Teacher Education and Development Study in Mathematics - IEA

PART 1: TEACHER RELATIVE PAY AND STUDENT PERFORMANCE
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and the percentage of the age cohort in lower-secondary education. The coefficient of
GDP per capita changed somewhat with the inclusion of total population, but the Gini
coefficient remained highly robust.
Table 4: National Mathematics Test Scores, Gini Coefficients, and GDP per Capita, by Test and Year
Variable TIMSS 1995 TIMSS 1999 TIMSS 2003 PISA 2000 PISA 2003
Gini -2.964*** -3.454*** -3.364*** -2.962*** -3.028***
Coefficient (x 100) (-3.18) (-3.12) (-2.90) (-2.85) (-3.59)
GDP per Capita 0.0010 0.0036*** 0.0027*** 0.0023*** 0.0021***
(PPP$) (1.22) (3.39) (2.79) (2.85) (3.41)
Intercept 608.7 565.4 569.9 534.4 545.0***
Adjusted R 2 0.30 0.47 0.39 0.42 0.50
N 36 35 36 38 36
Note: ***Statistically significant at 1% level.
Source: IEA TIMSS 1995, 1999, 2003; OECD PISA 2001, 2004.
One reason why average test scores might be higher in countries with more equal income
distribution is that students from low-income families in such countries are relatively
better off than students in economies with similar levels of average GDP per capita but
greater inequality. If, as Chiu and Khoo (2005) suggest, income inequality correlates
positively with inequality in the distribution of teacher qualifications across schools,
then shifting teacher skills from higher to lower income schools could have a substantial
impact on lower-income students in countries with greater income inequality. Highincome
students’ performance should be less affected by income distribution differences
among countries.
We tested this proposition in two ways. First, we estimated the difference between the
mathematics scale score of the highest scoring 10% of test-takers in each country and
the scale score of the lowest scoring 10% of test-takers relative to the overall mean of
the mathematics scale score. We then ran that computation against the country’s Gini
coefficient. Second, we estimated the equations given in Table 4 above for the average
test scores of the lowest 10% of performers, the lowest 25% of performers, and the
highest 10% of performers in each country.
The plot of the relative mathematics scale score differences from PISA 2003 against the
Gini coefficients of income distribution in 2000 (Figure 1) showed a highly significant
positive relationship (statistically significant at the 0.1% level). Table 5 shows that
the Gini coefficient related to the relative scale-score differences between the highestscoring
10% and the lowest-scoring 10% of test-takers was about 0.002. Thus, what
is evident to us, in addition to the positive relationship between mean test score and
greater income equality, is that the more equal the income distribution, the more likely
it will be that the difference in achievement scores between high- and low-scoring
students is low when those scores are compared against the mean achievement score.
For every 10 points of Gini (the difference between Sweden and the United States, for
example), we found that the relative difference in scale score between the lowest- and
the highest-scoring students increased by about two percent.