Child Labor Measurement:
Whom Should We Ask?
Sarah A. Janzen*
Abstract:
Funding decisions to support vulnerable children are regularly tied to reports of child
labor statistics, making accurate measurement of child labor critical. Yet measuring child
labor is a complex process with widespread inconsistency across studies. This paper analyzes
whether respondent type plays an important role in explaining variation in child labor statistics. Using data from two sites in Tanzania, the analysis shows that, using various definitions
of child labor, whom we ask matters considerably when estimating the prevalence of child
labor. The results suggest that estimates of child labor prevalence increase by approximately
35-65% when using child self reports rather than proxy reports from household heads. This
reporting bias affects 14-31% of the sample, depending on the indicator used. Discrepancies
decrease as the child ages and increase if proxy attitudes demonstrate opposition to child
labor.
Acknowledgements: This research was made possible using the WEKEZA (Wezesha Ustawi, Endeleza, Kiwongo cha Elimu Kuzuia Ajira kea Watoto) baseline survey data collected by Savannas Forever Tanzania
for the International Rescue Committee, World Vision, Kiota Women’s Health and Development, Tanga
Youth Development Association and the Institute of Development Studies with funding support from the
United States Department of Labor and assistance from National Institute for Medical Research-Muhimbili
Medical Research Centre. I would like to specifically acknowledge the contributions of Susan James, Jovit
Felix, Nai-Nancy Laiser, David Lawson, Andrew Ferdinands, Deborah Levison, Bernard Ngowi, and Nike
Start for research assistance and support during the survey implementation process. Mark Anderson, Paul
Glewwe, David Lawson, Carly Urban and audiences at the University of Montana and the Economics of
Global Poverty Conference provided helpful feedback. All errors are my own.
*Sarah Janzen ([email protected]) is Assistant Professor of Economics in the Department of Agricultural Economics and Economics at Montana State University.
Recent estimates from the International Labor Organization (ILO) suggest that approximately 168 million children are actively participating in the global economy through activities that are potentially harmful - mentally, physically, socially or morally - and commonly
interfere with the child’s education (ILO, 2013). Udry describes child labor as “an insidious evil,” because it frequently (though not always) limits the child’s future opportunities
(Udry, 2006). For this reason, an extensive literature has analyzed the determinants and
consequences of child labor. As highlighted by (Anker, 2000), policymakers often use child
labor statistics to set priorities, allocate resources, monitor progress and evaluate programs.
Yet, measuring child labor remains a complex process, leading to widespread inconsistency
across studies. In this paper I investigate the role of one critical factor - the survey respondent - in explaining variation in child labor statistics. The findings suggest that whom we
ask matters considerably when estimating the prevalence of child labor.
In measuring child labor the respondent can be either the child herself, or a proxy respondent who reports on the child’s behavior and time use. The ILO recommends that
questions be answered directly by the child, provided the child is at least 10 years old (ILO,
2004). Although these guidelines are widely acknowledged as the “gold standard,” evidence
supporting the exclusive use of child reports over proxy reports is weak. The cognitive development of children is likely to affect the quality of the information provided, and it’s not
entirely clear that child reports are more accurate than proxy reports. In practice, proxy
respondents, typically the household head or other knowledgeable adult, are routinely used
- it’s often easier and cheaper to interview an adult than it is to interview a child.
With these issues in mind I analyze whether the respondent matters in measuring child
labor prevalence. Specifically, I ask the following two questions: (1) Do the answers provided
by proxies differ significantly from the answer of the child on whose behalf they are answering? (2) Do child or proxy characteristics explain some of the difference between child and
proxy reports of child labor participation? The answers to these questions have important
implications regarding the cost of collecting accurate child labor statistics that are compara-
1
ble both geographically and intertemporally. Such statistics are regularly used to set targets
and monitor progress, and often have important implications for the allocation of resources.
Two recent studies seeking to answer these questions have produced conflicting results.
Dillon et al. (2012) use a randomized survey experiment in Tanzania and conclude that respondent selection, whether the child or a proxy respondent, does not matter for determining
rates of child labor. On the contrary, Dammert & Galdo (2013) find significant disagreement
between child and proxy responses. Despite studying a population most similar to Dillon
et al. (2012) - both their study and this one use data from Tanzania - the results reported
here provide further evidence in support of the Dammert & Galdo (2013) findings - child and
proxy reports of child labor vary significantly. Our results suggest that estimates of child
labor increase by approximately 50% when child self reports are used to construct indicators
of child labor prevalence, rather than proxy reports.
This analysis builds on both of these studies in three important ways. First, like Dammert
& Galdo (2013), our data allows us to estimate a “pure” proxy effect, defined as the difference
between child and proxy reports for the same child. This should provide a better estimate
than Dillon et al. (2012), who cannot compare data on the same person from proxy and self
response.
Second, in the analysis that follows, differences between reports of child labor participation are not limited to simple definitions of labor market participation. I also consider
the ILO’s more complex definition of child labor (described in section 1) which is used for
global child labor estimates and targeting. In addition, the analysis considers hours spent
performing economic activities and household tasks, and exposure to potentially hazardous
conditions. This disaggregation provides the opportunity to analyze whether the variation
between child and proxy responses depend on the type of work or its relative risks.
Third, I analyze the relationship between child labor reports and proxy attitudes regarding child labor. In this case, it’s expected that the local context will matter. Our study uses
data collected in two districts of Tanzania: Kigoma and Tanga. The study by Dillon et al.
2
(2012), which finds no discrepancy between proxy and child, also uses data from Tanzania
although different districts are considered. While Dillon et al. (2012) are forced to assume
that all Tanzanian households do not have negative perceptions toward child labor, I use
rich data regarding the child labor attitudes of adults to control for such perceptions. This
provides the opportunity to explicitly explore how attitudes are related to both child-proxy
agreement and disagreement.
Unlike either study, I provide some evidence that child labor attitudes may lead some
proxies to underreport the prevalence of child labor in the household. I also present evidence
that reports from young children may suffer from overreporting bias, likely due to cognitive
underdevelopment. Combined, these results suggest that both child and proxy reports are
susceptible to reporting bias.
The rest of this paper is structured as follows: The next section reviews some of the
key literature regarding the measurement of child labor statistics. Section 2 describes the
available data. Section 3 analyzes major discrepancies - a difference of roughly one to two
thirds - between child and proxy-based indicators of child labor prevalence, affecting up to one
third of the sample. Section 4 evaluates whether child or proxy characteristics (particularly,
child age and proxy attitudes) explain some of the difference between child and proxy reports
of child labor participation. Section 5 concludes.
