Crawford PhD Conference 2014

1. Shining a light on the Indonesian oil palm
and development debate, with ‘Big Data’
Ryan B. Edwards
Arndt-Corden Department of Economics
Crawford School of Public Policy
College of Asia and Pacific
Crawford PhD Conference 2014
18 November 2014
Source: Wikipedia

2. Does palm oil production make
people in Indonesia better off?
2

3. Indonesia now exports over 16 times the
palm oil it did in 2000…
Source: The Atlas of Economic Complexity, 2014 (http://www.atlas.cid.harvard.edu/ ) 3

4. .. to all over the world…
Source: The Atlas of Economic Complexity, 2014 (http://www.atlas.cid.harvard.edu/ ) 4

5. .. and is now the largest supplier
(by over 13,000,000,000,000 tonnes!)
5
Source: Indexmundi (2014),
http://www.indexmundi.com/agriculture/
?commodity=palm-oil&

6. But palm oil can have high ecological costs..
6

7. ..leading to boycotts, divestment, and more
7

8. … while poverty remains widespread..
Source: World Bank Indonesia (2014), http://www.worldbank.org/content/dam/Worldbank/Feature%20Story/EAP/Indonesia/Poverty%20infographic%20revised.png 8

9. ..and we have no systematic empirical
evidence on the development effects of
Indonesia’s recent palm oil expansion.
9

10. 10

11. What I do
Use the World Bank’s first public
sub-national public database to estimate
the short-run household welfare effects
of palm oil production in Indonesia,
at the district level
11

12. What I find
Q: Does palm oil production make
people in Indonesia better off?
A: Yes, on average, in the short run
But, it likely depends on who, how
12

13. Introducing the data:
INDONESIA DATABASE FOR
POLICY AND ECONOMIC RESEARCH
(DAPOER)
13

14. 14

15. Main variables
• Per capita palm oil production, by district
– Since 1997 from Tree Crop Estate Statistics of Indonesia, Ministry of
Agriculture, via DAPOER
– Denominated by district population, for consistency (e.g., reformasi)
– Non-producing districts coded as zeros; kept level to retain control
15

16. 16
National variation in palm oil production
intensity, by province, 2013
Source: http://www.simreg.bappenas.go.id/

17. Main variables
• Per capita palm oil production, by district
– Since 1997 from Tree Crop Estate Statistics of Indonesia, Ministry of
Agriculture, via DAPOER
– Denominated by district population, for consistency (e.g., reformasi)
– Non-producing districts coded as zeros; kept level to retain control
• Per capita household monthly expenditures, by district (IDR)
– Based on district aggregates in the National Socioeconomic Survey
(SUSENAS) from Statistics Indonesia, via DAPOER
– Reasonable proxy for average household welfare in each district,
i.e., not equal to local GDP, but increasing nationwide 1997-2010
– Put into natural logarithms for appropriate form and easier
semi-elasticity interpretations
17

18. 18
11 12 13 14 15 16
0 1 2 3 4 5
Per capita palm oil production, district (tons)
Log per capita HH expenditure Fitted values
A naïve comparison reveals a positive correlation
Source: DAPOER

19. IDENTIFICATION CHALLENGES
19
Time trend

20. IDENTIFICATION CHALLENGES
20
Time trend
Reverse causality
Expenditure may influence
decisions and ability to produce

21. IDENTIFICATION CHALLENGES
21
Time trend
Reverse causality
Expenditure may influence
decisions and ability to produce
Time-invariant omitted
variables
District and regional;
observable and unobservable

22. IDENTIFICATION CHALLENGES
22
Time trend
Reverse causality
Expenditure may influence
decisions and ability to produce
Time-invariant omitted
variables
District and regional;
observable and unobservable
Time-varying omitted
variables
Common shocks;
District- or region-specific

23. Estimated equation
푙표푔 푦푖,푡 = 훽1 + 훽2 푃푖,푡 + 훽3푇 + 푣푖 + 푒푖,푡
푦푖,푡 = outcome of interest, district i, time t
푃푖,푡= per capita palm oil production
푇 = time trend
푣푖 = district fixed effect
푒푖,푡 = district-clustered robust error term
23

24. Estimated equation
푙표푔 푦푖,푡 = 훽1 + 훽2 푃푖,푡 + 훽3푇 + 푣푖 + 푒푖,푡
푦푖,푡 = outcome of interest, district i, time t
푃푖,푡= per capita palm oil production
푇 = time trend
푣푖 = district fixed effect
푒푖,푡 = district-clustered robust error term
Main estimators (i.e., within, fixed effects) focus on the
yearly changes within each district (i.e., short-run)
24

25. Identifying assumption
Within-district palm oil production changes are
exogenous to changes in average household
expenditures in the same district, conditional
on time-varying common factors and district-specific
time-invariant factors
25

26. Plausibility of the identifying assumption
• Production takes many years, i.e., cannot be
contemporaneously endogenous to household spending
26

27. Plausibility of the identifying assumption
• Production takes many years, i.e., cannot be
contemporaneously endogenous to household spending
• District variation in palm oil production is mostly affected by
‘random’ centralized land use decisions and climatic conditions
27

