Child labour and school attendance in Palestine:

the role of local labour market and the effect of conflict with Israel

Michele Di Maio

University of Naples ’Parthenope’

Tushar K. Nandi∗

University of Siena

First Version: June 2008

This Version: September 2009

Abstract

This paper studies the impact of changes in the local labour market conditions and in

the Israeli-Palestine conflict on child labour and school attendance of Palestinian children

in the West Bank. We use a novel dataset obtained by matching the Palestinian Labour

Force Survey with a separate data set of children (10-14 years). We consider the period

between the beginning of the Second Intifada (September 2000) and 2006. Using a bivariate

probit model for child labour and school attendance outcomes, we find that an increase in

the border restrictions imposed by Israel increases child labour while it does not affect school

attendance. We also find that child labour increases when the labour market opportunities

improve. This substitution effect is much smaller during conflict periods when - following a

reduction in the market wage - child labour tends instead to increase.

Keywords: Palestine, Israel, child labour, school attendance, conflict, closure days

JEL Classification: J13, C35

∗Corresponding author: Michele Di Maio, Department of Economic Studies, Faculty of Economics, University

of Naples ’Parthenope’: E-mail: michele.dimaio@uniparthenope.it. We would like to thank Davide Castellani,

Eric Edmonds, Tommaso Frattini, Ira Gang, Gianna Giannelli, Marco Manacorda, Patrizio Piraino, seminar

participants at University of Siena, University of Bethlehem, University of Perugia, XXIII AIEL Conference and

the 2nd IZA Workshop on Child Labour in Developing Countries for comments and suggestions. All errors are

of course our own responsibility. Financial support from the FIRB Project ”Creation of a Centre for Advanced

Studies and Research in Cooperation and Development at Bethlehem University” is gratefully acknowledged.

1

Contents

1 Introduction 3

2 Child labour, labour market opportunities and the conflict 4

3 Estimation 7

3.1 Estimation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.1 Number of closure days and the evolution of the Palestinian labour market 9

4 Data 10

4.1 Descriptives statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5 Estimation results 14

5.1 Discussion of the results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.2 Robustness checks and other model specifications . . . . . . . . . . . . . . . . . . 19

6 Concluding remarks 20

7 Appendix 25

2

1 Introduction

The existence of child labour naturally raises a number of ethical, social and economic issues.

While the dimension of the phenomenon is well known, there is still a large disagreement con-

cerning its main determinants. One reason is that - as recent research indicates - child labour

is a more varied, country and context-specific phenomenon than usually believed. In particular,

the empirical evidence suggests that not only the child and household characteristics but also

the features of the labour market may play a fundamental role in determining the level and

dynamics of child labour. If the labour market is an important determinant of child labour, it

follows that any factor that modifies its functioning may have a large impact on child labour

too. This is especially true for weak and fragile developing countries whose labour market is

directly affected by a military conflict. This is the case of the Palestinian Territories.

Starting from the Six-Day War in 1967, the Palestinian Territories (West Bank and Gaza

Strip) have been occupied by Israel. Since then, periods of conflict of different intensity followed

one after the other. After a decade of (relative) amelioration of the economic and political

situation during the 90’s, the situation had dramatically worsen since the so-called Second

Intifada (September 2000). In response to re-surge of the conflict, Israel started increasingly

imposing on Palestinian workers a number of mobility restrictions through different means:

closures of borders, curfews and sieges.1 In particular, the closure of borders made impossible

for Palestinian workers employed in Israel to reach their job places. Given that those represent

a relevant share of Palestinian workers, the closures severely affected the performance of the

Palestinian labour market.

The starting point of the present paper is the presumption that these effects are likely to

reverberate on child labour and school attendance. The conflict can affect children’s outcomes

through a direct and an indirect channel. It directly impacts on children belonging to households

whose father is employed in Israel but it also affects child outcome through the modification of

the general economic condition of the region and in particular of the the labour market. The

closure of borders causes a sudden and unanticipated change in the earnings for Palestinian

workers employed in Israel. Repeated incidence of closure can encourage returning workers to

look for local jobs modifying the domestic labour market, increasing domestic unemployment

and reducing local wage. Households - either directly or indirectly affected by the mobility

restrictions - unable to smooth consumption during temporary shocks may decide to send their

children work. The luxury axiom indeed implies that that during sever economic down-turns

the drop in household income should induce an increase in child labour and a reduction in

school attendance. At the same time, closures affect the local labour market and modify the

child opportunity cost of not working. From a theoretical point of view, a change in latter is

predicted to have two opposite effects on child labour (and school attendance): the income and1Closure consists of banning the movement of labor and goods between the Occupied Territories and Israel,

as well as between, and within, the West Bank and the Gaza Strip. Siege implies the the closing off of a town.

3

the substitution effect. It is an empirical question which of these effects prevails.

In this paper we determine the response of children’s time allocation to changes in the local

labor market conditions, to the intensity of the conflict and to the changes induced by the latter

on the former. We use data on 10-14 years old children between the beginning of the Second

Intifada (September 2000) and the end of 2006 in the West Bank. Our results show that 1) the

intensity of the conflict - measured by the number of closure days - increases the probability of

child labour; 2) the probability of child labour is positively correlated with the labour market

opportunities measured by the local average market wage, i.e. the substitution effect prevails;

3) the conflict modifies the ”‘normal”’ relationship between the opportunity cost of not working

and child labour decision. When closure are in place, the income effect becomes stronger: this

suggests that child labour tends to increase as a reaction to the worsening of the economic

conditions.

Our paper is related to two lines of research on child labour - the effects of local labour market

dynamics on child labour (Wahba, 2006) - and the impact of aggregate shocks on children’

outcomes (see i.e. Duryea and Arends-Kuenning, 2003). To the best of our knowledge, this is

the first paper that analyzes how child labour and school attendance in West Bank are affected

by the intensity of the Israeli Palestinian conflict and how the latter impacts on the relation

between the labour market opportunities and child choice.

The paper is structured as follows. The next section review the two strands of literature to

which the paper is related to. Section 3 presents the estimation strategy. Section 4 describes

the dataset and the characteristics of child labour and school attendance in West Bank. Section

5 presents the estimation results and robustness checks. Section 6 concludes the paper.

2 Child labour, labour market opportunities and the conflict

This section reviews the two strands of literature that are relevant for our analysis. The first

concerns the interaction between child labour, school attendance and the local labour market in

developing countries. The second is related to the analysis of the evolution of the Palestinian

labour market and how it has been affected by the conflict with Israel.

