Trick or Treat: Does a Microfinance Loan Induce or Reduce the Chances of Spousal Violence against Women? Answers from India

Abstract

It is a common belief that microfinance plays a dual role of poverty alleviation and socioeconomic upliftment of its women participants. However, there are enough researches that negates the positive impact of microfinance loans on spousal violence. Recognized as one of the most predominant social evils, violence against women is not only a violation of their human rights but also an act of exploitation and denial of freedom. In the present study, we have tried to investigate if microfinance loan takers experience more spousal violence as compared to their counterparts by analyzing the National Family Health Survey IV, 2015–16. Our results indicate that 40.8% of women microfinance participants experience spousal violence. Additionally, the likelihood of microfinance participants to experience spousal violence is much higher than the non-microfinance participants (odds ratio = 1.35, p value = .000). Microfinance programs are designed to increase the individual agencies of women participants, which, in turn, reduce the chances of them becoming victims of spousal violence. However, if the credit program participation induces the woman to be a victim of spousal violence together with becoming financially autonomous, then such hidden costs need to be taken into account while evaluating the effectiveness of the gendered policy design.

Keywords

cultural context credit access microfinance spousal violence interpersonal violence India

There is one universal truth, applicable to all countries, cultures, and communities: violence against women is never acceptable, never excusable, never tolerable.

—United Nations Secretary-General, Ban Ki-Moon, 2008

Background

In almost all forms of violence, be it the tribulation, bloodshed and hostility of wars or self- destructive suicides, affliction in old age or bullying, molestation, and rapes, the one behind the doors poses the most vicious threat (WHO, 2002). Violence against women breaches their fundamental freedoms and human rights (Agarwal & Panda, 2007; Ansara & Hindin, 2011). As defined by the United Nations, it is “any act of gender-based violence that results in, or is likely to result in, physical, sexual, or mental harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or private life” (United Nations, 1993). Intimate partner violence (henceforth, IPV) is one of the most common forms of gender-based violence (Garcia-Moreno et al., 2006). Prevalent on a global scale, IPV cuts across country and community, and is thus a significant public health challenge (Peterman et al., 2015). For instance, globally, 30% of ever partnered women experience physical or sexual IPV; it ranges from 37.0% in Eastern Mediterranean to 25.4% in Europe and 37.7% in South East Asia (WHO, 2013).

The Microfinance Program

Gender and economic inequality is one of the root causes of IPV (Gibbs et al., 2017; Hughes et al., 2015). In recent years, in order to empower women economically, socially and financially several livelihood-microfinance programs and cash transfers have been launched (Buller et al., 2018; Hidrobo et al., 2016). Based on the strong tenets of Developmental Idealism (Weiss & Montgomery, 2005), microfinance provides collateral-free credit to rural women to alleviate financial distress (Mahtab, 2007) and promote their social and economic well-being (Basargekar, 2010; Hoque & Itohara, 2009). Several models across the globe are followed to deliver microfinance services out of which the Grameen Bank model of Bangladesh is the most popular. The Bolivian banking model and the Rotating Savings and Credit Association (ROSCA) model of South America are among the others.

Microfinance in India existed in the form of chit funds for a very long time until it was recently brought under the purview of the formal banking system with the development of the Cooperative system. It is based on the principles of cooperation, mutual help, and democratic functioning. Generally, the loan is given to a group of women who are from a humble socioeconomic background and have a common business idea. In India, these groups are called self-help group (henceforth, SHG), which is also the world’s largest microfinance program in terms of its client base and outreach (NABARD, 2017, 2018). The group members mutually agree to contribute to a common fund by regularly saving small amount. They rotate these small pooled savings among themselves as loans within the group. After six months to one year of disciplined functioning, the group becomes eligible for a loan from the bank (Baland et al., 2019; Shah et al., 2007). An account is opened in the name of the group as per the guidelines set by the Reserve Bank of India (India’s central bank). The group deposit and the joint liability acts as the collateral (Basu & Srivastava, 2005). Thereafter, the SHG gets effectively linked to the bank and to the mainstream credit market of the economy. The bank then sanctions loan which are usually in the ratio of loan to savings either 2:1 (Basu & Srivastava, 2005) or 4:1 (Roy & Sen, 2015). The SHG members take a loan from the bank and distribute among themselves to invest in microenterprise activities (Gaiha & Nandhi, 2008).

Utilizing loan for household wellbeing has been the cornerstone principle of microfinance loans. Such loans are used for food and nutrition, health, education of children (Gaiha & Nandhi, 2008), and miscellaneous expenses incurred during festivals, marriages, higher education, and medical emergencies (Chakraborty, 2008). Also, women who are members of old SHGs (for example, three years) try to start and develop their own microenterprises by taking up income-generating activities after their basic financial needs are met (Singh, 2008).

