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Womens Labor Force Participation and Child Health in Nepal: Not all work is the same By Sarah Brauner-Otto 1 McGill University Sarah Baird Draft do not cite George Washington University Dirgha Ghimire University of Michigan 1 Corresponding
Women’s Labor Force Participation and Child Health in Nepal: Not all work is the same By Sarah Brauner-Otto1 McGill University Sarah Baird George Washington University Dirgha Ghimire University of Michigan
1 Corresponding author: sarah.brauner‐otto@mcgill.ca; Subsequent authors are listed alphabetically.
Women’s Labor Force Participation and Child Health in Nepal ABSTRACT The increase in female labor force participation in the paid labor market since the mid-1900s is
may also increase stress and decrease time with children. We explore the consequences of this transition for children’s health in rural Chitwan, Nepal using newly collected data on child health
We model the selection of women into the formal labor market and account for other, dramatic social changes occurring at the same time as women increasingly entered the work force. Results show that the type of employment mother’s are engaged in is a crucial component of the story. Different individual, household, and community factors predict different types of mother’s employment, specifically salaried employment vs wage labor. Furthermore, mother’s employment is inversely related to child health but that is only true for salaried employment, not wage labor.
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Women’s Labor Force Participation and Child Health in Nepal Many countries prioritize issues surrounding women’s increasing participation in the work force and the subsequent effects this has on families. Dramatic changes in world economies have brought women out of the household into formal work across the globe.1 This increase in formal female labor force participation (FLFP) since the mid-1900s is one of the most pronounced transitions the family has seen and is increasingly a global phenomenon. While this transition took place much earlier in Western Europe and North America, FLFP rates are now at least 40- 50% in Southern Asia and Central America.2 This shifts the dynamic within the household and likely has profound implications for the health and educational outcomes of the women themselves as well as their children. This study focuses on the consequences of this change for children in rural Nepal. We investigate the relationship between FLFP and child health in part because existing theoretical frameworks and empirical evidence yield contrasting hypotheses. Increased household financial resources may enable families to purchase more/better food, use more preventative health services, and obtain treatment for sick children. This may result in better nutrition and physical health. On the other hand, women’s increased time out of the household may have negative effects on children’s health outcomes. If employment increases time constraints, employed mothers may have less time available for food preparation, home health care, or visits to health care providers. The vast majority of research on the consequences of FLFP for people other than the women themselves uses data on wealthy countries like the U.S. where FLFP has been over 40% since at least the late 1960s/early 1970s.41 Furthermore, the context in which women are engaged in the labor force in high-income countries is very different from that in low-income countries in
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particular in terms of childcare infrastructures, family and gender norms, and divisions of household labor. By focusing on a lower-income setting where female participation in non- family labor has only recently begun to be widespread we can learn more about the processes through which the current transformation of women’s labor experiences influences children’s health. Using the rich longitudinal data from Chitwan, we focus on identifying the total effect of FLFP that is separate from the concomitant individual, household, and community- level changes that are occurring. Household composition may condition whether women engage in non-family labor and most factors that would make labor force participation more likely (e.g. increasing women’s education and increasing access to employers) are also related to child outcomes. Using multilevel longitudinal data, we estimate models of FLFP and child outcomes accounting for prior individual, household, and neighborhood-level characteristics and assess the degree to which the observed relationship is independent of them. While this does not allow us to control for unobservables, the richness of the longitudinal data allows us to control for confounders that are often not available to the researcher. That said, we are careful in our discussion of results and make clear that these are well-identified associations. Background Women have been and continue to perform unpaid labor in the household and on family
is often done in addition to their unpaid, domestic work. We use the phrase “female labor force participation (FLFP)” to include women’s paid work, regardless of whether it occurs in or
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abbreviation FLFP both for ease to the reader and to highlight the connection to existing literature. We consider several competing, theoretically motivated hypotheses regarding the relationship between FLFP and child outcomes. First, consider household economics. Following from a rational choice framework, net of household assets and wealth, increasing FLFP leads to increases in household income (assuming the return to women’s labor is greater outside the home than inside), which should lead to an increase in resources devoted to children and better child
