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Natural Resource Sectorsand Human Development:
International and Indonesian Evidence
Ryan B. Edwards
a thesis submitted for the degree ofDoctor of Philosophy at the
Australian National University
April 2016
i
© Copyright by Ryan Barclay Edwards 2016All Rights Reserved
Declaration
This thesis is my own work.
A version of Chapter 2 is published in World Development:
Edwards, R. B. (2016), “Mining away the Preston curve”, World Development,78, February, pp. 22–36.
Ryan B. EdwardsJanuary 2016
i
Acknowledgements
I first and foremost thank Paul Burke, Chair of my PhD supervisory panel. Paul
has been an amazing supervisor and provided exceptional guidance throughout
my PhD journey. I would also like to thank my other two panel members, Budy
Resosudarmo and Robert Sparrow, who were always happy to discuss ideas and
provide useful feedback.
I was fortunate to complete my PhD in the Arndt-Corden Department of
Economics (ACDE) at the Australian National University (ANU), with its unique
focus on the economies of the Asia-Pacific region. Prema–chandra Athukorala,
Sommarat Chantarat, Max Corden, Sarah Dong, Hal Hill, Raghbendra Jha, Heeok
Kyung, Blane Lewis, Chris Manning, Ross McLeod, Kate Mclinton, Nurkemala
Muliani, Arianto Patunru, Daniel Suryadarma, Peter Warr, Ben Wilson, and
Sandra Zec have been fantastic and helpful colleagues.
My month of fieldwork supported by Budy and the Indonesia Project greatly
enhanced my PhD experience, allowed me to develop enduring friendships, and
laid the foundations for my ongoing research program. I thank Thomas Barano,
Ernawati Apriani, Yudi Agusrin, Margaretha Nurrunisa, and WWF-Indonesia for
kindly hosting me in Jakarta and accompanying me around Sumatra, and Daniel
Suryadarma at CIFOR/ICRAF, Matthew Wai-Poi at the World Bank, Kiki Verico
at the University of Indonesia, and Bank Indonesia for being such generous hosts.
Bill Wallace, Asep Suryahadi, Indira Hapsari, Meine van Noordwijk, Ernest
Bethe, Triyanto Fitriyardi, Dhanie Nugroho, and Ari Perdana also provided
useful discussions and feedback.
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ACDE PhD students provided a stimulating environment throughout my PhD
journey, particularly Lwin Lwin Aung, Ariun-Erdene Bayarjargal, Rohan Best,
Kimlong Chheng, Omer Majeed, Matthew McKay, Huy Nguyen, Manoj Pandey,
Rajan Panta, Umbu Raya, Marcel Schroder, Yessi Vadila, Samuel Weldeegzie, and
Agung Widodo.
ACDE is part of the Crawford School of Public Policy at the ANU. The Crawford
School was an ideal place for PhD study in many respects, allowing me to
develop my teaching skills, providing stimulating public policy events and
seminars, and providing a collegial environment of PhD students and academic
staff. I thank Fitrian Ardiansyah, Adriyanto, Shiro Armstrong, Bob Breunig,
Alrick Campbell, Bruce Chapman, Leo Dobes, Matt Dornan, Mark Fabian, Ippei
Fujiwara, Yusaku Horiuchi, Wee Koh, Stephen Howes, Llewelyn Hughes, Frank
Jotzo, Tom Kompas, Ida Kubiszewski, Belinda Lawton, Luke Meehan, David
Stern, Julia Talbot-Jones, Ariane Utomo, Peter Whiteford, and Terence Wood for
their friendship and support. I am also grateful to our excellent post-graduate
coursework students for keeping me on my toes when teaching and for their
usually constructive and positive feedback.
Kay Dancey and the CartoGIS unit at the ANU College of Asia and the Pacific
kindly assisted with maps and GIS training.
Indira Hapsari, Robert Sparrow, Kay Dancey, Arianto Patrunu, Agung Widodo,
Yessi Vadila, Budy Resosudarmo, Blane Lewis, Susmita Dasgupta, and the
Australian Data Archive kindly shared data.
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Useful feedback on the three chapters of this thesis was received from three
examiners, five anonymous reviewers, one editor, Fabrizio Carmignani, Andrea
la Nauze, Richard Denniss, and from many conference and seminar participants
at the Australian National University, the Australasian Development Economics
Workshop at Monash University, the Centre for International Forestry Research,
the World Bank, Bank Indonesia, the University of Indonesia, the Australian
Conference of Economists at Queensland University of Technology, the World
Congress of Environmental and Resource Economics in Istanbul, and the
Australian Agricultural Resource and Environmental Economics Society.
I gratefully acknowledge financial support for activities undertaken during
the course of my PhD study from the Indonesia Project, ACDE, ANU, Monash
University, and the Australian Government.
Last but certainly not least, I thank my family—Colette, Raymond and Karina—
and my wife Jessica for the motivation and for seeing me through.
Abstract
This thesis collects three papers on natural resource sector-led development.
The first paper examines the long-term health and education impacts of mining
dependence. Exploiting between-country variation in a large international
sample, causal effects are identified through instrumental variable estimation.
Results show that countries with economies more oriented toward mining on
average display poorer health and education outcomes than countries of similar
per capita income. Income from sectors other than mining tends to deliver
better health and education outcomes. Key channels explaining the lower social
productivity of mining sector activity include its impacts on non-mining sectors
and institutions. Similar patterns are observed across Indonesian districts,
suggesting this is not only a country-level phenomenon.
The second paper examines the poverty impacts of the world’s largest modern
plantation sector expansion, Indonesian oil palm in the 2000s. The paper
combines administrative data on local oil palm acreage at the district level
with survey-based estimates of poverty, using an estimation approach in
long-differences. Identification is achieved through an instrumental variable
strategy exploiting detailed geospatial data on crop-specific agro-climatic
suitability. The key finding is that increasing the oil palm share of land in a
district by ten percentage points contributes to around a forty percent reduction
in its poverty rate. Of the more than 10 million Indonesians lifted from poverty
over the 2000s, my most conservative estimate suggests that at least 1.3 million
of these people have risen out of poverty due to growth in the oil palm sector.
Similar effects are observed for different regions of Indonesia, for industrial and
v
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smallholder plantations, and at the province level. Oil palm expansion tends to
be followed by a small but sustained boost to the value of agricultural output,
manufacturing output, and total district output.
The final paper presents three quantitative case studies on the local economic
and welfare impacts of rapid natural resource sector expansion in Indonesia. The
paper focuses on three districts that have experienced notably large production
booms for Indonesia’s three largest primary exports: palm oil (Indragiri Hilir, in
Riau), coal (Tapin, in South Kalimantan), and natural gas (in Manokwari, West
Papua). Counterfactuals are constructed for each case study district through
synthetic control modelling. Results suggest that all three resource booms boosted
total economic output and altered the structure of the local economy. Oil palm
expansion in Riau raised agricultural, industry, and services output, while coal
mining in South Kalimantan reduced agricultural and services output. Oil palm
and coal mining booms both appear to have delivered strong poverty reduction.
The Tangguh natural gas project in West Papua delivered a massive increase in
local economic and industry output, but I find no evidence of any discernible
impacts on household welfare and poverty. The three case studies show that
natural resource sectors can make important contributions to poverty alleviation.
Relative to their size, sectors with more concentrated rents tend to provide less
broad-based benefits than diffuse resource sectors using labour more intensively.
Contents
Declaration i
Acknowledgements ii
Abstract v
List of Figures ix
List of Tables 1
1 Introduction 11.1 Natural resource sectors . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Curse or blessing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 A focus on Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.4 Thesis purpose and approach . . . . . . . . . . . . . . . . . . . . . . 111.5 Key research questions and results . . . . . . . . . . . . . . . . . . . 121.6 Methodological contributions . . . . . . . . . . . . . . . . . . . . . . 151.7 Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Mining away the Preston curve 182.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.2 Linking mining to health and education . . . . . . . . . . . . . . . . 222.3 Empirical approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 Instrumental variable strategy . . . . . . . . . . . . . . . . . 272.3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.4.1 Health and education effects of mining sector growth . . . . 362.4.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402.4.3 Health and education elasticities of income, by sector . . . . 412.4.4 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.4.5 Potential channels . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5 Within-country evidence from Indonesia . . . . . . . . . . . . . . . 482.6 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 522.7 Chapter 2 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
vii
Contents viii
3 Is plantation agriculture good for the poor? 683.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2 Indonesia’s oil palm expansion . . . . . . . . . . . . . . . . . . . . . 72
3.2.1 Linking oil palms to poverty . . . . . . . . . . . . . . . . . . 743.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.3.1 Oil palm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.3.2 Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773.3.3 Pemekaran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.4 Empirical approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803.4.1 Instrumental variable strategy . . . . . . . . . . . . . . . . . 81
3.5 Main results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853.6 Short-run and dynamic impacts . . . . . . . . . . . . . . . . . . . . . 923.7 A migration story? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963.8 Heterogeneity and wider impacts . . . . . . . . . . . . . . . . . . . . 102
3.8.1 Heterogeneity by region . . . . . . . . . . . . . . . . . . . . . 1023.8.2 Heterogeneity by sector . . . . . . . . . . . . . . . . . . . . . 1043.8.3 Wider impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1093.10 Chapter 3 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4 Local impacts of resource booms 1214.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.2 Synthetic control approach . . . . . . . . . . . . . . . . . . . . . . . . 125
4.2.1 Estimation and inference . . . . . . . . . . . . . . . . . . . . 1264.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1294.2.3 Identifying appropriate case studies . . . . . . . . . . . . . . 1324.2.4 Constructing each synthetic control . . . . . . . . . . . . . . 138
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1414.3.1 Oil palm expansion in Sumatra . . . . . . . . . . . . . . . . . 1414.3.2 Coal mining in Kalimantan . . . . . . . . . . . . . . . . . . . 1494.3.3 Natural Gas Extraction in West Papua . . . . . . . . . . . . . 152
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1564.5 Chapter 4 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
5 Conclusion 163
References 166
List of Figures
2.1 Preston Curve, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2 Educational Attainment, Income, and Mining, 2005 . . . . . . . . . 212.3 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . 232.4 Contribution of Mining to Value-Added, 2005 . . . . . . . . . . . . 312.5 Per Capita Fossil Fuels, 1971 . . . . . . . . . . . . . . . . . . . . . . 322.6 Health and Education Elasticities of Income, By Sector . . . . . . 432.7 Mining Share of District Regional Gross Domestic Product in
Indonesia, 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502.8 Global Infant Mortality, 2005 . . . . . . . . . . . . . . . . . . . . . 552.9 Global Years of Educational Attainment, 2005 . . . . . . . . . . . 56
3.1 Oil Palm Land as a Share of District Area, 2009 . . . . . . . . . . . 763.2 District Poverty Rates, 2010 . . . . . . . . . . . . . . . . . . . . . . . 783.3 Attainable Palm Oil Yield Across Indonesia . . . . . . . . . . . . . 823.4 Poverty Impacts of the 2001–2010 Oil Palm Expansion . . . . . . . . 873.5 Regional Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . 1023.6 Sector Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.1 Case Study Districts—Locations . . . . . . . . . . . . . . . . . . . . 1344.2 Case Study Districts—Treatments . . . . . . . . . . . . . . . . . . . 1364.3 Impacts of Oil Palm Expansion in Sumatra . . . . . . . . . . . . . . 1424.4 Consumption in Indragiri Hilir . . . . . . . . . . . . . . . . . . . . . 1474.5 Kernel Density Estimate–Consumption Distribution in Indragiri
Hilir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1484.6 Impacts of Coal Mining in Kalimantan . . . . . . . . . . . . . . . . 1504.7 Impacts of Gas Extraction in West Papua . . . . . . . . . . . . . . . 1544.8 Appendix–Impacts of Oil Palm Expansion in Sumatra, non-zero Y
axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1604.9 Appendix–Impacts of Coal Mining in Kalimantan, non-zero Y axis 1614.10 Appendix–Impacts of Gas Extraction in West Papua, non-zero Y axis162
ix
List of Tables
2.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.2 Health and Education Effects of Mining Dependence . . . . . . . 372.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.4 National-level Mechanisms . . . . . . . . . . . . . . . . . . . . . . . 462.5 Sub-national Evidence from Indonesian Districts . . . . . . . . . . 512.6 Main Results Instrumenting with Mineral, Oil, Gas, and Coal
Reserves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.7 Main Results Instrumenting with only Oil and Gas Reserves . . . 582.8 Main Results using Alternative Time Periods . . . . . . . . . . . . . 592.9 Main Results using Alternative Measures of Mining . . . . . . . . 602.10 Main Results without Resource-rich Mini-states and Other
Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612.11 Health and Education Elasticities of Income from Different
Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.12 Regional Sub-samples . . . . . . . . . . . . . . . . . . . . . . . . . . . 632.13 Sub-sample Estimation by Institution Type . . . . . . . . . . . . . . . 642.14 Local Average Partial Effects, By Commodity . . . . . . . . . . . . 652.15 National-level Mechanisms—OLS and IV . . . . . . . . . . . . . . 662.16 Main Results, By Gender . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.1 Poverty Impacts of the 2001–2010 Oil Palm Expansion . . . . . . . . 863.2 Impacts of the 2001–2010 Oil Palm Expansion on the Poverty Gap . 893.3 Additional Covariates and Robustness . . . . . . . . . . . . . . . . 913.4 Short-run and Dynamic Poverty Impacts . . . . . . . . . . . . . . . 943.5 Short-run and Dynamic Impacts on Poverty Depth . . . . . . . . . 973.6 Province level Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1003.7 Population, Poor People, and Production . . . . . . . . . . . . . . 1013.8 Regional Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . 1033.9 Heterogeneity by Plantation Type . . . . . . . . . . . . . . . . . . . 1053.10 Effects of Oil Palm Expansion on Sectoral and Total District GDP1083.11 Estimated Contribution to Poverty Reduction . . . . . . . . . . . . 1093.12 Panel Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . 112
x
LIST OF TABLES 1
3.13 Main Results—Linear-linear Functional Form . . . . . . . . . . . 1133.14 Main Results—Production Instead of Land . . . . . . . . . . . . . 1143.15 Determinants of Changing Oil Palm Land Shares . . . . . . . . . . 1153.16 Robustness—Panel Fixed Effects . . . . . . . . . . . . . . . . . . . . 1163.17 Robustness–Alternative Samples . . . . . . . . . . . . . . . . . . . . 1173.18 Heterogeneity–By Region . . . . . . . . . . . . . . . . . . . . . . . . 1183.19 Heterogeneity–By Sector . . . . . . . . . . . . . . . . . . . . . . . . 1193.20 Heterogeneity–By Land Quality . . . . . . . . . . . . . . . . . . . . 120
4.1 Case Study Districts— Descriptive Statistics . . . . . . . . . . . . . 1354.2 Impacts of Oil Palm Expansion in Sumatra . . . . . . . . . . . . . . 1414.3 Predictor Balance: Indragiri Hilir . . . . . . . . . . . . . . . . . . 1444.4 Synthetic Indragiri Hilir Weights . . . . . . . . . . . . . . . . . . . 1454.5 Impacts of Coal Mining in Kalimantan . . . . . . . . . . . . . . . . 1494.6 Impacts of Gas Extraction in West Papua . . . . . . . . . . . . . . . 153
Chapter 1
Introduction
1.1 Natural resource sectors
Natural resource sectors have been key drivers of the unprecedented economic
development of the 20th century, with the consumption of fossil fuel and forestry
products powering the industrial revolution, the enlightenment, and the global
human progress that followed.1 Natural resources are assets (i.e., raw materials)
occurring in nature that can be used for economic production or consumption;
natural resource sectors are industries extracting and processing them. Today
natural resource sectors remain important in the global economic landscape. The
total value of natural resource sector exports from developing countries in 2010
was 9.8 and 9 times larger than foreign aid and remittances (Jedwab, 2013; World
Bank, 2014).
In this thesis I focus on two critical natural resource sectors: mining, broadly
defined to include coal and mineral mining and oil and gas extraction2; and palm
oil. Palm oil is a unique export-oriented sector similar to mining in many respects.
Both sectors are characterised by economic enclaves and negative environmental
externalities. Unlike many other cash crops, fresh fruit from oil palms must be
processed shortly after harvest, requiring processing facilities and large up-front
infrastructure investments (e.g., transport) similar to mining. These up-front
1See, e.g., Ayres and Warr (2005; 2009), Cleveland et al (1984), Deaton (2013), Hall et al (2003),Mayumi (1991), Kander et al (2013), Kander and Stern (2014), and Wrigley (2010).
2Although the oil and gas industries have several different characteristics to coal and minerals,these sectors have many similarities and major resource sector companies tend to diversify acrosscommodities for scale and scope efficiencies.
1
§1.1 Natural resource sectors 2
investment requirements ensure the palm oil sector grows principally through
plantations, which are better able to recover large up-front investments and deal
with long gestational periods than family farms or smallholders (Hayami, 2010).
Smallholder oil palm typically emerges once this prerequisite infrastructure is in
place, much like artisanal mining around large mining facilities.
Natural resource sectors can have several unique characteristics requiring
special attention from policy-makers. Natural resource endowments (including
forestry resources) tend to be the property of the nation state, and resource
rents are often legally intended to be distributed amongst countries’ current
and future populations. A government allowing a nation’s natural resources
to be extracted without benefiting the national economy and its people is
unlikely to be a popular one, so natural resource sectors are often of interest to
politicians. Natural resource sectors are characterised by negative environmental
and public health externalities, giant infrastructure investments, and long project
life cycles requiring local and macro-level economic and political stability.
Compared to other sectors, there is a greater role for policy-makers in weighing
up the relative costs and benefits of large natural resource projects requiring
government approval (e.g., through concessions, licensing, permits, special
regulations) and often, for more political than economic reasons, obtaining
direct government support (e.g., through state-owned enterprises, favourable
financing arrangements, tax exemptions, and direct subsidies). Understanding
the contribution of natural resource sectors to human development is important
for informing development strategies and policy settings.
§1.2 Curse or blessing? 3
1.2 Curse or blessing?
Despite their important role in driving economic development in the 20th
century, the role of natural resource sectors for improving well-being today is less
clear. Many resource-rich countries have experienced sustained natural resources
sector-driven economic growth but little progress in improving development
outcomes. Nigeria’s oil revenues increased almost tenfold from 1965–2000, but
real income stagnated, and poverty and inequality increased (van der Ploeg,
2011). Papua New Guinea had an average GDP per capita growth rate of 8%
over the second half of the 2000s yet a virtually unchanged poverty rate of
nearly 40% (World Bank, 2014). Natural resource sector-led economic growth
swept Africa in the 2000s, but agricultural yields across the continent remain low,
manufacturing and services sectors remain relatively small and unproductive,
and poverty reduction has been disappointing (Caselli, 2005; Evenson and Gollin,
2003; Restuccia et al, 2008; Vollrath et al, 2015; and World Bank, 2013). Comparing
these recent experiences with those of resource-poor countries like South Korea,
Singapore, and Taiwan (or the manufacturing-led growth of China), natural
resource sector-driven economic growth appears to correspond to countries
developing distinctly differently.3 Much is known about how natural resource
sectors affect macroeconomic aggregates and political institutions, but many
questions about how natural resource sectors affect broader well-being remain
largely unanswered.
3For greater tractability in this introductory discussion, I focus on mining sectors, as naturalresources have been typically conceptualised in the literature. Palm oil is only produced in ahandful of developing countries and has some similar characteristics to mining (c.f. most countrieshave mining sectors).
§1.2 Curse or blessing? 4
The idea that natural resources can “curse” countries that own them has
been widely held. Early studies by Auty and Mikesell (1998), Gylfason,
Herbertsson, and Zoega (1999), and Sachs and Warner (1995; 2001) documenting
negative associations between resource abundance and economic growth led to
a burgeoning field of empirical research contesting the existence of the “resource
curse” (Torvik (2009), van der Ploeg (2011), and Wick and Bulte (2009) provide
reviews). This research focused on the relationship between natural resources
and aggregate economic activity, with institutional quality widely believed to
determine whether natural resources become a curse or a blessing (Collier and
Goderis, 2012; Mehlum et al, 2006; Torvik, 2002, 2009). As highlighted by van
der Ploeg (2011), cross-country evidence has been sensitive to sample period,
countries, variable definitions, omitted variables, measurement error, and other
factors.4 van der Ploeg (2011) concludes “the road forward might be exploiting
variation within a country where variables that might confound the relationship
between resources and macroeconomic outcomes do not vary and the danger of
spurious correlation is minimised”.
Within-country studies on the economic impacts of natural resources are now
common, and more recent national-level studies with improved identification
strategies tend to disprove the idea of an aggregate GDP resource curse.5 Smith
(2015) shows that countries that have become resource-rich since 1950 now have
significantly higher per capita incomes that they would have without the resource
discoveries. Cotet and Tsui (2013) show the same exclusively for oil. For diffuse
natural resources, Nunn and Qian (2011) show that areas more suitable for
agricultural production are significantly more developed today.4For example, studies following the Sachs and Warner (2001) approach arrive at different
results instrumenting for resource abundance (Brunnschweiler and Bulte, 2008), includingcountry fixed effects (Manzano and Rigobon, 2001), and using different measures of resourceintensity (Lederman and Maloney, 2007).
5Examples of within-country studies on the effects of natural resources and a boomingresource sector include Aragon and Rud (2013), Black et al (2005), Caselli and Michaels (2013),Domenech (2008), Dube and Vargas (2013), Fleming and Measham, (2014), James and James (2012)and James and Aadland (2011), Michaels (2010), and Papyrakis and Gerlagh (2007). Cust andPoelhekke (2015) provide a review.
§1.2 Curse or blessing? 5
Policy recommendations for managing natural resource wealth typically seek
to address the mechanisms through which a possible resource curse could
act. For example, improved governance and transparency policies are designed
to deal with the direct negative impacts of resource wealth on institutional
quality (e.g., the Extractive Industries Transparency Initiative). Different
macroeconomic policies are often used to manage potential Dutch disease
effects and macroeconomic volatility associated with commodity prices (e.g.,
sterilisation and sovereign wealth funds).6 Such policies have been in place in
several resource-rich countries since the early 2000s (Arezki et al, 2011), yet the
poor development performance of countries with natural resource sector-driven
economic growth seems unchanged.
I follow a different line of enquiry. If we are concerned with overall living
standards, we must look beyond economic aggregates and examine how natural
resource sectors affect well-being across society. Positive aggregate income effects
arising from resource wealth are unlikely to be spread evenly across society, as
natural resource sectors are closely linked to extractive institutions (Acemoglu
et al, 2001). Standard empirical relationships between higher income levels and
improved health (Summers and Pritchett, 1994), education (Barro and Lee, 1993;
2010; Taubman, 1989), and poverty outcomes (Dollar et al, 2016) may be different
in the context of income from natural resource sectors. In this thesis I examine the
links between natural resource sectors and human development, focusing on (a)
human capital, which I consider as as the combination of health and education
outcomes and an important channel for economic and social mobility in all
economies; (b) spillovers to non-resource sectors, which tend to generate greater
employment opportunities and productivity benefits due to higher labour and
skill intensities (discussed further below); and (c) poverty, the first and foremost
Sustainable Development Goal.
6van der Ploeg and Poelhekke (2009), and van der Ploeg (2011) discuss the importance ofvolatility in resource-dependent economies in detail. The Dutch disease is explained below.
§1.2 Curse or blessing? 6
The experiences of natural resource exporting countries in the post-war period
support the argument that “natural capital appears to crowd out human capital,
thereby slowing down the pace of economic development” (Gylfason, 2001).
While human capital has typically been seen as a channel for natural resources
to stunt economic growth (Gylfason and Zoega, 2006), primary commodities
abundance and dependence have been shown to lower broad measures of social
development (Bulte et al, 2005; Carmignani, 2013; Carmignani and Avom, 2010).
To understand how natural resource sector-led economic growth affects long-term
social development trajectories, it is instructive to revisit some of the earliest work
on effects of a booming primary sector on the rest of the economy: the “Gregory
thesis”, or as it is more commonly known, “Dutch disease” theory (Corden, 1984;
Corden and Neary, 1982; Gregory, 1976).
The Dutch disease occurs when growth in export-oriented natural resource
sectors reduce activity levels and employment in other tradable sectors
(e.g., manufacturing and highly-skilled services) by raising factor prices and
reducing competitiveness relative to foreign-produced substitutes (Corden, 1984).
Non-tradable services, usually low-skilled, tend to see an increase in activity
levels and employment. Lagging sectors in a Dutch disease affected economy
are those where profitability and investment incentives are trapped between
rising domestic costs and output prices set in world markets: some parts of
agriculture, skilled tradable services, and most of manufacturing. Dutch disease
theory initially found limited empirical support due to the sheer dominance of
the net income effects of natural resource booms and the diverse experiences
of resource rich countries (Auty, 2001). Today, natural resource sector-driven
growth continues to deliver higher per capita incomes and other signs of economic
development (e.g., urbanisation) without the industrialisation-related structural
change that historically accompanied broad development progress (Vollrath et al,
2015), just as the Dutch disease predicts.
§1.2 Curse or blessing? 7
But do these missing sectors matter? Export-oriented manufacturing
and skilled tradable services are characterised by technological dynamism,
information and productivity spillovers, and other agglomeration effects that
drive economic growth by making capital and other factors of production more
productive (Ellison et al, 2010; Greenstone et al, 2010; Hanson, 2012; Rodrik,
2015).7 Natural resource sectors and non-tradable services, by contrast, are not
skill-intensive, tend to experience less productivity growth, and deliver fewer
productivity spillovers to other industries.8
A sustained resource boom could thus reduce the growth of skilled jobs, lower
returns to existing human capital, and reduce incentives to invest in new human
capital, as predicted by Gylfason (2001) and highlighted in Grilches’ (1969) early
work on the complementarity of physical and human capital. In the short term,
there is likely to be downward pressure on enrolment, retention, and graduation
rates (particularly at higher levels) due to rising demand for labour in low-skilled
industries and the rising opportunity cost of attaining further education. In the
long term, the natural resource sector-driven economy will be poorly positioned
to transition into innovation- and skills-driven economic growth (Eichengreen,
Park, and Shin, 2013). Given the important role of education as a pathway to
economic mobility, structural change in the labour market shapes the distribution
of income and opportunities.9 In developing countries, formal opportunities
vary by sector and structural change redistributing employment across sectors
alters the probability of informal employment. Unskilled non-tradable services
sectors that often boom with natural resource sectors tend to have high levels of
informality and act as a sink for the underemployed, the unemployed, and the
7In explaining why resource booms could lower economic growth (i.e., the “resource curse”),it was often assumed that it was due to stagnating manufacturing (see, e.g., Matsuyama (1992)and Torvik (2001)), known for being an “escalator” for economic growth (Rodrik, 2015).
8Lederman and Maloney (2007), Corden and Neary (1982), Krugman (1987), Matsuyama(1992), and van Wijnbergen (1984) show why this may be the case. Michaels (2010) and Blacket al (2005) provide some recent evidence to the contrary.
9Cassing and Warr (1985) discuss the distributional impacts in a standard Dutch disease modelin more detail.
§1.2 Curse or blessing? 8
poor. A focus on sectors and structural change is thus critical to understand
the human development and distributional consequences of natural resource
sector-driven economic growth.
Distributional aspects of natural resource sectors, particularly mining, have
also been neglected by the literature (Ross, 2007), particularly relating to poverty.
In fact there is a strong negative relationship between a country’s dependence
on resource rents and the amount of data we have about its inequality and
poverty levels (Ross, 2007). Bhattacharyya and Williamson (2013) study Australia,
finding that the richest benefit disproportionately from resource booms, but not
agricultural booms, and note that “ the empirical literature on the inequality and
resource boom connection is relatively thin.” Gylfason and Zoega (2003) and
Goderis and Malone (2011) similarly find that resource booms increase inequality.
Carmignani and Avom (2010) and Carmignani (2013) find that natural resource
abundance and dependence increases income inequality and that this is the key
mechanism through which natural resources can hinder social development.
Turning to poverty, Smith and Wills (2015) exploit detailed global geo-spatial
data and find that oil booms around the world have not benefited the rural
poor. Bhattacharya and Resosudarmo (2015) find that province-level mining
sector growth accelerations actually increase poverty in Indonesia, and Aragon
and Rud (2015) find that gold mining in Ghana significantly worsens poverty in
surrounding areas. In a review paper, Gamu et al (2015) conclude that extractive
industries appear to make limited contributions to poverty reduction. Given the
strong environmental and climate change externalities associated with natural
resource sectors, particularly fossil fuels and palm oil, the development challenges
summarised in this subsection become even more pertinent when considered in
the context of the recently agreed Sustainable Development Goals and the Paris
climate change agreement.
§1.3 A focus on Indonesia 9
1.3 A focus on Indonesia
With a population of 253 million, Indonesia is the world’s third most populous
developing country after India and China. Indonesia’s per capita income was
$3,492 USD in 2014. This places Indonesia on the cusp of being classified as an
upper-middle income country, yet 28 million Indonesians (11.4%) lived below the
national poverty line in 2014 and a further 68 million remain vulnerable to poverty
(World Bank, 2014). Indonesia is the world’s largest natural resource-dependent
economy, with coal, natural gas, and palm oil its three largest exports and
responsible for most of the country’s recent economic growth (Garnaut, 2015).
Today Indonesia is the world’s largest exporter of thermal coal and palm oil, two
sectors at the centre of contemporary sustainable development policy debates.
Natural resource-driven economic growth from the 1970s to the early 2000s
supported Indonesia’s rapid industrialisation and provided broad-based benefits
and poverty reduction (Hill, 1996). Indonesia was often regarded as one of the
few developing countries to have not suffered from the “resource curse” for its
post-war development progress (Temple, 2003). The same cannot be said of
the last two decades. Like some African countries, the Indonesian economy
was buoyed through the global economic crisis of 2007–2008 by the commodity
boom of the 2000s (Burke and Resosudarmo, 2012; Hill et al., 2008). Demand
for Indonesia’s industrial crops and mined commodities—driven by economic
expansion in China and other large Asian economies—helped to sustain an
average annual per capita GDP growth of almost 5 percent over the decade, lifting
Indonesia’s income per capita from 15% of the world average in 2001 to 20% in
2011. The share of Indonesia’s main resource exports (oil, natural gas, coal, copper
and palm oil) in total merchandise exports rose from 34 percent to 46 percent,
driven almost entirely by coal and palm oil.10 The real exchange rate rose by
almost 4 percent per year during the 2000s and growth all of Indonesia’s key10The share of palm oil in merchandise exports rose from 2 percent to 9 percent, and coal from
3 to 14 percent.
§1.3 A focus on Indonesia 10
skill-intensive manufacturing industries slowed (Coxhead and Li, 2008; Coxhead,
2014), contributing to more than half of Indonesia’s GDP growth from 1990–1996
but less than a third from 2000. Following the persistent drop in investment after
the Asian financial crisis, capital stock shifted from mostly manufacturing before
2000 to mostly construction, particularly non-residential. The growth of capital
per worker (excluding construction) was very low, even negative, throughout the
2000s (van der Eng, 2009).
The boom of the 2000s was the first for which a labour-intensive agricultural
product like palm oil played a central role. Indonesia exhibits all the symptoms of
a modern Dutch disease-affected economy, continuing to grow and urbanise while
export-oriented manufacturing and skilled services sectors stagnate. Real wages
across all sectors have been flat since the early 2000s (Coxhead, 2014). Indonesia
performs poorly by most international health and education comparisons, with an
already under-educated labour force showing no discernible catch-up relative to
slower-growing economies (Newhouse and Suryadarma, 2011; Suryadarma and
Jones, 2013; World Bank, 2014). The pace of poverty reduction has slowed and
inequality continues to rise, but there remains little evidence on the role that the
resource boom of the 2000s has played in shaping these outcomes (Yusuf, 2013;
World Bank, 2014). In this thesis, I empirically examine how the mining and palm
oil sectors affect some of these development outcomes. This evidence could assist
countries like Indonesia to better align development strategies across different
economic, social, and environmental policy objectives. The environmental
impacts of natural resource sectors have been well documented and are beyond
the scope of this thesis.
§1.4 Thesis purpose and approach 11
1.4 Thesis purpose and approach
This thesis presents three self-contained empirical research papers on
how two critical natural resource sectors—mining and palm oil—affect
human development outcomes. Human development involves expanding
the opportunities and freedoms offered to all people, and is defined as the ability
to live a long and healthy life, attain knowledge, and have a decent standard of
living.
I focus on health, education, non-resource sectors, and poverty, and apply a
range of econometric techniques to identify the impacts of natural resource sectors
at the (a) country level, using a large international sample; (b) within-country
level, using a national sample of Indonesian districts; and (c) local level, through
three district-specific case studies in Indonesia.
Research methods vary by context and appropriateness for the research
questions at hand. The three research papers use observational data and
exploit quasi-experimental conditions for causal inference. Key methods include
(a) between estimators exploiting variation across units, for long-run effects;
(b) long-differences exploiting differential rates of changes across units, for
medium-run effects; (c) panel data estimators exploiting within unit variation, for
short-run effects; (d) instrumental variable estimators to deal with unobserved
heterogeneity and measurement error; and (e) synthetic control modelling, to
make causal inferences on single case study units.
§1.5 Key research questions and results 12
1.5 Key research questions and results
Chapter 2—Mining away the Preston curve
The first research paper asks the question: how does mining dependence affect
long-term health and educational development? I estimate the long-term national
health and education impacts of having a larger mining sector, instrumenting the
relative size of the mining sector with the natural geological variation in countries’
fossil fuel endowments to obtain causal effects. By comparing countries with
different structural compositions, I examine the “social productivities” of different
types of economic activity, focusing on fossil fuel extraction.
The findings suggest that countries with larger mining shares tend to have
poorer health and education outcomes than countries with similar per capita
incomes, geographic characteristics, and institutional quality. Doubling the
mining share of an economy corresponds to the infant death rate being twenty
percent higher, life expectancy being five percent lower, total years of education
being twenty percent lower, and seventy percent more people having no formal
education. Divergences from the Preston curve—the concave relationship
between cross-country income and life expectancy (Preston, 1975)—are thus
partly explained by the size of the mining sector. I test the generality of my
results by estimating an analogous model using a large cross-section of Indonesian
districts. Similar patterns are observed. I also provide evidence on key causal
mechanisms, finding that mining dependence is associated with lower levels of
non-mining income, lower health investment, and weaker democratic institutions.
The findings of this chapter provide support for a growing body of evidence
linking mining to poorer average living standards, particularly vis-a-vis other
types of income.
§1.5 Key research questions and results 13
Chapter 3—Is plantation agriculture good for the poor?
Chapter 3 turns to the world’s largest modern plantation-based agricultural
expansion. I ask whether Indonesian districts that converted more land into oil
palm plantations over the 2000s now have lower poverty as a result. I combine
administrative information on local oil palm acreage at the district level with
survey-based estimates of district poverty, and relate decadal changes in oil
palm plantation area to changes in district poverty rates to compare the poverty
elasticity of oil palm land against alternative uses for land (e.g., rice and forestry).
Causal effects are identified by instrumenting the size of each districts’ oil palm
expansion with its relative agro-climatic suitability for the crop.
The key finding is that districts with larger oil palm expansion have
achieved more poverty reduction than otherwise-similar districts without oil
palm expansion. The magnitude of the estimated poverty reduction from
increasing the district share of oil palm land by ten percentage points from
my preferred estimator is around 40% of the initial poverty rate. Poverty
gaps significantly narrow, suggesting not only those near the poverty line are
being lifted up. I assess short-term effects and dynamics using standard panel
estimators with distributed lags and I find no evidence of any major effect
heterogeneity when I disaggregate by large plantations and smallholders. Similar
effects are also observed across Indonesia’s major palm oil producing regions and
at the province level. I find evidence of minor spillovers to other local economic
activities. Oil palm expansion tends to be followed by a small but sustained
boost to the value of agricultural, manufacturing, and total district output. While
the links between agriculture and poverty have been widely studied, plantation
agriculture has received relatively little attention. To my knowledge, I provide
the first estimates of the poverty elasticities plantation-based agricultural growth.
That oil palm growth has been pro-poor in Indonesia is consistent with existing
§1.5 Key research questions and results 14
findings on agricultural-based growth.11 This new evidence will be able to inform
an ongoing policy debate on the future of palm oil across the developing world.
Chapter 4—Local impacts of resource booms
The final research paper asks: how does rapid natural resource sector
expansion affect a local district economy and its residents’ welfare? To answer this
question, I present three quantitative case studies. I focus on Indonesia’s three
largest export commodities at the heart of modern climate change debates and
exploit some of the largest and most sudden increases in district-level production:
palm oil in Indragiri Hilir, Riau; coal mining in Tapin, South Kalimantan; and a
giant natural gas project in the Bintuni Bay of West Papua. The three sectors have
all been argued to be economic enclaves, but have starkly different characteristics.
I use a relatively new empirical method—synthetic control modeling—to
construct a “synthetic” comparison district for each resource boom district.
This allows me to compare the booming districts’ key economic and human
development indicators—per capita economic output, its components, average
household expenditures, and poverty rates—with reasonable counterfactuals.
The findings suggest that all three sectors transform the local economy and
reduce poverty. Palm oil expansion in Indragiri Hilir delivered a small boost
to all sectors of the economy, and strong poverty reduction. Coal mining in
Tapin reduced agricultural and services sector output, but also delivered strong
poverty reduction. The Tangguh natural gas project in West Papua delivered
a massive increase in local economic output, but a more muted response to
average household welfare and poverty and a contraction in the agricultural
sector. Together the three case studies highlight how more diffuse natural
resource sectors tend to deliver more broad-based benefits for the local economy
and its residents. For natural resource sectors with highly concentrated rents (e.g.,
11See Dercon (2009) and Dercon and Gollin (2014) for reviews.
§1.6 Methodological contributions 15
natural gas), resource sector growth alone does not appear to deliver economic
development in other sectors or commensurate improvements to local residents’
welfare. The findings from these three case studies contribute to a nascent but
rapidly growing empirical literature on the local economic and poverty impacts
of booming natural resource sectors.12
1.6 Methodological contributions
A focus on sectors
Throughout the thesis, my analytical focus is on the impacts of natural resource
sectors. The literature’s traditional focus has been on resource endowments
exogenously determined by nature, and natural resource exports largely driven by
external demand. A focus on natural resource sectors has greater policy relevance,
as sector size is a function of policy choices and subject to a range of national
and subnational policy instruments. In each chapter I introduce new measures
of natural resource sector size and activity. In Chapter 2, I introduce a new
measure for country-level resource dependence: mining value-added and mining
value-added as a share of GDP. In Chapter 3 I measure district-level palm oil
sector intensity with the share of total district area used for palm oil plantations.
Focusing on land use change allows me to compare the development impacts of
more oil palm land against all alternative uses of land—the opportunity cost. My
three district-level case studies in Chapter 4 define local resource booms as events
where the district economy experiences a sharp and sudden increase in resource
sector output. Examining time series for resource sector output allows me to
identify episodes of resource expansion appropriately classified as dichotomous
treatments for event study.
12See Cust and Poelhekke (2015) and Gamu et al (2015) for reviews.
§1.6 Methodological contributions 16
Empirical strategies
The primary contributions of this thesis are the new empirical results
described above, but I also introduce several methodological innovations. The
empirical method used in Chapter 2 builds on the instrumental variable strategies
of van der Ploeg and Poelhekke (2010) and Carmignani (2013), who instrument
natural resource share of exports with estimated natural resource reserves. I
extend this approach to focus on the mining sectors and explicitly address
potential measurement error in observed historical resource endowments by
controlling for historical exploration effort. My novel proxy for exploration effort
is the total number of exploratory oil and gas wildcats drilled in each country over
the 20th century.
In Chapter 3 I introduce a new instrumental variable strategy to study causal
effects of agricultural sector growth. By exploiting detailed geo-spatial data on
agro-climatic suitability for oil palm for every field in Indonesia, I can identify the
local average partial effect of oil palm expansion arising from purely exogenous
agro-climatic conditions shaping the incentives to develop oil palm plantations.
Data are taken from the Global Agro-ecological Zones database of the Food and
Agricultural Organisation of the United Nations (Fischer et al, 2002). Each pixel
is matched to Indonesian district boundaries. By also controlling for potential
yields of other crops that could share agro-climatic suitability characteristics with
oil palm, I ensure the identifying variation relates only to oil palm and not other
types of agriculture.
An additional empirical innovation in Chapter 3 is use of plausibly exogenous
identifying variation for my panel fixed effects estimates. I exploit the fact that
district heads must apply to the central government for approval to convert
land into oil palm plantations. This generates uncertain (i.e., subject to some
degree of randomness) variation in the timing of approvals within districts
and the outcomes of the decisions across districts. This identification strategy
§1.7 Organisation 17
builds on Burgess et al (2012), who similarly argue that the timing of district
splits through Indonesia’s recent decentralisation is exogenous in a panel data
setting. Convoluted oil palm and forestry regulations across different levels of
government arguably strengthen the case for exogenous timing relative to the
more formalised arrangements for setting up new administrative units (Fitriani
et al, 2005; Resosudarmo, 2005).
My unique application of a relatively new method—the synthetic control
method—in Chapter 4 should also be of demonstrative utility. Sub-national
panel data are becoming increasingly available and policies and decision-making
are commonly devolved to sub-national administrative units. I show how the
synthetic control method can be applied to analyse the impacts of major policies
and economic shocks to single administrative units in the Indonesian context. This
is just the second sub-national application of the synthetic control method to study
the effects of resource shocks and the first in a developing country context.
