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Nutritional status and the allocation of time in Rwandese households
h
~
JOURNAL OF
Econometrics ELSEVIER
Journal of Economelrics 77 (1997) 277 -295
Nutritional status and the allocation of time in Rwandese households Alok Bhargava Department of Economics. University of Houston, Houston. TX 77204-5882. USA
Abstrae~ This paper analyzes the activity patterns of adult men and women in approximately 110 Rwandese households surveyed four times in 1982-83. Dynamic models are separately estimated for men and women for the time spent sleeping and resting, performing heavy aclivities, doing housework, and on agriculture. The models postulate simultaneity between men and women's activities and investigate the differential feedbacks. The main findings are that low incomes and high food prices reduce the households' energy intakes, thereby forcing the adults to spend additional time resting and sleeping. Second, both men and women share the workload in spite of poor nutritional status. Third, for women, there is substitution between housework and agriculture, the former tasks being relegated to other household members. Lastly, energy intakes of twice the Basal Metabolic Rate seem inadequate for the sustenance of active adults. The policy implications of the results are discus~,ed. Key words: Health; Nutrition; Time allocation; Dynamic models; Food policies JEL class!/ication: C23; !12; J24; O12
1. Introduction The measurement of human energy expenditures is an important topic in biological sciences; its relevance for defining energy requirements was underscored by the expcrt committee of the FAO/WHO/UNU (FAO, 1985). The FAO approach to energy needs is reflected in the phrase 'Requirements for what?'. Since Lavoisier's discovery of the role of oxygen in energy metabolism
This study was supported by Research Committee of the World Bank. The author thanks Q. Khan, C. Muller, M. Ravallion, and J. Yu for their help, but retains the responsibility for views in the paper. This revision has benefited from the comments of three referees. 0304-4076/97/$15.00 © 1997 Elsevier Science S.A. All rights reserved PII S 0 3 0 4 - 4 0 7 6 ( 9 6 ) 0 1 8 1 6-7
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and Rubner's work on the heat produced in oxidation of food, physiologists have developed sophisticated methods for measuring energy expenditures (Durnin, 1991). For example, modern calorimeters afford accurate measurement in laboratories and the doubly labelled water method is useful for free living poulations. A knowledge of energy expenditures of the inhabitants of less developed countries is useful for the design of food policies. Since resources available for data collection are limited, the expenditures are typically calculated from time allocation surveys. The data have provided some useful insights. For example, Berio (1984) suggests that women bear the greater burden of economic development. The behavioral mechanisms through which men and women share work are not explored in the analysis. Interpreting some biomedical evidence on G~mbian women, however, Beaton (1984) concludes that low energy intakes induce behavioral changes amongst the poor. Consequently, it is superficial to distinguish between the biological and social sciences approaches to time allocation; a unified framework is useful for data analysis. The importance of research in bio-chemistry of food for economic development policies was recognized by Leibenstein (1957) who argued that higher wages of workers will improve their nutritional status and hence productivity. The 'wage efficiency hypothesis' has since been refined (Mirrlees, 1975). In countries such as Rwanda, the population subsists on agriculture and energy deficiencies are prevalent. The link between wages and labor productivity is affected by the intra-household distribution of food; the earners' intakes need not increase proportionately with wages. Further, while higher wages ultimately affect productivity via improvements in health (Fogel, 1994), the lags underlying health processes are complex (Bhargava, 1994). Lastly, many adults work on their own hind and perform housework without receiving monetary compensation, An analysis of the determinants of time allocation in Rwandese households is therefore of interest. Modelling the relationship between the adults' time allocation and nutritional status presents several difficulties. Firstly, while recording daily activities at a disaggregated level is insightful, there is a large amount of internal variation in the data. Also, energy expenditures are available only for broad categories of activities (FAO, 1985). Secondly, individual food intakes are difficult to quantify in Rwanda since household members share the food from the same plate. Thirdly, alternative formulations for sharing the work between men and women are available in social sciences (e.g., Becker, 1991; Sen, 1983; Simon, 1986). Lastly, due to differences in data collection procedures, the effects of variables such as food prices on time allocation can be assessed only indirectly in a iongitudimd II~tHI~OI ~.......... K. ' The structure of this paper is as follows: Section 2 describes the data from Rwanda. Section 3 outlines a framework for analyzing the activities data and the dynamic model is briefly described. The empirical results for the households'
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energy intakes, adults' weights, and the time spent sleeping avd resting, on heavy activities, performing housework, and on agricultural activities are discussed in Section 4. Finally, in Section 5, the issue of 'Requirements for what?' is reexamined in light of the empirical results.
2. The data
The longitudinal study in Rwanda was conducted by the Ministry of Planning with assistance from the French government during 1982-83 (Republique Rwandaise, 1986). The food intakes in 270 households (three from each of the 90 'sectors') were recorded for seven consecutive days. The surveys were repeated four times at four-month intervals. The heights of adults were recorded once and the weights were measured in each round. The time spent by household members on over 600 activities for fourteen consecutive days was recorded in each survey round. Since food intakes are observed for seven days, the activity data on the same days are used in the analysis. Further, for comparisons with other studies and to reduce variation in the data, the activities were mapped into the 25 broad categories defined by FAO (1985). The activities that were difficult to match were put into 5 groups formed on the basis of energy expenditures, expressed as multiples of the Basal Metabolic Rate (BMR is the minimal energy necessary for sustaining life). The data for seven days were then averaged to produce a figure for an "average' day of the week, i.e., average activity levels in the four survey rounds are analyzed in this paper. Some information on education, land holding, and other variables is contained in the data set. Single observations for the survey period are available on the value of households' consumption and production. The prices of seven food groups and information on wages earned by a subset of household members are recorded once. The 'lead' young adult man and woman were selected from each household; the adults need not be a couple but were likely to be the ones performing the demanding tasks. Retaining individuals with four time observations, complete data were obtained on approximately 110 adults. The sample mean are reported in Table 1.
3. A framework for time allocation at the susistence level
3.1. NuO'ient deficiencies, work performance, and time allocation The uc,~,l,..,,,,,~' ....-"~" ;,o nrv.certain nutrients in the diet diminish the physical work capacity of individuals. For example, iron deficiencies are ki~own to reduce the maximum volume of oxygen consumed (Spurr, 1983); field studies have shown
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Table I Sample means of the variables in the four rounds of data from Rwanda Variable Proportion of time spent sleeping, resting, and sitting quietly Proportion of time spent on heavy activities Proportion of time spent on housework Proportion of time spent on agriculture Average energy expenditure Household size Age Total value of consumption Total value of production Energy intakes Protein intakes Weight Height Body mass index (BMI}
Men
Women
0.547 10.107) 0.187 (0.088) 0.014 (0.050) O.I00 (0.088) 1.992 (0.437) 5.991 (2.248)
0.50Q (0.086) 0.180 (0.073) 0.131 (0.069) 0.140 (0.079) 2.184 (0.298)
34.223 (19.315) 5188 I (25319) 43271 {31248) I 1161 15340) 361.6 1201.7} 48.817 (12.787) ! 57.259 (15.625) 19.350 (3.033)
32.017 (16.157)
48.269 (11.636) 152.975 (11.637) 20.333 (3.592)
The sample means are calculated using four time observations on 116 men and 119 women. Tables 3-6 analyze a subset of these data. Numbers in parentheses arc estimated standard deviations for the pooled samples. Heavy activities require at least thrice as much energy as sleep. Average energy expenditure is in terms of Basal Metabolic Rate. Consumption and prc[tuction are annual figures in Rwandese francs for the households. Households' energy and protein ii~takes per day are measured, respectively, in kcals and grams. Weight and height are in kilograms and centimeters, respectively.
positive effects of iron supplementation on labor productivity (Basta et al., 1979; Gardner et al., 1975). In countries such as Rwanda, energy deficiencies are of paramount importance since they restrict behavior and affect health by hindering the absorption of nutrients (Bhargava, 1991). For an historical perspective, see Fogel (1994).
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The physiological changes resulting from inadequate energy intakes are complex. Watedow {1986), lor example, has suggested that the (low) ratio of an individual's BMR to body weight (fat-free mass) in developing countries reflects 'metabolic economy'. However, this is not necessarily the case in an undernourished sample from India (Soares and Shetty, 1991). A high BMR to body weight ratio for the malnourished is consistent with a long-run elasticity of BMR with respect to weight that is less than unity (Bhargava and Reeds, 1995). The energy necessary for performing an activity can be expressed as a multiple of the individual's BMR. The latter is related to body weight (Schofield, 1986). From a policy standpoint, the effects of nutritional status on work performance are of interest. Since the Rwandese households spend most of their time on subsistence activities, the link between nutritional status and time allocation is important. i
3.2. Moa~elling the effects o f economic variables on time allocation
Myrdal (1968) observed the vicious circle of poverty and poor work performance in developing countries caused by chronic food shortages. Mirrlees (1975) extended the theoretical wage-consumption analysis, though emphasizing the need for an empirical treatment. Since improved nutrition enhances the 'capabilities' of individuals to undertake useful tasks (Sen, 1985; Anand and Ravallion, 1993; Dasgupta, 1993), modelling the determinants of time spent on various activities is of interest. The treatment of leisure (L) as an argument in the utility function yields the demand for leisure as a function of goods prices (p), wage rate (w), and nonlabor income (A), i.e., L = h(p, w, A).
