Download Cross-national studies of academic achievement have

Student Achievement and National Economic Growth
Francisco O. Ramirez, Stanford University
Xiaowei Luo, University of Illinois – Champagne/Urbana
Evan Schofer, University of Minnesota
John W. Meyer, Stanford University
April 2005
Word Count (including notes, references, tables): 9,600
Direct all correspondence to Francisco Ramirez, School of Education, Stanford
University, Stanford, CA 94305. This research was supported by a grant from the
Stanford Institute for the Quantitative Study of Society. We thank the members of the
Stanford Comparative Sociology Workshop for their helpful comments. We also thank
Eric Hanushek for making measures available to us and providing detailed information
regarding his statistical analyses. Lastly, we acknowledge with gratitude the criticisms
and recommendations for revision offered by two anonymous reviewers.
Student Achievement and National Economic Growth
Abstract
Educational policy, around the world, has increasingly focused on improving
aggregate student achievement as a means to increase economic growth. In the last two
decades, attention has focused especially on the importance of achievement in science
and mathematics. Yet, the policy commitments involved have not been based on
research evidence. The expansion of cross-national achievement testing in recent
decades makes possible longitudinal analyses of the effects of achievement on growth,
and we carry out such analyses here. Regression analyses appear to show some effects of
science and mathematics achievement on growth, but these effects are due mainly to the
inclusion of the four “Asian Tigers,” and are not consistent over time. These empirical
findings call into question educational policy discourse that emphasizes strong causal
links between achievement and growth.
Achievement and Economic Growth
Introduction
Historically, educational policies and reforms have been driven by political,
military, and religious forces more than by economic goals and interests. A country
might reform its educational system after losing a war, or facing expanded political
competition, or upon revolutionary regime change. Economic success or failure was less
often involved. Even as recently as the great depression of the 1930s, policy reform
efforts in the United States and around the world did not focus substantially on
educational improvement (Tyack, Lowe, and Hansot 1984; Dobbin 1993). The great
Sputnik crisis of 1957 did generate educational reform efforts in the United States, but
this crisis was defined more in military and political terms than economic ones.
Perhaps this overall historical situation arose because education was seen as
having a public face, and economic management was seen as part of the private sector.
That perspective has, of course, dramatically changed. Every national regime in the
world is under great pressure to scrutinize the national economy and its health as a
principal responsibility. And it is clear that education and its reforms are everywhere
seen in light of their supposed economic effects. It is also clear that the areas of
education given the most attention as relevant to economic goals have been science and
mathematics, the new keys to economic growth.
This paper undertakes an analysis of cross-national data to examine the
relationship between student achievement in mathematics and science and national
economic growth. We seek to answer the following questions: First, are countries with
higher levels of achievement more likely to have greater subsequent economic growth?
Second, is the relationship between achievement and growth stable over time and
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Achievement and Economic Growth
independent of particular cases? Third, do achievement levels have stronger positive
effects, as would be expected, in countries with higher levels of educational enrollment?
Finally, do achievement levels affect obvious intervening variables known to have
economic effects? That is, can we identify the intervening mechanisms through which
academic achievement results in economic growth? Our attempt to answer these
questions provides a bridge between two literatures, one focusing on international
surveys of academic achievement and the other examining the sources of economic
growth. Though some economists have recently addressed the academic
achievement/economic growth tie (Hanushek and Kimko 2000), there is no sociological
research that directly examines the evidence. There is, however, much sociological
literature on education and development (Rubinson and Browne 1994; Chabbott and
Ramirez 2000; Hannum and Buchmann, 2003) and it is to this literature that this paper
seeks to contribute.
In what follows we first briefly consider the comparative literatures on academic
achievement and on educational effects on development. Our goal is to make explicit a
set of assumptions that guide many of the debates within these literatures and inform the
questions this paper seeks to answer. Next, we describe the cross-national data and
statistical methods with which we assess the influence of academic achievement in
science and mathematics on economic growth. Finally, we report the results of our
analyses and discuss their implications.
Our cross-national quantitative analyses show the expected positive effect of
academic achievement on economic growth but this effect appears to be both case and
time sensitive. The achievement effect is sharply reduced or disappears altogether both
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Achievement and Economic Growth
when we delete the “Asian Tigers” from the analyses and in analyses between 1980 and
2000. These findings raise questions about some of the achievement and development
claims reviewed below.
Review of the Literature
Cross-national studies of academic achievement in mathematics and science have
generated much more policy and media attention than other comparative educational
inquiries. The most prominent metaphors used in the American discussion include “the
rising wave of mediocrity in the schools” and “a nation at risk” (U.S. National
Commission on Excellence in Education 1984) following the Second International
Mathematics Study. Following the Third International Mathematics and Science Study,
we find “a splintered vision“ (Schmidt et al. 1996), “a curriculum that is one mile wide
and one inch deep,” and the task of “facing the consequences.” (Schmidt, et. al., 1998).
By way of contrast there is considerably less dramatic imagery in discussions of the
results of international surveys of reading literacy or civic education achievement, studies
in which American students appear to fare better than in science and mathematics. The
discrepancy in “what counts” has been seized upon by some critics as evidence of a
conspiracy aimed at bashing education and teachers (Berliner and Biddle 1996; Bracey
1996; see also the debate between Baker and Westbury 1993). Others have lamented the
fact that bad news sells, and thus bad news gets greater coverage (Berliner 1997). But
such reactions may miss the big picture: economic development as a goal is increasingly
seen as achieved through scientific and technical developments in schools and in the
broader society (Drori, Meyer, Ramirez, and Schofer 2003). This phenomenon is evident
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Achievement and Economic Growth
in much development discourse rooted in both human capital and modernization theories.
Consider for example the implicit causal chain, which we spell out in Figure 1, sketched
in a National Research Council document:
“Several implicit causal assumptions underlie public and political interest in
cross-national studies of mathematics and science: (1) increased and improved
curricular emphasis on mathematics and science in precollegiate education for all
students will result in greater achievement for a greater number of students; (2)
this will result in a greater number of better prepared students entering the natural
sciences and engineering; and (3) this will in turn lead to a greater number of
more productive scientists and engineers in the labor force. A final assumption is
that more and better scientists and engineers will increase economic growth or
productivity. Each of these premises has its critics and each should be more
systematically evaluated.” (Guilford, ed. 1993, p. 10)
------ FIGURE 1 ABOUT HERE ------
The high stakes metaphors associated with the international studies of science and
mathematics achievement reflect this causal chain, minus the caveat that these are
premises that need to be systematically evaluated. Both human capital and
modernization theories presume that schools are settings within which pupils acquire
skills, knowledge, and value dispositions, which subsequently make them more
productive members of their societies. Problems of conceptualization and measurement
notwithstanding, there is widespread confidence in the linkage between schooling and
productivity at both individual and aggregate levels. Human capital and modernization
theories are pervasive in their worldwide influence on development professionals and
organizations (Chabbott 2003; World Bank 2002). To be sure, these theories generated
counter theories that emphasized the sorting, credentialing, and reproduction functions of
schooling and debunked its economic and modernization effects (Collins 1971; see
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Achievement and Economic Growth
Bowles and Gintis 2002 for a recent reaffirmation of their 1974 reproduction
correspondence theory).
There have also been efforts to go beyond “either-or” formulations and to argue
instead that sociologists need to identify the conditions under which education leads to
greater economic development (Fuller and Rubinson 1992; Rubinson and Browne 1994).
