1. Tricky Data Issues in the PEN Datasets
Monica Fisher & Arild Angelsen
March 26, 2009

2. Organization Introduce the four main tricky data issues.
Break into groups of 5-10 people to discuss each issue and the recommended solution. (Spend ~30 minutes in discussion.)
One volunteer from each group reports back on the pros/cons of the recommended solution to each data problem. Other recommended solutions are very welcome.
Discussion. 2 2

3. The Four Main Issues Missing data problems:
Wave nonresponse (quarter is missing)
Item nonresponse (fields missing)
Challenges to meaningful welfare comparisons:
Inter-household differences in size and composition
Inter-country (26 PEN countries) price level differences 3 3

4. Missing Data 1: Wave Nonresponse
What is the problem?
Possible solutions:
Case deletion
Single imputation
Simple sample mean or conditional mean
Hot deck (randomly matched to similar hh)
Regression
Multiple imputation
Estimates are uncertain -> several datasets
Recommended solution:
Case deletion for cases having less than three quarters of income data 4 4

5. Imputation of sectoral incomes
Multiple imputation
regression using income other quarters, and hh and village characteristics
Single imputation:
formula: forinc3,i (pred) = (forinc1,i + forinc2,i + forinc4,i) * forinc3,v /(forinc1,v + *forinc2,v + forinc4,v)
= forinc3,v * (forinc1,i + forinc2,i + forinc4,i)/(forinc1,v forinc2,v + forinc4,v)
(so HH forest income + seasonal adjustment) 5 5

6. Missing Data 2: Item Nonresponse
What is the problem?
Possible solutions (same as for wave nr):
Case deletion
Single imputation
Multiple imputation
Recommended solution:
Single imputation: Regression to derive a simple formula 6 6

7. Welfare Comparison 1: Household
What is the problem?
Some families are bigger than others
Some eat more than others
One TV is enough
Some possible solutions:
Per capita adjustment
Adult equivalence scales
Nutritional equivalence scales
Recommended solution:
Nutritional equivalence scale 7 7

8. Welfare Comparison 2: Country
What is the problem?
Different currencies used
Price levels differ
Recommended solution:
Purchasing power parity 8 8

9. Aggregation issue: Definitions of forest –env income
Origin of product
Cultivated
Collected
Forest
Forest income (incl. plantations)
Forest income
Non-forest
Agricultural income
Environmental income (non-forest env.inc.)
The forest product should depend on the existence of forests, but some in-between categories: Minerals? Fish?
Plantations? FAO definition?
Read PEN guidelines! *

10. Other issues Calculating net income Allocating agr costs
Negative income?
Uneven timing of surveys
Increasing income over survey methods
Data aggregation
Appropriate categories
Averages
Pricing subsistence products, part. firewood 10 10

11. Concluding remarks Get all issues on the table
Some experimental work needed:
the cost and benefit of more refined methods
getting a simple formula (optimal ignorance)
“Do things are simple as possible, but not simpler” (Albert Einstein) 11 11

12. Group discussion
Discuss and suggest solutions on how to deal with major issues outlined:
Missing values
Income categories
Firewood pricing
List any new ‘tricky’ data issues
30 min – group; plenary
I (far corner): born 1-7; II (near corner): 8-15; III (coffee table): IV(miombo); : 24-31 12 12

13. Group 1 Missing values: Firewood pricing:
Missing data reflects reality, careful to impute
-10: I don’t want to respond
Firewood pricing:
No market = zero price ?
Use value for price?
Underestimation of illegal activities
Categories of income:
Distinction forest and non-forest env. Income

14. Group 2 What is a forest? Be consistent Negative values:
Seasonality (timing costs and harvest)
Look at large input expenses in Q4, but also see how fit with income data in Q1
Poor harvest (ok)
No particular forest product dominates (except fuelwood)
Might be an aggregation problem (e.g. aggregate types of fruits)
Some products not considered forest products
Probing done by enumerators
Wage income and business income: disaggregate
Missing values: Do as Ronnie says
Income categories – adult eq.
Use regionally differentiated scales?
Firewood pricing
Compare PEN and official price figures
WTP – use ‘local estimated price’ instead; respondents have difficulties to put price on non-market items

15. Group 4 Area estimates, intercropping
Fuelwood prices: meta analysis , how priced?
Other fuelwood price studies?
Missing quarters
Simplest formula that we are confident in
Experiment with more advanced methods
High attrition rates: any biases?
Reprentativeness of studies:
“Meta study of case studies with good data”
Adult equivalents:
Agree on some simple ways to calculate that

16. Group 3

17. Additional New Tricky Issues
Timing of surveys vs time-value of money (USD): How do we compare the different surveys?
Allocation of input costs: what about subsistence inputs? Need to standardize these costs.
Definition of forest products? Need to standardize. Definitions are systematically applied.
How to deal with the site selection bias?
Cost estimation of other inputs e.g. Fodder.
How do we value increase in number of livestock and weight?

18. Fuelwood Pricing
Need to crosscheck proportion of energy income spent by other hhs away from forests vs those close to the forests.
Hypothesis: Proportion of energy spending for the latter < the former.

19. Income Categories Household vs village level costs/prices?
WTP vs market price?
Hypothesis: WTP <= market price