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The monster-in-law effect: Linking qualitative observations to quantitative analysis on household structure, migration and empowerment in Nepal
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This presentation was given by Audrey Pereira (International Food Policy Research Institute), as part of the Annual Scientific Conference hosted by the University of Canberra and co-sponsored by the University of Canberra, the Australian Centre for International Agricultural Research (ACIAR) and CGIAR Collaborative Platform for Gender Research. The event took place on April 2-4, 2019 in Canberra, Australia. Read more: https://www.canberra.edu.au/research/faculty-research-centres/aisc/seeds-of-change and https://gender.cgiar.org/annual-conference-2019/
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The monster-in-law effect: Linking qualitative observations to quantitative analysis on household structure, migration and empowerment in Nepal
- The Monster-in-law Effect: Linking qualitative observations to quantitative analysis on household structure, migration, and empowerment in Nepal Cheryl Doss, Ruth Meinzen-Dick, Audrey Pereira, Rajendra Pradhan & Sophie Theis April 4th, 2019 | Seeds of Change Conference | Canberra, Australia
- What’s on TV tonight?
- Daughters-in-law have little control over time, work, property (Pradhan et al. 2018) “We could never take the food by ourselves, but had to work very hard to plant corn, break earth, and so on but my mother-in-law was never satisfied. I did whatever my mother-in-law ordered me to do, went wherever she asked me to go. I was never allowed to go where I wanted to go. I was never allowed to do what I wanted.” “As daughters-in-law we are supposed to do what they (parents-in- law) tell us to do” Experiences of daughters-in-law in Nepal
- Unmarried daughter Young daughter-in-law in extended household Wife/mother in nuclear household Mother-in-law in extended household Pradhan, R., Meinzen-Dick, R. S., & Theis, S. (2018). Property Rights, Social location in the household
- Women’s empowerment is dynamic across the life course Social location and empowerment Social location even more significant than land ownership in women’s household decision making power (Allendorf 2007) Matters more than caste/ethnicity in household decision making (Singh 2016) Different domains of empowerment may be more important than others at different times Sraboni & Quisumbing (2018); Malapit & Quisumbing (2015); Sraboni et al. (2014) Empowerment across the life course
- Drawing on Intersectionality Social Location Caste/ Ethnicity Socioeconomic status Age Gender Collins, P. H., & Bilge, S. (2016). Intersectionality. John Wiley & Sons. An analytical tool that examines how power relations are intertwined and mutually constructing Multiple categories overlap and interact to define identities, shape experiences and influence outcomes “Interlocking systems of oppression”
- Importance of intersectionality Caste and ethnicity affect status of household and gender norms Household structure, social location within the household is critical: Narratives of “dukha” (suffering) of daughters in law Women experience a major increase in their empowerment when they split off from extended family Male migration has complex effect on households How generalizable are these findings? Qualitative findings
- How is a woman’s social location in the household associated with her empowerment? How does her husband’s migration status mediate these outcomes? Research questions
- Project and data Treatment 1 Technical Trainings Livestock (Goats) Cornerstones + Trainings Group Formation Treatment 3 Technical Trainings Livestock (Goats) Group Formation Treatment 2 Technical Trainings Cornerstones + Trainings Group Formation Technical Trainings Cornerstones + Trainings Group Formation Technical Trainings Livestock (Goats) vs. Control Research funding provided by USAID “BASIS Assets and Market Access” Innovation Lab RCT of a Heifer International program designed to replicate the Smallholders in Livestock Value Chain (SLVC) Program in Nepal Three arms: Physical capital (goats), technical training and group savings, social capital formation
- Context Project included 3,300 Households from 7 districts Mid-Hills (2 districts) Terai (3 districts) Earthquake (2 districts): removed from analysis Qualitative data: 4 villages: 2 in hills, 2 in Terai Two rounds Village-resident ethnography, focus groups, life histories, key informants Quantitative data: Midline survey data 1,803 currently married women; one respondent per household We use the Abbreviated Women’s Empowerment in Agriculture Index
- What in the world is WEAI? Women’s Empowerment in Agriculture Index Developed by USAID, IFPRI & OPHI Launched in 2012, now used in 53 countries Measures inclusion of women in the agricultural sector Survey-based index - interviews men and women in the same household
- A-WEAI Indicators Empowerment score is weighted sum of adequacy in the six A-WEAI indicators
- Indicator Adequacy definition Access to and decisions on credit Respondent solely or jointly makes at least one decision about at least one source of credit that their household used Asset ownership Respondent solely or jointly owns at least one large or two small assets Control over use of income Respondent has at least some input in decisions about income or feels they can make decisions about income, not including minor household purchases Group membership Respondent participates in at least one community group Input in productive decisions Respondent has some input in decisions about livelihood activity or feels they can make decisions in at least two areas of livelihood activities Workload Respondent worked less than 10.5 of the last 24 hours (includes productive and reproductive work) A-WEAI Indicators (contd.)
