Factors that Inform International Literacy Rates 1
Running Head: FACTORS THAT INFORM INTERNATIONAL LITERACY RATES
Factors that Inform International Literacy Rates
Class Project for Course EDA 381P
Quantitative Research Design and Analysis
Gloria Lenoir – [email protected]
Soojin Lim – [email protected]
Maria Luisa Illescas-Glascock
Jeannette Bellemeur
The University of Texas at Austin
Paper Presentation at the Annual Meeting of the University Council for Educational
Administration
Orlando, Florida
November, 2008
Factors that inform international literacy rates
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Abstract
This article explores the topic of literacy as it correlates to school life expectancy
in various economically developed and developing countries. This exploratory,
quantitative study uses United Nations (UN) and CIA Fact Book data in order to examine
correlations between Gini coefficient, Gross Domestic Product (GDP), school life
expectancy and literacy rate. The evidence examined here suggests that school life
expectancy may be a predictor of total percent literacy rate for the countries represented
in this study.
Introduction
Investigation of the correlation between individual literacy acquisition and school
success remains a focus of educational research. The national educational agenda in the
United States has been informed by reports such as the National Reading Panel Report
(2000) in order to determine policy regarding literacy acquisition and instruction in the
public schooling system. At the same time research has found strong correlations
between increased literacy rates and school success experienced by students (Meha,
Villanueva, Hubbard, & Linz, 1996). In addition, there is strong evidence that indicates
that individual economic success is directly related to level of education attainment.
According to the National Center for Education Statistics (NCES), comparative
indicators of education report (2007) “higher employment rates were associated with
higher levels of educational attainment.” Given this data it is clear that there is some
correlation between individual literacy acquisition and individual earning potential.
There is also a connection between the socio-economic status of students and their
propensity for pursuing higher educational goals. In other words, success during primary
and secondary school years does not necessarily indicate that a student will go on to
obtain a university degree. Rather, there is evidence that suggests that students who come
from lower socio-economic situations and/or fear debt are less likely to seek entrance into
a university program of study (Pennell et. al. 2005; Hatt et. al. 2005).
Given these data, this study seeks to expand focus from the individual to explore
the possibilities of the relationships of economic status, school life expectancy and
literacy rate variables among countries in economically developed and developing
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countries. Using the key concepts represented in Table 1.1, this study seeks to examine
the correlation between economic development of a country and the total percentage of
literacy in that country, as well as the possible correlations between economically
prosperous countries and school life expectancy in that country.
TABLE 1.1
Gross Domestic Product (GDP)
Gini Coefficient
(Gini, C. 1912)
Human Development Index (HDI)
Literacy
School Life Expectancy
The gross domestic product is the term
which describes a country’s prosperity
based upon goods and services produced
annually.
The Gini coefficient is a ratio that describes
income inequality for a given country. The
range used is from 0 to 1, where 0
represents total equality and 1 represents
total inequality.
The Human Development Index focuses on
three measurable dimensions of human
development: healthy life, education,
economic standard of living. These
measures are used by the United Nations in
order to draw comparisons in human
development conditions among various
countries.
Literacy in this study refers to the
percentage of people in a given country
over the age of 15 who are able to read and
write with proficiency.
School life expectancy refers to the
expected years of education (in a school
setting) for an average person in any given
country
The present study seeks to use the concepts listed above in order to analyze the
correlation between a given country’s Gini coefficient and total literacy rate in order to
discover whether or not a country’s overall wealth is related to the literacy rate of its
population. Secondly, this study seeks to identify any significant correlation between the
country’s literacy rate and the school life expectancy of the population.
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Research Focus
This quantitative research study examines the data available for 110 countries that
have varying Gini coefficients and GDP. The data set also includes the total literacy rate
for the population in the form of percentage and in 76 cases; it also includes school life
expectancy. The project is based upon data obtained from the CIA World FactBook and
The United Nations Human Development Programme, and the Human Development
Report (2006).
This exploratory project seeks to identify whether or not there is a correlation
between a given country’s Gini coefficient, GDP and/or school life expectancy and
overall literacy rate. Direct comparisons will be made using both the country’s Gini
coefficient, the GDP and total percentage of literacy in the given country. This study
includes data regarding school life expectancy for over half the cases and a descriptive
analysis will be run on this data in order to determine whether or not this data will
produce a relevant outcome.
The main hypotheses under investigation are:
1-1 Is there any difference in the “literacy rate” according to continent (9 subgroups
created)?
1-2 Is there any difference in the “literacy rate” according to the level of GDP for a given
country (3 groups created)?
The secondary hypotheses are:
2-1 Is there any correlation between school life expectancy, GDP, Gini coefficient and
literacy rate?
2-2 Which of the three variables selected is a predictor of literacy rate?
