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International Journal of Applied Research & Studies ISSN 2278 – 9480

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iJARS/ Vol. I/ Issue I/Jun-Aug, 2012/158

Research Article

Determinants of Household Decision Making among Women in Kolkata slum Areas: An Application of Multinomial Logistic

Regression

Authors:

Gitanjali Hajra

Address for Correspondence:

Assistant Professor, Economics & Statistics, Brainware Group of Institutions, Kolkata

Abstract: In today’s world women’s autonomy, which is one of the most important indicators of women empowerment, is very well-known concern regarding the gender issue for inclusive

growth of an economy. In most of the cases the role of women in family decision making is

negligible, where the male member of the family is the final decision maker, which is more

visible in the poor families of the developing countries. There are different dimensions of

women’s autonomy. The present paper identifies four such dimensions like decision in family

planning, Decision in savings, Decision in expenditure and decision in healthcare. This paper

attempts to find how far education, age and income have an impact on these four dimensions. In

our study the target respondents were only the women segment from Kolkata Municipal

Corporation’s slum areas and the primary data has been collected by applying multistage

sampling technique. Multinomial Logistic Regression was run through the statistical software

SPSS 15 to test the relationship of these variables to all four dimensions of Decision making.

Keywords: Decision making, developing countries, Multinomial Logistic Regression, Multi-Stage Sampling, Women’s Autonomy, Women Empowerment.

JEL Classification: C01, C12, I25.

1. Introduction: In a less developed country like India, women played an important role for achieving inclusive growth of the economy. In the approach paper of 12

th Five year plan (2012-

17) Planning Commission of India addressed economy’s transition to a higher and more

inclusive growth Path implying removal of Social Exclusion. Inclusiveness is a

Multidimensional concept; one of such dimensions is women’s autonomy. Dyson and Moore in

‘on kinship structures and Female Autonomy’ (1983) defined autonomy as the capacity to

manipulate one’s personal environment and the ability – technical, Social and Psychology to

obtain information and to use it as the basis of making decisions about one’s private concerns

and those of one’s intimates’. The National Family Health Survey (NFHS-3) Report in India of

1998-99 also measured female autonomy in terms of various indicators on household Decision

Making. Role of women in household decision making Process depends on various Social and

demographic factors like Income, Age, Occupation, Level of education and so on. In the present


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study finds out the different factors that can influence the household decision making progress of

women.

2. Review of Literature: There exist several studies conducted by the different researchers, which examined the determinants of Women’s Autonomy in household Decision Making.

Dev Raj Acharya, Jacqueline S Bell, Padam Simkhada, Edwin R van Teijlingen and Pramod Raj

Regmi in the study of ‘ Determinants of Women's Autonomy in Decision Making’(2010) aimed

to explore the links between women’s household position and their autonomy in decision

making.

In another research study by Nava Ashraf (Spousal Control and Intra-Household Decision

Making: An Experimental Study in the Philippines Harvard Business School) found that

household savings and investments typically depend on how decision making power distributed

between men and women. It also analyzed the fact that, financial decisions of the household are

greatly affected by the fact that the income is known to spouses or not.

Marie Furuta and Sarah Salway in their study of ‘Women’s Position Within the Household as a

Determinant Of Maternal Health Care Use in Nepal’ (2006) examined women participation

within their household—decision making, employment and influence over earnings, and spousal

discussion of family planning by running a logistic Regression Model and showed that there is a

strong association of women’s education with health.

M. Hemanta Meitei in the study titled ‘Education or Earning and Access to Resources

Determining Women’s Autonomy: An Experience Among Women of Manipur’ investigated

how far education or earning and access to resources have a significant impact on women’s

decision making power. He concluded that, most of the decisions are taken jointly (both husband

and wife) while working women take more of independent decisions than the non-working

women. Controlling effect of the other background variables work status of women turn out a

significant explanatory variable rather education.

Mosiur Rahman, Uzzal K. Karmaker and Abdur R. Mia in the study of ‘ Determinants of

Women Empowerment at Domestic and Non-domestic Issues: Evidence from Chapai Nawabganj

District in Bangladesh’ examined the determinants of women empowerment at domestic and

non-domestic issues in Bangladesh. From the logistic regression model considering decision-

making power for household affairs as the dependent variable, it had shown that as the level of

education of the respondents increases their decision making power also increases.

Siwan Anderson and Mukesh Eswaran in the article titled ‘What Determines Female Autonomy?

