ARE CHILDREN AND PARENTS SUBSTITUTES OR COMPLEMENTS IN THE FAMILY LABOR SUPPLY DECISION IN BANGLADESH?* Shahina Amin University of Northern Iowa, USA Shakil Quayes Arizona State University-Phoenix, USA Janet M. Rives University of Northern Iowa, USA ABSTRACT This research uses logistic regression to determine whether a parent (father or mother) and a child are substitutes or complements in the family labor supply decision in Bangladesh. We look separately at models for children’s market work and children’s household work. For market work, we test our models for eight demographic groups of children, namely, younger and older, rural and urban, boys and girls. Our results show that for market work, fathers and children may be substitutes or complements in supplying labor, whereas mothers and children are complements for all groups of children. Our study of household work pertains to girls only, because boys do not engage in household work in sufficient numbers for analysis. We find almost no connections between parents’ market work and daughters’ household work status. JEL Classifications: J130, J240, J820, O100 Keywords: Asia, Bangladesh, Child Labor, Adult Labor, Substitutes, Complements, Household Work, Market Work Corresponding Author’s E-mail:
[email protected]
INTRODUCTION Child labor has drawn much attention recently among researchers and policy makers. Although child labor can largely be explained by family poverty, social factors such as education, culture, and urbanization must also be considered. Because unemployment is widespread in most developing countries, a common question that arises is how child labor can exist in the face of significant unemployment. A follow-up question is whether children and adults substitute for one another in supplying their labor services to the market or in performing household chores or whether they work together as complements. The purpose of this research is to examine the family labor supply decision in Bangladesh with regard to child labor. We seek to determine whether a parent (father or mother) and a child are substitutes or complements in the family labor supply decision. Specifically, we would like to know whether the incidence of child labor, either market work or household work, increases or decreases in response to parents’ labor income. An inverse relationship would mean that children and parents are substitutes in their labor supply, and a positive relationship would mean that they are complements. The nature of the relationship between children’s and parents’ work has implications for efforts to limit the extent of child labor in developing countries. If children work because a parent cannot work and the family is thus in a state of poverty,
16 then policies to lessen child labor must recognize the need for economic assistance. If children engage in market work because they accompany a parent to work based on social and cultural mores, then policies to lessen children’s market work and increase children’s schooling must accommodate the cultural climate. Using data from the 1995-1996 Household Expenditure Survey (HES) of Bangladesh, we model child labor in Bangladesh as depending upon whether the child’s father or mother is engaged in market work. Father’s income and mother’s income are used as measures of a parent’s involvement in market work. We consider other socioeconomic child and family characteristics such as the child’s age and educational attainment, the parents’ educational levels, family size (number of male and female adults and number of younger and older children), and a measure of non-labor household income. We test our models for eight demographic groups of children, namely, younger and older, as well as rural and urban, boys and girls. A key feature of this study is that we look separately at models for children’s market work and children’s household work. Our results show that for market work, fathers and children may be substitutes or complements in the household labor supply decision. Mothers and children are complements in the family labor supply decision for all groups of children except for older rural girls. Our study of household work pertains to girls only, because boys do not engage in household work in sufficient numbers for analysis. We find almost no connections between parents’ work and daughters’ work status when we focus on girls’ household work. CHILDREN'S AND PARENTS' LABOR SUPPLY Substantial research investigates the determinants of child labor in developing countries (Amin et al. 2004; Delap 2001; Grootaert 1999; Ray 2000a and 2000b) and whether child labor deters schooling (Ray 2000a and 2000b; Psacharopoulos 1997; Patrinos and Psacharopoulos 1995). Few research efforts, however, examine whether parents and children are substitutes or complements in supplying their labor in the market. This issue was addressed in the seminal work of Basu and Van (1998) and Basu (1999). They examined the idea from the labor demand side and called it the Substitution Axiom. The Axiom says, "From a firm's point of view, adult labor and child labor are substitutes. More specifically, child labor can be substituted by adult labor” (Basu and Van 1998, p. 416). There have been a number of studies which look at the substitution or complementarity of parents and children from the labor demand side. Grant and Hamermesh (1981) found that youth and women in the U.S. are close substitutes in production in the sense that increases in adult women in the labor market reduce the demand for young persons’ labor. Diamond and Fayed (1998) found that in Egypt, adult male and child labor are complements while adult female labor is a substitute for child labor. In other words, employers would hire both more men and more children but would hire more children instead of more women.1 We follow the labor supply rather than the labor demand approach and provide an empirical investigation of whether children's work and parents' work are substitutes or complements in the labor supply decisions of families in Bangladesh. We begin with the basic labor supply model. The theory suggests that individuals derive utility (U) from 1) the consumption of goods (C) that are purchased with labor income and nonlabor income and 2) leisure time (L), where L includes non-market time. That is, utility is a function of C and L. The individual’s goal is to select that combination of C and L that yields the maximum utility subject to constraints of the individual’s income and time. Applying the theory for empirical studies, labor economists model labor supply as a function of own wage, non-labor income, and own characteristics (such as age and education). This simple labor supply model has many deficiencies. Recent research on labor supply that incorporates the roles of the family focuses on a household labor supply