(1)
It would be desirable to adopt a flexible approach in the analysis of longitudinal time allocation data from Rwanda. Firstly, a large proportion of the food consumed by households is their own produce or is received in the form of gifts. Also, energy deficiencies are apparent in Table 1 from the sample means of Body Mass Index (BMI is defined as weight in kilograms divided by the square of height in meters; James et al., 1988). In such circumstances, it is likely that energy expenditures are driven by the intakes. The food available to many households will be inadequate and can thus be viewed as pre-aUocated (Pollak, 1969). The indicators of nutritional status are important explanatory variables in time allocation models since they capture the effects of current and past nutritional deficiencies. Secondly, due to own-production of food and the gifts received, minor fluctuations in food prices may not have immediate effects on leisure. However, pric, . . . e.~ . . . .will . . . ~,~r~d'l'ail~Jh~,,~-,,~...,._~.~,,,,th,~,,,,,~l-abitual' leisut~e. Thirdly, since a single observation on wages is available for roughly a third of the households, it might
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seem difficult to examine the effects of wages on time allocation. However, one can control for earnings of the household members by including total consumption during the sample period as a regressor in the longitudinal models. The link between nutritional status and time allocation can be investigated using the present data set by splitting the estimation problems into two stages. At the first stage, the households' average energy intakes in the four survey rounds are explained in a cross-sectional framework by variables such as household size, regional dummy variables, food prices, and measures of household incomes. The estimated relationship will provide a large-scale view of the effects of economic variables on food availability. The equilibrium quantity of food consumed through market and nonmarket arrangements, however, may be insufficient for essential activities of the household members. The second stage, then, models the determinants of time allocated to leisure and productive activities in a longitudinal framework. Since the choice between work and leisure is affected by health, biological and behavioral factors play important role in model specification. In particular, the mechanism by which energy deficiencies restrict activities and factors underlying the sharing of work between men and women merit a systematic treatment.
3.3. ,4 dynamic fi'amework for longitudinal time allocation data The joint nature of household labor supply decisions was formulated from an empirical standpoint by Mincer (1962}. The analysis is suitable for time allocation decisions since, at the subsistence level, members share the work. The joint activity patterns, however, complicate the application of Becker (1965) type models of household production (Pollak and Wachter, 1975). It would seem difficult to specify a model for time allocation, analogous to the 'characteristics' model of demand (Pudney, 1981), in terms of latent price variables. The division of tasks within a household affects the relationship between time allocation and nutritional status for all the members. For example, women with young children may substitute agricultural activities by housework; the latter is less strenuous and older children can help in gathering wood, fetching water, etc. Further, men and women share the additional (seasonal) workload according to certain rules. Since fertility rates are high in Rwanda, a model of joint time allocation decisions would be useful for assessing if women face a disproportionate burden in times of growth (Berio~ 1984). Now, inadequate energy intakes will force a reduction in the individuals' energy expenditures. This might be achieved in the short run by decreasing the effort on strenuous tasks; a gradual increase in the proportion of time spent on lighter activities will follow. If energy deficiencies persist over time, the individuals will begin to lose weight and further curtail expenditures. The latter might entail avoiding heavy activities altogether; there is evidence that the undernourished cannot endure strenuous work (Gardner et al., 1975). The
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proportion of time spent resting and sleeping will show an upward tendency even in the short run. An apparent shortcoming in exclusively focusing on the equality between energy intakes and expenditures ir that important health aspects are omitted from the discussion. For examp,e, micro-nutrient intakes are essential for maintenance of the immune system; repeated sicknesses can diminish individuals' work capacity (see also Floud et al., 1991). In the present data, morbidity can be inferred from resting patterns; hence the dynamic aspects of time allocation and nutritional status are important. The nutritional status of an individual may be represented by height and weight in time allocation models. In response to work opportunities, however, adults might increase activities in spite of poor health. Higher energy and protein intakes facilitate expenditures in the short run. Also, given the interrelationships between height and weight, it is desirable to test if these variables can be combined (Bhargava, 1994). Lastly, an equation for weight is useful for studying the dynamics of nutritional status.
3.4. The model and its estimation The model for time allocation can be represented by the following equations (h = 1, .... J; i = 1.... ,N; t = 2,3,4): k
C,, = ~.. zh~flj + ul,,,
(2)
j=l ~Pl
N
W , = ~., z07; + ~ xot~ j + ei W . - n + 21Hi + u21~, ,i= I |
p"
P. = ~', zorl~ + ~ xotOj + =2P~t-t + 22Wi, + 23Hi + ua.. j=t
(3)
j= I
(4)
j=l
Here, Ch is the hth household's average energy intake in four survey rounds; there are J households on whom cross-sectional data are available. Wit is ith individual's weight in the tth survey, Hi is the height, and Pi, is the proportion of time spent on a certain activity in the tth survey round (the subscript for different activities is suppressed for brevity). Thus N individuals are repeatedly observed in four survey rounds. The z's and x's are, respectively, time-invariant and time-varying regressors; the coefficients are denoted by lower case Greek letters. The error terms affecting (2) are independently distributed with zero mean and finite variance. The errors in Eqs. (3) and (4) are assumed independent across individuals but correlated over time with a positive definite variance-covariance matrix (u2, and u3, can be correlated with one another). The random effects decomposition
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for u2i~, for example, is a special case, i.e., U2it : ~i "Jr"/J2it,
(5)
where ,$'s are individual-specific random effects and v's are independently distributed random variables. Note that lagged dependent variables (including initial observations W~I and P~I) are treated as endogenous in the system (Anderson arid Hsiao, 1981). Some ~of the time-varying regressors are also endogenous in the sense that they ~ire correlated with random effects. The exogeneity hypotheses can be tested using likelihood ratio statistics. Details of identification of model parameters and computation of maximum likelihood estimates are presented elsewhere (Bhargava and Sargan, 1983). It is important to explore the mechanism by which men and women share the work. For example, additional work available during a survey round is likely ~o be distributed amongst household members on the basis of their work capacity. Otherwise, the arrangements may not be sustainable and a breakdown could threaten members' survival. Since observations on some members are incomplete, results for the lead adult man and woman are presented in the next section.
4. The empirical results 4.1. T,l:e results Jbr households'average energy intakes Table 2 presents the results for households' average energy intakes in the four survey rounds. Since Rwanda is divided in six geographical zones, five dummy variables were included in the models: the variables for zones 3 and 5 and 6 were insignificant. The results in the first two columns assume the regressors to be exogenous; the third column treats the total value of consumption as an endogenous variable and reports the instrumental variables estimates. All variables were transformed into natural logarithms to reduce heteroscedasticity (e.g., Nelson et al., 1989). Household size is positively associated with energy intakes. While some nonlinearity is implicit in the logarithmic specification, square of household size was insignificant. The total value ofconsumption is an approximate measure for household incomes. The point estimate of income elasticity of energy intakes in the first column is 0.56. The total value of production, however, is insignificant. Note that since energy intakes influence productive activities, consumption may be correlated with the error term. This problem is tackled in the third column where the size of land owned by the household is used as an additional instrument. Land holding is fixed in Rwanda by the government and there is variation in land quality. The estimate of income elasticity is higher in column 3 though its standard error is larger as well.
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Table 2 Cross-sectional results for average household energy intakes Variables Constant
Specification 1
Specification 2
Specification 3
6.476 (0.509)
6.204 (0.661)
5.376 (1.909)
Zone 2
- 0.119 10.041)
- 0.107 (0.062}
- 0.110 (0.045}
Zone 4
- 0.186 (0.041)
- 0.197 (0.059)
- 0.179 (0.044) 0.154 CO.! 39) 0.667 (0.182}
- 0.120 (0.102)
Household size
Price beans
- 0.146 (0.091 }
0.253 (0.061) 0.545 (0.053} - 0.006 (0.032) - 0.047 (0.032} - 0.093 CO.130)
Price sweet potatoes
- 0.095 (0.046)
- 0.020 (0.062)
- 0.101 (0.048)
0.477 (0.0771
0.568 (0.114)
0.476 (0.077)
Total consumption Total production Average wage rate
Price traditional beers Sample size Adjusted R z
0.234 (0.041 } 0.558 (0.040} 0.002 (0.022) --
251
i 19
0.757
0.721
---
251 --
All v a r i a b l e s a r e in Iogal'ithn~:, Zone 2 and a ~ e indicator variables. Specification I and 2 are estimated by least squares. Speclficatiol~ 3 treats consumption as endogenous using land size as an additional instrument. Standard errors are in parentheses.
Higher prices of sweet potatoes significantly decrease households' long-run energy intakes. Due to poor quality of land in Rwanda, some quantities of staple foods are purchased at market prices. It is interesting to note that price of traditional beers has the opposite effect on energy intakes. Thus expenditures on beer (consumed mainly by men) appear to divert scarce resources from staple foods. Lastly, the results in second column include a measure for wages earned by household members; the wage variable is insignificant. The reduction in sample size decreases the precison of the estimates; prices of beans and sweet potatoes are insignificant as well. The treatment of wages and consumption as endogenous variables led to insignificance of all the regressors (the results are not reported). There are difficulties in finding suitable instruments for predicting wages in poor countries; shifts in demand for labor contribute to fluctuations in wages (of. Bliss
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and Stem, 1978; Deolalikar, 1988). However, measures of economic activity in the 90 sectors of Rwanda are not included in the data set.