Review of Literature
Literacy is considered to be a fundamental skill and is the focus of many school
programs and curricula. Literacy skills have long been used as both predictors and
indicators for school success. Additionally, there is an accepted correlation between the
level of education attainment and earnings potential among individuals (Miller, Sen,
Massey 2007). The NCES has reported that among the G-8 countries (those countries
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with the most wealth), the gap in employment rates among working age adults who had
completed some form of post-secondary education and those who had not was between
seven and 14 percentage points (Miller et. al. 2007). These studies reveal important
information regarding individual literacy rates, education attainment, earning potential
and employability.
There is also strong evidence in the literature that suggests that students who
come from lower socio-economic backgrounds are less likely to attend college (Cabrera
& La Nasa, 2001; Fitzgerald & Delaney, 2002; Perna, 2000; Plank & Jordan, 2001 cited
in Rowan-Kenyon, 2007). The reasons for these phenomena are reported to be directly
linked to both the family’s inability to pay for expenses and to the lack of information
provided regarding financial aid options. Hatt et. al. (2005) report that when students are
informed about “bursary” programs (UK term; equivalent to grants in the US); they are
more likely to pursue higher education opportunities.
While current literature indicates that literacy rates and socio-economic
background are key determinants for individual school life expectancy, it does so at a
micro level. This study seeks to examine these issues at the macro-level by looking at the
correlation between a country’s economic development and literacy rates. In addition it
will determine whether or not correlations can be expanded to include the average school
life expectancy in each country. This study’s goal is to add to the literature on educational
comparisons among countries and expand these comparisons beyond the G-8 countries.
Methods: Data, Variables, Analytic Framework
We have complete data sets for conducting correlations between Gini
coefficient/GDP and literacy rates, though our main focus for this project is the link
between Gini coefficient and literacy rate. We were not able to include the HDI
correlation in our analysis because of the limitations of the data in this study.
This study draws upon publicly accessible data from the United Nations
Development Programme 2006, The Human Development Report and The CIA World
Factbook 2006. The data contained in these reports include but are not restricted to Gini
coefficient, Gross Domestic Product, Human Development Index, Total Percentage of
Literacy, and School Life Expectancy.
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We used the individual country as our unit of analysis. Doing this enabled us to
run several comparative analyses of the data. We included the 110 countries for which
we acquired a full data set. Each data set included Literacy rate, Gini coefficient, and
GDP. School life expectancy data was available for 76 of the 110 countries. Obtained
data was originally formatted for use with the Excel program and had to be reformatted in
order to be run using SPSS tools for analysis. The full sample consisted of 32 Asian
countries (16.4%), 13 Middle Eastern (Asia) countries (6.7%), 45 European countries
(23.1%), 50 African countries (25.6%), three North American countries (1.5%), eight
Middle (Central) American countries (4.1%), 11 South American countries (5.6%), 16
Oceanic countries (8.2%) and 17 Caribbean countries (8.7%).
For getting a significant outcome with the SPSS program, the data was recoded
into a numerical identifier. At first glance, the data collected seems to be unbalanced due
to the high proportion of European and African countries represented. However, closer
examination reveals that Middle Eastern category is a subset of the Asian countries and
the Americas were divided into three regions.
The countries were recoded into four groups for purposes of analyses based on
their GDP level. We used quartile groups initially so that statistical significance could be
more easily identified. Because of the initial findings, the data was recoded into three
groups in order to facilitate comparisons between the top and bottom half. This
categorization provided more accurate results since initially there was no significant
difference between groups three and four. This study primarily employed each of the
country’s GDP, Gini coefficient and school life expectancy as independent variables. The
dependent measure in this study assessed each country’s total literacy rate.
The first set of quantitative analyses compared means for one-way ANOVA,
allowing for the testing of significant within-group and between-group differences on the
outcome measure across the four subgroup-countries of literacy. The descriptive results
helped to inform the next set of analyses that focused on causal relationships through
multivariate regression modeling. In the linear multiple regression model for this study,
independent variables were grouped in three blocks (i.e., GDP, Gini Coefficient and
School life expectancy).
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Results/Conclusions
The following results address research question 1-1. Comparing the continent
difference results using ANOVA, the total sample shows a significant difference in the
mean frequency of Literacy rate (F=20.277, p<.05). Among nine continents, North
America is represented by only three countries. North America shows the highest levels
of literacy rate (µ=97.33, S.D=.577), the next highest level is Europe(µ=95.17,
S.D=11.882). The lowest rated group is Africa (µ=55.57, S.D=18.034). Moreover, after
running post –hoc tests such as Tukey HSD, Scheffe and Dunnett T3, it is found that
Africa differs significantly from all of the other eight continents.
In order to address research question 1-2, another ANOVA was used to show the
significant difference of literacy rate with the GDP level of countries (F=7.516, p<.05).