17 model instead of on an individual’s labor supply. Becker (1965) noted that goods can be either bought from the market or produced at home. For example, a cake can be bought from the store or can be made at home. The household must decide whether to buy the good from the market or spend time baking a cake. Thus, it becomes important for the household to know how to allocate the time of various members of the family. Gronau (1973, 1976) developed a household labor supply model. He noted that members in a family have comparative advantage in the production of home goods and market goods, thus they allocate their time accordingly. That is, household members who are relatively efficient in home production would spend time producing goods at home, and those who are efficient in the labor market would spend time producing goods in the market or generating income that can be used to purchase market goods. As a result, the labor supply decision of person i depends on the labor supply decisions of other household members. Gronau's model shows that a household derives utility according to U = U (M, H, L1, L2), where M is the amount of market goods, H is the amount of home goods, L1 is the amount of the husband’s leisure and L2 is the amount of the wife’s leisure. An important factor that distinguishes this utility from individual utility is the possibility of exchange that can take place within a family. The family maximizes this utility subject to the family budget constraint and time constraint. Thus, labor supply decisions of the husband and wife are not made independently but, rather, in the context of the household. The exchange or dependency can take place in the following way: If the wife works more hours in the labor market and the husband also starts working more hours, then the husband and the wife are complements in supplying labor in the market and at home. If the husband decreases market work in response to increased hours of work by the wife (or vice versa), then the husband and wife are substitutes in their labor supply in the market and at home. Goldin (1979) noted that this kind of modeling cannot be applied to households in nineteenth century America because the household composition then was different from what it is today. Nineteenth century American households included not only husbands and wives, but their children as well as non-nuclear relatives, servants, and boarders who could alter the labor supply decisions of the individual family members. By the same token, the traditional household labor supply model as developed by Becker (1965, 1991) and Gronau (1973, 1976) cannot be applied in the context of developing countries today, because most families in developing countries (specifically in Bangladesh) still have an extended family structure in which children may work. These families must consider the allocation of time by all family members, not only the husband and wife. Time spent at home or in the market is not an independent decision; rather, it depends on the decisions of other family members based on the comparative advantage of each family member. Moreover, the presence of non-nuclear relatives might affect the economic well-being of the entire family. In addition, parents’ decisions about their child’s education, work, or leisure can complicate the household utility function. Following Goldin (1979) and extending the model of husband’s and wife’s allocation of household member’s time developed by Gronau (1973, 1976), we incorporate children’s time in the model. We note that most fathers in our sample work in the market for pay and only a few women work for pay in the market. Therefore, like Goldin (1979), we can assume that each family maximizes the following utility function: U = U (Z, Lc), (1) where Z= household produced goods, Lc = leisure of the children. Z, in turn, is produced by X (purchased goods), Hm, mother’s time working at home, and Hc, children’s time working at home.2 Thus, (2) Z = f (X, Hm, Hc). The budget constraints are: X = McWc + MfWf + V (3) where Mc = market time of the children, Mf = market time of the father, Wc = wage of the
18 children, Wf = wage of the father, and V is nonlabor income fo the family. The time constraint is T = Mi + Li + Hi. (4) where i stands for father, mother, or children. The maximization solution allows us to estimate the child labor models described in the model section. It is well established in the literature that parents send their children to work in order to lessen the burden of family poverty (Amin et al. 2004; Basu and Tzannatos 2003; Delap 2001; Grootaert 1999; Ray 2000a and 2000b). Non-working children are luxuries that poor families cannot afford. Thus, it is reasonable to assume that parents send children to work to maximize the utility of the household. Children may also accompany a parent to work for cultural as well as economic reasons. Because children are unlikely to decide for themselves whether to work or not, child labor decisions are made within the context of households. In light of efforts to lessen the extent of child labor in developing countries, it is important to see whether children's work increases or decreases as a result of parents' income from market work. Given the number of studies concerning child labor in developing countries, it is surprising that very few contemporary empirical studies have addressed the substitutability or complementarity of parents and children. Results found in the handful of empirical research studies in this area pertain to nineteenth and earlier twentieth century America. Goldin (1979) found that the father's full-time wage was negatively related to a child's probability of work in Philadelphia in the 19th century, while the mother's wage did not have any significant impact on a child’s labor supply. Manacorda (2003) examined the effect of children’s work status on parents’ work and found a positive and significant correlation between children's and mother's labor supply in the U.S. in 1920.3 He explained that this was probably due to the fact that mothers’ and children's labor supply are affected by similar circumstances. After disaggregating by gender, however, Manacorda found that there was no differential effect of a boy’s or girl’s market work on father's labor supply. He found, further, that boys' work affected mothers' labor supply negatively, while girls' work did not. Holleran (1997) found that girls' and fathers' labor supply were substitutes, in the sense that when fathers’ income increased, girls' market work decreased in Carolina cotton mills in 1899. The correlation between boys' labor supply and fathers' income was not found to be significant. Similarly, Rotella (1980) did not find any substitution between children's earnings and women's labor force participation in 1890 America. To our knowledge there exists only one study which addressed this issue from the labor supply side using recent data from developing countries.4 Ray (2000a, 2000b) changed Basu and Van's Substitution Axiom to the household labor supply context and called it the “Substitution Hypothesis,” stating that child labor and adult labor are substitutes (Ray 2000a). He tested this hypothesis for Peru and Pakistan. He found that, in Peru, adult wages and child labor were significantly negatively correlated, suggesting that parents and children are substitutes in the labor market; in Pakistan, adult female wages and child labor were positively related, suggesting that they are complements. Our paper investigates whether parents' market work (as measured by their labor income) and child labor (market work and household work) are substitutes or complements in Bangladesh. Studies of Bangladesh have established that poverty is a determinant of child labor in that country (see Delap 2001 and Amin et al. 2004). We seek to add to this literature by determining whether child labor would increase or decrease as a result of parents' working. Our results have implications for selecting policies appropriate to lessening the extent of child labor. MODELS AND DATA The Data: As noted earlier, we use data from the 1995-96 Household Expenditure Survey of Bangladesh, carried out by the Bangladesh Bureau of Statistics