expenses leads to better nutrition and health. Empirical findings demonstrate that when women work more household money is spent on food,14-17 which should mean a decrease in malnutrition and stunting. Also, to the extent that medical care is costly, children are more likely to receive that care when mothers are working for pay and have more autonomy.18; 19 Mother’s employment may also be important because as women engage in non-family activities such as work they are exposed to new ideas including the importance of education, information on the benefits of health services, and childhood as a period of investment.25-27 This theoretical perspective leads us to expect that children whose mothers engage in paid labor will have better physical health. The second theoretical approach centers on time investments and constraints and yields a contrasting hypothesis, namely that mother’s employment will be associated with poorer child
mothers spend more time in paid labor they are spending less time devoted to their children leading to worse child outcomes.28-30 This time shortage manifests itself in several ways. Most directly, we would expect less time to take care of important household tasks, such as food
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preparation, and less parental supervision and monitoring.31 In the U.S., children whose mothers work are more likely to be participating in school lunch programs, implying that mothers are investing less of their own time in meal preparation and nutrition.32 In Nepal where meal programs are not typically available we would expect to see an increase in child malnutrition and
are sick or for regular visits leading to worse health outcomes.33 Aside from the time constraint theory, there are other reasons to expect a negative relationship between mother’s employment and child health outcomes. Work is often stressful for the worker and that stress may have consequences for other family members. Research on intergenerational relationships more broadly has identified such spillover effects. This stress effect may also have a biological link to child health, particularly when we focus on the prenatal
Another possible pathway through which women’s employment may have negative consequences for children is childcare. When women are working someone still needs to tend to household tasks such as caring for younger children. This is of particular concern for low-income countries because even though there is increasing pressure and availability for mothers to work there has not been a similar increase in childcare options. Most research in wealthier settings has found that when mothers return to work children typical spend more time in non-family care (e.g. formal child care settings). Without this option, it is possible that young children will be left in the care of older children or other, less qualified caregivers, which may lead to poorer health
will have worse physical health. Note that some empirical research has found that mother’s employment has no effect on
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child health outcomes.11; 35; 36 One reason may be that mothers are not spending less time with their children today than in the past.5; 38-40 Another reason may be that the both the positive effect via income and the negative effect via time shortage occur and are balancing each other out. One study designed to separate these two mechanisms using a sample in Guinea, West Africa found that the negative effects dominated.12 Along with competing pathways, mixed findings may stem from methodological problems related to selection or endogeneity. 8-10 A major concern with all research on FLFP is that FLFP is never randomly assigned—women in some families choose to work while women in others do not and this choice is influenced by a range of interrelated factors at the individual, household, and macro-level. For example, education is a well-established correlate of both employment and child health and education itself is likely a result of other macro-level changes that may also have direct effects on employment and child health. Major society wide changes such as the spread of mass education, the shift to market-based economies from subsistence farming, and the spread of health services are all related to both increases in women’s employment and changes in child
In Nepal, and certainly within our data, there are no mothers living alone. This then means that their employment status is likely influenced by the presence and characteristics of other household members. For example, large households may have even greater domestic demands meaning women are less likely to look outside the household for paid employment. There are also additional potential employees in these households lowering the likelihood of any one woman living in them working outside the home. Because of gendered norms regarding domestic labor, in Nepal this effect is likely to be particularly strong. General female autonomy is another factor widely seen as influencing employment and
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child outcomes. Women with more autonomy are both more likely to work outside the home and their children have better outcomes. Another potential explanation for the lack of consensus among existing research may be the operationalizations of women’s employment used. When measuring women’s employment in low income countries, most studies measure simple status (e.g employed or unemployed) or perhaps account for level of employment (e.g. full or part-time). However, there is huge variation in the type of work women are engaging in, and the consequences of this work for their children likely varies by type. For example, hard physical labor clearly places greater demands on pregnant and lactating mothers than working in a shop or a health clinic. Additionally, although much of the increase in FLFP across the globe has been in unskilled labor, that is not the entire
