Examination of Elements Influencing Mothers’ Dynamic Capacity and Versatility: A Household-level Analysis

Abstract

The study aims at exploring the degree of dynamic capacities and versatility of mothers at the household level and to recognise its general determinants. The sample includes 280 households where mothers with school-going children are the respondents. Principal component analysis is applied to estimate a mother’s dynamic capacities and versatility level. The ordinary least-square regression model is developed to identify the factors that influence the mother’s dynamic capacities and versatility level. The outcome shows that the dynamic capacities and versatility of mothers shift fundamentally across families and in general, it is tolerably high. The further result shows that the higher the level of education and the higher the age at marriage the higher the mother’s capacity to taking decisions and move autonomously. Different elements that add to this are a place of residence, religion and housing conditions.

Keywords

Principal component analysis OLS mother’s dynamic capacities and versatility socio-economic and household characteristics

Introduction

In India, women have generally been considered an oppressed group in the society for a long period. According to Human Development Index 2021, India placed 131st out of 188 countries and received a score of 0.645 (UNDP, 2021). Gender Gap Report 2022 shows that India ranked 135th out of 146 nations in the world (World Economic Forum, 2022). The government has taken several policy initiatives to eliminate gender discrimination and provide equal opportunities for women. One of India’s eight Millennium Development Goals (MDGs) is the promotion of gender equality and women’s empowerment.

It is obvious that men and women both act as agents of economic progress, and the stagnation of one of these agents will impede the process of overall development. Different facets of society experience gender discrepancies that can be seen in the political, economic, workforce participation and educational arenas. It is well known that women are less likely than men to have access to assets and other resources within a family (Deere & Doss, 2006; Doss, 2013). Women often have uneven positions and prestige in practically every culture and area of life; as a result, it is important to empower them by offering them equal opportunities. Women’s empowerment is a key strategy for eradicating gender inequality in society (Duflo, 2012).

This study attempts to analyse the extent of women’s empowerment by exploring the dynamic capacities and versatility of women at the household level. Women’s dynamic capacities and versatility are complex phenomena that show women’s ability to make decisions independently and enjoy equal freedom and opportunities in all aspects of society. Women’s empowerment implies women’s capacity to make a choice for their own lives (Kabeer, 2001). The concept of empowerment refers to a process that develops people’s power to use their own lives, communities and societies via acting on topics they regard as crucial. It is crucial for promoting social, financial and political strengthening and includes many aspects such as dynamic capacity, capacity to move outside the home uninhibitedly, political interest and so forth.

The study focuses on the sufficient degree to quantify mothers’ dynamic capacity and versatility at the household level through their decision-making ability, mobility and participation, which can also be called empowerment of mothers within the family. The study estimates the mother’s dynamic capacity and versatility aspects with a chosen set of indicators and investigates the factors that impact the dynamic capacity and versatility of mothers.

The sample includes 280 women having children of school-going age. Factor analysis is used to identify different dimensions of dynamic capacity and versatility. To quantify the extent of women’s empowerment at a household level a dynamic capacity and versatility index (DCVI) was developed using principal component analysis (PCA). Further, the analysis employed an ordinary least-square (OLS) regression model in order to identify personal and household characteristics that affect overall empowerment at the household level.

Literature Review

Women’s Empowerment Through Decision-making Ability, Mobility and Participation

Women’s empowerment, according to Kabeer (1999), is the process by which people who have been denied the ability to make wise life decisions eventually develop it. Even if they are closing, there are still gender disparities in the world’s economic, political, health and educational results, particularly in emerging nations (World Economic Forum, 2017). According to Kabeer (1999), interventions aimed at empowering women should concentrate on expanding women’s access to and control over resources as well as cultivating their sense of agency to make decisions that would improve their welfare.

