PIAAC Cycle 1 (2012-2017)
The key results
Initial results were published internationally and nationally on October 8, 2013. Nine additional countries participated in the second round (2012-2016). The results for Round I and II, covering a total of 33 participant countries, were published in June 2016. Furthermore, as part of the first cycle, six more countries (Ecuador, Kazakhstan, Mexico, Peru, Hungary, United States) participated in the third round from 2016 to 2019. The results of the entire first cycle, including the third round, were published in November 2019. Between 2011 and 2018, a total of 39 countries participated in the first cycle of PIAAC. Data collection took place in three rounds.
The core of PIAAC Cycle 1 was Round 1, in which a total of 24 countries, including Germany, participated. This round provided an initial overview of the basic competencies of adults in an international comparison. The results from Round 1 were published in 2013. A detailed international presentation of the findings can be found in the OECD report (OECD, 2013); a results presentation with a focus on Germany is featured in the German PIAAC report (Rammstedt, 2013) and in the summary results brochure.
Nine additional countries participated in Round 2 of PIAAC Cycle 1; five more countries took part in Round 3. The results from these rounds, combined with Round 1 of PIAAC, were published starting in 2016 (e.g., OECD, 2016; Rammstedt, Zabal & Gauly, 2019) and 2019 (e.g., OECD, 2019; Rammstedt, Gauly & Zabal, 2021).
How does Germany perform in PIAAC?
The reading competence of adults in Germany was slightly above the average of the participating OECD countries from all three rounds of PIAAC Cycle 1.
The highest scores in average reading competence were achieved by the populations in Japan, Finland, and the Netherlands. Noticeably low reading competencies were found in Mexico, Peru, and Ecuador.
The comparatively low reading competencies of the OECD countries from Round 3 explain why the reading competence of the adult population in Germany is slightly above average when considering the results from all three rounds together, although it was slightly below average when only considering the countries from Round 1 (cf. Rammstedt et al., 2021).
In everyday mathematical competence, the adult population in Germany scored above the OECD average, both in Round 1 and when considering data from all three rounds. This can be attributed to the relatively high proportion of individuals with very high competencies. Similar to reading competence, Japan and Finland had the highest average everyday mathematical competence, while Mexico, Chile, Ecuador, and Peru had the lowest.
Technology-based problem-solving was, by definition, computer-based and therefore not assessed for all individuals. The results were presented as population shares in three competency levels (Levels I, II, and III). When looking at the population shares of competency levels II and III, representing those with high technology-based problem-solving competence, Germany was above the OECD average. Of all participating countries, New Zealand, Sweden, Finland, and the Netherlands had the highest population shares in Levels II and III. The lowest shares were found in Turkey, Peru, and Ecuador.
Competencies in different population groups
In the international comparison, significant differences in basic competencies between various population groups were also observed. For example, differences were seen in all countries depending on education level, age, migration background, or gender.
Adults with a university degree had, on average, significantly higher competencies than adults without a higher secondary education degree.
The data also showed that the age group of 25- to 35-year-olds had a slightly higher competency level across all three competency domains on average in the OECD compared to older individuals. This is not only due to biological aging processes but also because younger individuals or birth cohorts often have higher levels of education.
In almost all countries, adults with a migration background achieved, on average, lower competency scores than those without a migration background. This disparity can be explained by the fact that migration background was determined by the mother tongue, and competencies in PIAAC were assessed in the respective national languages.
Men and women differed only marginally in basic competencies across all countries, with the notable exception that gender disparities favoring men were relatively high in Germany.
Competencies in the labor market
Basic competencies are also relevant for participation in the labor market. For example, employed individuals in all participating countries – including Germany – had, on average, higher competencies than unemployed individuals and those not in the labor force. Higher competencies were also associated with higher incomes.