Does women's work improve their nutrition: Evidence from the urban

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Soc. Sci. Med. Vol. 43, No. 10, pp. 1475-1488. 1996

Pergamon S0277-9536(96)00046-9

Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0277-9536/96 $15.00 + 0.00

DOES WOMEN'S WORK IMPROVE THEIR NUTRITION: EVIDENCE FROM THE U R B A N PHILIPPINES EILENE Z, BISGROVE ~ and BARRY M. POPKIN 2. ~Family Health International, Box 13590, Research Triangle Park, NC 27709, U.S.A. and -'Department of Nutrition, School of Public Health and The Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516-3997, U.S.A. Abstract--Women's market work in developing countries is thought to improve their well-beingdirectly through increased income for health-related purchases and indirectly through elevating women's status within the household. While a number of studies have looked at the effects of women's work and the cost of women's time on child nutrition and welfare, the direct effects of women's work on their own welfare have been largely untested. Using data on 1963 urban Filipino women from the Cebu Longitudinal Health and Nutrition Survey, we examined the relationship between women's work and their dietary intakes of energy, protein, fat, calcium, and iron from home and commercially prepared foods. Determinants equations for home and commercial intakes were estimated simultaneously to adjust for non-independence. Appropriate methods were used to deal with selectivity,endogeneity, and unobserved heterogeneity. Nearly half (48%) of the women worked for pay, and commercially prepared foods made up an important part of working women's diets. Not only did women's work improve the quality of their diets, but there were strong distributional implications; lower-income women gained more than higher-income women. Employment sector also influenced women's dietary patterns. Informal non-wage work was associated with increased intakes, whereas formal sector work was associated with decreased intakes. Positive effects of work in the informal sector were greater for women from low-income households. Policy implications of the dietary benefits of informal non-wage work for low-incomewomen are discussed. Copyright © 1996 Elsevier Science Ltd Key words--women's work, income, nutrient intakes, Philippines


Extensive debate in development has focused on the role of women in development as it affects the welfare of their children, but little attention has been given to the welfare of women. The attention paid to this topic normally focuses on women within the context of childbearing and the focus is on the diet and health of women to improve infant health. The safe motherhood initiative of the World Bank and other international organizations has been part of a re-emphasis on the well-being of women. A crucial aspect of this is the nutritional status of women, a topic examined in this paper. Over the past two decades, one of the arguments of the "women in development" literature has been that increasing women's labor force participation in developing countries would improve their status and well-being [l~l]. Market work has been hypothesized to operate directly through increased income which will provide for greater health-related purchases, and indirectly through altering women's status within the household and their share of and autonomy over household resources [5]. One dimension of the discussion h a s focused on the relative difference of the effect of

*Author for correspondence. SSM 43/IO~B.~

market and non-market income on welfare with researchers arguing that market income has a greater impact [3]. In general, adult women in low-income countries consume diets which are relatively more inadequate compared with their physiological need than are the diets of men and children [6-8]. The few studies and reviews of this topic have pointed toward the need for improving women's diets with regard to their own well-being as well as the importance of women's roles in home and market production and the effects of improved maternal health on reproduction and lactation. A limited set of research has rigorously examined the relationship between women's work and women's nutrition. Studies exploring the relationship between the income or wage rate or market economic status of women and consumption are questioning the assumption that all sources of income will have the same effect on dietary intake and other sources of family welfare. Garcia found that all household incomes do not have the same effect on household expenditures pattern [9]. Senauer et al. found that Filipino women's predicted wage increased their proportion of total household energy intakes [10]. These studies provide a strong case for the need to explore these relationships with more complete models and larger, more representative samples.



Eilene Z. Bisgrove and Barry M. Popkin

Neither study examines the welfare implications for women themselves. A number of studies from low-income countries have looked at the effects of women's work and income on child nutrition [11-16].* Thomas and his colleagues have conducted a series of careful studies of the effects of the mother's and father's income on child nutritional status in Brazil. As noted above, the effects of women's work on their own welfare have been largely untested. In this study the relationship between women's work and dietary intake is explored by using a unique in-depth survey of about 2000 women and their households conducted in the Cebu region of the Philippines. In attempting to examine this relationship, the relationships underlying our hypotheses should be laid out. The woman-in-development literature has focused on the ways that women's work raises their direct control over money and also indirectly affects their status and influence regarding household resources. For this study, the increase in women's cash income is expected to increase women's overall dietary intakes through increasing their share of household foods and/or through women's increased purchases of foods for household and for their own consumption. Since the increased value of women's time outside the home also would increase the opportunity cost of preparing meals at home, one of the expected changes that occurs when women enter the labor force is an increase in away-from-home food consumption. Given the generally greater structure and higher prestige of formal sector work, in conjunction with typically longer periods of time away from home, work in the formal sector is likely to be associated with relatively greater increases in intakes of commercially prepared foods. With larger numbers of hours worked away from home, increases in away-from-home food consumption and possible decreases in home-prepared food consumption are also expected. To test these relationships, we examine separately the effects of change in work and income on diet from home and away-from-home sources and we separate work patterns in a manner that allows testing of the effects of sector of employment. Little is known about patterns and determinants of away-from-home food consumption patterns in low-income countries, though the extensive literature in higher income countries points to the conceivably large magnitude of this activity, its potentially sizable effect on diet, and to the substantial increase in commercial food consumption associated with