1.7 Organisation
The thesis has five chapters. Chapters 2–4 present the core research. Chapter
2 examines the long-term health and education impacts of economic dependence
on the mining sector, internationally across countries and across districts in
Indonesia. Chapter 3 turns to diffuse natural resources, studying the poverty
impacts of rapid growth in the palm oil sector in Indonesia. Chapter 4 presents
three district-level case studies of the local economic and welfare impacts of
growth in the palm oil, coal, and natural gas sectors in Indonesia. Chapter 5 briefly
discusses the implications of my findings and outlines some possible directions
for future research.
Chapter 2
Mining away the Preston curve
Abstract
I estimate the long-term national health and education impacts of having a
larger mining share in the economy. By instrumenting the relative size of
the mining sector with the natural geological variation in countries’ fossil fuel
endowments, I provide evidence suggestive of a causal relationship. The
findings suggest that countries with larger mining shares tend to have poorer
health and education outcomes than countries with similar per capita incomes,
geographic characteristics, and institutional quality. Doubling the mining
share of an economy corresponds to, on average, the infant death rate being
twenty percent higher, life expectancy being five percent lower, total years of
education being twenty percent lower, and seventy percent more people having no
formal education. Divergences from the Preston curve—the concave relationship
between cross-country income and life expectancy that has long been of interest
to economists, demographers, and epidemiologists—are thus partly explained by
the size of the mining sector. Within-country evidence from Indonesia paints
a similar picture. My results provide support for a growing body of evidence
linking mining to poorer average living standards, particularly vis-a-vis other
types of income. I also estimate the effects of national mining dependence on
non-mining income, health and education investment, and institutions.
18
§2.1 Introduction 19
2.1 Introduction
Resource-rich Equatorial Guinea had a gross national income of $14, 320 per
capita in 2013, yet more than three-quarters of its population lives below the
poverty line, and life expectancy at birth is 20 years less than other high-income,
non-OECD countries. Africa grew fast on the back of the global commodity boom
in the 2000s, but progress in reducing poverty has been disappointing (World
Bank, 2013). The fate of much of the world’s poor is tied to mining, with at least
half of the world’s known oil, natural gas, and mineral reserves in non OECD,
non-OPEC countries. Resource-driven economies are home to around 70 percent
of the world’s extreme poor.13
The economic and institutional effects of natural resources and a booming
resource sector have been well studied, but evidence on how extractive industries
relate to social outcomes remains thin (see van der Ploeg (2011) and Wick and
Bulte (2009) for reviews). Human capital is typically seen as a channel for
natural resources to stunt economic growth (Gylfason and Zoega, 2006), although
primary commodities can also directly impede social development (Carmignani,
2013). To my knowledge, an international study is yet to focus on mining sector
output nor examine its effects on national health and education outcomes.
In this chapter I compare countries with different structural compositions
to look at the “social productivities” of different types of economic activity,
focusing on fossil fuel extraction. I exploit geological variation in countries’
fossil fuel endowments to identify the long-term effects of mining on health and
education. I find that countries with more mining tend to have poorer health and
education outcomes than countries with similar per capita incomes, geographic
characteristics, and institutional quality. My estimates suggest that doubling the
mining share of an economy corresponds to the infant death rate being twenty
percent higher, life expectancy being five percent lower, total years of education13’Resource-driven economies’ are categorised according to the typology in McKinsey Global
Institute (2014), using World Bank (2014) data.
§2.1 Introduction 20
being twenty percent lower, and seventy percent more people having no formal
education. Within-country evidence from Indonesian districts reveals similar
patterns. Just some types of economic growth (e.g., agriculture) are better at
reducing poverty (Christiaensen et al, 2012), non-mining income is on average
better for health and education than income from the mining sector.
Figure 2.1: Preston Curve, 2005
The findings help to understand divergences from the Preston curve, the
concave relationship between cross-country income and life expectancy that has
long been of interest to economists, demographers, and epidemiologists (Deaton,
2013; Preston, 1975). In Figure 2.1, I plot life expectancy at birth against per capita
income, with countries weighted by contribution of mining to value added. I do
the same for years of education in Figure 2.2. Countries with larger mining sectors
tend to have poorer health and education outcomes than expected at their income
level.
§2.1 Introduction 21
Figure 2.2: Educational Attainment, Income, and Mining, 2005
The chapter proceeds as follows. In Section 2.2, I provide a conceptual
framework linking mining to health and education. Section 2.3 explains the
instrumental variable (IV) strategy used in my main estimates. Section 2.4
presents the national-level results, compares the health and education elasticities
of mining income with income from other sectors, and explores potential
mechanisms. Section 2.5 presents similar evidence from Indonesian districts.
Section 2.6 concludes.
§2.2 Linking mining to health and education 22
2.2 Linking mining to health and education
Why would the mining sector affect a country’s infant mortality rate or life
expectancy? The size of the mining sector can be linked to national health and
education outcomes through three main channels: income and Dutch disease
effects; investment in health and education, by individuals and governments; and
various institutional channels (Figure 2.3, dotted lines and clear boxes).
A larger share of mining in the economy could benefit health and education by
boosting income. While a substantial body of research argues natural resources
can hinder long-term economic growth, evidence remains mixed and recent
studies demonstrate clear long-term positive income effects from the discovery of
large resource wealth stocks (Mideksa, 2013; Smith, 2015). Such discoveries can
lead to significant health improvements (Cotet and Tsui, 2013). Net income effects
arising from a larger mining sector depend on the size of the mining expansion,
its effects on other sectors of the economy, and the distribution of mining and
non-mining income. Dutch disease occurs when resource exports generate large
balance of payments surpluses, appreciating the real exchange rate and increasing
relative prices for non-tradable inputs. Coupling these price and exchange rate
effects with higher demand from a mining boom, other trade-exposed sectors tend
to be less competitive and are often permanently displaced (Corden, 1984). In
extreme cases, booming mining sectors can have similar effects on non-resource
sectors as large tariffs (Gregory, 1976). Because manufacturing and other tradable
sectors tend to more intensively use human and physical capital, booming sector
dynamics often lead to less capital in the economy (Mikesell, 1997). Positive health
and education impacts of a mining-driven income boost are also likely to be offset
by the unequal distribution of new income, as countries with greater primary
commodity dependence tend to have higher inequality, which in turn affects social
development trajectories (Carmignani and Avom, 2010).
§2.2 Linking mining to health and education 23
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§2.2 Linking mining to health and education 24
The second main channel is human capital investment, which tends to be lower
in mining-dependent countries due to lower expected returns to skills, education,
and knowledge (Blanco and Grier, 2012). At the micro level, a booming mining
sector alters the incentives for human capital development. Trade-exposed
modern sectors are typically more labour and human capital intensive, with
higher wage premiums for educated workers and greater innovation. Conversely,
primary commodity sectors tend to use less skilled labour and have fewer linkages
to other sectors of the economy, effectively taxing human capital if they divert
people and resources away from higher skilled activities (Matsuyama, 1992). For
example, oil resources tend to orient university students towards specialisations
providing better access to resource rents (Ebeke et al., 2015).14 With poorer
micro-level incentives for investment in skills and education, private health
investment is unlikely to respond much differently. Long-term positive spillovers
from natural resources typically hinge on resource revenues strengthening
governments’ fiscal positions, enabling increased investment in health and
education and “spreading the benefits” (Arezki et al, 2011). Volatility—argued by
the van der Ploeg and Poelhekke, (2009) to be the “quintessence of any resource
curse”—-makes this difficult, as short-term political and economic horizons in
volatile economies provide little incentive to prioritise long-term health and
education investments. Rather, commodity price uncertainty often corresponds
to erratic and restrictive public spending and even long-run neglect (Gylfason
and Zoega, 2006; Mikesell, 1997). Acemoglu et al (2013) find the health spending
elasticity of resource-related income is well below one and there remains scant
evidence of resource revenues being converted into effective public investment
(Caselli and Michaels, 2013).
Extractive industries go hand in hand with the extractive institutions
historically associated with poorer health conditions (Acemoglu et al., 2001). The
challenges of managing natural resources tend to be less severe in the presence of14Note that some of these sectors can be highly skilled, or example geology and engineering.
§2.2 Linking mining to health and education 25
good institutions (Boschini et al., 2013; Farhadi et al., 2015; Mehlum et al., 2006),
but poorer institutions also directly harm health and educational development.
For example, a high level of corruption and poor government effectiveness
can lead to systemic health and education system failure. Resource-related
conflict can not only undermine health and education service delivery and the
incentives governing human capital accumulation, but cause sudden depletions
of human capital stock and long-term health and cognitive impacts (de Soyza
and Gizelis, 2013; Lei and Michaels, 2014; Williams, 2011). Political institutions
in mining-focused economies provide little incentive for broad-based human
capital investment. Increasing the average level of education and facilitating
the growth of an urban middle class undermines elites’ control of rents: a
dynamic underwritten by weaker democratic accountability in resource exporters
and reflected in their generally poorer health outcomes (Besley and Kudamatsu,
2006; Ross, 2001; Sokoloff and Engerman, 2000; Tsui, 2011). Lastly, rent
holders often use their political power to promote sub-optimal policies, resisting
industrialisation (and urbanisation) and reinforcing any Dutch disease and
investment effects (Auty, 1997).
A potential mechanism receiving attention in emergent work on the local
impacts of mining also bears a mention: pollution (see Cust and Poelhekke
(2015) for a review). Pollutants emitted from mining operations are some
of the most toxic, associated with premature births, lower birth weights and
weight-for-height ratios, stunting, anaemia, increased respiratory illness, malaria,
and less intelligence (Factor-Litvak et al, 1999; Iyengar and Nair, 2000; 2014; Saha
et al., 2011). Aragon and Rud (2015) study the impacts of 12 gold mines in
Ghana on local agricultural production, finding large decreases in productivity
and greater poverty likely to stymie human capital development (e.g., through
nutrition). While pollution mostly affects exposed communities (i.e., unlikely
to drive any national-level effects), cumulative impacts on cognitive and other
§2.3 Empirical approach 26
long-term development outcomes could be significant; it would be naive to
rule out their potential role in shaping national social development outcomes,
particularly in smaller economies.
In this chapter I focus on the reduced-form effects of mining on health
and education (solid lines, shaded boxes in Figure 2.3), holding income and
institutions constant (dotted lines, clear boxes). I focus on the mining share of
income and estimate partial elasticities, to compare the long-term health and
education effects of mining income with income from other sectors. I then extend
this approach to empirically test some potential channels outlined in this section.
2.3 Empirical approach
I relate the mining share of the economy to national health and education
outcomes with the equation:
ln(yc) = αln(Mc) + βX ′c + uc (2.1)
where yc is the general health conditions or educational attainment in country c;
M is the percentage contribution of mining to value-added; and X ′ includes per
capita gross domestic product (GDP), the absolute distance from the equator, an
index of institutional quality, and the total number of wildcats drilled in the 20th
century (exploratory drilling, known as wildcat drilling because of wild cats seen
in remote areas explored in the early 20th century). Standard errors are adjusted
for arbitrary heteroskedasticity. Logs account for skewness inM , y, and per capita
GDP, and provide convenient partial elasticity interpretations: when a country’s
mining share of total value-added increases by one percent, health and education
indicators are expected to rise or fall by α percent in the long run, holding all else
constant.
§2.3 Empirical approach 27
National health and education indicators are some of the slower moving
cross-country variables and exhibit strong serial correlation. The timing of
potential effects is difficult to correctly specify due to the long and non-uniform
lags in the system governing human capital accumulation, but doing so is critical
to detect any average effects and understand long-run magnitudes. My preferred
estimator is the classic “between” approach, ignoring short-run dynamics (unlike
static fixed-effects models) and providing long-run effects (Baltagi and Griffin,
1984; Burke and Nishitateno, 2015; Stern, 2010). By exploiting the long-run
equilibrium differences between countries in a large international cross-section in
2005, I obtain a natural long-run interpretation, retain the cross-country variation
of interest, and make few ad-hoc timing assumptions.
2.3.1 Instrumental variable strategy
Associative relationships between mining and health and education outcomes
cannot be taken as conclusive evidence more mining causes poorer health
and education outcomes. Countries with less human capital might tend
towards the primary sectors, “selecting” into mining (Alexeev and Conrad, 2009;
Brunnschweiler and Bulte, 2008). Estimation with ordinary least squares (OLS)
could lead to biased and inconsistent estimates, e.g., due to measurement error,
reverse causality, or correlation with other unobservable factors. For example,
economic activity in the mining sector may be affected by human capital stock and
capabilities, suggesting potential reverse causality through the supply of mining
engineers, exploration abilities, mining technology, cost competitiveness, and
capabilities in other sectors: factors mostly unobservable and difficult to control
for. National-level mining is affected by domestic policy settings and the decisions
of people and firms, so strong exogenous variation is needed to identify causal
effects.
§2.3 Empirical approach 28
My identification strategy exploits the fact that countries must be endowed
with natural resources before they can have a mining sector. I instrument the
contribution of mining to value-added in 2005 with national per capita fossil fuel
reserves (i.e., coal, oil, and gas) deeply lagged to 1971 to allow sufficient time to
evolve into mining sector activity.15
Instrument relevance and strength
Instrumenting mining share with deeply lagged per capita fossil fuel reserves
has important implications for the interpretation of my estimates. I estimate
the local average partial effect of a country having a larger mining share due to
greater initial fossil fuel reserves, i.e., relating more to dependence on coal mining
and oil and gas extraction than mineral extraction. First-stage coefficients are
positive and statistically significant at the one percent level, with around half
of the variation in 2005 mining shares explained by 1971 per capita fossil fuel
reserves. The combination of per capita coal, oil, and gas reserves provides the
broadest commodity coverage and strongest identification.16
A weak IV problem can be present even with highly significant first-stage
coefficients, so I provide weak IV diagnostics with all results. I report the
Kleibergen-Paap (2006) rk Wald F statistic with all IV estimates with the highest
Stock-Yogo (2005) critical value of 16.38 for one endogenous regressor, a single
instrument, and 10% maximal IV size. An excluded-F statistic greater than
10 is the more common benchmark for sufficient instrument strength (Staiger
and Stock, 1997). With a large international sample and a single strong IV, I
obtain consistent long-run causal estimates with the smallest bias if the exclusion
restriction is satisfied.15Using 1971 reserves as an instrument for the mining sector in a between-country setting is
the most appropriate use of this instrument, as it does not provide any temporal variation. As mydependent variable is slow-moving and my IV from 1971, I am also not overly dependent on anymoment in time. Estimates from alternative time periods and using country averages are similarand provided in the Chapter Appendix.
16By contrast, national mineral reserves in the 1970s are large in scale but only weakly correlatedwith mining dependence: an empirically irrelevant potential instrument.
§2.3 Empirical approach 29
Instrument validity
For the exclusion restriction to be satisfied, proven reserves in 1971 can have
no relationship with the dependent variables except through the mining sector,
holding other controls constant. If the geological allocation of fossil fuel reserves is
a product of nature and luck—as argued by Carmignani (2013) and van der Ploeg
and Poelhekke (2010)—then it is orthogonal to the unobservable and unmalleable
factors affecting my outcomes and the exclusion restriction is satisfied. Causal
pathways other than mining would be ruled out by design: the primary channel
for sub-soil reserves in 1971 to affect socioeconomic outcomes since 1971 is
through the current, past, and future size of the mining sector, with future size
likely to be a function of current size and endowments.17
That reserves are proven however gives them a non-random component.
While countries cannot decide and enact policies to create fossil fuel endowments,
they can enact policies that may increase the likelihood unknown resource
endowments will become known. If reserves are random but measured with error
(i.e., depending on exploration effort, income, institutions, and other historical
factors) then the exclusion restriction is valid conditional on including the factors
explaining any systematic measurement error in the reserves. By controlling
for per capita income, institutions, geography, and exploration effort, I seek
to isolate the natural geological component of measured reserves. The critical
identification assumption is that my covariates control for all relevant omitted
variables and systematic error: not an implausible assumption, but impossible
to prove in observational studies with external instruments. I present two
additional pieces of evidence suggesting the probability of my main identification
17Bazzi and Clemens (2013) show how many popular IVs can only be valid in one applicationdue to possible violation of the exclusion restriction, and highlight the common confusion betweenexogeneity and orthogonality in applied work (e.g., rainfall, price, geographical, and laggedinstruments). The exclusion restriction for a given instrument can only be not rejected afterconsidering all previous uses, then providing a convincing argument that: (a) past instrumentedexplanatory variables are part of the same causal chain; or (b) past usage is invalid. van der Ploegand Poelhekke (2010) and Carmignani (2013) use Norman (2009) reserves to instrument naturalresource exports, but mining comes before exporting in the same causal chain.
§2.3 Empirical approach 30
assumption being violated is likely to be low. Firstly, I adopt the common
heuristic that coefficient stability to additional control variables can be informative
about potential omitted variable bias (Altonji et al., 2005; Bellows and Miguel,
2009) and conduct sensitivity analysis using a wide range of variables plausibly
correlated with potential measurement error in fossil fuel reserves. Secondly, as
unobservable country-specific factors cannot be ruled out, I present analogous
within-country evidence from Indonesia.
2.3.2 Data
My health dependent variables are the mortality rate for infants aged one year
and below (per thousand births) and life expectancy at birth from the World
Bank (2014). Education variables are the average years of education attained
and the percentage of the population with no formal schooling for the total
population and youth from Barro and Lee (2010). The poorest performers in
terms of infant health conditions and years of schooling tend to be equatorial
low-income countries with poorer institutions (see maps in Chapter Appendix).
I estimate impacts on different indicators separately to allow for heterogeneity
across indicators and a more precise interpretation (c.f., interpreting effects on
composite indexes).
My explanatory variable of interest is the contribution of mining to
value-added, taken from the United Nations (UN) Environmental Indicators and
available for 1995–2008. Mining is defined following International Standard
Classification 0509 and includes the extraction of coal, lignite, metal ores, crude
petroleum and natural gas, as well as mining support services (UN, 2013). Mining
value-added is more useful than previous proxies for natural resources—namely,
exports (Sachs and Warner, 2001), estimated natural capital and resource rents
(Brunnschweiler and Bulte, 2008), and commodity prices (Collier and Goderis,
2012)—because it explicitly captures economic activity in the mining sector and
§2.3 Empirical approach 31
Figu
re2.
4:Co
ntri
buti
onof
Min
ing
toVa
lue-
Add
ed,2
005
§2.3 Empirical approach 32
Figu
re2.
5:Pe
rC
apita
Foss
ilFu
els,
1971
§2.3 Empirical approach 33
is affected by policy choices. To my knowledge, the UN data are only publicly
available national-level data for mining sector output across countries and not
yet used in any resource curse studies. Figure 2.4 illustrates the contribution of
mining to value-added around the world in 2005, with noticeable variation across
continents and income groups.
My national per capita fossil fuel reserves IV is taken from Norman (2009),
and generally regarded as some of the more reliable and exogenous of the
different natural resource stock measures currently available (van der Ploeg and
Poelhekke, 2010; Carmignani, 2013). Norman (2009) constructs her dataset by
adding extraction of oil, coal, natural gas, and minerals from 1970—2001 to proven
reserves in 2002. I convert Norman’s original measures into per capita terms
and combine the oil, coal, and natural gas components to obtain per capita fossil
fuel reserves in 1971, in 1971 prices, presented in Figure 2.5. Heavily-endowed
countries are from all continents and income groups, and not necessarily the
countries with the largest mining shares (c.f., Figure 2.4). The conversion of large
fossil fuel reserves to large mining shares is an outcome of choices.
Per capita GDP controls for a country’s income and level of development,
and is taken from the Penn World Tables in purchasing power parity, 2005
constant prices, and in per capita terms to scale for population size (Heston
et al., 2012). Latitude (i.e., the absolute distance from the equator) controls
for countries’ location, remoteness, the effects of tropical diseases, and regional
trade effects (Sala-i-Martin et al., 2004).18 My preferred proxy for institutional
quality is the World Bank’s government effectiveness index, strongly correlated
to traditional measures of institutions, but also picking up service delivery
capabilities (Kaufman et al., 2013). Lastly, I include the total number of wells
drilled in areas where no oil production exists (wildcats), summed over the
20th century (Cotet and Tsui, 2013). Wildcat drilling is a useful proxy for18Regional fixed effects yield similar results, but latitude achieves the same purpose more
parsimoniously and allows for a stronger first-stage in the IV estimates. Results with regionalfixed effects are provided in Column 1 of Table 2.3.
§2.3 Empirical approach 34
country-specific mining exploration effort, investment, history, and technological
capabilities.
My parsimonious set of controls explain substantial international variation in
health and education outcomes, and has implications for interpreting the mining
coefficients. The estimated partial effect of mining due to higher initial fossil
fuel endowments is from economies of comparable levels of GDP per capita,
location, institutional quality, and exploration effort, excluding effects through
these channels: I compare whether countries at a given income level have better
health and education outcomes because of the structure of their economies.
Summary statistics for mining share, per capita fossil fuel reserves, and key
dependent variables in 2005 are presented in Table 2.1. Column 1 includes the full
sample and the remaining columns split the sample by countries whose mining
share in GDP is larger or smaller than thirty percent. Health and education
outcomes tend to be worse in high-mining countries (Column 2).
§2.3 Empirical approach 35
Tabl
e2.
1:Su
mm
ary
Stat
istic
s
Varia
ble
All
coun
trie
sH
igh-
min
ing
Low
-min
ing
Col
umn
12
3
Con
trib
utio
nof
min
ing
tova
lue-
adde
d(p
erce
nt)
Mea
n0.
090.
520.
04
Std.
dev.
0.16
0.14
0.06
Cou
ntrie
s18
819
163
Mor
talit
yra
te,i
nfan
t(no
.per
’000
)M
ean
33.5
638
.61
32.4
4
Std.
dev.
30.8
432
.57
30.4
4
Cou
ntrie
s19
335
158
Ave
rage
year
sofs
choo
ling
atta
ined
Mea
n7.
87.
267.
87
Std.
dev.
2.74
1.98
2.83
Cou
ntrie
s13
917
122
Perc
enta
geof
popu
latio
nw
ithno
scho
olin
gM
ean
16.8
21.6
716
.12
Std.
dev.
18.8
216
.48
19.0
8
Cou
ntrie
s13
917
122
Valu
eof
perc
apita
foss
ilfu
elre
serv
esin
1971
USD
)M
ean
20.8
112.
391.
78
Std.
dev.
119.
5227
3.95
5.37
Cou
ntrie
s15
727
130
Hig
hm
inin
gre
fers
toco
untr
iesw
itha
cont
ribut
ion
ofm
inin
gto
valu
e-ad
ded
ofov
er30
perc
ent.
Low
min
ing
refe
rsto
coun
trie
sund
er30
perc
ent.
Sam
ple
isal
lava
ilabl
eco
untr
iesi
n20
05
§2.4 Results 36
2.4 Results
2.4.1 Health and education effects of mining sector growth
My main results are presented in Table 2.2. Panel A presents results for
children and youth and Panel B for the total population. OLS and IV estimates are
presented for all outcomes. Coefficients for mining are statistically significant in
all IV estimates, and the signs and magnitudes suggest the prominence of mining
contributes to large international differences in health and education outcomes
across countries.
In Panel A of Table 2.2, Columns 1 and 2 look at infant mortality. The estimated
coefficients in Column 2 suggest doubling the share of mining in the economy
related to greater initial fossil fuel endowments corresponds to, on average, an 18
percent increase in the infant mortality rate in the long run. Doubling the size of
the mining sector in a country with the mean infant mortality rate, i.e., 34 per 1000,
is expected to lead that country to settle at an infant mortality rate of 40 per 1000,
holding all else constant. The OLS estimate in Column 1 is slightly biased towards
zero, as expected.19 The rest of Panel A in Table 2.2 reports the effect of mining
on national educational outcomes for youth. Column 3 presents the OLS estimate
on average years of educational attainment: a small and statistically insignificant
negative effect. The IV estimate in Column 4 is less ambiguous. Doubling the
share of mining due to greater initial fossil fuel endowments leads to around
21 percent fewer years of schooling, or a reduction in educational attainment
from 7.7 to 6.2 years in the mean-educated country. Columns 5 and 6 look at
the percentage of the young people with no education, picking up completely
19Coefficients on mining share obtained from my IV estimates in Table 2.2 are best consideredlower bounds for four reasons. Firstly, I control for (hold constant) key potential channels.Secondly, IV estimates in finite samples tend to be biased the same direction as OLS (downwards,in this case). Third, between estimation detects only contemporaneous and long-run effects,making it difficult for treatment to affect the outcome variable in countries with recent miningexpansions. Finally, estimates on cohort-specific indicators tend to underestimate long-termpopulation effects, although I have used extensive-margin indicators (e.g., infant mortality, youtheducational attainment) to minimise this problem.
§2.4 Results 37
Tabl
e2.
2:H
ealt
han
dEd
ucat
ion
Effe
ctso
fMin
ing
Dep
ende
nce
Pane
lA:E
ffect
son
child
ren
and
yout
hLo
gde
pend
entv
aria
ble
Infa
ntm
ort.
(dea
thsp
er’0
00)
Avg
.yrs
educ
.,yo
uth
Perc
enta
geof
pop.
w/
noed
uc.,
yout
hEs
timat
orO
LSIV
OLS
IVO
LSIV
Col
umn
12
34
56
Log
min
ing
shar
e0.
108*
**0.
181*
**-0
.018
-0.2
08**
*0.
260*
**1.
095*
**(0
.018
)(0
.04)
(0.0
12)
(0.0
47)
(0.0
77)
(0.2
46)
Log
real
GD
Ppe
rcap
ita-0
.553
***
-0.5
94**
*0.
216*
**0.
338*
**-0
.800
***
-1.3
20**
*(0
.044
)(0
.064
)(0
.042
)(0
.072
)(0
.159
)(0
.28)
Latit
ude
-0.7
27**
*-0
.435
0.24
1*-0
.239
-1.8
98**
1.41
5(0
.199
)(0
.28)
(0.1
27)
(0.3
71)
(0.8
32)
(1.6
95)
Gov
ernm
ente
ffect
iven
ess
-0.2
13**
*-0
.171
**-0
.045
-0.2
05**
0.33
90.
838*
*(0
.059
)(0
.085
)(0
.034
)(0
.087
)(0
.228
)(0
.405
)
Wild
cats
(*10
00)
0.04
0***
0.03
0***
0.00
50.
010*
-0.1
40**
*-0
.190
***
(0.0
05)
(0.0
06)
(0.0
03)
(0.0
09)
(0.0
3)(0
.05)
Firs
t-sta
ge
Perc
apita
foss
ilfu
els1
971
0.00
3***
0.00
3***
0.00
3***
(0.0
01)
(0.0
01)
(0.0
01)
Excl
uded
-F16
.43
16.7
216
.72
Cou
ntrie
s15
113
312
711
212
611
2Pa
nelB
:Effe
ctso
nth
etot
alpo
pula
tion
Log
depe
nden
tvar
iabl
eLi
feex
p.(y
rs)
Avg
.yrs
educ
.Pe
rcen
tage
ofpo
p.w
/no
educ
.Es
timat
orO
LSIV
OLS
IVO
LSIV
Col
umn
78
910
1112
Log
min
ing
shar
e-0
.012
**-0
.042
***
-0.0
21*
-0.1
99**
*0.
120*
*0.
671*
**(0
.005
)(0
0.01
)(0
.013
)(0
.05)
(0.0
57)
(0.1
89)
Log
real
GD
Ppe
rcap
ita0.
010*
**0.
123*
**0.
242*
**0.
352*
**-0
.464
***
-0.7
89**
*(0
.012
)(0
.018
)(0
.045
)(0
.076
)(0
.12)
(0.2
09)
Latit
ude
0.07
20.
050.
351*
*-0
.192
-3.9
94**
*-1
.526
(0.0
46)
(0.0
8)(0
.139
)(0
.379
)(0
.619
)(1
.269
)
Gov
ernm
ente
ffect
iven
ess
-0.0
07-0
.039
-0.0
58-0
.189
**0.
128
0.43
5(0
.015
)(0
.025
)(0
.039
)(0
.089
)(0
.181
)(0
.302
)
Wild
cats
(*10
00)
-0.0
03**
-0.0
010.
010*
**0.
020*
**-0
.110
***
-0.1
50**
*(0
.001
)(0
.002
)(0
.003
)(0
.01)
(0.0
2)(0
.04)
Firs
t-sta
gePe
rcap
itafo
ssil
fuel
s197
10.
003*
**0.
003*
**0.
003*
**(0
.001
)(0
.001
)(0
.001
)Ex
clud
ed-F
16.5
216
.55
16.5
5C
ount
ries
150
132
122
107
122
107
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rce
ntle
vels
.Sa
mpl
eis
the
larg
estp
ossi
ble
2005
cros
s-se
ctio
nfo
rth
ese
varia
bles
.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tson
cons
tant
san
dfir
st-s
tage
cont
rols
are
not
repo
rted
.IV
estim
ates
inst
rum
entl
ogm
inin
gsh
are
with
perc
apita
foss
ilfu
elre
serv
esin
1971
.The
rele
vant
criti
calv
alue
fort
heK
leib
erge
n-Pa
apW
alk
rkF
stat
istic
srep
orte
d(i.
e.ex
clud
ed-F
)ist
heSt
ock-
Yogo
criti
calv
alue
of16
.38c
alcu
late
dfo
rone
endo
geno
usre
gres
sor,
one
inst
rum
ent,
10%
max
imum
IVre
lativ
ebi
as,a
ndi.i
.der
rors
.
§2.4 Results 38
discouraged, disengaged, or excluded young people. The OLS coefficient in
Column 5 is positive and statistically significant at the one percent level, rising to
1.095 in Column 6. Single equation estimates not accounting for the endogenous
response of mining to human capital appear to tend towards zero, consistent with
earlier conflicting findings (Gylfason 2001; Stijns, 2006).
In Panel B, I turn to life expectancy and educational attainment indicators for
the whole population. Columns 7 and 8 show countries with more mining in their
economies tend to have lower life expectancies than other countries at a similar
income level. Results for years of education in Columns 9 and 10 of Panel B
are similar to youth, as expected exploiting between country variation. Results
for the percentage of the population (Columns 11 and 12) with no education
are of a lesser magnitude than for youth, although economically quite large.
Applied to the sample mean of 17 percent, doubling the mining share results
in the uneducated share of the population rising to almost 30 percent in the
long run. The Kleibergen-Paap rk Wald F statistic of 16.43 exceeds the highest
Stock-Yogo critical value (16.38) across all estimates in Table 2.2, providing no
evidence of a weak IV. Per capita income is the most important control, statistically
significant at the one percent level and of a similar magnitude in all estimates.
Coefficients on constants, controls, and the excluded instrument are not reported
for the remainder of this chapter.
§2.4 Results 39
Tabl
e2.
3:Se
nsit
ivit
yA
naly
sis
1970
cont
rol
Regi
onFE
sLo
gG
DP
p.c.
Log
IMR
Log
year
sedu
c.D
emoc
racy
Exec
.con
st.
Pres
.C
olon
yFr
.leg
alLo
gse
t.mor
t.A
llN
orm
anIV
ASP
OIV
Col
umn
12
34
56
78
910
1112
Pane
lA:L
ogin
fant
mor
talit
y(d
eath
sper
’000
)
Log
min
ing
shar
e0.
15**
*0.
17**
*0.
14**
*0.
17**
*0.
17**
*0.
18**
*0.
21**
*0.
17**
*0.
19**
*0.
14*
0.16
***
0.08
**(0
.05)
(0.0
5)(0
.05)
(0.0
5)(0
.04)
(0.0
4)(0
.05)
(0.0
3)(0
.03)
(0.0
8)(0
.04)
-0.0
4Ex
clud
ed-F
2.32
58.
5110
.45
10.3
54.
665.
079.
785.
8416
.15
23.7
414
.23
12.1
3C
ount
ries
133
133
121
107
109
109
130
114
136
7513
312
5Pa
nelB
:Log
aver
agey
ears
ofed
ucat
iona
latta
inm
ent
Log
min
ing
shar
e-0
.16*
*-0
.13*
*-0
.15*
*-0
.03
-0.1
0*-0
.10*
-0.1
3***
-0.1
3***
-0.1
3***
-0.0
1-0
.13*
**-0
.06*
(0.0
8)(0
.07)
(0.0
6)(0
.05)
(0.0
6)(0
.05)
(0.0
4)(0
.05)
(0.0
3)(0
.03)
(0.0
5)(0
.03)
Excl
uded
-F3.
1510
.11
10.9
510
.35
5.94
6.47
13.1
65.
6116
.46
17.8
814
.17
11.9
4C
ount
ries
107
107
102
107
9494
108
9710
960
107
100
Pane
lC:L
ogpe
rcen
tage
ofpo
pula
tion
with
noed
ucat
ion
Log
min
ing
shar
e0.
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uded
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6116
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4C
ount
ries
107
107
102
107
9494
108
9710
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107
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tical
sign
ifica
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e20
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rmat
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max
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eter
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olum
ns1–
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men
tlog
min
ing
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ew
ithpe
rcap
itafo
ssil
fuel
rese
rves
in19
71,C
olum
n11
uses
the
sum
ofpe
rcap
itaoi
l,ga
s,co
al,a
ndm
iner
alre
serv
esfr
omN
orm
an(2
009)
,i.e
.,in
clud
ing
min
eral
s,an
dC
olum
n12
uses
actu
ales
timat
edoi
l“en
dow
men
t”fr
omC
otet
and
Tsui
(201
3).T
here
leva
ntcr
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lval
uefo
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eK
leib
erge
n-Pa
apW
alk
rkF
stat
istic
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orte
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the
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k-Yo
gocr
itica
lval
ueof
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8ca
lcul
ated
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neen
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esso
r,on
ein
stru
men
t,10
%m
axim
umIV
rela
tive
bias
,and
i.i.d
erro
rs.
§2.4 Results 40
2.4.2 Robustness
If the exclusion restrictions for my IV estimates are valid, potential omitted
variable problems are overcome. Satisfying the exclusion restriction relies on
the assumption that the covariates control for all relevant omitted variables
and systematic error in measurement of observed reserves. Table 2.3 presents
IV estimates with additional controls arguably related to potential error in
observed fossil fuels. I use the median-unbiased limited information maximum
likelihood (LIML) estimator with a Fuller parameter of one for improved inference
with weaker instruments (Fuller, 1977). Column 1 includes regional fixed
effects, Columns 2–4 deeply lagged values of income, health, and education,
and Columns 5–10 institutional factors potentially affecting the detection and
measurement of reserves. Columns 1–10 of Panel A show how further restricting
the variation in reserves exploited has little effect on the size or direction of the
infant mortality estimates. Column 11 instruments mining share with total per
capita reserves from Norman (2009), i.e., including minerals. Results are similar,
but with weaker identification. Column 12 shows that a similar result is obtained
instrumenting mining share with an alternative measure of oil endowment from
the Association for the Study of Peak Oil and Gas, estimated by geologists and
factoring in cumulative discovery and local geological conditions (Tsui, 2011;
Cotet and Tsui, 2013).
Panels B and C of Table 2.3 present sensitivity analysis for the main education
estimates. Including a deep lag for years of education (Column 4) renders
the main result insignificant for both indicators; the global mining sector was
already developed in 1971 and the distribution of years of schooling across
countries is persistent and has not changed significantly since. In Panel B, other
initial controls and institutional indicators do not significantly affect the result
for years of schooling, except for settler mortality, which halves the sample.
Instrumenting mining share with total per capita mineral, oil, coal, and natural
§2.4 Results 41
gas reserves (Column 11) gives a similar result, and the coefficient obtained
from instrumenting with the alternative oil endowment measure (Column 12) is
less significant but of the same sign. In Panel C of Table 2.3, estimates for the
percentage of the population with no schooling are similar to those for years of
schooling. Second-stage coefficients are relatively stable to the inclusion of a wide
range of additional covariates, suggesting further omitted variables are unlikely
to have any major effects on my results. Further robustness checks are provided
in the Chapter Appendix, including estimates using alternative periods, country
averages, alternative measures of the size of the mining sector, and dropping
resource-rich mini-states and other outliers.
2.4.3 Health and education elasticities of income, by sector
Until now I have focused on the share of mining in the economy, holding
per capita incomes constant. The net effects of mining are ambiguous if we
move from partial to general equilibrium, as crowding-out may be more than
one-for-one (e.g., as in the Dutch disease case), or learning-by-doing and upstream
spillovers may occur (as was the case for Norway (Torvik; 2001; Mideksa, 2013)).
I now compare the health and education elasticities of mining income with
income from other sectors by extending my main approach to level terms. I
multiply mining share by total per capita income in PPP terms to obtain per capita
mining income deflated by the economy-wide GDP deflator (c.f., sector-specific
deflators). I replace the per capita GDP control with per capita non-mining
income to hold income from other sectors constant. Per capita agricultural income,
non-agricultural income, manufacturing income, and non-manufacturing income
are constructed with the same UN (2014) national accounts to compare across
sectors. I instrument mining income with per capita fossil fuel reserves in
1971 and agricultural income with per capita arable land (World Bank, 2014).
Manufacturing is not instrumented.
§2.4 Results 42
The long-term health and education elasticities of income from mining,
agriculture, and manufacturing are presented in Figure 2.6.20 The bars represent
point estimates and the whiskers 95 percent robust confidence intervals. With
narrow confidence intervals, mining income, on average, appears to be of
significant net harm to long-term health and education outcomes when income
from other sectors is held constant. Coefficients on agricultural income show
imprecise negative effects, and non-mining and manufacturing income are
associated with better health and education outcomes. The large gap between
non-mining income and manufacturing income suggests services likely play a
major role (services are not a clear category in UN national accounts data, so
omitted from the analysis). When it comes to mining income, wealthier is
not necessarily healthier (c.f., Pritchett and Summers, 1996). Income from the
non-mining sectors tends to be better for health and educational development.
20The formal regression results behind each of the bars in Figure 2.6 are presented in theChapter Appendix. Bars represent the coefficients on sectoral income with robust 95 percentconfidence intervals and the standard covariates are included in all estimates. Mining andagricultural income coefficients are obtained from IV estimation and manufacturing from OLS.Mining is instrumented with per capita fossil fuel reserves in 1971 and agriculture with log percapita arable land, both strong instruments exceeding the highest Stock-Yogo critical value. Sampleperiod is 2005.
§2.4 Results 43Fi
gure
2.6:
Hea
lth
and
Educ
atio
nEl
asti
citi
esof
Inco
me,
BySe
ctor
§2.4 Results 44
2.4.4 Heterogeneity
Torvik (2009) argues “the most interesting aspect of the paradox of plenty is
not the average effect of natural resources, but its variation. For every Nigeria
or Venezuela there is a Norway or Botswana.” Two main types of heterogeneity
could be masked by my main results: different effects among those countries
with fossil fuel reserves, and different effects for different types of primary
commodities. I estimate my main results by sub-samples to explore variation by
region and institutional characteristics used to explain winners and losers in the
natural resource lottery (Torvik, 2009). Health results are consistent across regions
and institutions, but education results are more varied (see Chapter Appendix).
Estimated coefficients turn positive or insignificant in parliamentary democracies,
in countries with less than 80 percent of the population of one faith, and excluding
the Middle East and Africa, suggesting this region drives the main education
results.
My main IV estimates identify the local average partial effects of mining related
to fossil fuel abundance. While including minerals gives similar results (Table 2.3),
oil and natural gas are special point resources known to exacerbate the resource
curse. To explore potential heterogeneity in mining dependence related to each
type of reserves, I instrument mining share with separate per capita oil, gas,
mineral, and coal reserves. Oil and gas extraction have stronger negative health
and education effects than the main estimates and coal mining appears harmful
for health (see Chapter Appendix). Conclusions cannot be drawn in relation to
mining dependence arising from mineral abundance due to weak identification.
§2.4 Results 45
2.4.5 Potential channels
I now empirically test for direct links between mining dependence and
non-mining income, human capital investment, and institutions (dotted lines
and clear boxes in Figure 3). I use the same IV approach, with proxies for
these potential channels as dependent variables. Table 2.4 presents the results.
Column 1 of Panel A shows that doubling the mining share corresponds to a 10
percent reduction in the level of non-mining income. The non-mining economy
is responsible for, on average, 11 times more value-added than mining, making
magnitudes economically significant, particularly given the opportunity cost of
other welfare-improving modern sectors (UN, 2013). Columns 2 and 3 find more
mining corresponds to countries investing less in health per capita and as a share
of total government spending. Columns 4 and 5 provide no evidence a larger
mining share leads to less investment in education per capita or as a share of total
government spending, perhaps reflecting the recent policy shift by resource-rich
countries towards education investment and contradicting prior studies showing
that natural resources divert financial resources away from education (Gylfason,
2001).
In Panel B of Table 2.4 I examine governance-related institutional indicators:
government effectiveness, corruption, and gender equality. Columns 6 and
7 show that countries with a larger mining share score lower on indices of
government effectiveness and control of corruption (Kaufman et al, 2013).
Column 8 shows Transparency International’s Corruption Perceptions Index is
also considerably lower in mining-dependent countries. Gender equality has
been a focal area of modern governance reforms and anti-corruption policies, and
resource wealth could allow the persistence of cultural norms altering women’s
roles in society. In Columns 9 and 10 (Panel B) I look at gender equality, estimating
the effect of a larger mining share on the proportion of seats held by women
in national parliaments in Column 9 and on the World Bank’s CPIA gender
§2.4 Results 46
Tabl
e2.
