International Journal of Nutrition
ISSN: 2379-7835
Current Issue
Volume No: 1 Issue No: 3
share this page

Research Article | Open Access
  • Available online freely | Peer Reviewed
  • Employment and Nutritional Status Among Women in Rural Nepal

    Vanessa M. Oddo 1       Joanne Katz 1     Keith P. West Jr. 1     Steven C. LeClerq 1     Subarna K. Khatry 2    

    1Department of International Health, Johns Hopkins Bloomberg School of Public Health

    2National Society for the Prevention of Blindness in Kathmandu, Nepal

    Abstract

    This study sought to assess the impact of part-time employment on the nutritional status of women in rural Nepal. We used longitudinal data from a prospective cohort of women in the Sarlahi District of southeastern Nepal to assess whether part-time employment was associated with a change in mid-upper-arm circumference (MUAC) between baseline and five-years. The women enrolled in the study (n =715) had applied for part-time employment distributing weekly vitamin A supplements to married women of childbearing age. Over the five-years of follow-up, women received 900 Nepalese rupees ($15 USD) per month, for approximately five hours of work per week. The women who were hired (n =324) were younger and better educated than those who were not hired (n =391), but were otherwise similar. After baseline adjustments, change in MUAC (in cm) (β = 0.08; 95 % CI: -0.20, 0.36) was not associated with employment.  Also, changes in MUAC over time were inversely related to baseline MUAC, with better nourished women gaining less (MUAC of 23 - 24.99: β = -0.83; 95% Confidence Interval CI: -1.18, -0.48; MUAC of ≥ 25: β = -0.99; 95% CI: -0.99, -0.54) compared to thin women (MUAC <21). In this sample, women employed part-time did not have improved nutritional status as compared to their unemployed counterparts. Future research should explore the impact of women’s employment on the nutritional status of other members of the household, particularly children, and among women employed full-time.

    Received 30 Aug 2014; Accepted 05 Feb 2015; Published 10 Jun 2015;

    Academic Editor:Sadanand Naik, HOD (Clinical Biochemistry), K.E.M.Hospital, Pune, India

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright©  2015 Vanessa M.Oddo, et al.

    License
    Creative Commons License    This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Vanessa M. Oddo, Joanne Katz, Keith P. West Jr., Steven C. LeClerq, Subarna K. Khatry (2015) Employment and Nutritional Status Among Women in Rural Nepal. International Journal of Nutrition - 1(3):15-28.
    Download as RIS, BibTeX, Text (Include abstract )
    DOI10.14302/issn.2379-7835.ijn-14-608

    Introduction

    A common tenant in economic development is that participation among women in income generating activities often results in improved health and nutrition outcomes among themselves as well as their children, 1,2 ,3 ,4 ,5 as women and men may have different preferences for allocating food and nonfood resources. 6 ,7 Therefore, many interventions that aim to alleviate poverty and improve nutritional status in low- and middle-income countries (LMIC) target women as their main beneficiaries. Prior research has explored the impact of cash transfer and microfinance programs on nutritional status1, 2, 3, 4, 5, 8, 9, 10, 11however, the association between women’s employment and nutritional status in LMIC is less well understood.

    Several studies that examine similar relationships report a positive relationship between steady income from employment and nutritional status3, 4, 12, 13, 14, 15Steady and formalized work among women may accord benefits such as improved dietary diversity and well-being among themselves as well as other household members. For example, in a study of urban Filipino women, the quality of diet improved among employed women15 Similarly, in a random sample of approximately 900 households in rural Bangladesh, women in a cash-for-work program spent more money on food and their households consumed more fruit and protein-rich food (e.g. fish, meat, eggs and milk).4 Employment may also enhance women’s ability to independently access financial resources and increase their savings2 as well as increase their control and influence over household decision-making processes15, thus resulting in improved nutritional status. However, it is unclear if the positive effects of income or improved socioeconomic status (SES) are offset by negative time allocation effects among women participating in informal work or those with inadequate childcare support13, 16, 17, 18, 19