education and health such as teachers, teaching assistants, and community health workers engage a large portion of women employees. The ability to balance work and domestic tasks such as watching small children or breastfeeding may also vary by job type, and not necessarily in ways that correspond with the skills those jobs require. For example, both community health workers and field workers may be able to bring infants with them while working, whereas this may not be possible working in an office or a factory. Life course considerations [here add something about child outcomes over different life stages] We take advantage of a unique, multilevel, longitudinal data source from rural Nepal (the CVFS) that will allow us to estimate the strength of the association between mother’s employment and child outcomes, accounting for these major community and household-level
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changes, and family-level experiences. We use the CVFS specifically because the data make it remarkably suited for the study of FLFP. Methods Data To test our hypotheses we use multiple data sets from the Chitwan Valley Family Study (CVFS) conducted in rural Nepal. The CVFS launched in 1995 by selecting a systematic sample
key services (employers, roads, schools, health services, etc.). All individuals aged 15-59 and their spouses were interviewed in 1996. Since then additional individual interviews, neighborhood level data collection, demographic event registries, and extensive follow-up and inclusion of migrants have occurred (all key respondents are followed regardless of where they migrate). The study has had a response rate of over 95% throughout last 20 years of the data collection and mortality is the largest driver of attrition. We use several of these data collection efforts in our paper. First, we use data from a 2016 supplement focused on women’s labor force participation and child health which gathered health data on all 683 children 72 months or younger living in sample neighborhoods and their mother’s (N=613). Health measures included anthropometric measures for children 60 months or younger and subjective health and illness reports for all children aged 72 months or younger. Quarterly data on the women’s employment history from 2008-2016 including information on industry, income, and days worked were also collected. Second, we use information on household wealth from household level surveys. We preferred to use data from the 2006 survey as this measured household wealth before the women’s labor force participation of focus and before the birth of the children—this was
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possible for 56% of the children. However, not all children in the 2016 supplement were living in
2015 survey. Note, 14 children were in households that were not interviewed in 2006 but were in interviewed in 2001 and we use data from that year; 43 children were in households who had never been selected for household level interviews and are therefore excluded from our analyses. We use data from the monthly Demographic Event Registry to provide information on the presence and gender of other household members and data from Neighborhood History Calendars (NHCs) to provide information on community characteristics. NHCs are a mixed- method data collection tool used in the CVFS to capture detailed, time varying information on the location and types of community services available in each CVFS sample neighborhood. Measures Measures of FLFP. Our information on FLFP is time varying (coming from work history calendars) and captures whether a woman worked for pay at all (any work), in salaried employment, or in wage labor in a given quarter from 2008-2016. Work that did not result in any earnings (e.g. labor on the family farmland) is not captured by our measures. Wage labor in this setting was solely agriculture labor on another households’ land. 57% of mothers in our analyses had worked for pay during this period; 30% had held a wage labor position, and 16% had held a salaried job. Note, only 8 women had worked in both wage labor and a salaried position. An additional 10% of women had worked in other types of labor including having their own business (either in or outside of the home) or something else. Table 1 presents the descriptive statistics for these measures and all the measures used in the models predicting mother’s work at the mother-level.
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(Table 1, about here) In our analyses of child health, using FLFP as the key independent variable, we explored three time-sensitive measures of FLFP that correspond to different stages in the child’s life
employment only, and wage labor only. The time-sensitive measures are whether the mother worked before the child was born, during the child’s first 1000 days (prenatal period and first 2 years), and whether the mother worked last month. Table 2 shows the descriptive statistics for these employment variables at the child level. We also created continuous measures of mother’s earnings from those specific types of employment during that period. However, these were not significantly related to child health so we exclude them here for parsimony. Child Health. We explore three measures of child health across the life course. First, we consider birth weight, specifically whether the child had a low (<2500grams) birth weight. Table 2 shows descriptive statistics for the child health measures used in the analyses. 10% of children in our sample were categorized as low birth weight. Second, using anthropometric measurements we calculated gender-age standardized z-scores25 for height for age using median WHO international reference value65 as the standard. We categorized children as being stunted if their standardized scores fell 2 or more standard deviations below that value. 10% of children were stunted according to this benchmark. Only about 2% of all children were classified as having both low birthweight and being stunted. Third, mothers were asked a series of questions regarding their children’s health in the past 2 weeks. Specifically, whether the child had diarrhea in the last 2 weeks, meaning loose or watery stools at least 4 times in 24 hours, a fever in the last 2 weeks, or a cough accompanied by short, rapid breathing in the last 2 weeks. We created 1