Microfinance and Intimate Partner Violence

Though the microfinance program has been vigorously promoted as the ideal poverty alleviation model, the impact of its potential intervention to reduce the risk of IPV has generated ambiguous findings in different settings. For instance, in Bangladesh, while some studies support the notion that credit program participation reduces violence (Nawaz, 2015; Schuler et al., 1996), others oppose it (Ahmed 2005; Bajracharya & Amin, 2013; Bhuiya et al., 2003; Dalal et al., 2013). Murshid et al. (2016), found that in Bangladesh, women’s access to financial resources, especially in the context of poverty, increases their odds of experiencing IPV, thus compromising the potential empowering effects of such programs.

Results of a study examining the impact of training on women’s microenterprises in projects located in Ethiopia, India, Peru, and Sudan, by Leach & Sitaram (2002), are context driven. According to the research in India, the flow of income from the microenterprise determines spousal violence, more the income less the violence. Thus, one can certainly not conclude the effectiveness of microfinance interventions. However, by analyzing the panel data of Rojiroti, a small organization that runs microfinance programs for the weakest section of women in Bihar, India, Gordon 2016 concludes that microfinance results in the reduction of domestic violence. Similarly, studies based on South Africa (Kim et al., 2007; Pronyk et al., 2006), rural Guatemala (Cepeda et al., 2017), and Tanzania (Kapiga et al., 2019) show that microfinance based intervention reduce IPV.

Various theories try to understand the paradoxical relationship between women’s microfinance participation and IPV however end up presenting competing and conflicting views. The following results could be the rationale behind the reduction of spousal violence after taking a microfinance loan: (a) An increase in the women’s economic resources empowers her to bargain for a better situation for herself or to leave, therefore, reducing her risk of abuse (Holvoet, 2005); (b) Opening up of economic opportunities through access to credit, awareness-raising activities and skill training for income-generating activities improve the woman’s status within the household and her relationship with her husband thereby reducing IPV (Ahmed, 2005); (c) An increase in the household cash flow reduces conflict over money and resources (Abramsky et al., 2019), and finally (d) If the woman is the primary beneficiary of any economic safety net, designed to reduce poverty, then it affects the power dynamics within the household (Buller et al., 2018) and helps to reduce the violence against them (Aizer, 2010).

It has been observed that, the unintended outcome could be due to an increase in women’s resources which is seen as a threat to men who, in turn, use violence to reassert authority in the household and society at large (Eswaran & Malhotra, 2011). By applying a combination of economics and ethnographic techniques, Bloch & Rao, in 2002, explains how a husband is more likely to use violence as a weapon against his wife to extract material gains from her family. Same might hold true in case the wife is a microfinance participant where the husband resorts to violence to extract the money from his wife. Similarly, the Status Inconsistency theory suggests that access to credit leads to higher income and financial independence of a woman that challenges the preexisting marital norms laid and followed by the society for ages. The difference of status in the household leads to dysfunctional behavior (violence) by the one who usually enjoys a higher rank (Yick, 2001).

The Other Factors of Intimate Partner Violence

A large and growing body of literature documents adverse mental (Ansara & Hindin, 2011; Fortin et al., 2012; Nur, 2012; Walker et al., 2011; Wong et al., 2011; Woods et al., 2010) and physical health (Gerber et al., 2008; Loxton et al., 2006; Ruiz-Pérez et al., 2007; Schneider et al., 2009; Vives-Cases et al., 2011) as serious implications of IPV. Furthermore, women who experience IPV are more likely to face employment instability (Showalter, 2016), display poor work performances, experience tardiness, practice absenteeism (McFarlane et al., 2000) and experience negative economic well-being (Adams et al., 2012). However, while several studies have documented the aftermath of IPV, a few tried to decode its antecedents (Abramsky et al., 2019; Ackerson & Subramanian, 2008; Vameghi et al., 2018).

Demographic and socioeconomic characteristics are theoretically motivated predictors of IPV (Ackerson & Subramanian, 2008). For instance, by examining IPV during pregnancy across 19 countries, Devries et al., 2010, finds that prevalence of IPV is relatively constant within the age group 15–35 years, and after that tends to decline gradually. This suggests that younger women are more likely to experience violence. On the other hand, by examining data from National Violence Against Women Survey, conducted between 1995 & 1996, Wilke & Vinton, 2005 observes that older women are more likely to remain in an abused relationship than younger women since they are more accustomed to traditional gender roles. The authors further explain that older women live in the fear of being left alone without the prospect of remarriage and employment, whereas younger women are aware of the repercussions of violence and are soon able to leave an abusive relationship. In a developing setting like India, where divorce is still considered as a taboo (Luke & Munshi, 2011), members of the natal family force women to continue to live with their husbands in the name of family honor even if they are victims of spousal violence. Also, in the presence of traditional Indian norms that focus on gender-based discrimination, violence is well-known, accepted & justified (Ahuja et al., 2000; Surekha et al., 2016).