19 (BBS). These data were released for public use in 1998. The BBS followed a two-stage stratified random sampling technique under the framework of the Integrated Multipurpose Sample design. This design was developed on the basis of the Population and Housing Census of 1991. The focus of the HES was on the household, and data on all household members were collected. The BBS chose a total of 371 communities for the survey to be nationally representative, and they randomly selected twenty households from each of these communities. This procedure produced data for a total of 7,420 households with information on an individual’s location within Bangladesh, his or her economic activity, occupation, industry, age, educational background, household size, household income, household expenditures, consumption, changes in wealth, as well as health and sanitation. There are 11,367 children in the sample, aged 5 through 14, who live in 5,390 households. First, we divide the sample into sub-samples determined by age, gender, and urban or rural location. These divisions yield eight demographic groups. Younger children are 5 through 11 years of age; older children are 12 through 14. We make this distinction for three reasons. First, age 12 is a point where children may make a decision to continue with their education. Second, secluding girls from the public after puberty (around age 12) is important in Bangladesh. Third, our data show a marked increase in the proportion of children working at age 12. The Dependent Variable: We develop two models, one for children’s market work and one for children’s household work. The distinction between market and household work is important. Orazem and Gunnarsson (2004) noted that girls are more likely to work inside the home while boys are more likely to work outside the home. To determine whether a child is engaged in market work or not, we look at a survey question which asks about the child’s primary activity during the week prior to the survey. A respondent has the following choices: (1) employed and worked the previous week, (2) employed but did not work the previous week, (3) did household work, (4) searched for a job but did not find a job, (5) did not search for a job and had no interest in finding a job, (6) was a student, (7) was retired, (8) other. We define a child as working in the market (MKTWK equals 1) if the activity is (1) or (2) regardless of the past year’s income level or the activity is (4), (5), (6), (7), or (8) and income is greater than zero; otherwise, MKTWK equals zero. A child is defined as doing household work (HHWK equals 1) if the activity is (3), regardless of the income level; otherwise, HHWK is zero. The above definitions insure that market work and household work are mutually exclusive activities, since the choice of household work (activity 3) is excluded from the definition of market work.5 Independent Variables: There are eleven independent variables included in our models (See Appendix A, Table A1).6 We follow Goldin (1979), Horrell and Humphries (1995), Ray (2000a, 2000b), and Amin et al. (2004) in selecting independent variables. Our key variables are LFATHINC representing the natural log of father’s labor income, and LMOTHINC, representing the natural log of mother’s labor income.7 In rural families, father’s income constitutes 55 percent of family income and mother’s income makes up just under 7 percent of family income. In urban families, these figures are 59 percent and 8 percent, respectively. Positive labor income levels indicate, first, that the parent is engaged in market work. Furthermore, they reflect both the wage level for that work and the number of hours worked. Thus, when we find, for example, a negative relationship between child labor and father’s income, we can say that father’s status of working lessens the probability that the child will work. Further, we can conclude that the higher the level of labor income (reflecting both the wage level and hours worked), the lower the probability that the child will work. LNNONLIN is the natural log of non-labor income (for example, interest and rent). AGE is the child’s age in years, and EDUYR is the number of years of schooling
20 completed by the child. FATHEDU and MOTHEDU represent years of education of the father and mother, respectively. YNGCHLD represents the number of children in the household who are younger than the child in question; OLDCHLD is the number of children in the household who are older than the child in question. MALAD is the number of male adults (age 15 and over) in the household, and FEMAD is the number of female adults (age 15 and over).8 Tables 1 and 2 show that there is much similarity in these variables among the eight demographic “cases” but also some interesting differences. The most notable difference is in the rates of participation in market work and household work. Very few younger children participate in either of these activities, and the rates are particularly low for boys’ participation in household work. Girls are much more likely than boys to participate in household work. Older rural girls are especially likely to do this type of work, while urban girls show nearly the same rates of participation in market and household work. Older urban girls are much more likely to engage in market work than are older rural girls. TABLE 1 VARIABLE DEFINITIONS AND DESCRIPTIVE STATISTICS FOR THE RURAL MODELS: MEANS AND STANDARD DEVIATIONS (IN PARENTHESES) Dependent Variable MKTWK HHWK Independent Variables LFATHINC LMOTHINC LNNONLINC AGE EDUYR HEADFATH FATHEDU MOTHEDU YNGCHLD OLDCHLD MALAD FEMAD N
Boys Younger 0.07 (0.26) 0.005 (0.07)
Older 0.29 (0.46) 0.02 (0.15)
Girls Younger 0.05 (0.21) 0.03 (0.17)
Older 0.05 (0.22) 0.25 (0.43)
9.55 (2.76) 2.96 (4.16) 9.46 (1.30) 8.06 (1.81) 1.61 (1.72) 0.82 (0.38) 2.78 (3.78) 1.45 (2.56) 1.47 (1.20) 1.16 (1.02) 1.56 (1.03) 1.50 (0.81) 3,078
9.53 (2.98) 3.84 (4.31) 9.65 (1.24) 12.86 (0.86) 3.41 (3.18) 0.81 (0.39) 3.09 (3.95) 1.55 (2.63) 2.28 (1.62) 0.16 (0.39) 1.74 (1.09) 1.58 (0.84) 1,284
9.45 (2.89) 3.01 (4.20) 9.44 (1.31) 8.03 (1.81) 1.58 (1.76) 0.81 (0.39) 2.73 (3.80) 1.43 (2.61) 1.63 (1.29) 1.09 (1.03) 1.52 (1.03) 1.47 (0.78) 3,039
9.52 (3.10) 3.58 (4.38) 9.67 (1.34) 12.80 (0.83) 3.51 (3.24) 0.79 (0.41) 3.32 (4.14) 1.74 (2.85) 2.41 (1.63) 0.17 (0.41) 1.84 (1.17) 1.59 (0.86) 1,127
With regard to the independent variables, our demographic groups look very similar with a few exceptions. For both rural and urban children, the mean log of fathers’ income is much higher than the mean log of mothers’ income, regardless of the mean age of the children in the demographic group. Not surprisingly, the mean log of mothers’ income is higher for older children than for younger ones, since mothers are more likely to be at home caring for younger children rather than engaged in market work.9 Rural families have greater non-labor income, perhaps reflecting their land holdings. Both fathers’ and mothers’ mean education is higher in urban than in rural families. Family sizes are similar both in terms of the number of children in the family and the number of male and female adults. Our sample shows a heavier representation of rural than urban children, reflecting the distribution of the population of Bangladesh.
21 TABLE 2 DESCRIPTIVE STATISTICS FOR THE URBAN MODELS: MEANS AND STANDARD DEVIATIONS (IN PARENTHESES) Dependent Variable MKTWK HHWK Independent Variables LFATHINC LMOTHINC LNNONLIN AGE EDUYR HEADFATH FATHEDU MOTHEDU YNGCHLD OLDCHLD MALAD FEMAD N
Boys Younger 0.07 (0.25) 0.002 (0.046)
Older 0.27 (0.44) 0.01 (0.11)
Girls Younger 0.08 (0.27) 0.03 (0.18)
Older 0.13 (0.34) 0.14 (0.35)
9.76 (2.98) 3.44 (4.47) 8.16 (3.49) 8.11 (1.85) 1.94 (1.90) 0.81 (0.39) 5.52 (5.05) 3.58 (4.13) 1.27 (1.07) 1.04 (1.02) 1.59 (1.09) 1.57 (0.95) 943
9.77 (3.31) 3.96 (4.64) 8.54 (3.44) 12.90 (0.86) 4.69 (3.36) 0.74 (0.44) 5.94 (5.29) 3.63 (4.12) 1.82 (1.47) 0.21 (0.46) 1.90 (1.22) 1.82 (1.07) 468