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dichotomous variable for whether the mother reported any of these three conditions. Almost 30%
(Table 2, about here) Factors Known to Influence Women’s Employment and Child Health We control for a range of characteristics at multiple levels that previous research has found influence women’s employment and child health. For all of these measures we attempt to establish clear temporal ordering whenever possible such that these measures would capture events or experiences that occurred before the mother’s employment and the birth of the child. At the neighborhood level we control for access to a range of community characteristics previous research has found to be related to both employment and family outcomes (lots of Nepal cites here). Using data gathered from the NHCs we created a series of measures equal to 1 if the mother’s neighborhood had a school, health service, employer, market, or bus stop within a 3 minute walk in 2008 (this corresponds with the service being available within the neighborhood or in the next closest one). We then summed these measures to create an index of the number of community organizations. The index ranges from 0 to 5 with a mean number of about 1.5 organizations across both mothers and children (tables 1 and 2 present descriptive statistics for all the relevant control variables. Statistics do not vary much across samples because most women had only 1 child in the data collection). We control for several household level factors—specifically the composition of household
mother’s employment as having more men in the household means men are available to go out and work themselves. We created a measure for whether there were 0, 1, or 2or more men in the household.
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When men are out of the household it is typically because they have migrated for employment and are therefore sending remittances home, lessening the pressure of women to work and improving child outcomes. This is likely particularly true when it is the child’s father (mother’s husband) who has left the household. As such, we also created a dichotomous measure for whether the husband/father was living in the household. Since other women in the household can serve as potential caregivers when mothers are working we created a continuous variable for the number of women (aged 15 or older) living in the household. Because most women lived in households with only a few other women we recoded this measure in to 1, 2, 3, or 4 or more women in the household. These measures are all significantly correlated with each other (Pearson correlation coefficients were over .4 and significant at the 0.000 level) and including them all in the models created unstable estimates. We present the models showing the variable for the number of men in the household as it was the most stable, but using the other measures does not change the overall story in these analyses. We also control for household wealth using household characteristics and resources, and we created three such measures (whether the household owns the land their home is on, owns any farm land, and owns any livestock) and sum them into a wealth index ranging from 0-3. Additionally, because houses in more remote regions of the study area may be more disadvantaged than other households we include a control for the distance (as the crow flies) between that household and the main town, Naryanghat. A final household measure is ethnicity. In Nepal, these caste-ethnicity has been linked to variation in family formation behavior (Pearce, Brauner-Otto, & Ji, 2015) and to access to non- family experiences such as education and employment (Axinn and Yabiku 2001; cite Yabiku
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work paper). Our investigation differentiated among the five caste-ethnic groups that represent major race, ethnicity, and religious differences in our study site in Nepal: Brahmin-Chhetri (high-caste Hindus, reference group), Dalit (low-caste Hindus), Newer, Hill Janajati (Tibeto Burmese descendants of Hill residents), and Terai Janajati (Tharu and Bote), who are believed to be indigenous to northern India and the southern part of Nepal, including the study area). Caste- ethnic group is the same for all household members. We include several measures of mother’s characteristics and experiences including the mother’s age at interview (in years) and education. In Nepal, the SLC (School Leaving Certificate) is an important credential opening up a range of employment opportunities. Thus, we created a dichotomous measure for whether the mother had obtained an SLC or higher level of
captures whether the mother was ever a member of a youth group. These groups are important vehicles for connecting women to potential employers. 12% of mothers had participated in a youth group. We also created two measures of the marriage/spouse of the mother: her husband’s educational attainment (had an SLC or higher) and the amount of involvement the woman had in choosing her spouse. As is common in low-income countries, men have greater access to education and 43% of women had husbands who had obtained at least an SLC. Marriages in Nepal typically involve some parental involvement but the degree varies and those who had more involvement may also have more autonomy in the household which may lead to increased employment and improved child outcomes. Following previous research we create a scale ranging from 1, the parents chose her spouse without any involvement from the mother, to 5, the mother chose her spouse without any involvement from her parents.
