Youth Literacy in Canada:
Comparisons with the Past and Expectations for the Future
David A. Green and W. Craig Riddell
Department of Economics
University of British Columbia
June 2007
Youth Literacy in Canada: Comparisons with the Past and Expectations for the Future
David A. Green and W. Craig Riddell
Human capital has increasingly come to be seen as a key determinant of an economy’s
success. In a world with rapidly changing technologies, it is argued, having a workforce that is
both skilled and flexible enough to adopt new technologies is key. Thus, the best predictor of
how an economy will perform in the future may well be the skill level of its youth. Or, to reverse
the argument, a country that is not doing a good job of creating skills among its new generations
is likely to have troubles in the future. In this paper, we examine a set of direct measures of youth
skills in Canada: the scores of a representative sample of youth on tests designed to measure their
literacy, numeracy and problem solving abilities in a number of dimensions. The availability of
direct measures of literacy skills is a huge advantage in a realm where indirect measures of skills
(such as years of schooling) are usually all that are available. Literacy skill measures also have
the advantage of measuring something that is of direct interest in and of itself (as opposed to just
being of interest because of its role in promoting economic growth). Sen(1999) argues that
individuals need a set of capabilities in order to function as equal members of society. Key
among these capabilities is literacy since it opens up opportunities to take part in political
discourse as well as opportunities in many other fora. Thus, we are interested in characterizing
youth literacy and inequality in youth literacy because it is a direct measure of social equity.
A key issue in discussing the literacy among the current generation of youth in Canada
will be establishing a benchmark. The literacy scores we report, and even the degree of inequality
1
in them, will have little meaning in themselves.1 We use two main types of benchmarks in this
paper. The first consists of earlier generations of Canadians. Our prime group of interest will be
what we will call the current generation of youth, consisting of individuals aged 16 to 25 in 2003.
We will compare their literacy outcomes to the directly preceding generation of youth (people
who were aged 16 to 25 approximately a decade earlier) and to generations just before that. A
key feature of our investigation will be our attempt to separate differences across cohorts from
the effects of ageing on literacy levels. We could compare the literacy of current youth to that of
the directly preceding cohort by examining the literacy of youth in 2003 and the literacy of 26 to
35 year olds in 2003 since the previous cohort of youth are observed in the latter age range in
2003. However, such a comparison will reflect both differences across cohorts and the effects of
ageing on literacy. That is, we would not know whether the literacy of 26 to 35 year olds in 2003
is different because literacy levels are permanently different across cohorts or because of the fact
that this group is older when we observe them. To address this problem, we use a combination of
datasets - one from 1994 and one from 2003. In this way, we can compare literacy levels of the
current and previous generations at times when they were both youth. Moreover, we can follow
the literacy outcomes of the previous generation across the two datasets, allowing us to establish
the impact of ageing on literacy.
Our second benchmark consists of literacy outcomes for youth in two other countries:
1
The literacy data used here do include indicators for a set of 5 literacy “levels” or ranges
of literacy scores. These are then given an interpretation, e.g., “Level 3 is the desired threshold
for coping with the rapidly changing skill demands of a knowledge-based economy and society”
(The Daily, November 30, 2005). We view these characterizations of the associated ranges of
literacy scores as having limited empirical basis (see Blum et al (2001)) and choose to focus on
examining the whole literacy score distribution rather than artificially generated subsets of it.
2
Norway and the US. In part, this choice of comparator countries is dictated by data availability
but it also has some fortuitous elements. In particular, it is well known that the Nordic countries
fare particularly well both in terms of literacy levels and literacy equality. Thus, a comparison
with Norway sets a high standard. The US typically does not fare as well in international
comparisons but is Canada’s main economic partner and a point of constant comparison for
Canadian outcomes of all sorts. Given concerns about international comparability of test data
(particularly tests given in different languages) (Blum et al (2001)), we do not place a heavy
emphasis on comparison across the three countries in a particular year. Instead, we are interested
in whether the patterns we observe across time in Canada are also seen over time within the other
two countries.
Data holds a place of central importance in our investigation. We make use of the unique
International Adult Literacy surveys (IALS) which combine extensive survey questions on
respondent backgrounds and behaviours with scores on four broad literacy tests. Particularly
important for our investigation is the fact that the IALS literacy tests have been specifically
designed to be comparable over time and across countries. Thus, we are able to use direct
comparisons of scores from the 1994 and 2003 versions of the IALS for Canada to examine
cohort and ageing effects and to use comparisons to the 1994 and 2003 IALS for the US and the
1998 and 2003 IALS for Norway to construct consistent international comparisons. As we
mentioned earlier, though, there is some degree of contention about the cross-country
comparability of these data; a point we discuss in more detail in the paper.
Our investigation generates the following set of key conclusions. Canada’s current youth
have generally lower literacy levels than previous generations of Canadians. More precisely, the
3
probability that current Canadian youth suffer low levels of literacy is either no different or
slightly lower than previous generations. However, the probability that they attain high levels of
literacy is decidedly lower than for previous generations and this disparity increases as we move
higher and higher in the literacy distribution. This relatively inferior performance seems to us to
be a cause for concern. A second key conclusion is that literacy as measured on these tests
declines with age after leaving school. In some ways this is not surprising. Many parents have
had the experience of having their children spout facts or mathematical calculations about which
they have vague recollections from their school days but can no longer truly remember. This may
reflect a “use it or lose it” model of literacy in which literacy skills obtained during school
atrophy with lack of use after leaving school. Whether the result is expected or not, though, it
implies that if current youth are at relatively low levels of literacy today, they are only going to
move to even lower levels over time. In terms of international comparisons, Canada falls about
midway between Norway and the US both in terms of literacy levels and the extent of inequality
in their literacy distributions. Thus, there is potentially much to learn from the Norwegians but
we do appear to have an advantage over the Americans. Interestingly, all three countries show the
same pattern of literacy loss with age. Thus, whatever Norway is doing better it seems not to
have to do with institutions and opportunities associated with maintaining literacy levels after
leaving school. Or, to put it in the current policy vernacular, there is no reason to think, based on
literacy test scores, that Norway is better at “life long learning” than Canada. In terms of crosscohort patterns, the US shows much the same pattern as Canada while the Norwegian data does
not show any particular pattern of differences across cohorts. Thus, whatever Norway is doing
right, it has been doing it for a while and has been consistent. Both Canada and the US, on the
4
other hand, appear to face a growing problem with each successive generation.
Our investigation proceeds in sections. In section 2, we provide a brief overview of the
data. In section 3, we characterize the distribution of youth literacy in Canada and provide direct
comparisons with older age groups in 2003. In section 4, we break our comparisons down into
dimensions related to permanent differences across cohorts and ageing effects. In section 5, we
examine the role of education and introduce regression based examinations of the key patterns. In
section 6, we present the literacy distributions for Norway and the US and make direct
comparisons with Canada.
2) Data
Our data comes from the International Adult Literacy and Skills Survey (IALS03): a
combination survey and skills assessment carried out in several countries in 2003.2 We also use
the International Adult Literacy Survey (IALS94), an earlier survey of literacy skills also carried
out in a series of countries but in differing years for different countries. For Canada the earlier
IALS was carried out in 1994 (hence our use of the abbreviation, IALS94). This is also the year
of the comparable US dataset. However, Norway carried out its earlier version of IALS in 1998.
The IALS03 includes standard questions on demographics, labour force status and
earnings, but it also attempts to measure literacy and related cognitive skills in four broad areas:
Prose, Document, Numeracy, and Problem Solving (the latter is not included in the earlier IALS).
Perhaps of most importance for our discussion, both the IALS03 and the earlier version of IALS
2
The other countries participating in this first round of the IALSS03 were Bermuda, Italy,
Mexico, Norway, Switzerland and the U.S. The earlier IALS survey was carried out in over 20
countries during the period 1994 to 1998.
5
attempted to go beyond measuring basic abilities in math and reading to try to assess capabilities
in applying skills to situations found in everyday life. Thus, the Prose questions in the survey
assess skills ranging from items such as identifying recommended dosages of aspirin from the
instructions on an aspirin bottle to using “an announcement from a personnel department to
answer a question that uses different phrasing from that used in the text.” The Document
questions, which are intended to assess capabilities to locate and use information in various
forms, range from identifying percentages in categories in a pictorial graph to assessing an
average price by combining several pieces of information. The Numeracy component ranges
from simple addition of pieces of information on an order form to calculating the percentage of
calories coming from fat in a Big Mac based on a table.
In part of the work that follows, we use comparisons between the 1994 and 2003 data to
examine issues related to changes in literacy with age and differences across birth cohorts.
Unfortunately, the Numeracy component of the tests changed substantially between the earlier
version of IALS and the 2003 survey and, as a result, we cannot make comparisons in this
dimension. In contrast, the Document and Prose tests have substantial overlap in the two survey
years, with approximately 45% of the questions being identical across years. Statistics Canada
also renormalized test results from the remaining 55% of the questions in 2003 so that the overall
average test scores from 2003 bore the same relationship to the overall average in 1994 as do the
averages on the questions that are identical between the two years. Even with this, there is
potential room for non-comparability of the test results across the years. As Osberg (2000) points
out, the literacy scores in the IALS are constructed based on Item Response Theory. Essentially,
in this approach, questions are rated on their level of difficulty on a scale with a maximum value
6
of 500. The reported literacy score is a calculation for the individual of the level of difficulty a
respondent is capable of answering correctly 80% of the time (with the accompanying
assumption that they will get questions of lower levels of difficulty correct more often and
questions with higher levels of difficulty correct less often). This calculation is based on answers
to a set of literacy test questions but also involves some amount of imputation based on the
individual’s observable characteristics. Osberg states that this imputation can imply assigned
literacy scores that are actually outside the range of difficulty of the test questions that are asked.3
Given this, even with questions that are well matched across surveys, we could observe changes
in the literacy distribution between years arising simply because of a change in imputation
methods. To the best of our knowledge, no such change in methods occurred. Moreover, while
we document substantial changes in the literacy distribution between the two surveys for younger
aged individuals, there are no such changes for individuals in the 46 to 55 age group (Green and
Riddell(2006)). If there were general changes in procedures or question difficulty, however, we
would expect to see it for all age groups. Based on this, in the analysis that follows, we treat the
Prose and Document test scores as perfectly comparable between the two survey years.
The Canadian IALS94 sample contains observations on 5660 individuals while the
Canadian IALS03 is substantially larger at 23038 individuals. More importantly for our
purposes, the samples of youth (who we define as individuals who are age 16 to 25 in the sample
year) contain 3574 individuals in 2003 and 1193 in 1994. We include both males and females
3
Though, this appears to be more of a problem in the lower than the upper tail of the
distribution. Osberg(2000) presents evidence that 26% of Canadians in the 1994 IALS had
imputed scores below the lowest difficulty rating of any question on one part of the test but only
0.5% had imputed scores above the highest difficulty rating of any question. This is important in
our case since much of the movement we will discuss occurs in the upper tail.
7
throughout, dividing the analysis on gender lines in some places. Finally, we use the sample
weights provided with the data in all tables and estimation. Literacy scores within each of the
four domains are reported as 5 plausible values for each person. While documentation associated
with the IALS recommends first calculating a given statistic and then averaging across the
statistics, much of our investigation focuses on averages and regressions where results are
identical whether we first estimate the relevant statistics for each plausible score separately and
then average them or we first average the scores and then estimate. Thus, we will focus on the
average of the five plausible values for each individual throughout.
3) Youth Literacy in Canada
The first, immediate consideration in an examination of literacy among youth is to
establish a benchmark. One might imagine arguing that we would like it to be the case that
everyone in society should have the highest possible level of literacy. That is, our goal is to have
everyone achieving the top score on literacy tests. This would both promote equality in social
domains where literacy is important (such as participation in political discourse) and maximize
the skill set for use in production in the economy. In considering youth, our question would be
whether we will be able to achieve this goal from this generation forward, and our measure of the
job our education system (and our society, more generally) is doing in generating literacy is how
far short of this goal we are falling.
The main difficulty with trying to use this absolute benchmark as our measure of success
is that it is difficult to know how to aggregate the various individual shortfalls relative to the
benchmark. For example, consider a society of three people in which two are at the maximal
8
literacy level while one falls 200 points short of the top literacy test score and another society in
which one person is at the maximal level and two others are each 100 points short of the top
score. Should we view these societies differently in terms of literacy achievement? This is more
than idle speculation since it is related to how we should spend societal resources in trying to
meet literacy goals. If we feel that falling below some critical literacy level is a tremendous
disadvantage in society while falling a bit short of the top level leads only to small
inconveniences then we would rate the first society as being much lower in terms of literacy
success. We would also tend to focus our resources on raising the literacy of people below the
critical threshold. Put another way, the distribution of the literacy (as opposed to just the average
literacy level) is important for our deliberations on the literacy success of a particular generation.
In the empirical work that follows, we will present results in terms of features of the whole
distribution.
While establishing an absolute measure of the literacy success of a generation is a
daunting task, we can at least examine whether recent generations are obtaining higher levels of
literacy than earlier generations. If this is the case then there is reason for optimism and we can
investigate what has been working well in literacy creation. If it is not the case then there is
reason for alarm and we need to go back to figuring out what was done better in previous
generations. An additional benchmark comes from comparisons with other countries. A
particular advantage of the IALS datasets is the attention that has been paid to making them
internationally comparable. Thus, we can see whether youth in other countries fare better in
terms of literacy and, if so, set out to investigate how literacy creation mechanisms differ in those
countries. We will pursue both comparison strategies in this paper, comparing literacy outcomes
9
of youth in Canada to outcomes for previous generations of Canadians and to literacy outcomes
of youth in the United States and Norway.
It is worth noting, though, that comparisons of test scores from the IALS across countries
is not without controversy. Blum et al(2001) argue forcefully that IALS test scores are not
comparable because of problems in translation and cultural specificity of questions. They provide
direct examples of questions in the IALS for the UK and France which are intended to be the
same but are, in fact, less clear in the French version. It is noteworthy that the French respondents
recorded substantially lower literacy scores than those from other countries in the early 90's
version of the IALS. Indeed, their shortfall is very surprising. This view, however, is not
uniformly held. Tuijnman and Boudard(2001) quote independent reviews of the IALS showing
“adequate robustness” of the data. Overall, the arguments in Blum et al (2001) suggest caution
about the comparisons between Canada and Norway in the tables that follow. The comparisons
between Canada and the US, though, are likely more reliable since they are conducted in the
same language and within a relatively similar cultural reference. Moreover, we will focus, to
some extent, on the question of whether over time changes in the literacy distribution are similar
within each of the three countries. These types of comparisons are likely to be much more
reliable than simple comparisons of literacy levels.
3.1) Characterizing the Distribution of Youth Literacy in Canada
As stated in the data section earlier, we will define youth as being between the age of 16
and 25. However, we would view a level of literacy for a 17 year old differently if that person has
left school (and therefore is less likely to improve their literacy further) versus if he or she is still
in school. We will also present some results in which we eliminate from the sample all those who
10
are still in school to see whether this consideration is likely to make a difference. In addition, we
will present breakdowns with and without immigrants. As we will see, immigrant - nonimmigrant differences are important for understanding the overall literacy distribution. Moreover,
if we want to examine impacts of the Canadian education system we clearly need to remove
immigrants who were not educated in Canada.
