Economic Assessment of Lead (Pb) Pollution in Kabwe:
Objectives, Progress and Prospects

Speaker: Dr. Peter HANGOMA
School of Public Health, University of Zambia

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MEMBERS OF THE ECONOMICS TEAM
Bona Chita
Chrispin Mphuka
Peter Hangoma
ZambiaDaiju Narita
Masato Hiwatari

Daichi Yamada
Japan

2

Outline
I. Introduction
II. Progressa)

Socioeconomic Survey in Kabwe 2017: Survey Design and Descriptive
statistics

III. Prospectsa)
b)
c)
d)

Socioeconomic Conditions and Pb exposure
Economic Costs of Pb exposure
Impact Evaluation of Remediation
Cost-Benefit Analysis
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I. Introduction

4Gaps• Kabwe has one of the highest Pb exposure yet no study has
assessed how this could be associated with socioeconomicconditions.
• The value to society of possible remediation measures has
equally never been assessed.

5

Objectives
1. Ascertaining the economic consequences of the health aswell as the environment effects of the Lead (Pb) pollution
problem in Kabwe.
2. Undertake a cost-benefit assessment of possible
interventions. We will combine cost estimates of Pb
pollution and impact evaluation of possible remediation
programs.

6Interactive Relationship between Pb Exposure and Socio Economic Factors
Pb Exposure(Measured byBLLs)

EconomicConsequencesSoils
•
•••
•
•

Lost wagevegetation
Lost Labour times
Lost education attainment
Increased costs of medical careChildren’s IQ defect -> decrease in lifetime earning
Mortality⁞water

SocioeconomicConditions

Air•
•
••

Poor living conditions
Low nutrition
Inadequate lifestyle
Other daily activates⁞7II. Progress:
Socioeconomic Survey in Kabwe20178

Background• In August-September 2017, we conducted a baseline survey ofhousehold socio-economic conditions in Kabwe.
• The survey was designed to be representative of Kabwe district.
• Detailed information on about 900 randomly selected households
was collected.
• The socioeconomic survey was carried out in combination with ahealth survey that focused on blood lead levels (BLLs), quality of life
and other health conditions.

9Survey Design• Similar to the 2015 Living Condition Monitoring Survey (LCMS), weused the 2010 census sample frame.
• Kabwe district can be divided into 384 Standard Enumeration Areas(SEAs).
• Multistage sampling: 40 SEAs were randomly selected in the firststage. In the second stage, 25 households were randomly selectedfrom each SEA based on satellite information (or household lists).
• Computer-Assisted Personal Interviewing (CAPI) with the software“Survey Solutions” developed by World Bank was aused.
• 21 enumerators conducted face-to-face interviews to target
households from August 21 to September 8.
10Central Area

Geographical location of 40 SEAs in Kabwe District

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Questionnaire• The questionnaire mainly asks the same questions as in the 2015 LCMS, but
supplemented with original questions. It takes 2-3 hours for one interview.
• Structure of the Questionnaire• Section 1. Household Roster• Section 2. Biological Relationship• Section 3. Health (for all persons)• Section 4. Education (for all persons)• Section 5. Economic Activity and Non-Farm Income• Section 6. Agricultural Production• Section 7. Other Income• Section 8. Household Assets & Durables• Section 9. Household Amenities and Housing Conditions• Section 10. Household Expenditure• Section 11. Child Health• Section 12. Deaths in the household12

Preliminary Descriptive Statistics
Sex Distribution by Age1816141210864200-4

5-9

10-14

15-19

20-24

25-29

male

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65+

female13

Descriptive Statistics
Migration

has never moved
16%

0%
32%

4%

17%

different dwelling/ same
townshipdifferent township/kabwe district
different district/central
provincedifferent province

31%

different country
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Education
60

Highest Level of Education (for those who are notunder age)

Reasons for not attending school
dont know
otherunsafe to travel to school
school not important

50

illness / injury
no school / school too farno financial support40

expensive
could not get a place
300102030405060

20

Percentage reporting missing school by frequency(Number of days)

10very often (more than ten days per mont0
no education

pre-school

primary (1-7)

male %

secondary (8-12)

female %College/Universityoften (three to ten days per month)a little (one day or two per month)never0102030

40

50

1560

HealthSex
sick in last 2 weeks Prior
to Survey
male (%)
female (%) All Individuals (%)
yes sick
17.7
20.1
19
yes injured
0.8
0.30.5yes both
0.1
0.1
0.1
no81.479.4
80.3
dont know
00.1
0.1
Total
100 (N=2,378) 100 (N=2,519) 100 (N=4,897)

Continously ill in last 3
monthsyes
noTotalsex
male (%)
2.9
97.1
100 (N=2,378)

female (%) All individuals (%)
3.8
3.4
96.2
96.6
100 (N=2,519) 100 (N=4,897)

Continously Sick and able
to Carry out normal
activities
yesnoTotal

male %
57.1
42.9
100 (N=70)

female % All Individuals %
52.1
54.2
47.9
45.8
100 (N=96)
100 (N=166)

Self Reported Type ofSickness in last 2 weeks
Cough/cold/chest infection
headacheFever/malaria
Other
Abdominal pains
Hypertension
Diarrhoea without bloodbackacheToothache/mouth infection
Skin infection
Eye infection
Pneumonia
Constipation
AsthmaDiarrhoea with vomiting
Paralysis
Diarrhoea with blood
Diabetes
Bronchitis
Aneamia
Vomiting
CancerTuberculosis
StrokeBoils
Shingles/herpes zoster
Ear infection
measles
Total

