Table 4.8 above presents the results for the four models based on NLSS-III data. The coefficients for my key independent variable, ‘belongs to migrant household’, confirms the four hypotheses stated above.
Model 1: Household Work
The R-squared value for model 1 is 0.142; so the independent variables in my model ‘explain’ about 14.2 percent of the variation in time spent by women on household work. The positive and statistically significant coefficient for the variable for migrant household confirms my hypothesis (H1) that women in migrant households spend more time on household work than women in non-migrant households.
The coefficients for most of the explanatory variables in the model are consistent with the expectations discussed above. The results indicate a quadratic relation between age and household work, with household work increasing with age up to a certain maximum age and decreasing after that. Also, household heads are likely to have more household work and women who are employed for wages are likely to have less household work. The coefficient for education is positive indicating higher household work for women with more education. Though this result contradicts my expectation, the coefficient is not statistically significant.
I find that in households with more adult female members, the work responsibility for each individual woman is lower. Also, increase in number of dependent members increases women’s work responsibilities. Both these findings are consistent with my expectations. The coefficient for asset index is negative,
indicating lower household work for women from wealthier families. Also, ownership of land is associated positively with women’s household work, as expected. The coefficient for caste is positive indicating higher household work for women from high caste families; however the coefficient is not statistically significant. Women from rural areas have higher workload than women from urban areas; this is consistent with my expectation. The coefficients for the variables on Mountain and Hill belt are positive indicating that women in these regions have higher workload than women in Terai region. However, these coefficients are not statistically significant indicating that there might not be much difference in women’s household work responsibilities based on the ecological belt that they belong to.
Model 2: Self-employment in agriculture
The pseudo R-squared value for model 2 is 0.161, indicating that the explanatory variables in my model ‘explain’ about 16.1 percent of the variation in self-employment in agriculture. The coefficient for migrant household is positive and statistically significant here; this confirms my hypothesis (H2) that women in migrant households are more likely to be self-employed in agriculture than women in non-migrant households. Though some women in migrant households in my fieldwork sample claimed that they had either given up or cut down on agricultural work, the findings from NLSS data suggests that the overall participation among migrant households is higher than that in non-migrant households. This indicates that there are more women taking up their husbands’ work in the fields than those leaving the fields barren when their husbands migrate. Women in non-migrant
households may also have underreported their participation in agricultural work, since in the presence of men they may have considered their own participation as being marginal.20
The coefficients for age and age-squared indicate a quadratic relation between age and self-employment in agriculture. However, contrary to my expectation, I find self-employment in agriculture declines with age up to a certain age and then increases after that. This could be because women in their prime working age prefer other forms of employment than subsistence agriculture; hence, they may be working in the family fields either when they are younger and not ready to enter the job market or after they get older and retire from other forms of employment. Also, I find a negative and statistically significant coefficient for household headship; though this contradicts with my expectation, the negative coefficient may be indicative of female household heads taking up wage- employment or other forms of income-generating activities. Given the low productivity from agriculture, female heads may be incentivized to seek alternative forms of employment to be able to provide for the family. As expected, the results show that women with lower educational qualifications are more likely to be self- employed in agriculture.
For the household and regional characteristics, the results indicate that women from wealthier families are less likely to be self-employed in agriculture and women from households with larger land ownership are more likely to be self- employed in agriculture. Both these findings concur with my assumptions. I find that
women from high-caste families are more likely to be self-employed in agriculture, perhaps because the restrictive gender norms in these households limit them from participating in employment outside home. I also find that women from rural area are more likely to be self-employed in agriculture; this finding is supported by the fact that most rural households are agricultural. The results indicate that women in the Mountain and Hill regions have higher participation in self-employment in agriculture than women in Terai. This result contradicts with my expectation that women in Terai may be more likely to be self-employed in agriculture given the fertile land and stricter gender norms in this region.
Model 3: Self-employment in Non-Agriculture
The pseudo R-squared value for model 3 is 0.057; so my model is only able to ‘explain’ about 5.7% of the variation on women’s self-employment in non- agricultural sector. The coefficient for ‘belongs to migrant household’ is negative and statistically significant. This confirms my hypothesis (H3) that women in migrant households are less likely to be self-employed in non-agricultural sector.
As expected, I find a quadratic relation between age and self-employment in non-agricultural field. Also, I find that women who are household heads and those who have higher educational qualifications are less likely to be self-employed in non-agricultural sector. Though these findings contradict my expectation, the coefficients for both these variables are not statistically significant.
The results show that, as expected, the likelihood of women’s self- employment in non-agriculture is lower in households with more dependent members. Women from wealthier families are more likely to be self-employed in
non-agriculture; this is consistent with my assumption that women from wealthier households may have the resources to invest in capital and skill-training. Women from households with less land are more likely to be self-employed in the non- agricultural sector. Also, women from higher caste groups and women in rural areas are less likely to be involved in self-employment in non-agriculture. All of these findings concur with my expectations discussed above. Finally, I find that women from both Mountain and Hill region are more likely to be self-employed in non- agriculture than women from Terai; this could be because of the less restrictive gender norms in the Hills and Mountains.
Model 4: Wage-Employment in Agricultural or Non-Agricultural Sector
The pseudo R-squared value for model 4 is 0.061 indicating that the explanatory variables in my model ‘explains’ about 6.1% of the variation in wage- employment for women. The negative and statistically significant coefficient for ‘belongs to migrant household’ confirms my hypothesis (H4) that women from migrant households are less likely to be employed for wages. This is consistent with my argument that during men’s absence, women may be discouraged to participate in market work either because of increased household responsibilities and restricted mobility or because of increase in income through remittance reception.
The coefficients for all of the individual characteristics of women are consistent with my expectations and also statistically significant. Age has a quadratic relation with being wage-employed. Being the household head and having higher educational qualifications are associated with higher probability for participating in market work.
Most of the coefficients for the household and geographic characteristics also match my expectations. Increase in number of dependent members in the household decreases the probability of women being employed for wages. Women from wealthier families are less likely to be involved in wage-employment; this could be either because women from these families don’t feel the need to work for wages or because they are restricted from working for wages in the name of maintaining family honor. Also, higher land ownership is associated with lower participation in wage employment; perhaps because women in these households are involved in agricultural work or in animal husbandry. As expected, I find that women from higher caste households and women in rural areas have lower probability of participating in wage-labor. For the ecological zones, the results indicate that the probability of participation in market work is higher for women in the Mountain or Hill region than for women in the Terai belt; this could be because of the higher gender inequality and more restrictions on mobility for women in Terai region.