In Table 1, we present basic statistics characterizing literacy distributions in 2003 for all
16 to 25 year olds in Canada. Each column of the table corresponds to one of the literacy tests
(prose, document, numeracy, problem solving). Without a benchmark, it is difficult to know what
to think of the level and spread of this distribution. There is clearly a substantial difference in
performance on the tests between the top and bottom of the distribution. We will wait until we
present the distributions for various comparison groups before we discuss this distribution
further. Instead, we start by looking at the distribution within specific sub-groups of the
population. Table 2 presents literacy score distributions for all youth who are currently in school
(i.e., who respond that their current work situation is “in school”). The distributions are
extremely similar to the ones displayed in Table 1, indicating that we do not need to be
concerned about the fact that we observe some individuals before they have completed their
schooling. For the remainder of the paper, we will use all youth (whether they are in school or
not) in order to maintain a larger sample size.4
In Tables 3 and 4, we examine literacy for sub-groups who might be expected to have
lower literacy levels. The first of these is immigrants who arrived in Canada after age 11 and
4
The one exception to this will arise when we present the cohort based analyses broken
down by education. There we will need to focus only on individuals not in school in order to
make sure we have a consistent cohort definition across datasets.
11
who, as a result, must have had at least their early education outside Canada. The literacy
outcomes for this group are substantially below the overall figures from Table 2. In fact, as we
will see, the 5th percentile of the older age at arrival immigrants is lower than that for nonimmigrant, non-aboriginal youth by the equivalent of the standard deviation of the latter
distribution. This type of shortcoming is evident in all of the literacy dimensions. In all cases, the
disadvantage of older age at arrival immigrants is much smaller (though still substantial) in the
upper part of the distribution. This likely fits with language difficulties in answering the test for
those in the bottom part of the distribution. Indeed, for immigrant youth who arrived at an older
age with prose literacy scores below the 25th percentile, only 19% list English or French as the
language most often spoken at home while for those with literacy scores above the 75th
percentile, 42% report English or French as the language spoken at home. Ferrar et al(2005) find
strong evidence of language effects on literacy score outcomes among immigrants. They argue
that it is difficult - if not impossible - to disentangle literacy from language skills and that it is not
clear we want to. If we are interested in literacy as a measure of skills needed to succeed both at
work and in society more broadly in Canada (as opposed to a measure of intelligence) then those
skills should be measured in terms of their levels in either English or French. In comparison, the
literacy outcomes for aboriginal youth in Table 4 show better scores than those for immigrants
who arrive at older ages at the lower end of the distribution (the 5th and 25th percentiles) but
lower scores at the 50th percentile and above. Broadly speaking though, both of these groups have
similarly low results to those for other Canadians.
Table 5 contains the literacy distribution results for immigrants who arrived in Canada
before age 11. This group would both have completed their education in Canada and have a
12
substantial amount of time to acclimatize to Canada. Their outcomes are actually similar to what
we observe for the overall distribution in Table 1 in the lower tail and slightly worse than the
overall distribution in the upper tail. This implies that this group is “average” for the youth
population as a whole - performing better than aboriginals and older age at arrival immigrants but
worse than other Canadians. It is important to emphasize that these tables are constructed without
conditioning on education. Thus, standard effects of children of first generation immigrants
(whether born in Canada or abroad) obtaining higher levels of education (e.g.,
Bonikowska(2007)) are not enough to generate higher levels of literacy for this group.5
Table 6 contains descriptive statistics for literacy score distributions for youth who are
non-immigrant and non-aboriginal. This distribution dominates that for any of the other groups
examined, including immigrants who arrived at a young age. They are also the group whose
literacy will most reflect the impacts of the main education system and, for this reason, we will
use them as our baseline case.
In Tables 7 and 8, we investigate the gender dimension of literacy among youth. In these
tables, we focus attention on non-immigrant, non-aboriginal youth in order to make sure we do
not confuse gender effects with differences in these dimensions. Interestingly, gender differences
vary by the type of literacy test. Females have higher prose test scores across the whole
distribution with differences ranging from over 25 at the bottom to approximately 13 at the top
(i.e., between half and a third of a standard deviation). In document literacy, on the other hand,
5
In a regression of a dummy variable corresponding to being currently in school on age
and a dummy variable corresponding to immigrants who arrived before the age of 10, the latter
variable has a coefficient of .11 with a standard error of .034. Thus, immigrants who arrived as
young children are 10% more likely to be in school and thus end up with higher education.
13
females obtain higher scores by approximately a third of a standard deviation at the bottom of the
distribution but this advantage declines as we move up the distribution and by the 95th percentile
males and females have the same score. In numeracy, males actually have superior scores across
the distribution, though the difference is not as large as the female advantage in prose literacy.
Finally, in problem solving females again have an advantage. In some ways, this seems
surprising since one might have expected that the same type of logic present in math questions
would be a key element in problem solving.6 The gender results presented here echo those in
earlier papers (e.g., Willms(1998)).
3.2) Comparisons With Other Age Groups
As we discussed earlier, one way to benchmark the literacy levels of youth is to compare
them to older individuals. In this way, we can see whether Canada is making progress in terms of
literacy across generations. In Table 9, we present statistics on the literacy distribution for the
sample of non-immigrant, non-aboriginal individuals who are aged 26 to 35. We drop
immigrants and aboriginals in order to be sure that differences across age groups are not arising
just because of composition shifts in these dimensions.7 A comparison of Table 9 with Table 6
reveals that the older age group has slightly better literacy score distributions. For example, the
prose literacy distribution of the older group has values at the various percentiles that is generally
6
It is worth noting that these same patterns (i.e., that females have higher prose,
document and problem solving scores but lower numeracy scores) are replicated when we control
for age and education by regressing the individual types of scores on a female dummy, age, years
of schooling and a dummy corresponding to being in school.
7
We also examined this broken down by gender. The cross-age group patterns are very
similar for males and females separately, so we present results for males and females combined
in the paper for presentational simplicity.
14
on the order of 4 to 7 points higher than those for the youth distribution. The same description
can be used for the numeracy distributions. These differences are not large (they are typically on
the order of 1/6th of a standard deviation) but they do not point to declines in literacy across
successive generations. A comparison with the literacy distributions for 36 to 45 year olds8
shown in Table 10, on the other hand, is more mixed. The 36 to 45 year olds have worse prose
literacy outcomes at the very bottom of the distribution than the youth but quite similar prose
literacy scores for the 25th percentile and above. This, of course, also means that their literacy is
inferior to that of 26 to 35 year olds.
A key complication in these comparisons is differences in schooling. Thus, many youth
have not completed their education and, thus, given that schooling and literacy are positively
related, have not attained their highest literacy. To generate a cleaner comparison, in Table 11,
we present regression results separately by literacy types in which we regress literacy scores on
years of schooling, a dummy variable for whether the individual is currently in school, and
dummy variables corresponding to 26 to 35 year olds and 36 to 45 year olds (the base group is
youth). The samples used in these regressions are again restricted to non-immigrant, nonaboriginals. The regressions for all literacy types show strong effects of schooling, with
coefficients on the years of schooling variable that are both substantial in size and statistically
significant. The schooling coefficients indicate that an extra year of school is associated with 7 or
8 extra literacy score points, indicating that increasing years of education from 12 to 16 would be
associated with an increase in literacy approximately equal to the increase from moving from the
50th to the 75th percentile of the youth literacy distribution. In addition, being in school at the time
8
Again, the results shown are for a non-immigrant, non-aboriginal sample.
15
of the survey is also associated with higher literacy scores. Once we control for schooling, the
youth are the highest literacy age group with average literacy scores approximately 7 points
higher than for 26 to 35 year olds and 12 to 14 points higher than 36 to 45 year olds. Thus, our
surmise that youth are placed at a disadvantage by the simple comparisons in the previous tables
appears to be true.
4) Cohort and Ageing Dimensions
4.1) Cohort Effects
The results from the comparisons of Tables 6, 9 and 10 and from the regressions in Table 11 can
be interpreted in different ways. One possible interpretation is that the literacy experience of
older workers in the same survey can be used for predictions of what we should expect as the
current youth get older. Under this interpretation (controlling for schooling) we should expect
youth to lose their literacy levels as they age. Alternatively, we could see differences across the
age groups as reflecting differences in their schooling and experiences: literacy may be lower for
older workers (conditional on years of schooling) because schools were not as good at teaching
literacy when they were younger or, instead, because the jobs they held when younger did not
require as much use of literacy as do the jobs held by today’s youth. In reality, the differences we
observe in our single cross-section are likely some combination of the two factors. Older workers
may have different literacy levels because of some combination of changes in literacy that
happen to everyone as they age and differences in literacy levels across successive cohorts of
individuals. There is no way to untangle these factors in a single cross-section.
Solving the problem of untangling ageing and cohort effects requires some sort of panel
16
data. Thus, suppose we could follow a sample of individuals over time, re-testing their literacy at
various points in their life. Based on that, we could establish how literacy varies with age. If we
could do that with successive cohorts (say, start following a new sample of 16 to 25 year olds
every 10 years), we could also investigate whether literacy levels are changing across cohorts by
comparing literacy levels at the same age for different cohorts.9 We, unfortunately, do not have a
pure panel with literacy test scores (indeed, as far as we are aware, such a dataset does not exist
in any country). However, we can obtain consistent estimates of cohort and ageing effects from a
series of cross-sectional datasets under a specific set of assumptions. In our case, we have data
from the 1994 IALS and the 2003 IALSS for Canada.10
The idea behind “pseudo-panel” techniques constructed using a series of cross-sectional
datasets can be understood by considering the complete population of youth aged 16 to 25 in
1994. We will call this, cohort A. As we stated above, our preference would be to obtain literacy
test scores for all of these individuals in 1994 and then obtain a new set of test scores for them in
2003. Given that we cannot do that, consider, instead, drawing a random sample from that
9
Thus, for example, we could compare the literacy of 16 to 25 year olds in 2005 with that
of 16 to 25 year olds in 1995. As is well known from the panel and pseudo-panel literature,
literacy levels between these two groups could differ because of cohort effects (permanent
differences in literacy levels across different generations) or because of year effects (differences
in literacy levels across all age groups in the two years). In this case, year effects would amount
to the claim that literacy levels trend up or down for everyone in the society (regardless of their
age). This seems unlikely to us and so we assume that year effects do not exist. This allows us to
summarize all patterns over time as a combination of cohort and ageing effects. It is worth noting
that the assumption that there are no year effects cannot be proven because of perfect collinearity
among ageing, year and cohort effects. It can only be justified as the most reasonable assumption
in the circumstance.
10
Later in the paper, we will make use of the same type of data for the US and Norway in
order to provide benchmarks for our Canadian results.
17
population in 1994 and obtaining literacy test scores for them. Then, imagine drawing a different
random sample from that same population (now aged 25 to 34) in 2003 and, again, obtaining
literacy test scores. Since each sample (the one taken in 1994 and the one taken in 2003) is
representative of the overall population, each can provide consistent estimates of characteristics
of the literacy distribution for the entire population of cohort A. Thus, by examining those
characteristics from each sample, we obtain estimates of how the literacy distribution for cohort
A evolved as the members of the cohort aged from 16- 25 years of age to 25-34 years of age. As
a concrete example, we can obtain a consistent estimate of average prose literacy for cohort A
when it is aged 16-25 using the 1994 data. We can also obtain a consistent estimate of average
prose literacy for cohort A when it is aged 25 to 34 using the 2003 data. Comparing these, yields
an estimate of the impact of ageing for this cohort. The same approach can be used for examining
the evolution of other characteristics of the literacy distribution such as the median or the
standard deviation.
The conditions under which this approach yields consistent estimates of the evolution of
the literacy distribution with age for a given cohort follow naturally from the given example. We,
essentially, need the two surveys to be representative draws from the same population observed
at different ages. This would be violated if the population from which we are drawing changes
over time. Thus, if we examined average literacy for everyone in Canada who were age 16 to 25
in 1994 and for everyone in Canada age 25 to 34 in 2003, we would have problems because of
immigration. Some of the people present in the 25 to 34 year old age group in 2003 would be
immigrants who had arrived since 1994. Suppose, for the moment, that these new arrivals had
quite low literacy levels. In that case, we would see a lower average literacy level in the sample
18
of 25 to 34 year olds in 2003 than in the sample of 16 to 25 year olds in 1994 even if the literacy
level of those in the original sample did not change at all over time. To avoid this, we will focus
on non-immigrants in our cohort-based investigations. We also exclude observations from the
Territories in 2003 to make the data comparable to the 1994 data. Further, we require that the
samples at each point in time can be regarded as representative of the overall cohort population.
To insure this, we make use of the sample weights provided with the IALS surveys. Finally, we
require that the literacy tests are comparable over time. If, for example, the 2003 test were harder
than the 1994 test this would have obvious impacts on our attempts to make inferences about the
impact of ageing on literacy scores. In fact, the Prose and Document tests were designed to make
them comparable over time. Approximately 45% of the test questions in these two areas are
actually identical across the two years, with those common questions used as the basis of a renormalization designed to insure that the overall average test scores in the two surveys bear the
same relationship to one another as the averages on the common subset of questions. In addition,
in Green and Riddell(2006), we argue that the fact that differences in scores between the two
surveys differ by age group is evidence in favour of comparability. If one test were simply more
difficult than the other then one would expect to see uniformly lower scores for all age groups in
the more difficult test. Finally, it is worth noting that the Numeracy test changed substantially
between the 1994 and 2003 surveys and so cannot be used for the type of comparisons we are
considering. Further, the 2003 survey includes a new test - problem solving - that was not
implemented in 1994. For this reason, we focus our attention in this section on the Prose and
Document scores.
To this point, we have discussed how to use multiple cross-section datasets to obtain a
19
consistent picture of the evolution of literacy with age for a given cohort. We can also use these
datasets to answer questions about how literacy differs across cohorts. This is particularly
interesting for us since it will allow us to investigate whether current youth literacy can be
viewed as an improvement on that of earlier cohorts. The key to making comparisons across
cohorts is to insure that any comparison is not contaminated by differences in age. Thus, in our
discussion of what we can learn from a single cross-section, earlier, we argued that comparing
16-25 year olds to 26-35 year olds in, say, 2003, does not provide us with a clear picture of
cohort differences because any observed differences may partly reflect the fact that one group has
aged more than the other. To get around this, we need to compare the two cohorts at the same
age. Thus, comparing literacy scores for 16 to 25 year olds in 1994 to those for 16 to 25 year olds
in 2003 is effectively comparing literacy across successive cohorts. As before, a key assumption
is that the literacy tests are comparable across the two years.
We begin with the question of differences in literacy levels across cohorts. We will label
cohorts by the mid-point of the birth years that compose the given cohort. Thus, we will call the
cohort consisting of 16 to 25 year olds in 2003, the 1983 birth cohort; the cohort consisting of 16
to 25 year olds in 1994 (who are then the 25 to 34 year olds in 2003), the 1974 cohort; the cohort
consisting of 26 to 35 year olds in 1994 (and 35 to 44 year olds in 2003), the 1964 cohort; the
cohort consisting of 36 to 45 year olds in 1994 (and 45 to 54 year olds in 2003), the 1954 cohort;
and the cohort consisting of 46 to 55 year olds in 1994 (and 55 to 64 year olds in 2003), the 1944
cohort. Table 12 contains characteristics of document literacy distributions for pairs of cohorts
observed at the same age. We also provide an all-encompassing representation of the information
in these tables by presenting figures containing kernel density plots for the literacy distributions
20
for pairs of cohorts in Figures 1, 2 and 3.
