Poverty and other determinants of child labor in Bangladesh.1. Introduction
Americans have demonstrated strong disapproval of child labor child labor, use of the young as workers in factories, farms, and mines. Child labor was first recognized as a social problem with the introduction of the factory system in late 18th-century Great Britain. through their own history and in current movements to boycott boycott, concerted economic or social ostracism of an individual, group, or nation to express disapproval or coerce change. The practice was named (1880) after Capt. imports from countries employing child labor. (1) Wasserman Wasserman - A.I. Wasserman (Tony), president of IDE. (2000) demonstrated the parallels between the historical pattern of the decline in child labor in the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. and situations today in developing countries. She relied heavily on the notion that the greater the extent of poverty in a country, the greater the amount of child labor. She noted that this relationship holds within a country over time as well as across countries at a given point in time. Though poverty may be a determinant determinant, a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant. of child labor, it cannot be examined in the absence of cultural and social factors, such as education, culture, and urbanization.
Why should we care if poverty is a determinant of child labor in a developing country such as Bangladesh Bangladesh (bäng-lädĕsh`, băng–) [Bengali,=Bengal nation], officially People's Republic of Bangladesh, republic (2005 est. pop. 144,320,000), 55,126 sq mi (142,776 sq km), S Asia. ? As researchers have noted, boycotting exports of goods produced by children may actually worsen wors·en
tr. & intr.v. wors·ened, wors·en·ing, wors·ens
To make or become worse.
to make or become worse
worsening adjn the welfare and well-being of those children and their families, first by lowering their living standards living standards npl → nivel msg de vida
living standards living npl → niveau m de vie
living standards living npl and second by pushing children into dangerous work endeavors, such as begging and prostitution prostitution, act of granting sexual access for payment. Although most commonly conducted by females for males, it may be performed by females or males for either females or males. (Bissell Bissell may refer to:
In this research we examine poverty (as measured by income) and other child and family characteristics as determinants of child labor in Bangladesh. If we can understand the dynamics of poverty, education, and child labor, we may be in a position to create more effective ways of eliminating child labor through education and economic development efforts. First, however, we must understand the nature of child labor in Bangladesh and establish the connections that exist among child labor, poverty, education, and other socioeconomic so·ci·o·ec·o·nom·ic
Of or involving both social and economic factors.
of or involving economic and social factors
Adj. 1. factors.
2. Background: Child Labor in Bangladesh
Why Do Children Work?
Child labor is pervasive pervasive,
adj indicates that a condition permeates the entire development of the individual. in Bangladesh. According to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. reported statistics, as many as 19% of children ages 5 through 14 are in the labor force (Rahman, Khanam, and Absar 1999). As these authors state, "Child labor is deeply rooted in poverty and social customs" (p. 999). Thus, one reason for such high labor force participation is that working children are from impoverished im·pov·er·ished
1. Reduced to poverty; poverty-stricken. See Synonyms at poor.
2. Deprived of natural richness or strength; limited or depleted: families (Basu Basu is a common Indian surname. It may refer to:
There are also social and cultural explanations for widespread child labor in Bangladesh. Delap Delap is a town in the Marshall Islands. It is located in the east of Majuro Atoll. Along with Uliga and Darrit it forms what is known as the "D-U-D communities". (2001) concluded from a survey of families in three Dhaka Dhaka or Dacca (both: dăk`ə), city (1991 pop. 6,844,131), capital of Bangladesh, on a channel of the Dhaleswari River, in the heart of the world's largest jute-growing region. slums that purely economic explanations for child labor are not adequate to explain the phenomenon. One cultural factor motivating families to send their children to work is a fear that the children will be idle if they do not work. Delap reported that the majority of parents in a survey of the urban poor indicated that the income of working children was not critical to the family's survival, but the children worked because "it was improper
2. The vagrant act of 17 G. II. c. 5, which, with some modifications, has been adopted, in perhaps most of the states, describes idle persons to be those who, was deemed especially harmful to poor urban boys, whom parents feared would become involved in criminal activities.
Not only is idleness to be avoided, but children's work is also viewed favorably fa·vor·a·ble
1. Advantageous; helpful: favorable winds.
2. Encouraging; propitious: a favorable diagnosis.
3. as a means of preparing young people for work as adults. They can begin to learn the skills of farming or a trade. Rahman, Khanam, and Absar (1999) offered the example of employment in an engineering workshop that provides an opportunity for boys to learn an employable skill. For many girls, serving as maids in households prepares them for their future as wives and mothers.
Schooling does not necessarily limit child's work as it does in industrialized in·dus·tri·al·ize
v. in·dus·tri·al·ized, in·dus·tri·al·iz·ing, in·dus·tri·al·iz·es
1. To develop industry in (a country or society, for example).
2. countries. Ravallion and Wodon (2000) examined the work and schooling choices of rural Bangladeshi children to determine whether an enrollment subsidy subsidy, financial assistance granted by a government or philanthropic foundation to a person or association for the purpose of promoting an enterprise considered beneficial to the public welfare. plan was effective in limiting child labor. Because rural schools are open only part of the year and only part of the day, the authors contended that the time children spend in school would not necessarily limit time they would have spent working. The enrollment subsidy program did increase schooling of those offered the plan, but child labor did not decrease comparably. Rahman, Khanam, and Absar (1999) underscored these findings in noting that, for many children displaced displaced
see displacement. from garment factories, school was not an option. Instead, these children found work in the informal sector of the economy where jobs on the street are less secure, more poorly paid, and sometimes dangerous.
What Work Do Children Do?
The answer to this question depends on the age and gender of the child and whether the child lives in a rural or urban area. Table 1 shows the distribution of younger (ages 5-11) and older (ages 12-14) urban and rural boys and girls boys and girls
mercurialisannua. by general occupational category, based on answers to the 1995-96 Household Expenditure Survey (HES) of Bangladesh, the data source for our empirical study. These two age-groups are chosen for three reasons. First, by 12 years of age, girls have entered puberty puberty (py`bərtē), period during which the onset of sexual maturity occurs. , and secluding them from the public is important. Second, age 12 is a point where children may make a decision to continue with their education. Third, an examination of our empirical data shows a substantial increase in the proportion of children working at age 12. The reported percentages are calculated on the basis of the number of working children who indicated an occupation.
Some occupations shown in Table 1 are mostly urban (e.g., transport and communications workers), while some are mostly or completely rural (such as agricultural occupations). Likewise, some are mostly or completely boys' jobs (salesmen/businessman, transport and communications worker, and day laborer day labor
Labor hired and paid by the day.
day laborer n.
Noun 1. ). No major occupational categories shown represent completely girls' jobs, though specific employment within these occupations may be gender specific. Most urban boys are in the category of salesman/businessman. These boys work as shop assistants, street vendors, and tea and food vendors. Urban boys may also work as assistants in match, biscuit biscuit,
n the firing bakes, or stages (referred to as
low, medium, and
high), during the fusing of dental porcelain preceding the final, or glaze, bake.
in dogs, a grayish-yellow coat color. , shrimp, cigarette, and salt processing factories as well as in tanneries. Some boys also work in garment, hosiery hosiery
Knit or woven coverings for the feet and legs, worn inside shoes. In the 8th century BC, Hesiod referred to linings for shoes; the Romans wrapped their feet, ankles, and legs in long strips of leather or woven cloth. , and small engineering factories (Rahman, Khanam, and Absar 1999). They also work in smaller numbers as porters, cycle rickshaw pullers, ticket sellers (transport workers), servants, and day laborers. Additional employment for boys in the informal sector of the urban economy includes stone crushing Same as
See also: Stone and firewood collection. Rural boys' jobs are largely agricultural, with some rural boys selling items and some younger rural boys working as servants.
Delap (2001) emphasized the importance of the practice of seclusion seclusion Forensic psychiatry A strategy for managing disturbed and violent Pts in psychiatric units, which consists of supervised confinement of a Pt to a room–ie, involuntary isolation, to protect others from harm , or purdah purdah
Seclusion of women from public observation by means of concealing clothing (including the veil) and walled enclosures as well as screens and curtains within the home. , in explaining girls' jobs in Bangladesh. Urban girls aged 12 or older are restricted to jobs in which they are secluded se·clud·ed
1. Removed or remote from others; solitary.
2. Screened from view; sequestered.
se·clud from men. Table 1 shows two major occupations for girls: working in production, especially in garment factories where workers are primarily women or younger boys, and working as maids and domestic servants domestic servant n → sirviente/a m/f
domestic servant n → domestique m/f
domestic servant domestic n . Rural girls primarily work as domestic servants and maids. Some girls, especially older girls, work in the agricultural sector, and a few rural girls work as vendors or in production jobs.