    This study was part of a larger randomized controlled community trial, which aimed to assess the impact of a weekly dose of vitamin A or beta-carotene delivered to women of childbearing age in rural Nepal. This steady part-time employment offered the opportunity to better explore the extent to which women’s employment may impact the nutritional status among women in LMIC, by comparing those who received income from employment, with women who applied for the job but were not hired. Complementing a previous analyses by Katz and colleagues20, these analyses follow the same women over a five-year period and tests the hypothesis that employed women have a greater, positive change in nutritional status, as measured by mid-upper-arm circumference (MUAC). Evidence suggests that women are increasingly entering formalized employment in LMIC,21 therefore, a better understanding of the association between women’s employment and nutritional status is critical and may allow for informed development of policy and intervention strategies to support this trend.

    Methods

    Study Population

    This prospective cohort study followed women over a five-year period that applied for part-time employment during December 1992 through January 1993. Additional study details are described elsewhere2, 20 Briefly, employees were selected based on the results of a reading and writing test, relevant work experience, and an interview. Women made weekly visits to the homes of neighboring women to provide supplements, record pregnancy status, and record the receipt of supplements. The women received approximately 900 Nepalese rupees ($15 USD) per month for an estimated five hours of work per week.

    Study staff administered the baseline questionnaire during the job interview application and interview process and prior to their selection for employment. Using a structured questionnaire, women were asked to provide information on demographic and socioeconomic characteristics, literacy, education, caste, decision-making, and time spent on various household activities and tasks. Total weekly household food expenditures were ascertained (collected in Nepalese rupees), as well as for specific foods such as meat, clarified butter (ghee), fish, eggs, milk, and vegetables. The MUAC of all applicants was measured using a Ross insertion tape by trained anthropometrists, following standard procedures22.Women who completed the baseline questionnaire were subsequently contacted for follow-up and re-interviewed two- and five-years later. Using a structured questionnaire, similar information regarding demographic and socioeconomic features and household food expenditures were obtained during follow-up. MUAC was measured in the same manner as at baseline. Verbal consent for participation was sought from each respondent. Interviews and assessments were conducted only after consent was obtained. Ethical approval for the study was given by the Joint Committee on Clinical Investigation of the Johns Hopkins School of Medicine and by the Nepal Health Research Council.

    The original sample included 870 women (350 employees and 520 non-employees). An available case analysis approach was used to handle missing data, whereby all cases for MUAC and employment status (the primary variables of interest) were observed. Women with a missing MUAC value at baseline or five-years (n =22) or missing employment status (n = 2) were excluded. Excluded cases also included women who were lost to follow-up (n = 113), died (n = 6), or changed employment status during the study (n = 9). To prevent highly atypical observations from influencing the primary results, those with an extreme value for MUAC were also excluded (n=3). The analytic sample included 715 women, 324 employed women and 391 unemployed women, representing 82 percent of the baseline sample.

    Dependent Variable

    Prior research has established MUAC as an appropriate indicator of women’s nutritional status in non-pregnant women because of its high correlation with women’s body mass index (BMI)23, 24, 25 Similarly, the accuracy of MUAC in screening for underweight is well-documented26, 27 recent estimates suggest 89% sensitivity and 71% specificity in a sample of Asian women28 For these analyses, a summary measure was created to represent the change in MUAC between baseline and five-years, which served as the primary dependent variable. Change in MUAC was modeled as a continuous variable in centimeters.