Child labour and labour market opportunities Until recently, most of the analysis of

child labor has focused on testing the ’luxury axiom’ introduced by Basu and Van (1998) which

states that parents send their children to work when household income is low. While the luxury

axiom is considered the reference point in the theoretical literature, there is no consensus in the

empirical literature on the relationship between poverty and child labour. In fact, although child

labour is likely to be positively correlated to household poverty, it is also likely to be determined

by other socio-economic factors: access to school, intergenerational expectations, inequality

and employment opportunities. Indeed all these elements have been shown to be important

components in explaining child labour (Edmonds, 2008). In particular, recent research has

4

focused on the effect of opportunities provided by the labour market (see for instance Edmonds

et al, 2009). These are usually measured by the local average market wage which gives the

opportunity cost of not-working. From a theoretical point of view two effects that are in place

when the market wage change: the income and the substitution effect. The former prevails when

a lower market wage increases child labour. This is the case when the child works for reaching

a given level of consumption. On the contrary, the substitution - or own price effect - prevails

when an increase in the market wage - the opportunity cost of not working - induces the child

to work more. It is an empirical question which of the two effects prevails.

Empirical evidence from developing countries suggests the substitution effect to dominate

the income effect. Levison (1991) and Barros et al. (1994) find that child labour in Brazil is

higher in high income cities with thriving labour markets than in cities with higher poverty

rates. Duryea and Arends-Kuennings (2003) analyze child labour and schooling decision in

Brazil using a dataset comprising repeated cross-sections covering 1977-1998 to determine the

effect of changes in labour market opportunities on child labour and schooling. They find that

market wage of low-skilled workers (a proxy for child’s opportunity cost of not working) has a

positive and significant effect on child labour. Wahba (2006) uses individual data from the 1988

Egypt’s Labour Force Survey to study the sensitivity of household supply of child labour to

adult market wages. She finds that higher the adult male and female provincial wages relative

to the national average, lower is the probability of child labour, while child wages are negatively

correlated with child labour. Finally, Kruger (2007) documents an increase in child labor and

a decline in schooling in coffee-growing regions of Brazil during a temporary boom in coffee

exports.

While different as for methodologies and datasets used, all these studies point to two facts.

First, a strong country effect seems to be present (Ray, 2000). Second, child labor responds to

the availability of jobs and to the opportunity cost of not working.

Child labour and the closure of Israel-Palestine borders The Palestinian economy is

characterized by a peculiar labour market: Palestinian unemployment and domestic wage have

long strongly responded to job opportunities and wage dynamics in Israel (Angrist 1996; Kadri

and MacMillen, 1998; Sayre 2001).2 This is not surprising considering that by the late 1990s

- under conditions of relatively open but controlled borders - more than one-fifth of the total

labor force was commuting daily to Israel (Ruppert Bulmer, 2003).

The recent re-surge of Palestine-Israel conflict, started by the Second Intifada in September

2000, has added another dimension of complexity to the dynamics of the Palestinian labour

market. While the prohibition to enter Israel for Palestinians without a special work permit

has been there since the beginning of the 90s3, the closures that prohibit the movement of all2See also Farsakh (2002), Shaban (1993).3Until the late 1980s, Palestinians and Israelis could move freely between each other’s territories. Israel

introduced permit requirements in 1991 to control the movements of Palestinian workers. After the 1993 Oslo

5

Palestinians, including authorized workers, between the Palestinian Territories and Israel are

now increasingly used (together with a number of other measures) in order to militarily control

the area.

The closure of borders is intended to be a security measure taken in the presence of surges,

or expected surges, in the Israeli-Palestinian conflict (Miaari and Sauer, 2006). But their effects

obviously are much more pervasive. Recently academic researchers, international agencies and

NGOs have documented how the closures have impacted the Palestinian economy (see for in-

stance B’Tselem, 2007; OCHA, 2007; PCBS, 2001; United Nations, 2002; World Bank, 2003).

In particular, academic research has mostly focused on measuring the effect of changes in the

number of closure days on the performance of the Palestinian labour market. The reason is

twofold. First, the labour market is the part of the economy expected to be affected the most

by these measures since closures have a direct impact on Palestinian workers employment op-

portunities. Second, the availability of a detailed Labour Force Survey allows a careful analysis

of the phenomenon.

Ruppert Bulmer (2003) is the first study of the effect of changes in the Israeli border policy on

daily Palestinian labour flows to Israel, unemployment and wages. The calibrated model predicts

that closures would raise total unemployment in Palestine, increasing domestic employment with

decreasing wages. the latter effect is the result of the downward pressure caused by the return

of workers previously employed in Israel. Aranki (2004) and Miaari and Sauer (2006) both

estimated the effect of closures on the Palestinian labour market using data from the PCBS

Labour Force Survey. Their results suggest that closures have a negative effect on Palestinian

workers, not only on those who commute to Israel for work. In particular, closure increases

the probability of being unemployed and decrease the monthly earnings of Palestinian workers

regardless of their work location (Israel or Palestinian Territories). The only paper we are aware

of studying child labour and schooling in Palestine is Al Kafri (2003). It analyzes child labour

and schooling decision in Palestine comparing year 2000 and 2001 (i.e. before and after the

Second Intifada) focusing on supply factors at the household level. However, his analysis does

not include the number of closure days, the market wage and household income as explanatory

variables. The results of the sequential-response model show an increase in the probability for

male child to go to work and for female child to leave school following the beginning of the

Second Intifada.

Accords permit controls and other mobility restrictions (i.e., temporary border closures) were started to be strictly

enforced.

6

3 Estimation

3.1 Estimation strategy

This section describes the econometric model used to study the effect of changes in local market

opportunities and conflict on child labour and school attendance. Our estimation strategy is

based on the fact that the number of closure days - our measure for the intensity of the conflict

- is exogenous to the evolution of the Palestinian labour market condition. At the same time we

argue that the average district low-skilled market wage - our proxy for the opportunity cost of

child labour4 - is exogenous to the probability of child labour and school attendance. We argue

that the latter is exogenous to our dependent variables on two grounds. First, given the different

level of aggregation (district vs. individual) the relation between the two is not direct. Second,

in the Palestinian case child labour is so low that it is unlikely to have any effect on the average

market wage. Thus we conclude that the average district market wage can be consistently used

to estimate the effect of labour market opportunities on child labour and school attendance.

Following Duryea and Arends-Kuenning (2003), we assume that the decisions (work and

school) are interdependent.5 Accordingly, we use a bivariate probit model to estimate the prob-

abilities of child labour and school attendance. Indeed, an interesting feature of this model is that

it allows for the correlation of random components and it gives a measure of the interdependence

of the working and schooling decisions.