1
Measuring Child Labor
A large theoretical (most notably Basu & Van, 1998 and Baland & Robinson, 2000) and
empirical literature considers why and how children work, as well as the consequences of that
work. Edmonds (2008) and Basu (1999) provide reviews. Given the absence of a universally
accepted definition of child labor, these empirical analyses vary widely with regard to how
child labor is defined, in addition to how child labor is measured (Edmonds, 2009). The
most commonly used definition in applied research considers children involved in market
3
activities to be child laborers. This includes any work outside the household for wages or
compensation, or any work on the family farm or business. Edmonds (2008) suggests that
nearly half of all empirical studies considers this definition (including Beegle et al. (2006),
Fafchamps & Wahba (2006), and de Janvry et al. (2006)).
In 1998 the ILO’s Statistical Information and Monitoring Programme on Child Labour
(SIMPOC) was established in response to growing demand for accurate and comparable
child labor data. The definition of child labor proposed by SIMPOC (ILO, 2004) includes
work that is (a) mentally, physically, socially, or morally dangerous and harmful to children,
or (b) interferes with their schooling by depriving them of the opportunity to attend school,
obliging them to leave school prematurely, or requiring them to attempt to combine school
attendance with excessively long hours and heavy work. This represents a rather challenging
concept that can be extremely difficult to quantify. For example, what constitutes hazardous
or dangerous work for children? How should we define “excessively long hours?” Despite
SIMPOC’s efforts at standardizing methodologies for collecting child labor statistics (see
ILO, 2004 for more information), much variation still exists in how child labor is measured.
The concern is that different measurement techniques will result in different child labor
estimates, which are often used in policy decisions. Guarcello et al. (2008) studies the extent to which child labor statistics produced by different survey instruments are comparable.
Using 87 different datasets across 35 countries, the study finds that estimates do vary considerably across surveys of different kinds. Differences in observable survey characteristics
such as questionnaire type explain some of the variation in child labour estimates across
survey instruments, but much remains unexplained.
This paper analyzes whether respondent type explains some of the unexplained variation
in child labor estimates. SIMPOC recommends that children age 9 and above should answer
questions about their own work activities. Borgers et al. (2000) suggest that children in this
age range can answer well designed questions with some consistency, however data quality
improves with older children as cognitive, communicative and social skills continue to de-
4
velop. Children younger than 9 years may, in some cases, “have problems in comprehending
or answering questions,” and in these instances SIMPOC suggests that, “someone else in
the family, usually the mother or elder sister, may assist them.” (ILO, 2004). Dammert &
Galdo (2013) summarize the issue as follows:
On one hand, child-reported information may be more accurate than proxy responses, given that a child knows best how she allocates her time. This point
is particularly relevant for children who work outside the family farm or business. At the same time, the cognitive development of children may affect the
quality of the information provided, particularly for those aged 9 and younger.
Calculations for weekly hours worked, for example, could be an issue for younger
children. Similarly, the head of household may be familiar with the children’s
activities since many child laborers in developing countries work on the family
farm or enterprise. Still, the proxy respondent may tend to underreport the true
rate of participation if child labor is viewed as “bad” because of social norms and
cultural values. (Dammert & Galdo, 2013)
In practice, budgetary constraints often mean estimates are based on household surveys in
which a proxy respondent answers questions about a child’s work activities on their behalf. A
recent study in Tanzania by Bardasi et al. (2011) finds lower estimates of adult labor statistics
when measured by a proxy. However, perhaps surprisingly, results from the same survey
experiment differed when child labor statistics were considered (Dillon et al., 2012). For the
survey experiment, households from 7 districts across Tanzania were randomly selected into
two different types of survey treatments: proxy or child respondent, and long or short survey,
for a 2x2 randomized design - long survey with the child respondent, short survey with the
proxy respondent, long survey with the proxy respondent, or short survey with the child
respondent. The results of the experiment suggest that the respondent selection, whether
the child (10-15 years of age) or a proxy respondent (a randomly selected household member
who is at least 15 years old), does not matter for determining rates of child labor. There are
5
two critical limitations of the Dillon et al. (2012) study: 1) the study relies on a relatively
small sample size (566 children) cut into four different treatment groups. While a randomized
experiment is often ideal, this author’s post-hoc power analysis using the statistics reported
in their study suggests the study may suffer from an insufficient sample size for detecting
an effect, if one in fact exists, with inferential statistics. 2) the study does not have data on
the same child across two different respondents (the child and a proxy). For this reason, it
is unable to estimate what they call the “pure” proxy effect - that is, the difference between
child and proxy reports for the same child.
Building on the Dillon et al. (2012) study, Dammert & Galdo (2013) are able to identify
the “pure” proxy effect using a large dataset of 11,739 observations from Peru. The nationally
representative Peruvian data contains information on the same child from two different
sources (child and proxy) for every observation. The study finds significant disagreement,
affecting 20% of the sample, between child and proxy responses. Going beyond what the
(Dillon et al., 2012) study is able to do, the paper also analyzes whether attitudes and social
perceptions toward child labor affect discrepancies between child and proxy responses, and
finds no relationship.
Given the conflicting evidence, this paper uses a unique dataset to fill a critical gap in the
literature by analyzing the “pure” proxy effect in estimating child labor statistics in Tanzania,
and evaluating whether attitudes or other factors drive the estimated discrepancies.
2
Data
The data used for this analysis was collected as part of the WEKEZA (Wezesha Ustawi,
Endeleza, Kiwongo cha Elimu Kuzuia Ajira kwa Watoto) Baseline Survey conducted during the period of July-September, 2013. The WEKEZA project seeks to curb child labor
in Tanzania through provision of scholarships, school vouchers and job or financial management training programs. Eligible beneficiary households were selected from WEKEZA
6
program communities in Tanga and Kigoma Districts of Tanzania based on a screening and
wealth-ranking process designed to target households at risk of having child laborers. 520
households were then randomly selected for the survey from the list of eligible households
with children age 5-17. Note that in this way, the sample is not representative, rather, it
consists of the most vulnerable households in the selected communities.1 The questionnaire
was developed using the ILO’s SIMPOC guidelines for child labor survey-based data collection. The primary respondent (household head) was asked to answer questions related
to basic household demographics, the education levels of all household members, personal
attitudes regarding child labor, and detailed information regarding child employment and
the participation in household tasks of all children.
Table 1 reports household and child-specific summary statistics of basic demographic
information, using data reported by the household head. The average child age is 13, with
children having on average four years of education. Four out of every five children in the
sample are currently enrolled in school. 80% of households own some agricultural land; these
households are later defined as “rural”. 73% of household heads have some education, and
a large number - 50% are female. This high number of female household heads is likely due
to the sample frame consisting of eligible (i.e. “vulnerable”) households.
