economic development and gender equality a positive or negative relationship
TRANSCRIPT
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Economic Development and Gender Equality:
A Positive or Negative Relationship?
You can tell the condition of the nation by looking at the
status of its women. Jawaharlal Nehru
Introduction
It is commonly observed that the status of women in society
is related to socio-economic development. Many measures of
gender inequality support this. Looking at secondary school
education, one can see that for the poorest 25% of countries, only
5% of women has any secondary school education, whilst the
figures are 51% for the richest 25%1. Some have been more
explicit, arguing that economic development can lead to
improvement in gender equality2. Amongst many others, it has
been argued that an increase in per capita income will lead to
greater gender equality3.
However, many others have argued that economic
development has not increased gender equality. Of note is the
feminist school, which argues that economic growth increases the
vulnerability of women to gender inequality4. Which leads to the
question: which of these approaches are correct?
This question is of significant importance. Many studies have
shown that there are many positive effects on improving the
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increasing gender equality, affecting the family, society, the
economy and politics. Higher gender equality can lead to economic
growth through increased output per capita5, increase their
daughters survivals6, and reduces infant and maternal mortality7,
just to name a few.
This paper found that the relationship between economic
development and gender equality is not so clear. In some instances,
it has been found that one approach is supported, while in other
instances another approach is supported . This papers posits that a
generalizable theory between economic development and gender
equality is insufficient, and more research needs to be done to find
out what are the conditions that causes one approach to work in
one instance while causing another approach to work in another
instance.
Literature Review
Contemporary work on the impact development and growth
have on gender equality tends to fall under three schools of
thought. In the first school, called the modernisation-neoclassical
approach,believes that gender equality would improve as a country
develops8. It has been argued that gender equality tends to
improve with economic development, as gender equality in basic
rights decreases as regions of the world get poorer. Others have
suggested that increases in per capita income improve gender
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equality9. Chen postulates that economic development may affect
gender equality through wages. Wages increase with economic
development, which increases the opportunity cost of not working.
Others have stated that economic growth creates an increase in
employment opportunities and competition that would eventually
improve gender equality in education, finance and training10. As
opportunity cost increases, the formerly unemployed would be
compelled to work. In countries where gender equality is low, the
majority of the unemployed are women, thus women in this case
would be compelled to work11. Others have agreed that economic
growth together with increased access to education, health, credit,
legal rights and employment opportunities will increase womens
socio-economic role in developing countries.
A second school of thought is what is called the Boserup
approach, which believes that that economic growth and gender
equality have a relationship in the form of a U shape. Where a
country has no market economy, gender relations are more or less
equal. As growth and development takes place, equality falls initially
due to the specialisation of roles, with womens primary role as that
of childcare and mens as that of a breadwinner. As the economy
develops further, there would be an overall transformation in
society and roles, and the above can be reversed due to increased
economic opportunities and demand for female labour12.
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There is a third school, which raises objections to the above
contentions. This is know as the feminist approach, which believes
that institutions like patriarchal family structures play a major role
in reducing gender equality, and where economic growth is seen as
a factor that increases the vulnerability of women to gender
inequality. It has been argued in Morrison & Jutting that social
institutions constitute the most important single factor that
determines womens economic choices, both directly and indirectly
through their access to education and health care. They cited
evidence that in the Sub-Saharan Africa, South Asia, the Middle
East and North African regions, even as a country might experience
high per capita incomes, it might have low gender equality. In
further support, it shows that even in a same country, disparities in
gender equality exist, supporting that argument that a country can
develop even though gender inequalities persist13.
Contextualised Briefing
As this paper aims to do an Asia-wide study, and because
comparing every single Asian country is beyond the scope of this
paper, the main criteria is to select countries with the widest spread
of politics, geographical area and religious influence.
For political diversity, there are a mix of communist / socialist
and ex-communist / socialist states (China, Vietnam and Laos),
authoritarian and ex-authoritarian states (South Korea, Indonesia
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and Pakistan14), and democracies or quasi-democracies (India,
Japan and Malaysia).
For geographical diversity, there is a mix of East Asia (China,
South Korea and Japan), Southeast Asia (Vietnam, Indonesia, Laos
and Malaysia), and South Asia (India and Pakistan).
For religious diversity, there is a mix of Buddhist / Taoist /
Confucian majorities (Japan and Laos), Muslim (Indonesia, Malaysia
and Pakistan), Hindu (India) and no religion (China, Vietnam and
South Korea15).
