From 2006 to 2011 the International Association for the Evaluation of Educational Achievement (IEA), the organization that produces the TIMMS and PIRLS international assessments, worked with teacher education programs in seventeen countries to administer a survey to determine the depth of mathematics knowledge and skills of future teachers in those countries.  The study also assessed the quality assurance measures each nation has in place for recruiting and training teachers.  The seventeen countries that participated in this Teacher Education and Development Study in Mathematics (TEDS-M) were Botswana, Canada (four provinces), Chile, Georgia, Germany, Malaysia, Norway, Oman (lower-secondary teacher education only), the Philippines, Poland, the Russian Federation, Singapore, Spain (primary teacher education only), Switzerland (German-speaking cantons), Taiwan, Thailand, and the United States (public schools only).

The survey included a test of mathematics content knowledge of teachers in training in the last year of their primary- and lower-secondary teacher education programs.  The subdomains used to develop the test were derived from the subdomains used in the assessment frameworks for IEA’s Trends in Mathematics and Science Study (TIMSS) given every 4 years to students at grades 4 and 8 in more than 60 countries.  A different test was administered to students training to be primary teachers from the test given to students training to be lower-secondary teachers in order to reflect the level of mathematics they will eventually teach.

The charts below show the scaled mean score for teachers from each country, for primary teachers in Chart 1 and lower-secondary teachers in Chart 2.  Canada is not included in either chart as the number of responses from that country did not meet the sample size requirements for TEDS-M.

While not all countries represented in these charts participated in TIMSS 2011, it is clear that top TIMMS performers, such as Singapore and Taiwan, also did well on this measure of future teachers’ math content knowledge at both primary and secondary levels.

Along with the assessment gauging future teachers’ mathematics content knowledge, TEDS-M conducted a survey of teacher training programs in the countries studied to see what quality assurance procedures are in place for recruitment and selection of future teachers.  This study looked at three criteria for quality assurance in the recruitment and selection of future teachers: 1) The extent to which states control enrollment into teacher training programs, 2) The attractiveness and status of primary and secondary teaching as a profession, and 3) High selection requirements for entry into teacher training programs.  Quality assurance procedures from each country were ranked Strong, Moderately Strong or Limited.

The chart below demonstrates how each country scored for each category, according to the criteria developed by the TEDS-M study.  Among the countries that responded to the survey, only two countries were rated strong in all three categories: Taiwan and Singapore, both of which came out at the top of the league tables in the most recent administration of TIMSS Mathematics in 4th and 8th grade.  The United States, meanwhile, rated low on all three measures of quality assurance for recruitment and selection of teachers.

The results of the TEDS-M survey clearly show a large gap in future teachers’ mathematical content knowledge between countries that typically top the league tables in student performance on international tests, like Taiwan and Singapore, and low performing countries.  Top performing countries also have rigorous quality assurance measures for recruiting and training new teachers.  The findings of the TEDS-M survey also suggest that the diversity in teacher recruitment and training procedures represents a policy continuum, which can provide countries working to improve teacher quality with examples of systems that are working hard to improve the mathematics knowledge and skills of their teaching force.  The TEDS-M survey considered other factors in teacher training programs as well, including future teachers’ beliefs about mathematics learning and self-reported past performance in mathematics.  We encourage you to read the full report here.

In their September 2012 Global Employment Outlook, the International Labour Organization (ILO) drew particular attention to the plight of the young worker worldwide.  They project that the global youth unemployment rate (youth being defined as between the ages of 15 and 24) will climb from 12.7 percent in 2012 to 12.9 percent by 2017.  This is in contrast to the overall unemployment rate, which is expected to remain steady worldwide at 6 percent between 2012 and 2017.  The projected rates of youth unemployment vary, of course, by region.  In East Asia, the youth unemployment rate is projected to increase to 10.4 percent by 2017, up from 9.5 percent, while in the developed economies and the European Union, the rate is actually projected to decline from 17.5 percent in 2012 to 15.6 percent in 2017.  However, the latter figure is not actually cause for celebration – the report notes this is “principally because discouraged young people are withdrawing from the labor market and not because of stronger hiring activity among youngsters.”

We turn to additional ILO data to see what the picture looks like in some of the countries with top-performing education systems, to see if the strength of the primary and secondary systems mitigates to some degree the proportion of young people who are struggling to find work (Figure 1).  The results are somewhat surprising.  Finland, widely acknowledged as having one of the best primary and secondary education systems in the world, also has the highest unemployment rate for people aged 15 to 19 years, and one of the highest unemployment rates for people aged 20 to 24 according to the ILO data.  Singapore and the Netherlands, which have strongly integrated vocational and technical pathways available to students before the age of 18, on the other hand (and unsurprisingly), have quite low youth unemployment rates.

Figure 1

But before jumping to conclusions, it is important to dig deeper into how countries define youth unemployment, because this in and of itself can impact how well a country appears to be doing in terms of moving young people into the workforce.  For the chart above, the ILO definition of “unemployed” included people who were not in paid employment, were available for employment, and were seeking employment.  The ILO points out that these measures are difficult to compare across countries because education systems vary widely, and in some countries a young person may be considered “employed,” for example, if they are engaging in a vocational training program part-time.  In another country, the labor force may be considered as including only the youth who have dropped out of secondary school or who have earned a secondary degree.  This may result in inflated rates of “unemployment” in some countries, for example, Nordic countries, that have more modular vocational and post-secondary education programs and other strong supports for young people, resulting in young people pursuing a combination of part-time training, employment, or other activities such as international travel before settling into a career.