In addition to these basic demographics, the household head completed a survey module
designed to examine knowledge, attitudes and practices regarding child labor. These attitudinal responses are aggregated into a single “attitudes” index using principle components
analysis. This index will be used later to understand whether attitudes increase proxy bias.2
1
This is in contrast to both Dillon et al. (2012) and Dammert & Galdo (2013) who use representative
samples.
2
Responses to the following series of questions were used to construct the index of child labor attitudes.
Positive/negative weights are indicated accordingly. Response options for each question include: Strongly
Agree, Agree, Neutral, Disagree, Strongly Disagree.
1. Children involved in work often lack proper parental guidance and support. (+)
2. Parents should be prevented from allowing their children to work in hazardous jobs like sisal or tobacco.
(+)
3. It is OK to send your child to work as a domestic boy/girl if you need the money. (-)
4. It is more important for children to earn money for the family than attend school. (-)
5. Employers should be prevented from hiring children. (+)
7
Table 1: Household and Child Demographic Characteristics
Mean
Std. Deviation
Child Characteristics
Female (%)
Age
Years of Education Child
Currently Enrolled/Attending in School (%)
School Attendance (days missed in past week)
51
13.0
4.00
80
0.67
50
2.14
2.14
40
1.35
Household Characteristics
Household size
Rural Agricultural Household (%)
6.76
80
2.25
40
Household Head Characteristics
Any Education (%)
Age
Female (%)
73
47.3
50
45
14.4
50
Observations
573
To supplement the household survey, researchers interviewed 623 children age 10-17 who
were randomly selected from within the interviewed households. These children were asked
directly about their employment and household labor activities. In this way, the data set
contains the information typically used to estimate child labor statistics collected from two
different sources - proxy (household head) and child. This provides a unique opportunity to
investigate the importance of the respondent - child vs. proxy - in estimating child labor
statistics.
The child and proxy (household head) surveys were similar, but not identical. The primary difference affecting this study lies in the way work hours were collected. Proxies were
6. It is ok if a child works instead of going to school. (-)
7. Parents should be prevented from sending their children to work as domestic laborers. (+)
8. Children who are working in the domestic service are often exploited or abused. (+)
9. Parents who send their children into domestic service often cannot afford to take care of them. (+)
10. The work that a child must do should never interfere with their education. (+)
11. Children have more opportunities working as a house girl/boy for a family in town than staying in the
village. (-)
12. It is OK for children to do dangerous work sometimes. (-)
13. Adults should do dangerous work so that children dont have to. (+)
8
asked to estimate the total hours worked (disaggregated by housework and economic activities) by each child for every day of the week. This calculation was deemed too complicated
for children, who were instead asked to estimate the number of hours worked doing various
activities (again disaggregated into housework and economic activities) for every day of the
week. Total daily and weekly hours were then summed electronically. This could clearly
affect some of the results (at least any results based on hours worked)3 . Nonetheless, from
a practical standpoint such simplifications are often made for child surveys. While these
alterations make the two surveys less directly comparable from a researcher’s perspective,
from a practical standpoint, the results are all the more relevant since this most accurately
reflects the type of survey typically used when collecting these statistics from either type of
respondent.
3
Child/proxy discrepancies
This study seeks to quantify the systematic measurement error attributable to respondent
bias in collecting child labor statistics. Suppose there are two instruments available, one
for each respondent type, each thought to suffer from unique systematic error. A standard
difference in means t-test of the child labor indicator across instruments c (child-based) and
p (proxy-based) indicates whether the indicators are on average equivalent, even if they are
both biased.
If either instrument is systematically biased, then the goal is to discern whether systematic exaggeration (positive or negative) in either child or proxy reports is the source of bias.
This is not straightforward, as we do not observe the true value. First, theory cannot predict
the sign of the measurement error of a child’s report: υ c . We anticipate that child cognition
will affect accuracy of the child’s self-report, but it isn’t obvious whether this will result in an
exaggeration that is consistently positive or negative (or neither). Similarly, it is difficult to
3
Note that the “economically active” definition later defined does not require hours worked, and the
differences across surveys can then be ignored.
9
sign the measurement error of the proxy, υ p . A proxy may lack awareness about the child’s
actual activities, but it isn’t clear whether this would result in systematic overestimation or
underestimation of proxy-based measures of child labor. One might hypothesize that the
proxy-based measurement error will be positive if the proxy purposefully underreports the
true rate of participation of child labor (that is, if the proxy knows child labor is frowned
upon). If the difference between the child’s report and the proxy’s report of child labor is
positive, this provides some evidence that the proxy may indeed be purposefully underreporting. However, it still depends on the sign and magnitude of υ c , and it depends on other
factors contributing to the bias of υ p .
Moreover, measurement error could arise from the inevitable “fuzziness” of the question
the respondent is asked, given the complexity of the child labor definitions. If this is the case,
the difference between child and proxy reports may reflect the fact that their interpretation
of the question being asked varies. For example, one survey question posed to both child
and proxy seeks to ascertain whether the child is exposed to dust or fumes while working. A
child may walk outside and think they are exposed to dust, whereas the proxy might respond
affirmatively only if the child is exposed to heavy dust or strong fumes.
Table 2 summarizes data on key measures of child labor and education as reported by
the two different types of respondents. The first column presents proxy responses, while
the second column presents the child responses. In both cases, 1=yes and 0=no for either
respondent unless the variable is continuous (such as weekly hours worked, years of schooling
or weekly school absences). The third column presents the difference between child and proxy
reports. For the reasons previously noted, a difference in means t-test (significance from the
t-test is reported using asteriks in the table) is useful for identifying whether child and proxy
reports are equal, but it is limited in its ability to identify the source, direction, or magnitude
of bias.
Since the definition of child labor is not universal, I consider two prominent definitions.
The first row of Table 2 reports the number of estimated child laborers using the most
10
commonly used empirical definition: the economically active (EA) population. Under this
definition, a child laborer is economically active if he or she is engaged in any market activities
- including paid or unpaid work, and work in a family business or farm - for at least one
hour in the week prior to the activity. This definition, like most definitions of child labor,
does not include domestic work performed in the child’s own household, because housework
is considered noneconomic. Notably, this definition is not affected by the variation across
survey design for the different respondents.4
The second row reports statistics using the ILO’s SIMPOC criteria for child labor, referred to as ILO. Under the ILO criteria, a child under the age of 12/14/17 is defined as
a child laborer if he or she is economically active in the previous week for 1/14/43 hours
respectively, or if he or she participates in any activities that are hazardous. Under this
definition hazardous activities include, but are not limited to, those activities which are
considered the worst forms of child labor such as trafficked, bondage or forced labor, armed
conflicts, prostitution, pornography or illicit activities. However, children in this study who
were considered to be “exposed to hazards,” were exposed to far less serious hazards - such
as dangerous tools or fire (see Table 3 for a complete list)5 . Because this definition uses
hours worked, observed differences across respondents may also reflect small differences in
survey design as noted above.