women's labor force participation [17-23]. Two small studies from developing countries found that commercially prepared foods make up about one fourth of urban dwellers' diets in the Philippines [24, 25]. With increased modernization, the importance of the commercial food sector is expected to grow. The dietary impact of commercial food consumption has been rarely measured, let alone analyzed in developing countries. In fact, consumption of away-from-home commercially prepared foods is not measured in the household food consumption surveys in most low-income countries. In the United States (U.S.) and other higherincome countries, at-home and away-from-home consumption provides different levels of protein, fat and numerous micronutrients than do homeproduced foods. Too little is known about awayfrom-home consumption in low-income countries to allow us to come to the type of conclusions that many researchers in the U.S. have concerning the nutritional adequacy and value of away-from-home consumption. In the U.S. over 28.4% of the diet comes from such consumption [17, 18]. These U.S. studies show that away-from-home eating patterns may have an important effect on differences in the make-up and quality of consumers' diets [19-21]. The health consequences of altered nutrient intakes from commercial and home-prepared foods could be beneficial or detrimental depending on women's usual dietary intakes and on their nutritional and health status. For women with marginal intakes, increased energy from fat, and increased iron and calcium could have beneficial effects on their nutritional status and health. If women's work and income influence their home vs commercial food intake patterns with subsequent effects on their nutritional status, then knowledge about how these factors interrelate is of key importance in designing strategies to improve women's health and welfare. In this article, we organize the model into a system which examines the determinants of women's diets from foods prepared at home and away from home simultaneously. We develop a system which allows us to examine the effects of maternal work while controlling for unobserved heterogeneity and the potential endogeneity of the work and consumption decisions. The following Section II presents hypotheses to be tested. Section III presents our model and estimation techniques. Section IV presents the survey data. This is followed by the descriptive and analytic results and a discussion.

*Studies which have looked at the effects of women's work on child welfare outcomes have found mixed effects, most of which were dependent on the compatibility of the work with child care and the quality of available time and goods substitutes. In general this literature has ignored the endogeneity of women's work and many of these child welfare decisions, with there being a few exceptions. For example, see Blau et al. [43]. This analysis considers explicitly this topic at a later point.


(A) As women's cash income increases, their intakes of nutrients from commercially prepared foods increase. While this effect may not be linear, it is thought to be monotonic. (B) Women's work for pay increases intakes of home and commercially prepared foods. Work

Women's work and women's welfare in the Philippines for pay interacts with household income and effects are stronger for lower income households. (C) Women's work in the formal sector increases intakes of commercially prepared foods more than does work in the informal sector or piece work. (D) Increased hours at work increase intakes of commercially prepared foods and decrease intakes of home prepared foods. III. MODEL AND ESTIMATIONTECHNIQUES In developing the framework for modeling the effects of women's work and income on their diets, a demand system equation method was chosen since it allows modeling determinants of home and commercial intakes as alternate decisions using identical structural equations. A model was developed in which home and commercial nutrient intakes are a function of women's work, income, and sets of control variables identified as potential confounders or having explanatory linkages with women's dietary intakes. Control variables were included in the model based on careful review of related studies, hypothesized rationale for inclusion, and data-based testing of the model. The model used is as follows: HNI = f(WS,_ l, INC, - I, SES,- t, HHC,_ I, NS,_ I, HS,_ I, REP,_ 1, ENV) CNI = f(WS,_ I, INC,_ I, SES,_ I, HHC,_ I, NS,_ ], HS,_ I, REP,_ I, ENV) where HNI = Home nutrient intake CNI = Commercial nutrient intake WS = Vector of women's work status variables INC = Women's income SES = Vector of household socioeconomic variables (includes household income other than women's cash income) H H C = V e c t o r of household composition variables NS = Women's nutritional status HS = Vector of women's health status variables REP = Women's reproductive status ENV = Vector of environmental factors t - 1 = Lagged endogenous variables. Ordinary least squares (OLS) was used to estimate the home intake equation and Tobit was used to estimate the commercial intake equation (note: 26% of the sample had zero values for intakes of nutrients from commercial sources) [26]. The subsequent effects of determinants on combined intakes are examined by summing the home and commercial coefficients for each determinant. Because dependent and independent variables were measured using the same scales for the home (OLS) and commercial (Tobit) intake