4:N
atio
nal-
leve
lM
echa
nism
s
Pane
lA:I
ncom
ein
othe
rsec
tors
and
publ
ichu
man
capi
tali
nves
tmen
tLo
gde
pend
entv
aria
ble
Perc
apita
non-
min
ing
inco
me
Perc
apita
heal
thex
pend
iture
Hea
lthex
pend
iture
shar
ePe
rcap
itaed
ucat
ion
expe
nditu
reEd
ucat
ion
expe
nditu
resh
are
Col
umn
12
34
5
Log
min
ing
shar
e-0
.09*
**-0
.22*
*-0
.16*
-0.0
80.
03(0
.04)
(0.0
9)(0
.09)
(0.0
8)(0
.11)
Excl
uded
-F16
.37
16.4
316
.43
17.5
18.6
4C
ount
ries
133
133
133
108
122
Pane
lB:Q
ualit
yof
gove
rnm
enta
ndge
nder
equa
lity
Dep
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over
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teffe
ctiv
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sC
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olof
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tions
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nsC
PIA
gend
erC
olum
n6
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910
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ing
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.17*
**-0
.14*
**-0
.31*
**-4
.90*
**-0
.17*
**(0
.04)
(0.0
4)(0
.10)
(1.1
0)(0
.03)
Excl
uded
-F16
.05
16.7
318
.09
6.07
71.6
1C
ount
ries
134
137
103
132
55Pa
nelC
:Con
flict
and
stab
ility
Dep
ende
ntva
riabl
eYe
arso
fcon
flict
Stab
ility
inde
xPo
lity
IVsc
ore
Num
bero
fcou
psLo
gm
ilita
rysh
are
Col
umn
1112
1314
15
Log
min
ing
shar
e0.
29-0
.08*
-5.5
2***
0.74
***
0.44
***
(0.2
4)(0
.04)
(1.2
0)(0
.26)
(0.1
0)Ex
clud
ed-F
16.7
316
.73
13.2
316
.73
5.4
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ntrie
s13
713
711
913
711
6
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eter
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and
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,and
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-sta
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sion
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epor
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Pane
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alG
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,lat
itude
,gov
ernm
ente
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and
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20th
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Pane
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min
ing
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ew
ithpe
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itafo
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fuel
rese
rves
in19
71an
dth
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leva
ntcr
itica
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ulat
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ent,
10%
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imum
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lativ
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as,a
ndi.i
.der
rors
.
§2.4 Results 47
equality index in Column 10. I find that at a given level of income, doubling
the mining share corresponds to almost 50 percent less women in parliament and
a much lower score on the gender equality index. While more mining-oriented
economies appear to have higher levels of institutionalised gender inequality,
estimating my main results (Table 2.2) separately for males and females reveals
no statistically significant gender differences (see Chapter Appendix). Other
measures of institutional quality yield similar results and the direct institutional
“curse” of natural resources appears directly relevant for economic dependence
on mining. Partialling-out institutional effects in my main results (i.e., holding
government effectiveness constant) implies larger general equilibrium effects.
In Panel C of Table 2.4 I turn to conflict and stability. In Column 11, I
estimate my main equation using the number of years since 1970 (i.e., when the
instrument is measured) that a country has been classified as in a state of civil
war in the Uppsala Conflict Data Program / International Peace Research Institute
(UCDP/PRIO) Armed Conflict Dataset as a dependence variable (Centre for the
Study of Civil War, 2009). I find no evidence of any relationship between mining
dependence and conflict, consistent with related studies by Brunnschweiler and
Bulte (2009), Arezki and Gylfason (2013), and Blattman and Bazzi (2014). In
Column 12 I find a weakly significant relationship between mining dependence
and government stability (Kaufman et al, 2013). Columns 13 and 14 find
mining-dependent countries, after controlling for income level and distance from
the equator, tend to be less democratic and more likely to experience a coup d’etat.
This result is reinforced by the final estimate in Column 15, showing that doubling
the mining share of a country corresponds to around a 50 percent increase in the
military expenditure share of the national budget. My results for conflict, coups,
and military expenditure line up firmly behind the idea that resource rents can
be used to “buy” peace and stability (Arezki and Gylfason, 2013; Cotet and Tsui,
2013).
§2.5 Within-country evidence from Indonesia 48
2.5 Within-country evidence from Indonesia
Looking for similar patterns in different contexts is a useful way to gauge
generality. In this section I present new evidence from a large cross-section of
Indonesian districts in 2009, holding country- and province-specific observable
and unobservable factors constant, and using an estimation strategy analogous
to my international estimates.21 Indonesia’s large mining boom of the 2000s is
coming to an end and its long-term development implications are still not well
understood (Garnaut, 2015; Hill et al., 2008).
Consider the equation:
ln(yd) = αln(Md) + γp + βX ′d + ud (2.2)
where yd is a health or education outcome in district d in 2009. While my
international estimates look at national educational attainment, sub-national
data allow me to also explore participation and quality using enrolment rates
and test scores. I use net enrolment ratios for each district from the high
quality, district representative socioeconomic survey (SUSENAS) carried out
by Indonesia’s central statistics agency, Badan Pusat Statistik (BPS). I also use
average examination test scores (out of 100) for each district from the Ministry of
Education, reported by each school to the district education office and on-reported
up to the ministry. To my knowledge, there are no reliable district-level child
mortality or life expectancy data.22 Instead I look at (a) the percentage of the
births attended by a skilled health worker and (b) average household health and
education expenditures, both derived from SUSENAS.
21All data used in this section are taken from the Indonesia Database for Policy and EconomicResearch (World Bank, 2015), freely available for download and easily replicable.
22Child death is a rare event for the average household. With roughly 40 deaths out of every1000 births, a very large sample is needed to capture sufficient incidence and variation at thedistrict level. Existing household surveys are simply not large enough; census data must be used.
§2.5 Within-country evidence from Indonesia 49
Md is the mining and quarrying share of regional gross domestic product
(RGDP) in district d in 2009 from BPS.23 Consistent with the UN variable used
in my cross-country estimates, mining covers oil, natural gas, coal, and minerals;
quarrying refers to the quarrying of surface rocks, sand, and soil. District-level
mining dependence across Indonesia is presented in Figure 7.
γp is a fixed effect for each of Indonesia’s 33 provinces, capturing
province-specific factors jointly affecting mining dependence and social outcomes
and restricting my comparison to districts within the same province. X ′ includes
log per capita RGDP (combining BPS district RGDP and unpublished population
data) and a categorical variable (i.e., fixed effect) for the assessment given to each
district by the Indonesia Audit Board for their sub-national budgets, to proxy the
quality of local institutions. ud is a heteroskedasticity-consistent error term.
α has a causal interpretation if mining shares are exogenous to the outcomes of
interest conditional on province fixed effects, per capita incomes, and institutions,
i.e., there are no problematic omitted variables correlated with both mining share
and the outcomes of interest within provinces. Such factors cannot be ruled out,
so the following estimates are best interpreted as robust correlations.
Sub-national estimates are presented in Table 2.5. Panel A considers education,
health, and poverty, and Panel B household human capital expenditures. Column
1 of Panel A asks whether, at a given level of income and institutional quality,
children are less likely to participate in school in mining-dependent districts
than in neighbouring districts in the same province. I focus on senior secondary
enrolments because mandatory enrolment policies up to junior secondary remove
variation at the lower levels. Columns 1 and 2 find that a doubling in district
mining share corresponds to a three percent decrease in the net enrolment ratio,
and slightly lower test scores. Both are precisely estimated, statistically significant
23Sub-national accounts are imperfect and likely have some measurement and imputationerror, but Indonesian statistics are comparatively better than many other development countries’national accounts. See McCulloch and Sjahrir (2008) and McCulloch and Malesky (2011) for moredetailed discussions on Indonesian sub-national accounts.
§2.5 Within-country evidence from Indonesia 50
Figu
re2.
7:M
inin
gSh
are
ofD
istri
ctRe
gion
alG
ross
Dom
esti
cPr
oduc
tin
Indo
nesia
,200
9
§2.5 Within-country evidence from Indonesia 51
Tabl
e2.
5:Su
b-na
tion
alEv
iden
cefr
omIn
done
sian
Dist
rict
s
Pane
lA:H
ealth
,edu
catio
n,an
dpo
vert
y
Log
depe
nden
tvar
iabl
eSn
r.En
rolm
ent(
%)
Snr.
test
scor
es(/
100)
Skill
edbi
rth
atte
ndan
t(%
)Po
vert
yra
te(%
)
Col
umn
12
34
Log
min
ing
shar
e-0
.032
***
-0.0
04**
*-0
.024
***
0.05
2***
(0.0
07)
(0.0
01)
(0.0
08)
(0.0
12)
Dis
tric
ts44
745
544
744
5
Pane
lB:H
ouse
hold
(HH
)hum
anca
pita
linv
estm
ent
Log
depe
nden
tvar
iabl
eH
Hed
ucat
ion
expe
nditu
reH
H.h
ealth
expe
nditu
re
Leve
lSh
are
ofto
tal
Leve
lSh
are
ofto
tal
Col
umn
56
78
Log
min
ing
shar
e-0
.070
***
-0.0
37**
*-0
.049
***
-0.0
16*
(0.0
11)
(0.0
08)
(0.0
10)
(0.0
09)
Dis
tric
ts44
743
344
743
3
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
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ple
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2009
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tions
and
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darie
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ntro
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itaG
DP,
sub-
natio
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udge
taud
itas
sess
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riabl
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nd33
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ffici
ents
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uce
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abas
efo
rPol
icy
and
Econ
omic
Rese
arch
(DA
POER
).
§2.6 Concluding remarks 52
at the one percent level.24 Column 3 shows that people in more mining-dependent
districts are much less likely to have births attended by skilled health workers.
A district with twice as much of its output from mining, on average, also has
a poverty rate over five percent higher (Column 4), consistent with Bhattacharya
and Resosudarmo’s (2015) finding that non-mining economic growth significantly
reduces poverty in Indonesia but mining growth does not.
Panel B of Table 2.5 extends the national-level mechanism analysis to look
at average household human capital investment in mining-dependent districts.
Columns 5–8 show that households in mining-dependent districts tend to spend
less on education and health-related expenditures both in level terms and
as a share of total household expenditure, although the estimate for health
expenditure share is only statistically significant at the 10 percent level. Doubling
the mining share is associated with seven percent less household spending on
education and five percent less spending on health. The patterns observed across
countries thus appear more widely applicable and a promising area for more
detailed empirical study at the micro level.
2.6 Concluding remarks
This chapter documents how countries with larger mining sectors tend
to diverge below the Preston curve, with lower levels of general health and
educational attainment than expected for their income level. By instrumenting
the relative size of the mining sector with the natural geological variation in
countries’ historical fossil fuel endowments, I provide evidence suggestive of a
causal relationship. Similar patterns between mining, health, and education are
observed across Indonesian districts. My results provide support for a growing
body of evidence linking mining to poorer average living standards, particularly
24A greater mining share corresponds to poorer test scores across all levels of schooling:primary, junior secondary, and senior secondary (estimates available on author request).
§2.6 Concluding remarks 53
vis-a-vis other types of income (Bulte et al., 2005; Daniele, 2011; Gamu et al., 2015).
But a more systematic investigation of causal mechanisms and the conditions
under which mining can be less harmful for health and education is also needed.
Chapter 4 begins down this track, examining how district poverty, average
household expenditure, and different sectors of the local economy respond to coal
mining and natural gas booms in Indonesia. I hope that by highlighting the links
between mining intensity and long-term health and education outcomes at the
international and district levels, I encourage others to focus on these important
aspects of well-being when considering the impacts of mining, particularly in
comparison to other economic development strategies.
§2.7 Chapter 2 Appendix 54
2.7 Chapter 2 Appendix
§2.7 Chapter 2 Appendix 55
Figu
re2.
8:G
loba
lIn
fant
Mor
tali
ty,2
005
§2.7 Chapter 2 Appendix 56
Figu
re2.
9:G
loba
lYe
arso
fEdu
cati
onal
Att
ainm
ent,
2005
§2.7 Chapter 2 Appendix 57
Tabl
e2.
6:M
ain
Resu
ltsI
nstr
umen
ting
wit
hM
iner
al,O
il,G
as,a
ndCo
alRe
serv
es
Log
dep.
varia
ble
Inf.
mor
t.A
vg.y
rsed
uc.,
yout
hPe
rcen
tpop
.w/
noed
uc.,
yout
hLi
feex
p.A
vg.y
rsed
uc.
Perc
entp
op.w
/no
educ
.
Col
umn
12
34
56
Log
min
ing
shar
e0.
17**
*-0
.16*
**0.
87**
*-0
.03*
**-0
.14*
**0.
51**
(0.0
4)(0
.05)
(0.2
7)(0
.01)
(0.0
5)(0
.20)
Excl
uded
-F14
.23
14.3
614
.36
14.1
714
.17
14.1
7
Cou
ntrie
s13
311
211
213
210
710
7
This
tabl
esh
owst
hatt
hem
ain
resu
ltsar
esi
mila
rift
heag
greg
ated
Nor
man
(200
9)pe
rcap
itare
serv
es,i
nclu
ding
min
eral
sast
heIV
.The
smal
lerc
oeffi
cien
tsca
nbe
inte
rpre
ted
asa
com
bina
tion
ofw
eake
ride
ntifi
catio
nst
reng
than
dpo
tent
ially
mor
ehar
mfu
leffe
ctsf
rom
oil,
gas,
and
coal
than
min
eral
s.St
arsd
enot
est
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
per
cent
leve
ls.
Sam
ple
isth
ela
rges
tpos
sibl
e20
05cr
oss-
sect
ion
for
thes
eva
riabl
es.
Het
eros
keda
stic
ity-r
obus
tst
anda
rder
rors
inpa
rent
hese
s,an
dco
effici
ents
onco
ntro
ls,c
onst
ants
,and
first
-sta
geco
effici
ents
are
notr
epor
ted.
IVes
timat
esin
stru
men
tlog
min
ing
shar
ew
ithto
talp
erca
pita
oil,
gas,
min
eral
,and
coal
rese
rves
in19
71fr
omN
orm
an(2
009)
.Th
ere
leva
ntcr
itica
lval
uefo
rth
eK
leib
erge
n-Pa
apW
alk
rkF
stat
istic
sre
port
ed(i.
e.ex
clud
ed-F
)is
the
Stoc
k-Yo
gocr
itica
lval
ueof
16.3
8ca
lcul
ated
foro
neen
doge
nous
regr
esso
r,on
ein
stru
men
t,10
%m
axim
umIV
rela
tive
bias
,and
i.i.d
erro
rs.
§2.7 Chapter 2 Appendix 58
Tabl
e2.
7:M
ain
Resu
ltsI
nstr
umen
ting
wit
hon
lyO
ilan
dG
asRe
serv
es
Log
dep.
var.
Inf.
mor
t.A
vg.y
rsed
uc.,
yout
hPe
rcen
tpop
.w/
noed
uc.,
yout
hLi
feex
p.A
vg.y
rsed
uc.
Perc
entp
op.w
/no
educ
.
Col
umn
12
34
56
Log
min
ing
shar
e0.
18**
*-0
.22*
**1.
15**
*-0
.04*
**-0
.20*
**0.
69**
*
(0.0
4)(0
.05)
(0.2
5)(0
.01)
(0.0
5)(0
.19)
Excl
uded
-F17
.05
17.2
17.2
17.1
416
.99
16.9
9
Cou
ntrie
s13
311
211
213
210
710
7
This
tabl
esh
ows
the
mai
nre
sults
are
sim
ilar
ifon
lype
rca
pita
oila
ndga
sre
serv
esar
eus
edas
the
IV,i
.e.,
coal
isdr
oppe
d.Th
ela
rger
coeffi
cien
tsca
nbe
inte
rpre
ted
asa
com
bina
tion
ofim
prov
edin
stru
men
tstr
engt
han
dpo
tent
ially
mor
eha
rmfu
leffe
cts
from
oila
ndga
sth
anco
al.S
tars
deno
test
atis
tical
sign
ifica
ncea
tthe
10,5
,and
1pe
rcen
tlev
els.
Sam
plei
sthe
larg
estp
ossi
ble2
005
cros
s-se
ctio
nfo
rthe
seva
riabl
es.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tson
cons
tant
s,co
ntro
ls,a
ndfir
st-s
tage
coeffi
cien
tare
notr
epor
ted.
IVes
timat
esin
stru
men
tlog
min
ing
shar
ew
ithto
talp
erca
pita
oila
ndga
sres
erve
sin
1971
from
Nor
man
(200
9).T
here
leva
ntcr
itica
lval
uefo
rthe
Kle
iber
gen-
Paap
Wal
krk
Fst
atis
ticsr
epor
ted
(i.e.
excl
uded
-F)i
sth
eSt
ock-
Yogo
criti
calv
alue
of16
.38
calc
ulat
edfo
rone
endo
geno
usre
gres
sor,
one
inst
rum
ent,
10%
max
imum
IVre
lativ
ebi
as,a
ndi.i
.der
rors
.
§2.7 Chapter 2 Appendix 59
Tabl
e2.
8:M
ain
Resu
ltsu
sing
Alt
erna
tive
Tim
ePe
riod
s
Sam
ple
1995
2000
Betw
een
estim
ator
Ave
rage
min
ing
Estim
ator
OLS
IVO
LSIV
OLS
IVO
LSIV
Col
umn
12
34
56
78
Pane
lA:D
epen
dent
varia
ble,
log
infa
ntm
orta
lity
(dea
thsp
er’0
00)
Log
min
ing
shar
e0.
09**
*0.
22**
*0.
10**
*0.
22**
*0.
11**
*0.
22**
0.12
***
0.19
***
(0.0
2)(0
.05)
(0.0
2)(0
.05)
(0.0
2)(0
.09)
(0.0
2)(0
.04)
Excl
uded
-F17
.73
15.9
416
.65
Cou
ntrie
s14
913
315
113
321
0718
6115
213
4
Pane
lB:D
epen
dent
varia
ble,
log
aver
agey
ears
ofed
ucat
iona
latta
inm
ent
Log
min
ing
shar
e-0
.01
-0.2
4***
-0.0
2-0
.22*
**-0
.02
-0.2
2**
-0.0
2-0
.20*
**
(0.0
2)(0
.06)
(0.0
2)(0
.06)
(0.0
2)(0
.11)
(0.0
1)(0
.05)
Excl
uded
-F16
.53
15.2
716
.79
Cou
ntrie
s12
210
812
310
836
732
312
310
8
This
tabl
esh
ows
that
the
mai
nre
sults
are
unch
ange
dw
hen
diffe
rent
time
perio
dsan
dco
untr
yav
erag
esar
eus
edin
stea
dof
the
2005
cros
s-se
ctio
n.St
arsd
enot
est
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
perc
entl
evel
s.Sa
mpl
eis
the
larg
estp
ossi
ble
2005
cros
s-se
ctio
nfo
rthe
seva
riabl
es.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tsco
nsta
nts,
cont
rols
,an
dfir
st-s
tage
regr
essi
onsa
reno
trep
orte
d.A
lles
timat
esin
clud
elo
gre
alG
DP
perc
apita
,abs
olut
edi
stan
cefr
omth
eeq
uato
r,an
inde
xof
gove
rnm
ente
ffect
iven
ess,
and
the
tota
lnum
ber
ofw
ildca
tsdr
illed
inth
e20
thce
ntur
yas
cont
rols
.IV
estim
ates
inst
rum
entl
ogm
inin
gsh
are
with
per
capi
tafo
ssil
fuel
rese
rves
in19
71.
The
rele
vant
criti
calv
alue
for
the
Kle
iber
gen-
Paap
Wal
krk
Fst
atis
ticsr
epor
ted
(i.e.
excl
uded
-F)i
sthe
Stoc
k-Yo
gocr
itica
lval
ueof
16.3
8ca
lcul
ated
foro
neen
doge
nous
regr
esso
r,on
ein
stru
men
t,10
%m
axim
umIV
rela
tive
bias
,and
i.i.d
erro
rs.
§2.7 Chapter 2 Appendix 60
Tabl
e2.
9:M
ain
Resu
ltsu
sing
Alt
erna
tive
Mea
sure
sofM
inin
g
Log
depe
nden
tvar
iabl
eIn
f.m
orta
lity
Avg
.yrs
educ
.,yo
uth
Perc
entp
op.w
/no
educ
.,yo
uth
(dea
thsp
er’0
00)
Estim
ator
OLS
IVO
LSIV
OLS
IV
Col
umn
12
34
56
Pane
lA:M
inin
gin
com
eexp
lana
tory
varia
ble(
level)
Log
min
ing
inco
me
perc
apita
0.11
***
0.18
***
-0.0
2*-0
.20*
**0.
27**
*1.
05**
*
(0.0
2)(0
.04)
(0.0
1)(0
.05)
(0.0
7)(0
.24)
Excl
uded
-F15
.69
16.2
416
.24
Cou
ntrie
s15
013
212
611
112
511
1
Pane
lB:M
inin
gex
port
ssha
reex
plan
ator
yva
riabl
e
Log
min
ing
shar
eof
expo
rts
0.04
***
0.23
***
-0.0
2-0
.40*
*0.
28**
*2.
39**
*
(0.0
2)(0
.06)
(0.0
2)(0
.16)
(0.1
0)(0
.84)
Excl
uded
-F13
.09
6.44
6.44
Cou
ntrie
s13
311
511
710
211
610
2
This
tabl
esh
ows
that
usin
gm
inin
gin
com
ean
dm
inin
gex
port
ssh
are
ofto
tale
xpor
tsas
expl
anat
ory
varia
bles
yiel
dses
timat
esof
sim
ilar
sign
san
dco
mpa
rativ
em
agni
tude
s.St
ars
deno
test
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
perc
entl
evel
s.Sa
mpl
eis
the
larg
estp
ossi
ble
2005
cros
s-se
ctio
nfo
rthe
seva
riabl
es.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tsco
nsta
nts,
cont
rols
,and
first
-sta
gere
gres
sion
sar
eno
trep
orte
d.A
lles
timat
esin
clud
elo
gre
alG
DP
per
capi
ta,a
bsol
ute
dist
ance
from
the
equa
tor,
anin
dex
ofgo
vern
men
teff
ectiv
enes
s,an
dth
eto
taln
umbe
rof
wild
cats
drill
edin
the
20th
cent
ury
asco
ntro
ls.
IVes
timat
esin
stru
men
tlo
gm
inin
gin
com
epe
rca
pita
(UN
)and
log
min
ing
shar
eof
expo
rts
(Wor
ldBa
nk)w
ithpe
rca
pita
foss
ilfu
elre
serv
esin
1971
.Th
ere
leva
ntcr
itica
lval
uefo
rth
eK
leib
erge
n-Pa
apW
alk
rkF
stat
istic
srep
orte
d(i.
e.ex
clud
ed-F
)ist
heSt
ock-
Yogo
criti
calv
alue
of16
.38
calc
ulat
edfo
rone
endo
geno
usre
gres
sor,
one
inst
rum
ent,
10%
max
imum
IVre
lativ
ebi
as,a
ndi.i
.der
rors
.
§2.7 Chapter 2 Appendix 61
Tabl
e2.
10:M
ain
Resu
ltsw
itho
utRe
sour
ce-r
ich
Min
i-sta
tesa
ndO
ther
Out
lier
s
Log
depe
nden
tvar
iabl
eIn
f.m
orta
lity
(dea
ths/
’000
Avg
.yrs
educ
.,yo
uth
Perc
entp
op.w
/no
educ
.,yo
uth
Estim
ator
OLS
IVO
LSIV
OLS
IV
Col
umn
12
34
56
Pane
lA:D
ropp
ing
oil-r
ichm
ini-s
tate
sfro
mth
esam
ple
Log
min
ing
shar
e0.
11**
*0.
13**
*-0
.01
-0.1
5**
0.19
**0.
47**
*
(0.0
2)(0
.04)
(0.0
1)(0
.07)
(0.0
8)(0
.13)
Cou
ntrie
s14
612
812
310
812
210
8
Pane
lB:D
ropp
ing
oil-r
ichm
ini-s
tate
sand
outli
ersf
rom
thes
ampl
e
Log
min
ing
shar
e0.
11**
*0.
18**
*-0
.01
-0.1
8***
0.15
*0.
41**
(0.0
2)(0
.03)
(0.0
1)(0
.06)
(0.0
8)(0
.17)
Cou
ntrie
s14
112
311
910
411
810
4
This
tabl
esh
owst
hatd
ropp
ing
reso
urce
-ric
hm
ini-s
tate
sand
othe
rout
liers
from
the
sam
ple
does
nota
ltert
hem
ain
resu
lts.S
tars
deno
test
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
per
cent
leve
ls.
Sam
ple
isth
ela
rges
tpos
sibl
e20
05cr
oss-
sect
ion
for
thes
eva
riabl
es.
Het
eros
keda
stic
ity-r
obus
tst
anda
rder
rors
inpa
rent
hese
s,an
dco
effici
ents
cons
tant
s,co
ntro
ls,a
ndfir
st-s
tage
regr
essi
onsa
reno
trep
orte
d.A
lles
timat
esin
clud
elog
real
GD
Ppe
rcap
ita,a
bsol
ute
dist
ance
from
the
equa
tor,
anin
dex
ofgo
vern
men
teffe
ctiv
enes
s,an
dth
eto
taln
umbe
rofw
ildca
tsdr
illed
inth
e20
thce
ntur
yas
cont
rols
.IV
estim
ates
inst
rum
entl
ogm
inin
gsh
are
with
per
capi
tafo
ssil
fuel
rese
rves
in19
71.
Pane
lAdr
ops
Qat
ar,U
AE,
Brun
ei,K
uwai
t,Ba
hrai
n,an
dO
man
.Pan
elB
drop
sQat
ar,U
AE,
Brun
ei,K
uwai
t,Ba
hrai
n,O
man
Saud
iAra
bia,
Liby
a,Eq
uato
rialG
uine
a,Ir
aq,a
ndVe
nezu
ela.
§2.7 Chapter 2 Appendix 62
Tabl
e2.
11:H
ealt
han
dEd
ucat
ion
Elas
tici
ties
ofIn
com
efr
omD
iffer
entS
ecto
rs
Log
depe
nden
tvar
iabl
eIn
fant
mor
talit
y(d
eath
sper
’000
)A
vg.y
ears
ofed
ucat
ion
Perc
ento
fpop
.w/
noed
ucat
ion
Estim
ator
IVIV
OLS
IVIV
OLS
IVIV
OLS
Col
umn
12
34
56
78
9
Log
perc
apita
min
ing
leve
lval
ue-a
dded
0.07
***
-0.1
0***
0.44
***
(0.0
2)(0
.02)
(0.1
0)
Log
perc
apita
agric
ultu
re(A
HFF
)val
ue-a
dded
0.23
*-0
.06
-0.1
7(0
.12)
(0.0
5)(0
.18)
Log
perc
apita
man
ufac
turin
gva
lue-
adde
d-0
.23*
**0.
10**
-0.4
5***
(0.0
6)(0
.05)
(0.1
5)
Log
perc
apita
valu
e-ad
ded
inre
stof
econ
omy
-0.6
0***
-0.4
1***
-0.2
0***
0.39
***
0.03
**-0
.08
-1.0
9***
-0.0
50.
47**
*(0
.06)
(0.0
5)(0
.06)
(0.0
7)(0
.01)
(0.0
5)(0
.23)
(0.0
6)(0
.17)
Excl
uded
-F21
.16*
**38
.61*
**24
.1**
*53
.94*
**24
.1**
*53
.94*
**C
ount
ries
133
161
161
9912
712
799
127
127
This
tabl
epr
esen
tsth
ere
gres
sion
estim
ates
forF
igur
e2.
6in
the
mai
nar
ticle
.Sta
rsde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
are
inpa
rent
hese
s,co
effici
ents
onco
nsta
nts
and
first
-sta
geco
effici
ents
are
notr
epor
ted,
and
gove
rnm
ente
ffect
iven
ess
and
latit
ude
cont
rols
incl
uded
inal
lest
imat
es.
“Res
tofe
cono
my”
ÂĂ
Âİr
efer
sto
tota
lval
uead
ded
min
usth
ein
com
eal
read
yin
clud
edin
the
estim
ate
(i.e.
,min
ing,
agric
ultu
re,o
rman
ufac
turin
g).M
inin
gis
inst
rum
ente
dw
ithpe
rcap
itafo
ssil
fuel
rese
rves
in19
71an
dag
ricul
ture
with
log
perc
apita
arab
lela
nd.T
here
leva
ntcr
itica
lval
uefo
rthe
Kle
iber
gen-
Paap
Wal
krk
Fst
atis
ticsr
epor
ted
(i.e.
excl
uded
-F)i
sthe
Stoc
k-Yo
gocr
itica
lval
ueof
16.3
8ca
lcul
ated
foro
neen
doge
nous
regr
esso
r,on
ein
stru
men
t,10
%m
axim
umIV
rela
tive
bias
,and
i.i.d
erro
rs.T
heex
clud
edF
stat
istic
isco
mpa
red
toth
eSt
ock-
Yogo
criti
calv
alue
of16
.38
foro
neen
doge
nous
regr
esso
r,on
ein
stru
men
t,an
d10
%m
axim
umIV
rela
tive
bias
.
§2.7 Chapter 2 Appendix 63
Tabl
e2.
12:R
egio
nal
Sub-
sam
ples
Cou
ntry
grou
pO
ECD
No
OEC
DM
iddl
eEa
stan
dA
fric
aN
oSu
b-Sa
hara
nA
fric
aN
oM
iddl
eEa
stan
dA
fric
a
Estim
ator
OLS
IVO
LSIV
OLS
IVO
LSIV
OLS
IV
Col
umn
12
34
56
78
910
Pane
lA:D
epen
dent
varia
ble,
Log
infa
ntm
orta
lity
Log
min
ing
shar
e0.
07**
0.08
**0.
11**
*0.
17**
*0.
09**
0.02
0.10
***
0.18
***
0.09
***
0.08
***
(0.0
3)(0
.04)
(0.0
2)(0
.04)
(0.0
4)(0
.09)
(0.0
2)(0
.04)
(0.0
2)(0
.03)
Excl
uded
-F7.
7813
.37
1.4
17.4
712
.78
Cou
ntrie
s31
2612
010
761
5910
891
9074
Pane
lB:D
epen
dent
varia
ble,
Log
aver
agey
ears
ofed
ucat
iona
latta
inm
ent
Log
min
ing
shar
e0.
020.
06**
-0.0
4**
-0.2
1***
-0.0
2-0
.21*
-0.0
2-0
.13*
**0.
02-0
.03
(0.0
1)(0
.03)
(0.0
2)(0
.06)
(0.0
3)(0
.11)
(0.0
1)(0
.04)
(0.0
1)(0
.03)
Excl
uded
-F7.
7811
.17
3.33
13.1
211
.81
Cou
ntrie
s31
2691
8145
4493
7877
63
This
tabl
epr
esen
tsth
em
ain
estim
ates
from
Tabl
e2.
2in
the
artic
lees
timat
edby
regi
onal
sub-
sam
ples
.Th
eco
ntin
ento
fAfr
ica
iscl
early
driv
ing
muc
hof
the
aver
age
effec
tin
Tabl
e2.
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rce
ntle
vels
.Sa
mpl
eis
the
larg
estp
ossi
ble
2005
cros
s-se
ctio
nfo
rth
ese
varia
bles
.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tsco
nsta
nts,
cont
rols
,and
first
-sta
gere
gres
sion
sar
eno
trep
orte
d.A
lles
timat
esco
ntro
lfor
incl
ude
log
real
GD
Ppe
rca
pita
,lat
itude
,gov
ernm
ente
ffect
iven
ess,
and
wild
cats
drill
edin
the
20th
cent
ury,
and
use
the
limite
din
form
atio
nm
axim
umlik
elih
ood
(LIM
L)es
timat
orw
itha
Fulle
rpa
ram
eter
ofon
e.A
lles
timat
esin
stru
men
tlog
min
ing
shar
ew
ithpe
rca
pita
foss
ilfu
elre
serv
esin
1971
and
the
rele
vant
criti
calv
alue
fort
heK
leib
erge
n-Pa
apW
alk
rkF
stat
istic
srep
orte
dis
the
Stoc
k-Yo
gocr
itica
lval
ueof
16.3
8ca
lcul
ated
foro
neen
doge
nous
regr
esso
r,on
ein
stru
men
t,10
%m
axim
umIV
rela
tive
bias
,and
i.i.d
erro
rs.
§2.7 Chapter 2 Appendix 64
Tabl
e2.
13:S
ub-s
ampl
eEs
tim
atio
nby
Inst
itut
ion
Type
Cat
egor
ical
dum
my
Dem
ocra
cyPr
esid
entia
lsys
tem
Fren
chle
gals
yste
mC
olon
yM
ono-
faith
NY
NY
NY
NY
NY
Col
umn
12
34
56
78
910
Pane
lA:D
epen
dent
varia
ble,
log
infa
ntm
orta
lity
(dea
thsp
er’0
00
Log
min
ing
shar
e0.
19*
0.13
***
0.16
***
0.26
***
0.17
***
0.13
***
0.10
***
0.14
***
0.15
***
0.20
***
(0.1
0)(0
.05)
(0.0
5)(0
.08)
(0.0
5)(0
.04)
(0.0
4)(0
.04)
(0.0
5)(0
.04)
Excl
uded
-F1.
7936
.726
.65.
39.
2111
.78.
227.
5812
.28
14.9
2
Cou
ntrie
s52
7140
8866
6725
8774
59
Pane
lB:D
epen
dent
varia
ble,
log
aver
agey
ears
ofed
ucat
iona
latta
inm
ent
Log
min
ing
shar
e-0
.15
0.03
**0.
04**
*-0
.23*
**-0
.09*
*-0
.19*
**-0
.02
-0.1
8***
-0.0
4-0
.14*
**
(0.1
0)(0
.02)
(0.0
1)(0
.07)
(0.0
4)(0
.05)
(0.0
2)(0
.07)
(0.0
4)(0
.05)
Excl
uded
-F3.
7625
.69
25.3
28.
516.
2115
.21
12.8
26.
2612
.58
13.3
9
Cou
ntrie
s38
6634
7255
5223
7259
48
This
tabl
epr
esen
tsth
em
ain
estim
ates
from
Tabl
e2.
2in
the
artic
lees
timat
edby
sub-
sam
ples
cate
goris
edby
bina
ryin
stitu
tiona
lcha
ract
eris
tics
com
mon
lyus
edto
expl
ain
the
hete
roge
neou
sex
perie
nces
ofre
sour
ceric
hco
untr
ies.
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Sam
ple
isth
ela
rges
tpos
sibl
e20
05cr
oss-
sect
ion
fort
hese
varia
bles
.Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
inpa
rent
hese
s,an
dco
effici
ents
cons
tant
s,co
ntro
ls,a
ndfir
st-s
tage
regr
essi
ons
are
notr
epor
ted.
All
estim
ates
cont
rolf
orin
clud
elo
gre
alG
DP
perc
apita
,lat
itude
,gov
ernm
ente
ffect
iven
ess,
and
wild
cats
drill
edin
the
20th
cent
ury,
and
use
the
limite
din
form
atio
nm
axim
umlik
elih
ood
(LIM
L)es
timat
orw
itha
Fulle
rpa
ram
eter
ofon
e.A
lles
timat
esin
stru
men
tlog
min
ing
shar
ew
ithpe
rcap
itafo
ssil
fuel
rese
rves
in19
71an
dth
ere
leva
ntcr
itica
lval
uefo
rthe
Kle
iber
gen-
Paap
Wal
krk
Fst
atis
ticsr
epor
ted
isth
eSt
ock-
Yogo
criti
calv
alue
of16
.38
calc
ulat
edfo
rone
endo
geno
usre
gres
sor,
one
inst
rum
ent,
10%
max
imum
IVre
lativ
ebi
as,a
ndi.i
.der
rors
.
§2.7 Chapter 2 Appendix 65
Tabl
e2.
14:L
ocal
Aver
age
Part
ial
Effe
cts,
ByCo
mm
odit
y
Inst
rum
ent
Oil
Gas
Coa
lM
iner
als
All
Estim
ator
LIM
LLI
ML
LIM
LLI
ML
CU
E-G
MM
Col
umn
12
34
5Pa
nelA
:Dep
ende
ntva
riabl
e,lo
gin
fant
mor
talit
y(d
eath
sper
’000
)Lo
gm
inin
gsh
are
0.16
***
0.18
***
0.23
***
0.19
*0.
16**
*(0
.04)
(0.0
4)(0
.08)
(0.0
6)(0
.04)
Excl
uded
-F7.
3714
.88
3.36
5.18
10.4
1C
ount
ries
133
133
133
133
133
Pane
lB:D
epen
dent
varia
ble,
log
aver
agey
ears
ofed
ucat
iona
latta
inm
ent
Log
min
ing
shar
e-0
.16*
**-0
.16*
**0.
09*
0.14
**-0
.16*
**(0
.04)
(0.0
6)(0
.05)
(0.0
7)(0
.04)
Excl
uded
-F7.
2714
.38
2.9
3.13
8.48
Cou
ntrie
s10
710
710
710
710
7
This
tabl
epr
esen
tsth
elo
cala
vera
gepa
rtia
leffe
ctof
min
ing
depe
nden
ceco
nditi
onal
onea
chty
peof
natu
ralr
esou
rce
rese
rves
inN
orm
an(2
009)
.N
ote
that
coal
and
min
eral
sre
serv
esar
epr
actic
ally
unid
entifi
edan
dth
eref
ore
unin
terp
reta
ble.
Star
sden
ote
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
elsa
ndsa
mpl
epe
riod
2005
.Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
inpa
rent
hese
s,an
dco
effici
ents
cons
tant
s,co
ntro
ls,a
ndfir
st-s
tage
regr
essi
onsa
reno
trep
orte
d.A
lles
timat
esin
clud
edas
cont
rols
log
real
GD
Ppe
rcap
ita,l
atitu
de,g
over
nmen
teffe
ctiv
enes
s,an
dw
ildca
tsdr
illed
inth
e20
thce
ntur
y.C
olum
ns1–
4ar
ees
timat
edw
ithth
elim
ited
info
rmat
ion
max
imum
likel
ihoo
d(L
IML)
estim
ator
with
aFu
ller
para
met
erof
one
and
Col
umn
5us
esth
eco
ntin
uous
lyup
date
dge
nera
lised
met
hod
ofm
omen
tses
timat
or(C
UE-
GM
M).
The
inst
rum
ent
inth
eto
pro
wre
fer
toth
epe
rca
pita
rese
rves
ofth
isty
pein
2001
,and
Col
umns
5in
clud
esal
lfo
urty
pesa
ssep
arat
ein
stru
men
ts.T
here
leva
ntcr
itica
lval
uefo
rthe
Kle
iber
gen-
Paap
Wal
krk
Fst
atis
ticsr
epor
ted
aret
heSt
ock-
Yogo
criti
calv
alue
sof1
6.38
forC
olum
ns1–
4an
d5.
44fo
rCol
umn
5,bo
thfo
r10%
max
imum
IVre
lativ
ebi
asan
di.i
.der
rors
.
§2.7 Chapter 2 Appendix 66
Tabl
e2.
15:N
atio
nal-
leve
lM
echa
nism
s—O
LSan
dIV
Pane
lA:I
ncom
ein
othe
rsec
tors
and
publ
ichu
man
capi
tali
nves
tmen
t
Log
depe
nden
tvar
iabl
ePe
rcap
itano
n-m
inin
gin
com
ePe
rcap
itahe
alth
expe
nditu
reH
ealth
expe
nditu
resh
are
Perc
apita
educ
atio
nex
pend
iture
Educ
atio
nex
pend
iture
shar
e
Estim
ator
OLS
LIM
LO
LSLI
ML
OLS
LIM
LO
LSLI
ML
OLS
LIM
L
Log
min
ing
shar
e-0
.06*
**-0
.09*
**-0
.04*
*-0
.22*
*-0
.06*
*-0
.16*
-0.0
2-0
.08
0.01
0.03
(0.0
2)(0
.04)
(0.0
2)(0
.09)
(0.0
3)(0
.09)
(0.0
2)(0
.08)
(0.0
2)(0
.11)
Excl
uded
-F16
.37
16.4
316
.43
17.5
18.6
4
Cou
ntrie
s15
113
315
113
315
113
311
910
813
912
2
Pane
lB:Q
ualit
yof
gove
rnm
enta
ndge
nder
equa
lity
Dep
ende
ntva
riabl
eG
over
nmen
teffe
ctiv
enes
sind
exC
ontr
olof
corr
uptio
nin
dex
Cor
rupt
ion
perc
eptio
nsin
dex
Fem
ale
parl.
(p.c
.)C
PIA
gend
erin
dex
Estim
ator
OLS
LIM
LO
LSLI
ML
OLS
LIM
LO
LSLI
ML
OLS
LIM
L
Log
min
ing
shar
e-0
.11*
**-0
.17*
**-0
.15*
**-0
.14*
**-0
.31*
**-0
.31*
**-1
.09*
**-4
.90*
**-0
.10*
**-0
.17*
**
(0.0
3)(0
.04)
(0.0
3)(0
.04)
(0.0
7)(0
.10)
(0.4
0)(1
.1)
(0.0
3)(0
.03)
Excl
uded
-F16
.05
16.7
318
.09
6.06
671
.61
Cou
ntrie
s15
213
415
613
711
710
315
113
263
55
Pane
lC:C
onfli
ctan
dst
abili
ty
Dep
ende
ntva
riabl
eYe
arso
fcon
flict
Stab
ility
inde
xPo
lity
IVsc
ore
Num
bero
fcou
psLo
gm
ilita
ryex
pend
iture
shar
e
OLS
LIM
LO
LSLI
ML
OLS
LIM
LO
LSLI
ML
OLS
LIM
L
Log
min
ing
shar
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190.