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We also include a measure of the total number of children the mother had ever given birth to, coded as 1,2, or 3 or more. In models of mother’s employment we include a measure of the age of her youngest child. In the child health models we include measures of the child’s gender, birth order, and age in
Analytic strategy Our analysis proceeds in two stages. The first stage focuses on understanding FLFP. Because our dependent variables are all dichotomous we estimate logistic regression models of mother’s work. The second stage of our analysis focuses on isolating the relationship between FLFP and child health. To do this we estimate models with mother’s employment predicting child health. We first estimate base models We exclude children with missing data for any of the variables in the analyses resulting in a full analysis sample of 662 children with 594 mothers. Analytic sample sizes are reduced when estimating models that include measures of mother’s work during the child’s first 1000 days as children younger than 2 are excluded due to possible censoring and when estimating models predicting child stunting because anthropometric measures were only taken for children 60 months old or younger. Results Selection into FLFP Table 3 presents models predicting women’s participation in the formal, paid labor
first glance the analyses appear to reveal very little about women’s selection into employment. Neither community context, household structure, wealth, nor location were significantly related
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to whether women had ever worked for pay. Looking at ethnicity we see that Dalit and Terai janajati, widely recognized as the most disadvantaged groups in Nepal, had odds of working twice as high as Brahmin-Chhetri women. Turning to characteristics and experiences of the mother we see that only participation in a youth group is related to employment, doubling the
(Table 3, about here) However, when we disaggregate work by type of employment we begin to see a clear story of stratified labor opportunities. Column 2 shows the results of models of having a salaried job and Column 3 for wage labor. Although community context was not related to overall employment or having a salaried job, women in communities with more services had much lower odds of having worked as wage laborers. Similarly, those living in households with more men, men who likely have access to better paying jobs, also had lower odds of working in wage
higher likelihood of working among disadvantaged ethnic groups is because they are more likely to be working as wage laborers. This pattern is further supported with the education results. Women who had at least an SLC were 6 times more likely to work in salaried jobs and less likely to work as wage laborers. These results are likely not surprising as having an SLC can be a required credential for a salaried job (although this is clearly not the case for all positions such as community health workers). Furthermore, women whose husbands had an SLC were also less likely to be wage laborers. In some, results from these models highlight the social status differences in between salaried and wage labor. These findings may seem obvious but they warrant attention here because most of the literature on the consequences of FLFP in the developing world does not, in
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fact, differentiate between the type of employment women are engaged in. This may be because it is assumed that women in low income countries are only engaging in these low skill, low status
is questionable (Heath and Jayachandran 2017). FLFP and Child Health We now turn to stage 2 of our analyses where we examine the relationship between women’s work and child health, specifically accounting for the type of work women are engaged in and many of the observable factors that are influencing the likelihood that they are participating in that type of labor (Table 4). Panel A shows the relationship between FLFP and child health only controlling for the most basic controls (child gender, birth order, child age, and mother’s age). Panel B adds in measures of the full set of child, mother, household, and neighborhood level factors that are likely to be causing a spurious relationship. One major finding from these analyses is that the relationship between FLFP and child health appears to be largely independent of these other factors as the results between Panels A and B are virtually the
the factors that influence that “choice” or difference do not, in fact, alter the effect of that work
(Table 4, about here) A second major finding is that the type of work women are engaged does appear to matter when assessing the consequences of that labor market participation. Further, in connection to our findings above that controlling for factors known to influence the type of work women engage in does not change the overall FLFP-child outcomes relationship, we do not see evidence that the effect of work on children is a simple extension of social status. First, consider our
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findings regarding any work (rows i and iv). Looking at Model 1, we see that children whose mothers worked in any job before they were born were much more likely to be of low birth weight than children whose mothers did not work. Similar, we see that children whose mothers worked at any of the three critical life course stages were more likely to be stunted than those whose mothers did not work (Models 4-6, rows i and iv). When we look at the analyses by type
whose mothers had a salaried job before they were born, during their first 1000 days, or in the past month were much more likely to be stunted than children whose mothers did not have a salaried job. However, we see no significant relationship between work and stunting, or any of the child health outcomes, when considering wage labor. Conclusion This global transition of women into the paid, formal labor market has been dramatic, changes the dynamic within the household, and has profound implications for children’s health.3-
6 To date most research on these relationships has focused on wealthy countries, in particular
countries like the United States where the transition occurred decades ago. With women making up a growing share of the global labor market and the increase in low-skill, female-dominated jobs in poor regions further increasing demand for their labor,7 it is imperative that we understand how this transition has influenced children in all countries. Better documentation of associations between FLFP and child outcomes in poor countries not only advances our understanding of child wellbeing among the majority of the world population, it also has the potential to provide an important comparative perspective, thereby informing our theories and understanding of the consequences of FLFP in rich countries (e.g. the U.S). This paper will move us forward along these lines.