Fortunately, there is another international measure that allows us to compare the proportion of young people who are struggling to enter the workforce or the education sector.  That is the percent of youth not in employment, education or training, often abbreviated as NEET.  The OECD provides data on the percent of NEET youth in most of its member countries; below, we have again shown the data for the top performers (Figure 2).  The chart provides information for three different categories of young people: youth who are unemployed (that is, looking for work), and not in education or training; youth who are inactive (that is, not looking for work), and not in education or training; and the NEET rate, which includes youth who are either unemployed or inactive, and not in education or training.  The NEET rate is represented by the total length of the bar on the chart, as it is a combination of the two other measures.

Figure 2

The Netherlands, which has one of the lowest rates of youth unemployment by ILO measures, also has a very low NEET rate.  Notably, just 1.5 percent of youth in the Netherlands who are not in education or training and are actively seeking work are unable to find jobs.  This is just over 25 percent of the overall OECD rate of 5.8 percent, and significantly smaller than the EU27 (European Union) rate of 6.6 percent.  Denmark and Finland, two Nordic countries which, by overall youth unemployment measures, do not look particularly good, also have very low NEET rates.  These low rates are likely due to the fact that these countries, and particularly the Netherlands and Denmark, have very strong school-to-work pipelines, with multiple pathways for all types of students.  Students in these countries have access to various workplace learning experiences and apprenticeships, as well as a close relationship between industry and these training programs.  On the other end of the spectrum, the United States, New Zealand and the United Kingdom all have high NEET rates in addition to their high youth unemployment rates, suggesting that job training programs or pathways into the workforce in these countries are lacking.

One concern, however, is the possibility of a growing connection between youth unemployment rates and youth NEET rates.  The ILO points out in their Global Employment Outlook that as new economic sectors grow and old sectors decline, people who were either employed in or being trained for jobs in the old sectors will face the loss of these jobs with a sense of discouragement, meaning that NEET rates will rise following the rise in unemployment rates.  This is why it is so important to have education connected to current workplace skill requirements, and particularly, to ensure that vocational and technical education programs are linked closely to industry, so that youth are being prepared for the jobs of the future.

In December, the results of the 2011 administration of the Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) were published in three separate reports, each examining international performance in reading (at the fourth grade level), math (at the fourth and eighth grade levels) and science (at the fourth and eighth grade levels).  These assessments provide a picture of international student performance in the years before a student reaches the age of 15, which is the age at which students take the OECD’s Programme of International Student Assessment (PISA).  However, there are some central differences between the TIMSS/PIRLS and PISA assessments.  Michael Martin, the Co-Executive Director of the TIMSS and PIRLS Study Center at Boston College, notes that while PISA is intended to measure a student’s general skills in the arenas of reading, math and science, TIMSS and PIRLS are more focused on content mastery.  Additionally, Jack Buckley, the commissioner of the National Center for Education Statistics, has pointed out that the countries participating in both assessments do vary – the TIMSS and PIRLS groups are smaller and represent a mixture of countries at different levels of economic development as compared to the participants in PISA.

Because of the differences between the assessments, the countries that are in the top ten or fifteen of the TIMSS and PIRLS rankings are somewhat different than the top performers on the last incarnation of PISA in 2009.  While league tables of the top countries based on their average scores always garner the most press when the results of international assessments are released, we decided to take a more in-depth look at what level of proficiency students in the top fifteen countries are actually reaching in these subjects.

The IEA has established four “international benchmarks” on their score scale for these assessments.  While the score scale for both PIRLS and TIMSS runs from 0-1000, the vast majority of scores fall between 300 and 700.  The IEA has identified a score of 400 as the “low” international benchmark, indicating that students at this score point have been educated to a “basic” level.  Beyond that, there is a score of 475, or “intermediate;” a score of 550, or “high,” and a score of 625, or “advanced.”  Below, we have plotted the percent of students at each benchmark in the top fifteen countries on the 2011 administration of PIRLS and TIMSS.  This is useful when thinking about the top performers, because it shows, in a clearer way perhaps than the average scale score, what students in each country are really able to do.

In the fourth grade PIRLS reading assessment, a student who reaches the “low” international benchmark is able to “locate and retrieve an explicitly stated detail” in a literary text, and “locate and reproduce explicitly stated information … at the beginning of the text” in an informational text.  By contrast, at the “advanced” international benchmark, students are able to “integrate ideas and evidence across a text,” and “distinguish and interpret complex information from different parts of a text,” among other skills.

The chart above, like the others to follow, is organized from top to bottom in the order of average scale score.  However, the average scale score does not always correlate to the highest percentage of students reaching the “advanced” benchmark in each country.  In this case, it does not, though Hong Kong does have the highest proportion of students meeting either the “high” benchmark or “advanced” benchmark – 67 percent – while in the United States, just 56 percent of students meet those levels.  The tail of students either meeting the “low” benchmark or not meeting a benchmark is also significantly smaller in the top three countries – Hong Kong, the Russian Federation, and Finland (7, 8 and 8 percent, respectively), than in the majority of the other countries.  This more specific data on student performance is useful in terms of thinking about a country’s overall performance, because it gives a clearer sense, potentially, of the equity of the school system, and the ability of the system to educate all students – or any students – to high levels.  It also demonstrates that there are clear differences in student performance between the top handful of countries and the rest of the countries rounding out the top ten or fifteen.

For fourth grade math, in order to reach the “low” benchmark, a student must be able to demonstrate “basic mathematical knowledge,” such as adding and subtracting integers and being able to recognize familiar shapes.  At the “advanced” benchmark, a student must have an understanding of how to apply their knowledge, for example, by solving word problems with multiple steps, and they must show some understanding of more difficult concepts like fractions and decimals.