4.2. Resultsfor body weight The empirical results for the body weight of men and women are presented in Table 3. The height variable significantly affects weight; the short-run coefficient for women is almost thrice as large. Since fertility rates are high, these differences may be due to short-term accumulation of subcutaneous fat. The inclusion of household size and energy and protein intakes as regressors in the model affords an approximate treatment of the impact of food intakes on weight. It would be desirabl ~to include individual intakes and take into account ages of the household members. Since food is shared from a common plate in Rwanda, an alternative approach would be to use energy expenditures of members as surrogates for their intakes; members' weight and average activity Tables 3 Maximum likelihood estimates of the weight relationship in Rwanda Variable
Men
Women
Constant
- 3.063 (0.812) 0.004 (0,019)
- 7.078 (0.440) - 0.009 (0.025)
0.834 (0.249) 0.004 (0.019~ 0.007 (0.026) - 0.008
i.947 (0.077) 0.004 (0.022) 0.003 (0.030) - 0.026
(0.013)
(0.013)
tlousehold size Height Protein intakes Energy intakes Time dummy 3 Time d u m m y
4
- 0,025
- 0.007
Within variance
(0.013) 0.670 (0.123) 0.091 (0.152) 0.011
(0.013) 0.282 (0.040) 0.984 (0.221) 0.010
Chi-square (4)
9.9
0.7
Lagged dependent variable (Between/within) variance
All variables in logarithms. The numbers in parentheses are the estimated asymptotic standard errors. Chi-square(4) is the test for exogeneity of energy intakes. Time dummies 3 and 4 are, respectively, indicator variables for 3rd and 4th survey rounds (see the text).
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levels are relevant regressors. The estimation of such a formulation, however, is infeasible; activities of children were not recorded and observations on adults are often incomplete. The statistical insignificance of household size and energy and protein intakes should thus be interpreted with caution. The coefficient of previous weight is significant in equations for men and women. While the estimate for men is higher, the ratio of between to within variance is low, indicating small between-subject differences. Note, however, that maximum likelihood estimation using modest sample sizes often entails underestimation of the between variance. The indicator variables for survey rounds appear with negative signs; coefficients in fourth and third rounds are, respectively, significant for men and women (there is no dummy variable for the second survey round as the initial observation on the dependent variable has its own intercept and a constant is included). The results indicate a tendency of weight loss over time. Since work availability in the last two survey rounds is low, shortfalls in energy intakes seem responsible for weight changes. Lastly, exogeneity hypothesis for energy intakes is not rejected in the models for men and women. While within-subject variation is similar for the sexes, overall variation in the data is high. The acceptance of exogeneity hypotheses may in part be due to the wide confidence intervals for likelihood ratio statistics.
4.3. Results .for the resting and sleeping patterns The empirical results for the proportion of time spent by the adults resting, sleeping, and sitting quietly are in Table 4; logistic transformation of the dependent variable ensures a smooth relationship with explanatory variables. The adults' height and weight were initially introduced as separate regressors. The use of likelihood ratio statistics generally led to acceptance of the hypothesis that the two variables can be combined as the Body Mass Index (BMI). Moreover, in models where coefficients of height and weight were unrestricted, coilinearity amongst the regressors was evident; estimates with BMi are reported in the tables. First focusing on the results for men, the time spent resting increases with age (coefficient of age squared was insignificant). Also, resting time of the lead adult is positively associated with household size. Secondly, estimated coefficients of the value of household consumption and individuals' BMI are negative and significant. The coefficient of BM! is strong and this variable reflects cumulative effects of energy deficiencies ir the medium term. It therefore appears that adult males in poor households spend additional time resting and sleeping to avoid weight loss. Thirdly, the ~ime spent resting by men is negatively associated with average energy expenditure of the lead woman in the household. Since household consumption is taken into account, a possible explanati.on is that men decrease
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Table 4 Quasi maximum likelihood estimates for the proportion of time spent sleeping, resting, and sitting quietly Variable
Men
Constant
- 0.915 (0.876)
1.387 (!.356)
Age
O.129 (0.044)
- 0.006 (0.047t
Household size
0.149 {0.075}
0.095 (0.078)
- 0.308 (0.093)
0.038 t0.078)
0.042 10.075)
0.070 10.069)
Average men's or women's energy expenditure
- 0.316 (0.163)
- 0.165 10.094)
BM !
-
0.623 (0.161)
0.025 {0.139}
Energy retakes
- 0.031 10,102)
- 0.174 (0.105)
Time dunnny 3
0.21 ! 10.060}
0.153 10.044)
Time dununy 4
O+121 (0.067)
0. I 17 10.047)
Lagged del~¢ndent variable
0.322 (01|95)
0.021 10.0831
Between/within) variance
0.007 111,063)
0.364 (0.1631
Within variance
0.192
0.090
Total consumption Protein intakes
Chi-square (12)
15.8
Women
18,8
The dependent variable is the logistic transformation. Women's (men's} average energy expenditure is included in the equation for men Iwoment. Chi-square {12) is the test for the exogen~:ity of men's {or women's) average energy expenditure, BMI, and energy intakes.
their resting time to offset high demand for women's activities. This demonstrates the joint nature of activity patterns; a decline in men's leisure would facilitate subsistence tasks. While the type of work undertaken is explored below, marginal product ,of labor is likely to be low due to poor nutritional status. However, no further measures of adult productivity are available in the data set, Fourthly, energy intakes are statistically insignificant. Note that 24-hour recall data from developing countries exhibit high within-subject variation in
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intakes by the poor (Bhargava, 1992). This variation does not appear to diminish in the average intakes, presumably due to chronic food shortages in Rwanda. The likelihood ratio test accepts the joint exogeneity of women's average energy expenditures and men's BMI and energy intakes. However, if the latter variables are treated as exogenous, the exogeneity of women's (men's) expenditures was rejected in some of the models. We also estimated static versions of the models used in this paper since they require less restrictive exogeneity assumptions. However, the results were quite similar. Lastly, the coefficients of indicator variables for third and fourth survey rounds are positive and significant indicating low work availability. Also, time spent resting is significantly influenced by its lagged value; long-run effect of a change in an independent variable is about 1.33 times greater than the short-run impact. The between subject variance is insignificant, however. In contrast with the results for men, only a few variables are significant in explaining women's resting patterns. The average energy expenditure of men and women's energy intakes are estimated with negative coefficients that are marginally significant. The indicator variables for survey rounds and the between subject variance are significant. Notice that the estimated within subject variance is twice as large for men. This might be due to the fact that the agricultural tasks performed by men are seasonal in nature.
4.4. Resulls lbr heart activities Table 5 contains results for the proportion of time spent on heavy activities (with a continuity correction for extreme values; Cox, 1970) requiring at least thrice as much energy as the BM R. From the viewpoint of assessing tile effects of poor nutritional status on time allocation, this model complements the specification for resting patterns. Notice that proportion of time spent by men (women) on heavy activities is positively associated with time spent by women (men) (see also Ashenfelter and Heckman, 1974; Hausman, 1981; Lundberg, 1988). The coefficients are statistically significant. Also, BMi is significant tbr men and might have been significant in the model tbr women if the sample size were greater. The energy intakes are significant for men; dummy variables for survey rounds are mainly negative though their coefficients are insignificant. While the coefficients of lagged dependent variables are insignificant, between subject variances are significant. The statistical insignificance of some of the parameters may be due to greater variation in heavy activities. Unlike rest and sleep, these activities are strongly influenced by geographical location of the household, land quality, etc. In fact, within variances in Table 4 are about twice the corresponding estimates in Table 3.
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Table 5 Quasi maximum likelihood estimates for the proportion of time spent on heavy activities Varible Constant
Men
Women
0.635 (2.690)
2.506 12.207)
Age
- O. 166 10.081)
0.009 (0.076)
Household size
- O. 186
0.049
10.132)
10.133)
0.204 (0.138)
0.040 10.142)
- 0.056 (0.073)
- 0.016 (0. ! 061
Men's or women's time on heavy activities
O. i ! 3 (0.0641
0.142 (0.0431
BM!