9.78 (2.95) 3.67 (4.56) 8.18 (3.47) 8.12 (1.87) 1.80 (1.97) 0.79 (0.41) 5.20 (5.04) 3.38 (3.97) 1.37 (1.13) 1.06 (1.03) 1.62 (1.14) 1.59 (1.02) 960
9.96 (3.20) 4.15 (4.68) 8.90 (3.20) 12.90 (0.85) 4.60 (3.45) 0.74 (0.44) 5.68 (5.30) 3.81 (4.26) 2.12 (1.48) 0.23 (0.48) 1.85 (1.22) 1.81 (1.14) 468
Method and Model. We apply logistic regression techniques to determine the way in which the independent variables discussed above influence the probability that a child will be working in the market or doing household work. Because the dependent variable is binary, OLS estimates are not ideal. Instead, we use a logit model to estimate work status equations (Studenmund, 2001, p. 436.) We report marginal effects (partial derivatives) of each independent variable, evaluated at their means, as well as estimated coefficients and their standard errors and significance levels. The marginal effect of the probability of a particular independent variable is calculated as δP(y=1)/ δx = βP(1-P), where X is the independent variable, β is the logit estimate, P is the probability that y equals one, and (1P) represents the probability that y is zero (Maddala 1988, Liao 1994, Allison 1999). Some children in our sample are in the same households and, therefore, do not constitute independent observations; thus, standard errors of the coefficients have been corrected for clustering. The logistic regression model for market work is as follows: P(MKTWK) = b0 + b1 LFATHINC + b2 LMOTHINC + b3 LNNONLIN + b4 AGE + b5 EDUYR + b6 FATHEDU + b7 MOTHEDU + b8 YNGCHLD + b9 OLDCHLD + b10 MALAD + b11 FEMAD + error (5) Similarly, the logistic regression for household work is as follows: P(HHWK) = b0 + b1 LFATHINC + b2 LMOTHINC + b3 LNNONLIN + b4 AGE + b5 EDUYR + b6 FATHEDU + b7 MOTHEDU + b8 YNGCHLD + b9 OLDCHLD + b10 MALAD + b11 FEMAD + error (6)
22 RESULTS AND DISCUSSIONS Children’s Market Work The impact of parents’ income. Our logistic regression results for market work of rural boys and girls are shown in Table 3 which reports the coefficient of each variable in the estimated equation, its standard error and statistical significance, and the marginal effect of a one-unit change in each independent variable on the probability that a child will be engaged in market work.10 With respect to our key variables, father’s and mother’s income, we find that, in three of the four rural demographic groups, father’s income is statistically significant and negatively related to whether a child performs market work. Thus, rural fathers and their younger sons as well as their younger and older daughters are substitutes in the family labor supply framework. The father’s work provides income that allows these young children to be at home or at school rather than doing market work. For older rural boys, on the other hand, a father’s work and a boy’s market work are complements. A father’s income is positively related to an older rural boy’s status of engaging in market work. One explanation for this result is that older rural boys are much more likely to work (29 percent of them work as seen in Table 1) than are other rural children (5 to 7 percent of whom work). It is not surprising, therefore, to see that older rural boys participate in working along with their fathers. This result is consistent with the evidence found from Pakistan and Ghana (Bhalotra and Heady, 2003). One explanation of this as noted in the literature is that, as the families become wealthier, they own more land. More land requires more labor. The richer families can hire other adult labors; but in the presence of moral hazard with hired labor and imperfect land and labor markets in the rural areas, richer families might prefer to have their own older children do the work. Our results for urban children (shown in Table 4) are weaker than those for rural children. Only for younger urban boys is father’s income related to the child’s probability of doing market work, and the sign is negative. Younger boys’ market work substitutes for fathers’ work and income; a young urban boy from a household in which the father works has a lower probability of engaging in market work. For urban girls, both younger and older, the father’s income from market work has no bearing on a girl’s probability of doing market work.
23 TABLE 3 MARKET WORK: LOGISTIC REGRESSION RESULTS FOR THE RURAL MODELS Variables
Intercept
Boys Younger Logit Coeff.a -4.112*** (0.793)
MEb
LFATHINC
-0.136*** (0.034)
0.004
LMOTHINC
0.1634*** (0.025) -0.304*** (0.069)
0.004
0.461*** (0.053) -0.250*** (0.055)
0.013
FATHEDU
-0.071** (0.031)
0.002
MOTHEDU
-0.143** (0.061) -0.341*** (0.076)
0.004 0.009
0.269*** (0.085) 0.745*** (0.107) 0.260** (0.119) 3,078 -592.30
0.007
LNNONLIN
AGE EDUYR
YNGCHLD
OLDCHLD MALAD FEMAD
0.008
0.007
0.021 0.007
Older Logit Coeff. 6.881*** (1.329) 0.115*** (0.032)
0.055
Girls Younger Logit Coeff. 2.542*** (0.926) 0.216*** (0.037) 0.151*** (0.030) 0.241*** (0.065) 0.180*** (0.070) -0.015 (0.075)
0.015
-0.036 (0.039)
0.0005
-0.090 (0.063)
0.002
0.010 0.027
-0.067 (0.059) 0.746*** (0.121) 0.116 (0.101) 1.106*** (0.108) 0.182 (0.133) 3,039 -395.95
-0.001
-0.187 (0.130) 0.433*** (0.112) 0.570** (0.291) 0.567*** (0.149) 0.595*** (0.199) 1,127 -176.10
0.004 0.009
ME
0.019
0.168*** (0.020) -0.070 (0.068)
0.029
0.493*** (0.090) 0.324*** (0.033) 0.087*** (0.030) -0.058 (0.050) 0.160*** (0.052) -0.250 (0.254) 0.061 (0.087) -0.023 (0.101) 1,284 -591.95
0.084
0.012
0.043 0.010 0.004
ME
Older Logit Coeff. -3.218 (2.565)
ME
0.146*** (0.055) 0.032 (0.040) -0.178 (0.114)
0.003
0.0002
0.167 (0.187) -0.038 (0.055)
0.003 0.001
-0.003
0.002 -0.003
0.002
-0.010
0.002 0.014 0.002
0.007 0.004
0.010 0.010 0.01
N Log pseudolikelihood Chi-square 310.55*** 219.50*** 206.53*** 81.08*** Pseudo R2 0.27 0.24 0.31 0.20 a Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. b Marginal effects (ME) are calculated as δP(y=1)/ δx = βP(1-P). MEs are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level.
24 TABLE 4 MARKET WORK: LOGISTIC REGRESSION RESULTS FOR THE URBAN MODELS Variables
Intercept LFATHINC LMOTHINC LNNONLIN AGE EDUYR FATHEDU MOTHEDU YNGCHLD OLDCHLD MALAD FEMAD N Log pseudolikelihood Chi-square Pseudo R2
Boys Younger Logit Coeff.a -4.656*** (1.123) -0.1223** (0.052) 0.137*** (0.036) -0.051 (0.047) 0.341*** (0.107) -0.161* (0.099) -0.048 (0.053) -0.092 (0.082) -0.476*** (0.142) 0.186 (0.133) 0.529*** (0.148) 0.078 (0.153) 943 -186.93 98.50*** 0.20
MEb
0.004 0.004 0.002 0.011 0.005 0.002 0.003 0.015 0.006 0.017 0.002
Older Logit Coeff. -8.031*** (2.583) -0.039 (0.062) 0.169*** (0.035) -0.124*** (0.043) 0.677*** (0.196) -0.309*** (0.057) -0.090* (0.049) -0.240*** (0.078) -0.042 (0.107) 0.648** (0.289) 0.564*** (0.148) -0.125 (0.186) 468 -160.60 125.46*** 0.41
ME
0.004 0.017 0.013 0.069 0.032 0.009 0.025 0.004 0.067 0.058 0.013
Girls Younger Logit Coeff. -7.180*** (0.973) 0.019 (0.055) 0.177*** (0.041) -0.088* (0.047) 0.554*** (0.094) -0.338*** (0.114) -0.051 (0.063) -0.274*** (0.090) -0.535*** (0.147) 0.361* (0.194) 0.568*** (0.141) -0.091 (0.189) 960 -173.54 128.18*** 0.33
ME
0.0003 0.003 -0.002 0.010 -0.006 -0.001 -0.005 -0.010 0.007 0.011 -0.002
Older Logit Coeff. 0.639 (2.635) -0.010 (0.064) 0.081** (0.042) -0.004 (0.053) -0.094 (0.193) -0.146*** (0.054) -0.105* (0.052) -0.113 (0.087) -0.264** (0.123) -0.374 (0.358) 0.084 (0.170) 0.062 (0.150) 468 -152.01
ME
-0.001 0.006 0.0003 -0.007 -0.011 -0.008 -0.008 -0.019 -0.027 0.006 0.005
51.44*** 0.18
a
Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. b Marginal effects (ME) are calculated as δP(y=1)/ δx = βP(1-P). MEs are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level.