- Empowerment: Empowerment score Time spent on work in hours (total, productive, reproductive) OLS regressions Indicators of empowerment (binary): Input in productive decisions, asset ownership, control over use of income, access to and decisions on credit, group membership, workload Logistic regressions Quantitative analysis
- Social location in the household is defined as: Wife (of HoH): No in-laws in household Mother-in-law: at least one daughter-in-law in household Daughter-in-law: at least one parent-in-law in household Controls Individual: Age; caste; years of education Household-level: child <5 in HH; child <15 in HH; #adult men in HH; #adult women in HH; asset index Definitions
- Sample characteristics Wife - Husband in HH Wife - Migrant husband MIL - Husband in HH MIL - Migrant husband DIL - Husband in HH DIL - Migrant husband Mean (SE) Mean (SE) Mean (SE) Mean (SE) Mean (SE) Mean (SE) Age (years) 42.19 34.27 52.71 43.58 30.21 28.60 (0.48) (0.46) (0.46) (1.39) (0.53) (0.46) Years of schooling 2.04 3.35 0.47 1.10 5.08 5.77 (0.14) (0.23) (0.08) (0.36) (0.27) (0.27) Caste: Brahmin or Chhetri 0.26 0.22 0.25 0.16 0.27 0.27 (0.02) (0.03) (0.02) (0.07) (0.03) (0.03) Caste: Dalit 0.33 0.40 0.37 0.42 0.33 0.39 (0.02) (0.03) (0.03) (0.09) (0.03) (0.03) Caste: Janajati 0.13 0.10 0.12 0.19 0.09 0.10 (0.01) (0.02) (0.02) (0.07) (0.02) (0.02) Caste: Muslim 0.04 0.03 0.03 0.00 0.04 0.03 (0.01) (0.01) (0.01) (0.00) (0.01) (0.01) Caste: Tarai Middle 0.21 0.21 0.20 0.23 0.21 0.17 (0.02) (0.03) (0.02) (0.08) (0.02) (0.02) Other castes 0.04 0.04 0.03 0.00 0.06 0.03 (0.01) (0.01) (0.01) (0.00) (0.01) (0.01) Child under 5 years lives in HH 0.18 0.25 0.48 0.65 0.47 0.51 (0.02) (0.03) (0.03) (0.09) (0.03) (0.03) Child aged 5-18 years lives in HH 0.73 0.91 0.75 0.87 0.83 0.80 (0.02) (0.02) (0.02) (0.06) (0.02) (0.03) Number of adult women in HH 1.26 1.14 2.67 2.65 2.36 2.33 (0.02) (0.02) (0.05) (0.19) (0.05) (0.05) Number of adult men in HH 1.50 1.30 2.92 2.77 2.33 2.29 (0.03) (0.04) (0.05) (0.18) (0.07) (0.07) N 605 254 371 31 307 235
- Time spent on work Wife Mother-in-law Daughter-in-law Husband in HH Migrant husband Husband in HH Migrant husband Husband in HH Migrant husband Mean (SE) Mean (SE) Mean (SE) Mean (SE) Mean (SE) Mean (SE) Time spent on work (total hours/day) 8.88 9.24 8.96 8.34 9.01 9.09 (0.13) (0.23) (0.19) (0.66) (0.18) (0.22) Productive work (hours/day) 4.25 4.60 4.94 4.69 4.13 4.54 (0.14) (0.22) (0.18) (0.66) (0.19) (0.23) Reproductive work (hours/day) 4.66 4.69 4.02 3.83 4.90 4.64 (0.12) (0.18) (0.15) (0.51) (0.19) (0.21) N 605 254 371 31 307 235
- Time spent on work: MIL vs DIL Mother-in-law Daughter-in-law Test Total hours worked Mean 8.91 9.04 (SE) (0.18) (0.14) Productive work hours Mean 4.92 4.31 *** (SE) (0.18) (0.15) Reproductive work hours Mean 4.00 4.79 *** (SE) (0.15) (0.14) N 402 542 *** p<0.01, ** p<0.05, * p<0.1
- Social location Mother-in-law Daughter-in-law Empowerment Credit Group Income Asset ownership Input in Productive decisions Workload - Total time (hours) Productive work (hours) Reproductive work (hours) - Reference category: Wife Controls for age; caste; years of education; child <5 in HH; child <15 in HH; #adult men in HH; #adult women in HH; asset index Only estimates for at least p<0.1 Red: Less likely empowered/ adequate Green: More likely empowered/ adequate Red: Working less Green: Working more
