1. Factors influencing transition to marriage among females in the Kassena-Nankana District, Ghana University of Cape Coast – Navrongo DSS Collaborative Team Presentation at the 9 th INDEPTH Network AGM, Pune, 26-29 October 2009

2. Outline Introduction Background Objectives Methods Results Conclusions Appreciation

3. Introduction - I Between 1988 and 2003 the total fertility rate in Ghana declined from 6.4 to 4.4 This decline in fertility is not limited to a particular section of the society but cuts across all population subgroups Fertility decline may reflect changes in various aspects of reproductive behavior in Ghanaian society Marriage as an important context for reproduction Changes in marital practices have fertility implications

4. Introduction - II Age at first marriage and spousal age differences have featured prominently in discussions of reproductive behavior in sub-Saharan Africa Relative lack of data on marriage dynamics in the sub-region, especially in specific geographic areas Data from DSS sites provide an opportunity to explore issues relating to marriage and family formation over time UCC-NHRC examining this possibility using data from Navrongo DSS

5. Transition to Marriage Marriage as a major event in the life course –Transition to adulthood Timing of first marriage has several implications for society and the individual –Beginning of exposure to childbearing –Curtailment of socio-economic advancement –Individual status and autonomy Factors such as age, sex, education, place of residence affect the timing of first marriage In small communities evidence on the timing of marriage and its associated factors is rare

6. Objectives To examine trends in timing of first marriage among women 15-24 years –Proportions married –Mean age at first marriage To identify factors associated with the transition to marriage among a cohort of women 15-24 years To make suggestions for improvements in data collection

7. Methods Analysis of data collected by Navrongo DSS; exploit the longitudinal nature of the data DSS information on individuals –Information on mortality, migration and marriage –Marital information includes start and end dates Annual surveys of women 15-49 years old in 2003 –Background characteristics of respondents –Information on reproductive preferences and practices Primary interest in the behavior of women 15-24 years –Examine prevalence of marriage over time –Examine transition to first marriage among a cohort of unmarried women aged 15-24 to identify background factors associated with transition to marriage

8. Year of surveyNumber of respondents Respondents aged 15-24 years 199552781456 (27.6%) 199762022061 (33.2%) 199866782323 (35.0%) 199961622022 (32.8%) 200061362193 (35.7%) 200164042329 (36.4%) 200358292246 (38.5%)

11. Marital transition of cohort of never married 15-24 year olds 1509 females 15-24 years never married in 2003 survey Tracked their marital transition till July 2009 229 reported married during follow up –24 omitted because date of marriage earlier than date of interview in 2003 Examined the effects of 2003 background characteristics on odds of getting married –Age, religion, ethnicity, school attendance, ever given birth

12. Background characteristicEffect on odds of marriage Age+ve [ns] Currently in school-ve [sig] Ethnic group – nankam+ve [ns] Religion – christian-ve [ns] Ever given birth+ve [ns] Location - rural+ve [s]

13. Observations from data In the Navrongo DSS area majority of young women marry before age 20 Increasing proportions are marrying after age 20 Marriage is earlier for those out of school and those residing in the rural parts of the district

14. Conclusions Analysis is exploratory and indicative of what could be done with existing data at Navrongo Preliminary results appear consistent with existing literature –Marriage occurs earlier among rural women and those with no or little education In this area rural young women who are not attending school face considerable pressure to to marry Programs to keep girls in school longer could delay marriage

15. Next steps Further refine our analysis –Identify more background factors to include in the analysis –Explore use of hazard models Examine how deficiencies in existing data could be addressed in future data collection

16. Appreciation Rockefeller Foundation and Population Council for funding the DSS and Panel Surveys INDEPTH Network for funding the UCC-NHRC collaboration Navrongo DSS Community Staff of the Navrongo Health Research Centre

17. THANK YOU!