Let cl∗i and sc∗i denote two latent variables underlying the working and schooling decisions,

respectively, for child i in household j in district k. We represent the decision making process

as follows:

cl∗i = gi1 (·) + ui1 (1)

sc∗i = gi2 (·) + ui2

where [ui1, ui2] ∼ BN (0, 0, 1, 1, ρ) and BN stands for the bivariate normal distribution. The

index function for child z has the form:

gi (·) = αj + βk + γt + θλjt + δσt + X′jktφ l = 1, 2 (2)

where α, β and γ are household, district and quarter fixed effects, respectively. σ is the variable

which captures the intensity of the conflict as the number of days the borders between Israel4To measure the child’s opportunity cost of not working, the obvious candidate would be the average child

wage at the district level. This was not possible due to the etremely low number of observations on child wage in

our dataset.5The estimation method reflects the household decision making process about supplying child labour or sending

child to school. Conceptually four options are available: only school, only work, both, and neither of them. In the

child labour literature, children’s work and schooling are often treated either as two independent decisions or as a

sequential process. We deviate from these methods on the following grounds. First, a multinomial choice model

requires the a unlikely assumption that child labour and schooling are simultaneous and independent to hold.

Second, a sequential choice model requires a strong assumption on the hierarchy of the options which depends on

the different welfare perspectives adopted (Wahba 2006).

7

and Palestinian Territories remained closed in a quarter. The associated coefficient measures the

impact of the conflict on child outcomes. λ is our measure of the tightness of the labour market,

i.e. the average district level low-skilled market wage. In the baseline specification this is the

(log) wage of private sector workers with less than seven years of education.6 The coefficient θ

captures the extent to which changes in labour market opportunities induce a change in child

status. Xjt is a vector of control variables that account for individual, household and district

characteristics and the interaction factors. Its content varies across specifications. In the baseline

specification it includes child’s age, parents’ years of education - which controls for the long-run

household income, mother being employed, a categorical variable for household income (net of

child wage) and the interactions between the number of closure days and the average market

wage, the distance from the Israeli border and the household income. In section 5.2 we consider

a number of robustness checks and other model specifications.

Suppose now that cli and sci are the observed dummy variables for working and schooling

decisions, defined as:

cli

{= 1 if cl∗i > 0

= 0 otherwise

sci

{= 1 if sc∗i > 0

= 0 otherwise

The choice probabilities for child i are:

p11i = Pr[cli = 1, sci = 1] = Φ [gi1 (·) , gi2 (·) , ρ]

p10i = Pr[cli = 1, sci = 0] = Φ [gi1 (·) ,−gi2 (·) , ρ]

p01i = Pr[cli = 0, sci = 1] = Φ [−gi1 (·) , gi2 (·) , ρ]

p00i = Pr[cli = 0, sci = 0] = Φ [−gi1 (·) ,−gi2 (·) , ρ]

where Φ ( ) is the standardized bivariate normal distribution function. Define qi1 = 2cli − 1 and

qi2 = 2sci−1 such that qij takes value 1 or -1 depending on the value of the constituting dummy

variable. Define ρ∗i = qi1qi2ρ. The model is estimated maximizing the log likelihood function

that takes the following form.

LL =n∑

i=1

lnΦ [qi1gi1 (·) , qi1gi2 (·) , ρ∗i ]

Since one of our focus variables, local market wage, is constructed from the individual ob-

servations, the intra-cluster correlation of the error terms can affect the estimated standard

error. In our estimation we take into account and correct for this possible bias. Error correction

for other aggregate variables are used as robustness checks. This includes estimation of robust6Wages are in constant 1996 New Israeli Shekels (NIS), obtained by deflating nominal wages by the Consumer

Price Index in West Bank provided by the PCBS. In the robustness checks we consider other and more restrictive

definition of low-skilled wage. Note, however, that the average father’s years of education in our sample is 10.

8

standard errors taking into account household and other district level variables constructed from

individual observations.

One potential problem with using the market wage to capture the tightness of the local

labour market - and thus the opportunity cost of not working - is that the former is likely to be

correlated with un-observable characteristics at the district level. Indeed the level of the wage

is likely to be associated with the general economic condition of the district: e.g. a higher wage

would indicate a richer district. We use fixed effects at the district, household and individual

level to control for the time invariant heterogeneity.7

Another possible concern with this empirical strategy is potential endogeneity of closure

days. We now present some compelling evidence supporting our statement on the exogeneity of

closure days.

3.1.1 Number of closure days and the evolution of the Palestinian labour market

Since the end of the first Gulf War, Israel has used temporary closures of West Bank and Gaza

Strip to restrict the movement of Palestinians between Palestinian Territories and Israel (see

also Section 2). This was intended to be a security measure taken in the presence of insurgency,

or expected insurgency, in the Israeli-Palestinian conflict (Miaari and Sauer, 2006). In practice,

closures consist of restrictions on the movement of all Palestinians (including the Palestinian

workers with legal permit to work in Israel) and goods between the West Bank, Gaza, and Israel

(as well as third countries).

We used two sources for constructing the series of closure days for the West Bank. The

main source was the Office of the United Nations Special Coordinator (UNSCO) in Ramallah

which provided us with data for the period 1999-2004. UNSCO calculates the number of days

of effective closures by netting out of comprehensive closures Saturdays, half the number of

Fridays (labor and commercial flows are at about half their normal workday level on Fridays)

and Jewish and Muslim holidays. We complemented these data with the information collected

by B’Tselem on closure days for the period 2004 2006 checking also for consistency between the

two data series.

Figure 1 shows the number closure days imposed by Israel on the West Bank in each quarter

between 1999 and 2006. After the dramatic increase following the Second Intifada (fourth

quarter of 2000), the number of closures days have fluctuated with a rather high variance during

the period under consideration.

For our estimation strategy to be able to capture the effect of the conflict on our depen-

dent variables (child labour and schooling attendance) the number of closure days have to be

exogenous to both the dependent variables and to the other variables (in particular the average

market wage) in our model. In the following we show that the number of closure days: 1) do7The underlying assumption here is that there is no time-variant heterogeneity which influences the outcome

variables. Considering the evolution of the Palestinian economy in the period under consideration, we cannot

indicate any of these possible sources of heterogeneity.

9

Table 1: Determinants of the number of closure days

Number of closure days Coeff. p-value

wage lag -1.15 0.483

unemployment lag -1.12 0.309

closure lag -0.13 0.564

# 24

F 0.482

R-square 0.113

Note: OLS regression. Variable definition is reported in the Appendix.

not depend on the economic conditions or the labour market in Palestine; 2) are not systematic

across quarters (they are not seasonal) and do not have a trend. This evidence will support our

statement that in fact the number of closure days is exogenous to the evolution of the Palestinian

domestic labour market and only responds to military consideration.

As first piece of evidence, we have that the number of closure days are not correlated with

the average wage, the low skilled average wage or the unemployment rate in WB in the previous

period.8 As an additional check we also regressed the number of closure days on previous period

number of closure days, average wage and unemployment rate. The results are reported in the

Table 1. We also verified that the number of closure days does not have a seasonal component.9

These results confirms the view in Miaari and Sauer (2006) that the (temporary) closures of the

West Bank are not related to unobserved determinants of Palestinian labor demand or to the

evolution of the economic situation in the region. Finally, we find that the correlation between

closure days and the percentage of child labour at the district level is positive significant (0.18),

while the correlation with schooling is not statistically different from zero.