Unlike late nineteenth century America, we find that father’s altruistic behavior is pervasive in rural Bangladesh.11 The higher the father’s income level, the lower the probability that a child will engage in market work. Only for older rural boys are fathers and boys complements. Because the boys in this sample are aged 12 through14, it is likely that fathers are older, too. As a result, it is not unexpected that the fathers would take these boys to work with them and teach them on the job in an apprenticeship fashion. Thus, seeing fathers’ and sons’ work as complementary is not a deviation from the father’s altruistic behavior. In addition, according to Humphries (2003), "even altruistic parents may send their children to work (Humphries, 2003; p. 184)." This is especially true when the family is very poor. To meet the requirements of the household, child labor becomes necessary for the survival of the family.
25 In seven out of eight demographic groups of rural and urban children, mothers and children are complements with regard to children’s market work status; the sign on the coefficient of the variable LMOTHINC is positive and statistically significant. (The exception is for older rural girls, where a mother’s income has no effect on her daughter’s work status.) Complementarity between mother’s and children’s work was also found in Pakistan by Ray (2000a, 2000b). For older boys and girls, the complementarity between mother’s work and children’s work arises out of necessity. Typically, a low household income level that requires the mother to work would necessitate that older children work also. In addition to the poverty argument of complementarity between mother’s work status and children’s work, there is also the mother’s desire and need for her younger children to accompany her to work. The absence of in-home or other child care arrangements may prompt a mother to take her child to work with her, and the child may perform some tasks at the workplace. Delap (2001) noted that many mothers would not want to leave their children home alone, because of their fear that children might participate in criminal activities or become victims of such activities. Although it is feasible for younger urban boys to find independent jobs, this would not be true for younger girls. Moreover, the practice of purdah (the veiling of women in public) pushes mothers to take their older girls with them if they are working in primarily women’s occupations such as maids and garment workers. Our results of complementarity between mother’s and children’s work are in direct contrast to Goldin’s (1979) finding for late 19th century Philadelphia. In that setting, children were the first source of labor income after the father. Children and mothers were found to be substitutes, not complements, in the household labor supply decision. The contrast between Goldin’s findings and ours underscores the importance of the social and cultural milieu in which family labor supply is examined. The impact of child and family characteristics on children’s market work. In addition to parents’ income, another important family income component is non-labor family income (LNNONLIN), in other words income from assets. This component is not trivial. In rural families it accounts for 36 percent of total family income, and in urban families it constitutes just under 30 percent of family income.12 We would expect that, in families with greater income from assets, the probability of a child doing market work would be lowered; the sign on the coefficient of the variable LNNONLIN would be negative. This is the case in rural families for two of the four demographic groups: younger rural boys and younger rural girls. For these rural children, non-labor family income allows them to stay at home or go to school rather than participating in market work. Similarly, non-labor family income has a significant negative effect on the market work status of older urban boys and younger urban girls; the greater the level of income from assets, the less likely an older urban boy or younger urban girl will engage in market work. Instead these children will be able to continue their education or stay at home. This, too, reflects altruistic behavior on the part of parents; they save so their children do not have to do market work. The remaining control variables in the estimated logistical regression equations include child characteristics and other family characteristics. Following Horrell and Humphries (1995), a child’s age is included to represent availability for work. The sign on the coefficient of AGE is expected to be positive, since older children are more likely to be available for work. As expected, the coefficient on AGE is positive and statistically different from zero for rural and urban boys and for younger rural and urban girls. For older rural and urban girls, however, age has no bearing on their likelihood of doing market work. Given age, the years of education a child has acquired (EDUYR) are expected to be negatively related to the probability of being engaged in market work.13 This result is expected simply because, for any given age, more years of education indicate a greater chance that the child is still in school and has not dropped out to pursue market work.
26 Our findings are consistent with expectations with the exception of rural girls who have a very low probability of engaging in market work (only 5 percent do market work). For six of the eight demographic groups, higher levels of education, given one’s age, indicate a greater probability of still being in school and a smaller chance of being engaged in market work. Family characteristics, in addition to parents’ and non-labor family income measures, are also expected to influence a child’s probability of doing market work. We expect both father’s and mother’s education levels to influence family decisions involving children’s work (and schooling) choices. Specifically, higher education levels, especially for mothers, are expected to lower the probability that a child would be engaged in market work (Grootaert 1999, Hossain 1996, Ray 2000a, Amin et al. 2004). Higher levels of father’s education do lessen the probability of market work for rural boys but not for rural girls. Older urban boys and girls also benefit from the effect of higher father’s education. Higher educational levels of the mother are seen to deter the child’s participation in market work for younger rural boys, older urban boys, and younger urban girls. The limited effect of parents’ education levels on children’s market work status is surprising in light of other studies. Gronau (1973, 1976) and Horrell and Humphries (1995) noted that an individual child’s labor force participation decision will be influenced by each family member’s comparative advantage in the labor market. We capture this effect by following Goldin (1979) and looking at the number of children in the household who are younger and older than the child in question and by the number of male and female adults (in addition to parents) in the family. With respect to the number of children in the family, we would expect older children, particularly older girls, to bear some child care responsibilities. If so, the sign on YNGCHLD would be negative, since a larger number of younger children would imply that the child in question would be less likely to do market work and more likely to stay at home and care for younger children. This is the case for all rural children (Table 3) and for urban children with the exception of older urban boys. There are two possible outcomes regarding the number of older children in the household (OLDCHLD). First, a larger number of older children may imply that these older children will take on child care and income earning activities allowing the child in question to remain out of market work. If this were the case, the sign on the coefficient of OLDCHLD would be negative. Alternatively, larger family size as implied by more older children may require market work by more children in the family. Additionally, having more older children may mean that older children, as well as mothers, take younger children to work with them and that these younger children engage in market work tasks. In these cases, the sign on the coefficient of OLDCHLD would be positive. Our results show that when the coefficient of OLDCHLD is statistically significant, it is positive. This is true for half of the eight demographic cases: younger rural boys, older rural girls, older urban boys, and younger urban girls. These are the cases in which having more older children in the household means that the child in question is more likely to engage in market work. There are two alternative expectations regarding the way in which the number of adults in a household might influence the probability of market work for children. If male and female adults in the family (for example, older brothers, sisters, cousins, aunts or uncles) are working, this might allow children to remain free from market work and, instead, attend school or do household work. In this case the signs on the coefficients of MALAD and FEMAD would be negative. Alternatively, if the other adults are not working (as would be the case for children 15 and over who are still in school and for elderly grandparents) or if they are in an extended family for cost-sharing purposes, then the presence of other adults might put greater pressure on children to be engaged in market work. Having more mouths to feed within the household would require children to work in order to supplement family income. In this case, the signs on the coefficients of MALAD and FEMAD would be positive.
