Growth is Dead, Long Live Growth

The Quality of Economic Growth and Why it Matters

JICA Research Institute January 2015

Lawrence Haddad
Hiroshi Kato
Nicolas Meisel

Growth is Dead,Long Live Growth
The Quality of Economic Growthand Why it Matters
JICA Research Institute

JICA Research Institute

Edited by

Lawrence Haddad
Hiroshi Kato
Nicolas Meisel

Growth is Dead, Long Live Growth
The Quality of Economic Growthand Why it Matters
Edited by

Lawrence Haddad
Hiroshi Kato
Nicolas Meisel

January 2015
Agence Française de Développement (AFD)
Institute of Development Studies (IDS)
Japan International Cooperation Agency (JICA)

The views and opinions expressed in the articles contained in this volume do notnecessarily represent the official views or positions of the organizations theauthors work for or are affiliated with.
Agence Française de Développement (AFD)
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Institute of Development Studies (IDS)
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Japan International Cooperation Agency (JICA)
http://www.jica.go.jp/english/index.html
Haddad, Lawrence, Hiroshi Kato and Nicolas Meisel. 2015.
Growth is Dead, Long Live Growth: The Quality of Growth and Why it Matters.
Tokyo: JICA Research Institute.
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Growth is Dead, Long Live Growth
The Quality of Economic Growth and Why it Matters

Contents

Foreword ������������������������������������������������������������������������������������������������������������������������������������������������������� iii
Preface ������������������������������������������������������������������������������������������������������������������������������������������������������������� iv
List of Contributors ������������������������������������������������������������������������������������������������������������������������������ v

Introduction

Part I

Growth is Dead, Long Live Growth
The Quality of Economic Growth and Why It Matters ��������1
Lawrence Haddad, Hiroshi Kato and Nicolas Meisel

Growth: A Path towards Abundance or Depletion?

Chapter 1 What is the Quality of Growth?
Sustainability and Inclusiveness ������������������������������������������������������ 19
Michel Aglietta
Part II

The 20th Century Concept of Growth and Its Deliverables

Chapter 2 Poor Trends: The Pace of Poverty Reduction
after the Millennium Development Agenda �������������������������� 57
Richard Bluhm, Denis de Crombrugghe and Adam Szirmai
Chapter 3

Chronic Poverty in Rural Cambodia:
Quality of Growth for Whom?��������������������������������������������������������������� 85
Ippei Tsuruga

Chapter 4 Does the Quality of Income Growth AffectChild Nutrition Status?
Lawrence Haddad, Edoardo Masset and Lisa Smith ���������������� 121iChapter 5 Disability and Growth Elasticity of Poverty
in a Developing Country ����������������������������������������������������������������������� 137
Kamal Lamichhane and Damaru Ballabha PaudelPart III Different Growth, Different Outcomesin Sustainability, Inclusiveness, and Resilience
Chapter 6 Reflections on the Prospects for Pro-Poor Low-Carbon
Growth ���������������������������������������������������������������������������������������������������������������� 159
Dirk Willenbockel
Chapter 7

Structural Transformation, the Quality of Growthand Employment Outcomes������������������������������������������������������������������ 187
Xavier Cirera

Chapter 8 The Elusive Quest for Inclusive Growth in Sub-SaharanAfrica: Regional Challenges and Policy Options
Bruno Losch ������������������������������������������������������������������������������������������������������� 217
Chapter 9 What are the Macroeconomic Impacts of Natural
Disasters? - The Impacts of Natural Disasters on the
Growth Rate of Gross Prefectural Domestic Product inJapan ����������������������������������������������������������������������������������������������������������������������� 243
Go ShimadaPart IV Multiple Paths towards Quality Growth
Chapter 10 Industrial Transformation and Quality of Growth ��������� 267
Akio Hosono
Chapter 11 The Governance of Natural Resource Wealth: Some
Political Economy Considerations on Enhancing Social
Investment ���������������������������������������������������������������������������������������������������������� 301
Andrés Mejía Acosta
Index �������������������������������������������������������������������������������������������������������������������������������������������������������������� 331ii

Foreword
The three organisations behind this book—AFD, IDS, and JICA—have different
roles and responsibilities when it comes to global development, but each has
long advocated for a more nuanced debate on growth, emphasising, forexample, its distributional consequences, its human development impacts, andits environmental effects. As we enter into the post 2015 development era, all ofthese features of growth are becoming ever more integrated, and gaining
importance as decisive factors for realising sustainable development.
We are delighted that the authors of this book have come together, supported byour respective institutions, to make a significant contribution to inform ourdebate on this subject. In the 21st century our decisions and policies will
perhaps be judged less on the quantity of growth they help to achieve, but on its
quality—what it does for the things that we, as citizens of this planet, really careabout. This book will offer some guidance on what we have come to know about
the quality of growth, and it will help to identify the research gaps that mosturgently need to be filled.

Anne Paugam

Melissa Leach

Akihiko Tanaka

CEOAgenceFrançaisede Développement

Director
Institute ofDevelopment Studies

President
Japan International
Cooperation Agency(JICA)

(AFD)

(IDS)

iii

Preface
This book was written because the editors felt that insufficient attention is being
given to the quality of growth.
As the signs of negative impact of global climate change are becoming evident,
and especially in the aftermath of the global economic turndown, societies had achoice—either reframe their definition of growth, or try to pursue it— growth as
we have known it for many decades—via business as usual routes, using metrics
rooted in the 20th century. True, there are emerging signs of change. For example,
the debate on the post-2015 development agenda is slowly moving towards agreater consensus on the need to focus on growth that generates jobs, reduces
inequality, is consistent with a sustainable use of natural resources and generates
manageable amounts of greenhouse gases. By and large, however, despite suchdebate in international arena, politicians both in developed and developing
countries alike seem to be choosing the latter routes, pursuing growth as we have
known it via business as usual approaches and metrics.
What does the academic world have to say on these issues? Among the various
academic disciplines, economists in particular seem to have focusedinsufficiently on these issues: What are the dimensions of growth quality weshould most care about? How to measure such qualities? And what are thetradeoffs between them in the context of different policy regimes and governancestructures? And the list of such important research questions goes on.
This collection of papers represents an effort of the researchers of the three
institutions—AFD, IDS, and JICA—to contribute to the deepening andwidening of international debate on such issues. We hope it provokes policymakers to think twice about the blind pursuit of “growth at all costs,” and ourdevelopment community to think seriously about how their day-to-day
decisions and activities can contribute, both positively and negatively, torealizing a higher quality of growth.

For the AFD TeamFor the IDS TeamFor the JICA Team

Nicolas Meisel

Lawrence Haddad

Hiroshi Katoiv

List of Contributors
Acosta, Andrés Mejía

Senior Lecturer of Polical Economy,
International Development Institute,
Kings College London

Aglietta, Michel

Emeritus Professor of Economic Sciences,
University of Paris X Nanterre and Advisor toCentre d’Etudes Prospectives et d’Informations
Internationales (CEPII)

Bluhm, Richard

Assistant Professor, Leibniz University Hannover,Institute of Macroeconomics

Cirera, Xavier

Research Fellow, Globalisation Team,
Institute of Development Studies and Research
Associate at the Centre for the Analysis of Regional
Integration (CARIS) at the University of Sussex, UK

de Crombrugghe, Denis

Associate Professor, Maastricht University Schoolof Business and Economics

Haddad, Lawrence

Senior Research Fellow, International Food Policy
Research Institute (IFPRI)

Hosono, Akio

Senior Research Advisor, Japan International
Cooperation Agency Research Institute (JICA-RI)

Kato, Hiroshi

Vice President, JICA

Lamichhane, Kamal

Associate Professor, University of Tsukuba,

Losch, Bruno

Research Director, Centre de Coopération
Internationale en Recherche Agronomique pour leDéveloppement (CIRAD), UMR Art-Dev

Masset, Edoardo

Research Fellow, Institute of Development Studies(IDS)

and Former Research Fellow, JICA-RIv

vi

Meisel, Nicolas

Senior Economist, Research Directorate,
Agence Française de Développement (AFD)

Paudel, Damaru ballabha

Officer, Nepal’s Ministry of Finance

Shimada, Go

Senior Research Fellow, JICA-RI

Smith, Lisa

Senior Economist, TANGO International

Szirmai, Adam

Professorial Fellow, UNU-MERIT and Professor ofDevelopment Economics, Maastricht Graduate
School of Governance of Maastricht University

Tsuruga, Ippei

Former Deputy Assistant Chief, JICA-RI, andcurrently Representative of JICA U.S.A.

Willenbockel, Dirk

Research Fellow, Institute of Development Studies(IDS)

Introduction

Growth is Dead, Long Live Growth

The Quality of Economic Growth and Why It Matters
Lawrence Haddad, Hiroshi Kato and Nicolas Meisel“Speed is irrelevant if you are going in the wrong direction”(M� Gandhi)

Why the quality of growth?
The way that economic growth is conceptualised and measured has been
a topic of great debate for many years (Seers 1972). How can equity
considerations be incorporated? How can environmental externalities betaken into account? And, ultimately, how can we improve the ability of
economic growth to drive the development outcomes we most care about?
Many now talk about degrowth (Demaria et al. 2013), the end of growth(Heinberg 2011), or the forthcoming secular stagnation (Teulings andBaldwin 2014) —about how growth is no longer desirable, useful, or evenfeasible. This collection of papers prefers to frame the debate not as theend of growth, but as the end of growth as we know it. The 20th century
definition of growth must be left in the 20th century and 21st century
formulations adopted for the times we live in.
We are not the first to discuss the quality of growth. The debate on thequality of growth is primarily a debate about the quality of life. It is wellknown since the early works of Easterlin (1974) that the progress of GDP
per capita in Western countries does not mean a parallel progress inperceived happiness. Since 1990, the United Nations Development
Programme (UNDP) through both its Human Development Reports andthe Human Development Index has brought the idea of the quality ofgrowth into the mainstream policy discourse. An important piece of workon this issue by Thomas, et al. (2000) was also published by the WorldBank at the turn of the millennium. It argued that while economic growth
remains important among the factors that contribute to development, thequality of that growth is equally important. The report contended thatdevelopment is better served when quantity and quality of growth1Introduction

intertwine and explicitly focus on an agenda that addresses the human,social, environmental, and governance dimensions of development.1
In addition to the benefit of 15 more years of observation,
conceptualization, data and studies, our work advances Thomas et al.
(2000) in three important ways. First, the ramifications of the global
financial crisis of 2007-8 flow through many of the papers in this collection,
generating greater insight about what we should be measuring. Second,the evidence on climate change is more organized and compelling,
generating a new literature on the potential tradeoffs between economic
growth, poverty reduction and environmental costs. Finally, much morework has been done on the levels, drivers and consequences of inequality
and we draw on and aim to contribute to this literature.
Indeed, the trends and events of the last decade have moved thesedebates from largely academic exercises to urgent matters of public
policy. The dramatic increases forecast in greenhouse gas emissions, thepersistence of chronic poverty despite the increasing wealth of nations,and an increasing recognition of the interplay between different typesof growth and different types of fragilities have made the search for new
ways of framing, measuring, analysing and assessing economic growtheven more pressing. If not all forms of growth are equally valuable andsome are destructive of development, what growth do we need and how
do we get it?
Policy debates on the quality of growth have been very active around theworld. In Asia, for example, the quality of growth has been hotly debated.
This is understandable, for Asia is a region that has achieved remarkableeconomic development, but only with accompanying difficulties such as
inequality within the region and within countries, insecurity in terms of
the supply of food and energy, increased risk of infectious diseases, andthe middle-income poverty trap problem (Sumner 2013). Thus the APEC’s
growth strategy adopted in 2010 (Xia 2011) highlighted five pillars, that
included (1) balanced growth, (2) inclusive growth, (3) sustainable growth,(4) innovative growth, and (5) secured growth. Even China seems to haveclearly put an end to its policy orientation of “growth at all costs.” In 2011
Chinese Premier Wen Jiabao told a conference in Beijing2 that he believes
1. World Bank Institute (2001).
2. Annual Meeting of the New Champions (what is generally called “Summer Davos”
conference) in 2011.

2

Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Mattersthat “China’s economy can achieve longer term, better quality growth”.
The participants of the said conference came up with an understandingthat the following five elements constitute “quality growth:”
sustainability, inclusion, fairness, balance, and innovation.
In Africa we have heard the term “quality of growth” less frequently but
the current heated debate on African development seems to be centred
not simply on assuring the continent’s continued growth but more
importantly on economic transformation, i.e., away from the excessive
dependency on extractive mineral and energy sectors and toward more
broad based economic development. It centres on “inclusive growth” or
how to share the fruit of growth among the populace, and this is at thecore of the quality of growth debate. In Latin America, too, where
inequality has long been a major constraint to development, this is anagenda attracting attention from policy makers (Thorbecke 2013).
In Europe the debates have been around sustainable growth (Jackson
2011), degrowth (Demaria et al. 2013), measurement issues (Stiglitz, Sen
and Fitoussi 2013) and the connection between inequality and economic,
social, and ecological challenges (Alvaredo et al. 2013, Haddad 2015). Allof these refer to quality of growth issues.
In this volume we do not offer a precise definition of the quality ofgrowth. More importantly we focus on expanding the knowledge base on
three agreed key dimensions of quality: (1) growth that manages
environmental tradeoffs, (2) growth that supports equity andinclusiveness, and (3) growth that is less susceptible to shocks. Bybringing together these strands we aim to contribute to a unification of
the field and to identify any common patterns and synergies.
Environmental Sustainability and Climate Change
A few years earlier than the work of Easterlin (1974), the Meadows report(1972) on The Limits to growth drew the world’s attention to thecontradiction between exponential growth trends in population and GDP
and the finite resources and carrying capacities of our planet. The report
was ignored or ridiculed by the bulk of the economic profession at thetime of its publication. However, 35 years later Turner (2008) showed thatthe values predicted in 1972 by the ‘standard run’ (business as usual)scenario were disturbingly close to the observed data. In this scenario,
3Introduction

exponential growth is followed by a collapse of our economic system atsome point in the 21st century due to soaring global emissions ofgreenhouse gases (GHG, such as methane, nitrates and carbon dioxidefrom various sources related to human activity).
Likely climate change generated by greenhouse gas (GHG) emissions will
have both short and long run effects. First, via temperature-inducedchanges in water stock availability climate change would progressively
erode the natural capital base of our societies, hence adversely impacting
agricultural output and food security. A recent study by MIT scholar John
Reilly (2014), which was presented at the 11th AFD-Proparco-EUDN
conference on Energy for Development, shows that policy efforts aiming
to control climate change through (re)forestation for carbon sequestration,use of land for bioenergy production and increased energy costs, would
ultimately affect prices of food, hence generating systemic pressure on thealready fragile climate-agriculture link. Moreover, as a consequence ofaltered biophysical equilibria, life expectancy would shrink from current
average, due to, among other factors, rising health costs, emerging
pathologies and viruses, severe droughts, the destruction of
infrastructure and conflicts. In the longer run, and more indirectly,
geophysical evolutions such as changes in oceanic streams or releases of
GHG trapped under the permafrost might entail a substantial and sudden
rise in atmospheric temperature with dramatic ecological, economic andsocial consequences.
Whatever the exact nature and timing of such events, their likelihood is
rapidly increasing according to the International Panel on Climate
Change (IPCC, 2014) and the many research centres working on theseissues. A famous paper by Anderson and Bows (2011) from the Tyndall
Centre for Climate Change Research in the UK states: “The analysis within
this paper offers a stark and unremitting assessment of the climate change
challenge facing the global community. There is now little to no chance of
maintaining the rise in global mean surface temperature at below 2°C, despite
repeated high-level statements to the contrary. Moreover, the impacts associatedwith 2°C have been revised upwards, sufficiently so that 2°C now more
appropriately represents the threshold between dangerous and extremely
dangerous climate change.”
Western Europe and Japan have been experiencing near-stagnant growthfor some years now and the prospects remain moderate to say the least4

Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Matterswhile the centre of gravity of global growth has moved to Asia. Sharing thediagnosis of Jackson (2011) and many others, our societies face a dilemma
to reconcile economic progress, social stability and the preservation of ourecosystems. The primary constraint pertains to the link between social
stability and ‘growth as we know it’, and the dilemma has to do with thecontradiction between the quest for ‘growth as we know it’ and theresulting GHG emissions within viable limits as suggested by the IPCC
(2014). Let us shed some light on each of them in turn.
How to reconcile an indefinitely growing global economy fuelled by a
growing population (set to reach nine billion by 2050), always producing
and consuming more goods and services with the preservation of a viable
environment for humankind needed to ensure the reproducibility of oursocieties? CO2 emissions and global GDP have moved hand in hand for thelast two centuries, following not an arithmetical, nor a geometrical, but an
exponential growth trajectory. In the last decade according to the IPCC
(2014), population growth and economic growth outpaced emission
reductions from improvements in energy intensity. Increased use of coal
relative to other energy sources reversed the long-standing trend of
gradual decarbonisation of the world’s energy supply. The current share offossil fuels in energy consumption will increase with about 1200 new coalfired plants in the world according to the World Resources Institute (Yangand Cui, 2012), two thirds of which are in China and India. Given thisrising share of fossil fuels in the energy mix, keeping the extraction of nonrenewable materials (i.e. fuels and minerals), the production of waste andpollution, and the emission of GHG within ‘reasonable’ limits (i.e. avoid
overshooting the 2°C target of global warming beyond which extreme
events would become highly likely) would practically mean constraining
the economic system to a global average ‘growth as we know it’ close to 0.
Growth as we know it thus faces a physical impossibility that policymakers around the world do not seem to fully realize. In the words of
Daly (2005), one of the first ecological economists at the World Bank: “thebiosphere is finite, non-growing, closed (except for the constant input ofsolar energy), and constrained by the laws of thermodynamics. Any
subsystem, such as the economy, must at some point cease growing andadapt itself to a dynamic equilibrium, something like a steady state.”
Fully internalising the negative carbon externality over the short and longrun is a challenge to the tools of economists because of the uncertainty over5

Introduction

the magnitude of future shocks. Current market signals do not allow us to
identify the magnitude of the future market failures. Market signals would
likely remain too small until too late, i.e. when irreversibility thresholds
would have been crossed. The only way out of this massive “market failure”
is to think of government regulations changing market signals andincentives at the local, national and global levels. Examples would include
carbon markets with a global quota system setting the volume of expected
reductions in emissions, or a “carbon tax” setting a carbon price, with therisks that these mechanisms will be either insufficient or bypassed or, if
effective, will have strong recessive consequences.
Moreover there is little analysis of the politics of collective action required
to prevent catastrophic outcomes in the future, let alone confront them.
This refers the political difficulty of mobilising populations on abstract
and remote threats where the costs of political inaction are diffuse and in
the future, the benefits of inaction are concentrated in the hands ofpowerful sectors of the economy and the consequences of inaction impact
the majority of the population, usually the poorest disproportionately.
Job Creation, Equality and InclusivenessIn the presence of productivity gains, which are both the core engine andoutcome of economic growth, net job creation in our current economic
system depends on permanently increasing the size of the economy(Jackson 2011). Indeed, as soon as GDP growth stands below productivity
gains, jobs are destroyed, all other things being equal (size of working age
population, number of worked hours, and duration of working time). In
many parts of the world, the same mechanism and the concentration of
growth in capital-intensive enclaves can help explain “jobless growth”
and the ineffective calls for making it more “inclusive.” Maintaining or
improving the lot of the unemployed and of those who are not part of thelabour force depends either on the ability to grow the economy andinclude them, or on the ability of states to tax a flow of incomes andredistribute them through social transfers, public goods and public
services. Some put the emphasis on the first term (“Grow, dammit, grow!”
urged The Economist in October 2010), some on the second (e.g. Paul
Krugman pointing at Europe’s secret success, its welfare states). Social
stability and progress in both cases fundamentally rest on “growth as weknow it,” i.e. a growth in the production of goods and services. It is by6

Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Matterssustaining impressive levels of output growth over three decades that the
Chinese regime managed to pull 400 million people out of poverty.
Should growth stagnate or diminish, the whole social edifice is shaken as
is now the case in many European countries, triggering a vicious circle ofgrowing structural unemployment, lower consumer spending, lower
investment, rising social transfers, diminishing tax revenues, andwidening budget deficits that turn into rising debt and sooner or later
justify slashes in social spending. Our current economic system seems to
be doomed to indefinitely growing in size if it is to maintain stability.
The concept of inclusive growth is an idea that encompasses the centralityof job creation at its core but also others such as the issues of inequality ofaccess to social services and income inequality. One of the reasons that
this notion came to be highlighted is that the MDG framework did not pay
enough attention to the issue of inequality. World Bank President Zoellick
adopted this concept as part of the organization’s vision in 2007, and the
Asian Development Bank also adopted this concept as one of its main
agendas in its Strategy 2020.
Defined by the World Bank as a kind of growth that “allows people tocontribute to and benefit from economic growth,”3 this concept is broader
than “pro-poor growth” in that it emphasizes not only the benefits for
those living in poverty but also for other excluded groups of society, suchas the disabled, minorities, and those living in disadvantaged areas. In thecurrent debate on the upcoming post-2015 development agenda this issue
of inclusiveness is one of the issues receiving strong attention. As will bedemonstrated by one of the papers in this volume, investment in those
who are generally regarded as vulnerable and unproductive could pay off
not only from the humanitarian perspective but also from the perspectiveof economic welfare.
Exposure to Shocks and the Promotion of the Resilience of Growth
Another quality that the growth of the 21st century must embody isresilience: an ability to keep driving human development outcomes in thecontext of shocks and uncertainties (Spence 2011). Indeed progress
toward the MDGs has been critically hindered by shocks and crises suchas natural disasters, manmade disasters, economic and financial crises,
and conflicts (Conceicao et al. 2011).
3. World Bank (2009).

7

Introduction

The evidence base is thin in this area. There are several papers that linkshocks to growth: for example, Dell et al. 2012 find evidence thattemperature shocks are significantly associated with dips in economic
growth rates, especially in poorer countries. There are fewer papers that
identify the attributes of growth that make it more or less able towithstand shocks. One example is the work of Wen and Wu (2014),
comparing China’s post global financial crisis experience with othercountries. Wen and Wu argue that it is not the rate of growth pre-shockthat was important. They conclude that China’s growth was the mostresilient of all nations in the face of the 2008 global financial crisis due tosome of its features (the role of state owned enterprises) and some of its
policies (such as an aggressive fiscal stimulus).
However, the blind pursuit of resilience is not always going to beconsistent with other goals of growth such as poverty reduction. Whilethe benefits of adopting the concept of resilience as an analyticalframework to understand how systems respond to shock/stress have been
widely recognized in the social-ecological systems literature (see e.g.
Carpenter et al. 2001; Chapin et al. 2009) the evidence of benefit indevelopment is less clear. Indeed, Béné et al. (2012) argue that resilience is
not necessarily a pro-poor concept—countries, institutions and people
who are better off can invest in resilience—and that there is no automatic
link between poverty reduction and resilience.
Nevertheless, many conclude that it is better to be safe than sorry, andHoddinott (2014), in a wide ranging review, argues that the post 2015
development framework will need to give much more to identifying andpromoting policies to nurture the capabilities of more vulnerable
economies, people, and communities to deal with external shocks.
Understanding the characteristics of growth that make it more resilient toshocks while maintaining or improving its pro-poor features is an
important area for future research.
The Contributions of the Papers in This Volume
This collection of papers contributes to this debate on the quality ofgrowth in a number of ways.
First, we show how different
conceptualisations of growth, when measured, deliver very different
assessments of country performance over time, and between countries.
The paper by Aglietta shows that for many countries, what appears to be8Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Mattersstrong performance is revealed as a path towards depletion. The
unsustainability of extractive resource based economies is clearly
demonstrated, but so too the weak performance of economies that appear
robust but which place the greatest emphasis on individual as opposed tosocial welfare. The easiest thing to do is to continue with the façade ofmeasuring and reporting GDP per capita, but reporting real wealth or IWI
reveals the truer benefits and costs of resource allocation decisions.
Policymakers need to decide whether to choose between truth or
simplicity. The negative shocks of the last decade—and the promises ofever more frequent and severe shocks in the future—suggest that
simplicity has become merely simplistic and that truth cannot bedismissed for the sake of convenience.
Second, we show how 20th century conceptions of growth do not deliveron the issues we care about in the 21st century. Even the most reliablerefuge of those who protect current definitions of economic growth—its
ability to drive down extreme income poverty—will soon offer noprotection. Bluhm, de Crombrugghe and Szirmai show that even underthe most optimistic of scenarios there will be a slowdown in the abilityof growth to reduce $1.25 a day poverty. A much overlooked dimensionof the quality of growth is its spatial distribution—if poverty rates are tocontinue declining at historical rates, each of the countries of subSaharan Africa will have to grow at 4.5% per capita for the next 15 years.
In the past 10 years, they have—on average—achieved 2.5%, an excellent
performance, but not good enough for the next 15 years if we want tocontinue to drive down extreme poverty. The next three papers in thevolume remind us that even this historically good performance ofgrowth in driving down poverty has bypassed the most vulnerable
members of society. Tsuruga shows that in Cambodia, despite excellent
macroeconomic performance between 2004 and 2010 and extremely
rapid declines in income poverty rates, households with certain
attributes (primarily agrarian, who own little or no land) remain stuckin poverty.
Using global data, Haddad, Masset and Smith show that compared toincome poverty rates, infant stunting rates are much less responsive toeconomic growth and that this has implications for their own poverty as
adults and for the likely poverty of their children. Using a large dataset
from Nepal, Lamichhane, for the first time, is able to compare theresponsiveness of human development outcomes to income growth9Introduction

between individuals with and without disabilities. The paper makes it
clear that income growth delivers less for individuals with disabilities.
Third, we show that different components of growth deliver different
development outcomes. There are policy choices and they matter. What
attributes of growth do we need to pay more attention to make it a driver
not a destroyer of the kinds of development transformations we want tosee? We focus on growth that helps control greenhouse gas emissions,that generates employment, that does not increase susceptibility toexternal shocks, and that capitalises on fundamental demographictransitions in Africa. Willenbockel’s analysis makes it clear that without
low carbon growth in the low income countries global emission targets
will not be met, causing problems for all countries. He argues that it is in
the interests of low income countries—and everyone else—for them toavoid the “grow now, go green later” strategy. This, he stresses, is therationale for rich country investment in low carbon growth in the poorest
countries. Jobs are a driver of development and decent work is a vital
component of human wellbeing. Cirera analyses the links between
income growth components and different components of employment.
His analysis demonstrates the evidence gaps in our understanding of how
to direct growth towards better employment outcomes.
Using local government panel data from Japan, Shimada refutes thenotion that long run growth is spurred by the impact of natural crises.
He estimates significant short run and long run impacts of disasters on
growth. He also explores whether some growth patterns are better orworse at mitigating the impacts of a given disaster. The susceptibility ofdifferent types of growth to shocks is another key dimension of quality.
Focusing on Africa, Losch highlights the major demographic trends that
Africa will experience in the coming decades. In particular, over the
next 40 years the ratio of adults of working to nonworking age will
double—from one to two—presenting African countries with thepotential for a demographic dividend. This transition will represent a
dividend if employment opportunities can be created by investments
now. How to realise this potential and avoid a demographic nightmare?
For countries, where agriculture is an employer of a large percent of the
population, investing in agriculture is one way of stimulating rural
income, lowering the real price of food in urban settings, stimulating thedemand for non-farm goods and bidding up rural wages. It is also a propoor way of growing. But is this an adequate strategic response to the10Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Matterspotential demographic dividend? The paper also discusses thepossibility of a time window where wage rates in some Africancountries will become much more competitive with those in Asia. The
potential of these overlapping windows sharpens the focus on making
the right strategic choices about growth.
Finally, as the papers by Hosono and Mejia Acosta make clear, there is no
single path towards high quality growth. There are many policy choices
and many common ingredients, but the heterogeneity of context is greatand sequencing matters enormously. These papers also remind us, as
does Willenbockel, that single policy instruments should not be relied
upon to move forward multiple dimensions of growth quality. A range of
instruments should be employed to advance a range of growth attributes.
Trade-offs are inevitable and generate winners and losers, at least in termsof short run perceptions. This means that politics come into play—at the
global, national and subnational levels. Mejia Acosta examines the policy
choices that have been deployed to attempt to convert one key source ofgrowth—natural resource extraction—from a historically low quality
type of growth into a higher grade. He focuses on the oil and gas sectors
in ten low and middle income countries and highlights the complexities
and technical and political trade-offs involved in strategizing aroundstabilisation and savings funds, revenue sharing formulas (between
regions and between different levels of government) and cash transfers.
Hosono’s paper brings us back to an expanded definition of the quality ofgrowth, adding security to the standard component of innovation and thenewer recognition of inclusiveness and sustainability. His paper
examines four case studies that are widely recognised to exhibit some
attribute of high quality growth. He analyses Chile’s salmon industry, theCerrado in Brazil, the automobile industry in Thailand, and the garment
business in Bangladesh. The case studies vividly demonstrate thestructural transformations that took place, the vital role of learning,knowledge and institutional innovation, but the analysis also highlights
the vulnerabilities of even these success stories in terms of sustainability(Chile), security (Bangladesh).
Implications for Policy
Several implications for policy emerge from these papers, and from thework they build on.

11

Introduction

First, policymakers need to become more discerning about growth. Not
all types of growth are good. Some forms advance the humancondition—now and in the future—and some do not. Growth is acontingent means to an end, not an end in itself. The numbers in the paper
by Aglietta pull back the curtain on what growth has really been achieved
and at what cost. This is a difficult position for low-income governments
to take when increases in GDP per capita are (still) an effective way of
reducing poverty (Chen and Ravallion 2013). Thus the middle and highincome countries must lead the way in changing their behaviour towards
growth. They will not do this until their failure has some electoralconsequences. The gradual strengthening of the green movement and thehollowness of jobless growth will eventually bring about this awakening.
We can only hope it will be before some irreversible physical thresholdhas been crossed (Hughes et al 2013).
Second, we need to set out to design the kind of growth we want. If wewant growth to reduce poverty, to not destroy the environment, and tonot be fragile to shocks, we know what to do: at the very least prevent
inequality from worsening, make sure social and environmental costs areincorporated into benefit-cost ratios, and view growth through aresilience lens, where diversification is a key principle. The resource curse
literature has shown that governance is the difference between growththat is high quality and low quality (Moore 2011). The rules of the gamecan be influenced to make it more likely that higher quality growth will
be generated. Innovations that promote lower resource footprints per unitof growth need to be incentivised and shared via R and D spending, welldesigned public-private partnerships to shift incentives in the use of longterm investment resources, targeted tax breaks and international charters
which reduce transactions costs on high quality growth-promoting
intellectual property. As envisaged more or less explicitly in different
papers of the volume, notably Aglietta, the most coherent and ambitious
way forward should consist in a massive and coordinated global
investment initiative to accelerate the transition from low cost nonrenewable to low cost renewable energy sources. This would require
strong global political support and a financial commitment of long-term
resources backed by central banks in order to pool the risk on the path to
this energy transition.
Finally, we need to measure the things we care about. How much
employment is generated per unit of growth? How many resources are12Growth is Dead, Long Live Growth:
The Quality of Economic Growth and Why It Mattersused per unit of growth? How much emissions are produced per unit ofgrowth? There are many efforts to answer these questions (e.g. Schreyer
and Jorgenson 2013) and they must be supported. Until we have reliablemeasures of these dimensions, growth for growth sake will prevail. The
SDGs need to set the standard here for a new generation of indicators andtargets that fuse economic, social and environmental outcomes, guiding
domestic and external resources. Key here is that any development
spending targets expressed as percentages of income need to havedenominators that incorporate all dimensions of growth quality.
The papers in this collection do not underestimate the challenge ofthinking and acting differently about growth. For 80 years since thecreation of national accounts, massive investments have been made inconsolidating and refining that system. The creaking nature of thenational accounts infrastructure—and to some extent, how we measureincome at the national level 4—has only become widely apparent in thepast 15 years. It will take more evidence, more persuasion and, perhaps,
more shocks for this agenda to accelerate more rapidly. We hope that thiscollection contributes in some small way to that much needed
acceleration.

4. Even going so far as using night light data as an income proxy (Henderson et al. 2011).

13

Introduction

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The Quality of Economic Growth and Why It Mattersto a Social Movement.” Environmental Values 22 (2): 191-215.
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Krugman P. 2014. “Europe’s Secret Success.” New York Times, 25 May 2014.
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Rodrik, D. 2011. The Globalization Paradox: Democracy and the Future of theWorld Economy. W.W. Norton & Company: First Edition.
Schreyer, Paul, and Dale W. Jorgenson. 2013. “Measuring Individual
Economic Well-Being and Social Welfare within the Framework of
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Growth in a Multispeed World.” Macmillan.
Stiglitz, J. Amartya Sen, and Jean-Paul Fitoussi. 2013. “Is Progress
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Thomas, Vinod, Mansoor Dailami, Ashok Dhareshwar, Daniel
Kaufmann, Nalin Kishor, Ramon Lopez, and Yan Wang. 2000. The
Quality of Growth. New York: Oxford University Press.
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and Poverty in sub-Saharan Africa.” Journal of African Economies 22(S1): i15-i48.
Turner, G. 2008. “A Comparison of the Limits to Growth with Thirty Yearsof Reality.” CSIRO Working Paper Series 2008-09. Canberra:
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World Bank Institute. 2001. “The Quality of Growth.” Development
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Institute.

16

Part Ⅰ
Growth:A Path towards Abundanceor Depletion?

Chapter 1

What is the Quality of Growth?
Sustainability and Inclusiveness

Michel Aglietta

1� Introduction
The world is in the grip of a three-dimensional crisis: financial, socialand ecological. This lingering crisis provides a warning that the financeled growth regime dominant since the early 1980s is now worn-out. It is
plagued by ever-widening inequalities in income, the huge rent leviedby finance on the economy, the dearth of productive investment, thecrumbling social systems and the degradation of ecosystems. The
magnitude and persistence of the problems mean that the in-built modeof regulation of financialised capitalism is unable to correct the
distortions in the market economy.
Shareholder value, efficient market hypothesis and “fair value”
accounting are the principles that have precipitated deep havoc in every
part of the market economy. Shareholder value has given rise to
extravagant concentrations of wealth, made the cost of capitalprohibitive for many firms and has diverted profit from productive
investment. The efficient market hypothesis, supposed to reveal
objective fundamental values as a linchpin for market price adjustment,
has been invalidated by the financial cycle, much studied by the BIS,
which has led to the global financial crisis. Mark-to-market accounting
has exacerbated ample and long financial cycles driven by momentum
and interspersed by devastating financial crises, triggered by thereversal of debt-induced asset price bubbles. It follows that a longer-termview of the future of our economies is necessary to provide a basis foroverhauling the basic principles that underlie failing modes ofregulation.
It is now more and more accepted that the growth regime must be
overhauled in the direction of inclusiveness and sustainability.
19

Chapter 1

Defining inclusivenessOn a theoretical level, any relevant and useful understanding of society
cannot escape a definition of social welfare. If inclusiveness is a social
end worthy of pursuit, a process of social choice must provide guidancein the selection of relevant policies. In democratic societies that rest on
principles of market economies, welfare theorists might wish that social
choice could be based on individual preferences. However thisendeavour is a dead end, because it encounters Arrow’s impossibility
theorem1. There is no non-arbitrary social choice procedure regarding
minimal conditions of consistency in choices. This sweeping and very
powerful achievement stems from the impossibility of aggregating
heterogeneous individual preferences in any meaningful social welfare
function. It is why neo-standard models in macro economies are usually
dynamic stochastic general equilibrium models based upon a single
representative agent. They ipso facto ignore distribution problems. It
follows that neither absolute poverty nor relative inequalities can beconsidered in such a framework.
To overcome this dead end, equity must be defined in a way that permitsinterpersonal comparisons. One cannot rely on a majority vote to enact afair rule of income sharing. It excludes underrepresented minorities, asmuch as the market excludes people with no access to money. One
cannot be content with abstract and empty formulas, like so-called“human rights” much praised by Western politicians. Individuals areembedded in civil society with multiple belongings. A collective
expertise of social interdependencies, where economists shall have theirsay, is needed. However, to contribute valuably, economics must be
thought of as part of the social sciences without any pretence tosupremacy.
In order to address social welfare issues, it is impossible to bypass
ethical principles, as they provide a linchpin for social justice. To thisend, John Rawls has set up a cardinal principle regarding access to thebasic resources of society: primary goods of which no one should be
deprived. It follows that social development should be measured
according to improvements in the accessibility of primary goods
amongst the most disadvantaged people. In this respect China has
succeeded in raising 400 million people over the UN absolute poverty
1. See Arrow (1963) for more on the theorem, its meaning and the substance of thedemonstration.

20

What is the quality of growth?
Sustainability and inclusivenessthreshold (less than 1.25$ a day) in 30 years. Should this not also be
considered one of the highest achievements for human rights?
Rawls (2001) understands primary goods as a broad set of publicresources: material, educational and institutional. They encompass theaccessibility and quality of public health, primary education, basic civilrights and environmental goods, all of which are not market
commodities. Therefore, in setting his principle of justice, Rawls asserts
forcefully that equality between human beings worth to be pursued isfar from being only formal.2 It is a plain rejection of utilitarianism
attached to homo economicus. Inequalities can be justified only if they
help raise productivity in such way as to expand accessibility to primary
goods. The market can contribute if it is embedded under development
policies dedicated to that end.
Therefore the key concept of capability goes beyond Rawls’s principle, as
far as policies aiming at inclusiveness are concerned. He emphasizes theconversion factors of primary goods into life achievements. Indeed
equality in the space of primary goods cannot prevent per se serious
social inequalities, all of which can be magnified by runaway market
expansion. A few of them are evils of contemporaneous societies. Ethnic
discrimination, gender discrimination in social roles, structural
unemployment, power relationships in corporations and institutions are
all levers exploited in present-day capitalist societies. They help in
shaping labour markets so that real freedoms of many people aresubordinated to the paramount objective of shareholder value: extracting
maximum rent for the benefit of an elite, with the main outcome theextravagant rise in income inequalities over the past 30 years.
Defining sustainability
Sustainability is an intergenerational concept. It is the conservation andpossible improvement over time of social welfare as defined above. A
society cannot be sustainable if it is not inclusive. As we will see in thenext section, sustainability cannot be measured by GDP paths.
Because it involves time, sustainability is intrinsically intertwined with
2. In 2001, Rawls revisited his theory and clarified the link between social justice and equity(see Rawls 2001). Freedom of speech and of vote are formal liberties that pertain to primary
goods. But freedom for people that are starving and illiterate does not mean much. This is
the kind of problem that the “largest democracy in the world” encounters.

21

Chapter 1

finance. When one is evoking finance, one is confronted with the sacredcore of market fundamentalism in its most dogmatic belief: the efficient
market hypothesis in its strongest form. It stipulates that financial markets
reveal fundamental values of assets, i.e. the marginal contributions tosocial welfare of all types of capital. If it were true, the moving price systemin financial markets over time would be the most relevant expression ofwhat society values in pursuing its own perpetuation.
The problem raised with this assertion is profound indeed. Upheld by
most powerful financial interests, fostered by the ever-lasting
deregulation and globalization over more than 30 years, it has led todramatic policy failure leading up to the devastating financial crisis andits costly aftermath. Indeed, finance has moved under a momentous
dynamic for so long and generated a financial cycle so huge and longlasting that the efficient market hypothesis cannot stand under Karl
Popper’s reality principle. What is at stake is a much more fundamental
question than market imperfections, asymmetrical information andbounded rationality. It is the implicit assumption about what constitutes
economic time and what value means.
As everyone should notice, only the strong form of market efficiency is
relevant in sustainability, because that form is required to pretend thatmarket finance achieves the optimal allocation of saving overtime. Only
this assumption can amalgamate rational expectations and thefundamental value of assets. The basic question is the feedback of the
future (expectations) on the present economic equilibrium. No
mechanical or biological system can be said to be determined by the
future. Their workings and law of motions proceed from more or lesscomplex linkages that science has the mission to discover more or lessaccurately. Causal time is an arrow that is not reversible whatever theknowledge mustered on it. Social systems are different because humanbeings are capable of beliefs about the future. However the reflexivity offinancial expectations on observed economic variables cannot be calledcausal in any meaning of the word causation. However market
fundamentalism pretends that fundamental values have apredetermined objectivity (in logical sense of the word) external tofinancial markets that the market reveals. Such an assertion is the resultof a confusion in regard to the notion of time. It is postulated that thecausal time of objective processes is homogenous to the subjective timeof expectations. How can it be so?
22

What is the quality of growth?
Sustainability and inclusivenessLet us look at the fundamental value of an asset when all rational market
participants share all the available relevant information. The
fundamental value stemming from market efficiency is:
VFt = Et Rt+1+VFt+1 )/(1+x),
where VF is the fundamental value, E(R) the future expected income
from holding the asset and x the discount rate.
To assume that market participants make expectations in such way thatthe market is balanced at a price Pt =VFt, x must be known. However
this equation is just an arbitrage saying that in an efficient market there
cannot be excess returns. An arbitrage is just a condition equating thereturns on two assets. It can be used to determine the price of an asset
only if the return on the other is known. But the VF equation is a very
peculiar arbitrage that equates the return of the asset… with itself.
Indeed it can be rewritten:
Et (Rt+1+VFt+1) = (1+x )VFt
The left hand side term is the definition of the asset return. The righthand side is the required return x=r +ρ with r the riskless interest rateand ρ the risk premium of the asset. ρ is as much unknown as VF
itself. Therefore the efficiency hypothesis teaches us nothing as far as thedetermination of fundamental values is concerned, because it
encapsulates two unknowns: fundamental value and risk premium.
One has to specify a model able to determine x. But it has nothing to dowith market efficiency. There will be as many asset price dynamics as there
are a priori beliefs on the future of the economy that embodies those assets. The
core reason is the reflexive nature of the feedback of expectations onmarket prices. It is so because the subjective time of expectations iscounterfactual. It bears no logical homogeneity with objective time of past
events. The market creates values; it does not reflect pre-existing values. Values
depend irrevocably on beliefs. The relevant question is how beliefs arecoordinated through strategic interrelationships, gurus, prophets ormarket manipulators, focal points, self-generated fixed points inconverging mimetic processes. All kinds of processes can occur infinancial markets. A particular convergence of expectations defines avalue and, as a result, an economic equilibrium can ensue. A different
23Chapter 1

belief that gives rise to another focal point might also produce
equilibrium. Beliefs about the future are a priori unlimited.
Subsequently, reflexivity generates multiple equilibria. This is the very
nature of the coordination by the future.
Because financial markets have been allowed to get loose in the last 30
years or so, a powerful financial cycle encompassing real estate, equity,
fixed income and the associated derivatives has dominated financial
valuations. Momentum has been the mode of coordination of
expectations fuelled by leverage. The piling up of risky exposures in thebalance sheets of both asset owners and financial intermediaries hascreated an interlocking of fragilities that no supervisor can embraceeven if it were willing. Indeed regulators were not willing to look inside
the intricate web of counterparty risks, because they assumed that
finance was self-regulating by nature. They were not upset by theextreme of the momentum in the real estate market, believing that theextravagant levels reached by property prices were fundamental prices.
It follows that the turnaround of the momentum surprised them. Indeed
the precise date of the turnaround was unknowable, even if the burst of
the bubbles was certain!.
This phenomenon points to the theoretical distinction between risk anduncertainty3. The latter cannot be dissolved into the former. The future
pertains to counterfactual time because finance is nothing but tradingpromises. It is driven by fluctuating beliefs, migrating from one
equilibrium to another. How can a long-term horizon emerge in such aworld without strong regulation imposed by a public authority?
Therefore the mutation of the growth regime to one based onsustainability and inclusiveness is a daunting collective task that
requires an intellectual revolution to re-embed economics into social
sciences, a deep social reform to make the firm a locus of participativesocial contracts between stakeholders, a transformation of finance toallow investors with long-run view, a better say in social choices.
If sustainable growth is to be taken seriously, it will turn economics
upside down. Society comes first. There is no longer an axiomatic microfoundation of the macro-economy, but a social welfare theoretic
3. Hyman Minsky was the author that most forcefully elaborated on Keynes’s conception of
uncertainty. See H. P. Minsky (1992) for more on the theoretical formulation of his
thinking.

