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Effect of Climate Shock on Cognitive

Development of Children in Ethiopia

Tilahun Asmare, WVEand

Guush Berhane, ESSP, IFPRI 

 Addis Ababa, Ethiopia

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PRESENTATION OUTLINE

Introduction Problem statement Objective

Research Methodology Data Source Conceptual framework Model Specification

Results and Discussions Descriptive Analysis Econometric Analysis

Conclusion and recommendation Conclusion recommendation

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INTRODUCTION

• Climate-related shock, drought and flood threaten the livelihood of nations specially developing countries that have low coping mechanism and resilience capacity.

• Ethiopia faced by adverse climate shock (e.g drought) that leads to a loss of household income, a reduction in consumption, and/or a loss of productive asset (Darcon, 2005).

• Drought often defined as a continuous interval of time during which actual moisture supply at a given place is consistently less than normally expected (Roy and Hirway, 2007).

• Drought is covariant and slow onset shock

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INTRODUCTION

The short term impact of drought loss of food production and non-food

production, loss of employment; shortage of water, fodder and fuel wood; indebtedness, migration etc

long-term impact Low agriculture growth stunting, low cognitive development of children that it

turn negatively affects the development of the country as it lost its children intelligence.

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INTRODUCTION

Cognitive development is the construction of thought processes, including remembering, problem solving, and decision making, from childhood through adolescent and adulthood (cengage 2005).

Many researchers used Peabody picture vocabulary test (PPVT) as measurement of cognitive of children (desai (1989), Bau (1999,McCullah &Joshi (2002), Duc (2009), Nair (2009).

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STATEMENT OF THE PROBLEM

‘notwithstanding major investments in improving the numbers and the qualifications of teachers and the availability of equipment, student achievement has not sufficiently improved. (MOE, 2010).

The Evolution of scores obtained in NLA shows the composite score of grade 4 decreased to 40.9% in 2007/08 from its result 2000/01 that was 47.9% and the composite score of grade 8 decreased from 41.1% of 2007/08 to 35.6% in 2000/01 (NOE, 2008).

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STATEMENT OF THE PROBLEM:

Though drought is the most frequent challenge of the country, the national ESDP do not include it as a factor that determinants of children academic achievement.

Most researches undertake on cognitive development of children are mainly focused on students/individual factors, household/ family factors and school factors and give little or no attention to weather related shocks.

Most research is focused on the impact of idiosyncratic shocks (such as child labor) on children educational outcomes (test scores) (Woldehanna, 2012.

As far as my knowledge there is no any related researches undertaken in the country that can shows the impact of weather related shock such as drought on PPVT test score

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OBJECTIVES OF THE STUDY

The General objective of this study is to examine the effect of climate related drought shocks on children cognitive development and school outcomes. The specific objectives are:

To investigate the impact of climate related shock such as drought exposure on cognitive ability of children.

To examine the impact of drought on other academic achievement measurements such as reading and writing skills.

To measure the impact of productive safety net (PSNP) interventions on cognitive development of children aged 12 and 15.

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RESEARCH METHODOLOGY

Data source Secondary data (Young Lives longitudinal dataset Second and third round old cohort

Used purposive sampling strategy in selecting sentinel sites and a random strategy used in the household selection within each sentinel site (Woldehanna , 2011).

The sampling was undertaken in five regions (namely Amhara, Addis Ababa, Tigray, Oromia and SNNP

3 to 5 woredas were selected in each region, gives total 20 woredas.

50 old cohort children (of age 7.5 to 8.5 years) were selected randomly in each sentinel site.

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RESEARCH METHODOLOGY Children test score broadly depend on their initial skill

endowment, income and Time (Millett & Shah, 2012) .

Initial skill endowments include biological or initial characteristics of children such as sex, birth order, race, birth weight etc.

HH income has direct effect, determine the amount of investment on cognitive development children goods and services such as books that increases students test results up on consumption.