Women’s empowerment primarily consists of two components: decision-making and involvement in economic activity. Although it is not a guarantee, having control over resources can act as a ‘catalyst for empowerment’ (Malhotra et al., 2002). Economic, social and political resources are frequently crucial in ensuring that women are empowered, but they are not always enough. Resources cannot lead to empowerment if women are unable, individually or collectively, to recognise and use resources in their own interests (Malhotra & Mather, 1997).

Various factors that contribute to women’s empowerment have been extensively documented. These include giving women access to resources and knowledge (Roy et al., 2015; Valdivia, 2015), increasing the number of girls who can attend college (Geddes & Lueck, 2002; Spohr, 2003), and enacting laws that strengthen women’s inheritance rights and reserve seats in politics for women (Beaman et al., 2012; Bhalotra et al., 2018; Bose & Das, 2017).

Muhammad et al. (2021) observed that informed and independent working women are more engaged, and their job in dynamic interaction is extremely steady. Women’s financial strengthening assists with facilitating conjugal pressures as engaged women can make better decisions with regard to well-being (Cornish et al., 2021). Seidu et al. (2021) evaluated that women with decision-making power and confidence were more conscious about child health care.

Factors Affecting Women’s Empowerment

Many studies explored several socio-economic, personal and household factors that impact the empowerment level of women in a family. Hafsah et al. (2021) demonstrated that the position of women, care of legitimate privileges, clinical access and political investment positively affected women’s empowerment. Khan et al. (2021) found that women with education and employment are more empowered to make decisions regarding expenditure and travelling in the family; however, women with less education and from jointly structured families are less empowered. Sharaunga et al. (2016) found that women that are heads of the household and have higher levels of economic agency, physical capital, psychological strength and financial management skills were more likely to make better decisions.

Chaudhary et al. (2012) found that the consciousness of women about their rights, economic empowerment of women and women’s overall development have a positive and significant effect on women’s empowerment. Haque et al. (2011) found that women’s current age, place of residence, education, religion, media exposure, etc., are the important factors affecting women’s empowerment and autonomy. Islam et al. (2012) found that decision-making power and increased involvement in family affairs were significantly associated with women’s empowerment in rural areas. Nikkhah et al. (2010) found that education and marital status had a significant effect on women’s empowerment. Women’s education had a significant effect on decision-making regarding savings and family planning (Hajra, 2012).

Al-Shami et al. (2017) found that access to microcredit can play an important role in household financial decision-making. Thapa and Gurung (2010) established the fact that women’s age at marriage, husband’s work experience, education and occupation, economic status and positive attitude towards a job have a positive influence in enhancing women’s empowerment. Shettar (2015) found that access to education, employment and change in social structure are only enabling factors for women empowerment. Sultana and Hossen (2013) found that employed women were more empowered and the factors such as age, educational qualification, household income and employment had a statistically significant positive effect on women’s empowerment.

Methodology

Objectives

The objectives of the study are as follows:

To measure the extent of mothers’ dynamic capacities and versatility through decision-making ability, mobility and participation.

To identify the personal and household characteristics that affect the extent of dynamic capacities and versatility.

Area of Study and Sample

Assam, one of the eight states of North East India, is the largest state in the region on the basis of its population, while in terms of physical size, it is second only to Arunachal Pradesh. Assam’s attitude towards women differs from other states in a few key aspects. According to NHDR, there was more gender disparity in the state than there was in all of India; out of the 32 states and Union Territories in the nation, the state was ranked 29th. According to the Human Development Index 2019, the HDI value of Assam is 0.613, which is lower than all northern, western and southern states in the country. Hence, women’s empowerment is a serious concern in Assam for equal participation in society. Women in Karimganj, one of the remotest districts in Assam are lacking in education and other opportunities (Das & Dutta, 2015, 2016). This provided a strong basis for choosing the Karimganj district as an area for the study.