In the case of TIMSS fourth grade math, the percent of students reaching the “advanced” benchmark does correlate to the country’s average scale score, at least for the top six performers.  This chart indicates very clearly how well the East Asian countries do compared to the rest of the world in instilling advanced-level math skills in their students, even at an early age, with about a third of students or more reaching the “advanced” benchmark in Singapore, Korea, Hong Kong, Taiwan and Japan, and an overwhelming majority reaching either the “advanced” or “high” benchmarks in all cases.  These countries also have the smallest proportions of students who failed to meet the most basic level.  By contrast, starting with Northern Ireland, which is in sixth place in the overall league table in this subject, the other countries have higher proportions of students failing to reach at least the “intermediate” benchmark, and generally much lower proportions of students reaching the “advanced” benchmark.

In eighth grade math, students at the “low” benchmark “have some knowledge of whole numbers and decimals, operations, and basic graphs.”  At the “advanced” level, students are able to demonstrate many mathematical skills, such as solving linear equations, reasoning with geometric figures, and expressing generalizations algebraically.

The pattern in proficiency seen in the TIMSS fourth grade math results is continued in the TIMSS eighth grade math results.  Andreas Schleicher from the OECD and US Education Secretary Arne Duncan have commented on the drop in math and science skills from fourth grade to eighth grade in the United States, and the data bears this out.  In fourth grade, 47 percent of American students met either the “high” or “advanced” benchmarks; in eighth grade, just 30 percent of students did.  Furthermore, twice as many American students – 8 percent – failed to meet any benchmarks in eighth grade than in fourth grade.  In Singapore, however, the number of students meeting the “advanced” or “high” benchmark holds steady at 78 percent in both grades, and the other East Asian countries also do not lose any substantial ground.  Taiwan increases the number of students at the “advanced” level from 30 percent in fourth grade to about half (49 percent) in eighth grade.

In fourth grade science, students at the “low” benchmark “show some elementary knowledge of life, physical and earth sciences,” and “demonstrate knowledge of some simple facts … interpret simple diagrams, complete simple tables, and provide short written responses to questions requiring factual information.”  At the “advanced” benchmark, students can “apply knowledge and understanding of scientific processes … and show some knowledge of the process of scientific inquiry.”  Additionally, “they have a beginning ability to interpret results in the context of a simple experiment, reason and draw conclusions from descriptions and diagrams, and evaluate and support an argument.”

On the TIMSS fourth grade science assessment, the East Asian countries do not dominate in terms of student proficiency at the “advanced” benchmark as completely as they do in math, although perennial top performers South Korea and Singapore still top the list in this measure.  Fewer students overall, across the board, seem to have reached the “advanced” benchmark in science as compared to reading and math.  The United States seems to have a particular problem in this subject, with 19 percent of students either failing to meet any benchmark or only meeting the “low” benchmark.

At the eighth grade level in science, students meeting the “low” benchmark are expected to “recognize some basic facts from the life and physical sciences,” and can display this knowledge by “interpret[ing] simple diagrams, complet[ing] simple tables, and apply[ing] basic knowledge.  Students at the “advanced” level can “communicate an understanding of complex and abstract concepts in biology, chemistry, physics and earth sciences.”  They also “understand basic features of scientific investigation … [and] combine information from several sources to solve problems and draw conclusions, and … provide written explanations to communicate scientific knowledge.”

Like in fourth grade science, overall, there seem to be fewer students who reach the “advanced” benchmark across the board.  The United States sees a 5 percent decline in the number of students reaching the “advanced” benchmark from fourth to eighth grade, and a four percent decline in students reaching the “high” benchmark.  This is compounded by a large jump in the percent of students who either do not meet any benchmarks (7 percent compared to 4 percent) or meet only the “low” benchmark (20 percent compared to 15 percent) – more than a quarter of all US students, in fact.

A separate, but equally interesting, set of data from the 2011 PIRLS results is the level of proficiency of students in two types of reading – literary and informational – as compared to a country’s overall score.  Debates over the value of each type of reading as emphasized in a curriculum have been raging for some time now, and while the PIRLS data does not solve this debate, it does provide interesting new fodder to the discussion.

The chart above depicts the overall average reading score on PIRLS, which is administered to fourth grade students, for the top fifteen systems on that assessment, as well as the average score on the literary reading tasks and on the informational reading tasks.  The top performing countries (Hong Kong, the Russian Federation, Finland and Singapore) all have average informational reading scores that are higher than or equal to their overall reading score, with literary reading scores somewhat lower than or equal to both the overall score and the informational score.  By contrast, the United States, Northern Ireland, Denmark, Ireland, Canada and England all display the opposite trend – literary reading scores that are higher, often statistically significant, than either their informational reading scores or their overall scores.  There is also, in the case of the United States, Ireland and Northern Ireland, a statistical significance in the difference between the lower informational reading score and the overall score.

This suggests that informational reading may, in fact, help aid a student’s overall reading skills, at least as measured by the PIRLS assessment.  It is notable that several East Asian countries, including Singapore, Hong Kong, and Taiwan, all of which traditionally do very well in the math and science assessments, also have students who perform better on informational reading tasks than on literary reading tasks.  In the case of Hong Kong and Singapore, this results in a very high overall score.  In Taiwan, the informational reading score is extremely high compared to the literary reading score, and actually fairly comparable to Singapore’s informational reading score.  However, in this case the literary reading score of Taiwan’s students brings the overall score down, suggesting a need for balance.  In terms of balance, Finland seems to have gotten this just right; the informational, literary and overall scores are indistinguishable from one another, and are all very high.

