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Patterns of Change in Child Labour and Schooling inTurkey: The Impact of Compulsory SchoolingMeltem Dayioğlu a

a Middle East Technical University, Department of Economics , 06531, Ankara, TurkeyPublished online: 23 Jan 2007.

To cite this article: Meltem Dayioğlu (2005) Patterns of Change in Child Labour and Schooling in Turkey: The Impact ofCompulsory Schooling, Oxford Development Studies, 33:2, 195-210, DOI: 10.1080/13600810500137798

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Patterns of Change in Child Labourand Schooling in Turkey: The Impactof Compulsory Schooling

MELTEM DAYIOGLU

ABSTRACT Substantial improvements have taken place in the employment and schooling ofchildren in Turkey. Decomposition analysis based on data from two time periods shows that asubstantial part of the drop in child labour and over half of the increase in school enrolment can beattributed to the changing cost and benefit structures of work and schooling rather than to changingpopulation characteristics. This paper establishes that work and schooling are incompatibleactivities and that the negative association between them has increased over time. The observedchanges are attributed to the extension of compulsory schooling and the ban on child labour.

1. Introduction

One of the main reasons why child labour is so vigorously objected to is the belief that

work interferes with the schooling of children. The negative association between child

labour and schooling has been established in a number of developing countries (see, for

instance, Skoufias, 1994; Grootaert & Patrinos, 1999; Chernichovsky, 1985; Ray, 2000;

Psacharopoulos, 1997; Ravallion & Wodon, 2000; Assaad et al., 2001; Levison et al.,

2001). At the same time, there is also a small number of studies that show that children are

able to combine work and schooling without detrimental effects on their school

performance, and it might well be the case that the earnings from work make their

schooling possible (Patrinos & Psacharopoulos, 1997; Myers, 1989; Admassie, 2003).

Research on this subject is very limited in Turkey. The exceptions include: Tunalı (1996),

who investigated the determinants of child schooling and their labour market involvement

but did not enquire into the possible link between the two phenomena; Dayıoglu and

Assaad (2003), who looked at the determinants of child labour but not schooling; and

Tansel (2002), who studied the educational attainment of children but ignored the issue of

child labour. This paper contributes to the discussion on the interplay between child labour

and schooling by providing further evidence from Turkey. In particular, it seeks to analyse

the association between children’s schooling and work in two time periods, in 1994 and

1999, over which there was a substantial drop in child labour and a significant increase in

ISSN 1360-0818 print/ISSN 1469-9966 online/05/020195-16

q 2005 International Development Centre, Oxford

DOI: 10.1080/13600810500137798

I wish to thank Cem Baslevent, Sheila Pelizzon and an anonymous referee for their valuable comments and

suggestions. I assume responsibility for the remaining errors.

*Meltem Dayıoglu, Department of Economics, Middle East Technical University 06531, Ankara, Turkey.

Oxford Development Studies,Vol. 33, No. 2, June 2005

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child schooling. Data from two time periods not only allows us to determine the main

sources of improvement in the schooling and work outcomes of children over time, but

also makes it possible to test the robustness of the relationship between the two.

The most recent estimates based on the 1999 child labour survey (CLS) conducted by the

State Institute of Statistics (SIS) ofTurkey indicate that half amillion children (4.5%of the child

populationbetween the ages of7 and14) are actively engaged inmarket-oriented activities (SIS,

2002). Only 5 years earlier, a previous study of child labour showed that one million children

were engaged in market work (8.7% of the child population). These surveys estimate the

improvement in school enrolment to be 3.5 percentage points, from 88% in 1994 to 91.5% in

1999. This drop in the number and proportion of working children makes Turkey one of the

success stories in the fight against child labour. The success is due to several factors: over 100

projects conducted since 1992 under the ILO-initiated IPEC programme (International

Programme on the Elimination of Child Labour); the signing of the UN Convention on the

Rights of the Child (CRC) in 1994; the extension of compulsory basic education from 5 to 8

years in 1997; and the ratification of ILO Convention 138 in 1998, which is the minimum age

convention.By ratifying the latter,Turkey instituted age15as theminimumageof employment.

Prior to the convention, theminimumage of employment in industry and serviceswas 12 years,

but an age limit did not exist in agriculture or unpaid family work.

Among these initiatives, the one that caused the most discussion was the extension of

compulsory schooling. The previous system was based on a three-tiered structure with 5

years of primary school, 3 years of junior-high and 3 years of high school. Prior to 1997,

children were required only to finish the first tier. With the extension of compulsory

schooling, the first two tiers were combined so that children were required to stay in school

until age 15. One of the major groups that opposed the new education act was made up of

small informal establishments that made use of child labour. Despite their opposition and

problems with financing and infrastructure, the government enforced the new law in the

1997–98 school year. The fact that the extension of compulsory schooling and the ban on

child labour came after the first child labour survey but before the second one gives us an

excellent opportunity to assess whether they had any favourable effects on child labour

and schooling.

Improvements in child labour and schooling in Turkey took place despite unfavourable

economic conditions. In both survey years, 1994 and 1999, the economy shrank by 6.1%,

significantly affecting household well-being. While the main source of the 1994 downturn

was macroeconomic instability, in 1999 it was a devastating earthquake that hit the

Marmara region, the industrial heartland of Turkey. In analysing the determinants of child

labour and schooling and the relationship between the two, this paper will also consider the

economic standing of the household and attempt to determine its impact on the outcomes.