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Table 1. Descriptive statistics. Mother level. N=594. MEAN STD MIN MAX Mother's employment, 2008-2016 Any job for pay 0.57 0.50 1 Wage labor (agricultural work) 0.30 0.46 1 Salaried job 0.16 0.36 1 Neighborhood-level factor Number of community organizations within a 3 min walk in 2008 (school, health service, employer, market, bus stop) 1.53 1.52 5 Household-level factors Number of men in hh (0,1,2+) 1.18 0.75 2 Husband currently in the household 0.50 0.50 1 Number of women in hh (1,2,3,4+) 2.08 0.97 1 4 Wealth index (sum of owns land home is on,
2.30 0.96 3 Distance to Naryanghat 8.68 4.03 0.02 17.70 Ethnicity (reference group: Brahmin-Chhetri) 0.37 0.48 Brahmin-Chhetri 0.16 0.37 1 Dalit 0.06 0.23 1 Newar 0.22 0.41 1 Hill Janajati 0.20 0.40 1 Terai Janajati 1 Mother-level factors (as of 2016) Mother's age 26.50 4.93 17 43 Mother's education (has SLC) 0.36 0.48 1 Mother ever in youth group 0.12 0.33 1 Father/husband's education (has SLC) 0.43 0.50 1 Degree of spouse choice (5=self,1=parents) 2.79 1.79 1 5 Children ever born (1, 2, 3 or more) 1.69 0.71 1 3 Age of youngest child 41.55 18.17 9.233333 105.9
Table 2. Descriptive statistics. Child level. N MEAN STD MIN MAX Mother's work Any job Mother ever worked before child was born 662 0.38 0.49 1 Mother had worked 1000 days 550 0.35 0.48 1 Mother worked last month 662 0.26 0.44 1 Wage labor Mother ever worked before child was born 662 0.18 0.39 1 Mother had worked 1000 days 550 0.17 0.38 1 Mother worked last month 662 0.08 0.27 1 Salaried job Mother ever worked before child was born 662 0.12 0.32 1 Mother had worked 1000 days 550 0.08 0.27 1 Mother worked last month 662 0.04 0.19 1 Child health Low birth weight (<2500 grams) 634 0.10 0.30 1 Stunted 540 0.10 0.30 1 Child sick in past 2 weeks (at least 1 of diarrhea, fever, cough) 662 0.29 0.45 1 Neighborhood-level factor Number of community organizations within a 3 min walk in 2008 (school, health service, employer, market, bus stop) 662 1.52 1.51 5 Household-level factors Number of men in hh (0,1,2+) 662 1.19 0.75 2 Husband currently in the household 662 0.50 0.50 1 Number of women in hh (1,2,3,4+) 662 2.09 0.96 1 4 Wealth index (sum of owns land home is on,
662 2.31 0.96 3 Distance to Naryanghat 662 8.66 4.04 0.02 17.70 Ethnicity (reference group: Brahmin-Chhetri) Brahmin-Chhetri 662 0.37 0.48 1 Dalit 662 0.16 0.37 1 Newar 662 0.06 0.23 1 Hill Janajati 662 0.21 0.41 1 Terai Janajati 662 0.20 0.40 1 Mother-level factors (as of 2016) Mother's age 662 26.39 4.81 17 43 Mother's education (has SLC) 662 0.36 0.48 1 Mother ever in youth group 662 0.12 0.32 1 Father/husband's education (has SLC) 662 0.43 0.50 1 Degree of spouse choice (5=self,1=parents) 662 2.79 1.78 1 5 Children ever born (1, 2, 3 or more) 662 1.74 0.70 1 3 Child is female 662 0.45 0.50 1 Birth order 662 1.68 0.96 1 8 Child age (months) 662 43.23 18.35 9.23 105.9