24

What is the quality of growth?
Sustainability and inclusivenessapproach that derives macro conditions to be implemented byindividual agents through proper incentives. This paper can only
pinpoint theoretical problems and browse the main results from serious
attempts to measure sustainable development by international
institutions.

2� Conceptual issues and measurement problems
A social welfare approach involves a revolution in macroeconomics. It isakin to the revolution in economic thought that was triggered by WorldWar II. After Keynes’ (1940) memorandum to the British Chancellor of
the Exchequer on May 4, 1940, followed by another paper by Colin Clark(1940), the conceptual and measurement work to create national
accounting began because the British government wanted to know what
were the resources the country could muster for the war and how muchthey were worth. The research program achieved the first consistentsystem in the 1945 memorandum published by the UN in 1947 (Stone
1947). As a result, GDP was invented and measured for the first time.
The impulse for this breakthrough was entirely political: the urgent
need to muster and mobilize all the economic resources of the country
for the war effort on the one hand, and the fear that the great depression
would resume after the war on the other. To act efficiently thegovernment needed to measure the aggregate supply and demand of thecountry, something a decentralized market economy does not provide.
Nowadays climate change is a worldwide peril, threatening theecological foundation of economies, exacerbating precariousness andinequalities among countries and jeopardizing the welfare of future
generations. Nonetheless, even if political elites talk of inclusiveness andsustainability, it is just lip service. The sense of urgency is nowhereapparent in the West. Public opinion is indifferent at best, and rather
hostile in countries like France. Powerful vested interests in energyproducing and electricity-using industries pay armies of scientists to
spread climate-scepticism. A related scepticism arises on the ability tomeasure linkages between environmental processes and social
preferences. On that matter there is a strange de facto alliance between
industrial and financial lobbies on the one hand, and “fundamentalist”
ecologists on the other hand (Oreskes and Conway 2010). Both consider
that persistent and strenuous efforts to internalize externalities are not
worthwhile.
25

Chapter 1

The first group, the industrial and financial lobbies, follows its own
interests and disguises them under the claim that markets cannot fail. To
enhance private profitability it is better to deny that more costs should betaken account of in pursuing its own activities. This is the usual divorce
between private and social ways of assessing values while there aremarket failures. The second group, the “fundamentalist” ecologists,
pretends that ecosystems are so radically alien that their impact onhuman beings, regardless of damage or benefits, cannot be intrinsicallymeasured in value terms. This is pure nonsense because any factor that
impinges upon wellbeing always has a social marginal value or cost. Yet
what is true is that this social marginal value is not always revealed by amarket price, often by a wide margin. Renouncing the quest to evaluate
those social values amounts to denying that a global strategy forsustainable development is possible. This is not the way responsible
governments and vivid civil societies should behave. Measuring social
values is the best and most rational way to define and deliver common
goods and therefore to detect the best capital assets in which to invest. In
other words it is the indispensable input of social choices.
Starting from a very imperfect situation it is understandable that several
methods have been advocated to handle the problem. They differ inscope: macro or micro, all-encompassing or digging into specificquestions and using partial economic analysis. They also differ in theirtime span, dealing with urgent questions and setting specific objectives
or elaborating the theoretical basis of a sustainable growth regime in thelong run. Some possible ways forward have been explored in the Stiglitz
report (Stiglitz, Sen and Fitoussi 2009). Enriching GDP from a public
policy perspective would need to take account of inequalities,
completing GDP with an array of physical indicators without measuring
their social marginal values, broadening the scope of capital assets in an
extended accounting registered in satellite accounts, and developing a
new integrated social accounting system based upon a generalizedversion of capitalIn the next section, I will follow the way forward explored in the UN
project in improving the measurement of an extended definition ofcapital and its link with social welfare. I will also acknowledge theproximity and differences with the World Bank project. Bothapproaches are endeavours to link the theoretical framework of social
welfare to sustainability conditions. They differ in their dealings with26

What is the quality of growth?
Sustainability and inclusivenessexternalities to measure marginal social values. Then I will introduce a
problem left aside in the Stiglitz report. On one side, a macro model ofsocial capital growth is necessary to frame a long-run policy ofsustainable development. On the other side, capitalism will still prevailin allocating resources for the foreseeable future. Therefore processes toachieve social incentives will still be shaped by the pursuit of private
returns in decentralized firms. Therefore there is an inescapableproblem of incentives. Although social values are not reflected in market
prices, they should be. In one way or another, they impinge upon theprospective rates of return of the firms, which will invest in the types ofcapital that might produce those social values. It follows that firm
accounting must also be reformed to become consistent with social
accounting conditions. The literature on business accounting ignores the
problem entirely. Corporate management is content with the rhetoric ofsocial responsibility, an empty discourse without any meaningful
impact on the business model of the firms. Setting the problem has only
one virtue for the time being: displaying how far we are from the
beginning of a transition to sustainable growth. Correlatively, I will
sketch the conception of long-run financing investments driven bysustainability conditions, focusing on climate policy.

3� A social welfare-theoretic approach of sustainability
The present paper is not the place for a survey of the different
approaches for dealing with sustainability. As explained above, it takes
the view of those who base measurement upon monetary value, hence
who are concerned with valuing environmental and intangible assets, aswell as ecological services with no market values. International
institutions lead the investigations. The World Bank (2012) explores aweak condition of sustainability with its genuine saving concept. The
High Level Panel set up by the general secretary of the UN explores astrong condition summed up in the inclusive wealth indicator (IWI)(High Level Panel 2012). However both derive the sustainability
condition from the concept of social welfare not decreasing over time.
Let us first understand the theoretical underpinning of themeasurement methodology based upon an extended concept of social
capital. Many forms of this all-encompassing concept of capital are
public goods that boost the productivity of privately-owned capital.
Those relationships imply interdependence, viewed as strong or weak27

Chapter 1

depending on the way one defines social marginal productivity,
between public choices and private property rights. Measurement is
controversial because those social marginal productivities are shadow
prices, i.e. expected marginal contributions to social wellbeing of thedifferent forms of capital. Shadow prices are not observed; they arecounterfactual by their very nature, because they depend on the future
path of the economy.
Because society is a collective that pervades over time, well-being istrans-generational. Its productive base is economic development.
Sustainability is defined as a pattern of development that along withintergenerational well-being does not decline. There must be anaggregate measure of the productive base of a national economy, called
total national wealth. Social well-being is produced by its productive
base. There exists a generalized production function relating them.
Aggregate net investment is a measure of the rate at which the marginal
intergenerational well-being changes over time, provided that thedifferent types of capital comprising social wealth are measured at theirsocial marginal values in terms of welfare (shadow prices) and that the
shadow prices can be taken as constants to get a measure of the“volume” of the growth in wealth. Another way to look at it is by sayingthat aggregate wealth is the shadow value of the stocks of all assets
available in the economy. Box 1 sketches the basic model used by theHigh-Level Panel.
Therefore the strong condition of sustainability is the following: a longrun economic policy is sustainable if and only if aggregate net investment
measured at shadow prices is positive over time.

28

What is the quality of growth?
Sustainability and inclusivenessBox 1. Definition of the sustainability condition

V(t) = intergenerational well-being
Ki(t) =stock of i asset in t. K(t)={K1(t),.…, Ki(t),…Kn(t)} vector of capital
assets.
V(t)=V[K(t),t] function of intergenerational wellbeing
Shadow price of time: Q(t)=dV(t)/dt
Shadow prices of capital assets: Pi(t)=dV(t)/dKi(t) if the economy
does not cross a tipping point. If not, dV(t) is a finite step that must be
estimated directly
Because of externalities in the V function, shadow prices are notmarket prices. Estimating them implies ethical values, which in turndepend on the conception of equity, theories on environmental/social interactions, info on asset size, their distribution and theirsubstitutability.
One can define inclusive wealth:

And the sustainability condition: if shadow prices are constant, theduality theorem gives the following condition:

On a time line short enough so that shadow prices can be held as constant,social welfare does not diminish if and only if inclusive wealth does notdiminish.

29

Chapter 1

Figure 1 gives a stylized view of the approach.
Figure 1� National wealth and social well-being: strong concept of sustainability

Total real wealthSocial welfare

Capital inputs

Function of social well-being

・Tangible productive capital: equipment/ ・Private consumption adjusted for income
inequalities
structures/urban real estate・Public services imputed to households
・Intangible capital: human capital/and consumed in the same period
knowledge/ institutional
infrastructures/ social capital/ net ・Environmental services degrade bypollution/ exhaustion of fossil
foreign assets
resources/ damages to biodiversity
・Natural capital: fossil resources/
timber and other forest resources/
protected areas/ crops and pastures

Strong sustainability:
net investment in real wealthnon-decreasing

Discounted value of social welfare
non-decreasing

To adjust private consumption for inequality of income for the purposeof tracking inclusive growth, the social welfare function must be
increasing in average income growth and satisfy the transfer property:
any transfer from a richer person to a poorer one increases the value ofthe function (Mishra and Peiris 2013). It can be measured this way:
Inclusive income growth= average income growth + (average income) (Δmedian/average income).
The main problem is the measure of the services of ecosystems whose
substitutability to private consumption is low. Estimating shadow prices
is a tricky problem, while there is no market price equivalent because of
externalities. Shadow prices must be approximated with notional prices.
They are the outcomes of agreements among people with a social
consciousness to internalize particular externalities. Getting
agreements involves debates between partners concerned by the costs ofnegative and the advantages of positive externalities to be shared. Thosedebates will extend into a considerable time line while people better30

What is the quality of growth?
Sustainability and inclusivenessunderstand the challenge of ecological degradation for their life style.
While social preferences are going to change through experience, better
information and more political debates, improved valuation will bereflected in national accounts.
One challenge concerning the value of the services of ecosystems and of
valuing natural capital more generally is their non-linear dynamic.
Unknown thresholds can induce unknown discontinuous changes.
There are different regimes when tipping points are crossed (e.g.
destruction of fisheries and of the tropical rainforests). Some of theregimes may induce global systemic crises, massively destroying real
wealth and decisively transforming human civilization as we know it(e.g.an increase in average world temperature over 5°C) (Oreskes andConway 2014). The IPCC has argued convincingly about the nonnegligible probability of this catastrophic scenario by the end of the
century. Discontinuities in ecological processes should be reflected inshadow prices because the latter capture the substitutability between
capital assets in the present and the future. Crossing a tipping point
entails a discontinuous slump in substitutability between natural andother capital assets. It will provoke a violent surge in the shadow prices
associated with these assets, making it uneconomical to draw further onthem and forcing an immediate reinvestment in the worst possible
economic environment, because societies will have suffered the losses ofa systemic crisis. This is why the Stern Review (Stern 2007) has
advocated the use of a quasi-zero discount rate and some authors haveshown that there is a strong rationale to apply the precautionary
principle (Weitzman 2009).
The High-Level Panel of the UN Secretariat has been following this pathin the Inclusive Wealth Report that will be progressively reviewed every
two years (High Level Panel 2012). However, there are other lessdemanding ways. The World Bank has settled for a criterion of weaksustainability drawn from a more restrictive view of total real wealth,called comprehensive wealth, that leads to a criterion of sustainability
based upon an extended measure of national net saving, called genuinesaving. Such a measure is a weak criterion because it avoids the estimateof shadow prices. It is essentially a revised measure of GDP.
The World Bank has drawn upon pioneering work by Pearce andAtkinson (1993). Development depends on total wealth defined in a
31

Chapter 1

restrictive way compared to UN methodology, e.g. produced, human,social and natural capital. Sustaining total wealth is the key for viable
growth regimes. For the World Bank the different forms of capital aredefined in the following way:
Produced (tangible) capital= equipment + structures + urban land
Intangible capital= human capital +institutional infrastructures + social
capital + net foreign financial assets
Natural capital= subsoil assets + Timber resources + non-timber forest
resources +protected areas + crop land + pasture land

The sum of the three components is the real wealth of the nation. Thechange in real wealth has been named the adjusted net saving (orgenuine saving). If the different types of capital that make up the
productive base of the economy in a general ecological and economic
sense can be measured, the variation of total wealth per capita is thesum of the growth of total factor productivity and the increase in theaggregate growth in the volume of the different types of capital. Since
the variation of total net real wealth or genuine wealth is the net
investment of society, the condition of sustainability is that society does
not destroy its wealth in mustering enough adjusted saving or genuinesaving to match net investment. Therefore the sustainability condition
becomes the following: the development path of an economy is sustainable if,
at every date, adjusted social saving (or genuine saving) is non-negative. If it
becomes negative, it means that society is destroying its wealth.
The definition of genuine saving is the following:
Genuine saving = economic gross saving of the nation – fixed productive capitaldepreciation + change in value of human capital + change in value of social capital –depletion of mineral and energy fossil resources – net reduction of forests – damages
due to pollution in ≈ CO2

How do inclusive wealth and comprehensive wealth compare
methodologically? They have in common the intent to measure total
wealth. Both introduce estimates of how well they can value intangibles
and they both also try to measure the degradation in natural capital.
However they have differences too. In inclusive wealth accounts, wealth32

What is the quality of growth?
Sustainability and inclusivenessis measured directly from its productive base while notional prices havebeen estimated. No pre-assumption is made on sustainability.
Unsustainable trajectories are included. Inclusive wealth tries todisentangle ecosystem services (fisheries and water-related ecosystems).
Furthermore population is a critical factor of sustainability. Population
changes are directly estimated. In comprehensive wealth accounts
population is supposedly stationary or increasing at a constant growthrate. Furthermore the social welfare function is only related to private
consumption that is supposed to grow at a constant rate. Wealth is its
present value. A given path of consumption is deemed unsustainable if
adjusted net saving is negative for this path.

4� Comparing three measures of development: gross domestic
product, comprehensive wealth, inclusive wealth�
Table 1 compares the evolution of the three indicators over long-run
periods for some advanced and emerging market countries. In doing sothey improve the picture given by GDP markedly. This is definitely anirrelevant indicator in framing long-run policies. For all but advanced
countries the WB indicator is grosso modo between GDP and IWI. For the
emerging market economies (EMEs) it is closer to GDP than to IWI. The
reason is that natural capital weighs much more in total wealth in EMEs
than in advanced countries where the weight of intangibles and theirimpact on development is much larger. However, the WB
underestimates the losses in wealth due to the destruction of ecosystems
that the UN panel tries to capture. This is why the former undervalues
ecological losses.

33

Chapter 1

Table 1� Different measures of development
Countries

GDP/individual(% annual growthrate 1990-2008)

Real wealth/indiv(WB, % annual
growth rate
1995-2005)

IWI/individual(UN, % annual
growth rate
1990-2008)

Advanced countries:
Germany
France
US
UK
Japan

1.5
1.3
1.8
2.2
1.0

1.3
1.7
2.3
2.8
1.5

1.8
1.4
0.7
0.9
0.9

EMEs:
BrazilChina
India
South Africa

1.6
9.6
4.5
1.3

0.9
6.9
3.6
1.3

0.9
2.1
0.9
-0.1

Oil-exporting countries:
Nigeria
RussiaSaudi Arabia
Venezuela

2.5
1.2
1.3
1.3

-1.5
-0.8
-1.3

-1.9
-0.3
-1.1
-0.3

Based on these terms, the case of China is striking: massive expansion of
fixed productive capital, fuelled by over-accumulation of capital ininfrastructure and heavy industries, has produced outstanding growth
in GDP. According to the gauge of comprehensive wealth theperformance is reduced, but by not that much, because depreciation istaken into account (it is a net and not a gross concept like GDP) andbecause massive environmental damage is somewhat accounted for, butless than in inclusive wealth, which looks at the losses due to thedeterioration of the regulatory properties of ecosystems. The IWI still
attributes the best performance to China over the 30-year period or so,
but it is no longer considered an outstanding performance. On thepositive side the achievement is the eradication of absolute poverty – 400
million people have been taken out of absolute poverty in 30 years, thebest performance worldwide of all time. Investment in human capital
has also advanced substantially but it is still lagging in the rural sector.
However, China is the country where the negative gap (IWI-GDP) per
capita is the largest. It means that intensive growth in fixed capital has
entered a stage of fast-decreasing marginal return and that thedegradation in natural capital is destroying real wealth alarmingly. The
34

What is the quality of growth?
Sustainability and inclusiveness

new Chinese leadership has pledged to link the new urbanization drivewith environmental policies and has issued detailed directives to guidethe strategic planning for an overhaul of the growth regime. In India thesituation might be worse since the political system seems to be unable toinvest in infrastructure and in basic education for the larger masses ofthe population, while keeping enshrined crippling social
discriminations, not least against women. However bottom-up frugal
innovations are well under way, which save energy use and broaden therange of goods affordable by the nascent middle class.
In advanced countries, the comprehensive wealth indicator usually
depicts better performance than GDP, essentially thanks to its
measurement of intangible capital, something that has become the mostimportant factor of growth since the ICT revolution. However what is
striking is that the WB indicator veers toward GDP, rather than IWI in
the comparison between advanced countries. In particular, theperformance of Germany and France compared to the Anglo-Saxon
countries is reversed. The latter fare much better in GDP and muchworse in IWI. Remember that IWI is a measure of well-being. In the US,
public health is appalling in terms of life expectancy, morbidity andobesity, while costs are prohibitive. This boosts GDP per capita since
wages must be higher than in other countries just to pay for the rents
drawn by the medical and the insurance sectors on the population.
Therefore what is counted as a plus in GDP deteriorates IWI. Add to itthat the US has not invested sufficiently in their public infrastructures,
impairing the stock of public capital in the UN IWI. As for the UK, thatshare largely with the US the non-inclusive character of their growthmodel, especially the extreme inequality of income and the inefficiencyof their health care, the exhaustion of oil fields has not been redeployed
in real capital but in elusive foreign financial assets.
Furthermore, both the WB indicator and the IWI, as opposed to GDP,
concur to show that non-advanced oil-producing countries are on anunsustainable path. This is the well-known curse of primary resource
ownership for development. Be they increasing or decreasing in
population, densely or sparsely populated, those countries havegovernments that impoverish their people. This is because theappropriation of the scarcity rent is squandered or redistributed
according to the feudal (Saudi Arabia) or populist nature (Venezuela) of
the political systems of the countries. In any case it is not invested
35

Chapter 1

enough in wealth-producing forms of capital to offset the exhaustion offossil resources.

5. From macro to micro: how can firm accounting provide
the right incentives to contribute to sustainable development?
As was acknowledged at the beginning of this paper, sustainability is aproblem that stems from the dynamic of complex systems. Theinteractions between economy and ecology on the one hand, and theelaboration of policies for inclusiveness in societies impacted bymultiple conflicting interests on the other, raise the questions of the
incentives of economic agents that will make collective objectives comethrough. Because externalities are not exceptions but are dominant inenvironmental problems, because market prices are massively
incomplete and finance has proved to be more than inefficient but
systemic risk-prone, the macro–micro problem is both inescapable anddaunting. The welfare theoretic approach and the generalized wealthaccounting build tools for strategic planning to formulate societal longrun objectives. However in countries with vibrant civil societies, lifelong
goals come from the bottom and economic implementation of thosegoals raises enterprises to the fore. Innovations in measurement inmacro accounting must impact measurement in business accounting forpolicy goals to be conveyed into the right incentives. This is all the morechallenging as the present business model of most firms is still based
upon shareholder value, which is alien to the theoretical foundation ofsustainable development.

Shareholder value, market finance and the social interest

It has been commonly said, since the implicit contract view of the firmhas become most influential in financial elites and popular among
academics and politicians, that firms are agents of their shareholders4.
Meanwhile the average holding time of business equities in OECD
countries has dramatically declined from five years in the late 1960s to
five months in 2010. The reason is the spread of the Anglo-Saxon model
4. In 2001, Hansmann and Kraakman (2001, p.89) wrote that the rules of corporate
governance were being uniformed under shareholder value. The principle according towhich firms must be exclusively run for the sake of their shareholders had already reached avery large normative consensus. They added that the dominant ideology of shareholder
value would not be challenged in the future. According to their opinion, it amounted to the“end of history” in corporate governance. Hansmann H. and Kraakman R. (2001), “the End
of History for Corporate law”, Georgetown Law Journal, vol.89, pp.439-468.

36

What is the quality of growth?
Sustainability and inclusivenessof dispersed ownership in continental European countries where
diverse forms of governance used to prevail: insider, family or blockownership control. Obviously dispersed and tieless owners, obsessedwith liquidity, have neither the interest nor the means to control firm
strategies. Therefore the principal agent relationship is irrelevant as far
as individual shareholders are concerned. Dispersed ownership andcontrolling power are contradictory.
The basic question remains: how should firms be managed and toachieve what? The goal looks fairly obvious: maximizing the total returnof shareholders via share buybacks, dividend distributions and M&As.
The entity capable of disciplining firm management to conform to thosepredicaments is the stock market. As long as the circulation of property
rights is frictionless, the stock market is the principal of the firms since
the liquidity of shares homogenize shareholders. Firm managers areunder the threat of potential owners on the one hand and are induced toconform to shareholder value by the distribution of stock options on theother hand.
Therefore, if and only if equity markets are perfectly efficient, theanonymous control they exert achieves the social interest because alltypes of productive capital are represented and the equilibrium market
returns are equal to their marginal social costs. If one buys theseaxioms, one must accept the conclusion: shareholder value is relevant inmatching the macro–micro problem. Moreover the financial structuresof the firms are meaningless because all financial assets are perfect
substitutes in their risk-adjusted returns.
It is enough to spell out those conditions to understand how much they
are irrelevant for the macro–micro problem. Contemporary societies
must overcome the mutation from the failed growth regime offinancialised capitalism to bring their economies onto a sustainablegrowth track. In Section 1, the basic reasons grounded in the very natureof finance were provided to reject the strong efficiency hypothesis.
Correlatively, the assertion that the firm has no existence as anautonomous entity, being a knob of implicit contracts, does not hold.
Stakeholdership, the social interest and responsible shareholders
The failing of the implicit contract theory in equating shareholder value
and societal responsibility has two flaws regarding the firm on top of its
37

Chapter 1

idealised view of finance. The first is its inability to recognize that thecorporation is a legal entity of its own. In this respect the corporation isan entity in its capacity to make commitments on behalf of the enterprise.
Excluding slavery, the enterprise is not an object that can be possessed byanyone. It is a human gathering dedicated to the production of social
values. Its productivity depends crucially on the complementarity andcooperation of talents, as much as they are able to develop collective tacitknowledge. All bearers of intangible assets that contribute to the
productive capacity of the firm and that have no directly marketableproperty rights are stakeholders in the social value produced by theenterprise. They should be as much entitled to have their say in thestrategy of the corporation and to share the profit as the shareholders.
They have even more stake since they do not enjoy the liquidity of theassets they own. As a consequence, they are more interested in long-run
strategies that consider the corporation as a going concern.
Therefore, the quality of growth at the macro level depends onshareholder ship being replaced by a much larger stakeholders hip in
corporate governance (Mayer 2013). Stakeholders are all the people
bringing productive assets, be they tangible or intangible, to thecollective productive strength of the enterprise. Since the productive
capacity of the enterprise lies in the cooperation, individual marginal
productivity cannot be measured in full. Correlatively individual
marginal productivity cannot be measured entirely.
Stakeholders have multiple interests. With the stock market being
unable to determine the business model that aligns the corporation
governing the enterprise on the social interest, the business model must
be the outcome of a strategy debated and decided by an organ of apolitical nature, the board of directors. The board is not only acontrolling body working as the agent of a predetermined end,
shareholder value. It must define the finality of the corporation and its
associated strategy to make account of the multiple relationships of theenterprise both inside the organization and within its environment. In a
stakeholder corporation, the board must gather the delegates of all
stakeholders to elaborate the common interest. To establish the
responsibility of management, checks and balances must be embeddedin the structure of governance: separation between the chair of theboard and the chief executive officer, equal participation of employee
delegates in the board, pay and audit committees protected from the38

What is the quality of growth?
Sustainability and inclusivenesspressures of management, and objective criteria need to be linked to thestrategic objectives defined by the board to assess the performance ofmanagement.
Such a structure might be able to link the participation of human capital
to innovative investment projects, i.e. to make the achievements ofindividual “capabilities” fit with the larger finalities of the quality ofgrowth. Stakeholder corporations are inclusive due to the participationof employees, not only by redistribution that was a principle of the postwar growth regime labelled “Fordism”. They will be actors ofsustainability if their strategies are shaped by investments that conformto environmental and societal criteria. Those investments aim at curbing
the trends that are degrading the life of people: climate change, scarcity
of resources, giant inequalities, discriminations, structural
unemployment, and financial fragilities.
Those bad trends have noxious effects on long-run capital return
because externalities develop over time and are loaded withirreversibility. They are intrinsically non-linear. Therefore they generate
extra financial risks that must be converted into financial values. It is
why business accounting and economic calculus of investment returns
must be overhauled. The recognition of such needs requires long-term
investors acting as responsible shareholders in stakeholder corporations.
Governance matching corporate interests and social involvement
needs an overhaul in business accounting
Investment projects are selected according to their internal rates of return(IRR). The IRR of a project is the discount rate that cancels the netpresent value of future cash flows stemming from all revenues andexpenditures up to the horizon of the project. This measure does nottake account of the positive and negative externalities that impinge
upon the social value linked to the project. The social value of aninvestment is the net present value of all costs and benefits entailed by
the investment, whether this is comprised of money flows accruing to
the investment or external impacts (positive or negative). This is, forinstance, crucial for clean projects that abate a computable amount ofgreenhouse gases. They generate positive externalities in the amount ofabated GHGs. These externalities can be valued if society recognizes
that avoided GHG emissions are something of value and institutes a
notional price: the social value of carbon, for a unit of avoided carbon39

Chapter 1

equivalent. Therefore externalities must be valued from notional pricesthat should be agreed upon in non-market social procedures. Rigorously
the notional prices to guide investment choices of firms must be theshadow prices of the different types of capital on a sustainable trajectory,
computed as the shadow prices associated to this trajectory. This is themacro–micro consistency. Practically such a consistency is out of reach at
the present time: the development of numerical estimates of shadow
prices that can be used in computing expected rates of return. However
this normative consistency teaches a lot of what a price is all about.
Indeed, true market prices, i.e. prices whose determination follows a
Walrasian adjustment, exist only in centralized asset markets. A price ismuch more general than a market price. This is an implicit, tacitagreement between two or more parties in sequential trade, when for
instance consumers buy products at prices that are already posted in
shops or stores. Or it is the product of negotiations between
intermediaries (e.g. wages decided in collective bargaining), or it isnotional like transfer prices between sub companies of a multinational
corporation, or it is purely conventional – like accounting prices used inanalytical accounting. Therefore the argument that it is impossible tovalue what has no market is empty of meaning. If pollution is not valued
it is because public authorities have not instituted a carbon price andobliged firms to compute pollution costs in their operating accounts. The
reason why they do not do it is because the political dominant influencein financialised capitalism makes it self-evident that a narrow view ofproperty rights legitimates incentives of firm managers to maximize
shareholder value.
While sustainable growth has gained momentum as a primary finality
in the political debate, the need for consistency between the macro
accounting of total real wealth and business accounting should becomea requirement in order to fulfil incentives embodying environmental
and societal objectives in corporate governance. Under those new
incentives it will become necessary to correct the IRR and compute an
integral internal rate of return (IIRR), valuing the externalities producedby firms’ activities according to a generalized view of valuation. Such aview rests on the stakeholder view of the corporation where the board
must answer the following questions: who are the stakeholders to whommust the firm be accountable? Which performance criteria must be
accounted for? Under which procedures must they be accounted for? In
40

What is the quality of growth?
Sustainability and inclusivenessstakeholder-corporate governance, corporations would have to report totheir different stakeholders, so that it would be possible to identify andmeasure the global imprint of each firm on its natural, social andeconomic environment. In particular there should be a reporting
towards socially responsible investors who need to assess the potential
of investment projects according to IIRR.
In the first stage of implementing the new paradigm, one should not aim
at a unified reporting where extra-financial valorisation are integrated
in standard financial accounts. Extra-financial accounting would have to
be experimental in any first stage. There should be satellite accounts
whose ability to feed the extended calculus of the IIRR must be tested.
The enlarged accounting must be built as a new metric of societal responsibility.
But a metric it should be, which means prices defined in money as theuniversal unit of account. Business accounting must check whether
particular firms contribute to sustainable development, e.g. create atleast as much resources as they consume. To define prices that guidestrategic investment decisions capable of attracting long-run investors,
consultations between stakeholders interested by a particular domain of
externalities must be organized (Schoum, de Saint-Front and Veillard2012).
Considering social responsibility, since the capabilities of workers
acting as a team comprise the main productive asset of the firm,
expenditures to reproduce and expand them must not be treated as
operating costs, but as investments in human capital. Discounted
inflows and outflows of future wages due to the mobility of workers andrevalorization of wages due to expenditures in vocational training
would appear much more valuable in such accounting. Instead of
dealing with a wage policy as a cost to compress as much as possible,
wage policy would become investment policy to be anticipated as anintegral part of investment projects.

6. Long-term finance and sustainable growth: How to financeclimate policy?
Both the scope of possible human and material damages and theirirreversible character if the average temperature increases above 2°C(compared with pre-industrial times) are arguments in favour of urgent
and strong action by societies against climate change. The intervention
41

Chapter 1

should be much more energetic than what has been accomplishedduring the last 40 years to reduce the sources of emissions and increase
the absorption wells. The last report of the Intergovernmental Panel on
Climate Change (IPCC 2014”) indicates that the emissions from humanorigins have increased during the years 1970-2010 at a rate higher than
2% per year, and that the last decade 2000-2010 has known the biggest
increase in human history5. Past climate policies, which do not allow theincrease in temperatures to be reversed, have thus been largely
insufficient.
The uncertainties of the costs resulting from climate change cover
several types of realities: uncertainty regarding the scope of climate
damage with respect to the increase in temperatures; uncertainty about
the scope of technical change, also mostly irreducible, allowing us toreduce the costs of abatement activities; uncertainty on the discount rate
to be used today to evaluate damages that could occur in the very longrun.6 These different forms of uncertainties provide compelling reasonsfor societies to take early action against climate change, and eschew all
forms of delay. The fifth evaluation report of the IPCC, published in
2013-2014, strongly called for increases in the level and the changes instructure of annual productive investment in the period 2010-2030 tohelp mitigate climate change. These included a reduction of theinvestments in fossil fuels, an increase of around $150 billion forinvestments in renewable and nuclear energy, as well as capture andstorage of carbon emissions; and an increase of around $340 billion forinvestment in increasing energy efficiency in transport, housing andindustry. According to the International Energy Agency (IEA), theannual investments in energy efficiency and low carbon technologies
should reach $790 billion in 2020 and $2300 billion in 2035 in order tolimit the temperature increase to 2°C.

5. According to a new analysis by the UN Wprld Meteorological organization, CO2concentration rose 2.9 parts per million (ppm) between 2012 and 2013, the biggest annual
increase sinc 1984. The IPCC 2014 report (part 1) states that the last three decades have beensuccessively warner at the earth’s surface than any preceding decade since 1850.In the
Northern Hemisphere, 1983-2012was likely the warmest 30-yearperiod of the last 1400
years.
6. This uncertainty has polarized the debates on the costs of climate change after the Stern
report (Stern 2007.) There has been criticism of the choice of a very low pure time preference
that is not reflected in the discount values emerging from market prices (Nordhaus 2007).

42

What is the quality of growth?
Sustainability and inclusiveness

From Kyoto to Cancùn: a paradigm shift
Guiding the climate negotiations according to an ethical principle –leading the northern countries to finance the climate mitigation in thesouthern countries – does not amount to giving equal emission rights toeverybody. The allocation of emission permits is just a form of allocationof financial assets. In a world where wealth inequality reaches extreme
levels, the richest have soon bought their desired amount of permits on
the market for emission rights, circumventing the equity principle.
Emissions per head should be equalized in the very long run.7
Such an objective provides direction to the principle proposed by India
at the Cancùn Conference, that of “equal access to sustainable
development” (soon to be called the ‘Cancùn paradigm shift’). Logically,
this will require a massive increase in help from developed countries todeveloping ones. In this regard, the Cancùn Conference of Parties (COP16) can be understood as a real shift, translating international
negotiations from a top-down and insufficiently cooperative approach(a unique carbon price linked to a world market between states foremission reductions and burden sharing) based on the obligations ofstates towards an international climate regime based on the
responsibility of states to voluntarily promote nationally appropriate
mitigation actions8.
Among the economic instruments allowing the correction of the
distortions due to externalities, we usually distinguish between theprice policies (taxes or subsidies) to control the prices paid by polluters
and the quantity policies pretending to control the quantities of emitted
GHGs. The markets for the emission permits (such as the “European
Emission Trading Scheme”, or EU ETS) are among those. If futuredamages due to GHG emissions were knowable with certainty, -, taxes
and permits would be equivalent. In certain economic conditions it isalways possible to determine the quantities of permits such that themarket price is equal to a certain tax level. But the uncertainty of the realworld makes the equivalence disappear. The market gives certainty on
quantities; tax gives it on prices. The tax is more predictable only if thegovernment has a well-defined climate policy in the medium run,
7. As Stern (2007) clearly states, if the world must emit less than 20 billion tons of CO2eq in
2050 and the planet will have around 9 billion inhabitants at that time, this means thatemissions should be limited everywhere to 2 tons of CO2eq per head in 2050.
8. Nationally appropriate mitigation actions (NAMAs) for developing countries andNationally Determined Contributions (NDCs) for all countries.

43

Chapter 1

associated with a trajectory of carbon prices on which it is credibly
engaged. On the other hand, the emission rights market is an asset
market, and is thus accompanied by chaotic price trajectories, as theEuropean market has well illustrated. A market that is affected bymultiple externalities cannot be efficient. Price flexibility is a benefit only
for the speculators, except if the market is regulated by a public entity
able to insure a medium run trajectory in line with what would give a
tax in a credible abatement scenario on 5 to 10 years. The tax is thus apriori better than the market, which was not the reasoning of the Kyoto
Protocol. This advantage only exists however if the announced evolutionof the value of the tax is considered credible by all the actors. And weknow by experience that this is not the case. The political cost is so highthat if a tax is put in place, its level can only be too weak to direct the
new investments in a significant way.
The world of the perfect market is oblivious to weak environmental
policies that are reached without any conviction, and prone tounpredictable changes of direction, amplifying the risks linked to theinvestments. They generally are not very popular when they take theform of a tax or a carbon market establishing a price from one day to theother. When they are put in place seriously, they impose immediatetransition costs on entire sectors of the economy, early and indifferently
depreciating parts of the installed capital of the economy to give value toa capital that is yet to come, and they have certain redistributive effects
that are hard to quantify. The political economy arguments do not playin favour of these traditional tools, which do not seem to be preferred ata political level, compared with regulations, sector subsidies or otherforms of industrial policies.
In developing countries, nationally appropriate mitigation actions,(NAMAs) could lead to an emphasis on national objectives ofdevelopment: tightly linking low carbon technologies and the local
environment, investing first in human capacities and R&D using themacroeconomic policies to lower the arbitrages between technical andsocial costs. There are several potential bottom-up initiatives here. The
compatibility between many decentralized actions and the global goalof containing climate change becomes crucial (Guesnerie and Stern2012). The NAMAs allow the governments of developing countries tointegrate the governmental objectives into their national development
policies. But the GHGs emissions are a global externality. As Roger
44

What is the quality of growth?
Sustainability and inclusiveness

Guesnerie puts it, a global coordination for a global control of quantities
must be created. For that matter countries must agree on a global
emission level.
An international permits market would regulate the gaps between thepermits allocated to countries and emissions, creating an international
coordination at the margin (and not on each carbon unit emitted as in
the Kyoto Protocol), while the States and the regional groupings of States
would look for their internal objectives with the help of taxes andinvestment public policies. The compatibility between several
decentralized actions and global climate change can thus be insured.
Of course the installed “dirty” capital must be depreciated in order tomake room for “clean” technologies. But this must be done at the margin
by new investments accumulating over time. Developing a new
direction for the current investments and the investments to come is apriority that can be compared to the revaluation of the whole stock witha disruptive price. It can be done through a valuation of carbon througha notional price applied to investment categories that produce anabatement of GHGs, which independent agencies could validate.
We call such a level of abatement a carbon asset. Because it is not (or too
partially) raised by a tax incorporated into the price of the produced
goods, the return on these investments can be adjusted through theacquisition of carbon assets produced against monetary emissions.
Money is indeed something that is universally acceptable and thus
validates the product of all economic activities. It can answer thequestion of the financing of public investment policies in favour of the
carbon externality.

Confronting the funding gap

There is a huge funding gap in achieving a transition to a low-carbon
economy. To assess the funding gap one should not confuse the flow of
payments over the duration of the projects to cover capital and operation
costs and the upfront costs, i.e. the cash necessary to cover the cost of theequipment before it enters into operation. The latter might be two or
three times the former. Furthermore the financing need is not only what
will finance net investment flows to accumulate capital in clean
technology but must also cover the redirection from old production
capacities in existing energy systems to new ones in low-carbon energy
45

Chapter 1

systems. If, for instance, a renewable energy plant produces electricity ata cost 30% higher than a coal plant, the real amount of investment toreplace coal-fired electricity is 130%. Finally the total incremental costsof the changeover from one energy system to another must account for
redirecting investment in building and transportation to achieve higher
energy efficiency and lower energy demand permitted by changes in
consumer behaviour. For around $500bn of incremental investment
costs in 2020, a back-of-the-envelope calculation gives about $4100bn of
redirected investment (Aglietta et al. 2014).
Confronting this huge need for finance, the cash flow generated by theclean development mechanism is utterly insufficient. Moreover it yields
cash at the end of the project and thus is not designed to reduce theupfront investment cost. Public finance mechanisms do bring funds
during the incubation phase of the investments, but they cover only the
extra costs of low-carbon technology, not the bulk of the investment
projects. Not considering the uncertainty in the time line of the newindustrial revolution, they assume implicitly that without the extra
incremental costs the projects will spontaneously yield positive internal
returns.
Climate finance is fragmented for several reasons: the international
market for polluting rights does not exist; the resources must be
mobilized on a much larger scale and must be borrowed on highly
diverse financing channels; climate change must be integrated into thedevelopment strategies of each country, so that financing is predictable
and sustainable, contrary to the volatility of carbon finance. Only theappropriation of needs by the beneficiary countries will allow financingof overly narrow and divided projects to be avoided, because they aredefined from the outside by international institutions or donating
countries.
However market instruments are not available. The availability ofsavings can be found in public and private institutional investors, butthey usually hold easily tradable assets - exactly what infrastructure andgreen bonds are not. These are alternative assets the institutional
investors seldom possess, i.e. <1% of their portfolio for the pension
funds in countries of the OECD (OECD 2013), because these instruments
have the triple handicap of not being liquid, having high levels of risks,and dependant on tentative policies. So the energy policies in Europe
46

What is the quality of growth?
Sustainability and inclusivenessare chaotic and contradictory. The subsidies for new sources of energy
can be excessive and then suddenly disappear with devastating effects
on the cash flows of the on-going projects. From the point of view of thefinancing sources, a strong diversification of instruments and a change of scale
are vital.
Political uncertainty and the weakness of market structures to invest inenvironmental infrastructures are a double handicap. The obstacles toalternative investments are well-known: the competition of asset
managers for the quarters’ prize lists means that only the short term isvalued, with most investors facing regulatory restrictions on long-term
asset ownership. Competition policies that separate grid producers andservice producers force the investors to choose the property rights theywant to own without being able to incorporate the synergies in theirinvestments. And while the activities are technically and economicallyintegrated, there is no history of prices or benchmarks, forcing
producers to internalise the management of assets completely (withsupplementary costs). The green investments have supplementaryhandicaps. The most crippling argument is the inadequacy or even the
non-existence of a carbon price determined by the market for polluting
rights. This handicap is all the more striking given that innovations in“low carbon” investments bear both technological and ecological risks.
Without a sufficiently credible valuation of carbon, guaranteed by thegovernments and increasing over time, and without the cessation offossil fuel energies, these investments will continue to be dominated by
the existing infrastructure.
As a conclusion, these binding constraints force the need to find a
cutting-edge equilibrium. Engaging in industrial policies to mitigate
climate change requires reorienting several billions from energy andsoil without any existing cheap substitute for fossil fuels. To reorient
savings in low carbon investments, risk profiles must be lowered
without supplementary charges on the taxpayer. For that matter, theabatement of emissions has a monetary value that grows over time. But
this monetary value cannot be obtained at present by a tax or a market
that weakened economies after the crisis could bear (Hourcade,Shuklaet and Cassen 2014), One must thus think in a different way: thebase of financing the transition towards a low carbon economy can only
be monetary.