Is Poverty a Determinant of Child Labor in Developing Countries?
In examining factors that affect child labor in Bangladesh, we place special emphasis on poverty. Our interest in the role of poverty is attributed to Basu and Van's seminal seminal /sem·i·nal/ (sem´i-n'l) pertaining to semen or to a seed.
Of, relating to, containing, or conveying semen or seed. article in which they proposed a "luxury axiom" of child labor stating, "A family will send the children to the labor market labor market A place where labor is exchanged for wages; an LM is defined by geography, education and technical expertise, occupation, licensure or certification requirements, and job experience only if the family's income from non-child-labor sources drops very low" (Basu and Van 1998, p. 416). In other words Adv. 1. in other words - otherwise stated; "in other words, we are broke"
put differently , a child's leisure or nonwork is a luxury that these poor families cannot afford. Thus, the "luxury good" is a child at home rather than in the workforce. As family income increases, "consumption" of this luxury good would increase. This translates into a lower probability that a child would work for families above some minimum poverty level.
There are many challenges that researchers face in attempting to test the luxury axiom of child labor. First is to interpret what Basu and Van mean when they say that a "poor" household cannot afford to consume the luxury good of children's leisure. The luxury axiom itself defines poor in terms of "the family's income" (Basu and Van 1998, p. 416). Yet elsewhere, the authors expand their concept of poverty when they say, "More generally, all we want is to give primacy pri·ma·cy
n. pl. pri·ma·cies
1. The state of being first or foremost.
2. Ecclesiastical The office, rank, or province of primate. to the household or family wealth as a determinant of child labor" (p. 415). Thus, we must consider both income and wealth as dimensions of poverty.
A second challenge is to determine what level of income or wealth triggers sending a child into the workforce or removing a child from work. Basu and Van state that "a poor household cannot afford to consume this good [children's nonwork] but it does so as soon as the household income rises sufficiently" (Basu and Van 1998, p. 415). How low must income fall before a child works? Must the family exhaust Exhaust may refer to:
Given the multidimensional mul·ti·di·men·sion·al
Of, relating to, or having several dimensions.
multi·di·men aspect of poverty, a third challenge in examining Basu and Van's luxury axiom is to acquire both income and asset data, preferably pref·er·a·ble
More desirable or worthy than another; preferred: Coffee is preferable to tea, I think.
pref over time. Finding such data for developing countries is particularly difficult given severely limited data sources. Our information sources for Bangladesh limit us to income data for members of the household and do not provide adequate information on family assets or income from social programs. Defining poverty as low family income provides an initial approach to examining Basu and Van's idea that poverty is a determinant of child labor, but looking at income alone does not capture other dimensions Other Dimensions is a collection of stories by author Clark Ashton Smith. It was released in 1970 and was the author's sixth collection of stories published by Arkham House. It was released in an edition of 3,144 copies. of family poverty. Thus, establishing a negative link between income and child labor provides a necessary but not sufficient condition for the luxury axiom to hold.
Challenges in testing the luxury axiom have not deterred researchers from investigating the role of family poverty in child labor. Although Delap emphasized the importance of looking at social and cultural determinants of child labor in her 2001 study of children in Dhaka's slums, she did find an association between lower household income and children's labor force participation. Household poverty was the most frequent reason given for children to have engaged in income-generating work. In an appendix to her paper, Delap reported that lower household income was associated with a higher probability that a child would work.
Ray (2000a, b) provided empirical tests of the connection between family income and children's work in Peru and Pakistan Pakistan (păk`ĭstăn', päkĭstän`), officially Islamic Republic of Pakistan, republic (2005 est. pop. 162,420,000), 310,403 sq mi (803,944 sq km), S Asia. . First, he used logistic regression In statistics, logistic regression is a regression model for binomially distributed response/dependent variables. It is useful for modeling the probability of an event occurring as a function of other factors. to calculate the probability that a child would be working, based on a set of independent variables (Ray 2000a). In his second paper (Ray 2000b), he estimated a child labor supply function. In both studies, poverty was defined as family income below the poverty line. He showed that the luxury axiom, when applied to the probability of working, was rejected for Pakistan and showed weak support for Peru (Ray 2000a). He also found a significant positive relationship between a child's hours worked and poverty for Pakistan but not for Peru (Ray 2000b).
A third study looked directly at the influence of poverty on child labor for children in an African country. Grootaert (1999) used a sequential choice model to find the determinants of child labor. Using the Cote d'Ivoire Living Standards Survey 1985-1988, he separated the data into two groups, urban and rural, and then conducted a multivariate analysis multivariate analysis,
n a statistical approach used to evaluate multiple variables.
n a set of techniques used when variation in several variables has to be studied simultaneously. of the determinants of child labor force participation. He controlled for child characteristics, parent characteristics, and household characteristics. Grootaert included a dummy variable This article is not about "dummy variables" as that term is usually understood in mathematics. See free variables and bound variables.
In regression analysis, a dummy variable indicating whether household income fell in the lowest quintile quin·tile
1. The astrological aspect of planets distant from each other by 72° or one fifth of the zodiac.
2. Statistics The portion of a frequency distribution containing one fifth of the total sample. . He argued that inclusion of this variable captured the constraints CONSTRAINTS - A language for solving constraints using value inference.
["CONSTRAINTS: A Language for Expressing Almost-Hierarchical Descriptions", G.J. Sussman et al, Artif Intell 14(1):1-39 (Aug 1980)]. faced by the poorest households in terms of access to credit and insurance. Grootaert found some support for the idea that financial constraints affecting the poorest households were among the most important variables in determining child work and schooling outcomes for a sample of urban children. For rural children, household poverty rarely influenced the decision to work.
When we examine empirical studies Empirical studies in social sciences are when the research ends are based on evidence and not just theory. This is done to comply with the scientific method that asserts the objective discovery of knowledge based on verifiable facts of evidence. of child labor determinants for developing countries, we find some support for the importance of poverty. Given mixed results from the literature, the purpose of our research is to deepen deep·en
tr. & intr.v. deep·ened, deep·en·ing, deep·ens
To make or become deep or deeper.
to make or become deeper or more intense
Verb 1. the understanding of the connection between poverty and child labor by developing a model that includes economic as well as social and cultural information for different groups of children in Bangladesh. We contribute to the existing literature in several ways. We improve on the income variables used by Grootaert (1999), Ray (2000a, b), and Delap (2001) by dividing nonchild family income into quintiles Quintiles Transnational Corp. is a contract research organization which serves the pharmaceutical, biotechnology and healthcare industries. History
Quintiles was founded in 1982 by Dennis Gillings and as of 2007 it has 18,000 employees. . Unlike studies that focus on either urban or rural children, we examine both. Finally, we develop separate models for younger and older children to see if family poverty and other variables have stronger effects on younger children. (3)
3. Data, Variables, and Models
We use logistic regression to estimate whether a child works, based on information about the child and his or her family. Our dependent variable is binary Meaning two. The principle behind digital computers. All input to the computer is converted into binary numbers made up of the two digits 0 and 1 (bits). For example, when you press the "A" key on your keyboard, the keyboard circuit generates and transfers the number 01000001 to the ; either the child works or the child does not work. Data are drawn from the 1995-96 Household Expenditure Survey of Bangladesh The Survey of Bangladesh (SOB) is the National Mapping Authority of Bangladesh. The agency functions under the Ministry of Defence and is headed by the Surveyor General of Bangladesh. , conducted periodically by the Bangladesh Bureau of Statistics (BBS (1) (Bulletin Board System) A computer system used as an information source and forum for a particular interest group. They were widely used in the U.S. ). These data were released for public use in 1998. A two-stage stratified stratified /strat·i·fied/ (strat´i-fid) formed or arranged in layers.
Arranged in the form of layers or strata. random sampling technique was followed, under the framework of the Integrated Multipurpose mul·ti·pur·pose
Designed or used for several purposes: a multipurpose room; multipurpose software.
Adjective Sample (IMPS IMPS Instant Messaging and Presence Services (telecommunications)
IMPS Interface Message Processors
IMPS Infinite Monkey Protocol Suite (RFC 2795) :-)
IMPS Interactive Mathematical Proof System ) design which was developed on the basis of the Population and Housing Census of 1991. The primary sampling unit used in the HES is the household. Although there is an enormous amount of individual (household member) data available from the survey, the focus of the survey was on the household and data on all household members were provided in the household survey. A total of 371 communities were chosen for the survey to be nationally representative with twenty households randomly selected from each of these communities. This procedure produced data for a total of 7420 households.