29-0
.12*
**-0
.08*
-1.3
1***
-5.5
2***
0.27
*0.
74**
*0.
12**
*0.
44**
*
(0.2
2)(0
.24)
(0.0
3)(0
.04)
(0.3
1)(1
.2)
(0.1
6)(0
.26)
(0.0
4)(0
.1)
Excl
uded
-F16
.73
16.7
313
.23
16.7
35.
4
Cou
ntrie
s15
613
715
613
713
711
915
613
713
511
6
This
tabl
epr
esen
tsth
ena
tiona
l-lev
elm
echa
nism
anal
ysis
from
the
artic
le(T
able
2.4)
expa
nded
toin
clud
eth
ele
asts
quar
eses
timat
ebe
side
IVes
timat
e,fo
rcom
paris
on.S
tars
deno
test
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
perc
entl
evel
s.Sa
mpl
eis
the
larg
estp
ossi
ble
2005
cros
s-se
ctio
nfo
rthe
seva
riabl
es.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsin
pare
nthe
ses,
and
coeffi
cien
tsco
nsta
nts,
cont
rols
,and
first
-sta
gere
gres
sion
sar
eno
trep
orte
d.Pa
nelA
cont
rols
incl
ude
log
real
GD
Ppe
rcap
ita,l
atitu
de,g
over
nmen
teffe
ctiv
enes
s,an
dw
ildca
tsdr
illed
inth
e20
thce
ntur
y.Pa
nels
Ban
dC
cont
rolf
orin
clud
elo
gre
alG
DP
per
capi
ta,l
atitu
de,a
ndw
ildca
ts,i
.e.,
gove
rnm
ente
ffect
iven
ess
isom
itted
.A
lles
timat
esus
eth
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Chapter 3
Is plantation agriculture good for the
poor?
Abstract
I examine the poverty impacts of the largest modern plantation sector expansion,
Indonesian oil palm in the 2000s. Combining administrative data on local oil
palm acreage at the district level with survey-based estimates of poverty into
a balanced district panel, I estimate long-differences spanning the expansion.
Causal effects are identified through an instrumental variable strategy exploiting
detailed geo-spatial data on crop-specific agro-climatic suitability. The results
suggest that increasing the oil palm share of land in a district by ten percentage
points leads to around a forty per cent reduction in its poverty rate. Of the
more than 10 million Indonesians lifted from poverty over the 2000s, my most
conservative estimate suggests that at least 1.3 million people have escaped
poverty due to growth in the oil palm sector. Different panel data techniques
are used to assess short-run dynamics. I observe similar effects across different
oil palm producing regions, for industrial and smallholder plantations, and at the
province level. Oil palm expansion tends to be followed by a small but sustained
boost to the value of district-level agricultural output, manufacturing output, and
total output.
68
§3.1 Introduction 69
3.1 Introduction
Palm oil is the world’s most consumed vegetable oil. Crude palm oil is
derived from the reddish pulp of the fruit of the oil palm, a plantation-based,
labour-intensive cash crop originating from Africa (elaeis guineensis) and the
Americas (elaeis oleifera), mostly grown in developing countries today.25 Millions
of people across Asia, South America, and Africa earn income from oil palms,
yielding more oil per hectare than any other crop from relatively little inputs. Oil
palm is one of the most economically attractive uses for land in humid lowland
tropics (Butler et al, 2009) but the palm oil industry is one of the world’s most
socially contested due to deforestation, forest fires, endangered wildlife, displaced
people, and local conflicts.26
In this chapter I ask whether the world’s largest modern plantation-based
agricultural expansion has been pro-poor. I estimate the impacts of the
remarkable expansion in palm oil production in Indonesia on poverty over the
2000s using rich new longitudinal data. Blending administrative information on
local oil palm acreage at the district (kabupaten) level with survey-based estimates
of district poverty, I relate decadal changes in oil palm plantation area to changes
in district poverty over the same period, comparing the poverty elasticity of
oil palm land against alternative uses for land (e.g., rice and forestry). Causal
effects are identified through a novel instrumental variable (IV) strategy exploiting
detailed geo-spatial data on agro-climatic suitability for every field in Indonesia.
By controlling for potential yields of other crops that could share agro-climatic
suitability characteristics with oil palm, I ensure the identifying variation relates
only to oil palm and not other types of agriculture.
25A cash crop is typically an agricultural crop grown to sell rather than consume, usually toglobal export markets (c.f., locally consumed food and subsistence crops).
26See Corley and Tinker (2003) for history and physiology, and Rival and Levang (2014) andSayer et al (2012) for physiology and recent developments in Asia. Dennis et al (2005), Koh andWilcove (2007, 2008), Busch et al (2015), and Miriam et al (2015) discuss environmental impacts,and Barr and Sayer (2012), McCarthy et al (2011), Rist et al (2010), Cramb (2013), Gellert (2015),and Cramb and McCarthy (2016) discuss local social impacts.
§3.1 Introduction 70
The key finding is that districts with larger oil palm expansion have achieved
more poverty reduction than otherwise similar districts without oil palm
expansion. The magnitude of the estimated poverty reduction from increasing
the district share of oil palm land by ten percentage points from my preferred IV
estimator is around 40% of the poverty rate. A simple policy simulation based
on my most conservative estimate suggests that at least 1.3 million out of the
approximately 10 million people lifted from poverty over the 2000s have escaped
poverty due to growth in the oil palm sector. Poverty gaps significantly narrow,
suggesting not only those near the poverty line are being lifted up. I assess
short-term effects and dynamics using standard panel estimators with distributed
lags: dynamic effects reflect the oil palm life cycle. I find no evidence of any major
effect heterogeneity when I disaggregate by large plantations and smallholders,
despite the starkly different characteristics of the two sectors. Similar effects
are also observed across Indonesia’s major palm oil producing regions and at
the province level. I find some evidence of spillovers to other local economic
activities, with oil palm expansion usually followed by a small but sustained boost
to agriculture, manufacturing, and total district output.
The key contribution of this chapter is to provide new causal evidence on
impacts of growth in Indonesia’s palm oil sector on poverty. In providing these
estimates, I shed new light on the poverty elasticity of plantation-based cash
crop production. The role of the agricultural sector in economic development
and poverty reduction has been widely studied (Dercon (2009) and Dercon and
Gollin (2014) provide reviews), but little attention has been paid to plantation
agriculture or cash crops despite their ubiquity in developing countries (Barbier,
1989; Maxwell and Fernando, 1989; Pryor, 1982; Tiffen and Mortimore, 1990).
Agricultural growth tends to be pro-poor (Ravallion and Chen, 2003; Kraay,
2006; Ravallion and Chen, 2007; Christiaensen et al, 2012), but large-scale
agricultural development remains contested and plantation-based cash crops
§3.1 Introduction 71
have starkly different characteristics to other forms of agriculture (Quizon and
Binswanger, 1986; Anriquez and Lopez, 2007; Maertens and Swinnen, 2009;
Hayami, 2010). Unlike food crops and subsistence agriculture, plantation-based
cash crops seldom feed those employed in modern sectors (c.f., Lewis, 1954;
Schultz, 1964) and the potential for agricultural demand-led industrialisation
is ambiguous (c.f., Johnston and Mellor, 1961; Ranis and Fei, 1961; Adelman,
1984). On one hand, consumption linkages may be greater than other agriculture,
due to higher yields and profits. On the other hand, low technology, skill,
and processing requirements for most cash crops imply limited production
linkages. Plantation-based cash crops, however, are unique in this regard. The
plantation system arises due to the need for closer coordination between farm
production and large-scale processing due to the need to process some crops
shortly after harvest. Examples include black tea, sisal, and palm oil, which
must be milled within 24 hours of harvest (c.f., green tea, cocoa, coconuts, and
copra do not require much further processing or marketing, so are suitable
to independent smallholders and family farms). So although several key
mechanisms responsible for past agriculture-led poverty reduction and the “green
revolution”—agricultural technology growth, initial agricultural infrastructure,
and human capital conditions (Ravallion and Datt, 2002; Gollin et al, 2002)—are
generally less applicable for cash crops, this may not be the case for the plantation
system due to its infrastructure requirements (Gollin (2010) provides a useful
review of the theory and evidence linking agricultural production to poverty
reduction). To my knowledge, this is the first nationwide study of the link between
plantation-based cash crops and poverty.
§3.2 Indonesia’s oil palm expansion 72
I provide new evidence against a particularly salient policy debate on palm
oil across the developing world. While the environmental costs of oil palm
have been widely documented, whether Indonesia’s dramatic shift in land use
towards oil palm has brought benefits to the poorest is the subject of much
speculation but yet no systematic quantitative inquiry (McCarthy et al, 2011; Rist
et al, 2010; Cramb and Sujang, 2013; Cahyadi and Waibel, 2013; Budidarsono et
al, 2012). Existing qualitative narratives and geographically-narrow case studies
provide a rich source of descriptive evidence but little basis for causal inference,
as they tend to have weak and narrow internal validity. Although I focus
on Indonesia, findings are informative for other developing countries looking
towards plantation agriculture for poverty reduction. An additional contribution
is my use of a novel IV approach to study causal effects of agricultural sector
growth.
The chapter proceeds as follows. The next section provides a brief background
on Indonesia’s recent oil palm expansion and possible links to poverty. Section
3.3 explains the data and Section 3.4 my empirical approach. Section 3.5 presents
the main results. Section 3.6 estimates short-run effects using annual panel data.
Section 3.7 considers alternative explanations for the main results. Section 3.8
explores effect heterogeneity and spillovers to other sectors. Section 3.9 concludes.
3.2 Indonesia’s oil palm expansion
The world’s largest plantation-based agricultural expansion is taking place
in Indonesia. The third most populous developing country after China and
India, Indonesia supplied more than 40 per cent of the 60.54 million metric
tons of palm oil produced in 2014–15. Global palm oil production has doubled
every decade since the 1960s, surpassing soy bean oil in 2007 to become the
dominant vegetable oil (US Department of Agriculture, 2015). With a comparative
advantage in unskilled labour-intensive goods and proximity to India and China
§3.2 Indonesia’s oil palm expansion 73
(the largest purchasers), Indonesia was well-placed to capitalise on the growing
demand. Palm oil has been Indonesia’s largest agricultural export for the last two
decades, with its rapid increase in production coming almost exclusively through
land area expansion (92 per cent) rather than intensification and higher yields
(Gaskell, 2015). From 2001–2009 (my study period) oil palm land increased from
around five million hectares to just under 17 million hectares, or 8.7 per cent of
Indonesia.27 Oil palm expansion has had large opportunity costs, particularly in
terms of the environment (Busch et al, 2015; Carlson et al, 2013; Fargione et al,
2008; Gibbs et al, 2010; Hunt, 2010; Koh et al, 2011; Rival and Levang, 2014; and
Wheeler et al, 2013). Land use is the central policy issue but there is a paucity of
credible evidence on the welfare impacts of changing land use patterns.
Three decades of economic growth and structural change since the 1970s saw
broad-based benefits and poverty reduction across Indonesia (Hill, 1996). Rural
poverty reduction appears to have been mostly driven by agricultural growth,
including through the Green Revolution (Suryahadi et al, 2009; de Silva and
Sumarto, 2014; Rada et al, 2011).28 Since the Asian Financial Crisis and the fall
of Suharto in 1997, economic growth and poverty reduction have both slowed
(Pepinski and Wihardja, 2011). Rapid structural change and a steadily rising
manufacturing share of gross domestic product (GDP) slowed to a halt with
the contemporaneous mining and palm oil booms of the 2000s. The poverty
headcount has continued to fall, but it is unclear how much progress can be
attributed to oil palm. Almost 100 million Indonesians still lived below or near
the poverty line in 2014, of which 28 million, or 11.4% of the population, lived
below the poverty line.29
27Gatto et al (2015) discuss the links between the oil palm boom and land-use dynamics.28See Booth (1988), Fuglie (2010), and Rada et al (2011) for overviews of Indonesian agricultural
development in this period.29Hill, 2014; Manning (2010), Miranti (2010), Suryahadi et al (2003), Manning and Sumarto
(2011), and Wetterberg et al (1999) provide comprehensive accounts of the evolution anddeterminants of poverty in Indonesia.
§3.2 Indonesia’s oil palm expansion 74
3.2.1 Linking oil palms to poverty
The poverty elasticity of economic growth in different sectors depends on
sectors’ relative importance to the economy and poor people (Loayza and
Raddatz, 2010). Economic expansion in the oil palm sector is thus likely to be
pro-poor if poor people (a) are employed, (b) have access to land to become
smallholders, or (c) benefit from related economic development.
Labour intensity shapes the poverty elasticity of sectoral growth in most
countries and any poverty benefits from oil palm expansion could be a purely
labour income story for new smallholders or people working on plantations
(Thorbecke and Jung, 1996). Oil palm is a labour-intensive cash crop requiring
little skill or capital to grow and harvest. Farmers and plantation workers typically
earn more than other low skilled workers, with returns to labour estimated to
be 2–7 times the average agricultural wage (Budidarsono et al, 2012). Large
plantations employ roughly two people for every five hectares. In 2010, 1.7
million Indonesians worked on oil palm plantations (Burke and Resosudarmo,
2012). However almost half of Indonesia’s reported oil palm plantation area is
managed by smallholders, usually with 1–2 hectares each, generating significantly
more jobs per hectare. Smallholder plantation area has grown much faster than
company and state-owned plantation area since 2000 (McCarthy et al, 2011; Gatto
et al, 2015), accounting for a larger share of the oil palm-related labour market.
Smallholders tend to report improved yields, profits, nutrition, and incomes after
entering the sector (Budidarsono et al, 2012). While those living below the poverty
line are more likely to be landless and unable to legally become smallholders, they
often work on large industrial plantations. Existing studies typically argue oil
palm expansions bring little benefits to local communities (Obidzinski et al, 2014),
but palm oil is unique compared many other cash crops, combining high returns
to labour with the need for initial infrastructural outlays and processing facilities
(capital needed to actually grow oil palm and sell fresh fruit bunches is minimal
§3.3 Data 75
though). New roads and electrification needed to run palm oil plantations and
mills could alleviate some constraints to rural development. The question of
whether Indonesia’s large oil palm expansion has been good for the poor is
ultimately an empirical one.
3.3 Data
3.3.1 Oil palm
My main explanatory variable is official district oil palm acreage, measured
in hectares and taken from the Tree Crop Statistics of Indonesia for Oil Palm.
Produced by the Department of Agriculture annually since 1996, data cover land
of varying condition (damaged, immature, and mature plantation) and ownership
(private, state, and smallholder).30 While data on official oil palm land are likely
imperfect, focusing on plantation land declared by the Indonesian Government
has greatest tractability.31 I convert oil palm land area to a share of total district
area to focus on changing compositions: comparing oil palm land to other land
uses. As oil palm expansion has been predominantly in rural districts, the
comparison tends to be against other types of agriculture and rural livelihoods
(e.g., rice, rubber, coffee, and forestry). Oil palm land as a share of total district in
2009 is shown in Figure 3.1. Districts across Sumatra and Kalimantan use a greater
share of land for oil palm than those in Sulawesi, Java, and eastern Indonesia.
30Districts with no oil palm land are missing values in the original data, so I recode them aszeros to retain the baseline and control districts. Before recoding as zeros, I cross-checked dataagainst other sources for official plantation figures and gained strong anecdotal evidence frompublic officials that data are more or less nationally exhaustive. There are no large jumps from theimputed zero values. All increase gradually. Similar results are obtained if I drop all districts withno oil palm, focusing only on changes in districts with oil palm land. Unless otherwise stated,subnational data are taken from the World Bank (2015).
31Alternative satellite data are ill-suited for this study, as they cannot distinguish betweenmature oil palm plantations and natural or other forests. For the parts of Indonesia where satellitedata on plantation areas have been field-verified, strong anecdotal evidence from NGOs currentlyassessing these issues suggests small unofficial, informal, and illegal oil palm developments tendto locate alongside and proportional to officially declared plantations, as the same supply chaininfrastructure is needed.
§3.3 Data 76
Figu
re3.
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§3.3 Data 77
3.3.2 Poverty
My primary outcome variable is the district poverty rate from 2002 to 2010,
taken from Indonesia’s central statistics agency, Badan Pusat Statistik (BPS). The
poverty rate is a key social policy target for Indonesian governments, defined
as the share of total district population living below an expenditure-based
poverty line that varies by district and period, linked by a universal
consumption requirement (mostly caloric). Poverty figures are derived from
the consumption module of BPS’s district-representative national socio-economic
survey (SUSENAS), implemented at least annually and covering almost two
million people across all provinces in 2010. SUSENAS is agnostic to whether
consumption goods are purchased in formal or informal markets and a consistent
method has been used to calculate poverty rates for the period under study (i.e.,
the method changed in 1998 and 2011). The distribution of household expenditure
can be steep around the poverty line, so I also estimate impacts on the depth
of poverty measured by the poverty gap index: the average gap between the
expenditure of poor people and the poverty line. This allows me to assess whether
only people near the poverty line are affected or those further below, although the
depth of poverty is interesting in its own right. District poverty rates in 2010 are
presented in Figure 3.2. Most of the poor live in Java and poverty rates are highest
in the eastern periphery away from the north-western islands producing most of
Indonesia’s palm oil.
§3.3 Data 78
Figu
re3.
2:D
istri
ctPo
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2010
§3.3 Data 79
3.3.3 Pemekaran
Indonesian districts (kabupaten) are clearly defined legal and geographical
units with district-level administrations reflecting local economies. A district
panel provides temporal and spatial variation suitable to identifying aggregate
district-level impacts (Sparrow et al, 2015). Indonesia underwent one of the
world’s largest reconfigurations of a modern state with the fall of President
Suharto in 1997, democratising and decentralising power to around 300 district
governments. New political and fiscal powers drove the number of districts
to proliferate from 292 in 1998 to 514 in 2014, a process known as pemekaran.
Pierskalla (forthcoming) and Fitriani, Hofman, and Kaiser (2005) provide detailed
accounts of pemekaran, highlighting how district splits followed sub-district
(kecamatan) boundaries and did not affect neighbouring districts’ borders. I
combine the SUSENAS-derived estimates of district poverty with the official
district oil palm statistics and apply year-2001 district boundaries to obtain a
nationally-exhaustive balanced panel of 341 constant geographic units.32
32In most Indonesian data, districts retain the original names and codes after splitting andreducing in size. Care is needed to avoid applying district fixed effects to such units. Ininternational data, this equates to letting the USSR series continue without its former membersinstead of creating a new series for Russia. Alternative district definitions yield similar results,but constant land area units allow an uninterrupted panel dataset better suited to my researchquestion. Panel summary statistics are provided in the Chapter Appendix.
§3.4 Empirical approach 80
3.4 Empirical approach
I relate changes in shares of district area used for oil palms to poverty with the
long-difference equation:
ln(yd,2010) − ln(yd,2002) = β(Pd,2009 − Pd,2001) + δi + γXd,2000 + εd (3.1)
where ln(yd,2010) − ln(yd,2002) denotes the change in the log poverty rate from
2002–2010 in district d. Differenced log dependent variables are used to better
compare relative changes in poverty prevalence between districts with high and
low poverty rates.33 Pd,2009 − Pd,2001 is the 2001–2009 change in the share of
district area used for oil palm plantations, lagged by one year because poverty is
measured in the middle of the year and oil palm at the end. Palm oil land shares
are not logged to retain zero values. Differencing removes any time-invariant level
sources of bias jointly affecting land use and poverty (e.g., geography, climate,
history, institutions, and culture). β is the effect of an additional percentage
point of oil palm land as a share of total district land on the district poverty
rate (i.e., a semi-elasticity). δi are island fixed effects, capturing region-specific
factors and allowing different regional trends (e.g., due to different patterns
of economic development or large regional infrastructure investments). Island
groups are defined as Java, Sumatra, Kalimantan, and Sulawesi, with remaining
eastern islands grouped together. γXd,2000 includes initial log poverty and per
capita output, capturing convergence across regions with higher poverty rates and
allowing variable trends by initial conditions.34 Standard errors are adjusted for
33The logged dependent variable ensures districts with relatively low levels of poverty makingsimilar proportional gains to districts with higher relative levels of poverty are accountedfor similarly. Results are similar using linear-linear functional form (see Chapter Appendix),suggesting districts with relatively low levels of poverty are not driving my results.
34The presence of a lagged dependent variable in a short panel could bias coefficients onthe convergence term upwards and oil palm land towards zero (Barro, 2015). Estimating across-section of long differences appears to minimise Hurwicz-Nickell bias, as results are similar(a) without the lagged dependent variable, and (b) with the dependent variable and without islandfixed effects (available on request).
§3.4 Empirical approach 81
heteroskedasticity.
I opt for long differences because any poverty impacts arising from additional
oil palm land are not likely to be fully realised immediately. Plantation
companies must establish the necessary infrastructure, hire workers, prepare
land, plant oil palms, then harvest the first fruit at least two years later. Likewise
smallholder farmers need time to switch livelihood, prepare land, and plant oil
palms, although smallholders commonly intercrop and adopt mixed livelihood
strategies. It takes five to seven years for oil palm to reach a productive state and
the price paid for a fresh fruit bunch increases with tree maturity.35
A causal interpretation of β̂ relies on an assumption of parallel trends, common
to all difference-in-difference-type approaches. Consistent estimates are obtained
if no time-varying omitted variables systematically shift poverty trends within
island groups after allowing for differential trends by initial income and poverty
levels. While oil palm expansion is governed by administrative processes subject
to a high degree of randomness (discussed further in Section 4.6), unobservable
heterogeneity is impossible to rule out in non-randomised observational studies,
so I turn to IV estimation to identify causal effects.
3.4.1 Instrumental variable strategy
My main source of identifying variation is a rich geo-spatial dataset on
agricultural productivity: the Food and Agriculture Organisation’s (FAO)
Global Agro-Ecological Zones (GAEZ) data. I instrument the change in the
share of district area used for oil palm from 2001–2009 with average district
agro-climatically attainable oil palm yield. Exploiting the variation in oil palm
expansion arising from crop-specific agro-climatic suitability isolates the effect of
developing oil palm on areas where it is makes the most sense to develop it.
35Prices are set weekly and published in local newspapers, such that per hectare income likelyincreases with time. Differencing and allowing differential regional trends is likely sufficient tocapture any systematic differences across local markets.
§3.4 Empirical approach 82
Figure 3.3: Attainable Palm Oil Yield Across Indonesia
§3.4 Empirical approach 83
The GAEZ dataset uses state-of-the-art agronomic models and high-resolution
data on geographic characteristics and climatic conditions to predict attainable
yields for 1.7 million grid cells covering the Earth’s surface. Estimates are available
for different crops on every piece of land regardless of whether the land is
cultivated or growing the crop, informing farmers and policy makers on how
productive they would be at crops they are not currently growing. Key inputs
to the model are exogenous variables known for every grid cell: soil types and
conditions, elevation, land gradient, rainfall, temperature, humidity, wind speed,
and sun exposure. Inputs feed into agronomic models predicting how inputs
affect the micro-foundations of each crop’s growth processes, explaining how a
given set of growing conditions map to potential yields at each grid cell.36 GAEZ
provide different sets of productivity predictions for different input scenarios.
I opt for the median options: medium man-made inputs and rain-fed water
supply.37
Pixel-level data for attainable palm oil yield across every field in Indonesia is
presented in Figure 3.3. Each major region has some districts suitable for palm oil
production with only rainfall irrigation and a medium level of inputs, particularly
in the low-lying tropical parts of Sumatra, Kalimantan, and eastern Indonesia best
suited to tropical oil seed crops (i.e., around the equator). I mapped the gridded
data on attainable yield of each of Indonesia’s main agricultural commodities
to Indonesia’s district boundaries using geographical information systems (GIS)
then calculated district’s mean. The granularity of the data and the continuous
nature of this variable gives a rich source of variation: a different value for every
potential palm oil producing district.
36Time-varying variables (i.e., humidity, temperature, rainfall, windspeed) are measured at ahigh frequency and their levels and variation over time are used in the models. Predictions foryields at the end of the 20th century and beyond are based on a large number of past realisationsof these variables over the 20th century.
37Rain-fed irrigation minimises measurement error from historical changes in irrigationintensity and technologies (Nunn and Qian (2011). Alternative input assumptions give a similarspatial distribution, so do not affect my results. See Fischer et al (2002), Nunn and Qian (2011),and Costinot et al (2016) for further details on FAO GAEZ data.
§3.4 Empirical approach 84
Instrument relevance and strength
Oil palms only grow under certain agro-climatic conditions—humid low-land
tropics—and potential yields and profits in each district affect the likelihood
that district will have oil palms planted. The IV is thus theoretically relevant.
First-stage coefficients on potential palm oil yield are positive and statistically
significant at the 0.1 per cent level (presented with main results). As a weak
IV problem can be present even with highly significant first-stage coefficients
(Bound et al, 1995), I report the Kleibergen and Paap (2006) rk Wald F statistic
(the heteroskedasticity-robust analogue of the Cragg and Donald (1993) test
statistic) against the relevant Stock and Yogo (2005) critical values and use the
Fuller (1977) median-unbiased limited information maximum likelihood (LIML)
estimator for all IV estimates.38 Additional confidence intervals and hypothesis
tests are provided using Moreira’s (2003) conditional likelihood ratio (CLR)
procedures, which significantly outperform the traditional Anderson and Rubin
(1949) weak-instrument-robust-inference tests (Andrews, Moreira, and Stock,
2004, 2006).
Exogeneity and exclusion
A causal interpretation is only obtained if average attainable district palm
oil yields do not affect changes in poverty through any channel other than oil
palm expansion. GAEZ potential yield predictions do not involve estimating any
sort of statistical relationship between observed inputs, outputs and agro-climatic
conditions, so are exogenously determined with respect to district economic
and poverty conditions. The two theoretically endogenous factors shaping
GAEZ data—irrigation and man-made inputs—are set equal for all districts, so
uncorrelated with poverty trends across districts.
38I prefer the Fuller estimator over the standard two-stage least squares (2SLS) IV estimatorbecause (a) a few IV estimates have scope for a weak IV problem and LIML point estimates aremore reliable for inference under weak IV (Murray, 2006), and (b) I prefer to use the same estimatorthroughout. 2SLS gives similar results (available on request).
§3.5 Main results 85
The main concern is that a key input to the oil palm GAEZ productivity model
could affect productivity of similar tropical crops and therefore welfare through
agricultural productivity in other sectors. This is a common challenge with
external instruments, particularly those relating to weather and climate (Bazzi
and Clemens, 2013; Sarsons, 2015). Using a crop-specific instrument reduces this
threat, but I can go a step further. GAEZ attainable yield data are available for
most of Indonesia’s major agricultural crops. By controlling for other key crops’
potential yields, I further restrict the identifying variation to that relating only to
oil palms and not shared suitability characteristics with other crops (i.e., tropical,
humid, non-mountainous, lowlands with sufficient rainfall that are less suitable
for other tropical cash crops that could likely be grown in similar areas to oil palm).
Second-stage coefficient stability to the inclusion of attainable yields for all rice
types, tea, coffee, cocoa, and cassava, suggest the exclusion restriction is likely
satisfied.
3.5 Main results
My main result is presented in Figure 3.4 and Table 3.1. Districts that converted
more of their land to oil palm plantations in the 2000s have achieved more rapid
poverty reduction than districts of similar initial poverty levels and per capita
incomes in the same region. Oil palm is on average a better rural land use than
alternatives for poverty alleviation.39
39More precisely, my specification using the oil palm share of district area compares the effectof using additional oil palm land relative to the average of all other possible uses for land.
§3.5 Main results 86
Tabl
e3.
1:Po
vert
yIm
pact
soft
he20
01–2
010
Oil
Palm
Expa
nsio
n
Dep
ende
ntva
riabl
e∆
log
dist
rictp
over
tyra
teEs
timat
orO
LSIV
IVIV
IVIV
Col
umn
12
34
56
∆oi
lpal
mla
nd/
dist
ricta
rea
-0.0
12**
*-0
.034
***
-0.0
40**
*-0
.046
***
-0.0
78**
*-0
.033
***
(0.0
03)
(0.0
13)
(0.0
12)
(0.0
12)
(0.0
23)
(0.0
13)
CLR
95%
confi
denc
ein
terv
alN
/A[-0
.19,
-0.0
03]
[-0.1
33,-
0.00
8][-0
.12,
-0.0
2][-0
.20,
0.00
9][-0
.17,
0.00
6]C
LRp-
valu
e(H
0:B
=0)
N/A
0.03
0.01
00.
080.
08Is
land
fixed
effec
tY
NN
NN
NPo
tent
ialr
ice
and
casa
vayi
elds
NN
YN
YN
Pote
ntia
lcoff
ee,c
ocoa
,tea
yiel
dsN
NN
YY
NFi
rst-s
tage
coeffi
cient
sand
diag
nost
icsLo
gpo
tent
ialp
alm
oily
ield
N/A
0.69
0***
0.69
0***
0.89
3***
0.58
4***
Log
palm
oils
uita
bilit
yin
dex
N/A
0.50
6***
Kle
iber
gen-
Paap
Wal
drk
Fst
atN
/A13
.41
13.1
220
.25
9.08
10.8
330
perc
entm
axFu
llerb
iasc
ritic
alva
lue
N/A
12.7
112
.71
12.7
112
.71
12.7
110
perc
entm
axFu
llerb
iasc
ritic
alva
lue
N/A
19.3
619
.36
19.3
619
.36
19.3
6O
bser
vatio
ns33
530
830
330
330
330
9
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rce
ntle
vels
.A
llIV
estim
ates
use
Fulle
r’slim
ited
info
rmat
ion
max
imum
likel
ihoo
des
timat
orw
ithaF
ulle
rpar
amet
erof
one.
Sam
plei
sthe
long
-diff
eren
cecr
oss-
sect
ion
ofal
lava
ilabl
edis
tric
tsfr
om20
02–2
010.
2001
dist
rictb
ound
arie
sar
eus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar-2
001
pare
ntdi
stric
ts.
Cha
nges
insa
mpl
essi
zear
edu
eto
data
avai
labi
lity.
Oil
palm
land
isla
gged
one
perio
d(2
001–
2009
).H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsar
ein
pare
nthe
ses
and
cond
ition
allik
elih
ood
ratio
-bas
edco
nfide
ncei
nter
vals
insq
uare
brac
kets
.Isl
and
grou
ping
sare
defin
edas
Java
,Sum
atra
,Kal
iman
tan,
and
Sula
wes
i,w
ithre
mai
ning
dist
ricts
grou
ped
toge
ther
.Eac
hre
gres
sion
incl
udes
log
pove
rty
and
log
perc
apita
outp
utin
the
initi
alpe
riod
asco
ntro
lvar
iabl
es.
§3.5 Main results 87
Figure 3.4: Poverty Impacts of the 2001–2010 Oil Palm Expansion
-.15
-.1-.0
50
Least squares Oil palm yield IVOil palm yield IV + rice Oil palm yield IV + cash cropsOil palm yield IV + all crops Oil palm suitability index IV
Column 1 of Table 3.1 presents Equation 1 estimated with least squares. A
district that experienced a ten percentage point increase in the share of land
used for oil palm over the 2000s, at the mean (e.g., from 10 to 20 per cent of
district area), has a poverty rate 12 per cent lower than otherwise similar districts
in 2010. Columns 2–5 of Table 3.1 present the IV estimates, dropping island
dummies for stronger identification. Positive first-stage coefficients confirm oil
palm expansion has been most pronounced where most productive. Column 2
shows that a ten percentage point increase in district oil palm land share over the
2000s corresponds to over a thirty per cent greater reduction in the poverty rate.
That the estimate in Column 2 is almost three times the magnitude of least squares
is not surprising, as oil palm is likely to have more pronounced effects where more
productive.40 The CLR confidence interval reported under the main coefficient
40While oil palm in less suitable areas—notably in a few poorer mountainous areas—suchgrowers (i.e., “non-compliers”) account for a minuscule component of total oil palm area andproduction.
§3.5 Main results 88
does not overlap with zero, rejecting the null hypothesis that the coefficient equals
zero with 97% confidence.
In Column 3 of Table 3.1 I control for the attainable yield of two of Indonesia’s
most important non-cash crop agricultural commodities: rice (wet- and dry-land)
and cassava. The first-stage coefficient in Column 3 is virtually the same and
the second-stage coefficient slightly larger; this is expected, as rice is typically
produced in slightly different regions with different agro-climatic conditions
and so should not share much of the identifying variation with oil palms (e.g.,
compare Java to Kalimantan, or the rice-growing deltas of south-east Asia to
neighbouring tropical islands growing cash crops. See Hayami (2010) for further
discussion). In Column 4 I separately include average district-specific attainable
yields for three of Indonesia’s key tropical cash crops: cocoa, coffee, and tea.41
The first-stage coefficient increases to 0.89 and the excluded-F statistic to 20.25,
exceeding the Stock-Yogo critical value of 19.36 for ten per cent maximum Fuller
bias. The second-stage coefficient restricting the identifying variation to suitability
to oil palms but none of Indonesia’s other major cash crops (i.e., controlling
for agro-climatic suitability for cocoa, coffee, and tea) is 0.046, suggesting an
additional ten percentage point increase in oil palm land share where it is most
suitable can almost halve the poverty rate. The CLR test rejects the null that
the coefficient equals zero with almost 100% confidence. In Column 5, I include
potential yield for all six additional crops as controls. Identification is significantly
weaker, with an excluded-F statistic of 9.08, and the estimated coefficient on oil
palm expansion much larger at 0.078. The CLR test however still rejects the
null hypothesis that the coefficient is equal to zero at the ten per cent level.
Instrumenting oil palm expansion with GAEZ’s oil palm suitability index instead
of potential yield gives a similar result in Column 6. Figure 3.4 illustrates how
similar confidence intervals (at the 95% level) emerge from these alternative IV
specifications, most overlapping those from least squares.41To my knowledge, agro-climatic suitability data for rubber is unavailable.
§3.5 Main results 89
Tabl
e3.
2:Im
pact
soft
he20
01–2
010
Oil
Palm
Expa
nsio
non
the
Pove
rty
Gap
Dep
ende
ntva
riabl
e∆
log
dist
rictp
over
tyga
pin
dex
Estim
ator
OLS
IVIV
IVIV
IVC
olum
n1
23
45
6
∆oi
lpal
mla
nd/
dist
ricta
rea
-0.0
15**
*-0
.038
**-0
.038
**-0
.054
***
-0.1
32**
*-0
.046
**(0
.005
)(0
.019
)(0
.018
)(0
.018
)(0
.049
)(0
.020
)C
LR95
%co
nfide
nce
inte
rval
N/A
[-0.1
30,0
.013
][-0
.110
,0.0
06]
[-0.1
46,-
0.01
1][-0
.282
,0.0
17]
[-0.2
44,0
.017
]C
LRp-
valu
e(H
0:B=
0)N
/A0.
122
0.08
50.
016
0.08
40.
12Is
land
fixed
effec
tY
NN
NN
NPo
tent
ialr
ice
and
casa
vayi
elds
NN
YN
YN
Pote
ntia
lcoff
ee,c
ocoa
,tea
yiel
dsN
NN
YY
NFi
rst-s
tage
coeffi
cient
sand
diag
nost
icsLo
gpo
tent
ialp
alm
oily
ield
N/A
0.69
5***
0.82
***
0.90
2***
0.58
4***
Log
palm
oils
uita
bilit
yin
dex
N/A
0.50
8***
Kle
iber
gen-
Paap
Wal
drk
Fst
atN
/A13
.54
13.7
720
.53
9.08
10.8
330
perc
entm
axFu
llerb
iasc
ritic
alva
lue
N/A
12.7
112
.71
12.7
112
.71
12.7
110
perc
entm
axFu
llerb
iasc
ritic
alva
lue
N/A
19.3
619
.36
19.3
619
.36
19.3
6O
bser
vatio
ns33
530
830
330
330
330
9
Star
sden
otes
tatis
tical
sign
ifica
ncea
tthe
10,5
,and
1pe
rcen
tlev
els.
All
IVes
timat
esus
eFul
ler’s
limite
din
form
atio
nm
axim
umlik
elih
ood
estim
ator
with
aFu
llerp
aram
eter
ofon
e.Sa
mpl
eis
the
long
-diff
eren
cecr
oss-
sect
ion
ofal
lava
ilabl
edi
stric
tsfr
om20
02–2
010.
2001
dist
rict
boun
darie
sare
used
,with
new
dist
ricts
colla
psed
into
year
-200
1pa
rent
dist
ricts
.Cha
nges
insa
mpl
essi
zear
edu
eto
data
avai
labi
lity.
Oil
palm
land
isla
gged
one
perio
d(2
001–
2009
).H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsar
ein
pare
nthe
ses
and
cond
ition
allik
elih
ood
ratio
-bas
edco
nfide
nce
inte
rval
sin
squa
rebr
acke
ts.
Isla
ndgr
oupi
ngs
are
defin
edas
Java
,Sum
atra
,Kal
iman
tan,
and
Sula
wes
i,w
ithre
mai
ning
dist
ricts
grou
ped
toge
ther
.Ea
chre
gres
sion
incl
udes
log
pove
rty
and
log
per
capi
taou
tput
inth
ein
itial
perio
das
cont
rol
varia
bles
.
§3.5 Main results 90
The reduction in the poverty rate observed in Table 3.1 could be due to
people near the poverty line being lifted just above, with little effect on those
further down the income distribution. In this case, the poverty rate would fall
but the gap between the average poor person and the poverty line (i.e., poverty
depth) increase. I estimate impacts on poverty depth in Table 3.2. Results are
similar to Table 3.1. Oil palm expansion corresponds to reductions in poverty
depth. Decreases in the rate and depth of poverty confirm benefits from oil palm
expansion tend to reach the average person living below the poverty line.
Table 3.3 explores the robustness of the main long-difference estimate to
controlling for economic growth and changes in the natural environment,
partialling out effects through these two channels. In Column 1 of Table 3.3 I
control for the decadal change in log per capita output. The coefficient on RGDP
growth is statistically insignificant, and the coefficient on oil palm land share is
unchanged, implying palm oil production has been a particularly pro-poor (i.e.,
redistributive) economic activity.
Like many equatorial developing countries, Indonesia was mostly tropical
forest half a century ago and oil palms are planted on areas once primary
forest. The forestry landscape can often change alongside oil palm expansion.
Such changes could bias estimates through potential omitted variables.42 In
Column 2 I control for the initial level and 2000–2010 change in tree cover using
pixel-level Moderate Resolution Imaging Spectroradiometer (MODIS) satellite
imagery data.43 Results are similar to Column 1 of Table 3.1 and the coefficients
on tree cover variables statistically insignificant, suggesting conversion of primary
forest into more “economically productive” use is not driving my result (i.e.,
42For example, income from forestry and logging taking place in the same districts as oilpalm expansion could bias my estimates downwards, and social harms like conflict and malariaassociated with deforestation could bias estimates upwards. Note that if such factors arise due tooil palm expansion, this is included in the net effect in my main estimates.
43Data are taken from Wheeler et al (2013). While MODIS data cannot disentangle primaryforest from plantations (i.e., it is distinctly not a measure of deforestation in the Indonesiancontext), it is still a useful proxy for observed changes in forest and the natural environment.Burgess et al (2012) and Wheeler et al (2013) discuss the MODIS data in detail.
§3.5 Main results 91
Tabl
e3.
3:A
ddit
iona
lCo
vari
ates
and
Robu
stne
ss
Dep
ende
ntva
riabl
e:∆
log
dist
rictp
over
tyra
te
Col
umn
12
34
∆oi
lpal
mla
nd/
dist
ricta
rea
-0.0
12**
*-0
.011
***
-0.0
13**
*-0
.014
***
(0.0
03)
(0.0
04)
(0.0
04)
(0.0
04)
∆lo
gdi
stric
tGD
Ppe
rcap
ita(ID
R)-0
.048
(0.0
74)
∆tr
eeco
ver(
pixe
ls)
-0.0
001
(0.0
001)
Late
first
year
NN
YN
Early
final
year
NN
NY
Obs
erva
tions
335
335
336
334
Star
sden
ote
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Sam
ple
isth
elo
ng-d
iffer
ence
cros
s-se
ctio
nof
all
avai
labl
edi
stric
tsfr
om20
02–2
010,
with
oil
palm
land
lagg
edon
epe
riod.
2001
dist
rict
boun
darie
sar
eus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar-2
001
pare
ntdi
stric
ts.
Het
eros
keda
stic
ity-r
obus
tst
anda
rder
rors
are
inpa
rent
hese
s.A
lles
timat
esus
eor
dina
ryle
ast
squa
res
and
cont
rolf
oris
land
fixed
effec
ts,i
nitia
lpov
erty
,and
initi
alpe
rca
pita
inco
me.
Isla
ndgr
oupi
ngsa
rede
fined
asdi
stric
tsfr
omJa
va,S
umat
ra,K
alim
anta
n,an
dSu
law
esi,
with
rem
aini
ngdi
stric
tsgr
oupe
dto
geth
er.C
hang
ein
tree
cove
rref
erst
oth
ech
ange
pixe
lsof
tree
cove
rmea
sure
dby
MO
DIS
sate
llite
data
;ini
tialt
ree
cove
risa
lso
incl
uded
asa
cont
rolv
aria
ble
inC
olum
n2.
