Gender Inequality Around the World

For centuries, people have attempted to reach a social equilibrium throughout the world. In the past, the most dire problems were those of race and class. These problems have yet to be eradicated, and, subsequently, an existing but ignored inequality has surfaced as a significant global issue. This inequality is called gender or sexual inequality. According to Princeton University's WordNet 1.6, equality is "the quality or state of being the same in quantity or measure or value or status." In context of this research paper, the equality, or lack thereof, referred to here deals with that between male and females. Gender equality is an important concern for the international community in that its institutionalization is essential to the basic human rights guaranteed or proclaimed to be guaranteed to every individual. There is no reason why one certain group of individuals should be persecuted just for living. Inequality between men and women can arise in many aspects of political, social, and economic life. Politically, a woman is often denied the right to represent the citizens of her nation in a political position of power. While the restriction may not be in the law, it can be enforced by social practices. Additionally, one of the most fundamental rights that should be given to every individual of a nation is the right to vote. This basic right is not entirely universal for everyone. Women are either prohibited by law to partake in the practice or they are discouraged from doing so. Socially, we have seen women being pushed down to the level of mere animals. The former Taliban regime in Afghanistan was one of the greatest violators of gender equality in recent years. Its atrocious prohibitions on women were unthinkable and varied from requiring women to dress from head to toe in a burqa, denying the right of employment, education, and health care to women, and forbidding women to go outside their house without a close male relative accompanying them, among countless others. Similar violations of gender equality are present in many areas of the world. In the economic sector, education and employment statistics are probably the most central. The literacy rate, the rate of employment, and the number of women in political positions are all parts of the economy which must be looked at. What will be determined is what kinds of nations provide a high level of gender equality to their citizens, and what the factors that allow for this gender equality are. Additionally, I will look at the countries with a high level of inequality and determine what can be done to alleviate the problem.

Literature Review

The striving towards gender equality can be the outcome of problems apparent in the international community today; however, it can also be the root of various other problems within a cultural spectrum. Avigail Eisenberg, in her article entitled "Diversity and Equality: Three Approaches to Cultural and Sexual Difference," outlines the primary reasons for the new-found focus on women's rights around the world. She mentions that, regardless of the country, women, overwhelmingly, find themselves in a lower social, political, and economic status than men, and women can find themselves unequal in specific groups within a country as well. This can create a struggle between, as she calls it, "sexual equality" and "cultural autonomy." Which of the two has priority over the other is difficult to determine. Should cultural and religious groups have the ability to undermine individual human rights for the sake of advancement of their own group? Should the people within this group allow for the violation of their rights as humans in order to aid the cultural or religious group in which they were raised? The article cites several authors and feminists who attempt to answer these two questions. One of the biggest problems with this situation is whether or not there are universal, fundamental rights of humans. The article argues, generally, in favor of the notion of

fundamental rights; however, Eisenberg also claims that sometimes it is necessary to limit individual rights for the better of a group, although this does not mean that individual rights should be endlessly flexible. Two ways to alleviate the problem of gender inequality would be to allow the culture to become extinct or to require the culture to change its customs in order to accommodate the needs and fundamental rights of both genders. The change would require the equal representation of both men and women in the process to ensure equality in the result. Eisenberg cites court cases in which the women involved were a part of an Aboriginal group in Canada. The court ruled against the women, despite the non-discriminatory clause in the Canadian Bill of Rights, as there was a dilemma between cultural rights, first, and then women's rights, second.

Education, employment, and political participation are all important factors of gender equality that are researched in "'What Happened at Work Today?': A Multistage Model of Gender, Employment, and Political Participation" by three authors. They argue that there is a general gap within the sectors mentioned above. Employment and political participation are dependent upon education to determine whether an individual will be partaking in those activities. On the most basic level, those who are more educated are more likely to be in the workforce. In the United States, which is where the data was compiled for this research, there is little disparity between women and men in education. Where the gap commonly occurs is in the workforce. Men, generally, are the breadwinners in the family. There is a larger percentage of families with men working and women staying at home as opposed to women working and men staying at home, although this does happen. Assuming both the man and woman of the household work, men are more likely to be full-time employees as opposed to women as part-time employees. There are far fewer positions at the professional level for a part-time employee; thus, women who are part-time employees are more likely to do lower-level jobs. Further, assuming both men and women are both full-time employees at a professional level and have the same education, men are still more likely to be paid higher than women. It is an unfortunate fact that strays from the education variable and lends itself only to gender difference and, thus, gender inequality.