As mentioned before, all of the countries were recoded by the level of income into four
groups. Among the four country groups, the 1st group, which includes 26 countries,
shows the highest levels of literacy rate (µ=91.08, S.D=18.362). The 3rd /4th group which
include 56 countries, has the lowest (µ=72.45, S.D=22.383). Using post-hoc tests (Tukey
HSD, Scheffe and Dunnett T3), it is determined that the difference in literacy rate
between the 1st group and the 3rd/4th group is significant.
The bivariant results found that there are several variables which are correlated to
literacy rate, the major variable in this study. They include school life expectancy (.770)
surveyed by the UN, GDP (.507), Gini coefficient (-.300) by CIA. All three variables
(school life expectancy, GDP and Gini coefficient) can be correlated to the literacy rate.
School life expectancy has the strongest correlation.
Because there is a significant correlation between variables, a multiple linear
regression could be used to show the strongly significant predictive strength (R2=.582,
p<.05) of the three variables on literacy rate. The important indicator from the UN data
that enters the model as the most significant predictor of literacy rate is school life
expectancy (B=.783). This multiple-regression equation accounts for R2=58.2%, among
these, school life expectancy is the strongest variable to account for literacy rate. These
findings help to affirm the second hypothesis under investigation. All three of the
variables appear to have an effect on the total literacy of the given country, but the most
significant is school life expectancy.
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This study’s findings suggest that countries that offer opportunity for longevity in
schooling have higher literacy rates. In addition, it appears that countries with greater
GDP also have a higher literacy rate. These findings combined suggest that countries that
have the resources to offer schooling to the majority of the population have greater rates
of literacy. While the results of this study show a significant correlation between school
life expectancy and literacy rate, further study would be needed in order to identify other
contributing factors such as industrialized vs. non-industrialized, access to public
schooling, length of availability of schooling, and cost of schooling.
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References
Altbach, P. G. (Editor), Wright, P. (Reviewer) (2006). Decline of the guru: The
academia profession in developing and middle income countries. Higher
Education Quarterly, 60(4), 416-418.
Central Intelligence Agency. (2006). The world factbook 2006. Retrieved July 2006 to
December 2006.
Graveter, F., & Wallnau, L. B. (2007). Statistics for the behavioral sciences. Belmont,
California: Thomson Learning, Inc.
Hatt, S., Hannan, A., Baxter, A. (2005). Bursary and student success: A study of
students from low-income groups at two institutions. South West. Higher
Education Quarterly, 59(2), 111-126.
Lenoir, G. C. (2003). “Income and Culture.” Unpublished paper.
Meha, H.; Villanueva, I.; Hubbard, L.; Linz, A. (1996) Constructing school success: The
consequences of untracking low-achieving students. Books.google.com
(downloaded 12/01/07)
Miller, D. C.; Sen, A.; Malley, L. B. Comparative indicators of education in the United
States and other G-8 Countries: 2006. NCES 2007-2006. National Center for
Education Statistics. 92 pp. (Peer reviewed journal) 2007.
Pennell, H. & West, A. (2005). The impact of increased fees on participation in higher
education in England. Higher Education Quarterly, 59(2), 127-137.
Roman-Kenyon, H. (March/April 2007). Predictors of delayed college enrollment and the
impact of socioeconomic status. The Journal of Higher Education. (Peer reviewed
journal). 78 (2): 188-214.
United Nations. (2006). Human Development Reports. United Nations Statistic
Division. Retrieved July 2006 to August 2006.
The Statistical Package for the Social Sciences (SPSS) computer program for the
statistical calculations.
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Appendix 1: Data Methods
School Life
Expectancy
Major
variable:
Literacy rate
G.D.P
Gini
Coefficient
• Data
•
•
United Nations Human Development Reports
CIA World Factbook 2006
• 110 countries
• Countries classified into 4
subgroups
• Continents classified into 9
subgroups
• One-way ANOVA
• Linear Multiple Regression
• Post-hoc tests (Tukey HSD,
Scheffe and Dunnett T3)
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Appendix 2: Recoding “Continent” Variable into Number
•
•
•
•
•
•
•
•
•
1: Asia
2: Middle East
3: Europe
4: Africa
5: North America
6: Central America
7: South America
8: Oceania
9: Caribbean
Factors that inform international literacy rates
Group of
Continent
(Indepen.Var.)
Literacy rate
(Depen. Var.)
Country group
by Income
(Indepen.Var.)
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Appendix 3: Findings & Results (ANOVA I)
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Appendix 4: Post-hoc Test I (Tukey-HSD, Dunnett T3)
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Appendix 5: Findings & Results: ANOVA II
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Appendix 6: Post-hoc Test II (Tukey-HSD, Dunnett T3)
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Appendix 7: Correlation Between Literacy and GDP, Gini Coefficient, and Scholastic
Life
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Appendix 8: Regression
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