Studies linking education with experience of IPV establishes that women with lower levels of education are more likely to face spousal violence. Mariam, in 2014 tries to explore the association between uneducated or less educated women in rural Bangladesh and the degree of violence inflicted upon them. Her research shows that a woman’s earning capacity and maturity along with higher education could mitigate violence. The results of a study conducted in Saudi Arabia, show that education and occupation play a positive role in the life of Saudi women as it considerably reduces the levels of domestic violence (Shiraz, 2016). Furthermore, in a community based quantitative and qualitative study in rural Gujarat, India, Visaria (2000), observes that as women’s education increases their experience of violence decreases.

Attitude towards IPV remains one of the most critical challenges that influence the perpetration of violence. Exposure to mass media can affect this attitude towards violence and can bring a positive, nonviolent change. For example, by analyzing the data from 17 Demographic Health Surveys (DHS), Uthman et al. (2009) shows that access to media is associated with lower odds of justifying IPV. Similarly, in India, a secondary analysis of National Family Health Survey data, 2005–06, shows that regular exposure to newspapers or magazines has produced favorable outcome for women by disregarding wife-beating thereby leading to a decreased rate of spousal violence (Bhattacharya, 2016). The reason for reduction in acceptance of violence could be the changes in the norms about women’s status due to mass media exposure (Bhushan & Singh, 2014).

Women’s decision-making is considered to be a strong indicator of her empowerment, which results in multiple developmental outcomes. However, studies examining its role in reducing IPV continue to provide mixed results in India. For instance, a study investigating the relationship between spousal violence and women’s empowerment in a slum community in Mumbai, India, reports that women who are empowered are less likely to be at risk of domestic violence (Donta et al., 2016). Likewise, by analyzing prospective data in rural India, Sabarwal et al. (2014) finds out that financial autonomy, freedom of movement, and household decision making is successful in reducing the overall risk of marital violence but, not in the gender stratified regions of north India. The discrepancy in the study findings could be due to the following reasons. Firstly, in a conservative cultural set up in Northern India, husbands assume that they “own” their wives, based on norms and practices sanctioned by the society (Ahuja et al., 2000). Secondly, a deviation from the usual and predefined household gender roles in terms of decision making often leads to violence (Zegenhagen et al., 2019).

It has been observed that women’s economic frailty in the context of her dependence on men often aggravates violence against her. With no acceptable alternatives and norms widely sanctioned by cultural logic (Schuler et al., 1996), women tend to tolerate some level of violence in return for economic support (Bolis & Hughes, 2015) and as an acceptable facet of the institution of marriage (Murshid & Zippay, 2017). Unfortunately, some findings show working women experience more violence, despite the widespread belief that working women face less spousal violence due to raised social status and financial independence. In a multicentre study on married women between the age group 15 and 35 years, Mahapatro et al. (2012) shows the prevalence of spousal violence was common among working women as compared to the homemakers. In India, a study by Bhattacharya, 2015 illustrates that married women who experience spousal violence are more likely to seek employment to be financially self-reliant. This may be because of several possible reasons ranging from the possibility of male backlash (Luke & Munshi, 2011) to arguments posited by the Evolutionary Theory that suggests that husbands out of jealousy resort to spousal violence (Eswaran & Malhotra, 2011).

One of the underlying reasons for IPV in India is the rigid, immobile, and unjust caste system prevalent in the country. In addition to caste being a varna or jati, in India, it is also a political construct. The Indian Constitution recognizes three broad categories of caste for its reservation policies. First, the scheduled castes (SCs, includes predominantly ex-untouchables); second, the scheduled tribes (STs, includes geographically isolated groups) and finally third, the other backward classes (OBCs, includes castes and communities considered to be socially, economically or educationally backward) (Vaid, 2014). Though the Indian Constitution prohibits any kind of public discrimination based on caste, discriminatory social practices coupled with the absence of opportunities continue to prevail (Krishnan, 2005). The discrimination multiplies three folds for a Dalit (SC) woman because of her class (poor), caste (outcaste), and gender (female) (Surekha et al., 2016). In India, a study by Ackerson & Subramanian (2008) shows that women belonging to schedule caste or tribe are more likely to report IPV. Their study reveals that men of a particular reserved category tend to vent their emotional and financial distress on the women of the house by inflicting violence upon them.

Purpose of the Paper

The idea of this paper originated after we were motivated to carry out a research on the microfinance beneficiary system currently prevalent in India. The discrepancy in the existing theory predictions and inconclusive empirical findings on IPV prompted us to study the subject further. Secondly, although there is a wide recognition and understanding of the potential benefits of microfinance participation in India, there is little evidence on how to address spousal violence with regard to credit program participation. A few studies based on small scale sample tries to understand its association (Gordon, 2016; Leach & Sitaram, 2002), but the results cannot be generalized for the entire country. Thirdly, the data from the recent National Family Health Survey (henceforth, NFHS) IV reveals that one out of every three ever-married Indian women experience physical, sexual, or emotional violence (IIPS and ICF, 2017). However, though there are studies that try to explore the determinants of IPV in the Indian context (Ackerson & Subramanian, 2008; Bhattacharya, 2016; Sabarwal et al., 2014, Visaria, 2000), there are very few studies that try to understand the association of spousal violence and credit program participation in India. Currently, an understanding of this association is of utmost importance as financial inclusion is one of the most important policy objectives of the government of India. Fourthly, the Indian society is characterized by a patriarchal culture where male dominance is legitimized within the family and the society through superior rights, privileges, power, and authority (Visaria, 2000). Furthermore, the age-old cultural norms advocate women to be subordinate to men throughout their lives (Gundappa & Rathod, 2012). All these cultural traits make India a relevant setting to study IPV.