Democratization goes hand-in-hand with modernization. Overwhelmingly, the West proclaims to be democratic and, thus, wishes to spread its democratic ways to the rest of the world. Democracy promises and promises to protect the rights of all individuals, which include women. As gender equality is a major area of focus for democracy, the three authors of "Gender Equality and Democracy" have attempted to look at 70 proclaimed-democratic nations within the world which constitute 80% of the world's population. The article argues that, because women represent more than half of the entire population, democracy cannot be reached until this half has equality to the status of men. Interestingly, the article brings up several points crucial to this research. It seems as though Protestant-influenced nations are less conservative than Catholic-influenced nations in terms of gender equality. That is to say that Protestant nations gave suffrage to women much earlier than did Catholic nations. In addition, the authors mention that Islamic nations tend to restrict the involvement of women in Parliament. Despite the differences between Protestant and Catholic nations, the nations with traditionally Christian populations were found to have many more women in political positions as opposed to non-traditionally Christian populations. These non-traditional nations are those practicing Islam, Hinduism, Buddhism, and Confucianism. On the point of democracy, those nations claiming to be democratic tend to have more women in political positions as opposed to those nations claiming to be undemocratic. Exceptions do occur in this model, as China, generally authoritarian, has many women in parliament and as Japan, Ireland, France, and the United States, generally democratic, have relatively few women. Despite the exceptions, one of the main reasons for this low representation of women in the parliaments of undemocratic nations is that many of these nations still have the cultural bias towards men. They feel that men are more suited and better able to lead countries. Additionally, more prosperous countries with a high per capita GDP relative to less prosperous countries tend to have more women in parliament as well.

Susan Marshall's article on "Development, Dependence, and Gender Inequality" fits most aptly with the hypotheses of economics' effects on gender equality. She claims that throughout history we have distinguished the nations of the world between those which were developed and those which were not, those which were rich and those which were poor. Although social inequality has been a big topic emerging from the realization of economic different amongst countries, gender inequality had been pushed aside a bit. She proposes the idea that development does, in fact, play a crucial role in determining gender equality in a nation, although her analysis failed to provide accurate information on some of the more affluent countries. Within the past centuries, there has been a gradual increase of women's equality to men in the more-developed nations. There is hope that the same can occur in less-developed nations with the increase of foreign aid; however, this is not necessarily the case. To many, modernization may not be the key factor for improving the status of women. The gap between men and women still exists as women still have much less equality and rights than do men even in more-developed nations. Her analysis, thus, focused more on regional characteristics that could attribute to gender inequality in the world. This approach most definitely raised more questions, as the author pointed out herself.

Hypothesis

The questions still needing to be answered are what nations tend to give less equality to women and why? In order to determine this, I will need to hypothesize various theories and create a method for testing the hypotheses. I believe that gender inequality is attributed to multiple factors. For the purpose of this research, I will look at several different economic factors as I think these are the most closely linked to gender inequality. Socio-cultural and political factors inevitably affect gender equality; however, I believe they affect economics on a greater scale and that economics encompasses many of these factors and will be, in the end, a more accurate display of the countries that have gender inequality and the reasons for this inequality. The venues I will look at include the effects of the level of economic development of a country, the level of economic freedom, and the Gross Domestic Product per capita on illiteracy of women over the ages of 15, percentage of female professionals, and percentage of females in the lower house of the legislature. It will be interesting to see which of the economic factors will have the greatest impact on gender equality within nations.

Hypothesis #1: There is a positive relationship between gender equality and level of economic development.

The most basic assessment of level of economic development would be to divide those countries amongst three categories: least developed, developing, and industrial. These categories can also be substituted for terms such as First World, Second World, and Third World, although these terms have often been recognized as having negative definitions. The idea of economic development being a cause of greater or lower gender equality was presented in Susan Marshall's research, although she did not come to any clear answers to the problem. Although gender inequality is present in all countries, regardless of economic development, the level of inequality may be less in well-developed countries.

