1. Teachers’ Literacy & Numeracy skills Bart Golsteyn, Stan Vermeulen, Inge de Wolf Maastricht University, Academische Werkplaats Onderwijs

2. 2 Teacher quality: Essential for educational outcomes (Hanushek, 2011; Barber & Mourshed, 2007). Determinants not well-defined (Hanushek & Rivkin, 2006). Teacher cognitive skills  Important predictor of student performance (e.g. Hanushek, Piopiunik & Wiederhold, 2014; Metzler & Woessmann, 2012). Understanding which segment of the population chooses to become a teacher gives important information about the quality of the teacher corps. Relevant to determine the focus of interventions to improve average teacher quality. Background

3. 3 Aim: Investigate differences (in both mean and distribution) in reading and math skills between teachers and other individuals in various countries. Data: OECD international assessment of adult competencies (PIAAC and ALL). Representative samples of adult population. Tests measuring literacy & numeracy levels. Detailed information on occupation. Overview

4. 4 Results: Teachers score above country average in virtually all countries. Secondary school teachers outperform primary school teachers on average. Teachers outperform the other respondents (even other college graduates) mainly in the lower percentiles of the distribution. The best teachers are not outperformed by the best other respondents. Considerable differences between countries in terms of the shape of the distribution. Overview

5. 5 Prior research: Teacher skills are below college graduate average (Hanushek & Pace, 1995). Downward trend in teacher skills (Bacolod, 2007). Teaching profession is not attractive for highly skilled people (Chevalier et al., 2007). Our contribution: We extend previous research by focusing on the distribution of skills. The data permits cross-country comparisons. We show that depending on the shape of the skills distribution different policies are likely to be most effective in different countries. Contribution

6. 6 PIAAC & ALL  International adult skill surveys conducted by the OECD. ALL: First round: 2003; second round: 2006-2008 10 countries PIAAC: First round, first cycle: 2008-2013; second cycle: 2012-2016 24 countries Netherlands, Norway & Italy present in both datasets. Literacy & Numeracy tests are comparable. Exclude countries for which no teachers can be identified  18 countries left Data

7. 7 Data Descriptive statistics

8. 8

9. 9 Results

10. 10 Results

11. 11 Why look at the difference in distribution? Results

12. 12 Results

13. 13 Results

14. 14 Concerns: Teachers may use their skills more on average  Effect driven by practice. –PIAAC & ALL have questions on frequency of skill use at work –Controlling for skill use does not change results. High ability ‘old’ teachers may mask low abilities of ‘young’ teachers (Bacolod, 2007). Results

15. 15 Skills over age

16. 16 Are the results stable across the two datasets? Robustness

17. 17 Teachers outperform the other respondents (even other college graduates) mainly in the lower parts of the distribution. The highest skilled teachers are not outperformed by the highest skilled other respondents. Results are not driven by skill use or background characteristics, and are stable across two waves. The distribution of teacher skills differs across countries. Conclusions

18. 18 Depending on the shape of the distribution, different interventions seem most efficient in different countries. Increasing barriers to entry may prove inefficient in raising teacher quality in countries where low scoring teachers score relatively well. Improving on the factors that drive highly skilled persons into teaching may be more effective. Further research needed to discover the characteristics and motivations of highly skilled teachers. Implications

19. 19 What drives the highly skilled to enter the teaching profession? How can differences between countries be explained? (e.g. wages? (Hanushek, Piopiunik & Wiederhold, 2014)). Simulations to model the effects of interventions on the skill distributions. Are teacher skills and student outcomes linearly related across the distribution? Avenues for further research

20. 20 Bacolod, M. (2007b). Who teaches and where they choose to teach: College graduates of the 1990s. Educational Evaluation and Policy Analysis, 29(3), 155-168. Barber, M., & Mourshed, M. (2007). How the world's best-performing schools systems come out on top. McKinsey & Company. Chevalier, A., Dolton, P., & McIntosh, S. (2007). Recruiting and Retaining Teachers in the UK: An Analysis of Graduate Occupation Choice from the 1960s to the 1990s. Economica, 74(293), 69-96. Hanushek, E. A. (2011). The economic value of higher teacher quality. Economics of Education Review, 30(3), 466-479. Hanushek, E. A., & Pace, R. R. (1995). Who chooses to teach (and why)?. Economics of Education Review, 14(2), 101-117. Hanushek, E. A., Piopiunik, M., & Wiederhold, S. (2014). The Value of Smarter Teachers: International Evidence on Teacher Cognitive Skills and Student Performance. NBER Working Paper No. 20727. Hanushek, E. A., & Rivkin, S. G. (2006). Teacher quality. Handbook of the Economics of Education, 2, 1051-1078. Metzler, J., & Woessmann, L. (2012). The impact of teacher subject knowledge on student achievement: Evidence from within-teacher within-student variation. Journal of Development Economics, 99(2), 486- 496. Reference list

21. 21 Backup slides

22. 22 *table 3* Data Descriptive statistics

23. 23 Age graph highly educated

24. 24 PIAAC vs. ALL

25. 25 PIAAC vs. ALL