To increase household income the household needs many inputs such labor that can be own or hired agricultural labor.

The decision to use either of the two sources depends on the wage rate.

When the wage is high households may use own labor including child labor that in turn reduce children test score.

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RESEARCH METHODOLOGY Children time consumption: hours spent on study or

school time positively affects children test score.

The biological effect of drought on cognitive development is very straightforward.

Drought exposed children (either prenatal or postnatal exposure) are simply lower ability, so conditional on the same level of schooling, they still score lower on achievement tests because they attend less efficiently (Bryce & Manisha , 2012).

Drought also negatively affects the income of household. Hence any food or cash support to the drought affected

households helps to reduce the negative impact of drought.

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RESEARCH METHODOLOGY

The analysis is based on the work of (Millett & Shah, 2012) to measure the effect of drought on test scores and estimate the regression equation:

where Yit measures standard PPVT score of child ‘i’ in round t

constant term, Xit is self-reported indicator drought incidence in round t,

Zi measure other factors that influence cognitive development, αi is unobserved individual specific effect and εi unobserved random error term. β is our coefficient of interest and it is the causal impact of drought on the standard PPVT test scores (or cognitive ability).

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RESEARCH METHODOLOGY

We used Panel data that have following advantages over pooled data (Baltagi 2004)

(1) account for heterogeneity across individual units which is assumed away in pooled data

(2) deal with time-invariant omitted variables as we can and in pooled data

(3) are less likely to have problems with autocorrelation and multicollinearity as time series data

There are basically two types of panel models, the fixed effects and the random effects model.

They differ by their assumptions how the heterogeneity is captured and estimation techniques.

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RESEARCH METHODOLOGY

The fixed effect model assumes that individual heterogeneity is captured by the intercept term.

The random effects model assume in some sense that the individual effects are captured by the intercept and a random component.

This random component is not associated with the regresses on the right hand side and part of the error term.

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RESEARCH METHODOLOGY The critical difference between Fixed Effect and Random Effect is that FE

allowed for correlation between the unobserved individual specific effect and the explanatory variables whereas RE requires these to be uncorrelated.

  Having this, we run a basic fixed effect regression and random effect

regression of independent variables cognitive achievement outcome of interest (standard score in PPVT test).

To choose from FE and RE we used Hausman In this case, Random effects (RE) is preferred under the null hypothesis

due to higher efficiency, while under the alternative fixed effects (FE) is at least consistent and thus preferred.

Using the test of Hausman test we rejected the null hypothesis and accepted the alternative hypothesis that is fixed effect model preferred.

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DATA AND DESCRIPTIVE EXPLANATIONS

 Variable Obs Mean Std dv Min Max

Standard PPVT test core 1648 91.17658 26.38066 40 160

Dummy for drought affected 1663 .3968731 .4893964 0 1

Dummy for PSNP participation 1663 .439567 .4964837 0 1

Rural PSNP participating 1663 .3505713 .4772924 0 1

rural drought affected PSNP

beneficiaries

1662 .2178099 .4128817 0 1

dummy for Child health worse 1660 .086747 .2815488 0 1

Hours spent on paid work 1659 .2932188 1.339566 0 11

Dummy for missed more than a week 1511 .1250827 .3309222 0 1

Dummy for land ownership 1664 .9206731 .2703296 0 1

Hours spent on study 1659 1.794039 1.158055 0 8

Grade completed 1504 4.746676 1.917542 0 11

School distance in minute 1505 25.45116 19.5518 0 130

dummy for credit access 1663 .2958509 .4565616 0 1

Dummy household condition poor 1664 .6790865 .4669679 0 1

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RESULTS AND DISCUSSION

Drought shock and PPVT test score by region and year

Source: Author’s calculations based on Young Lives data

 Standard PPVT score Amhara Oromia

SNNP Tigray Total

not affected          round 2 83 96 88 96 90round 3 96 99 104 115 101

Change rate (%) 15.66 3.13 18.18 19.79 12.2affected          round 2 76 76 76 88 80round 3 79 82 84 98 88