4 Data

This paper uses individual level data from the Palestinian Labour Force Survey, carried out

by the Palestinian Central Bureau of Statistics (PCBS), for the period 1999-2006. Each round

of the survey consists of a nationally representative sample of 7,600 households in Palestinian

territories. The survey collects detailed information on employment and socio-economic charac-

teristics of individual household members aged 15 years or more. We merged this dataset with

separate data on 10-14 years old children, provided by the Palestinian Central Bureau of Statis-

tics (PCBS). The present analysis is based on children between 10 and 14 years old for whom

full information on schooling, labour participation and a number of parents’ characteristics is8The correlation coefficients between the number of closure days and average wage, low skilled average wage

and unemployment rate in WB in the previous quarter are −0.24, −0.09 and −0.28, respectively: all coefficients

are not statistically significantly different from zero.9Detailed results are available from the Authors upon request.

10

Figure 1: Number of closure days (quarterly).

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0:4

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1:3

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2:1

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2:2

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2:3

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2:4

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3:1

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3:2

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3:3

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4:1

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4:2

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4:3

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5:3

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6:3

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6:4

Quarter

Note: effective closure days are comprehensive closures net of Saturdays, half the number of Fridays and Jewish

and Muslim holidays. Source: UNSCO and B’Tselem.

available.10 We consider only male children for analysis since observations for female working

children are very few. A child is considered working if he worked and was remunerated for at

least one hour during the reference week of the survey, or was working as an unpaid family mem-

ber. This is the definition of child labour adopted by the International Labour Organisation,

and often followed in child labour literature. We also restricted the analysis to West Bank since

the data on child labour in the Gaza Strip seems not reliable.11

4.1 Descriptives statistics

Tables 2 presents descriptive statistics of the sample of children between 10 and 14 years old in

West Bank (WB). The first column presents the characteristics of the total sample. The sec-

ond and third columns present the distribution of child, parental and household characteristics

for school participants and non-school participants respectively. The fourth and fifth columns10The Palestinian Labour Law, effective since mid-2000, prohibits the employment of children before they reach

the age of 15. The Chapter on Juvenile Labour in the Palestinian Labour Law defines boys between 15-18 years as

working juveniles and accordingly allows them to work but prohibits their employment in industries hazardous to

their safety or health, in night work, or on official holidays (Birzeit University Development Studies Programme

and UNICEF (2004)).11The data for GS in the LFS are quite different from the data in the 2004 Child Labour Survey conducted by

the PCBS (PCBS, 2004). The analysis of the child labour and schooling decision for Palestinian in the GS are

available upon request from the authors.

11

Figure 2: Number of closure days (quarterly) against closure days in the previous quarter

2000:Q4-2006:Q4.

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0 20 40 60Number of Closure days

Note: effective closure days are comprehensive closures net of Saturdays, half the number of Fridays and Jewish

and Muslim holidays. Source: UNSCO and B’Tselem.

present the characteristics for working children (child labour) and non-working children respec-

tively.

Our analysis is based on a sample of 17,248 observations. In the sample school participation

rate is 97.9% and child labour is 3%. The sample consists of 61.1% in 10-12 years group and

38.9% in 13-14 years group. School participation is higher among younger children and child

labour is higher among older children. Going down the list of characteristics we observe that

there is considerable variation in terms of parental characteristics. Father’s education is higher

for the children who attend school than for two other groups - non-school participants and child

labour. It is interesting to note that among the school participants unemployed father is more

prevalent than self-employed father. Also note that among the child labour percentage of children

with self employed father is higher than that with unemployed father. For mother’s education, it

appears that mother’s education is lower for child labour than for school participants. Employed

mother is notably higher among the child labour as compared to school participants. In terms

of break down of household characteristics for different children outcomes, number of adult

unemployed is less for child labour than for school participants.

The last group of descriptives gives the district and year level break down of the sample and

different children outcomes. The first column gives the percentage of children from different

districts. Three districts - Hebron, Ramallah and Nablus - represent around half of the sam-

12

Table 2: Sample descriptive statistics

Total Working Non-Working School Non School

Sample Participants Participants

All (%) 100 3.0 97.0 97.9 2.1

Child’s characteristics (%)

Age (%)

10-12 years 61.1 34.2 61.9 61.8 27.813-14 years 38.9 65.8 38.1 38.2 72.3

Father’s characteristics (%)

Education

No education 2.7 3.1 2.7 2.6 6.61 to 4 years 6.5 7.8 6.5 6.3 13.75 to 8 years 29.8 31.3 29.8 29.4 48.69 to 12 years 31.8 29.1 31.9 32.1 20.1More than 12 years 29.2 28.7 29.3 29.6 11.0

Employment status

Unemployed 19.3 10.5 19.6 19.0 32.1Self employed 14.6 27.4 14.2 14.5 19.0Wage earner 66.1 62.1 66.2 66.5 48.9

Mother’s characteristics (%)

Education

No education 8.2 13.4 8.0 7.9 20.11 to 4 years 8.8 10.5 8.7 8.6 16.85 to 8 years 31.8 35.9 31.7 31.7 35.79 to 12 years 36.8 32.0 36.9 37.1 22.5More than 12 years 14.4 8.2 14.6 14.7 5.0

Employment status

Unemployed 74.6 34.8 75.8 74.5 78.6Employed 25.4 65.2 24.2 25.5 21.4

Household’s characteristics

Number of children: 10-14 years 2.2 2.4 2.2 2.2 2.2Number of children: 15-17 years 0.8 0.9 0.8 0.8 1.0Number of unemployed adults 0.4 0.2 0.4 0.4 0.3Size 6.5 7.0 6.5 6.5 6.9

District (%)

Jenin 8.8 12.8 8.7 8.8 8.5Tubas 4.1 4.3 4.1 4.2 3.6Tulkarm 6.3 8.2 6.3 6.3 6Nablus 12.0 20.8 11.7 12 11.8Qalqilya 4.7 3.9 4.8 4.8 3.6Salfit 3.7 3.5 3.7 3.8 2.5Ramallah 14.3 8.7 14.5 14.2 17Jericho 4.5 1.0 4.6 4.5 5.2Jerusalem 4.1 0.0 4.2 4.2 2.8Bethlehem 10.4 1.9 10.7 10.4 9.6Hebron 27.0 35.0 26.7 26.9 29.4

Year (%)

2000a 4.0 4.7 3.9 3.9 6.62001 16.4 8.5 16.6 16.3 21.42002 11.9 2.5 12.2 11.9 11.82003 14.1 12.6 14.1 14.1 142004 14.7 7.4 15.0 14.8 12.42005 19.1 20.4 19.1 19.2 12.42006 19.9 43.9 19.2 19.9 21.4

Sample size (N) 17,248 515 16,733 16,884 364

Source: Authors’ elaboration based on data from PCBS.