Analytical Framework
This paper chooses an empirical approach. Using this, the
paper is able to evaluate whether the different levels of economic
development corresponds with the varying levels of gender
inequality. Moreover, using this approach, this paper is able to
determine whether the strength of economic development
corresponds with an equally strong gender equality increase. An
example would be that 13% economic growth should see a higher
increase of gender equality scores, and 2% economic growth should
see a small increase in gender equality scores.
Next, the nine countries selected will be grouped together
based on the various measure of economic development to
determine which of these measures of economic development
matches corresponds closer with gender equality scores.
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Methodology
One can measure economic development in many ways. This
study would use both the static figures of per capita GDP, per capita
GNI, and proportion of population living below US$1 a day, and the
rates of change of per capita GDP, per capita GNI, and poverty
levels measured as proportion of population living under US$1 a
day, and GDP growth rates. These figures are selected because of
the ease of which they can be obtained.
Measuring gender equality is trickier. The quantitative
approach is to use indices, of which the UNDPs Gender-related
Development Index (GDI) is the most common. However, GDI alone
does not measure inequality; it is merely the Human Development
Index (HDI) adjusted for gender disparities. Gender equality is the
difference between the ratios of the two indicators16. Other ways to
measure gender equality quantitatively are by using education and
employment levels alone as proxies, as it was found that education
and earning independent income from paid employment reduces
anti-female bias and increase female voice and agency in the
home17. Education can be measured by looking at gender
differentials in enrolment rates18 or the access and achievement of
women in education19, amongst others. Employment is usually
measured by finding the female share of the labour force in a
country20. Other quantitative indicators include improvements in
health21.
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There are several criticisms for using just quantitative
measures. First, it has been shown that generally, gender equality
is improving in the world22. This creates problems as any form of
economic development will be correlated with an increase with GDI
scores. Second, the education and employment variables used in
the index are not contextualized and therefore not useful for
meaningful comparison across countries23. It has been argued that
the content of schooling matters24, and that it might require much
higher levels of education before a woman is exposed to ideas and
strategies necessary for questioning gender inequality25. Similarly,
whether gender equality increases after earning independent
income depends on whether they have control over it, and the pre-
existing power balance in the family26.
There are also qualitative measures, but its use is less
universal and limited due to difficulties in getting information and
disagreements on a standard set of measures. This is because
gender equality is a multi-faceted concept that measures many
things. These includes measuring the legal and economic parity of
women in society and marriage27, and measuring constraints like
social institutions through laws, social norms, codes of conduct and
traditions28.
For the purposes of this paper, the difference between HDI
and GDI scores (HDI-GDI) would be used as a proxy score for
gender equality. The main reason is that a comparative analysis
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requires the use of a measure that is easily obtainable. Though the
criticisms of using quantitative measures are strong, comparing
qualitative indicators is unrealistic due to the lack of data and
commonly accepted qualitative measure. Moreover, HDI-GDI scores
are truly global, and other ways of measuring gender equality have
not met universal subscription and have very specific and limited
usage, making it difficult to have any form of meaningful
comparison across countries.
In addition, due to the limitations of this paper, an analysis of
all the quantitative measure universally available is impossible.
Using HDI-GDI helps as it includes the three indicators of income,
education and life expectancy, hence encompassing a more holistic
approach to measuring gender equality.
This paper will circumvent the problem of generally increasing
GDI scores by testing if the intensity of economic development
(higher GDP growth rates, higher per capita increases in GDP and
GNI, higher poverty reduction rates) corresponds with higher
increases in HDI-GDI scores. If economic development does indeed
increase gender equality, then higher rates of economic
development should show a higher increase in gender equality.
For a static comparison, this paper would use per capita GDP,
per capita GNI and poverty levels against HDI-GDI, and grouping
countries according to their economic development indicators
against HDI-GDI. For a comparison based on the rate of change of
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economic development against the rate of change of gender
equality, this paper would use the rate of change of per capita GDP,
rate of change of per capita GNI, and rate of change of poverty
levels against the rate of change of HDI-GDI, and grouping
countries according to their economic development indicators
against the rate of change of HDI-GDI. Lastly, countries would be
grouped according to its political history, religious influence and
geographical areas to see if these factors are related to HDI-GDI.
Data
Historical data for economic development indicators are found
on the United Nations Statistics Division database online. Historical
data for HDI and GDI can be found on the United Nations Human
Development Reports online from 1996 onwards.
