Child labour and youth employmentas a response to household vulnerability: evidence from rural Ethiopia

Introduction

• Growing literature of the effect of household vulnerability on children’s work and youth employment;

•Idiosyncratic shocks and natural disasters apparently lead households to use children as a risk copying instruments

•There is robust evidence that shocks do in fact matter for housheold decision concerning children’s work and education;

•But shocks experienced by household can take a variety of forms and their consequences may depend on their specific nature;

•As a result, the policies required to help cope with risk might also vary depending on the type of shock;

Data and variable definition

•The Ethiopia Rural Household Survey (ERHS) is a longitudinal household data set covering households in a number of villages in rural Ethiopia.

• Data collection started in 1989;

•In 1994, the survey was expanded to cover 15 villages across the country.

• An additional round was conducted in late 1994, with further rounds in 1995, 1997, 1999, 2004, and 2009. In addition, nine new villages were selected giving a sample of 1477 households

• We use the 2004 and 2009 round

•The EHRS round 2004 and 2009 collectes informationon children involvememnt in employment starting from the age of 5 years

Data and variable definition

• The two rounds of the Ethiopia Rural Household Survey (ERHS) collect also information on occurence of shocks during the 5 years prior to the survey;

•Children’s work appears to be substancially higher for children belonging to household hit by a shock;

Data and variable definition

Percentage of children (5-14) in employment, belonging to household experiencing shocks by type of shock, and year

Year 2004 Year 2009Type of shock No Yes No YesNatural disaster 50.0 60.7 54.8 62.0Economic 60.8 53.9 54.5 68.6Other 58.0 63.2 60.5 53.7Lack demand/input 58.4 58.5 58.9 68.2

Note: Natural disaster (drought, pest-desease on crops, pest or desease on livestock); Economic shocks (input price increase, output price increase=; Other (land redistribution in PA, confiscation of assets); Lack demand input (lack of demand of agricultural products, lack of access to inputs).

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

Percentage of children (5-14) attending school, belonging to household experiencing shocks by type of shock and year

Year 2004 Year 2009Type of shock No Yes No YesNatural disaster 44.7 41.3 65.6 61.9Economic 42.4 41.3 60.4 66.2Other 41.3 51.1 63.0 51.9Lack demand/input 42.6 40.0 62.4 64.6

Data and variable definition

• On the contrary, the effect of shocks on children’s school attendance is not well defined;

Note: Natural disaster (drought, pest-desease on crops, pest or desease on livestock); Economic shocks (input price increase, output price increase=; Other (land redistribution in PA, confiscation of assets); Lack demand input (lack of demand of agricultural products, lack of access to inputs).

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

Percentage of youth (15-21) in employment, belonging to household experiencing shocks by type of shock, and year

Year 2004 Year 2009

Type of shock No Yes No YesNatural disaster 73.0 75.0 69.4 70.9Economic 73.6 76.5 68.2 73.6Other 74.6 74.1 70.7 61.1

Lack demand/input 73.4 78.0 70.0 73.3

Percentage of youth(15-21) attending school, belonging to household experiencing shocks by type of shock and year

Year 2004 Year 2009Type of shock No Yes No YesNatural disaster 58.4 48.4 62.4 61.1Economic 51.6 47.9 60.1 63.1Other 49.8 56.1 61.2 71.4

Lack demand/input 51.9 46.2 60.7 65.1

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

Effect of shocks on youth employment and school attendance are also not well defined;

Children’s work and school attendance in rural Ethiopia

Children’s work and school attendance in Ethiopia

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

Involvement in economic activity of Ethiopian children remain one of the highest in Africa region

Child activity status (age 5-14), by year

Activity status2004 2009

Male Female Total Male Female TotalEmployment only 35.5 26.7 31.1 23.4 15.2 19.4School only 9.4 20.2 14.8 11.1 32.4 21.5Employment and school 35.4 19.2 27.3 51.4 30.6 41.3Neither 19.7 34.0 26.8 14.1 21.7 17.8

100 100 100 100 100 100Total Employment 70.9 45.9 58.4 74.8 45.8 60.7Total schooling 44.8 39.4 42.1 62.5 63 62.8

Employment rate, by age and years

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 210

102030405060708090

100

20042009

age

% e

mpl

oym

ent

Employment rate

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

School attendance rate, by age and years

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 210.0

10.020.030.040.050.060.070.080.090.0

100.0

20042009

age

% sc

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att

enda

nce

School attendance rate

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

Theoretical Model

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Elasticity of child labour supply

First best solutionBorrowing constraints: no corner solution for child

labour supply

Borrowing constraints: corner solution for child labour supply

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Econometric analysisPreliminary Results

•Two approaches to assess the impact of shocks on household behaviour

•Non-Linear model : by regressing the outcome variable “employment” at time t on the employment at time (t-1), a set of individual and household characteristics at time (t), shocks experienced by the household;

•Non-Linear model with IVUsing past shocks and individual and household characteristics as instruments

    (1) (2)Variables employment (t) employment (t)

       Employment (t-1) 0.564*** 0.564***

(7.86) (7.85)