It is worth pausing for a moment to consider what is being presented in the kernel density
figures. One way to depict the distribution of a continuous variable (like literacy) is with a
histogram. Histograms are constructed by first dividing the range of the literacy variable into subsegments (e.g., from 200 to 220, 220 to 240, etc.). We would then calculate the proportion of the
total sample with observations within each of these segments. With this, we can see where in the
total range most of the observations are concentrated. The downside of this depiction is that is
tends to be very “jumpy”, with high values in one segment next to low values in the neighbouring
segments. This can be caused by the arbitrariness of where the edges of the segments are placed.
Kernel densities can be seen as smoothed histograms which can overcome this jumpiness. Thus,
as with histograms, ranges of literacy scores where the kernel density line is high represents
scores where there is considerable concentration of individuals.
With this in mind, we turn to examining the plots of the document literacy densities for
16 to 25 year olds in 1994 and for the same age group in 2003 shown in Figure 1. As we stated
earlier, this comparison shows the difference between literacy scores for current youth (what we
call the 1983 Cohort) relative to the previous cohort (the 1974 Cohort) at the same point in the
life cycle. The two densities are very similar in the lower range of scores (what is typically called
the left or lower tail of the distribution), implying that individuals in the two cohorts have
roughly the same probability of possessing low document literacy skills. However, the two
distributions part company in the upper range (the right tail). In particular, the most recent youth
cohort has a noticeably lower density in the right tail, implying that youth in the 1983 Cohort are
less likely to have high literacy scores. This is reflected in the statistics associated with the two
21
distributions presented in Table 12: while the 25th and 50th percentiles are very similar for the two
distributions, the 90th percentile of the 1983 Cohort distribution is 12 points lower and the 95th
percentile is almost 20 points lower than the comparable percentiles of the 1974 Cohort
distribution. Put in a different way, approximately 5% of individuals in the 1983 Cohort sample
have document literacy scores above 355 while over 10% of 1974 Cohort sample members have
scores above this value. Thus, the troubling implication from Figure 1 and the first columns of
Table 12 is that the most recent cohort of youth have lower literacy at the upper end of the
literacy score range.11
Figure 2 presents density plots for document literacy scores for the 1974 Cohort (the
cohort which was aged 16 to 25 in 1994) and the 1964 Cohort (the cohort who would have been
in the youth age range - 16 to 25 - in 1984). We can compare these two cohorts in the common
age range, 26 to 35. We observe the 1974 Cohort in this age range in 2003 and the 1964 Cohort
in this age range in 1994. The figure shows that the 1974 Cohort has a lower probability of
having very low literacy scores but also lower probabilities of having high literacy scores. Thus,
the 95th percentile of the 1974 Cohort document literacy distribution is approximately 30 points
lower than that for the 1964 Cohort. Similarly, in Figure 3, the 1964 Cohort has a similar median
to the 1954 Cohort but much lower 90th and 95th percentiles. Thus, the overall picture constructed
from Figures 1 through 3 is one of declining literacy across cohorts in the upper end of the
distribution, with the lower tail of the distribution changing very little across cohorts. Given that
more recent cohorts have more education (17% of the 1974 Cohort are university graduates
11
Note that we will provide standard errors on estimated cohort differences in order to
assess whether the observed differences are statistically significant in the regressions presented
below.
22
compared to 13% of the 1954 Cohort) this is both surprising and disturbing.
It is worth recalling, at this point, criticisms that have been levelled at the Item Response
Theory approach used to calculate the literacy scores we are presenting. Based on numbers
presented in Osberg(2000), imputation appears to be greater and, potentially, more problematic
in the lower than the upper tail of the distribution. If this is true then the observation that the
lower tails of the literacy score distributions are similar across cohorts could just be a reflection
of the people in this part of the distribution being imputed similar scores rather than a reflection
of a true lack of change. We currently have no means of assessing this possibility and it should
be kept in mind in the discussion that follows.
In Figures 4 through 6, we repeat our cross-cohort comparisons but use prose literacy.
These are backed up by statistics for the distributions in Tables 13. Both Figure 4 and the
statistics indicate that the 1983 and 1974 Cohorts have virtually identical prose literacy
distributions. The differences between the 1974 and 1964 Cohorts are similar to what we saw in
the document score distributions (though with the absolute differences being smaller): the 1974
Cohort has both lower probabilities of very low prose literacy scores and lower probabilities of
high literacy scores. The comparison between the 1964 and 1954 Cohorts is also similar to what
was observed with document literacy, with similar lower tails of the distribution but a noticeably
lower probability of observing high scores in the 1964 Cohort. Thus, there is again evidence that
literacy at the top of the distributions in recent relative to earlier cohorts. This difference, though,
is smaller in Prose than Document literacy and there is no difference across the most recent
cohorts in Prose literacy. Thus, the most recent cohort of youth are similar to the directly
proceeding cohort in reading literacy but worse at the upper end of the range in literacy tasks
23
related to finding and interpreting information in documents.
4.2) Effects of Ageing
We turn, next, to the question of how literacy levels change as an individual ages. As we
discussed earlier, the best way to track this would be with a true panel of individuals undergoing
literacy testing at different points in their lifecycle. Not having that, we employ the next best
option: following a cohort of individuals across successive cross-sectional datasets. With two
such datasets, we can only follow a given cohort through one 9 year period of their life.
Observing what happens to different cohorts (ageing through different lifecycle periods) will then
allow us to piece together the complete ageing profile under the assumption that profile has the
same shape for each cohort. Thus, our approach assumes that each cohort can have a different
overall literacy level (captured in the differences in literacy at a given age described in the
previous subsection) but the same shape for the profile showing how literacy changes with age.
In Figure 7, we plot the kernel densities for document literacy for the 1974 Cohort at the
two age ranges in which we observe them: 16 to 25 (their age in IALS94) and 25 to 34 (their age
in IALS03). This provides our best guess at what will happen to the literacy of today’s youth as
they age through the next 9 years of their lifecycle. The figure indicates that the lower tail of the
distribution was relatively unchanged across this period but there was a noticeable deterioration
in the right tail. In other words, as this cohort of youth grew older, the literacy skills at the bottom
of the distribution did not change but those at the top atrophied. The results in Table 14, where
we present characteristics of the two distributions, support this conclusion: the 25th percentile is
quite similar across the two age ranges but the 95th percentile is 18 points lower for this Cohort
observed at age 25 to 34 than it was for the same cohort observed at age 16 to 25. Put another
24
way, at age 16 to 25, over 10% of the people in the 1974 Cohort had a literacy score of 356 or
above while by the time they were age 25 to 34, only 5% of the people in this Cohort had scores
that exceeded this mark. It is worth pointing out that since we do not have a true panel, we cannot
know whether the people in each tail of the distribution are similar in the two age range
distributions (i.e., whether a person observed at the 5th percentile in the younger age distribution
is likely to also be observed at about the same percentile in the older age distribution). It is
possible (though not all likely) that as people age, those with the lowest literacy at the younger
age become the ones with the highest literacy at the older age and vice versa. However, the most
probable scenario is that the low literacy types at the younger age are also the lower literacy types
at the older age. If that is true then one would interpret this figure as saying those with the lowest
literacy levels do not lose their literacy as they age but the individuals with the highest literacy
levels do lose some of their skills over time. This would fit with a particular form of what is
sometimes referred to as a “use it or lose it” model of literacy in which basic literacy skills are
not lost over time (either because they are easier to retain or because they are actually used by
virtually everyone on a day to day basis) but higher level literacy skills are lost to some extent.
In Figure 8, we repeat this exercise but for the 1964 Cohort, for whom we observe
literacy scores at ages 26 to 35 (in 1994) and 35 to 44 (in 2003). The pattern is much the same as
in Figure 7, with the older age distribution having a similar left tail to what was observed for the
same cohort 9 years earlier but a noticeably lower right tail. The main difference relative to
Figure 7 is that the differences between the two distributions start a much lower level in the 1964
Cohort. Thus, for the 1974 Cohort, the median document literacy score (shown in Table 14) is
very similar at the two ages at which we observe them while for the 1964 Cohort, the median is 5
25
points lower at the older age. By the 95th percentile, the older age distribution is a full 27 points
(or, the equivalent of two-thirds of a standard deviation of the overall literacy distribution) lower.
While almost 10% of individuals in this Cohort have scores above 365 when they are age 26 to
35, only 1% have scores above this level when they are 35 to 44.
The same picture arises with the 1954 Cohort, observed at ages 36 to 45 (in 1994) and 45
to 54 (in 2003). The two densities plotted in Figure 9 and the statistics presented in Table 14
show a deterioration with age across virtually the whole literacy range. Thus, putting the results
from the ageing of these three cohorts together, one arrives at a picture in which document
literacy skills are at their peak levels in the youth (16 to 25) age range. In the ensuing years, these
skills deteriorate, with the higher level skills deteriorating first and to the greatest extent while
the lower levels are initially unaffected by ageing but eventually they, too, deteriorate. It is
possible that this fits with a “use it or lose it” scenario, i.e., one in which literacy obtained during
formal schooling deteriorates if it is not used on the job or in day-to-day activities later in life.
We will return to this point later.
In Figures 10 through 12 and Table 15, we repeat the exercise of examining ageing
effects using Prose literacy. In contrast to what we observed for document literacy, the prose
literacy distributions are virtually identical at ages 16 to 25 and ages 25 to 34 for the 1974
Cohort. In other words, while upper levels of document literacy begin to deteriorate as soon as
people leave formal schooling, the same is not true of prose literacy. This might be because prose
skills are more essential in the sense of being more widely used in day-to-day life and jobs. For
ageing beyond age 25, though, the same picture as we observed for document literacy emerges. In
particular, the 95th percentile for the 1964 Cohort deteriorates by 20 points between the ages of
26
26-35 and the ages of 35-44. This is smaller than the deterioration observed in the right tail of the
document literacy distribution but is still substantial. Similarly, the entire prose literacy
distribution deteriorates between ages 36-45 and ages 45-54 for the 1954 Cohort but to a smaller
degree than what we witnessed for document literacy. Thus, the same general pattern of
deterioration of literacy skills with age is evident for prose as for document literacy but to a lesser
degree and arising later in life.
It is interesting to compare these cohort and ageing patterns with what we observed in the
2003 cross-sectional data. Recall that in comparing literacy levels across age groups (not
conditioning on education), we found that 26 to 35 year olds had slightly better prose and
document literacy, particularly toward the top of the distributions. The results here indicate that
this arises because the 1974 Cohort (who are roughly aged 26 to 35 in 2003) has superior literacy
outcomes relative to 1983 Cohort (Youth in 2003) but that superiority has been reduced to some
extent by the effects of ageing.
5) Introducing the Education Dimension
5.1) Differences Within Education Groups Over Time
As we discussed earlier, differences in literacy across cohorts may be a reflection of
differences in education levels across those cohorts and, for Youth, may partly reflect incomplete
schooling. As a first step toward understanding the education dimension, we present kernel
density plots for four education groups, showing the 1994 and 2003 densities in the same figure.
The four education categories are based on highest level of education attained and consist of: 1)
education less than high school graduation; 2) high school graduation; 3) a post-secondary degree
27
or diploma less than a BA; 4) a BA or post-graduate degree.
Figure 13 contains the kernel density plots of document literacy for drop-outs of all ages
in both years. The plots indicate an improvement over time, with a lower probability of obtaining
quite low literacy scores offset by higher probabilities of obtaining what were above average
literacy scores for this group in 1994. More specifically, the 25th percentile of this distribution
increases from 125 in 1994 to 166 in 2003 and the 75th percentile increases from 281 to 294. This
is a heartening improvement. In contrast, though, Figure 14 shows declines over time for high
school graduates at all percentiles above the 25th. The 95th percentile for this group declines from
363 to 352 between 1994 and 2003. A pattern of stronger declines in the upper part of the
distribution for high school graduates is also evident in the plots for the Some Post Secondary
group in Figure 15. For that group, the 95th percentile of the document literacy distribution
declined from 387 to 350 between the two years - a very large decline, indeed. Finally, university
graduates show a similar pattern, with little difference between the two years in the lower half of
the distribution but strong declines in the probability of observing high literacy scores offset by
increased probabilities of seeing scores that were above average but still somewhat mediocre in
the 1994 distribution. This is reflected in a decline of the 90th percentile from 392 to 365.
Figures 17 to 20 contain the same set of educational breakdowns but for prose rather than
document literacy. The patterns portrayed in these figures are in broad agreement with what we
observed for document literacy. In particular, drop-outs experienced literacy improvements
(particularly at the bottom of their distribution) while all other educational groups experienced
declines (particularly at the top). The main difference is that the declines over time for the
university educated are more pervasive across the distribution of prose literacy than for document
28
literacy.
5.2) Regression Results
One way to proceed in understanding how these within-education group movements
relate to the cross-cohort differences would be to provide a complete set of breakdowns into
groups defined by education and cohort. The trouble with this approach is that many of the
education x cohort groups are quite small, making inference based on them difficult. Examining
such a large number of groups is also cumbersome. Instead, we adopt the approach of presenting
both mean and quantile regressions in which we pool the data from 1994 and 2003. With the
pooled data, we can identify cohort effects (average literacy scores for member of a cohort,
whichever sample they are observed in, holding all else constant) and ageing effects (which are
identified in the same way as in our discussion in the previous subsection, i.e., by the changes
across time within a given cohort). We estimate these effects holding constant (i.e., including as
additional covariates) dummy variables corresponding to the various education levels and to
gender. With the inclusion of these education controls, the estimated cohort effects show
differences across cohorts while holding constant education composition, i.e., abstracting from
the fact that the different cohorts have different educational compositions. For those who are not
interested in the details of the regressions, we provide a summary of the key findings from them
at the end of the section.
We begin with a simple mean regression in which we regress the log of the individual’s
literacy score on dummy variables corresponding to 5 cohorts: 1) the cohort who were 16-25 in
2003; 2) the cohort who were 16-25 in 1994; 3) the cohort who were 26-35 in 1994; 4) the cohort
who were 36-45 in 1994; and 5) the cohort who were 46 to 55 in 1994. The first of these cohorts
29
(the 1983 Cohort) is the omitted group so all cohort dummy coefficients can be read as showing a
difference relative to that cohort. In addition, we include a series of age dummy variables
corresponding to: 1) 16 to 25; 2) 26 to 35; 3) 36 to 45; 4) 46 to 55; and 5) 56 to 65. The first age
group is the omitted group in the regressions. We control for education using dummies
corresponding to the high school graduate, some post-secondary and university categories
mentioned earlier, with drop-outs forming the base group. We also include a set of dummy
variables for mother’s education and a separate set for father’s education. These pertain to the
same categories as for the respondent’s own education plus an additional category corresponding
to an answer that the respondent does not know his or her parent’s education. Finally, we include
a gender dummy variable. We experimented with further specifications in which we controlled
for whether each parent was an immigrant, regional dummies, parental occupation and
interactions of parental occupation and education but these did not yield substantial changes in
the results and so we present the simpler specification to aid in the readability of the results.