The top 10 countries according to the indicators are:

1.    Finland
2.    South Korea
3.    Hong Kong
4.    Japan
5.    Singapore
6.    United Kingdom
7.    Netherlands
8.    New Zealand
9.    Switzerland
10.    Canada

Unlike other recent indices, China was not ranked, nor was the province of Shanghai.  Australia is ranked 13th, and the US is ranked 17th.  Also unlike other league tables, the United Kingdom ranks high.  This is surprising given their average performance on international tests of student performance.  The diagram below shows how the top ten countries in the EIU index overlap with the ten top performers in the OECD Program for International Student Assessment and the World Economic Forum’s (WEF) education indices of health and compulsory education and higher education and training.

The report, like the Early Childhood Education report recently released by the Economist Intelligence Unit (Starting well: Benchmarking early education across the world), involved interviews with several experts in the field, including Chester Finn, Eric Hanushek, Lee Sing Kong, Andreas Schleicher and Ludger Woessmann.

The authors draw a few conclusions from their work – the central being that teacher quality and national culture surrounding education are two factors that do have a very big impact on the success of an education system.  They point out that the two top systems – Finland and South Korea – have extraordinarily different systems in many ways, particularly in regard to their approaches to testing and hours students spend studying (both in the classroom and out), but both countries put a lot of effort into creating a top-notch teaching force, and both countries consider education to be among the highest priorities.  This finding is consistent with Surpassing Shanghai, NCEE’s analysis of the common elements found in the top performing countries.  Surpassing Shanghai found a number of other reasons for strong student performance including aligned instructional systems, investment in early childhood education, and more.  To see those findings, click here.

Recently, the World Economic Forum (WEF) released their 2012-2013 Global Competitiveness Report.  In this report, the WEF ranks 144 countries based on a global competitiveness index; their definition of competitiveness encompasses “the set of institutions, policies, and factors that determine the level of productivity of a country,” with the understanding that productivity directly influences prosperity.  The index is based on 12 “pillars,” including institutions, infrastructure, macroeconomic environment, goods market efficiency, financial market development, labor market efficiency, technological readiness, market size, innovation and business sophistication.  All of these pillars speak to a country’s ability to promote both stability and growth among their institutions and workforce, strengthening their position in the global economy.

We have always believed that education is one of the most important factors affecting a country’s economy, and the WEF is in agreement.  Two of the twelve pillars used to calculate the overall score deal with a country’s ability to provide education for its children from the primary level through the postsecondary level, with one of the pillars also factoring in the health profile of a country.

So who came out on top?  Unsurprisingly, several of the countries with the strongest education systems as measured by the OECD’s PISA program cracked the top ten, with both Singapore and Finland in the top three.  The Netherlands, Hong Kong and Japan placed slightly lower but still performed well, coming it at fifth, ninth and tenth, respectively, though Japan did slip one spot from last year.  Hong Kong moved up two spots from the last ranking to enter the top ten, displacing Denmark, which slid from eighth place to twelfth.  Other PISA top performers’ results were mixed; Canada, Korea and Australia all made the top twenty (fourteenth, nineteenth and twentieth, respectively), while New Zealand came in at twenty-third, rising two places from last year, and China at twenty-ninth, dropping three places from last year (though it is important to note that China is not itself a PISA top performer – Shanghai is).  However, even twenty-ninth place in a field of 144 is fairly impressive.

The WEF, in addition to producing an overall ranking based on the 12 pillars included in their index, also provided rankings within each of the 12 pillars, making it possible to compare their education top performer lists with the PISA top performers.  In the health and primary education category, six of the top ten PISA performers made the top ten in the WEF analysis (Finland, Singapore, New Zealand, Netherlands, Canada and Japan).  Rounding out the category were European nations known for their ability to provide wide scale and equitable healthcare and basic education: Belgium, Iceland, and Switzerland, and one surprise, Cyprus, which is ranked fifty-eighth overall.  However, in the category of health and basic education, Cyprus received a score of 6.5 out of 7, primarily, it appears, due to a primary enrollment rate of 98.7 percent of children, a relatively high life expectancy (79.4 years) and a low incidence of certain diseases.  Indeed, the health and primary education category is much more focused on health than on primary education, with eight of the ten indicators within the category related to health, and just two, quality of primary education and primary education enrollment rate, related to education.

The other pillar dealing with education – higher education and training – does take into account many other factors that actually speak to the quality of the education system.  The indicators are secondary and tertiary enrollment, quality of the education system, quality of math and science education, quality of management schools, Internet access in school, availability of research and training services, and the extent of staff training.  In this category, four of the PISA top performers (Finland, Singapore, Netherlands and New Zealand) were rated among the WEF top ten.  The other countries included Switzerland, Belgium, Germany, Sweden, the United States and Taiwan.  The United States is world-renowned for its university system, and the European countries all have some of the strongest vocational and training systems in the world.  Taiwan has extremely high enrollment in both secondary education (100 percent) and tertiary education (83.4 percent, ranked seventh of all countries), as well as a high quality math and science education (ranked sixth of all countries).

Clearly, the countries that perform the best on PISA are among the most economically competitive in the world, for a variety of reasons.  But the WEF report, and their index, suggest that there are other factors beyond student performance worth considering in evaluating both competitiveness and education systems.  Their rankings place more emphasis both on the context of education – particularly the health of children and the workforce – and on the strength of non-academic education, and particularly workforce training.  Both of these factors are hugely important to the overall strength of the education system and can be clearly brought to bear on many of the other factors that add up to a competitive economy, including pillars like labor market efficiency, technological readiness and innovation.  Taken together, the PISA results and the WEF rankings indicate the continued predominance of systems like Singapore, Finland, the Netherlands and Hong Kong, who top the rankings in many respects, while more established advanced industrial economies like the United States appear, conversely, to be on the decline.