The material well-being of the household is expected to affect both child labour and

schooling not only because children might be required to contribute to family budget, but

also because the household might simply not be able to afford the direct cost of schooling.

Although public education in Turkey is “free”, parents still need to meet such schooling

expenses as books, supplies, transportation and the like. In the literature, mixed results

emerge on how poverty affects child work and schooling. While some studies found that

poverty adversely affects both, others found minimal effects or no negative effect.1

In Turkey, Tansel (2002) found that child schooling, measured in terms of highest grade

attained, improved with household income, while Dayıoglu & Assaad (2003) established

that it reduced child employment in urban areas.

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Section 2 presents the methodology employed. Section 3 describes the data sources and

discusses the characteristics of children and their parents on the basis of key variables.

Section 4 presents the results of the determinants of child labour and schooling and

attempts to establish the link between the two phenomena. In Section 5, a series of

simulation exercises is carried out to speculate on the main sources of improvement in

child labour and schooling over time. Section 6 concludes the paper.

2. The Model

Children’s time can be allocated into a number of activities, including market work and

schooling. Following Rosenzweig & Evanson (1977), the study analyses the time

allocation of children within a neo-classical model where the household maximizes a

common utility function subject to the full income budget constraint. The reduced form

demand equations for work and schooling can be expressed as functions of the

characteristics of the child, the parents, and the household and the community at large. The

determinants of child work and schooling include the age and the sex of the child, ages of

the parents, the level of parental schooling, employment status of the father, the number of

children2 of different ages in the household, existence of a household-based enterprise,

household material well-being and whether the household resides in urban or rural areas.

The age and the sex of the child, along with the existence of a household-based enterprise,

will determine the opportunity cost of child’s time. Similarly, the age and education level

of the parents will determine the opportunity cost of parental time. Assuming that the

father’s employment decision is taken independently of the child’s, we use the father’s

sector of employment to indicate his level of earnings. Rural as opposed to urban residency

is likely to affect the potential wages of children and their parents as well. The number of

children in the household is used to indicate the availability of resources per child.

Although the fertility decision of the household is potentially endogenous to the decision

of whether to send the child to school and/or to work, its use is justified on the grounds that

our main concern in this paper is to understand the changes in child labour and schooling

over the period 1994–99, which is too short a time-frame to allow for substantial

adjustments in fertility behaviour. We consider the poverty status of the household as a

more direct measure of household material well-being. This is derived from a wealth index

constructed on the basis of dwelling characteristics and facilities enjoyed by the household

using principal components analysis. On the basis of the wealth index, households are

divided into five equal groups. The lowest 20% are considered to constitute the poor.3

Since the dwelling as well as the facilities enjoyed will show variations between urban and

rural areas due to differences in needs and customs, we estimate the incidence of poverty

separately for urban and rural areas.4

Considering that the work and schooling decisions of children are possibly

interdependent, we model the two outcomes using bivariate probit analysis. This

technique has two main advantages: (1) it allows us to test directly whether or not the two

decisions are indeed interdependent; and (2) it does not require the ordering of the two

decisions. This property is desirable since a priori it is not possible to determine whether

children start working because they drop out of school or they drop out of school because

they need to work. The alternative estimation procedures could have been nested logit,

ordered probit or multinomial logit, which have also been widely used in the child labour

and schooling literature. The disadvantage of nested logit and ordered probit is that they

Child Labour and Schooling in Turkey 197

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require the ordering of decisions. Multinomial logit does not have this drawback.

Furthermore, it allows for the study of various time-use alternatives (e.g. work and

schooling, no work but schooling, no schooling but work, neither work nor schooling).

However, its use is justified when the independence of irrelevant alternatives (IIA)

assumption is not violated, which it often is.5 Notwithstanding these concerns and given

that we wish to estimate the possible interdependence between the two outcomes, bivariate

probit seems to serve our purposes the best. The model employed can be summarized as

follows:

W*t ¼ xtbt þ mt; t ¼ 1994; 1999

Wt ¼1 if xtbt þ mt $ 0

0 otherwise

S*t ¼ xtgt þ nt t ¼ 1994; 1999

St ¼1 if xtgt þ nt $ 0

0 otherwise

(;

where W*t and S*t are latent variables indicating the propensity of the child to engage in

work and to enrol in school, respectively. The child is observed to engage in market work

(or enrol in school) only if a certain (unobserved) threshold is surpassed. x denotes the

vector of child and household characteristics, b and g denote the parameter vectors, and m

and n are normally and independently distributed error terms that are allowed to be

correlated with each other.6 Marginal probabilities regarding child labour and schooling

are estimated using:

Pr½W ¼ 1� ¼ FðxtbtÞ; t ¼ 1994; 1999

Pr½S ¼ 1� ¼ FðxtgtÞ; t ¼ 1994; 1999;

where F is the univariate cumulative normal distribution function.

Once we estimate the reduced form demand equations for work and schooling at two

points in time, the next step is to carry out simulation exercises to determine the factors

that are instrumental in bringing about the observed changes. The drop in child labour and

increase in school enrolment might be the result of favourable changes in population

characteristics and/or work and schooling structures over time. In the absence of external

shocks, we expect the former to lead to a gradual fall in child labour and increase in child

schooling over time. However, an external shock would change the way the covariates

impact on the outcomes, giving rise to changes over and above what would be realized

with a mere change in population characteristics.