equations, coefficients can be added. Since women in the study consumed more then 96% of their total intakes from combined home and commercial sources, these combined intakes approximate total intake (note: other sources which made up about 4% of women's diets included celebrations, meals at friends' and relatives' homes, and food programs). One issue involved in using the demand system approach arises from the assumption of non-independence of decisions to consume home or commercially prepared foods. During the course of a day, one decision may be conditioned upon the other. For example, if a woman consumed a large nutrient-dense meal at a cafeteria for lunch, she may reduce her intake of home prepared foods in the evening. In addition there may be unobserved characteristics of women, such as strong personal preferences for prepared meals at home or for eating out which influence their consumption patterns of home or commercially prepared foods. Estimation of the home and commercial equations separately, assuming independence, would yield unbiased regression coefficients; however, these estimators would not be efficient if the error terms between the equations were correlated. Employing Zellner's "seemingly unrelated sets of regression" (SUR) approach, the home intake equation and the commercial intake equations were estimated simultaneously using H O T Z T R A N software [27, 28]. Unlike a model with two continuous and unlimited dependent variables where SUR and OLS would give the same results, this model represents a gain in efficiency when the two equations are estimated simultaneously. In estimating the models in this analysis, the coefficient of correlation in error terms between the two equations (psuR = - 0.06; T-value -- - 4.46, P < 0.01), was significant, thereby substantiating the assumption of non-independence. Hence, more efficient estimators are produced with this model. Two other issues arise in developing and estimating the model. The first is sample selection bias. Women were lost for a number of reasons from the sample between baseline and 14 months postpartum, the panel selected for this analysis. In order to discern whether estimation of the model was biased by these losses, a probit selectivity equation was estimated simultaneously; first with the home intake equation, then with the commercial intake equation. This maximum likelihood Heckman procedure is more efficient than the two-step Heckman approach which estimates the inverse of the Mills ratio and tests its estimated coefficient for significance. For the commercial Tobit equation p . . . . was not significant. For the home OLS equations, estimation of the equation was maximized at photo°= 0. In both cases, the results indicated that sample losses had not introduced serious bias. The second modeling issue is endogeneity and unobserved heterogeneity. Outcome variables and

Eilene Z. Bisgrove and Barry M. Popkin


some independent variables may be reciprocally related. For this study, time-lagged variables were considered predetermined with respect to women's current home and commercial intakes. An additional problem associated with endogeneity arises from joint causation. Endogenous independent variables and outcome variables may be determined by the same underlying factors which, if controlled for, present little problem. In addition, there may be unobserved characteristics correlated with both independent and outcome variables. An example of such an unobserved characteristic would be the "Healthy Worker Effect". Healthy women who may be more likely to work may also have greater appetites. To correct for unobserved heterogeneity, for variables a priori considered endogenous to the model, instruments were predicted from reduced form equations and tested for endogeneity using the Hausman Test [29]. Variables a priori considered endogenous included all work-related variables, women's cash income, women's weight, energy expenditure, pregnancy, week in gestation, recent term birth, lactation, and household structure and composition. Estimation methods for instruments and the core exogenous variables used to estimate instruments are listed in Tables l(a) and (b). In Table l(b), the statistical significance of each variable in predicting each endogenous variable is noted. For truncated endogenous variables such as income, Tobit models were estimated using HOTZTRAN software [30]. Also when instruments were estimated only for a sub-sample, for example, an instrument for the duration of gestation (in weeks) was estimated for pregnant women only, a probit

selectivity equation modeling the exogenous determinants of current pregnancy status was estimated simultaneously with the OLS reduced form equation for week in gestation. Selectivity equations (probit models of women's working status) were also included in preliminary estimations of instruments for the variables for the various dimensions of work (work sector, place of work, hours worked). Variables which tested endogenous were pregnancy, week in gestation for pregnant mothers, and living in an extended household. For work and income variables, the instruments were not more efficient estimators than the observed values. Based on the poor performance of the work and income instruments and the number of highly significant tests of the observed values, women's work and income were considered exogenous to the model. IV. SURVEY DATA

The study uses data from the Cebu Longitudinal Health and Nutrition Survey (CLHNS), in which data were collected specifically to examine the wide array of factors that influence maternal nutrition. The study site, metropolitan Cebu, is the second largest metropolitan area in the Philippines. A single-stage cluster sampling procedure was used to randomly select 33 communities or barangays (17 urban, 16 rural) from the metropolitan Cebu area. The barangays, which contained about 28,000 households, were completely surveyed in late 1982 and again in early 1983 to locate all pregnant women. Women of the selected barangays who gave birth between 1 May 1983 and 30 April 1984 are included in the sample.

Table I(a). Estimation techniques used for developing instruments for variables a priori considered to be endogenous Numerical order of instruments

Dependent endogenous variable

Estimation method

I 2 3 4 5 6 7 8 9 10 I1 12 13 14 15 16 17

Working for pay Women's cash income (pesos/week) Non-wage work Time wage work Piece wage work Lactating Pregnantb Week in gestation' Recent term birth Women's weight (kg) Respiratory infection Diarrhea prior weekd Energy expenditure (kcal/kg) Parity' Index baby alive~ Household size' Extended household'

Probit Tobit Probit S b Probit S~ Probit S b Probit Probit OLS b Probit OLS Na d Probit OLS OLS Probit OLS Prohit


Mother heads household'

Na d

0.05 (0, I)


19 20

Spouse present' Children age 0 to 12


0.98 (0, I) 3.0 (0-11)

NA R 2 = 0.24

Mean (range) 0.48 (0, 1) 52.3 (0-3791) 0.26 (0, I) 0.13 (0, 1) 0.09 (0, 1) 0.46 (0, I ) 0.18 (0, 1) 3.1 (0-37) 0.06 (0, 1) 46.5 (29-86) 0.01 (0, 1) 0.04 (0, I ) 36.2 (19-63) 2.2 (0-14) 0.96 (0, 1) 6.6 (2-19) 0.44 (0, 1)