Thus, by using nationally representative data, the present paper is perhaps the first attempt in India to examine the relationship between credit program participation and spousal violence. We also have used appropriate and reliable instruments for measuring spousal violence by taking into consideration all the forms of violence—physical, sexual, and emotional. Accordingly, the objectives of the paper are—first, to reiterate the impact of microfinance on spousal violence among those who access it in India; second, to identify the factors that determine spousal violence and try to investigate the possible reasons behind it; third, to understand the present scenario of spousal violence experienced by microfinance participants in India at large and its states in specific.

Data and Methods

Study Design and Sample

The study is based on accurate and legitimate data from NFHS-4 gathered during 2015–16, conducted under the stewardship of the Ministry of Health and Family Welfare (MoHFW), Government of India. It is the fourth series of the nationally important source of data for India’s 29 states and seven Union Territories. Also, for the first time, the survey gives an estimate for all 640 districts. The survey adopts a stratified two-stage sampling design in both rural and urban areas. Over 601,509 households were interviewed in NFHS-4, with a response rate of 98%. Among the interviewed households, 723,875 eligible women aged between 15 and 49 years were identified for the survey and were individually interviewed. Of them, 699,868 women were interviewed with a response rate of 97%. The detailed methodology, with complete information on the survey design and data collection has been published in the survey report (IIPS and ICF, 2017). The survey also provides information on sexual behavior; awareness and attitude on HIV/AIDS. It also provides details on domestic violence but only at the state level (in the state module). A sample of 15% of households was selected for the state module, and a questionnaire was administered that included all the questions needed for district-level estimates plus additional questions for the topics listed above.

The present study is restricted to the ever-married women selected for the domestic violence module (n = 79,729). Women, whose identity could not be secured or who were not present during the interview have been excluded from the survey. Furthermore, the study excluded never married (13,545) and married women whose gauna was not performed (171). Thus, the final sample size for the present study is 66,013 women aged between 15 and 49 years.

Outcome Variable—Spousal Violence

For the exclusivity of the subject we have defined violence as any form of destructive force— physical, sexual, and emotional caused to an ever-married women by her present or most recent husband. We have used the domestic violence module given in NFHS IV to measure spousal violence. The module administers a subset of questions on physical, sexual, and emotional violence on all ever-married women. For currently married women, the questions on violence were asked in reference to their present husband and the women who were previously married referred to their most recent husbands. Our dependent variable is a multidimensional measure of spousal violence. It indicates whether an ever-married woman has experienced any form of violence from her current or most recent husband in the last 12 months preceding the interview. This variable is based on the woman’s response to a set of three-part question designed to understand if she has experienced the following forms of violence:

Emotional: husband ever (a) humiliated her in front of others; (b) threatened to hurt or harm her or someone close to her; (c) insulted her or made her feel bad about herself.

Physical: husband ever (a) pushed or shook or threw something at her; (b) twisted her arm or pulled her hair; (c) slapped her; (d) punched her with his fist or with something that could have hurt her; (e) kicked, dragged or beat her up; (f) tried to choke or burn her on purpose; (g) threatened or attacked her with a knife, gun, or any other weapon.

Sexual: husband ever (a) physically forced her to have sexual intercourse with him even when she did not want to; (b) physically forced her to perform any other sexual acts that she did not want to; (c) forced her with threats or in any other way to perform sexual acts that she did not want to.

If the woman answered “no” to all these questions, our dependent variable was coded as 0. If she answered “yes” to any of these forms of violence, she was asked the frequency of the act in the last 12 months. If the woman answers “often” or “sometimes” to any one of the forms of violence listed, we coded our spousal violence variable as 1. If she answers “not at all” to all of them, we coded it as 0, grouping these women with those reporting never having experienced any of these forms of domestic violence.

Explanatory Variables

Microfinance Participation.

The key independent variable of the analysis is microfinance participation. Microfinance participation by women is constructed upon the question “Have you yourself ever taken a loan, in cash or in kind, from any of these programs, to start or expand a business?” This question is canvassed on a subsample of 15% of randomly selected households. Women who responded that they had taken the loan have been coded as “1” that is ‘microfinance participants’ and “0” as ‘non-microfinance participants’. The variable is dichotomous in nature.

Media Exposure.