§3.6 Short-run and dynamic impacts 92
similar poverty impacts from oil palm land expansion are observed holding tree
cover constant). Finally, as long-differences can sometimes be sensitive to start
and finish year, I use alternative start and finish years in Columns 3 and 4 of Table
3.3. Results are similar.44
3.6 Short-run and dynamic impacts
I have focused on the total changes in oil palm plantation land and poverty
over the 2000s. But the relationship between growth in the palm oil sector and
poverty could vary over the crop’s life cycle. In this section I use alternative panel
estimators to examine short-run impacts.
My preferred panel estimator takes the form:
ln(yd,t) = βPd,t−1 + δd + τi,t + γXd,t−1 + εd,t (3.2)
yd,t denotes poverty in district d at time t. Pd,t−1 is the oil palm land percentage
of total district area, with additional lags in some estimates.45 β is the effect of
an additional percentage point of oil palm land on poverty. δd are district fixed
effects, removing time-invariant district-specific sources of confoundedness. τi,t
are island–year fixed effects capturing time-varying shocks common to each island
group (e.g., economic growth and business cycles, international commodity
prices for an island’s commodities, political shocks, regional infrastructure
investments, and other major policy changes).46 Island-year fixed effects focus my44Following the common heuristic that coefficient stability to additional controls can be
informative about omitted variable bias (Oster, 2015; Bellows and Miguel, 2009; Altonji et al, 2005),the estimated parameter of interest is also similar if I include electricity-related variables proxyingeconomic capacity (Sparrow et al, 2015), fiscal and political variables, and the battery of othercorrelates of poverty available in the Indonesia Database for Policy and Economic Research (WorldBank, 2015). Such additional estimates are available on author request. Further robustness checksare provided in the Chapter Appendix, including using district per capita palm oil production intons instead of the district share of oil palm land, splitting the sample period, and omitting theisland of Java.
45Estimates including lead values of palm oil land, as in-time placebo tests, are included in theChapter Appendix.
46Social policy in Indonesia is strongly targeted towards the poor, but its spatial is relatively
§3.6 Short-run and dynamic impacts 93
comparison to districts within the same island group, relaxing the parallel trends
assumption to more flexible regional trends. γXd,t−1 is a vector of potential time-
and district-varying controls. Standard errors are adjusted for heteroskedasticity
and clustered by district to allow arbitrary correlation within districts over time.47
β̂ in Equation Two has a causal interpretation if there are no time and district
varying omitted variables correlated with yd,t and Pd,t−1 influential enough to
systematically shift poverty trends within island groups. Assuming changes
in official oil palm land are exogenous to changes in district poverty outcomes
conditional on district and island-by-year fixed effects is reasonable for two
reasons. Firstly, Equation Two focuses on the poverty response from the timing
of district oil palm expansions, so the critical issue is what determines the timing.
Oil palm land declared by the Department of Agriculture reflects plantation sector
land use decisions made through the large, decentralised bureaucracy: each step
in this process is influenced by idiosyncratic factors resulting highly unpredictable
delays.48 The second reason is that island-year fixed effects appear to eliminate
selection bias into oil palm in the short run: a wide range of time-varying
correlates of poverty do not explain changes in oil palm land when included in
the same panel regression model as island-year fixed effects, consistent with the
short-term changes in oil palm land—the timing—being subject to some degree
of randomness.49
unchanged from 2001–2010 and mostly captured by district fixed effects. New social programswere mostly implemented nationally (e.g., the Raskin rice subsidy, PNPM, unconditional cashtransfers, and scholarships) so captured by island–year fixed effects, or piloted in a few villagesbefore national roll-out.
47Bertrand et al (2004) discuss problems arising in panel estimates when serial correlationis unaddressed. I consider larger cluster robust errors a more conservative basis for inferenceand hypothesis testing, with weaker assumptions and better finite sample properties than moreefficient counterparts.
48Indonesian land use regulations are complicated. The Regional Autonomy Laws 1999 sawdistrict forest departments become answerable to bupatis (district heads) instead of the centralgovernment. Bupatis apply to the central government for approval to convert land into oil palmplantations, a process involving identifying areas for plantations, attracting investors, gainingdistrict parliament approval, making a formal request to the central government, central agenciesworking through the request, the district receiving approval, and land being converted. Burgesset al (2012) similarly highlight how administrative lags from central to district governments renderdistrict splits exogenous to province and district outcomes.
49Estimates provided in the Chapter Appendix show how many poverty correlates are
§3.6 Short-run and dynamic impacts 94
Tabl
e3.
4:Sh
ort-
run
and
Dyn
amic
Pove
rty
Impa
cts
Dep
ende
ntva
riabl
e:lo
gdi
stric
tpov
erty
rate
Estim
ator
Firs
t-diff
eren
ceW
ithin
FEFE
IV
Col
umns
12
34
56
78
Oil
palm
land
/di
stric
tare
a(%
)-0
.003
**-0
.004
***
-0.0
04**
-0.0
07**
*-0
.005
**-0
.007
***
-0.0
06**
*-0
.037
***
(0.0
01)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
14)
Firs
tlag
-0.0
03-0
.002
-0.0
03
(0.0
02)
(0.0
03)
(0.0
03)
Seco
ndla
g-0
.009
***
-0.0
08**
-0.0
07**
(0.0
03)
(0.0
04)
(0.0
03)
Third
lag
-0.0
06**
*-0
.005
*-0
.005
(0.0
02)
(0.0
03)
(0.0
03)
Σco
effici
ents
-0.0
22**
*-0
.019
***
-0.0
20**
*
Firs
tsta
geco
effici
ent
0.20
7***
Kle
iber
gen-
Paap
Wal
drk
Fst
at8.
79
Dis
tric
tfixe
deff
ects
NN
YY
YY
YY
Isla
nd-s
peci
fictim
etr
ends
NN
NN
NY
NN
Prov
ince
-spe
cific
time
tren
dsN
NN
NN
NY
N
Obs
erva
tions
3040
2371
2371
3386
2717
3386
3386
3386
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,
5,an
d1
per
cent
leve
ls.
Het
eros
keda
stic
ity-r
obus
tst
anda
rder
rors
are
inpa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
el.
Sam
ple
isan
annu
al34
1-di
stric
tpan
elfr
om20
02–2
010.
Oil
palm
land
isla
gged
one
perio
d.20
01di
stric
tbo
unda
ries
are
used
,with
new
dist
ricts
colla
psed
into
year
-200
1pa
rent
dist
ricts
.C
hang
esin
sam
ple
size
are
due
toda
taav
aila
bilit
y.Is
land
-yea
rfix
edeff
ects
are
incl
uded
thro
ugho
ut,w
ithis
land
grou
ping
sde
fined
Java
,Sum
atra
,Kal
iman
tan,
Sula
wes
i,an
dth
ere
st.T
hew
ithin
estim
ator
refe
rsto
the
mea
n-di
ffere
nced
(with
in-d
istr
ict)
fixed
effec
tses
timat
or.
The
FEIV
estim
ator
isa
mea
n-di
ffere
nced
Fulle
rlim
ited
info
rmat
ion
max
imum
likel
ihoo
dIV
estim
ator
,w
here
the
inst
rum
enti
sdi
stric
t-spe
cific
linea
rtre
nds
base
don
the
leve
lofi
nitia
loil
palm
land
inea
chdi
stric
t.Si
gnifi
canc
ere
port
edfo
rsum
ofth
eco
effici
ents
rela
test
oth
ete
stth
atth
esu
mof
the
coeffi
cien
tson
oilp
alm
iseq
ualt
oze
ro.
§3.6 Short-run and dynamic impacts 95
I estimate Equation Two with first-differences, mean-deviations (i.e., within
estimation), distributed lags, and IV. Column 1 of Table 3.4 presents the annual
first-difference. Assuming an effect within the same year, a ten percentage point
increase in the district share of land used for oil palm in one year corresponds to
a three per cent reduction in the poverty rate the next year, statistically significant
at the five per cent level. Assuming the land data is accurate and timely in its
reporting, such immediate effects must come through channels other than the
production and sale of the crop (e.g., payments to communities or waged labour
to establish plantations). In Column 2 I include the first three lags of the annual
first-difference. The second and third lags have much larger coefficients, reflecting
the oil palm life cycle. The sum of the coefficients on oil palm land is 0.022,
between long-difference estimates obtained from least squares and IV. As the
evolution of oil palm land has been gradual, in Column 3 I take first differences
and include district-fixed effects to extract the “shock” component of the changes
in oil palm land (Ciccone, 2011). Coefficients are similar.
In Columns 4–7 of Table 3.4 I adopt the mean-differenced “within” estimator.
Unlike the first-differences in Columns 1–3, coefficients reflect the effect of
variation over time within each district (c.f., at a particular point in time across
districts). As within estimation also picks up level effects, this is a more
appropriate flexible estimator than first-differences (i.e., due to the lags in the
palm oil production process). Column 4 of Table 3.4 presents my preferred within
panel estimate. A ten percentage point increase in the share of land used for oil
palm at the mean corresponds to a seven per cent reduction in the poverty rate
in the short-run. Column 5 includes lags, summing exactly to the least squares
long-difference in Column 1 of Table 3.1. Columns 6 and 7 include island- and
province-specific time trends. Results are almost identical with these rich control
vectors.
statistically significant determinants of changes oil palm land in pooled least squares and withinestimators with district and year fixed effects (Columns 1 and 2), but including island-year fixedeffects in Columns 3 renders them all statistically insignificant.
§3.7 A migration story? 96
The final column of Table 3.4 presents a panel fixed effects IV estimate,
exploiting demonstration effects arising from early adoption. I instrument the
level shares of oil palm land with a district-specific linear trend increasing in
districts’ initial oil palm land, as initial conditions matter for future growth
trajectories. Oil palms historically expanded more in areas where plantations
were already established due to better access to pre-existing knowledge networks,
materials, processing facilities, and other necessary infrastructure. Column 8 of
Table 3.4 shows that a ten percentage point increase in the share of district land
used for oil palm at the mean—exclusively due to that district’s steeper oil palm
land expansion trajectory because of existing oil palm activity—corresponds to
an almost 40 per cent reduction in the poverty rate. The larger IV estimates
suggest early adopters on average achieved more rapid poverty reduction, with
a magnitude similar to my long-difference IV estimates.50
Table 3.5 presents the same set of estimates for poverty depth. The main
coefficient is negative, of similar magnitude, and statistically significant across
all estimates. Panel estimates in Section 4.6 withstand in-time placebo tests and
the inclusion of a wide range of time-varying covariates (see Chapter Appendix).
3.7 A migration story?
District poverty rates can fall either due to real consumption growth for
the poor, or through changes in population. Population changes that would
contaminate my interpretation include inward migration of non-poor people
and outward migration of poor people. Both would also alter poverty rates
in my comparison pool if migration is to and from districts without oil palm
expansion. Critics of the palm oil sector highlight a story of displacement, where
“land-grabbing” drives forest-dwellers, indigenous people, and poor farmers off
50This local average partial effect is quite widely applicable, with 71 of the 341 districts acrossfour of the five island groups using some share of their land for oil palm plantations in 2000.
§3.7 A migration story? 97
Tabl
e3.
5:Sh
ort-
run
and
Dyn
amic
Impa
ctso
nPo
vert
yD
epth
Dep
ende
ntva
riabl
e:lo
gpo
vert
yga
pin
dex
Estim
ator
Firs
t-diff
eren
ceW
ithin
FEFE
IV
Col
umns
12
34
56
78
Oil
palm
land
/di
stric
tare
a(%
)-0
.010
***
-0.0
09**
*-0
.012
**-0
.014
***
-0.0
09**
-0.0
14**
*-0
.006
***
-0.0
21**
*
(0.0
03)
(0.0
03)
(0.0
05)
(0.0
04)
(0.0
04)
(0.0
04)
(0.0
02)
(0.0
07)
Firs
tlag
0.00
0-0
.005
0.00
1
(0.0
04)
(0.0
07)
(0.0
04)
Seco
ndla
g-0
.006
-0.0
10-0
.003
(0.0
04)
(0.0
07)
(0.0
05)
Third
lag
-0.0
11**
-0.0
14**
-0.0
10**
(0.0
04)
(0.0
06)
(0.0
05)
Σco
effici
ents
-0.0
26**
*-0
.041
***
-0.0
23**
*
Firs
tsta
geco
effici
ent
0.47
8***
Kle
iber
gen-
Paap
Wal
drk
Fst
at12
.17
Dis
tric
tfixe
deff
ects
NN
YY
YY
YY
Isla
nd-s
peci
fictim
etr
ends
NN
NN
NY
NN
Prov
ince
-spe
cific
time
tren
dsN
NN
NN
NY
N
Obs
erva
tions
2705
2036
2036
3051
2382
3051
3386
3051
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,
5,an
d1
per
cent
leve
ls.
Het
eros
keda
stic
ity-r
obus
tst
anda
rder
rors
are
inpa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
el.
Sam
ple
isan
annu
al34
1-di
stric
tpan
elfr
om20
02–2
010.
Oil
palm
land
isla
gged
one
perio
d.20
01di
stric
tbou
ndar
iesa
reus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar-2
001
pare
ntdi
stric
ts.C
hang
esin
sam
ple
size
ared
ueto
data
avai
labi
lity.
Isla
nd-y
earfi
xed
effec
tsar
einc
lude
dth
roug
hout
,with
isla
ndgr
oupi
ngsd
efine
dJa
va,S
umat
ra,
Kal
iman
tan,
Sula
wes
i,an
dth
eres
t.Th
ewith
ines
timat
orre
fers
toth
emea
n-di
ffere
nced
(with
in-d
istr
ict)
fixed
effec
tses
timat
or.
The
FEIV
estim
ator
isa
mea
n-di
ffere
nced
Fulle
rlim
ited
info
rmat
ion
max
imum
likel
ihoo
dIV
estim
ator
,whe
reth
ein
stru
men
tis
dist
rict-s
peci
ficlin
eart
rend
sbas
edon
the
leve
lofi
nitia
loil
palm
land
inea
chdi
stric
t.Si
gnifi
canc
ere
port
edfo
rsum
ofth
eco
effici
ents
rela
test
oth
ete
stth
atth
esu
mof
the
coeffi
cien
tson
oilp
alm
iseq
ualt
oze
ro.
§3.7 A migration story? 98
their previously occupied land (Gellert, 2015; Cramb and McCarthy, 2016). I do
not dispute the existence of such cases and have heard them first hand. But could
population movements–by choice or by force–explain the reductions in district
poverty rates documented in this Chapter?
Understanding the scale and scope of local migration in Indonesia is difficult,
as reliable internal migration data are collected only in the decadal population
census with questions only on inter-province movements and no information
on income level.51 I investigate the plausibility of a migration-based alternative
explanation in three main steps. I first identify the quantum of migration needed
to explain my main estimates in the context of official internal migration statistics
and relevant contextual information gathered from two field visits. I then pursue
province-level estimates, less exposed to the issue of migration given the larger
size of provinces vis-a-vis districts. I finally estimate district-level impacts of oil
palm expansion on population change and on the number of poor people in a
district (c.f., the poverty rate).
The 2010 Indonesian Population Census reported 2.5 per cent of the population
living in a different province to where they lived at the time of the previous census.
In resource-rich provinces, the rate can be higher (around 6 per cent in Riau and
East Kalimantan) or closer to the national average, even below (4 and 1.8 per
cent in Jambi and South Sumatra). The highest rate is in West Papua, the least
densely populated province, but still under 8 per cent. It is important to note
that magnitude of migration flows in the average oil palm district would have
to be around four times the national average rate of recent migrants to explain
my long-difference estimate (i.e., 10 per cent), a further four times that to explain
my preferred IV estimate, and predominately involve poor people leaving or
non-poor people coming. Contrast this to the tendencies of lower-income people
to move to booming regions seeking economic opportunities, and of wealthy51SUSENAS and the labour market surveys SAKERNAS do not include information on
migration. The Indonesian Family Life Surveys are not district-representative. Meng et al (2010)study recent internal migration patterns in Indonesia.
§3.7 A migration story? 99
beneficiaries of natural resource sectors to be based in capital cities.
Two other contextual issues bear a mention. First, the popular displacement
narrative relates to agro-industrial frontier expansion, but smallholders manage
around half of Indonesia’s planted oil palm area. The increase in planted oil palm
area over the period of this study was mostly from smallholders, thus accounting
for most of the identifying variation. Independent smallholders tend to be local
people without much government or company support, to be less affluent, but
also to be far more hesitant to move. Plasma scheme smallholders mostly moved
in during the transmigration program, which ceased in 2000.52
Second, a district is a large geographic unit, on average comprising over 200
villages. When villages are forcefully moved or formal relocation agreements
reached, communities tend to be relocated nearby or incorporated into plantation
activities within the same sub-district (kecamaten) or the existing village area if
large–often on unfavourable terms. Relocation to other districts is rare, and a
displaced poor individual is unlikely to move farther than the district or provincial
capital, in no small part due to financial constraints.
Estimating analogous models at a greater level of spatial aggregation
is a useful way to remove the influence of any within-province migration.
Province-level estimates are presented in Table 3.6. Columns 1 and 2 present
short-run effects, focusing on changes within each province over time. Column
1 includes island-specific poverty trends and Column 2 island-year fixed effects.
The magnitude of the estimate in Column 2 is similar to that from the analogous
district-level within estimator (Column 4 of Table 3.4). A long-difference estimate
with island fixed effects is presented in Column 3. Provinces with a ten percentage
point increase in their share of oil palm land have experienced, on average, a 13
per cent greater reduction in the poverty rate from 2002–2010. Province-level
estimates are similar to district-level estimates, suggesting that intra-province
migration is not substantially affecting my findings.52Bazzi et al (2016) detail Indonesia’s transmigration program.
§3.7 A migration story? 100
Table 3.6: Province level Results
Estimator FE FE LD
Column 1 2 3
Oil palm land / district area (%)-0.014* -0.007** -0.013**
(0.006) (0.003) (0.004)
Linear island trends Y N N
Island–year fixed effects N Y N
Island fixed effects N Y Y
Observations 319 319 30
Stars denote statistical significance at the 10, 5, and 1 per centlevels. Sample is an annual balanced panel of Indonesianprovinces from 2002–2010, with oil palm land lagged one period.Estimates are the within estimator with province fixed effects (FE)and the long difference estimator (LD). Heteroskedasticitiy-robuststandard errors are in parentheses, clustered at the province levelfor FE estimates and the island level for LD. Data taken from theWorld Bank (2015).
In Table 3.7 I present results from least squares fixed effects and long-difference
estimators (Equations 1 and 2) using logged population (Columns 1 and 2) and
logged number of poor people (Columns 3 and 4) as dependent variables. Column
1 provides no evidence of any short-term change in population size arising from
oil palm land expansion. Column 2 shows that over the nine years, districts with
greater oil palm expansion tend to now have slightly larger populations, although
this effect is statistically significant only at the ten per cent level. Columns 3 and
4 show more oil palm land corresponds to a large reduction in the total number
of poor people in each district.53 I cannot rule out poor people systematically
leaving oil palm districts and being replaced by non-poor inward migrants, but
the evidence presented above suggests that this is highly unlikely to fully explain
the falling poverty rates identified in this Chapter.
53Note that estimates in Columns 1–4 are simple decompositions of estimates in Tables 3.1 and3.4 using the log poverty rate as the dependent variable.
§3.7 A migration story? 101
Tabl
e3.
7:Po
pula
tion
,Poo
rPe
ople
,and
Prod
ucti
on
Dep
ende
ntva
riabl
eLo
gdi
stric
tpop
ulat
ion
Log
num
bero
fpoo
rLo
gdi
stric
tpov
erty
rate
Estim
ator
FELD
FELD
FELD
Col
umn
12
34
56
Oil
palm
land
/di
stric
tare
a(%
)-0
.000
70.
003*
-0.0
11**
*-0
.009
***
-0.0
04*
-0.0
07
(0.0
007)
(0.0
02)
(0.0
03)
(0.0
03)
(0.0
02)
(0.0
04)
Perc
apita
palm
oilp
rodu
ctio
n(to
ns)
-0.1
90**
*-0
.125
**
(0.0
54)
(0.0
54)
Dis
tric
tfixe
deff
ects
YN
YN
YN
Isla
nd-y
earfi
xed
effec
tsY
NY
NY
N
Initi
alco
nditi
onco
ntro
lsN
YN
YN
Y
Dis
tric
ts34
133
534
133
534
133
5
Obs
erva
tions
3689
335
3045
335
3386
335
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rce
ntle
vels
.Sa
mpl
eis
anan
nual
341
dist
rictp
anel
from
2002
–201
0.O
ilpa
lmva
riabl
esar
ela
gged
one
perio
d.20
01di
stric
tbou
ndar
iesa
reus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar-2
001p
aren
tdis
tric
ts.V
aria
tions
inth
esam
ples
izea
redu
eto
data
avai
labi
lity.
Estim
ator
sare
with
infix
edeff
ects
(FE)
and
long
diffe
renc
es(L
D).
Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
arei
npa
rent
hese
s,cl
uste
red
atth
edis
tric
tlev
elfo
rpan
eles
timat
ors.
Initi
alco
nditi
onco
ntro
lsre
fert
olo
gdi
stric
tper
capi
tain
com
e,lo
gpo
vert
yra
te,a
ndan
isla
ndgr
oup
fixed
effec
t.
§3.8 Heterogeneity and wider impacts 102
3.8 Heterogeneity and wider impacts
3.8.1 Heterogeneity by region
Existing qualitative studies examining the poverty implications of oil palm
expansion in Indonesia emphasise context-specific heterogeneity (McCarthy,
2010). I now explore potential heterogeneity by region and sector. Figure 3.5
presents the long-difference point estimates for each of Indonesia’s five main
regions. Tabulated results are presented in Table 3.8, for the full-sample within
and long-difference estimates, interacting the island dummies with my main oil
palm land share variable to provide marginal effects by region.54
Figure 3.5: Regional Heterogeneity
-.1-.0
50
.05
.1.1
5Se
mi-e
last
icity
of d
istri
ct p
over
ty ra
te to
oil
palm
land
sha
re
Java Sumatra Kalimantan Sulawesi Other
54I drop the main (not interacted) effects to allow a more straightforward interpretation ofthe estimated coefficients. Main results in Tables 3.1 and 3.2 are weighted averages of these.Sub-sample estimates are provided in the Chapter Appendix.
§3.8 Heterogeneity and wider impacts 103
Table 3.8: Regional Heterogeneity
Dependent variable Log poverty rate Log poverty gap
Estimator FE LD FE LD
Column 1 2 3 4
Java*oil palm land share0.013 -0.040 -0.063 -0.122*
(0.015) (0.041) (0.040) (0.065)
Sumatra*oil palm land share-0.006*** -0.010*** -0.012*** -0.011**
(0.002) (0.003) (0.003) (0.004)
Kalimantan*oil palm land share-0.014*** -0.029*** -0.027*** -0.039***
(0.004) (0.008) (0.007) (0.012)
Sulawesi*oil palm land share-0.045*** -0.052*** -0.044*** -0.031***
(0.007) (0.017) (0.008) (0.008)
Other*oil palm land share-0.036 0.060 -0.162 0.098
(0.128) (0.062) (0.174) (0.118)
District and year fixed effects Y N Y N
Initial conditions controls N Y N Y
Observations 3386 335 3051 335
Stars denote statistical significance at the 10, 5, and 1 per cent levels. Samplesample is an annual district panel from 2002–2010. Oil palm land is laggedone period. 2001 district boundaries are used, with new districts collapsed intoyear-2001 parent districts. Island groupings are defined as districts from Java,Sumatra, Kalimantan, Sulawesi, and with remaining islands grouped together.Estimators are within fixed effects estimator (FE) with district and year fixedeffects, and the long difference estimator (LD) with initial log poverty andlog per capita income controls. Heteroskedasticity-robust standard errors arein parentheses, clustered at the district level for FE estimates. Island*palminteraction terms interact the island dummy for each island with the main oilpalm land share variable. Main effects (not interacted) are dropped for a morestraightforward interpretation.
§3.8 Heterogeneity and wider impacts 104
Column 1 of Table 3.8 presents the within estimate including district and year
fixed effects. The coefficients on the interaction terms for regions with little oil
palm (i.e., Java and eastern Indonesia) are statistically insignificant. Across the
main oil palm growing regions of Sumatra, Kalimantan, and Sulawesi, districts
have experienced short-run poverty reductions as a result of oil palm expansion.
Districts in Sulawesi experienced the largest reductions in district poverty rates,
highlighting how it is not just regions with relatively low poverty driving
my results, but some districts with high poverty rates making commensurate
proportional poverty reductions. The long-difference estimate presented in
Column 2 paints a similar picture to the panel estimates. The only difference is
magnitudes for Kalimantan and Sulawesi are similar in the short and long runs,
but for Sumatra the long-run effects are twice the magnitude of the short-run
effects. A plausible explanation for this difference is that in Kalimantan and
Sulawesi most recent oil palm expansion has come through large industrial-scale
plantations, whereas Sumatran oil palm expansion has been predominantly
smallholders (discussed further below). Columns 3 and 4 of Table 3.8 present
impacts on the poverty gap by region. Results are similar in the short and longer
term across all palm oil producing regions.
3.8.2 Heterogeneity by sector
Indonesian smallholders are reported to have per hectare yields up to 40
per cent lower than industrial estates, struggle to exploit economies of scale,
and use inefficient practices restricting yields and incomes (Hasnah et al, 2004;
Burke and Resosudarmo, 2012; Lee et al, 2013; Alwarritzi et al, 2015). Industrial
plantations are usually between 5,000–20,000 hectares and intensively managed
to maximise efficiency (Corley and Tinker, 2003). Naturally the sectors could have
heterogeneous effects.
§3.8 Heterogeneity and wider impacts 105
Table 3.9: Heterogeneity by Plantation Type
Sector State Private Smallholder
Panel width Annual 4-year Annual 4-year Annual 4-year
Column 1 2 3 4 5 6
Panel A: log poverty rate
Oil palm land/district area (%)-0.011** -0.011** -0.012*** -0.011*** -0.004 -0.011**
(0.004) (0.004) (0.003) (0.004) (0.002) (0.004)
Panel B: log poverty gap index
Oil palm land/district area (%)-0.012** -0.015** -0.014*** -0.011** -0.004 -0.014**
(0.006) (0.006) (0.004) (0.005) (0.003) (0.006)
Observations 3009 1004 3009 1004 3009 1004
Stars denote statistical significance at the 10, 5, and 1 per cent levels. Sample is an annual 341 districtpanel (2002–2010) Oil palm land is lagged one period. 2001 district boundaries are used, with newdistricts collapsed into year-2001 parent districts. Heteroskedasticity-robust standard errors are inparentheses, clustered at the district level. A within estimator with district and island–year fixedeffects is used throughout.
Table 3.9 compares the poverty impacts of additional state, industry, and
smallholder-managed oil palm land. Data are taken from the Tree Crop Statistics
for Indonesia from the Department of Agriculture. Sub-sectoral oil palm land
data are strongly unbalanced, so I use the within estimator (i.e., Equation
Two) and shift between an annual and four-yearly panel to assess dynamics.
Columns 1–4 of Table 3.9 show similar coefficient magnitudes and dynamics
in large state-owned and private plantations, consistent in magnitude with the
long-difference presented in the Column 1 of Table 3.1. Effects for smallholders
are more variable. In Column 5, there is no detectable short-run relationship
between more smallholder land and district poverty rates. Large state and
company plantations, on the other hand, immediately hire labour to establish
and work on the plantations, often building local infrastructure and community
facilities for their workers. Independent smallholders bear these costs and usually
see little profit for two years. Column 6 extends the time-to-effect to four years:
the coefficient is similar to other sectors.
§3.8 Heterogeneity and wider impacts 106
Across all four-yearly panel estimates I find no evidence of any differential
impacts on the rate or depth of poverty, despite the different nature of the sectors
and their varying direct engagement with the poor. This is shown clearly for
the poverty rate in Figure 3.6, with similar point estimates and overlapping
confidence intervals for the three palm oil sub-sectors.55
Figure 3.6: Sector Heterogeneity
-.05
-.04
-.03
-.02
-.01
0Se
mi-e
last
icity
of d
istri
ct p
over
ty ra
te to
oil
palm
land
sha
re
State-owned Private Industry Smallholder
55Estimates exploring further heterogeneity by sector and by land quality and provided in theChapter Appendix.
§3.8 Heterogeneity and wider impacts 107
3.8.3 Wider impacts
In this section I use similar long-difference and fixed effects estimators to
assess whether oil palm expansion has generated district-level aggregate demand
spillovers. Table 3.10 estimates the local economic impacts of oil palm expansion
on the value of district agricultural, manufacturing, and aggregate output (all
official BPS data). First, note that while the estimated coefficients are mostly
positive and statistically significant, magnitudes are not large. Oil palm expansion
does not systematically correspond to local economic booms. For the two
sectors most directly involved—agriculture and manufacturing, which accounts
for milling—Columns 1–4 show small, persistent, and statistically significant
increases in the value of output. A ten percentage point increase in the share
of land used for oil palm plantations corresponds to a seven per cent increase in
the value of agricultural output and a four per cent increase for manufacturing.
Considering aggregate output in Column 5, an annual panel fixed effects estimate
finds no statistically significant immediate effect, implying short-run crowding
out and reallocation of factors of production. The long-difference estimate in
Column 6 shows increasing the share of district land used for oil palm by 10
percentage points corresponds to an average increase in non-oil and gas real
output of 2.4 per cent relative to districts without oil palm expansion. Any
crowding-out of other local economic activity appear at least fully offset in the
medium term, with net economic effects positive but small.
§3.8 Heterogeneity and wider impacts 108
Tabl
e3.
10:E
ffec
tsof
Oil
Palm
Expa
nsio
non
Sect
oral
and
Tota
lD
istri
ctG
DP
RGD
Pde
pend
entv
aria
ble(
IDR)
Log
agric
ultu
ralo
utpu
tLo
gm
anuf
actu
ring
outp
utLo
gG
DP
(exc
l.oi
l,ga
s)
Estim
ator
FELD
FELD
FELD
Oil
palm
land
/di
stric
tare
a(%
)0.
004*
*0.
007*
**0.
005*
**0.
004*
0.00
10.
002*
*
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
02)
(0.0
01)
(0.0
01)
Dis
tric
tfixe
deff
ects
YN
YN
YN
Isla
nd–y
earfi
xed
effec
tsY
NY
NY
N
Initi
alco
nditi
onsc
ontr
ols
NY
NY
NY
Obs
erva
tions
3410
342
3410
342
3410
342
Star
sden
ote
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Sam
ple
isan
annu
al34
1di
stric
tpan
elfr
om20
02–2
010.
Oil
palm
land
isla
gged
one
perio
d.20
01di
stric
tbou
ndar
ies
are
used
,with
new
dist
ricts
colla
psed
into
year
-200
1pa
rent
dist
ricts
.Es
timat
ors
are
with
infix
edeff
ects
(FE)
and
long
diffe
renc
e(L
D)
estim
ator
s.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsar
ein
pare
nthe
ses,
clus
tere
dat
the
dist
rictl
evel
forp
anel
estim
ator
s.In
itial
cond
ition
sco
ntro
lsre
fer
tolo
gdi
stric
tper
capi
tain
com
ean
dlo
gpo
vert
yra
tein
2000
,and
isla
ndfix
edeff
ects
.
§3.9 Conclusion 109
3.9 Conclusion
This chapter’s objective was to quantify the contribution of oil palm
expansion to local poverty reduction in Indonesia. While there have
been clear environmental consequences associated with Indonesia’s rapid
increase in palm oil production, Indonesian districts using more land for oil
palm tend to experience more rapid poverty reduction. Indonesia’s recent
smallholder-led oil palm expansion provides an important case study of how
geographically-dispersed pro-poor growth can reach remote rural regions. But
how significant is this contribution for national poverty reduction?
Table 3.11: Estimated Contribution to Poverty Reduction
District ∆ oil palm/areaPoverty rate, 2010
∆ poorActual Counterfactual
Column 1 2 3 4
Rokan Hulu 36 13 20 -28,526
Asahan 34 12 13 -47,012
Labuhan Batu 34 13 12 -43,457
Tanah Laut 24 5 7 -4,509
Deli Serdang 21 7 9 -50,281
Simalungun 20 11 15 -26,463
Kampar 19 10 13 -16,303
Kuantan Singingi 19 13 19 -11,003
Pasaman 17 10 12 -13,077
Langkat 17 11 16 -29,751
Σ estimated poverty reduction for all districts (no. poor people) -1,319,369
Districts are ten largest oil palm expansions, as measured by the 2001–2009change in district area allocated to oil palm and defined by 2001 districtboundaries. Counterfactual poverty rates are estimated by predicting eachdistrict poverty rate with oil palm expansion set to zero using the mostconservative least squares estimator (Column 1, Table 3.1). The estimatedpoverty reduction is calculated from the difference between the estimatedpoverty rate and its counterfactual. The sum in the final row is for all districtsfor which data are available.
§3.9 Conclusion 110
Table 3.11 presents the ten districts with the largest proportional oil palm
land expansions. Columns 3 and 4 compare the actual poverty rate to a
simulated counterfactual poverty rate without oil palm expansion based on my
first least squares estimate (i.e., setting oil palm expansion to zero and using a
semi-elasticity of 0.012). All but one of these districts reduced poverty below
its estimated counterfactual poverty rate in the absence of oil palm expansion.
Of the more than 10 million Indonesians lifted from poverty over the 2000s, my
most conservative estimate suggests that at least 1.3 million people have escaped
poverty exclusively due to growth in the oil palm sector.
In this chapter I focused on the macro-level, reduced-form impacts of oil palm
expansion on local poverty. My focus on effects within the same district tends to
miss spillovers across regions or nationally, positive and negative. My findings do
not imply that an oil palm boom is the best way to reduce national poverty. Detailed
mechanism analysis using individual-level data is now needed to understand
whether the observed poverty reduction is purely a labour income story for
those employed in the agricultural sector, or whether there are wider economic
spillovers. Recent work on local multipliers (e.g., Moretti, 2010; Hornbeck
and Keskin, 2015) provides a useful framework for such analysis and, to my
knowledge, has not yet been extended to a developing country context (i.e.
with imperfect substitutability between imports and local consumables, immobile
factors of production, and abundant unskilled labour). Moreover, Indonesia
has continued to rapidly urbanise since its 1998 decentralisation without much
further industrialisation—a phenomenon common to many resource-dependent
countries (Vollrath, Gollin, and Jedwab, 2015). Most palm oil companies are
based in capital cities and general equilibrium effects are not well understood,
particularly consumption linkages to cities’ non-tradable sectors where profits are
mostly spent. The longer-term economic and social consequences of pro-poor
primary sector growth also warrant further study.
§3.9 Conclusion 111
My main finding that oil palm expansion has tended to reduce local poverty
in Indonesia should be considered with four more widely-known facts. First,
given the immense environmental costs associated with converting tropical forests
to oil palm plantations, emissions and biodiversity loss in particular, a strong
environmental case can be made for any future expansion to focus on existing
agricultural or degraded land already identified as suitable for oil palm (Gingold
et al, 2012; Austin et al, 2015). Second, oil palms are one of the most productive
uses for land in humid low-lying tropics. There are large gains to be made
from farmers continuing to switch to more productive crops. Market failures
inhibiting crop switching, for example incomplete credit markets, insufficient
public infrastructure, or restrictive land use practices (e.g., relating to food
self-sufficiency policies) could be promising areas for further research or policy
development. Third, large differences in productivity remain (between Indonesia
and Malaysia, and within Indonesia) and improving smallholder productivity
is often as simple as adopting improved agricultural practices.56 Thus there
is scope for further oil palm-related poverty reduction through these three
avenues (extensification, intensification, and crop switching) without the large
environmental costs that have characterised the sector to date. Finally, Indonesia’s
uniquely large share of smallholders engaged in plantation-based agriculture are
central to this story. Generalising my findings to other countries with different
levels of smallholder engagement would be injudicious.
56Knowledge of good agricultural practices for growing oil palms are not widespread forsmallholders and the transfer of knowledge between nucleus and plasma schemes has beenproblematic.
§3.10 Chapter 3 Appendix 112
3.10 Chapter 3 Appendix
Table 3.12: Panel Summary Statistics
Variable 2002 2010 All years Mean difference
Palm oil land / district area (%)
Mean 0.58 2.65 1.3 2.07
SD 1.61 6.35 4.01 4.74
N 341 341 3386
District poverty rate (%)Mean 19.94 13.82 16.74 -6.12
SD 11.57 7.3 9.5 -4.27
N 335 341 3386
Summary statistics are for the balanced panel of constant geographic units, where districtboundaries are reset to those at the start of the panel period for consistency. Palm oil landas a share of district area is lagged by one year, as it is in my estimates. Data are officialIndonesian Government data, obtained through the World Bank’s Indonesian Databasefor Policy and Economic Research online public portal.
§3.10 Chapter 3 Appendix 113
Tabl
e3.
13:M
ain
Resu
lts—
Line
ar-l
inea
rFu
ncti
onal
Form
Dep
ende
ntva
riabl
ePo
vert
yra
te
Estim
ator
OLS
IVIV
IVIV
Col
umn
12
34
5
Palm
oill
and
/di
stric
tare
a(%
)-0
.108
***
-0.6
43**
-0.5
51**
*-0
.535
***
-0.6
08**
(0.0
37)
(0.2
61)
(0.2
12)
(0.1
74)
(0.2
58)
Isla
ndfix
edeff
ect
YN
NN
N
Initi
alco
nditi
onsc
ontr
ols
YY
Yy
Y
Potn
tialr
ice
and
casa
vayi
elds
NN
YN
N
Pote
ntia
lcoff
ee,c
ocoa
,tea
yiel
dsN
NN
YN
Suita
bilit
yin
dex
IVN
NN
NY
Kle
iber
gen-
Paap
Wal
drk
Fst
atN
/A12
.70
12.7
620
.97
10.7
6
Obs
erva
tions
335
308
303
303
309
This
tabl
esh
ows
the
that
the
mai
nre
sults
are
sim
ilar
ifa
linea
r-lin
ear
func
tiona
lfor
mis
used
(i.e.
,no
tlo
gpo
vert
y).
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,
5,an
d1
per
cent
leve
ls.
All
IVes
timat
esus
eth
etw
o-st
age
leas
tsqu
ares
inst
rum
enta
lvar
iabl
ees
timat
or.
Sam
ple
isth
elo
ng-d
iffer
ence
cros
s-se
ctio
nof
alla
vaila
ble
dist
ricts
from
2002
–201
0.20
01di
stric
tbou
ndar
ies
are
used
,with
new
dist
ricts
colla
psed
into
year
-200
1pa
rent
dist
ricts
.Cha
nges
insa
mpl
essi
zear
edu
eto
data
avai
labi
lity.
Oil
palm
land
isla
gged
onep
erio
d(2
001–
2009
).H
eter
oske
dast
icity
-rob
ust
stan
dard
erro
rsar
ein
pare
nthe
ses.
Isla
ndgr
oupi
ngs
are
defin
edas
Java
,Sum
atra
,Kal
iman
tan,
and
Sula
wes
i,w
ithre
mai
ning
dist
ricts
grou
ped
toge
ther
.Ea
chre
gres
sion
incl
udes
log
pove
rty
and
log
perc
apita
outp
utin
the
initi
alpe
riod
asco
ntro
lvar
iabl
es.
§3.10 Chapter 3 Appendix 114
Tabl
e3.
14:M
ain
Resu
lts—
Prod
ucti
onIn
stea
dof
Land
Dep
ende
ntva
riabl
eLo
gdi
stric
tpov
erty
rate
(%)
Log
dist
rictp
over
tyga
pin
dex
(IDR)
Estim
ator
FEFE
LIM
LLD
LDLI
ML
FEFE
LIM
LLD
LDLI
ML
Col
umn
12
34
56
78
Perc
apita
palm
oilp
rodu
ctio
n(to
ns)
-0.2
12**
*-0
.594
***
-0.1
95**
*-0
.427
***
-0.2
41**
*-0
.813
***
-0.2
62**
*-0
.565
***
(0.0
54)
(0.1
59)
(0.0
43)
(0.1
18)
(0.0
54)
(0.2
29)
(0.0
69)
(0.2
06)
Dis
tric
tand
year
FEs
YY
NN
YY
NN
Isla
nd–y
earF
EsY
NN
NY
NN
N
Initi
alco
nditi
onsc
ontr
ols
NN
YY
NN
YY
Excl
uded
-Fst
atis
tic8.
0312
.01
8.3
12.0
1
Dis
tric
ts34
134
133
527
434
134
133
527
4
Obs
erva
tions
3386
3386
335
274
3051
3051
335
274
This
tabl
esh
ows
the
effec
tsob
serv
edfo
rad
ditio
nalp
alm
oill
and
asa
shar
eof
tota
ldis
tric
tare
aca
rry
over
toac
tual
palm
oilp
rodu
ctio
nin
tons
,and
conv
erte
dto
perc
apita
term
sto
scal
e.St
arsd
enot
est
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
perc
entl
evel
s.Sa
mpl
eis
anan
nual
341
dist
rictp
anel
,200
2-20
10.
Palm
oill
and
isla
gged
one
perio
d(i.
e.,2
001-
2009
).20
01di
stric
tbou
ndar
ies
are
used
,with
new
dist
ricts
colla
psed
into
year
2001
pare
ntdi
stric
ts.