These efforts have given rise to the general contention that schooling that is more attuned
to the needs of the economy is more likely to foster its growth. This argument highlights
the quality of schooling, not merely its quantity. But what kind of schooling constitutes
high quality schooling? Without pretending to answer this broad question, many studies
proceed as if schooling that contributes to individual productivity and national
development is high quality schooling. Researchers have often assumed that more
technical and scientific forms and outcomes of schooling would be more likely to meet
the needs of the economy. These forms and outcomes thus serve as proxies for school
quality, and in some studies, via a process of extrapolation, even labor force quality
(Hanushek and Kimko 2000). Thus, several empirical studies have focused on different
aspects of scientization in curricular emphasis and enrollment levels and assessed their
effects on economic growth. Benavot (1992b), for example, provides some evidence
suggesting that a greater emphasis on science in the primary school curriculum positively
influences economic development. This study furthermore finds a positive interaction
effect between science curricular emphasis and secondary enrollment levels. That is, the
impact of more scientific emphasis in the curricula may be greater in countries with more
expanded secondary school enrollments. Another set of studies suggested that the
expansion of technical tracks in secondary schooling, in both France and Germany,
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Achievement and Economic Growth
positively affected economic development, while the expansion of classical curricular
tracks failed to do so (Garnier and Hage 1990; Hage, Garnier, and Fuller 1988). The
authors of these studies argue that the different economic impacts of expanded schooling
are due to differences in the emphasis and quality of different school systems.
Shifting to higher education, other cross-national studies examine the economic
impact of greater enrollments in the fields of science and engineering and find positive
effects (Schofer, Ramirez, and Meyer 2000; Ramirez and Lee 1995). The established
generalization across many studies is that higher educational enrollment expansion has
weak or even negative effects on economic growth (see Benavot, 1992b; but see Hannum
and Buchmann 2003 for evidence of a positive effect). But this finding may mask the
positive effect of science and engineering enrollments by confounding it with the
negative impact of enrollments in the non-science and engineering sector.
Taken as a whole these studies support the idea that science-oriented schooling
has greater economic payoffs than schooling in other areas. This is consistent with the
implicit causal chain that underlies the debates around achievement in science and
mathematics. But several studies raise evidentiary questions. Walters and O’Donnell
(1990) use time series data to analyze the influence of science and engineering graduates
on economic growth in the United States and fail to find a positive effect. Schofer et al.
(2000) show that some scientific output indicators such as scientific publications either
have no effect on economic growth, or actually have a negative effect (for similar
negative effects, see also Shenhav and Kamens 1991). Despite such studies,
conventional policy discourses treat all these indicators of scientific development as
important mechanisms producing economic growth (see Drori et al. 2003).
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Achievement and Economic Growth
Only one study directly gauges the impact of academic achievement in
mathematics and science on economic growth and that study finds a positive effect
(Hanushek and Kimko 2000). These researchers do not attempt to empirically ascertain
the mechanisms or intervening variables through which greater levels of academic
achievement lead to greater rates of economic growth, but generally assume that human
capital processes of the sort outlined in Figure 1 are involved. The focus of this study on
achievement, beyond simple enrollment levels, is indicative of the growing interest in
school quality and its consequences.
We build on the literatures discussed above, using nation-states as units of
analysis, to ascertain whether aggregate levels of student achievement in mathematics
and science positively influence national economic growth. We employ analysis models
and measures of the independent and dependent variables similar to those utilized in
Hanushek and Kimko (2000), and in Schofer, et al. (2000). We examine 1970-1990 and
1980-2000 periods to see if the relationships between achievement levels and economic
growth are stable over these two periods. We undertake analyses with and without
outliers, to see if the relationship is case sensitive. We also estimate the interaction
effects of achievement and enrollment on economic growth, since both theory and
research (Benavot 1992) would lead one to expect a positive interaction effect: variations
in average student achievement should have stronger effects in countries with more
students enrolled. Lastly, we gauge the effects of achievement levels on four different
measures of scientific development that could serve as intervening variables between
achievement and economic growth.
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Achievement and Economic Growth
Before we describe our data and the methods with which we propose to analyze
the data, a cautionary word about figure 1 is in order. This dramatic portrait of
achievement and development presupposes a world of enormous variation with respect to
curriculum, teaching, and achievement. But cross-national investigations actually show
considerably less variation with respect to curriculum, teaching, and achievement than
expected in policy discourse (Baker and Latendre 2005; see also Meyer, Kamens,
Benavot, and Cha 1992). This is especially the case when the weakest educational
systems in the world do not enter into analyses due to inadequate data. So, from a
research perspective one should not expect robust achievement effects on economic
growth. But the policy discourse reviewed earlier often presupposes robust effects and at
least one empirical study concludes that there is a clear achievement on economic growth
effect (Hanushek and Kimko 2000). Our goal thus is to ascertain whether the link
between collective national achievement and economic growth is stable over time and
across cases
Data and Methods
Modeling Economic Growth
The modeling approach, measures, and control variables we employ are chosen to
be consistent with prior high quality studies of national economic growth by economists
and sociologists (See, e.g., Barro 1991; Barro and Sala-i-Martin 1995; Levine and Renelt
1992; Schofer et al. 2000). We use OLS regression with robust standard errors to model
national economic growth. The unit of analysis is the country. Economic growth is
studied over a 20-year span, which is similar to prior research. This lag allows sufficient
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Achievement and Economic Growth
time for independent variables, such as academic achievement, to have an impact on
national economic performance. We examine both the 1970-1990 and the 1980-2000
periods to see whether the role of academic achievement is consistent over time. Data
limitations, particularly the lack of student achievement measures, prevent the analysis of
earlier periods.
Dependent Variable: National Economic Growth
We use real gross domestic product (GDP) per capita to measure the level of
economic development in nation-states. Data were collected from United Nations
Statistics Yearbook. Economic growth is measured as the annualized average GDP
growth rate from time t1 to t2, in keeping with prior research on national economic
growth (Barro 1991; Barro and Sala-i-Martin 1995). We examine GDP growth over 20
year time spans (1970-1990 and 1980-2000), calculated as: 1
Annualized GDP growth rate = (GDP per capita t2 / GDP per capita t1) 1/20 - 1
It is has become fairly standard in the literature to model GDP growth using OLS
regression, with independent predictors measured at or near the starting time, t1 (Barro
1 There are several other methods of calculating growth, such as a simple change score, (GDP t2–GDP
t1)/GDPt1, or by taking the log of the ratio of GDP at both time points divided by the time span to yield the
annualized change with continuous compounding. All produce virtually identical results.
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1991; Levine and Renelt 1992). 2 Just as in panel analysis, the use of independent
variables measured at time t1 minimizes the possibility that reverse causality will affect
results. Indeed, panel models of GDP per capita (not presented here) produce similar
findings to our models of GDP growth. However, we present models of GDP growth, as
it is the more common approach in the literature.
Independent variables are measured as follows:
Academic Achievement in Mathematics and Science
We measure academic achievement using an index based on international
standardized achievement tests. Hanushek and Kimko (2000) created the measure by
gathering data from all major international mathematics and science achievement tests
conducted before 1991 (there are six of them) and creating a composite score of student
achievement for each participating nation-state. 3 The six tests were either on
mathematics or science, and were given to students between the 8th grade and 12th grade. 4
2 Based on Hanushek and Kimko (2001), we know that achievement scores are fairly consistent over time.
Therefore we are confident in using their indicator to in models starting in both 1970 and in 1980.