47

Chapter 1

7. A carbon-based financial intermediation backed by money
Fundamental principles of the proposal

The first principle is an international agreement instituting a social
value of carbon. Better to do it at the COP21 in Paris 2015. The IPCC
defines the social cost of carbon as the price that equalizes the marginal
cost of reducing emissions and the marginal gain of avoided climate
damage, along a sustainable growth trajectory. It is neither a pricedetermined by a carbon market, nor a tax incorporated into the price of
current goods. It is a notional price, defined as the value of the avoided
ton of equivalent CO2, and applied to new investments, and not to theexisting stock of capital. The estimations of the available models indicatethat the social value of carbon is highly uncertain, because it relies on alarge ensemble of parameters of which some are unknown (Dumas,
Hourcade and Perrissin-Fabert 2010). This is the reason why it should be
defined by a political agreement. We know that it should increase withtime according to predefined agenda, which could be revised every five
years.
This proposal introduces a temporal distinction in climate policy by
distinguishing the valuation of new investments – that is the future
capital to be produced and the valuation of already installed capital andthe goods and services it produces. This distinction is made because theinvestments are urgent, uncertain and risky, while the introduction of atax or a market price at a sufficient level to make these investments
profitable is politically out of reach today in most countries. Thisdistinction thus solves a political deadlock, which has affected climate
negotiations until today, with the argument related to the highimmediate employment impacts and welfare costs of a carbon price. The
social cost of carbon, defined in monetary units, establishes a new space ofcommensurability, which is the space of carbon assets. These assets are thevalues applied to the volumes of avoided CO2-eq emissions thanks to“low carbon” investments in all economic activities. Carbon assets areproduced when the quantity of avoided GHGs is checked and certified
by competent and independent agencies.
The second principle is government guarantee. The government of eachparticipating country guarantees for a period of five years a certain
quantity of carbon assets as a contribution to the international climate
policy. Effective emission reductions will be validated in kind by48

What is the quality of growth?
Sustainability and inclusivenessindependent experts and give rise to a monetary value. Thus thisfinancial organization aims at eliminating the divorce between privateand social returns of investments, a drawback that plagues investments
involving high degrees of externality. The firms bearing the projects willfind advantage in the certainty of the rise in the social value of carbon,
since it increases the relative value of low-carbon investments. Theirlenders find the opportunity of a new source of credit for which the risks
due to the production of carbon assets are shared at a level linked to thevalidated carbon assets. The governments should be interested in giving
a guarantee on a certain level of carbon assets for their development
policies. However, the process can only be started through aninternational agreement on the social value of carbon, and theidentification of carbon assets must be accompanied by the expertise of
independent agencies. Therefore an international supervision bodyshould be instituted, to monitor the protocol followed by the
independent agencies in their investigation. In order to foster a firstwave of projects, it would be good that this international supervisionbody define the framework in which national states would be persuaded
to promote investments: the technologies, sectors, temporal horizons. It
could also propose the allocation rules of carbon assets, and thus theacceptability of the certificates by project type depending on the
anticipation of avoided GHGs. There would be a common guide for theparticipation of each State.
The third principle allows central banks to register the value of theguaranteed carbon assets on the asset side of their balance sheets. On
the liability side, the central bank can register carbon certificates. These
carbon certificates are reserves or collateral for the financial institutions(development banks, investment funds, private equity funds) that havefinanced the validated investment projects. The risk for the investor who
finances the projects is in a way socialized. It is diminished by theamount of carbon certificates on the guaranteed carbon assets.
The fourth principle has to do with time consistency. Monetaryfinancing can be understood as a temporary device to launch a wave ofinnovative investments as much as quantitative easing (QE) has been to
alleviate the impact of the financial crisis. As long as those investments
are implemented, the production structure will change towards clean
technology. The consumption structure will change with the use ofcapital while former “dirty” capital has been replaced. Therefore the49Chapter 1

resistance against a carbon tax or cap-and-trade market will wane. It
should be possible to come back to a form of standard taxation, in theframework of a new international agreement. The exit condition would
be the convergence in the long run of the valuation of the carbon
externality through the monetary tool, and the one from a future carbon
tax (or a carbon market such as the ETS). Without this convergencecondition, there would be a time inconsistency in the expected return ofinvestments during the transition from one tool to the other. In the longrun, the proposed financial policy can be institutionalized in a newmonetary system or can be thought of as temporary before theintroduction of more traditional tools.
Carbon assets in the monetary and financial systems
The monetary financing proposal for low carbon projects is not akin to
QE, which involves the purchase of already existing assets on secondary
markets. Our proposal involves the direct financing of new real
investments, creating carbon assets by monetization of credit. The
monetization only occurs for validated projects by independent andofficial agencies. There is no endogenous inflation, since the price ispredefined on the expected abatement trajectory and the counterpart ofthe monetary creation by the central bank is a real asset for which thestate has defined a total maximum amount for a determined period andguarantees its value. The only risk lies in possible errors from the certification
agencies, which may accept projects that do not produce the anticipated carbon
assets. There would thus be carbon asset destruction, cancellation of the money
created and loss for the bank who gave the loan and/or loss for the entrepreneur
who took the risk.
The balance sheet of this monetary intermediation appears on table 2.
Table 2. Bank balance sheets of a financial intermediation restingon carbon assets
Central Bank
AssetsLiabilities-Currency-Foreign-Bank deposits
exchange-Carbon-Bills and bonds
certificates-Carbon assets-Non-monetary
items

50

Commercial and development banks
AssetsLiabilities-Deposits and-Reserves
ordinary bond-Commercial
issuedloans and
-Bonds issued on
securities
low-carbon-Loans on lowinvestments
carbon-Capital
investments

What is the quality of growth?
Sustainability and inclusivenessA complementary mechanism can be designed to tap the large pools ofsavings collected by institutional investors. Indeed, not only banks but
also specialized non-bank financial investors can use the carbon-based
monetary facility to back climate-friendly financial products. The idea is
to create a financial intermediation to match the preference for low riskof the bulk of institutional investors worldwide and the involvement ofspecialized risk-taking funds. A green fund, backed by governmentsthat would provide the core of its capital base, could issue climate bonds
on carbon assets transferred by the specialized funds that had
contributed to finance the investments. Those bonds would be dedicatedto institutional investors. The accounting side of this intermediation
scheme is depicted in Table 3.
On the asset side of its balance sheet, the green fund would finance alarge array of financial specialists, which themselves finance diversified
projects. It could acquire liabilities of private equity funds, buy projectbonds, and lend to development banks.
Table 3� Financial intermediation via Green Funds
Specialized financial investors
AssetsLiabilities

Carbonassets fromvalidatedprojects

Other assets

Green Fund
AssetsLiabilities

Loans from
EGF

Loans tofinancespecialists

Other loans

Project
bonds

CapitalEquities

Bonds oncarbonassetsCapitalInstitutional investors
AssetsLiabilities
ClimateBonds oncarbonassetsCollective
saving(retirement
contracts,
life
insurance,
state
Other bonds funding of
SWFs)
Equities

CapitalTherefore green funds could be established in every country
participating in the international agreements on the notional carbon
price and related state backing of carbon assets. The funds can mediate
the financing of well-diversified investment projects, thereby creating
carbon assets. Thanks to the diversification of risk in its interventions
and the strong backing of its capital, the European green Fund ispresumed to get the highest rating and be able to issue high-rated bonds
with a high multiple of about 10 ($1000bn equivalent with a capital of$100bn). Institutional investors worldwide would be able to diversify
51

Chapter 1

their asset allocation with a new class of assets weakly correlated withexisting assets. Because the specialists financing individual projects canbe dispersed throughout the territories, the scheme can bedecentralized. It can finance industrial policy linked to urban
development, recycling processes and bio-agriculture that can reterritorialize industry, reducing heterogeneity and dependence on
imported carbon intensity via foreign energy dependencies.

8� Conclusion
The paper has emphasized the linkages between a conceptual
framework of social welfare improvement that can be called sustainable
development, and shown the need for deep reform in national
accounting to make operational the concept of total national wealthupon which long-run development policies can be implemented. It has
also indicated that deep changes in corporate governance and businessaccounting are required to provide incentives for private firms tocorrespond with strategic national planning goals. Finally, the paper hastaken the view that climate policy could be the decisive driver ofsustainable development objectives. It is the domain where investment
projects must be upgraded urgently. An international agreement on a
notional price of carbon as well as the commitment of governments toachieve a definite amount of carbon abatement in a finite period of time
is a precondition to define and run a new financial intermediation. This
will provide the monetary backup necessary to overcome the inabilityof financial markets to provide the huge amounts of credit needed toreorient the production system.

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54

Part Ⅱ
The 20th Century Concept of Growthand Its Deliverables

Chapter 2

Poor Trends: The Pace of Poverty Reduction
after the Millennium Development Agenda1

Richard Bluhm, Denis de Crombrugghe and Adam Szirmai

1� Introduction“We are at an auspicious moment in history, when the successes of past decades
and an increasingly favorable economic outlook combine to give developing
countries a chance, for the first time ever, to end extreme poverty within ageneration” (Jim Yong Kim, World Bank President, speaking at Georgetown
University, April 3, 2013)
Only 13 years after the Millennium Summit in September 2000 at whichworld leaders agreed on halving the 1990 global poverty rate at $1.25 a dayby 2015, the end of extreme poverty seems to be in sight. Recent estimatessuggest that the first Millennium Development Goal (MDG) was alreadyreached in 2010 and about 700 million people were lifted out of poverty. In
2013, the World Bank declared a new organizational goal of ending
extreme poverty by 2030; that is, reducing the $1.25 a day poverty rate to
3% by 2030. The last two decades clearly ushered in unprecedented
success, but is 2030 really likely to mark the end of extreme poverty? Ourmain contribution is to demonstrate that this is unlikely.
In this paper, we review the origins of the ‘dollar-a-day’ poverty line,
discuss progress over the last three decades, and forecast $1.25 and $2 a
day poverty rates until 2030. It is well known that regional trends inpoverty alleviation are very heterogeneous. In spite of rising inequality,
rapid growth in China was the driving force behind global progress
1. We have greatly benefited from discussions with several participants of the AFD/IDS/JICA workshop on the “Quality of Growth”. In particular, we would like to thank Lawrence
Haddad, Nicolas Meisel, Charles Kenny and Laurence Chandy for useful comments andsuggestions. We gratefully acknowledge financial support from the Agence Française deDéveloppement (AFD). The findings, interpretations and conclusions expressed in thispaper are solely the responsibility of the authors and do not necessarily represent policies
or views of Maastricht University, UNU-MERIT, AFD and/or other affiliated institutions.
All remaining errors are those of the authors.

57

Chapter 2

over the last two decades and accounts for more than three quarters of
the reduction in the number of people living below $1.25 a day.
However, most of the poverty reduction potential coming from China is
now exhausted. Poverty reduction in the developing world outside
China has been considerably slower, although economic growth has
accelerated significantly since 2000. In 2010, three-fourths of theextremely poor lived in sub-Saharan Africa and South Asia, as opposed
to approximately 40% in 1981. This changing regional composition ofworld poverty has important ramifications for future trends in poverty
reduction. Historically fast growing countries make up less and less ofthe global poor.
Building on a new method for estimating poverty elasticities andpredicting poverty headcount ratios developed in Bluhm, deCrombrugghe, and Szirmai (2013), we show that the pace of poverty
reduction at $1.25 a day is likely to slow down significantly after 2015.
Extreme poverty barely falls below 8% in the most optimistic scenario.
Ravallion (2013) first suggested the 3% target relying on the assumptionthat consumption in developing countries would continue to grow at
the average post-2000 trend, or 4.5% per year. We find this ‘equalgrowth’ assumption too optimistic. Poverty tends to be higher incountries with rapid population growth and lower than average
consumption growth. None of our scenarios predict a poverty rate near
3% once country-specific trends from 2000 to 2010 are used. However,the $2 a day poverty rate may fall below 20% in 2030, while a slowdown
happens only late during the forecast period or not at all. A distinct
advantage of our approach is that it is computationally inexpensive.
Hence, it can easily be used for benchmarking progress as new data
become available.
The paper is structured as follows. Section 2 discusses some of thecontroversies surrounding the setting and updating of international
poverty lines. Section 3 is a data-driven review of global poverty andinequality trends with a particular focus on China, India, Brazil andNigeria. Section 4 presents projections of global and regional poverty
rates until 2030 at the $1.25 and $2 a day poverty lines using different
growth and inequality scenarios. Section 5 concludes and offers somepolicy recommendations.

58

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

2� Drawing the line: international poverty lines
The dollar-a-day poverty line was first defined in a background paper tothe 1990 World Development Report (in 1985 PPPs), then updated to$1.08 (in 1993 PPPs) in 2000, and again updated to $1.25 (in 2005 PPPs) in2008. While the first update went by almost unnoticed, the most recentchange has sparked a controversy. Redefining extreme poverty as living
below $1.25 a day raised the global poverty headcount by about 10
percentage points and reclassified approximately 450 million people asextremely poor (Chen and Ravallion 2010). In this section, we briefly
review the origins of the $1 a day measure and discuss shortcomings ofthe current updating procedure.
The problem of setting a global poverty line is far from trivial. Even if
we could use a ‘basic needs’ or calorie-intake approach to devise aminimum consumption bundle for the entire world, it is inherently
difficult to apply any such bundle in international comparisons.
Subsistence needs, relative prices, and purchasing power vary acrosscountries and over time. Faced with these problems, Ravallion, Datt,and van de Walle (1991, henceforth RDV) suggested an original solution.
Since many national poverty lines are set using a basic needs or calorieintake method, there should be a universal lower bound among theabsolute poverty lines, which may be recovered from the data.
Converting 33 national poverty lines and the corresponding
consumption levels from the 1970s and 1980s into international dollars,
RDV showed that a poverty line of about $31 per month ($1.02 a day, in1985 prices) was shared by the six poorest countries in their sample,
while those of two other countries came close. They argued that arounded-off poverty line of $1 a day was a sensible threshold formeasuring global poverty, since any one poverty line is likely to be
estimated with error and the non-food allowance included in thesubsistence basket varies across countries. RDV also estimated a lower
line of $23 per month (about 76 cents a day) for the poorest country intheir sample. This lower line was close to India’s poverty line at the timeand became widely used as the international poverty line during the1970s and 1980s (e.g. Ahluwalia, Carter, and Chenery 1979).
Setting the poverty line in international prices has the advantage thatdomestic inflation is typically taken into account when average incomesor expenditures from surveys are converted into (base year)
59Chapter 2international dollars, so that the line itself does not have to be explicitly
updated annually. However, purchasing power parities (PPPs) changeover time as countries grow richer (due to the Balassa-Samuelson effect).
In addition, the quality of PPP estimates has been improving
substantially with each round of the International Comparison Program(ICP), so that updates are needed approximately every decade. When the1993 ICP data became available, Chen and Ravallion (2001) revised the$1 a day line to $1.08 in 1993 prices. Using the same data as in theoriginal study, they found that $1.08 a day was the median poverty lineof the ten poorest countries. However, when the 2005 ICP wascompleted, instead of converting the old poverty line to 2005 prices, newdata were collected and the poverty line was redrawn. Ravallion, Chen,
and Sangraula (2009, henceforth RCS) compiled a dataset of 74 national
poverty lines to update the original analysis. They found that national
poverty lines do not rise with per capita consumption until a certain
turning point (about $60 per month) but increase strongly thereafter (leftpanel, Figure 1). RCS set the global line as the average poverty line of the
15 countries below this threshold, or $1.25 a day in 2005 prices.
Figure 1� Poverty lines and consumption levels around 2000

Notes: Author’s calculations using the data reported in Ravallion et al. (2009) and following theillustration of Deaton (2010). The non-linear trends are estimated using a (weighted) local linear
smoother with bandwidth 0.8.

Deaton (2010), as well as Deaton and Dupriez (2011), take issue with thisapproach. They argue that updating the international poverty line basedon new data leads to “graduation effects” when countries move out of
the reference group. They illustrate their case using India and Guinea
60

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

Bissau as examples. India was part of the initial reference group in RDV,and both countries appear in RCS’s more recent reference group. India
has a relatively low poverty line ($0.90 a day in 2005 prices) and a
population of more than a billion people, whereas Guinea Bissau has ahigher poverty line ($1.51 a day in 2005 prices) and is home to less than
1.5 million people. As average consumption in India grew considerably
until 2000, it crossed the $60 threshold and is no longer part of thereference group. Even though the average Indian has become richer,both the international poverty line and the global poverty headcountincreased as a result of India dropping out of the average. With Guinea
Bissau the case is reversed. Its poverty line is currently part of theaverage. A move out of the reference group would entail a fall in the
global poverty line and a reduction in global poverty that is many times
greater than the population of Guinea Bissau. The left panel of Figure 1
illustrates this relationship. The bold horizontal line marks the $60 per
month threshold (labeled RCS’09). A related issue is that the ICP data areprimarily designed for comparing living standards of entire
populations, not just poor people. The typical consumption basket of thepoor, and the associated price level, may be very different than thereference basket used for computing PPPs.
To address both the graduation issue and the PPP issue, Deaton and
Dupriez (2011) propose using an alternative procedure. Linking
consumption surveys to ICP data for the 50 poorest countries, they
simultaneously estimate the poverty line and PPPs of those near thepoverty line (PPPs for the poor, or P4s). This procedure yields lower
poverty lines in between $0.92 and $1.19 a day. However, the effect of theP4s on the global poverty counts – at similar poverty lines – is relatively
small. The resulting estimates of global poverty are lower primarily due
to the lower poverty lines and not due to differences in relative prices.
To an extent, the Deaton-Dupriez criticism can be addressed within the
RCS approach by (1) weighting the national poverty lines by population
sizes, and (2) extending the reference group of “poorest” countries. The
Deaton-Dupriez proposal, labeled DD’11 below, is to select the 50
poorest countries to constitute a fixed reference group. Clearly, thethreshold of 50 countries is arbitrary. A possible alternative is to
replicate the RCS approach but estimate the consumption gradient using
population weights instead of equal weights (right panel, Figure 1). Thisis the approach proposed here. Examining the plot to find the point
61

Chapter 2

where the slope of consumption begins to be positive, we visually
identify a threshold of about 5 log dollars or $148.41 per month ( (cluster t= -2.40) percentage points, respectively. Using an alternative $2 a day
poverty line, the magnitudes and differences in speeds across regions
remain broadly similar.2
Figure 2� Population-weighted poverty trends by region, 1981 to 2010, $1�25 a day

Notes: Authors’ calculations based on surveys from PovcalNet.

An important question is whether consumption growth or
redistribution is driving the decline in poverty. Estimating the historicalcontributions of growth and changes in distribution during the 1980s
and 1990s, Kraay (2006) found that most poverty reduction was due toincome or consumption growth. Our analysis broadly corroborates thisfinding (although we do not explicitly estimate contributions). The
population-weighted growth rate of the survey means from 1981 to 2010
across all countries is a very robust 4% per year (cluster t = 3.03). Overthe same period, within-country inequality, as measure by the Gini
coefficient, actually increased slightly by about 0.7% per year (cluster t =1.64). This implies that, on average, changes in distribution may have infact moderately slowed the pace of poverty reduction. Poverty reduction
over the last three decades has mostly been due to income andconsumption growth. However, both the high average growth rate in
2. East Asia and Pacific (slope = -2.31, cluster t= -7.33), Europe and Central Asia (slope= -0.26,
cluster t= -2.33), Latin America and Caribbean (slope= -.55, cluster t = -5.74), South Asia (slope= -0.72, cluster t= -4.92), and sub-Saharan Africa (slope = -0.02, cluster t= -0.07).

62

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

the survey means and the apparent rise in within-country inequality
are driven by China. Excluding China, the survey means grew about
1.8% per year (cluster t = 2.45) and inequality barely moved (increased
0.047% per year, cluster t = 0.13). In other words, poverty reduction in thedeveloping world outside China has been steady but slow and has (onaverage) not been helped by improvements in distribution. 3
These findings are also in line with estimates of poverty at the $1.25 a day
poverty line reported by the World Bank (see Appendix Table A-1). Chen
and Ravallion’s (2010) estimates indicate rapid progress in China, little
improvement in sub-Saharan Africa, and moderate poverty reduction
elsewhere. The poverty headcount ratio in sub-Saharan Africa only fell by
about three percentage points over the entire period from 1981 to 2010,
and actually exceeded its 1981 value for most of the period. Combining
these trends with population growth rates reveals the dire absence of arobust positive trend in terms of the number of global poor outside of East
Asia. While China has lifted an astonishing 680 million people out ofpoverty between 1981 and 2010, the rest of the world has only about 50
million fewer extremely poor people in 2010 than in 1981. This trend is
owed to persistently high poverty rates coupled with strong population
growth in sub-Saharan Africa and India. This is most evident in subSaharan Africa where the number of extremely poor has roughly doubled
over three decades (in spite of the slight decrease in the headcount ratio).
The rise of China from a poor to a middle-income country also implies
that the relative composition of world poverty is changing rapidly. In 1981
about 40% of the world’s extremely poor lived in sub-Saharan Africa andSouth Asia, by 2010 their share has risen to 75%.
A very intuitive approach to illustrating past progress (or lack thereof) is
to approximate the shape of the income or expenditure distribution at
various points in time and examine how the features of the distribution(esp. quantiles) shift over time. Figures 3 and 4 plot the lower tail (up to$400) of the monthly income or expenditure distribution for the mostpopulous country of the four poorest regions – East Asia, South Asia,
Latin America, and sub-Saharan Africa – in 1985, 1990, 2000 and 2010.
The vertical lines are the $1.25 and $2 a day poverty lines in terms ofmonthly consumption. After lining up the survey data in time, weestimate the different density functions using a log-normal
3. Excluding India in addition to China from the sample does not qualitatively alter thisresult.

63

Chapter 2

approximation.4 While the assumption of log-normality has its
weaknesses5, it usually provides a useful first estimate of the shape of
the income distribution. A key advantage is that it only requires
knowledge of the mean and Gini coefficient.
We can illustrate a few essential concepts with these graphs. The area
under the curve to the left of the poverty line gives the fraction of the
population that is poor (the poverty headcount ratio), while the spreadof the distribution reflects inequality. The raw difference between two
such areas under the curve is the absolute change in the poverty
headcount ratio in percentage points and the relative difference gives
the percent change in the poverty headcount ratio. The sensitivity ofpoverty reduction to changes in income or inequality is often measured
in the form of elasticities or semi-elasticities. The income elasticity ofpoverty is the percent change in poverty for a one percent increase inincomes, and the income semi-elasticity of poverty is the percentage
point difference in poverty for a one percent increase in incomes. The
inequality elasticity and semi-elasticity are defined analogously. An
attractive feature of the semi-elasticity is that it first increases and thendecreases again during the development process. It measures the pace ofpoverty reduction in terms of the percentage of the population lifted outof poverty. Hence, it is usually more informative for policy-makers andmore useful than reporting relative changes.6
Figure 3 visualizes the tremendous progress in reducing poverty rates
in China over the last three decades.7 As noted before, poverty in China
4. We interpolate and extrapolate the data as follows. First, we project mean consumption
forward and backward using the corresponding growth rates of personal consumption
expenditures from the national accounts. Second, we linearly interpolate between theavailable Gini coefficients and extrapolate beyond the first or last available measure bykeeping inequality constant. The same data set (with all countries from PovcalNet) is later
used for computing the inequality indices in the developing world.
5. Log-normality typically works better with consumption surveys than with income
surveys (Lopez and Serven 2006), tends to underestimate the level of poverty (Dhongde andMinoiu 2013), and overstates the pace of poverty reduction (Bresson 2009).
6. In relatively rich countries with low percentages of people below the poverty line,
elasticities can be very misleading. Small reductions in the poverty headcount rate canmanifest themselves as very high elasticities. For a more detailed discussion of theproperties of elasticities and semi-elasticities of poverty see Bourguignon (2003), Klasen
and Misselhorn (2008), and Bluhm et al. (2013).
7. The implied poverty rates for China correspond well with the official World Bank
estimates. At the $1.25 a day poverty line, our estimates imply a poverty rate of 60.56% in
1985, 56.92% in 1990, 31.97% in 2000 and 9.75% in 2010.

64

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

at $1.25 a day fell rapidly over the entire period. The biggest gains
occurred early on, between 1985 and 2000, when the peak of the
distribution was close to the $1.25 and $2 poverty lines. By 2010, the peakof the distribution has moved considerably to the right of both poverty
lines and the overall spread has widened. A great many Chinese are
now considered part of a developing country ‘middle class’ (if defined
between $2 and $13 per day).8 However, this also implies that thepoverty reduction potential from China is largely exhausted. The
income semi-elasticity of the poverty headcount is far beyond its peakand steadily approaching zero. In addition, inequality has increased
remarkably over the same period. In 1985, the Gini coefficient was 0.28
and by 2010 it has risen to 0.44.
Figure 3� Estimates of the expenditure distribution: China and India, 1985–2010

Notes: Authors’ calculations. China’s expenditure distribution is estimated based on a weighted mean
and a rural-urban ln-mixture for the Gini coefficient. China’s surveys in PovcalNet are consumptionbased after 1987 and income-based before.

In India, on the contrary, there remains much greater potential forpoverty reduction in the medium-term future. While the mode of theincome distribution was near the $1.25 line around 1985 and 1990, thepeak of the distribution in 2000 and 2010 is located between the twopoverty lines. The process of “bunching up” in front of $2 a day
observed by Chen and Ravallion (2010) implies that, in the medium-term
8. Ravallion (2010) defines the size of the ‘middle class’ by developing country standards as
the proportion of the population living on at least $2 per day but less than $13 per day, where
the upper bound is the poverty line in the United States. Naturally, this is one of many
possible definitions.

65

Chapter 2

future, the pace of poverty reduction in India (defined as the absolutechange in the headcount) will be particularly fast at the $2 a day line andcontinue at a fast but decelerating pace at the $1.25 line. Put differently,
India’s income semi-elasticity around 2010 is very high and a moderate
rate of growth will immediately have a large (but decreasing) effect on
the poverty headcount ratio at both thresholds.
Figure 4 illustrates two very different cases. The left panel shows thatfrom 1985 to 1990 poverty reduction in Brazil was very slow, with some
progress at the $2 a day line but a nearly unchanged poverty rate at the$1.25 line. Yet, on average, Brazilians were already considerably better
off in the 1990s than their Chinese or Indian counterparts in 2010. After
1990, the pace of poverty reduction accelerates and by 2010 only 4.92% of
the population was below the $1.25 a day poverty line.9 Lifting theremaining people out of poverty will require sustained economic
growth, as both the income and distribution semi-elasticities of povertyin Latin America as a whole are rather low (Bluhm et al. 2013). With a
Gini of 0.56 in 1985 income inequality was initially very high in Brazil,
peaked at 0.61 in 2000 and then fell again to 0.55 by 2010, thus positively
contributing to poverty reduction after 2000. The right panel illustrates
that poverty in Nigeria was considerably higher in 2000 or 2010 than in
1985. Nigeria’s plight is characteristic for most of sub-Saharan Africa inthe 1980s and 1990s, as real consumption on the subcontinent was
declining at a pace of about 0.82% per year. Only after 2000 did
expenditures recover and the poverty headcount ratio began to decline.
Yet even by 2010, the peak of the expenditure distribution is still
noticeably to the left of the poverty line and the implied poverty rate at$1.25 a day is 65.96%.10 In addition, inequality in Nigeria increases over
the observed period, starting from a Gini of 0.39 in 1985 to 0.49 in 2010.

9. The World Bank estimates a poverty rate of 5.38% at $1.25 a day for Brazil in 2010.
10. The World Bank estimated a poverty rate of 67.98% at $1.25 a day for Nigeria in 2010.

66

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

Figure 4� Estimates of the expenditure distribution: Brazil and Nigeria, 1985– 2010

Notes: Authors’ calculations. Brazil’s distribution is based on incomes instead of expenditures. The
2010 data for Nigeria was revised in late 2014 (not incorporated here).

Taken together, these four distributions exemplify the changing
composition of global poverty and broadly represent the trends in theirrespective regions. Over the last three decades, most poverty reduction
occurred in East Asia where consumption growth was fastest, somepoverty reduction occurred in India where real consumption growth
was steady, and little poverty reduction occurred in sub-Saharan Africa
where real consumption growth was slow and volatile. This suggeststhat without significantly faster growth in sub-Saharan Africa than in
the past, possibly coupled with improvements in the income or
expenditure distribution, the global pace of poverty reduction will
inevitably slow down in the near future.
Another essential aspect of poverty analysis is studying the evolution of
inequality. In this part, we focus only on inequality among citizens ofdeveloping countries, as our interest is the changing relative position ofpeople in the developing world rather than their position vis-à-vis richcountries. Interestingly, many of the global trends are also evident evenwhen we restrict our attention to this truncated distribution. We
compute three measures of inequality by applying Young’s (2011)
mixture of log-normal distributions approach to the PovcalNet data.
‘Overall inequality’ is the Gini coefficient for citizens of developing
countries regardless of their country of residency. ‘Within inequality’ isa population-weighted summary measure of inequality within eachcountry. Last, ‘between inequality’ is the population-weighted Gini
67

Chapter 2

coefficient of average incomes among all developing countries. In otherwords, the first measure encompasses both the within-country andbetween-country components that make up overall inequality in thedeveloping world. Naturally, global inequality – including the citizens
of developed countries – is typically estimated to be considerably
higher. Recent estimates of the global Gini suggest that it is around 0.650.70, and may be even higher if underreporting of top-incomes is taken
into account (e.g. see Pinkovskiy and Sala-i-Martin 2009; Milanovic 2012;
Chotikapanich, Griffiths, Rao, and Valencia 2012; Lakner and Milanovic2013).
Table 2� Inequality in the developing world, 1980–2010
Gini coefficient
Year
1980
1985
1990
1995
2000
2005
2010
Δ 1980-2010 (in %)Δ 1990-2010 (in %)Δ 2000-2010 (in %)OverallWithin Between

0.596
0.555
0.578
0.559
0.537
0.535
0.554
-7.186
-4.17
3.066

0.356
0.353
0.367
0.385
0.395
0.399
0.404
13.632
10.095
2.2

0.486
0.421
0.449
0.411
0.374
0.372
0.399
-17.82-11.081
6.642

Mean
Consumption Population
73.29 2907.8
8379.05 3223.2
8695.98 4049.3
10498.41 4555.2
114
102.45 4931.4
121120.34 5285.6
123
150.72 5625.1
123
–
–
–
–
–
–
–
–
–

Notes: Authors’ calculations. The sample size varies over the years. A total of 124 countries arerecorded in PovcalNet but we lack PCE data for West Bank and Gaza. The results are very similar if
we constrain the developing world to consist of the 104 countries from which we have (interpolated)data from 1990 onwards. Due to the lower coverage, the results for the 1980s should be interpretedwith caution. For details on the ln-mixture calculations refer to Young (2011).

Table 2 reveals some interesting trends. Overall inequality in thedeveloping world has been falling between 1990 and 2005, but it exhibits
an increase in 2010. At the same time, within-country inequality has
been rising steadily since the mid-1980s. Between-country inequality
fell over most of the period but also shows a slight increase between
2005 and 2010. If we exclude China from the computations given that its
weight is very high, then these trends are considerably muted or even68

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

non-existent.11 Hence, two developments drive the overall change. First,
inequality of incomes within China has been increasing significantly
and second, its relative position among developing countries has beenchanging rapidly. Rising mean incomes in China from the 1980s
onwards initially implied a reduction of between-country inequality as
the average citizen in China was moving from the bottom towards the
middle of the developing country ranks, but they now put upward
pressure on overall inequality as incomes in China continue to growand the distance from incomes in sub-Saharan Africa increases.12

3 Going forward: poverty projections until 2030

As the expiration date of the MDGs is approaching quickly, new goals
will have to be selected. Picking from a wide range of possible
benchmarks invariably involves formulating expectations towards a
fundamentally uncertain future. Thus, it becomes important to ask:
what can the current data and methods tell us about the prospects forpoverty alleviation over the next two decades? The list of policy-relevant
questions is long. What level of poverty do we expect to prevail in 2030?
Will it be feasible to truly eradicate extreme poverty by 2030? Or, howquickly do we expect poverty rates under the $2 a day poverty line todecrease? Here, we provide both a glimpse into several likely futuresand some potential answers to these questions.
This section draws heavily on Bluhm et al. (2013), where we develop a‘fractional response approach’ for estimating income and inequality(semi-) elasticities of poverty. Among other things, the paper shows that
this new method can be used to easily forecast global poverty rates
using only two variables (the survey mean and the Gini coefficient). A
key advantage of this approach over, say, linear trend extrapolations, isthat it builds in the non-linearity of the poverty-income-inequality
relationship. Neither the income or inequality elasticity nor the income
or inequality semi-elasticity is assumed to be constant. The method
11. Overall inequality is estimated as 0.583 in 1990 and 0.584 in 2010, within-country
inequality is estimated as 0.384 in 1990 and 0.391 in 2010, and between-country inequality is
estimated as 0.450 in 1990 and 0.454 in 2010. Removing India in addition to China has little
effect on the trends in the inequality measures.
12. This trend is corroborated by the literature on global inequality. According to Laknerand Milanovic (2013), average incomes in sub-Saharan Africa were $742 in 1988 and just $762
in 2008 (in 2005 PPPs), while Chinese incomes increased by 228.9% and no longer make up alarge part of the lower tail of the global income distribution. They also show that inequality
within China has risen between 1988 and 2008.

69

Chapter 2

accounts for the fact that income growth will have an increasing effect invery poor countries, where the mass of the distribution is to the left ofthe poverty line, and less and less of an effect in rich countries, where
the mass of the distribution is far to the right of the poverty line.13
Similarly, the effect of changes in distribution will indirectly depend on
the prevailing levels of both income and inequality.
We are of course not the first to present poverty projections over the next
two decades. Ravallion (2013), for example, outlines an aspirational
scenario where an additional billion people are lifted out of extreme
poverty by 2025-2030. Karver, Kenny, and Sumner (2012) discuss the
future of the MDGs more generally and simulate poverty rates at the$1.25 and $2 a day poverty lines for 2030.14 Yet there are some importantconceptual and methodological differences between our approach andthese studies. First, the assumption that the developing world willcontinue growing at the accelerated 2000 to 2010 pace for another twenty
years (our optimistic scenario) is questionable. There is a well-known
instability of growth rates across decades that should not be ignored(Easterly et al. 1993), especially since the high average growth rates inthe developing world were driven by rapid growth in China. A moreconservative assumption is that countries will grow at rates much closer
to their individual long-run growth path. Second, the changing
composition of the countries contributing to global poverty matters a lot
for the expected speed of global poverty reduction. Unless there is apersistent acceleration of consumption growth in sub-Saharan Africa on
top of the post-2000 growth rates and sustained consumption growth inIndia, we can show that the pace of poverty reduction at the $1.25 line islikely to experience a pronounced slowdown in all of our forecast
scenarios (defined below). Third, pro-poor growth can potentially makea sizable difference in the expected poverty rates, while a rise in withincountry inequality will hasten the arrival of the slowdown. Fourth, ourmethod approximates the ‘official’ PovcalNet results at a fraction of the
13. The inability to account for countries that have relatively high incomes and zero poverty
at some point in time (typically the beginning or end of a spell) is a key weakness of studies
investigating poverty elasticities.
14. Karver et al. (2012) allow for country-specific growth rates but use older data (their
PovcalNet reference year is 2008) and disregard the difference between GDP per capita
growth and growth of the survey mean. This leads them to overestimate the speed ofpoverty reduction relative to our forecasts. A recent study by Chandy, Ledlie, andPenciakova (2013) echoes some of our results. They use GDP per capita rather thanconsumption expenditure data for most of the period, but apply a conversion factor, andreport lower poverty estimates.

70

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

computational cost, so that a variety of scenarios can be easily estimated(and frequently updated with the arrival of new data).
We define three different constant growth scenarios on the basis of thehistorical personal consumption expenditure (PCE) growth rates fromthe national accounts.15 An ‘optimistic’ scenario uses the average PCE
growth rate of each country from 2000-2010, during which period
growth rates were significantly higher than before 2000. A ‘moderate’
growth scenario uses the average PCE growth rate of each country from1980 to 2010 – the long run average over the entire dataset. Finally, a‘pessimistic’ growth scenario uses the 1980 to 2000 average PCE growth
rates. The latter scenario assumes that mean consumption in subSaharan Africa is shrinking at a rate of about 0.82% per year.16 Table A-2
in the Appendix reports the population-weighted average regional
growth rates over several different periods to illustrate the implied
regional income dynamics.
For each growth scenario, we also simulate three different inequality
patterns. ‘Pro-poor growth’ implies an annual decline in the Gini
coefficient of approximately -0.5%, ‘distribution-neutral growth’ keeps
inequality constant at the level prevailing in 2010, and ‘pro-rich growth’
implies an increase in the Gini coefficient of approximately 0.5% per
year.17 As an illustration, if a country’s Gini coefficient is 0.40 in 2010 and
we apply the pro-poor pattern, then by 2030 we project a Gini coefficientof about 0.36. If we apply the pro-rich pattern, then the Gini coefficient isabout 0.44 in 2030. Changes of this magnitude are in line with the
population-weighted regional trends obtained from the surveys.
We forecast the poverty rates until 2030 as follows. First, we estimate themodel outlined in Bluhm et al. (2013) for the $1.25 a day poverty line
using all nationally representative surveys recorded in PovcalNet overthe period from 1981 to 2010. Next, after lining up all surveys in 2010, we
apply each of the nine growth and distribution scenarios to project the
15. The term ‘national accounts’ refers to data from the World Development Indicators or thePenn World Table 7.1, whichever has more data over the 30 year horizon.
16. Owing to the post-communist transition, consumption and incomes in Europe andCentral Asia were shrinking over the same period. However, given the small number ofpoor in 2010, the influence of that region on the global poverty headcount in 2030 isminimal.
17. All reported growth rates (in percent) are computed as log differences if not otherwise
noted.

71

Chapter 2

income and inequality data forward to 2030, country by country.18 Then,
we predict the poverty headcount ratios in five-year intervals over theperiod 2015 to 2030, country by country. Finally, we calculate
population-weighted regional poverty rates and apply these to theprojected total population in each region. For consistency with
PovcalNet, the population projections are also taken from the WorldBank and the ‘developing world’ is defined as in 1990 – the countries
targeted by the MDGs – no matter how high we forecast the averagelevel of consumption to be in 2030. Contrary to the World Bank’s recent
redefinition of the denominator, we still focus on the percent of poor
population in the developing world and not the entire world.
Figure 5 plots the historical evolution of the poverty headcount from 1981
to 2010, a linear trend fitted through the observed data and thenextrapolated until 2030, and our different scenarios. The linear trend serves
as a reference for the non-linear projections. Several points are noteworthy.
First, only the linear extrapolation predicts a poverty rate in the vicinity ofzero by 2030. Regressing the global poverty rate at $1.25 a day on time one
obtains a slope of about one percentage point per year (see also Ravallion2013). 19 As the global poverty rate was about 20.6% in 2010, the linear trend
predicts that extreme poverty will have vanished by 2030. Second, all our
projections show a decelerating rate of poverty reduction. Even in the most
optimistic scenario, the pace of poverty reduction slows down. Most
forecasts show a decelerating trend early on. In the optimistic scenario theslowdown only becomes noticeable by about 2020. Third, all scenarios but
the optimistic pro-poor growth or optimistic distribution-neutral growthscenarios imply a poverty rate higher than 10% in 2030 at the $1.25 a day
line. The optimistic pro-poor growth and distribution-neutral scenarios
suggest a poverty rate in 2030 of 7.9% and 9.1%, respectively. In a nutshell,
2030 is not likely to mark the end of extreme poverty, even under very
optimistic assumptions. Our projections suggest that the World Bank’s
goal of 3% extreme poverty in 2030 is not likely to be reached.
18. To line up all surveys in 2010, we use the actually observed PCE growth rates from thenational accounts to extrapolate the survey means from the latest available survey. In doing
so, we keep inequality constant at the last observed Gini coefficient. In 2010, the average
year when the last survey was conducted is 2006.7, so about 3 years prior to 2010. More than
40% of the last surveys were conducted in 2009 or 2010.
19. This differs from the 1.5 percentage points estimated in the previous section as theglobal poverty rate is measured by lining up and weighting all surveys at reference years(three year intervals from 1981 onwards), whereas in the previous section we were using an
unbalanced panel of unequally-spaced, population-weighted survey data with a wide yetsomewhat selective coverage.

72

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

Figure 5� Actual and projected poverty headcount ratios at $1�25 a day, 1981–2030

Notes: Authors’ calculations based on Bluhm et al. (2013) and survey data from PovcalNet. The solid
black line beyond 2010 refers to the moderate (distribution-neutral) growth scenario in Table 3, while
the solid grey lines represent the distribution-neutral variants of the optimistic and pessimistic
scenarios. The pro-poor and pro-rich variants are shown as grey dotted lines and are located above
or below a solid line.

Table 3 provides the corresponding regional and total poverty rates in
2030 including the expected number of poor in the various scenarios.
Our moderate growth estimate suggests a global poverty rate of 13.2% in
2030, implying about 950 million poor versus 1.2 billion poor people in2010. The pace of poverty reduction will have slowed significantly bothin terms of relative changes and in terms of numbers of poor people. In
this scenario, about 70% of the world’s poor live in sub-Saharan Africa
and about 23% in South Asia by 2030. In contrast, the (distributionneutral) optimistic result suggests a poverty rate of 9.11%, with about 655
million people remaining extremely poor. About 76% percent live in
sub-Saharan Africa and about 17% in South Asia. The pessimistic casesuggests next to no progress at all. Given an unchanged distribution, thepoverty headcount ratio is estimated at 16.82% and the world is stillhome to 1.2 billion extremely poor people. Even if growth rates in subSaharan Africa were to double relative to the post-2000 trend, the global
poverty rate in 2030 is still projected to be 6.50% with pro-poor growth,
7.67% with distribution-neutral growth, and 8.81% with pro-rich growth.
All of these estimates imply that it will take considerably longer than 2030
to lift the remaining 1.2 billion people out of poverty. The good news is thatby 2030 extreme poverty in Europe and Central Asia, East Asia, Latin73

74
neutral

20.44
24.47
32.61
16.47
254.4
725.031076.35

0.94
5.17
4.59
3.72
12.76
51.75
14.96

28.00
27.16
38.77
21.15
318.89772.26
1209.95

1.29
5.74
5.46
4.77
15.99
55.12
16.82

neutral

37.87
30.45
46.05
26.83
394.16819.221359.23

1.74
6.44
6.48
6.05
19.77
58.47
18.89

pro-rich

Pessimistic (1980-2000)

pro-rich pro-poor

Change in Inequality (Gini)

pro-rich pro-poor

Panel (a) – Headcount at $1.25 a day in 2030 (in percent)
0.65
0.93
1.31
0.76
1.07
1.48
0.12
0.16
0.21
1.21
1.45
1.71
2.27
2.74
3.28
3.46
4.12
4.91
0.48
0.66
0.91
1.54
2.07
2.75
4.19
5.54
7.24
8.48
10.89
13.79
32.09
35.69
39.37
43.62
47.17
50.70
7.88
9.11
10.49
11.63
13.2
14.96
Panel (b) – Poor population at $1.25 a day in 2030 (in millions)
14.05
20.23
28.56
16.59
23.29
32.22
0.59
0.76
0.97
5.72
6.86
8.11
16.15
19.44
23.32
24.61
29.3
34.86
2.12
2.94
4.04
6.84
9.18
12.18
83.47
110.38144.35169.13217.08275
449.54499.97551.61
611.15660.85710.32567.2
655.36754.95836.34
949.491076.37

neutralCentral (1980-2010)

Notes: Authors’ calculations. Population projections are from the World Bank’s Health, Nutrition and Population Statistics database. The different scenarios are
estimated using the fractional response approach outlined in Bluhm et al. (2013) and the survey data reported in the World Bank’s PovcalNet database.