Note: aData refers only to the 4th quarter

13

ple. The schooling in Ramallah appears more than the sample proportion, and child labour in

Nablus and Hebron appears less than sample proportion. The last block of numbers shows the

composition of the sample in terms of year of survey. It shows that considerable variation exists

in the pool in terms of year of survey and children outcomes.

Table 3 presents the distribution of the four states that are observable for a child, namely

both work and school, only work, only school and neither. The first row shows that 2.5% children

are engaged in both work and school in West Bank. Less than 1% is involved in only working

and 1.6% in neither work nor school. A notable difference between the 10-12 years and 13-14

years groups is that the percentage of only school is much higher among the younger group. The

last block of the table shows the yearly pattern of these four states for children. Looking at the

only school column we note that schooling has increased until 2005 to then abruptly decrease.

Only work has steadily decreased over time in WB. Adding to it the values of both work and

study, we get the yearly percentage of child labour. Child labour followed a U-shaped path over

the years with an intermediate peak in 2003 reaching the 6% in 2006. As for children attending

school, it turns out that the percentage has gradually increased over time, from 96.4% in 2000

peaking 98.6% in 2005 to then decrease to 97.% in 2006.

Table 3: Working and school participation rates by age group

Working Working Studying Neither Child Labour School attendance

and studying only only (1)+(2) (1)+(3)

Total sample 2.5 0.5 95.4 1.6 3.0 97.9

Age (%)

10-12 years 1.6 0.1 97.4 0.9 1.7 99.013-14 years 3.9 1.2 92.2 2.8 5.1 96.1

Year (%)

2000 2.5 1.0 94.0 2.5 3.5 96.52001 0.9 0.7 96.4 2.1 1.6 97.22002 0.2 0.4 97.7 1.7 0.6 97.92003 2.2 0.5 95.7 1.7 2.7 97.92004 1.2 0.3 97.1 1.5 1.5 98.22005 3.0 0.2 95.7 1.2 3.2 98.62006 6.0 0.6 91.8 1.7 6.6 97.7

Source: Authors’ elaboration based on data from PCBS.

In the following sections we try to offer an explanation for these patterns focusing on the

role of the labour market and of the border closure by Israel.

5 Estimation results

Our objective is to estimate the effect of the Israeli-Palestine conflict on children outcomes and

to determine whether the conflict modifies the relation between the labour market conditions

and children’s outcome. Table 4 presents the estimated coefficients from the bivariate probit

14

model. The regressions pool together children across the period 2000:Q4-2006:Q4. All estimation

incorporates weights provided by the PCBS. The definition of variables is reported in Table 7

in the Appendix.

Regression (1) is the baseline specification. The error correlation between the child labour

and schooling attendance decision is −.485. The negative sign indicates that the unobservable

factors affect child labour and school attendance in opposite direction, i.e. there is a trade off

between the two outcomes. The relatively high value of the parameter indicates that in West

Bank child labour and schooling decisions are interdependent rather than being independent.

Estimation results in Table 4 show that the labour market situation (as proxied by the low-

skilled district level average market wage) strongly influences the probability of child labour.

The coefficient of the market wage is positive and very significant for all specifications: the

higher the market wage the higher child labour. This meas that in WB the substitution effect

prevails over the income effect. The coefficient of the market wage is instead not significant for

school attendance: this suggests that the latter decision is not influenced by the evolution of the

labour market and that other factors probably play a more important role in determining that

choice. Our estimation also indicates that child labour decision is influenced by the intensity of

the conflict as proxied by the number of closure days: an increase in the number of closure days

increases child labour. While the number of closure days has a direct effect on child labour, it

does not affect schooling attendance. Finally our results also indicate that the Israeli-Palestinian

conflict has an effect on the relationship that exists between the labour market condition and

child labour. The coefficient of the interaction between market wage and number of closure days

is negative and highly significant. We interpret this result as indicating that when the borders

are closed, i.e. when the intensity of the conflict is higher, a decrease in market wage tends

to increase child labour. This implies that the positive correlation between the opportunity

cost of not-working and the supply of child labour is much reduced when the conflict is more

severe. While the correlation between market wage and child labour may still be positive, this

result shows that the conflict has a strong impact on the child labour’s response to the labour

market conditions. As for the other control variables, results are as expected. Consistently

with previous studies (i.e. Desai and Jain, 1994), we find that mother’s education is associated

with a lower probability of child labour and a higher school attendance. The mother being

employed is positively associated with the probability of child labour12 while no correlation is

found with school attendance. As for household wage income13, we find that it is negatively

correlated with child labour. This is consistent with the luxury axiom (Basu and Van, 1998).12This result is consistent with Francavilla and Giannelli (2007) who found that mother’s labour market par-

ticipation is associated with child labour in India. Also Manacorda (2006) finds that child labour is positively

associated with the mother being employed.13We use a categorical variable defined as follows: low income is the upper bound of the 25th percentile, middle

income is between 25th and 75th percentile, high income is the lower bound of the 75th percentile of the full

sample. In section 5.2 we check our results with respect to alternative measurement of household well-being.

15

In addition, the coefficient of the interaction term indicates that the negative relation between

household income and child labour becomes weaker during closure days and it decreases more for

mid-income rather than for high income households. School attendance is positively correlated

with household income but the coefficients are smaller and less precisely estimated than for child

labour. Again the conflict results to impact more on the the middle-income households than on

the high-income ones: the interaction terms show that when closure is in place the probability

of school attendance decreases for the former but not for the latter.

Table 4 also report the regression results when we expand the set of control variables in

X in equation (2). In regression (2) we include a control for the father’s place of work: Israel,

another district in WB or the district of residence (reference category). We interact this variable

with the number of closure days to capture the possible changes in the effect of the father being

employed in Israel when the conflict is more severe. In regression (3) we also control for the

type of father’s employment (with father unemployed being the reference category). The type

of father’s employment turns out to be correlated with the probability of school attendance but

not with child labour. The father being employed increases the probability of school attendance

with the probability being highest if the father is employed in the public sector rather than in

the private sector or being self-employed. The interaction of the father’s type of employment

with the number of closure days is never significant. In regression (4) we include a number of

household characteristics: the number of siblings 10-14 and 15-17 years old in the household,

the ratio of female children in the household, the head of the family being a female, the number

of unemployed adults in the household and the household size. In regression (5) we consider the

household place of living, a refugee camp, a rural or an urban area. Finally in regression (6) we

also control for the characteristics of the labour demand at the district level. Since the informal

sector is the more likely candidate for employing children, we estimate the model controlling for

the share of informality and for the unemployment rate at the district level. While the latter is

never significant, the former is positively significantly correlated with child labor. Even if the

coefficient of the interaction term is small, it indicates that when closures are in place school

attendance is negatively correlated with the informality rate.