The first column of Table 16 contains the estimated coefficients and their associated
standard errors from this specification with the log of document literacy as the dependent
variable. The effect of education is as one might expect: literacy increases with education, with
university educated individuals having literacy scores that are approximately 11% higher than
high school grads and 25% higher than high school drop-outs. Having either a mother or father
who were themselves drop-outs is associated with about 3% lower literacy than if the
respondent’s parent was high school educated and these effects are statistically significant at any
conventional significance level. Interestingly, though, whether the parent has a high school, some
post-secondary or university education does not matter. Further, not knowing the education of a
30
parent has a negative effect on average literacy that is on the order of 5% and is statistically
significant. This may be picking up something about parenting and how close the child is to his
or her parents. Both parental immigrant status and gender do not have substantial or statistically
significant effects. Thus, these results point to formal schooling being particularly important in
literacy skill formation, with some negative impact of having a very low educated parent but
parental impact being otherwise much lower than that of schools.
Our main interest in this table is in the cohort and ageing effects. The coefficients on the
cohort dummy variables rise to some extent with the cohort number (which would fit with older
cohorts having higher average literacy levels) but these effects are neither large nor statistically
significant at the 5% level. This is perhaps not surprising since the figures we examined in
previous sections indicate that cross-cohort differences tend to be focussed in the top end of the
distribution rather than being pervasive across the distribution, implying that mean literacy scores
(which is what is being examined in these regressions) will move much less than the tails of the
distribution. On the other hand, a negative ageing effect is evident in a form that indicates a
relatively constant loss of literacy skills with age. These age effects are economically substantial
(with 46 yo 55 year olds having 9% lower document literacy than 16 to 25 year olds) and
statistically significant at any conventional significance level. This fits with the robust picture of
declining literacy with age within cohorts seen in the earlier figures.
The second column in Table 16 shows results from the same regression specification but
with using the log of prose literacy as the dependent variable. The results are broadly the same as
those obtained using document literacy except that most effects are somewhat smaller, fitting
with earlier results showing that prose literacy tends to have less variation across a number of
31
dimensions. The other main difference is that the gender effect is larger and statistically
significant, implying that females have somewhat higher prose literacy even though there is no
difference in document literacy across the genders. The cohort effect estimates do not point to
any consistent, cross-cohort patterns while the age effect coefficients again point to declining
literacy with age, though with smaller declines than we witnessed in document literacy.
In the third and fourth columns of the Table, we present results from a re-estimation of
the regressions with the inclusion of occupation dummy variables. The main patterns in our
estimates are not changed with the inclusion of these variables. Cohort effects are again muted
and there is still strong evidence of a decline in literacy with age. This implies that these patterns
are not due shifts in occupational composition across cohorts or with age. Thus, explanations for
the types of patterns we are observing lies elsewhere: with factors such as school quality for
cohort effects and for factors operating within occupations over time for the ageing effects.
We are interested, in part, in how these results vary with education. To investigate this,
we estimated an additional specification in which we fully interacted the cohort dummy variables
with the education variables, allowing different cohort effects for each education group. The
results from that estimation are presented in Table 17 The effects of other variables, such as
parental education, are not affected by the inclusion of these interactions so we do not report on
their coefficients for the sake of brevity. The first column contains results for document literacy.
The simple cohort dummy coefficients in this table correspond to the cohort effects for the base
education group (high school drop-outs). For that group, the cohort effects are negative and, at
times, statistically significant, implying that the current cohort of Youth have higher average
document literacy than earlier cohorts. This fits with the improvement (particularly at the bottom
32
of the distribution) that we saw in the kernel density plots for drop-outs in the previous section. It
seems very likely that this reflects rising years of education among drop-outs. Simple tabulations
from the 2001 Census show that of those with 10 or fewer years of education, 72% of 20 to 24
year olds had 9 or 10 years of education (with the remainder having completed fewer years of
school) but only 60% of those aged 45 to 54 had 9 or 10 years of schooling.
The next set of coefficients show the difference in the cohort effect for the second cohort
for the other education groups relative to drop-outs. These coefficients should be added to the
simple 1974 Cohort effect and to the coefficient associated with their specific education level
(e.g., the coefficient on the simple hs dummy, which shows the literacy of a high school graduate
relative to a high school drop out in the first cohort) to get the total effect for the 1974 Cohort for
each of the education groups. The fact that this total effect is positive for all three education
groups indicates that, in contrast to drop-outs, Youth literacy in these other education categories
is lower than the literacy level of the cohort that directly precedes the current Youth. This is even
more evident as we move to examining earlier cohorts. Thus, these results imply that the near
zero cohort effects we witnessed in Table 16 are actually due to offsetting negative effects for
drop-outs combined with positive effects for the other education levels. The age effects continue
to show strongly declining literacy with age. The results in the second column of Table 17, show
that much the same set of patterns exists for prose literacy. Once again, the results imply that the
current cohort of Youth drop-outs are better than previous cohorts of drop-outs in their average
literacy but the reverse is true for all other education groups.
Because much of what we observed in terms of cross-cohort and ageing effects in the
figures in the last section was unevenly distributed across the distribution, we turn next to
33
investigations using quantile regressions. In particular, we present results from quantile
regressions run for the 10th, 50th and 90th quantiles. This allows us to see what is happening in
each tail of the distribution as well as in the middle. It is important to clarify the interpretation of
coefficients in these regressions. The coefficient on, for example, the 1974 Cohort dummy
variable in the quantile regression corresponding to the 10th percentile shows the difference in the
10th percentiles between 1974 Cohort and the 1983 Cohort (the base group), holding constant the
effects of all other covariates in the regression.12
In Table 18, we present the estimated coefficients from cohort, age, education, gender and
parental education variables for the 10th, 50th and 90th quantile regressions for the log of
document literacy.13 The patterns in the 10th percentile quantile regression point to effects of the
respondent’s own education that are stronger than was observed in the mean regression.
Moreover, while literacy declines both as age and cohort number increases, the effects are
generally small and statistically insignificant. The only exception to this is for the oldest (age 5665) age group, whose 10th percentile is much lower than other cohorts. Overall, though, the
conclusion from this regression is that the lower tail of the document literacy distribution is
12
Standard errors reported in this table and all other quantile regression tables are
bootstrap standard errors. We performed our estimation in Stata. However, Stata does not allow
both weighting and bootstrap standard errors. To get around this, we first created a weighted
“fake” dataset in which we turned each observation into multiple observations according to its
weight. For example, an observation with a weight of 2.8 is viewed as representing 3 actual
observations (after rounding) so we simply replicated this observation twice more so that it is
reflected a total of 3 times in our final “fake” dataset. It is worth noting that we first normalize
the weights so they sum to the actual sample size. This way we do not create a dataset that seems
much larger (and thus yields more precision) than the actual dataset. Once we have created this
weighted dataset, we can use it in all of our estimations along with the bootstrap command.
13
We do not include region or parental immigration status variables in order to simplify
the exposition. Their inclusion does not alter our main results.
34
relatively constant across cohorts and relatively immune to ageing effects.
In contrast to what is observed for the 10th percentile, the third column shows that the
median reflects both cohort and age effects that are statistically significant. The cohort effects,
though are not large, with the 1954 Cohort having a median document literacy score that is
approximately 4% higher than current youth (the 1983 Cohort). The ageing effects are similar in
magnitude to what was observed in the mean regressions: a negative, relatively constantly
declining profile.
It is in the 90th percentile results that we see truly sizeable effects. Again, the results imply
that the current cohort of youth has the lowest 90th percentile of all the cohort literacy
distributions. The 1954 Cohort (the cohort which was 36-45 in 1994 and, thus, would themselves
have been youth in the early 1970s) have a 90th percentile for their literacy distribution which is
14% above that of current youth, holding constant education, gender and parental education.
Similarly, the ageing effects are much stronger at the 90th percentile than what we observed at the
median and in the lower tail. The implication is that whatever is different across cohorts, it is
focussed on the creation of very top level literacy skills. Similarly, the ageing process seems
largely to have to do with losing higher level literacy skills. It is worth noting, on the other hand,
that the effects of education decline strongly across the distribution: being a high school drop out
is associated with much lower minimum levels of literacy than what is observed for high school
graduates but has a smaller (though still substantial) effect at the top end of the distribution. Put
another way, the lowest literacy values observed in the distribution for the university educated
are well above those for drop-outs while the highest literacy values observed for the university
educated exceed those for the less educated groups but not by as much.
35
Table 19 contains the estimates from quantile regressions for the log of prose literacy.
The results at the 10th percentile differ from those for document literacy in that they show clearer
evidence of cohort effects, with those effects again pointing to current youth having the lowest
literacy values. As with document literacy, the main ageing effect on this part of the distribution
is a substantially lower value for the oldest age group we consider. As we have seen in several
situations, the estimated effects on the median are similar in pattern to those for document
literacy but are somewhat smaller in magnitude. The same is true at the 90th percentile. Prose
literacy scores show the same pattern of a substantially lower 90th percentile for current youth
relative to earlier cohorts and the 90th percentile shows a sharply declining pattern with respect to
age. These effects are slightly smaller than what we observe for document literacy but are still
substantial and still point to significant concern about what has been happening in the upper
literacy ranges.
We consider the impact of literacy more closely by re-estimating our quantile regression
specifications for two education sub-groups: those whose highest education level is high school
graduation or less (combining the high school drop-outs and the high school graduates in order to
obtain more substantial sample sizes), and the university educated. We present results from the
10th, 50th and 90th percentile quantile regressions for document literacy for both groups in Table
20. The results at the 10th percentile for the lower educated group point to the current youth
cohort (the 1983 Cohort) having the highest scores at this point in the distribution, though most
of the estimated cohort effects are not statistically significant. The ageing pattern at the 10th
percentile is neither consistent nor statistically significant. At the median, there is no consistent
cohort pattern discernable but there is a clear pattern of declining literacy with age. At the 90th
36
percentile, on the other hand, we observe a strong cross-cohort pattern with the current youth
having the lowest values in this part of the distribution. We again see evidence of a substantial
decline in literacy with age at the top of the distribution. Thus, even the lowest educated face a
deterioration with age and a decline across successive cohorts of the literacy skills which, for
them, are relatively high. For the university educated, on the other hand, the patterns of declining
literacy scores with age and across cohorts are evident in both tails of the distribution and are
quite strong.
In Table 21, we repeat the education break-down exercise using prose literacy. The main
patterns are similar to those observed for document literacy. The main differences are that cohort
and ageing patterns are not in evidence even up to the median for the low educated group and
that, for the university educated, the estimates point to the 26-35 age group having the highest
literacy level. The latter would fit with many of the youngest, 16-25 age group, not having
completed their education and, as a result, still having increases in literacy yet to come. In
general, the patterns evident in the prose literacy results are more muted than with document
literacy, though this is not always the case.
The results from the various regression exercises point to several key conclusions that
back up what we observed in the figures in the previous section. First, much of the movement in
the distributions, both across cohorts and with age, occur in the upper tails of the literacy
distributions. In contrast, the lowest levels of literacy are relatively constant across cohorts and
age groups, though there is some (relatively weak) evidence of improvements in the lower tail of
the distribution for current youth relative to earlier cohorts. Second, the cohort pattern that
emerges in the upper tail (and to some extent in the middle of the distribution) is one in which
37
literacy levels are lowest for current youth and are increasing the farther back in terms of cohorts
we go. Third, the age pattern that emerges both in the upper tail and in the middle of the
distribution is one with strongly declining literacy with age. Fourth, the declines with age and
across cohorts are particularly strong for more educated individuals. This fits with the fact that
we observe these movements mainly in the upper tail of the distribution when we do not control
for education. However, we also observe these trends in the upper tails of the distributions for the
least educated. For the latter group, there is some evidence of improvements in the minimum
literacy scores observed across successive cohorts, with the current youth having better low-end
literacy scores than previous generations. Fifth, education is a key determinant of literacy skills
and its importance is particularly strong in raising minimum literacy levels (as opposed to
generating very high literacy values). This is not meant to imply that formal schooling does not
have an impact in generating high literacy values - far from it - but its strongest effects are in
raising minimum levels. Sixth, parental education has an impact on literacy but mainly in the
form of worse literacy outcomes for those whose parents are high school drop-outs. Differences
across individuals whose parents are high school graduates, post-secondary graduates and
university graduates are small. The impact of parental education, in general, is much smaller than
that of the respondent’s own education. Unfortunately, in these surveys, we do not know
anything about reading and literacy in the home when the respondent was young so we cannot
make definitive statements about the role of home versus school in literacy generation but
comparing the relatively small impacts of parental education to the sizeable impacts of own
education point toward a conclusion that formal schooling is the most important venue for
literacy generation. Seventh, the patterns we observe tend to be more muted for prose than
38
document literacy. This may be because prose literacy is more commonly used - that it is, in
some sense, a more basic skill. As a result, it tends to deteriorate less with age and has less
variation across cohorts. Overall, the picture that emerges is one in which literacy skills are
generated in school but higher end literacy skills start to deteriorate as soon as people leave
school. Further, current youth are not suffering as low minimum levels of literacy as previous
cohorts but are also not attaining as high top end literacy levels as previous cohorts. Thus, current
youth can be characterized as having relatively lower levels of top-end literacy compared to
previous generations and there is every reason to expect their literacy levels will decline from
here, following the standard ageing pattern observed in earlier generations.
6) Comparisons to Other Countries
Another potential benchmark for the literacy of Canada’s youth is the literacy of youth in
other countries. We use Norway and the US as benchmarks in this paper. We chose both
countries because in both cases we have access to consistent data from both rounds of the IALS,
allowing us to make the same kind of cohort comparisons we carry out for Canada. In addition,
Norway provides an interesting benchmark because the Nordic countries tend to perform well in
these literacy comparisons. Thus, a comparison with Norway allows us to see how well Canada
compares to the “gold standard” in terms of what is attainable. The US is interesting since it is
the closest competitor for our workforce. The Norwegian and US samples are smaller than those
for Canada with 2522 in our usable 2003 sample for Norway and 1486 in our 2003 sample for
the US.
In Table 22, we recreate our statistics for document literacy from our overall Canadian
39
youth sample along with the same statistics from the 2003 Norwegian and American samples. It
is worth re-iterating that while the IALSS surveys were explicitly designed to allow direct
comparability of literacy levels across countries and over time, there is reason to be cautious,
particularly in the comparisons with Norway. As we expected, the Norwegian document literacy
distribution for youth in 2003 dominates that the youth distribution for Canada in the same year.
The 5th percentile is 15 points higher in the Norwegian than the Canadian distribution and both
the 50th and 95th percentiles are at least 12 points higher. The extent of inequality in literacy
among youth is also somewhat lower in the Norwegian sample, with a ratio of the 95th to the 5th
percentile of 1.64 for Norway and 1.69 for Canada. In comparison, the 5th percentile of the US
youth document literacy distribution is 9 points worse and the median is 14 points worse than
that for Canada. On the other hand, the 95th percentiles for Canada and the US only differ by 3
pointsl. Thus, Canada’s performance is comparable to the US at upper literacy levels but superior
in the lower half of the distribution. The obvious implication is that inequality in literacy is lower
in Canada than in the US. A reasonable summary of this evidence is that Canada sits between
Norway, which has both superior literacy levels and less inequality in literacy, and the US, which
has generally lower literacy levels and higher literacy inequality. This fits with evidence on
literacy levels and inequality across a set of countries in Wilms(1998).
Inequality in literacy is important both because it will lead to inequality in other outcomes
such as earnings and health (Green and Riddell(2006)) and because literacy is valuable in its own
right. Sen argues that literacy is a key determinant of full social inclusion and thus something we
should focus on for its own sake (Sen(1999)). In that case, greater inequality in literacy, and
particularly, low minimum levels of inequality, should be viewed as bad. Indeed, while some
40
argument can be made for positive effects from earnings inequality stemming from incentive
effects, no such argument can be made about inequality in literacy. Inequality in literacy is
simply bad. In this sense, Canada is doing better than the US but has much to learn from Norway.