The survey provides information on an individual's location within Bangladesh, his or her economic activity, occupation, industry, age, and educational background as well as household size, household income, (4) household expenditure, consumption, changes in wealth, and health and sanitation sanitation: see plumbing; sanitary science. . Our sample consists of 11,373 children aged 5 through 14 who live in 5394 households. We apply our empirical analyses to 11,282 of these children (5) and then to sub-samples of boys and girls from urban and rural areas.
Defining Child Labor
In order to examine the determinants of child labor in Bangladesh, we developed a definition of child labor based on the response to the question concerning "activity" of the child during the previous week. The choices in answering this question were as follows: (i) employed and worked the previous week, (ii) employed but did not work the previous week, (iii) did household work, (iv) searched for a job but did not find a job, (v) did not search for a job and had no interest in finding a job, (vi) was a student, (vii) was retired, or (viii) other.
As indicated earlier, the dependent variable in our logistic regression model is binary (see Table 2 for a summary of variables used in the model and descriptive statistics descriptive statistics
see statistics. for these variables). We define our dependent variable as Work; this variable takes on a value of I if a child works and a value of 0 otherwise. The estimated value of Work is the probability that a child will work, P(Work). Our definition of child labor takes cultural and societal so·ci·e·tal
Of or relating to the structure, organization, or functioning of society.
Adj. norms into account and, at the same time, recognizes the nature of the data obtained in the survey. Though the "activity" of an individual pertains to the previous week, the variables we will use to estimate the work status (a child worked or did not work) are defined on an annual basis. Under our definition, a child is considered to work if either he or she was employed the previous week (activities i and ii), whether income was earned during the year or not, or if he or she earned income Sources of money derived from the labor, professional service, or entrepreneurship of an individual taxpayer as opposed to funds generated by investments, dividends, and interest. during the year regardless of his or her activity status for the previous week. In other words, earning income over the past year becomes the criterion for being classified as working for those who did not report that they were employed during the previous week. (6)
A total of 1064 children are considered to be child laborers under this definition; this amounts to 9% of those in the sample. This figure is somewhat lower than reported in other studies (Rahman 1997; Ravallion and Wodon 2000) because our definition requires that a child who did not work during the previous week would have had to earn income during the previous year to be counted as child labor. Thus, our definition may underestimate the extent of child labor in Bangladesh. First, we purposely pur·pose·ly
With specific purpose.
USAGE: See at purposeful.
Adv. 1. omit o·mit
tr.v. o·mit·ted, o·mit·ting, o·mits
1. To fail to include or mention; leave out: omit a word.
a. To pass over; neglect.
b. children who were not paid for doing household chores; some may consider this child labor, but we do not in this study. We exclude these children because the definition of "labor" in the term "child labor" generally means that a child works on a regular basis for pay or is unpaid but produces output that will be sold in the market (Bachman Bachman - A proposed a style of Entity-Relationship model which differs from Chen's. 2000). The only children claiming the activity of household work who would be included in our study are those who earned income during the year; there was only one such child. We cannot determine from our data source whether this child's income was from household work or from other jobs this child held earlier during the prior year; thus, we retain this child in our sample.
Second, among those in our study who reported working the previous week, only 14% reported receiving income during the year. This tells us that there might be other children who worked during the year (but not during the previous week) who would not have earned cash income. (7) The main point is that these unpaid workers who did not report working during the previous week would not be included as child labor in our study because we cannot distinguish them from those who did not work at all during the previous year (see Ravallion and Wodon 2000 for an alternative measure of child labor).
Factors Influencing Child Labor
What factors determine whether a child works? Based on the work of Basu and Van (1998) as well as others, income (or, more precisely, poverty) is a key factor. One approach is to use the natural log of income as an independent variable since the negative influence of higher income on a child's likelihood of working would diminish as income increases. Although we estimated such models, we have not included the results in our tables because the log-of-income variable does not capture the fundamental idea that poverty is a determinant of child labor. (8)
Instead of using the log of income, we define five dummy variables as follows: Poorest takes on a value of 1 if the nonchild family income is in the lowest 20% of the household incomes in the sample of 11,282 children and a value of 0 otherwise. Similarly, the dummy variable Poor equals 1 for a child in the second-lowest income quintile, Middle equals 1 for a child in the third-lowest income quintile, Rich equals 1 for a child in the fourth-lowest income quintile, and Richest equals 1 for a child in the highest income quintile. The omitted category is Richest; thus, a positive sign on a dummy Sham; make-believe; pretended; imitation. Person who serves in place of another, or who serves until the proper person is named or available to take his place (e.g., dummy corporate directors; dummy owners of real estate). income category variable indicates that a child in this income category is more likely to work than a child in the omitted category (Richest); that is, the probability of work (our dependent variable) is higher for this income category than for the Richest category. We realize that poverty may exist at a low level of income; thus, we expect that some higher-income-category variables may have coefficients that are not statistically different from zero.
Table 2 shows additional determinants of child labor included in our model. Age indicates the age of the child in years; the average age is 9.45 years. We expect families to be more likely to send older children to work; thus, the coefficient coefficient /co·ef·fi·cient/ (ko?ah-fish´int)
1. an expression of the change or effect produced by variation in certain factors, or of the ratio between two different quantities.
2. on Age is expected to be positive. We also account for age by dividing children into two groups. Younger children are ages 5 though 11, and older children are ages 12 though 14. We make this distinction because the motivation for families to send children to work may be quite different for younger as compared to older children.
The variable Boy takes on a value of 1 if the child is a boy and 0 if the child is a gift. Table 2 shows that 51% of the children in our study are boys. Families are more likely to send boys to work; thus, we expect a positive sign on Boy. According to Delap (2001, p. 11), "household decision-makers are reluctant to send girls out to work and will do so when all other household members ... are working and extra income is still required." The variable Eduyr is the number of years of education completed by the child. (9) The average years of schooling for children in our study is 2.3 years. We would expect a negative sign on the coefficient of Eduyr since those children with more education at any given age are more likely to still be in school and, thus, less likely to be working. Urban takes on a value of 1 for a child living in an urban area and 0 if the child lives in a rural area. Only 25% of children in our study are from urban areas. This is generally consistent with the overall population distribution in Bangladesh, which is 85% rural (Rahman 1997). We expect a positive sign on Urban because child work outside the home is more prevalent in the informal sector in urban areas of Bangladesh (Rahman 1997).
The variable HHSize measures the household size as the number of people in the household; the mean household size is 6.56 individuals. We expect a positive sign on the coefficient of HHSize since the larger the family, the more mouths to feed and the greater the family's need to have children work for income. The variable Headmale takes on the value 1 if the head of the household is a man. Ninety-two percent of children in our study come from male-headed households. For 88.1% of children in male-headed households, the head is the child's father; in the remaining cases, the head is a grandfather, uncle, brother, or other male relative. For 87.7% of children in female-headed households, the head is the child's mother; in the remaining cases, the head is a grandmother, aunt, sister, or other female relative. We expect that male-headed households are less likely to have children working because of social status and because of greater income stability than in female-headed households. Thus, the expected sign on Headmale is negative. Finally, the variable MHeadedu represents the household head's education in years for a male household head, and FHeadedu represents the household bead's education in years for a female household head. Studies have shown that the more educated the parents, the more likely the children will attend school rather than work (Grootaert 1999; Ray 2000b). Hossain Hossain is a surname, and may refer to:
The model that we estimate is shown here:
P(Work) = [b.sub.0] + [b.sub.1] Poorest + [b.sub.2] Poor + [b.sub.3] Middle + [b.sub.4] Rich + [b.sub.5] Age + [b.sub.6] Boy + [b.sub.7] Eduyr + [b.sub.8] Urban + [b.sub.9] HHSize + [b.sub.10] Headmale + [b.sub.11] MHeadedu + [b.sub.12] FHeadedu + error.
This model is estimated for the full sample of 11,282 children (first all children and then younger and older children) and then for eight specific demographic groups described in the next section.
4. Method and Results
We apply logistic regression techniques to determine the way in which the previously discussed factors influence the probability that a child will work. Because the dependent variable is binary, ordinary least squares (OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
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OLS Online System ) estimates are not ideal. Instead, a logit The logit function is an important part of logistic regression: for more information, please see that article.