LIM
Lre
fers
toth
elim
ited
info
rmat
ion
max
imum
likel
ihoo
din
stru
men
talv
aria
ble
estim
ator
,FE
fixed
effec
ts,
and
LDlo
ng-d
iffer
ence
s.Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
are
inpa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
elin
forF
Ees
timat
ean
dat
the
prov
ince
leve
lfor
LDs.
FELI
ML
inst
rum
ents
oilp
alm
prod
uctio
nw
ithth
ein
itial
dist
ricts
hare
ofpa
lmoi
llan
din
tera
cted
with
atim
etr
end,
and
LDLI
ML
dist
ricta
gro-
clim
atic
suita
bilit
yfo
roi
lpal
m.
Initi
alco
nditi
onco
ntro
lsar
ein
itial
pove
rty
rate
s,in
itial
per
capi
tain
com
es,a
ndis
land
dum
mie
s.Ex
clud
ed-F
refe
rsto
the
Kle
iber
gen-
Paap
Wal
drk
Fst
atis
ticob
tain
edfr
omfir
st-s
tage
regr
essi
ons.
§3.10 Chapter 3 Appendix 115
Table 3.15: Determinants of Changing Oil Palm Land Shares
Dependent variable Palm oil land / district area (%)
Estimator Pooled OLS Within FE Within FE
Column 1 2 3
Lag electricity capacity-0.0004** -0.0002 -0.0002
(0.0002) (0.0002) (0.0002)
Lag electricity capacity nearby-0.0003* -0.0003* 0001
(0.0001) (0.0002) (0.0001)
Lag access to electricity-0.010 0.046*** 0.016
(0.010) (0.015) (0.019)
Lag human development index-0.180*** 0.243*** 0.220
(0.058) (0.086) (0.243)
Lag child immunisation rate-0.168*** 0.003 -0.0001
(0.036) (0.010) (0.010)
Lag adult literacy rate0.245*** -0.088** -0.036
(0.026) (0.036) (0.036)
Lag skilled birth0.046*** 0.001 0.005
(0.015) (0.013) (0.013)
District FEs N Y Y
Island-year FEs N N Y
Observations 1019 1019 1019
R-squared 0.14 0.10 0.20
Stars denote statistical significance at the 10, 5, and 1 per centlevels. Sample is an annual 341 district panel, 2002–2010. Palm oilland is lagged one period (i.e., 2001–2009). 2001 district boundariesare used, with new districts collapsed into year-2001 parent districts.Heteroskedasticity-robust standard errors are in parentheses, clustered atthe district level. Covariates are all taken from the World Bank (2015)Indonesia Database for Economic and Policy Research.
§3.10 Chapter 3 Appendix 116
Tabl
e3.
16:R
obus
tnes
s—Pa
nel
Fixe
dEf
fect
s
Dep
ende
ntva
riabl
ePa
lmla
ndLo
gdi
stric
tpov
erty
rate
Col
umn
12
34
56
78
9
Palm
oill
and
/di
stric
tare
a0.
001
-0.0
06**
*-0
.008
***
-0.0
10**
*-0
.009
***
-0.0
07**
*-0
.011
***
-0.0
04*
(0.0
01)
(0.0
02)
(0.0
02)
(0.0
03)
(0.0
03)
(0.0
02)
(0.0
03)
(0.0
02)
Lag
log
pove
rty
rate
0.23
50.
412*
**
(0.3
62)
(0.0
57)
2nd
lag
log
pove
rty
rate
0.45
1
(0.6
17)
In-ti
me
plac
ebo
NY
NN
NN
NN
N
Lag
pove
rty
cont
rols
NN
YN
NN
NN
N
Elec
tric
ityco
ntro
lsN
NN
YN
NN
NN
Polit
ical
,fisc
al,o
il,ga
scon
trol
sN
NN
NY
NN
NN
Fore
st&
polit
ical
cont
rols
NN
NN
NY
NN
N
Inco
me
and
reve
nue
cont
rols
NN
NN
NN
YN
N
Cor
rela
teso
fpov
erty
cont
rols
NN
NN
NN
NY
N
Dis
tric
t-by-
dist
rictt
ime
tren
dsN
NN
NN
NN
NY
Dis
tric
tand
isla
nd-y
earF
EsY
YY
YY
YY
YY
Obs
erva
tions
2359
2704
2699
2321
1445
1445
3386
1333
3386
Star
sden
ote
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Sam
ple
isan
annu
al34
1di
stric
tpan
el,2
002–
2010
.Pal
moi
llan
dis
lagg
edon
epe
riod
(i.e.
,200
1–20
09).
2001
dist
rictb
ound
arie
sar
eus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar-2
001
pare
ntdi
stric
ts.
With
ines
timat
orw
ithdi
stric
tand
isla
nd–y
earF
Esus
edfo
rall
estim
ates
.Het
eros
keda
stic
ity-r
obus
tsta
ndar
der
rors
arei
npa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
el.C
olum
n1
regr
esse
spov
erty
lags
onpa
lmla
ndan
dth
ein
-tim
epla
cebo
test
inC
olum
n2
uses
futu
repa
lmoi
llan
dva
lues
.Ele
ctric
ityco
ntro
lsre
fert
odi
stric
tand
neig
hbou
ring
dist
rictp
ower
capa
city
,tak
enfr
omSp
arro
wet
al(2
015)
.Pol
itica
l,fis
cal,
oil,
and
gas
cont
rols
are
take
nfr
omBu
rges
set
al(2
012)
and
fore
stco
ntro
lsfr
omW
heel
eret
al(2
013)
.All
othe
rcov
aria
tes
are
take
nfr
omth
eW
orld
Bank
(201
5)In
done
sia
Dat
abas
efo
rEco
nom
ican
dPo
licy
Rese
arch
.
§3.10 Chapter 3 Appendix 117
Tabl
e3.
17:R
obus
tnes
s–A
lter
nati
veSa
mpl
es
Dep
var:
log
dist
rictp
over
tyra
te
Sam
ple
No
Java
2006
–201
020
01–2
005
Estim
ator
FEFE
LIM
LLD
LDLI
ML
FEFE
LIM
LFE
FELI
ML
Col
umn
12
34
56
78
Palm
oill
and
/di
stric
tare
a(%
)-0
.007
***
-0.0
39**
-0.0
12**
*-0
.012
**-0
.006
**-0
.056
*-0
.002
-0.1
16**
*
(0.0
02)
(0.0
16)
(0.0
03)
(0.0
06)
(0.0
03)
(0.0
34)
(0.0
02)
(0.0
34)
Dis
tric
tand
year
fixed
effec
tsY
YN
NY
YY
Y
Isla
nd–y
earfi
xed
effec
tsY
NN
NY
NY
N
Add
ition
alco
ntro
lsN
NY
YN
NN
N
Excl
uded
-Fst
atis
tic7.
770
14.9
84.
185
17.0
6
Obs
erva
tions
2295
2295
230
175
1683
1683
2044
2044
Col
umns
1–4
show
that
my
mai
nes
timat
esin
Tabl
e3.
2an
dth
ead
ditio
nali
nstr
umen
talv
aria
ble
estim
ates
are
robu
stto
incl
udin
gIn
done
sia’
sm
ostp
opul
ous
isla
ndw
ithlit
tlepa
lmoi
llan
d,Ja
va.
Col
umns
5–8
use
the
first
and
seco
ndha
lfof
the
sam
ple
perio
d,as
ther
ew
asan
incr
ease
inov
eral
lpal
moi
lpro
duct
ivity
betw
een
the
perio
ds.
Star
sde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rce
ntle
vels
.Sa
mpl
eis
anan
nual
341
dist
rictp
anel
,200
2-20
10.
Palm
oill
and
isla
gged
one
perio
d(i.
e.,2
001-
2009
).20
01di
stric
tbo
unda
ries
are
used
,with
new
dist
ricts
colla
psed
into
year
2001
pare
ntdi
stric
ts.
LIM
Lre
fers
toth
elim
ited
info
rmat
ion
max
imum
likel
ihoo
din
stru
men
talv
aria
ble
estim
ator
,FE
fixed
effec
ts,a
ndLD
long
-diff
eren
ces.H
eter
oske
dast
icity
-rob
usts
tand
ard
erro
rsar
ein
pare
nthe
ses,
clus
tere
dat
the
dist
rictl
evel
info
rFE
estim
ate
and
atth
epr
ovin
cele
velf
orLD
s.FE
LIM
Lin
stru
men
tsoi
lpal
mpr
oduc
tion
with
the
initi
aldi
stric
tsha
reof
palm
oill
and
inte
ract
edw
itha
time
tren
d,an
dLD
LIM
Ldi
stric
tagr
o-cl
imat
icsu
itabi
lity
foro
ilpa
lm.I
nitia
lcon
ditio
nco
ntro
lsar
ein
itial
pove
rty
rate
s,in
itial
perc
apita
inco
mes
,and
isla
nddu
mm
ies.
Excl
uded
-Fre
fers
toth
eK
leib
erge
n-Pa
apW
ald
rkF
stat
istic
obta
ined
from
first
-sta
gere
gres
sion
s.
§3.10 Chapter 3 Appendix 118
Table 3.18: Heterogeneity–By Region
Dependent variable: log district poverty rateSample Island Island AllEstimator FE LD FEColumn 1 2 3Panel A: Java
Palm oil land / district area 0.015 -0.035 -0.007***(0.016) (0.047) (0.002)
Island–palm interaction 0.021(0.015)
N observations 1091 105 3386Panel B: Sumatra
Palm oil land / district area -0.007*** -0.011*** -0.016***(0.002) (0.004) (0.005)
Island–palm interaction 0.010**(0.005)
N observations 960 96 3386Panel C: Kalimantan
Palm oil land / district area -0.007* -0.009** -0.006***(0.003) (0.004) (0.001)
Island–palm interaction -0.008**(0.004)
N observations 379 37 3386Panel D: Sulawesi
Palm oil land / district area -0.05*** -0.05*** -0.006***(0.008) (0.016) (0.002)
Island–palm interaction -0.039***(0.007)
N observations 450 45 3386Panel E: Other islands
Palm oil land / district area -0.039 0.345** -0.007***(0.130) (0.171) (0.002)
Island–palm interaction -0.026(0.128)
N observations 506 51 3386
This table provides consonant sub-samples estimates by region tosupplement to the full-sample estimates with interaction termspresented in the paper. Stars denote statistical significance atthe 10, 5, and 1 per cent levels. Full sample (Column 3) is anannual 341 district panel, 2002–2010. Palm oil land is lagged oneperiod (i.e., 2001–2009). 2001 district boundaries are used, withnew districts collapsed into year 2001 parent districts. Estimatorsare the within estimator (FE) with district and year fixed effects,and the long-difference (LD) estimator with initial log povertyand log per capita income controls. Heteroskedasticity-robuststandard errors are in parentheses, clustered at the district level.
§3.10 Chapter 3 Appendix 119
Tabl
e3.
19:H
eter
ogen
eity
–By
Sect
or
Dep
var:
log
pove
rty
rate
Pane
lwid
thA
nnua
l4-
year
lyA
nnua
l4-
year
lyA
nnua
l4-
year
lyA
nnua
l4-
year
ly
Col
umn
12
34
56
78
Stat
e-ow
ned
palm
oill
and
-0.0
11**
-0.0
11**
-0.0
03-0
.002
(0.0
04)
(0.0
04)
(0.0
05)
(0.0
06)
Priv
ate
palm
oill
and
-0.0
12**
*-0
.011
***
-0.0
12**
*-0
.009
**
(0.0
03)
(0.0
03)
(0.0
03)
(0.0
05)
Smal
lhol
derp
alm
oill
and
-0.0
04-0
.011
**0.
002
-0.0
04
(0.0
02)
(0.0
05)
(0.0
02)
(0.0
06)
Dis
tric
tand
isla
nd-y
earF
EsY
YY
YY
YY
Y
Obs
erva
tions
3009
1004
3009
1004
3009
1004
3009
1004
This
tabl
epr
esen
tsth
esa
me
sect
oral
estim
ates
asth
ose
inth
epa
per,
buta
dditi
onal
Col
umns
7an
d8,
whi
chsh
owth
atth
epo
vert
yim
pact
sofp
rivat
epa
lmoi
llan
ddo
min
ate
thos
efr
omot
hers
ecto
rsw
hen
alls
ecto
rsar
ein
clud
edin
the
one
regr
essi
on.
Palm
oill
and
varia
bles
are
allp
lant
atio
nar
eash
ares
ofto
tald
istr
icta
reas
.Sta
rsde
note
stat
istic
alsi
gnifi
canc
eat
the
10,5
,and
1pe
rcen
tlev
els.
Sam
ple
isan
annu
al34
1di
stric
tpan
el,2
002-
2010
.Pa
lmoi
llan
dis
lagg
edon
epe
riod
(i.e.
,200
1-20
09).
2001
dist
rictb
ound
arie
sar
eus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar20
01pa
rent
dist
ricts
.Het
eros
keda
stic
itiy-
robu
stst
anda
rder
rors
are
inpa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
el.P
anel
fixed
effec
tses
timat
orus
edth
roug
hout
,with
dist
rict,
year
,and
isla
nd-y
earfi
xed
effec
ts.
§3.10 Chapter 3 Appendix 120
Tabl
e3.
20:H
eter
ogen
eity
–By
Land
Qua
lity
Dep
ende
ntva
riabl
e:lo
gdi
stric
tpov
erty
rate
Palm
oill
and
qual
ityD
amag
edIm
mat
ure
Mat
ure
Pane
lwid
thA
nnua
l2-
year
ly4-
year
lyA
nnua
l2-
year
ly4-
year
lyA
nnua
l2-
year
ly4-
year
ly
Col
umn
12
34
56
78
9
Palm
oill
and/
dist
ricta
rea
(%)
-0.1
42**
-0.1
41**
-0.0
74-0
.025
***
-0.0
25**
*-0
.035
***
-0.0
14**
*-0
.014
***
-0.0
11**
(0.0
66)
(0.0
69)
(0.0
83)
(0.0
09)
(0.0
09)
(0.0
13)
(0.0
04)
(0.0
05)
(0.0
04)
Obs
erva
tions
3009
1674
1004
3009
1674
1004
3009
1674
1004
This
tabl
esh
owsh
owin
crea
sing
the
shar
eof
dist
rictl
and
used
ford
amag
edpl
anta
tions
hast
hela
rges
tsho
rt-te
rmim
pact
son
dist
rictp
over
ty,
tape
ring
offov
ertim
e.Im
mat
ure
and
mat
ure
plan
tatio
nsha
vebr
oadl
yco
nsis
tent
impa
cts
over
4ye
ars:
the
form
ersl
ight
lyin
crea
ses
(per
haps
from
“lea
rnin
gby
doin
gan
dth
ein
crea
sed
prod
uctiv
ityfr
omtr
eesc
omin
gof
age)
and
the
latte
rslig
htly
decr
ease
s(as
pove
rty
gain
swou
ldha
veal
read
ybe
enm
ade)
.St
ars
deno
test
atis
tical
sign
ifica
nce
atth
e10
,5,a
nd1
per
cent
leve
ls.
Sam
ple
isan
annu
al34
1di
stric
tpan
el,2
002-
2010
.Pa
lmoi
llan
dis
lagg
edon
epe
riod
(i.e.
,200
1-20
09).
2001
dist
rict
boun
darie
sar
eus
ed,w
ithne
wdi
stric
tsco
llaps
edin
toye
ar20
01pa
rent
dist
ricts
.Het
eros
keda
stic
itiy-
robu
stst
anda
rder
rors
are
inpa
rent
hese
s,cl
uste
red
atth
edi
stric
tlev
el.T
hew
ithin
pane
lest
imat
orw
ithdi
stric
tan
dis
land
-by-
year
fixed
effec
tsis
used
inal
lest
imat
es.
Chapter 4
Local impacts of resource booms
Abstract
I study the local economic and welfare impacts of three of Indonesia’s largest
natural resource sector booms. Applying the synthetic control method to
district-level data over the 2000s, I construct non-parametric counterfactual
estimates of the local impacts of resource booms in three districts experiencing
some of the largest and steepest increases in resource sector output. I examine
palm oil in Sumatra, coal mining in Kalimantan, and natural gas extraction in
West Papua. All three resource booms boosted total economic output. Oil
palm expansion in Riau raised agricultural, industrial, and services output. Coal
mining in South Kalimantan reduced agriculture and services output. Oil palm
and coal mining booms appear to have delivered strong local poverty reduction.
The Tangguh natural gas project in West Papua delivered a massive increase in
industry and aggregate output, but appears to have had indiscernible impacts on
household welfare or poverty.
121
§4.1 Introduction 122
4.1 Introduction
Indonesia is the world’s largest exporter of coal and palm oil, but the long-term
development implications of Indonesia’s sustained resource-driven growth are
still not well understood (Burke and Resosudarmo, 2012; Garnaut, 2015; Hill
et al., 2008; van der Eng, 2014). With some of the world’s most decentralised
governance arrangements, understanding region-specific impacts of booming
resource sectors is critically important for economic, social, and environmental
policy and longer-term development strategies.
This chapter asks how rapid natural resource sector expansion affects a local
district economy and its residents’ welfare. I present a quantitative case study
on each of Indonesia’s three largest natural resource exports–coal ($22.9B; 11% of
merchandise exports in 2014), natural gas ($17.4B; 8.5%), and palm oil ($16.5B;
8.1%)–and exploit some of the largest and most sudden increases in district-level
production for each commodity: palm oil in Indragiri Hilir, Riau; coal mining in
Tapin, South Kalimantan; and a giant natural gas project in the Bintuni Bay of
West Papua. All three sectors have been argued to be economic enclaves, but have
starkly different characteristics. Oil palm is labour- and land-intensive and spread
amongst government, private, and smallholder farmers. Natural gas extraction
and coal mining are capital-intensive, generate relatively little employment, and
have highly concentrated rents. My key hypothesis is that the more diffuse oil
palm sector should generate broader-based benefits than point resource sectors,
particularly natural gas. An appropriate control group is needed to compare the
observed district outcomes with the same district’s unobserved counterfactual
outcomes (“untreated”). I use a relatively new method—synthetic control
modelling—to construct a “synthetic” comparison district for each resource boom
district, allowing me to compare the booming districts’ observed outcomes with
reasonable counterfactuals. Each synthetic control district is a weighted average
of untreated districts with similar pre-treatment observable characteristics and
§4.1 Introduction 123
outcome behaviour to the treated district. Optimal weights are determined
through a data-driven algorithm minimising the mean squared prediction error
over the pre-treatment period, resulting in a synthetic control more closely
resembling the treated district than any other single comparison district or group
of districts.
I find that all three resource booms boosted total economic output and
significantly altered the structure of the local economy. Oil palm expansion
in Indragiri Hilir raised agricultural, industrial, and services output. The
poverty rate declined substantially while average household expenditure fell
below its counterfactual—a puzzle I discuss further in the chapter. A major
coal mining expansion in Tapin reduced agriculture and services sector output
while delivering strong poverty reduction. Natural gas extraction in West
Papua delivered a giant boost to mining, industry, and total output in one of
Indonesia’s most developmentally lagging regions. Small, positive services sector
spillovers are observed, but agricultural output diverged below its estimated
counterfactual. No change in the pre-existing poverty and average household
expenditure trends implies negligible welfare impacts in light of the massive ramp
up in district per capita output. My three case studies collectively suggest that
more diffuse natural resources tend to generate broad-based, inequality-reducing
economic development. The benefits from extracting point resources appear
more concentrated, suggesting a greater role for social policy and economic
diversification in areas dominated by these sectors.
Existing studies on the local impacts of natural resources typically fall into
two categories: small-scale qualitative case studies (see, e.g., McCarthy et al
(2010; 2011), Rist et al (2010), and Budidarsono et al (2012) for palm oil), or
larger-scale statistical studies (see Cust and Poelhekke (2015), Torvik (2009),
van der Ploeg (2011), and Wick and Bulte (2009) for mining). Qualitative
narratives and geographically-narrow case studies provide a rich source of
§4.1 Introduction 124
descriptive evidence, but are generally unable to quantify impacts and provide
little basis for causal inference. Econometric studies—while sometimes providing
a causal interpretation of the data—can often lack context, assume homogeneous
treatments, and mask heterogeneity across treated units. One of the few points
of agreement in the resource curse literature is that the development impacts of
natural resources have been heterogeneous across time, commodities, country
characteristics, and local conditions.
My main contribution in this chapter is to provide new quantitative case
study evidence of the local economic and welfare impacts of rapid resource sector
expansion in Indonesia’s three largest export commodities: coal, natural gas, and
palm oil. In doing this, I provide new micro-level evidence on the poverty and
welfare elasticity of resource sector growth, and on its potential inter-sectoral
linkages (i.e., spillovers). With sub-national panel data becoming increasingly
available, I also show how the synthetic control method can be a useful tool to
analyse the impacts of major policies and economic shocks to single administrative
units, particularly in Indonesia where many decisions are made at the district and
province levels.
The chapter is structured as follows. Section 4.2 explains synthetic control
modelling and its application to sub-national data from Indonesia. Section 4.3
presents the results for my three case studies. Section 4.4 concludes.
§4.2 Synthetic control approach 125
4.2 Synthetic control approach
To make causal inferences about the local impacts of palm oil plantations,
mines, or other resource-related activities through case study-based research
we must ask what the area would look like without these activities—a difficult
question to answer. Many factors affect local economic outcomes, from
government policies, to private investment, to weather. Finding a single
comparison unit free of the treatment but still sufficiently similar to the treated
unit is often problematic. Before-and-after analyses tend to be contaminated by
other changes in the local economy, and differencing out trends cannot account
for systematic differences in growth paths.
One approach to deal with these problems is the synthetic control method
proposed by Abadie and Gardeazabal (2003). The synthetic control method is a
systematic way of choosing case study comparison units, allowing quantitative
causal inferences in small samples, usually a single “treated” unit. It uses
data from multiple comparison units to construct a single synthetic comparison
unit—a weighted average—that most closely resembles the unit of interest before
the event under study, i.e., exhibiting the same pre-treatment dynamics. If a
synthetic district can be generated based on a set of similar districts and valid
predictor variables, and a resource boom has substantial impacts on the local
economy, then the economic trajectories of the district experiencing the resource
boom and its synthetic control should be similar before the resource boom but
diverge after.
The usefulness of the synthetic control method has been demonstrated
across a variety of applications. At the national level, Pepinski and Wihardja
(2011) examine the economic impact of Indonesia’s decentralisation in 1999.
Mideksa (2013) quantifies the positive economic impact of Norway’s natural
resource endowment over a long time period. Smith (2015) applies the same
approach to many resource-rich countries, finding that countries that have
§4.2 Synthetic control approach 126
become resource-rich have subsequently attained higher per capita incomes.57 My
study relates closely to Mideksa (2013) and Smith (2015) but at a sub-national level
and focusing on indicators beyond per capita output.
Within-country applications are becoming popular as well. Synthetic control
analysis is particularly well-suited to micro-level studies, as within-country units
share some of the same external shocks (e.g., related to national-level policies
and the economy). In the seminal study introducing the method, Abadie and
Gardeazabal (2003) estimate the economic effects of terrorism in the Basque
Country of northern Spain. The closest studies to mine are those of Munasib
and Rickman (2015), who use the synthetic control method to look at the regional
economic and welfare impacts of shale gas in a few US counties, and Sills et al
(2015), who use sub-national data from Brazilian municipalities to examine the
effectiveness of a local anti-deforestation policy.58 To my knowledge, mine is the
second application of the synthetic control method to sub-national data from a
developing country after Sills et al.’s (2015) Brazilian study.
4.2.1 Estimation and inference
Consider d = 1 as the district experiencing a resource boom (the treated
district) in a sample ofD+ 1 districts. d = 2 to d = D+ 1 are potential comparison
units in a comparison pool. Yd,t is the outcome of interest for district d at time
57Additional national level studies include Abadie, Diamond and Hainmueller’s (2014) studyof the impact of German reunification in 1990 on West Germany’s economic growth, Horiuchiand Mayerson’s (2015) study of the economic costs of conflict in Israel following from the SecondIntifada, Billmeier and Nannicini’s (2013) study of the impacts of trade liberalisation on per capitaoutput, and Karlsson and Pichler’s (2015) work on the demographic impacts of HIV.
58Other sub-national applications include Montalvo’s (2011) study of the electoral impacts ofthe 2004 Madrid bombings, Abadie, Diamond and Hainmueller’s (2010) study of the impact of alarge-sale tobacco control program on cigarette sales in California, and Bohn et al’s (2014) study onthe impact of the 2007 Legal Arizona Workers Act on the state’s population composition. Chan et al(2014) construct synthetic academics and estimate the impact of the John Bates Clark Medal andthe Fellowship of the Econometric society on academic performance, Bauhoff (2014) look at thedietary impacts of nutritional standards in schools, Barone and Mocetti (2014) look at the regionaleconomic impacts of two earthquakes in Italy, Pinotti (2015) examines the impacts of mafia activityin Italy, Ando (2015) looks at the local impacts of new power plants in Japan, and Krief et al (2015)re-examine a difference-in-difference evaluation of a major UK health policy without assumingparallel trends.
§4.2 Synthetic control approach 127
t. A synthetic control district for d = 1 is constructed as a weighted average of
comparison pool districts, using a (D × 1) vector of weights:
W = (w2, ..., wd+1)′ (4.1)
with 0 ≤ wd ≤ 1 for d = 2, ..., D + 1 and w2 + ...+wd+1 = 1. W ∗ is selected through
an optimisation procedure minimising:
(X1 −X0W )′V (X1 −X0W ) (4.2)
where X1 is a vector of pre-treatment variables for my case study district, X0 is
the corresponding matrix of the same variables for D comparison pool districts,
and V a diagonal matrix reflecting the relative importance of the different Xs.
A data-driven algorithm minimises the pre-treatment differences between the
outcomes of interest and results in a single comparison district resembling
the treated district—in outcome levels, outcome behaviour, and observable
covariates—better than any single comparison district or group of districts. If a
good pre-treatment fit between the case study district and its synthetic control is
achieved, differences in post-treatment outcomes can be assumed to be a result of
the treatment. The treatment effect for district d = 1 in the post-boom period is:
Y1,t − ΣD+1d=2 w
∗dYd,t (4.3)
where w∗d are the optimal weights.
A key strength of the synthetic control method is that it is a generalised
difference-in-difference approach allowing quantitative causal inference on a
single treated unit. But unlike difference-in-difference and panel fixed effects
estimators capturing only time-invariant confounders, well-constructed synthetic
controls do not rely on a parallel trends assumption and are robust to time-varying
§4.2 Synthetic control approach 128
unobservable confounders (Abadie et al, 2010). A principal reason to use synthetic
controls is to control for the effect of unobservable factors potentially influencing
time trends in the treated and control units (Acemoglu et al, 2014; Smith,
2015). Unlike regression and matching on baseline covariates, synthetic controls
compare units similar in terms of baseline covariates plus the behaviour of the
outcome of interest in the pre-treatment period and how the covariates shape
this behaviour. The credibility of the method depends on being able to construct
a close comparison unit falling within the convex hull of the treated unit, then
arguing the divergence in outcomes between the treated unit and the synthetic
control is due to the intervention of interest (i.e., the treatment is the only major
change affecting the treated unit).59
The key limitation of the synthetic control approach is that conventional
methods of assessing statistical significance (i.e., based on large-sample inferential
techniques) are not available due to the smaller number of time periods and
control pool observations. The three cases in this Chapter are no exception.
However, non-parametric treatment effects obtained from synthetic control
analyses can be subjected to a range of falsification tests, leading to what Abadie et
al (2010) term “exact inference” from examining the full distribution of treatment
effects obtained by iteratively applying the treatment to each unit in the sample
(akin to random permutation testing and inference). Such placebo tests can be
conducted across time periods (Bertrand et al, 2004), across donor pool units, on
the synthetic control itself, and on alternative outcomes plausibly unaffected by
the treatment.
59See Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010; 2011;2014) for more technical treatments of the synthetic control method, including proofs andalternative approaches to constructing V .
§4.2 Synthetic control approach 129
Applying the synthetic control method to district-level resource booms in
Indonesia is arguably more challenging than previous applications for two
reasons. Sub-national data from developing countries tend to be more porous
and volatile than the national accounts and within-country data used in previous
studies. This is important because the approach has historically required clean,
slow-moving data for the optimisation procedure to function well and separate the
treatment effect from idiosyncratic variations in the outcome of interest. Abadie
et al (2010) go as far as recommending smoothing volatile outcome variables
to minimise idiosyncratic variation. Given the imperfections already laden in
the data, I avoid this practice.60 Secondly, many districts of Indonesia have
experienced resource booms of varying size and scope. Care must be taken to
identify resource expansions appropriately interpreted as discrete shocks (i.e.,
dichotomous treatments), and large restrictions must be placed on the comparison
pool to ensure sufficiently similar but untreated districts.
4.2.2 Data
A balanced panel of Indonesian districts
The constrained optimisation procedure to obtain the W ∗ weights requires
data for matrices X1 and X0. Balanced panel data are needed for the outcome
variables for the treated unit and all potential control units in the comparison
pool. Additional covariates (predictors) must also be available for all units for at
least one pre-treatment period. All data for this study are taken from the World
Bank’s (2015) Indonesia Database for Policy and Economic Research (DAPOER).
DAPOER is a public sub-national database covering over 200 socio-economic
variables across Indonesia’s 33 provinces and over 500 districts (kabupatens).
60McCulloch and Sjahrir (2008) McCulloch and Malesky (2011) discuss some of themeasurement issues associated with Indonesian subnational accounts data. Indonesian statisticscan however be considered relatively reliable by developing country standards.
§4.2 Synthetic control approach 130
My three case studies are conducted at the district level to identify the
aggregate impacts of resource booms on the local economy and residents’ welfare.
Indonesian districts are clearly defined legal and geographical units with district
administrations reflecting local economies, but the number of districts has not
remained constant over time. Power was decentralised to around 300 district
governments with the fall of Suharto in 1998, driving the number of districts to
proliferate from 292 in 1998 to over 500 in 2015 (Fitriani et al, 2005). To obtain
a nationally-exhaustive balanced panel of constant geographic units, I apply
year-2001 district boundaries to my dataset. The average district in my balanced
panel had a population of 1.4 million people in 2011, with the smallest district
having a population of just 21,000.
The study period is as long as the data permit for each estimate,
as longer treatment periods improve the accuracy of estimation (Abadie,
Diamond, and Hainmueller, 2010).61 Most district-level data are only available
post-decentralisation, so I focus on resource expansions from 2005 to allow a
sufficient pre-treatment window to fit the synthetic controls. Post-treatment
periods are extended as long as data permit to allow treatment effects to emerge
gradually and to examine dynamics.62
61Recall the credibility of the approach depends on how well the synthetic control tracks thetreated unit before the treatment, with this demonstrated for as many periods as possible. Aneffective synthetic control does not only match the level and the trend of the treated unit in thepre-treatment period, but its behaviour and sensitivity to time-varying conditions (e.g., businesscycles).
62The study period includes commodity price booms and the global financial crisis. Districtswithout commodities would not benefit from their prices, so the total treatment effect naturallyincludes some price effects. Commodity price booms and financial crises only bias results ifresponsible for a structural break in how unobservables map to the outcome after the treatment.
§4.2 Synthetic control approach 131
Outcomes of interest
My economic outcomes of interest are real regional gross domestic product
(RGDP) per capita and its components. I follow standard United Nations
sectoral classifications. The primary sector comprises agriculture, forestry, fishing
(one item in Indonesian sub-national accounts), and mining and quarrying.
Manufacturing, construction, and electricity, gas, water, and sanitary services
(utilities) are the secondary sector (industry). The tertiary sector—broadly
defined as services—consists of transport, storage, and communications,
wholesale and retail trade, banking, insurance and real estate, ownership of
dwellings, public administration and defence, services, and all other activities. I
am interested in the effects of resource booms on the rest of the primary sector (i.e.,
agriculture in the case of mining), industry, and services (excluding government).
Examining impacts on different sectors allows me to gauge any local structural
change and spillovers through: (a) production linkages (producing commodity
sector inputs or processing raw materials); (b) consumption linkages (greater
demand for non-resource sector output from resource sector income); or (c)
crowding-out non-resource sectors (i.e., (a) and (b) in reverse).63 Output variables
are converted to per capita terms to take population size into account and to
better facilitate matching. RGDP in million Indonesian rupiah (IDR, constant
2000 prices) and total district population data are taken from Indonesia’s central
statistics agency, Badan Pusat Statistik (BPS). While most national accounts are
subject to some measurement and imputation error, Indonesian statistics are
generally better than many other developing countries’ national accounts data
(McCulloch and Sjahrir, 2008; McCulloch and Malesky, 2011).
63Fiscal linkages, the third of Hirschman’s (1981) linkages in addition to production andconsumption linkages, are beyond the scope of this study.
§4.2 Synthetic control approach 132
To assess broader welfare and distributional impacts I also examine average
per capita monthly household expenditure and the district poverty rate. Per
capita household expenditures (monthly IDR) are derived from the consumption
module of BPS’s high quality, district-representative national socio-economic
survey (SUSENAS). SUSENAS is implemented at least annually (usually at a
similar time each year) and covered around 1.2 million people in 2011.64 The
poverty rate is the share of the total district population whose expenditure level is
below the poverty line: the central social policy target for Indonesian governments
and development agencies. The expenditure-based poverty line varies by district
and period, linked by a universal consumption requirement.
A key concern when selecting outcome indicators is that sufficient time is
allowed for impacts to be quantitatively seen. Although the full impacts of
any resource booms will not likely be realised for years, output, poverty, and
household expenditure should be relatively responsive. The synthetic control
method allows me to evaluate their short-run dynamics over time. Long-term
effects may be different.
4.2.3 Identifying appropriate case studies
Resource booms must be of sufficient magnitude to cause noticeable impacts
on the outcomes of interest relative to any idiosyncratic shocks, particularly in
comparative case studies where small effects are often indistinguishable from
random shocks.
I separately sorted district-year observations by the two-year change in mining
and quarrying output, oil and gas output, and palm oil production to identify the
district-level resource boom for each of Indonesia’s three largest natural resource
64I leave household expenditure in nominal terms. This may be misleading if a resource boomleads to local (district-specific) inflation, for example due to local Dutch disease dynamics. As pricedata are available infrequently for only major cities, I cannot assess year-to-year inflation in mycase study districts and in comparison with other districts. However, there is no reason to expectthe law of one price not to hold, and cities (kotas) nearest to my case study districts (Pekanbaru,Banjarmasin, and Manokwari) show no abnormalities relative to other cities and national trends.
§4.2 Synthetic control approach 133
export commodities most suitable for synthetic control analysis.65
The synthetic control approach relies on a sharp discontinuity or event (i.e., a
structural break) to split the observed outcome from the simulated counterfactual.
To create a dichotomous treatment environment from my continuous treatment
variables, I identify districts with consistently low or non-existent resource
production in the early 2000s and a large and rapid production scale-up followed
by persistently high production. I visually inspect the time series for districts with
the largest short-term changes in mining, oil and gas, and palm oil production to
restrict potential case studies to those where there is just the one major production
increase during the period under study. Assuming I fit valid synthetic controls,
differences between the treated districts’ outcomes and the synthetic controls in
the post-treatment periods can be interpreted as causal effects if the resource
boom is the only major event affecting the treated unit. Thus districts with
resource booms for other commodities (e.g., with oil and palm oil, as is the case in
some districts in Riau province) and other district-specific economic shocks (e.g.,
natural disasters) are removed.
I identify (a) palm oil expansion in the Indragiri Hilir district of Riau province,
(b) coal mining expansion in Tapin district of South Kalimantan, and (c) the
commencement of natural gas extraction in Manokwari, West Papua, as the three
district-level resource booms most suitable for synthetic control study (see Figure
4.1). Table 4.1 compares case study districts’ characteristics to each other and
national averages in 2005, the last common pre-boom year.
65Sorting was done in level changes in production output and checked against growth rates(i.e., the percentage change in the second lag). Mining and quarrying, and oil and gas output,are official BPS sub-national accounts data. Mining captures economic activity extracting oil,natural gas, coal, and minerals, and preparing them for further processing. Quarrying refers tothe quarrying of chemical elements, mineral, and recess rock sediments just below the earth’ssurface, excluding metal, coal, petroleum, and natural gas. Manufacturing includes processingthese materials mechanically, chemically, or manually into finished or semi-finished products.District-level palm oil production in tons is taken from the Tree Crop Statistics of Indonesia forOil Palm produced annually by the Department of Agriculture.
§4.2 Synthetic control approach 134
Figu
re4.
1:C
ase
Stud
yD
istri
cts—
Loca
tion
s
§4.2 Synthetic control approach 135
Tabl
e4.
1:C
ase
Stud
yD
istri
cts—
Des
crip
tive
Stat
istic
s
Dis
tric
tIn
drag
iriH
ilir
Tapi
nM
anok
war
iN
at.a
vg.
Trea
tmen
tyea
r20
0820
0520
08N
/A
Com
mod
ityO
ilpa
lmC
oal
Nat
ural
gas
N/A
Pove
rty
rate
(%)
168
5118
Avg
.mon
thly
HH
expe
nditu
re(ID
R)21
2,93
641
5,68
525
8,74
326
0,58
9
Out
put(
mill
ion
IDR)
4,65
4,04
568
5,34
21,
215,
282
4,77
8,09
6
Out
putp
erca
pita
(mill
ion
IDR)
7.4
4.6
5.2
7.5
Agr
icul
ture
outp
utsh
are
(%)
5049
5232
Are
a(k
m)
12,6
152,
701
37,9
015,
636
Popu
latio
n63
0,86
315
0,00
023
5,60
863
0,12
0
Rura
lpop
ulat
ion
shar
e(%
)88
8595
67
Agr
icul
ture
empl
oym
ents
hare
(%)
3464
6545
Adu
ltlit
erac
yra
te(%
)97
9587
90
All
2005
exce
ptth
eem
ploy
men
tsha
re(2
008)
.A
vera
gein
clud
esal
l334
inth
eba
lanc
edpa
nelw
ith20
01di
stric
tbou
ndar
ies.
Ave
rage
mon
thly
hous
ehol
dex
pend
iture
isex
pres
sed
inno
min
alID
Rte
rms.
Out
puta
ndco
mpo
nent
sare
alli
nre
alte
rms.
§4.2 Synthetic control approach 136
Figure 4.2: Case Study Districts—Treatments
0.5
11.
5
Out
put p
er c
apita
(ID
R)
020
0000
4000
0060
0000
Pal
m o
il pr
oduc
tion
(ton
s)
2002 2005 2008 2011Year
Indragiri Hilir palm oil (LHS) Tapin mining (RHS)Manokwari oil & gas /10 (RHS)
Figure 4.2 presents each case study’s time series for resource sector output.
Oil and gas output per capita for Manokwari is by far the largest boom, so
I divide it by ten to present the three together. By identifying the resource
booms through a component of output prior to assessing impacts, I validate
a dose-response relationship between resource production and the economy.
This implies the resource “shock” is the key driver of any observed changes,
rather than some other factor coinciding with the boom (Mideksa, 2013). By
focusing on production, the main challenges is that the two possible states for the
treated district are pre-production and post-production. For the mining booms,
pre-production periods may be different to periods with no mining activity (e.g.,
due to construction and other activities, reflecting the mining life cycle). However
if a tight match is achieved on the pre-production period, the synthetic control
comparisons are valid: the counterfactual is just based on a synthetic control
mimicking a pre-production state rather than a purely untreated state. Relatedly
§4.2 Synthetic control approach 137
reverse causation can also threaten the validity of comparisons. Resource booms,
and in particular, private investment in the resource sector, could be triggered by
anticipation of future growth prospects. If such growth expectations are captured
by the unobservable heterogeneity included in the model—demonstrated by a
tight pre-treatment fit and the synthetic control and the actual outcome moving
together—this should not bias estimates. Using earlier treatment years—to
include pre-production activities in the estimated treatment effects and account
potential anticipation effects—gives similar results.66
66Although the synthetic control method handles unobservable heterogeneity, resource shocksarguably can be treated as exogenous in any case. The geological placement of naturalresource reserves is fundamentally random (Carmignani, 2013; Edwards, 2016; van der Ploegand Poelhekke, 2010) and the development of Indonesia’s natural resources tends to proceedregardless of local socioeconomic conditions (Resosudarmo, 2005). Likewise the process of oilpalm expansion is subject to a high degree of arbitrariness, similar to that described in Burgess etal. (2012).
§4.2 Synthetic control approach 138
4.2.4 Constructing each synthetic control
Identifying relevant predictors
Optimal weights used to construct synthetic controls (W ∗) depend critically
on V , so predictor variables in X0 and X1 must be considered carefully. Valid
synthetic controls for my three case study districts must be based on predictors
effectively (a) describing the three districts’ pre-treatment economic profiles, and
(b) predicting their post-treatment trajectories. I identify output per capita, the
adult literacy rate, population density, agricultural employment share, industry
employment share, average household expenditures, the poverty rate, the poverty
gap index, the agricultural share of output, manufacturing share of output, and
lagged outcome variables as relevant predictors.67 I ensure lagged outcome
variables are omitted for a few pre-treatment periods (to allow sufficient influence
of the other predictors) and follow Abadie and Gardeazabal (2003) to choose
V through the purely data-driven method described earlier.68 There are likely
other relevant predictors not included in my model, but tight pre-treatment fits
(i.e., the synthetic control tracking the movements of the actual outcome) and
strong predictor balance suggest my predictors capture most of the unobserved
heterogeneity in the pre-treatment periods.