Using the EA definition, an estimated 37% of children are classified as child laborers using
proxy reports, whereas 61% of children would be considered child laborers using child selfreports. This is a 65% increase over the proxy report, and a statistically significant difference
of 24 percentage points. Because so many children in the population are reportedly exposed
to potentially hazardous working conditions, both child and proxy-based estimates increase
when the ILO definition is considered, but the difference between child and proxy reports
(24 percentage points) remains statistically significant: 66% of children are classified as child
laborers using proxy reports, compared to 89% using child self-reports. This is a 35% increase
4
5
This distinction is important, as Dillon et al. (2012) report variation across survey design.
These hazards match the list of hazards actually used for the baseline report of the WEKEZA project.
11
Table 2: Mean of Child Labor and Education Measures by Type of Respondent
Proxy Child Difference
Child Labor Indicators
EA1
ILO2
Exposure to hazards
Weekly work hours (Total)
Weekly work hours (Market)
Weekly work hours (Household)
0.37
0.66
0.64
16.7
5.18
11.6
0.61
0.89
0.88
26.2
8.40
18.2
0.24***
0.24***
0.23***
8.71***
3.15***
6.18***
Sector
Agricultural
Non-Agricultural
0.16
0.25
0.24
0.49
0.075***
0.24***
Education
Currently attending school
Years of completed schooling
School days missed (past week)
0.80
4.67
0.67
0.76
4.72
0.82
-0.0037
0.030
0.18***
573
573
573
Observations
*** p<0.01, ** p<0.05, * p<0.1
1
2
Economically Active: engaged in any market activities in the prior week.
SIMPOC: Economically active in the previous week for x hours, or hazardous work.
12
Table 3: Mean of Reported Child Exposure to Hazards by Type of Respondent
Proxy Child Difference
Exposure to any hazards
0.64
0.88
0.23***
Dust or fumes
Fire, gas, flames
Loud noise or vibration
Extreme cold or heat
Dangerous tools
Work underground
Work at heights
Work in water
Dark or confined workplace
Insufficient ventilation
Chemicals
Explosives
0.61
0.08
0.18
0.36
0.31
0.01
0.01
0.01
0.01
0.00
0.01
0.01
0.78
0.38
0.20
0.48
0.64
0.01
0.03
0.08
0.05
0.03
0.04
0.00
0.17***
0.31***
0.02
0.12***
0.32***
0.00
0.02***
0.073***
0.043***
0.023***
0.023***
-0.0018***
Observations
573
*** p<0.01, ** p<0.05, * p<0.1
573
573
over the proxy-based estimate.
In estimating the number of child laborers using the ILO definition, any activity thought
to be potentially mentally, physically, socially or morally harmful is considered. For this
reason, the third line of Table 2 summarizes child exposure to potentially hazardous conditions. Both the child and proxy survey contained a series of questions asking about the
child’s exposure to any of the following conditions while working in the past year: dust,
fumes, fire, gas, flames, loud noise, vibration, extreme cold, extreme heat, dangerous tools,
work underground, work at heights, work in water, work in a dark or confined workplace,
insufficient ventilation, or exposure to chemicals or explosives. If the child or proxy answered
“yes” to any of these conditions, the child is considered to have been exposed to “potentially
hazardous conditions”. As with the traditional child labor measures, children report higher
levels of exposure to potentially hazardous conditions. Table 3 further breaks this down
by each particular hazard. Across almost all potential hazards, children consistently report
higher exposure rates.
13
In lines 4-6 of Table 2, I consider work hours as another indicator of child labor. In
both the proxy and child survey, respondents were asked, “For each day worked during the
past week, how many hours did [child] actually work, including the main and other work?”
Two work schedules were in fact collected. The first schedule collected information on hours
worked in the past week doing economic activities. The second schedule collected information
on any hours spent completing household tasks. These hours are summed to create three
separate indicators based on both proxy and child reports: market activity weekly work
hours, household activity weekly work hours, and total weekly work hours. These indicators
are all reported in Table 2. Once again, child self-reports are at least 50% higher, with
children reporting 26 total hours per week on average, 8 hours of market-related activities
and 18 hours spent doing household tasks. These differences between child and proxy are
statistically significant for all three categories of work hours.
In the second panel of Table 2 child work is disaggregated by the agricultural and nonagricultural sector. Here we see that children are 50% more likely to report working in
the agricultural sector (a difference of 8 percentage points) and 100% more likely to report
working in the non-agricultural sector (a difference of 24 percentage points).
In the third and final panel of Table 2 reporting differences on educational outcomes
are considered. This serves two purposes: First, any work that interferes with a child’s
education is considered to be child labor by the ILO, so education outcomes are integrally
tied to the issue of child labor. Second, for basic educational indicators we would not expect
children and proxies’ reports to differ significantly. In this way, testing for differences across
educational indicators serves as a data quality check. I find no detectable difference in basic
educational outcomes between child and proxy. Both respondents provide the same answer
when asked, “Is [child] currently in school? ” and “Which grade? ”. Not surprisingly, some
disagreement between child and proxy responses is observed for school attendance, with
children reporting a higher number of days missed in the previous week.
Another way to compare child and proxy responses is to consider agreement and disagree-
14
Figure 1: Agreement and Disagreement between Proxy and Child on Various Child Labor
Indicators
EA
Both say "Yes."
ILO
Both say "No."
Exposure to Hazards
Proxy says "Yes,"
Child says "No."
Agricultural Work
Proxy says "No,"
Child says "Yes."
Non-Agricultural Work
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ment in responses. If child and proxy responses agree, then we can be more confident that the
measure is accurate for a given observation. The summary statistics just presented suggest
that proxy respondents may largely underestimate child labor relative to child self-reports,
or alternatively, that children may largely overestimate their own child labor participation.
For this reason, we may be particularly interested in knowing how commonly we observe
this misreporting in the data. On the contrary, we would expect to rarely observe proxychild disagreement such that the proxy reports the child as a laborer while the child herself
does not. Figure 1 reports proxy-child agreement and disagreement on various indicators
of child labor. For all indicators, the child and the proxy respondent agree 60-80% of the
time. As expected, proxy over-reporting (relative to child) is rarely observed, whereas proxy
under-reporting (or child over-reporting) is substantial.
