Presented By: Yagya Prasad Subedi PhD Student, University of Aberdeen

1. University of Aberdeen, Centre for Sustainable International Development(CSID) -Nepal group presentation on 13th Feb 2013Factors Associated with Women’s Decision Making in Nepal (A Research Carried Out from Nepal Demographic and Health Surveys Data) Presented By: Yagya Prasad Subedi PhD Student, University of Aberdeen The research was a part of MPS degree in IIPS, Mumbai, India in 2006. The data are recalculate d and updated from NDHS 2011. This was guided by: USHA RAM, PhD former Associate Professor of IIPS Mumbai –  At present Postdoctoral Fellow at Centre for Global Health Research (CGHR), University of Toronto, Canada

2. Background Study Setting: The following findings are documented in many studies conducted in Nepalese and in South-Asian contexts. • Nepal is a multi-ethnic and multi-cultural country. The country’s cultural landscape is extremely diverse, more than 50 known languages and 100s of ethnic groups (CBS, 2001) • These groups are divided largely into Indo-Aryan and Tibeto-Burmangroups on the basis of the languages they speak (CBS, 1991). • In Hindu tradition, marriage is an essential step in life for men and women. • Girls are encouraged to marry in their early teens or even earlier by their parents, so the average marriage age for Nepalese girls is 18 years (Achrya, 1998). • Marriage in the Hindu tradition is a social contract rather than an emotional bond between two people, women rarely have any say in the choice of their own life partners. (Kishor, 1996)

3. Background… Study Setting… • Child marriages and arranged marriages are still widely followed, and a restriction on widows remarrying is also common even if law allows to remarry (Acharya, 1996) • Dowry system is widely prevailing (daughters birth in poor family is perceived as a suicidal event) (Kishor, 2009) • Domestic violence (wife beating, threatening and sexual assaults) in a society are common (Acharya, 1996) • Women’s are losing jobs every year from growing industrial and service sectors - lack of opportunities for education and training (6,000 women in 5 year) (NLFS, 2008) • Household activities - cooking, cleaning, child caring, etc. are considered primarily as women’s responsibilities - are not paid (Shrestha, 2001). • Only 10% of Nepalese women are employed in modern industrial and service sectors for paid work, whereas 40% of men are in these sectors (NDHS, 2011). • Nepal had been involved in internal conflicts for a decade, women were also seriously affected from that.

4. Research questions… This study tries to seek the answers of the following questions: • How can we view the women’s decision making from DHS data? • Is there any relationship between women’s employment, reproductive responsibilities and women’s decision making? If yes, what are the mechanisms? • What are the other important factors influencing women’s decision making?

5. Seven-Decisions: Domestic Cooking Daily Needs Durable Items Socio-economic Health Care Mobility Earning/Spending Reproductive When to have Sex? Using Contraception Deciding the length of Birth intervals Women’s Decision Making Dimensions in DHS

6. Decision making composite index • High Level of invelvment – women who reported as “involved” for making majority decisions from every sphere (66% to 100% decisions). • Medium Level of involvement – women who reported as “involved” for making one decision from each sphere but not “involved” for making majority of decisions (between 33% to 66% decisions). • Low Level of involvment – women who reported as “not-involved” even for making one decision from each sphere (less than 33% decisions)

7. Definition of Women’s Employment in DHS The study classifies the women according to the nature of their employment/ jobs to see the impact in their decision making : • The women who do not have employment in any paid job (but they are cooking, cleaning & childrearing at home) • The women who have an employment in own agriculture farm (but don’t get monetary payment) • The women who have an employment in other’s agricultural farm (and get payment) • The women who have an employment in non-agricultural (industry and service) sectors

8. Methods of Analysis of DHS data • Step: 1 Started with approximately 20 possible variables, which were prominent to incorporate in the conceptual frame-work from available DHS data. • Step: 2 Observed their significance of association using chi-square test; and those variables, which were significant at least 5% level were retained. • Step: 3 Coupled in different combinations by observing their correlations; and made representative index variables and cross tabulated for levels and differentials. • Step: 4 Run binary logistic regression and compared minus 2 LL difference with chi-square value, only then the variable was included in the model. • Step: 5 Explained exponential beta coefficient results for Decision Making in three model: firstly, including work related variables, secondly, including cultural variables; finally, with spousal differentials and other players

9. Objectives • to develop the women’s decision making index from available Nepalese DHS data • to examine the role of ecological, demographic, socio-cultural and economic factors on women’s decision making • to investigate the relationship between women’s employment and women’s decision making at various levels.