Goodness of fit, -2LLR = R ~ = 0.23 R 2 = 0. I 0 R 2 = 0.19 R: = 0.15 - 2LLR = -2LLR = R: = 0.13 -2LLR = R ~ = 0.29 NA - 2LLR = R 2 = 0.06 R 2 ffi 0.59 - 2LLR = R: = 0.14 - 2LLR =


189.5 116.5 60.0


51.97 267.8

•Measures of goodness of fit are the R 2 for OLS. pseudo R 2 for Tobit and selectivity corrected Prohits, and - 2 Log Likelihood Ratio for non-selectivity corrected probits. bS refers to selectivity corrected models. ~Variables which tested as endogenous using the Hausman Test. aDue to small variance, reduced form equations could not be estimated. 'Variables which were dropped from the final model. Unless required as a control variable, variables with 7" statistics less than 1 in both home and commercial intake equations were dropped from the model.

Women's work and women's welfare in the Philippines


Table l(b). Core exogenous variables used in estimating reduced form equations for instruments Dependent endogenous variables in numerical order from Table l(ap Exogenous variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Household income x x x X X X X X X Household assets x X X Woman's age x x x X X X X X X X X Woman's education in years x x x x X X X X X X Woman is literate x x Woman's height X X X Woman has insurance x Father's education if present X X Father's age if present x x x X X X X X Baby alive at 1 year X Electricity in household x x X X Stove in household x x x x X X X X X X X Refrigerator in household x X X Television in home X X Radio in home X X X Price of corn x X X Price of kerosene X Price of cooking oil x Price of evaporated milk x Price of infant formula x Price of condensed milk x x X Price of powdered milk Availability of formula x x X X Time to private health care facility x X X Time to public health care facility x X X X Time to traditional health care facility x Barangay population density x x x x x X Distance from house to nearest road x X Availability of municipal water X X X Modernization indexb x X Time in days from beginning of survey X Days postpartum x x x x x X X X X Wet season x Cumulative rain Religion Cebuano ethnicity ~All x's denote that the exogenous variable had a statistical significanceof P < 0.05 in the estimation of this endogenous variable. bA modernization index, scaled from 1 to 14, was based on infrastructure, utilities, and services surrounding and available to the household.

A baseline interview was c o n d u c t e d a m o n g 3327 w o m e n d u r i n g the 6--7th m o n t h o f pregnancy so t h a t all births, including pre-term births, could be identified. Subsequent surveys t o o k place immediately after birth, then at b i m o n t h l y intervals for 24 m o n t h s . Each survey collected detailed health, nutrition, d e m o g r a p h i c a n d socioeconomic data. I n - d e p t h dietary intake d a t a o n the m o t h e r s were collected d u r i n g pregnancy, 3 a n d 14 m o n t h s p o s t p a r t u m . A m a j o r focus o f training for dietary data collection was o n the mitigation o f interobserver variability. Using the 24-hour recall method, dietary i n f o r m a t i o n was gathered by trained interviewers using questionnaires designed for o b t a i n i n g precise i n f o r m a t i o n o n each food item c o n s u m e d including the place o f p r e p a r a t i o n , m e t h o d o f preparation, meal, a n d day o f week. A 2-week training course o n dietary data collection m e t h o d ology a n d tests of inter-observer reliability was used to ensure the quality o f the data. In addition, there were periodic training in-services a n d reliability tests t h r o u g h o u t the survey period. Interviewers were

*The survey occurred during a period of rapid inflation and two large devaluations which created added exogenous variability in income and market work opportunities for sample women.

rotated to minimize the likelihood o f systematically biasing the intakes of any one subpopulation. W o m e n ' s average energy expenditures (kcal/kg) d u r i n g a 24-hour period were estimated by applying previously estimated activity expenditure values to the time w o m e n allocated to various types o f activities performed in h o m e a n d m a r k e t production, as well as rest a n d leisure [31]. It is i m p o r t a n t to note t h a t we f o u n d t h a t m u c h of the variability in w o m e n ' s energy expenditures is related to h o m e p r o d u c t i o n a n d there is a n insignificant correlation between o u r various work a n d income variables a n d w o m e n ' s energy expenditure [31]. Detailed occupation a n d income d a t a were collected o n m o t h e r s a n d o t h e r h o u s e h o l d m e m b e r s at baseline, 12 a n d 24 m o n t h s p o s t p a r t u m [32-34]. Also w o m e n ' s occupations a n d h o u r s worked during the previous week were collected bimonthly. Price data, collected o n a b i m o n t h l y basis, were used to deflate the income a n d earnings data.* F o r the purpose o f this study, work is defined as work for pay during the previous week. Various dimensions o f work m e a s u r e d include: work sector (where n o n - w a g e or informal work is w i t h o u t c o n t r a c t u a l agreements to h o u r s a n d wages, e.g. self-employment; time wage or formal work is with contractual agreements to h o u r s a n d wages; a n d piece