    Independent Variables

    Unchanged employment status at baseline and five-years was used as a surrogate indicator for consistent part-time employment throughout the five-year period of follow-up. Using appropriate cutoffs, categorical variables were created for maternal education (no education, 1 to 9 years of schooling, 10 years of schooling or more), MUAC (< 21 centimeters (cm), 21.0 – 22.9 cm, 23.0 – 24.9 cm, 25 cm or more), parity (1-2 births, 3-5 births, more than 5 births), age (< 20 years, 20-29 years, 30-39 years, 40-49 years, 50 or more years) and household ownership (quintiles based on 17 items where quartile 1 indicates those with few items owned and quartile 4 represents ownership of more items).

    Confounders and Effect Measure Modifiers

    Factors that we hypothesized might impact both employment and change in MUAC were initially selected a priori on the basis of their known association with maternal nutrition,29 including characteristics such as age, education, parity, tobacco use, household composition, women’s decision-making, and food expenditures. Based on previous literature30 we hypothesized that the relationship between employment and change in MUAC may be different depending on a woman’s baseline MUAC. Therefore, baseline MUAC (modeled as a categorical variable) was explored as an effect measure modifier using an interaction between baseline MUAC X employment. In addition, we explored the relationship between employment and nutritional status stratified by SES. SES was defined based on households’ ownership of items (e.g. cattle, rice patties, bicycles, radios), as is common in LMIC. Those above the median score for household ownership of goods were categorized as high SES and those at or below the median score were defined as low SES.

    Statistical Analysis

    We used linear regression to explore the association between employment and change in MUAC over the five-year period of follow-up. Maternal and household characteristics that were significantly different between the employed and unemployed group at baseline or were hypothesized confounders a priori were used to fit several multivariable models, regressing change in MUAC on employment status. Only age, education, smoking status, having household servants, and baseline MUAC were significant and retained in the final model. The interaction between baseline MUAC and employment was tested against the final model, but was not statistically significant, and therefore was removed.

    Multivariable linear regression models were checked by studentized residuals, variance inflation factors, and leverage. In robustness checks, we tested whether results would substantively change if influential points were excluded. The data are presented as crude and adjusted mean changes for the primary outcome. All analyses were performed using Stata version 13.1. Alpha for main effects was set to 0.05 and to 0.10 for the interaction.

    Results

    Table 1 displays key demographic, household, and economic characteristics of the sample. The women who were hired were significantly younger than those who were not (25.18 versus 29.76 years; p <0.001). They were also more likely to be literate (p <0.001), have more years of formal schooling (p <0.001), have household servants (p <0.001); be multiparous (p <0.001) and to make decisions within the household. Only age, education, smoking status, and having household servants remained significantly different between the employed and unemployed women at baseline in the multivariable model. Those who were hired and those who were not hired were comparable with respect to caste, household size, household ownership of items, and food and non-food expenditures. The distribution of MUAC was comparable in the two groups of women (p= 0.31). The average MUAC was 22.8 cm among those who were hired and 22.9 cm among those who were not.