Evidence from Bangladesh’ (2005) examined the determinants of female autonomy within

households in Bangladesh, a developing country. They investigated the relative contributions of

earned versus unearned income in enhancing women’s autonomy and the role of employment

outside of their husband’s farm.

Data: For the present study, Primary data have been collected from the slum areas of Kolkata Municipal Corporations. In this survey each woman was asked the questions based on structured

questionnaire to measure their role in family decision making directly. We have taken a sample

of 100 women from Kolkata Slum areas.


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3. Variables under the Study: In general there are two types of variables: dependent and independent.

Independent Variable: Independent variables under the study was –

a) Age of an Woman b) Educational Qualification of a woman, which is a categorical variable. This variable is categorized into six groups: Illiterate, Primary, Secondary, Higher Secondary, Graduate and Post

Graduate.

c) Income of a woman is categorized into three: < Rs 5000, Rs 5000-Rs.10000 and >Rs 10000.

Dependent Variable: The dependent variable in this study consists of four types of household

decision making of women which has three aspects:

a) Decision in family planning b) Decision in family saving c) Decision in family expenditure d) Decision in healthcare Expenditure The above four variables are categorized into three levels: 1=High, 2=Low and 3=No Role.

4. Objective of the study: The specific objective of the present study is to identify the relationship between:

a) Role of level of education of women and decision in family planning, family savings, family expenditure and healthcare.

b) Role of Age of Women and decision in family planning, family savings, family expenditure and healthcare.

c) Role of Income of Women and decision in family planning, family savings, family expenditure and healthcare.

5. Research Methodology:

Multinomial Logistic Regression (MLR): To understand the role of women education, age and Income in family decision making process

we have used several statistical and analytical tools. In this study we have Multinomial logistic

regression (MLR) and to predict the presence or absence of a characteristic or outcome based on

values of a set of predictor variables.

When the dependent variable is categorized then we can’t use Ordinary Least Square Method

(OLS) to predict the dependent variable. In that case Logistic Regression is applied for

predicting the dependent variable.

Logistic regression is useful for situations in which we want to be able to predict the presence or

absence of a characteristic or outcome based on values of a set of predictor variables. It is similar

to a linear regression model but is suited to models where the dependent variable is dichotomous

(0 and 1).

The multinomial (polytomous) logistic regression model is an extension of the Binomial logistic

regression model, the dependent variable is not restricted only to two categories and the

independent variables may be categorized or may be continuous. It is used when the dependent

variable has more than two nominal or unordered categories, in which Independent variables can

be factors or covariates. In general, factors should be categorical variables and covariates should

be continuous variables. In our study each of the four dependent variables is categorized into

three levels: High (Code1), Low (Code 2) and no role (Code 3). Among the independent


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variables level of education is categorized into six dummies: 0 for illiterate, 1 for primary, 2 for

secondary, 3 for Higher Secondary, 4 for Graduate and 5 for the others.

The overall test of relationship among the independent variables and the dependent was based on

the reduction in the likelihood values for a model without any independent variables and the

model with the independent variable. This difference in likelihood followed a chi-square

distribution. The Logistic Regression equation by using the Maximum Likelihood Estimation is given by:

Log-odds=A+B(X)

Or, Odds= Antilog (A+B(X))

Where, the Odds ratio (p/(1 − p) indicates the ratio of the Proportion of Occurrences to the

Proportion of non-occurrences.

The Wald statistic is used to test the significance of individual logistic regression coefficients for

each independent variable (that is, to test the null hypothesis in logistic regression that a

particular logit (effect) coefficient is zero). Wald statistic is the squared ratio of the

unstandardized logistic coefficient to its standard error.

Results of Multinomial Logistic Regression Model:

6. Factors affecting the level of savings as one dimension of women decision

making: Multinomial Logistic Regression Analysis:

6.1 Overall Test of the relationship

6.1.1 Model Fitting Information: The first analysis of Multinomial Logistic Regression is to

describe the overall Test of the relationship i.e. the relationship between the dependent and the

dependent variable. This relationship is based on the statistical significance of the final model

Chi-Square. Chi-Square Statistic is the difference in 2 Log-Likelihood between the final model

and the reduced model.

Table 6.1

From the log-likelihood table the log likelihood function (-2Log likelihood) will decrease if the

independent variables have a relationship with the dependent variable. In our analysis (Table 6.1)

the initial Log Likelihood value (with no independent variable) is 171.513 and the final Log

Likelihood (including the independent variable) is 138.805. The difference between the two is a

Chi-Square and its significance value is 0.001 (which is less than 0.05). So, there is significant

relationship between the dependent variable (level of savings) and the independent variables

(Income, age and level of education). So, the final model is outperforming the null model.