Hypothesis #2: There is a positive relationship between gender equality and level of economic freedom.

Economic freedom basically entails the notion of government control over the economics of a country. What are the key components of economic freedom? The list can be extensive; however, we can agree that the allowances of opportunities to compete, succeed in monetary terms, trade, and be protected from incorrigible economic hardships are all components. These can all be results of government control over the economy to permit such opportunities, but economic freedom also depends on restraint on the part of the government. How this relates to gender equality can be seen in the control or restraint of the government in economics, such as whether a government allows for women to have the same chances as men to succeed and/or be active members of the economy. The reason for following the first hypothesis with this one is that the level of economic freedom can be either directly or indirectly related to the level of economic development, and development is often the outcome of a free economy. Although not always the case, in general, the freer the country, the more developed it is.

Hypothesis #3: There is a positive relationship between gender equality and Gross Domestic Product per capita.

The Gross Domestic Product per capita does not necessarily mean how rich a country is, but how high the quality of life may be for a certain individual. This can result in social equality, and, of course, gender equality. A high per capita indicates that there is a better opportunity for individuals to reach monetary success, discussed in the previous hypothesis. A country which has a high per capita does not necessarily have a high Gross Domestic Product overall, but this country is able to allocate a high income to

individuals. Generally, a country with a low Gross Domestic Product but a high per capita has a smaller population for which it has to account. Indeed, there are very wealthy individuals and very poor individuals in all societies; however, the suggestion here is that the country has a pretty stable, prosperous economy that meets most or many of the needs of a majority of its citizens.

Methods

The method of testing for these hypotheses will be to look at statistics relevant to the above-mentioned economic factors and observe the impacts the independent variables have on the dependent variables. In this case, the economic factors are the independent variables, and the levels of gender equality are the dependent variables. Although I have used a general notion of gender equality for the dependent variables in the hypotheses, I will look more in-depth at the subdivisions of gender equality also mentioned above. I will use regression analysis of these relationships, alongside scatter plot models, to determine what nations have gender inequality and why.

Terms

In order to understand the data analysis for the relationships between the three independent variables and each of the four dependent variables, a few terms must be defined. For the research, I will be using regression analysis, which indicates the dependent variable as a function of the independent variable. The relationship will be shown in a scatter plot. The degree of relationship between the variables is measured by the Pearson correlation coefficient, noted as "r." In addition to the degree of relationship, this coefficient also determines the direction of the relationship, whether it is positive or negative. The closer the absolute value is to 1.0, the stronger the relationship. As the value goes closer to 0, the relationship weakens. At 1.0 the relationship is perfect, and at 0 the relationship does not exist. As for direction, if the coefficient is negative, there is a negative direction. The opposite is true for a positive coefficient. The Pearson correlation coefficient is important to data analysis because it tells us how much the independent variable affects the dependent variable, if at all. The PRE Interpretation gives us the variation of the dependent variable as explained by the independent variable, and we get this number by squaring the Pearson correlation coefficient and multiplying it by 100 to get a percent value. This value helps determine the degree of the relationship as well.

Data Analysis

Hypothesis #1

Looking at Figure 1.1 on the following pages, we see that there is a substantial difference in the literacy rate of developed, developing, and underdeveloped countries. In the scatter plot, underdeveloped is coded as 1, developing as 2, and developed as 3. We can see there is a much greater concentration of illiteracy for the underdeveloped countries in which the majority of the countries have at least a 40% illiteracy rate. This number is astronomical when realizing that almost half or more than half of the women in a country cannot read and write at the basic level. Of the most illiterate of the underdeveloped countries, we can see that many of the countries are in Africa and Southeast Asia, Niger being the most illiterate. As for the developed countries, of the countries in the study, all had illiteracy rates of less than 30% and the majority was concentrated around the 10% or less range. These countries consisted mostly of European countries. The developing countries are a bit interesting because we can see that there are no definite clusters around a particular percentage. For the most part, developing countries have illiteracy rates of less than 50% but a few managed to get a little higher.

The Pearson's correlation coefficient for this relationship is -0.696, and the PRE Interpretation is 48.4%. This indicates that there is a strong relationship between level of economic development and the illiteracy rate of women ages 15 and up; however, this relationship is negative. It is negative because illiteracy is an indicator of inequality, and the more developed nations should tend to have lower levels of inequality. The relationship is statistically significant at the .01 level.