Source: OECD. (February 2011). PISA in Focus 1: Does participation in pre-primary education translate into better learning outcomes at school?)

The new report, Starting well, by the Economist Intelligence Unit (EIU) and the Lien Foundation profiled in our Global Perspectives section this month urges the importance of having strong early childhood education systems in place in order to ensure future success in school.  The OECD provides data that backs up this assertion in a 2011 PISA in Focus brief, Does participation in pre-primary education translate into better learning outcomes at school?, which outlines the correlation between participation in pre-primary education and later PISA performance.  The OECD defines pre-primary education as different from preschool – this category encompasses “all forms of organized and sustained center-based activities,” including both preschools and daycare centers, so their definition is somewhat different from that in Starting well, which makes a clearer distinction between daycare and preschool.  The data is unequivocal.  After accounting for socio-economic background, they found that students who attended more than one year of pre-primary education had, on average, a 33 point advantage on PISA over students who had not attended pre-primary education for more than a year.  The average difference between students who had attended for more than one year and students who had not attended at all was even higher, at 54 points on the reading portion of the test.  With 39 PISA points being equivalent to a year of schooling, this difference is quite significant.  Of the 65 countries participating in PISA, students attending more than a year of pre-primary education had at least a small advantage over students who did not in all but one case.

Some countries have a particularly large gap on PISA; in France, Belgium and Israel, the gap between students from similar backgrounds ranges from about 60 to 80 points.  And the brief’s authors found that when socioeconomic status was not taken into account – that is, when all students who attended pre-primary school for more than a year were simply compared to all students who had not – there was an even greater gap of more than 100 points in all three cases.  This is perhaps unsurprising in the case of Belgium and France, given that these two countries rank highly on the Starting well index, and Belgium is in fact ranked first in the world in terms of availability of early childhood education.  If a country has a high quality, widely available preschool system, it is not surprising that students who did not take advantage of that system would fare poorly compared to their classmates who had.

The OECD data lends credence to the assertion by the Starting well authors that inclusion is one of the most important factors of a strong preschool system.  Not only does the system need to be high-quality, it must be available to all children in order to raise the entire student population’s educational performance; according to the OECD, the correlation between PISA performance and pre-primary attendance is highest in countries that provide more access to pre-primary education.  Furthermore, in many of the countries, the brief finds, participation in early childhood education is more effective for immigrant students in closing the performance gap than for native students.  On this front, the authors of Starting well investigated whether countries with greater income equality were more likely to have an affordable preschool system.  They plotted each country’s affordability ranking with its Gini coefficient, which we have recreated below.  The Gini coefficient is a measure of a country’s income equality, expressed either as a number between 0 and 100.  A country with a Gini coefficient of 0 has perfect income equality, while a country with a Gini coefficient of 100 has perfect income inequality.  The report’s authors found that countries with greater affordability in early childhood education were also more likely to have greater income equality (figure 1).  However, affordability was not the only aspect of early childhood education ranked by the Economist Intelligence Unit.  In addition to affordability, the Economist Intelligence Unit also ranks countries’ early childhood education systems in terms of quality and availability, and produces an overall ranking that takes all three of these categories, as well as social context, into account.  We plotted the availability, quality and overall rankings with the countries’ Gini coefficients in order to determine whether income equality was likely to predict these other features of an early childhood education system, as well.

Source: The Economist Intelligence Unit. (2012). Starting well: Benchmarking early education across the world.; The CIA World Factbook

Figure 2 indicates that the lower the Gini index, the higher the ranking is likely to be in terms of position in the ranking – a low ranking number means a high spot on the league table.  Like affordability, quality also correlates fairly strongly to income equality (figure 3), as does availability (figure 4), though availability correlates to income equality less so than do affordability and quality.  The overall ranking actually has the strongest correlation to income equality (figure 5), possibly because this ranking takes into account not just affordability, availability and quality, but also social context.  Social context is measured in this case by the prevalence of malnutrition, the mortality rate of children under the age of five, the DPT immunization rate, the gender inequality index and the adult literacy rate.  It follows that countries with high income equality, such as the Nordic countries and other Western European countries, would have strong rankings in these areas, while countries with a higher income inequality such as South Africa, Thailand and Mexico may not fare as well.

Source: The Economist Intelligence Unit. (2012). Starting well: Benchmarking early education across the world.; The CIA World Factbook

Notable, too, is that the countries with the greatest income equality and generally the best early childhood education systems are not the countries that spend the most on this service.  Finland spends just $5,334 annually per student, well below the United States, which spends a staggering $10,070 per student per year, and is ranked solidly in the middle of the 45 countries in the Economist Intelligence Unit league tables.  However, the other top-ranked countries, except Belgium, spend a little more than the OECD average on these services.  Across the

Source: The Economist Intelligence Unit. (2012). Starting well: Benchmarking early education across the world.; The CIA World Factbook

OECD, countries spend on average $6,210 per student per year.  The countries rounding out the top five in the overall ranking along with Finland – Sweden, the United Kingdom, Norway and Belgium – for the most part spend a little more, but not substantially so.  Belgium spends just a bit more than Finland at $5,732, while Sweden, the United Kingdom and Norway spend $6,519, $7,119 and $6,572, respectively.  Chile has been working hard in recent years to improve their early childhood education

Source: The Economist Intelligence Unit. (2012). Starting well: Benchmarking early education across the world.; The CIA World Factbook

programs, and is now ranked at number 20 – several spots above the United States – in the overall rankings.  They spend just $3,951 per year, and have achieved their rapid improvement through expanding access (the number of preschools increased by 550% between 2006 and 2009) through public and private providers, and establishing national curriculum guidelines.  While quality preschool education cannot apparently be had at incredibly low prices, it appears that social context and access are more important in building a high-quality system than spending alone.