East Asia and Pacific
Europe and Central Asia
Latin America and Caribbean
Middle East and North Africa
South Asia
Sub-Saharan Africa
Total

East Asia and Pacific
Europe and Central Asia
Latin America and Caribbean
Middle East and North Africa
South Asia
Sub-Saharan Africa
Total

pro-poor

Optimistic (2000-2010)

Average PCE Growth

Table 3� Projected poverty headcount ratios and poor population at $1�25 a day in 2030, by region

Chapter 2

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

America, and Middle East and North Africa may virtually disappear(projected to be less than 5% in most forecasts). However, we predict a strong
increase in the (relative) share of global poverty located in sub-SaharanAfrica, which suggests that a non-trivial fraction of extreme poverty may beconcentrated in ‘fragile states’. Whether these countries will overcome civil
strife, political instability and corruption will ultimately decide whether
there is a lower bound at which extreme poverty will continue to exist.
Gradual changes in inequality raise or lower the overall headcount in
between 1.2 and 2.1 percentage points and account for about 100 million
poor people more or less. Contrary to suggesting that inequality does
not matter (we only assume slow changes), this finding hints at two
crucial points. First, if the developing world as a whole is to truly
maintain the impressive record in poverty reduction of the last decades,
then this requires both sustained high growth at the level experienced
since 2000 and improvements in distribution. Second, any systematic
worsening of within-country inequality, particularly in large andlargely poor countries like India or Nigeria, will reinforce the slowdown
and thus more strongly decelerate the global rate of poverty reduction.
Readers may wonder why these results are so different from the
projections reported in Ravallion (2013). Our results differ mainly because
Ravallion (2013) uses the average growth rate of the developing world toproject poverty in countries with very different track records, while we use
country-specific average growth rates. Otherwise there are only minor
differences in the data used and our method closely approximates results
obtained using PovcalNet. Ravallion (2013) calculates that a PCE growthrate of 4.5% per year may bring the global poverty rate down to 3% by 2027.
However, he makes the (in our view implausible) assumption that all
developing countries will continue to grow equally fast at this common
rate of 4.5%. Likewise, the linear projection of the global poverty rate ontime ignores all issues of aggregation and provides an overly optimistic
picture of the medium-term future.20 Yet composition matters, even if weincorporate the optimistic assumption that the post-2000 trend willcontinue. The average hides that rapid growth is less likely in somecountries than in others. As we have shown, this has direct consequences
20. We do not mean to imply that Ravallion (2013) is not aware of the aggregation issues. In
fact, he uses PovcalNet precisely to confirm that his ‘optimistic scenario’ is possible once theintrinsic non-linearity of the poverty-income-inequality relationship is accounted for. Our
point is rather that he envisions “the best possible world” to be used as a benchmark for
future progress while we also focus on other, more likely, scenarios.

75

Chapter 2

for when a slowdown will be observed and how strong the deceleration
will be. However, even if we assume a uniform growth rate for all
developing countries, a deceleration appears sooner or later within thenext two decades (although it may actually be preceded by a brief
acceleration if we assume growth rates in excess of 5% p.a.). It is comforting
that, in line with Ravallion (2013), our method implies that if consumption
in the entire developing world grew at a distributional-neutral pace of 7.6%
per year, then extreme poverty would indeed virtually disappear by 2030
(fall to 1.1%). The 3% target can be reached with a uniform distributionneutral growth rate of approximately 5.5% per year.21
We repeat this exercise at the $2 a day poverty line. The results arereported in Table 4 and Figure A-1 in the Appendix. Interestingly, thelinear projection is a much better approximation of progress at the $2
poverty line than at the $1.25 poverty line. This is not due to a slower
historical poverty reduction record: a regression of the global poverty rate
at $2 a day on time also yields a slope of approximately one percentage
point per year. However, the composition of countries (or people) near the$2 a day poverty line in 2010 is more reminiscent of its $1.25 counterpart in
the early 2000s. At the start of the decade in 2010, the total $2 poverty rate
is 40.67% – roughly double the $1.25 poverty rate. Fast growing East Asiaand moderately fast growing South Asia still make up more than half ofglobal poverty, implying that progress in these two regions will have alarge effect on the overall poverty headcount.
Our moderate growth scenario predicts that about 1.87 billion people(26%) live on less than $2 a day in 2030 versus about 2.4 billion people in2010. Considerably greater gains are possible. Global poverty at the $2
line falls below 20% in the optimistic distribution-neutral and pro-poor
scenarios. If this occurs in 2030, then more than one billion people will
have left poverty at the $2 a day line – undeniably a remarkableachievement. In most scenarios we also observe a slowdown at the $2 a
day line but this slowdown tends to occur later and is less pronounced
than at the lower threshold. In the most optimistic scenario, the rate ofpoverty reduction actually accelerates somewhat to about 1.16
percentage points per year, while the moderate growth scenario gives atrend of 0.73 percentage points per year over the projection period.
Examining the regional distribution, we find that poverty in East Asia is
21. Interestingly, a recent working paper by Yoshida et al. (2014), independently and using
different methods, comes to very similar conclusions.

76

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

likely to fall to around 5% by 2030, down from 29.7% in 2010. Nearly
everyone in East Asia will have entered the middle class (by developing
country standards), but this forecast partially hinges on fast growth inChina. In fact, some observers suggest that there is reason to believe that
China runs a non-negligible risk of falling into a ‘middle-income trap’(Eichengreen, Park, and Shin 2013) which might make it harder toachieve less than 10% poverty at $2 a day by 2030.22 Progress in South
Asia is also likely to be rapid. According to our moderate growthestimate the expected poverty rate is 35.9% in 2030, implying about 716
million poor, down from 66.7% and about 1.1 billion poor in 2010. In the
optimistic pro-poor growth case, the headcount ratio falls by about one
third to less than 20% and the number of poor decreases to less than 400
million. As a stark contrast, the $2 a day poverty rate in sub-SaharanAfrica is expected to remain very high. Our moderate growth scenario
predicts a poverty rate of about 66%, down from 69.9% in 2010, which atcurrent population projections implies almost one billion poor in subSaharan Africa alone. Even in the optimistic distribution-neutral growthscenario, we project a poverty rate of about 55% and more than 750
million poor. This is underlined by the analysis in the preceding section
where we suggest that the mass of the consumption distribution is far tothe left of the $2 a day poverty line in 2010 for most of the subcontinent.
Poverty alleviation in sub-Saharan Africa remains the primarydevelopment challenge of the first half of the 21st century.

4 Conclusion and policy recommendations
The main contribution of this paper is to forecast global poverty rates until
2030. To set the stage, we first highlighted that there is a fundamental
uncertainty about the precise levels of extreme poverty. For the sake ofcomparison, we selected the standard poverty lines of $1.25 and $2 a day(in 2005 PPPs). We then discussed a robust set of global poverty andinequality trends. The global MDG of halving the 1990 extreme poverty
level was reached in 2010 but this apparent progress hides substantial
regional heterogeneity. Most of the global success was driven by rapid
growth in China. Inequality among the citizens of developing countries
and between developing countries was declining until 2005, while average
within-country inequality was rising steadily until 2010.
22. However, our estimates suggest that this would require an exceptionally large
slowdown. For poverty in East Asia to remain above 10% at $2 a day, growth needs to be lessthan half of the 2000-2010 trend.

77

78
neutral

95.44
44.25
60.90
57.02
804.56993.712065.12

4.39
9.35
8.57
12.86
40.35
70.93
28.71

115.26
48.63
70.99
65.95
897.121027.192235.43

5.31
10.28
9.99
14.88
45.00
73.32
31.07

neutral138.11
53.50
82.42
75.91
991.461059.592412.3

6.36
11.31
11.60
17.12
49.73
75.63
33.53

pro-rich

Pessimistic (1980-2000)

pro-rich pro-poor

Change in Inequality (Gini)

pro-rich pro-poor

Panel (a) – Headcount at $2 a day in 2030 (in percent)
3.79
4.61
5.55
4.03
4.90
5.90
0.45
0.56
0.69
2.80
3.13
3.49
4.00
4.73
5.59
6.29
7.39
8.66
2.85
3.55
4.39
7.20
8.62
10.25
19.83
23.12
26.74
31.60
35.88
40.39
51.62
54.56
57.46
63.67
66.36
68.98
17.36
19.23
21.24
23.73
25.94
28.26
Panel (b) – Poor population at $2 a day in 2030 (in millions)
82.42
100.2
120.61
87.49
106.39128.23
2.12
2.63
3.26
13.24
14.79
16.49
28.39
33.63
39.73
44.71
52.52
61.50
12.64
15.74
19.47
31.94
38.22
45.46
395.33461.03533.21629.97715.46805.25723.19764.47805.00892.01
929.76966.481249.19
1383.61
1528.081707.21866.01
2033.35

neutralCentral (1980-2010)

Notes: Authors’ calculations. Population projections are from the World Bank’s Health, Nutrition and Population Statistics database. The different scenarios are
estimated using the fractional response approach outlined in Bluhm et al. (2013) and the survey data reported in the World Bank’s PovcalNet database.

East Asia and Pacific
Europe and Central Asia
Latin America and Caribbean
Middle East and North Africa
South Asia
Sub-Saharan Africa
Total

East Asia and Pacific
Europe and Central Asia
Latin America and Caribbean
Middle East and North Africa
South Asia
Sub-Saharan Africa
Total

pro-poor

Optimistic (2000-2010)

Average PCE Growth

Table 4� Projected poverty headcount ratios and poor population at $2 a day in 2030, by region

Chapter 2

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

The changing composition of global poverty has profound implications
for the medium-term future. After 2010, fast growing East Asia willcontribute less and less to global poverty reduction, while the share of
the global poor residing in sub-Saharan Africa and South Asia willcontinue to rise. All of our projections show that the global rate ofpoverty reduction at $1.25 a day will slow down markedly between 2020
and 2025. None of our nine scenarios predicts a poverty rate near zero by2030. This stands in stark contrast to earlier studies and the ‘3% by 2030’
target recently announced by the World Bank. The Bank’s target can
only be reached if we make the unrealistic assumption of equally rapid
growth in all developing countries. Once country-specific growth rates
are used, even our most optimistic scenarios suggest a poverty rate of
between 7.9% and 10.5%, depending on the evolution of inequality. At $2
a day, the slowdown will occur much later and remarkable gains are
possible if the post-2000 growth trends continue. An optimistic estimate
suggests that the $2 a day poverty rate may fall below 20% by 2030,
implying one billion fewer poor people than in 2010.
We propose two new ‘twin targets’ on the basis of these findings. An
aspirational but realistic benchmark for progress would be to “reduce theproportion of the population living below $1.25 to 8% by 2030 and reduce theproportion of the population living below $2 a day to 18% by 2030.” Both ofthese targets are firmly anchored in our optimistic pro-poor growthscenarios. The $2 a day poverty line should receive more attention in the
future to better track continued progress in East Asia and, later on,
South Asia. Partly for the same reason, China has recently raised its own
national poverty line to about $1.80 a day.
These targets can be reached in a variety of ways but not only through acontinuation of the current path. They will require either an additional
acceleration of growth in poorer countries, or improvements indistribution. Reversing the trend of rising within-country inequalities
would speed up the pace of poverty reduction and still ensure progress
at more moderate growth rates. The returns to redistribution are
increasingly high in East and South Asia, and remain relatively large in
Latin America. However, in some regions growth takes precedent.
Rapid poverty alleviation in sub-Saharan Africa still requires asignificant and sustained acceleration in consumption growth.

79

Chapter 2

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Appendix A
Table A-1� World Bank poverty estimates by region, 1981 to 2010 (selected years)
Year
1981
1990
1999
2005
2010
Panel (a) – Headcount ratio at $1.25 a day (in percent)
East Asia and Pacific
77.18
56.24
35.58
17.11
12.48
China
84.02
60.18
35.63
16.25
11.62
Europe and Central Asia
1.91
1.91
3.79
1.33
0.66
Latin America and Caribbean
11.89
12.24
11.86
8.66
5.53
Middle East and North Africa
9.56
5.75
5.01
3.45
2.41
South Asia
61.14
53.81
45.11
39.43
31.03
India
59.83
51.31
45.62
40.82
32.67
Sub-Saharan Africa
51.45
56.53
57.89
52.31
48.47
Total
52.16
43.05
34.07
25.09
20.63
Panel (b) – Poor population at $1.25 a day (in millions)
East Asia and Pacific1096.5926.42 655.59 332.08 250.9
China
835.07 683.15 446.35 211.85 155.51
Europe and Central Asia
8.21
8.87
17.83
6.26
3.15
Latin America and Caribbean
43.33
53.43
60.1
47.6
32.29
Middle East and North Africa
16.48
12.96
13.64
10.47
7.98
South Asia
568.38617.26619.46 598.26 506.77
India
428.68 448.34 472.74 466.3
400.08Sub-Saharan Africa
204.93 289.68 375.97 394.78 413.73
Total
1937.83 1908.45 1742.53 1389.2 1214.98
Total excl. China
1102.76 1225.3 1296.18 1177.35 1059.31
Notes: Based on PovcalNet and Chen and Ravallion (2010, 2013).

82

Poor Trends: The Pace of Poverty Reduction after the Millennium
Development Agenda

Table A-2� Population-weighted regional PCE growth rates over various periods

East Asia and Pacific
Europe and Central Asia
Latin America andCaribbean
Middle East and North
AfricaSouth Asia
Sub-Saharan Africa
Overall average

2000–
2010
5.906
(0.813)6.085(0.989)2.444(0.239)3.495(0.443)4.448(0.489)2.382(0.689)4.544(0.152)
123

1990–
2010
5.772
(0.653)2.755(0.412)2.219(0.140)2.532(0.440)3.612(0.388)1.419(0.470)3.809(0.132)123

Period
1980–
2010
5.598
(0.725)2.558(0.411)
1.445(0.098)1.851(0.293)3.179(0.351)0.698(0.472)0.132(0.114)
123

1980–
2000
5.377
(0.677)-0.769(0.916)0.677(0.171)
0.495(0.545)2.173(0.284)-0.818(0.540)2.565(0.161)
122

1990–
2000
5.608
(0.508)-1.225(1.027)1.931(0.337)1.253(0.648)2.511(0.294)0.016(0.688)2.862(0.225)122

83

Chapter 3

Chronic Poverty in Rural Cambodia:
Quality of Growth for Whom?

Ippei Tsuruga

1. Introduction
For the past decade, policy makers and researchers have paid great
attention to pro-poor growth, focusing in particular on what types ofgrowth would decrease poverty. With the post-2015 era approaching
and with smaller poverty headcounts compared to the past, debates
surrounding poverty have started seriously considering the quality ofgrowth to eliminate extreme poverty in the coming decades, rather thanjust its decrease. Zero poverty cannot be realised without tackling
structured poverty. The Chronic Poverty Report (Shepherd et al. 2014)
calls for the implementation of a comprehensive set of protective andpreventative measures for those living in chronic poverty, or thosemoving in and out of poverty over time due to limited capacities. The
paradigm shift from reduction to elimination requires future growth tobe aware of whose poverty counts, or quality of growth for whom. Highgrowth and consumption increases are likely to benefit many of thepoor, but what of the chronic poor who structurally remain in long-term
poverty. One of the critical questions concerns the effects of past
growth, particularly in terms of structured poverty, and implications forthe quality of growth in the new era.
Taking the case of Cambodia, this paper aims to assess the effects of past
growth on the chronic poor by estimating the remaining population inchronic poverty, and analysing the structural characteristics that keepthem in poverty indefinitely. Cambodia still faces significant challenges
in its poverty reduction policy. After the devastating destruction ofphysical, social and human capital throughout the Pol Pot regime andthe following period of unstable recovery, the country is finally enjoying
steady development. With a favourable macroeconomic environment,
the country achieved a dramatic improvement in consumption by the85

Chapter 3

poor between 2004 and 2010. With more people emerging from poverty,
there are more people concentrated near or just above the poverty line.
The latest poverty assessment has emphasised the need to prevent thesepeople from falling back into poverty (World Bank 2013a). Despite
experiencing an excellent pro-poor growth period, households withcertain attributes still live in poverty. What is missing in the poverty
discussion in Cambodia, mainly due to lack of data and analysis, is thecritically important focus on chronic poverty as well as the transientpoor.
This paper attempts to make two major contributions: one is to fill the
research gap on chronic poverty. Recent works by the Cambodia
Development Resource Institute (CDRI) are the only widely publishedstudies that have assessed persistent poverty (Tong 2012a, 2012b, 2012c).
The second is to provide an estimated chronic poverty headcount withlocally meaningful multidimensional criteria. The research by Tong
provides a better understanding of chronic poverty througheconometric analysis using panel data but has limited data coverage. In
the absence of nationwide panel data, I estimate a nationally
representative figure. The findings of this study could potentially be of
benefit in identifying and targeting programmes for the chronic poor.

2. Growth and poverty in Cambodia(1) Pro-Poor Growth and Distribution
This section assesses the extent to which pro-poor growth has beenachieved from the following perspectives. The definition of pro-poor
growth in this study is simply “growth with poverty reduction,” aswidely adopted in the development community (DFID 2004). Pro-poor
growth can be achieved in several ways, such as income growth (Dollar
and Kraay 2001, Kraay 2006), distribution change (White and Anderson
2001; Ravallion 2004) and the favourable sectoral pattern of growth(Eastwood and Lipton 2000).
Quantitative data for poverty analysis has been derived from a series of
Cambodia Socio-Economic Surveys (CSES). Following the SocioEconomic Surveys of Cambodia (SESC) conducted in 1993 and 1996,
CSES was initiated in 1997, and data is available from 1997, 1999, 2004,
and every year from 2007 onwards. Since 2004, the questionnaire has
been improved to provide more information. The sample size was 12,000
86Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

households in 2004 and 2009, and 3,600 households in the other years.
National poverty indicators have been calculated using CSES. In April
2013, the Cambodian government modified the official calculation
method of consumption aggregation and redefined national poverty
lines based on CSES 2009 (Cambodia. Ministry of Planning 2013). The
food poverty line is now calculated based on an equivalent foodconsumption of 2,200 K-calories per person per day. The total poverty
line is calculated by adding an allowance for non-food items to the foodpoverty line. Taking price differences into consideration, separate
poverty lines are defined in three geographic areas: Phnom Penh (KHR
6,347), other urban (KHR 4,352), and rural (KHR 3,503) areas.
Applying the new method and adjusting poverty lines for inflation,
Table 1 shows estimated poverty indicators. All the poverty indicatorspresent a very positive improvement between 2004 and 2010. The
poverty headcount ratio dropped significantly from 62.82 percent to20.02 percent at the poverty line and 31.67 percent to 3.38 percent at the
food poverty line. In terms of depth and severity of poverty, poverty
indicators show a large improvement across the nation as well. The
poverty gap dropped from 22.38 points to 4.17 points, whilst thesquared poverty gap followed the same trend from 10.34 points to 1.32
points.

87

Chapter 3

Table 1. Poverty Estimation
Indicatorp0

p1

RegionFood Poverty Line

Poverty Line

Obs.

2004

2010

2004

2010

2004

2010

Other Urban

22.89

2.70

53.50

15.90

8,685

2,938

Rural

35.50

3.83

66.61

21.54

45,25810,011

Phnom Penh

6.88

0.38

39.09

11.93

5,909

3,561

Cambodia

31.67

3.38

62.82

20.02

59,852

16,510

Other Urban

5.96

0.45

19.02

3.36

8,685

2,938

Rural

8.98
1.79

0.05

12.05

2.63

4.47

45,25810,011

Cambodia

8.03

0.55

22.38

4.17

59,852

16,510

Other Urban

2.30

0.10

9.03

1.12

8,685

2,938

Rural

3.27

0.14

11.05

1.40

45,25810,011

Phnom Penh

0.75

0.01

5.34

0.82

5,909

3,561

Cambodia

2.95

0.12

10.34

1.32

59,852

16,510Updated Other Urban

1,774

2,694

2,962

4,498

–

–

povertylines(riels)

Rural

1,565

2,377

2,384

3,620

–

–

Phnom Penh

2,124

3,226

4,319

6,559

–

–

Phnom Penh
p2

0.62

23.94

5,909

3,561

Region (Rural Only)

Headcount ratio(%)

Regional Share
(%)

2004

2010

2004

2010

2004

2010

Mekong Plain

63.92

18.02

54.95

43.57

26,5485,485

Tonle Sap

69.29

16.66

26.8

21

11,384

2,659

CoastalNorth and Northeast
MountainCambodia

58.22

17.99

5.64

5.58

2,840

665

79.83

45.63

12.61

29.85

4,486

1,202

66.61

21.54

100

100

45,25810,011

Obs.

Source: Author’s calculations based on CSES
Note: Poverty lines have been adjusted by consumer price index (CPI) in Phnom Penh equally acrossthe regional poverty lines because the regional CPI breakdown is not available. Fixing the averaged
CPI October/December 2009 as 100 points, CPI for the other reference years was 0.68 for 2004 and 1.03
for 2010 (Carpenter 2012, National Institute of Statistics). All presented indicators have been assigned
population weights provided by each survey, calculated based on General Population Census 1998
and 2008 respectively for CSES 2004 and 2010. Poverty indicators are calculated based on the FosterGreer-Thorbecke method (1984).

88Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

This trend is likely a result of the favourable macroeconomic
environment and growth pattern. Between 2004 and 2010, even during
the global financial crisis, the country enjoyed 6.17 percent annual
growth in GDP per capita. Sectoral growth took place almost evenly inagriculture, which most poor people rely on, at 6.81 percent, as well as
manufacturing and services, at about 8 percent (Appendix 1). The
consumption growth of the poor was higher than that of growth rate inmean and at median in all regions, increasing annually by 9.11 percent
in Phnom Penh, 11.66 percent in other urban and 10.34 percent in rural
areas (Appendix 2). The growth incidence curve clearly indicates thatgrowth and distribution patterns were pro-poor in the rural settings.
Moreover, human development has improved substantially. The net
enrolment ratio presents an upward trend at all levels, primary, lower
secondary and upper secondary whilst school attendance and adultliteracy follow similar trends. Child mortality has also improved interms of neonatal, infant and under-5 age groups. Overall, thequantitative data both on macro and micro confirm that the country has
achieved pro-poor growth.
Looking closely into the socioeconomic data in 2010, geographic
distribution and economic activities illustrate poverty characteristics
further. All poverty indicators are noticeably higher in rural regions
than urban settings. Rural poverty was 21.54 percent whilst urban
poverty was even lower, 11.93 percent in Phnom Penh and 15.9 percent inother urban areas. Although the difference in poverty rates between
rural and urban areas might be seen as small, it is significantly different
in absolute terms. In spite of increasing urbanisation, rural areas are stillhome to 80 percent of the population and over 86 percent or 2.3 millionof the poor. This geographic distribution correlates with the trends ineconomic activities: 72 percent of poor people depend on agriculture1 fortheir livelihoods in Cambodia, and the rate increases to 78 percent inrural settings. Of the poor agrarians, crop farmers account for 80
percent. Although agricultural labourers, livestock farmers, forestry
workers, fishery workers and hunters constitute a minority (12 percent),
these groups contribute a higher share to poverty (18 percent) than their
population share. Poverty rates are relatively higher among them too. In
rural areas, 33 percent of fishery workers and 40 percent of agriculturallabourers live below the poverty line. On the other hand, urban poverty
1 Main economic activities of households are defined by time that household members
spend on particular sectors. A detailed definition is discussed later.

89

Chapter 3

shows quite different patterns in economic activities, with 95 percent of
the poor in Phnom Penh and 52 percent in other urban areas working insecondary or tertiary sectors.
To sum up, Cambodia seems to have achieved pro-poor growth between
2004 and 2010. Both macroeconomic environment and humandevelopment progress were steadily positive, consumption growth ofthe poor increased more than that of other socio-economic groups, andpoverty indicators dramatically improved.
(2) Existing Literature on Chronic Poverty
Quantitative regional studies provide fruitful insights on chronic
poverty. Tong (2012a) conducted a dynamic poverty study using CDRI
panel data of 793 households, collected in 2001, 2004/05, 2008 and 2011,
from nine villages in seven provinces in four geographic regions,
including Tonle Sap, Mekong Plain, Plateau and Coastal areas. The data
include information on household demographics, consumption, asset
ownership and economic activities. The study applies principal
component analysis to construct a wealth index from mixed asset
ownership for ranking households (Filmer and Pritchett 2001).
Households always below the set poverty line (40th percentile or 60th
percentile) are regarded as the chronic poor. The study finds that mostpoverty was transient during the period. It also finds the following
characteristics of the chronic poor households. Compared to non-poorhouseholds, the chronic poor households are likely smaller and havemore children under-six years, fewer adults aged 15-64, and thehousehold heads tend to be younger, less educated, female and single.
They likely lack agricultural land, non-land assets, livestock, andconnection with their community than other households.
Using the same dataset, Tong (2012b, 2012c) reassesses chronic poverty
with consumption measurements to compare results. It confirms all the
major findings above except for household size and age of household
heads. While the asset approach finds that the chronic poor tend to be insmaller households and their heads are likely young, the consumption
approach finds larger households and no significant trends in head’s
age among them. There is no further analysis of these particular
contradictions.
These findings provide a valuable foundation for understanding chronic
90Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

poverty in Cambodia. In relation to the previous studies by Tong, thispaper potentially makes an important contribution by confirming someof his major findings from a different approach and at the national level.
There are certain differences in methodologies and scopes. For example,
while Tong’s studies cover households with all the occupations in theselected areas, this paper limits its scope of analysis to agrarians in rural
areas across the nation. While Tong’s study uses consumption poverty
and a wealth index, this study defines chronic poverty by local
perspectives through PPA as discussed later.

3. Methodology for identifying chronic poverty
The methods for measuring the persistence of poverty have beendisputed. In contradistinction to transient poverty, chronic poverty iscommonly defined by poverty over long durations and regarded asintergenerational transmission of poverty through transferred capital orassets (Hulme and Shepherd 2003). For the identification of chronic
poverty in practice, one of the most common approaches is quantitative
assessment using a set of panel data (McKay and Lawson 2002; Haddadand Ahmed 2003; Wadugodapitiya and Baulch 2011). Comparing income
or consumption of the same households over time, it provides
informative analysis with figures. On the other hand, Hulme, Moore
and Shepherd . (2001, 34) argued that monetary measurements cannot
fully reflect the complexity of chronic poverty; therefore such analyses
need to take into account the multidimensional characteristics ofchronic poverty and can benefit from qualitative or subjective
assessment by poor people themselves. White (2002) also argued that
productive synergy can be established between them in poverty
analyses.
Building upon these ideas, Howe and McKay (2007) developed an
innovative framework for identifying chronic poverty by combining
both quantitative and qualitative approaches. They pointed out thatpanel data analysis provides a narrow understanding of chronic
poverty within the capacity of data availability or questionnaires,
although it provides the numeric results that policy makers prefer tohave for decision making. It can also assess relatively short spans and isusually sensitive to measurement errors. They also acknowledge thepros and cons of a qualitative approach. It provides narrative
information based on rich local knowledge and experience that is91

Chapter 3

usually missed by purely numerical methods. Data can be ambiguous
and therefore difficult for policy makers to use the results, as there arefewer objective figures and macro perspectives. To overcome thelimitations and maximise the advantages, they innovated by combining
the methodologies. The major value of the resulting methodology is thatit does not require panel data – instead, it uses cross-sectionalsocioeconomic data at a single point in time and qualitative informationcollected through participatory poverty assessments (PPA). They
proposed undertaking several steps of analysis: firstly, selecting criteriaof chronic poverty defined by the poor people themselves in the PPA,translating those qualitative criteria onto nationally representative
household data, and finally checking the robustness and sensitivity ofthe estimation.
As Howe and McKay (2007) acknowledge, the sustainable livelihood
approach (SLA) is a useful framework to understand qualitative
information in a vicious poverty cycle at the stage of criteria selection(Ellis 2000, 2006). In SLA, livelihood is conceived as a cycle of three main
components: assets, activities and outcomes. Assets consist of five or
more types such as human capital (skills, education, health), physicalcapital (goods), financial capital (savings, access to loans), natural capital(land, water, forest), and social capital (kinship, friendship). Households
are considered to mobilise those assets to produce outcomes throughdifferent types of economic activities, and invest the outcomes toaccumulate assets again. In terms of vulnerability in SLA, households
are considered to utilise assets to practise risk management and coping
strategies. If they manage the sequence successfully, they are able tobuild up assets. If unsuccessful, they deplete assets. Institutions andpolicies are involved in the framework to reduce vulnerability. In
relation to chronic poverty, destitute households may live in a vicious
cycle of asset-activity-outcome.
In this study, I adopt the approach of Howe and McKay (2007) to identify
chronic poverty. The approach is relevant for this study in two reasons.
The first reason is data availability. Cambodia does not have nationwide
panel data but socioeconomic survey data and the results of PPA are
available. Secondly, the methodology allows estimating the chronic
poverty headcount at two points in time, enabling analysis of the extent
to which pro-poor growth benefits the chronic poor over the period. As
Howe and McKay admit, this is not the most rigorous way to estimate
92

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

chronic poverty and it has a tendency to underestimate the population
but it is still considerably useful in assessing chronic poverty given theabsence of panel data.

4. Qualitative insights into chronic poverty in Cambodia
This section reviews qualitative information to identify thecharacteristics of chronic poverty. The qualitative information is derived
from the PPA conducted across the nation by the Asian Development
Bank between October and December 2000 (ADB 2001). The PPA
compiled local voices through focus group discussions (FGDs),
formulated in geographically targeted poor regions based on
quantitative surveys (National Population Census and CSES) andselection by local authorities, community or village members andnongovernmental organisations. Locally selected poor people
participated in 169 FGDs in 154 villages in 70 communes in all 24
provinces and in additional 15 urban areas. The regional share of FGDs
was 47 percent in the Mekong Plain, 29 percent in Tonle Sap, 12 percent
in the North and Northeast Mountain regions, and 12 percent in coastal
areas. The participants included a variety of vulnerable groups: women,children, rural farmers, fisher folk, ethnic minority groups, femaleheaded households, demobilised soldiers, orphans, street children, sex
workers, plantation workers, garment workers, garbage collectors andcyclo-drivers. Females and ethnic minorities accounted for over 50
percent and 13 percent respectively. The PPA paid particular attention to
the process to have real voices from those socially weaker groups. For
instance, the team members conducted separate discussions withwomen in situations where they could not openly explore gender issues
in the FGD where men were present.
As designed, the PPA provides deep insights about the livelihoods anddemographic characteristics of poor households. It found that foodinsecurity is a primary concern for all poor households regardless ofregion and ethnic group. Poverty means they spend a large amount oftime looking for food, potentially causing loss of other opportunities
such as participation in village activities. It also found that food foraging
activities are often undertaken by women and children; therefore
children, particularly girls, in poor families potentially have a higher
risk of missing educational opportunities. Most PPA participants
generate their livelihoods through agriculture. Rice farming was listed93

Chapter 3

as the most important economic activity by 83 percent, with market
gardening second at 16 percent, and raising livestock third at one
percent. Some households certainly mixed those activities but very few
households had other supplemental activities for livelihood. Moreover,the PPA found a vicious poverty cycle in relation to asset deprivation. In
rural Cambodia, the poorest families tend to sell assets to cope withmajor shocks like natural disasters, sickness or death of household
members, resulting in low levels of asset ownership. As ownership of
productive land was listed as very important for their lives, the coping
strategy of selling land is certainly not an easy choice for the poor.

94

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Table 2. Household Characteristics Identified in Participatory Poverty Assessment
Category

PoorestKror bamphot:
Extremely poor
Toal: People whohave no way out of
their present
situation

Poor

Household Characteristics

1. Little or no land (0.8-1.2 ha)
2. Perhaps one draft animal but no farming implements
3. Housing made of thatch in very poor condition
4. Few household utensils
5. Live on hand-to-mouth basis (food shortages for up to eightmonths)
6. Much reliance on natural resources to meet subsistenceneeds
7. Accumulated debts and inability to repay or borrow
additional amounts
8. No kinship support
9. Large young families with 5-12 children

1. Less than 2 ha of land in unfavorable locations
2. Usually have at least a pair of draft animals and some farm
Kror: Literally poor
implements
Kror thomada:
3. Houses made of thatch sometimes with tile roof and
Typical poverty
bamboo walls
4. Limited number of household utensils
5. Food shortages for 3-6 months
6. Able to borrow money for rice farming
Lower medium
incomeKror imom:
Reasonably poor
Kandal: Medium
Middle income
Mathyum: Average
Kandal: Medium

Least poor (NonPoor)
Throuthear: Fully
self-sufficient
without any debts;
Neak leu: Living
above poverty

1. Less than 3 ha of land
2. Draft animals and farm implements
3. Houses made of wood or bamboo, thatched roof and walls
and tile roof
4. Limited number of household utensils
5. Food shortages for 3-4 months
6. Able to borrow money for rice farming

1. Land holdings of up to 6 ha
2. 2-4 draft animals, some livestock and all farm implements
3. Houses made of wood with either bamboo or wooden floors
and tile roof
4. Reasonable number of household utensils
5. No food shortages except when major crisis or ritual occurs
6. Limited cash savings
7. Small-scale business
8. Old motorbike or boat

1. More than one hectare of very productive agricultural land
2. At least two draft animals and many other livestock andfarm implements
3. Houses made of permanent building materials, including
corrugated iron and tiles
4. Full food security with limited surplus for lending, sale orlabor exchange
5. Well-furnished households, often with television
6. Able and willing to lend money to other villagers

Source: Summarised by the author based on the PPA (ADB 2001)

95

Chapter 3

The PPA identified five broad livelihood ranks and those characteristics(Table 2), enabling a chronic poverty threshold to be defined. The firstcategory clearly implies chronic poverty. The literal description in local
language is Toal, people who have no way out of their present situation,
and Kror Bamphot, extremely poor. The identified characteristics also
confirm a significant deprivation – namely, lack of food security most of
the time (eight or more months per year), relying on subsistent
livelihoods (living on a hand-to-mouth basis), no productive assets or
kinship support. Spending most months hungry and unable to escapethe situation, those households in this category can be clearly identified
as being in chronic poverty. The second and third categories are too
ambiguous to be regarded as chronic poverty. Households in these twocategories have land at unfavourable locations and limited farming
implements with relatively long term food shortages. The PPA describes
their marginalised situation but does not provide a clear definition or
characteristics of poverty persistence. In order to avoid ambiguity andsubjectivity for selection criteria, this study regards only the firstcategory as chronic poverty.
Reflecting the limitations in coverage of the PPA, the above criteria
potentially underestimate chronic poverty in particular groups. Firstly,
the PPA provides little information about urban chronic poverty.
Although there are some related descriptions such as lack of in-housetoilet, mobile phone, car or motorcycle, or child’s education, it does notlink to those characteristics to poverty persistence. Due to this lack ofclear definition, this study is unable to estimate chronic urban poverty
and therefore the following analysis focuses on rural areas. Secondly,
the PPA does not provide sufficient information about characteristics ofchronic poverty in secondary and tertiary sectors; therefore, the study’s
scope is limited to chronic poverty in agriculture in terms of livelihood.
Given the lack of information, it also potentially underestimates thechronic poor who rely on non-farming agricultural subsectors. For
instance, as resource-based livelihoods are reported to have very
different characteristics from farming (Ballard et al. 2007), households
relying on fishery and forestry would not be rigorously identified inchronic poverty by the single set of criteria. Similarly, chronic poverty
among ethnic minorities cannot be easily identified by the single
selection criteria because each tribe has a variety of perceptions ofpoverty. For example, whilst the Stieng and the Tumpoun recognise lossof cultural identity as a characteristic of poverty, the dominant lowland
96

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Khmer, the Cham or the Vietnamese do not have such perceptions. Stieng
participants stated that they do not even have a term to describe poverty
and would not compare life with others in their culture. A tribal elder of
the Tumpoun in Ratanakiri defines poverty based on situations in which
they would be unable to protect and hand over their land to the next
generation, and they would not be rich even if they had enough money.
Such differences in values cannot be taken into account in this study.
Lastly, on a possible critique for using the PPA conducted over a decadeago, I would argue that the validity can be reasonably confirmed,
because Tong’s work (Tong 2012a, 2012b, 2012c), conducted throughout
the decade after the PPA, also found similar demographic characteristics
of chronically poor households, including lack of agricultural land, nonland assets, livestock, and networks with their community.
In summary, this study defines chronic poverty by the first category of
Table 2 and focuses solely on rural areas. With limited information, thisstudy potentially underestimates chronic poverty among households
who make their livelihood through non-farming activities, the urban
poor and ethnic minorities. Further studies could explore such
categories. Nevertheless, this study is still of value because theproportion of the population in urban settings, forestry, fishery or ethnic
minority groups is relatively small and the vast majority of rural
populations are covered in the following estimation.

5. Chronic poverty estimation
I will now combine the quantitative information and the qualitative data
to estimate chronic poverty. In order to identify the chronic poor, thegeneral principle of criteria selection here is to translate as many local
definitions as possible to household survey data. One critique of thiscombining method is that selected criteria are loosely associated with
PPA results (Shaffer 2013, 49). Therefore, it is crucial for this study to test
the robustness and sensitivity of the estimation result. This section
reviews the descriptive statistics of each dimension that the PPA
identifies, followed by an estimated chronic poverty headcount andfinally, a robustness and sensitivity analysis.
Descriptive Statistics and Discussion on Selection Criteria
Concerning economic activities, 77.34 percent of rural people relied on
97Chapter 3

agriculture in 2004 and 72.87 percent in 2010 (Table 3). The data allows
further breakdown into agricultural subsectors, but the categorisation is
not fully comparable over the two datasets at the subsector level. Amongagrarians in 2010, most of them lived on crop farming (87.53 percent),
while others relied on agricultural labour (7.5 percent), livestock raising(2.37 percent), fishery (1.35 percent) and forestry (0.26 percent).
In terms of asset ownership among agrarians in each survey year
respectively, 58.81 percent and 57.34 percent owned one hectare or less ofland for any agricultural activities such as vegetable gardening, crop
cultivation, livestock raising or private forestry; 42.19 percent and 46.48
percent owned one or no draft animals, which included cattle, buffaloes,
horses and ponies but excluded other types of livestock like pigs, sheep,goats, chickens, ducks or quails; 82.35 percent and 75.44 percent owned
no high value farming implements, such as tractors, bulldozers,
threshing machines, hand tractors, rice mills, or water pumps; 58.49
percent and 57.06 percent lived in houses where the walls or roof are
made from bamboo or thatch.
Some particular dimensions need further discussion in order todetermine selection criteria. Firstly, the PPA found that the chronic poor
tend to have 0.8 hectare to 1.2 hectares of agricultural land, as shown in
Table 4, while also indicating that owning productive cropland is one
criterion for non-poverty. The major difference between these two
descriptions is quality of land. Unfortunately, there is no translatable
quantitative data available to distinguish the quality of land. Therefore,
defining chronic poverty by taking land ownership of between one
hectare and 1.2 hectares potentially includes households withproductive land who are not poor according to the PPA. To avoid theinclusion error, this study takes one hectare as a threshold.
Secondly, the farming implement criterion is disputable. Selecting
households who have ‘no farming implements’, as the PPA indicates,
identifies only 4.61 percent and 0.51 percent in respective years. It may
cause significant underestimation of chronic poverty. On the other
hand, selecting households with ‘a few farming implements’ potentially
identifies those with productive agricultural machines. As the PPA
implies that the poor rely on low productive activities, they are unlikely
to own such modern farming tools. In order to minimise both inclusionand exclusion errors, this study adopts ownership of low productive98

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

farming implements including an animal cart, plough, harrow, rake,hoe, spade, axe or none as a criterion. In other words, households withhigh productive farming implements are excluded from this category.
The number of owned implements is not considered here because the
PPA does not specify the extent of ownership.
Thirdly, the PPA identifies that housing in chronically poor households
is likely made of thatch in very poor condition. Although it does not
specify what types of housing materials are indicated, roofs and walls
are repeatedly mentioned in the other categories. As the household
survey data do not allow division of bamboo and thatch and thosequalities, those two materials, in any conditions, are treated as acriterion in this study. Finally, the other listed characteristics in the firstcategory are not precisely translatable due to either limitation of thesurvey data or the PPA description. They mostly provide richunderstanding of chronic poverty but are insufficient as identification
criteria. A wide range of utensil variables is actually available in thesurvey data but the PPA provides little indication of what types ofdurables the participants meant. Although utensil ranking and assigned
weights can probably be inferred through statistical techniques likeprincipal component analysis (Filmer and Pritchett 2001), the result
would not reflect the self-rated characteristics of the poor people in thisstudy.
Similarly, the narrative description of “live on hand-to-mouth basis” or“food shortages” is not directly translatable into the survey data, butalternatively, consumption data are available. Variables for debt
accumulation and kinship support are not available in the household
survey data. Lastly, the dimension of “large young families with 5-12
children” is too ambiguous to be taken as a criterion and partiallyconflicts with the survey data. The survey data show no families withmore than nine children and very few of them, 1.44 percent in 2010, have
five children or more. This contradiction is probably because children in
the PPA period have grown up and the household size norms havechanged. However, this assumption cannot be verified with theavailable information. Some of these indicators will be used to test
estimation robustness later.

99

Chapter 3

Table 3. Main Economic Activities and Asset Ownership in Rural Cambodia
Main Household Economic Activities (%)

2004

Crop farmers

63.50

Livestock farmers
Forestry workers

Fishery workers or hunters

1.73

77.34

Agricultural labourer
Mixed agriculture

2010

0.19

0.99

5.47
0.99

Non-agricultural activities

22.05

26.73

Obs.

45,25810,011

0.8 ha or less

44.91

45.09

1 ha <
Obs.

41.19

34,786

42.66

None

32.56

37.2

57.81

53.52

1.55

0.78

None

Land (among agrarian) (%)
0.8< & <=1 ha

Draft Animal (among agrarian) (%)
One

Two or above

Obs.

Farming Implement (among agrarian) (%)
High farm implements only

0.60

13.90

9.63

34,786

0.40

12.25
7,317

9.28

7,317

Both high and low value farm implements

16.10

23.77

None

4.61

0.51

Low value farm implements only

77.74

74.93

Obs.

34,786

Wall and Roof made of Bamboo or Thatch

16.50
41.99

43.22

Others (Tiles, Fibrous cement, Concrete etc.)
Obs.