10. Propositions • Working-women in modern and service sectors are more likely to be more influential in decision making • Early age at marriage, sex of the first-child and other cultural practices influence women’s decision making. • Working women would have much longer birth intervals than their non-working counterparts. • Wider spousal gap in age, education and occupation negatively effects women’s participation in decision making.

11. Employment Status: -Not working -Working but not paid -Working in agriculture (paid) -Working in non-agriculture (paid) -Geographic variations - ecological zones -Socio-Demographic -Cultural Practices - Education status Women’s Decision Making: -Domestic, social and reproductive levels Conceptual Framework

12. Mechanism from “Women’s Work” to “Influence in Decision Making” • Firstly, likely to have direct access and control over financial resources from paid-works; • Secondly, likely to visit with people other than the immediate family; • Thirdly, likely to be able to translate her working relationship into the better social relationship; • Fourthly, likely to be more knowledgeable about the world , and; • Hence, be able to influence in decision makingat domestic, societal and reproductive levels.

13. Decision Making by Ecological Factors The figures indicate that mountain and hill regions women are more likely to influence in decision-making. This empowerment in decision may have achieved as a result of remittance earning from their partners, particularly pension of Gorkha Army and remittance from international labor market (labor force survey, 1998).

14. Decision Making by Partner’s Age-Gap The figures indicate that the women with same age cohort group’s partner are more likely to influence in decision-making in compared to the women with different age cohort groups partner.

15. Decision Making by Partner’s Education Gap The figures indicate that the husband and wife with same level of education are more likely to participate in decision-making in compared to the couple with different level of education attainments. Generally, education is likely to liberate women from certain traditional values and customs and facilitate to support their decision making (Kishor, 1995).

16. Decision Making by Occupation Gap Between the Partners The figures indicate that the husband and wife with same level of occupations are more likely to participate in decision-making in compared to the couple with wider gap in occupation levels. Generally, employment of partner is more likely to provide the maximum opportunity for broadening their attitude (Kishor, 2001).

17. Decision Making by Current Age of Women The figures indicate that the women of higher/ older ages are more likely to influence in decision-making in comparison to the lower ages women.

18. Decision Making by Number of Children The figures indicate that the women with fewer children are more likely to influence in decision-making in comparison to the women having more number of children.

19. Decision Making by Household Headship The figures indicate that the women in female headed house are more likely to influence in decision-making in comparison to male headed house.

20. Decision Making by Sex of the First Child A woman having first male child is likely to influence in Decision Making in comparison to the women having first female child. This fact is also consistent with the prevailing son-preference in the Nepalese culture (Acharya, 1998)

21. Decision Making by Age at Marriage The figures indicate that a lower age at first marriage may imply that a woman may actually have little influence for decision making. This may have negative effect on women’s opportunities for job, their exposure to outside world and their influence in decision making, etc.

22. Decision Making by Birth Interval The figures indicate that the women with long-first-birth interval are more likely to influence in decision-making. The reason could be the short-first-birth interval may hamper the education and career opportunities.

23. Decision Making by Employment Status The figures indicate that the women employed in modern industrial and service sectors are more likely to influence in decision-making in comparison to non-employed and agricultural un-paid women. There is positive association between women’s paid-work and women’s Decision Making.

24. Decision Making by Standard of Living The figure indicate that standard of living index and women’s Decision Making index are varying positively. Based on these observations, it can be inferred that women’s decision making is directly related to sharing of prosperity.

25. Decision Making by Land Ownership In agriculture sector, female’s ownership of land ensures higher Decision Making in many cases. The right of property ownership and access of economic recourses provides the new form of authority to make decision.

26. Work Status by Types of Decisions Non-employed women are generally restricted only at domestic sphere and, hence, they are confined to make domestic decisions mainly. However, the women who are working in modern and service sector may make, besides domestic decisions, reproductive and economic decision as well.

27. Logistic Regression Results

28. Logistic Results..

29. Logistic Results…

30. Conclusions • The hill and the mountain region’s women are relatively more influential in decision making than the Terai region’s women. • Spousal education and employment in modern sector occupations is also likely to be associated with higher Decision Making. • Larger the spousal gaps in age, education and occupation, lowerthe influence in Decision Making. • Lower age at first marriage and the short first birth interval may hamper education, career opportunities, and finally lower the influence in Decision Making. • The cultural taboos will have negative effect on women’s opportunities for job, their exposure to outside world and their Decision Making etc. • The extent of female participation in workforce is a crucial factor for influencing social and economic Decision Making.

31. Thank you