Predicted probabilities Tabel 5 presents the predicted probabilities from the estimates of

our baseline model. Each column reports the yearly percentage change in the dependent variable

following one standard deviation increase in the explanatory one. Our results indicates that a

one standard deviation increase in the average district market wage increases the probability of

child labour by 8% on average over the period 2000-2006. To compare our results to previous

ones, we also computed the effect of a 10% increase in the wage. In our model the latter would

increase child labour by 3.5%. Rosenzweig (1981), using aggregate data, finds that in rural India

a 10% increase in adult male wages reduces male children’ labour supply by 10%. In Duryea and

Arends-Kuenning (2003), the relationship between the market wage and child labour is positive

but smaller in our case: a 10% increase in the market wage increasing child labour probability by

16

Tab

le4:

Bip

robi

tre

gres

sion

resu

lts

vari

able

(1)

(2)

(3)

(4)

(5)

(6)

Child

Labour

School

Child

Labour

School

Child

Labour

School

Child

Labour

School

Child

Labour

School

Child

Labour

School

avera

ge

dis

tric

t(l

ow

skille

d)

mark

et

wage

0.6

1**

-0.2

40.6

2**

-0.2

20.6

3**

-0.2

40.6

4**

-0.2

50.6

3**

-0.2

60.7

5***

-0.1

3

no.c

losu

redays

0.0

7***

0.0

10.0

7***

0.0

10.0

7***

0.0

10.0

7***

0.0

10.0

7***

0.0

10.0

8***

0.0

2

no.c

losu

redays×

mark

et

wage

-0.0

1***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0

no.c

losu

redays×

dis

tance

0.0

0-0

.00***

0.0

0-0

.00***

0.0

0-0

.00***

0.0

0-0

.00***

0.0

0-0

.00***

0.0

0-0

.00***

age

0.2

4***

-0.2

0***

0.2

4***

-0.2

0***

0.2

4***

-0.2

0***

0.2

4***

-0.2

2***

0.2

4***

-0.2

2***

0.2

4***

-0.2

2***

fath

er

educati

on

0.0

10.0

4***

0.0

10.0

4***

0.0

10.0

3***

0.0

10.0

3***

0.0

10.0

3***

0.0

00.0

3***

moth

er

educati

on

-0.0

4***

0.0

4***

-0.0

4***

0.0

4***

-0.0

4***

0.0

4***

-0.0

5***

0.0

5***

-0.0

5***

0.0

5***

-0.0

5***

0.0

5***

moth

er

em

plo

yed

0.8

3***

0.0

00.8

3***

0.0

00.8

0***

0.0

00.8

0***

0.0

00.8

1***

-0.0

20.8

1***

-0.0

2

hh

incom

e(m

ediu

m)

-0.4

4***

0.2

9**

-0.4

5***

0.3

0**

-0.4

9***

0.2

9**

-0.5

2***

0.3

0**

-0.5

4***

0.2

7**

-0.5

2***

0.2

7**

hh

incom

e(h

igh)

-0.5

5***

0.2

3*

-0.6

0***

0.2

3-0

.64***

0.2

3-0

.60***

0.2

1-0

.63***

0.1

9-0

.60***

0.2

0

hh

incom

e(m

ediu

m)×

no.c

losu

redays

0.0

1**

-0.0

1*

0.0

1**

-0.0

1*

0.0

1**

-0.0

1*

0.0

1**

-0.0

1*

0.0

1***

-0.0

1*

0.0

1**

-0.0

1

hh

incom

e(h

igh)×

no.c

losu

redays

0.0

10.0

00.0

10.0

00.0

1*

0.0

00.0

1*

0.0

00.0

1*

0.0

00.0

1*

0.0

0

fath

er

work

soth

er

dis

tric

t0.4

1*

0.0

50.3

8*

0.0

40.3

60.0

50.3

80.0

20.3

5-0

.02

fath

er

work

sin

Isra

el

0.1

5-0

.01

0.3

00.0

10.2

50.0

90.2

60.0

80.2

50.0

7

fath

er

work

soth

er

dis

tric

t×no.c

losu

redays

-0.0

1-0

.01

0.0

0-0

.01

-0.0

1-0

.01

-0.0

1-0

.01

0.0

0-0

.01

fath

er

work

sin

Isra

el×

no.c

losu

redays

0.0

00.0

00.0

00.0

00.0

00.0

00.0

00.0

00.0

00.0

0

fath

er

self-e

mplo

yed

0.2

50.3

0*

0.1

20.3

7**

0.1

00.3

5**

0.1

70.3

8**

fath

er

public

secto

rem

plo

yed

0.3

8*

0.3

3**

0.2

00.4

6***

0.1

80.4

4***

0.2

10.4

6***

fath

er

pri

vate

secto

rem

plo

yed

0.0

60.1

9-0

.12

0.3

2**

-0.1

40.3

1**

-0.0

90.3

4**

fath

er

self-e

mplo

yed×

no.c

losu

redays

0.0

00.0

00.0

0-0

.01

0.0

0-0

.01

0.0

0-0

.01

fath

er

public

secto

r×no.c

losu

redays

0.0

00.0

00.0

00.0

00.0

00.0

00.0

00.0

0

fath

er

pri

vate

secto

r×no.c

losu

redays

0.0

00.0

00.0

00.0

00.0

00.0

00.0

00.0

0

fem

ale

house

hold

head

-5.5

8***

5.5

1***

-5.7

9***

5.4

8***

-5.5

6***

5.2

2***

hh

childre

ngender

rati

ora

tio

-0.0

40.0

0-0

.04

0.0

0-0

.03

-0.0

1

hh

no.c

hildre

n10-1

40.1

2***

-0.0

40.1

3***

-0.0

40.1

3***

-0.0

4

hh

no.c

hildre

n15-1

70.0

2-0

.01

0.0

2-0

.01

0.0

3-0

.01

hh

unem

plo

yed

adult

s-1

.98***

1.0

2***

-1.9

8***

1.0

0***

-1.9

4***

0.9

9***

hh

size

-0.0

20.0

4***

-0.0

20.0

4***

-0.0

20.0

4***

urb

an

0.2

7**

0.1

7**

0.2

7**

0.1

9**

rura

l0.1

70.2

4***

0.1

70.2

5***

dis

tric

tunem

plo

ym

ent

0.0

00.0

0

dis

tric

tunem

plo

ym

ent×

no.c

losu

redays

0.0

00.0

0

%dis

tric

tin

form

ality

0.0

2***

0.0

0

%dis

tric

tin

form

ality

×no.c

losu

redays

0.0

0-0

.00***

const

ant

-7.9

1***

4.8

1***

-8.0

2***

4.7

3***

-8.2

3***

4.6

6***

-8.2

5***

4.4

4***

-8.4

0***

4.3

2***

-9.1

7***

3.7

3***

hh

fixed

effect

YES

YES

YES

YES

YES

YES

dis

tric

tdum

mie

sY

ES

YES

YES

YES

YES

YES

quart

er

dum

mie

sY

ES

YES

YES

YES

YES

YES

#17,248

17,248

17,248

17,248

17,248

17,248

F−

test

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

ρ−

.489∗∗

−.4

89∗∗

−.4

98∗∗

.483∗∗

−.4

83∗∗

.486∗∗

Wald

test

Ch

i2(9

2)

=6884.7

3C

hi2

(100)

=7746.2

1W

Ch

i2(1

12)

=6762.8

8C

hi2

(124)

=16273.6

9C

hi2

(128)

=18018.2

6C

hi2

(136)

=22024.0

5

Note

:B

ivari

ate

pro

bit

model,

weig

hts

use

d.