There can be little question that early childhood education programs can provide strong educational advantages for students later in their education.  But building and strengthening these programs seems to have more to do with ensuring that high proportions of children are included in them, rather than what is spent on them.

Source: Accenture. (2011). No Shortage of Talent: How the Global Market is Producing the STEM Skills Needed for Growth; McKinsey Global Institute. (2012). The world at work: Jobs, pay, and skills for 3.5 billion people.

Judging by the headlines in many developed economies—especially the United States and the United Kingdom—there is growing concern that they are not producing enough STEM workers (people with degrees in science, technology, engineering or math) to meet the growing need for such people.  We often hear of a global “skills mismatch”; millions of new workers are entering the labor force every year, and millions of new jobs are being created, but many jobs are going begging because the available workers do not have the skills for the new jobs.  A recent report from Accenture, “No Shortage of Talent: How the Global Market is Producing the STEM Skills Needed for Growth,” refocuses this argument, contending that the skills mismatch is one of location, rather than overall supply and demand.  The authors of this report argue that while jobs requiring STEM knowledge and skills are growing at nearly twice the rate of other occupations in the United States, just 13 percent of American college students choose a STEM major.  In China, on the other hand, more than 40 percent of college graduates have STEM degrees; this figure is nearly 50 percent in Singapore (see figure 1).  In addition to the East and South Asian power players in the STEM field, countries like Brazil are also experiencing a rapid increase in the number of students who choose to pursue STEM degrees; by 2016, Brazil will have surpassed the United States in the number of engineering PhDs produced every year.  Furthermore, countries like Germany, with strong vocational education programs at the secondary level, are holding their own in terms of STEM degree production, with more than a quarter of students in higher education choosing a degree in these fields.

The benefits of producing a strong STEM workforce are myriad.  In another recent report, “The world at work: jobs, pay and skills for 3.5 billion people”, the McKinsey Global Institute found that in the United States, a STEM worker will earn, on average, $500,000 more over a lifetime than a non-STEM worker.  However, despite the benefits to both STEM workers and to national economies, the authors of this report also found that countries approach the issue of creating STEM workers very differently.  In the United States, there is a laissez-faire approach; students are free to choose their majors or specializations after being admitted into a university, and the vast majority of students do not choose STEM majors.  Many other countries, by contrast, require students to apply for places within a college or a university in a specific specialization in order to be admitted, thereby allowing the country to have a greater degree of control over degree production.  In Singapore, for example, the government estimates the fields in which workers will be needed and the number that will be needed in each field and then allocates the slots in its first year classes in its higher education institutions accordingly, in an effort to align supply and demand as closely as possible.  Individual students can still choose freely among careers for which they want to train, but the government controls the number of slots available in any given field.  This policy clearly has a bearing on the Singapore’s position on the league table above.  This capacity to align supply and demand this way is associated with countries that pay all or most of the cost of higher education—which happens in some countries but by no means all.  Several countries that have such policies, including Singapore, also have in place bonding schemes where the government pays for a student’s higher education in exchange for the student’s agreement to work in the country, sometimes in the public sector, for a certain number of years following graduation.

Source: McKinsey Global Institute. (2012). The world at work: Jobs, pay, and skills for 3.5 billion people.

The issue of a skills mismatch does not end with STEM degrees.  The McKinsey report estimates overall future job shortages and worker surpluses for the global workforce in 2030.  They suggest that there will be an overall shortage of nearly 40 million high-skill workers, or 13 percent of the global demand for people with higher education, as well as a shortage of 45 medium-skill workers (15 percent of the total demand) and a surplus of about 95 million low-skill workers, all of which means large number of people out of work and employers unable to fill positions unless more is done to raise the skills of low-skilled workers, entice more students to enter STEM and other high demand fields, and match employers with the workers they need.  The same countries that are producing high numbers of STEM workers, particularly China and India, are also adding the majority of new workers to the workforce.  China and India alone added enough new workers between 1990 and 2010 to represent 37 percent of the total workforce growth of 706 million; between 2010 and 2030, China’s workforce growth is expected to decline slightly to just 13 percent of all new workers, while India’s workforce growth is expected to grow to 28 percent of all new workers.  Young developing economies including Bangladesh, Pakistan and many African nations, along with young middle-income economies (such as Brazil, Mexico, Vietnam and Indonesia) added half of new workers between 1990 and 2010, while advanced economies (for example, the United States, Japan, Hong Kong and Australia) contributed just 11 percent (see figure 2).  The primacy of developing economies in workforce growth will continue through 2030; in this period, advanced economies are projected to add just 5 percent of new workers to the global workforce (see figure 3).

Source: McKinsey Global Institute. (2012). The world at work: Jobs, pay, and skills for 3.5 billion people.