27 Tables 3 and 4 show that when the coefficient on MALAD is statistically significant, the sign is positive. This is true for all demographic groups except older rural boys and older urban girls. For six demographic groups, having an additional male adult in the household increases a child’s probability of engaging in market work. This is consistent with the knowledge that older adults contribute little to family income; thus, extended families tend to require market work by children. The influence of the number of adult females in the family is quite different. For only one demographic group (younger rural boys) does the number of adult females influence the child’s probability of working and the influence is positive. As with adult males, having more adult females in the family is associated with a higher labor force participation of younger rural boys.14 Children’s Household Work A key feature of this study is that we separate children’s market work from household work. We do this anticipating that these two different sorts of “child labor” may be influenced differently by parents’ labor income and by other child and family characteristics. Table 5 shows the results of the logistic regressions. Our results apply only to girls, because the few cases of rural and urban boys engaged in household work (see Table 1) make it impossible to estimate labor supply equations for them. This is not peculiar to Bangladesh. Connelly, DeGraff, and Levison (1996) find that in Brazil teenage girls do more household chores, especially when there are younger siblings and their mother is working. Tables 1 and 2 show that, for Bangladesh, the greatest participation in household work comes from older rural and urban girls. A child is defined as being engaged in household work if, regardless of income, he or she answered the survey question by indicating that the main activity the previous week was household work. In developing countries, particularly in rural areas, domestic chores can be extremely time consuming. Given household technology (for example, no running water, no refrigeration, or no prepared foods) and household composition, there is a minimum amount of work that family members need to do to keep the household running (Levison and Moe, 1998). The choices for girls in our study would be to do household work, to do market work, or to attend school. We have established through our definitions that market work and household work are substitute activities for children and that household work and school are also substitutes as primary activities for children. Our key interest is in whether father’s and/or mother’s income (and, hence, work status) help to explain girls’ engagement in household work as their main activity. Parents may earn income from market work although such work might be performed in the home and then sold on the market. Using data from the HES, we cannot distinguish exactly where the parent works. We might expect a girl to be engaged in household work alongside her mother if the mother is not working or is doing market work at home. Thus, we cannot predict the direction of influence of mother’s income on girls’ household work. Similarly, the influence of father’s income on girls’ engagement in household work is not clear. To the extent that a working father implies a more economically stable household, we might expect girls to be free from any work, market or household, and able to attend school. Thus, a father’s income and a daughter’s household work would be substitutes in the family labor supply decision. If mothers are engaged in market work, we might expect daughters to follow them to the workplace or to do market work with them at home. Or perhaps the mother might do market work at home and leave the household chores to the daughter. The substitutability or complementarity of a mother’s work and her daughter’s household work is not clear a priori.15 Literature on discrimination against girls suggests that when a mother performs a job outside of the home, she removes her daughters from school so that they can do the household work. When her labor income rises sufficiently, she may be financially able to send the girls back to school. Thus, the relationship between mother's wage and the rate of girl's participation in household work could be in the form of an inverted U.16
28
TABLE 5 HOUSEHOLD WORK: LOGISTIC REGRESSION RESULTS FOR GIRLS ONLY Variables
Intercept
LFATHINC LMOTHINC LNNONLIN AGE EDUYR
FATHEDU
MOTHEDU YNGCHLD OLDCHLD MALAD FEMAD N Log pseudolikelihood Chi-square Pseudo R2
Rural Younger Logit Coeff.a 9.892*** (1.252) 0.025 (0.054) 0.073** (0.034) 0.015 (0.095) 0.758*** (0.092) 0.538*** (0.122) -0.142** (0.058) -0.330** (0.147) 0.177* (0.093) 0.064 (0.153) -0.029 (0.124) -0.279 (0.185) 3,039 -308.92 167.21*** 0.26
MEb
0.0001 0.0005 0.0001 0.005 -0.004
-0.001
-0.002 0.001 0.0004 0.0002 -0.002
Older Logit Coeff. 8.085*** (1.481) -0.019 (0.040) 0.036 (0.025) -0.012 (0.067) 0.673*** (0.107) 0.409*** (0.037) 0.113*** (0.037) -0.118** (0.061) -0.049 (0.063) 0.004 (0.263) 0.093 (0.101) -0.090 (0.124) 1,127 -463.05 190.22*** 0.27
ME
0.002 0.005
Urban Younger Logit Coeff. -10.855*** (1.776)
ME
0.0001 0.0001 0.0002
0.052
-0.016 (0.082) -0.029 (0.050) 0.059 (0.068) 0.861*** (0.157) -0.827*** (0.202)
0.014
-0.040 (0.092)
0.0001
0.015 0.006 0.001
-0.229* (0.131) 0.032 (0.233) -0.324 (0.296) 0.541*** (0.199) -0.088 (0.218) 960 -88.26
-0.001
0.002 0.086
0.012 0.011
109.96*** 0.39
0.003 -0.003
0.001 -0.001 0.002 0.0003
Older Logit Coeff. 6.720*** (2.335) -0.005 (0.058) 0.006 (0.039) 0.019 (0.044) 0.496*** (0.179) 0.227*** (0.056) -0.057 (0.066) -0.136 (0.088) -0.089 (0.092) 0.112 (0.328) -0.011 (0.148) -0.093 (0.148) 468 -153.76
ME
0.0004 0.0004 0.001 0.037 -0.016
-0.004
-0.010 -0.007 0.008 -0.001 -0.007
54.58*** 0.19
a
Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. Marginal effects (ME) are calculated as δP(y=1)/ δx = βP(1-P). MEs are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level.