0.709 t0.309}
0.314 (0.217)
Energy intakes
0.136 (0.0671
- 0.162 (0.152)
Time dummy 3
- 0.069 10.0801
- 0.045 t0.061 )
Time dummy 4
= 0,106 10.0801
0.008 10.(162)
Total consumption Protein intakes
0,051
0.013
(0,(178)
(0.086)
(Belween/withit~) variance
0.303 (0.138)
0.637 (0.253)
Within variance
0.342
0.179
Lagged dependent variable
Chi-square (I 21
16.7
17.6
7t'he dependent variable is the logistic transformation with a continuity correction for extreme values. Women's (men's) time on heavy activities is included in the equation for men (women). Chisquare (12) is the test for the exogeneity of men's (or women'sl heavy activities, BMI, and energy intakes,
4.5. Results lbr agricultural and househoht actil,ities The determinants of time allocated to agricultural and household activities are investigated in Table 6. The models underscore joint nature of time allocation decisions; men's agricultural actvities depend on their effort on housework and on time spent by women on agriculture. The women's housework is influenced by their agricultural activities, in contrast, women's agricultural
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Table 6 Quasi maximum likelihood estimates for the proportion of time spent on agricultural and household activities Men
Women
Variable
Agriculture
Housework
Agriculture
Constant
0.858 (1.384)
0.917 (2.496)
1.635 (I.763)
Age
0.331 (0.107)
0.139 (0.094)
0.518 (0.097)
- 0.148
- 0.360
- 0.116
(0.164)
(0. ! 55)
(0.154)
0.149 (0.067)
- 0.076 (0.1651
0.234 10.163)
- 0.203 (0. ! 20)
- 0.070 (0. I 16)
- 0.084 10.126)
0.239
- O. I 13
0.153
(0.062)
(0.049)
Household size Total consumption Protein intakes Men's or women's agriculture
(0.041 )
Housework
0.(132 (0.090l
BMI
0.937 (0.327)
0.773 (0.247)
0.773 10.194)
Energy intakes
0.226 (0.115)
0.253 (0.166)
..... 0.134 t0.181)
Time dummy 3
0.117 (0.(191)
-~ 0.122 (0.0651
..... 0.150 (0.072)
Time dmmny 4
.... 0.176 (0 090)
.... 0,089 (0.067)
0.023 (0,075)
Lagged dependent variable
0.068 10.081 )
0,009 10,067)
.... 0.027 10.074)
(Between/wit hin} variance
0.633 (0.233)
0.802 (0.278)
0.624 (0.225)
Within variance
0.427
0.208
0256
Chi-square
18.7(16)
- 0.152 (0.0601
7.7(12)
32.8116)
Men's (women's) agriculture appears in tile equation for women's (men's) agriculture. Women's agriculture is included in tile women's housework equation. Degrees of freedom of tile ehi-squ;u'e tesl for the exogeneity of agriculture, housework, BMI, and energy intakes are in parentheses.
activities are influenced by their housework and by the time spent by men on agriculture. Since It small proportion of men's time is spent on household activities, the empirical results for housework were poor and are omitted from the table.
292
A. Bhargat,a / Joucnal o]'Ecommte~ri('s 77 (1997) 277-295
First considering the results for men's agriculture, age, total value of consumption, BMI, energy intakes, women's agriculture, time dummies, and between-subject variance are all significant. The coefficient of age is positive and contrasts with the negative estimate obtained for heavy activities ..~ g ~ c ~i. ~iac inclusion of square of age indicated a nonlinear relationship though the additional term was not significant at 5% level. The housework performed by men is insignificant; dropping this variable from the model did not alter the results. In the model for women's housework, coefficient of household size is significant; a greater number of members in the household is associated with reduction in the lead woman's effort on housework. This phenomenon was explored fl~rther by controlling for the number of children in age groups I-5 and 5-10 years. The estimates were less precise as the sample size was reduced to 85 women because of missing data. However, the results confirmed that children in the household enable women to increase their resting time. Since poor nutritional status diminishes adult productivity, larger families might be viewed favorably in Rwanda; children enhance the subsistence capacity of the household (Schultz, 1973). The coefficients of women's agriculture and BMI are significant in explaining housework which contrasts with some of the findings in previous tables. The household and agricultural activities seem to cover salient aspects of women's role in subsistence agriculture. Notice the substitution between agricultural and household activities. This is not the case for men. Thus men share additional work by performing tasks outside the hot~se. The remaining tasks are completed by other members including children. Finally, the results for women's agriculture are broadly consistent with the estimates obtained for housework. The coefficient of men's agriculture is significant and positive whereas that of housework is negative. The subsititution between agricultural and household activities is of a similar order of magnitude in the two models The BMI is positively associated with agriculture; household size is insignificant. The coefficients of dummy variables for third survey rounds are negative and significant in the two columns. The coefficients of lagged dependent variables are insignificant though the between-subject variances are invariably significant.
5. Conclusion
This paper has proposed a dynamic framework for analyzing the determinants of time allocation at the subsistence level. The models for energy intakes, body weight, and activity patterns capture different aspects of the relationship. In spite of exchange of food amongst households, high food prices cause energy deficiencies. Further, poor nutritional status hampers the capacity of adults to undertake subsistence tasks. Since weight loss was evident during the sample
A. Bhargava / Jo,wnal q/'Et'on,m~etrics 77 (1997) 277-295
293
period, energy intakes of twice the BMR appear inadequate for subsistence activities of the lead adults. The productivity of adults will benefit from programs e.Jleviating energy deficiencies. This might be achieved by subsidizing foods such as sweet potatoes and beans. Significant improvements in health and productivity will facilitate family planning programs in Rwanda, once the present conflict is resolved. Since men and women share the work, special programs for women might not be necessary. At a general level, the results indicate that it would be fruitful to discuss the issue of'Requirements for what?' (FAO, 1985) in a dynamic framework. Firstly, in populations facing energy deficiencies, intakes determine the expenditures. At a given point in time, energy expenditures cannot afford a full assessment of the nutrient levels necessary for maintaining long-term health. Dietary guidelines for poor countries should reflect the importance of nutrients for various human functions. Secondly, in situations where energy needs are satisfied, modern intervention programs address protein and micronutrient deficiencies. The latter increase morbidity and reduce physical work capacity. The diminished capacity of individuals together with limited market opportunities will gradually lower their expenditures and intakes. The =oexistence of marginal undernutrition and poverty results in suboptimal energy expenditures. It is important to formulate the dynamic interactions between the energy, protein, and micronutrient intakes. Such considerations will enhance the elticacy of dietary recommendations (see also Bhargava and Reeds, 1995). Finally, for populations enjoying unrestricted food supplies, a knowledge of ~mergy expenditures is useful. While epidemiologic research in developed countries has focused on habitual intakes (e.g., Willetl, 1990), recent studies suggest underreporting (or underconsumption on the days sampled) of nutrients such as fat and cholesterol (Bingham, 1994). Future work, verifying the intakes data by energy expenditures and biological markers, can filcilitate the prevention of diseases associated with overnutrition.
References Anant',, S. and M. Ravallion, 1993, Human development in poor ¢ouulries: On tile role of private incomes and public services, Journal of Economic Perspectives 7, 133 150. Anderson, T.W. and C. Hsiao, 198!, Estimation of dynamic models with error components, Journal of the American Sl:ltislical Association 76, 598 606. Ashenf¢lter, O. and J. Heckman, 1974, The estimation of income and substitutmn effects in a model of family labor supply, Econometrica 42, 73 85. Basra, S.S, M.S. Soekirman, D. Karyadi, and N.S. Scrimshaw, 1979, Iron deticiency anemia and the productivity of adult males in Indonesia, American Journal of Clinical Nulr[tion 32, 916 925.
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Beaton, G.H., 1984, Adaptation to and accomodation of long term low energy intake, in: E. Pollitt and P. Amante, eds, Energy intake and activity {Alan R. Liss, New York, NY) 395-403. Becker, G.S., 1965, A theory of allocation of time, Economic Journal 75, 493-517. Becker, G. S., 1991, A treatise on the family (Harvard University Press, Cambridge, MA). Berio, A.-J., 1984, The analysis of time allocation and activity patterns in nutrition and rural development planning, Food and Nutrition Bulletin 6, 53-68. Bhargava, A., 1991, Estimating short and long run income elasticities of foods and nutrients for rural south india, Journal of the Royal Statistical Society A 154, 157-174. Bhargava, A., 1992, Malnutrition and the role of individual variation with evidence from India and the Philippines, Journal of the Royal Statistical Society A 155, 221-231. Bhargava, A.. 1994, Modelling the health of Filipino children, Journal of the Royal Statistical Society A 157, 417-432. Bhargava, A. and P. Reeds. 1995, Requirements for what? Is the measurement of energy expenditure a sufficient estimate of energy needs?, Journal of Nutrition 125, 1358-1362. Bhargava, A. and J.D. Sargan, 1983, Estimating dynamic random effects models from panel data covering short time periods, Econometrica 51, 1635-1660. Bingham, S.A., 1994, The use of 24-h urine samples and energy expenditure to validate dietary assessment, American Journal of Clinical Nutrition 59, 227S-231S. Bliss, C. and N. Stern, 1978, Productivity, wages and nutrition, I and II, Journal of Development Economics 5, 33 I- 362 and 363-398. Cox, D.R., 1970 Analysis of binary data {Chapman and Hall, London). Dasgupta, P., 1993, An inquiry into well-being and destitutkm (Oxford University Press, New York, NYL Deolalikar, A.B., 1988, Nutrition and labor productivity in agriculture: Estimates for rural south India, Review of Economics and Statistics 67. 406 413. Durnin. J., 1991, Practical estimates of energy requirements, Journal of Nutrition 121, 1907 1913. FAO/WlolO/IJNU, 1985. Energy and protein requirements, World Health Organization technical report series 11o. 724 (WHO, GenewtL F'loud, R., K. Wachler. and A. Gregory, 1991, tleight, health and hisiory (Cambridge University Press, Cambridgel. |,ogel, R.W., 1994, Economic growth, population Iheory and physiology: The hearing of long-term processes on the making of economic policy, American Economic Review 84, 369395. (]ardner, G.W., V.R, Edgerton, B. Senewiratne, J.R. Bernard, and Y. Ohira, 1975, Physical work capacity and metabolic stress in subjects with iron deticiency anemia, American Jourmd of Clinical Nutrition 30, 910.917. Hausman, J.A., 1981, Labor supply, in: H. Aaron and J. Pechman, eds., How taxes affect economic behavior (Brookings Institution, Washington, DC). James, W.P.T., A. Ferro-Luzzi, and J.C. Waterlow, 1988, Definition of chronic energy deliciencies in adults, European Journal of Clinical Nutrition 42, 969981. Leibenstein, H., 1957, Economic backwardness and economic growth {Wiley, New York, NYI. Lundberg, S,, 1988, Labor supply of husbands and wives: A simultanoeous equations approach, Review of Economics and Statistics 67, 224 234. Mincer, J., 1962, Labor force participation of married women, in: G.H. Lewis, ed., Aspects of labor economics (Princeton: University Press, Princeton, NJJ. Mirrlees, J,, 1975, A pure theory of underdeveloped economies, in: R. Reynolds, ed.. Agriculture in development theory IYale University Press, New Haven, NJL Myrdal, (3., 1968, Asian drama: An enquiry into the poverty of nations (Allen Lane, Middlesexl. Nelson, M, A.E. Black, J.A. Morris, and T.J. Cole, 1989, Between-and-within subject variation in nutrient intake fl'om infancy to old age: Estimating the number of days It) rank dietary intakes with desired precision, American Journal of Clinical Nutrition 50, 155 167.