34,772

7,317

Housing Material (among agrarian) (%)
Wall or Roof made of Bamboo or Thatch

41.50

7,317

13.84

42.94

Source: Author’s calculations based on CSES
Note: Main economic activity is defined by share of time that household members spend on eachactivity. The sum of months that household members spend in agriculture is divided by the total sumof months in all occupations to obtain the share of agricultural activity for each household. Then, maineconomic activity is identified in agriculture if the share is 50 percent or above. All the presented data
are population-weighted.

100

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

(2) Selection Criteria and Estimation Result
As discussed above, this study adopts the following criteria for
identifying chronic poverty, and regards households that meet all ofthese criteria as clearly chronically poor according to the local
definition. The identified households would not have sizable-enoughland to harvest sufficient food for household subsistence needs. They
have very limited farming assets to increase the productivity andefficiency of farming activities, although they invest most time andlabour in agriculture throughout the year. The vicious poverty cycle canbe observed in the SLA framework as well. The criteria take into
consideration physical capital (draft animals and farming implements),
natural capital (agricultural land) and human capital (labourers). This
provides a convincing enough picture of the negative spiral of poverty
in the household:• Main household economic activity is agriculture,• Household owns agricultural land of one hectare or less,• Household owns one draft animal or none,• Household owns no high value farming implement, and• Household walls or roof is bamboo or thatch.
Of the total rural population, these criteria identify chronic poverty
rates of 11.53 percent and 11.34 percent in the reference years (Table 4).
Although there are a few variations across different regions, it is notablethat the chronic poverty headcount almost levelled off over thefavourable period for economic growth and reduction in consumption
poverty.
Table 4. Estimated Chronic Poverty Headcount in Rural Cambodia
Region (Rural Only)

Headcount ratio(%)

Regional Share(%)

Mekong Plain

11.04

12.24

54.8

7.39

9.56

2004

Tonle Sap

13.63

North and Northeast
Mountain

11.64
11.53

CoastalCambodia

2010

10.25

2004

2010

Obs.
2004

2010

11,384

2,659

56.19

26,548

5.63

2,840

5,485

30.45

24.53

10.98

10.62

13.64

4,486

1,202

11.34

100

100

45,25810,011

4.13

665

Source: Author’s calculation based on CSES

101

Chapter 3(3) Robustness Analysis
In order to see the robustness of estimation, I compared the other indicatorsof poverty and human development, specified by PPA participants,
between two groups: the chronic poor and non-chronic poor in the same
economic activity at the 95 percent confidence level. The PPA claims that ill
health and education access are major determinants of poverty and foodshortages are important factors in defining chronic poverty. In relation tothese descriptions, there are rich quantitative data available to create
indicators of human development and consumption.
In general, the result shows that the estimation is robust across different
indicators (Table 5). The education indicators of the chronic poor,
including school enrolment, attendance and adult literacy, aresignificantly lower than those of the others in the same economic
activity, except for primary net enrolment ratio in 2010 (for which thedifference is statistically not significant). Looking over time, although
the primary net enrolment ratio has improved equally and is evenslightly higher among the chronic poor, the gap becomes much moreevident in secondary education. Whilst both lower and upper secondary
net enrolment ratio improved remarkably among the non-chronic poor,
the results were different for the chronic poor. Only 8.93 percent ofchronic poor children in the relevant ages go to lower secondary school,
compared to 27.19 percent in non-chronic poverty in the same activity,and 35.53 percent for the non-agrarians. Moreover, the share ofhousehold members who ever attended school and the proportion of
adult members who are able to read and write show considerable
deprivation among the chronic poor.
Similarly, prevalence of illness or injury tends to be slightly higher
among the chronic poor. The share of people who seek advice or carefrom health practitioners is not very different between the groups. Thatis probably because access to health care services improved equally andmost people now seek health care services when they become ill. In
terms of consumption, the estimation is also robust. The result presents
a large proportion of chronic poor identified in the bottom consumption
quintile, 34.32 percent and 32.83 percent, and few in the highest quintile,
5.99 percent and 3.16 percent, respectively; and most of the other chronicpoor are concentrated in second and third lowest quintiles. The
comparison of food consumption also follows the same distribution
pattern. Overall, almost all indicators demonstrate a significant
102

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

difference between the chronic poor and the others both in 2004 and2010. It may provide an indication of the robustness for the estimation.
Table 5. Comparison of Socio-Economic Indicators
2004
Indicators (%)(2)(1)
Non-CP
Chronic
in same
Poor (CP)activity

Net enrolment ratio (ages 6-11)

65.60

Net enrolment ratio (ages 12-14)

Net enrolment ratio (ages 15-17)

5.42

4.54

61.52

72.59

81.39

73.39

11.08

13.92

18.20

16.99

-2.31

-3.75

3.64

64.50

Seek care during the survey period

13.64

11.70

8.75

13.95

10.97

11.76

-1.94

64.36

64.70

5th quintile, Food consumption

8.86

11.21

24.07

9.41

25.40

Food consumption

34.32
5.99

1,351

24.63

24.58
1,459

Total consumption

1,920

2,270

2010
Indicators (%)(2)(1)
Non-CP
Chronic
in same
Poor (CP)activity

1.10

66.07

66.87

27.37

4.67

77.39

Seek care when ill or injured

5th quintile, Total consumption

18.19

64.76

-2.51

13.61

23.22

-9.74

-13.43

12.64

3.42

2,568

350

12.84

All
other

Total

(2) - (1)Diff.

t-value

-5.57

-1.95

Net enrolment ratio (ages 15-17)

2.74

11.72

23.20

13.94

81.31

72.65

14.93

17.38

-3.76

78.05

Prevalence of illness or injury

23.36

18.60

Adult literacy (ages 15+)

56.50

Seek care during the survey period

19.97

Seek care when ill or injured

85.51

1st quintile, Food consumption

32.83

5th quintile, Food consumption

5.54

1st quintile, Total consumption

5th quintile, Total consumption

Food consumption (constant 2004)

Total consumption (constant 2004)

38.46
3.16

1,937

2,978

71.43
16.21
87.18

23.51

35.53

85.94

21.46

18.96

88.36

18.26

79.14

9.75

6.06

-4.76

-3.47

1.68

0.65

19.91

87.30

8.98

4.67

3.69

7.37

-2.92

-9.31

-6.17

19.44

23.91

-15.26

-9.77

8.78

18.81

2,132

27.43

23.94

16.64

3,780

9.88

21.19

9.12

23.20

9.22

1,547

3,761

108

5.38

1,903

84.77

68.30

-1.56

-3.98

2.35

90.48

Ever attended school (ages 5+)

-3.68

-2.74

13.86

81.95
27.19

13.91

22.73

87.51
8.93

1.12

14.85

Net enrolment ratio (ages 6-11)

Net enrolment ratio (ages 12-14)

5.34

12.17

20.51

1st quintile, Total consumption

9.24

t-value

21.34

50.55

1st quintile, Food consumption

74.70(2) - (1)
Diff.

10.26

Adult literacy (ages 15+)

Prevalence of illness or injury

79.72

Total

4.84

2.55

Ever attended school (ages 5+)

74.84

All
other

2,398

4,570

10.75
10.86

2,182

3,904

3.58
5.62

195

802

4.63
9.04
7.39

13.15

Source: Author’s calculation based on CSES

103

Chapter 3(4) Characteristics of Chronic Poverty, Regional Distribution and Key
Factors
The previous sections have shown some key characteristics of thechronic poor – they have limited assets and relatively lower humandevelopment. Table 6 shows additional demographic characteristics.
Chronically poor households are more likely to be headed by femaleswith 31.96 percent, compared to 17.72 percent for the non-chronic poor in
the same economic activities. The proportion of either elderly- or ethnicminority-headed households is not statistically significant. Notably,households in chronic poverty tend to have higher dependency, inparticular child dependency, mainly due to fewer working aged
members, and household size is significantly smaller compared to othergroups. The chronic poor also tend to be younger. The average age ofhousehold heads and members is about two years younger than thenational average.
These results support some of the key findings of the previous studies.
As Howe and McKay (2007) in Rwanda and Tong (2012a, 2012b, 2012c) in
Cambodia found, chronically poor households are likely female-headed
and smaller in rural Cambodia. As Tong also found, the chronic poor
are liable to have fewer adults, younger members and less educatedhousehold heads. From a different approach, this paper confirms thatchronically poor households seem to have structural challenges toaccumulate human capital and make a living with fewer economically
active members and high child dependency.

104

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Table 6. Demographic Characteristics of Chronic Poor in 2010
Indicators(2)(1)
Non-CP
Chronic
in same
Poor (CP)activity

All
other

Total

(2) - (1)Diff.

t-value

HH head: female (%)

32.01

17.53

24.79

21.35

-14.48-4.82

HH head: elderly 65+ (%)

10.27

11.13

11.29

11.06

0.86

0.43

HH head: ethnic minority (%)

4.19

4.95

2.65

4.24

0.76

0.59

HH head: age

43.66

45.81

46.67

45.76

2.15

2.28

HH member: age

24.55

26.82

26.98

26.60

2.26

3.60

HH head: school attainment (year)

3.03

3.99

5.29

4.22

0.96

4.79

Average HH size

3.98

4.65

4.62

4.55

0.67

5.65

Average number of working age

2.27

2.92

2.99

2.85

0.64

8.15

Average number of children 0-14

1.54

1.51

1.41

1.49

-0.03

-0.31

Average number of elderly 65+

0.17

0.22

0.22

0.21

0.05

1.86

Dependency ratio (%)

85.13

73.45

67.18

73.30

-11.68-2.24

Child dependency ratio 0-14 (%)

75.91

62.21

59.02

63.12

-13.70-2.83

Aged dependency ratio 65+ (%)

9.22

11.24

8.16

10.17

2.02

1.02

Source: Own calculations based on CSES

Looking at regional distribution, the criteria seem to capture the chronicpoor better in some regions than in others. Most notably, the criteria
possibly underestimate chronic poverty in the North and Northeast
Mountain region. The consumption poverty headcount ratio was 45.63
percent in the region in 2010, compared to 16.66 percent to 18.02 percent
in the other regions. This regional disparity does not appear on theestimated distribution of chronic poverty, which is almost at the samelevel across the four regions. This estimation gap between the two
measurements was also observed in 2004. One possible reason for theunderestimation is diversity within the region. As discussed later,
estimation in the region is relatively more sensitive to housing and landownership criteria than the other regions. It possibly reflects thediversity of ethnicity, livelihood and concept of value, which
standardised criteria cannot capture. To overcome this potential
underestimation, more information both from quantitative andqualitative sides is necessary.
Regarding key factors, ownership of high value farming implements isthe most influential variable among the four criteria (Appendix 5). With
105

Chapter 3

the highest contribution rate, lack of a high value farming implement
explains chronic poverty most, followed by non-ownership of land, lackof draft animals and type of housing materials. In other words, owninghigh value farming implements has the largest impact on decreasingchronic poverty among the four variables; and worsening housingmaterials to thatch, and loss of draft animals to zero potentially increasechronic poverty most. Moreover, in the North and Northeast Mountainregion, the ranking is clearly different from the others: farming
implements, housing, land and draft animals in order. This implies againthat particular attention needs to be paid to the unique and diverse
characteristics of the Mountainous region in identifying chronic poverty.
(5) Chronic Poverty and Consumption Poverty
In practice, poverty targeting commonly focuses on consumption
poverty; it is useful to know whether consumption poverty can provide
a good proxy for chronic poverty. Comparison between consumption
poverty and chronic poverty shows that measurement based on thenational poverty line cannot capture a large proportion of the chronicpoor identified in this study (Table 7). The total poverty line identifies
only 36.32 percent of the chronically poor. Nevertheless, in terms ofhuman development, the rest of the chronic poor (chronic poor but notconsumption poor) are also greatly deprived. Their education indicators
are as low as the consumption poor in lower and upper secondary
enrolment, school attendance and adult literacy, except for the primaryenrolment ratio. Health indicators show that the chronically poor
become ill slightly more frequently than the consumption poor but
access health care services almost equally. One interesting question forfurther studies at this point is how much the chronic poor spend on
health services. They might be trapped in a vicious cycle of long-term
poverty because of high prevalence of illness and health expenditure.
In addition, some counterintuitive differences in demographic
characteristics are worth noting. Unlike in other low-income countries,
female-headed households are not a particular phenomena among theconsumption poor in Cambodia but more evident amongst the chronic
poor. The chronic poor tend to have smaller families while theconsumption poor have larger ones, although both groups face higher
dependency in common compared to the average.
Inconsistencies between the two measurements likely become greater
when the population below the poverty line is smaller. In fact, the106

87.51
8.93
2.74
68.30
56.50
23.36
85.51
32.01
10.27
4.19
43.66
24.55
3.03
3.98
2.27
1.54
0.17
85.13
75.91
9.22
1,937
2,978
1,152

(A) + (C)Chronic
Poverty
82.90
24.62
10.63
76.59
69.28
19.34
86.87
20.10
10.98
4.82
45.42
26.46
3.82
4.53
2.80
1.52
0.21
75.48
64.59
10.89
2,102
3,655
7,317

Total

Source: Author’s calculation based on CSES
Note: (C) is a group categorised as not being in consumption poverty at the national poverty line but identified as being in chronic poverty by the definition of thisstudy.

Net enrolment ratio (ages 6-11)
Net enrolment ratio (ages 12-14)
Net enrolment ratio (ages 15-17)
Ever attended school (ages 5+)
Adult literacy (ages 15+)
Prevalence of illness or injury
Seek care when ill or injured
HH head: female (%)
HH head: elderly 65+ (%)
HH head: ethnic minority (%)
HH head: age
HH member: age
HH head: school attainment (year)
Average HH size
Average number of working age
Average number of children 0-14
Average number of elderly 65+
Dependency ratio (%)
Child dependency ratio 0-14 (%)
Aged dependency ratio 65+ (%)
Food consumption (constant 2004)
Total consumption (constant 2004)
Obs. (individual)

Indicators (%)

(A)
(B)
(C)
(D)
(A) + (B)Consumption Consumption Non- Consumption Non-Consumption Consumption
Poverty
Poverty
Poverty
Poverty
Poverty
and
but
but
and
CP
Non-CPCP
Non-CP
86.28
71.32
88.64
86.45
75.04
2.33
14.75
14.90
30.65
11.47
2.99
3.95
2.57
13.62
3.74
69.31
68.87
67.73
80.33
68.97
54.63
60.29
57.37
73.77
58.96
21.09
13.59
24.65
19.91
15.42
88.17
80.81
84.21
88.32
83.26
20.67
16.76
36.18
17.68
17.78
2.30
8.79
13.20
11.59
7.10
2.19
8.77
4.93
4.21
7.05
40.50
43.51
44.82
46.25
42.72
21.33
22.47
26.40
27.95
22.19
3.06
3.00
3.02
4.19
3.02
5.37
5.89
3.46
4.40
5.76
2.77
3.13
2.09
2.88
3.03
2.49
2.56
1.19
1.31
2.54
0.12
0.21
0.19
0.22
0.18
122.75109.1070.64
66.42
112.68115.22
99.96
60.77
54.76
103.96
7.53
9.14
9.88
11.66
8.72
1,308
1,297
2,295
2,351
1,299
1,858
1,879
3,617
4,278
1,874
412
1,230
740
4,935
1,642

Table 7. Comparison between Consumption Poverty and Chronic Poverty among Rural Agrarians in 2010

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

107

Chapter 3

consumption poor overlapped 79.44 percent of the chronic poor in 2004,
compared to only 36.32 percent in 2010. It works even better to look at
consumption poverty in relative terms – by adjusting consumption
quintiles, chronic poverty can be identified more successfully. Looking
cumulatively from the bottom, there are 38.46 percent in the lowest
quintile, 65.41 percent in the second and 87.41 percent in the third
bottom quintile in 2010. The result was almost identical in 2004, with
80.35 percent in the third bottom quintile. The national poverty line fails
to identify the majority of the chronic poor but most of them are
identified in the third cumulative consumption quintile.
The challenge of consumption poverty measurement is that it
potentially underestimates the chronic poverty identified in this study,
and that the applied criteria here overestimate it by including better-off
people in consumption term. There is no doubt that consumption
poverty measurement is useful for chronic poverty identification
because households with the most vulnerable demographic
characteristics appear in a group identified both in consumption
poverty and chronic poverty. This study therefore suggests that
consumption based targeting programmes should apply the criteria to
identify chronic poverty in a mutually complementary manner. For
example, this method can be used to identify potentially chronicallypoor households above the consumption poverty line, and to divide theconsumption poor into the persistent poor and the others.
(6) Sensitivity Analysis
There are three types of sensitivity analysis to be considered, including
sensitivity to selection of different criteria, level of identified criteria,
and combination of identified criteria. Testing sensitivity to criteria
selection is irrelevant here because chronic poverty is defined bysatisfying all the criteria that the PPA specifies, so there are no unused
criteria left. The other two sensitivity analyses are tested below.
Firstly, sensitivity to level of identified criteria can be tested through thecomparison of the chronic poverty headcount ratio when changing thelevel of dimensions. There are 16 possible combinations generated by theabovementioned four variables, which have two alternative levels foreach (Appendix 4). The alternative levels are associated with ambiguity
that the PPA leaves as it defines the dimensions with ranges. The resultshows that the hovering trend changes little at the national level no108

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

matter which levels of dimensions are adopted: the chronic poverty
headcount almost stagnates between 2004 and 2010. It also indicates thatthe estimation is most sensitive to the ownership of farming implements
followed by housing materials. Applying a criterion of no farming
implements, the estimation comes up closer to zero for any
combinations. Although the PPA recognises that the chronic poor tend
not to have any farming implements, the household survey result showsthat there are very few farmers who meet this criterion. Adopting
farming implement ownership as a criterion, future research or
targeting policy would need to be careful about the level, which may
have strong effects on project outcomes. Excluding the farming
implement variable, which hides the effects of other criteria, looking byregions reveals interesting tendencies. In the Mekong Plain, theestimation is sensitive to housing criteria. The Coastal and the Tonle Sap
are not sensitive to the level of any of these selection criteria. In the
North and Northeast Mountain region, the estimation is sensitive toboth housing materials and land size.
Secondly, sensitivity to combining method can be tested using thecounting method to examine whether the combination of identified
criteria affect the trend (Appendix 5). In this method, cutoffs are definedfor each dimension, and one point is assigned for each person below thecutoff. The process is repeated for other variables. It is then aggregatedto obtain the total value of deprivation points (A). Then, the headcountratio (H), which is the share of people below a set cutoff (k), is calculated.
The adjusted headcount ratio (M0) is then calculated by the formula H
times A divided by the number of deprivation criteria. In general, choiceof different methods has little effect on the trend over the period. At
almost all cutoff levels from 1 to 4 in every region, particularly at thehigher or stricter cutoffs, little improvement in headcount ratio isobserved (Figure 1).

109Chapter 3

Figure 1. Adjusted Headcount Ratio with Different Cutoffs in Rural Cambodia

Source: Author’s calculation based on CSES

Furthermore, over the different combining methods, the relevance of
the intersection selection may be justified for two reasons: thecomparison with consumption poverty and conceptual framework.
Firstly, only cutoff 4 shows a lower chronic poverty headcount ratio thanconsumption poverty. The headcount rates between cutoff 1 and 3 areeven higher than the consumption poverty headcount both in 2004 and2010. The estimation at cutoff 4 is also close to the estimation of Tong
(2012a), 6 to 10 percent. From this point, the intersection method is likely
to be a more suitable option among four alternative cutoffs. Secondly, in
relation to the SLA framework of a vicious poverty cycle, theintersection method may be more appropriate than applying othercutoff levels. The main question mark for applying the other cutoff
levels is on the determination of selected criteria and assigned weight. In
this study, I attempt to draw locally meaningful definitions and criteria
as strictly as possible, so chosen dimensions must be drawn from the
PPA and be able to delineate a vicious cycle of poverty with the chosen
criteria. From this point, with the PPA information, there are no reasons
to justify the application of other cutoffs and weightings. Hence, theintersection method may be the most relevant combining method withgiven information availability.

6. Conclusions and Policy Implications
Despite the achievement of pro-poor consumption growth, this study
110Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

concludes that chronic poverty in rural Cambodia, based on criteria
defined by the poor, barely improved between 2004 and 2010. The result
implies that rapid economic growth has certainly raised theconsumption of chronically poor households by 43 percent in food and55 percent in total, but done little to help them accumulate productive
assets and human capital to break structural constraints of persistentpoverty.
Regarding policy implications, one of the major findings is that
consumption measurement based on the national poverty line cannot
identify a majority of the chronic poor. In other words, targeting
programmes or poverty analysis based on the poverty line would
potentially ignore the chronic poor, which may result in them being left
behind in the country’s development process. More concretely, when thegovernment attempts to implement social assistance, social insurance
and public works to reduce poverty and vulnerability under theumbrella of the National Social Protection Strategy (Cambodia. Royal
Government of Cambodia. 2011), its targeting mechanisms largely rely
on consumption measurement. The application of defined criteria in thisstudy may help the programmes related to the strategy to identify thechronic poor. Furthermore, the findings show that the consumption
poor and the chronic poor have a lot of similar characteristics but somedifferences, such as household size and the sex of the household head.
As the social protection strategy is expected to play a key role in ending
poverty in Cambodia, these features of chronic poverty should beunderstood in order to implement programmes more effectively.

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Appendix 1. Macroeconomic and human development indicators
Macroeconomic Indicators

2004-2010

Agriculture, ave. annual growth (%)

6.81

Manufacturing, ave. annual growth (%)

7.96

Industry, ave. annual growth (%)

7.52

GDP, ave. annual growth (%)

7.74

Services, ave. annual growth (%)

7.93

GDP per capita, ave. annual growth (%)

6.17

Inflation, ave. annual change (%)
Human Development Indicators

Net enrollment ratio (ages 6-11) (%)*

Net enrollment ratio (ages 12-14) (%)*

Net enrollment ratio (ages 15-17) (%)*
Ever attended school (ages 5+) (%)*
Adult literacy (ages 15+) (%)*

Mortality rate, neonatal (per 1,000 live births)
Mortality rate, infant (per 1,000 live births)

Mortality rate, under-5 (per 1,000 live births)

7.80

2004

75.98

16.37

30.80

75.92

81.70

8.53

17.50

69.78

76.28

56.6

37.3

27.2

70.3

Source: Author’s calculation based on WDI (World Bank 2013b) and CSES
Note: Education indicators (*) are calculated based on CSES.

114

2010

85.60

19.7

43.8

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Appendix 2: Consumption growth between 2004 and 2010
Indicators

Other Urban

Rural

Phnom Penh

Growth rate at median (%)

11.05

9.81

7.86

Consumption growth of the poor (%)

11.66

10.34

Growth rate in mean (%)

Mean percentile growth rate (%)
Corresponding poverty rate (%)

1.05
9.52

53.50

7.23

9.27

66.61

6.31

7.37

9.11

39.09

Source: Author’s calculation based on CSES
Note: The calculation is based on the method of Ravallion and Chen (2003). The growth rate is calculated
in real terms. Aggregate consumption has been adjusted by Phnom Penh consumer price index. Growth
incidence curves elaborate how much the actual consumption by the poor grew over time (Appendix
3).

115

Chapter 3

Appendix 3. Growth incidence curve in Cambodia

Source: Author’s calculation based on CSES

116

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Appendix 4. Sensitivity to level of selected criteria
Possible combination
Land

1 ha

Draft FarmHouse
Animal Tool

One

Low

OR

Region(Rural Only)
Cambodia

Mekong Plain

One

11.64

10.98

Mekong Plain

5.15

4.36

2.71

6.05

CoastalLow AND Tonle Sap
Coastal

None

OR

None

OR

None

None

4.38 100.00 100.00
52.09

51.85

4.53

30.65

28.07

3.37

14.18

10.85

60.45

43.00

5.12

0.00

3.09

9.23

0.15

100.00 100.00

0.32

23.28

0.08 100.00 100.00

1.19

1.39

0

11.16

57.00

0.00

Mekong Plain

0.76

0.02

56.99

Coastal

0.41

0

3.49

Cambodia

9.32

8.95 100.00 100.00

Mekong Plain

0.75
1.02

8.57

0.24

85.91

0

14.12

0.00

9.29

52.65

11.7

8.74

32.34

N. & NE. Mountain

10

8.73

11.30

Mekong Plain

4.1

5.37

4.67
5.83
1.88

7.65

30.17

15.03

10.24

60.69

43.00

4.28

0.00

4.5

1.2

0.12

Coastal

0.75

0

1.28

5.71

13.74

32.11

3.55

Mekong PlainN. & NE. Mountain

26.49

4.01

2.62

1.02

54.06

100.00 100.00

6.67

Tonle Sap

3.72

0.00

3.61

N. & NE. Mountain

1.13

14.09

25.40

Tonle Sap

CoastalCoastal

OR

5.63

13.64

0.76

Low AND Tonle Sap

None

24.53

0

Cambodia

1 ha

4.13

10.62

56.19

1.04

Cambodia

1 ha

1.31

30.45

CoastalTonle Sap

None AND Tonle Sap

Low

7.63

9.56

54.80

0.12

N. & NE. Mountain

1 ha

6.74

10.25

1.39

Cambodia

One

5.66

2010

Mekong PlainN. & NE. Mountain

1 ha

7.39

2004

11.34 100.00 100.00

12.24

N. & NE. Mountain

Cambodia

One

11.53

11.04

2010

13.63

N. & NE. Mountain

1 ha

2004

Tonle Sap

Cambodia

1 ha

CP Headcount Regional Share

50.27
2.59

51.24

8.34

0.15

100.00 100.00

0.32

23.16

0

11.88

57.00

0.00

117

Chapter 3

Cambodia

1 ha

None

Mekong Plain

None AND Tonle Sap
CoastalN. & NE. Mountain
Cambodia

0.8 ha

One

Low

OR

Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia

0.8 ha

One

One

None

OR

OR

118

None

13.51

56.29

4.18

5.56

24.64

6.17

8.65

4.61

4.01 100.00 100.00

7.69

Cambodia

1.11

Mekong Plain
Tonle Sap
Coastal

5.17

5.06
1.24

0.81

4.11
2.95

28.93

27.80

11.55

10.38

63.89

43.00

6.05

0.00

0.15

100.00 100.00

0.32

18.74

0.12

57.00

1.04

0

0.61

0.08 100.00 100.00

1.2

0

11.32

60.10

0.00

Mekong Plain

0.64

0.02

Coastal

0.41

0

7.75

8.4

100.00 100.00

8.13

29.83

6.64

8.31

9.01

13.95

3.72

3.35

55.42

51.48

1.59

4.5

2.67

Mekong Plain
Tonle Sap
CoastalLow AND Tonle Sap
Coastal

OR

8.49

58.93

8.56

Mekong Plain

None

9.96

0.00

2.88

N. & NE. Mountain

0.8 ha

28.40

0.00

2.33

5.14

Cambodia

None

9.43

11.23

14.42

2.06

N. & NE. Mountain

0.8 ha

10.51

85.91

CoastalNone AND Tonle Sap

Low

10.39 100.00 100.00

9.81

14.09

24.39

53.26

Cambodia

None

9.53

0

58.86

56.63

N. & NE. Mountain

0.8 ha

0

0.91

0.24

4.1

Cambodia

One

0.24

0.63

4.56

N. & NE. Mountain

0.8 ha0.08 100.00 100.00

0.02

Mekong Plain

Low AND Tonle Sap

N. & NE. Mountain

0.8 ha

0.66

0.68

0.47
0.91
7.74

8.98

4.82

3.84

4.45

8.66
7.65

3.8

1.1

0.12

0.75

0

Tonle Sap

0.74

N. & NE. Mountain

1.2

14.09

19.96

85.91

15.61

0.00

4.34

57.14

4.02

0.00

53.69

26.28
6.08

3.39 100.00 100.00

2.2

1

Coastal0

4.4

Cambodia

Mekong Plain

0.24

29.84

30.46

12.07

9.17

8.89

0.15

100.00 100.00

0.32

19.14

0

63.36

43.00

4.87

0.00

12.63

57.00

0.00

Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?

Cambodia

0.8 haNone

0.57

0.08 100.00 100.00

Mekong Plain

0.6

0.02

Coastal0.240None AND Tonle Sap

N. & NE. Mountain

0.45
0.91

0.24

0

60.30

14.09

20.30

85.91

16.70

0.00

2.70

0.00

Source: Author’s calculation based on CSES
Note: The land ownership criterion varies between 0.8 hectares and one hectare; the draft animal
criterion is one or none; the farming implements criterion is low productive implements or none; andthe housing material criterion is walls or roof, or walls and roof.

Appendix 5: Sensitivity to selection method
2004

Headcount(H)cutoff(k)

Adjusted
Deprivation
Headcount
(A)
(M0)
Land

Cambodia (Rural Only)
1

72.85

3

33.26

2

4

56.62

1

3
4

Tonle Sap

27.83

2.39
2.79

3.35

11.53

4.00

71.18

43.02

2.42

56.79

33.08
11.04

1

71.73

3

34.06

2

39.51

11.53

Mekong Plain
2

43.57

39.42
27.57

11.04
43.14

2.78

3.33

4.00
2.41

26.10
27.14

27.09

25.00
29.07

29.36

28.09

Contribution (%)
Draft
Farm
Housing
Animal
Tool
18.72
19.73

23.46

18.63
19.71

20.29

36.55
33.42
29.16

25.00

25.00

25.00

18.94

17.39

34.59

19.80

23.77

17.94

19.06

25.00

25.00

25.00

21.57

20.85

20.03

37.55

38.50

2.90

23.53

13.63

13.63

4.00

25.00

25.00

25.00

21.43

28.88

1

80.07

44.43

2.22

28.37

17.68

11.36

42.59

3

31.30

25.32

3.24

27.18

25.36

16.87

30.60

Coastal
2

4

58.95
7.39

28.95

39.15

2

3

4

80.20

30.99

24.32

25.00

25.00

25.00
40.53

47.04

2.35

20.16

13.47

25.84

28.04

3.35

26.03

18.61

25.90

11.64

11.64

2.72

4.00

21.76

25.00

25.00

12.89

25.00

42.68

32.95

19.80

4.00

62.80

33.50

2.66

25.37

7.39

North and Northeast Mountain
1

3.40

21.69

29.09

25.00

53.15

4

21.84

32.90

14.66

25.00

28.13

25.00

36.31

35.46
29.47

25.00

119

Chapter 3

2010

Headcount(H)cutoff(k)

Adjusted
Deprivation
Headcount
(A)
(M0)
Land

Cambodia (Rural Only)

Contribution (%)
Draft
Farm
Housing
Animal
Tool

1

66.68

40.53

2.43

25.77

20.89

3

32.02

26.85

3.35

26.72

22.71

2
4

52.06
11.34

Mekong Plain
1

2

65.22

36.87

2.83

26.38

11.34

4.00

25.00

40.05

2.46

28.40

50.69

36.42

2.87

28.87

4

12.24

12.24

4.00

25.00

1

66.20

39.02

3

29.43

3

Tonle Sap
2
4

Coastal
1

32.05

31.75

26.20

4

9.56

2.73

9.56

4.00

71.32

44.43

2.49

37.07

30.55

3.30

2

58.37

4

10.98

3

41.20

10.98

Source: Author’s calculation based on CSES

120

2.40

38.76

North and Northeast Mountain

19.80

33.11

3.30

2.82

4.00

25.00
27.53

27.53

25.00
25.00
21.21

20.84

22.47

25.00

31.81

21.82

28.57

25.00

20.80

20.55

25.00

25.00

24.27

16.94

32.89

21.33

28.27

26.38

4.00

42.10

19.72

3.35

10.25

70.24

25.00

22.89

10.25

28.70

25.00

2.36

24.59

21.88

20.38

22.00

2.79

24.63

33.91

21.43

28.43

35.03

56.86

1

3.38

50.22

2

3

27.10

19.43

23.93

18.52

25.00

25.00

19.40

13.76

23.85

19.80

31.22

25.00

31.45

25.00
37.93

14.59

34.32

25.00

25.00

25.00

19.50

25.26

36.56

22.63

23.72

24.12

20.09

25.00

19.83

25.63

25.00

29.47

33.10

29.45

25.00

Chapter 4

Does The Quality of Income Growth
Affect Child Nutrition Status?
Lawrence Haddad, Edoardo Masset and Lisa Smith*1� Introduction
The goal of this paper is to explore the relationship between the qualityof income growth and child nutrition status. Understanding more about
the relationship between income growth and nutrition status isimportant for a number of reasons. There are currently 162 million
stunted children under 5 (Black et al. 2013). These children havestandardized heath-for-age scores (HAZ) that fall below a threshold (i.e.
they are stunted) for which there are serious consequences for them andtheir societies. In addition to the moral case for protecting andrespecting children’s rights to food, care and a safe health environment,
there are severe functional consequences of stunting. Black et al. (2013)
estimate that 45 percent of child deaths under the age of 5 are due tochild undernutrition. The economic consequences are also severe.
Children who are stunted are more likely to learn less in school and tolive in poverty as adults (Adair et al. 2013; Hoddinott et al. 2013).
Knowing the features of income growth that make it more likely to
improve child nutrition status helps to invest strategically in nutrition
programmes to reduce undernutrition. The smaller the magnitude of
the income effect, the more strategic and proactive the scale-up of direct(nutrition specific, such as breastfeeding promotion, Bhutta et al. 2013)
and indirect (such as social protection, Ruel and Alderman 2013)
nutrition interventions needs to be for a given nutrition reduction target.
Estimating the relationship between income growth and nutrition
status has a long history (Behrman, Deolalikar and Wolfe 1988, Strauss
and Thomas 1998, Haddad et al. 2003, Headey 2012, Smith and Haddad2014). However, there is no literature assessing the quality of income* We would like to thank two anonymous reviewers for their comments. Any remaining errors are ours.

121Chapter 4

growth on nutrition outcomes (Gillespie et. al. 2013). This paper aims to
make an early contribution. The main challenge the paper faces is how
to define the quality of growth in an empirically practical way. To
generate sufficient variation in the quality of growth, however defined,
we opt for a cross-country descriptive approach.
We explore the quality of growth in three ways. The first two ways
assess the relationship between economic growth and nutrition under
different contexts. First, we survey the literature for comparable
country-level estimates of the relationship between income growth andnutrition outcomes. We then describe the magnitude of the estimates by
the level of income inequality in each country at the time. The
relationship between income growth and poverty reduction ismoderated by income inequality – the higher the inequality the lower
the poverty reduction for a given rate of economic growth (see Ferreira
and Ravallion 2008 for a review) – and this provides a useful clue for ourpaper. Second, we build on Smith and Haddad (2014) and employ crosscountry econometrics to estimate the relationship between national
income growth and stunting rates under different governance regimes.
The third way of exploring the quality of growth uses measures of
economic growth that fully embody some dimensions of quality.
Unfortunately these data do not yet exist for many countries and so weare only able to undertake some preliminary descriptive analysis.
The paper is organized into the following sections. First, the paper
presents the results of a systematic search of the literature estimating
the relationship between income growth and z-score of height for age(HAZ) of children under 5. We identify several estimates and explore
the association with income inequality as measured by the Gini
coefficient. Second, we explore the role of governance in shaping therelationship between income and nutrition status using cross country
regressions. Is the effect of income on nutrition larger (and therefore ofhigher quality) or smaller in different governance regimes? Third, for asmall set of countries for which data are available, we compare therelationship between nutrition status and GDP per capita with therelationship between nutrition and the Inclusive Wealth Index (IWI).
The IWI emerged from the post 2008 interest in measuring what matters,
and represents an attempt to measure the quality of growth in terms ofhuman development, equity and sustainable resource use (Smit andSteendjik 2014). The final section concludes with a discussion of the122Does The Quality of Income Growth Affect Child Nutrition Status?

results and suggestions for further research in this area.

2� Income and nutrition elasticity estimates:
do they vary by income inequality?
This section summarizes the findings of selected papers on theestimated magnitude of the relationship between height for age z-scoresand traditional measures of income from household surveys. We
searched the literature on income and nutrition, adopting the Rapid
Evidence Assessment (REA) method1. This method applies simple
exclusion criteria and circumscribes the limits of the reviewed literaturein such a way that the process is conducted much more quickly than in astandard systematic review. The selection of the reviewed papers went
through three stages.
In stage one, we searched the published literature on income andnutrition using the Citation Indexes at the Web of Science database (ISI)through Endnote software. This limited the search to papers that were
published in peer-reviewed journals. We searched for all publications inEnglish from 2000 to beginning of July 2012 using the following searchstrings: income OR expenditure AND nutrition OR undernutrition. Thissearch delivered 1,836 papers. We then read all paper titles and removed
all papers that were not from low and middle income countries by theWorld Bank classification, and that were not looking at the relationshipbetween income and nutrition in an obvious way. This process reduced
the number of publications to 307.
In stage two, we read all 307 abstracts and removed all publications that
did not estimate the relationship between income (measured by either
household income or expenditure) and nutrition (measured by height
for age Z-scores). At this point we increased the number of selectedstudies in two ways: (1) by back-referencing we included studies
referenced by the latest publications that escaped our search, and (2) byconsulting experts. In stage 3 we downloaded all studies and removed
those whose methodology did not address fundamental issues for thecorrect “identification” of income elasticities (i.e. they did not allow for
unbiased independent effects of the association between income andnutrition status). This left us with the 6 papers in the Appendix Table,
1. UK Civil Service 2012. http://www.civilservice.gov.uk/networks/gsr/resources-andguidance123Chapter 4

yielding 14 elasticity estimates which are presented in Table 1.
The explanatory variable employed by the 6 studies reviewed is almost
invariably the log of per capita expenditure. This specification allows forthe calculation of a semilogarithmic elasticity that varies with the value ofthe dependent variable (various indicators of undernutrition). The outcome
nutrition indicator used varied greatly by study. We chose standardized
height-for-age Z-scores (HAZ) among under-5s (6 papers, 14 estimates) as
the preferred indicator. We choose HAZ because we have the mostestimates for this indicator and it has become the preferred nutrition statusindicator, largely because it more accurately predicts long short and longterm consequences of undernutrition (Adair et. al. 2013).
From Table 1 the miroeconometric HAZ-pc income (or expenditure)elasticities range from 0 (not significantly different from zero) to 0.17. A
doubling of income will, at most, raise height for age z-scores by 17%.
For more detail on the studies and the calculations of the elasticities, see
the Annex Table.
Table 1� Summary of elasticities from the microeconometric andcross-country studies, by nutrition outcome indicator
Nutrition
indicator

Z-score Height
for age (HAZ level)124

Microeconometric studies
0 (n.s.) (China 1991-1993/Osberg et. al. 2009)
0 (n.s.) (China 1997-2000/Osberg et. al. 2009)
0 (n.s.) (China 1997/Chen et. al. 2007)
0.063 (Ethiopia 1995-98/Christiansen et. al. 2004)
0.063 (Tanzania 1991-94/Alderman et. al. 2006)
0.07 (Vietnam 1993/O’Donnell et. al. 2009)
0.07 (China 1993/Chen et. al. 2007)
0.098 (Vietnam 1998/O’Donnell et. al. 2009)0.12 (China 1989/Chen et. al. 2007)
0.13 (China 2000/Chen et. al. 2007)
0.13 (Vietnam 1993/Wagstaff et. al. 2003)
0.14 (China 1993-97/Osberg et. al. 2009)0.15 (China 1991/Chen et. al. 2007)
0.17 (Vietnam 1998/Wagstaff et. al. 2003)

Does The Quality of Income Growth Affect Child Nutrition Status?

Figure 1 plots the elasticities by Gini coefficient in the survey year and we
can see a downward slope: as inequality increases elasticities decline. Notethat the R-squared is low at approximately 0.095 and that we cannot reject
the hypothesis that the estimated slope coefficient is zero.
Figure 1� Elasticities (absolute values, vertical axis) for HAZ levels and PC
expenditure by Gini coefficient in survey year (horizontal axis)

Data source: http://data.worldbank.org/indicator/SI.POV.GINI?page=2

3� Does governance affect the nutrition-income relationship?
Having too few elasticity estimates to explore conclusively the
relationship between elasticities and the Gini coefficients, we turn tomore plentiful sources of data, national level GDP per capita andstunting rates. The twist in our analysis is that we intend to explore thecross-country relationships between these two variables in different
governance contexts.
In the past decade, governance more broadly has risen up the agenda interms of both health (Halleröd et. al. 2013; Farag et. al. 2013) andnutrition (Nishida 2009; Pelletier et. al. 2012; Mejia-Acosta and Fanzo
2012; Haddad 2012; Gillespie et. al. 2013). This reflects an increased
recognition that the ability of governments to be responsive andresponsible has a profound influence on a number of factors that
determine nutrition status. Smith and Haddad 2014 found that nearly all
the governance variables were associated with reductions in stunting,even when GDP per capita was included in the regressions.
125

Chapter 4

To measure the quality of governance for the countries in our sample weemploy International Country Risk Guide (ICRG) indicators published by
the Political Risk Services Group (PRS 2013). The indicators are indexes
corresponding to five dimensions of governance: (1) bureaucratic quality;(2) law and order; (3) political stability; (4) restraint of corruption; and (5)democratic accountability.2 The data are available from 1982 onwards and,to render them comparable across countries and over time, are compiled
based on PRS experts’ subjective analyses of political information
organized on the basis of pre-specified “risk components”.
How might these five dimensions be relevant to facilitate or impede
efforts to accelerate reductions in child undernutrition? 3 Smith andHaddad (2014) briefly surveyed the literature and concluded thefollowing:・ Bureaucratic quality concerns the quality of public services and thecivil service, including policy formulation and implementation, andregulation of the private sector. It is important for effectively
providing public services and programs that support childnutrition status such as safe water, sanitation, education and public
food safety net programs. Effective functioning of countries’
bureaucracies is particularly important to child undernutrition
because addressing it requires a multisectoral effort and vertical
integration of different levels of government. It thus puts strong
demands on public agencies. Similarly, a strong regulatoryenvironment is necessary as the private sector produces a numberof products that if marketed irresponsibly can harm the nutrient
consumption of children under two years of age—effective
regulation and enforcement of that regulation is vital for thenutrition status of the most vulnerable.
・ A strong system of law and order is founded on a solid and impartial
legal system in conjunction with popular observance of the law.
・ Political stability rests on a government’s ability to carry out its
declared programs when in office and to gain office and stay inoffice through constitutional and non-violent means. Both are
2. The actual names of the ICRG indicators are: bureaucracy quality, law and order,
government stability, corruption, and democratic accountability.
3. The definitions and descriptions of each dimension given here are from PRS (2013).