6.1.2 Goodness of Fit:

Model Fitting Information

171.513

138.805 32.707 12 .001

Model

Intercept Only

Final

-2 Log

Likelihood

Model

Fitting

Criteria

Chi-Square df Sig.

Likelihood Ratio Tests


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Pearson and Deviance Goodness of Fit measure how the model adequately fits the data. The Null

Hypothesis in this case is given by-

H0: There is no Difference between Observed and Expected Frequencies, i.e. the model is good

fit.

If the significance value is >0.05, it is not statistically Significant and the model is good fit. In

table 6.2 the significant value of Chi-Square is greater than 0.05. So, we can say that, the data

Table 6.2

Are consistent with the model assumptions i.e. the model adequately fits with the data.

6.2 Strength of the Multinomial Logistic Regression Relationship:

6.2.1 Classification Matrix as a measure of Model accuracy:

The classification Table shows the practical results of using MLR Model. This cross tabulation

of the observed response categories with the predicted response categories helps to assess the

predictive performance of the model. Cells along the diagonal represent numbers of correct

predictions. Cells off the diagonal represent numbers of incorrect predictions.

Table 6.3

The Table 6.3 classifies correctly 62.2 out of 100 respondents who state that, they have a high

role in family household savings. The model gives better accuracies for ‘High’ role group and

‘Low’ role group but not for the ‘No Role’ group.

6.2.2 Relationship between Dependent Variable and Independent variables (likelihood

Ratio Test):

Likelihood Ratio Test Checks the contribution of each independent variables to the dependent

variable. If the significance of the test is small (i.e., less than 0.05) then the effect contributes

significantly to the model.

Goodness-of-Fit

112.984 130 .856

114.368 130 .834

Pearson

Deviance

Chi-Square df Sig.

Classification

23 13 1 62.2%

12 37 0 75.5%

1 12 1 7.1%

36.0% 62.0% 2.0% 61.0%

Observed

High

Low

No Role

Overall Percentage

High Low No Role

Percent

Correct

Predicted


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Table 6.4

In table 6.4 only level of education is significant, because the significance value of chi-Square

for this independent variable is 0.005(


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Savings(a) B Std. Error Wald df Sig. Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound Upper Bound

High Intercept 20.901 2.318 81.281 1 .000

Age -.069 .053 1.700 1 .192 .933 .841 1.035

[Income=1.00] -.425 .982 .187 1 .666 .654 .095 4.484

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] -18.305 1.886 94.254 1 .000 1.12E-008 2.79E-010 4.52E-007

[Leveledu=1.00] -16.200 2.123 58.239 1 .000 9.21E-008 1.44E-009 5.91E-006

[Leveledu=2.00] .488 2852.015 .000 1 1.000 1.628 .000 .(c)

[Leveledu=3.00] -16.845 1.505 125.311 1 .000 4.83E-008 2.53E-009 9.23E-007

[Leveledu=4.00] 0(b) . . 0 . . . .

Low Intercept 19.728 1.849 113.887 1 .000

Age -.072 .046 2.407 1 .121 .931 .850 1.019

[Income=1.00] .361 .958 .142 1 .706 1.435 .220 9.377

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] -16.518 1.364 146.572 1 .000 6.70E-008 4.62E-009 9.72E-007

[Leveledu=1.00] -14.920 1.672 79.603 1 .000 3.31E-007 1.25E-008 8.78E-006

[Leveledu=2.00] .698 2852.015 .000 1 1.000 2.010 .000 .(c)

[Leveledu=3.00] -17.073 .000 . 1 . 3.85E-008 3.85E-008 3.85E-008

[Leveledu=4.00] 0(b) . . 0 . . . .

a The reference category is: No Role. b This parameter is set to zero because it is redundant. c Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing.

From table 6.5 the EXP(B) is 0.933 for ‘High’ group i.e. for each year difference in age, the

women is 0.933 (7%) times less likely to have ‘high role’ in family savings compared to ‘No

Role’ Group , similarly, the respondent is 0.931 times less likely to have low role in family

savings compared to ‘No Role’ Group.