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Pollak, R., 1969, Conditional demand functions and consumption theory, Quarterly Journal of Economics 83, 70-78. Pollak, R. and M. Wachter, 1975, The relevance of household production functions and its implications for the allocation of time, Journal of Political Economy 83, 255 277. Pudney, S., 1981, Instrumental variable estimation of a characteristics model of demand, Review of Economic Studies 48, 417-433. Republique Rwandaise, 1986, Enquete national sur le budget et la consommation des manages (Ministry of Planning, Rwandal. Schoefield, W.N., 1986, Predicting basal metabolic rate, new standards and review of previous work, Human Nutrition Clinical Nutrition 39C, Suppl. 1, 5-41. Schultz, T.W., 1973, Fertility and economic values, in: T.W. Schultz, ed., Economics of the family (Chicago University Press, Chicago, IL). Sen, A., 1983, Economics of the family, Asian Development Review I, 14-26. Sen, A., 1985, Commodities and capabilities (North-Holland, Amsterdam). Simon, H.A., 1986, Rationality in psychology and economics, Journal of Business 59, $209--$224. Soares, M.J. and P.S. Shetty, 1991, Basal metabolic rates and metabolic economy ill chronic undernutrition, European Journal of Clinical Nutrition 45, 363-373. Spurr, G.B., 1983, Nutritional status and physical work capacity, Yearbook of Physical Anthropology, 1-35. Waterlow, J.C., 1986, Metabolic adaptation to low intakes of energy and protein, Annual Review of Nutrition 6, 495- 526. Willett, W., 1990, Nutritional epidemioiogy (Oxford University Press, Oxford).
~
JOURNAL OF
Econometrics ELSEVIER
Journal of Economelrics 77 (1997) 277 -295
Nutritional status and the allocation of time in Rwandese households Alok Bhargava Department of Economics. University of Houston, Houston. TX 77204-5882. USA
Abstrae~ This paper analyzes the activity patterns of adult men and women in approximately 110 Rwandese households surveyed four times in 1982-83. Dynamic models are separately estimated for men and women for the time spent sleeping and resting, performing heavy aclivities, doing housework, and on agriculture. The models postulate simultaneity between men and women's activities and investigate the differential feedbacks. The main findings are that low incomes and high food prices reduce the households' energy intakes, thereby forcing the adults to spend additional time resting and sleeping. Second, both men and women share the workload in spite of poor nutritional status. Third, for women, there is substitution between housework and agriculture, the former tasks being relegated to other household members. Lastly, energy intakes of twice the Basal Metabolic Rate seem inadequate for the sustenance of active adults. The policy implications of the results are discus~,ed. Key words: Health; Nutrition; Time allocation; Dynamic models; Food policies JEL class!/ication: C23; !12; J24; O12
1. Introduction The measurement of human energy expenditures is an important topic in biological sciences; its relevance for defining energy requirements was underscored by the expcrt committee of the FAO/WHO/UNU (FAO, 1985). The FAO approach to energy needs is reflected in the phrase 'Requirements for what?'. Since Lavoisier's discovery of the role of oxygen in energy metabolism
This study was supported by Research Committee of the World Bank. The author thanks Q. Khan, C. Muller, M. Ravallion, and J. Yu for their help, but retains the responsibility for views in the paper. This revision has benefited from the comments of three referees. 0304-4076/97/$15.00 © 1997 Elsevier Science S.A. All rights reserved PII S 0 3 0 4 - 4 0 7 6 ( 9 6 ) 0 1 8 1 6-7
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A. Bhargava / Journal of Econometrics 77 (1997) 277-295
and Rubner's work on the heat produced in oxidation of food, physiologists have developed sophisticated methods for measuring energy expenditures (Durnin, 1991). For example, modern calorimeters afford accurate measurement in laboratories and the doubly labelled water method is useful for free living poulations. A knowledge of energy expenditures of the inhabitants of less developed countries is useful for the design of food policies. Since resources available for data collection are limited, the expenditures are typically calculated from time allocation surveys. The data have provided some useful insights. For example, Berio (1984) suggests that women bear the greater burden of economic development. The behavioral mechanisms through which men and women share work are not explored in the analysis. Interpreting some biomedical evidence on G~mbian women, however, Beaton (1984) concludes that low energy intakes induce behavioral changes amongst the poor. Consequently, it is superficial to distinguish between the biological and social sciences approaches to time allocation; a unified framework is useful for data analysis. The importance of research in bio-chemistry of food for economic development policies was recognized by Leibenstein (1957) who argued that higher wages of workers will improve their nutritional status and hence productivity. The 'wage efficiency hypothesis' has since been refined (Mirrlees, 1975). In countries such as Rwanda, the population subsists on agriculture and energy deficiencies are prevalent. The link between wages and labor productivity is affected by the intra-household distribution of food; the earners' intakes need not increase proportionately with wages. Further, while higher wages ultimately affect productivity via improvements in health (Fogel, 1994), the lags underlying health processes are complex (Bhargava, 1994). Lastly, many adults work on their own hind and perform housework without receiving monetary compensation, An analysis of the determinants of time allocation in Rwandese households is therefore of interest. Modelling the relationship between the adults' time allocation and nutritional status presents several difficulties. Firstly, while recording daily activities at a disaggregated level is insightful, there is a large amount of internal variation in the data. Also, energy expenditures are available only for broad categories of activities (FAO, 1985). Secondly, individual food intakes are difficult to quantify in Rwanda since household members share the food from the same plate. Thirdly, alternative formulations for sharing the work between men and women are available in social sciences (e.g., Becker, 1991; Sen, 1983; Simon, 1986). Lastly, due to differences in data collection procedures, the effects of variables such as food prices on time allocation can be assessed only indirectly in a iongitudimd II~tHI~OI ~.......... K. ' The structure of this paper is as follows: Section 2 describes the data from Rwanda. Section 3 outlines a framework for analyzing the activities data and the dynamic model is briefly described. The empirical results for the households'
A. Bhargava / Journal of Economeo'ics 77 (1997) 277-295
279
energy intakes, adults' weights, and the time spent sleeping avd resting, on heavy activities, performing housework, and on agricultural activities are discussed in Section 4. Finally, in Section 5, the issue of 'Requirements for what?' is reexamined in light of the empirical results.
2. The data
The longitudinal study in Rwanda was conducted by the Ministry of Planning with assistance from the French government during 1982-83 (Republique Rwandaise, 1986). The food intakes in 270 households (three from each of the 90 'sectors') were recorded for seven consecutive days. The surveys were repeated four times at four-month intervals. The heights of adults were recorded once and the weights were measured in each round. The time spent by household members on over 600 activities for fourteen consecutive days was recorded in each survey round. Since food intakes are observed for seven days, the activity data on the same days are used in the analysis. Further, for comparisons with other studies and to reduce variation in the data, the activities were mapped into the 25 broad categories defined by FAO (1985). The activities that were difficult to match were put into 5 groups formed on the basis of energy expenditures, expressed as multiples of the Basal Metabolic Rate (BMR is the minimal energy necessary for sustaining life). The data for seven days were then averaged to produce a figure for an "average' day of the week, i.e., average activity levels in the four survey rounds are analyzed in this paper. Some information on education, land holding, and other variables is contained in the data set. Single observations for the survey period are available on the value of households' consumption and production. The prices of seven food groups and information on wages earned by a subset of household members are recorded once. The 'lead' young adult man and woman were selected from each household; the adults need not be a couple but were likely to be the ones performing the demanding tasks. Retaining individuals with four time observations, complete data were obtained on approximately 110 adults. The sample mean are reported in Table 1.