    Table 1. Baseline Characteristics
    Employed (n=324) Unemployed (n=391) p value 1
    n (%) n (%)
    Age (years) 2 25.18 ± 6.09 28.76 ± 7.98 <0.001
    Education
    No schooling 6 (1.85) 123 (31.46) <0.001
    1-9 years of schooling 242 (74.69) 218 (55.75)
    ≥ 10 years of schooling 76 (23.46) 50 (12.79)
    Literacy 317 (98.14) 311 (79.74) <0.001
    MUAC (centimeters)
    < 21 51 (15.74) 58 (14.83) 0.309
    21.0 - 22.9 126 (38.89) 150 (38.36)
    23.0 - 24.9 108 (33.33) 117 (29.92)
    ≥ 25.0 39 (12.04) 66 (16.88)
    Smoking 3 8 (2.47) 52 (13.30) <0.001
    Parity 2 1.11 ± 0.63 1.28 ± 0.60 <0.001
    Household Size 2 7.35 ± 3.80 7.21 ± 3.64 0.631
    Caste 3
    Brahmin 99 (30.56) 114 (29.23) 0.248
    Chhetri 56 (17.28) 54 (13.85)
    Vaishya 157 (48.46) 195 (50.00)
    Shudra 7 (2.16) 12 (3.08)
    non-Hindu 5 (1.54) 15 (3.85)
    Household Servants 113 (34.88) 77 (19.69) <0.001
    Household Ownership Scale 4
    Quartile 1 51 (15.74) 80 (20.46) 0.249
    Quartile 2 93 (28.70) 121 (30.95)
    Quartile 3 90 (27.78) 96 (24.55)
    Quartile 4 90 (27.78) 94 (24.04)
    Food Expenditures 5
    All Food 316 (97.53) 374 (95.65) 0.173
    Meat 197 (62.35) 255 (65.22) 0.426
    Ghee 152 (46.91) 159 (40.66) 0.093
    Fish 133 (41.05) 159 (40.66) 0.917
    Eggs 86 (26.54) 101 (25.83) 0.829
    Milk 169 (52.16) 181 (46.29) 0.118
    Vegetables 296 (91.36) 362 (92.58) 0.547
    Women’s Decision Making
    Buying Foods 115 (35.49) 176 (45.01) 0.010
    Selling Foodstuffs 59 (36.20) 104 (63.80) 0.008
    Buying Children’s Clothing 111 (34.26) 169 (43.22) 0.015
    Children Seeking Medical Care 145 (44.75) 212 (54.22) 0.012
    Borrowing Money 140 (43.21) 197 (50.64) 0.048

    Missing values: age (n=3) literacy (n=2); caste (n=1); food expenditure (n=17); meat expenditure (n =8); ghee expenditure (n =8); fish expenditure (n =8); egg expenditure (n =8); milk expenditure (n =9); vegetable expenditure (n =4); borrowing money (n=2).
    1 Based on χ2 test for binary outcomes or t-test for continuous data
    2 Mean ± standard deviation
    3 p-value determined using fishers exact test due to small cell sizes
    4 Score based on 17 items where quartile 1 indicates those with few items owned with quartile 4 representing ownership of more items
    5 Presented as a dichotomous outcome indicating if money spent during the past week in Nepalese rupees

    At the five-year follow-up, 29 of the 391 women (7.1 percent) who had not been employed by the project reported that they had been employed in other jobs for which they were paid cash. Similarly, 22 of the 324 (6.8 percent) employed women reported additional cash employment. However, the amount of the cash payments these women received was not determined and additional cash employment was not associated with change in MUAC (β = -0.09, 95% Confidence Interval CI = -0.58, 0.41) (data not shown).

    After adjustment for the baseline differences, employment was associated with a non-significant increase in change in MUAC (β = 0.08; 95% CI: -0.20, 0.36) (Table 2). As hypothesized, those women with a higher baseline MUAC value gained less in arm tissue over time than those with a baseline MUAC < 21 cm (MUAC 23 - 24.99: β = -0.83; 95% CI: -1.18, -0.48; MUAC ≥ 25: β = -0.99; 95% CI: -1.44, -0.54). Similarly, those women with 10 or more years of schooling had a greater positive change in MUAC as compared to those with no schooling (β = 0.59, 95% CI: 0.12, 1.06). Those women who smoked at baseline had a significantly lower change in MUAC as compared to those women who did not smoke (β = 0.54; 95% CI: -0.97, -0.10).

