Data collection in difficult environments: measuring economic recovery in post-

conflict Bougainville

Satish ChandUNSW@ADFA

Three messages

1. Data for policymaking absent in most post-conflict situations

2. This deficiency undermines capacity to ‘prevent, prepare for, and respond to disasters’ (APCOE mission)

3. The data deficiency can be addressed.

Papua New Guinea – Where?

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Enga

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Papua Highlands Momase Islands Region

0

500

1000

1500

2000

2500

Per Capita Income by Province in Papua New Guinea (1983)

Economic impact of the conflict

Panguna, in 1989, accounted for:• 45 percent of PNG’s export income; • 17 percent of internally generated govt. revenues; and, • 12 percent of GDP.

At the height of conflict, one third (approx. 70K) of population were in care centres.

Toniva, Kieta

Rebound in the economy! How much?

Evidence of a rebound in economic activity: By how much?

26/02/2011

Gold smelting

The Gold Smelter The product

Approx. 2Kg smelted per week @ $57/gram = $114,000.

Green gold!

Access to basic services

Private versus public provision of basic services

The Research Team: Well Trained Locals

Survey locations

Buka (33)

Selau(34)

Arawa(74)

Buin(40)

Survey Sites: Arawa

Pre-crisis # houses: 1850Post-crisis (02/2011) #: 740

23/02/2011

Data collected on• Income – how much and source?• Investments – age and types of housing?• Own or rent house?• Access to basic services

• Education/Health? • Water• Telephone• Electricity• Toilets• TV

Interview in progress

21/02/2011

Table 1: Summary statisticsVariable Obs Mean Std. Dev. Min MaxAge of household head 181 45 11 20 81

Male household head 181 58% 0.49 0 1

Moved house due to conflict 179 30% 0.46 0 1

Number of people per house 181 5.70 3.03 1 16

Number of males per house 180 2.85 1.76 0 9

Number of dependents per house 179 1.78 1.70 0 8

Number in school per house 179 1.89 1.84 0 8

Proportion owning house 181 56% 0.50 0 1

Year house built 112 1996 13.77 1960 2011

Weekly household income 180 416 317 <100 >1000

Table 2: Population distribution by ageAge group Females Males percent Cum %

1yr -5yrs 50 53 10.2% 10.2%

6yrs - 10yrs 53 58 10.9% 21.1%

11yrs - 15 yrs 55 74 12.7% 33.8%

16yrs - 20yrs 81 59 13.8% 47.6%

21yrs-25yrs 61 55 11.4% 59.1%

26yrs - 30yrs 42 42 8.3% 67.4%

31yrs - 35 yrs 41 38 7.8% 75.1%

36yrs - 40yrs 26 23 4.8% 80.0%

41yrs - 45yrs 24 21 4.4% 84.4%

46yrs - 50yrs 30 26 5.5% 89.9%

51yrs+ 49 53 10.1% 100.0%

Calculated from a total population of 1,014 individuals made up of 502 males and 512 females from the 181 households surveyed. NSO Census 2000, table 1.6 North Solomons Urban Population less than 15 years = 34.8 percent (rural = 43.8%).

Table 3: Zero years of schooling

Birth yearNumber of

FemalesNumber of

MalesProportion w/o

schooling

1982 or earlier 6 0 2%

Between 1983 and1991 2 2 2%

Between 1992 and 2005 12 33 13%

Notes: 35 percent of those between 6 and 10 years of age as of 2011 were not in school => displacement of young by older children in primary schools. Source: Calculated from survey of 181 households comprising 911 individuals born in or before 2005 from Arawa, Buka, Buin and Selau (data collected in February 2011).

Figure 2: Highest schooling for 20+ yr population

85

2

16 16 16

103

11

58

25

117

1

24

77

1014

10

22

1 1 1

05

01

001

50F

req

uenc

y

0 5 10 15 20Yschool

Pr(6 year-old completing Year 12) = 11 percent!

Investments: Three types of houses!

Material = 3Material = 1

Construction activity over time- Buka & Selau

01

23

4F

req

ue

ncy

1980 1990 2000 2010Ybuilt

Preliminary findings1. Approx half of urban population < 20yrs of age.2. One in three urban children of 6~10yrs not in school3. Per capita income for urban Bougainville = K3,863 at

2010 prices (or K783 at 1983 prices).4. Large regional disparities in rates of and prospects

for economic growth5. Ex-combatants turned entrepreneurs in post-conflict

Bougainville – a growing constituency for peace.

Fun of fieldwork in difficult environments

Conclusions

1. Data can be collected in difficult environments.2. Involvement of local authorities crucial for

success.3. Costs of data collection are high - sampling and

a staggered approach to surveys.4. Honesty/transparency/publicity helps with

fieldwork.5. Local graduate students are invaluable for all of

the above!