3 Hanushek and Kimko (2001) did not include the Third International Mathematics and Science Study
(TIMSS), which was administered in 1995-96 with 45 countries and regions participating. Although
TIMSS has so far involved the largest number of nation-states, it was excluded because Hanushek and
Kimko (2001) ended their analysis in 1990, several years prior to the TIMSS data. However, we
conducted exploratory analyses using TIMSS data (see below) in large part due to the design weaknesses
inherent in early achievement tests, which may affect the Hanushek and Kimko index.
4 Four tests were administered by the International Association for the Evaluation of Education (IEA).
These included tests done in: 1964-66, 1966-73, 1980-82, and 1983-86. Two tests were administered by
the International Assessment of Educational Progress (IAEP) in 1988 and 1991. The logic for focusing on
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Achievement and Economic Growth
Some nation-states participated in all six tests, while others participated in only a subset
of them. The resulting measure reflects an average of standardized scores for all
available tests, and is available for nearly 40 countries. Specifically, Hanushek and
Kimko used the following procedure: The six major tests generated a total of 26 score
variables, as some tests were administered to multiple age groups, or involved multiple
tests in different subjects. They used multiplicative transformations to standardize each
score variable on a mean of 50. The final index reflected a weighted average of available
tests score measures, with each score weighted by the inverse of the country-specific
standard error. Hanushek and Kimko (2000) find that nation-state performance in these
tests has been very consistent, suggesting that student achievement in countries is quite
stable over time in the post-WWII period.
Hanushek and Kimko also computed “estimated” values of student achievement
for countries that did not participate in the international tests. They derived these
estimates from the relationships between test scores and other variables, such as
secondary school enrollment, national expenditure on education as a percentage of the
Gross Domestic Product, and other factors found to correlate with test scores. Models
presented below do not use estimated test scores, due to concerns that the imputation
process may bias the achievement measure – bias that is likely to be correlated with other
variables in our models, such as secondary enrollments. All models presented below are
based on actual test score data. However, we did conduct exploratory analyses using the
math and science comes from the policy literature, as well as work by economists such as Romer (1990)
and Bishop (1992) who argue for the importance of math and science in economic growth, either via
increased R&D activities or by increasing individual productivity.
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Achievement and Economic Growth
estimated data for approximately 70 countries. Results (not presented here) were
consistent with models presented below.
Initial Level of Real GDP Per Capita. Studies of GDP growth invariably control for a
nation’s starting point (Barro 1991; Barro and Sala-i-Martin 1995). In models of
economic growth, initial GDP is typically negative, as large economies tend to grow
more slowly than smaller ones (Barro 1991). Data is taken from the Penn World Tables,
version 5.6 (See Summers and Heston 1991). It is common to compute the log of GDP
per capita in cross-national studies, in order to reduce skewness. We use the un-logged
version for two reasons: 1) GDP is not highly skewed in our small sample of mostly
industrialized countries; and 2) We wish to be consistent with Hanushek and Kimko’s
(2000) study. In any case, logging the variable does not alter our findings.
Investment Rate. Economic theory and research identify capital investment as a core
contributor to future economic growth (see Barro 1991; Levine and Renelt 1992).
Consistent with prior research, we measure the rate of investment as total annual capital
investment as a proportion of a nation’s overall GDP (Source: Penn World Tables
version 5.6; Summers and Heston 1991).
Secondary Education
Studies of economic growth have generally shown that the expansion of
secondary education has robust and stable effects on economic growth (Barro and Sala-iMartin 1995; Levine and Renelt 1992). Therefore, we include controls for the gross
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secondary enrollment ratio. 5 Data were collected from UNESCO Statistical Yearbook
(UNESCO 1974, 1982, 1992).
Interaction: Achievement * Secondary Enrollment
The impact of educational achievement on the economy may be conditional on
the size of the educational system. The economic benefits of high achievement should be
greater if more students go through the educational system before entering the labor
force. Therefore, we included a variable for the interaction between achievement and
measures of educational enrollment. We computed a continuous interaction term and
also a version based on dummy variables that indicated countries above the median on
each measure. We present the latter below, as it reduces the potential for
mulicollinearity, but both versions produce the same results.
Additional Control Variables
Finally, we examined a range of additional control variables. (Models are not
presented here, but they are available from the authors on request.) Levine and Renelt
(1992) argue that investment and secondary education are the main variables that
consistently affect economic growth in the late 20th century, and that additional controls
are generally not needed. Indeed, the vast majority of factors thought to increase growth,
5 Hanushek and Kimko (2001) used a slightly different measure of educational expansion: average years of
schooling in labor force to measure the general expansion of education. When we used their measure in
place of secondary enrollment for our models, and results are substantively the same. We present models
with secondary education to be consistent with most prior studies in the economic growth literature.
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such as trade, tax rates, or technology, do not in fact affect economic growth (Levine and
Renelt 1992). Nevertheless, we considered a variety of other control variables in our
models: measures of trade, democracy, savings rates, technology (patents), the
prevalence of labor unions, and state centralization. The addition of these variables to
our models did not alter findings substantially. 6
Intervening Mechanisms
In addition to our main analysis of economic growth, we model several possible
intervening variables that may mediate the effect of achievement on economic growth.
That is, we wish to determine whether student achievement in mathematics and science
does, in fact, have a positive effect on likely intervening factors suggested by economists
and policymakers (see Romer 1990; Bishop 1992). We consider the following
intervening variables: size of the scientific labor force, tertiary enrollment in science and
engineering, scientific publication rates per capita, and patents granted per capita. 7 The
initial level of the dependent variable and other relevant control variables are included in
the panel analyses. We model these intervening variables using panel analysis over 10year periods. To study intervening factors in growth from 1970-1990, for instance, we
model intervening variables from 1970-1980. In theory, this allows ten years for the
6 We did not include all of these variables in analyses at the same time because too many cases would be
lost due to missing data. Therefore, we added additional control variables in small groups or singly.
7 Some of these variables – such as patents and science publications – have not been shown to strongly
influence economic growth in empirical studies (See Schofer et al. 2000). We nevertheless include these
variables in our study because of the importance placed on them in the literature.
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Achievement and Economic Growth
intervening variable to influence economic growth. We do not present models of the
final path between intervening measures and economic growth, as these effects are wellstudied in prior sociological research (though we did replicate those findings with our
dataset) (see Schofer et al. 2000).
Scientific/Technical Research
It is commonly argued that increased student achievement may benefit the
economy due to improved national research capacity (but see Schofer et al. 2000 for
criticisms of this view). We use both scientific publication and number of patents
granted to indicate nation-state scientific research activities. Both variables are measured
per capita (logged), and are collected for 1970, 1980 and 1990 from the UNESCO
Statistics Yearbook, the Science Citation Index (ISI 1973, 1982, 2000), and the World
Intellectual Property Organization website.
Science and Technology Labor Force
Science and mathematics achievement may affect the economy by producing
greater human capital in the fields of science and technology. We use the number of
scientists and engineers in research and development, per million people (logged), to
measure the scientific human capital in the labor force. Data were collected for 1970,
1980, and 1990 from UNESCO Statistics Yearbooks (UNESCO 1974, 1982, 1992).
Students in Higher Education Science and Engineering
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Along similar lines, expanded higher education in science and technical fields
may mediate the effect of achievement on the economy. The enrollment of students in
tertiary science and engineering disciplines (as a percentage of the 20-24 age cohort) was
collected for 1970, 1980, and 1990 from the UNESCO Statistics Yearbook (UNESCO
1974, 1982, 1992).
Further Methodological Considerations
We employed a number of methodological checks to determine the robustness of
our results. These included the examination of residuals and outliers, checks for
multicollinearity, consideration of measurement error and alternative indicators,
comparison with varying samples, and experimentation with alternate model
specifications. In general, results were fairly robust despite the small sample size.