The study depends on essential information which is created through a family overview where the mother of school-going children is the respondent. The survey is conducted in Karimganj district of Assam and a convenient sampling is used for identifying the households where women have children of school-going age. The initial sample included 300 respondents, but 20 responses were discarded due to incomplete data. The final sample of the study includes 280 responses where 90 families are from metropolitan regions and the rest 190 are from rustic areas which are also called the rural areas of Karimganj district. The study uses 17 indicators to measure women’s empowerment, a sample of 280 households is sufficient to meet the minimum sample criteria of 1:10 suggested by Hair et al. (2019).

Study Design

Women’s empowerment is a broad concept and has been measured using different dimensions by different researchers. Several researchers have measured women’s empowerment along social, political and economic aspects through women’s decision-making ability, mobility and engagement at both the household and community levels (see Malhotra et al., 2002; Roy et al., 2015; Valdivia, 2015).

Since women’s empowerment is such a broad concept, it is exceedingly challenging to examine all of its facets in a single study. This study focuses on mothers’ dynamic capacity and versatility at the household level through their decision-making ability, mobility and participation across economic and social aspects, which can also be called empowerment of mothers within the family.

The term ‘dynamic capacities and versatility’ has been used as the index captures a mother’s ability to make decisions, move freely and participate in a variety of facets of life. The index includes decision-making regarding, family health, routine purchases, savings and investments, and children’s education along with mobility to parental, friends and relatives’ home, local doctor clinics and local markets for different purposes and participation in social and religious activities.

Mother’s DCVI is constructed as a measure of overall empowerment using 17 indicators identified through a review of different articles and reports on the measurement of decision-making ability, mobility and participation of women. These indicators are listed in Table 1; the first 10 indicators measure the mother’s decision-making power in family matters and the rest 7 indicators measure her mobility and participation in different activities.

Table 1.

Indicators for Decision-making Ability, Mobility and Participation of the Mother.

Decision-making of the mother regarding	Mother’s mobility to
1. Family health care (FHC), 2. Family size (FS), 3. Family day-to-day expenditure (FDTDE), 4. Spending personal income (SPI), 5. Purchasing jewellery/bonds/shares (PJBS), 6. Purchasing clothes and makeup articles (PCMA), 7. Control the saving (CSFU), 8. Education/expense on children (E/EC), 9. Gifts for relatives (PGFR), 10. Purchase of land (POL),	1. Local market for day-to-day purchases (LMFP), 2. Local health care/local doctor clinic (LHC/LDC), 3. Home of relatives/home of friends (HOR/HOF), 4. Visiting parental home (VPH), mother’s participation in 5. Cultural programme (MPICP), 6. Religious programme (MPIRP), 7. Meetings of women organisation (MWO).

Source: Chaudhary & Nosheen (2009) and UNDP (2010, 2015).

Table 2.

List of Explanatory Factors.

Explanatory Variables
1. Respondent age (RA), 2. Respondent age at marriage (RAAM), 3. Respondent’s yearly income (RYI), 4. House condition (HC) is a categorical variable, where the condition of a house is assessed as ‘good’ = 3, ‘average’ = 2, ‘below average’ = 1, through observation keeping in view the material used in construction. Here below-average category is the reference group. 5. Health condition of the respondent (HCOR) is also a categorical variable which is categorised as follows: The condition of the health of the respondent is assessed as ‘very good’ = 4, ‘good’ = 3, ‘bad’ = 2 and ‘very bad’ = 1, through observation. Here the category ‘very bad’ is the reference group. 6. Place of residence (POR), a dummy; 1 if urban; 0 otherwise, 7. Family type (FT), 1 if nuclear; 0 otherwise,	8. Religion (RLGN), 1 if Hindu; 0 otherwise 9. The education level of the mother (MED) is categorised into four different categories. MED1 (representing education level attained 13 years and above), MED2 (representing education level attained 11 years to 12 years), MED3 (representing education level attained 8 years to 10 years), MED4 (representing education level attained 0 years to 7 years). Here, MED4 is the reference group.

