Sample and Variable Definition

Sample: The NLSS survey collects information from 10,288 women who are above the age of 15 years. Since my research question is centered on understanding the changes in women’s well-being due to her husband’s migration, I only include

married women in the sample. Also, women who are not in the labor force either because they are still in school or because they are too old, sick or disabled are excluded from my sample. The analysis presented here is based on a subsample of 6,243 women, all of whom are married and in the labor force.

Dependent Variables: Table 4.6 below presents the definitions for the dependent variables in my models.

Table 4.6: Dependent Variables for Models on Women's Work (NLSS-III)

Variable Name Definition

Household work

(Model 1) Total number of hours per week spent in household work, measured as following:

Household work = Hours spent on (Fetching water + Collecting firewood + Collecting fodder + Animal care + Knitting /tailoring + Processing preserved food + Household repair + Cooking/ Serving food + Clearing house + Shopping + Caring for elderly/sick + Child care + Community Service/ other volunteer) Self-employment in agriculture (Model 2) Binary variable with value 1 for any woman who has spent at least an hour in the past week working in family-owned field for no wages, and 0 otherwise. Self-employment in non-agriculture (Model 3) Binary variable with value 1 for any woman who has spent at least an hour in the past week working in family-owned business or home-production, 0 otherwise. Wage-employment

(Model 4) Binary variable with value 1 for women who are employed in agricultural or non-agricultural activities and received wages or in-kind payments for this work, and 0 otherwise.

Independent Variables: Table 4.7 below presents brief definitions of the independent variables in my models. This is followed by a discussion of the expected relationship of each of the independent variables with the four dependent variables. Since the same set of variables is expected to affect the different kinds of work I am studying here, most of the variables used for the four models are same. Variables, when used only for specific models, have been identified as such in the discussion presented.

Table 4.7: Independent Variables for Models on Women's Work (NLSS-III) Variable Name Definition Belongs to migrant household Binary variable with value 1 for women who belong to migrant households, 0 otherwise. Remittance-receiving households Binary variable with value 1 for women who belong to households that receive remittances, 0 otherwise Age Woman’s age Education Years of schooling Relation to household head Binary variable with value 1 for household heads, 0 otherwise Wage-employed Binary variable with value 1 for women who are employed for wages, 0 otherwise Number of adult female members Number of women above the age of 15 present in the household Number of dependent members Sum of the number of children below the age of 15 and number of elderly members above the age of 60 present in the household. Asset Index Index representing asset ownership of a household Land Ownership Size of land owned by a household Caste Binary variable with value 1 for high caste group (Brahmin, Chhetri and Newars), 0 otherwise Location Binary variable with value 1 for households in rural areas, 0 otherwise Ecological Zone Ecological zone is divided into the following three regions: Mountains, Hills and Terai. Two dummy variables, Mbelt with value 1 for mountain region and 0 otherwise and Hbelt with value 1 for Hill region and 0 otherwise, are used to represent the ecological zones. Woman belongs to migrant household: Since my main goal is to look into differences in women’s work between migrant and non-migrant households, the key independent variable in all my models is ‘whether a woman belongs to migrant household or not.’ Based on my discussion above, the coefficient for this variable is expected to be positive for models on household work and self-employment in agriculture and negative for the models on self-employment in non-agriculture and wage-employment.

Age: Women are expected to take on more responsibilities, as they get older. So, women’s work in all four categories is expected to increase with age. However, after a certain age, some of the work responsibilities may be transferred to younger members in the household. Hence, a quadratic relation is expected between age and

women’s work, with the coefficient for age being positive and that for age-squared being negative, in all the four models. Education: Women with higher educational qualifications are likely to have a better bargaining position within the household. They are also more likely to be employed for wages. Hence, household work and self-employment in agriculture are expected to be lower and self-employment in non-agriculture and wage-employment are expected to be higher for women with higher educational qualifications. The coefficient for education is expected to be negative for models 1 and 2 and positive for models 3 and 4.

Relation to household head: Women who are heads of their household are the primary caretakers, so they may have higher work responsibilities within the house as well as in the family-owned fields. These women are also expected to have higher participation in market work (either through self-employment in non-agriculture or through wage-employment) to be able to earn and provide for the family. Hence, the coefficient of being household head is expected to be positive in all the four models. Wage-employed: This variable is only used for the model on household work (model 1). Since wage-employed women contribute to household income, it is likely that they have higher bargaining power and less household work. Hence, a negative coefficient is expected for being wage-employed.