In Table 23, we repeat the cross-country comparison for youth using prose rather than
document literacy. The comparison of Canada with Norway for prose literacy again points to
superior literacy in Norway. Interestingly, though, while Norway dominates Canada in the lower
end of the literacy distribution to roughly the same extent we observed in document literacy, the
95th percentile of the Canadian distribution is only 3 points below that for the Norwegian
distribution. Once again, this points to greater inequality in the Canadian distribution but it at
least indicates that Canadian youth have, at the top end, comparable prose literacy relative to a
country that is known to perform well in literacy. Relative to the US, the Canadian prose literacy
distribution is again dominant in the lower half of the distribution but the two have similar 95th
percentiles. Thus, all three countries attain similar levels of prose literacy at the top end of the
distribution but quite different levels at the low end.
Finally, in Table 24, we repeat the comparisons for numeracy scores. Once again, the
ordering runs from Norway as the best to the US as the worst in terms of literacy scores at the
bottom of the distribution. However, all three countries have identical scores at the 95th percentile
of the distribution, pointing to the Norwegian superiority in reducing inequality in literacy scores.
The fact that Canada performs as well as Norway - a known high-achieving country in literacy on numeracy in the top half of the distribution is heartening. Note that we do not present results
for problem solving because the US data does not include scores for this test and the Norwegian
problem solving scores take odd sizes.
41
6.1) Norway
We turn next to a closer examination of outcomes in the two comparison countries, with a
particular emphasis on the question of whether the cross-cohort patterns and ageing patterns are
similar to those we observed for Canada. In this subsection, we examine the results from
Norway. Recall that we can only examine prose and document literacy in the cross-cohort
comparisons because only those scores are comparable across surveys. For Norway, we have the
2003 survey plus an earlier survey from 1998 to use in our comparisons. We will only present
figures from Prose literacy to save on space since the document and prose outcomes are similar.
We will, however, present tables based on both measures. Figure 21 contains the kernel density
plots for 16 to 25 year olds in 1998 and 2003, allowing a cross-cohort comparison between the
current youth and youth from approximately a half cohort earlier. Underneath the figure, we
present a summary table showing characteristics of each distribution. The two distributions are
essentially identical, implying no substantial, cross-cohort changes. Figure 22 plots the two
distributions corresponding to a comparison of what we have called the 1974 Cohort with the
1964 Cohort. In this case, the newer cohort (who are 26 to 35 in 2003) have a similar distribution
in the lower tail but a superior distribution in the upper tail (or, in other words, people in the
newer cohort have a greater probability of attaining high literacy scores). The same is true,
though to a smaller extent, in a comparison of the 1964 Cohort with the 1954 Cohort in Figure
23. Thus, these plots suggest that Norwegian literacy has been moving in the opposite direction
relative to Canada, with improvements rather than declines across successive cohorts. As in the
Canadian data, the changes are occurring mainly in the upper tail of the distribution. Given that
the upper tails of the current Canadian and Norwegian distributions of prose literacy for youth are
42
similar, the implication is that Canada had superior prose literacy distributions to Norway in the
past but the two have converged to the middle. If these patterns continue, Canada would be
projected to fall behind at the top end of the distribution (as they already are behind in the lower
end) within a generation or two.
In Figure 24, we plot the prose literacy densities for individuals in the 16 to 25 year age
group in 1998 both in 1998 and 5 years later. This allows us to see the impact of ageing on
literacy for a given cohort, though we are only able to follow them for 5 years rather than the 9
that was possible for Canadians. The figure and the table of statistics beneath it indicate that
there is actually an improvement over time for this cohort, with the largest improvements at the
bottom of the distribution. Particularly since we only follow this group for 5 years, we may just
be picking up the direct effects of ongoing schooling for this group. Thus, the next figure (25),
which shows the ageing effects for the people who were age 26 to 35 in 1998 is more likely to
reveal pure ageing effects. This figure shows little change at the very bottom of the distribution
but improvements with age over the rest. Following those who were age 36 to 45 in 1998 (Figure
26) shows a decline in literacy with ageing at the bottom of the distribution but no effect at the
top. Overall, these pictures suggest the opposite pattern to that observed in the Canadian data:
literacy levels that are either relatively unchanging or generally increasing with age.
To examine these implications more systematically, in Table 25, we present the estimated
coefficients from our standard OLS specification plus quantile regressions for the 10th, 50th and
90th percentiles. The cohort effects are generally insignificant and small in these regressions. On
the other hand, the age effects reveal a strongly declining pattern with age across the whole
distribution. Indeed, they show a pattern similar in magnitude to that seen in the Canadian data.
43
The fact that the estimation and the plotted figures show such different results stems from the
short time between cross-sectional observations. Because these observations are only 5 years
apart, a person who is 16 to 25 in the first dataset might still be in the same category in the
second dataset. In this case, when we simply plot literacy distributions for 16 to 25 year olds in
1998 and in 2003, the latter group will include some people from the initial cohort (i.e., the
people who are 16 to 25 in 1998). When we estimate, on the other hand, we are able to be precise
about who belongs to which cohort since we define cohorts based on ages groups in 1998. This
means that both our cohort and ageing effects are more accurate in the regression analysis. Thus,
we conclude that Norway does not show any consistent pattern in literacy across cohorts but does
show a strong decline in literacy with age, much like in the Canadian data. Similarly, education
shows effects that are similar in magnitude to those observed for Canada and which, again,
decline across the literacy distribution. The main difference relative to Canada in this dimension
is that the penalties in literacy to having less than a high school education are smaller in Norway.
This is likely what is behind the superior left hand tail of the Norwegian prose literacy
distribution relative to that for Canada. Table 26 presents the same types of results but for
document literacy. The pattern of results is very similar to that for prose literacy but, as we often
observed in the Canadian data, the estimated effects are typically larger in magnitude than those
for prose literacy.
6.2) United States
Next, we repeat our analysis of literacy data for the US samples. Figure 27 plots the
44
kernel densities for prose literacy for 16 to 25 year olds in the 1994 and 2003 data.14 Thus, this is
a very similar depiction of the differences between the 1984 Cohort (current youth) and the 1974
Cohort to what we created from the Canadian data. As in the Canadian data, this figure shows a
deterioration of literacy in the upper half of the distribution between the two cohorts. The same is
true in the comparison of the 1974 Cohort and the 1964 Cohort in Figure 28: the younger cohort
(the 1974 Cohort in this case) has a superior literacy distribution. This is particularly the case at
the top end of the distribution, with the 95th percentile of the 1974 Cohort distribution being 12
points higher than that for Cohort 3. This same pattern is even clearer in the comparison of the
1964 and 1954 Cohorts in Figure 29. The 95th percentile of the 1964 Cohort distribution is nearly
30 points lower than that in the 1954 Cohort distribution.
Figures 30 through 32 show the impact of ageing on prose literacy for the 1974, 1964 and
1954 Cohorts, respectively. Figure 30 shows some deterioration in literacy scores in the middle
of the distribution as the 1974 Cohort ages from ages 16-24 in 1994 to 25-33 in 2003. For the
1964 Cohort, ageing 9 years is associated with declines in all percentiles above the 10th, with
their 95th percentile being nearly 20 points lower in 2003 than in 1994. A similar ageing pattern
is evident for the 1954 Cohort, except that the declines at the very top of the distribution are even
greater.
We, again, confirm these patterns using OLS and quantile regressions, presented in Table
27 for prose literacy and 28 for document literacy. The prose literacy regressions show the same
kind of pattern that we have seen for the Canadian data and, to some extent, the Norwegian data.
14
Note that the age groupings in the US data dictate a slightly different set of groups than
in the Canadian data (i.e., the youngest age group is 16 to 24 rather than 16 to 25).
45
In particular, the estimated coefficients point to the current cohort of youth having the lowest
literacy level, with this pattern being particularly strong at the top of the distribution. The results
also match those from the Canadian data in showing strong declines with age that are, again,
strongest at the top of the distribution. The main difference relative to the Canadian results is that
these cohort and ageing patterns are evident even at the bottom of the distribution for the US,
which was not the case for Canada. This is not the case, though, for the US document literacy
results in Table 28, where the 10th percentile quantile regression does not show a consistent
pattern either across cohorts or with age. Interestingly, the patterns at the 90th percentile are also
not as strong for US document literacy. Finally, education effects are important for the US,
having similar magnitudes to what is observed in the Canadian data.
Overall, comparisons to both Norway and the US indicate that the ageing pattern we
identified for Canada is also present in countries with quite different literacy levels. In all three
countries, literacy declines strongly with age for any given cohort. This may indicate that literacy
has a “use it or lose it” nature in all of these economies. The countries are also similar in the
importance of formal schooling for generating literacy and in the fact that schooling plays a
particularly strong role in raising minimum literacy levels. In contrast, there are differences
across countries in the cohort patterns of literacy. Both the US and Canada have experienced a
pattern of declining literacy across successive generations, with particularly strong declines at the
top end of the literacy distributions. Norway, on the other hand, has not experienced any clear
pattern: recent cohorts have all attained similar literacy levels (conditional on their schooling
levels and the schooling levels of their parents). The fact that the ageing patterns are similar
across the three economies suggests that the impact of post-schooling institutions is similar in all
46
three. That is, none of the countries has established a superior system in terms of maintaining
post-schooling literacy levels. Cohort effects, on the other hand, are related to “permanent”
differences associated with people who were born and went through schooling at different times.
Differences in cohort patterns are thus reflections of institutions which have persistent effects on
literacy, with differences in the efficacy of formal schooling being the most likely candidate.
Under this interpretation, Norway is not only doing something better with its schooling (in that it
is generating both higher overall literacy levels and less literacy inequality), it has maintained its
schooling effectiveness over time. In contrast, cohort patterns in the Canada and the US may
indicate a reduction in the efficacy of schooling over time, particularly in terms of generating
high end literacy.
7) Exploring Cohort Effects: Why Have Current Canadian Youth Fallen Behind in
Literacy?
We turn, next, to trying to understand the emerging differences in literacy across cohorts
in Canada. As a first step, we examine differences in literacy correlates between generations in
the IALS03. The survey includes questions about literacy use at work. The literacy use at work
questions ask about frequency of performing reading, writing and mathematical tasks. Thus, for
reading, questions are asked about 5 tasks and there are also questions on 5 writing tasks and 5
math tasks. We construct dummy variables equalling 1 if the individual responded that he or she
performed 4 or 5 of the reading related tasks at least once a week and similar variables for the
writing and math tasks. We also constructed dummy variables corresponding to performing one
to three of the tasks at least once a week for each of reading, writing and math. Finally, we
constructed a dummy variable corresponding to individuals who answered that they performed
47
all of the tasks in a given area (e.g., reading) “rarely”. In Table 29, we show the proportions in
each of these categories for the 1983 Cohort (those aged 16-25) and the 1974 Cohort (those aged
26-35) (note there is an omitted category which includes all other possible responses). For this
part of the table, we restrict our attention to individuals who are either high school drop-outs or
high school graduates, otherwise the results will naturally favour the 1974 Cohort since all the
university graduates in that cohort will be at work while at least some of the university graduates
in the 1983 Cohort will still be in school. This would make it appear that the 1974 Cohort has
more university related jobs.
The patterns of literacy and numeracy at work are extremely different between the two
groups. In particular, the 1974 Cohort is over twice as likely to claim that they use reading skills
frequently at work and is, similarly, much more likely to claim that they use writing and math
skills in their work.15 The 1974 Cohort is also more likely to read a newspaper or a book at least
once a week and less likely to agree to the statement that they read only when they have to. Thus,
the 1974 Cohort appears to use literacy related skills much more often both on and off the job
(though, interestingly, their tendency to watch tv is not very different from the 1983 Cohort).
Whether this is a result of their higher literacy levels or a cause of literacy differentials between
the two cohorts cannot be discerned from this data. The differences we observe may, also, be a
function of ageing rather than true differences across cohorts (i.e., people may read more as they
move beyond the youth years, and we are observing the 1983 Cohort in the youth years and the
15
As a side point, note that people are generally more likely to claim they using reading
skills on their job than the other two types of literacy/numeracy skills. This fits with our
conjectures, earlier, that prose literacy tends to vary less across cohorts and age groups because it
is a more commonly used type of literacy.
48
1974 Cohort in a later age range). Unfortunately, differences in questions between the IALS94
and IALS03 make it difficult to compare the two cohorts at the same age.
8) Conclusions
In this paper, we use data from International Adult Literacy Surveys (IALS) for pairs of
years for Canada, Norway and the US. Our focus is on the literacy levels and extent of literacy
inequality among Canadian youth (individuals aged 16 to 25). We find that Canada’s current
youth have generally lower literacy levels than previous generations of Canadians. More
precisely, the probability that current Canadian youth suffer low levels of literacy is either no
different or slightly lower than previous generations. However, the probability that they attain
high levels of literacy is decidedly lower than for previous generations and this disparity
increases as we move higher and higher in the literacy distribution. This relatively inferior
performance seems to us to be a cause for concern. A second key conclusion is that literacy as
measured on these tests declines with age after leaving school. This may reflect a “use it or lose
it” model of literacy in which literacy skills obtained during school atrophy with lack of use after
leaving school. Importantly, this implies that if current youth are at relatively low levels of
literacy today, they are only going to move to even lower levels over time.
In terms of international comparisons, Canada falls about midway between Norway and
the US both in terms of literacy levels and the extent of inequality in their literacy distributions.
Thus, there is potentially much to learn from the Norwegians but we do appear to have an
advantage over the Americans. Interestingly, all three countries show the same pattern of literacy
loss with age. Thus, whatever Norway is doing better it seems not to have to do with institutions
and opportunities associated with maintaining literacy levels after leaving school. Or, to put it in
49
the current policy vernacular, there is no reason to think, based on literacy test scores, that
Norway is better at “life long learning” than Canada. In terms of cross-cohort patterns, the US
shows much the same pattern as Canada while the Norwegian data does not show any particular
pattern of differences across cohorts. Thus, whatever Norway is doing right, it has been doing it
for a while and has been consistent. Both Canada and the US, on the other hand, appear to face a
growing problem with each successive generation.
Taken together, comparisons across generations within Canada indicate that we are at
least doing no worse and may be improving our performance in terms of raising the literacy
levels at the low end. That is, literacy policies aimed at basic literacy seem to be working to some
extent. However, a comparison with Norway indicates that we can still do much better in this
regard. It is at the other end - the top - that we see real declines for current youth relative to
earlier generations. This is something that would need to be addressed with a different type of
policy. In particular, it focuses attention on the efficacy of post-secondary education. The next
step in an investigation of these patterns would be to use the fact that we know the province
where individuals lived during their high school years in the IALS to try to relate patterns to
differences in education policy across provinces and over time. Thus, we could investigate
whether fiscal problems in various provinces in the 1990s are at the heart of the cross-cohort
declines. On the face of it, this is unlikely to provide the main explanation since the declines we
observe are evident for the cohort which attended school in the 1980s relative to the previous
cohorts as well as for the most recent cohort relative to all previous cohorts. Whether the decline
reflects greater presence of ESL students and other students requiring extra help in the classroom
or changes in educational philosophy toward a focus on helping the weakest students or some
50
other change will be interesting to investigate but is beyond the scope of the current data.