As the main sources of structural change in time-use patterns, we consider the extension

of compulsory schooling and the ban on child labour. The difficulty of enforcing the latter,

due to the employment of most children either in household establishments or in the

informal sector, leads us to consider the former as the main engine of change. It might be

claimed that the various projects conducted within the IPEC programme and the economic

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downturns of 1994 and 1999 have also acted as sources of structural change. Given that the

majority of the IPEC projects were of micro-scale, it is unlikely that they could have

produced an impact on time-use patterns at such scale. The impact of the changing

economic environment on the work and schooling outcomes of children is more difficult to

ascertain. However, the similarity in the macroeconomic environments at two survey

periods possibly works to minimize the effect of the downturns.

The first simulation exercise asks what the incidence of child labour and school

enrolment would have been in 1994 had the mean child and household characteristics

observed in 1999 prevailed in 1994. The second simulation exercise does the opposite. It

determines the incidence of child labour and school enrolment in 1994 based on

population characteristics that prevailed in 1999. We can illustrate these simulation

exercises and the resulting changes in the incidence of child labour using the estimated

equation for the propensity to engage in market work as follows:

P1 ¼ DPr½W ¼ 1� ¼ Fð�x1994b1994Þ2Fð�x1999b1999Þ;

P2 ¼ DPr½W ¼ 1jx1994� ¼ Fð�x1994b1994Þ2Fð�x1994b1999Þ;

P3 ¼ DPr½W ¼ 1jx1999� ¼ Fð�x1999b1994Þ2Fð�x1999b1999Þ;

where P1 is the observed change in the incidence of child labour between 1994 and 1999,

and P2 and P3 are the resulting changes under the above scenarios. If we find P2 (or P3), for

instance, to be much smaller than P1, then we would conclude that the main reason for the

drop in child labour between 1994 and 1999 had been the favourable changes in the mean

characteristics of the population. If, on the other hand, P2 (or P3) is at a level around P1 we

would conclude that there had been favorable changes in the structure of employment.

3. Data

The data for this study come from the 1994 and 1999 CLSs conducted by the SIS of

Turkey within the framework of the IPEC programme. The two surveys are similar in

many respects. Both were launched as modules in the October round of the Household

Labour Force Surveys (HLFSs). Except for a few questions added to the 1999 survey, they

employed the same questionnaire. The 1994 CLS included 9822 children between the ages

of 7 and 14 from 5686 households. The 1999 CLS used an extended frame covering 11 801

children from 7241 households. From the former, 8944 children and, from the latter, 11

002 children belonging to the household head are drawn.7

We consider the child to be employed if he/she has done at least 1 hour of market work

in the reference week. The work status of the child is determined directly from the answers

given by the child to a series of labour market questions on possible forms of market work

he/she might have carried out during the reference week, such as wage work, work without

pay within the family establishment, apprenticeship, petty trade and the like. The

schooling status, on the other hand, is determined by asking the child whether he/she is

presently enrolled in school. Since the reference week for both surveys was the last week

in October and schools start around mid-September, all school age children were expected

to be enrolled in school at the time of the survey.

Child Labour and Schooling in Turkey 199

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The drop in child labour and the increase in child schooling over time are illustrated in

Figure 1.8 Statistical tests confirm that changes in both variables are significant at

conventional levels. The improvement in child schooling is most visible in the 12–13-year

age category, which marked the end of compulsory schooling in 1994. Traditionally, the

break in child schooling came at around 12 years when children completed their

compulsory primary schooling. The enactment of the new education law seems to have

reduced the drop drastically, though it is still the case that school enrolment goes down

with age. The relatively small improvement observed for the 14-year-olds can be

attributed to the timing of the new schooling act vis-a-vis the 1999 survey. The extension

of compulsory schooling in 1997 would most strongly have affected those children who

were, at the time, still enrolled in primary school, since integrating school drop-outs back

into the system is relatively harder. Not only would they be older for their grade, which

might impose non-pecuniary costs on them, but if they had already started working, going

back to school would entail a higher opportunity cost. The fact that the most substantial

improvement in school enrolment is observed for the 12–13-year-olds gives support to the

conjecture that any structural adjustment in the schooling and, possibly, employment

equations of children would be due to the legal machinery, rather than the economic

downturns realized or the IPEC projects conducted.

The age-employment profile of children is almost a mirror image of their enrolment

profiles. Consequently, in parallel with the improvements observed in the school

enrolment of children, there is a drop in their employment. Figure 1 illustrates that there is

very little child labour among the very young. In 1994, the first significant jump came at

around 12 years of age. From there on, children’s employment gradually increased. By

1999, age 12 had lost its old importance in being a turning point in children’s lives.

However, this had the effect of magnifying increases in child labour in later years.

Further descriptive statistics on children and their parents are presented in Table 1. One

striking aspect of child labour in Turkey is that only a small proportion of working children

are enrolled in school. While this ratio was 40% in 1994, it went down to 34% in 1999.