    Table 2. Multivariable Linear Regression Results of Change in Mid-Upper-Arm Circumference (in cm) Between Baseline and Five Years
    Change in Mid-Upper-Arm Circumference 1 (n=715)
    β (95% Confidence Interval) p value
    Employment Status 0.08 (-0.20 0.36) 0.58
    Age (years)2
    < 20 Reference
    20 – 29 0.39 (-0.12, 0.89) 0.13
    30 – 39 0.67 (0.13, 1.20) 0.01
    40 – 49 0.29 (-0.48, 1.07) 0.46
    ≥ 50 0.16 (-1.11,1.43) 0.81
    MUAC (centimeters)2
    < 21 Reference
    21-22.9 -0.25 (-0.58, 0.09) 0.15
    23-24.99 -0.83 (-1.18, -0.48) <0.001
    ≥ 25 -0.99 (-1.44, -0.54) <0.001
    Education
    No schooling Reference
    1-9 years 0.11(-0.24, 0.45) 0.54
    ≥ 10 years 0.59 (0.12, 1.06) 0.01
    Smoking -0.54 (-0.97, -0.10) 0.02
    Household Servants -0.10 (-0.38, 0.19) 0.51

    1 Adjusted for age, education, smoking status, having household servants, and baseline MUAC, using robust standard error
    2 Age was modeled as categorical variable as it was not linearly associated with change in MUAC

    Overall, results were similar when stratified by SES. After adjustment for baseline differences, employment was not significantly associated with change in MUAC among low SES (β = -0.05; 95% CI: -0.46, 0.37) nor high SES (β = 0.18; 95% CI: -0.21, 0.57) women (Table 3). Those women with a baseline MUAC > 25 cm exhibited the smallest increase in MUAC compared to those with a baseline MUAC < 21 cm (low SES: β = -0.93; 95% CI: -1.63, -0.24; MUAC ≥ 25 cm: β = -1.10; 95% CI: -1.74, -0.47). High SES women whose baseline MUAC was 23-24.99 cm also had a significantly lower change in MUAC. Among low SES women, education ≥ 10 years (β = 1.04; 95% CI: 0.38, 1.70) and smoking (β = -0.63; 95% CI: -1.17, -0.09) were significantly associated with change in MUAC. Robustness checks indicated that without influential points, the association between employment and change in MUAC changed direction (i.e. positive to negative), though results were not statistically significant (β = -0.53, 95% CI: -2.99, 1.93).

    Table 3. Multivariable Linear Regression Results of Change in Mid-Upper-Arm Circumference (in cm) Between Baseline and Five Years by Socioeconomic Status
    Low Socioeconomic Status (n= 34 5) High Socioeconomic Status (n= 370)
    β (95% C onfidence Interval) p value β (95% C onfidence Interval) p value
    Employment Status -0.05 (-0.46, 0.37) 0.82 0.18 (-0.21, 0.57) 0.36
    Age (years)2
    < 20
    20 – 29 0.72 (-0.03, 1.45) 0.06 0.23 (-0.44, 0.90) 0.50
    30 – 39 0.83 (0.06, 1.60) 0.04 0.76 (<0.01, 1.53) 0.05
    40 – 49 0.47 (-0.64, 1.59) 0.41 0.08 (-1.03,1.19) 0.89
    ≥ 50 0.37 (-1.69, 2.44) 0.36 -0.07 (-1.43, 1.28) 0.91
    MUAC (centimeters)2
    < 21
    21-22.99 -0.17 (-0.64, 0.31) 0.49 -0.30 (-0.78, 0.18) 0.22
    23-24.99 -0.49 (-1.01, 0.03) 0.07 -1.12 (-1.61, -0.64) <0.001
    ≥ 25 -0.93 (-1.63, -0.24) <0.001 -1.10 (-1.74, -0.47) 0.001
    Education
    No schooling
    1-9 years 0.25 (-0.20, 0.69) 0.27 -0.34 (-0.94, 0.26) 0.27
    ≥ 10 years 1.04 (0.38, 1.70) <0.001 0.06 (-0.67, 0.79) 0.88
    Smoking -0.63 (-1.17, -0.09) 0.02 -0.33 (-1.10, 0.44) 0.40
    Household Servants -0.24 (-0.76, 0.28) 0.36 -0.13 (-0.50, 0.23) 0.47

    1 Adjusted for age, education, smoking status, having household servants, and baseline MUAC, using robust standard error
    2 Age was modeled as categorical variable as it was not linearly associated with change in MUAC

    Discussion

    This study compared women with themselves before and after five-years of part-time employment, controlling for observed baseline differences and hypothesized confounders. In this sample of rural Nepalese women, part-time employment was not associated with a change in nutritional status, as measured by MUAC. Consistent with previous findings that document and inverse relationship between baseline MUAC and weight gain30 we find that women with a higher baseline MUAC value had a significantly lower change in MUAC during follow-up.