For Income, the odds ratio (EXP(B)) is 0.654 for group 1( < 5000 Rs per month), i.e. the women

within the group 2 ( Rs. 5000- Rs.10,000) are 1.52 times (Odds Ratio is 1.52, reciprocal of

0.654) are more likely to have high role compared to no role and . The women within the group 1

(


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The Final Log Likelihood value (-2Log Likelihood) decreases and the significant value of chi-

square is 0.002 i.e. less than 0.05. So, the Null Hypothesis that there is no significant difference

between the model without independent variables and the model with independent variables is

rejected. The existence of a relationship between the dependent and independent variables is

supported.


Table 7.2

The significance values of Chi-Square of Table 7.2 for both Pearson and Deviance are >0.05,

indicating the acceptance of the Null Hypothesis that the model adequately fits the data.


7.2.1 Classification Matrix as a measure of Model accuracy:

Table 7.3

The Classification table 7.3 indicates that, the overall percentage of accurate predictions is 70%.

The model gives the better accuracies for the ‘Low’ Role group and ‘No Role’ Group.

7.2.2 Likelihood Ratio Test:

Table 7.4


139.720

108.288 31.433 12 .002

Model

Intercept Only

Final

-2 Log

Likelihood

Model

Fitting

Criteria

Chi-Square df Sig.


Goodness-of-Fit

123.248 130 .650

92.169 130 .995

Pearson

Deviance

Chi-Square df Sig.

Classification

0 2 1 .0%

0 23 17 57.5%

0 10 47 82.5%

.0% 35.0% 65.0% 70.0%

Observed

High

Low

No Role

Overall Percentage

High Low No Role

Percent

Correct

Predicted


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The Significance value of Chi-Square for Age and Level of Education are less than 0.05, so these

two explanatory variables have significant effect on the level of family planning. But, Income is

not statistically significant at 5% level (though significant at 10% level), since the significance

value of Chi-Square for Income is greater than 0.05 (less that 0.1).

7.2.3 Identifying the Statistically Significant Predictors:

Table 7.5

Parameter Estimates

Planning(a) B Std. Error Wald df Sig. Exp(B)



High Intercept -38.805 3108.506 .000 1 .990

Age .111 .107 1.064 1 .302 1.117 .905 1.378

[Income=1.00] 17.315 3108.504 .000 1 .996 33109894.429 .000 .(b)

[Income=2.00] 0(c) . . 0 . . . .

[Leveledu=.00] 13.769 2.047 45.267 1 .000 954757.843 17293.269 52711983.952

[Leveledu=1.00] -1.740 6196.687 .000 1 1.000 .175 .000 .(b)

[Leveledu=2.00] 16.180 1.769 83.674 1 .000 10634781.576 332001.098 340657244.878

[Leveledu=3.00] 17.823 .000 . 1 . 55030877.101 55030877.101 55030877.101

[Leveledu=4.00] 0(c) . . 0 . . . .

Low Intercept -2.851 1.635 3.041 1 .081

Age .109 .039 7.695 1 .006 1.116 1.033 1.205

[Income=1.00] 1.001 .615 2.644 1 .104 2.720 .814 9.085

[Income=2.00] 0(c) . . 0 . . . .

[Leveledu=.00] -3.465 1.405 6.087 1 .014 .031 .002 .490

[Leveledu=1.00] -1.678 1.386 1.464 1 .226 .187 .012 2.828

[Leveledu=2.00] -1.062 1.346 .623 1 .430 .346 .025 4.833

[Leveledu=3.00] -1.316 1.465 .807 1 .369 .268 .015 4.736

[Leveledu=4.00] 0(c) . . 0 . . . .

a The reference category is: No Role.

Likel ihood Ratio Tests

108.288a .000 0 .

117.680 9.392 2 .009

113.736 5.448 2 .066

132.433 24.145 8 .002

Ef fect

Intercept

Age

Income

Leveledu

-2 Log

Likelihood of

Reduced

Model

Model Fitting

Criteria

Chi-Square df Sig.


The chi-square statistic is the dif f erence in -2 log-likelihoods

between the f inal model and a reduced model. The reduced

model is formed by omitting an ef f ect f rom the f inal model. The

null hypothesis is that all parameters of that ef f ect are 0.

This reduced model is equivalent to the f inal model

because omitting the ef f ect does not increase the

degrees of f reedom.

a.


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b Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing. c This parameter is set to zero because it is redundant.

Log Odds Ratio for Age i.e. EXP(B) for age is 1.117, i.e. for each difference in age, women are

1.117 times more likely for ‘High’ Role Group compared to ‘No Role’ group and for each

difference in age women are 1.116 times more likely for ‘Low’ Role Group compared to ‘No

Role’ Group.