A parallel phenomenon is observed among the school-going children, only 2–4% of

whom are found to be engaged in market work. We therefore deduce that, unlike some

other developing counties, schooling and market work are not compatible in Turkey. This

is despite the fact that the two activities are not mutually exclusive. Public schools in

0102030405060708090100

7 8 9 10 11 12 13 14age

%

94 employed99 employed94 student99 student

Figure 1. Changes in children’s employment and schooling rates over time.

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Table 1. Descriptive statistics for children by employment and work status

1994 1999

Employed Student Employed Student

No Yes Yes No No Yes Yes No

Age 10.31 (2.2) 11.96 (1.91) 10.18 (2.16) 12.58 (1.47) 10.31 (2.22) 12.60 (1.76) 10.23 (2.19) 12.60 (1.89)

Student (%) 93.21 40.17 95.13 34.03

Employed (%) 4.0 46.01 1.67 39.15

Non-wage earner (%)

Hours of work per week 38.24 (17.22) 40.63 (20.22)

Father’s age 39.85 (7.43) 43.41 (8.62) 39.75 (7.40) 43.41 (8.40) 40.02 (6.87) 44.14 (7.65) 39.92 (6.80) 43.59 (7.79)

Father’s years of schooling 6.10 (3.49) 3.99 (2.21) 6.16 (3.49) 3.94 (2.27) 6.38 (3.41) 4.04 (2.29) 6.45 (3.42) 4.20 (2.41)

Father employed (%) 88.89 92.0 89.70 84.98 89.63 89.51 90.27 81.79

Father public sector employee (%) 23.41 7.16 23.75 8.03 18.90 3.30 19.01 8.27

Father private sector wage earner (%) 25.66 12.80 24.72 22.93 28.84 17.90 28.31 28.72

No/absent father (%) 3.74 3.10 3.58 4.50 3.75 3.74 3.74 3.90

Mother’s age 35.69 (6.56) 38.78 (7.01) 35.59 (6.52) 38.81 (7.06) 35.89 (6.31) 39.55 (6.96) 35.81 (6.28) 39.09 (6.86)

Mother’s years of schooling 4.02 (3.39) 2.18 (2.40) 4.08 (3.40) 2.13 (2.36) 4.50 (3.45) 2.10 (2.47) 4.59 (3.44) 1.96 (2.45)

No. of children aged 0–6 0.68 (0.93) 0.75 (0.98) 0.66 (0.91) 0.85 (1.12) 0.61 (0.82) 0.70 (1.04) 0.59 (0.80) 0.84 (1.09)

No. of children aged 7–14 2.17 (1.01) 2.51 (1.07) 2.16 (1.01) 2.51 (1.11) 2.03 (0.97) 2.5 (1.22) 2.01 (0.96) 2.54 (1.20)

No. of children aged 15–17 0.47 (0.69) 0.71 (0.74) 0.46 (0.69) 0.73 (0.73) 0.33 (0.58) 0.63 (0.73) 0.33 (0.58) 0.59 (0.68)

HH agricultural establishment (%) 17.82 62.30 19.54 38.73 19.79 53.86 20.81 27.75

HH non-agricultural establishment (%) 14.61 8.43 14.16 13.69 21.30 12.55 21.19 17.50

Poor urban householda (%) 12.85 6.74 12.17 13.37 14.61 13.08 13.46 27.52

Poor rural householda (%) 8.36 19.84 8.60 15.34 8.51 17.93 8.37 15.72

Rural household (%) 41.16 79.50 42.01 64.02 37.32 74.15 38.19 48.64

No. of observations 8268 676 7969 975 10 534 468 10 059 943

a Poor households are those that fall within the bottom 20% of the wealth quintiles in urban and rural areas.Figures in parentheses are standard deviations.HH stands for household.

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Turkey run for only half a day, so children could work after school hours. The other

striking aspect of child labour is the long hours of work, which might partly explain why

the two phenomena are not often observed together. On average, working children put in

almost 40 hours per week, while the average work week for adults, estimated at 50 hours,

is not that much higher. Over time, an increase in the working hours of children is

recorded, which probably stems from a higher likelihood of wage work that comes with a

longer working week.

Working children in 1999 were, on average, older than their counterparts in 1994. Two

factors were responsible for this result: younger children pulled out of the labour market

faster than older children; and the age at which children entered employment increased

over time. As expected, parents of working children are less educated than parents of non-

working children.9 A similar disparity is observed between the parents of school-going and

non-school-going children. Father’s employment in the public sector is also less likely for

working children and those who are not enrolled in school. In contrast, the likelihood of

finding a household-based establishment (proxied by adult males who work on their own

account, either in agricultural or non-agricultural activities) is higher for these groups

of children. Over time, there is a slight improvement in the human capital endowment of

parents, which is expected to affect positively the schooling and work outcomes of

children. Migration out of rural areas,10 which has been going on since the 1960s, is also

evident from the data. In parallel, and possibly due to the declining domestic terms of trade

against agriculture, the proportion of households with family-run agricultural establish-

ments has also declined over time, reducing the proportion of children working as non-

wage earners.

Over the period studied there was a slight increase in the proportion of children living in

poverty in urban areas (from 12.3 to 14.5%) and a slight drop (from 9.4 to 8.9%) in rural

areas. Despite these minor (though statistically significant) changes, urban areas recorded

substantial increases in the proportion of working children and school drop-outs11 living in

poverty (see Table 1). These observations suggest that the association between poverty,

child labour and schooling might have grown stronger for urban children over time.