The independent variables used are GDP growth rates, per
capita GDP, rate of change of per capita GDP, per capita GNI, rate
of change of per Capita GNI, poverty levels, and rate of change of
poverty levels.
Table 1 shows the GDP growth estimates. As there are no
gender equality data for 1996, the GDP growth estimate for 1996
will be dropped. Hence, the GDP growth estimates for 1997 will be
revised by the formula below.
1996 GDP growth + 1997 GDP growth2
GDP growth estimate for 1997 =
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Table 2 shows the revised GDP growth estimates applying the
formula above.
Table 3 shows the per capita GDP and the rate of change of
per capita GDP in US$. The rate of change of per capita GDP in US$
is calculated as follows:
Rate of change of per capita GDP =
currentyears per capita GDP previous years per capita GDPprevious years per capita GDP
Table 4 shows the per capita GNI and the rate of change of
per capita GNI in US$. The rate of change of per capita GNI in US$
is calculated as follows:
Rate of change of per capita GNI =currentyears per capita GNI previous years per capita GNI
previous years per capita GNI
Table 5 shows the available figures of poverty levels and the
rate of change of poverty levels. The rate of change of poverty is
calculated as follows:
Rate of change of poverty levels =
previous available years poverty levels subsequent available years povertylevels
The dependent variable used is Gender Equality. Table 6
shows the gender equality score and the rate of change of gender
equality for each of the countries chosen. The gender equality
scores would get smaller as gender equality increases in the
country. If the rate of change is positive, it means gender equality
is increasing, and vice-versa. The formulas for calculations are as
follows:
Gender Equality = HDI scores GDI scores
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Rate of change of Gender Equality =
previous years Gender Equality current years Gender Equality
For comparisons of poverty reduction rates and the rate of
change of gender equality, due to the lack of poverty reduction
rates for every year, the rate of change of gender equality is
calculated as follows:
Rate of change of Gender Equality =previous corresponding years Gender Equality subsequent corresponding years
Gender Equality
Analysis of Data
GDP Growth Rates and Gender Equality Scores
Table 7 shows GDP growth rates and the corresponding
gender equality scores for each country each year. Figure 1 shows
the relationship on a scatter plot. The wide dispersal of plots
suggests that there seems to be no relationship between GDP
growth rates and gender equality scores.
Table 8 shows the countries grouped together according to
their GDP growth rates and the corresponding averaged gender
equality scores, and Figure 2 shows the relationship on a line chart.
The results show that for GDP growth rates below 0% and more
than 10%, the averaged gender equality scores are 0.0076 and
0.0060 respectively, whereas for GDP growth rates between 0%
and 10%, the averaged gender equality scores range from 0.0130
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to 0.0194, supporting the Boserup approach that economic
development and gender equality has a U relationship. The U is
inverted in this case as gender equality is measured using zero as
the perfect gender equality
GDP Growth Rates and Rates of Change of Gender Equality
Scores
Table 9 shows the GDP growth rates and the corresponding
rate of change of gender equality scores for each country each year.
Figure 3 shows the relationship on a scatter plot. The tendency of
the plots to remain near the x-axis, and the high instances of
negative rates of change of gender equality scores despite an
increase of GDP growth rates, suggests that there seems to be no
relationship between GDP growth rates and the rates of change of
gender equality scores.
Table 10 shows the countries grouped together according to
their GDP growth rates and the corresponding averaged rates of
change of gender equality scores, and Figure 4 shows the
relationship on a line chart. Likewise, results suggest that there
seems to be no relationship between GDP growth groups and the
averaged rates of change of gender equality scores.
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Per capita GDP and Gender Equality Scores
Table 11 shows the per capita GDP and the corresponding
gender equality scores for each country each year. Figure 5 shows
the relationship on a scatter plot. The results show that at low
levels of per capita GDP, there is a wide and uniform dispersal of
plots, suggesting that per capita GDP at the low levels does not
affect gender equality. However, at the per capita GDP increases,
the plots are uniformly near the X-axis, except for some outlying
cases, suggesting that generally, as per capita GDP increases,
gender equality would remain low, supporting the modernisation-
neoclassical approach
Table 12 shows the countries grouped together according to
their per capita GDP performance and the corresponding averaged
gender equality scores, and Figure 6 shows the relationship on a
line chart. The results show that gender equality scores are lowest
when the country is in the upper middle-income bracket, supporting
the Boserup approach that economic development and gender
equality have a U shaped relationship.
Rates of Change of per Capita GDP and Rates of Change of
Gender Equality Scores
Table 13 shows the rates of change of per capita GDP and the
corresponding rates of change of gender equality scores for each
country each year. Figure 7 shows the relationship on a scatter plot.