For educational qualification for ‘High’ Income Group, Group 0(Illiterate) and Group 2(

Secondary Education) are statistically significant and for ‘Low’ Income Group only Group

0(Illiterate) is highly significant when we take our reference category as ‘No Role’ Group.

Having no education (Illiterate: Group 0) make a woman 97% (100%-3%) less likely to choose

‘Low’ Role group over ‘No Role’ Group.

8. Factors affecting the level of Family Expenditure as one dimension of

women decision making: Multinomial Logistic Regression Analysis

8.1 Overall Test of the Relationship:

8.1.1 Model Fitting Information:

Table 8.1

Table 8.1 of Model Fitting Information indicates that, the value of -2 Log Likelihood Ratio (LR)

of Chi-Square decreases for final model against the initial model, when we only consider the

intercept. Ie. There is a relationship between the dependent variable and independent variables,

but since the significant value of Chi-Square (Chi-Square is the difference between the -2Log

Likelihood of the initial Model and the Final Model) is greater than 0.05, so we can say that,

there is no significant relationship between the dependent variable and independent variables.


Table 8.2

Table 8.2 indicates that, the model adequately fits the data, since both the values of Chi-Square

in the table are greater than 0.05.



173.484

157.552 15.932 12 .194

Model

Intercept Only

Final

-2 Log

Likelihood

Model

Fitting

Criteria

Chi-Square df Sig.


Goodness-of-Fit

128.118 130 .530

133.926 130 .389

Pearson

Deviance

Chi-Square df Sig.


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Table 8.3

The Table 8.3 of the Likelihood Ratio Test shows that, none of the independent variables are significant, since all the significance value of Chi-Square are greater than 0.05. It indicates that,

there is no significant relationship between any of the independent variables (Age, Income and

Level of Education) and the dependent variable.

8.2.2 Estimating the individual Parameter:

Table 8.4 Parameter Estimates

Expenditure(a) B Std. Error Wald df Sig. Exp(B) 95% Confidence Interval for Exp(B)


High Intercept -.419 1.856 .051 1 .821

Age -.008 .043 .035 1 .853 .992 .911 1.080

[Income=1.00] 1.015 .764 1.766 1 .184 2.760 .618 12.334

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] .268 1.573 .029 1 .865 1.308 .060 28.536

[Leveledu=1.00] 19.320 1.534 158.715 1 .000 245699683.079 12163814.092 4962944501.598

[Leveledu=2.00] 2.135 1.678 1.619 1 .203 8.457 .315 226.839

[Leveledu=3.00] 2.093 1.838 1.296 1 .255 8.105 .221 297.556

[Leveledu=4.00] 0(b) . . 0 . . . .

Low Intercept .038 1.884 .000 1 .984

Age -.026 .045 .344 1 .558 .974 .892 1.063

[Income=1.00] 1.032 .796 1.679 1 .195 2.806 .589 13.359

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] .355 1.580 .051 1 .822 1.427 .064 31.597

[Leveledu=1.00] 19.290 .000 . 1 . 238610630.073 238610630.073 238610630.073

[Leveledu=2.00] 1.531 1.708 .804 1 .370 4.624 .163 131.361

[Leveledu=3.00] 1.064 1.932 .303 1 .582 2.898 .066 127.749

[Leveledu=4.00] 0(b) . . 0 . . . .


157.552a .000 0 .

158.008 .457 2 .796

159.538 1.986 2 .370

172.417 14.865 8 .062

Ef fect

Intercept

Age

Income

Leveledu

-2 Log

Likelihood of

Reduced

Model

Model Fitting

Criteria

Chi-Square df Sig.









a.


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a The reference category is: No Role. b This parameter is set to zero because it is redundant.

The interpretation of the table 8.4 is that, having a Primary Educational Degree makes a woman

245699683 times more likely to choose ‘High’ Role group over ‘No Role’ Group.

9. Factors affecting the level of Family Expenditure as one dimension of

women decision making: Multinomial Logistic Regression Analysis 9.1 Overall Test of the Relationship:


Table 9.1

The Model Fitting Information Table (Table 9.1) indicates that, though there is a relationship

between the dependent variable and the independent variables since the -2Log Likelihood Ratio

decreases in case of final Model from the model with intercept only. But this relationship is not

significant, since the significance value of Chi-Square is greater than 0.05.