126

Does The Quality of Income Growth Affect Child Nutrition Status?

essential for providing reliable public services, creating anenvironment conducive to the economic stability of households,
and the functioning of markets for essential nutrition inputs such as
food. Much like natural disasters, violence due to conflict isestimated to have large and permanent effects on nutrition status.
Both law and order and political stability allow governments tofulfill their role of protecting citizens from such violence.
・ Restraint of corruption, that is, restraint of the exercise of public
power for private gain, is important as many nutrition interventions
involve the transfer of valuable commodities, such as food anddrugs, at subsidized rates, which creates multiple opportunities forleakage.
・Finally, democratic accountability, including respecting and protecting
the rights and civil liberties of all citizens, represents how
responsive a government is to its people. The irreversibility of earlychildhood undernutrition means that public responsiveness insupporting families to meet the needs of young children is vital.
Democratic accountability and its herald, transparency, areparticularly important for nutrition as most forms of undernutrition
are invisible, both because the clinical signs are not obvious unlessat their most extreme and because of infrequent collection ofnutrition data. Hence public awareness of the magnitude andconsequences of the problem is low, and voice is essential tostimulate timely action. In addition, nutrition resource flows, being
fragmented across multiple authorities, are also notoriously
nontransparent, undermining accountability mechanisms.
To test whether the relationship between income and stunting rates
differs by governance indicator level, we estimate panel regression
coefficients (using instrumental variables when necessary) for variantsof the form:
Stunting = f (lnGDP per capita, governance indicator,
lnGDP per capita*governance indicator, demographic variables)
While Smith and Haddad (2014) found that governance and income haveseparate effects on stunting, interactions were not explored. The fivegovernance indicators are too correlated with each other to simultaneously
127Chapter 4

include all of them and their interactions within the regression framework.
So first we include an aggregated governance indicator (of the fivecomponents) and its interaction with income. Then we include each of the
five-component governance indicators and their interactions, one by one.
We find that none of the interaction terms are significantly different
from zero (regressions not reported). In other words, the governancevariables we include do not seem to modify the relationship between
stunting and income. This may be because the relationship does notexist, or the variables are too crude, or the model is incorrectly specified.
To address the last possibility we conduct a range of specification tests,
we use spline methods to detect any nonlinearities and we run the
regressions for separate sets of observations described by different
combinations of governance variables. Our specification tests do notsignal misspecification and our spline and subset analyses yielded nosignificant estimates on the interaction terms. These results suggest no
governance modification to the stunting-income relationship. Whether
this signals an absence of a relationship or insufficiently refinedindicators remains to be determined by future studies.

4� Comparing stunting with GDP per capita andthe Inclusive Wealth Index
The Inclusive Wealth Index has been developed by UNEP and the UNU
and comprises human capital, produced capital, natural capital andhealth capital. Prices are assumed to be constant so changes in wealthreflect real changes not short term price bubbles (UNU-IHDP and UNEP
2012, Smits and Steendjik 2014). This stock of wealth is then adjusted bytaking into account (a) carbon damages (estimated emissions multipliedby social costs), (b) oil capital gains (i.e. gains stemming from oil priceincreases into account and (c) total factor productivity—the gains in
output that cannot be accounted for by increases in inputs. Comparisonsof the growth of IWI per capita over the past 2 decades with GDP per
capita growth show that IWI per capita does not grow as fast as GDP per
capita and that the correlation between the two is low (UNU-IHDP and
UNEP 2012).
The IWI has only been calculated for 20 or so countries. Of these, only a
few have significant undernutrition issues. Table 3 identifies thesecountries, and for the last two years in which they have had nutrition
128

Does The Quality of Income Growth Affect Child Nutrition Status?

surveys it matches stunting data with GDP per capita and IWI per
capita. It is clear that the percentage change in IWI per capita is smaller
than the percentage change in GDP per capita. It is also clear that in
Nigeria, Colombia and Venezuela, all mineral extracting countries, IWI
per capita actually declines despite increases in per capita GDP,
indicating non sustainable growth.
Table 2� Stunting, GDP per capita and IWI per capita for countries
with significant undernutrition
CountryYear

BrazilBrazilChina
China
Colombia
Colombia
India
India
Kenya
Kenya
Nigeria
NigeriaSouth Africa
South Africa
Venezuela
Venezuela

1996
2006
2000
2008
1995
2005
1999
2005
1998
2008
2003
2008
1999
2008
1996
2006

Stunting, GDP/cap, Inclusive % change % change % change(Countdown
PPP
Wealthin
in GDP/incountry(World Index/cap stunting
cap
IWI/cap
profiles or Bank, in(IWI
WHO Global 000s)
report in
Database)
0,000s)

14
7
18
10
20
16
54
48
36
35
43
41
30
24
19
16

5.11
5.79
0.95
3.41
2.53
3.39
0.46
0.74
0.48
0.79
0.51
1.38
3.1
5.6
3.03
6.75

3.449
3.806
1.203
1.503
2.707
2.6
0.45
0.483
0.305
0.319
0.644
0.592
3.64
3.743
10.99
10.91

-50.00

13.31

10.35

-44.44258.9524.94-20.00

33.99

-3.95

-11.11

60.87

7.33

-2.78

64.58

4.59

-4.65

170.59-8.07-20.00

80.65

2.83

-15.79122.77-0.73

Table 2 calculates the arc elasticities for stunting with respect to GDP per
capita and IWI per capita. The population weighted mean arc elasticities
show that the stunting-IWI per capita elasticity is more negative than
the stunting-GDP per capita elasticity. This suggests that when wemeasure economic growth more fully we are measuring something thatis more strongly associated with undernutrition (specifically, stunting).

129

Chapter 4

Table 3� Arc elasticities, stunting, GDP per capita and IWI per capita
Country, time period

Brazil 1996-2006
China 2000-2008
Colombia 1995-2005
India 1999-2005
Kenya 1998-2008
Nigeria 2003-2008
South Africa 1999-2008
Venezuela 1996-2006
Arc elasticity, population
weighted meanarc elasticity, stunting/GDP arc elasticity, stunting/IWI
pc
pc

-3.76
-0.17
-0.59
-0.18
-0.04
-0.03
-0.25
-0.13

-4.83
-1.78
5.06
-1.52
-0.61
0.58
-7.07
21.69

-0.38

-1.16

However, these arc elasticities are problematic for at least two reasons.
First, while there is no nutrition component within IWI, there are healthrelated components, so the stronger correlation might be driven by this.
Second, the IWI per capita elasticities are higher because IWI per capita
does not grow as fast as GDP per capita, but the effort of increasing GDP
per capita and IWI per capita by the same percentage is not equivalent.
So while intriguing, the IWI per capita arc elasticities need to beinterpreted with extreme caution. Until we have IWI per capita for morecountries, single country observations will continue to exert a large
influence and we need to continue to rely on more convention measures
to explore the relationship between growth and nutrition status.

5� Conclusions
We have undertaken an exploratory analysis of the association between
economic growth and its quality on nutrition outcomes. First, we
conducted a systematic search for estimates of the elasticity between
height for age of under 5s and income. We found several estimates andtried to identify pattern for their magnitude with reference to income
inequality. Second, we explored the role of governance dimensions inshaping the relationship between income and nutrition status using crosscountry regressions to examine whether the effect of income on nutrition
was larger or smaller under different governance regimes. Finally, for thesame set of countries, we compared the patterns of changes in nutrition
status and GDP per capita with those of nutrition status and the Inclusive
Wealth Index per capita.
130

Does The Quality of Income Growth Affect Child Nutrition Status?

Our conclusions are as follows:First, there are very few studies published in peer-reviewed journals, withgood methodologies, estimating the relationship between nutrition status
and income. This is a pity, because the lack of studies prevents somepotentially interesting meta analyses, exploring an area that would be
useful for policy making. Given only 14 elasticity estimates (from only 6
studies and 4 countries) we were restricted to undertaking scatter plots
with Gini coefficients. Elasticities do reduce in magnitude as inequality
increases but the relationship is not statistically significant.
Second, we find that different governance levels do not modify the
relationship between GDP per capita and stunting rates. Income growth
seems to be important in reducing stunting rates over a wide range ofgovernance dimensions and levels.
Third, the arc elasticities between stunting and GDP per capita look very
different when we substitute IWI per capita for the latter. The IWI estimates
are much larger, but with so few country-level observations we are
reluctant to draw any conclusion other than this is an avenue of analysis
worth pursuing as more IWI per capita estimates become available.
From these three different types of analysis, the relationship between thequality of income growth and child growth, at least in the ways we havedefined quality, is weak. From the cross-country regressions, there is no
indication that the relationship between economic growth and stunting is
modified by governance. There may be some sets of observations where
governance does modify the stunting-income relationship, but we have notbeen able to identify them in this paper. The other two avenues of analysis
will become more revealing when the IWI is calculated for more countries
and when more HAZ-income elasticities are generated. More research isneeded along all three lines.
One thing is clear from the wider literature, however, namely that
economic growth as currently measured, on average, is not sufficient for
rapid stunting reductions. Just as “zero poverty” targets will prove to be
increasingly difficult to attain as poverty rates decline (Bluhm et. al. 2014,
this volume), so too will “zero stunting” targets, because stunting has aneven weaker relationship with GDP per capita than does $1.25 a day
poverty rates (Ruel and Alderman 2013). Identifying which components of131

Chapter 4

growth are most important for stunting reduction, and the conditions
under which they are most powerful, will be vital if the world is to meetand exceed the World Health Assembly target of reducing the number ofstunted under 5s from 165 million today to 100 million in 2025.

Annex Table� Microeconometric estimates of the relationship between HAZ
and income
Study

HAZ levels

Country and Dependentyear
and explanatory variable

Elasticity or other effectsize

Alderman 4 wave panel Under 5The coefficient of log of per
et al. (2006) Tanzania
height-forcapita expenditure ranges
1991-92-93-94 age Z-scores from 0.1-0.2 depending on
the specification. A douHousehold
bling of household income
per capita
would produce increase in
expenditure Z-score of 0.1-0.2 SD
Elasticity (OLS) = 0.206/
mean HAZ (-1.64)= 0.125

Christain- 3 survey
senroundset al. (2004) Ethiopia
1996-97-98

Other effects

In comparison
the presence of anutritionintervention in
the communities increases
the Z-scores by
0.3 SD

Elasticity of HAZ (preferred estimate)=0.103/
mean HAZ (-1.64)= 0.063
Under 5The coefficients of log of
In comparison
Height-forhousehold income range
an additional
age Z-scores from 0.16-0.19 depending
year of educaon the survey round.
tion of the mostLog of per
educated female
adult equiva- Elasticity = 0.16/mean HAZ household
lent houseof 2.54 = 0.063
member inhold expencreases theditureZ-score by 0.03
SD.
The communitylevel ability todetect stunting
increases theZ-score by .24
SD132

Does The Quality of Income Growth Affect Child Nutrition Status?

O’Donnell 2 crosset al. (2009) sections
Vietnam
1993-98

Osberg etal. (2009)

Three wave
panel
China
1991-93-97

Chen et al.
(2007)

5 surveyroundsChina 1989
19991-93-97
2000

Wagstaff(2003)

Under 5The coefficients of the log of Decomposition
Height-forper capita expenditure
analysis showsage Z-scores increase the HA Z-scoresthat 39% of theby 0.14 (1993) and 0.16 1998). change in
Log of per
stunting occurcapita
Elasticity1993=0.14/meanring between
household
HAZ (-2.03) =-0.07
the two rounds
expenditure
was the result of
Elasticity1998=0.16/mean
an increase in
HAZ (-1.64)=-0.098
householdconsumption.
Changes inwater andsanitation andhouseholdstructure had asmaller impact.
Under 5The coefficient of log of per Additional
Height-forcapita expenditure ranges years of educaage Z-scores from 0.0 to 0.17 depending tion of motherson the survey period.
and fathers do
Log of per
Elasticity 1993-97=-0.17/
not have aadult equiv mean HAZ (-1.25) = 0.135
statisticallyhh expendi- Elasticities in 1991-93 andsignificant
ture1997-2000 = 0 as a zero
impactestimated coefficientcannot be rejected
Height forage Z-score.
Log ofhouseholdincome2 wave panel Under 5VietnamHeight-for1993-98
age Z score
Log of percapitahouseholdexpenditure

The coefficients of log ofhousehold income range
from 0.09-0.19 dependingon the survey round.
Elasticity 1989 =
0.157/1.315= 0.12
Elasticity 1991 =0.192/1.315= 0.15
Elasticity 1993 =0.085/1.218=0.07
Elasticity 1997 = 0
Elasticity 2000 =0.122/0.971=0.13

In comparison
the coefficient ofyears of education of the headof household isonly 0.01-0.02.

The coefficient of log of per In comparison
capita expenditure is 0.26
years of schooland 0.27 depending on the ing of mothers
survey period.
and fathers &Elasticity in 93= -0.26/mean access to safe
HAZ (-2.036)= 0.13
water do not
Elasticity in 98=0.27/mean have an impact.
HAZ(-1.608)=0.17

133

Chapter 4

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136

Chapter 5

Disability and Growth Elasticity of Poverty
in a Developing Country
Kamal Lamichhane and Damaru Ballabha Paudel

1. Introduction
Growth as one of the central components for inclusive and sustainable
development. However, developing countries are struggling toaccomplish the goal of sustainable growth. Ensuring good quality
growth covering all strata of the society is important for the reduction ofpoverty and the achievement of social inclusion. When marginalized
people are not brought into mainstream development, it is unlikely thatgrowth can be achieved and then made sustainable. For example,
despite the significant progress on the Millennium Development Goals(MDGs), the systematic exclusion of disability issues is likely to be oneof the causes of the failure to achieve MDGs by 2015. This is aparticularly important omission, as individuals with disabilities
represent nearly 15 percent of the world’s population (WHO & WB 2011)
and comprise not only one of the most marginalized but also one of thelargest minority groups. As a result, people with disabilities are
frequently left behind and remain the poorest among the poor.
However, as we are heading for the post-2015 development goals, it ishigh time that we attempt to bring marginalized groups, including
people with disabilities, into the mainstream of development in order toachieve quality growth, reduce poverty, and make development
sustainable and inclusive. It has been said that if growth is distributed inan equitable manner, it helps reduce poverty. However, if the opposite isdone, this simply increases inequality and may result in a need forredistribution (Besley and Burgess 2003).
We are thankful to the Central Bureau of Statistics of the Government of Nepal for providing the Nepal
Living Standards Survey 2010/2011 dataset through which this paper has become possible. The
authors would also like to thank Alberto Iniguez and Takayuki Watanabe for their research
assistance. Additionally, we would also like to acknowledge support from the Research Institute of theJapan International Cooperation Agency (JICA-RI), the Institute of Development Studies (IDS) of the
University of Sussex, and the French Development Agency (AFD) for their helpful comments while
presenting the preliminary draft in the joint workshop held by these three organizations in AFD Parison February 2014.

137

Chapter 5

Furthermore, recognizing poverty as a global threat, governments andinternational development agencies have been making efforts to reduce
it. The MDGs have also focused on this as a priority issue by positioning“eradicate extreme poverty and hunger” as the primary goal. Despite
governments and international development agencies investing theirbudgets in different sectors aimed at lifting people out of poverty, key
questions still remain: why is progress so slow and how can we makegrowth more ‘pro-poor’?
Most projects by international agencies have been aimed at the
population nearest to the poverty line. It is likely that these agencies
have an incentive to direct their focus toward this part of the
population, as it is comparatively easier to show progress in poverty
reduction if the transitory poor are able to rise out of poverty, even witha small push (Barder 2009). However, if the same trend continues, what
will happen to the majority of the more marginalized population,
including those with disabilities? In the absence of inclusive policies andstrategies, the likelihood is that these people will remain below thepoverty line and chronically poor. Therefore, if these groups are to betargeted, widening the efforts, increasing investment, and making such
efforts more inclusive, is necessary. To do so, inclusive policies, institutions
and growth are required.
In this paper, based on findings on the growth elasticity of poverty
between people with and without disabilities in Nepal, w e will
explain why people with disabilities should be an importantcomponent in considering the quality of growth and inclusive
development. In other words, this paper is a preliminary attempt at
examining the impact of economic growth upon poverty between
people with and without disabilities, using the nationally
representative Nepal Living Standard Survey (NLSS) conducted in 2010.
Literature review
Studies on disability and poverty are rare. Some have focused on therole of education through findings on high returns to education for
persons with disabilities (Lamichhane and Sawada 2013), while others

138

Disability and Growth Elasticity of Poverty in a Developing Country

have studied the employment gap and wage differential between
individuals with and without disabilities (Mitra and Sambamoorthi2008), as well as the economic profile of persons with disabilities in theless-developed countries (Mitra et al. 2013). Lamichhane et al. (2014)
studied the factors associated with poverty between people with andwithout disabilities in Nepal. Another study on Africa examined theliving conditions of persons with disabilities (Loeb et al. 2008). However,none have focused on the growth elasticity of poverty for persons withdisabilities.
Among the 15% of people with disabilities in the world, nearly 80% live
in developing countries (WHO & WB 2011). Additionally, it is also
estimated that people with disabilities make up 15 to 20% of the poor indeveloping countries (Elwan 1999). This means that the worldwide
population of people living with disabilities constitutes one of thepoorest and most marginalized segments of society (ILO 2007; DFID
2000). While there are multiple factors contributing to poverty amongpeople with disabilities, unequal and poor access to education andemployment, as well as the unequal distribution of other resources, arelikely to be among the major causes of their poverty (Lamichhane et al.
2014). While inequality, exclusion, and discrimination are widespread,the needs of people with disabilities are not yet considered to be an
important component in poverty reduction strategies.
In the literature that reflects on growth alone, the terms “inclusive
growth”, “shared growth”, “broad-based growth” or “pro-poor growth”,
are used to convey similar concepts. The World Development Report
1990 (World Bank 1990) coined the concept of “broad-based growth” asgrowth that could reduce poverty in a rapid manner through theinclusion of all strata of the society. On the other hand, there is arelatively rich literature related to the growth elasticity of poverty, an
important economic term that is strongly related to the abovementioned concept of pro-poor (inclusive) growth. For instance,
Ravallion and Chen (1997) estimated the growth elasticity of poverty fordeveloping and transitional economies. Additionally, when looking at
pro-poor growth, Ravallion and Datt (1996, 1999 and 2002) studied thegrowth elasticity of poverty in different states of India. Moreover,among the most recent studies on the growth elasticity of poverty,
differing estimates of the elasticity of poverty have been presented for agreat variety of developing countries when considered as a whole139

Chapter 5(Adams 2004; Bourguignon 2003; Ram 2006), as well as for individual
country-level studies, such as India (Lenagala and Ram 2010; Ram 2011),
and different regions of the world (Besley and Burgess 2003; Kalwiji andVerschoor 2007), by analyzing the growth-poverty relationship fordifferent poverty lines and growth spells.
Despite the large amount of research during the last decades on thegrowth-poverty issue, to our knowledge, none of the studies has
touched upon the issue of disability and growth so far. The reason forthis important gap in the literature may be attributed primarily to thelack of data on disability, as well as to the lower level of priority given
to this issue by governments and development agencies.

2. Dataset from Nepal
The nationally representative data set (NLSS III) published by theCentral Bureau of Statistics of the Government of Nepal, has been used
(CBS 2011a). This household survey was conducted by CBS withtechnical assistance from the World Bank. The survey contains a wide
range of information from sample households such as: demographic
characteristics of the head and other members of the household;housing; access to facilities; education; health services; agriculture;
consumption; income; and employment status.
Altogether, information from 5,988 households was collected in thissurvey. In this paper, we use an adjusted sample of 4,840 households,
with the household head having an economically active age of 15-59
years. For the first time, NLSS has included questions that capture
information on disability. First author of this paper met CBS officials
twice when the survey was in the design phase. At this time the author
requested that disability specific questions be included in thequestionnaires. Nepal’s disability-related organizations also made aneffort to include disability in the survey. Due to these collective efforts,
the following two questions were included in the final version ofsurvey: 1) whether participants have a disability or not, and 2) if yes,
what type of disability. The types of impairments included in thissurvey were: physical impairments, visual impairments, hearing
impairments, deaf blindness, speech problems, intellectual disability,
and multiple disabilities. Based on this information on disability, it ispossible to analyze growth elasticity of poverty between those withand without disabilities.
140

Disability and Growth Elasticity of Poverty in a Developing Country

In this study, the consumption-based national poverty line calculated by
the CBS is used. According to CBS (2011b), the national poverty line for
Nepal is Nepalese Rupees (NRs) 19,261.18 – a figure based on the Cost of
Basic Needs (CBN) approach. In this approach, the poverty line can be
defined as the expenditure value (in local currency) required by anindividual to fulfill his/her basic needs in terms of both food and nonfood items. While the poverty line in the previous round of the survey(NLSS II), undertaken in 2003-04, was an update of prices for the same
BNB previously estimated in 1995-96 (NLSS I), the poverty line for 201011 is based on a new BNB for the poor that reflects changes in well-being
over time.

3. Empirical strategy
For the analysis of poverty, we use the Foster-Greer-Thorbecke (FGT)
poverty measures (Foster et al. 1984), which are referred to as the headcount index (P0), the poverty gap index (P1), and the severity of poverty
index (P2). The generalized FGT poverty measures are defined as:(1)
where y is the household per capita consumption expenditure, f(y) is its
density (roughly the proportion of the population with a consumption
level y), z denotes the poverty line, and α is a nonnegative parameter.
For Nepal, the national poverty line, based on per-capita household
consumption, is 19,261.18 NRs. Higher values of the parameter α
indicate a greater sensitivity of the poverty measure to inequality
among the poor or a greater emphasis to the poorest of the poor (Foster
et al. 1984). We estimate poverty measures Pα for α = 0, 1, and 2,
which define P0, P1 and P2, respectively.
The growth elasticity of poverty is the total percentage change inpoverty with respect to the total percentage change in per-capita
income. In this paper, the analysis relies on per-capita consumption,
instead of income, as preferred welfare indicator. According to theWorld Bank (2013), the growth elasticity of poverty is defined as:

141

Chapter 5(2)
Since we do not have time series or panel data on poverty and growth
focusing on disability, the methodology developed by Araar and Duclos(2013) to compute growth elasticity of poverty for household crosssectional data is used here. Based on this methodology, the elasticity ofeach FGT measure is estimated using DASP: Distributive Analysis Stata
Package version 2.2. According to Araar and Duclos (2013), the overall
growth elasticity (GREL) of poverty, when growth comes exclusively
from growth within a group k (namely, within that group, inequality
neutral), is given by:(3)

whereas, similar to above, z is the poverty line, k is the population
subgroup in which growth takes place, f (k, z) is the density function atlevel of income z of group k, and F(z) is the headcount index. The upper
case of equation (3) is for the condition α = 0 and the lower is for thecondition α > 0 (the values of α are 1 and 2). Moreover, this kind ofgrowth elasticity of poverty is group-specific and can be applied tocomparisons among categorical groups. This type of analysis is therefore
useful in understanding the differing rates at which poverty is reduced
among different, well-identified groups due to the particular growth oftheir incomes or consumption expenditures. Additionally, a study likethis will allow us to identify how these specific groups could be bettertargeted for the purpose of improving their living or welfare condition.
Definition and Mean of Variables
Household per-capita consumption expenditure is used as the welfare
indicator. The consumption aggregates are constructed by adding
together the various goods and services consumed by each household
over a period of 12 months. Various components of consumption aregrouped into three main categories: consumption of food items;
consumption of housing; and consumption of other non-food items.
Household level consumption in monetary terms is divided by the size ofthe household to find the household per capita consumption expenditure.
142

Disability and Growth Elasticity of Poverty in a Developing Country

Other variables are grouped into different categories such as sex ofhousehold head, age of household head (different age groups ranging from
15 years to 59 years) grouped into five categories, education of household
head (illiterate with 0 years to highest 17 years of schooling, split into threegroups), region (rural or urban), land assets (landless to large household
land size), a household’s access to facilities (roads, schools, the market
center, hospitals, electricity, and piped water) and ethnicity. A detailed
definition of variables is presented in Table 1. Although the definition
presented in Table 1 is self-illustrative for most of the variables, I havefurther elaborated on the variable of ethnicity based on Nepal’s ethnic
demographics, as the country has multiple and diverse ethnic groups.
According to the National Population and Housing Census Report 2012
(GON 2012b), the majority of Nepalese (81.3% of the population) arefollowers of the Hindu religion. Hindu societies are divided into ahierarchy based on the caste system. The same report further states that
there are 125 caste/ethnic groups in Nepal. I have categorized these 125
castes into five major ethnic groups for the purpose of this study. The
first group is the so-called ‘high castes,’ including the Brahmin and
Chhetri castes of both Hills and Terai areas. High caste people arescattered all over the country and they are considered to be thehistorically privileged caste. The second group is Mongoloids, which
includes Magar, Tamang, Rai, Gurung, Limbu, Sherpa, Thakali, Jirel, Dura,
Lepcha and Sunuwar castes. People from this group reside mainly in the
Hills and Mountains area. The third group is Newar. Newar is the casteof people who are settled in most of the cities, including Kathmandu
valley, and are engaged in trade and commerce. The fourth group is
Madheshi, which includes the Yadav, Rajbanshi, Kalawar, Kanu, Tajpuria,
Dhimal, Sudhi, Santhal/Satar, and Gangai castes but excludes the Brahmin
and Chhetri from Terai. The last group is the low castes, which includes
the so-called low castes of the Hills such as Kami, Damai, and Sarki, andthe low castes of Terai such as Chamar, Dusad, Paswan, Musahar, Lohar,
and Tatma. The so-called low caste people are historically the mostdiscriminated against and deprived group in Nepal. People in this caste
suffer from a lack of access to the benefits of development. Previous
studies such as (Lamichhane et al. 2014) have also analyzed poverty in
Nepal through a similar caste-based classification of the population.
The last two columns of Table 1 show the mean values of the variables
used for the growth elasticity of poverty estimates for persons with andwithout disabilities. Out of a total of 4,840 observations, 167 had
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Chapter 5

disabilities. The lower percentage of disability prevalence can beconnected to factors such as failure to address disability-related
components adequately in the survey questionnaires, problems withdefining disability, and enumerators (interviewers) not having proper
training on how to ask disability-related questions. Additionally, thepossibility of poor understanding by enumerators of the disability issue
may have caused them to cover only those people whose impairments
are severe. Factors like these may exclude other people whose
impairments may be moderate or mild. Although inclusion of disability
in the survey is a very positive step, further improvement of the survey
design is required so that many people with disabilities that are
currently excluded can be covered in the future. A similar explanation
may account for the 1.94% disability prevalence rate given by theGovernment of Nepal National Population Census in 2012.
The average household per capita consumption is NRs 44,184.52 forpersons with disabilities, whereas that is slightly higher (46,290.79 NRs)
for their counterparts without disabilities. According to the census, theaverage household size is 4.21 and 4.39 respectively for persons with andwithout disabilities. In both cases, the vast majority of the households(84% and 90% respectively) are headed by a male, and the remaining 14
and 10% are headed by women with and without disabilities. The
majority of the household heads have a lower level of schooling of below
5 years, with 84% and 81% of those with and without disabilities
respectively falling into this category. Moreover, 10% of household
heads have schooling of medium level (6-10) years and only 9% haveschooling of higher level (11 years and above). Additionally, regardlessof disability status, nearly two thirds (68%) are from rural areas.
In relation to land assets, 10% of those with disabilities and 12% of those
without are landless; another 12% and 14% have only marginal land(less than 0.15 hectares (ha); 10% of both groups have medium landownership (1.00ha-4.00ha), which is not different from the percentage
owned by marginal-land or the landless groups. When looking athouseholds with larger amounts of land (above 4.00 ha), the percentage
decreases by more than 20 points when compared to households having
small land assets. With regard to having access to facilities, except forelectricity, both groups of people still have low-level of access to vehicle
roads, hospitals, primary schools, piped water and market centers
within a thirty-minute walking proximity.
144

Disability and Growth Elasticity of Poverty in a Developing Country

4. Estimation Results
Table 2 shows estimations for poverty and inequality. It can be seen in
the table that all poverty measures (P0, P1 and P2) are relatively higher
for persons with disabilities than for their nondisabled counterparts.
The figures, respectively, for those with and without disabilities are P0 =0.28 and 0.24; P1= 0.08 and 0.05; P2= 0.03 and 0.02. Furthermore, the Gini
coefficient indicates that both consumption and land-asset inequalities
are also higher for persons with disabilities than for persons without
disabilities. However, the data shows that the distribution of land assets
is particularly more unequal for persons with disabilities (0.77) than for
their nondisabled counterparts (0.65).
Table 3 shows calculations of the growth elasticity of poverty bydifferent categories of people. For these groups, elasticity coefficients arepresented based on all three measures of poverty: P0, P1 and P2. Row 1of Table 3 shows the results by sex. Growth elasticity based on P0 is -1.68
and -1.08 for males and females with disabilities respectively. Thismeans that a one percent increase in household per capita consumption
will reduce poverty by 1.68 and 1.08% for males and females withdisabilities respectively. The coefficients are -2.31 and -1.55 for males andfemales without disabilities. Similarly, the growth elasticities based on
P1 are -2.92 and -1.21, respectively for males and females withdisabilities, and -3.47 and -2.67 for males and females without
disabilities. In the same vein, the growth elasticities based on P2 are-3.71 and -1.63 respectively for males and females with disabilities, and-3.95 and -2.81 for males and females without disabilities.
These results show clearly that the growth elasticities of poverty are lower
for persons with disabilities regardless of the poverty measures used.
Similarly, we can see in the same table, that the elasticities are also
considerably lower for females in each of the two analyzed groups. Given
that the growth elasticity of poverty is a decreasing function of thedevelopment level of a country and of the degree of inequality in the
distribution of income/consumption (Borguignon 2003), the results
indicate that the level of inequality facing individuals with disabilities
and females is higher than the one that is present among people without
disabilities and that of males. This means that regardless of the rate of
growth in the country, the reduction of poverty will always be smaller forthe more disadvantaged groups, including those with disabilities.
145

Chapter 5Consequently, in order to attain a higher level of poverty reduction for thewhole Nepal, as well as for the more disadvantaged population, in thiscase for people with disabilities, and make development more sustainable
and inclusive, public policies should strategically focus on improving the
distribution of consumption further for these groups through specifictargeted programs.
Row 2 of Table 3 shows the results according to the age of theparticipants. For both of the analyzed groups, the growth elasticities
differ considerably between them. For example, with regard to persons
with disabilities, P0 is highest in the 24-32 year old age group and lowest
in the 15-23 year old group. For persons without disabilities, it is highest
in the age group of 51-59 year olds and lowest in the 42-50 year oldgroup. The case is however, different for P1 and P2 indices. For persons
with disabilities, the poverty condition of groups covering ages 15-23 to
33-41 years old shows they are more sensitive to growth than thepoverty experienced by other age groups, as their growth elasticities
exhibit an absolute value equal or above 3.5 in all cases. However, forpersons without disabilities, the growth elasticity of poverty does notshow a clear pattern and is only higher than 3.5 in the case of the agegroups 24-33 and 33-41 years old for P1 and P2, as well as the 51-59 years
old group when the squared poverty gap is analyzed.
The previous results suggest that the impact of growth on poverty is thehighest among relatively young and middle-age people with disabilities,
and therefore that the economic well-being of those individuals is
improved the most as the economy expands. The reason for this to be thecase is that, as shown in Lamichhane et al. (2014), the same age groups(15-23, 24-32 and 33-41) are those with the highest levels of poverty in
Nepal – they are generally in school or have just completed their
university education and are searching for jobs. Consequently, this
implies that even a small growth of the Nepalese economy or small
investments in the human capital of these people with disabilities, who
are considered one of the disadvantaged groups, will bring about agreater economic benefit to them. Lamichhane and Sawada (2013), whoanalyzed the returns on investment in education for people withdisabilities in Nepal, likewise estimated a two or three times higher
return on education for these people.
Comparing the growth elasticity of poverty between urban and rural
146

Disability and Growth Elasticity of Poverty in a Developing Country

regions, we can see in Table 3 that the urban population without
disabilities has a lower elasticity. Persons with disabilities in the urban
population exhibit an elasticity of -0.81 and those in the rural areas havea -1.73 elasticity based on P0. The other two indicators also show asimilar trend albeit with higher elasticities in absolute value.
The results by region indicate that, regardless of disability status,
reducing urban poverty through growth in Nepal is more difficult than
improving the condition of the rural population by the same means. In
other words, even a small amount of growth can help to reduce poverty
in rural areas to a much greater extent than seems possible to achieve inthe urban centers with a similar amount of growth. The main
explanation for this growth-elasticity pattern may be the fact thatpoverty is much more prevalent and severe in rural than in urban areas
as shown in Lamichhane et al. (2014). Consequently, it is likely that thepoverty-reducing impact of growth will be stronger in the rural areas,
where the majority of the total population resides, and a greater
proportion is considered extremely poor and more disadvantaged thantheir urban counterparts.
Row 4 of Table 3 shows the elasticity of poverty estimates based on thelevel of education. Years of schooling are grouped into three categories:
lower level education (0-5 years); middle level education (6-10 years); andhigher level education (11 years and above). According to the results, in thecase of persons with disabilities, the growth elasticity of poverty (P0) for
persons with 0-5, 6-10 and 11 and above years of education is -1.83, -0.69
and -0.13 respectively. On the other hand, when analyzing P1 and P2 forpersons with disabilities, it is possible to corroborate that the growthelasticity for people with more than 5 years of education is zero, while theone of individuals with an elementary level of schooling is -3.08 and -3.94
respectively. The zero elasticity that was found for the more educatedgroups reflects the non-poor economic condition of the people withdisabilities that was also mentioned by Lamichhane et al. (2014).
Contrastingly, given the fact that the less educated groups are those withhigher levels of poverty in Nepal (Lamichhane et al. 2014), the contributionof growth to achieve a permanent reduction in the poverty level is indeed
crucial as implied by the high elasticities that were obtained.
The results obtained for persons without disabilities with respect to P0
resemble our elasticity estimates for persons with disabilities, according147

Chapter 5

to their educational status. Consequently, the growth elasticities ofpoverty are found to be higher the lower the level of education. These
results are in line with the idea, supported by our previous results aswell, that growth tends to have a stronger, positive impact on theeconomic well-being of people or groups whose levels of poverty arehigher than the rest of the population.
Moreover, row 5 of Table 3 shows the results according to different land
asset categories. For persons with disabilities, the highest elasticity basedon P0 is found for landless households (-1.9), followed by households withsmall land assets (-1.82), households with marginal land assets (-1.42),
households with medium land assets (-1.02), and lastly households withlarge land assets (-0.74). In the case of persons without disabilities,
households with marginal land holdings, followed by households withsmall land and the landless exhibit higher elasticities (-2.92, -2.57 & -2.56)
than those in the other groups. We can observe a similar trend for thecases of P1 and P2, corroborating again our previous findings about thepositive relationship that exists between higher levels of poverty andhigher growth elasticities (in absolute value). The results imply that,irrespective of disability status, persons that possess no or few assets are
the ones who benefit the most as the economy expands, given that theirpoverty condition is reduced faster with a given level of growth.
Moreover, based on the particularly high elasticities obtained, the results
suggest that landless persons with disabilities are more vulnerable to fall
into poverty than any other group in Nepal and, therefore, that specificpolicies or programs directed to them should be implemented to improvetheir economic well-being.
In row 6 of Table 3, results are presented based on individual access tovarious facilities that are within a 30-minute walking distance fromtheir households. When analyzing P0, persons not having access to avehicle road near their households have a higher elasticity (-1.63) thanthose who do have access (-1.2). In comparison, for those without
disabilities, the figures are -2.26 and -1.87 respectively. A similar trend isobserved for P0, P1 and P2 for most of the facilities in this analysis,
except for the cases of vehicle road (P1 & P2) and school (P0, P1 & P2) inthe case of persons with disabilities. These results are mainly related to
the levels of poverty that were estimated for each particular group but,in general, they are in line with our general conclusion that when higher
levels of poverty are observed, the absolute value of thegrowth148

Disability and Growth Elasticity of Poverty in a Developing Country

elasticity of poverty is higher too, indicating the potentially strong
impact that growth has upon poverty and the more disadvantaged
groups in Nepal. For instance, based on the growth elasticity of P2 forpeople with disabilities who do not have electricity at home, a one
percent increase in mean consumption reduces the severity of poverty
by around six percent (see Table 3, row 6, column 3), while, in contrast,
the reduction of poverty is only 2% for all persons who do have access toelectricity (see also Table 3, row 6, column 6).
Finally, the last row of Table 3 shows the results by ethnic category.
Regardless of disability status, all types of poverty (P0, P1 and P2) havethe highest growth elasticity for households within the low caste. Afterthe low caste households, Madheshi families have more elasticity,followed by Mongoloids, high caste, and Newar groups. Sincehouseholds within the Newar ethnicity are generally less poor thanhouseholds within the high caste, their corresponding growthelasticities are considerably lower. According to these results,
households that belong to any of the ethnicity groups of low caste,
Madheshi and Mongoloids should to be targeted in order to alleviate
their poor condition faster through growth-redistributive programs.

5. Concluding remarks
Using a nationally representative dataset of Nepal (NLSS), growthelasticities of poverty were estimated for people with and without
disabilities. Based on the results by disability status, it is not possible toidentify a clear growth-elasticity pattern between the different analyzed
categories. However, in some particular cases such as gender, educationand land assets, growth elasticities for people with disabilities tend to belower than those observed for their non-disabled counterparts. Given
that poverty is determined by both growth and inequality (Datt andRavallion 1992: Bourguignon 2003) and that, the higher the level ofinequality that is prevalent in the economy, the lower will be the impactof growth upon poverty (Ravallion 1997), our results suggest that people
with disabilities generally face higher levels of inequality in specific,
relevant areas of development than the one experienced by people
without disabilities. Consequently, it is advised that growthredistributive programs, targeted to people with disabilities and othermarginalized groups in Nepal, are implemented with the main purposeof reducing the persistent inequalities that are present between people
149Chapter 5with and without disabilities (see Table 2), so that the former of thesegroups can be equally benefited by the gains of growth, ultimately
resulting in an increase in their economic well-being.
Moreover, regardless of disability status, our findings indicate that thepoverty-reducing impact of growth accruing to the poorest and mostdisadvantaged people in Nepal (like the less educated, the landless andthe low caste groups) is considerably higher than that observed for the
rest of the population. As explained in the previous section, higher
elasticities are related to higher levels of poverty. Therefore, it may bepossible to affirm that the benefits of growth are greater for the groups
whose levels of poverty are higher in the economy, implying that thegrowth of the Nepalese economy is pro-poor generally speaking.
Unfortunately, as explained above, pro-poor growth has not beenreaching people with disabilities at the same pace. This seems to be
essentially an equity matter, which, if properly addressed, will bringabout important benefits not only to people with disabilities but to theeconomy as a whole. It should be fully acknowledged that every sectionof the society is equally important if the economy is to succeed in its
path to development. Therefore, neglecting any population group is not
only unfair but is also a threat to sustainable and inclusive development.
Hence, it is of extreme importance that disability-inclusive poverty
reduction strategies are implemented with the aim of achieving equitybased growth in the foreseeable future. Finally, as the inequality aspectsof growth have not been analyzed in this paper, further research isneeded to shed light on this issue.

150

Disability and Growth Elasticity of Poverty in a Developing Country

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Kalwiji, A. and A. Verschoor. 2007. “Not by Growth Alone: The Role ofDistribution of Income in Regional Diversity in Poverty Reduction.”
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———. 2013. “Disability and Barriers to Education: Evidence from
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Chapter 5

Table 1. Definition and mean of variables
Personswith
disabilities

Persons
without
disabilities

44184.52

46290.79

4.21
0.89

4.39
0.92

1 if male, 0 otherwise
1 if female, 0 otherwise

0.86
0.14

0.90
0.10

1 if having age group (15-23) years, 0 otherwise
1 if having age group (24-32) years, 0 otherwise
1 if having age group (33-41) years, 0 otherwise
1 if having age group (42-50) years, 0 otherwise
1 if having age group (51-59) years, 0 otherwise

0.04
0.13
0.24
0.29
0.30

0.04
0.20
0.29
0.26
0.21

Variable
Per capita
Consumption
Household Size
MarriedSex of HH
Male
FemaleAge of HH(15-23) years(24-32) years(33-41) years(42-50) years(51-59) years
Education of HH(0-5) Years(6-10) Years

Definition
Household per capita consumption
in Nepalese Rupees (NPR)
Size of household.
1 if married, 0 otherwise

1 if HH having education of (0-5) years, 0 otherwise
1 if HH having education of(6-10) years, 0 otherwise

0.84

0.81

0.10

0.10

11 Years and above

1 if HH having education of 11 years or more,
0 otherwise

0.07

0.09

RegionUrban
RuralLand Assets Group

1 if from urban region, 0 otherwise
1 if from rural region, 0 otherwise

0.31
0.69

0.31
0.69

Landless(0.00 ha)

1 if having 0.00 hectare of land,
0 otherwise

0.10

0.12

1 if having 0.00-0.15 hectares of land,
0 otherwise

0.12

0.14

0.49

0.44

0.10

0.10

1 if having 4.00 & above hectares of land, 0 otherwise

0.20

0.20

1 if household has access to vehicle road, 0 otherwise
1 if household has access to school, 0 otherwise
1 if household has access to market center,0 otherwise
1 if household has access to hospital, 0 otherwise
1 if household has access to electricity, 0 otherwise
1 if household has access to piped water, 0 otherwise

0.07
0.05

0.09
0.07

1 if caste is Brahmin & Chhetri, 0 otherwise
1 if from Mongoloids caste, 0 otherwise
1 if caste is Newar, 0 otherwise
1 if from Madheshi caste, 0 otherwise
1 if from Low Caste, 0 otherwise
Persons with Disabilities + Persons without
Disabilities

Marginal(0.00 ha-0.15 ha)
Small(0.15 ha-1.00 ha)
Medium(1.00 ha-4.00 ha)
Large(4.00 ha & above)
Access to facility
Vehicle Road
SchoolMarket Centre
Hospital
Electricity
Piped water
Ethnicity
High Caste
Mongoloids
Newar
Madheshi
Low Caste
Total Observations

1 if having 0.15-1.00 hectares of land,
0 otherwise
1 if having 1.00-4.00 hectares of land,
0 otherwise

Source: Authors’ Calculations
Note: HH = household head

154

0.02

0.06

0.04
0.66
0.23

0.04
0.75
0.29

0.33
0.32
0.07
0.17
0.11

0.35
0.29
0.09
0.15
0.12

167

4673

Disability and Growth Elasticity of Poverty in a Developing Country

Table 2 . Poverty and inequality measures
Variable(Mean Values)
Per capita
Consumption

Personswith
disabilities

Confidence
interval(95%level)

Persons
without
disabilities

44,184.52

Confidence
interval(95%level)

46,290.79

Headcount index(P0)0.28(0.0383)

0.21

0.36

0.24
(0.0105)

0.22

0.26

Poverty-gap index(P1)

0.08(0.0128)

0.05

0.10

0.05
(0.0034)

0.05

0.06

Squared poverty-gap
index(P2)

0.03(0.0061)

0.02

0.04

0.02
(0.0016)

0.02

0.02

Gini coefficient of
per capita consumption

0.41(0.0286)

0.35

0.46

0.38
(0.0060)

0.37

0.40

Gini coefficient ofland-asset ownership

0.77(0.0521)

0.67

0.87

0.65
(0.0085)

0.63

0.66

Total Observations

167

4673

Note: Standerd errors are shown in parenthesis.
Source: Authors’ Calculations.

