Variables That Influence Women in Parliament Essay Sample
- Pages: 4
- Word count: 1,088
- Rewriting Possibility: 99% (excellent)
- Category: gender
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Introduction of TOPIC
Women are consistently underrepresented in political systems around the world. In this research, I examine factors such as the gender equality scale, education, ratio of female to male income and cultural diversity and their impact on the percentage of women in government. My findings reinforce my hypotheses; all four independent variables have statistically significant effects on women in parliament, with the ratio of female to male income as the largest. Introduction:
The percentage of women in Canadian parliament has stagnated to 21% in recent decades, and 17% for the rest of the world. Which variables impact the representation of women? I want to know if the income gap between men and women, years of education, the cultural diversity of a country, and gender equality scale affects the percentage of women in parliament. I would expect when controlling for political knowledge, the more equal in opportunity a country is, the more women there are in parliament. My regression analysis propounds my hypotheses. While there are some shortcomings to my research, for example, I would have liked to the use age as an independent variable but it was not available in the dataset. Furthermore, my dependent variable only measures women’s representation in parliamentary regimes, thus it cannot be applied to countries with presidential regimes.
Is it necessary for women to obtain mirror representation in parliament? Liberal feminists believe that only trustee representation is necessary in democratic countries because just because an MP is a female, does not mean she will address women’s issues. Especially since party discipline is so strict in parliamentary regimes, female MPs will almost always conform to party cohesiveness than her constituency interests (Young, 2000). But on the other hand socialist feminists argue that democracy cannot achieve legitimacy if formerly oppressed groups such as women aren’t adequately represented. Also women are physiologically different than men, thus they care about women’s issues the most, and their presence in government is a symbolic achievement (Trimble and Arscott, 2003). While both schools of thought identify numerous reasons why women are underrepresented in government, little research is dedicated to which variable is the most important and is the largest barrier for women to enter politics.
I am using the Quality of Government Dataset, this dataset is very convenient
for regression analysis because most of the variables are ordinal or interval. My dependent variable
My third independent variable is an interval measure, Cultural Diversity (fe_cultdiv). I hypothesize the more culturally diverse (language, ethnicity) a country is, the more likely women are better represented in parliament. My rationale is that women are a minority in government, and minorities groups would more likely identify with other minority groups.
My fourth independent variable is Ratio of Female to Male Income (gid_rfmi). I hypothesize that the higher the ratio (more equal) of income between the sexes, the more women enter government. I included this variable because running campaigns cost a large sum of money, thus it is logical to assume that income affects a person’s ability to run for office.
My fifth and last independent variable is Average Schooling Years (Female) (bl_asyf15). I hypothesize that the more educated the female population is in a country, the higher the percent of female representation in parliament. Because being politicians require high levels of education, thus the more educated a subset group of the population is, the more likely they can achieve equal political representation.
Thus, my regression equation is such:
Women in Par =β1 +β2 Gender Equality+β3Cultural Diversity+β4Income Ratio+β5Education+ε
This is my multivariate regression analysis
So, by interpreting these results, we can see which independent variable has the greatest effect on women in parliament. It is the Ratio of Female to Male Income, with a coefficient of 32.15 and it is strongly statistically significant at p<0.00. This is interpreted as: when controlling for all other variables, if the ratio of female to male income increases by one point, then the women in parliament increases by 32.15 points. This variable obviously also have the biggest effect (beta) at .41 points.
The only variable not statistically significant is my control variable: how often do you discuss politics? At p>0.1 Furthermore the R-Square value at 0.65, it is an indicator that my model is a good predictor of some variables that affect the percentage of women in parliament. It means that 65% of the variance in all the cases are explained by my model.
Finally, my regression equation is statistically significant at p<0.00. Thus I can reject the null hypothesis that these independent variables have no effect on the dependent variable.
To conclude, the regression analysis confirms the hypotheses I posited above regarding the four independent variables, gender equality, cultural diversity, ratio of female to male income, and education which would all affect the number of women in parliament. But, the regression analysis also shows that when controlling for other variables, money (ratio of female to male income) has the largest impact. This could imply that the absence of equality in income is the biggest barrier for women to enter politics. In future research, I would like to further explore the effect that income has the number of women in politics, as well as other variables such as female candidates used as gatekeepers, stigma of politics, and gender stereotypes.