However, the removal of certain outliers affected results, an issue which we discuss at
length below.
We examined residuals to identify heteroskedasticity and potential outliers. We
found little evidence of heteroskedasticity, but we identified four cases that appear to be
influential cases/outliers in models of economic growth, with Cook’s D statistics above
the critical value of 4/N: Hong Kong, Singapore, South Korea, and Taiwan. These four
cases form a distinct cluster of outliers, clearly visible on partial regression plots.
Sometimes called the “Four Asian Tigers,” these countries achieved unusually high
economic growth in previous decades. We replicate our findings without these cases to
determine whether results remain consistent without these cases (see below).
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Second, we explored issues of multicollinearity by examining correlation
coefficients, tolerances/VIFs, and coefficient stability under changing model
specifications. Tolerances were moderate (topping out at just over .5 for some
independent variables) and coefficients were generally stable. We did not find evidence
that results are substantially affected by multicollinearity. Issues of multicollinearity are
especially acute for interaction terms, which are necessarily highly correlated with other
variables in the model. Although tolerances were acceptable, coefficients for interaction
terms should be interpreted with caution given the relatively small sample size in our
study.
Third, we took steps to address potential measurement issues. Measurement error
is always a concern in cross-national models. Even basic statistics such as GDP are
difficult to accurately and comparably measure across a diverse set of economies. We
explored alternative indicators, when available, to ensure that results were not an artifact
of our measures. We examined two different measures of GDP – one based on exchange
rates and another based on purchasing power parity. Likewise, we examined net
secondary enrollment ratios, in addition to gross secondary enrollment ratios. 8 Also, we
examined different achievement score measures, including a trichotomous grouping
based on the Hanushek and Kimko data, as well as a measure from the 1995 TIMSS data
(see below). In all cases, results were consistent. We also examined models in which we
excluded East-European Communist countries (in our sample, Hungary and Russia),
8 The net enrollment ratio is generally considered a higher quality measure, but is available for fewer cases.
In 1980 the two measures correlate at .95. Results are similar in statistical models, so we used the measure
that was available for a larger sample of countries.
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based on concerns that economic production statistics and test scores might have been
upwardly biased. 9 The removal of these cases did not alter our results.
Finally, we employed a variant of our analyses to address concerns regarding
sample size. Due to the limited availability of international test score data, our sample is
constrained to roughly 38 countries, most of which are highly industrialized Western
democracies. We also replicated our results using Hanushek and Kimko’s (2000)
estimates of student achievement based on imputed data for countries that did not
participate in cross-national achievement tests, yielding a sample size of over 70
countries. Results were consistent with the findings presented below.
Results
Tables 1 and 2 present the economic growth models for the periods 1970-90 and
1980-2000 respectively. Table 1, Model 1 shows the baseline relationships involved. As
is conventionally found, economic growth rates are negatively affected by initial levels of
wealth: large economies tend to exhibit lower growth rates. Investment rates and school
enrollments show positive effects, although these do not reach statistical significance in
this (relatively small) data set. The core finding is that student achievement in
mathematics and science shows positive effects on economic growth.
------ TABLE 1 ABOUT HERE ------
9 We thank an anonymous reviewer for bringing to our attention the possibility that achievement test
scores from Russia and Hungary may have reflected a “creamed” sample, rather than a representative
sample of students in those countries.
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`
Model 2 differs from Model 1 insofar as we exclude from the analyses four
outliers: the “Asian Tiger” countries of South Korea, Taiwan, Hong Kong, and
Singapore. The student achievement variable continues to show a positive and
significant effect, but the magnitude of this effect is reduced by more than half and its
significance level declines. Model 3 adds a dummy variable for these four cases. The
dummy variable has a large and significant positive effect, reflecting the extraordinary
economic growth of the “Asian Tigers”. The dummy variable has a similar outcome as
removing the cases from the sample: the effect of student achievement is reduced by half
and declines in level of significance.
We also examined a trichotomous version of our test score measure, dividing
cases into clusters of “low”, “mid”, and “high”, because it has been observed that
countries fall into only a few statistically meaningful groups. We observe that the “mid”
and “high” groups experience greater economic growth than countries with the lowest
test scores. If we choose “mid” as the omitted reference category, the effect of “high” is
positive, but not statistically significant. This suggests that much of the achievement
effect can be attributed to the worst performers on international tests. Moving from the
“middle of the pack” to the top provides less of an economic boost. This is a striking
finding that calls into question the disproportionate attention (and envy) focused on those
few countries with the very highest achievement scores. Such countries do not
experience substantially greater economic growth than countries which are merely
average in terms of achievement.
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Model 4 investigates the question of whether the effect of student achievement on
economic growth is conditional on the size of the educational system. We include an
interaction term between student achievement and secondary enrollment rates. The
literature predicts a positive interaction: the economic benefits of high student
achievement should be larger in countries with high levels of enrollment. Results in
Model 4, however, show that the interaction term is negative and significant. This result
should be interpreted with caution given the small sample size and potential issues of
multicollinearity. However, tolerances did not suggest a problem. Moreover, the effect
is not limited to secondary schooling. We also found a negative interaction between
achievement levels and tertiary enrollment. 10 School achievement levels appear to have
a greater influence on economic growth in countries with lower levels of enrollment. It is
difficult to interpret this effect, as it runs counter to all expectations. Apparently a
greatly expanded school system weakens the link between achievement and economic
outcomes. Or, perhaps enrollment expansion somehow obviates the need for (or
compensates for) high achievement, meaning that high achievement is most important in
nations with small educational systems. Or, the result may simply be an artifact of
multicollinearity and/or our small sample size, which can cause inefficient estimation of
coefficients.
Overall, the results of Table 1 provide limited support for the hypothesis that
aggregate achievement leads to economic growth. Much of the effect is due to the four
“Asian Tigers.” And we find a puzzling statistical interaction effect in the opposite
direction than implied in the education, science, and development literature.
10 Analyses not presented here, but available on request from the authors.
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Achievement and Economic Growth
We venture, here, the interpretation that much of the achievement “effect” is not
really causal in character. It may be, rather, that nation-states with strong prodevelopment policies, and with regimes powerful enough to enforce these, produce both
more economic growth and more disciplined student achievement levels in fields (like
science and mathematics) especially perceived to be development-related. This idea
would explain the status of the "Asian Tigers," whose regimes have been much focused
on both producing economic growth and achievement oriented students in math and
science (Cummings 1997; but see Baker and Holsinger 1996). This idea would also help
explain the absence of a positive interaction between achievement and enrollment. If
student achievement is an indicator of national commitments to development rather than
a means to this development, there is no reason to expect a positive interaction with the
number of students involved. And there might be a negative interaction effect resulting
from the suppression of enrollment produced by very high achievement standards
We turn now to Table 2, which examines the 1980 to 2000 time period. Models 5
through 8 directly parallel Models 1 through 4, except at a later point in time. The effect
of achievement in Model 5 is positive, but not quite statistically significant. The
coefficient is .086 in the 1980-2000 period, versus .123 in the 1970-90 period. This
decrease makes sense given the faltering economic performance of the “Asian Tigers” in
the 1990s, which are responsible for much of the achievement effect in the earlier period.
Other effects decrease in both magnitude and significance. 11 In Model 6 we observe that
11 This is not purely the result of small sample size. The effects of investment and secondary education
diminish in this period even in large sample analyses, reflecting a historical change in the factors associated
with growth.