Another interesting observation relates to the incidence of poverty among working and

non-working children. In both years, a smaller proportion of working children came from

poor families as opposed to non-working children. Since children’s work is often home-

bound, poverty might indicate the absence of a household-based establishment where

children could work. We now test the validity of these conjectures after controlling for the

effect of other relevant factors.

4. Determinants of Child Labour and Schooling at Two Points in Time

4.1. Estimation Results

The estimation results for the schooling and work outcomes of children at two points in

time are given in Table 2. In both years, the correlation coefficient (rho) between the two

outcome equations is significant and negative, indicating that the unobservable factors that

make children’s employment more likely also reduce their odds of enrolling in school. The

magnitude of the correlation coefficient clearly shows that the two activities are rather

incompatible and that this relationship is not unique to a given year. As suggested earlier,

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Table 2. Bivariate probit coefficient estimates for probability of work and schooling

Child labour Schooling Child labour Schooling

1994 1994 1999 1999

Child’s age 0.206*** 2 0.369*** 0.262*** 2 0.336***

[0.012] [0.015] [0.019] [0.019]

Female child 2 0.227*** 2 0.460*** 2 0.138* 2 0.606***

[0.051] [0.047] [0.071] [0.066]

Father’s schooling 2 0.054*** 0.090*** 2 0.074*** 0.053***

[0.013] [0.012] [0.019] [0.014]

Father’s age 2 0.04 2 0.009 0.066 2 0.026

[0.038] [0.032] [0.052] [0.040]

Father’s age squared (1/100) 0.05 0.005 2 0.061 0.018

[0.041] [0.033] [0.055] [0.042]

Father public sector employee 2 0.137 0.430*** 2 0.415*** 0.283***

[0.114] [0.093] [0.140] [0.108]

Father private sector wage earner 0.004 2 0.034 2 0.042 0.049

[0.087] [0.079] [0.107] [0.093]

No father 2 0.988 0.149 1.368 2 0.279

[0.911] [0.765] [1.226] [0.961]

Mother’s schooling 2 0.053*** 0.064*** 2 0.060*** 0.097***

[0.014] [0.011] [0.019] [0.014]

Mother’s age 0.039 0.024 2 0.068 0.068*

[0.043] [0.035] [0.054] [0.039]

Mother’s age squared (1/100) 2 0.06 2 0.019 0.071 2 0.079

[0.051] [0.043] [0.066] [0.048]

No. of children aged 0–6 years 2 0.02 2 0.113*** 0.099** 2 0.191***

[0.033] [0.027] [0.045] [0.034]

No. of children aged 7–14 years 0.065* 2 0.076*** 0.121*** 2 0.139***

[0.034] [0.025] [0.040] [0.031]

No. of children aged 15–17 years 0.031 2 0.082** 0.187*** 2 0.145***

[0.045] [0.035] [0.060] [0.048]

HH agricultural establishment 0.779*** 2 0.145* 0.320*** 0.261**

[0.087] [0.080] [0.121] [0.121]

HH non-agricultural establishment 2 0.002 0.038 2 0.004 0.123

[0.102] [0.083] [0.117] [0.097]

Poor urban household 0.176** 2 0.303*** 0.239*** 2 0.560***

[0.088] [0.076] [0.092] [0.073]

Poor rural household 0.015 2 0.011 2 0.114 2 0.205*

[0.096] [0.089] [0.123] [0.122]

Rural household 0.443*** 2 0.364*** 0.613*** 2 0.278***

[0.076] [0.066] [0.099] [0.081]

Constant 2 3.584*** 5.115*** 2 4.956*** 4.913***

[0.708] [0.695] [1.023] [0.973]

Rho 2 0.733 2 0.803

Wald test for rho ¼ 0 ( p-value) 0.000 0.000

log likelihood 2 3554.355 2 3221.527

No. of observations 8944 11 002

***Significant at 1%; **significant at 5%; *significant at 10%.Figures in parentheses are Huber–White standard errors.HH stands for household.

Child Labour and Schooling in Turkey 203

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the long hours of market work is a possible reason for the incompatibility of the two

activities.

The results of the bivariate probit analyses also show an increase in the magnitude of the

correlation coefficient over time, indicating that the work and schooling outcomes of

children have become even more negatively correlated over the period studied. The

increase may be explained by the extension of compulsory schooling, the stricter

enforcement of the basic education law,12 the ban on child labour and the resulting change

in the composition of working children and school drop-outs. If working children in 1999

were composed of those for whom work was more imperative, their schooling would have

been likely to suffer more. The descriptive statistics presented in Section 3 regarding the

drop in the proportion of children who managed to combine work and schooling, the

increase in the working hours of children and the stronger correlation between the poverty

status of the household and child employment in urban areas support the above conjecture.

Another explanation might be that working children are made up of those who are less

likely to be successful in school. The statistics presented above are also consistent with this

conjecture. If the household could afford to send only one of the children to school, it

would probably be the one most likely to succeed in school. Unfortunately, our model does

not differentiate between the two conjectures.