51
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Bownikowska, A. (2007). “Explaining the Education Gap Between Children of Immigrants and
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Buchinsky, M. (1997). “Recent Advances in Quantile Regression Models: A Practical Guideline
for Empirical Research,” The Journal of Human Resources, 33(1), pp. 88-126.
Canada. Statistics Canada. Census of Canada, 2001: “Total, Average and Median Years of
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Crompton, S (1996). “The Marginally Literate Workforce,” Perspectives on Labour and Income,
Statistics Canada, Summer.
Ferrer, A., D.A. Green and W.C. Riddell (2006). “The Effect of Literacy on Immigrant Earnings,”
Journal of Human Resources, Spring, pp. 380-410.
Green, D.A. and Riddell, W.C. (2003). “Literacy and Earnings: An Investigation of the
Interaction of Cognitive and Unobserved Skills in Earnings Generation,” Labour Economics 10
(April) 165-84.
Green, D.A. and W.C. Riddell (2006). “Literacy and the Labour Market: The Generation of
Literacy and its Impact on Earnings,” Report prepared for Statistics Canada.
Osberg, L. (2000). “Schooling, Literacy and Individual Earnings,” Statistics Canada, Catalogue
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Sen, A. (1999). Development as Freedom. New York: Anchor Books.
Tuijnman, A. And E. Boudard (2001). “Adult Education Participation in North America:
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52
Table 1. All Youth, 2003: Distributions of Literacy Scores
Prose literacy
Document
Numeracy
literacy
literacy
5th percentile
210.9077
208.9329
199.0734
th
25 percentile
258.5269
265.1976
248.7537
50th percentile
291.7701
292.7859
283.012
th
75 percentile
318.5801
320.1128
312.5327
95th percentile
349.4128
353.5218
349.9275
Mean
287.1755
290.3056
279.1742
Standard
42.96934
42.71123
46.87337
deviation
Number of
3574
3574
3574
observations
Problem
solving literacy
210.6055
258.0384
287.251
312.0545
343.921
284.0523
40.7942
3574
Table 2. All Youth not currently in school, 2003: Distributions of Literacy Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
5th percentile
211.7186
212.5642
195.9011
209.2827
th
25 percentile
257.4533
265.1878
248.0647
257.4167
50th percentile
291.8838
291.9083
281.2377
285.5068
th
75 percentile
317.1489
319.575
310.5335
311.6965
95th percentile
348.2553
351.1317
345.2617
342.6533
Mean
286.5293
289.7571
277.2776
283.1576
Standard
42.72131
42.31721
46.9206
40.9399
deviation
Number of
2384
2384
2384
2384
observations
53
Table 3. Immigrant Youth whose age at immigration was over 10, 2003: Distributions of
Literacy Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
5th percentile
175.0877
176.6836
146.4709
177.3325
th
25 percentile
217.891
228.2136
217.4049
226.5175
50th percentile
255.0026
268.5102
255.9726
253.8476
th
75 percentile
292.7519
294.395
295.1372
287.3729
95th percentile
329.9397
342.2929
331.4167
320.53
Mean
255.3356
263.5364
253.6294
252.6483
Standard
50.56662
51.17622
52.54771
47.3772
deviation
Number of
254
254
254
254
observations
Table 4. Aboriginal youth, 2003: Distributions of Literacy Scores
Prose literacy
Document
Numeracy
literacy
literacy
5th percentile
191.0428
192.8186
170.7711
th
25 percentile
230.6976
227.7727
216.2931
50th percentile
258.7505
264.3837
245.1619
th
75 percentile
289.7625
290.8277
275.7117
th
95 percentile
323.5422
330.8159
314.8333
Mean
258.5063
260.5814
244.0102
Standard
42.1194
42.85846
45.87844
deviation
Number of
304
304
304
observations
54
Problem
solving literacy
194.5074
225.8704
255.3114
284.3848
317.4493
255.9818
39.50166
304
Table 5. Immigrant Youth whose age at immigration was 10 or under, 2003: Distributions
of Literacy Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
th
5 percentile
188.1311
190.4963
185.9063
189.6137
25th percentile
237.3374
251.2311
234.447
239.4582
th
50 percentile
275.4353
283.9462
266.4867
272.4555
75th percentile
303.7608
312.5289
308.5161
305.2515
th
95 percentile
340.1601
346.874
349.5515
333.1534
Mean
270.5121
278.1956
268.436
268.4988
Standard
46.24143
46.82592
48.74372
43.66114
deviation
Number of
186
186
186
186
observations
Table 6. Non-immigrant, non-aboriginal youth, 2003: Distributions of Literacy Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
th
5 percentile
220.8727
222.7078
203.2564
224.4111
25th percentile
266.8139
269.9175
256.0006
264.6787
th
50 percentile
295.119
295.8677
286.8163
290.1752
75th percentile
321.9458
323.9247
315.8259
315.7706
th
95 percentile
351.2164
354.8656
350.284
346.3427
Mean
292.2965
294.5618
283.179
289.1168
Standard
40.21067
40.34996
45.29129
37.98856
deviation
Number of
2470
2470
2470
2470
observations
55
Table 7. Female, non-immigrant, non-aboriginal youth, 2003: Distributions of Literacy
Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
th
5 percentile
229.5333
217.3028
203.2564
228.3848
25th percentile
272.5641
270.6931
250.7837
266.3936
th
50 percentile
302.7395
296.0048
283.012
293.3438
75th percentile
328.3363
327.5212
312.0671
319.4827
th
95 percentile
357.2094
355.2672
342.4908
346.3427
Mean
298.9263
295.9567
279.8304
291.5042
Standard
39.9142
40.87085
43.55226
37.75761
deviation
Number of
1235
1235
1235
1235
observations
Table 8. Male, non-immigrant, non-aboriginal youth, 2003: Distributions of Literacy
Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
5th percentile
212.2286
223.3568
204.6953
218.6879
th
25 percentile
261.1498
269.8997
258.6088
264.272
50th percentile
290.8956
295.8677
290.5585
288.692
th
75 percentile
311.9637
319.3583
318.4716
311.6142
th
95 percentile
343.9744
352.607
354.8716
346.8442
Mean
285.9816
293.2331
286.3686
286.8427
Standard
39.48408
39.81818
46.68208
38.08315
deviation
Number of
1235
1235
1235
1235
observations
56
Table 9. Non-immigrant, non-aboriginal 26 to 35 years olds, 2003: Distributions of
Literacy Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
th
5 percentile
228.6403
227.0782
216.9315
224.4427
25th percentile
275.4052
276.4366
263.8186
269.6664
th
50 percentile
303.6976
304.2396
293.3011
296.1187
75th percentile
328.1208
330.8532
322.1561
322.3264
th
95 percentile
360.3748
361.0248
363.8924
352.6783
Mean
300.3751
301.4102
291.7783
293.7479
Standard
40.16061
41.9833
44.34693
39.25177
deviation
Number of
2180
2180
2180
2180
observations
Table 10. Non-immigrant, non-aboriginal 36 to 45 years olds, Distributions of Literacy
Scores
Prose literacy
Document
Numeracy
Problem
literacy
literacy
solving literacy
5th percentile
209.4869
203.9987
194.6071
205.9367
th
25 percentile
263.6475
262.931
251.4567
257.7397
50th percentile
294.1848
292.3627
282.9394
285.4711
th
75 percentile
323.0555
324.7113
316.6167
313.3466
th
95 percentile
355.7727
359.4344
353.1294
355.7798
Mean
290.6646
289.769
280.9003
283.6141
Standard
45.20643
47.76224
49.01289
44.7549
deviation
Number of
3215
3215
3215
3215
observations
57
Table 11: Regressions of Literacy Scores on Age, Education and Gender
Non-Immigrant, Non-Aboriginals, 16 to 45 Years Old
Fem ale
26-35
36-45
Years of
schooling
Prose literacy
7.0865***
(1.8127)
-4.2735**
(1.9097)
-9.6365***
(2.3937)
Docum ent
literacy
-3.1131*
(1.7568)
-6.6429***
(1.858)
-13.4596***
(2.4507)
Num eracy
literacy
-13.9275***
(1.8398)
-4.4201*
(2.2272)
-9.9657***
(3.1532)
Problem solving
literacy
-0.0477
(1.4094)
-7.2048***
(2.1806)
-12.9801***
(2.5493)
6.9525***
(0.2563)
7.3192***
(0.274)
7.6200***
(0.2869)
6.6899***
(0.3109)
Currently in
school
1.5452
-0.1641
3.6946
(2.7576)
(3.1492)
(3.3944)
Constant
201.5889***
204.7470***
193.7657***
(3.5695)
(3.9655)
(5.0406)
Observations
7865
7865
7865
R-squared
0.26
0.25
0.26
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
58
1.6923
(2.5832)
205.1251***
(4.4923)
7865
0.24
Table 12: Cross-Cohort Comparisons, Document Literacy
Percentiles Age 16-25
Age 26-35
Age 36-45
1983
1974
1974
1964
1964
1954
Cohort Cohort Cohort Cohort Cohort Cohort
th
213
227
188
204
208
5 percentile 223
th
270
276
266
263
259
25 percentile 270
th
303
304
299
292
292
50 percentile 297
th
324
334
331
339
325
333
75 percentile
th
374
361
387
359
380
95 percentile 355
observation 2662
1193
2180
937
3215
932
s
Table 13: Cross-Cohort Comparisons, Prose Literacy
Percentiles Age 16-25
Age 26-35
Age 36-45
1983
1974
1964
1954
1974
1964
Cohort Cohort Cohort Cohort Cohort Cohort
229
216
209
207
5th percentile 224
219
th
268
275
262
264
268
25 percentile 267
th
296
296
304
294
294
298
50 percentile
th
321
328
327
323
328
75 percentile 323
th
360
360
366
356
372
95 percentile 352
observation 2662
1193
2180
937
3215
932
s
59
Table 14: Effects of Ageing for Specific Cohorts, Document Literacy
Percentiles 1974 Cohort
1964 Cohort
1954 Cohort
16-25
25-34
26-35
35-44
36-45
45-54
th
188
208
5 percentile 213
231
206
199
th
270
277
266
263
259
256
25 percentile
th
305
299
294
292
287
50 percentile 303
th
331
339
325
333
317
75 percentile 334
th
374
361
387
360
380
354
95 percentile
observation 1193
2108
937
3132
932
3314
s
Table 15: Effects of Ageing for Specific Cohorts, Prose Literacy
Percentiles 1974 Cohort
1964 Cohort
1954 Cohort
16-25
25-34
26-35
35-44
36-45
45-54
th
216
207
5 percentile 219
230
213
202
276
262
264
268
262
25th percentile 268
th
306
294
295
298
291
50 percentile 296
th
321
328
327
324
328
318
75 percentile
th
360
366
356
372
353
95 percentile 360
observation 1193
2108
937
3132
932
3314
s
60
Table 16: OLS Regressions W ith Cohort Effects
Docum ent P r o s e Document
literacy
literacy
literacy
High school
graduates
0.1417***
0.1316***
0.1302***
(0.0099)
(0.0188)
(0.0113)
Som e postsecondary
0.1767***
0.1717***
0.1577***
(0.0088)
(0.0169)
(0.0093)
U niversity
graduates
0.2474***
0.2340***
0.2119***
(0.0189)
(0.0198)
(0.0158)
Fem ale
-0.0006
0.0377***
0.0021
(0.0054)
(0.0082)
(0.006)
Age groups
26-35
36-45
46-55
56-65
Mother’s
education
L e s s th a n
high school
P o s t secondary
Not reported
Father’s
education
L e s s th a n
high school
P o s t secondary
Not reported
1974 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
P r o s e
literacy
0.1229***
(0.0205)
0.1563***
(0.017)
0.2013***
(0.017)
0.0374***
(0.008)
-0.0299***
(0.0104)
-0.0564***
(0.0166)
-0.0861***
(0.0293)
-0.1190***
(0.0315)
-0.0189**
(0.009)
-0.0331
(0.0239)
-0.0516*
(0.0302)
-0.0651
(0.049)
-0.0433***
(0.0107)
-0.0756***
(0.0178)
-0.1114***
(0.0302)
-0.1332***
(0.0295)
-0.0291***
(0.0103)
-0.0469*
(0.0276)
-0.0710*
(0.035)
-0.0763
(0.0493)
-0.0424***
(0.0048)
-0.0355***
(0.0051)
-0.0374***
(0.0041)
-0.0316***
(0.0052)
-0.0024
(0.0065)
-0.0599***
(0.0189)
0.0051
(0.0086)
-0.0487***
(0.0167)
0.0001
(0.0067)
-0.0573**
(0.021)
0.0064
(0.009)
-0.0467**
(0.0189)
-0.0381**
(0.0172)
-0.0257
(0.0168)
-0.0370**
(0.0173)
-0.0247
(0.0175)
0.009
(0.0188)
-0.0522***
(0.0117)
0.0082
(0.0125)
0.0192
(0.0207)
0.042
(0.0329)
0.0281
0.0127
(0.0175)
-0.0286**
(0.0134)
0.0016
(0.0139)
0.0042
(0.03)
0.0258
(0.0369)
-0.002
0.0104
(0.0185)
-0.0457***
(0.0124)
0.0076
(0.0131)
0.0189
(0.0218)
0.0469
(0.0346)
0.0384
0.0138
(0.0173)
-0.0236*
(0.0135)
0.0015
(0.0141)
0.0035
(0.0317)
0.0293
(0.0404)
0.0056
61
(0.0334)
Occupation
Constant
(0.0548)
(0.0324)
Yes
5.6456***
(0.021)
16924
0.32
5.5851***
5.5550***
(0.0176)
(0.0215)
Observations 16924
16924
R-squared
0.3
0.27
Standard errors in
parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
62
(0.0564)
Yes
5.6053***
(0.0207)
16924
0.28
Table 17: OLS Regressions with Schooling-Cohort Interactions
Education
High school graduates
Som e post-secondary
University graduates
1974 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
1974 Cohort, high school
graduates
Docum ent
Prose
0.0651***
(0.0144)
0.0920***
(0.0135)
0.1629***
(0.0187)
-0.0212
(0.0237)
-0.0749
(0.0525)