Figure 1.2 examines further gender equality in nations by using the effect of the level of economic development on the percentage of females in the lower or single house of the legislature, the second dependent variable. I say single here because not all countries have a bicameral legislature. Again, we see a similar pattern as Figure 1.1; however, the relationship here is opposite. The underdeveloped section has significant clusters below the 10% mark, meaning that these countries' lower or single house are made up of less than 10% female. This implies that women have little say in the political process of the country as their voices are few and insignificant in the lower or single house of the legislature. What is surprising here is that none of the countries in this data have a high percentage of women in the legislature. For the developing countries, the biggest clusters are found under the 20% mark. Even for the developed countries, far fewer than 50% of the people in the legislature are women. This shows that there is not a very strong relationship between level of economic development and percentage of females in the lower house of the legislature.

The Pearson's correlation coefficient for this relationship is 0.354, and the PRE Interpretation is 12.5%. This indicates that there is a moderate relationship between level of economic development and the percentage of women in the lower or single house of the legislature. The absolute value of the coefficient for this relationship is much lower than the previous one, thus weaker. The direction is positive as the higher the economic development, the higher the percentage of women in the lower or single house tends to be. The relationship is, however, statistically significant at the .01 level, despite the fact that it is relatively weaker.

The last dependent variable is number of women in professional and technical areas of employment. There are more countries used as cases in this scatter plot than in the previous one so it is easier to see the relationship between the two variables. Once more, Figure 1.4 follows a similar pattern as the previous two figures. The clusters for the underdeveloped countries fall low on the y-axis which means that there are a small number of females working in professional jobs. There is a difference between the underdeveloped and developed nations in this model. The clusters for the developed section tend to be around the middle to upper area of the scatter plot. There are far greater numbers of females in professional positions in developed nations as opposed to underdeveloped nations. Developing nations are generally in the region between underdeveloped and developed nations, so, hopefully, their numbers will increase as they continue to develop more.

The Pearson's correlation coefficient for this relationship is 0.551, and the PRE Interpretation is 30.4%. It is positive because the higher the level of development, the higher the number of women in professional jobs. This indicates that there is a strong relationship between level of economic development and the number of women in professional jobs. It is slightly lower than the initial relationship of level of economic development and illiteracy rate but much higher than the relationship with the percentage of women in the legislature. The relationship is statistically significant at the .01 level.

Hypothesis #2

Figure 2.1 is different from the previous ones in that the data points are more scattered as opposed to straight up from three different points on the x-axis. This is because the independent variable is ordinal meaning that there is direction and there are infinite numbers of possibilities available. No one country had a perfect rating of 1 for the highest economic freedom and no one country had a rating of 5 for the lowest economic freedom. Most of the countries hovered around the 2-4 range. The countries that had low illiteracy rates tended to be European and South American, while the highest were African and Southeast Asian countries. Because there seems to be little difference between nations with regards to their level of economic freedom, we can assume that the relationship between these two variables is not very strong. There are countries with ratings of 4 that had very high illiteracy rates and very low illiteracy rates.

The Pearson's correlation coefficient for this relationship is 0.324, and the PRE Interpretation is 10.5%. It is positive because of the way the independent variable is coded. The number 1 indicates the freest nations and the number 5 indicates the least free nations. This indicates that there is a weak relationship between level of economic freedom and the illiteracy rate of women ages 15 and up. The relationship is statistically significant at the .01 level.

It is interesting to note that Figure 2.2 also does not seem to have a very strong relationship as the data points cluster around the middle of the graph. It is also interesting that Singapore, which has the freest economy, and Iran, which has the least free economy, both have about the same percentage of women in the legislature; this percentage is also quite low. Sweden, with the highest percentage of women in the legislature, is not, by far, the freest nation economically. Again, the most free nations tend to be European and American nations, and these are also the nations with some of the highest percentages of women in the legislature.

The Pearson's correlation coefficient for this relationship is -0.331, and the PRE Interpretation is 11.0%. It is negative because of the way the independent variable is coded, as with the previous relationship. The coefficient indicates that there is a weak relationship between level of economic freedom and the percentage of women in the legislature. The relationship is statistically significant at the .01 level.

