1. Youth Bulge, Civil Society, and Conflict Transformation Shayna McCready sm8482a@american.edu American University School of International Service

2. Background Info or Lit. Review Theory and findings paper #1 – Theory: The ‘youth bulge’ phenomena impacts social cohesion and economic development in developing countries. – Findings : Theory and findings paper #2 – Theory: Developing countries experiencing imbalanced &/or large youth populations are predisposed to violent political action in a dysfunctional civil society. This is even more prevalent amongst youth minority groups and males who are unemployed. – Findings : Theoretical and/or empirical gap/s in the existing literature – Theoretical gap/s : The ‘youth bulge/crisis’ theory misappropriates the structure of demographics in developing & post conflict countries so as to be able to generalize youth as inherently violent. An alternative PYD/SJYD approach for engaging young people as positive actors for peace within civil services operations more appropriately address communities’ expectations for youth inclusive and youth led support. – Empirical gap/s: Current applications disregard the contextual factors of conflict transformation and development such as current youth participatory roles for social cohesion operating within civil society functionality. Programming in developing countries needs to shift toward alternative approaches in order to garner the most effective interventions for engaging the ‘demographic dividend’.

3. INTRO: Research Question & Hypothesis Research Question(s) : – Across countries experiencing booming youth populations, do individual perceptions of civil society impact social cohesion? What are the factors that drive confidence in the civil services? Do those factors implicate justification for violent political actions? Research hypothesis/hypotheses: – H0 =There is no relationship between individual perceptions of civil society and any of the factors that drive confidence in the civil services? – H1 =Controlling for age, employment, social class, education, race, and gender, individual perceptions of civil society do impact social cohesion at the country level. Confidence in civil services will be highest amongst youthful minority groups who depend on them most Justification of violence for political action will be lowest amongst those who have been positively engaged by their community.

4. Key Variables & Data Source Unit of analysis/study : – Case study at the country level (South Africa) Source of the data: – The World Values Survey (WVS) 1981-2008 Wave 5 Official Aggregate for South Africa published in 2009 Reliability of the data: – Very Reliable. The WVS’s 5th wave aggregated data includes surveys conducted by the WVS from 1981 to 2008 in 87 societies, totaling more than 256.000 interviews. Dependent variable/s – Y Dependent Variable = Social Cohesion = Overall confidence in the civil services (yes/no) – X Independent Variable = X1 age (categorical, years :15-24/24-34/35-44/35-44…..) X2 race (categorical: black/other) X3 education level (categorical: low, mid, high) X4 employment status (categorical: employed part& full/unemployed) X5 social class (categorical: upper, upper middle, lower middle, working, lowest) X6 justification for the use of violence in political action (categorical: agree/disagree)

5. Descriptive Statistics Table or/and Graphics

17. Report Table with Gama/Lambda/Chi square statistic for association of ordinal/nominal data. Report Table with T test/F test statistic for association of ordinal/nominal dependent variable and I-R variables. Interpretation of reported statistics in these tables: – i) Does the association/ exists, i.e. is your measure of association/correlation statistically significant? – ii) If the association/correlation exists how strong it is? Check the value of the gamma or lambda statistic; – iii) What is the direction of the association? Remember that this is for gamma statistic, for I_R and ordinal LOM variables only. Remember that chi square and lambda/gamma statistics may show ambiguous results, i.e. On of the measures suggest associations and the other suggests no association.

18. Y - Gamma: Y – Chi 2 : T/F testsResearch hypothesis educationG= 0.2153 (0.0?)N = 10674 χ 2 = 266.7183 (0.0?)N = 10674 t=? Reject the H0. Y and X1 are associated ?????? incomeG=0.3090 (0.0?), N = 2552 χ 2 =167.8350 (0.?) N = 2552 t=? Fail to reject the H0. Y and X2 are not associated ???? X3 age χ 2 =1.3562 (0.?) N = 12280 t=? Mean of X3 for Y=0 is equal to Mean of X3 for Y=1 is equal to X3 has different means for are associated Y=0 and Y=1 ???? X4 race χ 2 =567.1505 (0.?) N = 12280 ????? X5 Violence G=0.0036 (0.0?), N = 2501 χ 2 =24.9615 (0.?) N = 2501 ?????? unemployme nt

19. Regression Analysis, Probit Marginal Effects Estimates, The Dependent Variable is Confidence: the civil services, 0=Yes, 1= No Interpretations: i)Does the association, i.e. is your coefficient statistically significant? Look this value. Is it <.05? ii) If the association/correlation exists (sig <.05), what is the direction of the association/correlation, i.e.. What is the sign of the coefficient? iii) interpret the value of each and every statistically significant coefficient. For example, if the dependent and independent variables are not in log-level, and if b1=0.11, we can interpret this coefficients in the following way “one unit change in the independent variable X1 leads to 0.11 units changes in the dependent variables Y.” You can not interpret values of coefficients that are not significant since they are statistical zeros. iv) Make sure you interpret adj. R square statistics.

21. Findings & Policy Implications of the research Findings: Did you accept your research hypothesis? – Finding #1 – Finding #2 What are the policy implications of your findings?