-0.0314
(0.0374)
-0.0566
(0.0433)
0.0635***
(0.0149)
0.0984***
(0.0137)
0.1449***
(0.0161)
-0.0146
(0.0212)
-0.0796
(0.0724)
-0.0401
(0.0424)
-0.091
(0.0833)
0.0527*
(0.0269)
0.03
(0.0268)
0.0497*
(0.0276)
0.0321
(0.028)
0.0435
(0.0294)
0.039
(0.0241)
0.1148***
(0.0393)
0.1011*
(0.056)
0.1292***
(0.0464)
0.1122*
(0.0584)
0.1412***
(0.0489)
0.1300**
(0.05)
0.0985***
(0.0284)
0.0863***
(0.0238)
0.1132***
(0.0262)
0.0873***
(0.0231)
0.0984***
(0.0353)
0.1069***
(0.0257)
0.1293***
(0.0253)
0.1299**
(0.0514)
0.1168***
0.1223***
1974 Cohort, som e postsecondary
1974 Cohort, university
graduates
1964 Cohort, high school
graduates
1964 Cohort, som e postsecondary
1964 Cohort, university
graduates
1954 Cohort, high school
graduates
1954 Cohort, som e postsecondary
1954 Cohort, university
graduates
1944 Cohort, high school
graduates
1944 Cohort, som e postsecondary
63
1944 Cohort, university
graduates
Fem ale
Age groups
26-35
(0.0289)
(0.0363)
0.1291***
(0.0286)
-0.0009
(0.0054)
0.1506***
(0.0454)
0.0374***
(0.0083)
-0.0226**
(0.0109)
36-45
-0.0526***
(0.0183)
46-55
-0.0833**
(0.0307)
56-65
-0.1167***
(0.0331)
Constant
5.6337***
(0.0172)
Observations
16924
R-squared
0.32
Standard errors in parentheses
* significant at 10%; ** significant at 5%; ***
1%
-0.011
(0.011)
-0.0279
(0.0245)
-0.047
(0.0315)
-0.0628
(0.0525)
5.5982***
(0.0173)
16924
0.28
significant at
64
Table 18: Docum ent Literacy, Quantile Regressions
10 th percentile
50t percentile
Education
High school graduates
Som e post-secondary
University graduates
Fem ale
90 th percentile
0.2140***
(0.0115)
0.2480***
(0.0092)
0.3567***
(0.0111)
0.0039
(0.0061)
0.1155***
(0.0036)
0.1487***
(0.004)
0.2087***
(0.0035)
-0.0050**
(0.0023)
0.0784***
(0.004)
0.1100***
(0.0044)
0.1682***
(0.0044)
-0.0073**
(0.0033)
-0.0117
(0.0144)
-0.0189
(0.0227)
-0.012
(0.0293)
-0.0813**
(0.0361)
-0.0207***
(0.0052)
-0.0472***
(0.0064)
-0.0737***
(0.0088)
-0.1317***
(0.0165)
-0.0526***
(0.0071)
-0.1071***
(0.0141)
-0.1875***
(0.0149)
-0.2264***
(0.0161)
-0.0514***
(0.0117)
-0.0011
(0.0085)
-0.1186***
(0.0144)
-0.0388***
(0.0038)
0.0059**
(0.0028)
-0.0562***
(0.01)
-0.0168***
(0.004)
0.0171***
(0.0046)
-0.0333***
(0.0054)
Age groups
26-35
36-45
46-55
56-65
Mother’s education
Less than high school
Post-secondary
Not reported
Father’s education
Less than high school
-0.0452***
(0.0075)
Post-secondary
-0.0005
(0.0073)
Not reported
-0.0496***
(0.0131)
1974 Cohort
-0.019
(0.0155)
1964 Cohort
-0.0345
(0.0217)
1954 Cohort
-0.0296
(0.0284)
1944 Cohort
-0.0414
(0.0331)
Constant
5.3609***
(0.0134)
Observations
16072
* significant at 10%; ** significant at 5%;
-0.0389***
(0.0041)
0.0064
(0.0051)
-0.0540***
(0.0072)
0.0152**
(0.0065)
0.0297***
(0.008)
0.0468***
(0.0101)
0.0653***
(0.0174)
5.6116***
(0.0059)
16072
*** significant
65
-0.0256***
(0.0039)
0.005
(0.0045)
-0.0283***
(0.005)
0.0315***
(0.0098)
0.0916***
(0.0141)
0.1532***
(0.0162)
0.1631***
(0.0168)
5.7702***
(0.0062)
16072
at 1%
Table 19: Prose literacy, Quantile Regressions
10thpercentile 50thpercentile
Education
High school graduates
Som e post-secondary
University graduates
Fem ale
Age groups
26-35
36-45
46-55
56-65
Mother’s education
Less than high school
Post-secondary
Not reported
Father’s education
Less than high school
Post-secondary
Not reported
1974 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
Constant
Observations
90 th percentile
0.2080***
(0.0103)
0.2584***
(0.011)
0.3480***
(0.0128)
0.0375***
(0.006)
0.0940***
(0.0054)
0.1354***
(0.0047)
0.1877***
(0.0053)
0.0299***
(0.0031)
0.0655***
(0.0038)
0.0910***
(0.0046)
0.1427***
(0.0063)
0.0266***
(0.0021)
-0.0082
(0.0149)
-0.0177
(0.0182)
-0.0333
(0.0207)
-0.1099***
(0.0253)
-0.0074
(0.0057)
-0.0266***
(0.0082)
-0.0483***
(0.0082)
-0.0798***
(0.0111)
-0.0170**
(0.007)
-0.0618***
(0.0106)
-0.0939***
(0.0138)
-0.1433***
(0.0141)
-0.0353***
(0.0077)
0.0218***
(0.0069)
-0.0581***
(0.0178)
-0.0302***
(0.0035)
0.0133***
(0.0051)
-0.0486***
(0.0077)
-0.0172***
(0.0044)
0.0231***
(0.0047)
-0.0129*
(0.0072)
-0.0383***
(0.0082)
0.0008
(0.0069)
-0.0612***
(0.0121)
-0.009
(0.0109)
-0.015
(0.0168)
0.0092
(0.0225)
0.027
(-0.023)
5.3195***
(0.0142)
16072
-0.0259***
(0.0032)
0.0110***
(0.0037)
-0.0278***
(0.0073)
0.002
(0.004)
0.0124*
(0.0075)
0.0410***
(0.0074)
0.0394***
(0.0114)
5.5910***
(0.0056)
16072
-0.0103***
(0.0039)
0.0246***
(0.0038)
-0.0272***
(0.0091)
0.0118
(0.0078)
0.0564***
(0.0118)
0.0935***
(0.0126)
0.1158***
(0.0155)
5.7334***
(0.0058)
16072
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
66
Table 20: Docum ent Literacy, Quantile
High school or less
10 th
50 th
percentile percentile
Fem ale
0.0360*** -0.0035
(0.0102)
(0.0048)
Regressions, Education Breakdowns
University or m ore
90 th
10 th
50 th
percentile percentile percentile
-0.0016
-0.0092
0.0113*
(0.0045)
(0.008)
(0.0062)
90 th
percentile
-0.0077*
(0.0041)
Age groups
0.0108
(0.0596)
0.0258***
(0.0098)
0.0655***
(0.0121)
0.1025***
(0.0138)
0.1277***
(0.0184)
0.0709***
(0.0091)
0.0997***
(0.0097)
0.1906***
(0.0226)
0.1984***
(0.0266)
0.1068***
(0.0165)
0.0684***
(0.0056)
-0.0183*
(0.01)
0.1665***
(0.0332)
26-35
-0.0076
(0.0193)
36-45
0.0137
(0.0356)
46-55
-0.0063
(0.0399)
56-65
Mother’s
education
Less than
high school
Postsecondary
Not reported
Father’s
education
Less than
high school
Postsecondary
Not reported
1974 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
Constant
-0.0299
(0.0345)
0.0803***
(0.03)
0.0136
(0.0165)
-0.0997**
(0.0395)
0.2067***
(0.0445)
-0.0129
(0.0206)
0.0864***
(0.0289)
-0.0144
(0.0124)
0.1068***
(0.0207)
0.1568***
(0.0199)
0.1955***
(0.0214)
0.0424***
(0.0066)
0.0495***
(0.013)
0.0312***
(0.0061)
0.0127*
(0.0068)
0.0032
(0.0068)
0.0744***
(0.0115)
0.0191*
(0.0106)
0.0352***
(0.0113)
0.0180*
(0.0099)
-0.0152
(0.0508)
0.0127**
(0.0061)
0.0762***
(0.0259)
0.0960***
(0.0199)
0.0470***
(0.0064)
-0.0116*
(0.0065)
-0.0059
(0.0105)
-0.0023
(0.0067)
0.0198***
(0.0046)
0.0145
(0.0164)
0.0966***
(0.0215)
0.0491***
(0.0139)
-0.0093
(0.0408)
0.0068
(0.0418)
-0.1165**
(0.0593)
5.4899***
(0.01)
0.0173***
(0.0054)
0.0881***
(0.0073)
0.0492***
(0.0094)
0.0687***
(0.0107)
0.1017***
(0.0139)
0.0713***
(0.0181)
5.6883***
(0.0061)
0.0119*
(0.0071)
0.0350***
(0.0089)
0.0588***
(0.0075)
0.0955***
(0.0098)
0.1718***
(0.019)
0.1462***
(0.0265)
5.8213***
(0.0109)
0.0058
(0.0092)
0.0095*
(0.005)
0.0001
(0.0081)
-0.0349
(0.1666)
0.0323
(0.0311)
0.0803**
(0.0343)
0.0830**
(0.0369)
0.1246***
(0.0438)
5.6655***
(0.0192)
-0.0476**
(0.0207)
-0.0093
(0.0152)
0.0064
(0.0167)
-0.0166
(0.0197)
0.0304
(0.0227)
5.7838***
(0.0072)
0.0135
(0.0217)
0.0571***
(0.0149)
0.1539***
(0.0221)
0.1792***
(0.0226)
0.1917***
(0.0226)
5.8581***
(0.0123)
67
0.023
(0.0214)
0.0413***
(0.0062)
0.0419
(0.0405)
Observations
9259
9259
9259
2846
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
68
2846
2846
Table 21: Prose literacy, Quantile Regressions, Education Breakdowns
High school or less
University or m ore
10 th
50 th
10 th
50 th
10 th
percentile percentile percentile percentile percentile
Fem ale
0.0607*** 0.0398*** 0.0393*** 0.0016
0.0335***
(0.0128)
(0.0045)
(0.0037)
(0.0086)
(0.0045)
Age groups
26-35
-0.0124
(0.0184)
-0.0107
(0.0102)
36-45
-0.0165
(0.0297)
-0.0145
(0.0176)
46-55
0.0264
(0.0363)
-0.0374*
(0.0217)
56-65
-0.0209
(0.0522)
-0.0463*
(0.0268)
-0.0121
(0.0091)
0.0513***
(0.0183)
0.0889***
(0.0203)
0.1302***
(0.0236)
0.0933***
(0.012)
0.0392***
(0.0048)
0.0035
(0.0148)
0.1654***
(0.0247)
Mother’s
education
Less than
high school
Postsecondary
Not reported
Father’s
education
Less than
high school
Postsecondary
Not reported
1974 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
Constant
50 th
percentile
0.0210***
(0.0048)
0.0328**
(0.0144)
-0.009
(0.0113)
0.0204***
(0.0057)
0.0187
(0.0185)
0.0006
(0.0204)
-0.0421
(0.0266)
0.1621***
(0.0283)
0.0016
(0.0206)
0.0639***
(0.0226)
0.1096***
(0.0217)
0.0378***
(0.0049)
0.0738***
(0.0157)
0.0251***
(0.0069)
0.0231***
(0.0069)
0.0256***
(0.0065)
0.0550***
(0.0106)
0.0095*
(0.0054)
0.0218***
(0.0053)
0.0303**
(0.0121)
0.0180***
(0.0046)
0.0430***
(0.0093)
-0.0197
(0.0651)
-0.0391
(0.0261)
0.0504***
(0.0103)
0.0788***
(0.0157)
0.0392***
(0.008)
-0.0094*
(0.0049)
-0.0028
(0.0092)
-0.0073
(0.007)
0.0095
(0.0131)
0.0814***
(0.0153)
0.0416**
(0.0171)
0.0162
(0.0309)
-0.0015
(0.0357)
-0.075
(0.0458)
5.4661***
(0.0145)
0.0157**
(0.0064)
0.0537***
(0.0063)
0.0276***
(0.0095)
0.0129
(0.0163)
0.0512**
(0.0201)
-0.0041
(0.0241)
5.6460***
(0.006)
0.0182***
(0.0053)
0.0487***
(0.0053)
0.0168*
(0.0093)
0.0587***
(0.0173)
0.0906***
(0.0183)
0.0981***
(0.023)
5.7877***
(0.0054)
-0.0128
(0.0099)
0.0215***
(0.0083)
-0.0028
(0.0054)
0.0188**
(0.0077)
-0.0348
(0.2085)
-0.0135
(0.0191)
-0.0318
(0.021)
0.0363
(0.0237)
0.1568***
(0.0263)
5.6570***
(0.0132)
-0.0428*
(0.0241)
0.0282**
(0.0125)
0.0383**
(0.0184)
0.0685***
(0.0221)
0.0844***
(0.0233)
5.7465***
(0.0094)
0.032
(0.0209)
0.0309***
(0.0109)
0.0375*
(0.0194)
0.0816***
(0.0248)
0.1116***
(0.0375)
5.8116***
(0.0112)
69
-0.0457*
(0.0246)
-0.0853**
(0.041)
Observations
9259
9259
9259
2846
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
70
2846
2846
Table 22: Cross country comparison, All youth, Document literacy
Canada
Norway
U.S.
th
5 percentile
209
198
224
th
25 percentile
265
249
274
th
50 percentile
293
279
305
75th percentile
320
313
332
th
95 percentile
354
351
368
Mean
290
277
301
Standard deviation 43
48
45
Number of
3574
320
516
observations
Table 23: Cross country comparison, All youth, Prose literacy
Canada
U.S.
th
5 percentile
211
198
th
25 percentile
259
241
th
50 percentile
292
276
75th percentile
319
306
th
95 percentile
349
346
Mean
287
272
Standard deviation 43
46
Number of
3574
320
observations
Norway
225
272
300
325
352
296
39
516
Table 24: Cross country comparison, All youth, Numeracy literacy
Canada
Norway
U.S.
th
5 percentile
199
178
207
th
25 percentile
249
227
254
th
50 percentile
283
271
286
75th percentile
313
307
317
th
95 percentile
350
350
350
Mean
279
266
284
Standard deviation 47
53
44
Number of
3574
320
516
observations
71
Table 25: Prose Literacy Regression, Norway
variable
1983 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
Age group
16-24
35-44
45-54
55-65
Education
Less than HS
College degree
BA or more
female
Mother’s education
Less than high
school
Ps, univ. or more
None reported
Father’s education
Less than high
school
Ps, univ. or more
None reported
constant
observation
OLS
.005
(.0121)
.015
(.0195)
.005
(.0205)
-.031
(.0352)
10th quantile
-.009
(.0198)
.032***
(.0120)
.028
(.0278)
-.015
(.0364)
50th quantile
.018**
(.0082)
.006
(.0078)
-.017
(.0145)
-.054***
(.0176)
90th quantile
.013
(.0137)
.004
(.0132)
.001
(.0212)
.017
(.0229)
.024*
(.0137)
-.004
(.0079)
-.047***
(.0133)
-.076***
(.0183)
.044**
(.0171)
-.015
(.0137)
-.094***
(.0268)
-.111***
(.0361)
.002
(.0098)
-.000
(.0084)
-.033***
(.0123)
-.058***
(.0157)
.020
(.0154)
.001
(.0120)
-.034**
(.0135)
-.073***
(.0231)
-.083***
(.0169)
.055***
(.0067)
.103***
(.0094)
.019
(.0114)
-.122***
(.0179)
.064***
(.0147)
.125***
(.0124)
.022**
(.0100)
-.072***
(.0073)
.050***
(.0055)
.084***
(.0057)
.020***
(.0036)
-.025*
(.0137)
.043***
(.0096)
.075***
(.0106)
.021***
(.0058)
-.011*
(.0063)
-.003
(.0157)
-.055**
(.0227)
-.010
(.0093)
-.009
(.0186)
-.041**
(.0177)
-.011*
(.0055)
.001
(.0067)
-.081***
(.0177)
.003
(.0064)
.010
(.0089)
-.011
(.0239)
-.018
(.0107)
.014*
(.0075)
-.006
(.0143)
5.677***
(.0121)
3709
-.017
(.0113)
.028***
(.0105)
-.026*
(.0157)
5.540***
(.0149)
3890
-.016***
(.0045)
.020***
(.0045)
.016
(.0214)
5.694***
(.0077)
3890
-.013**
(.0060)
.012
(.0105)
-.013
(.0155)
5.787***
(.0103)
3890
Note:Number if observations in quantile regressions is from the re-created datasets.