See

equati

ons

2and

3.1

.∗∗∗,∗∗,∗

stand

for

signifi

cant

at

1%

,5%

and

10%

level,

resp

ecti

vely

.C

orr

ecti

on

for

err

or

corr

ela

tion

for

mark

et

wage

clu

ster

and

robust

standard

err

or

for

all

oth

er

vari

able

suse

d.

All

regre

ssio

ns

have

254

clu

ster

for

mark

et

wage.

Refe

rence

cate

gori

es:

aunem

plo

yed;

bem

plo

yed

inth

edis

tric

tofre

sidence;

cre

fugee

cam

p.

17

Table 5: Predicted probabilities (% changes)

Base Number Av. district Household Mother

Closure days market wage Income Education

Child labour2000 2.8% 61.3 -3.8 -14.0 -28.8

2001 1.3% 77.6 1.4 -15.2 -31.4

2002 0.6% 87.5 39.1 -33.2 -33.6

2003 2.8% 73.4 12.0 -20.2 -28.1

2004 1.6% 91.6 5.9 -20.8 -30.2

2005 3.4% 66.7 2.0 -15.4 -26.9

2006 6.8% 58.9 -2.0 -12.5 -24.0

Weighted Averagea 3.0% 73.8 7.3 -18.3 -28.6

School attendance2000 97.0% -0.5 -0.3 0.4 0.8

2001 97.6% -0.4 -0.3 0.3 0.7

2002 98.0% -0.2 -0.3 0.1 0.6

2003 98.0% -0.4 -0.3 0.2 0.6

2004 98.3% -0.4 -0.3 0.3 0.5

2005 98.6% -0.2 -0.2 0.2 0.4

2006 97.7% -0.4 -0.3 0.4 0.7

Weighted Averagea 97.9% -0.3 -0.3 0.3 0.6

Note: Each variables increased by one standard deviation. Weights are year percentage from Table 2: column 2 for school

attendance and 4 for child labour. aWeights are share of each year observation in the total sample.

0.7% with respect to their baseline. As for closures, results show that if the number of closures

days in a quarter increases by one standard deviation, the probability of child labour increases

by around 74%.14 This is quite a large effect.

5.1 Discussion of the results

Our analysis indicates that the probability of child labour in West Bank is related, in addition

to a number of other elements, to the characteristics of the local labour market. In particular,

the substitution effect prevails over the income effect: the lower the wage the less is child labour.

Moreover the intensity of the conflict, proxied by the number of closure days, affects the prob-

ability of child labour and the relationship between the labour market opportunities and child

outcome. The closure of the Israeli-Palestinian borders leads to more domestic unemployment,

lower wages and (on average) lower household income.15 Our results show that the closures also

increases the probability of child labour and that this effect is not restricted only to children

whose father is employed in Israel. This means that the effect of closure reverberates on chil-14Over the period 2000-2006 the average number of closure days in quarter is 33.4 with a standard deviation of

19.4. This implies that increasing by the 1% the number of closure days increases the probability of child labour

by 1.6%.15Miaari and Sauer (2006) estimates that doubling of the frequency of closures reduces the mean monthly

earnings of Palestinians from the West Bank by 2.5%.

18

dren’s time allocation decision through its effect on the whole West Bank economy. Interestingly

this result is in line with the model presented in Epstein and Kahana.16 Finally, our estimation

results show that during the conflict periods, the relationship between the labour market con-

ditions and child labour is modified with respect to normal days. During the closure days, the

lower the market wage the higher child labour, i.e. as the condition of the labour market worsen

child labour increases. This indicates that child labour is in part a response to the worsening of

the general economic condition as caused by closures.

Our results also highlight the existing differences between the determinants of child labour

and school attendance decision. It clearly emerges from our analysis that, although the two

decisions are correlated, there are probably different determinants at play. Indeed, school at-

tendance appears not to be related either to market wage or to the number of closure days. We

speculate that an analysis of the determinants of school attendance would probably require also

additional information on the quality of education, the accessibility or the cost of education,

etc. Unfortunately, all these demand for school attendance variables are at the moment not

available. The use of district dummies, while being only possible control, is admittedly not

sufficient to account for all that characteristics that may strongly influence school attendance

decision. Further analysis and data collection is needed on this important aspect.

5.2 Robustness checks and other model specifications

Table 6 reports the results from a set of robustness checks. Regression (a) checks our results with

respect to our measure of the opportunity cost of not working. We run the the full specification

(regression (6) in Table 6) where the opportunity cost of not-working is measured as the (log)

market wage of workers employed in the private sector with less than 5 years of education as in

Duryea and Arends-Kuenning (2003). In regression (b), we run the full specification excluding

from the sample households living in Jerusalem Est, given the peculiar status of Palestinians

living in that area rather than in other district in West Bank. In regression (c), we run the

full specification including also an individual fixed effect. To control for a long-run possible

effect of the number of closure days, in column (d) we run our preferred specification including

the number of closure days of previous period (one quarter lag). Regression (e) we estimated

the full model including a dummy variable for the summer period to control for a possible

cyclical component in the child labour supply related to the school holidays. The summer

variable turns out to be significant for child labour with all other variables maintaining their

sign and significance. Finally, in regression (f) and (g) we consider alternative controls for the

household income. In regression (f) we use the (log) household wage income net of child wage.

The household income variable has the expected sign but it is not significant while our main16 The model predicts that - in a Basu-Van setting, emigration of fathers may move the economy from a

equilibrium-with child labour with an equilibrium without it. Our results indicate that the Palstinin case would

be an example of a reverse situation: the return of fathers - caused by the closure - increases child labour in the

economy.

19

variables of interest maintain the same sign and significance as in the full specification. Finally

in regression (g) we estimated the model using a set of proxies for the household income: the

ratio of employed adults in the household, the ratio of household members employed in the

private sector, in the public sector and self employed. The reason for using these proxies is

that they give a measure of household well-being which does not require household members to

declare a wage - which in some case may be missing or inaccurately reported. Again the sign

and significance of the market wage, number of closure days and their interaction do not change.