28. Plausibility of the identifying assumption
• Production takes many years, i.e., cannot be
contemporaneously endogenous to household spending
• District variation in palm oil production is mostly affected by
‘random’ centralized land use decisions and climatic conditions
• Remaining threat is time-variant district-specific OVB
– Province-year and island-year fixed effects yield similar results
– Diff-GMM and Sys-GMM yield similar results ; instrumenting with palm
oil price (weak), total arable land, and palm oil land yield similar results
– Province rainfall, humidity, and temperatures are weak IVs
– Work underway on alternative external IVs and identification strategies
28

29. Main results
29
Dependent variable Log per capita household expenditure (IDR)
Column (1) (2) (3) (4) (5)
Estimator OLS BE GLS RE FE FE
Per capita palm oil production
(tons)
0.09*** 0.11** 0.06*** 0.06*** 0.05**
(0.02) (0.06) (0.02) (0.02) (0.02)
Time trend (T) 0.13*** 0.1*** 0.13*** 0.13***
(0.00) (0.01) (0.00) (0.00)
Year dummy N N N N Y
District fixed effects N N N Y Y
N observations 3939 3939 3939 3939 3939
N districts 459 459 459 459 459
Avg. obs. per district 8.6 8.6 8.6 8.6 8.6
Overall F 3160 55 9955 4836 1169
R-squared (within) 0.55 0.18 0.55 0.81 0.82

30. LOOKING BELOW THE AVERAGE
30

31. Distributional results
31
0
0.05
0.1
0.15
0.2
0.25
Elasticity, percentage change from an additional
ton of palm oil production per capita, district level
Log bottom 20% TOTAL
household expenditure
Notes
• All years, all districts
• Within estimator
• District fixed effects
• Year fixed effects
• Robust district-clustered
90 percent confidence
intervals
Log poverty rate

32. SECTOR HETEROGENEITY
(preliminary results)
32

33. Private and smallholder sectors are similar sized
33
0
50000
100000
150000
200000
250000
300000
350000
2005 2010
Average district production, total (tons)
Total
Private
State-owned
Smallholders

34. Productivity is similar across sectors
34
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Private State-owned Smallholder
Average district yied (kg/ha)
2007 2008

35. Main results, by sector
35
Smallholder
N=952, D=159
Private
N=516, D=120
State-owned
N=207, D=53
Notes
• Bars represent semi-elasticity
point
estimates
• Whiskers represent 90
percent robust district
clustered confidence
intervals
• All years and districts
where data; no
‘controls’, estimates for
producing districts
• Missing data across
years and districts
• No reason to suspect
missing data are zeros
• Generalised least
squares, with district-level
random effects
• Time and province-level
fixed effects
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Elasticity, % change in avg. household expenditures
from an extra ton of palm oil production per capita

36. Distributional results, by sector
Notes
• Bars represent semi-elasticity
point estimates
• Whiskers represent 90
percent robust district
clustered confidence
intervals
• All years and districts where
data; no ‘controls’, estimates
for producing districts
• Missing data across years
and districts
• No reason to suspect missing
data are zeros
• Generalised least squares,
with district-level random
effects
• Time and province-level
fixed effects
36
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6 Elasticity, % change from ton of palm oil production per capita,
district level, by sector
private
state-owned
smallholder
Bottom 20% total
household
expenditure
Poverty rate

37. Implications so far
• In short run, increasing palm oil production in all sectors has
tended to be good for average household well-being
throughout the income distribution
• Slowing production is, in the short run, unambiguously harmful
for average household welfare at the district level
37

38. Implications so far
• In short run, increasing palm oil production in all sectors has
tended to be good for average household well-being
throughout the income distribution
• Slowing production is, in the short run, unambiguously harmful
for average household welfare at the district level
• Different sectors are likely to have differential effects at the
lower end of the income distribution
• Key policy challenge environmental / human trade-off,
maintaining production without adverse environmental effects
38

39. 39

40. Next steps
For this paper
• Obtain ‘purer’ short-run causal estimates, i.e., improve dataset, add
covariates, and add improved instruments
• Examine lagged effects and dynamics, and regional dynamics (i.e., by island)
• Disaggregate expenditure effects by expenditure type and on savings
40

41. Next steps
For this paper
• Obtain ‘purer’ short-run causal estimates, i.e., improve dataset, add
covariates, and add improved instruments
• Examine lagged effects and dynamics, and regional dynamics (i.e., by island)
• Disaggregate expenditure effects by expenditure type and on savings
Research agenda (related to this topic)
• Medium and long-run effects
• Effects on labour markets and employment, and on other sectors
• Effects on human capital (i.e., decreased secondary participation)
• Efficiency analysis, i.e., how to increase output holding land constant?
41

42. Acknowledgements
42

43. Contact
Email: ryan.edwards@anu.edu.au
Twitter: @ryanbedwards
Web: https://crawford.anu.edu.au/people/phd/ryan-barclay-
edwards
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