Download In this paper, we will be applying research methods and statistical

In this paper, we will be applying research methods and statistical skills to explain
our dependent variable, which is economic development. We will be examining why
some countries are more economically developed than others and the relationship
between economic development and female education.
Current statistics show that more than 800 million people are illiterate and eightyfive percent of them live in 34 countries, concentrated in regions affected by
poverty. Additionally, more than two-thirds of the illiterate population is female1.
The primary explanatory independent variable we will be using is female
education. Women’s education plays an important role in the overall economic and
political development of a country, yet throughout many parts of the world there is an
education gap because males receive more schooling than females. This education gap
likely has a strong causal relationship to GDP per capita.
There are various other factors, which also may causally affect economic
development. Political freedom and civil liberties in a country may affect a country’s
level of economic advancement, as greater political freedom is likely correlated to greater
economic freedom, which according to liberal theory translates into greater wealth. A
country’s respect for individuals’ freedoms is thus likely causally related to economic
development.
A second possible rival cause may be historical precedent of gender equality.
Countries with higher levels of gender equality may have higher levels of economic
development, as women are more likely to play an important role in the work force.
US Department of State. “Laura Bush Announces Global Literacy Conference”. April 2006.
http://usinfo.state.gov/scv/Archive/2006/Apr/25-37813.html
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The main hypothesis proposed in this paper is: in comparing countries, those with
a high ratio of female to male adult literacy rates will have a higher GDP per capita than
those countries with a low ratio of female to male literacy rates. In making this assertion,
it is important to define certain terms. First, we conceptualize “adult” as any person over
the age of 15. Next, literacy is defined by UNESCO as “the ability to identify,
understand, interpret, create, communicate and compute, using printed and written
materials associated with varying contexts."2 In order to measure the ratio of female to
male literacy, a percentage is taken of female literacy rates compared to male literacy
rates. These variables would effectively relate gender equality and economic success. By
understanding exactly what the hypothesis is referring to, the correlation between adult
female literacy rates as a percent of adult male literacy rates and GDP per capita becomes
easier to explain.
The hypotheses presented in this paper are in line with modernization theory, a
theory that attempts to identify particular social variables that will in turn explain social
progress and development. In the face of changing technology and employment, societies
must adapt in order to keep up. The increasingly interdependent world demands a certain
amount of communication skills; without them, a nation’s economy could be significantly
affected. Literacy rates in a country are thus a very relevant variable in trying to
understand education’s role on GDP; a report by The National Center for Education
Statistics shows that in the U.S., individuals with lower literacy rates earn less3.
2
UNESCO, <http://www.unesco.org>
“Adult Literacy in America.” The National Center for Educational Statistics, 22 Feb
2008. U.S. Department of Education<http://nces.ed.gov/pubs93/93275.pdf>
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Especially interesting are women’s roles in fiscal development. Their increasing
inclusion in both the classroom and the workforce are important additions to human
capital, and a good indicator of the relative success of the nation’s economy. Conversely,
partial or total exclusion of women could not only foster and perpetuate negative gender
stereotypes, but also severely impair the nation’s ability to compete in international
markets. Undervaluing half the potential labor force due to more male-favorable social
attitudes is a danger many modern nations face and in fact succumb to; this invariably has
negative economic consequences. Indeed, research points to a link between gender
equality and long term economic development by comparing gender attitudes with birth
rate in a nation4.
Examining the impact of female adult literacy (as a percentage of male literacy
rates) on GDP per capita would be an effective way of measuring both the reliability of
modernization theory and relative importance of female education in today’s economy.
There are several variables that should be controlled for when analyzing the
relationship between GDP per capita and female adult literacy as a percentage of male
literacy rates. Civil liberties are defined as freedoms that protect the individual from the
government to a certain extent5. It could be hypothesized that in countries with high civil
liberties, GDP would increase more when literacy also increased because of this
emphasis on protecting the individual’s rights. The strong importance of civil liberties in
4
Roger Mörtvik, Does Gender Equality Spur Growth?, OECD Observer, October 2005.
<http://www.oecdobserver.org/news/fullstory.php/aid/1664/Does_gender_equality_spur_
growth_.html>
5
"Civil liberties." Wikipedia, The Free Encyclopedia. 24 Feb 2008. Wikimedia
Foundation, Inc. 27 Feb 2008
<http://en.wikipedia.org/w/index.php?title=Civil_liberties&oldid=193798308>.
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GDP determinant would lead one to expect to see an additive and slightly interactive
relationship in this situation, taking into account all our variables.
The second variable that might need to be controlled for is year of female
enfranchisement. Countries that enfranchised women relatively recently are
correspondingly less likely to afford them equal rights in other aspects of daily life. These
aspects might be anything from education (thereby leading to higher female literacy
rates) or equal opportunity in the employment sector. With less considerate gender
attitudes, countries might consequently have a lower GDP per capita than a country that
enfranchised its women earlier and benefit from a longer tradition of inclusiveness.
Again, an interactive relationship would be likely in this case because it is reasonable to
expect that at high levels of female literacy compared to male literacy, year of female
enfranchisement would have a greater effect on GDP than at low levels of female literacy
compared to male literacy.
The dependent variable used to measure level of economic development is GDP
per capita. The data is from the year 2002 and was released in a 2004 report by the
United Nations Development Program. It includes measures of GDP per capita expressed
in U.S. dollars of 177 countries from all
regions of the world. The variable is interval
Table 1 – GDP per capita (US$) 2002
N
Missing
level data and the unit of analysis is countries.