Of course, not all degrees – STEM or otherwise – are created equal.  A separate 2005 McKinsey report, “The emerging global labor market: The supply of offshore talent in services – Part II” found that just 10 percent of Chinese engineers and 25 percent of Indian engineers are educated to a global standard – that is, suitable for hiring by a multinational corporation, whereas about 80 percent of engineers educated in the United States are considered globally suitable.  This finding is corroborated by the 2011 Aspiring Minds National Employability Report, which found that the majority of Indian engineering degrees are not awarded from the top 100 universities, which tend to be the main institutions that large, multinational corporations recruit from.  Other datasuggest that large portions of these degrees are what the world would consider “sub-baccalaureate.”  However, despite the concerns over the quality of some of the millions of STEM degrees being awarded in China and India, Accenture calculates that even if just 20 percent of Chinese STEM graduates are qualified to a world standard, this would represent more than 700,000 graduates by 2015, as compared to just 460,000 in the United States.  Additionally, while both McKinsey and Accenture recommend putting policies in place to facilitate the immigration of STEM workers to the countries with large STEM shortages, this strategy seems unlikely to address the skills mismatch in the long term.  Developing economies that want to progress by creating successful technology-driven companies within their own borders, must invest in raising the quality of their own education systems and do this while providing the vast majority of their populations with the opportunity to excel in high quality learning environments.  Countries with historically strong economies must work to produce STEM majors at a much higher rate by giving students the knowledge, skills and tools they will need to succeed in STEM courses in compulsory

Source: McKinsey Global Institute. (2012). The world at work: Jobs, pay, and skills for 3.5 billion people.

education.  However, as Marc Tucker has written in his Education Week blog, Top Performers, it is virtually impossible for a country to produce large numbers of high quality STEM graduates from mass education systems that were designed to produce mainly relatively low-skilled graduates overall.  It may be useful to think about the developed world as containing two categories of countries.  In one category there are nations with education systems that are still designed to produce large numbers of students with little more than a basic education and relatively small numbers of students who have what could be termed elite skills, the United States and the UK are in this category.  In the other category are countries that have redesigned their systems to educate all their students to the elite skills standard.  Countries like Finland, Japan, Korea and Singapore are in this category.  Countries in the first of these two categories will find it very difficult to greatly increase the proportion of high quality STEM graduates without redesigning their education systems using the strategies employed by the countries in the second category to provide elite skills to all their students.  This is a tall order for developing countries, and that is the reason that the highly industrialized countries, though small in population relative to the largest developing countries, are likely to have a disproportionate number of high-quality STEM graduates for a while.

In their recent report published in February of this year, the Grattan Institute examined the school systems of several East Asian countries with a view towards drawing policy recommendations from what they learned for Australia. In the report, titled Catching Up: Learning from the best school systems in East Asia (and featured in last month’s International Reads) the authors look to Hong Kong, Shanghai, Korea and Singapore and focus particularly on teacher education, professional development, and approaches to learning. It is the combination of these factors, the authors believe, that produces such impressive results whenever their students are compared to their counterparts in other countries on international assessments such as PISA.

One of the most striking findings in this report is the rate at which students in Singapore, Shanghai, Hong Kong and Korea are learning as compared to their counterparts in the United States, the United Kingdom and, of course, Australia. Using the conversion rates utilized by Thompson et al. in their 2010 ACER publication, Challenges for Australian education: results from PISA 2009: the PISA 2009 assessment of students’ reading, mathematical and scientific literacy (based on OECD analysis of PISA score levels and student competencies), the authors of Catching Up were able to produce a table demonstrating how many months ahead students in Shanghai, Hong Kong, Singapore and Korea were compared to students in the US, the UK, Australia and the EU21. A difference in reading of 39 points represents a full year of difference in learning; the number is similar in math (41 points) and in science (38 points). Thus the difference in student learning between the 2009 PISA top performer (Shanghai) and the bottom performer (Kyrgyzstan) is six to six and a half years of learning in all three subjects.

What emerges from viewing the PISA scores in this way is it shows how far ahead students in Shanghai are, even compared to their top-performing East Asian counterparts. In mathematics, students in Shanghai are more than two and a half years ahead of the average OECD student, with students in Singapore and Hong Kong about a year behind them. Students in the UK and the US, on the other hand, lag a few months behind the average OECD student. In science, again, students in Shanghai have a huge leg up on most others: they are nearly two years ahead of the average OECD student and at least half a year ahead of the other top performers, whereas in the UK, students have just a tiny advantage over the average OECD student, while the United States remains average. Finally, in reading, Shanghai is still the frontrunner by far, but the overall gaps are smaller. Students in Shanghai are “only” about a year and a half ahead of the average OECD student, and again about half a year ahead of the next-best top performer. The United States performs better, by a handful of months, than the average OECD student, while the UK is approximately on par with average.

A common perception about education in Asian countries is that students – and particularly teenagers like those tested in PISA – spend the vast majority of their time either in school or in “cram schools” in order to compete for spots at selective universities. While it is certainly true that the culture of “cram schools” persists in these countries, the authors of Catching Up argue that it is not the extra hours spent studying that lead to these massive gaps in student achievement between East Asian countries and other OECD countries. Instead, these gaps emerge from “effective education strategies that focus on implementation and well-designed programs that continuously improve learning and teaching” (12), which are in place in the top performing countries. As evidence for this statement, they point to Hong Kong, which leaped from 17th place in PIRLS to 2nd place in just five years. Cram schools and Confucian values, they contend, cannot explain that rise.  Neither can system size; although Hong Kong and Singapore are relatively small (Singapore has just under half a million students while Hong Kong has about 700,000), South Korea has 7.2 million students and is also a top performer. Rather, Hong Kong, like Singapore and other rapidly-improving East Asian countries, took it upon itself to implement a series of effective and well thought out reforms with a focus on teacher education, teacher professionalism, and funding equity to get to the top of the pack.