Refining the pool of potential comparison districts
The comparison unit is supposed to approximate the counterfactual of the
unit of interest without any treatment, so selecting a comparison pool of districts
sufficiently similar to the districts of interest is critical to construct a valid synthetic
67Predictors are taken from the World Bank (2015), statistically relevant according to simplepooled ordinary least squares and panel fixed effects estimates, and identified from existingresearch on Indonesian sub-national development, e.g., Arndt (1984), Hill (1996), Hill et al (2012),Suryahadi et al (2003), Suryahadi et al (2009), Wetterberg et al (1999), Resosudarmo (2005), Miranti(2010), Manning (2010), Manning and Sumarto (2011), and De Silva and Sumarto (2014).
68Including all outcome lags as separate predictors is common to improve the pre-treatmentfit; see, e.g., Bohn et al (2012) and Billmeier and Nanncini (2012). I avoid this practice, as it canlead to over-fitting and poorer post-treatment performance, rendering other predictors irrelevantirrespective of how important they are in describing the pre-treatment (Abadie et al, 2010).
§4.2 Synthetic control approach 139
control. With 336 districts in 2001, my balanced panel provides rich cross-sectional
variation to draw an appropriate comparison pool. However using an excessive
number of observations tends to give unreasonably large weights to observations
similar to the treated unit in the pre-treatment period in some characteristics, but
wildly different in others. Abadie et al (2010; 2014) emphasise the importance
of limiting the comparison pool to units whose outcomes are driven by the
same structural process as the outcome of the unit of interest and not subject
to structural shocks to the outcome in the period of the study. Restricting the
comparison pool ensures appropriate comparison units are used, helps avoid
such interpolation biases, and minimises over-fitting the data to idiosyncratic
variations in a large sample of unaffected units.
Three empirical issues must be addressed in restricting the comparison
pool. Districts in the comparison pool must be: (a) unaffected by similar
treatments or any other large idiosyncratic shocks that could affect the outcome
of interest during the period of the study; (b) not affected by spill-overs from the
treatment on the treated unit (otherwise known as the stable unit treatment value
(SUTVA) assumption or no interference); and (c) sufficiently similar observable
characteristics and outcomes to the treated districts.
To ensure the districts in the comparison pool are not “treated” by similar
resource sector shocks, I restrict the comparison pool to districts with an
average total (not per capita) mining output of less than 10,000 million IDR
and palm oil production less than 1,000 tons over the whole period. I use the
full sample period for this restriction (c.f., pre-treatment period) because other
treated districts cannot be in the control group. My difference-in-difference-style
comparisons are therefore between districts with little to no resource production
expanding rapidly and staying expanded, and a synthetic district with little to no
resource production without any expansion. While this procedure raises concerns
about the comparability of resource-rich districts with non-resource-rich districts,
§4.2 Synthetic control approach 140
ensuring potential comparison units are untreated is the first-order issue.69
Oil palm can be processed in mills in neighbouring districts and companies
tend to be head-quartered in provincial capitals (Pekanbaru in this case) and
Jakarta. Indonesia’s unique resource revenue sharing arrangements often see
mining revenues distributed to other districts in the same province (see Agustina
et al (2012), Fadliya and McLeod (2010), and Lewis and Smoke (2015) for
detailed discussions of Indonesia’s fiscal arrangements). Both scenarios could bias
estimates. To minimise contamination from economic spillovers in potentially
affected neighbouring districts, I remove districts from the same province as the
treated district from each comparison pool.
Comparison districts sufficiently similar in observable characteristics and past
outcome behaviour are selected using the data-driven algorithm described earlier
(i.e., dissimilar units receive zero weights). Ideally I would also restrict the
comparison pool to districts in the same region (i.e., island group) to ensure
“common geographic support”, but this is only feasible for oil palm expansion in
Indragiri Hilir. The goal is to strike a balance between how similar the observable
characteristics of districts within the comparison pool are and the size of the
comparison pool. Tapin is a relatively small district and other non-resource-rich
districts on the island are not sufficiently comparable. Likewise Manokwari is a
national outlier in many respects and it is necessary to draw from a larger pool
to get a good match. Losing place-based comparability and running the risk
of interpolating across dissimilar regions, I gain the benefit of drawing from a
sufficiently large comparison pool with districts more comparable to Tapin and
Manokwari in observables and pre-treatment outcome levels and behaviour. My
three case study districts are rural but I retain cities (kotas) in the comparison pools
to better capture local business cycle conditions.
69The synthetic control method also deals with this concern by generating a comparison unitfor the untreated district with the natural resources but without the boom in natural resourceproduction. Natural gas and coal reserves were known for the whole study period, as was the factthat oil palm could plausibly grow in the areas where it was later planted.
§4.3 Results 141
4.3 Results
4.3.1 Oil palm expansion in Sumatra
Palm oil is the world’s most consumed vegetable oil and Indonesia its
largest producer. Over the past few decades the Indonesian landscape has been
transformed by oil palm expansion, with most of the increase in production
coming from land conversion (Gaskell, 2015).70 Indragiri Hilir is one such district
that has been recently transformed by oil palm. Indragiri Hilir’s palm oil mill
(Teluk Bakau POM, in Rotan Semelur village) was recently certified by the
Roundtable on Sustainable Palm Oil (RSPO) and the district area land used for
oil palm plantations almost tripled in from 2007–2008. Production then increased
sevenfold from 2007–2009 (see Figure 4.2).
Table 4.2: Impacts of Oil Palm Expansion in Sumatra
Treatment minus synthetic controlRMSPE N
Pre-T T T+1 T+2 T+3 T+4
Column 1 2 3 4 5 6 7 8
Total output -0.01 0.16 0.28 0.85 0.67 0.87 0.04 35
Agriculture 0.00 0.29 0.36 0.76 0.78 0.87 0.02 35
Industry 0.00 0.07 0.12 0.25 0.25 0.32 0.02 35
Services 0.00 0.06 0.10 0.31 0.25 0.35 0.02 35
Poverty (%) 0.01 1.18 -0.19 -1.55 -2.57 -2.07 0.00 100
HH exp. -21 2764 7713 -77372 6415.00 225
Poverty matched from 2002 up to the pre-treatment period 2007. All other variablesmatched from 2001–2007. Pre-T is the average difference between the observedoutcome and the synthetic control in the pre-treatment matching period, RMSPEis the root mean squared prediction error, and N is the number of districts in thecomparison pool. HH exp. refers to average monthly household expenditure percapita in nominal IDR.
70Corley and Tinker (2003) discuss the oil palm’s history and physiology, and Rival and Levang(2014) recent developments in Asia. Dennis et al (2005) and Koh and Wilcove (2007, 2008) discussenvironmental impacts. McCarthy et al (2010; 2011) and Rist et al (2010) discuss local socialimpacts.
§4.3 Results 142
Figure 4.3: Impacts of Oil Palm Expansion in Sumatra
(a) Aggregate Output
02
46
810
12
Per
cap
ita R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(b) Agriculture
01
23
45
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009 2011
Year
Indragiri Hilir synthetic control
(c) Industry
0.5
11.
52
2.5
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(d) Services
01
23
4P
er c
apita
ser
vice
s R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(e) Household expenditure
010
0000
2000
0030
0000
4000
00
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
res
(ID
R)
2001 2003 2005 2007 2009Year
Indragiri Hilir synthetic control
(f) Poverty
02
46
810
1214
1618
Pov
erty
rat
e (%
)
2002 2004 2006 2008 2010 2012Year
Indragiri Hilir synthetic control
§4.3 Results 143
Economic impacts
The local impacts of oil palm expansion in Indragiri Hilir are presented in
Table 4.2. The units for total output, its components, and average household
expenditure are per capita Indonesian rupiah. Poverty is in percentage points.
2008 is the treatment year, and the synthetic control is constructed using data from
2001–2007. Results are presented up to 2012 or as far as the data permit (2010
for household expenditure). The first column of Table 4.2 presents the average
difference between the observed Indragiri Hilir and its estimated synthetic control
over the pre-treatment period. Columns 2–6 show the difference between the
observed Indragiri Hilir and its estimated synthetic control in the treatment and
post-treatment years. The pre-treatment differences between actual Indragiri Hilir
and its synthetic control and the root mean squared prediction error (loss of fit)
are small relative to the outcome and its variation and grow in the post-treatment
period, as should be the case if valid synthetic controls have been constructed and
impacts increase over time. The final column presents the number of districts in
each restricted comparison pool.
Figure 4.3 graphically compares Indragiri Hilir’s observed outcomes for the
period 2001–2012 with the outcomes of counterfactual Indragiri Hilir. Panel A
shows output per capita increased steadily above its counterfactual, suggesting
the oil palm expansion was not offset by any local “Dutch disease” effects, at least
in aggregate terms.
Table 4.3 compares the average values of the predictor variables for Indragiri
Hilir and the estimated synthetic control for per capita output. Outcomes and
predictors for the synthetic control closely approximate Indragiri Hilir in the
pre-treatment period: coupled with the tight pre-treatment fit, this suggests the
synthetic control captures unobservable heterogeneity.
§4.3 Results 144
Table 4.3: Predictor Balance: Indragiri Hilir
Predictor Treated Synthetic
Output per capita (2008) 8.72 8.56
Output per capita (2006) 7.79 7.83
Output per capita (2004) 7.04 7.06
Average output per capita 7.15 7.15
Adult literacy rate 97.51 96.94
Agricultural employment share 4.076 3.26
Average Household expenditures 215, 516 261, 980
Poverty rate 16.77 17.31
Poverty gap index 3.27 3.05
Industry employment share 8.77 2.77
Agriculture output share 0.50 0.41
Manufacturing output share 0.16 0.15
RMSPE: 0.076
Table 4.3 compares the predictor variables for Indragiri Hilirand its synthetic control for the impact estimate on district percapita output in Panel A of Figure 4.3 and row one of Table4.2. Variables other than lagged outcomes are averaged overthe pre-intervention period 2001–2008. RMSPE refers to the rootmean squared prediction error.
§4.3 Results 145
Weights used to construct the synthetic control in Panel A of Figure 4.3 are
presented in Table 4.4. Dairi (North Sumatra) and Palembang, (South Sumatra)
account for most of synthetic Indragiri Hilir. The synthetic control comprising
just a few districts with large weights suggests the model is not overfitted (c.f.,
most of the sample getting very small weights suggests a potential overfitting
problem). None of the districts contributing to the comparison group in Table
4.4 have experienced a rapid increase in oil palm production and they span the
island of Sumatra, as intended. I do not present predictor balance and weights
for the other 17 synthetic controls in the chapter.
Table 4.4: Synthetic Indragiri Hilir Weights
Weight District name Province
0.5 Dairi North Sumatra
0.31 Palembang South Sumatra
0.06 Sabang Nanggroe Aceh Darussalam
0.06 Tanggamus Lampung
0.03 Banda Aceh Nanggroe Aceh Darussalam
0.03 Medan North Sumatra
0.01 Aceh Besar Nanggroe Aceh Darussalam
0.01 Lampung Timur Lampung
Table 4.4 presents the weights used to calculate the total per capitaoutput synthetic control for Indragiri Hilir, Riau, behind Panel Aof Figure 4.3 and the top row of Table 4.2. The comparison pool is36 districts in Sumatra but outside Riau province. 28 districts areassigned zero weights.
§4.3 Results 146
Panels B and C of Figure 4.3 show substantial impacts on per capita agriculture
and industry output, the two sectors capturing the growing and milling of palm
oil.71 Industry classifications in Indonesian sub-national accounts do not allow
me to disentangle whether any of the positive impact on industry output is due to
activities beyond processing palm oil at the mills. In Panel D I turn to services
output and find limited evidence of any major economic spillovers to formal
services, with only a small increase relative to the counterfactual. Given the size of
the informal services sector in rural Indonesia and its systematic underestimation
in official data, I cannot rule out further consumption spillovers in the informal
economy.
Welfare impacts
Panels E and F of Figure 4.3 present the broader welfare impacts of palm oil
expansion in Indragiri Hilir. Average per capita household expenditures and the
district poverty rate tell a different although not necessarily inconsistent story.
Average household expenditures track the synthetic control until the final year,
when it plateaus off below the synthetic control (Panel E). The pace of poverty
reduction picks up with around a four percentage point reduction relative to the
counterfactual four years into the oil palm expansion (Panel F).72 But why do
average nominal household expenditure and the poverty rate both decline, rather
than move in opposite directions? The poverty rate falling without an increase
in the average implies changes to the distribution of consumption (e.g., holding
the distribution constant and substantially increasing the average consumption,
the poverty rate must fall). First, household expenditure and the poverty rate
are derived from the same source so data issues are unlikely to explain the
71The synthetic control for agriculture also diverges from its pre-treatment trend wheneach district in the comparison pool is sequentially dropped and when a wider comparisonpool is used (c.f., Sumatra). This highlights the risk of assuming linear or parallel trends indifference-in-difference studies, as pre-trends can indeed change during the treatment period.
72I draw from a larger nationwide comparison pool to attain a closer pre-treatment fit for thesetwo estimates (c.f., restricting to Sumatra).
§4.3 Results 147
divergence.73 Second, growth in the palm oil sector could just be pro-poor
and redistributive, as rural poor mostly work in agriculture and agricultural
sector growth has historically been pro-poor (Suryahadi et al, 2009; de Silva
and Sumarto, 2014). Third, the cumulative distribution function of household
expenditure can also be quite steep around the poverty line, so a small change
in the distribution can correspond to a large change in the poverty rate. This is
indeed what is observed in the individual-level data. Figure 4.4 plots cumulative
distribution function of household expenditure before and during the palm oil
boom, and Figure 4.5 plots the two corresponding kernel density estimates.74
The curves both shift slightly to the right and flatten out a little.
Figure 4.4: Consumption in Indragiri Hilir
Poverty line
0
.2
.4
.6
.8
1
Cum
ulat
ive
Prob
abilit
y
0 1000000 2000000 3000000Per capita household monthly expenditure (constant 2004 IDR)
2004 2010
73Counting the wealthiest Indonesians in SUSENAS has been a persistent problem and leadsto substantial measurement error when studying consumption inequality, but this is a challengein most years and regions so unlikely to drive my results.
74Figures 4.4 and 4.5 do not present any counterfactual, only the pre-post comparison. Percapital household monthly expenditure is adjusted into constant 2004 terms using the nationalGDP deflator.
§4.3 Results 148
Figure 4.5: Kernel Density Estimate–Consumption Distribution in IndragiriHilir
0 1000000 2000000 3000000Per capita household monthy expenditure (constant 2004 IDR)
2004 2010
Results presented in Figure 4.3, Table 4.2, and results to follow are similar
across a wide variety of sensitivity analyses and falsification exercises, which
I omit for brevity due to the 6 × 3 outcomes analysed in this Chapter. These
robustness checks are summarised in the Chapter Appendix. Some smaller
treatment effects are statistically insignificant at conventional levels (i.e., one and
five per cent) when ranked against the full distribution of treatment effects. This
is as expected given that my analysis utilises such small donor groups.
§4.3 Results 149
4.3.2 Coal mining in Kalimantan
My second case study is of coal mining. Flying over the giant island of
Kalimantan (Borneo) you cannot help but notice the landscape is littered with
large open-cut mines. Many of these mines have been active for decades or are in
districts with contemporaneous oil, gas, and palm oil booms, precluding them
from this study. However in the Tapin district of South Kalimantan—home to
one of the region’s largest coal terminals—mining and quarrying output more
than tripled in 2005 due to a large expansion in coal mining operations (Figure
4.2). Like many districts in Kalimantan, there has also been an oil palm expansion
since 2009, so I focus on the impacts of the mining boom in the years leading up
to oil palm production in 2009.
Table 4.5: Impacts of Coal Mining in Kalimantan
Treatment minus synthetic controlRMSPE N
Pre-T T T+1 T+2 T+3 T+4
Column 1 2 3 4 5 6 7 8
Total output 0.01 0.01 0.72 0.76 0.74 0.73 0.00 93
Agriculture 0.00 -0.06 -0.05 -0.07 -0.18 -0.03 0.02 93
Industry 0.00 0.00 0.03 0.04 0.09 0.11 0.00 93
Services 0.00 0.01 -0.04 -0.01 -0.05 -0.06 0.00 93
Poverty -0.09 -0.83 0.67 -0.26 -2.62 -3.10 0.00 105
HH exp. 386 289 93267 178072 -73769 28291 0 98
Poverty matched from 2002 up to the pre-treatment period 2004. All other variablesmatched from 2001–2004. Pre-T is the average difference between the observed outcomeand the synthetic control in the pre-treatment matching period, RMSPE is the root meansquared prediction error, and N is the number of districts in the comparison pool. HHexp. refers to average monthly household expenditure per capita in nominal IDR.
§4.3 Results 150
Figure 4.6: Impacts of Coal Mining in Kalimantan
(a) Aggregate Output
02
46
Per
cap
ita R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(b) Agriculture
0.5
11.
52
2.5
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009
Year
Tapin synthetic control
(c) Industry
0.2
.4.6
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(d) Services
0.5
11.
52
Per
cap
ita s
ervi
ces
RG
DP
(m
illio
n ID
R)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(e) Household Expenditure
020
0000
4000
0060
0000
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
re (
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(f) Poverty
02
46
810
Pov
erty
rat
e (%
)
2002 2004 2006 2008Year
Tapin synthetic control
§4.3 Results 151
Economic impacts
Results for Tapin are presented in Table 4.5 and Figure 4.6. A good
pre-treatment fit is achieved throughout, even though there is just four years lead
to fit the synthetic control before the treatment year 2005. Aggregate and industry
output increase with the rapid scale-up of mining production, as expected. Just
like palm oil milling in the previous case study, an increase in industry output
does not necessarily provide evidence of production linkages. Washing and
transporting the rocks at Tapin’s large coal facility is captured in industry output.
Agricultural and services output better identify spillovers. Both sectors declined
below the synthetic control, consistent with localised Dutch disease-type impacts
on non-tradable sectors (Corden, 1984; Matsuyama, 1992). Negative impacts on
local agriculture from coal mining are also consistent with Aragon and Rud’s
(2015) study of gold mines in Ghana, and Fleming and Measham’s (2014) study
finding no significant manufacturing or agricultural spillovers from Australian
mining. Agriculture recovers with Tapin’s first oil palm yield in 2009, but the
small negative impact on services appears more permanent.
Welfare impacts
Panels E and F of Figure 4.6 present effects on poverty and average household
expenditures. Panel E of Figure 4.6 shows how average household expenditure
moved above then below synthetic Tapin during the mining boom, suggesting
limited sustained impacts on the welfare of the average Tapin resident. Tapin’s
poverty rate (Panel F) diverges away from its synthetic control in the treatment
period (2005), spikes in 2006, then diverges off far below the counterfactual until
2009, halving from ten to five percent. A positive poverty impact is different to
what has been reported on average across Indonesia, as mining growth typically
does not correspond to poverty reduction (Resosudarmo and Bhattacharya, 2015)
and mining-dependent districts tend to have higher poverty rates (Chapter 2). In
§4.3 Results 152
a related study, Salami et al (2014) highlight the health impacts of mining in South
Kalimantan, finding children have decreased pulmonary function due to greater
intake of harmful particulates. Studies like this complement mine, highlighting
the health/wealth trade-off often faced by mining communities in developing
countries.
4.3.3 Natural Gas Extraction in West Papua
My final case study examines one of the world’s archetypical resource
enclaves: West Papua. I study the British Petroleum (BP) Tangguh project,
Indonesia’s first fully vertically integrated (i.e., from raw material to final product)
liquified natural gas (LNG) operation. Two unmanned offshore production
platforms tap the giant Tangguh LNG gas fields in Bintuni Bay (the “Bird’s head”
in Figure 4.1), pump the gas through subsea pipelines to the LNG processing
facility located in the village of Tanah Merah, then deliver LNG to markets across
Asia and the United States. The gas fields contain over 500 billion cubic meters
of proven natural gas reserves (with an extra 300 billion cubic meters estimated)
and are being developed by an international consortium led by BP. The facility has
operated at full capacity (seven million tons per year) since it became operational
in 2009 and is expected to expand with the addition of a third LNG train before
2020.
West Papua provides a unique case study in many respects. It is one of the least
populated and poorest parts of Indonesia. Papua and Irian Jaya were retained
by the Dutch following independence in 1945, seized by Indonesia in 1961, and
today remain one of Indonesia’s last areas of active separatism and civil unrest. In
2000, West Papua province contained three districts, but like many resource-rich
areas of Indonesia, the three districts Balkanised into twelve today. Using 2001
district boundaries (the district named Manokwari in 2001), my estimates relate
to a generous geographic area covering several modern-day districts. Despite the
§4.3 Results 153
larger area, the relative economic importance of this economic shock to the region
dwarves my other two case studies. Manokwari extracted no oil or natural gas
prior to the Tangguh project (see Figure 4.2), and the natural resource riches in the
Bintuni Bay could potentially transform one of Indonesia’s most developmentally
challenged regions.
Production at the Tangguh facility officially began in 2009. I use 2008 as the
treatment year to capture related economic activity prior to the facility becoming
operational (e.g., advance company payments to communities, building the
necessary infrastructure around the facility) and because the longer lead time
allows. The synthetic control is fitted from from 2001–2007.75
Table 4.6: Impacts of Gas Extraction in West Papua
Treatment minus synthetic controlRMSPE N
Pre-T T T+1 T+2 T+3 T+4
Column 1 2 3 4 5 6 7 8
Total output 0.02 0.02 1.67 8.34 14.91 19.26 0.15 96
Agriculture 0.00 0.14 0.13 0.00 -0.05 -0.16 0.06 96
Industry -0.01 0.14 1.58 7.72 14.14 17.07 0.03 96
Services 0.00 -0.01 0.01 0.14 0.12 0.04 0.02 96
Poverty 0.47 -0.02 -0.42 -3.85 -0.08 -3.38 2.13 108
HH exp. 533 14276 53997 10235 10190 101
Poverty matched from 2002 up to the pre-treatment period 2007. All other variablesmatched from 2001–2007. Pre-T is the average difference between the observedoutcome and the synthetic control in the pre-treatment matching period, RMSPE is theroot mean squared prediction error, and N is the number of districts in the comparisonpool. HH exp. refers to average monthly household expenditure per capita in nominalIDR.
75The land was acquired in 1999, with many resettlement agreements. The village relocationwas completed in 2004, so unlikely to affect my synthetic control modelling.
§4.3 Results 154
Figure 4.7: Impacts of Gas Extraction in West Papua
(a) Aggregate Output
05
1015
2025
Per
cap
ita R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(b) Agriculture
0.5
11.
52
2.5
33.
5
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009 2011
Year
Manokwari synthetic control
(c) Industry
05
1015
20
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(d) Services
0.5
11.
52
2.5
3
Per
cap
ita s
ervi
ces
RG
DP
(m
illio
n ID
R)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(e) Household Expenditure
010
0000
2000
0030
0000
4000
0050
0000
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
res
(ID
R)
2001 2003 2005 2007 2009Year
Manokwari synthetic control
(f) Poverty
010
2030
4050
Pov
erty
rat
e (%
)
2002 2004 2006 2008 2010 2012Year
Manokwari synthetic control
§4.3 Results 155
Economic impacts
The local economic and welfare impacts of the Tangguh project are presented
in Table 4.6 and Figure 4.7. Panels A and C of Figure 4.7 show dramatic boosts
to total and industry output, expected given the facility’s vertically-integrated
nature. Output in the agricultural sector—the largest sector before the mining
boom and still accounting for most Papuans’ livelihoods (Resosudarmo et al,
2013)—paints a less positive picture of the Tangguh project (Panel B). Agricultural
output breaks from its long-term trend as the large gas project became operational.
Panel D shows a small increase in services sector output relative to the
counterfactual, although some services may be part of the project (e.g., hotel
accommodation for expatriates).
Welfare impacts
Manokwari’s poverty rate has steadily declined from 2002–2012, falling by an
impressive 20 percentage points (Panel F of Figure 4.7). But there is no sharp
discontinuity or change in trajectory as the LNG facility became fully operational
in 2009. Similarly Panel E of Figure 4.7 shows how the commencement of the
facility’s operations corresponded to an immediate boost in average household
expenditures, but convergence with the synthetic control shortly after. The
quadrupling of per capita output has not been met by a commensurate increase
in welfare or poverty reduction: any impacts are slight in context of the massive
scale of the output ramp up. Unlike for coal mining in Tapin, this result is
consistent with the higher poverty rates that are observed on average across
mining-dependent districts in Chapter 2.
§4.4 Conclusion 156
4.4 Conclusion
The objective of this chapter was to quantify the local economic and welfare
impacts of resource booms in three Indonesian districts. I studied Indonesia’s
three largest primary exports and used the synthetic control method to construct
counterfactual paths for districts’ per capita economic output and its components,
as well as average household expenditure and poverty. Each resource boom
substantially altered the structure of the local economy. Palm oil expansion in
Indragiri Hilir delivered a small boost to all sectors of the economy and strong
poverty reduction. Coal mining in Tapin reduced agricultural and services sector
output, but also delivered strong poverty reduction relative to the estimated
counterfactual. The Tangguh natural gas project in West Papua delivered a
massive increase in local economic output, but no major impacts on average
household welfare and poverty and a contraction in the agricultural sector.
According to the Indonesian constitution, Indonesia’s natural resources are
to “be controlled and utilised by the State for the maximum prosperity of the
people” (Gandataruna and Haymon, 2011). My three case studies highlight how
natural resource sectors can make important contributions to increasing district
GDP per capita and reducing poverty in Indonesia. Relative to their size, sectors
with more concentrated rents (e.g., natural gas) appear to provide fewer benefits to
local residents than more diffuse, labour-intensive sectors. For regions depending
on resource sectors with highly concentrated rents, natural resource sector-led
economic growth alone should not be expected to improve the average residents’
welfare and reduce poverty. More active fiscal and social policies may be needed.
§4.5 Chapter 4 Appendix 157
4.5 Chapter 4 Appendix
The 18 synthetic control estimates presented in the chapter were subject to the
robustness checks / sensitivity analyses summarised below. Results are omitted
for brevity, but similar and readily produced from the public data or available on
request.
Sensitivity analyses
• Leave-one out analysis. I estimate the synthetic control iteratively leaving
out each of the three comparison pool districts assigned the largest weights
to gauge sensitivity to a particular unit being included in the comparison.
Like permutation testing, a distribution of leave-one-out treatment effects is
evaluated.
• Population effects. I checked that changes in population are not driving the
results on per capita economic output and that similar results are obtained
using total and log levels (i.e., not in per capita terms).
• Alternative comparison pools:
– Regional (island) group vs. full sample; and
– Dropping districts where the average outcome of interest is fifty
per cent larger or smaller than that for the treatment district in the
pre-treatment period, as in Horiuchi and Mayerson (2015).
• Varying the predictor set:
– Parsimonious predictors, as used in my main estimates;
– All lagged outcomes only; and
– A more extensive set of predictors taken from DAPOER (World Bank,
2015), allowing the data-driven synthetic control procedure to instead
assess performance and relevance.
§4.5 Chapter 4 Appendix 158
• Using alternative techniques to estimate the V matrix.
Placebo / falsification tests
• In-time placebo tests involve treating years without the oil palm expansion
as the treatment years. See Bertrand et al (2004).
• In-space placebo tests or permutation inference, where the treatment is
iteratively applied to every unit in the comparison pool to compare the
estimated treatment effect on the unit of interest against the distribution
of treatment effects obtained for untreated units in the comparison pool.
This leads to what Abadie et al (2010) call “exact inference”, akin to random
permutation testing. Although this test is becoming standard practice, its
interpretation is ambiguous when large restrictions are placed on the donor
pool (as I have done), so I have omitted it from the chapter. This exercise also
showed that prediction errors for the untreated units are roughly similar in
the pre-and post-treatment periods, providing no evidence of over-fitting.
• On estimated synthetic controls and on alternative outcomes plausibly
unaffected by the treatment, i.e., fiscal transfers unrelated to natural
resources, precipitation and temperature, and lagged outcomes.
Replications
Replications using similar cases identified through my case study selection
procedure—the runner-ups—gave broadly similar results. Replication cases
include: palm oil in Kalimantan and Sumatra; oil and gas in East Java; mining
in Sulawesi and Kalimantan; and West Papua estimated at the province level (c.f.,
district).
§4.5 Chapter 4 Appendix 159
Main Results Graphs with Non-zero Y Axes
§4.5 Chapter 4 Appendix 160
Figure 4.8: Appendix–Impacts of Oil Palm Expansion in Sumatra, non-zero Y axis
(a) Aggregate Output
67
89
1011
Per
cap
ita R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(b) Agriculture
33.
54
4.5
5
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009 2011
Year
Indragiri Hilir synthetic control
(c) Industry
11.
52
2.5
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(d) Services
1.5
22.
53
3.5
4P
er c
apita
ser
vice
s R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Indragiri Hilir synthetic control
(e) Household expenditure
1500
0020
0000
2500
0030
0000
3500
0040
0000
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
res
(ID
R)
2001 2003 2005 2007 2009Year
Indragiri Hilir synthetic control
(f) Poverty
810
1214
1618
Pov
erty
rat
e (%
)
2002 2004 2006 2008 2010 2012Year
Indragiri Hilir synthetic control
§4.5 Chapter 4 Appendix 161
Figure 4.9: Appendix–Impacts of Coal Mining in Kalimantan, non-zero Y axis
(a) Aggregate Output
44.
55
5.5
6
Per
cap
ita R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(b) Agriculture
2.1
2.2
2.3
2.4
2.5
2.6
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009
year
Tapin synthetic control
(c) Industry
.4.4
5.5
.55
.6.6
5
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(d) Services
1.4
1.5
1.6
1.7
1.8
1.9
Per
cap
ita s
ervi
ces
RG
DP
(m
illio
n ID
R)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(e) Household Expenditure
1000
0020
0000
3000
0040
0000
5000
0060
0000
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
re (
IDR
)
2001 2003 2005 2007 2009Year
Tapin synthetic control
(f) Poverty
56
78
910
Pov
erty
rat
e (%
)
2002 2004 2006 2008Year
Tapin synthetic control
§4.5 Chapter 4 Appendix 162
Figure 4.10: Appendix–Impacts of Gas Extraction in West Papua, non-zero Y axis
(a) Aggregate Output
510
1520
25P
er c
apita
RG
DP
(m
illio
n ID
R)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(b) Agriculture
2.6
2.8
33.
23.
43.
6
Per
cap
ita a
gric
ultu
re R
GD
P (
mill
ion
IDR
)2001 2003 2005 2007 2009 2011
Year
Manokwari synthetic control
(c) Industry
05
1015
20
Per
cap
ita in
dust
ry R
GD
P (
mill
ion
IDR
)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(d) Services
11.
52
2.5
3
Per
cap
ita s
ervi
ces
RG
DP
(m
illio
n ID
R)
2001 2003 2005 2007 2009 2011Year
Manokwari synthetic control
(e) Household Expenditure
1000
0020
0000
3000
0040
0000
5000
00
Avg
. mon
thly
hou
seho
ld e
xpen
ditu
res
(ID
R)
2001 2003 2005 2007 2009Year
Manokwari synthetic control
(f) Poverty
3035
4045
5055
Pov
erty
rat
e (%
)
2002 2004 2006 2008 2010 2012Year
Manokwari synthetic control
Chapter 5
Conclusion
This thesis has implications for policy and suggests several promising avenues
for future research. Each chapter shows that natural resource sectors have
important effects on other sectors of the economy. These effects differ for
different types of natural resource sectors. At the international level, more
mining income in the economy leads to lower levels of non-mining income in
the long run (Chapter 2). Within Indonesia, oil palm expansion does not appear
to crowd out other economic activity at the district level (Chapters 3). In a
case study of a booming oil palm district in Riau (Chapter 4), I find small but
positive impacts on all sectors, consistent with the pattern observed nationally
in Chapter 3. For Indonesia’s mining sectors, two case studies suggest that coal
mining and natural gas extraction have increased total economic output and
output in sectors directly involved in resource extraction and processing, but
substantially reduced agricultural output (Chapter 4). These findings highlight
how Indonesia’s resource boom has led to major local structural change across its
diverse regions. Growth in the oil palm sector exhibits minor positive effects on
other sectors and total economic output. Mining sector growth, on the other hand,
crowds-out non-tradable agriculture, suggesting a local Dutch disease. Future
research could investigate possible links between natural resource sectors and
urbanisation, employment and labour market outcomes, and other aspects of
structural change in Indonesia or elsewhere.
That natural resource sectors alter the structure of national and sub-national
economies has implications for social development trajectories. Chapter 2 showed
163
164
that non-mining income is on average better for health and education than income
from the mining sector. Mining-sector growth is likely to undermine national and
sub-national economic growth and prosperity due to the mining sector’s weaker
links to human capital development and lower social productivity than alternative
sectors. Countries and sub-national regions committed to mining sector-led
economic growth may wish to consider strategies to diversify the economy
towards more human capital-intensive modern sectors or better utilise mining
rents for broad social outcomes. Further investigation of the mechanisms and the
conditions under which mining can be more beneficial for health and education
outcomes could consider returns to education and skills under mining booms.
Examining the long-term health and education impacts of more labour-intensive
natural resource sectors like palm oil could also be worthwhile.
The first Sustainable Development Goal is to “end poverty in all its forms
everywhere”. In this thesis I find that natural resource sectors can make important
contributions to poverty reduction, but these contributions depend on the type of
natural resource activity and the concentration of resource rents.
Growth in Indonesia’s palm oil sector has, on average, made strong
contributions to poverty reduction over the period 2001–2010 (Chapter 3). This
finding may influence debates on oil palm and development. In 2011 former
World Bank President Robert Zoellick directed the World Bank Group to cease any
financing and operations related to the palm oil industry due to environmental
issues. My thesis encourages policy-makers to instead seek a middle ground
balancing environmental conservation with the pro-poor growth opportunities
presented by oil palms. As mentioned Chapter 3, there are ways to further
increase palm oil production without clearing pristine primary rainforest. Future
research could investigate how different palm oil sector business models affect the
diffusion of benefits through local communities, compare plantations and mills
that have attained sustainability certification with those that have not, and extend
165
my analysis to other countries with growing palm oil sectors (e.g., Ghana and
Cameroon).
In contrast to the poverty impacts of palm oil expansion, districts more
dependent on the mining sector tend to have significantly higher poverty rates
(Chapter 2). Looking beyond these averages, I find that a large coal mining
expansion in South Kalimantan reduced poverty while a giant natural gas project
in West Papua had no discernible effect (Chapter 4).
The local economic benefits of natural resource sectors thus appear to be
spread in accordance with the concentration of the rents. For natural resource
sectors with highly concentrated resource rents, expansions in output alone
should not be expected to reduce poverty. Policies to ensure broad-based benefits
might be needed. Further research into how such policies can be designed
and implemented in the developing country context may be beneficial. Further
research could also examine the impacts of natural resource sectors on poverty
using international data or alternative poverty measures (e.g., electrification,
nutrition and food security, or asset poverty).
There is still much to learn about how natural resource sectors affect human
development outcomes across and within countries. Theory and empirical
evidence on how natural resource sectors affect poverty and inequality, labour
markets and human capital, and industrialisation remains thin. I hope the
research presented in this thesis prompts other researchers to investigate these
issues further, develop new theories to explain the effects documented in this
thesis, and extend my analysis using rich new spatial and micro datasets.
Integrated assessments factoring in environmental and climate impacts beyond
the economic and social impacts documented in this thesis will also be important.
References
[1] Abadie, A. and Gardeazabal, J. (2003), “The economic costs of conflict: A case
study of the Basque Country.” American Economic Review, 93:1, pp. 113–132.
[2] Abadie, A., Diamond, A. and Hainmueller, J. (2010), “Synthetic control
methods for comparative case studies: Estimating the effect of California’s
Tobacco Control program.” Journal of the American Statistical Association,
105:490, pp. 493–505.
[3] Abadie, A., Diamond, A. and Hainmueller, J. (2014), “Comparative Politics and
the Synthetic Control Method.” American Journal of Political Science, 29:2, pp.
495–510.
[4] Abadie, A., Diamond, A. and Hainmueller, J. (2011), “Synth: An R Package for
Synthetic Control Methods in Comparative Case Studies.” Journal of Statistical
Software 42:13, pp. 1–17.
[5] Acemoglu, D., Johnson, S., and Robinson, J. (2001), “The Colonial Origins of
Comparative Development: An Empirical Investigation”, 91:5, pp. 1369–1401.
[6] Acemoglu, D., Finkelstein, A., and Notowidigdo, M. (2013), “Income and
health spending: evidence from oil price shocks”, Review of Economics and
Statistics, 95:4, pp. 1079–1095.
[7] Adelman, I. (1984), “Beyond export-led growth”, World Development, 12, pp.
937–949.
[8] Agustina, C.D., Fengler, W., and Schulze, G.G. (2012), “The regional effects
of Indonesia’s oil and gas policy: options for reform”, Bulletin of Indonesian
Economic Studies, 48:3, pp. 367–395.
166
167
[9] Alexeev, M. and Conrad, R. (2009,) “The elusive curse of oil”, Review of
Economics and Statistics, 91:3, pp. 586–598.
[10] Altonji, J., Conley, T., Elder, T., and Taber, C. (2005), “Selection on Observed and
Unobserved Variables: Assessing the Effectiveness of Catholic Schools,” Journal
of Political Economy, 113:1, pp. 151–184.
[11] Alwarritzi, W., Nanseki, T., and Chomei, Y. (2015), “Analysis of the factors
influencing the technical efficiency among oil palm smallholder farmers in
Indonesia”, Procedia Environmental Sciences, 28, pp. 630–638.
[12] Anderson, T.W. and Rubin, H. (1949), “Estimators of the Parameters of a Single
Equation in a Complete Set of Stochastic Equations”, Annals of Mathematical
Statistics, 21, pp. 570–582.
[13] Ando, M. (2015), “Dreams of urbanization: Quantitative case studies on the
local impacts of nuclear power facilities using the synthetic control method”,
Journal of Urban Economics, 85, pp. 68–85.
[14] Andrews, D., Moreira, M., and Stock, J. (2006), “Optional Two-Sided Invariant
Similar Tests for Instrumental Variables Regression”, Econometrica, 74:3, pp.
715–752.
[15] Andrews, D., Moreira, M., and Stock, J. (2004), “Performance of Conditional
Wald Tests in IV Regression with Weak Instruments”, Journal of Econometrics,
139, pp. 116–132.
[16] Anriquez, G., and Lopez, R. (2007), “Agricultural growth and poverty in an
archetypical middle income country: Chile”, Agricultural Economics, 36:2, pp.
1910–202
[17] Aragon, F.M. and Rud, J.P. (2013), “Natural resources and local communities:
evidence from a Peruvian gold mine”, American Economic Review: Economic
Policy 5: 2, pp. 1–25.
168
[18] Aragon, F.M. and Rud, J.P. (2015), “Polluting Industries and Agricultural
Productivity: Evidence from Mining in Ghana”, Economic Journal, forthcoming.
[19] Arezki, R., and Gylfason, T. (2013), “Resource Rents, Democracy, Corruption,
and Conflict: Evidence from Sub-Saharan Africa”, Journal of African Economies,
22: 4, pp. 552–569.
[20] Arezki, R., Gylfason, T., and Sy, A. (2011), “Beyond the Resource Curse:
Policies to Harness the Power of Natural Resources”, International Monetary
Fund, Washington, D.C.
[21] Arndt, H.W. (1984), “The Indonesian Economy: Collected Papers”, Chopman
Publishers, Singapore.
[22] Austin, K., Kasibhatla, P., Urban, D., Stolle, F., and Vincent, J. (2015),
“Reconciling Oil Palm Expansion and Climate Change Mitigation in Kalimantan,
Indonesia”, PLoS ONE 10:5, e0127963.
[23] Auty, R.M. (1997), “Natural Resource Endowment: The State and Development
Strategy”, Journal of International Development, 9:4, pp. 651–663.
[24] Auty, R.M and Mikesell, R. (1998), “Sustainable Development in Mineral
Economies”, Oxford University Press, Oxford.
[25] Auty, R.M. (2001), “Resource Abundance and Economic Development”,
Oxford University Press, Oxford.
[26] Auty, R.M. (2001), “The political economy of resource-driven growth”,
European Economic Review, 45, pp. 839–846.
[27] Ayres, R., and Warr, B. (2005), “Accounting for growth: the role of physical
work”, Structural Change and Economic Dynamics, 16, pp. 181–209.
[28] Ayres, R., and Warr, B. (2009), “The Economic Growth Engine: How Energy
and Work Drive Material Prosperity”, Edward Elgar, Cheltenham.
169
[29] Baltagi, B. H. and Griffin, J. M. (1984), “Short and Long Run Effects in Pooled
Models”, International Economic Review, 25:3, pp. 631–645.
[30] Barbier, E., (1989), “Cash Crops, Food Crops, and Sustainability: The Case of
Indonesia”, World Development, 19:6, pp. 879-895.
[31] Barone, G., and Mocetti, S. (2014), “Natural disasters, growth, and institutions:
a tale of two earthquakes”, 84, pp. 52–66.
[32] Barr, C.M., and Sayer, J.A. (2012), “The political economy of reforestation
and forest restoration in Asia-Pacific: critical issues for REDD+”, Biological
Conservation, 154, pp. 9–19.
[33] Barro, R.J. (2015), “Convergence and Modernisation”, Economic Journal, 125,
pp. 911–942
[34] Barro, R. J. and Lee, J. W. (1993), “International comparisons of educational
attainment”, Journal of Monetary Economics, 32, pp. 363–394.