b
The logistic regression results substantiate the ambiguous connections between parents’ income from market work and girls’ involvement in household work. Table 5 shows that for only one of the four demographic groups, younger rural girls, is there a statistically significant relationship. If the girl is in a household in which the mother engages in market work, the young rural girl is more likely to be engaged in household
29 work. The girl may be doing chores that the mother would do were she not working (either at home or elsewhere). This is consistent with what Levison and Moe (1998) find in Peru. Another family income source, income from assets, has no bearing on a rural or urban girl’s household work status. The child characteristics measured by AGE and EDUYR do influence a girl’s housework status. The older a girl is, the greater the probability that she will be engaged in household work; this applies to all four groups of girls. The more years of education she has (given her age), the less likely she is to be engaged in household work. This result indicates that she is more likely to still be in school or possibly to be involved in market work. (Recall that, according to our definitions, household work and market work are substitute activities as are household work and school.) Other child and family characteristics are helpful in explaining whether a girl is engaged in household work as her primary activity. Mothers’ and fathers’ education levels make a difference for rural girls. As the educational level of the parent increases, the probability that a younger or older rural girl will engage in household work decreases. Mother’s education has this same effect for younger urban girls. In these cases, more educated parents are more likely to choose school over household work for their daughters. The number of younger and older children and the number of male and female adults in a household rarely has an effect on the household work status of girls. One exception is the case of younger rural girls. For these girls, a greater number of younger children means a greater likelihood that the girl in question will be engaged in household work. This seems logical considering the greater amount of household work to be done in a family with more young children. The number of adult females in the household, has no bearing on the probability that a girl will engage in household work as her primary activity. The number of adult males in the household increases the likelihood that a young urban girl will perform household work. Thus, in only one of eight cases (four demographic groups times two adult variables) does the number of adults in the household influence a girl’s probability of doing household work.17 Our results are to some extent consistent with Levison and Moe (1998) in that we, too, find girls doing more household chores when the household has more younger siblings or adult males. In summary, we find that parents’ income does very little to explain the participation of Bangladeshi girls in household activities, and non-labor family income has no effect. Some social and demographic variables (a girl’s age, education, and parents’ education level) are important in explaining why girls may be engaged in household labor. SUMMARY AND POLICY IMPLICATIONS This study of rural and urban children in Bangladesh shows that, when the relationship between father’s income and children’s market work status is statistically significant, the conclusions are mixed. Fathers and children tend to be substitutes in the family labor supply model for younger rural boys and girls and for younger urban boys. When fathers are working, the family may be more likely to enjoy a level of economic security such that children do not need to engage in market work. One notable exception is that rural fathers and their older sons are complements in the family’s labor supply decision. This may reflect the culture of Bangladesh where it is expected that young rural males will be working out of the household by age twelve, perhaps alongside their fathers who teach them through on-the-job training. For the remaining three demographic groups, fathers’ income from market work has no bearing on the child’s market work. Our results show that mothers and children tend to be complements in the family’s market labor supply decision. This is the case for seven of the eight demographic groups. Our explanation for these results is twofold. First, according to
30 Basu and Van’s luxury axiom, families will send children into the labor market only if family poverty warrants it; having a working mother within a family is an indication of such poverty. Second, even if the mother is earning income sufficient to lessen the family’s economic needs, social customs and mores dictate that boys should be kept busy in order to stay out of trouble and girls should not be left home alone if a mother is working. Both of these cultural factors suggest that having a working mother would be associated with having children work as well, often alongside their mothers. In addition, there is some indication from our study that higher levels of nonlabor income provide a level of economic security sufficient to protect children from the need to engage in market work. Some other control variables, such as a child’s age and educational level, the number of younger and older children in the family have expected influences on the probability that a child will do market work. In addition, the greater the number of adult males (and in some cases adult females) in the household, the greater the probability that children will do market work. We argue that the effect of having more mouths to feed outweighs the limited income contribution of these adults. Two control variables, mother’s and father’s education, have some influence on children’s market work but less than we might expect based on other studies. Our attempt to estimate household work equations for all children was thwarted by the small numbers of boys engaged in household work. We estimate the household work status equations only for girls. In doing so, we find that mother’s and father’s income from work have little bearing on girls’ participation in household work. Other factors such as the child’s age and education and the parents’ educational levels were sometimes helpful in predicting the probability that a girl would do household work. The demographic make-up of the family in terms of the numbers of older and younger siblings and male and female adults rarely explains girls’ household work status. The results of this study augment previous studies of child labor in Bangladesh, in that this study concentrates on the issue of children’s market work separately from household work. We can see that some factors which encourage parents to keep children out of market work, most notably parents’ income, do not have a strong effect on children’s participation in household work. We are able to confirm that fathers and children may be substitutes or complements in market work, while mothers and children are complements. This finding suggests that family income stability may be an important factor in allowing children to be free from market work. If the family’s income status is such that mothers must work (as they do in about a third of rural households and more than 40 percent of urban households), then children are more likely to engage in market work as well. Although the income of a working mother adds to the family’s financial well being, it is not sufficient to deter a child’s market work either for financial reasons (because the mother’s income is minimal) or for cultural reasons (because children accompany mothers to work or join them in doing market work at home). We note that when a higher income level permits a family to save and to earn income from their savings, then child labor is lessened. These findings should be helpful in designing policies intended to deter child labor in Bangladesh, because they demonstrate the importance of the economic and demographic variables as well as social and cultural context in which labor markets operate. Banning child labor without addressing the underlying cause would help neither the children nor the families, especially in cases where the poverty level of the family necessitates some income contribution from children. Researchers studying child labor have reached near consensus that trade sanctions against countries using child labor fail to reduce child labor. Such trade bans have been shown to have the unintended consequence of pushing children into dangerous jobs (see Bissell and Sobhan 1996; Rahman, Khanam, and Absar 1999). Moreover, trade bans apply only to exported goods and, generally, only to urban children. We find that, as income increases, child labor decreases; thus, eliminating poverty could help deter child labor. One approach is suggested by Ranjan (2001), who
31 proved that inefficient child labor arises due to credit constraints. Thus, making credit available could help families lessens both poverty and child labor. In Bangladesh, microcredit has had a substantial impact in eliminating poverty and increasing employment, especially for women who tend to invest more in their children’s education (Wahid 1999, Haddad 1996). Another government policy to deter child labor would be to lessen the extent of income inequality within the country. This is suggested in light of research by Ranjan (2001) who found a positive relationship between income inequality and child labor. We argue, following Parsons and Goldin (1989) and Goldin (1979), that as was the case in nineteenth century America, child labor in developing countries would decrease with technological improvements. Parsons and Goldin (1979, p. 657) noted that, “advances in industrial technologies find little value in the unskilled labor of children.” Goldin (1979) indicated that there were many reasons to believe that in the industrial world, child labor laws were passed and were effective only after child labor started to decline due to technological progress in these countries. Encouraging science and technology in the third world, thus, would help eliminate child labor. Another policy area is the promotion of education as an alternative to child labor, as examined by Ravillion and Wodon (2000). Although they found that participants in the plan increased their schooling, there was not a commensurate decrease in child labor. Other policies to encourage schooling might have more promising results, with a goal of combining light work with schooling. Still another policy approach would address the fact that urban mothers tend to take their children to work with them for cultural reasons. Programs might be developed to provide day care or education for children at the parent’s place of work or nearby. The initiation of these suggested policies should be accompanied by follow-up case studies to determine the consequences, intended and unintended, on children’s work behavior and family well-being.