The factors identified in the literature emerge as significant determinants of child labour

and schooling in the Turkish case as well. Notably, the child’s age and sex, parental

education, the father’s sector of employment, the number of children in the household, the

existence of a household-based agricultural establishment and rural residency turn out to

be important determinants of both schooling and work. As the child gets older, the

likelihood that he/she will drop out of school and engage in market work increases. Female

children have a lower likelihood of engaging in market-oriented work and enrolling in

school. This probably reflects their higher propensity to engage in domestic chores. Higher

parental schooling, on the other hand, reduces the likelihood of work and increases child

schooling. The father’s public sector employment is a strong deterrent to child

employment. There might be two reasons for this: public sector work does not provide an

environment whereby children can work alongside their fathers or as substitutes for them;

and it implies a higher earning potential for the father. The end result is a lower likelihood

of child employment and higher likelihood of school enrolment.

With respect to household characteristics, larger numbers of children in a household

reduce the school enrolment of school-aged children. In the case of children’s work, a

consistent relationship does not emerge in the sense that, while in 1994 only children of the

same age group increased the likelihood of work for the 7–14-year-olds, in 1999 children

of all ages increased their propensity to engage in market-oriented activities. The existence

of a household-based agricultural establishment, on the other hand, works to increase the

likelihood of child employment. It also reduced the likelihood of child schooling in 1994,

but not in 1999. The existence of a non-agricultural establishment does not impact on child

labour or schooling. This may be to do with the nature of the work being less “fitting” for

child labour.

The bivariate probit analysis also reveals that children of poor urban backgrounds face a

higher probability of employment and a lower probability of school enrolment, in line with

Basu & Van (1998), who suggested that not sending the child to work is a luxury for poor

households. Having a poor rural background does not seem to affect child employment,

though it reduced school enrolment in 1999. The weak relationship between poverty and

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work in rural areas can be explained by the conjecture that children’s employment is part

of the normal rhythm of rural life irrespective of the socio-economic status of the

household. Erturk (1994) related how in rural areas children naturally become part of the

economic production process as they grow into adulthood. However, rural residency also

increases the likelihood of child employment and decreases their schooling. This must be

because it is easier to integrate children into the work activities of the household, which

often take the form of agricultural work. Notwithstanding this conjecture, to the extent that

we control for household-based agricultural establishments, there may be additional

factors that differentiate the work and schooling outcomes of rural children from their

urban counterparts. The lower returns to schooling due to limited work opportunities

outside agriculture in rural areas and higher demand for children’s time, together with

cultural factors, possibly work to reduce their schooling.

The coefficients on the determinants of child labour and schooling presented in Table 2

show changes over time, although some changes are not statistically significant. In the

work equation, the age of the child becomes a stronger determinant of his/her likelihood of

employment. This is not surprising since, with the extension of compulsory schooling and

the ban on child labour, the most visible drop in child labour was observed for the 12–13-

year-olds. The faster withdrawal of relatively younger children from the labour market

further increased the likelihood of employment at older ages. Another set of variables that

exerted differential effects in 1999 are children of different age groups and the existence of

a household-based agricultural establishment. The former played a stronger role in 1999 in

pushing children into the labour market. The latter increased child employment in both

years but became less of an important determinant in 1999.

With respect to child schooling, being a girl brought about a bigger disadvantage in

1999 than it did in 1994. Mother’s education, on the other hand, played a more significant

role in making school attendance more likely. The opposite observation is made for

father’s education. Though it still increased the schooling of children, higher levels of

paternal education brought smaller increases in child schooling. The effect of household-

based agricultural establishments on child schooling has also changed over time. While it

impacted negatively on the schooling of children in 1994, by 1999 the effect had turned

positive. The decline in household-based establishments and, consequently, the number

and proportion of children engaged in such work, have possibly changed the composition

of working children. This change and the fact that home-based work requires fewer hours

and is more flexible than wage work, might have allowed children to enrol in school, while

simultaneously contributing to household income through non-wage work. Indeed, a

closer look at the data reveals that a larger proportion of children who were engaged in

household-based agricultural work managed to combine work and schooling in 1999 than

they did in 1994. Children from poorer families also faced a lower likelihood of school

enrolment in 1999 than in 1994. These observations indicate that the drop-outs consisted

more of those for whom schooling is a “luxury”.

4.2. Sensitivity Analysis

We now see if our results are sensitive to model specification. In particular, we check if the

inclusion of children of various age groups in our model (i.e. the fertility decision of the

household) has an impact on the estimated correlation coefficient (rho). Another test

relates to the poverty status of the household. If poverty is the essential link between child

Child Labour and Schooling in Turkey 205

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labour and schooling, controlling it should reduce the correlation coefficient in a

significant way.

We begin with a constant-only model where all the covariates are excluded. The

correlation coefficient between child work and schooling is highly negative and

significant, and is on the rise over time (see Table 3). When all variables of interest,

excluding the number of children and the poverty status of the household, are included, the

correlation coefficient drops slightly, though it remains highly negative and significant.

When children of different ages are added to the model, the correlation coefficient hardly

changes. Including the poverty status of the household does not produce a sizeable change

in the magnitude of the correlation coefficient either. These findings indicate that our

results are robust to model specification and that the poverty status of the household is not

the essential link between the two outcomes.