In mathematics, especially as applied in statistics, the logit model is used to estimate work status equations. (10)
We report estimated coefficients and their standard errors as well as marginal effects (partial derivatives partial derivative
In differential calculus, the derivative of a function of several variables with respect to change in just one of its variables. Partial derivatives are useful in analyzing surfaces for maximum and minimum points and give rise to partial differential ) of independent variables in the logistic lo·gis·tic also lo·gis·ti·cal
1. Of or relating to symbolic logic.
2. Of or relating to logistics.
[Medieval Latin logisticus, of calculation model. The marginal effect of the probability of a particular independent variable is calculated as [delta]P(y = 1)/[delta]x = [beta]P(1 - P), where x is the independent variable, [beta] is the logit estimate, P is the probability that y equals 1, and (1 - P) represents the probability that y is 0 (Maddala 1988; Liao Liao (lyou), principal river of NE China, c.900 mi (1,450 km) long, rising in Inner Mongolia and flowing east then south through the fertile Liao alluvial plain to the Gulf of Liaodong. 1994; Allison
Allison, which may come from a medieval Norman nickname for Alice, meaning "noble type", or from the Irish name "Iseult", meaning "fair lady". 1999). Some children in our sample are in the same households and therefore do not constitute independent observations. Because of this, standard errors of the coefficients have been corrected for clustering.
Regression regression, in psychology: see defense mechanism.
In statistics, a process for determining a line or curve that best represents the general trend of a data set. Results for the Full Sample
Results of the logistic regression analysis for the full sample of children are presented in Table 3, which reports the coefficient of each variable, 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 work. (11) To test the overall statistical reliability of each model, we calculate the log likelihood and subject it to a chi-square test chi-square test: see statistics. . As Table 3 indicates, the chi-square chi-square (ki´skwar) see under distribution and test.
n. value is statistically significant for all three estimated equations.
The estimated models for the full sample of children in the study show statistically significant (beyond the .10 level) coefficients and expected signs for all child and household variables. Of particular interest are the positive signs on the coefficients for Age, Boy, and Urban. The older the child, the greater the probability the child works; each additional year adds .02 to the probability of working. Moreover, boys are more likely to work than girls; being a boy increases the likelihood of working by almost .04. Similarly, being from an urban area adds .03 to the probability a child will work.
It is apparent from Table 3 that some factors have different effects on the probability of working for younger versus older children. Because of these differences and because of the influence of age and gender on the probability that a child will work, we estimate separate equations for younger and older boys and gifts in urban and rural areas. Rather than providing additional discussion of income as well as child and family characteristics for the full sample, we present detailed discussions for each of the eight equations we have estimated for demographic subgroups.
Work Behavior Work behavior is a term used to describe the behavior one uses in the workplace and is normally more formal than other types of human behavior. This varies from profession to profession, as some are far more casual than others. of Urban Children
Table 4 provides descriptive statistics for four groups of urban children. The mean value of the variable Work shows the probability that a child will work. For younger urban boys the probability is 7%, and for older urban boys it is 25%. Only 6% of younger urban girls work, while the figure is 11% for older urban girls. Mean values for other variables are shown in Table 4.
Logistic regression results for the four groups of urban children are shown in Table 5. Differences are apparent between older and younger children and between boys and girls. Coefficients of child and family variables are statistically significant and with expected signs for older urban boys. The older the child, the greater the probability that he will work; moreover, each added year increases the probability of working by over .05. Having more years of education (given one's age) lowers the probability of working; each additional year of education decreases that probability by about .04.
Household size is a positive and statistically significant determinant of whether an older urban boy works. The probability that an urban family will send an older boy to work increases by .07 for a one-person one-per·son
1. Consisting of a single person.
2. Designed for or restricted to one person.
Adj. 1. increase in family size. Having a male head of household decreases an older urban boy's likelihood of working by .23, a much larger effect than for any other urban subgroup sub·group
1. A distinct group within a group; a subdivision of a group.
2. A subordinate group.
3. Mathematics A group that is a subset of a group.
tr.v. of children. When the male household head has a higher level of education, the probability that an older urban boy will work decreases by .01. The education level of a female household head has a negative effect of .06 on an older urban boy's likelihood of working, confirming our expectation that more educated parents choose not to send their children to work. Results for younger urban boys are similar except that neither the child's age, education, nor the male household head's education is a statistically significant determinant of child labor.
Of primary concern in this study is whether poverty (i.e., low nonchild family income) plays a role in the decision to send a child to work. Our models support the link between family poverty and child labor. For younger urban boys, the positive coefficient on Poorest is statistically significant; thus, boys from the poorest income quintile are more likely to work than are those from the richest income quintile. Boys in families from all other income quintiles are no more likely to be sent to work than are boys in families from the richest quintile. For older urban boys, the coefficient is positive and statistically significant on Poorest and on Middle, again showing the influence of low income on child labor. For older urban boys, being in the poorest income quintile increases the probability of working by almost .26 compared with older boys in the richest income quintile. This is the most important predictor of work status for older boys followed closely by Headmale. For younger urban boys, the marginal effect of Poorest is only about .08, whereas the marginal effect of Headmale decreases the probability of work by .17. In summary, we cannot reject the hypothesis that family poverty influences the child labor decision for both younger and older urban boys. In addition, being in a male-headed household has a strong negative impact on the probability that urban boys will work.
Logistical lo·gis·tic also lo·gis·ti·cal
1. Of or relating to symbolic logic.
2. Of or relating to logistics.
[Medieval Latin logisticus, of calculation regression results for younger urban girls, those ages 5 through 11, show statistical significance and expected signs on Age but not on Eduyr. However, most family variables are statistically significant with expected signs; only female household head's educational level does not influence the decision to send a young urban girl to work. The probability that young urban girls will work decreases (by .03) for male-headed households, but the effect is smaller than for young urban boys (.17). A young urban girl's probability of working decreases by a negligible This article or section is written like a personal reflection or and may require .
Please [ improve this article] by rewriting this article or section in an . amount (.004) as the male household head's educational level increases. For these young urban girls, the effect of poverty on child labor is evident from the positive signs and significance levels for Poorest and Poor. Being in the poorest income quintile, for example, increases the probability that a young girl will work by about .07 as compared to a young girl in the richest income quintile; this marginal effect is about the same as for young urban boys.
By contrast, for older urban girls (ages 12-14), most child and family variables have no impact on the girl's employment status. Only Headmale and MHeadedu have a statistically significant and negative influence on the probability that an older urban girl will work, and neither of these factors decreases the probability of working by very much. Being older, more educated, from a larger household, or from a household whose female head is more educated do not increase the likelihood that an older urban girl will work. Family poverty, however, has a strong positive marginal effect (of about .23 for those in the poorest income quintile) on older urban girls' probability of working, an effect that is similar to that for older urban boys and stronger than the effect for younger urban girls. Poorest is the variable with the largest marginal effect on older urban girls' work status. We conclude that family poverty motivates older urban girls to work, but many of the child and family characteristics that influence the probability of working for children in other urban demographic groups do not affect older urban girls.
From our earlier observation of urban girls' occupations (see Table 1), we see that a high proportion of older girls worked as maids (12) or as production workers, primarily in the garment industry. Girls working in factories may be earning money for dowries that will be required when they marry. Although paying dowries is illegal in Bangladesh, it is still widely practiced. Also, urban girls may be in families who have moved from rural areas and continue to send money earned in the city back to their extended families in rural areas. Finally, when urban mothers work outside the home, they often take their daughters to work with them, or they find appropriate employment for their daughters so that the daughters are not left home alone. All these are offered as explanations of the motivations of urban girls to work that go beyond the child and family explanatory ex·plan·a·to·ry
Serving or intended to explain: an explanatory paragraph.
ex·plan variables in our model.
Work Behavior of Rural Children
Four models are estimated for rural children. Descriptive statistics are shown in Table 6, which reveals a range of employment rates: 7% of younger rural boys, 29% of older rural boys, 5% of younger rural girls, and only 6% of older rural girls. Female seclusion is especially important for older rural girls; thus, we see lower percentages of girls at work than in urban areas. Though average years of education is slightly higher for younger rural boys than for younger girls, the reverse is true for older rural children.
Table 7 reports logistical regression results for younger and older rural boys and girls. Signs and significance levels of the coefficients of child and family variables are as expected for younger and older rural boys, with one exception: being from a male-headed household does not affect the probability that an older rural boy will work. The marginal effects of Age and Eduyr are more pronounced for older than for younger rural boys. An additional year of age, for example, adds .10 to the probability that an older rural boy will work.