Cohort 2 (omitted category) are people who were from 26 to 35 in 1998, Cohort 1 are people who
were from 16 to 25 in 1998, Cohort 3 are people who were from 36 to 45 in 1998, Cohort 4 are people
who were from 46 to 55 in 1998, and Cohort 5 are people who were from 56 to 65 in 1998.
72
Figure 26: Document Literacy Regressions, Norway
-.001
(.0146)
.023
(.0315)
.022
(.0439)
-.026
(.0648)
10 th
quantile
-.029
(.0278)
.036*
(.0185)
.030
(.0259)
.004
(.0428)
50 th
quantile
.009
(.0092)
.013
(.0090)
-.013
(.0132)
-.055**
(.0231)
90 th
quantile
.008
(.0094)
-.008
(.0113)
.013
(0136)
.000
(.0220)
.033*
(.0163)
-.013
(.0111)
-.664**
(.0263)
-.110**
(.0419)
.072**
(.0283)
-.032
(.0203)
-.101***
(.0269)
-.173***
(.0348)
.016
(.0102)
-.008
(.0093)
-.041***
(.0120)
-.076***
(.0159)
.014
(.0094)
.004
(.0127)
-.041***
(.0155)
-.101***
(.0206)
-.106***
(.0174)
.063***
(.0097)
.105***
(.0107)
-.030***
(.0061)
-.158***
(.0324)
.068***
(.0172)
.133***
(.0136)
-.032**
(.0125)
-.084***
(.0132)
.069***
(.0066)
.093***
(.0048)
-.028***
(.0060)
-.046***
(.0118)
.035***
(.0100)
.066***
(.0067)
-.017***
(.0050)
-.010
(.0076)
-.002
(.0262)
-.050
(.0334)
-.030***
(.0108)
-.025
(.0206)
-.061*
(.0340)
-.015**
(.0074)
.011
(.0084)
-.051***
(.0157)
.017***
(.0064)
.007
(.0086)
-.009
(.0175)
-.011
(.0089)
.016*
(.0128)
-.009
(.0145)
5.723***
(.0096)
3709
-.004
(.0123)
.033**
(.0168)
-.027
(.0257)
5.580***
(.0140)
3890
-.011**
(.0051)
.019***
(.0050)
.010
(.0152)
5.739***
(.0074)
3890
-.013*
(.0073)
.025***
(.0072)
-.022
(.0205)
5.854***
(.0091)
3890
Variable
OLS
1983 Cohort
1964 Cohort
1954 Cohort
1944 Cohort
Age group
16-24
35-44
45-54
55-65
Education
Less than HS
College degree
BA or more
female
Mother’s education
Less than high
school
Ps, univ. or more
None reported
Father’s education
Less than high
school
Ps, univ. or more
None reported
constant
observation
Note:Number if observations in quantile regressions is from the re-created datasets.
Cohort 2 (omitted category) are people who were from 26 to 35 in 1998, Cohort 1 are people who
were from 16 to 25 in 1998, Cohort 3 are people who were from 36 to 45 in 1998, Cohort 4 are people
who were from 46 to 55 in 1998, and Cohort 5 are people who were from 56 to 65 in 1998.
73
Table 27: Prose Literacy Regressions, US
-.033**
(.0142)
.030
(.0245)
.067
(.0402)
10 th
quantile
-.040*
(.0241)
-.009
(.0220)
.056
(.0378)
50 th
quantile
-.027*
(.0151)
.037***
(.0138)
.075***
(.0192)
90 th
quantile
-.012
(.0196)
.049***
(.0152)
.095***
(.0249)
.081***
(.0137)
-.030
(.0249)
-.060
(.0408)
-.113**
(.0544)
.133***
(.0242)
-.031
(.0228)
-.040
(.0407)
-.091*
(.0547)
.093***
(.0205)
-.031***
(.0104)
-.054***
(.0178)
-.108***
(.0289)
.021
(.0169)
-.015
(.0188)
-.066***
(.0204)
-.125***
(.0313)
-.175***
(.0254)
.049**
(.0195)
.135***
(.0080)
.038
(.0256)
-.248***
(.0390)
.054*
(.0285)
.169***
(.0220)
.046***
(.0156)
-.173***
(.0215)
.054***
(.0104)
.127***
(.0088)
.021***
(.0070)
-.114***
(.0117)
.031**
(.0155)
.095***
(.0123)
.019*
(.0101)
-.052***
(.0133)
.009
(.0080)
-.076**
(.0371)
-.048***
(.0119)
.023**
(.0113)
-.057***
(.0209)
-.012
(.0096)
.017*
(.0098)
-.077***
(.0241)
5.640***
(.0107)
2138
-.017
(.0128)
.006
(.0139)
-.060***
(.0145)
5.800***
(.0138)
2138
variable
OLS
1983 Cohort
1964 Cohort
1954 Cohort
Age group
16-24
35-44
45-54
55-65
Education
Less than HS
College degree
BA or more
female
Mother’s education
Less than high
-.052*** -.077***
school
(.0110)
(.0237)
Ps, univ. or more .011
.010
(.0165)
(.0185)
None reported
-.086*** -.062
(.0210)
(.0384)
Father’s education
Less than high
-.026
-.017
school
(.0171)
(.0258)
Ps, univ. or more .015*
.033
(.0086)
(.0207)
None reported
-.076*** -.116***
(.0210)
(.0358)
constant
5.627*** 5.424***
(.0158)
(.0214)
observation
2162
2138
Note:
Number of observations in quantile regressions is from the re-created datasets.
Cohort 2 (omitted category) are people who were from 26 to 35 in 1994, Cohort 1 are people who
were from 16 to 25 in 1994, Cohort 3 are people who were from 36 to 45 in 1994, and Cohort 4 were
people who were 46 to 56 in 1994.
74
Table 28: Document Literacy Regressions, US
variable
1983 Cohort
1964 Cohort
1954 Cohort
Age group
16-24
35-44
45-54
55-65
Education
Less than HS
OLS
-.006
(.0152)
-.003
(.0245)
.020
(.0397)
10 th quantile
.022
(.0358)
-.016
(.0332)
.013
(.0704)
50 th quantile
-.031*
(.0177)
.034**
(.0133)
.046**
(.0193)
90 th quantile
-.012
(.0168)
.024
(.0164)
.030
(.0245)
.066***
(.0149)
-.014
(.0243)
-.022
(.0387)
-.063
(.0528)
.051
(.0472)
.000
(.0270)
.009
(.0506)
-.035
(.0823)
.067***
(.0207)
-.041***
(.0152)
-.069***
(.0158)
-.119***
(.0238)
.022
(.0138)
-.022
(.0158)
-.050*
(.0263)
-.081**
(.0386)
-.307***
(.0614)
.068*
(.0399)
.183***
(.0204)
.035**
(.0149)
-.172***
(.0257)
.052***
(.0147)
.138***
(.0092)
-.002
(.0087)
-.151***
(.0160)
.012
(.0127)
.091***
(.0142)
-.017*
(.0089)
-.103***
(.0256)
.028
(.0259)
-.111
(.0823)
-.052***
(.0104)
-.000
(.0100)
-.067*
(.0369)
-.059***
(.0174)
-.009
(.0193)
-.047**
(.0208)
.004
(.0216)
.045**
(.021)
-.103**
(.0420)
5.370***
(.0328)
2138
-.028**
(.0123)
.019*
(.0106)
-.114***
(.0196)
5.664***
(.0102)
2138
.004
(.0111)
.018**
(.0082)
-.057***
(.0169)
5.830***
(.0149)
2138
-.198***
(.0372)
College degree .043**
(.0137)
BA or more
.137***
(.0110)
female
.009
(.0326)
Mother’s
education
Less than high -.063***
school
(.0153)
Ps, univ. or
-.008
more
(.0298)
None reported
-.095***
(.0200)
Father’s
education
Less than high -.032*
school
(.0170)
Ps, univ. or
.029**
more
(.0133)
None reported
-.091***
(.0201)
constant
5.629***
(.0172)
observation
2162
Note:
Number of observations is from the re-created datasets.
Cohort 2 (omitted category) are people who were from 26 to 35 in 1994, Cohort 1 are people who
were from 16 to 25 in 1994, Cohort 3 are people who were from 36 to 45 in 1994, and Cohort 4 were
people who were 46 to 56 in 1994.
75
Table 29: Literacy Related Activities, 2003 IALS
Variable
1983 Cohort (Age 16-25)
1974 Cohort (Age 26-35)
Reading Tasks at Work
4 or 5 at least once/week
15.8%
34.7%
1 to 3 at least once/week
25.1
33.7
8
6.5
4 or 5 at least once/week
4.1
14.1
1 to 3 at least once/week
24.8
42.9
rarely
18.8
14.7
4 or 5 at least once/week
15.8
28.8
1 to 3 at least once/week
29.4
39.7
5.1
6.5
rarely
Writing Tasks at Work
Math Tasks at Work
rarely
Less than 1 hour per day
watching tv
27
26.2
Read a newspaper at least
once a week
66.7
72.5
Read a book at least once a
week
38.1
45.6
Agree or strongly agree that
read only when have to
27
21
76
.006
.004
0
.002
Density
.008
.01
Figure 1: Document Literacy, Age 16-25
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 2: Document Literacy, Age 26-35
0
100
200
300
400
newx
Literacy Score
IALS94
IALS03
500
.004
0
.002
Density
.006
.008
Figure 3: Document Literacy, Age 36-45
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 4: Prose Literacy, Age 16-25
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
.006
.004
0
.002
Density
.008
.01
Figure 5: Prose Literacy, Age 26-35
0
100
200
pnewx
300
400
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 6: Prose Literacy, Age 36-45
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
.006
.004
0
.002
Density
.008
.01
Figure 7: Document Literacy, Cohort 2 (16-25 in 1994)
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.004
.002
0
Density
.006
.008
Figure 8: Document Literacy, Cohort 3 (26-35 in 1994)
0
100
200
300
400
newx
Literacy Score
IALS94
IALS03
500
.004
0
.002
Density
.006
.008
Figure 9: Document Literacy, Cohort 4 (36-45 in 1994)
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 10: Prose Literacy, Cohort 2 (16-25 in 1994)
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
.006
.004
0
.002
Density
.008
.01
Figure 11: Prose Literacy, Cohort 3 (26-35 in 1994)
0
100
200
pnewx
300
400
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 12: Prose Literacy, Cohort 4 (36-45 in 1994)
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
.006
.004
0
.002
Density
.008
.01
Figure 13: Document Literacy, Less than High School
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.006
.004
.002
0
Density
.008
.01
Figure 14: Document Literacy, High School Graduates
0
100
200
300
400
newx
Literacy Score
IALS94
IALS03
500
.006
.004
0
.002
Density
.008
.01
Figure 15: Document Literacy, Some Post-Secondary
0
100
200
300
400
500
newx
Literacy Score
IALS94
IALS03
.005
0
Density
.01
Figure 16: Document Literacy, University Graduates
0
100
200
300
400
newx
Literacy Score
IALS94
IALS03
500
.006
.004
0
.002
Density
.008
.01
Figure 17: Prose Literacy, Less than High School
0
100
200
pnewx
300
400
Literacy Score
IALS94
IALS03
.005
0
Density
.01
.015
Figure 18: Prose Literacy, High School Graduates
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
.005
0
Density
.01
.015
Figure 19: Prose Literacy, Some Post-Secondary
0
100
200
pnewx
300
400
Literacy Score
IALS94
IALS03
.005
0
Density
.01
.015
Figure 20: Prose Literacy, University Graduates
0
100
200
pnewx
300
Literacy Score
IALS94
IALS03
400
0
.005
Density
.01
.015
Figure 21: Prose Literacy (Norway), Age 16-25
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
237
235
th
282
282
th
304
306
th
326
328
th
95 percentile
356
352
mean
301
302
Standard deviation
36
37
observations
336
521
25 percentile
50 percentile
75 percentile
400
0
.005
Density
.01
.015
Figure 22: Prose Literacy (Norway), Age 26-35
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
237
238
th
279
281
th
301
311
th
320
328
th
95 percentile
348
355
mean
299
303
Standard deviation
32
38
observations
327
521
25 percentile
50 percentile
75 percentile
400
0
.005
Density
.01
.015
Figure 23: Prose Literacy (Norway), Age 36-45
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
244
232
th
276
276
th
299
304
th
316
323
th
95 percentile
344
351
mean
296
297
Standard deviation
31
37
observations
326
526
25 percentile
50 percentile
75 percentile
400
0
.005
Density
.01
.015
Figure 24: Prose Literacy (Norway), 16-25 in 1998
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
237
249
th
282
287
th
304
311
th
326
332
th
95 percentile
356
354
mean
301
307
Standard deviation
36
36
observations
336
433
25 percentile
50 percentile
75 percentile
400
0
.005
Density
.01
.015
Figure 25: Prose Literacy (Norway), 26-35 in 1998
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
237
238
th
279
277
th
301
308
th
320
325
th
95 percentile
348
353
mean
299
299
Standard deviation
32
37
observations
327
553
25 percentile
50 percentile
75 percentile
400
0
.005
Density
.01
.015
Figure 26: Prose Literacy(Norway), 36-45 in 1998
100
200
300
Literacy Score
IALS94
th
5 percentile
IALS03
IALS94
IALS03
244
224
th
276
269
th
299
296
th
316
318
th
95 percentile
344
346
mean
296
292
Standard deviation
31
37
observations
326
531
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
.01
Figure 27: Prose Literacy (U.S.), Age 16-24
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
169
201
th
262
246
th
288
276
th
314
306
th
95 percentile
358
347
mean
284
274
Standard deviation
54
44
observations
192
283
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
Figure 28: Prose Literacy (U.S.), Age 25-34
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
206
205
th
261
251
th
292
280
th
325
308
th
95 percentile
362
350
mean
288
279
Standard deviation
51
45
observations
241
296
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
.01
Figure 29: Prose Literacy (U.S.), Age 35-44
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
180
198
th
251
253
th
294
277
th
330
311
th
95 percentile
382
349
mean
290
279
Standard deviation
59
46
observations
269
332
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
Figure 30: Prose Literacy (U.S.), 16-25 in 1994
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
169
205
th
259
251
th
288
280
th
314
308
th
95 percentile
359
350
mean
284
279
Standard deviation
53
45
observations
205
296
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
.01
Figure 31: Prose Literacy (U.S.), 26-35 in 1994
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
199
198
th
261
253
th
293
277
th
328
311
th
95 percentile
366
349
mean
290
279
Standard deviation
53
46
observations
254
332
25 percentile
50 percentile
75 percentile
400
0
.002
Density
.004
.006
.008
.01
Figure 32: Prose Literacy (U.S.), 36-45 in 1994
0
100
200
Literacy Score
IALS94
th
5 percentile
300
IALS03
IALS94
IALS03
186
192
th
252
242
th
297
283
th
330
309
th
95 percentile
384
341
mean
292
276
Standard deviation
59
46
observations
267
321
25 percentile
50 percentile
75 percentile
400