3. A framework for time allocation at the susistence level
3.1. NuO'ient deficiencies, work performance, and time allocation The uc,~,l,..,,,,,~' ....-"~" ;,o nrv.certain nutrients in the diet diminish the physical work capacity of individuals. For example, iron deficiencies are ki~own to reduce the maximum volume of oxygen consumed (Spurr, 1983); field studies have shown
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A. Bhargat,a / Journal of Econometrics 77 (1997) 277-295
Table I Sample means of the variables in the four rounds of data from Rwanda Variable Proportion of time spent sleeping, resting, and sitting quietly Proportion of time spent on heavy activities Proportion of time spent on housework Proportion of time spent on agriculture Average energy expenditure Household size Age Total value of consumption Total value of production Energy intakes Protein intakes Weight Height Body mass index (BMI}
Men
Women
0.547 10.107) 0.187 (0.088) 0.014 (0.050) O.I00 (0.088) 1.992 (0.437) 5.991 (2.248)
0.50Q (0.086) 0.180 (0.073) 0.131 (0.069) 0.140 (0.079) 2.184 (0.298)
34.223 (19.315) 5188 I (25319) 43271 {31248) I 1161 15340) 361.6 1201.7} 48.817 (12.787) ! 57.259 (15.625) 19.350 (3.033)
32.017 (16.157)
48.269 (11.636) 152.975 (11.637) 20.333 (3.592)
The sample means are calculated using four time observations on 116 men and 119 women. Tables 3-6 analyze a subset of these data. Numbers in parentheses arc estimated standard deviations for the pooled samples. Heavy activities require at least thrice as much energy as sleep. Average energy expenditure is in terms of Basal Metabolic Rate. Consumption and prc[tuction are annual figures in Rwandese francs for the households. Households' energy and protein ii~takes per day are measured, respectively, in kcals and grams. Weight and height are in kilograms and centimeters, respectively.
positive effects of iron supplementation on labor productivity (Basta et al., 1979; Gardner et al., 1975). In countries such as Rwanda, energy deficiencies are of paramount importance since they restrict behavior and affect health by hindering the absorption of nutrients (Bhargava, 1991). For an historical perspective, see Fogel (1994).
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281
The physiological changes resulting from inadequate energy intakes are complex. Watedow {1986), lor example, has suggested that the (low) ratio of an individual's BMR to body weight (fat-free mass) in developing countries reflects 'metabolic economy'. However, this is not necessarily the case in an undernourished sample from India (Soares and Shetty, 1991). A high BMR to body weight ratio for the malnourished is consistent with a long-run elasticity of BMR with respect to weight that is less than unity (Bhargava and Reeds, 1995). The energy necessary for performing an activity can be expressed as a multiple of the individual's BMR. The latter is related to body weight (Schofield, 1986). From a policy standpoint, the effects of nutritional status on work performance are of interest. Since the Rwandese households spend most of their time on subsistence activities, the link between nutritional status and time allocation is important. i
3.2. Moa~elling the effects o f economic variables on time allocation
Myrdal (1968) observed the vicious circle of poverty and poor work performance in developing countries caused by chronic food shortages. Mirrlees (1975) extended the theoretical wage-consumption analysis, though emphasizing the need for an empirical treatment. Since improved nutrition enhances the 'capabilities' of individuals to undertake useful tasks (Sen, 1985; Anand and Ravallion, 1993; Dasgupta, 1993), modelling the determinants of time spent on various activities is of interest. The treatment of leisure (L) as an argument in the utility function yields the demand for leisure as a function of goods prices (p), wage rate (w), and nonlabor income (A), i.e., L = h(p, w, A).
(1)
It would be desirable to adopt a flexible approach in the analysis of longitudinal time allocation data from Rwanda. Firstly, a large proportion of the food consumed by households is their own produce or is received in the form of gifts. Also, energy deficiencies are apparent in Table 1 from the sample means of Body Mass Index (BMI is defined as weight in kilograms divided by the square of height in meters; James et al., 1988). In such circumstances, it is likely that energy expenditures are driven by the intakes. The food available to many households will be inadequate and can thus be viewed as pre-aUocated (Pollak, 1969). The indicators of nutritional status are important explanatory variables in time allocation models since they capture the effects of current and past nutritional deficiencies. Secondly, due to own-production of food and the gifts received, minor fluctuations in food prices may not have immediate effects on leisure. However, pric, . . . e.~ . . . .will . . . ~,~r~d'l'ail~Jh~,,~-,,~...,._~.~,,,,th,~,,,,,~l-abitual' leisut~e. Thirdly, since a single observation on wages is available for roughly a third of the households, it might
282
A. Bhargava /Journal of Econometrics 77 (1997) 277-295
seem difficult to examine the effects of wages on time allocation. However, one can control for earnings of the household members by including total consumption during the sample period as a regressor in the longitudinal models. The link between nutritional status and time allocation can be investigated using the present data set by splitting the estimation problems into two stages. At the first stage, the households' average energy intakes in the four survey rounds are explained in a cross-sectional framework by variables such as household size, regional dummy variables, food prices, and measures of household incomes. The estimated relationship will provide a large-scale view of the effects of economic variables on food availability. The equilibrium quantity of food consumed through market and nonmarket arrangements, however, may be insufficient for essential activities of the household members. The second stage, then, models the determinants of time allocated to leisure and productive activities in a longitudinal framework. Since the choice between work and leisure is affected by health, biological and behavioral factors play important role in model specification. In particular, the mechanism by which energy deficiencies restrict activities and factors underlying the sharing of work between men and women merit a systematic treatment.
3.3. ,4 dynamic fi'amework for longitudinal time allocation data The joint nature of household labor supply decisions was formulated from an empirical standpoint by Mincer (1962}. The analysis is suitable for time allocation decisions since, at the subsistence level, members share the work. The joint activity patterns, however, complicate the application of Becker (1965) type models of household production (Pollak and Wachter, 1975). It would seem difficult to specify a model for time allocation, analogous to the 'characteristics' model of demand (Pudney, 1981), in terms of latent price variables. The division of tasks within a household affects the relationship between time allocation and nutritional status for all the members. For example, women with young children may substitute agricultural activities by housework; the latter is less strenuous and older children can help in gathering wood, fetching water, etc. Further, men and women share the additional (seasonal) workload according to certain rules. Since fertility rates are high in Rwanda, a model of joint time allocation decisions would be useful for assessing if women face a disproportionate burden in times of growth (Berio~ 1984). Now, inadequate energy intakes will force a reduction in the individuals' energy expenditures. This might be achieved in the short run by decreasing the effort on strenuous tasks; a gradual increase in the proportion of time spent on lighter activities will follow. If energy deficiencies persist over time, the individuals will begin to lose weight and further curtail expenditures. The latter might entail avoiding heavy activities altogether; there is evidence that the undernourished cannot endure strenuous work (Gardner et al., 1975). The
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283
proportion of time spent resting and sleeping will show an upward tendency even in the short run. An apparent shortcoming in exclusively focusing on the equality between energy intakes and expenditures ir that important health aspects are omitted from the discussion. For examp,e, micro-nutrient intakes are essential for maintenance of the immune system; repeated sicknesses can diminish individuals' work capacity (see also Floud et al., 1991). In the present data, morbidity can be inferred from resting patterns; hence the dynamic aspects of time allocation and nutritional status are important. The nutritional status of an individual may be represented by height and weight in time allocation models. In response to work opportunities, however, adults might increase activities in spite of poor health. Higher energy and protein intakes facilitate expenditures in the short run. Also, given the interrelationships between height and weight, it is desirable to test if these variables can be combined (Bhargava, 1994). Lastly, an equation for weight is useful for studying the dynamics of nutritional status.
3.4. The model and its estimation The model for time allocation can be represented by the following equations (h = 1, .... J; i = 1.... ,N; t = 2,3,4): k
C,, = ~.. zh~flj + ul,,,
(2)
j=l ~Pl
N
W , = ~., z07; + ~ xot~ j + ei W . - n + 21Hi + u21~, ,i= I |
p"
P. = ~', zorl~ + ~ xotOj + =2P~t-t + 22Wi, + 23Hi + ua.. j=t
(3)
j= I
(4)
j=l
Here, Ch is the hth household's average energy intake in four survey rounds; there are J households on whom cross-sectional data are available. Wit is ith individual's weight in the tth survey, Hi is the height, and Pi, is the proportion of time spent on a certain activity in the tth survey round (the subscript for different activities is suppressed for brevity). Thus N individuals are repeatedly observed in four survey rounds. The z's and x's are, respectively, time-invariant and time-varying regressors; the coefficients are denoted by lower case Greek letters. The error terms affecting (2) are independently distributed with zero mean and finite variance. The errors in Eqs. (3) and (4) are assumed independent across individuals but correlated over time with a positive definite variance-covariance matrix (u2, and u3, can be correlated with one another). The random effects decomposition
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A. Bhargat,a / Journal of Econometrics 77 (1997) 277-295
for u2i~, for example, is a special case, i.e., U2it : ~i "Jr"/J2it,
(5)
where ,$'s are individual-specific random effects and v's are independently distributed random variables. Note that lagged dependent variables (including initial observations W~I and P~I) are treated as endogenous in the system (Anderson arid Hsiao, 1981). Some ~of the time-varying regressors are also endogenous in the sense that they ~ire correlated with random effects. The exogeneity hypotheses can be tested using likelihood ratio statistics. Details of identification of model parameters and computation of maximum likelihood estimates are presented elsewhere (Bhargava and Sargan, 1983). It is important to explore the mechanism by which men and women share the work. For example, additional work available during a survey round is likely ~o be distributed amongst household members on the basis of their work capacity. Otherwise, the arrangements may not be sustainable and a breakdown could threaten members' survival. Since observations on some members are incomplete, results for the lead adult man and woman are presented in the next section.