Poverty is a determinant of child labor for both younger and older rural boys. For younger rural boys in each of the four lowest income quintiles and for older rural boys in the poorest and poor income quintiles, the probability of working is higher than for those in the richest quintile. Most families are compelled by their poverty to send even young boys to work, largely in farm labor jobs. For both younger and older rural boys, Poorest has the largest marginal effect of any of the variables on work status (.19 and .12, respectively). Headmale is second in importance for younger rural boys, indicated by a decrease of. 12 on the probability of working.
For both younger and older rural girls, age and years of education do not influence the probability that the girl will work; moreover, male household bead's educational level is not a factor for younger rural girls. Otherwise, child and family characteristics are statistically significant with expected signs. Headmale has a particularly strong marginal effect for both younger and older rural girls, decreasing the probability of working by. 14 for young rural girls and. 12 for older rural girls. For both age-groups of rural girls, poverty is a very important determinant of child labor. The impact on the probability of working of being in the Poorest income quintile is quantitatively similar for both younger and older rural girls, an increase of. 12 for younger girls and. 14 for older girls.
5. Summary and Conclusions
In this study, we develop a logistic regression model and estimate it using data for Bangladesh. We define income quintiles as a means of measuring family poverty and add child and family characteristics to our model. We estimate the likelihood that a child will work, using separate models for younger and older boys and girls in urban and rural areas. Our results support the notion that a family's poverty affects the probability that a child will work; keeping children away from work is a luxury these families cannot afford. In most models, being in a male-headed household is the second most important determinant of a child's work status.
Older urban girls show the greatest divergence divergence
In mathematics, a differential operator applied to a three-dimensional vector-valued function. The result is a function that describes a rate of change. The divergence of a vector v is given by from our expectations. For these girls, aside from poverty, only two variables, being from a male-headed household and a male household head's educational level, produced expected results. We conclude that these girls are motivated mo·ti·vate
tr.v. mo·ti·vat·ed, mo·ti·vat·ing, mo·ti·vates
To provide with an incentive; move to action; impel.
mo by factors not captured in our model.
Based on the results of our study, we recommend that researchers with access to data from other countries examine the role of poverty (in both its income and its wealth dimensions, if possible) and other socioeconomic factors to determine whether the poverty-child labor link holds internationally. If poverty is, indeed, a determinant of child labor in Bangladesh (and perhaps other countries) and if child labor is an undesirable social and economic condition, then policymakers must turn their attention to alleviating poverty. If other researchers find groups of children for whom poverty is not a determinant of child labor, alternative solutions need to be examined.
Industrialized countries' policies that ban imports of goods manufactured by children have come under heavy criticism since these policies have pushed children into more dangerous work activities. More promising than banning imports are efforts, already under way, to increase educational levels of Bangladeshi children. Emphasis on education could decrease child labor directly by increasing the time children devote to school and indirectly through investments in human capital that will improve productivity and family income. Future generations would be endowed en·dow
tr.v. en·dowed, en·dow·ing, en·dows
1. To provide with property, income, or a source of income.
a. with the attributes that we have found give families the luxury of keeping their young children out of the workplace, namely, greater parental education and higher family income.
Table 1. Percentages (a) of Working Children by Major Occupations (b) Urban Boys Girls Occupation Younger Older Younger Older Farmworker 0.00 3.81 0.00 0.00 Fisherman 0.00 1.90 0.00 0.00 Forest and livestock worker 0.00 0.00 0.00 0.00 Servant/maid 21.05 12.38 68.75 43.18 Salesman/businessman 52.63 29.53 0.00 0.00 Production worker 15.79 21.90 25.00 43.18 Transport/communication worker 5.26 7.62 0.00 0.00 Day laborer 5.26 2.86 0.00 0.00 Other 0.01 20.00 6.25 13.64 Total 100.00 100.00 100.00 100.00 Rural Boys Girls Occupation Younger Older Younger Older Farmworker 38.75 55.40 6.67 29.63 Fisherman 6.25 5.83 6.67 3.70 Forest and livestock worker 10.00 2.04 0.00 0.00 Servant/maid 13.75 7.00 53.33 40.74 Salesman/businessman 12.50 12.83 6.67 3.70 Production worker 5.00 7.00 6.67 3.70 Transport/communication worker 2.50 1.46 0.00 0.00 Day laborer 5.00 1.46 0.00 0.00 Other 6.25 6.98 19.99 18.53 Total 100.00 100.00 100.00 100.00 (a) These percentages are based on the number of working children who provided information about their current occupations. (b) Occupational titles are from the 1995-1996 Household Expenditure Survey. Table 2. Variable Definitions and Descriptive Statistics for the Full Sample Definition Dependent Variable Work 1 if child works; 0 otherwise Independent variables Poorest income 1 if income is in the lowest < 15,800 taka quintile; 0 otherwise Poor income [greater than 1 if income is in the or equal to] 15,800, second-lowest <28,600 taka quintile; 0 otherwise Middle income [greater than 1 if the income is in or equal to] 28,600, the third quintile; <43,745 taka 0 otherwise Rich income [greater than 1 if the income is in or equal to] 43,745, the fourth-lowest <72,429 taka quintile; 0 otherwise Richest income 1 if the income is [greater than or in the top quintile; equal to] 72,429 taka 0 otherwise Age Child's age in years Boy 1 if boy; 0 otherwise Eduyr Child's education in years Urban 1 if urban; 0 otherwise HHSize Household size Headmale 1 if head of household is male; 0 otherwise MHeadedu Male head's years of education FHeadedu Female head's years of education N = 11,282 Mean (Standard Deviation) Dependent Variable 0.09 (0.29) Work Independent variables 0.20 (0.40) Poorest income < 15,800 taka Poor income [greater than 0.20 (0.40) or equal to] 15,800, <28,600 taka Middle income [greater than 0.20 (0.40) or equal to] 28,600, <43,745 taka Rich income [greater than 0.20 (0.40) or equal to] 43,745, <72,429 taka Richest income 0.20 (0.40) [greater than or equal to] 72,429 taka Age 9.45 (2.71) Boy 0.51 (0.50) Eduyr 2.30 (2.54) Urban 0.25 (0.43) HHSize 6.56 (2.45) Headmale 0.92 (0.27) MHeadedu 4.79 (4.60) FHeadedu 3.22 (1.57) N = 11,282 Table 3. Logistic Regression Results for the Full Sample Full Sample Logit (a) Younger Children Variables Coefficient ME (b) Logit Coefficient ME Intercept -6.664 (0.291) -5.999 *** (0.441) Poorest 1.312 *** (0.168) 0.102 1.900 *** (0.277) 0.128 Poor 0.929 *** (0.161) 0.064 1.369 *** (0.266) 0.079 Middle 0.777 *** (0.163) 0.051 1.181 *** (0.268) 0.065 Rich 0.457 *** (0.158) 0.028 0.780 *** (0.257) 0.038 Age 0.328 *** (0.015) 0.017 0.208 *** (0.029) 0.008 Boy 0.791 *** (0.086) 0.043 0.062 (0.115) 0.002 Eduy -0.154 *** (0.016) -0.008 -0.041 (0.030) -0.002 Urban 0.449 *** (0.102) 0.027 0.322 ** (0.140) 0.013 HHSize 0.330 *** (0.027) 0.018 0.456 *** (0.038) 0.017 Headmale -1.128 *** (0.132) -0.092 -1.641 *** (0.158) -0.123 MHeadedu -0.070 *** (0.011) -0.004 -0.064 *** (0.016) -0.002 FHeadedu -0.280 *** (0.044) -0.015 -0.354 *** (0.058) -0.014 Log likelihood -2898.91 -1589.27 Chi-square 946.85 *** 357.63 *** N 11,282 7986 Older Children Variables Logit Coefficient ME Intercept -7.301 *** (0.822) Poorest 0.969 *** (0.213) 0.124 Poor 0.648 *** (0.204) 0.076 Middle 0.551 *** (0.202) 0.063 Rich 0.301 (0.192) 0.032 Age 0.376 *** (0.059) 0.038 Boy 1.627 *** (0.136) 