Table 9.2

The significance value of Pearson Chi-Square Test Statistic is 0.026, which is less than 0.05, so

the model does not adequately fit with the given data.



Table 9.3


155.313

148.607 6.705 12 .876

Model

Intercept Only

Final

-2 Log

Likelihood

Model

Fitting

Criteria

Chi-Square df Sig.


Goodness-of-Fit

163.204 130 .026

133.639 130 .396

Pearson

Deviance

Chi-Square df Sig.


148.607a .000 0 .

148.712 .105 2 .949

148.793 .186 2 .911

155.221 6.614 8 .579

Ef fect

Intercept

Age

Income

Leveledu

-2 Log

Likelihood of

Reduced

Model

Model Fitting

Criteria

Chi-Square df Sig.









a.


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Likelihood Ratio Tests indicate that, none of the independent variables are statistically

significant at 5% level of significance.

9.2.2 Parameter estimation:

Table 9.3 Parameter Estimates

Healthcare(a) B Std. Error Wald df Sig. Exp(B)



High Intercept -.236 1.859 .016 1 .899

Age .014 .045 .105 1 .746 1.015 .929 1.108

[Income=1.00] -.213 .794 .072 1 .788 .808 .170 3.829

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] -1.302 1.561 .696 1 .404 .272 .013 5.793

[Leveledu=1.00] -2.770 1.815 2.328 1 .127 .063 .002 2.199

[Leveledu=2.00] -2.325 1.649 1.989 1 .158 .098 .004 2.476

[Leveledu=3.00] -2.047 1.796 1.298 1 .255 .129 .004 4.365

[Leveledu=4.00] 0(b) . . 0 . . . .

Low Intercept .087 1.723 .003 1 .960

Age .003 .037 .007 1 .932 1.003 .933 1.078

[Income=1.00] -.251 .640 .153 1 .695 .778 .222 2.730

[Income=2.00] 0(b) . . 0 . . . .

[Leveledu=.00] -.761 1.513 .253 1 .615 .467 .024 9.061

[Leveledu=1.00] -1.551 1.596 .944 1 .331 .212 .009 4.841

[Leveledu=2.00] -1.537 1.552 .980 1 .322 .215 .010 4.506

[Leveledu=3.00] -2.020 1.789 1.274 1 .259 .133 .004 4.427

[Leveledu=4.00] 0(b) . . 0 . . . .

a. The reference category is: No Role.

The interpretation of the table 9.3 is that, increases in age of women make 1.015 times more

likely to have high role in Healthcare over No Role Group. Similarly, increases in age of women

make 1.003 times more likely to have Low Role in Decision making of Health care over ‘No

Role’ Group.

Women within the income Group 1 (Monthly Income Rs.


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determinants of Women Empowerment and also have tried to find out the various factors

responsible for taking a role in household Decision making among women.

We have applied the Multinomial Logistic Regression (MLR) Model that best explains the

Decision making Progress in household based on the data collected from the women of Kolkata

Municipal Corporation Slum areas.

In this study we have shown that, level of education of women have significant effect on

decision in level of savings and family planning (The significance value of Chi-Square is less

than 0.05), whereas, level of education has no significant effect on decision in family

expenditure and Healthcare (the significance value of Chi-Square is greater than 0.05). Age is a

significant determinant in decision of family planning but age of a woman does not play any

significant role in taking decision in family savings, family expenditure and Healthcare

expenditure. But income does not play any significant role in any one of the four dimensions of

family decision making progress within the household among woman.

References:

1) Siwan Anderson and Mukesh Eswaran. What Determines Female Autonomy? Evidence from

Bangladesh. Bureau for Research and Economic Analysis of Development Working Paper No.

101, September 2005.

2) M. Hemanta Meitei. Education or Earning and Access to Resources Determining Women’s

Autonomy: An Experience Among Women of Manipur.

3) Acharya et al. Determinants of Women's Autonomy in Decision Making Reproductive Health

2010, 7:15, http://www.reproductive-health-journal.com/content/7/1/15.

4) Marie Furuta and Sarah Salway. Women’s Position Within the Household as a Determinant Of

Maternal Health Care Use in Nepal. International Family Planning Perspectives, 2006, 32(1):17–27.

5) Anu Rammohan and Meliyanni Johar. The Determinants of Married Women's Autonomy in

Indonesia. Feminist Economics. 16 Oct 2009. http://www.tandfonline.com/loi/rfec20.

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