Teacher Education and Development Study in Mathematics - IEA

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PART 1: SALARY RELATIVITIES 35 However, our measure of relative salaries should still tell us something about people with mathematics aptitude who want to enter teaching as a profession, and how much these people influence the quality of teaching, at least in mathematics. It is possible that countries willing to pay teachers higher relative salaries may also be the ones that invest more in teacher-education programs, and demand more of such programs in terms of the quality of the teaching professionals they produce. Although the P-TEDS results are limited to only six countries, they provide evidence that this is the case. Attracting better-qualified individuals into teaching and then educating them to be better teachers may be a good strategy to raise the quality of education, but it is also probably very costly. That cost may be worthwhile if we are convinced that it yields results in the form of higher student academic achievement. In the next section, we relate these salary ratios and other variables to student performance.
36 TEACHER PAY AND STUDENT MATHEMATICS ACHIEVEMENT 3. Teacher Relative Pay and Student Performance Income distribution across countries and student performance If better student performance is positively related to higher relative teacher pay because of a more equal income distribution, we could expect, for example, that societies with a more equal income distribution will generally be societies with relatively equitable pay scales across occupations. Thus, differences between teacher pay and pay in other occupations are likely to be smaller in these societies than in societies marked by large income inequalities. One question we consequently decided to explore was this: Does student performance across countries and regions of countries relate to income inequality? We considered that an answer to this question would provide a clue as to whether the main story about student performance and teacher pay is about income distribution rather than societal emphasis on paying teachers more. To test the proposition that student performance may relate to income inequality at the national level, we used the reported results of two international tests of mathematics— Trends in International Mathematics and Science Study (TIMSS), administered by the International Association for the Evaluation of Educational Achievement (IEA), and Progress in International Student Achievement (PISA), administered by the Organisation for Economic Co-operation and Development (OECD). The TIMSS assessment measures have been given to fourth and eighth graders in many countries over several iterations: 1995 (also given to seventh graders in this year), 1999, 2003, and 2007. TIMSS focuses on mathematics and science. The PISA assessment measures have also been administered in many countries—to 15-year-olds—with iterations occurring in 2000, 2003, and 2006. PISA focuses on mathematics, science, and reading. The two programs of study have somewhat different purposes: TIMSS is more interested in assessing the effect of curriculum differences across countries; PISA is more concerned with how well students are learning critical thinking skills needed for working in the 21st-century labor market. We estimated the relationship of the average test scores on the studies’ tests to income inequality, controlling for GDP per capita and using income inequality data from all countries participating in TIMSS and PISA for which such data were available. There are many measures of income inequality; each has its particular advantages and disadvantages in estimating the distribution of income. For our estimates, we used the Gini coefficient. We took our estimates of the coefficients from two large databases assembled by the World Bank (World Bank, 2005; see also Deininger & Squire, 1996). We regressed the average mathematics scores in each test by country on the most recent Gini coefficient (multiplied by 100) available for that country and the GDP per capita in purchasing power parity dollars (PPP$) for the year 2000. For each test, the relationship between average test score and the Gini coefficient was negative and statistically significant. The coefficient for the GDP per capita was positive and usually statistically significant (except for TIMSS 1995). Table 4 presents the results. The results suggest that the more equal the income distribution and the higher the GDP per capita in a country, the more likely it will be that students attain the higher average test scores. We also tried other specifications, including controlling for the country’s total population
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