21
Achievement and Economic Growth
the effect of student achievement shrinks further when the “Asian Tigers” are removed
from the analysis. South Korea, Singapore and Hong Kong were removed from the
analysis, and Taiwan was not in Model 5 to start with because of missing data in the
1980-2000 period. The inclusion of an “Asian Tigers” dummy in Model 7 has the same
effect. The interaction effect in Model 8 is insignificant and again in the opposite
direction from the expected one. Again, tolerances suggest that multicollinearity is not
responsible for the finding.
------ TABLE 2 ABOUT HERE ------
Some reflection may suggest reasons for the period-sensitivity. For reasons
entirely exogenous to our arguments and analyses here, economic growth was very
different in the 1970-90 period from growth in the 1980-2000 period. In particular, many
Asian countries experienced much lower growth in the 1990s. These countries
(including the Tigers, but also Japan) continued to have high academic achievement
scores, yet they experienced much lower economic growth due to structural and regional
problems. An effect of this sort would obviously lower the coefficient reflecting the
apparent effect of academic achievement on growth.
To investigate this possibility, we conducted exploratory analyses of economic
growth in the narrowed time period of 1990-2000, looking for possible effects of our
achievement variable. This is a plausible approach, since national achievement scores
show much consistency over time. In these exploratory analyses of economic growth
during a period that has been something of a disaster for a number of Asian countries and
22
Achievement and Economic Growth
others, the coefficient associated with academic achievement in science and mathematics
entirely disappears. In fact, it turns to an insignificant negative number. Results can be
found in Appendix C. We also conducted a similar analysis using the TIMSS math and
science achievement data, rather than the measure provided by Hanushek and Kimko, as
cross-national test score measures have improved over time. 12 We observe a nonsignificant positive effect of student achievement based on TIMSS data (results not
presented here; available from the authors upon request).
Exploring Intervening Processes
In Tables 3 and 4 we estimate the effects of our student achievement variable on
four potential intervening variables that might mediate the impact of achievement on
growth: numbers of scientists and engineers per million people (logged), the tertiary
science and engineering enrollment ratio (logged), logged scientific publications per
capita, and logged patents granted per capita. These indicators are suggested by the
causal chain discussed earlier (Figure 1), and by the literature on education, science, and
development. At issue is whether we can identify some mechanisms through which
school quality might influence economic growth. Table 3 looks at the 1970-1980 period
while Table 4 covers the 1980-1990 period. In these panel models the control variables
are the dependent variable measured at the earlier time point and the log Gross Domestic
Product per capita as a control for economic development.
12 Analyses based on 1994/5 TIMSS student achievement scores were used in panel models of GDP
growth from 1991-2001.
23
Achievement and Economic Growth
In Table 3, we find that student achievement in mathematics and science has
essentially no effect on the level of science and engineering enrollments (Model 10). On
the other hand, achievement has a positive and significant effect on scientists and
engineers in the labor force (Model 9), scientific publications (Model 11), and patents
granted (Model 12). Economic development shows modest and insignificant effects
throughout these analyses (recall that levels of these dependent variables ten years earlier
are controlled).
The results for the 1980-1990 period are shown in Table 4. Again, student
achievement in mathematics and science has no effect on tertiary enrollment in science
and engineering (Model 14). The positive effect of achievement on scientific research
persists across both time periods (Model 15). But, in contrast to Table 3 results, student
achievement fails to have a significant effect on scientists and engineers in the labor force
(Model 13) and patents granted (Model 16). Just as the general linkage between
achievement and growth weakens after 1980, so do the links between achievement and
intervening variables.
------ TABLES 3 AND 4 ABOUT HERE ------
This mixed bag of findings does not provide robust evidence regarding the
mechanisms that link mathematics and science achievement to economic growth. Prior
cross-national research shows that both scientific “input” indicators, levels of science and
engineering in higher education and in the labor force, positively influence economic
growth (Schofer, et al. 2002; Ramirez and Lee 1995). In Tables 3 and 4, however,
24
Achievement and Economic Growth
student achievement affects these measures in only one instance: in 1970-90,
achievement is associated with the growth of scientists and engineers in the labor force.
The effect is not stable over time. In the same studies, measures of scientific “output”,
such as scientific publications, do not show general positive effects on economic growth,
and are thus unlikely to serve as intervening variables in achievement effects (Schofer et
al. 2000). Thus, taken as a whole the results in Tables 3 and 4 do not identify clear
mechanisms through which academic achievement leads to economic growth.
To summarize, student achievement scores in science and mathematics positively
influence economic growth for 1970-1990. But these effects diminish or disappear when
the Asian Tigers are excluded from the analysis or in analyses of 1980-2000. The results
also run contrary to the expectation that the student achievement effect would be stronger
in more educationally developed countries. Lastly, the analyses do not provide us with
much supportive evidence about mechanisms through which achievement might
influence growth for both periods.
Discussion
Our results call into question a long-established pattern in education and
development policy discourse, a pattern that involves greater confidence in the effects of
education than is often empirically warranted. Since World War II economic success has
increasingly been viewed as produced by mass, and later even higher, educational
expansion. More recently, school quality and especially the quality of mathematics and
science teaching have been seen as central. There are scholarly skeptics to be sure and
these include some development economists (cf: Easterly, 2001; Pritchett, 1996) in
25
Achievement and Economic Growth
addition to the usual sociological suspects. But within national and international policy
circles educational investments, aid packages, and curricular and pedagogical reforms
proceed as if the broad economic consequences of education were well known. The fine
points of human capital theory are debated within universities and learned societies, but
human capital theory is taken for granted within government agencies and international
organizations in the quest for development (Chabbott 2003).
Early in the post-War period educational enrollment targets preoccupied
education and development decision makers and their national and international advisors
(Coombs 1968). More recently, the science and mathematics reform wave, focusing
mainly on mass education, has been the main focus. This wave emerged in the 1980s,
fueled by slow economic growth in some core countries, especially the United States, and
by an increased inclination to see education in a technical and economistic light. The
reform movement has long since gone worldwide, supported by all sorts of international
institutions, such as the World Bank and the OECD. More and more countries participate
in international surveys of mathematics and science achievement. And, of course, more
and more surveys are designed and administered. These surveys give rise to international
achievement report cards almost as routinized as economic development indicators
(Baker and Latendre 2005).
Confidence in the economic import of achievement in mathematics and science
was bolstered by the use of evidence highlighting exemplary cases, not as a result of
substantial cross-national comparisons or time series analyses. In the spirit of “selecting
on the dependent variable,” Asian, and especially Japanese economic success led to
beliefs that something about Japanese primary and secondary education was responsible
26
Achievement and Economic Growth
for the economic miracles of the 1980s (Stevenson and Stigler 1992; Rohlen 1983). That
“something” was called the Japanese model of human resource development, a model
that influenced the other East Asian countries as well (Cummings 1997; for a critique of
this perspective, see Baker and Holsinger 1996). Since the Asian economic failures of
the 1990s this literature has receded. A new and smaller literature now focuses on Asian
economic decline, and it too generates an arbitrary accounting, emphasizing the ways in
which Japanese education, or Asian education in general, stifles independence and
creativity (cf: Parameter 2000).
As these waves of reform pass through the educational systems of the world, one
research task is to try to see empirically what effects the variables they focus on may
actually have. That is the research task pursued in this paper. Another and more difficult
task is to try to understand the waves of reform in the first place. Educational
researchers, it seems, have a rather poor record of predicting which new reforms will
become fashionable down the road. Some sociological theories, however, predict that
successful reforms, like the push for achievement in science and mathematics, become
worldwide in their impact. That is because education is no longer mostly a local matter
shaped by societally endogenous factors but instead a world institution influenced by
universalistic ideas about education as means to development, which itself is now a
standardized world goal (Meyer, Boli, Thomas, and Ramirez 1997). Further research is
needed to evaluate this prediction, perhaps identifying the conditions under which some
educational reforms spread more rapidly and/or more extensively than others.