Eilene Z. Bisgrove and Barry M. Popkin

work is self explanatory), hours worked, place of work (home or away), and travel time to work in minutes. Women's income is defined as cash income. This value is based on the 12-month longitudinal survey which averages women's cash income from market labor over a 4-month period. The estimated value of women's non-market-related home production is not included in this analysis. There are several rationales for defining women's income as cash income only. One aspect relates to measurement. The non-cash component of income is a measure of household activity and since many household members may work on these activities in this population, imputation of an in-kind income to the women would add error to the estimate of women's income that is actually attributed to the household. Another relates to behavior. Often economists do not attribute in-kind household production to one individual since many members work on these activities and the family is felt to place a lower value on participation in home production activities. That is, it is felt that cash income may be more strongly associated with decision-making power and autonomy in spending income. In addition, cash income is typically required for commercial food purchases. Household income excludes women's cash income, but includes cash income from other household members and in-kind income from all home and market production (including the raising of livestock and poultry, gardening and farming). These measurement and behavioral issues underlie our reason for handling women's cash income in this manner, namely that we want to test the hypothesis that women's cash income matters. To combine cash and in-kind income would cloud our ability to test this hypothesis. The sample used for this analysis included 1963 urban women at 14 months postpartum.* Urban women were selected for the study because, compared with rural women, they consume more commercially prepared foods, and they have higher rates of labor

*From the sample of 1971 urban women with dietary data at 14 months, a total of 8 women were excluded. One woman who had a stillbirth and 7 who had miscarriages within the 2 months prior to the interview were excluded on the basis that a recent stillbirth or miscarriage could affect work and dietary patterns differently than a live birth, and the numbers were too small to meaningfully control for. tFor the purpose of comparability with other international studies, the WHO/FAO/UNU 1985 recommended standards for energy and protein intakes (reference woman moderately active and weighing 45 kg) were used for this analysis. The Philippines recommended intakes for energy are comparable to the WHO/FAO/UNU levels (e.g. 1920 and 1950 kcal respectively for nonpregnant, non-lactating women), whereas the Philippines recommendation for protein is substantially higher than the more accepted WHO/FAO/UNU levels (e.g. 54 and 34.5 g respectively for non-pregnant, non-lactating women).

force participation. Data from 14 months postpartum were used since that was the longest duration dietary data were collected from the index birth and provided greater variability in women's work patterns and reproductive status. By 14 months postpartum, women were likely to have returned to full participation in market labor. Women who were pregnant with or had recently given birth to a child following the index birth were thought to be less likely to be fully engaged in market labor. The analysis adjusts for current reproductive status. In developing the final model, unless a variable was required as a control variable, variables with T statistics less than 1.0 in both home and commercial intake equations were dropped. Women's height and weight were tested jointly and individually. Weight contributed explanatory power to the model, whereas height did not. These two variables were correlated (r = 0.47) and height was dropped from the model. In the following results, statistical difference is denoted. All tests of differences were two-tailed with the significance level ~ = 0.05. V. RESULTS

Descriptive analysis Fourty-eight percent of the sample (938 women) worked for pay during the previous week. These women were primarily engaged in sales and handicrafts. Sales work predominated in the nonwage and time wage sectors, whereas handicraft work predominated in the piece wage sector. Over half of the working women (55%) were in the non-wage sector (see Table 2). The lowest percentage (18%) were piece workers. Time wage workers put in slightly more hours than non-wage workers, earned higher incomes and came from households with substantially higher incomes. Non-wage workers came from the households with the lowest income. Time wage workers also had completed the highest years of education. Women's energy intakes were below the WHO/ F A O / U N U recommended levelst and the calcium and iron intakes were below the Philippines recommended levels (see Table 3). Protein intakes were below standards for all but non-pregnant non-lactating women. Overall non-pregnant, nonlactating women had the highest intakes, whereas pregnant and lactating women (the highest nutritional risk group) had the lowest intakes. In response to the low mean intakes found among certain sub-groups, the records of women with intakes less than 800 kcal were examined for types and amounts of foods consumed. These women primarily consumed corn grits (a local staple which is filling but has about one third less calories than an equal amount of cooked rice), with small amounts of vegetables and added flavorings. They typically consumed three reasonably well-balanced meals a day, but in very small quantities.

Women's work and women's welfare in the Philippines


Table 2. Characteristics of urban Cebuano women working for pay by sector of participation at 14 months postpartum (N = 938) Work secto~ Variable Time wage Piece w a g e Non-wage N 252 170 516 % worked at home 14.7 66.5 40.3 % worked away 85.3 33.5 59.7 Hours worked 36.2 (1.5)b 26.8 (1.6) 35.5 (1.4) Travel time (minutes)~ 14.9 (1.3) 8.5 (1.6) 11.7 (0.8) Cash income (pesos/week)a 110.7 (13.2) 43.1 (5.4) 90.0 (5.8) Household income (pesos/week) 168.9 (14.5) 149.2 (18.9) 1007.2 (12.1) Woman's education (years) 9.2 (0.3) 5.7 (0.2) 7.2 (0.1) ~Work sector categories are exclusiveand are based on women's primary jobs. ~Standard error of the mean is in parentheses. cAway-from-homeworkers only. din 1983, I peso was approximately equal to U.S. $0.05. W o m e n ' s mean energy intakes stratified by place of preparation and by work status, by work sector, and by work sector and place o f work are presented in Figs 1-3. As shown in Fig. 1, differences in total energy intakes and energy intakes from homeprepared foods were not significantly different between working women and women who did not work for pay. However, there were significant differences in energy intakes from commercially prepared foods between these two groups (P < 0.01). Working women offset small decreases in energy and nutrient intakes from home-prepared foods with increases from commercially prepared foods. There were striking differences by work sector and place o f work. Non-wage workers had the highest total energy intakes and the highest commercial intakes (see Fig. 2). Total and commercial energy intakes for piece workers were significantly lower than for women in the other two work sectors (P < 0.01). As Table 3. Urban Cebuano women's mean energy and nutrient intakes compared with recommended dietary allowances by pregnancy and lactation status Reproductive status Observed Recommended' Energy intake (kcal)