Explanatory Variables

1. Respondent age (RA),
2. Respondent age at marriage (RAAM),
3. Respondent’s yearly income (RYI),
4. House condition (HC) is a categorical variable, where the condition of a house is assessed as ‘good’ = 3, ‘average’ = 2, ‘below average’ = 1, through observation keeping in view the material used in construction. Here below-average category is the reference group.
5. Health condition of the respondent (HCOR) is also a categorical variable which is categorised as follows:
The condition of the health of the respondent is assessed as ‘very good’ = 4, ‘good’ = 3, ‘bad’ = 2 and ‘very bad’ = 1, through observation. Here the category ‘very bad’ is the reference group.
6. Place of residence (POR), a dummy; 1 if urban; 0 otherwise,
7. Family type (FT), 1 if nuclear; 0 otherwise,

8. Religion (RLGN), 1 if Hindu; 0 otherwise
9. The education level of the mother (MED) is categorised into four different categories. MED1 (representing education level attained 13 years and above), MED2 (representing education level attained 11 years to 12 years), MED3 (representing education level attained 8 years to 10 years), MED4 (representing education level attained 0 years to 7 years). Here, MED4 is the reference group.

All these indicators are measured on a Likert scale. In the case of decision-making ability, if the mother alone takes the decision value assigned is 2, if both mother and her husband take a decision jointly, then the value assigned is 1 and if mother does not take any decision and husband or other member(s) of the family takes decision then the value assigned is 0.

Similarly, in the case of the mother’s mobility and participation, if the mother moves frequently to above-mentioned places and participates frequently in the above-mentioned activities, then the value assigned is 2; if the mother rarely moves to above-mentioned places and rarely participates in the above-mentioned activities, then the value assigned is 1; but if she does not move to the above-mentioned places and does not participate in the above-mentioned activities, then the value assigned is 0.

Several personal and household factors that might influence decision-making ability, mobility and participation are explored through an OLS regression model in order to analyse their impact on the overall empowerment level. Table 2 provides a list of assumed factors that are identified from the review of the literature (Section 2.2).

Method

Mother’s DCVI has been constructed using PCA. Before using PCA, a multivariate factor analysis has been conducted to find the association among the set of observed variables. The validity of factor analysis is tested using Bartlett’s test and Kaiser–Meyer–Olkin (KMO) measure.

Here four factors are extracted, the first factor explains 33.8% of variations, the second factor explains 7.9% of variations, the third factor explains 7% of variations and the fourth factor explains 6.5% of variations. A composite index is developed as a weighted sum of scores for each household, the weight being the percentage of the variation explained by the factors. If this percentage for the kth factor is denoted by S²_k, then the index for the jth household was calculated using the formula:

H_{j} = \sum S^{2}_{j} f_{j} j =_{} 1, 2, \dots, j .

This index measures the mother’s empowerment at the household level through decision-making abilities, mobility and participation. The index value is calculated for each household. The value of the index can be negative or positive and can measure one value relative to the other. Further, for comparison, the index is standardised on a scale of 0 to 100. The standardised index of jth household is

I_{j} = (H_{j} - H_{\min i m u m}) / (H_{\max i m u m} - H_{\min i m u m}), \times 100, j = 1, 2, \dots, j .

The composite index value is used as a dependent variable in the OLS regression model for analysing the impact of personal and household characteristics on overall empowerment level.

Result and Discussion

Factor analysis results of the households are presented in Tables 3, 4 and 5. The validity of the factor analysis is tested using Bartlett’s test and KMO measure (Table 3). Bartlett’s test provided sufficient statistical significance to apply factor analysis and the KMO value (0.895) supports the same (Table 3). The factors are extracted using PCA. Four factors are extracted, the total variance explained and the rotated component matrix are shown in Tables 3 and 4, respectively.

Table 3.

KMO and Bartlett’s Test.