5. Simulations

As the descriptive statistics and estimation results revealed, both the mean characteristics

of the children and their parents as well as the coefficient estimates on the covariates

employed have changed over the period studied, giving rise to the observed drop in child

labour and increase in schooling. In order to disentangle the two effects, we carry out a

series of simulation exercises as described in Section 2, by fixing the population

characteristics either at 1994 or 1999 means, though any other synthetic data could have

been used. Evaluated at mean characteristics, the marginal probabilities of work and

schooling in 1999 are estimated at 0.9% and 98.4%, respectively. For 1994, the relevant

figures are 3.7% and 96.6% (see Table 4). The first set of decomposition results reveal that,

had the 1999 child population characteristics prevailed in 1994, the mean probability of

work and school enrolment would have been 3% and 97.3%, respectively. These figures,

which are calculated by plugging the mean characteristics of the population in 1999 into

the schooling and work equations estimated for 1994, indicate a 0.7 percentage point

improvement in the employment and school enrolment of children. Since the overall

improvement from 1994 to 1999 is estimated to be 2.8 and 1.8 percentage points, these

figures indicate that only 25% of the improvement in child labour and 39% of the

improvement in child schooling stem from the improvement in the mean characteristics of

the population. In other words, the bulk of the improvement in child labour and schooling

has stemmed from the change in the structure of work and schooling, which we interpret as

the effect of compulsory schooling and the ban on child labour.

Table 3. Correlation coefficient estimates under different model specifications

1994 1999

All covariates included 20.733 20.803No children 20.730 20.805No poverty 20.734 20.796No children þ no poverty 20.731 20.780All covariates excluded (constant only model) 20.766 20.812

Note: Wald test for rho ¼ 0 yields p , 0 for all specifications.

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The alternative simulation exercise, where we assume the 1994 characteristics still to

prevail in 1999, produces similar results, supporting the conclusion that it is the changing

structure of work and schooling that has given rise to the decline in child labour and

increase in schooling. Plugging the mean population characteristics to the estimated work

and schooling equations in 1999 reveals that changing population characteristics are

responsible for only 14% and 39% of the improvements in child labour and schooling,

respectively (see Table 4).

It might be interesting to look at the effect of the changing incidence of poverty on child

employment and schooling by running additional simulations. Since the poverty status of

the household is effective in reducing the employment probability of urban children only,

we focus on this group. The probability of employment for a child of average

characteristics from a poor urban household in 1994 was 2.6%. By 1999, this figure had

dropped by a half. Had the poor children in 1994 been more like their counterparts in 1999,

the resulting child labour would have been higher, estimated at 3%. In other words, the

attributes of the poor families and their children have changed in a way that results in

more, rather than less, child labour over time.13 The descriptive statistics indeed indicate

that the average poor child in 1999 had a younger and less educated father, who was less

likely to hold a public sector job. Although the mother was older, she had less schooling

and it was more likely for the poor household to have a household-based agricultural

establishment. These findings support our earlier conjecture that children who continued

to be employed in 1999 were made up of those for whom work was more imperative.

With regard to the schooling of poor urban children, we estimate a drop from 94.8% in

1994 to 93.1% in 1999. When we assign the poor children in 1994 the average

characteristics of their counterparts in 1999, the resulting drop is smaller, the enrolment

being estimated at 94.3%. In other words, a substantial part of the drop is due to the

changing coefficient estimates. What these results support is that, holding the mean

characteristics constant, the poor urban households in 1999 were less inclined to send their

children to school than in 1994, probably as part of their survival strategy. In the case of

poor children in rural areas, there was an increase in school enrolment from 89.4 to 93.3%.

Evaluating the schooling equation estimated for 1994 by the average characteristics of

poor children in 1999 results in a predicted enrolment rate of 90%. In other words, we

again arrive at the conclusion that a substantial part of the improvement in child schooling

has stemmed from the change in the attitude of the families, rather than from their

characteristics.

Table 4. Simulation results

Probability of employmentevaluated at:

Probability of school enrolmentevaluated at:

X94*B94 X99*B94 X99*B99 X94*g94 X99*g94 X99*g99Spec I 3.7 3.0 0.9 96.6 97.3 98.4

X94*B94 X94*B99 X99*B99 X94*g94 X94*g99 X99*g99Spec II 3.7 1.3 0.9 96.6 97.7 98.4

Note: Estimated probabilities are based on average characteristics and coefficient estimates reported inTable 2.

X denotes the vector of child and house holdcharacteristics;B and g denote the parameter vectors.

Child Labour and Schooling in Turkey 207

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6. Conclusion

In this paper, we have established that work and schooling are incompatible activities for

children in Turkey. The negative association between work and schooling seems to have

strengthened over time as evidenced by the increasing magnitude of the correlation

coefficient from 1994 to 1999. We have attributed this to the extension of compulsory

schooling, the increase in the legal working age, the stricter application of the basic

education law and, consequently, to the changes in the cost and benefit structures of work

and schooling. We conjecture that, following the increase in the cost of employment and

not sending the child to school, children for whom the two outcomes have been somewhat

weakly associated are pulled out of the labour market and placed into schools. Those who

continue to be employed are therefore made up of children for whom the two outcomes are

more closely associated. This conjecture is supported by the results of the simulation

exercises, which have identified the changing structure of work and schooling as the main

sources of improvement in the work and schooling outcomes of children.