32 APPENDIX TABLE A1 VARIABLE DEFINITIONS Dependent Variable MKTWK HHWK Independent Variables LFATHINC LMOTHINC LNNONLIN AGE EDUYR FATHEDU MOTHEDU YNGCHLD
1 if the child works in the market (paid or unpaid), 0 otherwise 1 if the child works in the household (paid or unpaid), 0 otherwise Log of father's labor income Log of mother's labor income Natural log of non-labor income of the household Child's age in years Child's years of education completed Father's years of education Mother's years of education Number of children in the household younger than the child in question Number of children in the household older than the child in question Number of other adult males (age 15 and over) in the household Number of other adult females (age 15 and over) in the household
OLDCHLD MALAD FEMAD
TABLE A2 FATHERS’ AND MOTHERS’ WORK STATUS AND LABOR INCOME Demographic Groups Rural Boys Younger Rural Boys Older Rural Girls Younger Rural Girls Older Urban Boys Younger Urban Boys Older Urban Girls Younger Urban Girls Older
Proportion Engaged in Market Worka Fathers Mothers 0.93 0.34 (0.25) (0.48) 0.92 0.45 (0.27) (0.50) 0.92 0.35 (0.27) (0.48) 0.91 0.41 (0.28) (0.49) 0.92 0.38 (0.26) (0.49) 0.91 0.43 (0.26) (0.50) 0.93 0.49 (0.26) (0.49) 0.91 0.45 (0.28) (0.50)
Annual Labor Incomeb Fathers Mothers 40,504 4,650 (59,170) (16,233) 43,249 5,871 (45,697) (17,244) 38,843 5,102 (45,154) (16,962) 46,744 6,201 (55,119) (17,395) 71,057 8,863 (157,180) (31,108) 82,129 11,418 (149,997) (35,054) 70,683 10,504 (169,510) (34,414) 89,917 11,425 (107,200) (34,899)
Notes: a Means and standard deviations (in parentheses). Means and standard deviations (in parentheses) expressed in Bangladeshi takas.
33 ENDNOTES *We wish to thank Imam Alam, Lisa Jepsen, and an anonymous referee for their helpful comments. The College of Business at the University of Northern Iowa provided support to Shahina Amin for this research through reassigned time. 1 In looking at adults and children as substitutes from the labor demand perspective, it is instructive to consider the jobs children perform in Bangladesh. According to Amin et al. (2004), some children’s market jobs are primarily urban (transport and communications workers, for example); some, such as agricultural jobs, are mostly or completely rural. Some children’s jobs are mostly or completely done by boys (salesmen/businessman, transport and communications worker, and day laborer); there are no job classifications completely represented by girls. Urban boys tend to work as salesmen/businessmen, specifically as shop assistants, street vendors, and tea and food vendors. Urban boys also work as assistants in match, biscuit, shrimp, cigarette, and salt processing factories and in tanneries as well as in garment, hosiery, and small engineering factories (Rahman, Khanam, and Absar 1999). Boys are found in fewer numbers working as porters, cycle rickshaw pullers, ticket sellers (transport workers), servants, and day laborers. Boys work in the informal sector of the urban economy crushing stone and collecting firewood. Rural boys’ jobs are primarily agricultural with some rural boys selling items and some younger rural boys working as servants. Most girls work in production, especially in garment factories. Girls also work as maids and domestic servants. Most rural girls work as domestic servants and maids, while some rural girls, especially older girls, work in the agricultural sector. A few rural girls work as vendors or in production jobs. 2 The notations closely follow Goldin (1979). 3 Note that we are looking at the opposite causation, namely the effect of parents’ work status on choices about children’s work. 4 Basu and Tzannatos (2003) present a transition from Basu and Van’s (1998) Substitution Axiom where adult and child work are substitutes from the labor demand viewpoint to a theoretical model in which adults and children are substitutes in supplying their labor. The authors do not undertake an empirical test of their theory. 5 The definitions of market and household work have a bearing on the substitutability of work and schooling. First, a child who engages in market work may also be in school (activity 6 and positive income). Thus, market work and schooling may be substitute or complementary activities. If the child’s primary activity is household work, then it cannot be school; thus, household work and schooling are mutually exclusive primary activities and can be viewed as substitutes. 6 All variables have been examined for multicollinearity in all eight regression models, and no problems were found. 7 We follow Goldin (1979), Ray (2000a, 200b), and Horrell and Humphries (1995) in using a measure of parents’ earnings. We convert our annual labor earnings measures to natural logs in order to control for the variance of earnings. 8 Some authors (Ray 2000b, Amin et al. 2004) include a variable to indicate the gender of the household head. We have excluded this variable because the log of father’s income is not independent of the gender of the household head, according to a statistical test. 9 Table A2 provides descriptive data on the percent of parents who engage in market work (i.e., whose labor income is positive) as well as data on annual labor income measured in Bangladeshi takas for the eight demographic groups. 10 Marginal effects are interpreted as follows using Table 3. The marginal effect of AGE on the
34 probability that a young rural boy will be engaged in market work is 0.01, meaning that a one-year increase in age will add 0.01 to the probability. 11 Parsons and Goldin (1989) found that parents in nineteenth century America did not demonstrate altruism toward their children in terms of child labor. They found that, even in rich families, child labor was common. 12 Parents’ labor income plus non-labor income accounts for 98 percent of rural families’ income and 97 percent of urban families’ income. The remainder of family income is contributed by children and other adults (in addition to parents) living in the household. 13 Due to reporting errors, some children showed years of education exceeding age minus 4 (the earliest age at which formal education could begin). For these cases, we defined EDUYR as Age minus 4. 14 In response to an anonymous referee’s suggestion, we examined the question of whether the presence of more adults in the household would affect the influence of mother’s income on the child’s probability of engaging in market work. The results were disappointing. For only two of sixteen possible cases (two variables, MALAD and FEMAD, and eight demographic groups shown in Tables 3 and 4) did the number of adults alter the influence of mother’s income on child labor. For older rural boys, an increase in the number of adult males increases the probability that the boy will engage in market work but lessens the complementary effect of mother’s income on child’s market work. For younger urban girls, the number of female adults in the family has no direct impact on a girl’s likelihood of working but lessens the complementary effect of mother’s income on girls market work. Because there were so few significant cases using interaction variables, the full regression results are not reported in this paper. They are available from the corresponding author upon request. 15 Deere (1983) finds that in Peru, until a girl is about 10 years old, she works in the household alongside her mother. After that age, young girls work independently in the household in such activities as cooking, thus allowing the mother to perform other types of work. 16 We thank an anonymous referee for pointing out this relationship. 17 An examination of interaction effects between the number of male and female adults in the family and mother’s income revealed two significant cases. First, the greater the number of female adults in the family, the less is the impact of mother’s market income on a girl’s probability of doing household work. Perhaps the adult females undertake the household responsibilities and, thus, lessen the need for girls to do household work when the mother works as was found to be the case in Peru (Levison and Moe, 1998). Second, the greater the number of males in the household, the greater the effect of mother’s market income on the probability that older urban girls will be engaged in household work. Perhaps the greater number of adult men creates a greater need for household work and increase the requirements for older urban girls to perform this work when their mothers are engaged in market work activities. It should be noted that no interaction effects are observed for the remaining six of the eight cases. Again, detailed statistical results are available upon request.
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