- Social location & Migration Mother-in-law Daughter-in- law Migrant husband Mother-in- law*Migrant husband Daughter-in- law*Migrant husband Empowerment + + - Credit - Group + + - Income + - Asset ownership + - Input in Productive decisions + Workload - Total time (hours) Productive work (hours) Reproductive work (hours) Reference categories: Wife Husband in HH Controls for age; caste; years of education; child <5 in HH; child <15 in HH; #adult men in HH; #adult women in HH; asset index; Only estimates for at least p<0.1
- Women experience a shift in empowerment when moving from joint to nuclear HHs Different domains contribute to empowerment in different ways Increased workload – less sharing of work; but more control over income Husband’s residence is “protective” of daughter-in-law’s status surprising finding! Time use vs. agency over time use Discussion
- Limitations We do not consider never-married or divorced, widowed, or separated women Other domains of empowerment may play a crucial role HH remittances: Who are they sent to? Limited sample sizes from intersectionality Need to be aware of overlapping social categories Implications for the design of interventions Conclusion
- • Allendorf, K., 2007. Do women’s land rights promote empowerment and child health in Nepal?. World development, 35(11), pp.1975-1988. • Collins, P. H., & Bilge, S. 2016. Intersectionality. John Wiley & Sons. • Janzen, S., Magnan, N., Sharma, S. and Thompson, W.M. 2016. Evaluation of the Welfare Impacts of a Livestock Transfer Program in Nepal Midline Data Pre-analysis Plan. • Malapit, H. J. L., and A. R. Quisumbing. 2015. What Dimensions of Women’s Empowerment in Agriculture Matter for Nutrition in Ghana? Food Policy 52:54–63 • Pradhan, R., Meinzen-Dick, R.S. and Theis, S., 2018. Property Rights, Intersectionality, and Women’s Empowerment in Nepal (Discussion Paper 1702). Intl Food Policy Res Inst. • Sraboni, E. and Quisumbing, A., 2018. Women’s empowerment in agriculture and dietary quality across the life course: Evidence from Bangladesh. Food Policy. • Sraboni, E., H. J. Malapit, A. R. Quisumbing, and A. U. Ahmed. 2014. “Women’s Empowerment in Agriculture: What Role for Food Security in Bangladesh?” World Development 61:11–52. Acknowledgments & References With many thanks to: Sarah Janzen Nicholas Magnan Agnes Quisumbing Emily Myers Elena Martinez Seeds of Change conference organizers CG Collaborative Platform for Gender Research; GAAP2 project; IFPRI
- gender.cgiar.org We would like to acknowledge all CGIAR Research Programs and Centers for supporting the participation of their gender scientists to the Seeds of Change conference. Photo: Neil Palmer/IWMI
Editor's Notes
- Good morning, everyone, and thank you for being here today. My name is Audrey Pereira and I’m a research analyst at the International Food Policy Research Institute. Today I’ll be presenting some joint work with Cheryl Doss, who’s at the university of Oxford, Ruth Meinzen-Dick who is also with IFPRI, and Rajendra Pradhan who is with the Nepa School of Social Sciences and Humanities in Kathmandu.