Change rate (%) 3.95 7.89 10.53 11.36 10Gap between affected and non-affected

         

round 2 7 20 12 8 10round 3 17 17 20 17 13

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RESULT AND DISCUSSION

Drought shock and PPVT test score by region round

Source: Author’s calculations based on Young Lives data

Amhara Oromia SNNP Tigray0

20

40

60

80

100

120

140

76 76 76

88

79 82 84

98

83

96

88

9696 99104

115

affected PPVT score round 2

affected PPVT score round 3

non affected PPVT score round 2

non affected PPVT score round 3

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RESULT AND DISCUSSION

Drought enrolment and grade completed

Source: Author’s calculations based on Young Lives data

 

outcomes

Full

sample

(R2)

Non

Affected

(R2)

Affecte

d (R2)

Full

sample

(R3)

non

Affected

(R3)

Affected

(R3)

Enrollment (%) 97.37 97.30 97.50 89.60 92.16 85  

grade completed

(progress)

4.25 4.3 3.54 5.74 5.7 5.15 

school

Attendance

5.32 5.24 5.52 10.64 9.53 14.09 

Taken grade 8 21% 27% 11%  

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RESULT AND DISCUSSION old cohort children time use on typical day by drought shock

 

Source: Author’s calculations based on Young Lives data

on a typical days hours spent on

Round 2 Round 3Full

sample

non Affected

Affected Full

sample

non Affecte

dAffected

caring for others 0.65 0.65 0.66 0.7 0.75 0.74Domestic work 2.3 2.34 2.2 2.7 2.67 2.75Farming activity 1.69 1.42 2.17 1.57 1.31 1.88Paying work 0.15 0.11 0.25 0.42 0.36 0.51School 5.15 5.27 4.93 5.52 5.31 5.05Study outside school

1.72 1.81 1.56 1.86 2 1.7

Sleep 9.04 8.91 9.26 8.65 8.56 8.77 Leisure 2.71 2.91 2.33 2.81 3.02 2.52child labor 4.79 4.52 5.28 5.39 5.09 5.88

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RESULT AND DISCUSSION old cohort children reading and writing skill by drought incidence

 

Source: Author’s calculations based on Young Lives data

can

't

read

read

s

lett

ers

read

s

word

s

read

s

sen-

ten

ce

No

wit

h

diffi

-cu

lty

wit

hou

t d

iffi-

cu

lt

Reading level Writing skills

0%

10%

20%

30%

40%

50%

60%

70%

80%

9% 12% 12%

67%

9%

29%

62%

14%19% 19%

47%

16%

39%45% not Af-

fected by drought

Affected by drought

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RESULT AND DISCUSSION

Households affected by drought and PSNP participation

 

Source: Author’s calculations based on Young Lives data

Region Affected PSNP participated R2)

affected but supported (R2)

cohort mean PPVT (R2)

Affected PSNP participated (R3)

affected but supported (R3)

cohort mean PPVT (R3)

response capacity

growth

Amhara 77% 78% 80.57 20% 72% 88.31 -7.60%

Oromia 70% 73% 88.29 53% 97% 99.33 32.60%

SNNP 61% 73% 85.69 7% 86% 95.54 18.70%

Tigray 93% 93% 91.82 69% 97% 102.95 5.00%

Total 78% 81% 91.29 40% 89% 102.95 8.90%

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RESULT AND DISCUSSION

During round two more than 78 percent of the households was PSNP participants but reduced to 40 percent during round three that partly may be due graduation.

drought responding capacity almost fifty percent reduction.

other food/cash support from small projects (may be as emergency relief programs) increased as a result percentage of drought affected but supported households increased in round three.