Non-pregnant/non-lactating Non-pregnant/lactating Pregnant/non-lactating Pregnant/lactating

1444 (26)b 1335 (22) 1370 (41) 1188 (63)

1950 2450 2250 2450`

46.4 (1.0) 40.9 (0.7) 43.6 (1.8) 37.2 (2.2)

34.5 51.5 60.0 60.0"

372 (10) 379 (9) 370 (I 8) 329 (26)

500a 1000a 500d 1000~

Protein intake (g)

Non-pregnant/non-lactating Non-pregnant/lactating Pregnant/non-lactating Pregnant/lactating Calcium intake (rag)

Non-pregnant/non-lactating Non-pregnant/lactating Pregnant/non-lactating Pregnant/lactating Iron intake (rag)

Non-pregnant/non-lactating 12.1 (0.4) 18d Non-pregnant/lactating 12.1 (0.3) 18d Pregnant/non-lactating 11.3 (0.5) 18d' Pregnant/lactating 10.4 (0.7) 18ca' 'WHO/FAO/UNU, 1985. For reference woman weighing45 kg and moderately active. bStandard error of the mean is in parenthesis. el'here are no standards for lactating pregnant women, the highest standard is used as a proxy. OFNRI, 1980. For reference women weighing 48 kg. "Minimum intake for pregnancy, additional 30-60 mg iron supplement recommended.

shown in Fig. 3, non-wage workers and piece workers who worked at home obtained significantly more of their energy intake from home-prepared foods than did women in these two sectors who worked away from home (P < 0.05). Time wage workers who worked away from home consumed significantly more energy from commercially prepared foods than did time wage workers who worked at home (P < 0.01). The mean percentage of women's intakes from commercially prepared foods varied by nutrient. The percentage o f energy from commercial foods was the lowest at 23% and the percentage of fat from commercial foods was nearly twice as high at 45%. Determinants analysis

The full determinants model tested effects o f women's working for pay, cash income, and income squared, while adjusting for a number o f relevant control variables (see Table 4). In unreported work, we tested quadratic and other relationships between women's income and diet and found that this functional form provided the best fit. Using d u m m y variables with non-workers as the reference group, the alternate models tested various dimensions of work including work sector, place of work, and hours worked. During the model-building phase, work effects were found to be unstable across the specifications until interactions for work and household income were added. These interactions were highly significant and substantially improved the interpretability of the models. Results from the full model for energy intake showed that women's cash income had a significant positive effect on their commercial intake, and that these effects were non-linear (see Table 4). The positive effect of women's income on commercial intakes was consistent for energy, and the four nutrients examined protein, fat, calcium, and iron [see Table 5(a)]. (Note: results for the control variables are not shown in Tables 5(a, b) since these were generally stable across nutrients and across models.) W o m e n ' s work was associated with significantly increased commercial intakes; however, contrary to the relationship hypothesized in Section II(B) above, home intakes decreased. With respect to commercial


Eilene Z. Bisgrove and Barry M. Popkin 1400



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o 600



0 • l ,.,a| v~,.nnn ~



v v ,.,.nuI'~,unu ~

Fig. 1. Urban Cebuano women's energy intake by work status, aTotal intake includes home, commercial, and other sources (e.g. celebrations, friend's homes, food programs). **Differences in means between women who were working and not working for pay was significant at P < 0.01. (Based on two-tailed test of significance.) Actual values in kcal are in parentheses.

intake there was a significant interaction between work and household income. The effect of working on women's protein and energy intakes from commercially prepared foods was greater for women from low-income households. The opposite was true for fat. As household income increased, the marginal effect for fat also increased as might be expected from studies on income and the structure of the diet [35]. Results from model 2 [see Table 5(b)] show that sector of work significantly altered home vs commercial intakes of energy and the four nutrients. Non-wage work was associated with small reductions in home intake which were offset by significant increases in commercial intake and resulted in increased combined intakes, Time wage work, contrary to the hypothesized relationship in Section II(C), was associated with significant reductions in home intake which were not offset by increases in commercial intake and resulted in decreased combined intakes. Piece work had small and primarily insignificant effects on intakes. Not only is there a work sector effect but the magnitude of this effect varies considerably by household income level. Non-wage workers from low-income households increased their commercial intakes of energy, protein, and iron more than non-wage workers from higher income households. In contrast, time wage workers from higher income households increased their home