    Despite being well correlated with BMI, few studies have used MUAC to explore the impact of women’s employment on nutritional status. In particular, Mascie-Taylor and colleagues4explored the impact of a cash-for-work program on improved nutritional status among women in rural Bangladesh. Results indicated that women in the intervention group increased in MUAC (mean +2.29 mm), whereas MUAC among women in the control group decreased. The authors indicated that participants worked approximately full-time; the program provided approximately 2.6 million person-days of paid work to men and women from September to December 2007. Notably, we may not have observed a significant association between employment and nutritional status among our sample in Nepal because the employment was part-time.

    In addition, Bisgrove and colleagues15 report differential implications of employment by SES; in particular, improved diet was greater among women from low-income households. As noted by Katz and colleagues in previous work,20 the women who applied for the part-time employment in Nepal were better educated and came from higher SES backgrounds than the general population of Sarlahi. For example literacy was very high among both women who were employed (98 percent) and those not employed (80 percent) by the study, while the overall literacy rate among women of childbearing age in Sarlahi was 14 percent at the time this study was conducted.31 Similarly, a considerable segment of this population was from the highest caste (Brahmin).

    Intra-household food allocation practices may also play a role32, 33 In a study of intra-household food allocation in rural Nepal, women had a late position in household serving order and often allocated special foods to males and children.32This study also reported that women had a lower total energy intake, as compared to their male counterparts. It is possible that increased maternal income translates into higher quality foods and a greater quantity of food for other household members, but not for the women themselves. However, indicators of nutritional status among others in the household were not measured at five-year follow-up.

    The initial analysis of Katz and colleagues20suggested that longer follow-up might result in impacts on nutrition that were not evident after two-years of part-time employment. However, these five-year follow-up results offer an alternative hypothesis; maternal part-time employment alone may not result in improved nutritional status. It may be that additional cash-income is spent on non-food expenditures, such as child health and education. A complementary analysis looked at the impact of employment (among these same women) on family health and economic well-being.2 In particular, employed women were more likely to save cash, buy jewelry, and buy certain discretionary household goods, as compared to those who were not hired.2Expenditures on children's clothing also increased among employed women, and their children were more likely to be in private schools at follow-up.

    Limitations of this study warrant consideration. It is possible that there were changes in other anthropometric indicators, such as absolute weight or BMI. However, additional indicators were not measured in this sample. MUAC was only collected among the mothers at follow-up, prohibiting the exploration of the impact of maternal employment on the others members of the household, primarily children. Other studies have suggested that increased maternal income may improve diet diversity among children4, 13 However, dietary consumption and total energy intake was not explored in this sample. An additional limitation of this longitudinal study design is the lost-to follow-up among participants, introducing the possibility that those who remain in the sample are different than those who were lost. However, to result in biased coefficients, the relationship between employment and change in MUAC would have to be different between those observed and those lost and we have no reason to believe this would be the case. We have a relatively high retention, with 82 percent of the original sample.

    Conclusions

    Despite results suggesting that part-time employment is not associated with changes in the nutritional status of women, women’s employment may have other benefits in LMIC settings, including improved decision-making power among women and increased household SES. Future research should explore if these pathways impact the nutritional status of other members of the household and investigate this relationship among women employed full-time.

    Acknowledgments:

    This study was carried out under cooperative agreement No. DAN 0045-A-5094 between the office of Nutrition, US Agency for International Development (USAID), the Center for Human Nutrition (CHN), and the Dana Center for Preventive Ophthalmology (DCPO) at Johns Hopkins University, with additional support through Task Force Sight and Life (now Sight and Life, Basel, Switzerland).