[35] Barro, R. J. and Lee, J. W (2010), “A new data set of education attainment in the
world, 1950–2010”, Journal of Development Economics, 104, pp. 184–198.
[36] Bauhoff, S. (2014), “The effect of school nutrition policies on dietary intake and
overweight: a synthetic control approach”, Economics and Human Biology, 12,
pp. 44–55.
[37] Bazzi, S., Gaduh, A., Rothenberg, A., and Wong, M. (2016), “Skill
Transferability, Migration, and Development: Evidence from Population
Resettlement in Indonesia”, American Economic Review, forthcoming.
[38] Bazzi, S. and Clemens, M.A. (2013), “Blunt instruments: Avoiding common
pitfalls in identifying the cause of economic growth”, American Economic
Journal: Macroeconomics, 5:2, pp. 152–86.
170
[39] Bellows, J., and Miguel, E. (2009), “War and local collective action in Sierra
Leone,” Journal of Public Economics, 93:11-12, pp. 1144–1157.
[40] Bertrand, M., Duflo, E., Mullainathan, S. (2004) “How much should we trust
difference-in-differences estimates?” Quarterly Journal of Economics, 119:1, pp.
249-275.
[41] Besley, T., and Kudamatsu, M. (2006), “Health and Democracy”, American
Economic Review, 96:2, pp. 313–318.
[42] Bhattacharyya, S. and Resosudarmo, B.P. (2015), “Growth. Growth
Accelerations, and the Poor: Lessons from Indonesia”, World Development, 66,
154–165.
[43] Bhattacharyya, S. and Williamson, J. (2013), “Distributional Impact of
Commodity Price Shocks: Australia over a Century”, CSAE Working Paper
Series, 2013–11, Centre for the Study of African Economies, University of Oxford.
[44] Billmeier, A. and Nannicici, T. (2013), “Assessing Economic Liberalization
Episodes: A Synthetic Control Approach.” Review of Economics and Statistics
95:3, pp. 983–1001.
[45] Black, D., McKinnish, T., and Sanders, S. (2005), “The economic impact of the
coal boom and bust”, Economic Journal, 115: 503, pp. 449–476.
[46] Blanco, L. and Grier, R. “Natural resource dependence and the accumulation
of physical and human capital in Latin America”, Resources Policy, 37:3, pp.
281–295.
[47] Blattman, C., and Bazzi, S. (2014), “Economic Shocks and Conflict: Evidence
from Commodity Prices”, American Economic Journal: Macroeconomics, 6:4,
pp. 1–38.
171
[48] Booth, A. (1988), “Agricultural Development in Indonesia”, Allen and Unwin,
Sydney.
[49] Bohn, S., Lofstrom, M., Raphael, S. (2014), “Did the 2007 Legal Arizona
Workers Act reduce the state’s unauthorized immigrant population?” Review
of Economics and Statistics, 96:2, pp. 258–269.
[50] Boschini, A., Pettersson, J., and Roine, J. (2013) “The Resource Curse and its
Potential Reversal”, World Development, 43, pp. 19–41.
[51] Bound, H., Jaeger, D., and Baker, R. (1995), “Problems with Instrumental
Variables Estimation when the Correlation Between the Instruments and the
Endogenous Explanatory Variables is Weak”, Journal of the American Statistical
Association, 90:430, pp.443–450.
[52] Brunnschweiler, C. and Bulte, E. H. (2008), “The resource curse revisited
and revised: A tale of paradoxes and red herrings”, Journal of Environmental
Economics and Management, 55:3, pp. 248–264.
[53] Brunnschweiler, C. and Bulte, E. H. (2009), “Natural resources and violent
conflict: Resource abundance, dependence, and the onset of civil wars”, Oxford
Economic Papers, 61:4, pp.651–674.
[54] Budidarsono, S., Dewi, S., Sofiyuddin, M., Rahmanulloh, A. (2012),
“Socioeconomic Impact Assessment of Palm Oil Production”, Technical Brief No.
27: palm oil series. Bogor, Indonesia. World Agroforestry Center–ICRAF, SEA
Regional Office.
[55] Budidarsono, S., Rahmanulloh, A., and Sofiyuddin, M. (2012), “Economic
assessment of Palm Oil Production”, Technical Brief No. 26: palm oil series.
Bogor, Indonesia. World Agroforestry Center–ICRAF, SEA Regional Office.
172
[56] Burgess, R., Hansen, M., Olken, B. A., Potapov, P., and Sieber, S. (2012),
“The Political Economy of Deforestation in the Tropics”, Quarterly Journal of
Economics, 122:1, pp. 73–117.
[57] Bulte, E., Damania, R., and Deacon, R. (2005), “Resource Intensity, Institutions,
and Development”, World Development, 33:7, pp. 1029–1044.
[58] Burke, P. and Nishitateno, S. (2015), “Gasoline prices and road fatalities:
international evidence”, Economic Inquiry, 53:3, pp. 1437–1450.
[59] Burke, P. and Resosudarmo, B.P. (2012), “Survey of Recent Developments”,
Bulletin of Indonesian Economic Studies, 48:3, pp. 299–324.
[60] Busch, J., Ferreti-Gallon, Engelmann, J., Wright, M., Austin, K., Stolle, F.,
Turubanova, S., Potapov, P., Margono, B., Hansen, M., and Baccini, A. (2015),
“Reduction in emissions from deforestation from Indonesia’s moratorium on
new oil palm, timber, and logging concessions”, Proceedings of the National
Academy of Sciences, 112:5.
[61] Butler, R.A., Koh, L.P., and Ghazoul, J. (2009), “ REDD in the red: palm oil could
undermine carbon payment schemes”, Conservation Letters, 2:2, pp. 67–73.
[62] Cahyadi, E.R. and Waibel, H. (2013), Is contract farming in the Indonesian oil
palm industry pro-poor? Journal of Southeast Asian Economies, 30:1, pp. 62–76.
[63] Carlson, K., Curran, L., Asner, G., McDonald Pittman, A., Trigg, S., Adeney,
M. (2013), “Carbon emissions from forest conversion by Kalimantan oil palm
plantations”, Nature Climate Change, 3, pp. 283–287.
[64] Carmignani, F. (2013), “Development outcomes, resource abundance, and
the transmission through inequality”, Resource and Energy Economics, 35, pp.
412–428.
173
[65] Carmignani, F. and Avom, D. (2010), “The social development effects of
primary commodity export dependence”, Ecological Economics, 70:2, pp.
317–330.
[66] Caselli, F. (2005), “Accounting for Cross-Country Income Differences, in
Handbook of Economic Growth, Vol 1., ed. Aghion, P. and Durlauf, S., Chapter
9, pp. 679–741, Elsevier.
[67] Caselli, F. and Michaels, G. (2013), “Do Oil Windfalls Improve Living
Standards? Evidence from Brazil”, American Economic Journal: Applied
Economics, 5:1, pp. 208–238.
[68] Cassing, J. and Warr, P. G. (1985), “The distributional impacts of a resource
boom”, Journal of International Economics, 18, pp. 301–319.
[69] Centre for the Study of Civil War (2009), “Uppsala Conflict Data
Program/Peace Research Institute Oslo Armed Conflict Dataset”, Version
4-2009.
[70] Chan, H., Frey, B., Gallus, J., and Torgler, B. (2014), “Academic honors and
performance”, Labour Economics, 31, pp. 188–204.
[71] Cramb, R. and McCarthy, J. (2016), “The Oil Palm Complex: Smallholders,
Agribusiness and the State in Indonesia and Malaysia”, National University of
Singapore Press.
[72] Christiaensen, L., Demery, L., and Kuhl, J. (2011), “The (evolving) role
of agriculture in poverty reduction—an empirical perspective”, Journal of
Development Economics, 96:2, pp. 239–54.
[73] Ciccone, A. (2011), “Economic Shocks and Civil Conflict: A Comment”,
American Economic Journal: Applied Economics, 3, pp. 215–215.
174
[74] Cleveland, C. J., Costanza, R., Hall, C., and Kaufmann, R. (1984), “Energy and
the U.S. economy: a biophysical perspective”, Science, 225, pp. 890–897.
[75] Collier, P. and Goderis, B. (2012), “Commodity prices and growth: an empirical
investigation”, European Economic Review, 56, pp. 1241–1260.
[76] Corden, W.M. (1984), “Booming Sector and Dutch Disease Economics: Survey
and Consolidation”, Oxford Economic Papers, 36:3, pp. 359–380.
[77] Corden, W.M. and Neary, P. (1982), “Booming sector and de-industrialisation
in a small open economy”, Economic Journal, 92: 368, pp. 825–848.
[78] Corley, R., and Tinker, R. (2003), “The Oil Palm”, 4th Edition, Blackwell,
Oxford.
[79] Cotet, A. and Tsui, T. (2013), “Oil and Conflict: What Does the Cross Country
Evidence Really Show?” American Economic Journal: Macroeconomics, 5:1, pp.
49–80.
[80] Cotet, A. and Tsui, T. (2013), “Oil, Growth, and Health: What Does the
Cross-Country Evidence Really Show?” Scandinavian Journal of Economics,
115:4, pp. 1107–1136.
[81] Costinot, A., Donaldson, D., Smith, C. (2016), “Evolving Comparative
Advantage and the Impact of Climate Change on Agricultural Markets: Evidence
form 1.7 Million Fields around the World”, Journal of Political Economy,
forthcoming.
175
[82] Coxhead, I. and Li, M. (2008), “Prospects for skills-based exports in
resource-rich developing countries: Indonesia in comparative perspective”,
Bulletin of Indonesian Economic Studies, 44:2, pp. 199–228.
[83] Coxhead, I. (2014), “Indonesia: returns to occupations, education, and ability
during a resource export boom”, in Ahtukorala, Patron, and Resosudarmo
(ends), “Trade development and Political Economy in East Asia: Essays in
Honour of Hal Hill”, Singapore, ISEAS.
[84] Cragg, J.G. and Donald, S.G. (1994), “Testing Identifiability and Specification
in Instrumental Variable Models”, Econometric Theory, 9, pp. 222–240.
[85] Cramb, R.A. (2013), “Palmed off: incentive problems with joint-venture
schemes for oil palm development on customary land”, World Development, 43,
pp. 84–99.
[86] Cramb, R.A., and Sujang, P.S. (2013), “The mouse deer and the crocodile: oil
palm smallholders and livelihood strategies in Sarawak, Malaysia“, Journal of
Peasant Studies, 40:1, pp. 129–154.
[87] Cust, J., and Poelhekke, S. (2015), “The Local Economic Impacts of Natural
Resource Extraction”, Annual Review of Resource Economics, 7:5.1–5.18.
[88] Daniele, V. (2011), “Natural resources and the quality of development”, Journal
of Development Studies, 47:4, pp. 545–573.
[89] De Silva, I., and Sumarto, S. (2014), “Does Economic Growth Really Benefit
the Poor? Income Distribution Dynamics and Pro-poor Growth in Indonesia,”
Bulletin of Indonesian Economic Studies, 50:2, pp. 227–242.
[90] de Soysa, I., and Gizelis, T. (2012), “The natural resource curse and the spread
of HIV/AIDS, 1990–2008”, Social Science and Medicine, 77, pp. 90–96
[91] Deaton, A. (2013), “The Great Escape”, Princeton University Press.
176
[92] Dennis, R., Mayer, J., Applegate, G., Chokkalingam, U., Colfer, C., Kurniawan,
I., Lachowski, H., Maus, P., Permana, R., Ruchiat, Y. (2005), “Fire, people and
pixels: Linking social science and remote sensing to understand underlying
causes and impacts of fires in Indonesia”, Human Ecology, 33, pp. 465–504.
[93] Dercon, S. (2009), “Rural poverty: old challenges in new contexts”, World Bank
Research Observer, 24, pp. 1–28.
[94] Dercon, S., and Gollin, D. (2014), “Agriculture in African Development:
Theories and Strategies”, Annual Review of Resource Economics, 6, pp. 471–492.
[95] Dollar, D., Kleineberg, T., and Kraay, A. (2016), “Growth still is good for the
poor”, European Economic Review, 81, pp. 68–85.
[96] Domenech, J. (2008), “Mineral Resource Abundance and Regional Growth in
Spain, 1860–2000”, Journal of International Development, 20, pp. 1122-1135.
[97] Dube, O. and Vargas, J. (2013), “Commodity Price Shocks and Civil Conflict:
Evidence from Columbia”, Review of Economic Studies, 80, pp. 1384–1421.
[98] Ebeke, C., Omgba, L., and Laajaj, R. (2015), “Oil, governance and the (mis)
allocation of talent in developing countries”, Journal of Development Economics,
114, pp. 126–141.
[99] Edwards, R.B. (2016), “Mining away the Preston curve”, World Development,
78, pp. 22–36.
[100] Eichengreem B., Park, D., and Shin, K. (2013), “Growth slowdowns redux: new
evidence on the middle-income trap”, NBER Working Papers No. 18673.
[101] Ellison, G., Glaeser, E., and Kerr, W. (2010), “What cause Industry
Agglomeration? Evidence from Conglomeration Patterns”, American Economic
Review, 100: 3, pp. 1195–1213.
177
[102] Evenson, R. E. and Gollin, D. (2003), “Assessing the impact of the Green
Revolution, 1960–2000”, Science, 300: 5620, pp. 758–762.
[103] Factor-Litvak, P., Wasserman, G., Kline, J., and Graziano, J., (1999), “The
Yugoslavia Prospective Study of Environmental Lead Exposure”, Environmental
Health Perspectives, 107:1, pp. 9–15.
[104] Fadliya and McLeod, R. (2010), “Fiscal transfers to regional governments in
Indonesia”, Arndt-Corden Department of Economics Working Papers in Trade
and Development, Working Paper No. 2010/14.
[105] Fargione, J., Hill, J., and Tilman, D. (2008), “Land clearing and the biofuel
carbon debt”, Science, 319, pp. 1235–1238.
[106] Farhadi, M., Islam, M. R., and Moslehi, S. (2015), “Economic freedom and
productivity growth in resource-rich economies”, World Development, 72, pp.
109–126.
[107] Fitriani, F., Hofman, B., and Kaiser, K. (2005), “Unity in diversity? The creation
of new local governments in decentralising Indonesia”, Bulletin of Indonesian
Economic Studies, 74:1, pp. 70–93.
[108] Fischer, G., van Nelthuizen, H., Shah, M., and Nachtergaele, F. (2002), Global
Agro-ecological Assessment for Agriculture in the 21st Century: Methodology
and Results, Food and Agriculture Organisation, Rome.
[109] Fleming, D.A., and Measham, T.G. (2014), “Local job multipliers of mining”,
Resources Policy, 41, pp. 9–15.
[110] Fuglie, K.O. (2010), “Source of growth in Indonesian agriculture”, Journal of
Productivity Analysis, 33, pp. 225–240.
[111] Fuller, W. (1977), “Some Properties of a Modification of the Limited
Information Estimator”, Econometrica, 45:4, pp. 939–954.
178
[112] Gamu, J., Le Billon, P., and Spiegel, S. (2015), “Extractive industries and
poverty: a review of recent findings and linkage mechanism”, Extractive
Industries and Society, 2, pp. 162–176.
[113] Gandataruna, K. and Haymon, K. (2011), “A dream denied? Mining legislation
and the Constitution in Indonesia”, Bulletin of Indonesian Economic Studies,
47:2, pp. 221–231
[114] Garnaut, R. (2015), “Indonesia’s Resources Boom in International Perspective:
Policy Dilemmas and Options for Continued Strong Growth”, Bulletin of
Indonesian Economic Studies, 51:2, pp. 189–212.
[115] Gaskell, J. C. (2015), “The Role of Markets, Technology, and Policy in
Generating Palm-Oil Demand in Indonesia”, Bulletin of Indonesian Economic
Studies, 51:1, pp. 29–45.
[116] Gatto, M., Wollni, M., and Qaim, M. (2015), “Oil palm boom and land use
dynamics in Indonesia: the role of policies and socioeconomic factors”, Land
Use Policy, 46, pp. 292–303.
[117] Gellert, P. (2015), “Palm Oil Expansion in Indonesia: Land Grabbing
as Accumulation by Dispossession”, in Jon Shefner (ed.) States and
Citizens: Accommodation, Facilitation and Resistance to Globalization (Current
Perspectives in Social Theory, Volume 34), Emerald Group Publishing Limited,
pp.65 –99.
[118] Gingold, B., Rosenbarger, A., Muliastra, Y., Stolle, F., Sudana, I., Manessa,
M., Murdimanto, A., Tiangga, S., Madusari, C., and Douard, P. (2012), “How
to identify degraded land for sustainable palm oil in Indonesia.” Working Paper,
World Resources Institute and Sekala, Washington D.C.
179
[119] Gibbs, H. K., Ruesch, A. S., Achard, F., Clayton, M. K., Holmgren, P.,
Ramankutty, N., and Foley, J. A. (2010), “Tropical forests were the primary
sources of new agricultural land in the 1980s and 1990s”. Proceedings of the
National Academy of Sciences, 107:38, pp. 16732–16737.
[120] Goderis, B. and Malone, S.W. (2011), “Natural resource booms and inequality:
Theory and evidence”, Scandinavian Journal of Economics, 113: 2, pp. 388–417.
[121] Gollin, D. (2010), “Agricultural Productivity and Economic Growth”, in
Handbook of Agricultural Economics, pp. 3825–3866.
[122] Gollin, D., Parente, S., and Rogerson, R. (2002), “The Role of Agriculture in
Development”, American Economic Review, 92:2, pp. 160–164.
[123] Greenstone, M., Hornbeck, R., and Moretti, E. (2010), “Identifying
Agglomeration Spillovers: Evidence from Winners and Losers of Large
Plant Openings”, Journal of Political Economy, 119: 3, pp. 536–598.
[124] Gregory, R.G. (1976), “Some implications of the growth of the mineral sector”,
Australian Journal of Agricultural Economics, 20:2, pg. 71.
[125] Griliches, Z. (1969), “Capital–skill complementarity”, Review of Economics
and Statistics, 51: 4, pp. 465–468.
[126] Gylfason, T. (2001), “Natural Resources, Education, and Economic
Development”, European Economic Review, 45, pp. 847–859.
[127] Gylfason, T., Herbertsson, T., and Zoega, G. (1999), “A mixed blessing: natural
resources and economic growth”, Macroeconomic Dynamics, 3, pp. 204–225.
[128] Gylfason, T. and Zoega, G. (2003), “Education, social equality, and economic
growth: a view of the landscape”, CESifo Economic Studies, 49: 4, pp. 557–579.
[129] Gylfason, T. and Zoega, G. (2006), “Natural Resources and Economic Growth:
The Role of Investment”, World Economy, 29:8, pp. 1091–1115.
180
[130] Hall, C., Tharakan, P., Hallock, J., Cleveland, C., and Jefferson, M. (2003)
“Hydrocarbons and the evolution of human culture“, Nature, 426, pp. 318–322.
[131] Hanson, G. (2012) “The rise of middle kingdoms: emerging economies in
global trade”, Journal of Economic Perspectives, 26: 2, pp. 41–64.
[132] Hasnah, Fleming, E., and Coelli, T. (2004), “Assessing the performance of a
nucleus estate and smallholder scheme for oil palm production in West Sumatra:
a stochastic frontier analysis”, Agricultural Systems, 79, pp. 17–30.
[133] Hayami, T. (2010), “Plantations Agriculture”, in Handbook of Agricultural
Economics, pp. 3305–3322.
[134] Heston, A., Summers, R., and Aten, B. (2012), “Penn World Table Version 7.1”,
Center for International Comparisons of Production, Income and Prices at the
University of Pennsylvania.
[135] Hill, H. (1996), “The Indonesian Economy Since 1996: Southeast Asia’s
Emerging Giant”, Cambridge University Press, London.
[136] Hill, H. (2014), “Regional Dynamics in a Decentralized Indonesia”, Vol. 501,
Institution of Southeast Asian Studies.
[137] Hill, H., Khan, M.E., and Zhuang, J. (2012), “Diagnosing the Indonesian
Economy: Toward Inclusive and Green Growth”, Anthem Press and the Asian
Development Bank, Manilla.
[138] Hill, H., Resosudarmo, B.P., and Vidyattama, Y., (2008), “Indonesia’s Changing
Economic Geography”, Bulletin of Indonesian Economic Studies, 44:3, pp.
407–435.
[139] Hirschman, A.O. (1981), “Essays in Trespassing: Economics to Politics and
Beyond”, Cambridge University Press, New York.
181
[140] Horiuchi, Y. and Mayerson, A. (2015), “The Opportunity Cost of Conflict:
Statistically Comparing Israel and Synthetic Israel”, Political Science and
Research Methods, May, pp. 1–10.
[141] Hornbeck, R., and Keskin, P. (2015), “Does Agriculture Generate Local
Economic Spillovers? Short-run and Long-Run Evidence from the Ogallala
Aquifer”, American Economic Journal: Economic Policy, 7:2, pp. 192–213.
[142] Hunt, C. (2010), “The costs of reducing deforestation in Indonesia”, Bulletin of
Indonesian Economic Studies, 46:2, pp. 187–192.
[143] Iyengar, G., and Nair, P. (2000), “Global outlook on nutrition and the
environment: meeting the challenges of the next millennium”, The Science of
the Total Environment, 249:1–3, pp. 331–346.
[144] James, A. and Aadland, D. (2011), “The curse of natural resources: an empirical
investigation of US countries”, Resource and Energy Economics, 33, pp 440–463.
[145] James, A. and James, R. (2011), “Do resource dependent regions grow slower
than they should?”, Economics Letters, 111, pp. 194–196.
[146] Jedwab, R. (2013), “Urbanisation without structural transformation: evidence
from consumption cities in Africa”, mimeo, George Washington University.
[147] Johnston, B.F., and Mellor, J.W. (1961), “The Role of Agriculture in Economic
Development”, American Economic Review, 51:4, pp. 566–593
[148] Kander, A., Malanima, P., and Warde, P. (2013), “Power to the People–Energy
and Economic Transformation of Europe over Four Centuries”, Princeton
University Press, Princeton.
[149] Kander, A., and Stern, D.I. (2014), “Economic growth and the transition from
traditional to modern energy in Sweden”, Energy Economics, 36, pp. 56–65.
182
[150] Karlsson, M., and Pichler, S. (2015), “Demographic consequences of HIV”.
Journal of Population Economics, 28, pp. 1097–1135.
[151] Kaufmann, D., Kraay, A., and Mastruzzi, M. (2013), “The Worldwide
Governance Indicators Project”, World Bank, Washington, D.C.
[152] Kleibergen, F., and Paap, R. (2006), “Generalized reduced rank test using the
singular value decomposition”, Journal of Econometrics, 127:1, pp. 97–126.
[153] Koh, L. P., Miettinen, J., Liew, S. C., and Ghazoul, J. (2011), “Remotely sensed
evidence of tropical peatland conversion to oil palm”. Proceedings of the
National Academy of Sciences, 108:12, pp. 5127–5132
[154] Koh, L., and Wilcove, D. (2007), “Cashing in palm oil for conservation”, Nature,
448, pp. 993–994.
[155] Koh, L., and Wilcove, D. (2008), “Is oil palm agriculture really destroying
tropical biodiversity?”, Conservation Letters, 1, pp. 60–64.
[156] Kraay, A. (2006), “When is growth pro-poor? Evidence from a panel of
countries”, Journal of Development Economics, 80, pp. 198–227.
[157] Krief, N., Grieve, R., Gangartner, D., Turner, A., Nikolova, S., and Sutton,
M. (2015), “Examination of the synthetic control method for evaluating health
policies with multiple treated units”, Health Economics, forthcoming.
[158] Krugman, P. (1987), “The Narrow Moving Band, the Dutch Disease, and
the Competitive Consequences of Mrs. Thatcher”, Journal of Development
Economics, 27, pp. 41–55
[159] Lederman, D. and Maloney, W. (2007), “Natural resources: neither curse nor
destiny”, Stanford University Press, Stanford, CA.
183
[160] Lee, J., Ghazoul, J., Obidzinski, K., and Koh, L. (2013), “Oil palm smallholder
yields and incomes constrained by harvesting practices and type of smallholder
management in Indonesia”, Agronomy for Sustainable Development, 34:2, pp.
510–513.
[161] Lei, Y. and Michaels, G. (2014), “Do giant oilfield discoveries fuel internal
armed conflicts?”, Journal of Development Economics, 110, pp. 139–157.
[162] Lewis, A.W. (1954), “Economic Development with Unlimited Supplies of
Labor”, Manchester School, pp. 139–91.
[163] Lewis, B. and Smoke, P. (2015), “Intergovernmental Fiscal Transfers and Local
incentives and Responses: The Case of Indonesia”, Fiscal Studies, forthcoming.
[164] Loayza, N.V., and Raddatz, C. (2010), “The composition of growth matters for
poverty alleviation”, Journal of Development Economics, 93, pp. 137–151.
[165] Maertens M, and Swinnen, J. (2009), “Trade, standards, and poverty: evidence
from Senegal”, World Development, 37:1, pp. 161–178.
[166] Manning, C. (2010), “Labor market adjustment to Indonesia’s economic crisis:
context, trends, and implications”, Bulletin of Indonesian Economic Studies, 36:1,
pp. 105–136.
[167] Manning, C., and Sumarto, S. (2011), “Employment, Living Standards, and
Poverty in Contemporary Indonesia”, Indonesia Update Series, Institute of South
East Asian Studies, Singapore.
[168] Manzano, O, and Rigobon, R. (2001), “Resource curse or debt overhang?”
NBER Working Paper 8390, July.
[169] Matsuyama, K. (1992), “Agricultural Productivity, Comparative Advantage,
and Economic Growth”, Journal of Economic Theory, 58, pp. 317–334.
184
[170] Maxwell, S., and Fernando, A. (1989), “Cash Crops in Developing Countries:
The Issues, the Facts, the Policies”, World Development, 17:11, pp. 1677–1708.
[171] Mayumi, K. (1991), ‘Temporary emancipation from land: from the Industrial
Revolution to the present time”, Ecological Economics, 4, pp. 35–56.
[172] McCarthy, J. (2010), “Processes of inclusion and adverse incorporation: oil
palm and agrarian change in Sumatra, Indonesia”, Journal of Peasant Studies,
37:4, pp. 821–850.
[173] McCarthy, J., Gillespie, P., Zen, Z. (2011), “Swimming Upstream: Local
Indonesian Production Networks in “Globalised” Palm Oil Production”, World
Development, 40:3, pp. 555–569.
[174] McCulloch, N., and Malesky, E. (2011), “Does Better Local Governance
Improve District Growth Performance in Indonesia”, IDS Working Paper 369.
[175] McCulloch, N., and Sjahrir, B. (2008), “Endowments, Location, or Luck?
Evaluating the Determinants of Sub-National Growth in Decentralized
Indonesia”, World Bank Policy Research Working Paper 4769.
[176] McKinsey Global Institute (2013), “Reverse the curse: Maximizing the
potential of resource-driven economies”, McKinsey Global Institute Report.
[177] Mehlum, H., Moene, K., and Torvik, R. (2006), “Institutions and the Resource
Curse”, Economic Journal, 116, pp. 1–20.
[178] Meng, X., Manning, C, Shi, L, and Effendi, T. (2010), “The Great Migration:
Rural?Urban Migration in China and Indonesia”, Edward Elgar Publishing.
[179] Michaels, G. (2010), “The long term consequences of resource-based
specialisation”, Economic Journal, 121, pp. 31–57.
[180] Mideksa, T. (2013), “The economic impact of natural resources”, Journal of
Environmental Economics and Management, 65, pp. 277–289.
185
[181] Mikesell, R.F. (1997), “Explaining the resource curse with special reference to
mineral-exporting countries”, Resources Policy, 23:4, pp. 191–199.
[182] Miranti, R. (2010), “Poverty in Indonesia 1984-2002: the impact of growth and
changes in inequality”, Bulletin of Indonesian Economic Studies, 46:1, pp. 79–97.
[183] Miriam, E., Marlier, I., DeFries, R., Kim, P., Koplitz, S., Jacob, D., Mickley, L.,
and Myers, S. (2015), “Fire emissions and regional air quality impacts from fires in
oil palm, timber, and logging concessions in Indonesia”, Environmental Research
Letters, 10: 8.
[184] Montalvo, J.G.,(2011) “Voting after the bombings: a natural experiment on
the effect of terrorist attacks on democratic elections”, Review of Economics and
Statistics, 93:4, pp. 1146–1154.
[185] Moreira, M.J. (2003), “A Conditional Likelihood Ratio Test for Structural
Models”, Econometrica, 71(4), pp. 1027–1048
[186] Moretti, E. (2010), “Local Multipliers”, American Economic Review: Papers
and Proceedings 100, pp. 373–377.
[187] Munasib, A. and Rickman, D. S. (2015), “Regional economic impacts of the
shale gas and tight oil boom: A synthetic control analysis”, Regional Science and
Urban Economics, 50, pp. 1-17.
[188] Murray, M.P. (2006), “Avoiding Invalid Instruments and Coping with Weak
Instruments”, Journal of Economic Perspectives, 20:4, pp.111–132.
[189] Newhouse, D., and Suryadarma, D. (2011), “The value of vocational education:
high school type and labor market outcomes in Indonesia”, Bulletin of
Indonesian Economic Studies, 25: 2, pp. 296–322
[190] Norman, C.S. (2009), “Rule of Law and the Resource Curse: Abundance Versus
Intensity”, Environmental and Resource Economics, 43, pp. 183–207.
186
[191] Nunn, N., and Qian, N. (2011), “The potato’s contribution to population
and urbanization: evidence from a historical experiment”, Quarterly Journal of
Economics, 126, pp. 593–650.
[192] Obidzinski, K., Dermawan., A., and Hadianto, A. (2014), “Oil palm plantation
investments in Indonesia’s forest frontiers: limited economic multipliers and
uncertain benefits for local communities”, Environment, Development, and
Sustainability, 16, pp. 1177–1196.
[193] Oster, E. (2015), “Unobservable Selection and Coefficient Stability: Theory and
Evidence”, Mimeo.
[194] Papyrakis, E., and Gerlagh, R. (2007), “Resource abundance and economic
growth in the United States”, European Economic Review, 51, pp. 1101-1039.
[195] Pepinski, T.B., and Wihardja, M.M. (2011), “Decentralization and Economic
Performance in Indonesia”, Journal of East Asian Studies, 11:3, pp. 337–371.
[196] Pierskalla, J.H. (2016), “Splitting the Difference? The politics of district creation
in Indonesia”, Comparative Politics, 42:2, forthcoming.
[197] Pinotti, P. (2015) “The economic costs of organised crime: evidence from
southern Italy”, Economic Journal, 125:586, pp. F203–F232.
[198] Preston, S.H. (1975), “The changing relation between mortality and level of
development”, Population Studies, 29:2, pp. 231–248.
[199] Pritchett, L. and Summers, L. H. (1996), “Wealthier Is Healthier”, Journal of
Human Resources, 31, pp. 841–868.
[200] Pryor, F. (1982), “The Plantation Economy as an Economic System”, Journal of
Comparative Economics, 7, pp. 288–317.
187
[201] Quizon, J., and Binswanger, H. (1986), “Modelling the Impact of Agricultural
Growth and Government Policy on Income Distribution in India,” World Bank
Economic Review, 1:1, pp. 103–48.
[202] Rada, N., Buccola, S.T., and Fuglie, K.O. (2011), “Government policy and
agricultural productivity in Indonesia”, American Journal of Agricultural
Economics, 93:3, pp. 867–884.
[203] Ranis, G., and Fei, J. (1961), “A theory of economic development”, American
Economic Review, 51, pp. 533–565.
[204] Ravallion M, and Chen, S. (2007), “China’s (uneven) progress against poverty”,
Journal of Development Economics, 82:1, pp. 1–42
[205] Ravallion, M., and Chen, S. (2003), “Measuring pro-poor growth”, Economic
Letters, 78, pp. 93–99.
[206] Ravallion, M., and Datt, G. (2002), “Why has economic growth been more
pro-poor in some states of India than others?”, Journal of Development
Economics, 68, pp. 381–400.
[207] Resosudarmo, B. P. (2005), “The Politics and Economics of Indonesia’s Natural
Resources”, Indonesia Update Series, Institute of South East Asian Studies,
Singapore.
[208] Restuccia, D., Yang, D. T., and Zhu, X. (2008), “Agriculture and Aggregate
Productivity: a Quantitative Cross-Country Analysis”, Journal of Monetary
Economics, 55: 2, pp. 234–250.
[209] Rist, L., Feintrenie, L., and Levang, P. (2010), “The livelihood impacts of
oil palm: smallholders in Indonesia”, Biodiversity and Conservation, 19:4, pp.
1009–1024.
188
[210] Rival, A., and Levang, P. (2014), “Palms of controversies: Oil palm and
development challenges”, Center for International Forestry Research, Bogor.
[211] Rodrik, D. (2015), “Premature industrialisation”, IAS School of Social Science,
Economics Working Papers, No. 107, Princeton University.
[212] Ross, M.L. (2001), “Does OIL hinder democracy?” World Politics, 53, pp.
325–61.
[213] Ross, M.L. (2007), “How mineral-rich states can reduce inequality”, Escaping
the resource curse, pp 237–75.
[214] Sachs, J. and Warner, A. (1995), “Natural resource abundance and economic
growth”, NBER Working Paper 5398, December.
[215] Sachs, J. and Warner, A. (2001), “The curse of natural resources”, European
Economic Review, 45, pp. 827–838.
[216] Saha, S., Pattanayak, E., and Sills (2011), “Undermining Health: Environmental
Justice and Mining in India”, Health and Place, 17:1, pp. 140–148.
[217] Sala-i-Martin, X., Doppelhofer, G., and Miller, R.I (2004), “Determinants
of long-term growth: a Bayesian Averaging of Classical Estimates (BACE)
approach”, American Economic Review, 94, pp. 813–835.
[218] Salami, I., Zulfikar, A., Marselina, M., and Roosmini, D. (2014), “Respiratory
health risk assessment of children living close to industrial areas in Indonesia”,
Reviews on Environmental Health, 29, pp. 1–2.
[219] Sarsons, H. (2015), “Rainfall and conflict: A cautionary tale”, Journal of
Development Economics, 115, pp. 62–72.
[220] Sayer, J., Ghazoul, J., Nelson, P., Boedhihartono, A. (2012), “Oil palm expansion
transforms tropical landscapes and livelihoods”, Global Food Security, 1, pp.
114–119.
189
[221] Schultz, T.W. (1964), “Transforming Traditional Agriculture”, Yale University
Press, New Haven, Connecticut.
[222] Sills, E., Herrera, D., Kirkpatrick, J., Brandao, A., Sickson, Y., Hall, S.,
Pattanayak, S., Shoch, D., Vedoveto, M., Young, L., Pfaff, A. (2015), “Estimating
the Impacts of Local Policy Innovation: The Synthetic Control Method Applied
to Tropical Deforestation”, PLoS ONE, 10:7, e0132590.
[223] Smith, B. (2015), “The resource curse exorcised: Evidence from a panel of
countries”, Journal of Development Economics, 117, pp. 57–73.
[224] Smith, B. and Wills, S. (2015), “Left in the Dark? Oil and Rural Poverty”,
OxCarre Research Paper 164, Oxford University.
[225] Sokoloff, K.L. and Engerman, S.L. (2000), “History Lessons: Institutions, Factor
Endowments and Paths of Development in the New World”, Journal of Economic
Perspectives, 14:3, pp. 217–232.
[226] Sparrow, R., Grimm, M., and Tasciotti, L. (2015), “Does Electrification Spur
the Fertility Transition? Evidence from Indonesia”, Demography, 52:5, pp.
1773–1796.
[227] Staiger, D. and Stock, J. H. (1997), “Instrumental variables regression with weak
instruments”, Econometrica, 65:3, pp. 556–586.
[228] Stern, D.I. (2010), “Between Estimates of the Emissions-Income Elasticity,”
Ecological Economics, 69, pp. 2173–2182.
[229] Stijns, J.P. (2006), “Natural Resource Abundance and Human Capital
Accumulation”, World Development, 34:6, pp. 1060–1083.
190
[230] Stock, J. H., and Yogo, M. (2005), “Testing for weak instruments in linear IV
regression,” in Donald W. K. Andrews and James H. Stock (Eds.), Identification
and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg,
Cambridge, UK: Cambridge University Press.
[231] Suryadarma, D. and Jones, G.W. (2013), “Meeting the education challenge”,
in Suryadarma and Jones (eds), “Education in Indonesia, Singapore, ISEAS, pp.
1–14.
[232] Suryahadi, A., Sumamto, S., and Pritchett, L. (2003), “Evolution of poverty
during the crisis in Indonesia”, Asian Economic Journal, 17:3, pp. 221–241.
[233] Suryahadi, A., Suryadarma, D., and Sumarto, S. (2009), “The effects of
location and sectoral components of economic growth on poverty: Evidence from
Indonesia”, Journal of Development Economics, 89:1, pp. 109–117.
[234] Taubman, P. (1989), “Role of Parental Income in Educational Attainment”,
American Economic Review, Papers and Proceedings, May 1989, pp. 57–61.
[235] Temple, J. (2003), “Growing into trouble: Indonesia after 1966”, in Rodrik,
D. (ed.), “In search of prosperity: analytic narratives on economic growth”,
Princeton University Press, Princeton.
[236] Thorbecke, E., and Jung, H. (1996), “A Multiplier Decomposition Method
to Analyze Poverty Alleviation”, Journal of Development Economics, 48:2 pp.
253–277.
[237] Tiffen, M.m and Mortimore, M. (1990), “Theory and Practice in Plantation
Agriculture: an Economic Review”, Overseas Development Institute, Russell
Press, London.
[238] Torvik, R. (2001) “Learning by doing and the Dutch disease”, European
Economic Review, 45, pp. 285–306.
191
[239] Torvik, R. (2002), “Natural resources, rent seeking, and welfare”, Journal of
Development Economics, 65, pp. 455–470
[240] Torvik, R. (2009) “Why do some resource-abundant countries succeed while
others do not”, Oxford Review of Economic Policy, 25:2, pp. 241–256.
[241] Tsui, K. (2011), “More oil, less democracy: evidence from worldwide crude oil
discoveries”, Economic Journal, 121, pp. 89–115.
[242] United Nations (2014),“National Accounts Estimates of Main Aggregates”,
United Nations Statistics Division.
[243] United Nations (2013), “Contribution of mining to total value added”,
Environmental Indicators: Energy and Minerals.
[244] United States Department of Agriculture (2015), “Oilseeds: world markets and
trade”, Washington, D.C.
[245] van der Eng, P. (2009), “Capital formation and capital stock in Indonesia,
1950–2008”, Bulletin of Indonesian Economic Studies, 45:3, pp. 345–371.
[246] van der Eng, P. (2014), “Mining and Indonesia’s Economy: Institutions and
Value Adding, 1870–2010”, Poverty Reduction, Institutions, Markets and Policies
in Developing Countries (PRIMCED) Discussion Paper Series, No. 57. Institute
of Economic Research, Hitotsubashi University.
[247] van der Ploeg, F. (2011), “Natural Resources: Curse or Blessing?” Journal of
Economic Literature, 49:2, pp. 366–420.
[248] van der Ploeg, F. and Poelhekke, S. (2010), “The Pungent Smell of Red Herrings:
subsoil assets, rents, volatility and the resource curse”, Journal of Environmental
Economics and Management, 60:1, pp. 44–55.
[249] van der Ploeg, F. and Poelhekke, S. (2009), “Volatility and the natural resource
curse”, Oxford Economic Papers, 61:4, pp. 727–760.
192
[250] van Winjnbergen, S. (1984), “The Dutch Disease: a disease after all?” Economic
Journal, 94: 373, pp. 41–55.
[251] Vollrath, D., Gollin, D., and Jedwab, R. (2015), “Urbanization with and without
Industrialization”, Journal of Economic Growth, forthcoming.
[252] Wetterberg, A., Sumarto, S., and Pritchett, L. (1999), “A national snapshot of
the social impact of Indonesia’s crisis”, Bulletin of Indonesian Economic Studies,
35:3, pp. 145–152.
[253] Wheeler, D., Hammer, D., Kraft, R., Dasgupta, S., and Blankespoor, B.
(2013), “Economic dynamics and forest clearing: a spatial econometric analysis”,
Ecological Economics, 85, pp. 85–96.
[254] Wick, K. and Bulte, E. (2009), “The Curse of Natural Resources”, Annual
Review of Economics, 1:1.1-1.17.
[255] Williams, A. (2011), “Shining a light on the resource curse: an empirical
analysis of the relationship between natural resources, transparency, and
economic growth”, World Development, 39:4, pp. 490–505.
[256] World Bank (2013), “Africa’s Pulse”, The Office of the Chief Economist for the
Africa Region. October 2013, Volume 8, World Bank, Washington, D.C.
[257] World Bank (2014), “World Development Indicators”. World Bank data portal,
Washington, D.C.
[258] World Bank (2014), “Indonesia: Avoiding the Trap”, 2014 Development Policy
Review, World Bank, Jakarta.
[259] World Bank (2015,) “Indonesia Database for Economic and Policy Research”,
World Bank data portal, Washington, D.C.
[260] Wrigley, E. A. (2010), “Energy and the English Industrial Revolution”.
Cambridge University Press, Cambridge.
193
[261] Yusuf, A.A. (2013), “The evolution of inequality in Indonesia, 1990–2012”,
Department of Economics, Padjadjaran University, Working Papers in Economics
and Development Studies, No. 2013114, Bandung.