presented by: yagya prasad subedi phd student, university of aberdeen

University of Aberdeen, Centre for Sustainable International Development(CSID) -Nepal group presentation on 13th Feb

2013

Factors 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

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-Burman groups 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)

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.

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?

Women’s Decision Making Dimensions in DHS

Socio-economic Sphere

Reproductive Sphere

Domestic Sphere

Seven-Decisions:1. Domestic

– Cooking– Daily Needs– Durable Items

2. Socio-economic– Health Care– Mobility– Earning/Spending

3. Reproductive– When to have Sex?– Using Contraception– Deciding the length

of Birth intervals

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)

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

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

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.

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.

Conceptual Framework

Employment Status:-Not working-Working but not paid-Working in agriculture (paid)-Working in non-agriculture (paid)

Women’s Decision Making:-Domestic, social and reproductive levels

-Geographic variations - ecological zones

-Socio-Demographic-Cultural Practices- Education status

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 making at domestic, societal

and reproductive levels.

Decision Making by Ecological Factors

Characteristics Percentage of women participating in Decision Making

Total

EcologicalLess than 30%

decisions30% to 66%

decisions

More than 66%

decisions

1 Mountain 8.8 76.6 14.6 100% (1188)

2 Hill 12.3 73.4 14.3 100% (3243)

3 Terai 15.5 77.5 7.0 100% (4295)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).

Decision Making by Partner’s Age-Gap

Characteristics Percentage of women participating in Decision Making

Total A. Spousal Age gap

Less than 30% decisions

30% to 66% decisions

More than 66% decisions

1Less than 10 years 6.6 76.8 16.5

100% (6137)

2Ten years & Above 28.5 62.6 8.8

100% (1583)

3 Wife is older than husband 7.0 76.3 16.8

100% (603)

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.

Decision Making by Partner’s Education Gap

Characteristics Percentage of women participating in Decision MakingTotal

Spousal Educational gap




1 Both are equal 14.6 23.1 62.2

100% (3242)

2 5 Years’ gap 60.4 23.7 15.9100% (2868)

3 10 Years and above gap 64.3 21.1 14.6

100% (2548)

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).

Decision Making by Occupation Gap Between the Partners


Spousal Occupational gap




1Husband has better occu. 63.2 22.3 14.5

100% (5539)

2Wife has better occu. 23.1 33.3 43.6

100% (239)

3Both are equal 15.9 23.9 60.2

100% (2921)

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).

Decision Making by Current Age of Women

Percentage of women participating in Decision Making

Total

Present Age of women Less than 30%

decisions30% to 66%

decisionsMore than 66%

decisions

1 Below 25 yrs 32.5 64.6 2.9100%

(2567)

2 25 - 39 yrs 8.7 80.2 11.1 100%

(4288)

340 - 49 years 5.0 81.3 13.6

100% (1871)

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.

Decision Making by Number of Children

Percentage of women participating in Decision MakingTotal

Number of children living




1 No Child 5.3 75.2 19.5 100%

(2939)

2 One Child 8.6 76.9 14.5 100%

(2650)

3Two

Children 10.2 77.8 12.0100%

(2177)

4Three and

more 26.6 70.6 2.8 100% (960)

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.

Decision Making by Household Headship


HeadshipLess than 33%

decisions33% to 66%


decisions

1 Male head 15.8 80.7 3.5 100% (7533)

2 Female head 9.2 45.4 45.3 100% (1193)

The figures indicate that the women in female headed house are more likely to influence in decision-making in comparison to male headed house.

Decision Making by Sex of the First Child

Sex of first children


Total




1 Female 67.3 21.1 11.6 100% (4721)

2 Male 19.4 25.1 55.5 100% (4005)

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)

Decision Making by Age at Marriage

Age at first marriage


Total




1 Less than 18 years 63.4 21.7 14.8

100% (6106)

2 18 – 25 Years 16.1 58.1 25.8 100% (2517)

3 More than 25 years 14.0 24.3 62.1 100% (103)

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.

Decision Making by Birth Interval

First birth interval


Total




1Less than 18 months 63.6 21.7 14.7

100% (4834)

2More than 18 months 15.8 24.5 59.7

100% (3892)

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.

Decision Making by Employment Status

Employment Status of Women


Total

Less than 33%

decisions33% to 66%


decisions

1 Not working 74.8 18.3 6.8 100% (1377)

2Working but not paid 16.3 75.1 8.5 100% (5246)

3Working in modern / service sectors 2.1 78.8 20.1 100% (1483)

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.

Decision Making by Standard of Living

Standard of Living Index (SLI)


Total




1 High SLI 7.9 80.1 12.0 100% (546)

2 Medium SLI 8.5 76.1 15.3 100% (4539)

3 Low SLI 15.5 74.9 9.7 100% (3641)

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.

Decision Making by Land Ownership

Owns Land


Total

Less than 33%

decisions33% to 66%


decisions

1 No ownership 16.3 75.8 7.9 100% (4856)

2 Owns alone 3.1 75.5 21.4 100% (837)

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.

Work Status by Types of Decisions

Percentage of Women Making Following Decisions

TotalDomestic

DecisionsReproductive

DecisionsEconomic

Decisions

1 Not working 43.1 36.6 29.8 1377

2 Working but not paid 39.6 29.8 29.3 5246

3Working in agriculture & paid job 51.9 41.5 44.2 1483

4 Working in modern industrial and service sectors jobs 76.5 77.2 78.1 620

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.

Logistic Regression Results

Model: I Model: II Model: III

Exp(B) Exp(B) Exp(B)

1 Not Working ®

Working but not paid 0.91 0.74** 0.83**

Working in agriculture & paid 1.32*** 1.59*** 1.67***

Working in non-agriculture & paid 3.33*** 2.49*** 2.46***

2Age at first marriage < 18 years ®

18 – 25 years 1.51*** 1.46***

More than 25 years 2.41*** 2.38***

3First open birth interval <= 18 month ®

First open birth interval > 18 month 1.62*** 1.46***

4 Male first child [female ®] 1.93*** 1.67***

5 Female head [male ®] 3.35*** 2.86***

Logistic Results..

6Spousal age gap >= 10 years ®

Model: III (Exp (B)

Spousal age gap < 10 years 1.35**

Wife age > husband age 1.05*

7Spousal education gap>10 yrs ®

Both are equal 1.91***

5 years gap 1.03**

5 - 10 years gap 1.01*

8Occupational Gap > =2 level ®

Both are equal or 1 level gap 2.61***

Wife has better occupation 1.78**

Logistic Results…

9 Terai ecological zone ® Model: III Exp (B)

Hill zone 1.46***

Mountain zone 1.38***

10Low SLI®

Medium SLI

1.52***

High SLI

1.58***

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, lower the 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.

Thank you

presented by: yagya prasad subedi phd student, university of aberdeen

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