4. The empirical results 4.1. T,l:e results Jbr households'average energy intakes Table 2 presents the results for households' average energy intakes in the four survey rounds. Since Rwanda is divided in six geographical zones, five dummy variables were included in the models: the variables for zones 3 and 5 and 6 were insignificant. The results in the first two columns assume the regressors to be exogenous; the third column treats the total value of consumption as an endogenous variable and reports the instrumental variables estimates. All variables were transformed into natural logarithms to reduce heteroscedasticity (e.g., Nelson et al., 1989). Household size is positively associated with energy intakes. While some nonlinearity is implicit in the logarithmic specification, square of household size was insignificant. The total value ofconsumption is an approximate measure for household incomes. The point estimate of income elasticity of energy intakes in the first column is 0.56. The total value of production, however, is insignificant. Note that since energy intakes influence productive activities, consumption may be correlated with the error term. This problem is tackled in the third column where the size of land owned by the household is used as an additional instrument. Land holding is fixed in Rwanda by the government and there is variation in land quality. The estimate of income elasticity is higher in column 3 though its standard error is larger as well.
A. Bhargava / Journal of Econometrics 77 (1997) 277-295
285
Table 2 Cross-sectional results for average household energy intakes Variables Constant
Specification 1
Specification 2
Specification 3
6.476 (0.509)
6.204 (0.661)
5.376 (1.909)
Zone 2
- 0.119 10.041)
- 0.107 (0.062}
- 0.110 (0.045}
Zone 4
- 0.186 (0.041)
- 0.197 (0.059)
- 0.179 (0.044) 0.154 CO.! 39) 0.667 (0.182}
- 0.120 (0.102)
Household size
Price beans
- 0.146 (0.091 }
0.253 (0.061) 0.545 (0.053} - 0.006 (0.032) - 0.047 (0.032} - 0.093 CO.130)
Price sweet potatoes
- 0.095 (0.046)
- 0.020 (0.062)
- 0.101 (0.048)
0.477 (0.0771
0.568 (0.114)
0.476 (0.077)
Total consumption Total production Average wage rate
Price traditional beers Sample size Adjusted R z
0.234 (0.041 } 0.558 (0.040} 0.002 (0.022) --
251
i 19
0.757
0.721
---
251 --
All v a r i a b l e s a r e in Iogal'ithn~:, Zone 2 and a ~ e indicator variables. Specification I and 2 are estimated by least squares. Speclficatiol~ 3 treats consumption as endogenous using land size as an additional instrument. Standard errors are in parentheses.
Higher prices of sweet potatoes significantly decrease households' long-run energy intakes. Due to poor quality of land in Rwanda, some quantities of staple foods are purchased at market prices. It is interesting to note that price of traditional beers has the opposite effect on energy intakes. Thus expenditures on beer (consumed mainly by men) appear to divert scarce resources from staple foods. Lastly, the results in second column include a measure for wages earned by household members; the wage variable is insignificant. The reduction in sample size decreases the precison of the estimates; prices of beans and sweet potatoes are insignificant as well. The treatment of wages and consumption as endogenous variables led to insignificance of all the regressors (the results are not reported). There are difficulties in finding suitable instruments for predicting wages in poor countries; shifts in demand for labor contribute to fluctuations in wages (of. Bliss
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286
and Stem, 1978; Deolalikar, 1988). However, measures of economic activity in the 90 sectors of Rwanda are not included in the data set.
4.2. Resultsfor body weight The empirical results for the body weight of men and women are presented in Table 3. The height variable significantly affects weight; the short-run coefficient for women is almost thrice as large. Since fertility rates are high, these differences may be due to short-term accumulation of subcutaneous fat. The inclusion of household size and energy and protein intakes as regressors in the model affords an approximate treatment of the impact of food intakes on weight. It would be desirabl ~to include individual intakes and take into account ages of the household members. Since food is shared from a common plate in Rwanda, an alternative approach would be to use energy expenditures of members as surrogates for their intakes; members' weight and average activity Tables 3 Maximum likelihood estimates of the weight relationship in Rwanda Variable
Men
Women
Constant
- 3.063 (0.812) 0.004 (0,019)
- 7.078 (0.440) - 0.009 (0.025)
0.834 (0.249) 0.004 (0.019~ 0.007 (0.026) - 0.008
i.947 (0.077) 0.004 (0.022) 0.003 (0.030) - 0.026
(0.013)
(0.013)
tlousehold size Height Protein intakes Energy intakes Time dummy 3 Time d u m m y
4
- 0,025
- 0.007
Within variance
(0.013) 0.670 (0.123) 0.091 (0.152) 0.011
(0.013) 0.282 (0.040) 0.984 (0.221) 0.010
Chi-square (4)
9.9
0.7
Lagged dependent variable (Between/within) variance
All variables in logarithms. The numbers in parentheses are the estimated asymptotic standard errors. Chi-square(4) is the test for exogeneity of energy intakes. Time dummies 3 and 4 are, respectively, indicator variables for 3rd and 4th survey rounds (see the text).
A. Bhargat,a / Jots, ,~al of Econometrics 77 (1997) 277-295
287
levels are relevant regressors. The estimation of such a formulation, however, is infeasible; activities of children were not recorded and observations on adults are often incomplete. The statistical insignificance of household size and energy and protein intakes should thus be interpreted with caution. The coefficient of previous weight is significant in equations for men and women. While the estimate for men is higher, the ratio of between to within variance is low, indicating small between-subject differences. Note, however, that maximum likelihood estimation using modest sample sizes often entails underestimation of the between variance. The indicator variables for survey rounds appear with negative signs; coefficients in fourth and third rounds are, respectively, significant for men and women (there is no dummy variable for the second survey round as the initial observation on the dependent variable has its own intercept and a constant is included). The results indicate a tendency of weight loss over time. Since work availability in the last two survey rounds is low, shortfalls in energy intakes seem responsible for weight changes. Lastly, exogeneity hypothesis for energy intakes is not rejected in the models for men and women. While within-subject variation is similar for the sexes, overall variation in the data is high. The acceptance of exogeneity hypotheses may in part be due to the wide confidence intervals for likelihood ratio statistics.
4.3. Results .for the resting and sleeping patterns The empirical results for the proportion of time spent by the adults resting, sleeping, and sitting quietly are in Table 4; logistic transformation of the dependent variable ensures a smooth relationship with explanatory variables. The adults' height and weight were initially introduced as separate regressors. The use of likelihood ratio statistics generally led to acceptance of the hypothesis that the two variables can be combined as the Body Mass Index (BMI). Moreover, in models where coefficients of height and weight were unrestricted, coilinearity amongst the regressors was evident; estimates with BMi are reported in the tables. First focusing on the results for men, the time spent resting increases with age (coefficient of age squared was insignificant). Also, resting time of the lead adult is positively associated with household size. Secondly, estimated coefficients of the value of household consumption and individuals' BMI are negative and significant. The coefficient of BM! is strong and this variable reflects cumulative effects of energy deficiencies ir the medium term. It therefore appears that adult males in poor households spend additional time resting and sleeping to avoid weight loss. Thirdly, the ~ime spent resting by men is negatively associated with average energy expenditure of the lead woman in the household. Since household consumption is taken into account, a possible explanati.on is that men decrease
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A. Bhargava / Journal of Econometrics 77 (1997) 277-295
Table 4 Quasi maximum likelihood estimates for the proportion of time spent sleeping, resting, and sitting quietly Variable
Men
Constant
- 0.915 (0.876)
1.387 (!.356)
Age
O.129 (0.044)
- 0.006 (0.047t
Household size
0.149 {0.075}
0.095 (0.078)
- 0.308 (0.093)
0.038 t0.078)
0.042 10.075)
0.070 10.069)
Average men's or women's energy expenditure
- 0.316 (0.163)
- 0.165 10.094)
BM !
-
0.623 (0.161)
0.025 {0.139}
Energy retakes
- 0.031 10,102)
- 0.174 (0.105)
Time dunnny 3
0.21 ! 10.060}
0.153 10.044)
Time dununy 4
O+121 (0.067)
0. I 17 10.047)
Lagged del~¢ndent variable
0.322 (01|95)
0.021 10.0831
Between/within) variance
0.007 111,063)
0.364 (0.1631
Within variance
0.192
0.090
Total consumption Protein intakes
Chi-square (12)
15.8
Women
18,8
The dependent variable is the logistic transformation. Women's (men's} average energy expenditure is included in the equation for men Iwoment. Chi-square {12) is the test for the exogen~:ity of men's {or women's) average energy expenditure, BMI, and energy intakes.
their resting time to offset high demand for women's activities. This demonstrates the joint nature of activity patterns; a decline in men's leisure would facilitate subsistence tasks. While the type of work undertaken is explored below, marginal product ,of labor is likely to be low due to poor nutritional status. However, no further measures of adult productivity are available in the data set, Fourthly, energy intakes are statistically insignificant. Note that 24-hour recall data from developing countries exhibit high within-subject variation in
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289
intakes by the poor (Bhargava, 1992). This variation does not appear to diminish in the average intakes, presumably due to chronic food shortages in Rwanda. The likelihood ratio test accepts the joint exogeneity of women's average energy expenditures and men's BMI and energy intakes. However, if the latter variables are treated as exogenous, the exogeneity of women's (men's) expenditures was rejected in some of the models. We also estimated static versions of the models used in this paper since they require less restrictive exogeneity assumptions. However, the results were quite similar. Lastly, the coefficients of indicator variables for third and fourth survey rounds are positive and significant indicating low work availability. Also, time spent resting is significantly influenced by its lagged value; long-run effect of a change in an independent variable is about 1.33 times greater than the short-run impact. The between subject variance is insignificant, however. In contrast with the results for men, only a few variables are significant in explaining women's resting patterns. The average energy expenditure of men and women's energy intakes are estimated with negative coefficients that are marginally significant. The indicator variables for survey rounds and the between subject variance are significant. Notice that the estimated within subject variance is twice as large for men. This might be due to the fact that the agricultural tasks performed by men are seasonal in nature.