Figure 2.3 follows the same pattern as 2.2. The relationship again seems to be weak, but that will be determined later by the Pearson's correlation coefficient. The data points are clustered around the middle of the scatter plot with a few outlier cases. Kazakhstan happens to be the country with the most number of women in professional jobs. The other high rates come from European countries and the lowest from Middle Eastern and African countries.

The Pearson's correlation coefficient for this relationship is -0.321, and the PRE Interpretation is 10.3%. It is negative because of the way the independent variable is coded, as with the previous relationships. The coefficient indicates that there is a weak relationship between level of economic freedom and the number of women in professional jobs. The relationship is statistically significant at the .01 level.

Hypothesis #3

It can be assumed that many of the countries with a low level of gender equality will also have a low level of gender equality when testing for GDP per capita. That seems to be the case here. Figure 3.1 shows the relationship between GDP per capita and the illiteracy rate of females. The cluster in the upper left corner indicates a large percentage of illiterate females in the population, and the cluster is made up of primarily

African and Asian countries. These countries are some of the poorest in terms of GDP per capita. Although not the highest in terms of GDP per capita in the world, the United Arab Emirates has the highest per capita in this specific model, with Italy in second. There is a quite significant gap between the two countries as the UAE has about a 20% illiteracy rate and Italy only less than 5%. A huge majority of the countries in this model have a relatively low GDP per capita of less than $10,000. Of these countries, about half have a low illiteracy rate, mainly from Europe and South America.

The Pearson's correlation coefficient for this relationship is -0.536, and the PRE Interpretation is 28.7%. It is negative because the dependent variable of illiteracy is an indicator of gender inequality as opposed to equality. The higher the GDP per capita, the lower the percent of illiterate women. The coefficient indicates that there is a strong relationship between GDP per capita and the illiteracy rate. The relationship is statistically significant at the .01 level.

As we have seen before, the percentage of women in the legislature is not very substantial, even in developed countries or countries with a high level of economic freedom. The ratio of females to males in politics seems to be low wherever one is. In the case of GDP per capita, the same is true. Although many of the Nordic countries have a relatively high GDP per capita and relatively high percents of women in the legislature, their numbers are still below 50%. There is a huge cluster in Figure 3.2 of countries with a really low GDP per capita that also have a low percent of women in the legislature. This shows that there is a relationship between the two variables. Interestingly, in this model, the countries with the lowest percentage of women in the legislature are not only African and Asian countries but also countries in Eastern Europe and South America. Luxembourg, with its highest per capita in the world, has less than 20% of its legislative seats filled by women.

The Pearson's correlation coefficient for this relationship is 0.482, and the PRE Interpretation is 23.2%. The direction here is positive because a high GDP per capita indicates a high percent of women in the legislature. The coefficient indicates that there is a strong relationship between GDP per capita and the percentage of women in the legislature. However, it is a weaker relationship than the previous one. The relationship is statistically significant at the .01 level.

The dependent variable of women in professional jobs is affected very similarly by the independent variable as the percentage of women in the legislature. There are fewer clusters with relatively few cases having a similar pattern. In Figure 3.3, we see that the general pattern is the same as Figure 3.2. The countries with the lowest number of women in professional jobs, however, have gone back to being mainly African countries. It is interesting to note in this model that regions tend to cluster around the same general area. That is to say that African countries both have similar GDP per capita as well as number of women in professional jobs, and the same is true for European, South American, and Asian countries.

The Pearson's correlation coefficient for this relationship is 0.336, and the PRE Interpretation is 11.3%. The direction here is positive because a high GDP per capita indicates a high number of women in professional positions. The coefficient indicates that there is a weak relationship between GDP per capita and the number of women in professional positions. It is notably weaker than the other two relationships. This means that GDP per capita does not affect the number of women in professional positions that much. The relationship is statistically significant at the .01 level.