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RESULT AND DISCUSSION Economic Estimation PPVT test estimation

Dependent variable standard PPVT test score Fe model Re model

Dummy for drought affected -6.732429*** -8.048916***-2.92 -4.93

dummy for Child health worse -4.658618 -5.031659**-1.53 -2.31

Hours spent on paid work -2.010522* -2.463492***-1.75 -3.26

rural drought affected PSNP beneficiaries 6.428558** 3.934902**2.28 2.02

Dummy for missed more than a week -3.907601 -2.166414  -1.59 -1.21Dummy for landownership 3.887701 -11.25698***  0.88 -4.88Hours spent on study per day 2.248315 2.562031***

2.74 4.36Grade completed 3.503539*** 4.636148***  3.91 12.86School distance in minute -0.0535196*** -0.0553434*

-1.04 -1.74

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RESULT AND DISCUSSION Economic Estimation PPVT test estimation

Dependent variable standard PPVT test score Fe model Re model

dummy for credit access 2.29007 0.15412551.31 0.12

Dummy household condition poor -4.268674*** -3.525394***-2.22 -2.76

R-sq: within 0.137 0.1193 between 0.2468 0.3153 overall 0.1974 0.2425Number of obs 1484 1484Number of groups 812 812F test that all u_i=0 1.71sigma_u 19.439141 10.434154sigma_e 19.718549 19.718549Rho 0.49286489 0.21875258Hausman test of FE vs. RE; 2(17)=28.81, p=0.0024Robust z statistics and t value in parentheses for RE and Fe respectively * significant at 10%; ** significant at 5%; *** significant at 1%

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RESULTS AND DISCUSSION

We found that drought shock affected cognitive development of children negatively. (Similar to the findings of Aderman, 2006 and Mill and Shah (2012)

The PPVT test score of old cohort children affected by drought shock reduced by almost 7 points than that of old cohort children not affected by drought shock.

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RESULTS AND DISCUSSION

We found that child health worse has negative effect on PPVT score similar to the findings of Spernak 2006

hours spent on paid work, land ownership,

hours spent on study and grade completion have positive effect test score similar to the findings of Ray and Lancaster, 2003

Meanwhile old cohort children from poor household registered lower results than that of non-drought affected old cohort children.

The impact of drought dummy for missed class more than a week with PPVT test score is negative thought it is not statistically significant.

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RESULTS AND DISCUSSION

We found that drought affected rural children whose household participated in the productive safety nets program scores 6 points higher than of non participant drought affected rural children .

We found that child health hours spent on paid work, land ownership, hours spent on study, grade completion have a significant and positive relationship with standard PPVT test score.

Meanwhile old cohort children from poor household registered lower results than that of non-drought affected old cohort children.

The impact of drought dummy for missed class more than a week with PPVT test score is negative thought it is not statistically significant.

dummy credit access has positive relationship children.

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CONCLUSIONS AND RECOMMENDATIONS

  Conclusions

Drought shock has a negative impact on children PPVT test score. Old cohort children affected by drought scores PPVT test score 7 point lower than those not affected.

Household condition poor and school distance in minutes negatively affect PPVT score

Drought also negatively affects school enrollment , grade completion school attendance, hours spent on study outside school.

Children who are exposed to drought shock score significantly worse in reading and writing skills.

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CONCLUSIONS AND RECOMMENDATIONS

  Conclusions

productive safety net programme participation has a positive and significant impact on drought affected children cognitive development.

Moreover productive safety improves children cognitive development by improving household economic condition. So drought mitigation measures such as food aid reverses help to improve cognitive development of children affected by the drought.

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CONCLUSIONS AND RECOMMENDATIONS

Recommendations

Strengthening prevention and mitigation measures (DRR) the identification of efficient agricultural management

practices, and provision of timely advice to farmers improving drought responding capacity/ emergency response

(especially Amhara region) as strengthen their early warning systems such as Short

message system (SMS) throughout all woredas of the region. Moreover microfinance institution can also create access for

credit service to the drought affected households. National Learning Assessment has to include as factor that

affects children academic achievement.

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Thank you!