intakes of protein and fat more than time wage workers from low-income households. While not shown in a table, increased time at work was associated with significant decreases in home energy intakes (P < 0.05) and increases in commercial energy intakes (P < 0.01) among non-wage and time wage workers. Time at work was not associated with changes in home or commercial intakes among women working on a piece wage basis. The estimation of this set of alternate at-home and commercial dietary intake models generates large numbers of estimated coefficients. To aid in the interpretation of the results, we utilize a simple simulation approach that allows us to quantify the effects of changes in the independent variables on each component of the women's diet and on total diet. While not shown in a table, the first simulation uses adjusted coefficients from model 1 [Table 5(a)], changing only women's income while all other values were held at the mean. Increasing women's income by 100 pesos per week (about U.S.$5 which nearly doubles women's cash income) had differential effects on combined home and commercial intakes of energy and the four nutrients. As a percentage of mean intake, combined fat intake increased the most (14%), followed by iron (8%) and protein (7%). Energy and calcium intake increased by 4 and 3%, respectively.

Women's work and women's welfare in the Philippines

1600 1


• totala • home • commercial


1400-1 1200IO00Q

8oom o o


400 ~r'k




time wage


piece wage

Fig. 2. Urban Cebuano woman's energy intake by sector of work. aTotal intake includes home, commercial, and other sources (e.g. celebrations, friend's homes, food programs). **Differences in means between non-wage workers vs piece wage workers and differences between time wage workers vs piece wage workers were significant at P < 0.01. (Based on two-tailed test of significance.) Actual values in kcal in parentheses.

The second simulation uses the adjusted coefficients estimated in model 2 [see Table 5(b)] to calculate the changes in combined home and commercial energy intake that would be experienced by a woman who had not been working for pay entering each of the three work sectors (see Table 6). Mean values are supplied for women's cash income by work sector and for low vs high household income (note: these household incomes are the mean values for the lowest and highest household income quartiles). For a woman entering the non-wage sector, having a low household income would result in a 174 kcal increase in energy intake (about a 13% increase given the mean intake of 1360kcal). The same low-income woman would experience decreases of 70 kcal by entering the time wage sector and 165 kcal if she engaged in piece work. Entering the non-wage sector and having a high household income would still result in an increase in energy intake (109kcal), compared to a small decrease with entering the time wage sector ( - 23 keal), and a small increase with engaging in piece work (36 kcal). VI. DISCUSSION AND C O N C L U S I O N S

This study represents one of a limited number of efforts to examine the relationship between women's

work and their own welfare. This analysis uses models which allow us to model differentially the effects of work on at-home and away-from-home consumption and to consider the potential endogeneity of many of the work and consumption decisions. With respect to urban women of reproductive age, four important sets of findings emerge. The first relates to the general effects of women's work and income on overall dietary quality. Nearly half of the women in the study worked for pay, and women working in the non-wage and time wage sectors worked long hours (average of 36 hours per week). In support of the "women in development" hypothesis that women's labor force participation has a beneficial impact on their well-being, this analysis showed that women's work and income have strong and significant effects on their patterns of home and commercial food intakes with subsequent positive effects on the overall nutrient quality of women's diets. The second finding relates specifically to the effects of work on women from various income levels. This study shows that not only can women's work positively impact their diet, but that there are strong distributional implications, namely that lowerincome women gain more than higher-income women. The greater benefit of work on low-income

Eilene Z. Bisgrove and Barry M. Popkin

1484 1600




I ~total a BIhome • commercial

1200 !

1ooo 800

8 o , m

600 400


0 _~m~_]home away nonwage

home away time wage

home away piece wage

Fig. 3. Urban Cebuano women's energy intake by sector and place of work. ~Total intake includes home, commercial, and other sources (e.g. celebrations, friend's homes, food programs). *Within sector differences in means between workers at home vs workers away from home were significant at P < 0.05. (Based on two-tailed test of significance.) **Differences significant at P < 0.01.

women's commercial intake may mean that, for these women, working and earning their own income can translate more directly into increased purchases of commercial foods for their own consumption. This phenomenon may reflect a greater demand for food among cash-earning low-income women with marginal intakes. While we have very little data from other studies on women's cash income and their food consumption for comparison, cross-national studies have observed repeatedly the general demand relationship that low-income households spend a larger proportion of their income on food, and, as income increases, increases in intake are considerably greater for low-income households than for moderate income households [36-38]. Another possible explanation is that, compared with women from high-income households, low-income working women may experience a more dramatic shift in their autonomy over income which might empower them to purchase even more food for their own consumption. This interpretation would lend strength to the "women-in-development" literature argument that women's labor force participation has a favorable impact on their status and autonomy with subsequent effects on well-being [1--4]. The third finding regards the need to consider at-home and away-from-home consumption. As

reported in studies from the U.S., this study found that consumption of foods prepared away from home represents an important component of the diet of women engaged in market work [19, 20] Urban Filipino women's nutrient intakes from commercial sources comprised a substantial proportion of their diets (about 25% of their energy intakes and about 45% of their fat intakes). Past surveys and studies which assessed only home intakes may have seriously underestimated women's total nutrient intakes. Working women from low-income households increased their intakes of commercially prepared foods more than women from higher-income households. By overlooking commercial food intake, conclusions from studies examining the relationship of women's labor force participation and intrahousehold allocation of food and resources would be misleading. This result is supported by recent work by Bouis et al. [39] which demonstrated the importance of including foods consumed away from home when estimating food demand using expenditure data. The fourth finding is the effect of work in different sectors. While results from the full model showed that women's work mattered, the alternate model provided a more meaningful picture about the importance of work sector as a key determinant of