0.164 Eduy -0.206 *** (0.021) -0.021 Urban 0.643 *** (0.132) 0.072 HHSize 0.192 *** (0.038) 0.019 Headmale -0.530 *** (0.198) -0.062 MHeadedu -0.083 *** (0.015) -0.008 FHeadedu -0.199 *** (0.059) -0.020 Log likelihood -1217.75 Chi-square 445.70 *** N 3296 (a) Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. (b) Marginal effects (ME) are calculated as [delta]P(y = 1)/[delta]x = [beta]P(1 - P) and are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level. Table 4. Variable Definitions and Descriptive Statistics for the Urban Models (a) Boys Younger Older Dependent variable Work 0.07 (0.24) 0.25 (0.43) Independent variables Poorest (b) (1 if income 0.23 (0.42) 0.19 (0.39) <18,610 taka; 0 otherwise) Poor (1 if income [greater than or 0.21 (0.41) 0.18 (0.38) equal to] 18,610, <36,000 taka; 0 otherwise) Middle (1 if income [greater than or 0.21 (0.40) 0.18 (0.39) equal to] 36,600, <59,650 taka; 0 otherwise) Rich (1 if income [greater than or 0.18 (0.39) 0.19 (0.40) equal to] 59,650, <105,600 taka; 0 otherwise) Richest (1 if income [greater than or 0.18 (0.38) 0.26 (0.44) equal to] 105,600 taka; 0 otherwise) Age 8.10 (1.85) 12.89 (0.86) Eduyr 1.94 (1.90) 4.75 (3.34) HHSize 6.30 (2.38) 6.51 (2.47) Headmale 0.92 (0.27) 0.90 (0.30) MHeadedu 6.64 (5.04) 7.47 (5.10) FHeadedu 2.65 (1.48) 2.76 (1.57) N 939 457 Girls Younger Older Dependent variable 0.06 (0.23) 0.11 (0.31) Work Independent variables 0.23 (0.42) 0.13 (0.33) Poorest (b) (1 if income <18,610 taka; 0 otherwise) 0.19 (0.39) 0.17 (0.38) Poor (1 if income [greater than or equal to] 18,610, <36,000 taka; 0 otherwise) 0.20 (0.40) 0.23 (0.42) Middle (1 if income [greater than or equal to] 36,600, <59,650 taka; 0 otherwise) 0.20 (0.40) 0.24 (0.43) Rich (1 if income [greater than or equal to] 59,650, <105,600 taka; 0 otherwise) 0.18 (0.38) 0.23 (0.42) Richest (1 if income [greater than or equal to] 105,600 taka; 0 otherwise) 8.08 (1.86) 12.90 (0.84) Age 1.84 (1.97) 4.80 (3.39) Eduyr 6.49 (2.50) 6.83 (2.48) HHSize 0.92 (0.27) 0.91 (0.29) Headmale 6.88 (4.96) 7.92 (5.00) MHeadedu 3.78 (1.58) 3.96 (1.67) FHeadedu 940 450 N (a) Data are means (standard deviations in parentheses). (b) The income quintiles are on the basis of all urban households, so the means in each subgroups will not necessarily be 20%. Table 5. Logistic Regression Results for the Urban Models Boys Younger Variables Logit Coefficient (a) ME (b) Intercept -3.987 *** (1.201) Poorest 1.288 * (0.717) 0.077 Poor 0.233 (0.693) 0.010 Middle 0.299 (0.653) 0.013 Rich 0.620 (0.623) 0.031 Age 0.138 (0.089) 0.006 Eduyr -0.018 (0.091) -0.001 HHSize 0.399 *** (0.100) 0.016 Headmale -1.921 *** (0.420) -0.171 MHeadedu -0.061 (0.040) -0.003 FHeadedu -0.396 ** (0.181) -0.016 Log likelihood -194.85 Chi-square 57.80 *** N 939 Boys Older Variables Logit Coefficient ME Intercept -6.610 *** (2.057) Poorest 1.490 *** (0.574) 0.264 Poor 0.623 (0.591) 0.096 Middle 0.966 * (0.548) 0.158 Rich 0.850 (0.531) 0.135 Age 0.396 *** (0.153) 0.053 Eduyr -0.312 *** (0.047) -0.041 HHSize 0.549 *** (0.105) 0.074 Headmale -1.257 *** (0.448) -0.227 MHeadedu -0.081 *** (0.031) -0.011 FHeadedu -0.450 *** (0.152) -0.060 Log likelihood -182.42 Chi-square 126.39 *** N 457 Girls Younger Variables Logit Coefficient ME Intercept -6.705 *** (1.169) Poorest 1.532 * (0.812) 0.069 Poor 1.758 ** (0.810) 0.088 Middle 0.668 (0.730) 0.023 Rich 0.518 (0.728) 0.017 Age 0.261 *** (0.076) 0.007 Eduyr -0.098 (0.086) -0.003 HHSize 0.361 *** (0.096) 0.010 Headmale -0.850 * (0.477) -0.034 MHeadedu -0.148 *** (0.040) -0.004 FHeadedu -0.054 (0.183) -0.002 Log likelihood -171.5 Chi-square 96.02 *** N 940 Girls Older Variables Logit Coefficient ME Intercept -2.067 (2.709) Poorest 2.082 ** (0.905) 0.225 Poor 1.313 (0.873) 0.103 Middle 1.644 * (0.866) 0.135 Rich 1.095 (0.916) 0.076 Age 0.018 (0.201) 0.001 Eduyr -0.004 (0.057) -0.0002 HHSize 0.030 (0.146) 0.002 Headmale -0.942 * (0.482) -0.070 MHeadedu -0.199 *** (0.041) -0.010 FHeadedu 0.032 (0.186) 0.002 Log likelihood -122.77 Chi-square 43.60 *** N 450 (a) Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. (b) Marginal effects (ME) are calculated as [delta] P(y = 1)/[delta]x = [beta]P(l -P) and are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level. Table 6. Variable Definitions and Descriptive Statistics for the Rural Models (a) Boys Younger Older Dependent variable Work 0.07 (0.26) 0.29 (0.45) Independent variables Poorest (b) (1 if income <15,308 taka; 0 otherwise) 0.21 (0.41) 0.17 (0.38) Poor (1 if income [greater than or equal to] 15,308, <27,200 taka; 0 otherwise) 0.20 (0.40) 0.19 (0.39) Middle (1 if income [greater than or equal to] 27,200, <40,270 taka; 0 otherwise) 0.20 (0.40) 0.20 (0.40) Rich (1 if income [greater than or equal to] 40,270, <64,800 taka; 0 otherwise) 0.20 (0.40) 0.21 (0.41) Richest (1 if income [greater than or equal to] 64,800 taka; 0 otherwise) 0.19 (0.39) 0.23 (0.42) Age 8.05 (1.81) 12.86 (0.86) Eduyr 1.61 (1.72) 3.44 (3.18) HHSize 6.54 (2.41) 6.59 (2.45) Headmale 0.93 (0.26) 0.92 (0.27) MHeadedu 3.92 (4.10) 4.37 (4.23) FHeadedu 2.71 (1.40) 2.70 (1.40) N 3072 1270 Girls Younger Older Dependent variable Work 0.05 (0.21) 0.06 (0.23) Independent variables Poorest (b) (1 if income <15,308 taka; 0 otherwise) 0.21 (0.41) 0.16 (0.36) Poor (1 if income [greater than or equal to] 15,308, <27,200 taka; 0 otherwise) 0.21 (0.41) 0.18 (0.38) Middle (1 if income [greater than or equal to] 27,200, <40,270 taka; 0 otherwise) 0.20 (0.40) 0.20 (0.40) Rich (1 if income [greater than or equal to] 40,270, <64,800 taka; 0 otherwise) 0.19 (0.40) 0.21 (0.41) Richest (1 if income [greater than or equal to] 64,800 taka; 0 otherwise) 0.18 (0.39) 0.25 (0.43) Age 8.03 (1.81) 12.81 (0.83) Eduyr 1.58 (1.76) 3.53 (3.24) HHSize 6.55 (2.43) 6.81 (2.55) Headmale 0.92 (0.27) 0.91 (0.29) MHeadedu 3.83 (4.10) 4.50 (4.49) FHeadedu 3.74 (1.48) 3.69 (1.52) N 3035 1119 (a) Data are means (standard deviations in parentheses). (b) The income quintiles are on the basis of all rural households, so the means in each subgroups will not necessarily be 20%. Table 7. Logistic Regression Results for the Rural Models Boys Younger Variables Logit Coefficient (a) ME (b) Intercept -7.230 *** (0.623) Poorest 2.266 *** (0.405) 0.193 Poor 1.638 *** (0.387) 0.117 Middle 1.321 *** (0.400) 0.086 Rich 1.304 *** (0.366) 0.085 Age 0.319 *** (0.042) 0.014 Eduyr -0.113 ** (0.046) -0.005 HHSize 0.436 *** (0.056) 0.019 Headmale -1.519 *** (0.244) -0.123 MHeadedu -0.078 *** (0.023) -0.003 FHeadedu -0.244 *** (0.086) -0.011 Log likelihood -683.5 Chi-square 200.04 *** N 3072 xx Boys Older Variables Logit Coefficient ME Intercept -7.442 *** (1.125) Poorest 0.623 ** (0.272) 0.124 Poor 0.506 * (0.259) 0.099 Middle 0.275 (0.275) 0.052 Rich 0.124 (0.254) 0.023 Age 0.541 *** (0.082) 0.098 Eduyr -0.282 *** (0.028) -0.051 HHSize 0.082 * (0.049) 0.015 Headmale 0.284 (0.270) 0.048 MHeadedu -0.062 *** (0.021) -0.011 FHeadedu -0.194 ** (0.083) -0.035 Log likelihood -632.52 Chi-square 202.38 *** N 1270 Girls Younger Variables Logit Coefficient ME Intercept -4.824 *** (0.729) Poorest 2.112 *** (0.445) 0.121 Poor 1.447 *** (0.443) 0.066 Middle 1.484 *** (0.439) 0.070 Rich 0.591 (0.441) 0.021 Age 0.060 (0.055) 0.002 Eduyr 0.075 (0.053) 0.002 HHSize 0.574 *** (0.059) 0.017 Headmale -2.050 *** (0.253) -0.144 MHeadedu -0.008 (0.029) -0.0002 FHeadedu -0.604 *** (0.090) -0.018 Log likelihood -499.71 Chi-square 152.37 *** N 3035 Girls Older Variables Logit Coefficient ME Intercept -5.882 *** (2.078) Poorest 1.970 *** (0.608) 0.138 Poor 1.734 *** (0.574) 0.107 Middle 0.893 (0.638) 0.040 Rich 0.441 (0.552) 0.017 Age 0.144 (0.155) 0.005 Eduyr 0.048 (0.047) 0.002 HHSize 0.424 *** (0.088) 0.015 Headmale -1.743 *** (0.414) -0.123 MHeadedu -0.088 ** (0.040) -0.003 FHeadedu -0.332 *** (0.129) -0.012 Log likelihood -209.75 Chi-square 55.20 *** N 1119 (a) Logit coefficients are reported with standard errors shown in parentheses. Standard errors are adjusted for clustering. (b) Marginal effects (ME) are calculated as [delta]P(y = 1)/[delta]x = [beta] P(1 - P) and are evaluated at the mean. *** Significant at 0.01 level, ** significant at 0.05 level, and * significant at 0.10 level.