Conclusion
27
Achievement and Economic Growth
There has been, in recent decades, a worldwide effort to measure student
achievement levels in science and mathematics, on the grounds that achievements in
these areas are instrumental in improving economic development. The worldwide effort
has generated measures now reaching far enough back in time to permit analyses of their
effects on subsequent economic growth. We have conducted such analyses, for the 19702000 period for a small set of countries for which there is data on the relevant
independent and dependent variables.
We find that countries with high science and mathematics achievement scores
tend to grow somewhat more rapidly than other countries. This finding is consistent with
the main inference reported in Hanuschek and Kimko (2000) and very much in line with
mainstream educational policy discourse in the United States. But we further find that
this effect is reduced when the four “Asian Tigers,” with high growth and high scores
during the period, are removed from the analysis. Moreover, the effect weakens in the
recent period, when a number of Asian countries went into a slow-growth phase for
reasons unrelated to matters of educational achievement. The special status of the "Asian
Tigers," and the additional finding that the apparent achievement effect decreases (rather
than increases, as we expect) with the increased level of educational enrollments in a
country, suggests that the overall effect may not be a causal one. Perhaps regimes
making a push for development can also make a push for disciplined student
achievements in areas like science and mathematics, which less pressured students might
choose to avoid. From this perspective achievement and development are outcomes of a
regime but not really causally related to each other.
28
Achievement and Economic Growth
Alternatively the Asian Tigers in the earlier era may exemplify exactly the kind of
human capital oriented school system emphasized in sociological theories that discuss
the conditional relationship between schooling and economic growth (Fuller and
Rubsinson 1992; Rubinson and Browne 1994). These arguments suggest that when
schooling is organized and chartered to produce human capital development, schooling is
more likely to result in economic growth. The diminished effect of achievement on
economic growth in the later period may reflect subtle changes in their school systems,
from a singular focus on human capital formation to a broader set of socialization goals
for their students (see Koh 1999 for the case of Singapore.) These changes may soften
the impact of achievement on economic growth, an impact in part drive by the Asian
Tiger cases. Ironically these subtle changes may signal the beginning of the
transformation of the Asian Tigers themselves, from narrower economic growth to
broader human development regimes. This shift may result in weaker ties between
academic achievement and educational and occupational aspirations and attainments,
(Buchmann and Dalton 2002), thereby weakening the effects of achievement on
economic growth. That is, if you get more Asian school children whose aspirations are
less grounded in their test scores and more attuned to individual personal tastes, then you
should expect to find less achievement impact on the economy.
From our study the main conclusion is that the relationship between achievement
in science and mathematics in school children and national economic growth is both time
and case sensitive. Moreover, the relationship largely reflects the gap between the
bottom third of the achievers and the rest; the middle of the pack does not much differ
from the rest. If there are economic consequences that a nation will face in the future due
29
Achievement and Economic Growth
to its current achievement profile, it is the nations in the bottom third of the educational
distribution that are at risk. These are countries competing below a minimum standard,
not those competing at higher levels. But much of the obsession with the achievement
“horse race” proceeds as if beating the Asian Tigers in mathematics and science
education is necessary for the economic well being of the United States or other
developed countries. Our analysis offers little support for this obsession.
High scores in standardized international mathematics and science achievement
tests may be important indicators of the quality of schooling. Educational reform to
upgrade the quality of mathematics and science education in the United States makes
good sense. But achievement indicators do not capture the extent to which schooling
promotes initiative, creativity, entrepreneurship, and other strengths not sufficiently
curricularized to warrant cross-national data collection and analysis. Unfortunately the
policy discourse that often follows from international achievement races involves
exaggerated causal claims frequently stressing educational “silver bullets” for economic
woes. Our analyses do not offer definitive answers but they raise important questions
about the validity of these claims. In an era that celebrates evidence based policy
formation it behooves us to carefully weigh the evidence, rather than use it simply as a
rhetorical weapon.
30
Achievement and Economic Growth
References
Baker, David. 1993. “Compared to Japan, the U.S. is a Low Achiever, Really: New
Evidence and Commentary on Westbury” Response to Baker by Westbury and Rejoinder
by Baker. Educational Researcher, 19:18-26.
Baker, David and Don Holsinger. 1996. “Human Capital Formation and School
Expansion in Asia: Does a Unique Regional Model Exist?” International Journal of
Comparative Sociology, 37: 159-173.
Baker, David and Gerald LeTendre, 2005. National Differences, Global Similarities:
World Culture and The Future of Schooling.. Stanford: Stanford University Press.
Barro, Robert J. 1991. “Economic Growth in a Cross Section of Countries.” Quarterly
Journal of Economics, 106, 2 (May): 407-443.
Barro, Robert J. and Xavier Sala-i-Martin. 1995. Economic Growth. New York:
McGraw Hill.
Benavot, Aaron. 1992a. “Curricular Content, Educational Expansion, and Economic
Growth.” Comparative Education Review, 36: 150-174.
Benavot, Aaron. 1992b. “Educational Expansion and Economic Growth in the Modern
World, 1913-1985. pp. 117-134 in Bruce Fuller and Richard Rubinson, eds. The Political
Construction of Education. New York: Praeger.
Berliner, David. 1997. “If It Bleeds, It Leads: The Natural Alliance Between School
Critics and the Media” Paper presented at the annual meetings of the American
Education Research Association.
Berliner, David and Bruce Biddle. 1996. The Manufactured Crisis. Reading, MA:
Addison-Wesley.
Bowles, Samuel and Herbert Gintis. 1976. Schooling in Capitalist America. New York:
Basic Books.
Bowles, Samuel and Herbert Gintis. 2002. “Schooling in Capitalist America Revisited.”
Sociology of Education, 75:1-18.
Bracey, Gerald. 1996. “International Comparisons and the Condition of American
Education.” Educational Researcher, 25:5-11.
Buchmann, Claudia and Ben Dalton. 2002. “Interpersonal Influences And Educational
Aspirations In 12 Countries: The Importance Of Institutional Context.” Sociology of
Education, 67: 184-198.
31
Achievement and Economic Growth
Chabbott, Colette and Francisco O. Ramirez. “Development and Education” pp. 163-187
in Maureen Hallinan, ed. Handbook of Sociology of Education. New York: Plenum
Chabbott, Colette. 2003. Constructing Education for Development: International
Organizations and Education for All. New York: Routledge Falmer.
Collins, Randall. 1971. The Credential Society. New York: Academic Press.
Coombs, Phillip. 1968. The World Educational Crisis. Oxford: Oxford University Press.
Cummings, William. 1997. “Human Resource Development: The J-Model.” pp. 275292 in William Cummings and Philip Altbach, eds. The Challenge of Eastern Asian
Education. New York: SUNY Press.
Cummings, William and Philip Altbach (eds.) 1997. The Challenge of Eastern Asian
Education. New York: SUNY Press.
Dobbin, Frank. 1993. "The Social Construction of the Great Depression." Theory and
Society, 22:1-56.
Drori, Gili, John W. Meyer, Francisco O. Ramirez, and Evan Schofer. 2003. Science in
the Modern World Polity: Institutionalization and Globalization. Stanford: Stanford
University Press.
Easterly, William. 2001. The Elusive Quest for Growth: Economists' Adventures and
Misadventures in the Tropics. Cambridge, MA: MIT Press.