Consider the EA definition: proxies under-report (relative to the child) 31% of the time.
Under the ILO’s definition, 27% of observations based on the proxy’s report would suggest the
child is not a laborer, while the child’s own self-report would suggest that he is indeed a child
laborer. If we were to focus on exposure to potentially hazardous conditions, disagreement
exists between 27% of observations such that the proxy reports no exposure while the child
15
indicates that he or she has indeed been exposed to hazards. The same trend shows up when
the sectors are disaggregated: the proxy reports no sector-specific agricultural labor while
the child reports working in agriculture 14% of the time. Similarly, the proxy reports no
non-agricultural child labor while the child reports working outside the agricultural sector
29% of the time.
These summary statistics suggest that considerable differences exist between child and
proxy reports of child labor. In the next section, I consider whether these differences are
affected by characteristics of either respondent.
4
Determinants of child/proxy discrepancies
As a first step toward evaluating the relationship between respondent characteristics and the
realized data discrepancies between child and proxy reports, Table 4 parallels the statistics
reported in the first panel of Table 2, but the summary statistics are further disaggregated
by child sex and age, and proxy attitudes related to child labor. Table 4 shows that the
child-proxy difference is relatively similar across boys and girls, but the gap appears to
shrink among older children relative to younger children, and is larger when proxy reports
are acquired from proxies with strong reported attitudes against child labor. To further
unpack the differences between child and proxy reports of child labor participation, In this
section I estimate the non-causal relationship between child-proxy differences and various
child and proxy characteristics using regression analysis.
To assess whether certain characteristics are correlated with differences in child-based
and proxy-based instruments, let child labor, as measured by instrument k, be a function of
observable determinants xi and random error εi :
yik = x0i β + εki
(1)
Including a dummy variable d indicating whether the dependent variable is measured using
16
Table 4: Mean of Child Labor Measures by Respondent, Child Sex and Age, Proxy Attitudes
Boys
Girls
Proxy Child Difference Proxy Child Difference
EA
0.42
0.66
0.24***
0.32
0.57
0.25***
ILO
0.64
0.88
0.24***
0.67
0.90
0.23***
Exposure to hazards
0.63
0.86
0.23***
0.66
0.89
0.24***
Weekly work hours (Total)
13.0
21.5
7.67***
20.3
30.4
9.65***
Weekly work hours (Market)
5.39
8.81
3.35***
4.99
8.01
2.96***
Weekly work hours (Household)
7.6
13
5.12***
15.4
22.9
7.13***
Observations
280
280
280
293
293
293
Proxy
EA
0.33
ILO
0.62
Exposure to hazards
0.59
Weekly work hours (Total)
13.6
Weekly work hours (Market)
3.77
Weekly work hours (Household) 9.78
Observations
261
Age 10-12
Child Difference
0.61
0.28***
0.87
0.25***
0.84
0.25***
23.1
9.00***
7.4
3.58***
15.7
5.38***
261
261
Weak Attitudes1
Proxy Child Difference
EA
0.42
0.63
0.21***
ILO
0.71
0.9
0.19***
Exposure to hazards
0.69
0.89
0.2***
Weekly work hours (Total)
16.4
25
7.92***
Weekly work hours (Market)
5.24
7.59
2.36***
Weekly work hours (Household) 11.2
17.9
6.35***
Observations
272
272
272
*** p<0.01, ** p<0.05, * p<0.1
1
Proxy
0.40
0.69
0.69
19.4
6.37
13.0
312
Strong Attitudes2
Proxy Child Difference
0.32
0.59
0.27***
0.61
0.89
0.28***
0.6
0.87
0.27***
17
27.4
9.48***
5.14
9.12
3.86***
11.8
18.5
6.01***
301
301
301
Weak attitudes denotes a proxy who earned a below average attitudes index,
(to be interpreted as weakly against - or unaware of - child labor)
2
Strong attitudes denotes a proxy who earned a below average attitudes index,
(to be interpreted as strongly against child labor)
17
Age 13-17
Child Difference
0.61
0.21***
0.91
0.22***
0.91
0.22***
28.9
8.47***
9.23
2.79***
20.4
6.84***
312
312
child or proxy reports allows estimation of the marginal effect γ of using either the child or
proxy report:
yik = x0i β + γd + εki
(2)
Now suppose the respondent bias is correlated with factors that also affect child labor.
Then the estimate of β̂ will be sensitive to the instrument used, which may affect our
understanding of the determinants of child labor. Several methods can be used to test for
this. First, interaction terms could be included. For example, d could be interacted with
child age or proxy attitudes related to child labor if we anticipate that these variables affect
the report of child labor differentially, depending on the respondent used. The results of this
“pooled model” are presented in Section 4.1.
Alternatively, Equation 1 could be estimated separately using both instruments. This
alternative specification allows us to take advantage of the “pure” proxy effect. Using this
specification, we then test whether β̂ c = β̂ p . To allow for the possibility that the errors
are correlated across the equations, the model can be estimated using seemingly unrelated
regression. The results of this specification are presented in Section 4.2.
Rather than analyze the determinants of child labor, we may be more interested in the
determinants of differences between child and proxy reports: ∆y = yic − yip . Writing this
difference as a function of observable determinants and errors yields:
∆y = x0i δ + εi
(3)
A significant coefficient is similar to testing β̂ c = β̂ p , a test of whether the systematic
measurement bias depends on xi . If the dependent variables are discrete, this is akin to
analyzing determinants of agreement and disagreement. Section 4.3 evaluates the “pure”
proxy effect through use of child-proxy agreement and disagreement as dependent variables.
Section 4.4 analyzes differences in continuous variables (specifically work hours).
18
4.1
Pooled Model
In this first step of the regression analysis the data is pooled across both child and proxy
reports. Following Equation 2, an indicator of child labor is regressed on a dummy variable
equal to 1 if the outcome variable was obtained from a child’s own self-report and a vector of
covariates. This specification allows for testing not only the hypothesis that the age of the
child and the attitudes of the proxy might influence the probability that the child is working,
but can also test whether those characteristics impact the report of child labor differentially
depending on whether a child or proxy report is used. To test this latter hypothesis, a dummy
variable for child/proxy report is interacted with child age and proxy attitudes respectively.
