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Engendering War

Engendering War. Planning Female Agency in Post-Conflict States. Jeffrey D. Zimmer SIS, American University 4400 Massachusetts Ave NW Washington DC 20016 jz9391a@american.edu http://eagle1.american.edu/~jz9391a/ SIS 600-05 Int'l Affairs Stats & Methods – Dr. Assen Assenov.

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Engendering War

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  1. Engendering War Planning Female Agency in Post-Conflict States Jeffrey D. Zimmer SIS, American University 4400 Massachusetts Ave NW Washington DC 20016 jz9391a@american.edu http://eagle1.american.edu/~jz9391a/ SIS 600-05 Int'l Affairs Stats & Methods – Dr. Assen Assenov

  2. Research Question & Hypothesis • Why am I studying this? • US alone spends over $6 billion a year on gender-based foreign aid • Gender demographic shifts in war? • Empowerment (i.e. Rosie the Riveter Effect) • Isolation (i.e. widowhood, exile, declining investment) • Hypothesis:In post-conflict states, female empowerment will increase within the first 10 years of the end of war.

  3. Scholarship – What’s Out There? • Dr. Rajasingham-Senanayake • Between Reality and Representation: Women’s Agency and War in Post-Conflict Sri Lanka • Important? • Civil War • Limited External Factors • Result? • Increases due to “new war” paradigm • Women must contribute and succeed due to constant threats • Dr. Moghadam • Organizing Women: The New Women’s Movement in Algeria • Important? • Civil War • External Factors • Result? • Potential for increases • Societal upheaval = societal reconstruction and reform • Carpe Power in a new order

  4. Data • Pippa Norris • Democracy Time Series Data Set from JFK School  specific year 2000 • Why 2000? • Reliability? • Unit of Analysis: Country • Independent Variable: Conflict • Dependent Variables: Combined Gross Enrolment Ratio for Primary, Secondary and Tertiary Level Schools (female) (CGER-F) • Why is CGER-F my variable for female empowerment ?

  5. Descriptive Statistics Conflict’s CT? = Mode (0) CGER-F CT? = Median (72.000) CGER-F Range? = 120 Uni-modal 93% Data Present (177 of 191 CGER-F) Look at that nice curve!

  6. Bivariate Analysis Pearson’s R2: .100489 increases my prediction rate by 10%! Correlation? Yes; indirect (-) and highly significant (.000) Number of observations: 177

  7. Regression Analysis • What Am I Looking At? • The association (coefficient) is highly significant • Thanks to the (-), we know that it is a downward sloping line/indirect association • My Adjusted R2 let’s us know that I am approximately 10% closer to an accurate prediction.

  8. Conclusion Findings? • Yay for post-conflict female enrollment • Gender-based after conflict is a good idea – it turns out that it will likely be used and be a great resource for recovery and societal change. Explanations & Future Research Potential? • Perhaps this would change with interstate vs. intrastate conflicts? • i.e. destabilize society and it’s too chaotic for women to be empowered • i.e. education is a non-threatening domestic investment field (or even conditionality for aid) • i.e. education is a chance for indoctrination to combat or amplify the conflict’s outcome • i.e. since so many men are killed – potentially even young boys – there is a skewed population demographic Policy Recommendations? • Increase (or at least maintain) gender-based foreign aid • Gender-based aid could potentially have amplified effects if done in the form of education assistance (especially a long-term empowerment solution) • In order to empower women, do gender-based aid to men as well to educate them on how it is in their interest/what they can do.

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