What explains variation in teacher effectiveness?

These findings are in line with the meta-analyses conducted on school resources and teacher characteristics: Hanushek covered a significant number of studies from the US in three reviews. He compiled 147 separately estimated educational production functions from 33 publications (Hanushek, 1986). Later he updated this survey twice. In his 1989 study he compiled 187 estimations from 38 articles (Hanushek, 1989) and in 1997 he covered a set of publications available through 1994. This set included 90 publications and 377 separate estimates (Hanushek, 1997). The selection criteria to be included in these surveys were to be published in a book or refereed journal, relating some objective measure of student output to family and school characteristics, and providing information about the statistical significance of the estimated relationships.

Hanushek draws three main conclusions from these meta-analyses: First, the estimates obtained from educational production functions do not look promising for teacher education. These studies fail to establish a strong and consistent relationship between teacher education and higher student achievement. Second, the majority of the estimated coefficients for teacher experience point in the correct direction and 30 percent of the estimated coefficients exceed the conventional statistical significance level of 5 percent. However, the results for teacher experience are hardly

overwhelming; the relationship between teacher experience and student achievement is strong only relative to other school inputs10. Hanushek (1986, 1989, 1997) claims that the positive association between teacher experience and student achievement may be due to possible selection effects. He stresses that this finding may be a result of experienced teachers being allowed to select schools and classroom with higher achieving students. Third, as Table 3 shows, among the entire array of teacher characteristics considered, higher teacher test scores are most consistently associated with stronger learning outcomes.

Table 3: Percentage Distribution of Estimated Effect of Teacher Characteristics on Student Performance 1986 1989 1997 Statistically significant & positive Positive Statistically significant & positive Positive Statistically significant & positive Positive Teacher education 6 30 7 35 9 42 Teacher experience 30 60 29 60 29 59

Teacher test score 28 78 26 58 37 64

Source: Author’s calculations from the studies of Hanushek (1986, 1989, 1997)

Note, however, that these meta-analyses cover only studies based on US data and the evidence from other developed countries is scarce. There is no meta-analysis available for developed countries other than for the US. However, the study by Hanushek and Luque (2003) gives some insight on this front. They analyze TIMSS data for 18 developed and developing countries11 and suggest that the findings are very similar to those yielded by the meta-analyses that only include studies from the US.

10

Hanushek also summarizes estimated coefficients for student teacher ratio, per pupil spending, facilities and administrative inputs.

11

Canada, Cyprus, Czech Republic, Greece, Hong Kong, Ireland, Japan, Latvia, Netherlands, New Zealand, Norway, Portugal, Thailand, Scotland, United States and Slovenia

As for developing countries, the first comprehensive meta-analysis dates back to 198712. Fuller (1987) reviewed 60 studies investigating student achievement in developing countries. Later Fuller and Clarke (1994) expanded Fuller’s 1987 study by surveying an additional 47 studies published between 1987 and 1993. These meta-analyses included studies that used at least some measure of student’s social class as a control variable. A year later, Hanushek (1995) conducted a larger survey with the same criteria for the selection of studies based on his previous research (Harbison & Hanushek, 1992). Lastly, more recently, Glewwe et al. (2011) conducted a meta- analysis on developing countries and extracted estimates from studies published between 1990 and 2010. They filtered a very large pool of studies according to their methodological

approaches and ended up with 79 studies which use at least simple OLS with at least one family background, school expenditure, teacher and one additional school variable as controls. A total of 43 of these 79 studies had a more complicated identification strategy, such as randomized controlled trials, difference-in-differences, regression discontinuity and matching designs.

These meta-analyses, which cover the last three decades, consistently show that teacher test scores are a stronger indicator of teacher effectiveness than teacher education and teacher experience and this distinction appears to be more pronounced in developing countries than in developed countries (see Table 4). Therefore, especially in developing countries, teacher test scores may provide valuable input for policy makers in designing teacher selection and hiring mechanisms.

12

Relatively smaller surveys appeared earlier (Heyneman & Loxley, 1983; Schiefelbein & Simmons, 1981; Simmons & Alexander, 1978).

Table 4: Percentage Distribution of Estimated Effects of Teacher Characteristics on Student Performance in Developing Countries (statistically significant and positive)

Glewwe et al. (2011) Fuller (1987) Fuller and

Clarke (1994)

Hanushek

(1995) inc. OLS exl. OLS

Teacher education 46 54 56 33 15

Teacher experience 43 40 35 27 18

Teacher test score 100 100 - 55 65

Source: Authors calculations from the studies of Fuller (1987), Fuller and Clarke (1994), Hanushek (1995) and Glewwe et al.(2011)

However, it should be noted that the methodology and findings of these meta-analyses have been disputed. Hedges et al. (1994) conclude that there is a systematic positive relation between resource inputs and school outcomes after analyzing the same set of studies surveyed by Hanushek. They claim that vote counting, the methodology which Hanushek employed, is problematic. Similarly, Dewey et al. (2000) claim that the inclusion of parental income in educational production function estimates leads to misspecification and this avoids the detection of a consistent relationship between resources and outcomes. Lastly, the weighting of the studies surveyed was also an issue: Krueger (2002) notes that given that Hanushek equally weights each estimate his selection rule automatically extracts more estimates from publications which

conducts subsample analyses by grade, race, gender etc. The shortcomings of these meta - analyses should be kept in mind.

III. Teacher selection regimes in Turkey