Fuller Bruce and Richard Rubinson, eds. 1992. The Political Construction of Education:
The State, School Expansion ,and Economic Change. New York: Prager.
Garnier, Maurice and Jerald Hage. 1990. “Education and Economic Growth in
Germany” Research in Sociology of Education and Socialization, 9:25-53.
Guilford, Dorothy. 1993. A Collaborative Agenda For Improving International Studies
in Education. Washington, D.C.: National Academy Press.
Hage, Jerald, Maurice Garnier, and Bruce Fuller. 1988. “The Active State, Investment
in Human Capital, and Economic Growth, France, 1825-1975.” American Sociological
Review, 53: 824-837.
Hannum, Emily and Claudia Buchmann. 2003. The Consequences of Global
Educational Expansion. Cambridge, MA: American Academy of Arts and Sciences.
Hanushek, Eric and Denise Kimko. 2000. “Schooling, Labor Force Quality, and the
Growth of Nations.” American Economic Review, 90, 5 (December):1184-1208.
32
Achievement and Economic Growth
Institute for Scientific Information (ISI) 1973, 1982, 2000. Science Citation Index.
Philadelphia, PA. Institute for Scientific Information.
Koh, Chin Thong. 1999. “Shaping Instincts and Attitudes: Moral Education in a
Singaporean School.” Unpublished Masters Thesis. School Of Education, Stanford
University.
Levine, Ross and David Renelt. 1992. “A Sensitivity Analysis of Cross-Country Growth
Regressions.” American Economic Review, 82,4 (September): 942-963.
Meyer, John, John Boli, George Thomas, and Francisco O. Ramirez. 1997. “World
Society and the Nation-State.” American Journal of Sociology, 103:144-181.
Meyer, John W., Francisco Ramirez, and Yasemin Soysal. 1992. “World Expansion of
Mass Education, 1870-1970.” Sociology of Education, 65, 2, 1992: 128-149.
Meyer, John W., David Kamens, Aaron Benavot, and Young Kyoung Cha. 1992. School
Knowledge for the Masses: World Models and National Primary Curricular Categories
in the 20th Century. Washington DC: Falmer Press.
Parameter, Lynne. 2000. “Internationalization in Japanese Education: Current Issues
and Future prospects.” pp. 237-254 in Nelly Stromquist and Karen Monkman, eds.
Globalization and Education: Integration and Contestation Across Cultures. New York:
Rowman and Littlefield Publishers.
Pritchett, Lant. 1996. “Where Has All The Education Gone?” World Bank Policy
Research Working Paper #1581. Washington, D.C.: World Bank.
Ramirez, Francisco and Molly Lee. 1995. "Education, Science, and Economic
Development." pp. 15-39 in Gerard Postiliogne and Lee Wing-On (eds). Social Change
and Educational Development in Mainland China, Taiwan, and Hongkong. University of
Hongkong, Center for Asian Studies.
Rholen, Thomas P. 1983. Japan’s High Schools. Berkeley: University of California
Press.
Rubinson, Richard and Irene Brown. 1994. “Education and the Economy” pp. 583-599
in Neil Smelser and R. Swedborg, eds. The Handbook of Economic Sociology. Princeton:
Princeton University Press.
Schofer, Evan, Francisco O. Ramirez, and John W. Meyer. 2000. “The Effects of
Science on National Economic Development, 1970 to 1990.” American Sociological
Review, 65:866-887.
Schmidt, William H., Curtis C. McKnight, and Senta Raizen. 1996. A Splintered Vision:
An Investigation of Science and Mathematics Education. Kluwer Academic Publishers.
33
Achievement and Economic Growth
Schmidt, William H., Curtis C. McKnight, Leland S. Cogan, Pamela M. Jakwerth, and
Richard T. Houang. 1998. Facing The Consequences: Using TIMSS for a Closer Look at
U.S. Mathematics and Science Education. Kluwer Academic Publishers.
Shenhav, Yehouda and David Kamens. 1991. “The ‘Costs’ of Institutional Isomorphism
in Non-Western Countries.” Social Studies of Science, 21: 427-545.
Stevenson, Harold and James Stigler. 1992. The Learning Gap: Why Our Schools Are
Failing and What We Can Learn From Japanese and Chinese Education. New York;
Simon and Schuster.
Summers, Robert and Alan Houston. 1991. “The Penn World Table (Mark 5): An
Expanded Set of International Comparisons, 1950-1988.” Quarterly Journal of
Economics 106: 327-68.
Tyack, David, Robert Lowe, and Elisabeth Hansot. 1984. Public Schools in Hard Times:
The Great Depression and Recent Years. Cambridge, MA: Harvard University Press,
1984.
United Nations Education, Science, and Cultural Organizations (UNESCO) 1974, 1982,
1992. UNESCO Statistical Yearbook. Paris, France: United Nations Education, Science,
and Cultural Organization.
U.S. National Commission on Excellence in Education. 1984. A Nation At Risk.
Cambridge, Mass: USA Research.
Walters, Pamela Barnhouse and Paul J. O’Connell. 1990. “Post World War II Higher
Educational Expansion, The Organization of Work, and Changes in Labor Productivity in
the United States.” Research in Sociology of Education and Socialization, 9:1-23.
World Bank. 1992. World Tables. Baltimore, MD: Johns Hopkins University.
World Bank. 2002. Education and Development. Washington, D.C. World Bank.
Electronic document: http: //www1.worldbank.org/education/pdf/EducationBrochure.pdf,
accessed August 2002.
World Intellectual Property Organization. 1983. 100 Years Protection of Industrial
Property. Geneva, Switzerland: World Intellectual Property Organization.
34
Achievement and Economic Growth
Table 1. Coefficients (with robust estimators) From the Regression of Annualized
Growth of Real GDP Per Capita, 1970-1990, on Student Achievement and Additional
Control Variables.
Model 1
Model 2
‘Asian
Tigers’
Excluded
Model 3
Model 4
-.283**
(.088)
-.177***
(.066)
-.167**
(.063)
-.356***
(.070)
Investment Share of GDP, 1970
(%*100)
.032
(.053)
.064
(.041)
.048
(.034)
.040
(.039)
Secondary School Enrollment
Ratio, 1970 (*10-3)
.001
(.012)
.008
(.008)
.009
(.008)
.030*
(.013)
Mathematics and Science
Achievement
.123**
(.040)
.048*
(.023)
.055*
(.023)
.142***
(.038)
Real GDP Per Capita in 1970
($1,000)
4.018***
(.554)
“Asian Tiger” Dummy
Large Secondary System *
Math and Science Achievement
-2.37***
(.552)
Constant
-2.44
(1.37)
-1.08
(1.23)
-1.112
(1.280)
-3.75**
(1.34)
r-squared
.48
.41
.73
.65
N
38
34
38
38
* p<.05, ** p<.01, *** p<.001 (two-tailed test), robust standard errors in parentheses.
35
Achievement and Economic Growth
Table 2. Coefficients (with robust estimators) From the Regression of Annualized
Growth of Real GDP Per Capita, 1980-2000, on Student Achievement and Additional
Control Variables.