The growing association between schooling and work, in turn, implies that greater

incentives would need to be offered to the children’s families and the children themselves

to keep them at school and away from work. We have also found evidence of the growing

negative impact of poverty on the schooling of female children and that households, in

general, have a stronger preference for the schooling of boys. These findings indicate that

addressing the needs of the poor families becomes even more crucial if children, especially

females, are to be kept at school and away from work. The newly initiated “conditional

cash transfer” programme administered by the Social Solidarity Fund under the Prime

Ministry may offer a viable alternative in increasing the schooling of children and

reducing their probability of work, especially if directed more towards female children.

The programme targets poor households and the receipt of the periodic cash transfers

(made to the mother of the child) is conditional on children’s school enrolment. The

impact assessment of the programme is yet to be carried out, but it is likely to produce

favourable effects provided that transfers are high enough to induce families to take part in

the programme. Another newly initiated state programme is the free distribution of school

books, which can also reduce the cost of schooling and therefore reduce the burden on the

family budget. Additional initiatives may include free school meals, after-school study

hours and recreational activities that keep children in school for longer hours. Innovative

measures must especially be considered for school drop-outs as integrating them back to

the system is relatively harder.

Notes

1 For a review of the impact of household income on schooling, see Behrman & Knowles (1997). For

more recent studies on the interplay between household welfare and child labour and schooling, see, for

instance, Bhalotra (2001), Blunch & Verner (2000), Borooah (2000), Canagarajah & Coulombe (1997),

Maitra & Ray (2002), Ray (2000) and Ravallion & Wodon (2000).2 We consider all the children in the household rather than just siblings since the “other” children are also

likely to affect the way in which resources are allocated within the household.3 In the data sets employed, information on household income and expenditures is also provided.

However, it is collected via a limited number of questions and as a sum of the contribution of all

members. Since wealth measures are less prone to reporting error and to endogenous labour supply

decisions, we have determined the poverty status of the households using wealth measures.

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4 We use the first component in the principal components analysis to construct the asset weights. The

variance accounted for by the first principal component is 20.6% in urban areas and 21.6% in rural

areas in 1994 and 26.9% and 31% in 1999, respectively.5 Multinomial probit constitutes yet another option. It has the advantage of not requiring the IIA

assumption. However, due to computational difficulties it is rarely used.6 We consider the child as the unit of analysis. Since more than one child may come from the same

household, we correct for the standard errors using households as clusters.7 In 1994, 93% of the 7–14-year-olds and, in 1999, 95% of the 7–14-year-olds were the children of the

household head. We have opted to maintain these children only, since the coding system does not reveal

who the parents of the other children are. Other exclusions include children with “multiple mothers” and

childrenwho aremarried.We also dropped childrenwithoutmothers present in the household (83 cases in

total) since they constituted too small of a group to produce meaningful estimates.8 Expansion factors are used in all tables and figures to inflate the sample figures to population figures.9 Years of schooling are calculated by assigning values to categorical education levels such that

illiterates are assumed to have 0 years of schooling, functional literates 2 years, primary school

graduates 5 years, junior-high school graduates 8 years, high school graduates 11 years and university

and higher diploma holders 15 years of schooling.10 Rural areas are defined to be those with a population of less than 20 000.11 We are using the term school “drop-outs” loosely to refer to children who do not go to school. Whether

they ever started school is not clear from the data. However, we suspect the number of such children to

be rather small since only 1.4% of the 10–14-year-olds were illiterate in 1999.12 In a number of cases, the local authorities are reported to have arrested the parents of children who

have been found to send their children to work rather than to school. The imposition of heavy fines has

also been observed. Although these incidences, which appeared in the newspapers, are possibly

marginal events, it is generally true that greater effort has been shown by local authorities to generate

funds from public and private sources to reduce the cost of schooling through the free distribution of

uniforms and books.13 The alternative simulation exercise produces similar results. In the interest of brevity, we do not report them.

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Appendix

Table A1. Marginal effects on the probabilities of work and schooling

Work School Work School1994 1994 1999 1999

Child’s age 0.016 20.028 0.006 20.014Female child 20.018 20.037 20.003 20.026Father’s schooling 20.004 0.007 20.002 0.002Father’s age 20.003 20.001 0.002 20.001Father’s age squared (1/100) 0.004 0.000 20.001 0.001Father in public sector 20.010 0.027 20.007 0.009Father in private sector 0.000 20.003 20.001 0.002No father 20.037 0.010 0.136 20.015Mother’s schooling 20.004 0.005 20.001 0.004Mother’s age 0.003 0.002 20.002 0.003Mother’s age squared (1/100) 20.005 20.001 0.002 20.003No. of children aged 0–6 years 20.002 20.009 0.002 20.008No. of children aged 7–14 years 0.005 20.006 0.003 20.006No. of children aged 15–17 years 0.002 20.006 0.004 20.006HH agricultural establishment 0.094 20.012 0.010 0.009HH non-agricultural establishment 0.000 0.003 0.000 0.005Poor urban household 0.016 20.029 0.007 20.035Poor rural household 0.001 20.001 20.002 20.010Rural household 0.038 20.029 0.018 20.012Predicted probability at mean 0.037 0.965 0.009 0.984

Note: Based on coefficient results for children reported in Table 2. For dummy variables, marginal effectsare calculated by comparing the probability when the dummy variable is one and when it is zero.For continuous variables, marginal effects are calculated at the means.HH stands for household.

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