    The study was a joint undertaking by CHN/DCPO, the National Society for the Prevention of Blindness in Kathmandu, Nepal, and the Kedia Seva Mandir in Sarlahi District, Nepal, under the auspices of the Social Welfare Council of His Majesty’s Government, and the Nepal Health Research Council.

      

    References

    1.Duflo E. (2003) Grandmothers and granddaughters: Old-age pensions and intrahousehold allocation in South Africa. World Bank Econ Rev. 17(1), 1-25.
    2.Katz J, P West Jr K, E K Pradhan, S C Leclerq, S K Khatry et al. (2007) The impact of a small steady stream of income for women on family health and economic well-being. Global Pub Health. 2(1), 35-52.
    3.J F Lamontagne, P L Engle, M F Zeitlin. (1998) Maternal employment, child care, and nutritional status of 12-18-month-old children inManagua. , Nicaragua. Soc Sci Med; 46(3), 403-414.
    4.C G Mascie-Taylor, M K, Goto R, Islam R. (2010) Impact of a cash-for-work programme on food consumption and nutrition among women and children facing food insecurity in rural Bangladesh. Bull World Health Organ. 88(11), 854-860.
    5.Yoong J, Rabinovich L, Diepeveen S. Science Research Unit, Institute of Education, University of London (2012) The impact of economic resource transfers to women versus men: a systematic review. Technical report. London: EPPI-Centre,Social Available at: http://r4d.dfid.gov.uk/pdf/outputs/systematicreviews/economicttransfer2012yoong.pdf
    6.Hoddinott J, Haddad L. (1995) Does female income share influence household expenditures: evidence from. , Côte 57(1), 77-96.
    7.Richards E, Theobald S, George A, J C Kim, Rudertc C. (2013) Going beyond the surface: Gendered intra-household bargaining as a social determinant of child health and nutrition in low and middle income countries. , Soc Sci Med 95, 24-33.
    8.L C Fernald, Hidrobo M. (2011) Effect of Ecuador&apos;s cash transfer program (bono de desarrollo humano) on child development in infants and toddlers: A randomized effectiveness trial.SocSciMed;72(9):. 1437-1446.
    9.L C Fernald, P J Gertler, Neufeld L M. (2008) Role of cash in conditional cash transfer programmes for child health, growth, and development: An analysis of Mexico&apos;s Oportunidades. , Lancet 371(9615), 828-837.
    10.Das N, Yasmin R, Ara J, Kamruzzaman M, Davis P et al. (2013) How do intra-household dynamics change when assets are transferred to women?. International Food Policy Research Institute,Washington, DC. Available at: http://www.ifpri.org/publication/how-do-intrahousehold-dynamics-change-when-assets-are-transferred-women-0 .
    11.Marquis G, Colecraft E. (2013) Microcredit with entrepreneurial and nutrition education intervention improved women&apos;s incomes and children&apos;s animal source food intake and nutrition in Ghana. Ann Nutr Metab. 63-143.
    12.P L Engle. (1991) Maternal work and child-care strategies in peri-urban Guatemala: Nutritional effects. , Child Dev 62(5), 954-965.
    13.Tucker K, Sanjur D. (1988) Maternal employment and child nutrition in panama. Soc Sci Med;26(6);605-612.
    14.I M Rathnayake, Weerahewa J. (2005) Maternal employment and income affect dietary calorie adequacy in households in Sri Lanka. Food Nutr Bull. 26(2), 222-229.
    15.Bisgrove E, B M Popkin. (1996) Does women&apos;s work improve their nutrition: Evidence from the urban Philippines. Soc Sci Med. 43-10.
