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

Does palm oil production make people in Indonesia better off?

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Indonesia now exports over 16 times the palm oil it did in 2000…

3 Source: The Atlas of Economic Complexity, 2014 (http://www.atlas.cid.harvard.edu/ )

.. to all over the world…

4 Source: The Atlas of Economic Complexity, 2014 (http://www.atlas.cid.harvard.edu/ )

.. and is now the largest supplier (by over 13,000,000,000,000 tonnes!)

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Source: Indexmundi (2014), http://www.indexmundi.com/agriculture/?commodity=palm-oil&

But palm oil can have high ecological costs..

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..leading to boycotts, divestment, and more

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… while poverty remains widespread..

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

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

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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

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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

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Introducing the data: INDONESIA DATABASE FOR

POLICY AND ECONOMIC RESEARCH (DAPOER)

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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

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National variation in palm oil production intensity, by province, 2013

Source: http://www.simreg.bappenas.go.id/

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

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0 1 2 3 4 5Per capita palm oil production, district (tons)

Log per capita HH expenditure Fitted values

A naïve comparison reveals a positive correlation

Source: DAPOER

IDENTIFICATION CHALLENGES

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Time trend

IDENTIFICATION CHALLENGES

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Time trend Reverse causality

Expenditure may influence decisions and ability to produce

IDENTIFICATION CHALLENGES

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Time trend Reverse causality

Expenditure may influence decisions and ability to produce

Time-invariant omitted variables

District and regional; observable and unobservable

IDENTIFICATION CHALLENGES

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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

Estimated equation

𝑙𝑜𝑔 𝑦𝑖,𝑡 = 𝛽1 + 𝛽2 𝑃𝑖,𝑡 + 𝛽3𝑇 + 𝑣𝑖 + 𝑒𝑖,𝑡

𝑦𝑖,𝑡 = outcome of interest, district i, time t

𝑃𝑖,𝑡= per capita palm oil production

𝑇 = time trend

𝑣𝑖 = district fixed effect

𝑒𝑖,𝑡 = district-clustered robust error term

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Estimated equation

𝑙𝑜𝑔 𝑦𝑖,𝑡 = 𝛽1 + 𝛽2 𝑃𝑖,𝑡 + 𝛽3𝑇 + 𝑣𝑖 + 𝑒𝑖,𝑡

𝑦𝑖,𝑡 = outcome of interest, district i, time t

𝑃𝑖,𝑡= per capita palm oil production

𝑇 = time trend

𝑣𝑖 = district fixed effect

𝑒𝑖,𝑡 = district-clustered robust error term

Main estimators (i.e., within, fixed effects) focus on the yearly changes within each district (i.e., short-run)

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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

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Plausibility of the identifying assumption

• Production takes many years, i.e., cannot be contemporaneously endogenous to household spending

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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

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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

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Main results

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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

LOOKING BELOW THE AVERAGE

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Distributional results

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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

SECTOR HETEROGENEITY (preliminary results)

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Private and smallholder sectors are similar sized

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0

50000

100000

150000

200000

250000

300000

350000

2005 2010

Ave

rage

dis

tric

t p

rod

uct

ion

, to

tal (

ton

s)

Total

Private

State-owned

Smallholders

Productivity is similar across sectors

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0

500

1000

1500

2000

2500

3000

3500

4000

4500

Private State-owned Smallholder

Ave

rage

dis

tric

t yi

ed

(kg

/ha)

2007 2008

Main results, by sector

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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

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

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-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

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

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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

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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

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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?

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Acknowledgements

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Contact

Email: ryan.edwards@anu.edu.au

Twitter: @ryanbedwards

Web: https://crawford.anu.edu.au/people/phd/ryan-barclay-edwards

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