Women’s exposure to mass media has been defined by considering how often they read newspapers, listen to the radio, and watch television. Responses on the frequencies of these sources of media were recorded as: almost every day, at least once a week, less than once a week, or not at all. The present study classifies women as having any exposure to mass media if they had exposure to any of these sources and as having no exposure if they responded with “not at all” for all three sources of media.

Household Decision Making.

Respondents were asked “who usually makes decisions” on the following: own healthcare, major household purchases and visiting family and friends, and five options of the respondent, husband, respondent, and husband jointly, someone else, and others were given. This variable has been categorized as along with partner and respondent alone.

Other Independent Variables.

Other variables include—Age of the women is categorized as 15–24, 25–34, and 35 years and older. Education of the women based on the number of years of schooling is classified as: illiterate, primary, secondary, and higher. Women’s occupation (not working and working) and husband’s occupation is grouped as not working, nonagricultural (includes professional/technical/managerial, clerical, sales, and services), agricultural and skilled & unskilled. Religion is categorized as Hindu, Muslim, and others (includes Christian, Sikh, Buddhist/Neo-Buddhist, Jain, Jewish, Parsi/Zoroastrian, no religion, and other). Caste is divided into four categories: scheduled caste, scheduled tribe, other backward class, and other castes. Place of residence is either urban or rural. The wealth index is calculated in the survey by combining household amenities, assets, and durables and then by characterizing households in a range varying from the poorest to the richest, corresponding to wealth quintiles ranging from the lowest to the highest.

Statistical Analysis

Data was analyzed in three stages. Firstly, bivariate analysis was done to look into the association of spousal violence among ever-married women and also ever-married microfinance participants. Secondly, binary logistic regression analysis was carried out to assess the association between microfinance participation and spousal violence. The unadjusted and adjusted odds ratio from the logistic regression analysis have been calculated. Finally, since spousal violence surpasses cultural, social, and regional boundaries, we used maps to accomplish the understanding. The analysis was carried out in STATA 13.0, and the maps were done with the help of ARC-GIS. Appropriate sampling weights have been used in the estimations.

Results

Prevalence of Spousal Violence and Sample Characteristics

Table 1 describes the background characteristics of ever-married women and ever-married microfinance participants who have faced spousal violence. Overall, 31.03% (n = 66,013) of ever-married women and 40.76% (n = 4871) of ever-married microfinance participants have faced spousal violence. Though the experience of spousal violence among ever-married microfinance participants is more or less uniformly distributed in all the age groups, it shows a positive association with the age of ever-married women. In both cases, the experience of spousal violence shows a significant and negative relation with education. For example, 50.01% of microfinance participants who have no education experience maximum spousal violence followed by those who have primary education (47.09%), secondary education (34.58%) and finally higher education (29.81%). Both ever-married microfinance participants (44.65%) and ever-married women (39.64%) who worked face more spousal violence compared to their nonworking counterparts. Women whose husbands are engaged in agricultural activities experience maximum spousal violence (microfinance participants—44.81% and ever-married—35.04%). Ever-married microfinance participants who have no media exposure (46.99%) and take household decisions alone (52.25%) experience more spousal violence. One-fourth of ever-married microfinance participants who live in rural areas, belong to schedule caste (45.90%) and are Hindus (41.48%) have experienced more spousal violence. Experience of spousal violence among ever-married microfinance participants tend to decline with the wealth of the household—highest among poorest women (50.88%) and least among the richest women (28.75%). The observation is similar to the experience of spousal violence among ever-married women.

Table 1.

Prevalence of Spousal Violence among Ever-married Women and Ever-married Microfinance Participants Aged 15–49 Years by Background Characteristics, India, 2015–16.

Background Characteristics	Spousal Violence among Ever-married Women		Spousal Violence among Ever-married Microfinance Participants
Background Characteristics	(%)	N	(%)	N
Ever taken a loan	p < .0001
No	30.11	20,955
Yes	40.76	4,871
Age	p < .0001		p = .717
15–24	27.47	10,489	41.36	374
25–34	30.99	27,568	40.88	1,938
35–49	32.42	27,956	40.61	2,559
Educational level	p < .0001		p < .0001
No education	40.65	22,028	50.01	1,442
Primary	36.15	9,669	47.09	824
Secondary	25.72	28,187	34.58	2,288
Higher	15.01	6,129	29.81	317
Women’s occupation	p < .0001		p < .0001
Not working	27.03	44,346	36.82	2,416
Working	39.64	21,667	44.65	2,455
Husband's occupation	p < .0001		p < .0001
Not working	33.24	2,674	37.35	194
Nonagricultural	25.10	21,577	36.92	1,449
Agricultural	35.04	22,363	44.81	1,688
Skilled and unskilled	33.38	19,399	40.88	1,540
Mass media exposure	p < .0001		p = .001
No exposure	39.86	14,507	46.99	674
Any exposure	28.73	51,506	39.85	4,197
HH decision power*	p < .0001		p = .012
Along with partner	30.21	60,795	39.48	4,272
Alone	40.78	1,921	52.25	260
Place of residence	p < .0001		p = .249
Urban	25.90	19,469	38.73	1,463
Rural	33.55	46,544	41.86	3,408
Caste	p < .0001		p < .0001
Scheduled caste	38.14	11,686	45.90	1,088
Scheduled tribe	32.09	12,108	37.87	708
Other backward class	33.17	25,574	41.40	2,142
Others	22.72	16,645	35.26	933
Religion	p < .0001		p = .071
Hindu	32.20	49,546	41.48	4,042
Muslim	27.91	8,614	35.42	394
Other	26.61	7,853	39.29	435
Wealth index	p < .0001		p < .0001
Poorest	44.56	12,838	50.88	788
Poorer	37.45	13,992	47.60	1,089
Middle	31.96	13,790	42.03	1,223
Richer	26.53	13,142	36.40	1,158
Richest	17.80	12,251	28.57	613
Total	31.03	66,013	40.76	4871