As shown in Table 1, the mean GDP per capita
is $6018.19, however this is not the best
Valid
177
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Mean
6,018.19
Median
1,897.00
Skewness
2.149
Std. Error of Skewness
measure of central tendency as the data is
.183
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positively skewed.6 A more accurate measure of central tendency is the median of
$1897.00. The largest concern regarding this data is the possibility of systematic
measurement error. As measurements of GDP fail to include black market transactions
and do-it-yourself work, the value presented in this data will consistently understate the
true value of goods and services produced in a given country. This will be especially true
of less developed countries where the black market and do-it-yourself work is more
common. This is a serious validity concern.
The primary independent variable that will be used in this study is female literacy
rate as percent of male literacy rate in a country. This variable will operationalize the
concept of female education and gender equality. This data is also from the year 2002 and
was released in a 2004 United Nations Development Program report. The variable
contains ordinal level data divided into three categories. Low female literacy is defined as
literacy rates between zero and seventy percent of male rates, medium female literacy is
between seventy and ninety percent of male rates and high female literacy is between
ninety and a hundred percent of male rates. These bounds were designed so that a
sufficient portion of the population is included in each category, with attention to where
naturally significant lines of differentiation can be drawn between countries. A country
such as Bangladesh, where female literacy is 50.3% of male literacy falls into the low
female literacy category, while Turkey, where female literacy is near even to male at
99%, falls in the highest category.
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Skewness / Std. error of skewness = 11.7. 11.7 > 2, therefore data is skewed.
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Table 2 - Female Literacy as % of Males
Cumulative
Frequency
Valid
Valid Percent
Percent
low female literacy
20
10.5
17.2
17.2
medium female literacy
30
15.7
25.9
43.1
high female literacy
66
34.6
56.9
100.0
116
60.7
100.0
75
39.3
191
100.0
Total
Missing
Percent
System
Total
The variable includes data from 116 countries, considerably less than the
GDP/capita data. This presents a validity concern, as it may be inferred that countries for
which data is missing do not represent a random sampling and would be more likely to
fall into the lower two categories. This is because literacy data is most difficult to collect
in less developed countries, where fewer females receive education. There may also be
reliability problems with the data, as literacy measurements are not conducted in a
universal way. Different measurement techniques among countries could lead to random
measurement error in the data.
A second independent variable, which is used as a control variable in this study, is
civil liberties. This variable is used to operationalize the broad concept of the level of
citizens’ freedom in a country. The data is from Freedom House’s 2002 report and
includes 191 countries. According to Freedom House, the specific freedoms measured are
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those of association, assembly, education and religion. The degree of protection these
freedoms receive under the law is also accounted for.7
Table 3 - Freedom House Civil Liberties
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
high civil liberties
75
39.3
39.3
39.3
low civil liberties
116
60.7
60.7
100.0
Total
191
100.0
100.0
The variable is ordinal and divided into two categories. The first category
includes all countries that score either a one or two in civil liberties. These countries are
embedded democracies with only minor deficiencies in their protection of civil rights;
among them are Canada and Israel. The second category includes all countries with
scores of three or less. These countries are fundamentally different from those in the first
category, though the degree of failure to protect freedom varies among them; all of these
countries suffer from a degree of civil repression, which debilitates society in someway.
Among this group is Haiti and Jordan. Because Freedom House is reliant on outside
sources of information in calculating these ratings, the data is susceptible to random
measurement error and imprecision.
The second control variable used is
Table 4 - Year women were first enfranchised
N
Valid
Missing
175
16
Mean
1946.84
Median
1949.00
Mode
7
1918
enfranchisement of women. This variable
operationalizes the concept of historical
acceptance of women’s equality in
society. The variable is ordinal and
Freedom House Methodology, 28 February 2008
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divides 175 countries into two equal categories. The first category is those countries
where women’s suffrage was achieved before 1950 and the second category is countries
where women gained the vote after 1950. The mean and median are both just before
1950. France is included in the first group, as French women received voting rights in
1919, while Paraguay is in the second group because women became able to vote in
Paraguay in 1961. This data is both precise and valid.
Table 5 shows the relationship between female literacy levels and GDP per capita,
controlling for civil rights. Both the mean comparison and the line graph show a clear
positive relationship between female literacy and GDP/capita.
Table 5 - GDP per capita (US$) 2002
Female literacy compared
Civil Liberties
to Male literacy
high civil liberties
low female literacy
low civil liberties
Total
Mean
N
411.00
1
medium female literacy
2,542.50
2
high female literacy
4,773.32
22
Total
4,420.36
25
397.32
19
medium female literacy
1,584.59
27
high female literacy
4,302.21
42
Total
2,625.30
88
398.00
20
medium female literacy
1,650.66
29
high female literacy
4,464.16
64
Total
3,022.43
113
low female literacy
low female literacy
Among countries with high civil liberties, mean GDP/capita rises from $411 to $4773 in
going from low female literacy to high female literacy, similarly, mean GDP/capita rises
from $398 to $4302 among low civil liberty countries. It is also important to note that
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there is only one country that has both high civil liberties and low comparative female
literacy.
The linear interactive relationship between these three variables is most evident in
the graph above. It shows that among high civil liberty countries, GDP per capita is more
positively related to female literacy than among low civil liberty countries. While both
lines begin at roughly the same point, GDP/capita rises to a higher level among high civil
liberty countries. This shows that at higher literacy rates, the relationship between civil
liberties and GDP/capita becomes positive, whereas it is not at the lowest literacy level.
Enfranchisement of women has a clear effect on GDP/capita. As shown in the
line graph, there is an interactive, curvilinear relationship between these three variables.
Among countries enfranchised before 1950, GDP/capita is more positively related to
female literacy than it is among those countries that enfranchised women after 1950, with
the most significant shift being the change from medium to high literacy.
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