Youth and Their Money: What Drives Youth Financial Behavior Megan Gash of Freedom from Hunger

1. Youth and Their Money: What Drives Youth Financial Behavior Megan Gash of Freedom from Hunger Investing in African Youth 2012 Conference, Dakar, Senegal

2. Agenda Financial Diaries - AIM Youth Project with Freedom from Hunger Survey Methodology Data analysis activity Key Results

3. Introduction • Freedom from Hunger Initiative • Advancing Integrated Microfinance for Youth (AIM Youth) • Provide 37,000 young people (22,000 in Mali and 15,000 in Ecuador) with financial services integrated with youth learner-centered financial education • Mali – NGOs: savings group program; MFIs: group savings accounts. • Ecuador - cooperatives and credit unions: individual savings accounts.

4. Implementing NGO: CAEB • Youth savings groups with Conseils et Appui pour l’Education à la Base (CAEB) • Mixed and single gender • 12-15 members each • Save same amount each week (50 CFCA or ~10-15 cents), kept in locked box, periodic loans; distribution after 9-12 months • Kolokani and Bougouni • Key research questions: income, expenditures, savings, loans, financial attitudes, financial knowledge

7. What are financial diaries? • Frequent surveys on financial topics • How differ from other research methods? • More accurate, more detail on lives of youth • Strengths: can identify seasons of low and high income, when would need more loans, different financial services, fluctuations in migration

8. Considerations • Human subjects protection protocol: university, national statistics office • Parental consent • Budget • Research firm? Employ university students? • Survey frequency: every 2 weeks, or 3 or 4 • Proper introductions, gifts, availability

9. Study Design • Study design • 72 respondents: 36 control, 36 treatment, 2 areas in Mali • 3 villages in each area; 6 respondents per village (18 per area) • Purposeful yet random selection; represent geography, age, gender • Survey design: • Length, topics, age appropriate questions • Surveys filled with data from last time; use same enumerators • Data analysis: • Systematization, periodic receipt • Analyst

10. Youth Context • Parental consent and availability • Incentives to participate: small gifts (300 FCFA value: soap, tea, sugar, trinkets), learn about money management • Availability: nights and weekends • Replacements for migration • Can forget expenses at first, learn to remember • Age-appropriate questions; sensitivity to reluctance • Personal details, learn about lives, attitudes

11. Lessons Learned • Labor intensive • Cut costs: less frequent surveys, smaller sample • Flexibility, gifts • Migration: replacement respondents • Control villages could learn about intervention • Same enumerators, ICT data collection if possible • Systematize the data analysis; designate an analyst • Qualitative follow-up: extra insight, case studies

12. Data Analysis Activity

13. Data Analysis Activity Instructions: Form small groups of 2-3 people Review data and discuss meaning Plenary discussion of conclusions

14. Key Results • Results for July – November, January • Analysis by Paloma Pineda • Mixture of descriptive and impact analysis • Preliminary analysis; some questions unanswered

15. Key Results: Demographics • 36 Treatment respondents; 36 control • 84% are 13-17; 16% are 18-24 years old • 86% unmarried • 55% in school • Food Insecurity: • 44% in July; 67% in October; 20% in January • Below National Poverty Line: • 63% in July, 71% in October, 68% in January

16. Income • INCOME: • Agricultural labor most common, then selling agricultural products • Amount of work depends on the season; low season in January • Youth earn more themselves rather than receive gifts • Earn approximately: US$11-16 every 3 weeks (or US$3.50-5.50/week) • MIGRATION: • 22% migrated by October; equal among males and females • Length of time: 10 days on average; seasonal variation • Day labor, housekeeping/child care • Make more money (US$54 every 3 weeks)… • … but have more expenses (US$36 every 3 weeks); money to parents

17. Expenses • Spend money on variety of items (US$6 per 3 weeks) • Medical expenses • highest total amount • Business costs • Clothes

18. Savings: Amount Treatment group are saving more & building more assets than control group

19. Savings: Location • 79% saved at home • 72% with a guardian • 63% have saved w/ livestock • 62% with a group • 3% with other assets • Proportion: • Livestock • Guardian at home • Savings group (amt) • Other Assets

20. Savings: Goals • Livestockboth means & goal • Clothes • Dowry/trousseau

21. Loan Use • Loans from savings groups: • US$2-4 • Business costs • Clothes • Family support: access to • credit for family is • highly valuable • 64% took loan Jul-Oct

22. Attitudes & Knowledge Increase in knowledge: identifying safe places to save, long term strategies & using savings to pay for things Increase in confidence: saving longer than a month, feeling safe about location of savings, less regret about purchases, ability to save regardless of friends and family asking, protecting long term savings from unexpected expenses, paying for unexpected expenses, speak up at home, affording daily expenses No change or decrease: none, although savings strategies vary

23. Summary of Findings • Youth earn significant amounts of their own money • Save even during times of scarcity • Seasonal migration: higher income during these times • Highest expenses are medical • Youth are using several tools for saving • Youth need access to loans: risk management; reaction to shocks • Youth savings groups have been able to increase savings for youth in rural and remote areas • Several positive knowledge & attitude changes!

24. Implications for Program Design Can identify seasons of low and high income, biggest expenses Risk management: do they need greater access to loans? Seen as problematic if taking loans for adults? Still saving much at home, with guardians: Seeing the minimum as a maximum? Still have savings needs?

25. Questions? Other implications? Experiences to share?

26. Thank you! For more information, contact Megan Gash at mgash@freedomfromhunger.org Some resources for this presentation came from: http://stats.stackexchange.com/questions/423/what-is-your-favorite-data-analysis-cartoon