To take into account the observation in Moulton (1990) about the effect on standard errors

of using aggregate variables in individual regression, we considered alternative ways of correcting

standard errors. While in our baseline estimation we clustered errors around the district market

wage, as checks we used robust standard errors without any clustering variable, and robust

standard errors clustered around district unemployment and household income. Our results do

not change.

We also considered alternative model specifications. First we run two separated probit

regressions - one for child labour and one for school attendance. While points estimates are

different from the ones obtained with our model - the probit model does account for the trade-

off with the other outcome for child decision - the sign and the significance of our two main

variables are consistent with the bi-probit model results. Results do not change also including

school attendance and child labour as control variable, respectively. Then we considered a

Seemingly Unrelated (SUR) version of our model and we also estimated a tobit model for the

hours of child work. In both cases the coefficients for the number of closure days and the

average market wage are precisely estimated and the sign remains the same as in the baseline

specification.17

6 Concluding remarks

In this paper we have analysied the determinants of child labour and school attendance for

Palestinian children in West Bank between the beginning of the Second Intifada and 2006. In

particular, we studied how the characteristics of the local labour market and the intensity of the

conflict are related to the children’ outcome.

We find that the conflict with Israel had a large impact on child labour. Our results show

that the increase in the number of closure days increases the probability of child labour and

that this effect is not limited to households whose members are employed in Israel. We also find

a highly significant positive correlation between the opportunity cost of not working and child

labour. This indicates that the job opportunities in the local labour market are an important

determinant of child labour in the region. While this is the normal relation between the labour

market situation and child choice, it changes during conflict period. When borders are closed,

the substitution effect is much weaker than when they are open. This meas that during the17Results are available upon request from the Authors.

20

Tab

le6:

Rob

ustn

ess

chec

ks:

Bip

robi

tre

gres

sion

resu

lts

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Av.d

istr

ict

wage

Exclu

din

gIn

div

idual

No.c

losu

reSum

mera

House

hold

bEm

plo

ym

ent

18-3

5,

edu

<5years

)Jeru

sale

mEst

fixed

effect

days

lag

incom

era

tio

Vari

able

CL

SC

CL

SC

CL

School

CL

SC

CL

SC

CL

SC

Mark

et

wage

0.4

6**

0.0

60.7

1***

-0.1

70.7

3***

-0.2

40.7

2***

-0.2

40.6

9**

-0.2

50.6

7***

-0.2

50.6

6***

-0.2

3

no.c

losu

redays

0.0

5**

0.0

20.0

7***

0.0

20.0

7***

0.0

10.0

7***

0.0

10.0

8***

0.0

10.0

7***

0.0

10.0

7***

0.0

1

no.c

losu

redays×

Mark

et

wage

-0.0

1***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0-0

.02***

0.0

0

no.c

losu

redays

lag

0.0

00.0

0

Sum

mer

0.3

5***

0.0

3

House

hold

wage

incom

ec

-0.1

30.0

6

Em

plo

ym

ent

rati

od

-2.9

4***

0.3

8

Em

plo

ym

ent

rati

o(s

elf-e

mplo

yed)e

2.7

8***

-0.7

3

Em

plo

ym

ent

rati

o(p

ublic

secto

r)-0

.92

-2.0

8***

Em

plo

ym

ent

rati

o(p

rivate

secto

r)0.5

6-1

.02**

indiv

idualfixed

effect

NO

NO

NO

NO

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

#obse

rvati

on

14,8

17

16,5

38

17,2

48

17,2

48

17,2

48

17,2

48

17,2

48

F−

test

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

ρ-.504

-.487

-.486

-.485

-.468

-.489

-.517

clu

ste

rs

189

238

254

254

254

254

254

Wald

test

chi2

(134)=

60196.4

8chi2

(132)=

18140.2

7chi2

(136)=

21236.6

0chi2

(134)=

21303.1

4chi2

(102)=

15494.0

4chi2

(130)=

20871.1

5chi2

(138)=

40104.1

8

Note

:B

ivari

ate

pro

bit

model,

weig

hts

use

d.∗∗∗,∗∗,∗

stand

for

signifi

cant

at

1%

,5%

and

10%

level,

resp

ecti

vely

.A

llre

gre

ssio

ns

inclu

de

the

set

ofcontr

olvari

able

sas

insp

ecifi

cati

on

(6)

ofTable

4.

ain

stead

of

quart

er

dum

mie

syear

dum

mie

sare

use

d.

bth

ecate

gori

calvari

able

for

house

hold

incom

euse

din

specifi

cati

on

(6)

isexclu

ded.

clo

gof

the

house

hold

wage

incom

enet

of

child

wage.

dth

era

tio

betw

een

em

plo

yed

mem

bers

ofth

ehh

and

size

ofth

ehh.

eth

era

tio

betw

een

self

em

plo

yed

(em

plo

yed

inth

epublic,pri

vate

secto

r)m

em

bers

ofth

ehh

and

size

ofth

ehh,re

specti

vely

.C

orr

ecti

on

for

err

or

corr

ela

tion

for

mark

et

wage

clu

ster

and

robust

standard

err

or

for

all

oth

er

vari

able

suse

d.

21

conflict periods, the choice of working is more due to a worsening of the economic condition

rather than to the increase in the opportunity cost of not working. In a situation of conflict with

decreasing wages, closure tends to increase child labour in West Bank also through this channel.

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24

7 Appendix

Table 7: Variables definition

Variable Definition

Child labour = 1 if a child worked and was remunerated for at leastone hour during the reference week of the survey,or was working as unpaid family member; 0 otherwise.

School = 1 if a child is attending school; 0 otherwise.

Child’ characteristics

Age age of the child

Father’s characteristics

Education father’s years of education.

Self employed = 1 if father is self employed; 0 otherwise.

Public sector wage earner = 1 if father is wage earner in the public sector; 0 otherwise.

Private sector wage earner = 1 if father is wage earner in the private sector; 0 otherwise.

Employed in other WBGS district = 1 father is not employed in the district the household lives.

Employed in Israel = 1 if father is employed in Israel

Mother’s characteristics

Education mother’s years of education.

Employed = 1 if mother is employed; 0 otherwise.

Household’s characteristics

Female household head = 1 if head of the household is female; 0 otherwise.

No.children: 10-14 years number of children in the age group 10 - 14 years.

No.children: 15-17 years number of children in the age group 15 - 17 years.

Children gender ratio ratio between female children in the hh and the total number of children (< 17).

No.male unemployed in the hh number of unemployed male adults (18-64) in the household.

Size number of people living in the household.

Rural = 1 if the household lives in a rural area; 0 otherwise.

Urban = 1 if the household lives in an urban; 0 otherwise.

Household income household income net of children wages

District characteristics

Market wage log the district level average dailywage of male (18-35) with less than 7 years of education

Average unemployment average unemployment at the district level

Closure days number of days in which mobility between Israel andPalestinian Territories is prohibited also for workerswith permission to work in Israel

Distance distance of the district capital to the closest Israeli border (Km).

25