Note. *Variable includes those who have taken household decision alone or along with partner/husband.

All p-values are based on chi-square test.

Determinants of Spousal Violence

Table 2 shows the results of multivariate analysis. The objective of the logistic regression is to find out the determinants of spousal violence among ever-married women with special reference to microfinance participation. The unadjusted result shows that the likelihood of spousal violence among ever-married women who participated in microfinance activity is higher (unadjusted odds ratio (uOR: 1.65; CI: 1.548–1.759) compared to those who did not participate. Furthermore, after controlling the effect of various socioeconomic and demographic factors, the odds of spousal violence among ever-married women who participated in microfinance activity is still higher (adjusted odds ratio [aOR]: 1.35; CI: 1.259–1.448) compared to those who did not participate. Age has a significant positive effect on spousal violence. Women aged between 25 and 34 and between 35 and 49 years are more likely (uOR: 1.16; CI: 1.00–1.214 and uOR: 1.20; CI: 1.143–1.261, respectively) to experience spousal violence compared to women aged between 15 and 24 years. However, the adjusted odds ratio decreased between the age 25 and 34 years (aOR: 1.10; CI: 1.006–1.198), and the result for women of age group between 35 and 49 years is not significant. Odds of spousal violence decrease with the increase in the level of education of women. Women with secondary (aOR: 0.64; CI: 0.597–0.696) and higher (aOR: 0.40: CI: 0.352–0.456) education are less likely to experience spousal violence than women with no education. However, working women reported more (aOR: 1.48; CI: 1.392–1.569) spousal violence compared to their nonworking counterparts. Similarly, women who took household decision alone experienced higher spousal violence (aOR: 1.44; CI: 1.245–1.677) than women who took the decision along with her partner. Women living in rural areas reported more odds (uOR: 1.32; CI: 1.275–1.372) of spousal violence compared to the ones living in an urban area (aOR was 0.79 CI: 0.737–0.847). Women belonging to ST (aOR: 0.66; CI: 0.598–0.728), OBC (aOR: 0.93; CI: 0.857–1.002), and others caste group (aOR: 0.68; CI: 0.617–0.741) were less likely to experience spousal violence compared to SC caste. Though not significant Muslim women are less likely to experience spousal violence (aOR: 0.94; CI: 0.855–1.031) than Hindu women. Wealth quintile has a significantly negative effect on spousal violence. The likelihood of experiencing spousal violence is found to be less among the women belonging to poorer (aOR: 0.83: CI: 0.754–0.911), middle (aOR: 0.65; CI: 0.589–0.721), richer (aOR: 0.53; CI: 0.474–0.592), and richest (aOR: 0.37; CI: 0.327–0.424) wealth quintile than poorest.

Table 2.

Unadjusted and Adjusted Effects (ORs, 95% CIs) of Microfinance Participation and Other Background Characteristics on Spousal Violence among Ever-married Women Aged 15–49 Years, India, 2015–16.