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The results show that the plots congregate near the X-axis, except
for a few outliers, suggesting that the rates of change of per capita
GDP do not affect the rates of change of gender equality scores.
Table 14 shows the countries grouped together according to
their rates of change of per capita GDP and the corresponding
averaged rates of change of gender equality scores, and Figure 8
shows the relationship on a line chart. The results show that in
times of negative rates of change of per capita GDP, the rates of
change of gender equality scores is increasing, moving towards 0%.
As the rates of change of per capita GDP increases from 0%, the
rates of change of gender equality scores decreases, to a point
where the rates of change of gender equality scores is negative at
high rates of change of per capita GDP
This suggests that economic development increases gender
equality at a faster rate only at a point when the rates of change of
per capita GDP is negative and rising towards 0%, supporting the
modernisation-neoclassical approach. As economic development
continues from where the rates of per capita GDP is positive and
increasing, gender equality scores increases at a slower rate.
Finally, the rates of change of gender equality scores turns negative
as economic development is at the high stages; when the rates of
change of per capita GDP is more than 20%. This supports the
feminist approach that economic development decreases gender
equality.
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Per capita GNI and Gender Equality Scores
Table 15 shows the per capita GNI and the corresponding
gender equality scores for each country each year. Figure 9 shows
the relationship on a scatter plot. The results show that at low
levels of per capita GNI, there is a wide and uniform dispersal of
plots, suggesting that per capita GNI at the low levels does not
affect gender equality scores. However, at per capita GNI increases,
the plots are uniformly near to the X-axis, except for some outlying
cases, suggesting that generally, as economic development
increases, gender equality remains high, supporting the
modernisation-neoclassical approach.
Table 16 shows the countries grouped together according to
per capita GNI and the corresponding averaged gender equality
scores, and Figure 10 shows the relationship on a line chart. The
results shows that there is no clear relationship between per capita
GNI and gender equality scores, only that gender equality scores is
highest when the country is in the upper middle-income bracket.
This supports the Boserup approach that economic development
and gender equality have a U shaped relationship.
Rates of Change of per capita GNI and Rates of Change of
Gender Equality Scores
Table 17 shows the rates of change of per capita GNI and the
corresponding rates of change of gender equality scores for each
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country each year. Figure 11 shows the relationship on a scatter
plot. The results show that the plots congregate near the X-axis,
except for a few outliers, suggesting that the rates of change of per
capita GNI do not affect the rate of change of gender equality.
Table 18 shows the countries grouped together according to
their rates of change of per capita GNI and the corresponding
averaged rates of change of gender equality scores, and Figure 12
shows the relationship on a line chart. The results show that in
times of negative rates of change of per capita GNI, the rates of
change of gender equality is increasing, moving towards 0%. As the
rates of change of per capita GNI increases from 0%, the rates of
change of equality decreases, to a point where the rates of change
of gender equality is negative at high rates of change of per capita
GNI
This suggests that economic development increases gender
equality at a faster rate only at a point when rates of change of per
capita GNI is negative and rising towards 0%, supporting the
modernisation-neoclassical approach. As economic development
continues from where the rates of per capita GNI is positive and
increasing, gender equality scores increases at a slower rate.
Finally, the rates of change of gender equality scores seem to
decrease as economic development is at the high stages; when the
rates of change of per capita GNI is more than 20%. This supports
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the feminist approach that economic development decreases gender
equality.
Poverty Levels and Gender Equality
Table 19 shows the available data for poverty levels for each
country with the corresponding gender equality scores for the year.
Figure 13 shows the relationship on a scatter plot. Results show a
wide dispersal of plots regardless of instances of poverty,
suggesting that poverty levels do not affect gender equality.
Table 20 shows the countries grouped together according to
their poverty levels and the corresponding averaged gender equality
scores. Figure 14 shows the relationship on a line chart. The group
for poverty levels between 10% to 20% is discarded as there is only
one data point. The results show that as poverty rates decrease,
gender equality scores increases. This supports the feminist
contention that as economic development increases, gender
equality decreases.
Poverty Reduction Rates and Rates of Change of Gender
Equality Scores
Table 19 shows the available data for the poverty reduction
rates each country with the corresponding rates of change of
gender equality scores for each available year. Figure 15 shows the
relationship on a scatter plot. Results show that the plots hover
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mostly above X-axis regardless of the poverty reduction rate,
suggesting that poverty reduction rates do not affect gender
equality.