Conclusion

From the data analysis, we can see that the level of economic freedom does not have a big impact on gender equality. All three of the dependent variables tested had relatively weak relationships. On the contrary, the level of economic development and the Gross Domestic Product per capita have large impacts on gender equality. The coefficients showed that there were strong relationships between the independent and dependent variables. If the GDP per capita is high, the level of economic development is usually relatively high as well. When both are high, gender equality is, thus, high. However, there is no complete equality between men and women in any of the data. Women hold significantly less number of positions in the workforce at the professional level and in the legislature than men, regardless of economic status. The illiteracy rate is harder to determine because we would need a comparative analysis of the illiteracy rate for men as well. However, it is assumed that women are, on the whole, more illiterate than men. This can be attributed to societal, familial, and educational restrictions placed on them. From this research, we can at least hope that, as countries develop and become richer, they will give their women more equality. The initial step that needs to be taken is to help the poorer and/or less-developed nations through either foreign aid or educational and assistance programs. This is a huge task and is a goal very difficult to reach. Although this research did not discuss every aspect of society that can affect gender equality, it touched upon a few very important ones. What political scientists will need to continue doing is look at several different variables including politics, conflict, religion, region, and many more to figure out distinct patterns of gender inequality. This way, specific geographic or social areas can be targeted for improvement. I think the most vital area to target is education. The more women are educated or at least become literate, the more they will understand that their situation as an unequal is unfair, unjust, and needs to be changed. They will have better abilities to rise above the inequality and will also be more qualified to work in professional and technical jobs as well as work as politicians. Adequate education is the first step towards equality for not only women but all minority groups in this world.

Bibliography

Canadian International Development Agency. "Gender Equality." 1 April 2004. http://www.acdi-cida.gc.ca/equality

Corbett, Michael and Michael Le Roy. Research Methods in Political Science. Canada: Thomson Wadsworth, 2003.

Development Gateway. "Gender Equality." 2 April 2004. http://www.developmentgateway.org/node/130685/special/gender/

Dowling, Mike. "Interactive Table of World Nations." 28 April 2004. http://www.mrdowling.com/800gdppercapita.html

Eisenberg, Avigail. "Diversity and Equality: Three Approaches to Cultural and Sexual Differences." The Journal of Political Philosophy, Vol. 11, No. 1 (2003): 41-64.

French, Joan. "Gender Equality and the Rights of Women and Girls." Development, Vol. 6, No. 44:2 (2001): 47-51.

Gender in Development. "Monograph Series." 1 April 2004. http://www.sdnp.undp.org/gender/resources/monograph.html

Human Rights Watch. "Women's Rights." 5 April 2004. http://hrw.org/women/

Inglehart, Ronald; Norris, Pippa; and Christian Welzel. "Gender Equality and Democracy." Comparative Sociology, Vol. 1, No. 3-4 (2002): 321-343.

Inter-Parliamentary Union. "Women in National Parliaments." 28 April 2004. http://www.ipu.org/wmn-e/classif.htm

Johnson, Janet Buttolph and Richard Joslyn. Political Science Research Methods. Washington D.C.: Congressional Quarterly Inc, 2001.

Kerr, Joanna. Association for Women's Rights in Development. "International Trends in Gender Equality Work." 24 March 2004. http://www.genderatwork.org/updir/Joanna-internationaltrends.htm

Marshall, Susan. "Development, Dependence, and Gender Inequality in the Third World." International Studies Quarterly, Vol. 29, No. 2 (1985): 217-240.

Millenium Development Goals. "Gender Equality." 20 March 2004. http://www.developmentgoals.com/Gender_Equality.htm

Schlozman, Kay; Burns, Nancy; and Sidney Verba. "'What Happened at Work Today?': A Multistage Model of Gender, Employment, and Political Participation. The Journal of Politics, Vol. 61, No. 1 (1999): 29-53

UNESCO. "Gender Mainstreaming." 21 March 2004. http://portal.unesco.org/en/ev.php@URL_ID=3160&URL_DO=DO_TOPIC&URL_SECTION=201.html

UNICEF. "Gender Equality." 5 April 2004. http://www.unicef.org/gender/

United Nations Inter-Agency Network on Women and Gender Equality. "Women Watch." 24 March 2004. http://www.un.org/womenwatch/

United Nations Population Fund. "Promoting Gender Equality." 20 March 2004. http://www.unfpa.org/gender/faq_gender.htm

United Nations Statistics Division. "Demographic, Social, and Housing Statistics." 20 March 2004. http://unstats.un.org/unsd/demographic/ww2000/pop2000.htm