Table 5 reports the results of this specification. Three outcomes of interest are used: the
EA and ILO definitions of child labor described previously (using probit), and work hours
relating to economic (not household-related) activities (using OLS). Additional covariates
include variables typically associated with vulnerability as it relates to a household sending
children into the work force: education and age of the household head, whether the head
is male or female, household size, rural, wealth, and whether the household has recently
experienced a covariate (defined as any country or community-wide problem in the last
year) or income shock (defined as a direct income shock, loss of employment, bankruptcy,
illness preventing work, death or abandonment of a working family member, criminal act of
a household member, loss of cash support or in-kind assistance, declining prices of products
important to the family business, harvest loss or livestock loss).6 The regression is first
estimated using the full sample (columns 1-3), and then disaggregated by boys (columns
4-6) and girls (columns 7-9).
Similar to the mean comparisons of Section 2, Table 5 suggests that child self-reports of
child labor are higher than proxy reports. This effect is consistently positive across specifications and often, though not always, statistically significant. Older children are more likely to
6
Jacoby & Skoufias (1997), Yang (2008), Beegle et al. (2006) and Duryea et al. (2008) provide examples of
studies that have shown households use child labor to cope with various types of (covariate or idiosyncratic)
income shocks.
19
20
yes
1,088
0.101
2.122***
(0.769)
-0.077
(0.052)
-0.132**
(0.059)
0.093***
(0.026)
-0.036
(0.031)
0.106
(0.199)
-0.029
(0.228)
-0.011
(0.010)
-0.017
(0.011)
0.950***
(0.212)
-0.087***
(0.027)
-0.462*
(0.253)
-0.198
(0.373)
-1.989***
(0.575)
yes
1,088
0.220
1.960
(1.484)
-0.016
(0.119)
-0.141
(0.131)
0.178***
(0.051)
-0.057
(0.096)
-0.078
(0.289)
-0.035
(0.298)
-0.010
(0.007)
0.050***
(0.019)
1.133***
(0.300)
-0.078**
(0.038)
0.393
(0.320)
-0.612**
(0.258)
-1.479
(1.269)
(2)
Pooled
ILO
yes
1,084
0.090
yes
534
0.139
1.909
(1.229)
-0.058
(0.080)
-0.160***
(0.022)
0.120**
(0.053)
-0.085
(0.066)
0.347
(0.469)
0.248
(0.278)
-0.018
(0.014)
0.028
(0.029)
1.464**
(0.664)
-0.066
(0.069)
-0.765
(0.473)
0.219
(0.532)
-3.025
(2.178)
EA
Hours1
5.553***
(0.815)
-0.168*
(0.072)
-0.511**
(0.180)
0.927***
(0.078)
0.132
(0.168)
0.811
(0.726)
-0.555
(0.983)
0.001
(0.046)
0.226
(0.193)
3.453***
(0.776)
-0.467***
(0.103)
-0.404
(0.670)
-1.843
(1.422)
-11.519*
(4.584)
(4)
(3)
Cluster robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
1 denotes work hours related to economic activities, not household activities
District Fixed Effects
Observations
R-squared
Constant
Income Shock
Covariate Shock
Wealth Index
Rural Agricultural Household
Household size
Age Household Head
Female Household Head
Any Education Household Head
Attitudes index
Age Child
Attitudes * Proxy Report
Age Child * Child Self-Report
Child Self-Report
EA
(1)
yes
534
0.191
2.278*
(1.377)
-0.048
(0.112)
-0.076
(0.145)
0.189***
(0.065)
-0.086
(0.120)
0.264
(0.343)
0.034
(0.274)
-0.014
(0.012)
0.033
(0.029)
1.038*
(0.534)
-0.065
(0.040)
0.372
(0.315)
-0.691**
(0.328)
-1.544
(1.968)
(5)
Boys
ILO
yes
533
0.135
7.684
(4.662)
-0.300
(0.404)
-0.574
(0.392)
1.247***
(0.269)
-0.434
(0.273)
1.760
(1.121)
0.093
(0.448)
-0.015
(0.032)
0.273
(0.293)
5.594**
(1.603)
-0.332*
(0.156)
-1.859**
(0.695)
0.127
(1.888)
-17.842**
(5.479)
Hours1
(6)
Table 5: Determinants of child labor outcomes using pooled data
yes
554
0.980
2.428***
(0.831)
-0.099
(0.061)
-0.099
(0.111)
0.092**
(0.044)
-0.001
(0.046)
-0.188
(0.158)
-0.267
(0.242)
-0.007
(0.011)
-0.033**
(0.015)
0.632**
(0.269)
-0.108***
(0.039)
-0.080
(0.328)
-0.607*
(0.338)
-1.762*
(0.910)
EA
(7)
yes
554
0.273
1.576
(1.615)
0.026
(0.129)
-0.239
(0.165)
0.183***
(0.061)
-0.004
(0.128)
-0.464
(0.339)
-0.130
(0.394)
-0.008
(0.009)
0.062*
(0.034)
1.192***
(0.070)
-0.098**
(0.040)
0.474
(0.509)
-0.645**
(0.327)
-1.351
(1.187)
(8)
Girls
ILO
yes
551
0.111
3.239
(4.341)
-0.020
(0.351)
-0.489
(0.341)
0.705**
(0.197)
0.572**
(0.221)
-0.302
(1.270)
-1.489
(2.469)
0.016
(0.053)
0.107
(0.247)
1.944**
(0.708)
-0.583
(0.303)
1.759
(1.484)
-3.920
(2.247)
-7.147
(4.994)
Hours1
(9)
be child laborers, as are rural children from poorer households. Although there is very little
evidence that proxy attitudes directly influence whether a child is engaged in child labor,
there is some evidence to suggest that if a proxy is strongly against child labor, then the
proxy’s report of child labor tends to be lower (as indicated by the consistently negative coefficient on the interaction between proxy attitudes and a proxy report). While older children
are more likely to be engaged in child labor, a consistently negative coefficient (although
not statistically significant) on the interaction between child age and a child’s report may
suggest a dampening effect as the child age: younger children may be overreporting.
4.2
Pure Proxy Effects
This dataset is unique in that it contains duplicate information on the same child as reported
from two different sources: the child and a proxy. This allows estimation of a “pure” proxy
effect. Table 6 presents the results of estimating Equation 1 separately for child and proxy
reports. The first two columns report probit results using EA as the dependent variable,
columns 4-5 report probit results using the ILO’s definition of child labor as the dependent
variable. Columns 4 and 6 report p-values for the test of equality of coefficients between the
child-based and proxy-based models.