Number of adult female members: This variable is only used for the model on household work, since women living in the same household may be able to share household responsibilities. With the increase in the number of adult female

members, household work for each individual woman is likely to decrease; hence, a negative coefficient is expected here.

Number of dependent members: Increase in dependent members in the household increases women’s household work, as women are primarily responsible for providing care to these members. Increased care responsibilities at home could deter women from participating in market work. This variable is only included for models 1, 3 and 4, and the coefficient for this variable is expected to be positive for the model on household work (model 1) and negative for the models on self- employment in non-agriculture and wage-employment (models 3 and 4).

Asset index: Asset index is an indicator of the household’s economic status. Households with higher asset index are likely to have better access to water, electricity and cooking fuel. This eliminates tasks such as fetching water and collecting firewood and reduces women’s work. Also, wealthier households are more likely to be able to hire someone to help with household and agricultural work. So, household work and self-employment in agriculture is expected to be lower for women from wealthier households. However, participation in self- employment in non-agriculture may be higher for women from wealthier families as they may have the resources to invest in capital and start their own production. Participation in wage-employment may also be higher for women from wealthier families since they may have better access to education and other resources. This relation is not as clear though, since less economic pressure in wealthier households may discourage women from participating in wage-employment.

Land ownership: Higher land ownership is likely to be related to more household work and higher self-employment in agriculture, since this land may be used to keep livestock and to do agricultural work, and women are likely to be involved in these tasks. Also, women in agricultural households are likely to be involved in processing agricultural output (such as cleaning and packaging grain), which could also add on to their work responsibilities. So, the coefficient of land ownership is expected to be positive for the models on household work (model 1) and subsistence agriculture (model 2). Lower land ownership could push women to take up wage-employment or to be involved in self-employment in non-agriculture, so the coefficient of land ownership is expected to be negative for models 3 and 4.

Caste: Gender norms, that limit women’s activities within the house, are followed more strictly in households from high caste groups; hence, women from higher caste families are expected to have more household work, higher participation in subsistence agriculture and lower participation in wage-employment and self- employment in non-agricultural activities.

Location: Access to resources such as water, electricity and gas are more limited in rural areas, hence tasks such as fetching water and collecting firewood add on to household work for women in rural households. Also, most rural households are involved in subsistence agriculture, indicating higher participation of rural women in self-employment in the agricultural sector. Participation in wage-employment or self-employment in non-agricultural sector is expected to be lower for rural women because of their lower access to market and to employment opportunities in manufacturing or services sector. Hence, the coefficient for location is expected to

be positive for household work and self-employment in agriculture and negative for wage-employment and self-employment in non-agricultural sector. Ecological zone: Ecological zone is included in the models to capture differences in women’s work due to the socio-cultural differences in geographic location. Among the three ecological zones in Nepal, gender inequality is highest in Terai because the social norms in this region are highly influenced by conservative Hindu beliefs that encourage patriarchy. The social norms in the Hills and Mountains are more influenced by Tibeto-Burman culture that has less restrictive norms for women (Bennett & Acharya, 1983). So, women in Terai region are expected to have higher household work than women in Hill or Mountain regions. However, this relation is not as clear; because of the land terrain in the Hill and Mountain regions, access to facilities such as piped water and gas are more limited meaning women may have to spend more time on tasks such as fetching water and gathering fodder. Self- employment in agriculture is also expected to be higher in Terai since the land in Terai region is the most fertile and suitable for agriculture; more than 55 percent of the food crops produced in the country comes from the Terai region.18 _{The relation}

of ecological zone with wage-employment and with self-employment in non- agriculture is also not certain. On the one hand, access to labor market is better in Terai, suggesting higher participation in these activities for women in Terai. On the other hand, the more restrictive gender norms in the Terai region imply that women may not be able to take up these forms of market employment. 18 _{Based on the author’s calculation using data from Statistical Pocketbook 2014,} provided by the Central Bureau of Statistics in Nepal. Here, food crops include paddy, wheat, maize, millet and barley.