    16.Toyama N, Wakai S, Nakamura Y, Arifin A. (2001) Mother&apos;s working status and nutritional status of children under the age of 5 in urban low-income community. , Surabaya, Indonesia, J Trop Pediatr 47-3.
    17.Nair M, Ariana P, Webster P. (2014) Impact of mothers&apos; employment on infant feeding and care: A qualitative study of the experiences of mothers employed through the Mahatma Gandhi National Rural Employment guarantee act. , BMJ Open 4(4), 004434.
    18.Nakahara S, K C Poudel, Lopchan M, Ichikawa M, Poudel-Tandukar K. (2006) Availability of childcare support and nutritional status of children of non‐working and working mothers in urban Nepal. , Am J Hum Biol 18(2), 169-181.
    19.B K Yeleswarapu, Nallapu S S. (2012) A comparative study on the nutritional status of the pre-school children of the employed women and the unemployed women in the urban slums of Guntur. , J Clin Diagn Res 6(10).
    20.Katz J, P West Jr K, Kimbrough Pradhan E, S C LeClerq, Shakya T R. (2001) Impact of providing a small income on women&apos;s nutritional status and household food expenditures in rural Nepal.Food Nutr Bull;22(1):. 13-18.
    21.Population Reference Bureau. (2011) The World&apos;s Women and Girls:Data Sheet. Available at: http://www.prb.org/Bookstore/ItemDetail.aspx?sku=IDS11WWENG&fromCategory=topic&fromSubCategory=Youth.
    22.R S Gibson. (2005) Principles of nutritional assessment.OxfordUniversityPress.
    23.James W P, Mascie-Taylor G C, Norgan N G, Bistrian B R, Shetty P S et al.The value of arm circumference measurements in assessing chronic energy deficiency in third world adults. , Eur J Clin Nutr 48(12), 883-894.
    24.Baqui A H, Arifeen S E, Amin S, Black R E. (1994) Levels and correlates of maternal nutritional status in urban bangladesh. , Eur J Clin 48(5), 349-357.
    25.World Health Organization. (1995) Maternal Anthropometry and Pregnancy Outcomes: A WHO Collaborative Study. Available at: http://whqlibdoc.who.int/bulletin/1995/supplement/bulletin_1995_Vol73_supp_(part1).pdf .
    26.Rodrigues V C, Rao R S, Lena A. (1994) Utility of arm circumference as a screening instrument to identify women at nutritional risk. , Trop 24(4), 164-166.
    27.Khadivzadeh T. (2002) Mid upper arm and calf circumferences as indicators of nutritional status in women of reproductive age. East Mediterr HealthJ;8(4-5):. 612-618.
    28.Nguyen P, Ramakrishnan U, Katz B. (2014) Mid-upper-arm and calf circumferences are useful predictors of underweight in women of reproductive age in northern Vietnam. , Food & Nutrition 35(3), 301-311.
    29. (2009) United Nations Children&apos;s Fund. Tracking progress on child and maternal nutrition. Available at: http://www.childinfo.org/files/Tracking_Progress_on_Child_and_Maternal_Nutrition_EN.pdf .
    30.N M Dale, Myatt M, Prudhon C, Briend A. (2013) Using mid-upper arm circumference to end treatment of severe acute malnutrition leads to higher weight gains in the most malnourished children. , PloS one 8(2), 55404.
    31.Katz J, P West Jr K, Wu L, Khatry S K Kimbrough Pradhan, E. (2002) Determinants of maternal vitamin A or beta-carotene supplementation coverage: Village-based female distributors in Nepal. , Am J Public Health 92(7), 1105-1107.
    32.Gittelsohn J. (1991) Opening the box: Intrahousehold food allocation in rural Nepal. Soc Sci Med. 33(10), 1141-1154.
    33.Gittelsohn J, Thapa M, L T. (1997) Cultural factors, caloric intake and micronutrient sufficiency in rural Nepali households. Soc Sci Med. 44(11), 1739-1749.