Focusing on these p-values, statistically significant differences in means between child
and proxy-based coefficients for the two variables of theoretical interest: child age and proxy
attitudes. These differences highlight the importance of this analysis: if the outcome we
seek to measure (in this case child labor) depends on characteristics of the respondent,
then the measures will be differentially biased depending on who we as researchers select
as respondents. This is thus a very important consideration when we consider aggregating
child labor statistics across time and space.
21
22
yes
544
0.109
0.020**
(0.009)
-0.033***
(0.011)
-0.091**
(0.039)
-0.000
(0.049)
-0.030
(0.043)
-0.003**
(0.002)
-0.006
(0.010)
0.181***
(0.065)
-0.016
(0.010)
-0.126**
(0.050)
-0.062
(0.063)
0.239
(0.195)
yes
544
0.065
0.002
(0.010)
-0.008
(0.012)
-0.087**
(0.041)
0.038
(0.051)
0.018
(0.045)
-0.002
(0.002)
0.002
(0.010)
0.228***
(0.067)
-0.021*
(0.011)
-0.065
(0.052)
-0.038
(0.065)
0.457**
(0.203)
(2)
EA
Proxy Report
0.368
0.757
0.325
0.716
0.565
0.492
0.506
0.366
0.531
0.936
0.070*
0.106
P-value
(3)
Seemingly unrelated regression with correlated standard errors in parentheses
*** <0.01, ** p<0.05, * p<0.1
District Fixed Effects
Observations
R-squared
Constant
Income Shock
Covariate Shock
Wealth Index
Rural Agricultural Household
Household size
Age Household Head
Female Household Head
Any Education Household Head
Female Child
Attitudes index
Age Child
Child Report
(1)
yes
544
0.202
0.035***
(0.009)
-0.029***
(0.011)
0.030
(0.037)
-0.021
(0.046)
0.025
(0.040)
-0.003*
(0.001)
0.010
(0.009)
0.241***
(0.061)
-0.016
(0.010)
0.040
(0.047)
-0.119**
(0.059)
0.212
(0.183)
Child Report
(4)
yes
544
0.127
0.009
(0.006)
-0.006
(0.007)
0.014
(0.025)
0.014
(0.031)
-0.033
(0.028)
-0.000
(0.001)
0.003
(0.006)
0.165***
(0.041)
-0.010
(0.007)
0.055*
(0.032)
-0.061
(0.040)
0.671***
(0.125)
(5)
ILO
Proxy Report
Table 6: Determinants of child labor outcomes, “pure” proxy effects
0.025**
0.377
0.772
0.594
0.265
0.442
0.111
0.199
0.503
0.683
0.057*
0.009***
P-value
(6)
4.3
Child-Proxy Agreement and Disagreement
The analysis presented thus far has shown considerable disagreement between child and proxy
on child labor participation reports. Figure 1 showed that 14-31% of observations yielded
different reports between child and proxy, depending on the indicator. In this section, I
consider each of the possible scenarios described in Figure 1: a) Both say “Yes,” b) Both say
“No,” c) Proxy says “Yes,” but Child says “No,” d) Proxy says “No,” but Child says “Yes.”
Columns 1-3 of Table 7 report the results for a regression where the dependent variable
equals 1 if both the proxy and child agree that the child is an active participant in the labor
force using the EA (column 1) and ILO (column 2) definitions of child labor, or is exposed to
potentially hazardous conditions while working (column 3). Column 4-6 reports the results
using a dependent variable equal to 1 if both the proxy and child agree that the child is not
a child laborer (columns 4-5 using EA and ILO respectively) or exposed to potential hazards
(column 6). Finally, columns 7-9 report similar regression results using a dependent variable
equal to 1 if the proxy does not report child labor or exposure to hazards, while the child’s
self report does (for EA, ILO and hazards respectively). All results presented in this table
are estimated as a seemingly unrelated regression with correlated errors. Few observations
exist for the fourth scenario- proxy reporting “yes” while the child reports “no” - so it is
left out as the base scenario (it cannot be estimated jointly due to matrix singularity). This
scenario is also theoretically the least interesting and the most difficult to interpret.
The results presented in Table 7 support the previous findings. Suppose proxy-child
agreement are indicative of “true” child labor measures. The first 6 columns suggest that
older males living in rural areas are most likely to be engaged in child labor. Economic factors
obviously play a role, lower wealth households are more likely to have children engaged in
child labor. Attitudes also matter: proxy respondents with strong negative attitudes towards
child labor are more likely to have child-proxy agreement: their children are less likely to
be counted as engaged in child labor either by proxy or child report. In other words, people
with strong attitudes against child labor are less likely to have children engaged in child
23
24
yes
544
0.098
0.018**
(0.009)
-0.023**
(0.011)
-0.112***
(0.038)
0.052
(0.047)
-0.023
(0.041)
-0.002
(0.001)
-0.008
(0.009)
0.162***
(0.062)
-0.017*
(0.010)
-0.082*
(0.048)
-0.103*
(0.060)
0.148
(0.186)
EA
yes
544
0.197
0.037***
(0.009)
-0.026**
(0.011)
0.033
(0.038)
-0.014
(0.047)
0.016
(0.041)
-0.002
(0.001)
0.011
(0.009)
0.236***
(0.062)
-0.017*
(0.010)
0.045
(0.048)
-0.106*
(0.060)
0.123
(0.187)
ILO
yes
544
0.232
0.044***
(0.009)
-0.026**
(0.011)
0.032
(0.037)
-0.012
(0.046)
0.035
(0.041)
-0.002
(0.001)
0.014
(0.009)
0.249***
(0.061)
-0.021**
(0.010)
0.048
(0.048)
-0.080
(0.060)
-0.070
(0.185)
Hazards
yes
544
0.091
-0.004
(0.009)
0.018*
(0.011)
0.067*
(0.039)
0.015
(0.048)
-0.011
(0.042)
0.003**
(0.001)
-0.004
(0.009)
-0.247***
(0.063)
0.020*
(0.010)
0.110**
(0.049)
-0.002
(0.062)
0.452**
(0.191)
EA
yes
544
0.149
-0.007
(0.005)
0.008
(0.006)
-0.011
(0.021)
-0.007
(0.026)
0.023
(0.023)
0.000
(0.001)
-0.002
(0.005)
-0.169***
(0.034)
0.008
(0.006)
-0.051*
(0.027)
0.074**
(0.033)
0.240**
(0.104)
ILO
Seemingly unrelated regression with correlated standard errors in parentheses
*** <0.01, ** p<0.05, * p<0.1
District Fixed Effects
Observations
R-squared
Constant
Income Shock
Covariate Shock
Wealth Index
Rural
Household size
Age Head
Female Head
Education Head
Female Child
Attitudes index
Age Child
(5)
(6)
yes
544
0.152
-0.013**
(0.005)
0.009
(0.006)
-0.018
(0.022)
0.005
(0.028)
0.034
(0.024)
0.001
(0.001)
-0.003
(0.005)
-0.168***
(0.037)
0.007
(0.006)
-0.061**
(0.028)
0.053
(0.035)
0.326***
(0.110)
Hazards
Both say “No”
(4)
Both say “Yes”
(3)
Proxy and Child Agree
(2)
Proxy and Child Agree
(1)
(8)
(9)
yes
544
0.022
-0.017*
(0.009)
0.015
(0.011)
0.024
(0.040)
-0.015
(0.050)
0.041
(0.044)
-0.000
(0.002)
0.010
(0.010)
0.065
(0.066)
-0.003
(0.011)
0.017
(0.051)
0.064
(0.064)
0.309
(0.198)
yes
544
0.117
-0.028***
(0.009)
0.020*
(0.010)
-0.020
(0.036)
0.028
(0.045)
-0.048
(0.040)
0.002
(0.001)
-0.008
(0.009)
-0.071
(0.060)
0.007
(0.010)
0.011
(0.047)
0.045
(0.058)
0.548***
(0.181)
yes
544
0.127
-0.028***
(0.009)
0.020*
(0.010)
-0.009
(0.036)
0.007
(0.045)
-0.078**
(0.040)
0.001
(0.001)
-0.010
(0.009)
-0.085
(0.060)
0.011
(0.010)
0.011
(0.046)
0.037
(0.058)
0.647***
(0.180)
Proxy and Child Disagree
Proxy says “No”
Child says “Yes”
EA
ILO
Hazards
(7)
Table 7: Determinants of child labor agreement and disagreement between child and proxy reports
labor, and their reports regarding their own child’s engagement in child labor is more likely
to be accurate than similar reports from those lacking a strong negative opinion. These
relationships are as we would expect.