We wish to thank Lisa Jepsen, Ken Brown, M. Imam Alam (language) ALAM - A language for symbolic mathematics, especially General Relativity.
See also CLAM.
["ALAM Programmer's Manual", Ray D'Inverno, 1970]. , Stan STAN Stanchion
STAN Stärke- und Ausrüstungsnachweis (German)
Stan Standard Man (human patient simulator)
STAN SEMCIP Technical Assistance Network
STAN System Trace Audit Number
STAN Star Trek Area Network Lyle Lyle may refer to:
Referees are usually appointed by a judge in the district in which the judge presides. for their helpful comments.
(1) Bachman (2000, p. 32) pointed out that the definition of child labor "is fraught fraught
1. Filled with a specified element or elements; charged: an incident fraught with danger; an evening fraught with high drama.
2. with anomalies and contradictions, reflecting a tangle of international standards, national laws, cultural practices and social expectations." We define child labor later in the paper.
(2) The proposed bill was a great enough threat to Bangladesh's export industries that the Bangladesh Garment Manufacturers and Exporters Association signed a Memorandum of Understanding A Memorandum of Understanding (MoU) is a legal document describing a bilateral or multilateral agreement between parties. It expresses a convergence of will between the parties, indicating an intended common line of action and may not imply a legal commitment. with the ILO ILO
International Labor Organization
Noun 1. ILO - the United Nations agency concerned with the interests of labor
International Labor Organization, International Labour Organization and UNICEF UNICEF (y`nĭsĕf'), the United Nations Children's Fund, an affiliated agency of the United Nations. in July July: see month. 1995. As a result, employers in the Bangladeshi garment industry eliminated 50,000 jobs held by children (Rahman, Khanam, and Absar 1999). For more discussion of the Harkin Bill, see McClintock Mc·Clin·tock , Barbara 1902-1992.
American genetic botanist. She won a 1983 Nobel Prize for discovering that genes are mobile within the chromosomes of a plant cell. (2001).
(3) We thank an anonymous referee for suggesting separate models for younger and older children.
(4) The survey requested information for all household members on their total income and the relevant sources of such income. A separate section of the questionnaire collected detailed information about the household's income from agricultural production. The income measure is fairly reliable since the discrepancy DISCREPANCY. A difference between one thing and another, between one writing and another; a variance. (q.v.)
2. Discrepancies are material and immaterial. is not substantial when matched with expenditure and savings data. However, it should be noted that income is traditionally underreported in most surveys conducted in developing countries.
(5) We omitted 91 children from our empirical analysis because they were reported as household members in households in which they were employed. Thus, the data on family characteristics, such as the household head's education, applied to the head of the household employing these children rather than to their own father or other relative. In these cases, the link between household characteristics and the decision to have the child work is obscured. Fifty-three of the omitted children are girls, 13 from rural areas and 40 from urban areas. Thirty-eight are boys, 23 from rural areas and 15 from urban areas.
(6) We initially also defined child labor more narrowly as children who reported that they worked the previous week (category 1) and who reported positive income for the past year. There were only 78 working children according to this definition, which represents only 0.7 of 1% of those in the sample. This small number indicates the narrowness of this definition in light of the cultural norms in Bangladesh.
(7) This does not mean that children who do not report income are not paid; rather, their payment could come in the form of food, lodging Lodging or holiday accommodation is a type of accommodation. People who travel and stay away from home for more than a day need lodging mainly for sleeping. Other purposes are safety, shelter from cold and rain, having a place to store luggage and being able to take a , or clothing. This would apply to servants and maids, for example. Agricultural workers who do not receive cash payments may he paid with crops, fruit, or vegetables.
(8) Results show that when the natural log of income is used instead of dummy income quintile variables, the coefficient of the log of income is negative and statistically different from 0 at the 5% level or better for all models reported in Tables 3, 5, and 7. Income is thus negatively related to the probability that a child will work, and the effect lessens as the income level increases.
(9) Reporting errors resulted in cases for which years of education exceeded age minus 4 (the earliest age at which formal education could begin). For these cases, we defined Eduyr as Age -4.
(10) Studenmund (2001, p. 436) outlined several reasons for not using an OLS model. First, the unrestricted OLS model can predict a negative value or a value greater than 1 as the probability of labor force participation. Second, the OLS estimates are less efficient than the logit estimates because of heteroscedasticity heteroscedasticity
an irregular scattering of values in a series of distributions; accompanied by a comparable scatter of variances. in the disturbances. Third, the standard errors of the OLS parameters are not consistent, whereas asymptotic efficiency and consistency are well established properties of the maximum likelihood estimates.
(11) Marginal effects can be interpreted as follows using Table 3. The marginal effect of Age on the probability of working for the full sample is 0.0174. This means that a one-year adj. 1. completing its life cycle within a year.
Adj. 1. one-year - completing its life cycle within a year; "a border of annual flowering plants"
phytology, botany - the branch of biology that studies plants increase in age will add about .02 to the probability that a child will work. For dummy variables, the interpretation of the marginal value Marginal value is a term widely used in economics, to refer to the change in economic value associated with a unit change in output, consumption or some other economic choice variable. is as follows using the dummy variable Boy from the same equation in Table 3 (Allison 1999). The marginal effect is 0.0425, meaning that, on average, a boy's probability of working is 0.04 of a percentage point higher than a girl's. There are alternative approaches to interpreting the marginal effects of dummy variables (Liao 1994).
(12) Forty urban girls were among the 91 cases omitted because they were reported as members of households where they were employed. These are cases where younger or older urban girls were employed as maids or servants. Thus, the maids and servants remaining in our sample of urban girls are those reported as members of their own and not their employers' households.
Allison, Paul Paul, 1901–64, king of the Hellenes (1947–64), brother and successor of George II. He married (1938) Princess Frederika of Brunswick. During Paul's reign Greece followed a pro-Western policy, and the Cyprus question was temporarily resolved. D. 1999. Logistic regression using the SAS system (1) Originally called the "Statistical Analysis System," it is an integrated set of data management and decision support tools from SAS that runs on platforms from PCs to mainframes. : Theory and application. Cary Car·y
A town of east-central North Carolina, an industrial suburb of Raleigh. Population: 98,000. , NC: SAS Institute SAS Institute Inc., headquartered in Cary, North Carolina, USA, has been a major producer of software since it was founded in 1976 by Anthony Barr, James Goodnight, John Sall and Jane Helwig. Inc.