W o m e n ' s w o r k a n d w o m e n ' s welfare in the Philippines


Table 4. Full model for urban Filipino women's energy intakes: tests work, income and income squared Home intake (OLS) Full model variable name Physiological Control Variables Weight (kg) Energy expenditure Respiratory illness Pregnant' Week in gestation' Term birth Lactating Pregnant x lactating Work and Income Variables Works for pay Work x household income Women's cash income (100 pesosb) (Cash income)~ Household income (100 pesos)

Commercial intake (Tobit)





4.3 4.3 - 107.0 303.0 - 0.1 28.2 74.2 - 115.2

2.7* 1.0 - 1.2 1.0 - 0.1 0.6 2.8 * - 1.7

2.7 7.6 -50.0 - 382.4 0.9 115.5 - 14.0 -40.7

1.9 1.9 -0.6 - 1.2 0.6 2.5* - 0.6 -0.7

-58.8 6.5

-2.0* 0.9

73.1 -15.5

2.6* -2.4*

-5.1 - 0. l -5.3

0.3 - 0. l - 1.3

69.8 - 6.7 11.7

3.1"* - 3.2** 3.2**

3.8 - 11.9 21.4 -9.8 181. I 88.7 31.2 109.4 10.9 1.7 65.4 - 104.0 2 I. 1 -l.l 28.2 213.3 -0.3 124.3 - 598.0 429.6

3.8** -2.3* 5.0** -1.3 1.2 2.2* - 0.8 2.1" 4.1"* 2.4* 2.5* - 2.2* 0.8 -1.1 1.2 1.2 -l.l 1.8 - 1.9 52.6**

Related Control Variables Minimum years urban Women's age Women's education No. children under 12 Extended household' Spouse present Cebuano ethnicity Catholic religion Modernization (0.1 unity Density (100/kin2) Electricity Refrigerator Stove Corn price (100 pesos) Time in survey (100 days) Wet season Wet season x time in survey Sunday Constant Sigma~

-0.7 7.2 12.1 10.2 268.8 43.4 - 105.4 -2.2 -2.7 -0.8 29.2 125. l 58.2 -0.0 -76.0 -440.2 0.6 50.5 780.9 495.2

-0.7 1.3 2.5* 1.2 1.6 1.0 - 2.4* -0.0 -0.9 - I. I I. 1 2.4* 2.0* -0.0 -3.1"* -2.3 2.0* 0.6 2.2* 62.8**

psu~': Coeff. = -0.1; T-value ffi -4.5**. 'Variables which tested as endogenous to the model using the Hausman Test. bin 1983, 1 peso was approximately equal to U.S.$0.05. cA modernization index, scaled from I to 14, was based on infrastructure, utilities, and services surrounding and available to the household. ~Disturbance in the error term. 'Correlation of error terms between home and commercial intake "seemingly unrelated regression (SUR)" equations. T-values are significant based on a two-tailed test at the following level: *P < 0.05; **P < 0.01.









Results from this study indicate that while women in

in the formal time wage sector as


more advantaged

the formal time wage sector came from households

than non-wage

with similar incomes as non-working

women, and in

workers based on factors such as greater job security,

addition received the highest wages of women

b e n e f i t s , a n d h i g h e r m o r e p r e d i c t a b l e w a g e s [40, 41].

sector, their participation in the time wage sector was










of working

formal sector may not translate into improved

related to reduced combined

in the







in any

intakes of energy and



sector (the largest percentage

in the

of workers)

Table 5(a). Overview of model 1 testing work and income as determinants of urban Filipino women's intakes of energy, protein, fat, calcium, and iron Energy Variables Working for pay Work x household income Cash income (Cash income)2 Household income Energy expenditure

Home' - ~



Comm b



+ ~ + + . + +


+ -











+ + .

Calcium Comm

+ .


+ .

+ +

. +

+ +

• Home intake equations estimated using OLS. bCommercial intake equations estimated using Tobit. ,Plus or minus signs indicate positive or negative coefficients; T-values are significant based on a two-tailed test at the following level: ( + or - ) P < 0.05; ( + + or - - ) P < O . O I .

Eilene Z. Bisgrove and Barry M. Popkin


Table 5(b). Overview of model 2 testing work sector and income as determinants of urban Filipino women's intakes of energy, protein, fat, calcium, and iron Energy Variables Non-wage work Wage time work Wage piece work Non-wage work x household income Wage time work x household income Piece work × household income (Cash income) 2 Household income Energy expenditure

H o m e~

Comm b

Protein Home

++c - -


Fat Home


Calcium Comm







Comm +


+ + + + m

+ + + +


' H o m e intake equations estimated using OLS. ~ o m m e r c i a l intake equations estimated using Tobit. cPlus or minus signs indicate positive or negative coefficients; T-values are significant based on a two-tailed test at the following level: ( + or - ) P < 0 . 0 5 ; ( + + or - - ) P