such as Competencies in Later Life (CiLL) and the longitudinal study PIAAC-L.

However, the PIAAC data presents challenges due to imputed competency scores (plausible values) and country-specific complex sample techniques. The RDC PIAAC provides information on analytic methods and the available analysis tools. It also offers workshops to familiarize users with the data and to teach them how to analyze the PIAAC data.

Given the brevity of the PIAAC data release, an impressive number of research papers were published that use PIAAC data. Research with PIAAC focuses, for example, on the returns to skills (e. g., Hanushek et al. 2015), skill and wage inequality (Paccagnella 2015), skill mismatch (Allen et al. 2013a; Perry et al. 2014), non-monetary outcomes, such as trust (Borgonovi/Burns 2015), and also methodological aspects, such as incentives in large-scale assessments (Martin et al. 2014).

This paper aims to present central aspects of PIAAC, analytical procedures for the competence measures and the complex sample design, as well as data, information and services provided through the RDC PIAAC at GESIS.