Bachman, S. L. 2000. The political economy of child labor and its impacts on international business. Business Economics, July, pp. 30-41.
Bangladesh Bureau of Statistics. 1998. Household Expenditure Survey, 1995-96. Dhaka, Bangladesh: Bangladesh Bureau of Statistics.
Basu, Kaushik. 1999. Child labor: Cause, consequence, and cure, with remarks on international labor standards. Journal of Economic Literature 37:1083-119.
Basu, Kaushik, and Pham Hoang Van. 1998. The economics of child labor. American American, river, 30 mi (48 km) long, rising in N central Calif. in the Sierra Nevada and flowing SW into the Sacramento River at Sacramento. The discovery of gold at Sutter's Mill (see Sutter, John Augustus) along the river in 1848 led to the California gold rush of Economic Review 88:413-27.
Bissell, Susan SUSAN Smallest Univalue Segment Assimilating Nucleus
SUSAN Sub Saharan African Network
SUSAN Smart Ultrasonic System for Aircraft NDE , and Barbar Barbar is a Belgian beer brand. It's unique for its honey based flavor. The honey is used with the malted wheat and the hops in the fermentation process yielding a sweeter and richer flavored beer. Sobhan. 1996. Child labour and education programming in the garment industry of Bangladesh: Experiences and issues. Dhaka, Bangladesh: UNICEF Education Section, Occasional Papers.
Delap, Emily EMILY Early Money Is Like Yeast
EMILY Electronic Membrane-Information Library
EMILY Every Moment I Love You . 2001. Economic and cultural forces in the child labour debate: Evidence from urban Bangladesh. Journal of Development Studies 37:1-22.
Grootaert, Christiaan Christiaan may refer to:
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of : St. Martin's St. Martin's or St. Martins may refer to:
Harkin, Tom. 1999. The United States should ban imports of products made by children. In Child labor and sweatshops, edited by Mary Mary, the mother of Jesus
Mary, in the Bible, mother of Jesus. Christian tradition reckons her the principal saint, naming her variously the Blessed Virgin Mary, Our Lady, and Mother of God (Gr., theotokos). Her name is the Hebrew Miriam. E. Williams. San Diego San Diego (săn dēā`gō), city (1990 pop. 1,110,549), seat of San Diego co., S Calif., on San Diego Bay; inc. 1850. San Diego includes the unincorporated communities of La Jolla and Spring Valley. Coronado is across the bay. : Geenhaven Press, pp. 38-42.
Hossain, B. B. M. Jaber Jaber may refer to:
Liao, Tim Futing. 1994. Interpreting probability models: Logit, probit In probability theory and statistics, the probit function is the inverse cumulative distribution function (CDF), or quantile function associated with the standard normal distribution. , and other generalized linear models Not to be confused with general linear model.
In statistics, the generalized linear model (GLM) is a useful generalization of ordinary least squares regression. It relates the random distribution of the measured variable of the experiment (the . Thousand Oaks Thousand Oaks, residential city (1990 pop. 104,352), Ventura co., S Calif., in a farm area; inc. 1964. Avocados, citrus, vegetables, strawberries, and nursery products are grown. , CA: Sage Publications This article or section needs sources or references that appear in reliable, third-party publications. Alone, primary sources and sources affiliated with the subject of this article are not sufficient for an accurate encyclopedia article. .
Maddala, G. S. 1988. Introduction to econometrics econometrics, technique of economic analysis that expresses economic theory in terms of mathematical relationships and then tests it empirically through statistical research. . New York: Macmillan Macmillan, river, c.200 mi (320 km) long, rising in two main forks in the Selwyn Mts., E Yukon Territory, Canada, and flowing generally W to the Pelly River. It was an important route to the gold fields from c.1890 to 1900. .
McClintock, Brent Brent, outer borough (1991 pop. 226,100) of Greater London, SE England. The area is a rail and industrial center. Its manufactures include automobile parts, clocks and watches, and electrical equipment. . 2001. Trade as if children mattered. International Journal of Social Economics 28:899-910.
Rahman, Mohammad Noun 1. Mohammad - the Arab prophet who, according to Islam, was the last messenger of Allah (570-632)
Mahomet, Mahound, Mohammed, Muhammad Mafizur, Rasheda Khanam, and Nur Uddin Absan 1999. Child labor in Bangladesh: A critical appraisal Noun 1. critical appraisal - an appraisal based on careful analytical evaluation
appraisal, assessment - the classification of someone or something with respect to its worth of Harkin's bill and the MOU-type schooling program. Journal of Economic Issues 33:985-1003.
Rahman, Wahidur. 1997. Child labour situation in Bangladesh: A rapid assessment. Dhaka, Bangladesh: International Labour Organization.
Ravallion, Martin, and Quentin Quentin (Latin Quintinus, from Quintus) is a Latin-derived given name meaning "the fifth". People
People commonly known solely as Quentin include:
tr.v. dis·placed, dis·plac·ing, dis·plac·es
1. To move or shift from the usual place or position, especially to force to leave a homeland: schooling? Evidence on behavioral behavioral
pertaining to behavior.
see psychomotor seizure. responses to an enrollment subsidy. Economic Journal 110:C158-75.
Ray, Ranjan. 2000a. Analysis of child labour in Peru and Pakistan: A comparative study. Journal of Population Economics 13:3-19.
Ray, Ranjan. 2000b. Child labor, child schooling, and their interaction with adult labor: Empirical evidence for Peru and Pakistan. World Bank Economic Review 14:347-67.
Studenmund, A. H. 2001. Using econometrics: A practical guide. 4th edition. Boston Boston, town, England
Boston, town (1991 pop. 26,495), E central England, on the Witham River. Boston's fame as a port dates from the 13th cent., when it was a Hanseatic port trading wool and wine. Having recovered from a decline in the 18th and 19th cent. : Addison-Wesley Longman Longman was a publishing company founded in London, England in 1724. It is now an imprint of Pearson Education. History
The Longman company was founded by Thomas Longman(1) (1699-1755), the son of Ezekiel Longman (d. 1708), a gentleman of Bristol. .
Wasserman, Miriam Miriam (mĭr`ēəm), in the Bible.
1 Sister of Moses and Aaron. After the crossing of the Sea of Reeds, she led the women in the song of Miriam. . 2000. Eliminating child labor. Regional Review (Federal Reserve Bank of Boston The Federal Reserve Bank of Boston is responsible for the First District of the Federal Reserve, which covers Connecticut (excluding Fairfield County), Massachusetts, Maine, New Hampshire, Rhode Island and Vermont. It is headquartered in Boston, Massachusetts. ), 2nd quarter, pp. 8-17.
Shahina Amin AMIN Arabic Media Internet Network , * M. Shakil Quayes, ([dagger]) and Janet Janet: see Clouet, Jean.
JANET - Joint Academic NETwork M. Rives Language
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Rives is the name of several places: France
Rives is the name of 2 communes in France:
A reference mark () used in printing and writing. Also called diesis.
Noun 1. ])
* Department of Economics, University of Northern Iowa The University of Northern Iowa, in Cedar Falls, Iowa, was founded in 1876, as the Iowa State Normal School. It has colleges of Business Administration, Education, Humanities and Fine Arts, Natural Sciences, and Social and Behavioral Sciences, and a graduate school. , Cedar Falls Cedar Falls, city (1990 pop. 34,298), Black Hawk co., N Iowa, on the Cedar River; inc. 1854. It developed as a milling center in the late 19th-century after the coming of the railroad; its name is derived from the cedar tree. , IA 50614-0129, USA; E-mail Shahina.Amin@ uni Uni (`nē), fl. c.2325 B.C., Egyptian official of the VI dynasty. His career is known through his private inscription. .edu See .edu.
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([dagger]) Department of General Business, Southeastern Louisiana University Southeastern Louisiana University is a state-funded public university that is located in the city of Hammond, Louisiana. It was originally founded in 1925 by Linus A. Sims, the principal of Hammond High School, as Hammond Junior College, located in a wing of the high school , Hammond Hammond.
1 City (1990 pop. 84,236), Lake co., extreme NW Ind., bounded by Lake Michigan, the Ill. state line, and the Little Calumet River, and traversed by the Grand Calumet River; settled 1851, inc. 1884. , LA 70402, USA; E-mail Mquayes@selu.edu.
([double dagger]) Department of Economics. University of Northern Iowa, Cedar Falls. 1A 50614-0129, USA; E-mail Janet. Rives@uni.edu.
Received July 2002; accepted June 2003.