Our primary interest lies in columns 7-9, which considers the case where the proxy does
not report the child to be a laborer, whereas the child’s own self report suggests he or she
should be counted as a child laborer. Although the model explains very little variation in
the data, we see some evidence that two factors, both grounded in theory, may be driving
child-proxy disagreement. 1) Older children are less likely to disagree with the proxy. 2)
Proxies with strong attitudes in opposition of child labor are less likely to report child labor
even if the child self-report indicates that he or she is a laborer. This evidence supports
underreporting of child labor (especially hazard exposure) by proxies with strong attitudes
in opposition of child labor. These two results combined suggest that both child and proxy
reports are likely to be biased in opposite directions: young children may overreport, and
proxies with strong attitudes against child labor may underreport.
4.4
Child-Proxy Differences
In a final step of the analysis, Table 8 shows the determinants of differences between child
and proxy reports of weekly hours worked. The first two columns are joint estimates using
seemingly unrelated regression. The third column is estimated separately.7 Here we observe
additional evidence of proxy underreporting and child overreporting. The gap between child
and proxy reported work hours, at least for economic activities, shrinks as the child ages.
Stronger attitudes appear to increase the spread between market-related work hours reported
by the child and proxy, but they have no impact on the difference reported for household
activities. This may indicate an opinion that children should not be engaged in market
activities, whereas household tasks are deemed appropriate.
7
Total work hours cannot be estimated as part the system due to matrix singularity since it is constructed
as the sum of economic and household activity hours.
25
Table 8: Determinants of differences between child and proxy reports of weekly hours worked
Age Child
Attitudes index
Female Child
Education Head
Female Head
Age Head
Household size
Rural Household
Wealth Index
Covariate Shock
Income Shock
Constant
District Fixed Effects
Observations
R-squared
(1)
(2)
(3)
Economic
Activity
Work Hours
Household
Activity
Work Hours
Total
Work Hours
-0.456*
(0.245)
0.730**
(0.287)
-0.179
(1.029)
1.058
(1.285)
2.071*
(1.117)
-0.020
(0.040)
0.380
(0.245)
-0.441
(1.741)
-0.298
(0.263)
-2.741**
(1.294)
2.503
(1.661)
5.910
(5.133)
0.227
(0.301)
-0.297
(0.354)
1.985
(1.266)
-0.296
(1.582)
0.718
(1.375)
-0.019
(0.049)
0.570*
(0.302)
0.688
(2.143)
0.001
(0.324)
-1.865
(1.593)
2.194
(2.045)
-5.762
(6.319)
-0.229
(0.289)
0.433
(0.415)
1.807
(1.705)
0.762
(2.114)
2.788***
(0.581)
-0.039
(0.039)
0.950*
(0.441)
0.247
(3.493)
-0.296
(0.454)
-4.607
(2.680)
4.697
(4.348)
0.148
(6.496)
yes
445
0.056
yes
445
0.072
yes
445
0.061
Seemingly unrelated regression with correlated standard errors in parentheses
*** <0.01, ** p<0.05, * p<0.1
26
5
Conclusion
In this paper I analyzed whether respondent type plays a role in explaining variation in child
labor statistics. The results presented suggest that it most certainly does. Estimates of
child labor increase by approximately 35-65% when child self reports are used to construct
indicators of child labor prevalence, rather than proxy reports. Disagreement between the
child and proxy exist in 14-31% of observations. This finding is noteworthy in that it
contradicts the findings of Dillon et al. (2012) who study a similar population and find
no difference between child and proxy.
I also analyzed whether certain characteristics contribute to the reporting bias. The
analysis provides some evidence that younger children may overreport their child labor status.
This could suggest that younger children, even at age 10, are not as capable cognitively
as is commonly argued, and may actually overreport their engagement in the labor force.
In addition, some proxies who demonstrate an attitude against child labor may choose to
underreport child labor in their household if the child is in fact working. These findings
are somewhat nuanced, and future research should explore these issues in greater detail.
However, it does suggest that the “gold standard” of child self-reporting may not be as clear
cut as previously anticipated. The answer to the question, “Whom should we ask?” is not
straightforward if both reports are biased, albeit in opposite directions.
Because funding decisions are regularly tied to reports of child labor statistics, measuring
these statistics accurately is of critical importance. A recent ILO report claims that 168
million children are child laborers. Assuming the estimates are largely based on proxy reports
(since proxy reports are easier to acquire), this analysis suggests that the estimates would be
much higher if children were asked to report on their own child labor. Yet, these estimates are
routinely used by the ILO and others to target policy reform, institutional capacity building
efforts, and awareness raising campaigns. This analysis suggests that respondent choice is
crucial for accurate measurement, cross-study comparability, and efficient targeting.
27
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