KMO Measure of Sampling Adequacy.		0.895
Bartlett’s test of sphericity	Approx. Chi-square	1,479.957
	Df	136
	Sig.	0.000

Table 4.

Total Variance Explained.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	5.749	33.818	33.818	5.749	33.818	33.818	3.924	23.080	23.080
2	1.347	7.922	41.740	1.347	7.922	41.740	2.537	14.922	38.003
3	1.198	7.046	48.786	1.198	7.046	48.786	1.620	9.527	47.529
4	1.099	6.466	55.252	1.099	6.466	55.252	1.313	7.723	55.252
5	0.951	5.597	60.849
6	0.859	5.053	65.903
7	0.843	4.957	70.859
8	0.715	4.204	75.063
9	0.659	3.874	78.937
10	0.615	3.618	82.555
11	0.539	3.169	85.724
12	0.495	2.912	88.635
13	0.451	2.653	91.288
14	0.427	2.512	93.800
15	0.413	2.427	96.227
16	0.355	2.088	98.315
17	0.287	1.685	100.000

Note: Extraction method: PCA.

Table 5.

Rotated Component Matrix.

Indicators	Component
Indicators	Factor 1	Factor 2	Factor 3	Factor 4
FHC	0.479	0.469	0.183	−0.020
FS	−0.154	−0.137	−0.064	0.679
FDDE	0.630	0.245	0.243	0.208
SPI	0.613	0.424	0.200	0.057
PJBS	0.115	0.801	0.022	−0.027
PCMU	0.685	0.036	−0.035	−0.053
CSFU	0.715	−0.030	−0.042	−0.109
E/CE	0.717	0.152	0.112	0.096
PGR	0.521	0.296	0.121	−0.057
PL	0.088	0.707	0.149	0.107
LMP	0.635	0.383	0.242	0.060
LHCD	0.147	0.556	0.109	−0.131
HRF	0.226	0.164	0.637	−0.294
PHV	0.011	0.114	0.795	0.096
MPICP	0.584	0.295	0.397	0.364
MPIRP	0.427	0.014	0.395	0.485
MWO	0.334	0.469	−0.092	0.530

Note: Extraction method: PCA.

Rotation Method: Varimax with Kaiser Normalisation.

Using the factor score values, the average value of the composite index of the mother’s DCVI is estimated to be 66.6 on a 0 to 100 scale. The result indicates a moderately high level of the mother’s decision-making ability, mobility and participation.

To identify the factors affecting the dynamic capacity and versatility of mothers, a multiple regression analysis is used where mother’s DCVI is the dependent variable and place of residence (POR), family type (FT), religion (RLGN), education level of mother (MED), respondent’s age (RA), respondent’s age at marriage (RAAM), respondent’s yearly income (RYI), house condition (HC) and health condition of the respondent (HCOR) are the independent variables. The descriptive statistics and the regression results are presented in Tables 6 and 7, respectively.

Table 6.

Descriptive Statistics, N = 280.

Variables	Mean	Std. Deviation
MDMAMI	66.6387	22.93304
POR	0.3214	0.46786
FT	0.6179	0.48678
RLGN	0.7500	0.43379
MED1	0.1429	0.35055
MED2	0.2000	0.40072
MED3	0.4821	0.50058
RA	43.7214	5.76675
RAAM	21.2143	3.61951
RYI	50,857.1429	96,473.48672
HC	2.4357	0.65266
HCOR	2.9821	0.45951

The descriptive statistics in Table 6 reveal that for a sample size of 280, the mean DVCI value is 66.6 with an SD value of 22.9. These figures indicate that on average the mother’s dynamic capacity and versatility are moderately high. But high SD value is pointing towards the fact that there is wide household-to-household variation in DVCI values which justifies the regression analysis to identify the determinants of DVCI. The average RYI is also found to be on the higher side; however, there exists a high level of income disparity as reflected by the SD value of RYI. All the independent variables together explain 61.8% of the variations in DVCI. The F-value is found statistically significant at 1% implying a good model fit.