155

Chapter 5

Table 3. Growth elasticity of poverty

Source: Authors’ Calculations

156

Part Ⅲ
Different Growth,
Different Outcomes in Sustainability,
Inclusiveness, and ResilienceChapter 6

Reflections on the Prospects for Pro-Poor
Low-Carbon Growth

Dirk Willenbockel

1� Introduction
Eradicating extreme poverty from the face of the earth once and for all is
set to be a central goal of the emerging post-2015 development agenda.
Without a rapid transition of the world economy to a low-carbon growth
path over the next few decades, this ambitious goal will remain elusive.
Under current greenhouse gas (GHG) emission reduction pledges, theworld is not on track to limit the average global temperature rise to +2℃
above pre-industrial levels. Failure to meet this agreed target threatens
to impede future progress and roll back past achievements in poverty
alleviation. Prospective impact assessments indicate that for poor
populations in tropical and sub-tropical hotspot regions in particular,
the combination of high direct exposure, dominance of climate-sensitive
sectors in economic activity and low autonomous adaptive capacity
entail a high vulnerability to the predominantly harmful effects ofclimate change on agricultural productivity, food security, water
resources, health, physical infrastructure and ecosystems.
Irrespective of the responsibility of the “Global North” for the bulk of
atmospheric GHG concentration levels accumulated in the past, most of
the growth in energy demand and global GHG emissions over coming
decades will arise from today’s developing countries. To avoid
catastrophic climate change, a transition to a low-carbon growth path intoday’s large fast-growing middle-income countries is imperative andmitigation efforts in other developing countries are also required.
Yet developing countries are unlikely to adopt a low-carbon
development strategy if such a strategy is perceived to be in conflict withdomestic near-term poverty reduction aspirations. Thus, a better
159

Chapter 6

understanding of the potential distributional implications of different
conceivable pathways to low carbon development is required to ensure
the social acceptability and political viability of low carbon policy
reforms. The growing recognition that the aims of equitable or pro-poor
growth and low-carbon growth need to be addressed together 1 has led
to efforts in the literature to identify potential synergies and trade-offs
between pro-poor and low-carbon growth. This chapter provides a
selective review of this literature along with some critical reflections.
To underpin the stated premises of this chapter concerning the need forclimate change mitigation action in developing countries, the following
section provides some quantitative background information about global
GHG emission projections and targets, and about the geographical andsectoral composition of current emissions. A simple back-of-the envelope
calculation is used to challenge the prevailing view that the emissions oftoday’s least developed countries are irrelevant from a climate
stabilization perspective. Section 3 contrasts arguments in favor of anearly adoption of low-carbon growth strategies in low-income countries
put forward in the recent literature with the dominant notion that thesecountries should focus on achieving growth and poverty reduction along
conventional lines first and start efforts at cutting carbon emissions at alater stage. Section 4 looks at the potential direct poverty implications ofadopting low-carbon development strategies in energy, agriculture andforestry, and Section 5 draws conclusions.

2� Some Unpleasant Greenhouse Gas Arithmetric
GHG Emission Projections and Targets
To maintain a reasonable chance of limiting the average global surface
temperature rise to +2℃ above pre-industrial levels while keeping
mitigation costs at manageable levels, annual global GHG emissions
would have to peak before 2020 and then drop to around 44 gigatons of
CO2 equivalent (GtCO2e) in 2020, 35 GtCO2e in 2030 and 20 GtCO2e in
2050 (UNEP 2013; OECD 2012; Rogelj and Meinshausen 2010; Stern 2009).
These figures are based on recent estimates of the least-cost emission
trajectories consistent with the +2℃ goal, and are necessarily sensitive toassumptions about technical progress and learning effects in low-carbon
technologies. They are also contingent on the current state of knowledge
about climate sensitivity to atmospheric GHG concentrations.
1. E.g. Stern (2009), Urban (2010a, 2010b).

160

Reflections on the Prospects for Pro-Poor Low-Carbon Growth

Current global emissions are around 50 GtCO2e / year. A full
implementation of present voluntary pledges for mitigation action
submitted by developed and developing countries under the 2009
Copenhagen Accord is projected to lead to 2020 emissions of 52-56
GtCO2e in 2020, suggesting an emission gap of 8-12 GtCO2e in relation to
the least-cost trajectory (UNEP 2013). In this case far higher rates of
mitigation effort than implied by this least-cost scenario would be
required beyond 2020, which – if technically feasible at all – will raise
total mitigation costs substantially. As a case in point, about 80 percentof the power stations likely to be in use in 2020 are either already built or
under construction (IEA 2010). A large fraction of these plants is fossil
fuel powered and will continue to pour out carbon for decades.
Prematurely closing or retrofitting such plants is a very costly option.
Thus, a large fraction of the global energy-related emissions still
permissible under a +2℃ scenario is already locked in by the existing
infrastructure (IEA 2013b). Every year of delaying decisive mitigation
action exacerbates this lock-in problem. Moreover, delayed action entails
a higher reliance on the large-scale deployment of potentially risky
negative emission technologies in the second half of the 21st century. In
short, the door to achieving the required emission cuts at a manageable
cost is rapidly closing. As a result, mitigation action needs to be stepped
up without further delay in the run-up to 2020, the earliest date at which
a comprehensive post-Kyoto climate agreement covering the major
emitters might optimistically take effect, or we run the risk that the +2ºC
goal is missed by a wide margin.
A recent study by the Potsdam Institute for Climate Impact Research for
the World Bank suggests that under the current mitigationcommitments and pledges, there is roughly a 20 percent likelihood of
exceeding 4°C by 2100, and if these pledges are not met, a warming of
4°C could occur as early as the 2060s (PIK 2012). Similarly, the latest
OECD Environmental Outlook baseline scenario, which likewise
assumes no mitigation efforts beyond current pledges, projects increases
in the global mean temperature of +2.0ºC to +2.8ºC by 2050 and of +3.7ºC
to +5.6ºC by 2100 above pre-industrial (OECD 2012). The International
Energy Agency’s new World Economic Outlook 2013 central scenario,
which likewise takes account of mitigation measures alreadyannounced by governments, sees the world on a trajectory towards along-term average temperature increase of +3.6 °C (IEA 2013a).

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These projections are broadly in line with the synthesis of results acrossthe whole range of state-of-the-art global circulation models reported in
the new 5th Assessment Report (AR5) of the Intergovernmental Panel on
Climate Change (IPCC 2013). For those two of the four representative
concentration pathways (RCPs) that are consistent with low or moderate
effective mitigation efforts considered in IPCC (2013), namely RCP6.0
and RCP8.5, the report states: “global temperatures averaged in the
period 2081–2100 are …likely to exceed 2°C above preindustrial for
RCP6.0 and RCP8.5 (high confidence). Warming above 4°C by 2081–2100 is
unlikely in all RCPs (high confidence) except for RCP8.5 where it is as likely
as not (medium confidence)”.2 The 5-95 percent range of across model
projections towards the last decades of the century is +2.0 to +3.7°C
under RCP6.0 and +3.2 to +5.4 °C under RCP8.5.
It is worth noting that projected temperature increases are notuniformly distributed across the globe. Projected increases for someregions are far higher than the global average. In view of these recent
climate projections, scientists are starting to get serious about
contemplating human development prospects in a +4°C world.3
The Decomposition of Present GHG Emissions by Region of
Origin and Sector
Table 1 displays global GHG emissions in 2010 by country or countrygroup of origin and their shares of world emissions ordered by emission
volume for the 21 top emitters (countries that account for 80 percent of
total emissions and for 70 percent of the world population). Importantly
for the purposes of the present chapter, the figures include emissions
from agricultural activity and land use change.
On a production basis, developed countries now account for less than 40
percent of total emissions with a declining trend, while the four top
developing country emitters - China, India, Brazil and Indonesia – alone
account for 33 percent of global emissions with a rising trend. It isevident from the Table that a significant contribution to GHG mitigation
efforts from these and other developing countries is required to bring
2. In IPCC terminology, the terms very likely, likely, and as likely as not mean likelihoods of
66-100, 33-66, and 0-33 percent respectively. Note that among all no-mitigation baseline
scenarios reviewed in the AR5 “none is consistent in the long run with the pathways in thetwo most stringent RCP scenarios …, with the majority falling between the 6.0 and 8.5
pathways” (Clarke and Jiang 2013, 17).
3. See e.g. New et al. (2011), Thornton et al. (2011), PIK (2013).

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Reflections on the Prospects for Pro-Poor Low-Carbon Growthemissions on a sustainable path. Even if the developed Annex I
countries do hypothetically cut emissions to zero over night, the total
would remain well above – and diverge further from – the least-costsustainable path outlined above without further mitigation efforts
beyond current developing country pledges. It is noteworthy in thiscontext that the developing country origin share of cumulative GHG
emissions (again including those from agriculture and land use change)
since 18504 is now roughly equal to the developed country share, and it
has been estimated by den Elzen et al. (2013) that this share will reach
56% by 2020.
To avoid any misinterpretations here: this is not a normative discussion
about climate justice. The sole purpose is to show where geographicallya large portion of future mitigation action must necessarily take place toachieve the climate stabilization goal – it is not an argument about who
is morally responsible to pay for such mitigation action.
Are LDC Emissions Really Irrelevant from a Mitigation Perspective?
The least developed countries contribute a small but non-negligible 5%(Table 1). However, this share is bound to rise significantly over the firsthalf of the 21st century as a result of population and income growth, andmore so if a carbon-intensive growth path is taken. This can bedemonstrated with a simple back-of the-envelope calculation: Let E andN denote total annual LDC emissions and population respectively.
Proportional differentiation of the accounting identity E = (E/N)∙N yields
gE = gE/N +gN. Defining the elasticity of per-capita emissions with respect
to per-capita income Y/N as ε = gE/N / gY/N, we obtain gE = ε∙ gY/N + gN.
The latest UN medium-variant population projections see the total
population of today’s LDCs grow from 832 (Table 1) to 1,726 million overthe period 2010-50, which equates to an average annual growth rate ofgN = 0.0184, i.e. 1.84 percent. With an assumed moderate annualized percapita LCD income growth of gY/N = 0.03 over the same period5 andsetting ε to a moderate 0.36, the average annual emission growth rate
4. Cumulative emissions are significant because they determine current atmospheric GHG
concentration levels and hence climate change,
5. Many LDCs reported far more impressive growth rates in recent years.
6. Under this assumption, a one-percent increase in per-capita income is associated with a
sub-proportional 0.3 percent increase in per-capita emissions. See e.g. Jobert, Karanfil andTykhonenko (2013) for recent empirical evidence on the relation between income andemissions at low-income levels and further reference to the empirical literature. Estimatesof ε vary widely across studies and countries.

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would be gE = 0.0274. Thus, total LDC emissions would nearly triple
from 2.3 (Table 1) in 2010 to 2.3∙(1+gE)40 ≈ 6.8 GtCO2e / year in 2050, eventhough emissions per head rise only moderately from 2.8 (Table 1) to 3.3
tCO2e under these assumptions. With only a slight increase in ε to 0.4
and in gY/N to 0.04, the same calculation would lead to LDC emissions of
9.0 GtCO2e / year with per-capita emissions of 5.2 in 2050 - that is nearly
half of the total global emissions permissible in 2050 under the least-cost
mitigation path outlined above.
These basic calculations indicate that the widely held view that LDC
emissions are largely irrelevant from a global mitigation perspective
does not hold up well to closer scrutiny. Moreover, the widespread
perception that these countries must focus exclusively on the promotionof growth unconstrained by low-carbon considerations and on
adaptation measures to bolster their future resilience to climate change
impacts should also be reconsidered.
An even simpler way to demonstrate the basic problem with this is viewis to just calculate the global average GHG emission levels per head in
2050 consistent with the goal to reach 20 GtCO2e by then: with aprojected world population of 9.3 billion (UNDESA 2011a; medium
variant) that is 2.15 tCO2e / head. As shown in the last column of Table 1,
the LDCs as a group are already slightly above that level, and any
significant increase in that level would necessitate even deeper cuts byother countries and would further increase the likelihood that the +2°C
goal is missed by a wide margin.

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Reflections on the Prospects for Pro-Poor Low-Carbon Growth

Table 1� Greenhouse Gas Emissions 2010 by Region
Emissions Share

China
United States
European Union (27)
Russian Federation
India
BrazilJapan
Indonesia
Australia
Iran
CanadaMexicoSouth Korea
South Africa
Saudi Arabia
Argentina
Venezuela
UkraineTurkeyMalaysia
Pakistan
Sum (Average)
World
Annex I Countries
Non-Annex I Countries
Least Developed

Gt CO2e

10.1
6.8
4.8
2.3
2.3
2.1
1.3
1.2
0.7
0.7
0.7
0.7
0.7
0.6
0.5
0.5
0.4
0.4
0.4
0.3
0.3
37.8
47.2
17.7
29.5
2.3

%

21.4
14.4
10.2
4.9
4.9
4.5
2.8
2.5
1.6
1.5
1.5
1.5
1.4
1.2
1.1
1.0
0.8
0.8
0.8
0.7
0.7
80.2
100.0
37.5
62.5
5.0

Cumulated Population Share Emissions
Share
per capita
%

21.4
35.7
45.9
50.9
55.7
60.3
63.0
65.5
67.1
68.6
70.1
71.6
73.1
74.3
75.4
76.4
77.2
78.0
78.8
79.5
80.2
100.0

Mill

1,341
310
500
143
1,224
195
127
240
22
74
34
113
48
50
27
40
29
45
73
28
174
4,837
6,869
1,207
5,689
832

%

19.4
4.5
7.3
2.1
17.7
2.8
1.8
3.5
0.3
1.1
0.5
1.6
0.7
0.7
0.4
0.6
0.4
0.7
1.1
0.4
2.5
70.1
100.0
17.5
82.5
12.1

t CO2e/head

7.5
21.9
9.6
16.2
1.9
11.0
10.2
4.9
33.5
9.8
21.4
6.3
14.2
11.2
20.1
11.3
13.3
8.5
4.8
12.0
1.9
(7.8)
6.8
14.7
5.2
2.8

Source: World Resources Institute CAIT 2.0 Data Base (accessed January 2014).
Note:“Annex I” countries are the established industrialized countries (i.e. OECD members as of 1992
plus Eastern European economies in transition) with emission reduction obligations under the Kyoto
protocol, while “Non-Annex I” countries are developing countries without such obligations.

The Decomposition of Present GHG Emissions by Sector
The two panels of Figure 1 show the 2010 GHG emission shares by broadtype of activity for the world as a whole and for LDCs respectively.
While on a global scale, anthropogenic emissions are dominated byenergy-related activities, in LDCs as a group, agriculture and land usechange account for nearly 80 percent of the total. It should be noted thatemissions from agriculture and land-use change are more difficult tomeasure than energy emissions, and some estimates in the literature
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record far higher global shares for the former than the World Resources
Institute estimates reported here. The future evolution of these sharesdepends obviously on the qualitative features of the growth paths takenover the coming decades.
Figure 1� Greenhouse Gas Emissions by Source 2010
(Percentage shares in total GHG emissions)

Source: Author’s calculations based on World Resources Institute CAIT 2.0 Data Base(accessed January 2014).

3� The Case for Low-Carbon Growth in Low-Income Countries
A key message of the previous section is the necessity to reconsider thedominant notion that poorer countries in general and LDCs inparticular should focus on achieving growth and poverty reduction
along conventional lines first and start worrying about cuts in carbon
emissions at a later stage. This notion rests primarily on the argumentthat the adoption of low-carbon technologies raises costs and consumer
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Reflections on the Prospects for Pro-Poor Low-Carbon Growth

prices in relation to conventional alternatives, entailing adverse impacts
on growth and hence poverty alleviation. A slow down in growthdriven poverty reduction implies in turn a higher vulnerability to
climate change impacts. Moreover, early mitigation action would divert
attention, funding and scarce planning capacity away from morepressingly needed adaptation investments. Further arguments insupport of the “grow first – decarbonise later” view are that low-carbon
technologies can be expected to be available at lower costs in the future,and that on intertemporal equity grounds, the current poor should not
be obliged to incur consumption sacrifices in favor of future, supposedly
wealthier, generations.
However, this view requires qualification for a number of reasons. Most
importantly, it violates basic economic efficiency principles – and indoing so ignores a potentially large source of gains from international
exchange for low-income countries: to achieve a given global GHG
emission target at minimum cost, the marginal GHG abatement costs
would have to be equalized across all regions of the world. In plain
language, it makes no sense to install low-carbon technologies in richregion A at a cost of $50 per ton of CO2e avoided as long as the sameamount of emission reduction can be achieved at a cost of $10 per ton inpoor region B. Such mitigation cost differences imply the presence ofpotentially large mutual gains from carbon credit transactions between
developed and developing countries. Quantitative estimates show ahuge potential for these mutual gains. A recent model-based global
study by Akimoto et al. (2010) indicates a far larger near-term low-costemission reduction potential up to 2020 in the developing non-Annex I
countries than in the in the developed Annex-I countries (Table 2). Thisfinding is consistent with the meta analysis of results from similar
model-based studies with a time horizon towards 2030 by van Vuuren etal. (2009), which finds unanimous agreement among existing estimatesthat the largest mitigation potentials are in non-OECD countries.

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Table 2� Estimated GHG Emission Reduction Potentials in 2020
(Deviations from 2020 baseline in GtCO2 e/year at abatement costs <0 and <20 US$/tCO2 e)

USA
EU27
All Annex-I
China
India
Brazil+Indonesia+South Africa
Other Non-Annex I
World< 0 $/tCO2e
1.7
1.2
3.3
6.9
3.3
1.2
3.9
20.2

< 20 $/tCO2e
2.4
1.8
4.3
10.6
4.3
1.7
6.2
30.1

Source: Akimoto et al (2010: Table 4) and author’s calculation.
Notes:“Annex I” countries are developed countries with emission reduction obligations under the
Kyoto protocol, while “Non-Annex I” countries are developing countries without such obligations.
The baseline assumes constant emission intensities.

In the absence of a full global cap-and-trade system, the Kyoto flexible
mechanisms including the Clean Development Mechanism (CDM) as
well as the upcoming REDD+ (Reducing Emissions from Deforestation
and Degradation) scheme are in conception key devices for therealization of the mutual gains arising from abatement cost differences
across regions and must be scaled up significantly under the emerging
post-Kyoto climate finance architecture. As far as the envisaged newmarket-based mechanisms that will supersede or complement the CDM
provide full funding and technical assistance for early mitigation actionin poorer countries from developed country sources, domestic growthprospects need not be adversely affected, and the recipient regions may
benefit from additional ancillary benefits as elaborated below. A
sufficiently high carbon price is a necessary precondition for theeffectiveness of any such market-based mechanism in mobilizing lowcarbon investment flows from richer to poorer countries at scale. This inturn will require a global climate agreement with stringent binding
targets for the major emitters. Proposals favoring a system of voluntary
pledges are in this respect “about as useful as a chocolate tea pot”, as
former UK Climate Secretary Chris Huhne has put it.7
Taken at face value, the presence of mitigation options with a negative
abatement cost in Table 2 would mean possibilities for immediate
mitigation measures with positive net gains even in the absence of
payments flows from rich countries. The presence of such negative-cost
mitigation options in poor countries undermines in itself the basic
7. Cited in Harvey (2011).

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Reflections on the Prospects for Pro-Poor Low-Carbon Growthpremises of the “develop first – decarbonize later” position.8
A further argument in favor of the early adoption of a low-carbon
growth path – that applies specifically to the least developed regionswith a rudimentary present energy infrastructure, emphasizes thebenefits of avoiding a high-carbon technology lock-in right from thestart. Given the long-lived nature of energy plant and infrastructure
investments and the associated high cost of premature scrapping,choosing low-carbon modes of development now will reduce theeconomic burden of GHG gas mitigation in the future (OECD 2013;
Byrne et al. 2011; Bowen and Fankhauser 2011).9 Moreover, as technicalprogress in the energy sector will shift to low-carbon technologies, oncea global climate deal is eventually reached, LDCs that have embarked ona traditional high-carbon development trajectory will be stuck with astagnant type of technology and will not be able to benefit from thistechnical progress (Bowen and Fankhauser 2011).
Another economic reason for embracing a low-carbon growth strategy isthe avoidance of prospective adverse impacts on future export growth
performance. Such adverse trade impacts would arise if countries withstrong mitigation efforts impose border tax adjustments on carbonintensive imports from countries on a high-carbon growth path, or if
better-off consumers – including households belonging to the growing
middle class in developing countries – switch their preferences to green
low-carbon alternatives (e.g. Rowlands 2011; Bowen and Fankhauser 2011).
The literature identifies a further range of co-benefits for countries
adopting a low-carbon development strategy. Such ancillary benefits
include the effects of reduced air pollution on mortality and morbidity,
greater energy security and the relaxation of foreign exchange due toreduced dependence on fossil fuel imports, and potential positive net
employment effects that arise if “green” low-carbon job creation in the
8. Economists tend to be sceptical about models and arguments that suggest the existence of
unpicked dollar bills on the sidewalk. Capital market imperfections and incomplete
information are among the most obvious explanatory factors for the presence of
unexploited negative-net-cost investment opportunities, given that upfront investments
are required to reap the stream of future net negative operating costs (that arise, forexample, due to large fuel cost savings as a result of investments in energy efficiency or aswitch to renewables).
9. Essentially the same lock-in argument applies to non-energy infrastructure investments
and planning decisions with a largely irreversible character such as in the areas of transport
and urban development– see Pye et al. (2010).

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course of the low-carbon transformation exceeds “brown” job
destruction.
Such co-benefits figure prominently in the existing literature on propoor low-carbon development. Indeed much of this literature appears to
be entirely fixated on the identification and propagation of win-win
measures that promise the simultaneous achievement of mitigation andpoverty reduction objectives by co-incidence.10 From a political economy
perspective, this fixation is understandable, given that prospects fordouble or triple wins open up opportunities for the formation ofalliances among multiple policy actors with differing priorities insupport of such measures. However, an exclusive focus on suchsynergetic measures unduly narrows the space of potential policy
options by ignoring a basic insight from the theory of economic policy inthe tradition of Jan Tinbergen (1952), joint winner of the first Nobel Prizein Economics back in 1969: the best possible achievement of multiple
policy objectives generally requires the combination of multiple policy
instruments.
In the present context, this means that a narrowly conceived pro-poor
low-carbon growth strategy that rules out policies with a highmitigation potential but without co-incidental pro-poor benefits - orwith adverse primary distributional side effects - is likely to generateinferior outcomes compared to a strategy that combines such mitigation
policies with the application of feasible redistributive measures.

4� How Pro-Poor is Low-Carbon Growth? A Closer Look
Poverty Implications of Low-Carbon Growth in the Energy Sector
Any consideration of the poverty implications of low-carbon growthmust start from the fact that presently some 1.3 billion people lack accessto electricity (IEA 2013a), another billion people only have access tounreliable electricity (Casillas and Kammen 2010), and about 2.6 billion
people rely on traditional biomass, such as wood, dung and charcoal forcooking and heating purposes (IEA 2013a).
Given that achieving universal access to modern energy services is a co10. Proponents of “climate-smart” development strategies go one step further bypromoting the pursuit of “triple win” policies that generate adaptation co-benefits on top of
mitigation and poverty reduction benefits.

170

Reflections on the Prospects for Pro-Poor Low-Carbon Growthrequisite for the eradication of extreme poverty, these figures might atfirst sight suggest a fundamental conflict between the global climate
stabilization and poverty elimination goals. However, a recent scenario
analysis by the International Energy Agency (IEA 2010) suggests that it
is technically and economically feasible to reach universal access to abasic level of modern energy services by 2030 with an increase in CO2emissions of merely 0.8 percent relative to a 2030 baseline scenario in
which 1.2 billion remain without electricity access.11
In the IEA universal access scenario all urban and peri-urbanhouseholds are connected to the national grid by 2030, while in rural
areas 70 percent of the new access is provided via decentralized minigrid and off-grid systems including solar photo-voltaic, mini-hydro,
wind, biomass and geothermal systems. In the recent low-carbon
development literature, the deployment of such decentralized
renewable energy systems is widely seen as a promising andeconomically viable approach to reduce energy poverty in remote rural
areas. Apart from the direct poverty reduction impacts associated withelectrification in general (such as the extensions of hours available forincome generation and education activities), the provision of thesesystems is seen to generate further pro-poor growth benefits by creating
local jobs in related hardware manufacturing, distribution, installationand maintenance.12
A recent study by Deichmann et al. (2011) for Ghana, Ethiopia and
Kenya uses spatial modeling in combination with engineering costestimates to determine where stand-alone renewable energy generation
is a cost-effective alternative to centralized grid supply. The results
indicate that decentralized renewables are competitive mostly in remote
11. Global electricity generation rises by 2.9 percent and oil demand by one percent relative
to the 2030 baseline levels. In the baseline scenario, global CO2 emissions in 2030 are 21
percent higher than in 2008. Chakravarty and Tavoni (2013) consider a more ambitious
global energy poverty eradication scenario that includes the provision of electricity andfuels to increase productivity in agriculture, commercial activities and transport to 3.4
billion energy-poor people, in addition to universal access to basic energy services to satisfy
basic human needs for 1.8 billion people by 2030. In this scenario, global final energy
consumption rises by 7 percent relative to the baseline.
12. See e.g. UNEP/UNCTAD/UN-OHRLLS (2011), World Bank (2012), UNDESA (2011b),
Casillas and Kammen (2010), Grantham Institute (2009). Of course, the extent to which suchgreen job creation is directly pro-poor depends in particular on the skill intensity of thesejobs and the empirical evidence for developing countries is weak in this respect (Dercon2014). For a detailed systematic review of the empirical evidence on the benefits of increased
renewable energy capacity for poor people see Pueyo et al. (2013).

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rural areas, while grid-connected supply is the cheaper option in more
densely populated areas where the majority of households in thesecountries reside. These findings confirm that decentralized renewableenergy can play an important role in expanding rural energy access in
Sub-Saharan Africa (SSA), but the results also underscore the need to pay
attention to the evolution of the fuel mix for centralized power generation.
As Collier and Venables (2012) point out in this respect, Africa is wellendowed with potential for hydro and solar power, but lacks capital,
skills and governance capacity. Since distributed solar power is very
capital and skill-intensive, the authors conclude that the international
community must provide support by increasing Africa’s supply of thesescarce factors. In line with the argument in section 2, the CDM is seen as
one of the appropriate instruments to provide this support.
Timilsina et al. (2010) investigate the potential of reducing energyrelated GHG emissions via the CDM across 44 countries in SSA throughthe CDM. The study looks at a wider range of low-carbon technology
options and finds that over 3,200 CDM projects that meet the eligibility
criteria could be developed in the region. The cumulated GHG
mitigation potential over the 10-21 year CDM project cycle is estimatedto amount to 9.8 GtCO2. However, the realization of this potential iscontingent on effective assistance in overcoming a range of
implementation barriers that partly explain the very low number of
CDM projects in the region up to the present. The barriers to
implementation identified in this study include inter alia lack ofinfrastructure, institutional capacity and local skilled labor as well as
potential foreign investors’ perceptions of SSA as a high-risk region.
In addition to electrification, the IEA (2010) Universal Access Scenario
also envisages the provision of access to clean cooking facilities (in theform of LPG stoves or advanced biogas/biomass stoves) for the 2.8
billion people still relying on traditional biomass in the 2030 baseline
scenario. Recent estimates suggest that the associated emission
reductions could exceed 1 Gt CO2e per year (Lee et al. 2013) – that is asignificant figure in relation to the global emission targets outlined in
Section 2. The pro-poor co-benefits of a suitably subsidized roll-out ofclean efficient stoves is evident. According to World HealthOrganization estimates, currently in-door air pollution causes 2 million
premature deaths per year (WHO 2011), or more annual deaths than are
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Reflections on the Prospects for Pro-Poor Low-Carbon Growthcaused by malaria, tuberculosis or HIV (UNDESA 2011b). In addition to
the direct health impacts and health-related productivity gains, the propoor ancillary benefits include fuel collection and cooking time savings.
A global cost-benefit analysis of a hypothetical intervention thatprovides access to clean stoves for 50 percent of the population lacking
such access in 2005 by Hutton, Rehfuess and Tediosi (2007) reports abenefit-cost ratio on the order of sixty.
Distributional Implications of Fossil Fuel Subsidy / Tax ReformTo achieve the transition to a low-carbon growth trajectory in marketbased economies, relative prices between fossil fuels and low-carbon
energy sources play a decisive role. It is critical that the fossil fuel prices
faced by market participants reflect the long-run marginal social costs
associated with GHG emissions in order to incentivize the required
structural transformation of the energy system as well as to induce
energy efficiency investments and shifts to less carbon-intense demand
patterns. Fossil fuel subsidies distort relative prices and pervert
incentives exactly in the opposite direction. Their swift phasing-out
must be part of any effective low-carbon growth strategy.
According to the latest IEA (2013a) estimates, fossil-fuel consumption
subsidies worldwide amounted to US$544 billion in 2012, a large portionof which is attributable to developing and emerging countries. Separate
estimates for OECD countries based on a different methodology suggest
indirect public support measures for fossil fuel production and use indeveloped countries on the order of US$45-75 billion per annum inrecent years (OECD 2011). At a global scale, fossil fuel subsidies are six
times higher than the financial support given to renewables (IEA 2013b).
Phasing out these subsidies could provide around half the emissions
reductions needed over the next decade to reach a trajectory that would
limit global warming to +2°C.
Fossil fuel subsidies in developing countries are commonly justified as ameans to make modern energy services affordable to the poor, and theirremoval is widely seen to hurt poor households disproportionally. Fact is,however, that fuel subsidization is a grossly inept instrument to target thepoor. Using data for a sample of 20 developing countries, Arze delGranado, Coady and Gillingham (2012) show that on average acrosssample countries, households in the top income quintile receive 42.8
percent of the benefits from fuel subsidies while the bottom quintile
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Chapter 6

receives only 7.2 percent. This implies that the average burden to
government budgets of transferring one dollar to the poor quintile is amindboggling US$13.89, as nearly 93 percent of the subsidy leaks to thehigher quantiles.13
This is not to deny that the direct impacts of fuel subsidy cuts on fuelprices and on the prices of other goods via input-output linkages viewed
in isolation will hit the poor, along with better-off households andproduction sectors intense in the use of fossil fuels or fossil-fuel-based
power. But the ultimate distributional impact of such cuts, as well as
their political feasibility, depends crucially on how governments use theadditional fiscal space created by the reduced burden on government
budgets.
As illustrated by a recent background study by Willenbockel and Hoa(2011) for UNDP (2012), adverse distributional and growth side effects offossil fuel subsidy cuts are by no means inevitable. A dynamic scenario
analysis based on a general equilibrium suggests in particular thatadverse impacts on poor households can be neutralized (or turned into
pro-poor impacts) by using part of the government savings arising fromthe subsidy cut for compensating cash transfers. Using the additional
fiscal space to foster additional productive and more energy-efficientinvestments may actually raise income and consumption for all
households in the medium run. The same argument applies to the
hypothetical introduction of a carbon tax, also considered in this study,as the direction of the first order effects is essentially equivalent to thatof a fossil fuel subsidy cut. The general tenor of these findings is broadly
in line with the results of similar ex-ante general equilibrium simulation
studies surveyed by Ellis (2010) and Boccanfuso et al. (2008). In short:fossil fuel subsidies are definitely not pro-poor – their elimination canbe.
Poverty Implications of a Low-Carbon Transition in the Agriculture
and Forestry Sector
Direct GHG emissions from agriculture (Figure 1 above), exclusive of
13. The average figures mask even more extreme cases of bad policy targeting (taking the
pro-poor motive for such subsidies at face value) that become apparent by looking at thedisaggregated results for country groups and fuel types in Arze del Granado, Coady and
Gillingham (2012: Table 12). E.g. in Africa, only 2.2 percent of gasoline subsidies reach thebottom quintile, implying a budgetary burden of over US$45 to transfer a single dollar tothe poorest quintile through this instrument.

174

Reflections on the Prospects for Pro-Poor Low-Carbon Growthforest conversion into agricultural land, consist primarily of N2O(nitrous oxide) associated with fertilizer use, MH4 (methane) associatedwith enteric fermentation emissions from livestock and emissions fromrice paddies and manure, and to a lesser extent, of net CO2 fluxes to theatmosphere associated with degradation of organic soils in tropicalregions (Smith et al. 2008, 2013; Olander et al. 2013).
Potential mitigation measures include various changes in land andlivestock management practices as detailed in Smith et al. (2008; 2013)
and Lal (2011). As far as adverse direct and indirect land use change
effects that could lead to a net increase in emissions can be avoided,
biofuel production may be seen as a further option for mitigation actionin the agricultural sector.
Smith et al. (2008) estimate the technically feasible global technical GHG
mitigation potential from agriculture excluding fossil fuel offsets from
biofuels by 2030 to be in the order of 5.5 to 6 Gt CO2e per year witheconomic potentials of 1.5 to 1.6, 2.5 to 2.7 and 4.0 to 4.3 at carbon prices of
up to US$20, 50 and 100 per ton of CO2e respectively. The additional
economic mitigation potential of replacing fossil fuels by biomassenergy from agriculture is estimated to be 0.64, 2.24 and 16.0 Gt CO2e per
year for the same three marginal abatement cost ranges.
However, any consideration of the potential contributions of agriculture
in developing countries to GHG mitigation must take into account thatthe combination of population growth and rising per-capita incomes thatwill be accompanied by a shift towards more livestock-intense diets inparts of the world will translate into a substantial increase in the demand
for agricultural output between now and the middle of the century.
These demand-side drivers are bound to intensify the competition forland and water, particularly in low-income regions with high population
growth and a high present incidence of undernutrition.14
The need to adapt to the emerging impacts of climate change that are
already locked into the system even under the most optimistic
assumptions about future mitigation efforts adds to the pressure forlow-income regions. Long-run agricultural productivity trends as well
as short-run yield variability are directly affected by climate change andthe associated expected increases in extreme weather events. A growing
14. Government Office for Science (2011); Godfray et al. (2010).

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number of studies suggest that climate change may well reduce theproductivity of farming in precisely those regions of the world where
undernutrition is already most prevalent.15
Moreover, climate change mitigation policies aimed at the energy sector
that raise fossil fuel prices would drive bioenergy demand upwards
even in the absence of biofuel mandates and further intensify thecompetition for land. An extensification response in the form of
converting forestland to farmland is obviously not a sustainable option,
as net forest conversion would further add to emissions and reinforce
the problem.
It is precisely this confluence of pressures on agricultural systems that
led the UK Government Foresight Report on the future of food andfarming (Government Office for Science 2011) to the conclusion that theincrease in the global food supply must be based on sustainable
intensification. Sustainable intensification means simultaneously raisingyields, increasing the efficiency with which inputs are used andreducing the GHG emissions associated with food production. It is acore principle of the wider notion of a climate-smart agriculture (CSA)
that seeks to “(i) sustainably intensify production systems to achieve
productivity increases, thereby supporting the achievement of national
food security and development goals; (ii) increase the resilience of
production systems and rural livelihoods (adaptation); and (iii) reduce
agriculture’s GHG emissions (…) and increase carbon sequestration(mitigation)” (Branca et al. 2012).
A gradual move to a growth path based on this conception in the rural
areas of SSA and South Asia with support from developed countries isin principle a pro-poor strategy, as it would raise returns to agriculturallabor over time and speed up the structural transformations required toachieve the longer-term aim of eradicating extreme poverty. Pretty,
Toulmin and Williams (2011) review 40 sustainable intensification
projects with a coverage of 12.8 million ha of land across 20 Africancountries. They report an average yield increase across these projects bya factor of 2.13, benefiting 10.4 million farmers and their families.16
15. See Willenbockel (2014) for further reference. See also Nelson et al. (2014).
16. For further reference to empirical case study evidence on yield improvements andpoverty impacts associated with the adoption of sustainable intensification practices seeinter alia UNEP (2011), Cooper et al. (2013), ILO (2012: Ch.2), World Bank (2012: Ch.5),
Shames et al. (2012).

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Reflections on the Prospects for Pro-Poor Low-Carbon Growth

The potential role of biofuel production within pro-poor low-carbon
growth strategies remains a highly contested issue. The suitability of
traditional food-crop-based first-generation biofuels, except for sugar
cane based fuels, for the achievement of significant net GHG reductions
is increasingly being called into question. However, various recent
global scenario studies exploring feasible emission pathways to achieve
the +2℃ goal see an indispensable role for second-generation lignocellulosic biofuels in the future energy mix.17 These emergent advanced
second-generation biofuels are based on non-edible inputs, including
crop and forest residues, grasses (switchgrass, miscanthus) or fastgrowing trees (poplar, willow, eucalyptus) that can be grown on
marginal and degraded land not suitable for food crop production andare expected to have a far higher net GHG reduction potential thanconventional first-generation bioenergy feedstocks (Lotze-Campen et al.
2014; OECD 2013).
For parts of SSA in particular, the hope is that a carefully regulated
allocation of marginal land to next-generation bioenergy production
could avoid the risks of harmful effects on poor people widely
associated with a large-scale conversion of land for commercial firstgeneration biofuel production.18 This includes risks such as theuncompensated loss of access to land for smallholders with precarious
customary land tenure rights and adverse food security impacts. Lyndand Woods (2011) envisage a large future potential of pro-poor benefits
for Africa offered by an integration of second-generation bioenergy
conversion technologies into agricultural value chains and outline therequirements for a realization of these benefits.
Through their implications for land use and access to land, mitigation
measures in agriculture are closely interconnected with measures toachieve reduced emissions from deforestation and forest degradation(REDD), and the potential poverty impacts of the latter are likewise
subject to controversial debate. A major bone of contention in thevoluminous pertinent literature is how alternative design options for the
REDD+ carbon finance scheme under a global post-Kyoto climateagreement affect the livelihoods of poor forest users. There are concerns
as to how safeguards can be established to ensure that the interests ofnational elites and international investors do not override the rights of
17. See Lotze-Campen et al. (2014) for reference to these scenario studies.
18. See e.g. Cotula, Dyer and Vermeulen (2008) and Mitchell (2011).

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local forest communities and that an equitable share of the REDD+
benefits reaches the poorest members of these communities.19
Meanwhile, the UNFCCC 19th Conference of Parties in December 2013 hasagreed upon the Warsaw Framework for REDD+, which specifies the key
design features of the future scheme that would take effect if a global deal
is reached in 2015. The Framework includes safeguard clauses that aim toensure that REDD+ is implemented in equitable ways and in accordancewith a country’s sovereignty. Notably, it contains a requirement forrecipient countries of carbon finance to publish periodic information on
how these safeguards are being addressed and respected.

5� Concluding Remarks
The point of departure and motivation for this chapter is the joint
proposition that there will be no lasting global poverty eradication
without low-carbon growth and no global low-carbon transition
without poverty reduction. Or as Nicholas Stern (2009) has put itemphatically, “(t)he two defining challenges of our century are
overcoming world poverty and managing climate change. If we fail onone, we fail on the other.” Thus, the aims of equitable or pro-poor
growth and low-carbon growth are intrinsically linked and need to beaddressed together.
A closer look at the basic merciless algebra of global GHG emissions andlow-cost mitigation pathways reveals an important message: theprevailing view that the emission paths of today’s least developed
countries are largely irrelevant from a global mitigation perspective isdemonstrably mistaken. In fact, average LDC emissions per capita arealready higher now than the maximum average global per-capita
emissions permissible in 2050 if the +2°C target is to be reached atmanageable mitigation costs. Thus, the widely held view that over the
next few decades these countries should focus exclusively on the
promotion of growth unconstrained by low-carbon considerations andon adaptation measures to bolster their future resilience to climatechange impacts, needs to be reconsidered.
19. E.g. Brown, Seymour and Peskett (2008) and Funder (2009) fur further elaboration. For
alternative critical perspectives see Leach and Scoones (2013) and Fairhead, Leach andScoones (2012).

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Reflections on the Prospects for Pro-Poor Low-Carbon Growth

Fortunately, there are a number of good economic reasons why it could bein LDCs’ own self-interest to adopt a low-carbon growth strategy at an
early stage. In particular, (i) the large potential for low-cost mitigation
measures in the developing world including LDCs provides opportunities
for substantial mutual gains from carbon credit transactions between
developed and developing countries; (ii) choosing low-carbon modes ofdevelopment now will reduce the economic burden of GHG gas mitigationin the future by avoiding a high-carbon technology lock-in; (iii) the
avoidance of potential adverse impacts on future export growth
performance in the case of border tax adjustments and shifts in consumer
preferences to low-carbon varieties in other countries; (iv) the realization ofgains from lower outdoor / indoor pollution; and (v) improvements inenergy security and the relaxation of foreign exchange constraints due toless dependence on fossil fuel imports.
But how pro-poor is the transition to a low-carbon growth path? The
existing literature on pro-poor low-carbon development identifies arange of clear synergies between mitigation and poverty reduction
objectives, but as the selective discussion of the prospects for thetransition to a pro-poor low-carbon path in the agriculture, forestry andenergy sectors of low-income countries in this chapter indicates there
are also trade-offs. A narrow conception of pro-poor low carbon growthstrategies that focuses exclusively on the implementation of coincidental
win-win measures aims too short, if redistributive measures are feasiblethat could reverse the ultimate equity outcomes of policies with highmitigation impact but adverse primary impacts on poverty. The removalof fossil fuel subsidies and the introduction of a carbon tax discussed inthis chapter illustrate the point. The direct price impacts of thesemitigation policies are bound to hit the poor along with the better-off,but the joint use of distributive measures could in principle generate apositive net impact on the poor. More generally, multiple policy
objectives call for the use of multiple policy levers.
The key message of this chapter is that a success of the adoption of propoor low-carbon strategies by low-income countries with significant
mitigation potential along the lines outlined above requires
development cooperation efforts between high- and low-income
countries on an unprecedented scale. This includes the completion andimplementation of a comprehensive global climate agreement withbinding targets and effective and sufficiently funded mechanisms for
179

Chapter 6

the transfer of carbon finance flows as an essential prerequisite.