Model 5
Model 6
‘Asian Tigers’
Excluded
Model 7
Model 8
Real GDP Per Capita in 1980
($1,000)
-.168
(.155)
-.107
(.164)
-.139
(.157)
-.201
(.157)
Investment Share of GDP,
1980 (%*100)
-.001
(.036)
-.019
(.036)
-.021
(.033)
-.014
(.035)
Secondary School Enrollment,
1980 (*10-3)
-.004
(.027)
-.003
(.031)
.003
(.028)
.014
(.030)
Mathematics and Science
Achievement
.086
(.053)
.065
(.069)
.062
(.070)
.105*
(.051)
1.935
(1.215)
“Asian Tiger” Dummy
Large Secondary System *
Math and Science
Achievement, 1980
-1.340
(.828)
Constant
2.647
(1.556)
3.395*
(1.727)
3.362
(1.726)
-1.619
(1.663)
r-squared
.17
.08
.23
.23
N
37
34
37
37
* p<.05, ** p<.01, *** p<.001 (two-tailed test), robust standard errors in parentheses.
36
Achievement and Economic Growth
Table 3. Coefficients (with robust estimators) From the Regression of Various Indicators
of Scientific Development on Student Achievement and Additional Control Variables,
1970-80
Scientists and
Engineers
Model 9
Scientists and Engineers Per
Capita, 1970 (logged)
Dependent Variable
Tertiary
Science and
Scientific
Engineering
Publications
Enrollment
Model 10
Model 11
Patents
Model 12
.667***
(.138)
Tertiary Science and
Engineering Enrollments 1970
(per age 20-24 pop, logged)
.773***
(.148)
Science Publications Per
Capita, 1970 (logged)
.827***
(.065)
Patents Granted Per Capita,
1970 (logged)
.624**
(.095)
Real GDP Per Capita 1970
($1,000)
.022
(.029)
.019
(.015)
.030
(.030)
.038
(.056)
Mathematics and Science
Achievement
.042**
(.015)
.007
(.018)
.021*
(.009)
.041**
(.015)
Constant
.365
(.699)
.689
(.924)
-1.183*
(.450)
-2.974
(.757)
r-squared
.88
.78
.969
.87
N
30
26
33
29
* p<.05, ** p<.01, *** p<.001 (two-tailed test), robust standard errors in parentheses.
37
Achievement and Economic Growth
Table 4. Coefficients (with robust estimators) From the Regression of Various Indicators
of Scientific Development, 1980-90, on Student Achievement and Additional Control
Variables.
Scientists and
Engineers
Model 13
Scientists and Engineers Per
Capita, 1980 (logged)
Dependent Variable
Tertiary
Science and
Scientific
Engineering
Publications
Enrollment
Model 14
Model 15
Patents
Model 16
.740***
(.157)
Tertiary Science and
Engineering Enrollments 1980
(per age 20-24 pop, logged)
.776***
(.093)
Science Publications Per
Capita, 1980 (logged)
.902***
(.024)
Patents Per Capita, 1980
(logged)
.754*
(.296)
Real GDP Per Capita, 1980
($1,000)
.078*
(.036)
.021
(.02)
-.037*
(.016)
.145*
(.058)
Mathematics and Science
Achievement
.016
(.017)
.004
(.012)
.028***
(.007)
.027
(.030)
Constant
.632
(.679)
-6.077***
(.514)
.174
(.221)
-2.470
(2.227)
r-squared
.88
.87
.97
.87
N
29
27
37
30
* p<.05, ** p<.01, *** p<.001 (two-tailed test), robust standard errors in parentheses.
38
Achievement and Economic Growth
Figure 1. Commonly Assumed Causal Factors Linking Science Education to National
Economic Development.
A. National Curricular and
Pedagogical Modernization
B. Math and Science Interest
and Achievement
C. Scientists and Engineers in
Higher Education
D. Scientists and Engineers in
the Labor Force
E. National Economic
Development
39
Achievement and Economic Growth
Appendix A: Math and Science Achievement Score Index (Source: Hanushek and
Kimko 2001).
Country Name
Japan
China
West Germany
Switzerland
Hong-Kong
Netherlands
Singapore
Taiwan
South Korea
France
United Kingdom
USSR
Hungary
Belgium
New Zealand
Israel
Poland
Norway
Spain
Finland
Australia
Ireland
Canada
Sweden
Italy
Portugal
USA
Thailand
Luxembourg
Jordan
Swaziland
Philippines
Nigeria
Brazil
Chile
Mozambique
India
Iran
Math and Science
Achievement Index
60.65
59.28
59.03
57.17
56.93
56.84
56.51
56.28
56.21
54.15
53.98
53.89
53.85
53.25
52.44
51.29
50.28
49.60
49.40
48.76
48.13
47.59
47.57
47.41
44.59
44.09
43.43
39.83
39.45
39.38
35.46
34.35
34.15
33.91
26.30
24.26
21.63
20.79
40
Achievement and Economic Growth
Appendix B: Means and Standard Deviations of Variables Used In Analyses.
Variable
Real GDP per capita 1970 ($1,000)
Real GDP per capita 1980 ($1,000)
Real GDP per capita 1990 ($1,000)
Real GDP per capita 2000 ($1,000)
Annualized growth rate of GDP per capita, 19701990
Annualized growth rate of GDP per capita, 19802000
Annualized growth rate of GDP per capita, 19902000
Investment ratio (% GDP) 1970
Investment ratio (% GDP) 1980
Investment ratio (% GDP) 1990
Secondary enrollment ratio 1970
Secondary enrollment ratio 1980
Secondary enrollment ratio 1990
Math and science achievement test score index
Scientific publication per capita 1970 (logged)
Scientific publication per capita 1981 (logged)
Scientific publication per capita 1990 (logged)
Patents granted per capita 1970 (*1,000, logged)
Patents granted per capita 1980 (*1,000, logged)
Patents granted per capita 1990 (*1,000, logged)
Scientists and engineers in labor force per million
population 1970 (logged)
Scientists and engineers in labor force per million
population 1980 (logged)
Scientists and engineers in labor force per million
population 1990 (logged)
Ratio of science and engineering tertiary enrollment
1970 (age cohort 20-24, logged)
Ratio of science and engineering tertiary enrollment
1980 (age cohort 20-24, logged)
Ratio of science and engineering tertiary enrollment
1990 (age cohort 20-24, logged)
Asian tiger dummy
Number of
Standard
Cases
Mean
Dev.
38
5680.87 3702.48
38
6490.36 3966.34
12067.7
38
7117.96
2
18219.0
38
5
11173.01
38
2.54
2.12
38
5.28
1.99
38
38
38
38
38
37
37
38
33
37
37
30
30
32
4.18
24.58
23.97
23.33
53.44
68.31
77.42
46.37
-2.70
7.54
8.01
-1.60
-1.84
-1.73
2.18
9.31
7.32
7.92
25.52
24.88
26.60
10.95
1.99
2.40
2.26
1.89
1.66
2.15
31
2.48
1.26
30
6.86
1.38
32
6.93
1.45
33
2.85
1.27
32
3.42
1.17
32
38
-3.05
0.11
1.00
0.31
41
Achievement and Economic Growth
Appendix C. Coefficients (with robust estimators) from the Regression of Annualized
Growth of Real GDP Per Capita, 1990-2000, on Student Achievement and Additional
Control Variables.
Full Sample
‘Asian Tigers’
Excluded
Real GDP per capita in 1990
($1,000)
-.042
(.123)
-.031
(.131)
Investment share of GDP, 1990
(%*100)
.106
(.067)
.088
(.083)
Secondary school enrollment ratio,
1990 (%*100)
-.018
(.031)
-.014
(.036)
Math and Science Achievement
.0003
(.071)
-.012
(.085)
Constant
3.580*
(1.808)
4.009
(2.106)
r-squared
.10
.06
N
37
34
* p<.05, ** p<.01, *** p<.001 (two-tailed test), robust standard errors in parentheses.
42