Background Characteristics	Odds Ratio (95% CI)(Unadjusted)	Odds Ratio (95% CI)(Adjusted)
Ever taken loan
No®
Yes	1.65***(1.548–1.759)	1.35***(1.259–1.448)
Age
15–24®
25–34	1.16***(1.00–1.214)	1.10**(1.006–1.198)
35–49	1.20***(1.143–1.261)	1.07(0.975–1.17)
Educational level
No education®
Primary	0.84***(0.797–0.880)	0.94(0.859–1.026)
Secondary	0.53***(0.510–0.550)	0.64***(0.597–0.696)
Higher	0.26***(0.246–0.285)	0.40***(0.352–0.456)
Women’s occupation
Not working®
Working	1.74***(1.679–1.798)	1.48***(1.392–1.56)
Husband’s occupation
Not working®
Nonagricultural	0.71***(0.649–0.772)	0.93(0.795–1.079)
Agricultural	1.11**(1.022–1.213)	1.019(0.868–1.177)
Skilled and unskilled	1.12***(1.031–1.225)	1.04(0.892–1.206)
Mass media exposure
No exposure®
Any exposure	0.65***(0.623–0.673)	1.05(0.965–1.151)
HH decision power
Along partner®
Alone	1.54***(1.400–1.684)	1.44***(1.245–1.677)
Place of residence
Urban®
Rural	1.32***(1.275–1.372)	0.79***(0.734–0.847)
Caste
Scheduled caste®
Scheduled tribe	0.73***(0.696–0.775)	0.66***(0.598–0.728)
Other backward class	0.80***(0.766–0.839)	0.93*(0.857–1.002)
Others	0.48***(0.454–0.503)	0.68***(0.617–0.741)
Religion
Hindu®
Muslim	0.80***(0.757–0.838)	0.94(0.855–1.031)
Other	0.73***(0.690–0.768)	1.01(0.915–1.118)
Wealth index
Poorest®
Poorer	0.74***(0.703–0.775)	0.83***(0.754–0.911)
Middle	0.59***(0.558–0.616)	0.65***(0.589–0.721)
Richer	0.45***(0.429–0.476)	0.53***(0.474–0.592)
Richest	0.28***(0.266–0.298)	0.37***(0.327–0.424)

Note. ®= Reference category. *p < .10. **p < .05. ***p < .001.

Figures 1a and 1b show the prevalence of spousal violence among ever-married women and ever-married microfinance participants, respectively. While Figure 1a shows spousal violence is more common in the south-eastern states of India, Figure 1b shows the experience of spousal violence among ever-married microfinance participants is prevalent all over India in varying range. For instance, though the prevalence of spousal violence in Maharashtra fall in the range of 13.8%–24.1% (Figure 1a) but it increases among microfinance participants and falls in the range of 25.3%–37.9% (Figure 1b). On the other hand, in the state of Andhra Pradesh, where spousal violence lies in the range of more than 44.7% (Figure 1a), it decreases among microfinance participants in the range of 37.9%–50.7% (Figure 1b).

Figure 1a.

Spousal violence among ever-married women aged 15-49 years, India 2015–16.

Figure 1b.

Spousal violence among ever-married microfinance participants aged 15–49 years, India 2015-16.

Discussion

There is considerable inconsistency in literature on whether microfinance as a tool can help reduce spousal violence or not. Our main motive in the present paper was to reiterate the impact of microfinance on spousal violence in India vis-a-vis to look into the potential determinants of spousal violence. While on one hand, studies indicate a reduction of spousal violence due to credit program participation (Cepeda et al., 2017; Kapiga et al., 2019; Nawaz, 2015), on the other hand, few supports the opposite notion (Bajracharya & Amin, 2013; Dalal et al., 2013). Though largely our bivariate and multivariate result conforms with the latter proposition that microfinance participants are more likely to experience spousal violence, the results of our GIS analysis reveal that credit program participation reduces violence (although for a few states only).

The multivariate results indicate that women who have access to credit are significantly more likely to be victims of spousal violence. The finding adds to the extant literature, which debates that women’s access to microfinance loan increases violence against them (Bhuiya et al., 2003; Dalal et al., 2013). This unintended outcome may take place because of the change in the woman’s status in her household due to an increase in her economic resources (via microfinance loan) that threatens the traditionally prescribed role of the man in the household (Eswaran & Malhotra, 2011) or if the man wants to take away the money (Bloch & Rao, 2002).

The above finding becomes further apparent when we look into the results of our GIS analysis. For a better understanding of the regional variation of spousal violence, we divided the states into two broad categories of reporting (a) more spousal violence and (b) less spousal violence after taking a microfinance loan. Our findings reveal that all the states, except for the states of Madhya Pradesh, Andhra Pradesh, Arunachal Pradesh, Mizoram, and Sikkim, reported an increase in spousal violence after taking the microfinance loan. The most vital reason which led to the decrease in spousal violence for the credit program participants is her empowerment and improvement in status both in the household and society at large. (Ahmed, 2005; Holvoet, 2005). On the contrary, for the majority of the states, the bargaining model of economics failed to establish the notion that access to economic resources helps the woman to have a better bargaining position in the household, thereby reducing violence. In fact in reality, better economic or social outcome of women challenges the socially prescribed roles of men that trigger a male backlash (Eswaran & Malhotra, 2011). Consequently, we argue that microfinance, as a socioeconomic empowerment tool to reduce spousal violence, is highly contextual and culture driven.

Age is a critical demographic variable that helps us to understand in which age cohort violence occurs. In our study, the likelihood of experiencing spousal violence increases with age. Though our finding contradicts Devries et al., 2010, it has exhibited similarity with the study results proposed by Wilke & Vinton (2005). The outcome ensures that due to cultural norms, attitudes towards violence, and marriage dynamics, women report experiencing violence at older ages (Ahuja et al., 2000; Luke & Munshi, 2011; Surekha et al., 2016). Hence, the role of education and mass media exposure is crucial in changing the attitude towards violence.