There are many different measures of teacher pay, from a strict comparison of actual salaries in USD PPP, to percentage of per capita GDP, to a comparison of teachers’ salaries with those of workers in other fields with the same level of education in a given country.  Looking at international comparisons of teachers’ salaries and workers in the same country with the same level of education, we find that across the board in top-performing countries, teachers make about what their counterparts with a similar amount of education make.  This is particularly true for upper secondary teachers, and in the Netherlands and Finland, these teachers actually make more than other similarly-educated workers.  Across the OECD as a whole, the proportion is smaller; upper secondary teachers make just over 80 percent of what other workers make, while primary and lower secondary teachers fall shy of the 80 percent mark.  In the United States, by contrast, upper secondary teachers make just over 60 percent of what similarly-educated workers make, while primary and lower secondary teachers make even less.  Other benefits aside, it is clear that providing teachers who could go elsewhere with comparable salaries does help to retain high-quality candidates.

It is difficult to discuss teachers’ salaries without a broader discussion of the overall cost of education systems, particularly because teachers’ salaries tend to represent a majority of the spending in most education systems.  Last month, the OECD posed the question of whether money buys strong performance on PISA in one of their “PISA in Focus” briefs.  What they found was that it is not how much a country spends, but how they spend it, that correlates to higher PISA scores.  They found that once countries spend more than  $35,000 on total student expenses from the ages of 6 to 15, any additional money spent does not seem to pay off in student performance.

One of the highest-spending countries, the United States, has one of the lowest average PISA reading scores, whereas Shanghai and New Zealand, economies which both spend less than half of what the United States spends on individual students, have far better student performance. However, there is clear correlation between investment in teachers’ salaries and PISA performance. Countries in which teachers have higher purchasing power, as measured by their salaries as a proportion of GDP, also tend to have much higher student performance on PISA, as indicated by the second chart.

In many of the countries, and notably in Korea, lower secondary, mid-career teachers are paid, on average, more than the average GDP per capita.  However, while in Korea teachers are paid twice the GDP per capita, they are paid only about 80 percent of what similarly-educated workers in other fields are paid, suggesting that while teachers are paid very well compared to the average worker, their pay is still some distance from that of the highest status professionals.  Overall, the data would suggest that nations that want a first class teaching force need to be prepared to pay enough to take compensation “off the table” as a major consideration for talented young people making career decisions, but need not pay at the top of the professional scale.

In every newsletter, we will highlight a particular set of international education statistics.  In this newsletter, we focus on the most recent OECD statistics relating reading ability to students’ socioeconomic background and the socioeconomic background of the other students in the school.  The question of particular interest is the degree to which socioeconomic background predicts student academic performance.  This material is based on the OECD publication, PISA 2009 Results: Overcoming Social Background—Volume II.

Socioeconomic background, as OECD defines that term, refers to the characteristics of a student’s family that describe its social, economic and cultural status.  It includes the occupational status of the father or mother, whichever is higher; the level of education of the mother or father, whichever is higher, converted into years of education; and a measure of family wealth which is constructed on the basis of the families’ home possessions, including books.

It turns out that socioeconomic background does not have to determine a student’s academic achievement.  In fact, OECD reports, “[T]he mean index of socioeconomic background is almost identical for the country with the lowest mean reading performance, Kyrgyzstan, and the economy with the highest mean reading performance, Shanghai-China.”

Nor is it necessarily the case that wide disparities in socioeconomic background in a country are matched by equally wide disparities in student achievement:  “equity in the distribution of learning opportunities is only weakly associated with a country’s underlying income inequality . . . [I]n general, cross-national differences in equalities of performance are associated more closely with the characteristics of the education system than with the underlying social inequalities or measures of economic development.”

This is, of course, heartening news for educators.  It means that one’s socioeconomic status is not destiny; education can make a big difference.  It can greatly reduce the differences in student academic achievement that might otherwise be the result of differences in socioeconomic status.  If that is true, then it is also true that differences in the design of national education systems results in real differences in the degree to which education can help students overcome initial differences in parents’ education and family wealth.

One of the most interesting OECD findings has to do with the difference that the socioeconomic background of the students in a school makes in the academic performance of the students in that school. “[R]egardless of their own socioeconomic background, students attending school in which the average socioeconomic background is advantageous tend to perform better than when they are enrolled in a school with a disadvantaged socioeconomic intake.”  In fact, the relationship between the socioeconomic status of the students in a school and their academic performance is stronger than the relationship between an individual student’s socioeconomic status and that student’s academic performance in the same school.

As the authors of the PISA volume point out, this should not surprise us.  Schools serving students from more advantaged families are more likely to have better teachers, a more challenging curriculum, higher teacher morale, fewer disciplinary problems, better teacher-student relations, and so on.

But the influence of a school’s socioeconomic performance on student achievement is not the same for all countries, a fact that is clearly demonstrated by the chart above.  The chart displays the variation in reading performance explained by schools’ socioeconomic background in PISA 2009, expressed as a percentage of the average variance in student performance in OECD countries.   It displays the data for the United States and the top ten performers. For the rest of the list of nations and their performance on this index, see the OECD volume referenced above, Figure 11.5.4.

The OECD document provides a clue concerning one source of these differences.  In the countries in which school socioeconomic background is a more powerful predictor of student academic performance, schools are more likely to be segregated by the socioeconomic background of the students they serve; there are fewer schools serving students of mixed socioeconomic background and more serving students of a homogenous socioeconomic background.  But the authors of the OECD document point out that there are many other aspects of the design of national education systems that also influence the degree to which school and student socioeconomic background predict student academic performance.