Percent
39.3
15.7
9.2
7.6
4.2
3.0
2.9
2.8
2.3
2.1
2.1
1.3
1.1
1.0
0.9
0.9
0.6
0.6
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.1
0.1
100 (N=933)

Self Reported Type ofContinous Sickness in
Last 3 Months
Other
Cough/cold/chest infection
headacheFever/malaria
Hypertension
Abdominal pains
Eye infection
backacheParalysis
Diabetes
CancerTuberculosis
Skin infection
Bronchitis
Diarrhoea without blood
Piles/hamoroids
StrokeToothache/mouth infection
Pneumonia
Diarrhoea with blood
Constipation
Boils
Total

Percent
19.88
12.65
12.65
10.24
6.63
5.42
5.42
5.42
3.61
3.61
2.41
1.81
1.81
1.2
1.2
1.2
1.2
1.2
0.6
0.6
0.60.6100 (N=166)

16Economic Activity Status
Current Economic Activity Status (12 yrs old or above)
AllTotal
100 (N=3,251)
Working
39.7
Wage employment
13.7
Non-farm business
13.7
Farming
6.0
Fishing
0.3
Forestry
0.1
Piecework
5.9
Not working
60.3
Unpaid family worker
0.7
Not working but looking 12.7
for work
Not working and not 4.8
looking for work (butavailable)
Full-time student
21.8
Home maker
9.2
Retired
3.5
Too young
6.6
Other1.0

Male
100 (N=1,532)
48.1
18.4
11.8
6.9
0.7
0.1
10.351.90.9
12.8Female
100 (N=1,719)
32.1
9.5
15.4
5.2
NaNa
2.0
67.9
0.6
12.7

3.6

5.9

23.6
0.1
3.0
6.7
1.2

20.2
17.3
3.9
6.6
1.0

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Household Income, YearlyIncome

Household income, Yearly
Per capita income, Yealy
Labor income (incl. business)Among those with non-zero labor income (N=710)
Agricultural income (sales – costs)
Among those with non-zero agricultural income
Remittances and gift
Among those receiving remittances and gifts
Other
Among those with non-zero other income (N=259)

Monthly Household Income
Our Survey in Kabwe 2017
• Average in Kabwe: K 3,347• Median in Kabwe: k1,220

34,62043,664
1,143
2,842
1,209
2,824
1,209
8,550

68,952
74,832
19,63530,907
5,496
8,131
5,496
35,921

3,347
736

6,458
1,695

0

1.0e-042.0e-04

3.0e-04

4.0e-04

5.0e-04

Household income, monthly
Per capita income, monthly

Density

2015LCMS• Average in Zambia: K1,801• Average in Urban: K3,152

Mean Std. dev
40,167 77,494
8,830 20,334

05000

10000
hhincome

15000

20000

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Household Expenditure, Monthly

Expenditure

MeanTotal Expenditure

182.8
41.1
651.6
1136.21236.5
36.0

418.7
131.8
1028.12257.93480.3
156.6

5.6%
1.3%
19.8%
34.6%
37.6%
1.1%

15000

20000

3.0e-04

4.0e-04

100.0%

2.0e-04

Density

5110.51.0e-04

2015LCMS• Average in Zambia: K1,588• Average in Urban: K2,680

Education
Self-Consumption of Agricultural Production
Housing Amenities (rent, electricity, etc.)
Food
Non-Food
Remmittance3284.30Monthly Household Expenditure
Our Survey in Kabwe 2017
• Average: K 3,284• Median : K 1,938

Std. Dev. Propotion

05000

10000
Expenditure

19Socioeconomic Status and Blood Lead Levels (BLL)• BLL higher in children (29.4 for 0-9 years, 23.4 for 10-19years, and 16.3 for older than 19)• BLL negatively correlated with total consumption but
relationship not significant• BLL positively correlated with total income=>we need tocontrol for occupation• It appears BLL is negatively correlated with wage andbusiness income while the correlation with farm income ispositive, but these correlations are insignificant• Those who lost more work days due to illness had
significantly higher BLL level.
20III. Prospects

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i. Socioeconomic Conditions and Pb exposure• We systematically examine how socioeconomic conditions are associated withexposure to Pb.
 Low-income families may be more likely to dwell on polluted lands with poor housings andengage in jobs in polluted industries. →Negative association between income (expenditure)and Pb exposure.
 On the other hand, people may prefer to live in a polluted area if they find more jobs withhigher wages in that area.→ Positive association between income and Pb exposure.

ii. Economic Costs of Pb exposure• We estimate the negative costs of Pb Exposure on economic outcomes.
 Various health risks can be attributable to Pb pollution, such as IQ loss, hypertension,
anaemia, digestive problems, early deaths, disabilities, pain and discomfort, etc.
 As a result, these effects may cause a decline in lifetime earnings, a loss of productivity,
increased costs of medical care, an increased need for special education, etc.
22iii. Impact Evaluation of Remediation Programs• By using the information of possible interventions (e.g. soilreplacement, medical treatment) taken from the project
activities or other sources(e.g. World Bank), we will estimate
the impacts of remediation programs.

iv. Cost-Benefit Analysis• Combining the cost estimates of Pb pollution and theinformation of the impact evaluation of possible remediation
programs, we conduct a cost-benefit assessment of possible
interventions.
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Suggestion & Request• We would like to encourage the project members to utilize our survey
data as baseline data to test the impacts of remediation programs or
any other experimental trial.
• For rigorous impact evaluations of remediation programs by ourproject or the World Bank, we would like to share our information in
the stage of designing the programs. Particularly, we have a keeninterest in sampling of target population for any program.
• The detailed map information of pollution(soils, water, air, vegetationetc.) will be useful for our analysis to identify the casual effects of BLL
on economic outcomes.
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