4.4. Resulls lbr heart activities Table 5 contains results for the proportion of time spent on heavy activities (with a continuity correction for extreme values; Cox, 1970) requiring at least thrice as much energy as the BM R. From the viewpoint of assessing tile effects of poor nutritional status on time allocation, this model complements the specification for resting patterns. Notice that proportion of time spent by men (women) on heavy activities is positively associated with time spent by women (men) (see also Ashenfelter and Heckman, 1974; Hausman, 1981; Lundberg, 1988). The coefficients are statistically significant. Also, BMi is significant tbr men and might have been significant in the model tbr women if the sample size were greater. The energy intakes are significant for men; dummy variables for survey rounds are mainly negative though their coefficients are insignificant. While the coefficients of lagged dependent variables are insignificant, between subject variances are significant. The statistical insignificance of some of the parameters may be due to greater variation in heavy activities. Unlike rest and sleep, these activities are strongly influenced by geographical location of the household, land quality, etc. In fact, within variances in Table 4 are about twice the corresponding estimates in Table 3.
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A. Bhargat,a / Journal o] Econometrics 77 (1997) 277-295
Table 5 Quasi maximum likelihood estimates for the proportion of time spent on heavy activities Varible Constant
Men
Women
0.635 (2.690)
2.506 12.207)
Age
- O. 166 10.081)
0.009 (0.076)
Household size
- O. 186
0.049
10.132)
10.133)
0.204 (0.138)
0.040 10.142)
- 0.056 (0.073)
- 0.016 (0. ! 061
Men's or women's time on heavy activities
O. i ! 3 (0.0641
0.142 (0.0431
BM!
0.709 t0.309}
0.314 (0.217)
Energy intakes
0.136 (0.0671
- 0.162 (0.152)
Time dummy 3
- 0.069 10.0801
- 0.045 t0.061 )
Time dummy 4
= 0,106 10.0801
0.008 10.(162)
Total consumption Protein intakes
0,051
0.013
(0,(178)
(0.086)
(Belween/withit~) variance
0.303 (0.138)
0.637 (0.253)
Within variance
0.342
0.179
Lagged dependent variable
Chi-square (I 21
16.7
17.6
7t'he dependent variable is the logistic transformation with a continuity correction for extreme values. Women's (men's) time on heavy activities is included in the equation for men (women). Chisquare (12) is the test for the exogeneity of men's (or women'sl heavy activities, BMI, and energy intakes,
4.5. Results lbr agricultural and househoht actil,ities The determinants of time allocated to agricultural and household activities are investigated in Table 6. The models underscore joint nature of time allocation decisions; men's agricultural actvities depend on their effort on housework and on time spent by women on agriculture. The women's housework is influenced by their agricultural activities, in contrast, women's agricultural
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291
Table 6 Quasi maximum likelihood estimates for the proportion of time spent on agricultural and household activities Men
Women
Variable
Agriculture
Housework
Agriculture
Constant
0.858 (1.384)
0.917 (2.496)
1.635 (I.763)
Age
0.331 (0.107)
0.139 (0.094)
0.518 (0.097)
- 0.148
- 0.360
- 0.116
(0.164)
(0. ! 55)
(0.154)
0.149 (0.067)
- 0.076 (0.1651
0.234 10.163)
- 0.203 (0. ! 20)
- 0.070 (0. I 16)
- 0.084 10.126)
0.239
- O. I 13
0.153
(0.062)
(0.049)
Household size Total consumption Protein intakes Men's or women's agriculture
(0.041 )
Housework
0.(132 (0.090l
BMI
0.937 (0.327)
0.773 (0.247)
0.773 10.194)
Energy intakes
0.226 (0.115)
0.253 (0.166)
..... 0.134 t0.181)
Time dummy 3
0.117 (0.(191)
-~ 0.122 (0.0651
..... 0.150 (0.072)
Time dmmny 4
.... 0.176 (0 090)
.... 0,089 (0.067)
0.023 (0,075)
Lagged dependent variable
0.068 10.081 )
0,009 10,067)
.... 0.027 10.074)
(Between/wit hin} variance
0.633 (0.233)
0.802 (0.278)
0.624 (0.225)
Within variance
0.427
0.208
0256
Chi-square
18.7(16)
- 0.152 (0.0601
7.7(12)
32.8116)
Men's (women's) agriculture appears in tile equation for women's (men's) agriculture. Women's agriculture is included in tile women's housework equation. Degrees of freedom of tile ehi-squ;u'e tesl for the exogeneity of agriculture, housework, BMI, and energy intakes are in parentheses.
activities are influenced by their housework and by the time spent by men on agriculture. Since It small proportion of men's time is spent on household activities, the empirical results for housework were poor and are omitted from the table.
292
A. Bhargat,a / Joucnal o]'Ecommte~ri('s 77 (1997) 277-295
First considering the results for men's agriculture, age, total value of consumption, BMI, energy intakes, women's agriculture, time dummies, and between-subject variance are all significant. The coefficient of age is positive and contrasts with the negative estimate obtained for heavy activities ..~ g ~ c ~i. ~iac inclusion of square of age indicated a nonlinear relationship though the additional term was not significant at 5% level. The housework performed by men is insignificant; dropping this variable from the model did not alter the results. In the model for women's housework, coefficient of household size is significant; a greater number of members in the household is associated with reduction in the lead woman's effort on housework. This phenomenon was explored fl~rther by controlling for the number of children in age groups I-5 and 5-10 years. The estimates were less precise as the sample size was reduced to 85 women because of missing data. However, the results confirmed that children in the household enable women to increase their resting time. Since poor nutritional status diminishes adult productivity, larger families might be viewed favorably in Rwanda; children enhance the subsistence capacity of the household (Schultz, 1973). The coefficients of women's agriculture and BMI are significant in explaining housework which contrasts with some of the findings in previous tables. The household and agricultural activities seem to cover salient aspects of women's role in subsistence agriculture. Notice the substitution between agricultural and household activities. This is not the case for men. Thus men share additional work by performing tasks outside the hot~se. The remaining tasks are completed by other members including children. Finally, the results for women's agriculture are broadly consistent with the estimates obtained for housework. The coefficient of men's agriculture is significant and positive whereas that of housework is negative. The subsititution between agricultural and household activities is of a similar order of magnitude in the two models The BMI is positively associated with agriculture; household size is insignificant. The coefficients of dummy variables for third survey rounds are negative and significant in the two columns. The coefficients of lagged dependent variables are insignificant though the between-subject variances are invariably significant.
5. Conclusion
This paper has proposed a dynamic framework for analyzing the determinants of time allocation at the subsistence level. The models for energy intakes, body weight, and activity patterns capture different aspects of the relationship. In spite of exchange of food amongst households, high food prices cause energy deficiencies. Further, poor nutritional status hampers the capacity of adults to undertake subsistence tasks. Since weight loss was evident during the sample
A. Bhargava / Jo,wnal q/'Et'on,m~etrics 77 (1997) 277-295
293
period, energy intakes of twice the BMR appear inadequate for subsistence activities of the lead adults. The productivity of adults will benefit from programs e.Jleviating energy deficiencies. This might be achieved by subsidizing foods such as sweet potatoes and beans. Significant improvements in health and productivity will facilitate family planning programs in Rwanda, once the present conflict is resolved. Since men and women share the work, special programs for women might not be necessary. At a general level, the results indicate that it would be fruitful to discuss the issue of'Requirements for what?' (FAO, 1985) in a dynamic framework. Firstly, in populations facing energy deficiencies, intakes determine the expenditures. At a given point in time, energy expenditures cannot afford a full assessment of the nutrient levels necessary for maintaining long-term health. Dietary guidelines for poor countries should reflect the importance of nutrients for various human functions. Secondly, in situations where energy needs are satisfied, modern intervention programs address protein and micronutrient deficiencies. The latter increase morbidity and reduce physical work capacity. The diminished capacity of individuals together with limited market opportunities will gradually lower their expenditures and intakes. The =oexistence of marginal undernutrition and poverty results in suboptimal energy expenditures. It is important to formulate the dynamic interactions between the energy, protein, and micronutrient intakes. Such considerations will enhance the elticacy of dietary recommendations (see also Bhargava and Reeds, 1995). Finally, for populations enjoying unrestricted food supplies, a knowledge of ~mergy expenditures is useful. While epidemiologic research in developed countries has focused on habitual intakes (e.g., Willetl, 1990), recent studies suggest underreporting (or underconsumption on the days sampled) of nutrients such as fat and cholesterol (Bingham, 1994). Future work, verifying the intakes data by energy expenditures and biological markers, can filcilitate the prevention of diseases associated with overnutrition.
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