Table 3. Logistic regression models predicting women's employment between 2008-2016. Any work Held salaried job Held wage labor job 1 2 3 Neighborhood-level factor 0.97 1.1 0.79** (-0.51) (1) (-2.53) Household-level factors 0.86 1.13 0.69** (-1.22) (0.66) (-2.4) 0.94 0.98 1.05 (-0.59) (-0.12) (0.37) 1.01 1.01 0.98 (0.26) (0.21) (-0.63) Ethnicity (reference group: Brahmin-Chhetri) 2.02** 0.86 5.15*** (2.38) (-0.35) (4.62) 1.14 0.18* 1.75 (0.31) (-2.08) (1.05) 0.97 0.98 1.84* (-0.1) (-0.05) (1.75) 2.44** 0.69 6.44*** (3.08) (-0.85) (5.39) Mother-level factors (as of 2016) 1.01 1.07* 0.93** (0.28) (1.82) (-2.57) 1.3 6.31*** 0.28*** (1.03) (4.98) (-3.74) 2.05** 2.49** 1.34 (2.43) (2.84) (0.75) 1.15 1.14 0.55* (0.6) (0.37) (-2.11) 1.02 0.94 1.01 (0.4) (-0.74) (0.14) 0.81+ 0.67+ 1 (-1.35) (-1.58) (-0.02) Child-level factor 1.01+ 1 1.01* (1.35) (-0.51) (1.99) 0.91 0.02*** 2.23 (-0.13) (-3.59) (0.96) N 594 594 594 Number of community organizations within a 3 min walk in 2008 (school, health service, employer, market, bus stop) Intercept Distance to Naryanghat Mother ever in youth group Children ever born (1, 2, 3 or more) Age of youngest child Number of men in hh (0,1,2+) Father/husband's education (has SLC) Degree of spouse choice (5=self,1=parents) Terai Janajati Mother's age Dalit Newar Hill Janajati Mother's education (has SLC) Wealth index (sum of owns land home is
Multilevel logistic regression models of the relationship between mother's employment and child health.
Mother ever worked before child was born Mother had worked 1000 days Mother worked last month Mother ever worked before child was born Mother had worked 1000 days Mother worked last month Mother ever worked before child was born Mother had worked 1000 days Mother worked last month
1 2 3 4 5 6 7 8 9 Panel A. Base models 1.58* 1 0.73 1.95* 1.86* 2.42** 1.13 1.23 0.78 (1.68) (0.01) (-0.96) (2.2) (1.78) (2.78) (0.67) (1.02) (-1.19) 1.31 1.45 1.47 1.48 0.93 1.16 1.01 1.07 1.43 (0.8) (1.01) (0.77) (1.09) (-0.14) (0.22) (0.04) (0.24) (1.02) 1.81 0.77 1.59 2.08* 2.56* 3.55* 1.47 2.17* 1.31 (1.51) (-0.41) (0.71) (1.94) (1.89) (2.24) (1.45) (2.29) (0.62) Panel B. Full models 1.67* 1.02 0.73 1.86* 1.89* 2.11* 1.12 1.33 0.8 (1.77) (0.08) (-0.9) (1.93) (1.68) (2.17) (0.57) (1.37) (-0.99) 1.56 1.64 1.85 1.26 0.97 1.06 1.03 1.16 1.47 (1.19) (1.22) (1.17) (0.55) (-0.06) (0.07) (0.1) (0.53) (1.06) 2.05 0.84 1.94 2.68* 2.97* 3.18* 1.4 2.19* 1.43 (1.61) (-0.26) (0.98) (2.21) (1.81) (1.91) (1.14) (2.13) (0.78) Obs 634 523 634 540 428 540 662 550 662 Note: Table shows odds multipliers with asymptotic z-ratios in parentheses. * p< .05; ** p< .01; *** p < .001 one tailed tests. Stunted Child sick in past 2 weeks (at least 1
Low birth weight (<2500 grams)