- For this presentation, and to help explain the topic, I asked my friends and family to send me the names of any tv shows they knew of, in which there was an evil mother in law. And I got a LOT of responses from countries across the world, including Mexico, Colombia, the Netherlands, Egypt, India, Nepal, south Korea, Tanzania and Nigeria. My second favorite one over here is evil mother in law season 1; because my favorite is evil mother in law season 2. But, in many contexts, there is the stereotype that mothers in law are evil, specifically to their daughters in law, and that the daughters in law are overworked and over burdened.
- This is true in Nepal as well. From qualitative work, we found that daughters in law have little control over their time, work and property. Daughters in law really felt like their mothers in law made them work very hard, always told them what to do, and that they had no say in the decisions on what they did, and their mothers in law had a lot of power.
- Before we proceed further, I’d like to show you how women’s social location in the HH changes over time. She starts of as a daughter, living with her parents and siblings. When she gets married, she moves in with her husband’s family, and is a daughter in law in an extended household. Eventually, she and her husband save up enough money to split off from the extended household and start their own household. In this point of time, she becomes a “wife” (as a wife to the head of household), and lives with her husband and children. When her children get older and get married, she becomes a mother in law, when her daughter in law comes to live with her. As you can probably see already, the level of empowerment of women in these different social location categories varies – mothers in law and wives tend to have more agency, and daughters in law and daughters less agency.
- In the literature as well, we find that women’s empowerment is dynamic across the life course. Social location is more important than caste or land ownership in women’s household decision making power. We also see that different domains of empowerment matter more than others for women’s empowerment at different points in time.
- For this work, we draw on intersectionality, which captures how power relations are intertwined and mutually constructing. It’s the idea that there are multiple categories, for example age, gender, caste, that overlap and interact to define identities, shape experiences, and influence outcomes. For example, a 25-year old Brahmin woman who lives with her parents has a very different experience and level of empowerment than a 60-year Dalit widow who lives with her son.
- The qualitative findings really stress the importance of intersectionality in this context, both outside and within the household. Caste and ethnicity affect HH status and gender norms, while HH structure, a woman’s social location and whether her husband is present in the household also interact Taking these qualitative findings, we really wanted to see whether they were generalizable
- The research questions that we want to test quantitatively then are: How is a woman’s caste associated with her empowerment? How is a woman’s social location in the household associated with her empowerment? And how does her husband’s presence in the household, i.e. whether or not he is a migrant, affect empowerment outcomes?
- For this analysis, we are using data from a randomized control trial of a Heifer International Project. I’m not going to go too into detail on the RCT itself since we are using just one round of the data.
- The project included 3300 households from 7 districts, but 2 districts were dropped following the earthquake. There were two rounds of qualitative work, which included life histories, focus groups, and key informant interviews. For the quantitative analysis, we are using only the midline survey data from 2017. We have about 1800 currently married women in our sample – and just one respondent per household. And we use the Abbreviated WEAI to measure empowerment.
- So what is the WEAI? It was launched in 2012 as a collaboration among IFPRI, OPHI and USAID. It’s currently in use in 53 countries, and it measures the inclusion of women in the agricultural sector.
- The survey itself is composed of two sub-indices. The first is the five domains of empowerment index, which is a direct measure of an individual’s empowerment in five domains. The second is the Gender Parity Index which measures the relative achievements between women and men in the same household. For our analysis, we use the 5DE as the main measure of empowerment.
- The A-WEAI includes six indicators across five domains - Production, Resources, Income, Leadership and Time. And for our main outcome of empowerment, we use a score which is the weighted sum of adequacies across the six indicators.