Shocks

drought 0.185** 0.187**(2.23) (2.25)

pest or desease on crop 0.281*** 0.282***(3.23) (3.24)

Lack of access to inputs 0.0741 0.0734(0.63) (0.62)

input price increase 0.141** 0.141**(1.99) (1.99)

output price increase -0.163 -0.164(-0.88) (-0.88)

lack demand agricultural product -0.167 -0.168(-0.64) (-0.64)

land redistribution in PA -0.574** -0.569**(-2.01) (-2.00)

confiscation of assets -0.114 -0.118(-0.22) (-0.23)

pest or desease on livestock -0.117 -0.118(-1.25) (-1.26)

dummy: zero per capita consumption in Kcal (cereals) 0.579 0.583(0.88) (0.89)

Log per capita consumption in Kcal (cereals) 0.0209 0.0211(0.39) (0.40)

variance ratio deficiency 0.541**(2.04)

variance per capita consumption in Kcal (cereals) 0.00993**(2.06)

Constant 0.108 0.114(0.13) (0.13)

Obs. 1,732; z-statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Regression analysis on employment at time t, without instrumental variable

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

    (1) (2)Variables employment (t) employment (t)

       Employment (t-1) -0.213 -0.207

(-0.83) (-0.81)

Shocks

drought 0.187** 0.189**(2.40) (2.41)

pest or desease on crop 0.231*** 0.231***(2.72) (2.72)

Lack of access to inputs 0.0713 0.0698(0.65) (0.63)

input price increase 0.153** 0.155**(2.30) (2.33)

output price increase -0.144 -0.146(-0.82) (-0.83)

lack demand agricultural product -0.158 -0.160(-0.64) (-0.65)

land redistribution in PA -0.511* -0.507*(-1.88) (-1.87)

confiscation of assets -0.113 -0.117(-0.23) (-0.24)

pest or desease on livestock 0.0339 0.0326(0.38) (0.37)

dummy: zero per capita consumption in Kcal (cereals) 0.497 0.513(0.81) (0.83)

Log per capita consumption in Kcal (cereals) 0.0215 0.0227(0.43) (0.45)

variance ratio deficiency 0.515**(2.07)

variance per capita consumption in Kcal (cereals) 0.00938**(2.08)

Constant -0.758 -0.752(-0.88) (-0.88)

Obs. 1,732; z-statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.1

IV Regression analysis on employment at time t

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

    (1) (2)Variables School attendance(t) School attendance(t)

       School attendance (t-1) 0.580*** 0.580***

(6.84) (6.84)

Shocks

drought 0.0705 0.0673(0.80) (0.76)

pest or desease on crop -0.0381 -0.0464(-0.42) (-0.51)

Lack of access to inputs 0.411*** 0.411***(3.02) (3.02)

input price increase 0.154** 0.155**(2.03) (2.04)

output price increase -0.106 -0.112(-0.50) (-0.53)

lack demand agricultural product -0.452* -0.452*(-1.69) (-1.69)

land redistribution in PA -0.0698 -0.0711(-0.22) (-0.22)

confiscation of assets -0.557 -0.552(-1.04) (-1.03)

pest or desease on livestock 0.165 0.167*(1.63) (1.65)

dummy: zero per capita consumption in Kcal (cereals) 0.0126 0.0597(0.02) (0.08)

Log per capita consumption in Kcal (cereals) 0.0269 0.0311(0.46) (0.54)

variance ratio deficiency 0.304(1.09)

variance per capita consumption in Kcal (cereals) 0.00334(0.67)

Constant -1.095 -1.106(-1.19) (-1.20)

Obs. 1,675; z-statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Regression analysis on school attendance at time t, without instrumental variable

Source: Author’s calculations based on Ethiopia ERHS 2004-2009

    (1) (2)Variables School attendance(t) School attendance(t)

       School attendance (t-1) 0.498 0.458

(1.21) (1.11)

Shocks

drought 0.108 0.105(1.23) (1.18)

pest or desease on crop -0.0454 -0.0537(-0.50) (-0.59)

Lack of access to inputs 0.407*** 0.407***(2.99) (3.00)

input price increase 0.164** 0.165**(2.15) (2.15)

output price increase -0.126 -0.133(-0.60) (-0.63)

lack demand agricultural product -0.491* -0.491*(-1.83) (-1.83)

land redistribution in PA -0.0685 -0.0687(-0.22) (-0.22)

confiscation of assets -0.544 -0.538(-1.04) (-1.03)

pest or desease on livestock -0.148 -0.147(-1.49) (-1.48)

dummy: zero per capita consumption in Kcal (cereals) 0.137 0.180(0.19) (0.26)

Log per capita consumption in Kcal (cereals) 0.0381 0.0422(0.66) (0.73)

variance ratio deficiency 0.346(1.25)

variance per capita consumption in Kcal (cereals) 0.00415(0.83)

Constant -1.330 -1.381(-1.32) (-1.38)

Obs. 1,675; z-statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.1

IV Regression analysis on school attendance at time t

Source: Author’s calculations based on Ethiopia ERHS 2004-2009