The experience of spousal violence is inversely related to education and mass media exposure. The results are consistent with the findings of other studies (Bhattacharya, 2016; Mariam, 2014; Shiraz, 2016; Uthman et al., 2009; Visaria, 2000). The inverse in relationship shows that the role of education and mass media cannot be denied in lowering the odds of spousal violence. Awareness of spousal violence and gender equality through school-based programs and programs on television, radio, and newspaper can bring about a favorable change against violence by negating the acceptability of violence in our societies.

Additionally, our study has also revealed that working women are more likely to experience spousal violence and it is in consistent with previous studies (Bhattacharya, 2015; Eswaran & Malhotra, 2011; Luke & Munshi, 2011; Mahapatro et al., 2012;). Similarly, the experience of spousal violence in case of household decision making is found to be more among the ones who take decisions alone than the ones who consult their partners. Our finding contradicts with the existing shreds of evidence (Donta et al., 2016; Sabarwal et al., 2014). Thus, to validate our findings, we rely on the Status Inconsistency theory (Yick, 2001) where higher income and female independence that follows post microfinance participation threatens or destabilizes marital norms, with implications in the form of increased violence. Since India has seen and practiced years of patriarchy any change in status, whether economic, social, or financial may lead to violence against the woman who is believed to be the subordinate to her husband.

Other risk factors of spousal violence that are found to be significant in our study are women’s caste, religion, place of residence, and wealth index. Corroborating with earlier studies conducted in India (Ackerson & Subramanian, 2008; Ahuja et al., 2000; Mahapatro et al., 2012), our study findings state that women mostly from backward castes, low income groups and rural settings are more likely to experience spousal violence which depicts the severity of a women’s condition from a poor economic background. The provision of economic and educational opportunities might help them to raise their voice against such atrocities (Krishnan, 2005).

Spousal violence is the most vicious form of violence that does not get enough attention in the society and in the eyes of the law probably because it occurs within the four walls of a household which is largely believed to be a safe place, characterized by love, support, and bonding. The consequences of IPV may not be apparent, but it causes many adverse health outcomes and behaviors. In most cases, nonfatal outcomes are common than fatal results that permeates every aspect in the victim’s life. Moreover, violence thwarts the hopes of economic and social development of the victim. Thus, drawing our attention to intervention and support.

We strongly believe that microfinance, as an economic and social intervention, can prove to be an effective mechanism in mitigating the problem of violence. However, India possesses several communities that are unique in their geography, language, and culture, which make the uniform impact of the microfinance program difficult to achieve. Our research shows that the complete impact of such interventions can only be deduced, if the cultural milieu of each regions, providing loan is taken into account. To achieve this goal, the first step is to raise awareness and counsel the male members of the family on expected gender roles and rigid patriarchal clichés. This will bring a positive change in the power dynamics between the husband and the wife instead of the probable male backlash due to female credit program participation.

Furthermore, since microfinance is a grassroots program targeting rural women, integrating community health workers with the program can accomplish the goal of prevention of violence and provide the much-needed social support. The health workers can educate the women on various forms of violence, taboos related to it, how to identify and respond to it. Thus, a multisectoral approach will ensure a more comprehensive and holistic way of freedom from spousal violence.

Finally, after more than two decades of research on domestic violence and microfinance participation where quantitative studies across the world have produced mixed evidence, it is time that we now shift our focus on ethnographic studies. It calls for an in-depth investigation of the dynamics and management of credit entering into the household, and the relationship between power inequalities and violence. A well-designed qualitative instrument will deepen our understanding and throw more light upon the determinants of spousal violence in the context of microfinance program participation. Also, more researches are needed to understand spousal violence at an individual, familial, and community levels.

Limitation

The study has its limitations too. Firstly, while exploring the contours of spousal violence, we could not look over other vital determinants of spousal violence like the spousal age gap, education, occupation, toxic behavior or addiction like alcoholism. Secondly, a more detailed analysis of the precipitating factors of violence would have helped us to identify the factors that instigates husbands to inflict violence upon their wives. Thirdly, due to the limitation in the cross-sectional data, we could not analyze the cause and effect relationship. However, backed by appropriate statistical data, facts and relevant theories, our findings seem to have made substantial contribution the existing literature on spousal violence.

Footnotes

Acknowledgments

The authors are grateful to International Institute for Population Sciences, Mumbai, India to provide the data of National Family Health Survey (NFHS-IV, 2015–16). The authors are also indebted to Aparajita Chattopadhyay for her constant motivation. Special thanks to Arkajyoti Bose & Priya Das for their support.

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Debashree Sinha

Author Biographies

Debashree Sinha is a PhD scholar at the International Institute for Population Sciences in Mumbai, India. She has a master’s degree in Economics and Population Sciences. Her research interests include economic development, gender and development, and ageing.

Pradeep Kumar completed M.Phil in Population Studies and is currently pursuing PhD in Population Studies at the International Institute for Population Sciences in Mumbai, India. His areas of interest include ageing, adolescent, maternal and child health.

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