- Here are the definitions of what we consider adequate in each of the six indicators. For example, a respondent is adequate in or satisfies the group membership variable if she was a member in at least one group in the community. These are all binary indicators
- For the quantitative analysis, we use the weighted empowerment score, and time spent on work in hours, looking at total work, productive work, and reproductive work as our outcome in OLS regressions. We also look at the six individual indicators as outcomes using logistic regressions And I’ll focus only on some modules and results today.
- These are the different social locations we consider: You’re a Wife: if there are no in laws in your household You’re a mother in law if you live with at least one daughter in law in the HH and you’re a daughter in law if you live with at least one parent in law in the HH, so either father in law or mother in law In all our regressions, we control for age, caste, education, presence of a child, number of adults, and asset index.
- Here’s what our sample looks like. Overall, MIL tend to be much older, and DIL are the youngest, which makes sense given the life cycle On average, DIL tend to have the most schooling, followed by wives, and then MIL And extended HHs tend to have more children under 5
- Here are some statistics on time spent on work We find that on average, wives with migrant husbands, spend the most time on work overall, likely because they do the productive and reproductive work and there are fewer members in the HH to share the burden with. But in extended households, MIL are doing more productive work and DIL are doing more reproductive work.
- If we check for differences, these are means, and the test for statically significant differences, between the time spent on work for MIL vs DIL, we find that there’s no statistically significant differences in overall time spent on work, but we do find that these differences are statistically significant when you look at type of work. MIL tend to work more on productive work, and DIL on reproductive work.
- All our regressions control for the individual and household level variables I showed you earlier. And I’m only highlighting the results that were statistically significant at at least the 10% level. Red shows less likely to be empowered and green shows more likely to be empowered for the empowerment score and six indicators. And for the time spent on work, these are in hours, so we see increases in work in green and decreases in work in red. When we look at social location, without taking into account husband’s migration status, we find that MIL are more likely to be inadequate in workload compared to the reference group, which is wife. This means that MIL are more likely to work more than 10.5 hours per day. And we find that DIL are working less on reproductive work than wives, likely because in an extended HH, you may be able to share childcare responsibilities which isn’t always available for wives (in nuclear households).
- So what happens if we include husband’s migration status as a mediator? The reference categories here are wife and husband in HH. In the first column, we have MIL (with her husband in the HH), and we find that compared to a wife, she’s less likely to be adequate in credit and workload, likely because she’s still managing the household and her DIL’s time. When we look at DIL with her husband in the HH, we find that she’s more likely to be empowered, and more likely to be adequate in group membership, compared to a wife. In the third column, for a wife with a migrant husband, she is more empowered, more likely to be adequate in group membership, control over use of income, asset ownership and input in productive decisions compared to a wife who lives with her husband. This makes sense. Because she’s taking over all the management of the household in her husband’s absence. But for a daughter-in-law, whose husband is away, we’re seeing the opposite effect. She’s less likely to be empowered, and less likely to be adequate in group membership, control over use of income, and asset ownership. So, not having your husband in the HH when you live with your in laws is associated with being more disempowered. The last thing I want to point out is that there were no statistically significant results in time spent on work in any of the categories.
- So, what did we learn? We found from the quant and qual that women experience a shift in empowerment when there’s a change in HH structure, e.g. moving from extended to nuclear HHs. Different domains contribute to empowerment in different ways. For example, a wife may have more work because there are fewer HH members to share the burden with, but she also may have more control over income. We found that when a DIL lives with her in laws, her husband’s absence is negative. And finally, the question you’ve been waiting for me to answer: do monster in laws exist? Well, I’m sure they do, but based on our sample in terms of the amount of work, we don’t find that. We find that they’re equally burdened as DIL. But this may be an issue of what we are measuring: time use vs. agency over time. Nobody likes being told what to do, and DIL have very little say in what they do. This comes out quite strongly in the qual work as well.
- A few limitations of this work: We do not consider never marroed, divorced, widowed or separated women who may have very different experiences. There may be other domains of empowerment that are not captured here and are crucial. With intersectionality, it’s easy to run into limited sample sizes because you can only cut the data so many ways. And finally to wrap up, we need to be aware of these overlapping social categories, particularly what it means for intervention design. Thank you!
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