Table 7.

Regression Result.

Model Variables	Coefficients		t	Sig.	Collinearity Statistics
Model Variables	B	Std. Error	t	Sig.	Tolerance	VIF
(Intercept)	−81.61	16.291	−5.01	0.000***
POR	3.739	1.078	3.47	0.006**	0.837	1.195
FT	−3.725	2.783	−1.339	0.182	0.922	1.085
REGN	39.949	3.545	11.268	0.000***	0.712	1.404
MED1	24.796	6.187	4.007	0.000***	0.358	2.795
MED2	27.797	5.184	5.362	0.000***	0.395	2.529
MED3	12.214	4.205	2.905	0.004**	0.381	2.625
RA	0.399	0.294	1.355	0.177	0.583	1.714
RAAM	2.359	0.586	4.022	0.000***	0.374	2.673
RYI	0.0039	0.0168	0.232	0.817	0.625	1.601
HC2	1.967	2.71	0.726	0.469	0.466	2.145
HC3	0.437	1.965	0.222	0.824	0.317	3.152
HCOR2	1.888	6.868	0.275	0.784	0.355	2.819
HCOR3	0.83	4.403	0.189	0.851	0.366	2.733
HCOR4	0.132	3.479	0.038	0.97	0.295	3.39

Note: Dependent variable: DVCI, R ²= 0.638, adjusted R² = 0.618, F-value: 33.18 (significant at 1%).

From the regression result, it is found that all the variables except family type have a positive influence on mother’s DCVI. POR, when urban, has a significant positive influence on DVCI at a 1% significance level. Religion has also a significant positive influence on DVCI implying that mothers of the Hindu religion are more empowered with regard to their dynamic capacity and versatility than Muslims. In the case of the education level of mothers, the three categories have a significant positive influence on DVCI implying that mothers having a critical level of schooling (8 years and above) enjoy greater decision-making power, mobility and participation and the impact of higher education is found to be stronger. RAAM is also found to have a positive significant impact on the dependent variable at a 1% significance level. The other variables, i.e., family type, respondent’s age, respondent’s yearly income, house condition and health condition of the respondent have no significant influence on DVCI.

Conclusion

Women’s empowerment has been viewed as the cure for all social evils in society and a real force for female progression, thus it is important to examine its causes in order to promote it. The study is important in the sense that it has considered three crucial aspects, namely, the ability to take decisions independently, freedom of mobility and participation which are important components of women’s empowerment.

The study reveals that in a traditionally orthodox backward society, the mothers are moderately empowered to take decisions independently and move outside the home in order to interact with society at will. It is needless to say here that the higher the ability of the mothers in these areas of activity, the higher would be their empowerment level. Therefore, the need of the hour is to take affirmative steps to enhance the ability of the mothers to make decisions and move independently. The findings of this study contain significant contributions and policy implications for NGOs and decision-makers. Given the consequences, policymakers and governments can take action that may ultimately prove significant for the empowerment of women, especially rural women in developing regions.

Due to cultural and societal limitations, a significant female population is unable to make a social or economic contribution and is not meeting their potential for sustainable growth. Furthermore, the majority of educated individuals in rural regions work in the public sector; however, the public sector in rural regions offers few prospects for the rural population. The result is rural women are less empowered than urban women. The POR in regression results supports this fact. The regression results establish the importance of a mother’s education in this respect. The higher the education level of the mother, the higher is her ability to taking decisions and move independently.

The other factors that contribute to this are urban residence, being Hindu by religion and the respondent’s age at marriage. The results support the view that the higher the women’s age at marriage, the higher her ability to take decisions and move out independently. Women’s empowerment is not achievable until the economy creates equal opportunities for investment, employment and entrepreneurship. Government and policymakers should take all these factors into account while making policies regarding gender equality and women empowerment, this might contribute significantly to reducing gender disparity and promoting social and economic development.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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