Table 21 shows the countries grouped together based on
poverty reduction rates and the corresponding averaged rates of
change of gender equality scores. Figure 16 shows the relationship
on a line chart. The results show that the averaged rates of change
for gender equality are negative for both periods of increasing
poverty rates and poverty reduction rates above 20%. This
suggests that economic development increases gender equality
when the rate of change of poverty is moving from negative to a
positive 10% rate, supporting the modernisation-neoclassical
approach. However, when the rate of change of poverty more than
positive 10%, the rates of change of averaged gender equality
scores is negative, supporting the feminist approach.
Summary of Key Results & Conclusion
Table 22 shows the summary of the findings. This paper
shows that all the approaches are supported at one point or
another, and therefore, the verdict of the relationship between
economic development and gender equality is still out. In addition,
whether the relationship supports one of the approaches or none at
all depends on the economic indicators one uses, and on which
stage of economic development one looks at.
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This paper suggests that a generalizable economic theory
explaining the relationship between economic development and
gender equality should not be the end and focus of research in this
area. More research needs to be done to discover why one approach
is supported in one instance but another approach supported in
another.
This paper readily agrees that there are limitations inherent in
this paper. Amongst some of this is the small sample size of nine
countries spread over the breadth of Asia. Future research can
embark on, if the information is available, a sampling all the Asian
countries, or a sampling of Asian countries based on regions,
religions, type of government, or other types of classifications, to
come up with a more generalizable and better supported validation
of the approaches mentioned.
Another possible future research strategy would be to look at
a few individual countries to see why one approach works in one
country but another approach works in another. These strategies
can focus on the institutions of the individual countries and the
effect on gender equality, grouped according to similar experiences
of economic conditions. Examples of research could include legal
institutions and the instances of discriminatory laws against women,
and social institutions affecting economic activities favouring men
over women.
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Notes
1 Dollar, D. & Gatti, R. (1999). Gender Inequality, Income and Growth: Are GoodTimes Good for Women?2 Chen, D. H.C. (2004). Gender Equality and Economic Development: The Role forInformation and Communication Technologies.3 Supra, note (1), at pp. 17.4 Morrison, C. & Jutting, J. (2004). The Impact of Social Institutions on theEconomic role of Women in Developing Countries, at pp. 6 & 8.5 Esteve-Volart, B. (2004), Gender Discrimination and Growth: Theory andEvidence from India.6 Bourne, K. L. & Walker, G. M. Jr (1991), The Differential Effect of MothersEducation on Mortality of Boys and Girls in India.7 UNICEF (undated), Factsheet: Early Marriage, at pp. 1.8 Supra, note (4), at pp. 8. Retrieved on 1st February 2009 from
http://www.oecd.org/dataoecd/23/43/31652310.pdf.9
Supra, note (2), at pp. 11.10 Supra, note (4), at pp. 811 Supra, note (2), at pp. 13.12 Supra, note (4), at pp. 8-9.13 Ibid, at pp. 6 & 8.14 Even though Pakistan is formally a federal republic since its independence in
1947, it has seen military rule three times lasting a total of thirty-three years. Formore information, see Story of Pakistan found onhttp://www.storyofpakistan.com.15 China and Vietnam are officially communist, and hence there is no statereligion. South Koreas official census in 1995 shows 49% of the population
choosing no religion as the answer. However, it is arguable that these countries
experience a strong influence from Buddhism, Taoism and Confucianism throughhistory.16 UNDP. (undated). Measuring Inequality: Gender-related Development Index(GDI) and Gender Empowerment Measure (GEM).17 Mukhopadhyay, S. & Sudarshan, R. M. (1997).Gender Discrimination and its
Indicators: A Research Agenda at pp. 3.18 Supra, note (2), at pp. 5.19 Ibid, at pp. 11.20 Ibid, at pp. 5.21 Ibid, at pp. 11.22 Ibid, at pp. 4.23 Supra, note (17), at pp. 13.24 Ibid, at pp. 3.25 Ibid, at pp. 4.26 Ibid.27 Supra, note (2), at pp. 11.28 Supra, note (4), at pp. 8.
http://www.oecd.org/dataoecd/23/43/31652310.pdfhttp://www.oecd.org/dataoecd/23/43/31652310.pdfhttp://www.storyofpakistan.com/http://www.storyofpakistan.com/http://www.storyofpakistan.com/http://www.oecd.org/dataoecd/23/43/31652310.pdf