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Chapter 7

Structural Transformation, the Quality of
Growth and Employment Outcomes

Xavier Cirera1

1� Introduction
The emphasis in economic growth as the main driver of economic
development has been shifting in the last decade to include a morenuanced view of the characteristics of the economic growth process. As
a result, more attention is being paid to understanding the “quality” of
economic growth in terms of facilitating development outcomes. Chiefamong these elements is the impact of economic growth on the qualityof employment.
Traditional economic development models a-la-Lewis (1954) link theprocess of economic development to the type of economic growthexperienced, which translates into changes in standards of living
through a process of structural change. In these models, economic
development can be characterized by the reallocation of employment
from low productivity “traditional” sectors, such as agriculture, to highproductivity “modern” sectors, such as manufacturing. As productivity
growth in agriculture accelerates, labor is transferred from agriculture
to industry. Accordingly, when the share of employment in higher
productivity sectors grows, more people are employed in higher wage
sectors and enjoy better labor conditions. Structural change, therefore,
allows the linking of economic growth to the quality of employment bylooking at the type of sector productivity growth and reallocation offactors of production that drives the economic growth process.
One critical driver of economic growth in the last decade in developing
countries, especially in Africa and Latin America, has been the large export
boom in primary commodities. This has resulted in further specialization
1. I would like to thank Antonio Martuscelli for superb research assistantship, ananonymous referee for useful comments, and JICA and AFD for financial assistance.

187

Chapter 7of the production base of these countries in natural resources andagriculture. One risk of this pattern of growth is that it potentially locks
employment in some of the poorer countries into low productivity sectors,
minimizing the transformational potential of employment growth inreducing poverty and vulnerability. This type of “negative” structuralchange has been documented empirically by some authors (see for example
McMillan and Rodrik 2011 for Latin America and Africa).
The objective of this paper is to shed some light on the link between
structural change and employment outcomes, such as unemployment andsome indicators of the quality of employment. We complement theexisting literature by analyzing structural change beyond the reallocationof employment across sectors and investigate the impact of structuralchange on different labor outcomes. Specifically, we focus on two key
elements. First, we test whether the pattern of growth in developing
countries is one of “jobless” growth. Second we analyze one dimension oflabor quality where there is data available, security of tenure and careerprospects (UNECE 2010), using indicators such as unemployment,
informality of employment or vulnerability of the labor force.
The paper is structured as follows: Section 2 briefly summarizes some of
the literature on structural change and the links to labor outcomes.
Section 3 describes the dataset used. Section 4 describes the evolution ofseveral indicators of employment quality and based on the results,
estimates a measure of structural change. Section 5 empirically analyzes
the link between the type of growth pattern and employment quality
indicators. The last section provides some brief conclusions.

2� Structural transformation, economic growth andlabor outcomes
Several studies have analyzed the process of structural change indeveloping countries. These are mainly motivated by a concern about
the patterns of production common in some developing countries thatare based on primary commodities and low sophistication/technological intensity products. Key among these concerns is thecontinuous decline in the importance of the manufacturing sectors in
the economy in some countries.
Growth in the manufacturing sector is credited with having significant
positive economic and social spillover effects into other sectors and the188Structural Transformation, the Quality of Growth and Employment Outcomesentire economy. Kaldor (1967) stated through his “growth laws” thatgrowth in the industrial and manufacturing sector is correlated withlarger economy-wide growth. Increasing returns to scale and learningby doing imply that growth in the manufacturing and industrial sectortranslates into significant productivity growth (Kaldor 1967). In
addition, other authors have suggested that manufacturing employment
growth helps to create a middle class that forces institutional
improvements, resulting in further growth and better living standards.
As a result, a strand of the development economics literature hasemphasized the importance of the manufacturing sector as the criticalelement for structural change and a key engine for the improvement oflabor standards and institutions.
Szirmai (2012), for example, looks at the issue of structural change acrossregions. The author suggests that Africa and some Latin Americancountries have become an exception in terms of structural change.
While between 1980 and 2005 the share of manufacturing in total output
continued to increase in many Asian economies, there has been aprocess of deindustrialization in Latin America and Africa. Concretely,in the 22 African countries for which data was available, manufacturing
output ranged between 8.5% and 13.3% of GDP, with an 11% average forthe continent. Memedovic and Lapadre (2010), focusing mainly on subSaharan countries, identified three different periods of structuralchange. The first, in the 1970s, corresponded to strong increases in value
added, extractive industries. The second occurred in the period 19801995 with a large expansion of the services sector, which was later
negated after 1995, as regional specialization in raw material production
deepened, to the detriment of manufacturing and services.
In a recent study McMillan and Rodrik (2011) have empirically analyzed
the issue of structural change for a sample of developing countries. The
authors calculate labor productivity changes for a set of countries withavailable information for the period 1990-2005. The authors follow thestandard decomposition of aggregate labor productivity growth in two
components (see Section 3 for a detailed explanation). The firstcomponent is the growth that is accounted for within-sector
productivity growth, which is related to increases in sector efficiency
over time and rationalization of productive units as countries face, forexample, more competition from opening up markets and integration
into the world economy.
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The second component, between-sector growth, is related to thecontribution of labor productivity growth that corresponds to labor
shifting to higher productivity sectors – the structural change component.
This labor reallocation measure is the critical element in understanding
the direction of labor flows and the potential impact on labor outcomes.
McMillan and Rodrik (2011) find a negative contribution of the structuralchange component - the between-sector measure- to average laborproductivity growth in Latin America and Africa, while this relationshipis found to be positive in Asia. This would suggest that labor reallocationsin the Latin American and African regions are mainly directed to lessproductive sectors. The authors show that the main factors that determine
this “negative” structural change pattern are high commodity prices andthe pattern of comparative advantage in these regions.
These findings are, however, contested by other authors. While there is asignificant amount of evidence indicating the loss of importance of the
manufacturing sector in most economies, other studies suggest that thestructural change component is positive, although small, in mostdeveloping countries. Regional aggregate results mask significant
differences across countries. For example, the estimated structuralchange component in McMillan and Rodrik (2011) for Africa is positivein four out of the nine countries of the sample. More recently, Kucera
and Roncolato (2012) estimate the structural change decomposition for asample of countries and find that the structural change component hascontributed positively to labor productivity growth in most countries. A
similar result is reached in the World Development Report (World Bank2013), which implies that some of the regional average results might bedriven by a few large countries. Kucera and Roncolato (2012) re-estimate
the labor productivity growth decomposition with the McMillan andRodrik (2011) dataset adding an interactive term, “within*between”
components, to capture whether the reallocation of labor happens tosectors with declining or growing labor productivity. The results
suggest that with the interaction term, the negative structural change
finding in McMillan and Rodrik (2011) is significantly reduced.
In addition to whether the structural change component is positive or
negative, it is important to compare its contribution to the within-sector
growth component. This is especially important when considering
potential labor outcomes. Kucera and Roncolato (2012) emphasize that thewithin-sector component tends to be larger for most countries than the190

Structural Transformation, the Quality of Growth and Employment Outcomes

structural change component. This finding is also corroborated
empirically by Ocampo et al. (2009) as well as Timmer and de Vries (2009).
The relevance of these results is related to the fact that often within-sector
productivity growth is explained by a significant adjustment of operating
firms in the sector. More productive firms expand and less productive
firms exit the market (Foster et al. 2008). This is translated into significant
reallocation of workers, with some exiting the labor market or becoming
employed elsewhere. The total net effect is unclear, and whenemployment creation is low in most productive firms, the resultant
pattern of productivity growth is one of potential “jobless” growth.
The hypothesis of “jobless” growth has been analyzed in the literature,but it does not appear to be supported by the evidence, which finds a
positive correlation between output growth and employment growth.
Nevertheless, Kucera and Roncolato (2012) find a weaker relationshipbetween output growth and employment growth and a negative
relationship between productivity growth and employment growth indeveloping countries. The authors suggest that “jobless” growth may be
a problem for some developing countries, especially in Asia.
One problem with some of this literature is the fact that the dichotomy
between manufacturing, modern sectors, and agriculture, natural
resources assumed in some studies, has become blurred in the last
decades with the emergence of services. The services sector is the largest
sector in terms of creating added value and employment in almost all
developed and developing countries, and only in some countries withlarge agriculture and natural resource sectors such as DRC, Ethiopia or
Liberia, does the services sector not dominate. In addition, the services
trade has expanded significantly in recent years in line with the
emergence of global production fragmentation. This implies that whenanalyzing structural change, rather than looking at manufacturing
shares only as the “modern” or high productive sector, one must
consider more generally the reallocation of employment from low tohigh productivity sectors. Including services makes the analysis
challenging, because some knowledge services are highly productive
while some other sector services are likely to have low productivity.
Overall, this literature suggests that there is a link between the pattern of
economic and productivity growth and labor outcomes. Most of thestudies, however, focus on the impacts on employment creation. One
191

Chapter 7

assumption when it comes to considering the quality of employment isthat employing more people in higher productivity sectors not only
creates employment and raises wages but also reduces vulnerability or
informality by strengthening labor market institutions. In addition,
employing more people in natural resource-related sectors might increase
worker vulnerability and adversely affect labor institutions in themedium run by for example increasing income inequality and reducing
the size of the middle class. The final labor outcome, however, depends on
the interaction between labor sector changes and labor institutions.
When thinking about labor outcomes it is important to consider quality
attributes. Employment changes can have different implications
depending on whether they are based on bad working conditions, lackof security or child labor. Therefore, it is important to analyze whether
the impacts on labor outcomes from structural change translate into
better or worse labor quality indicators.
Defining labor quality, however, is a complex task since it has many
dimensions. UNECE (2010) defines seven dimensions of the quality of
employment: 1) safety and ethics of employment, 2) income and benefits
from employment, 3) working hours and balancing work and nonworking,4) security of employment and social protection, 5) social dialogue, 6) skills
development and training, and 7) workplace relationships and work
motivation. Although, including most of the potential dimensions arenecessary in providing an overall picture of employment quality, the maindifficulty of implementing this framework is the lack of available
indicators, especially for developing countries. In addition, one
additional dimension of quality that also needs be considered is the (lackof) opportunities in the labor market for new entrants.
In this paper we operationalize the concept of labor quality by using a setof available indicators for developing countries produced by the ILO:
specifically youth unemployment, informal employment and vulnerable
employment. These correspond mainly to elements of job security and job
opportunities, although they also have embedded some elements such associal protection. In the next sections we look empirically at this issue andanalyze the impact of different growth patterns on labor outcomes
associated to unemployment and these quality of employment indicators.

192

Structural Transformation, the Quality of Growth and Employment Outcomes3� Data and evolution of labor outcomes indicators
3.1 Data sources
We use two main sources of data for the empirical analysis. Labor
outcomes data is from the ILO Key Labor Market Indicators (KLMI)
available at LABORSTAT.2 We focus on four main indicators:
unemployment, youth unemployment, employment in the informal
sector and vulnerability of employment.
The first indicator measures the unemployment rate and helps to test the
hypothesis of “jobless” growth: whether the impact of productivity
growth is mainly in reducing labor intensity with an overall zero or
negative effect on employment. The second indicator is the youth
unemployment rate, those unemployed between 15 and 24 years old,which we use to analyze whether certain patterns of economic growth
are more conducive to allowing entry for younger people into the labormarket. The third indicator, which we use to analyze the impact on thequality of employment, is the number of people employed in theinformal sector.3 This allows us to analyze the effect of growth onreallocating labor from the informal to the formal sector. The lastindicator used to measuring the quality of employment is the share ofvulnerable employment in total employment. Vulnerable employment isdefined as those self-employed without employees and thosecontributing to family labor. Although self-employment should notnecessarily be considered a more vulnerable category of employment,
especially in developed countries, the evidence suggest that as countries
develop, fewer people are self-employed in traditional activities or
employed in helping with family activities, and more are employed inindustry and services.4 As discussed above, while this set of indicators is
not a comprehensive list of proxies that could capture all dimensions of
the quality of employment, they measure some of the most importantdimensions around the quality of employment, and more importantly,
provide enough data to allow some empirical analysis.
Table 1 tabulates basic statistics for unemployment and the quality of
employment variables in the dataset by income group as determined in
2. www.ilo.org/kilm
3. The ILO defines informal employment as “the estimated number of persons in informal
employment to the total number of employed persons in the non-agricultural sector”.
http://laborsta.ilo.org/informal_economy_E.html
4. www.ilo.org/kilm

193

Chapter 7

2010. The most complete series for a larger number of countries are
unemployment rates, followed by youth unemployment. Data forvulnerable employment is available for 162 countries. In the case of
informal employment, data is scarce and only available for 41 countries,
15 of which have more than one observation over time and only three of
which are high-income countries. Some of the data for low-income
countries is rarely available and in some cases presents quality problems.
As a result, in the empirical analysis we use one low-income group that
includes low- and low-middle income countries. Also, one importantelement to clarify is the need to contextualize some definitions of thevariables. For example, low unemployment rates in low-income countries
are more the result of having fewer people satisfying the definition of
being formally unemployed, while being underemployed in rural areas.
While unemployment is not likely to be a good measure ofunderemployment, it is a critical measure to test the hypothesis of“jobless” growth and the transition of employment to the formal sector.
Table 1� Basic statistics on quality of employment variables
Income group

Stats

High income MeanN
std. Dev
Low income MeanN
std. Dev
Low middle Mean
incomeNstd. Dev
Upper middle Mean
incomeNstd. Dev
All
Mean
N
std. Dev

unemployment

7.69
1046
4.26
4.95
78
4.25
10.57
403
9.13
11.29
635
7.21
9.19
2162
6.60

Youth
InformalVulnerable
unemployment employment employment

16.62
932
8.81
10.06
42
9.46
20.82
241
13.98
23.43
449
13.37
18.90
1664
11.53

32.96
15
10.98
67.08
6
10.50
56.27
41
13.68
46.49
85
12.85
48.67
147
14.83

12.07
778
7.05
79.30
51
13.83
49.36
287
15.78
30.46
448
13.12
26.37
1564
20.51

Source: Author’s own elaboration from KILM (ILO 2013)

Data on value added, labor shares, GDP, population, endowment andsector shares has been obtained from the World Development Indicators(World Bank 2013). We use data for the period 1990-2011, covering aperiod of more than two decades and all countries. Data on employment
shares by aggregate sector is very limited to a few observations and not
194Structural Transformation, the Quality of Growth and Employment Outcomesfor all countries, which makes estimating measures of structural changevery challenging. All GDP variables are in 2005 US$ prices. In order tocapture endowments we use population, for labor endowments, andnatural resource rents as a percentage of GDP, for natural resource
endowments.
Since labor market outcomes also depend on labor regulations, we use
the rigidity of the labor market regulations index developed by the
Fraser Institute as part of the Economic Freedom Indicators (Gwartney
et al. 2012). This indicator ranges from 0 to 10, with higher scoresindicating less rigidity of labor regulations. The index is a compositethat includes rigidity in hiring regulations and minimum wage, hiringand firing regulations, centralized collective bargaining, working hour
regulations, mandated cost of worker dismissal and conscription.
Unfortunately the dataset is available year by year only from 2000.
Therefore, the empirical analysis using this variable uses mainly datafrom 2000 onwards.
3.2 Evolution of labour outcome indicators
Table 2 shows the incidence of the quality of labor indicators in terms of
top and bottom countries, while Table 3 shows the evolution over time ofthese indicators by income level group. Regarding unemployment rates,
these appear above 30% in a few developing countries from all regions.
However, when looking at countries with the lowest unemployment
rates the data indicates that Gulf States and some low-income SSA
countries have rates close to zero. While this is likely to reflect the reality
for Gulf States; for low-income SSA countries this suggests the extent of
unemployment discussed earlier. This makes comparisons between
countries in this region difficult, suggesting a need to focus on changes
over time of these indicators. When looking at evolution over time, wefind that unemployment rates are larger in the last five-year period thanin the first five-year period for high-income and upper-middle countries.
This is largely related to the impact of the 2008 financial crisis, but it also
shows significant weaknesses of the growth pattern in these countries inrelation to employment creation.
In regard to vulnerable employment, this is concentrated mainly in lowincome agriculture intensive countries in SSA, where most people do
not have access to formal employment and work in subsistence
agriculture. On the other hand, it is lower in Gulf States where most195

Chapter 7

people are formally employed by companies. Vulnerable employment
rates are low in high-income countries and very high, more than 50% in
low-income countries, suggesting that as countries develop
vulnerability of employment decreases. Looking over time, vulnerable
employment rates have decreased across countries at all income groups,
however, this reduction has been disappointing, especially for low
income countries with a modest weighted average decrease of 1.18
points. Youth unemployment follows a similar pattern tounemployment rates in terms of country incidence. Youthunemployment tends to be larger than adult unemployment. It also has
increased in high-income countries as compared to the second part ofthe 1990s.
Table 2� Top and bottom 5 countries based on selected labor indicators
Unemployment (%)

Informality (%)

Vulnerable
employment (%)

Youthunemployment (%)

Kosovo72.64
Marshall Islands 63.40
Macedonia 60.25
Guadeloupe 56.49
55.38
BosniaDjibouti
51.50 India
Kosovo47.43 Mali
Macedonia 33.93 Bolivia
Armenia
32.33 Madagascar
Solomon Islands 31.90 Peru

83.50
81.80
75.10
73.60
73.33

Burundi
94.40
Chad
93.70
Burkina Faso 93.03
92.50
RwandaSierra Leone 92.40

1.10 Timor-Leste
1.05 Moldova
0.98 Macedonia
0.70 Poland
0.50 Serbia

17.80
15.90
12.60
10.95
6.10

Bahrain
KuwaitTuvaluU. A. E.
Qatar

Benin
RwandaKuwaitChad
BurundiSource: Author’s own elaboration from KILM (ILO 2013)

196

2.11
2.10
2.00
1.40
0.43

Nepal
Qatar
Benin
BurundiRwanda

3.00
1.45
1.25
0.70
0.70

Upper middle income

3.87
1.20

17.52 13.15 21.64 16.83 18.62 14.58 18.35 21.83 19.56 21.73 19.02 20.00 23.21 25.69 23.03 19.66 15.52

19.64 22.34 22.11 23.86 26.10 23.08 25.79 25.21 24.37 24.85 24.28 24.27 22.92 22.81 23.95 25.93 24.47

Low and low middle income

Upper middle income

47.56 57.32 46.12 53.09 53.10 55.47 57.24 54.87 60.62 57.40 62.04 55.98 50.01 48.12 50.11 53.66 49.38 -1.18

31.91 32.61 30.97 31.15 32.98 32.43 30.87 29.93 32.33 30.86 31.19 29.55 28.67 27.99 26.80 26.93 23.33 -5.18

Low and low middle income

Upper middle income

Source: Author’s own elaboration from KILM (ILO 2013)12.07 12.79 12.00 12.53 12.73 12.30 12.39 12.29 12.35 11.68 11.50 11.68 11.07 10.82 10.82 10.84 10.73 -1.57

High income

Vulnerable employment

17.19 17.60 16.70 17.03 17.02 15.53 16.50 17.26 17.39 16.91 15.82 14.62 13.83 13.74 18.45 19.33 19.09 -0.22

High income

Youth unemployment

8.41 8.27 11.29 7.60 9.43 8.33 10.86 13.32 9.49 11.30 8.75 10.50 10.10 12.14 10.14 7.27 6.97

10.56 10.85 12.27 11.81 12.78 12.00 12.42 11.99 12.98 12.26 11.14 11.16 10.56 10.92 10.85 10.81 10.76 -0.87

Low and low middle income

0.32

8.24 8.74 8.07 8.06 8.21 7.63 7.56 7.87 7.90 7.55 7.12 6.45 5.76 5.76 7.97 8.66 8.35 -0.96

High income

Unemployment

Difference
2007-11/1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1995-99

Table 3� Evolution of labour indicators 1995-2010
Structural Transformation, the Quality of Growth and Employment Outcomes197

Figure 1� Evolution of informality countries with data available

Chapter 7198

aWithin component relates to productivity growth within the sector, structural component is the growth component from reallocation across sectors.

Figure 2� Value added per worker growth- withina and structural components
Structural Transformation, the Quality of Growth and Employment Outcomes

199

Chapter 7

Regarding informality of employment, this is very large especially in somemiddle income countries such as India or Peru and other low income
countries across all continents, suggesting that informality might beembedded in particular institutional contexts. In terms of low-informality
for the countries with data available, which mainly excludes high income
countries; this appears low in Eastern Europe and former Soviet Union
republics. Figure 1 shows the evolution of informality for those countries
with more than one observation over time. Interestingly, informality
appears to have increased or stayed at similar levels in most countries.
These indicators suggest that the pattern of economic growth has not
resulted in substantial improvements in the quality of employment
indicators used in this analysis. This is likely the result of the recent
financial crisis, but also it suggests that some of the high growth indeveloping countries have perhaps not had the full transformative
potential desired for these countries.

4� Quality of labor and structural change across developed anddeveloping countries
4.1 Measuring Structural change
In order to characterize structural change we calculate direct measuresof structural change following the decomposition of labor productivity
growth suggested in the literature (McMillan and Rodrik 2011). The
decomposition separates the aggregate growth in value added per
worker into two components (equation 1); where VL is value added per
worker in sector i and year t and s is the employment share in sector iand year t. The first component, the within-sector productivity growth,
captures the part of value added growth per worker that corresponds tosectoral productivity growth. The second component, the between or
structural change growth, captures the part of the productivity growththat corresponds to labor shares being reallocated to higher or lower
productivity sectors. When the structural change component isnegative, labor is being reallocated into lower productivity sectors.
(1)
A critical element in calculating the decomposition in (1) is the choice ofsectors. Data on sector employment share is very scarce. Some studies
200Structural Transformation, the Quality of Growth and Employment Outcomes

use a sample of countries with detailed sector data from existing inputoutput tables (McMillan and Rodrik 2011; Kucera and Roncolato 2012;
Timmer and de Vries 2009). The advantage of using more sectors is that itallows a more nuanced description of the productivity growth pattern.
However, it restricts the analysis to a very small number of countries,
usually larger and higher income countries.5 Therefore, in order to havea measure that we can extend to as many countries as possible and canbe used in the econometric analysis, we estimate a simpler version of thedecomposition based on only two sectors, agriculture vs. nonagriculture. This reflects a more traditional structural change measureof reallocation away from agriculture, but also implies masking large
within-sector heterogeneity in the non-agriculture sector.
To estimate equation (1) we use sector employment shares and value
added for the agricultural and other sectors available in WDI (2013). We
use those countries with at least two years of information available. In
total we have information for 117 countries, 41 high income, 37 upper
middle-income, 28 low middle-income and 11 low-income countries. The
period analyzed varies country by country, but in around 70% of cases
we are able to measure value-added peer worker change from late 1990s
or early 2000s to the period 2007-2011.
4.2 Structural change measures
Table 4 shows the results aggregated by income group of applying thedecomposition proposed in equation (1) to the dataset. For the analysis,
we have excluded outlier countries with very low or very large
components – values below the 1st percentile and above 99th percentile
since these are likely to indicate the bad quality of the data.6 The
estimates are a weighted average using country GDP as weights.

5. McMillan and Rodrik (2011) carry out the analysis for 11 countries, primarily middleincome countries.
6. Almost all of these outliers are in the low-income group and therefore only affect theresults for this group. When including outliers, the results for the low-income groupsuggest a structural change component of 5% and within growth of 6.25%.

201

Chapter 7

Table 4� VA decomposition by income groupa (GDP weighted)
High income
Low income
Low middle income
Upper middle income

VA/L%

1.54%
11.94%
15.57%
4.78%

within1.65%
4.24%
12.30%
3.41%

structural-0.11%
7.70%
3.28%
1.37%

Excludes outliers defined as those countries with structural change component in the 1st and 99th
percentile. Source: Author’s own elaboration from WDI.

a

Value added per worker increased very modestly in high-income
countries and very significantly in low middle-income and low-income
countries. In general within-sector productivity growth is larger thanstructural change, with the exception of low-income countries. The sizeof the structural change coefficients is consistent with economic
development models where structural change components are larger atlower levels of development, since more people are reallocated to higher
productivity sectors outside agriculture. In the case of high-income
countries the structural change term is even negative. These results aredifferent than McMillan and Rodrik (2011) and more in line with Kucera
and Roncolato (2012) aggregates. In both cases, these use a moredisaggregated sector dataset, although for a smaller set of countries.
Structural change estimates are also positive for SSA in our dataset,
where value added per worker increased by 3.18%: 2.45% corresponding
to within-sector growth and 0.72% to structural change.
Individual results are plotted in Figure 2 above, excluding countries withextreme values, and the results for top and bottom performing countries
are summarized in Table 5. India is the country with the largest value
added per worker growth, mainly explained by high productivity growthof the non-agricultural sector, services. A similar pattern, although withlower growth is followed by Maldives. In terms of structural change
Cameroon, Bhutan, Ethiopia, Nepal and Oman are the countries withlarger positive structural change; and the Dominican Republic, Morocco,
Korea, Zambia and Brunei the ones with the larger negative structuralchange. In 47 countries structural change is negative, while in theremaining 64 structural change is positive, which suggest large diversityof results regarding structural change. Finally and in line with theliterature, within productivity growth is the main driver of value added
per worker growth. In 97 of the 117 countries with data available, within
productivity growth is larger than the structural change component.
202

Structural Transformation, the Quality of Growth and Employment Outcomes

Table 5� Top and bottom countries’ VA decomposition
VA/L%
withinIndia
31.53% India
Burkina Faso
17.65% Maldives
Maldives
15.53% Zambia
Cambodia13.11% Armenia
Tanzania
12.79% Senegal
Ukraine-0.69% Bhutan
Kyrgyz Republic -1.52% Ukraine
Brunei-8.09% Belarus
Belarus-8.37% Gabon
Gabon-10.30% Cameroon

Structural change
25.53% Cameroon
20.65%
13.65% Bhutan
12.64%
12.83% Ethiopia
10.64%
11.07% Nepal
10.45%
10.57% Oman
10.33%- 2.15% Dominican Rep. -1.77%-3.79% Morocco- 2.10%-8.82% Korea, Rep.
-2.43%-17.15% Zambia-2.77%-19.68% Brunei-6.42%

Source: Author’s own elaboration from WDI

These results suggest significant heterogeneity of experiences regarding
structural change. This tends to be larger in low-income countries, but
within each income group there are significant differences. More
importantly, within-sector productivity growth tends to be the main
driver of productivity growth.
4.3 Unemployment, quality of employment and economic growth
Before looking more formally at how different patterns of economic
growth affect the quality of employment, we briefly explore whether
countries changes in labor outcomes indicators are highly correlatedwith economic growth. Figure 3 shows non-parametric plots of changes
in the four labor quality indicators and the average rate of growth.
Specifically, we compute the year-to-year rate of change of the four
variables average in the 1990s and the 2000s, and plot these rates against
the average rate of growth for the period for each country. This allows us
to explore whether it is likely that economic growth is the main driver ofthe path in these variables.
Panels (a) and (c) show the plots for unemployment rates and youth
unemployment rate changes. Although, some of the points lie outside
the confidence interval, the plots depict a potential negative
relationship, and suggest that economic growth might be an importantdriver in reducing both types of unemployment. Panels (b) and (d) for
informality and vulnerable employment, on the other hand, show noclear relationship between economic growth and changes in thesevariables. This suggests that other factors might be important drivers ofchange in these labor outcomes.
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Chapter 7

Figure 3� Non-parametric plots(a) Unemployment rate change
and GDP growth(b) Youth unemployment rate change
and GDP growth(c) Informality rate change and
GDP growth(d) Vulnerable rate change and
GDP growth

The relationship between growth, structural change and labour
outcomes are further explored in the next section.

5� Growth pattern, structural change and labor outcomes
5.1 Specifications
In this section we examine empirically the impact of the pattern of204

Structural Transformation, the Quality of Growth and Employment Outcomesgrowth and structural change on different quality of labor outcomes.
We use a two-stage methodology. In the first stage, we estimate areduced form equation where labor market outcomes are a function ofincome per capita, income growth and structural characteristics of theeconomy. In the second stage we analyze directly the impact ofstructural change measures on labor outcomes.
Specifically in the first stage we estimate equation (2) below:(2)Each labor outcome (L) in country i in year t is explained by a set ofvariables. We proxy structural characteristics of the economy (Str) using
labor endowments (population), rents from natural resources as a shareof GDP, manufacturing value added as a share of GDP and agriculture
value added as a share of GDP. These variables attempt to capture how
differences in the pattern of sectoral growth (i.e. growth via
manufacturing sector or agriculture) impact quality of labor outcomes.
We also use an augmented specification that takes into account the degreeof flexibility of the labor market as defined in section 3. Unfortunately, forthis variable we only have data available from 2000, which represents asignificant drop in the number of observations. This augmented
specification also includes an interactive term between GDP growth and adummy for SSA and a dummy for the Latin America and the Caribbean
Region. This interactive term aims at capturing whether the effect of
economic growth is different in these regions given the more natural
resource-based economic growth prevalent in these economies.
The final dataset covers the period 1990-2001 and includes more than 160
countries. Data availability for labor outcomes is, however, problematicfor many low income and developing countries, limiting, therefore, thesize of the dataset to be used for the estimations. Equation (2) isestimated in logarithms and using country fixed effects and year
dummies. In order to estimate directly the impact of structural change
on labor outcomes in a second stage we average the dataset and add asregressors the different structural change components estimated fromequation (1). The advantage of this approach is that it allows the use of
direct structural change measures. The main disadvantage, however, isthe significant loss of observations since we lose the panel structure and205Chapter 7

rely only on a cross-section of countries.
Specifically, we estimate equation (3):(3)
We use different proxies for economic growth and sources of growth.
The first specification uses GDP growth, the second value added per
worker growth; then we use the measure of structural change calculated
above and the measure of within-sector productivity growth. These two
decomposition measures are then interacted with a dummy for lowincome countries to test whether their effects on labor outcomes aredifferent at lower levels of development. All variables are averaged forthe period 1990-2011. In total, we have data for around 100 countries,
with the exception of informal employment that given the low
availability of data, only 36 observations, we omit from the analysis in
this section.
5.2 Results
Table 6 shows the main results for the four types of labor outcomes.
Looking at the estimates of the base line specification that maximizes
the number of observations (odd columns), both the level of GDP per
capita and the rate of economic growth reduce unemployment andyouth unemployment. Interestingly and in line with the non-parametricplots in the neither previous section, neither income per capita nor GDP
growth appear to be robust predictors of employment in the informal
sector or vulnerable employment.
The results regarding the structure of economic growth andendowments are also interesting. Countries with larger laborendowments measured by population appear to have larger
unemployment, youth unemployment, informal employment andvulnerable employment; although for youth unemployment andvulnerable employment the estimated coefficients are only marginallysignificant. The coefficient on natural resource rents to proxy for the sizeof the extractive sector is positive but only marginally significant foryouth unemployment. A more puzzling result is related to the impact ofmanufacturing value added. The estimated coefficients are positive andmarginally significant for unemployment and youth unemployment.
This would suggest that increases in manufacturing productivity might
reduce labor intensity and generate unemployment, although the low
206Structural Transformation, the Quality of Growth and Employment Outcomessignificance of the coefficients suggests that we should interpret theresults with caution. Finally, agriculture value added does not appear to
explain any of these labor outcomes.
Looking at the augmented specifications (even columns) allows us toanalyze the impact of different labor institutions, proxied by the labor
regulations freedom index. Including this variable, however, reduces
significantly the number of observations to half of the sample. The labor
institutions index does not appear to be a significant predictor of theselabor outcomes; and it is only negative and marginally significant for theunemployment rate specification, suggesting that more flexible labormarkets have lower unemployment rates.

207

208

-6.673(6.630)
1764
0.155
161

-0.594**(0.268)-0.00923**(0.00388)
0.839**(0.327)0.0167(0.0142)0.199*(0.111)-0.114(0.0878)-1.272***(0.297)-0.0108**(0.00415)
0.425(0.612)0.0160(0.0243)
0.218*(0.127)-0.170(0.103)-0.221*(0.114)0.00172(0.0301)-0.00114(0.00649)
6.253(10.30)
691
0.393
113

(2)
log
(unemp)-1.237(6.506)
1359
0.204
142

-0.847***(0.189)-0.0145***(0.00386)
0.666*(0.358)0.0270*(0.0150)
0.314**(0.121)-0.127(0.0856)(3)
log(youth_
unempl)-1.536***(0.285)-0.0164***(0.00442)
0.762(0.706)0.00721(0.0336)
0.209(0.140)-0.170(0.110)-0.0986(0.142)-0.0223(0.0252)
0.0123(0.00767)
3.924(12.16)
568
0.372
97

(4)
log(youth_
unempl)-27.02**(9.997)
143
0.508
39

-0.369(0.263)0.00529*(0.00263)
2.085***(0.607)0.0411(0.0422)-0.0812(0.117)-0.117(0.0900)(5)log(informal)-0.786(0.582)0.0124(0.0242)1.326*(0.692)0.0767*(0.0384)-0.638***(0.112)0.116(0.100)-0.0217(0.143)-0.121*(0.0630)-0.00415(0.0190)-10.43(10.77)
74
0.628
34

(6)
log
(informal)

All specifications with country and year fixed effects. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Observations
R-squared
Number of i

Constant

GDP_gr*LAC

GDP_gr*SSAlog(lab_freedom)log(agri_VA/GDP)log(manuf_VA/GDP)log(natres_rents)log(popul)

GDP_growthlog(GDP_cap)(1)log(unemp)

Table 6� Fixed effects estimates: Quality of labor indicators-3.018(4.331)
1297
0.075
136

-0.220(0.141)0.00198(0.00186)
0.496*(0.266)-0.00289(0.0131)0.0129(0.0618)
0.00206(0.0568)(7)log(vulnerab)-0.166(0.215)0.00408(0.00342)
0.417(0.439)0.0199(0.0214)-0.0859(0.0649)0.0890(0.0641)0.112(0.0698)
0.00311(0.0137)-0.00739**(0.00299)-2.439(7.384)
583
0.089
95

(8)
log
(vulnerab)

Chapter 7

(1)

134
0.096

0.559*(0.327)8.596***(2.057)-0.000115***(2.56e-05)-0.523(0.331)

(2)

107
0.134

0.755**(0.377)7.500***(2.141)-31.71**(14.51)-0.000138***(3.00e-05)

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Observations
R-squared

Constant

lab_freedom

Within*low income

withinStruct*low income

structural

VA/L gr

GDP_grGDP/cap

Table 7� OLS estimates: Unemployment rates

107
0.140

0.564
(0.368)7.039***(2.069)-10.03***(3.505)(3)-0.000103***(2.72e-05)

107
0.142

0.528
(0.367)7.241***(2.043)-10.58***(3.853)5.647(12.66)(4)-0.000103***(2.74e-05)

107
0.105

0.563
(0.378)6.798***(2.088)6.970***(1.999)(5)-9.96e-05***(2.78e-05)

107
0.147

9.060***(2.525)-26.11**(10.09)0.501(0.367)7.372***(2.057)(6)-0.000107***(2.75e-05)

Structural Transformation, the Quality of Growth and Employment Outcomes

209

210

0.709
(0.638)19.14***(4.116)
125
0.082

(1)
-0.000188***(4.65e-05)-0.878(0.745)

1.391**(0.626)15.32***(3.820)104
0.140-58.87***(17.14)(2)-0.000238***(5.13e-05)

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Observations
R-squared

Constant

lab_freedom

Within*low income

withinStruct*low income

structural

VA/L gr

GDP_grGDP/cap

Table 8� OLS estimates: Youth unemployment rates

1.065(0.686)
14.58***(3.872)104
0.079-6.408(5.134)(3)-0.000184***(5.33e-05)

1.479**(0.676)13.03***(3.868)104
0.140-2.933(3.486)-124.0**(56.29)(4)-0.000218***(5.90e-05)

1.132(0.691)
14.26***(3.865)
104
0.070

0.583
(2.966)(5)-0.000187***(5.42e-05)

0.713(2.639)-240.5***(36.97)1.461**(0.654)13.66***(3.716)
104
0.194

(6)
-0.000236***(5.52e-05)

Chapter 7

211-1.835(1.463)54.46***(8.966)
124
0.410

(1)
-0.00114***(0.000132)
1.882*(0.981)-1.124(1.467)45.79***(7.703)100
0.417

137.2**(67.35)(2)-0.000915***(0.000149)

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Observations
R-squared

Constant

lab_freedom

Within*low income

withinStruct*low income

structural

VA/L gr

GDP_grGDP/cap

Table 9� OLS estimates: Vulnerable employment rates-0.526(1.426)48.19***(8.182)100
0.341-2.421(47.35)(3)-0.00103***(0.000147)-0.556(1.423)48.36***(8.138)100
0.341-7.792(50.49)10.50(96.46)(4)-0.00103***(0.000148)-0.380(1.426)43.97***(7.808)100
0.384

74.35**(34.27)(5)-0.000968***(0.000145)

10.68(58.43)123.3*(73.84)-0.323(1.394)44.82***(8.020)
100
0.420

(6)
-0.000965***(0.000140)

Chapter 7

The impact of the interactive GDP growth*region dummies does notappear statistically significant for unemployment and nonunemployment rates. In the case of informal employment the SSA
interactive term suggests that, at least in SSA, economic growth reduced
informal employment. Similarly, economic growth appears to reduce
vulnerable employment in Latin America. Finally, manufacturing value
added growth reduces informality when we reduce the sample to the
2000s.
Overall, these results suggest that economic growth does not result in“jobless” growth since on average it reduces unemployment rates. This
is in line with recent evidence for East Asian countries (Hanusch 2012)
and consistent with recent estimates of the elasticities of employment toeconomic growth in OECD countries (Cazes et al. 2011). It is possible,
however, that when TFP growth in the manufacturing sector is large,
this results in fewer jobs. On the other hand, large agriculture value
added or dependency on natural resources does not seem to impact
unemployment rates. Informal and vulnerable employment does notseem to be determined by income growth or income per capita; only by
the population size.
Tables 7 to 9 show the OLS estimates for each labor outcome separately.
Starting with unemployment rates in Table 7, the results suggest that, as
expected, countries with larger income per capita tend to have lower
unemployment rates. Interestingly, growth rates do not significantly
impact unemployment rates, which is likely the result of growth rates
tending to be more extreme, both positive and negative, in developing
countries with larger unemployment rates. On the other hand, countries
with larger TFP growth tend to have lower unemployment. When we
look at the decomposition of this growth, the results suggest thatcountries with larger positive structural change tend to have lower
unemployment rates, while countries with larger within-sector
productivity growth tend to have more unemployment; with the
exception of low-income countries where both components reduce
unemployment. This suggests that when most of the productivity
growth is not reallocated to higher productivity sectors, theunemployment outcomes are worse. Finally, the labor freedom index is
positive but only statistically significant in the specification using value
added per worker.

212Structural Transformation, the Quality of Growth and Employment Outcomes

Table 8 shows the estimates for youth unemployment. The results are
somehow similar to total unemployment. Countries with higher income
per capita and TFP growth tend to have less youth unemployment. The
structural change coefficients are not statistically significant, whichsuggests that youth unemployment depends on other factors not related
to structural change. The interactive term suggests that structuralchange and within-sector productivity reduces youth unemployment
only for low-income countries, but this could be explained by otherfactors. One potential explanation is the fact that population growth andnew entrants to labor markets are much larger in low-income countries,
and this is being captured by the interactive term.
For the case of vulnerable employment in Table 9, the results are morepuzzling to interpret. As expected countries with lower income percapita tend to have more vulnerable employment, but countries withlarger GDP, TFP growth and within-sector productivity growth tend tohave more vulnerable employment. This may be related to the definition
used of vulnerable employment - those self-employed, without other
employees, and those contributing to family labor – where growth
processes are increasing self-employment and, therefore, the measure ofvulnerability. Structural change does not appear to impact vulnerabilityof employment.
Overall, these results suggest that productivity growth is likely to be thecritical element to reduce unemployment. More importantly, a growth
pattern of productivity growth based on positive structural change,
reallocation of workers to more productive sectors is also key in
reducing unemployment rates, while within-sector productivity
appears positive for low income countries only. On the other hand, thetype of structural change does not appear to be a relevant element
affecting the quality of labor indicators used in the paper.

6� Conclusions
This paper has analyzed one important aspect of the quality of growth;
its capacity to deliver higher quality of employment. One key element
arising from traditional development economic theories is theimportance of structural change and the reallocation of workers from
low to high productivity sectors in explaining improvements in labormarkets and standards of living. The concept of structural change is213

Chapter 7

significant in the reallocation of workers from low to high productivity
sectors. Given the observed pattern of commodity-based economic
growth in many countries in the developing world in the last decades,
especially in Africa and Latin America, some concerns about how thistype of growth has been impacting employment in developing countries
and the possibility that these countries experience “jobless” growth
have emerged.
Our findings suggest that economic growth might not result in “jobless”
growth since, on average, it appears to reduce unemployment rates overtime. As expected, countries with larger income per capita tend to havebetter quality of labor outcomes. More importantly, TFP growth andpositive structural change appear to be critical elements in reducing
unemployment, while within-sector productivity might increase
unemployment in higher income countries via reducing labor intensity,
but decreasing unemployment in low income countries.
In addition, the paper complements the existing literature on structuraltransformation by analyzing an unexplored dimension of structuraltransformation, the impact on labor quality indicators such as youth
unemployment, informality and vulnerability. The important result of
the paper is that while the effect of the growth process on employment
levels is significant, the impact on vulnerable and informal employment
does not appear to be explained by the type of economic growth pattern.
This suggests that other unexplained factors, such as the quality of labor
institution, might be a more important factor explaining the quality of
employment than economic growth.
In terms of policy implications, the findings emphasize the importanceof productivity growth in reducing unemployment and the significant
contribution of structural change, which suggests the importance ofguarantee labor opportunities in higher productivity sectors. This canbe problematic in countries with large comparative advantages in terms
of primary commodities. In terms of the quality of labor indicators,
more empirical work is necessary to fully understand these
determinants and the role of labor market institutions.

214

Structural Transformation, the Quality of Growth and Employment Outcomes

References
Cazes, Sandrine, Sher Verick, and Fares Al Hussami. 2011. "Diverging
Trends In Unemployment In The United States And Europe:
Evidence From Okun's Law And The Global Financial Crisis."
Employment Sector: Employment Working Paper 106. Geneva:
International Labour Office.
Foster, Lucia Smith, John C. Haltiwanger, and Chad Syverson. 2008.
“Reallocation, Firm Turnover, and Efficiency: Selection on
Productivity or Profitability?” American Economic Review 98 (1):394-425.
Gwartney, James; Robert Lawson and Joshua Hall. 2012. “2012 Economic
Freedom Dataset.” Economic Freedom of the World: 2012 Annual
Report. Vancouver: Fraser Institute http://www.freetheworld.com/
datasets_efw.html
Hanusch, M. 2012. “Jobless Growth? Okun’s Law in East Asia.” WorldBank Policy Research Working Paper 6156. Washington DC: WorldBank.
ILO. 2013. Key Indicators of the Labour Market (KILM). Geneva: ILO.
Kaldor, N. 1967. Strategic Factors in Economic Development. Ithaca, NY:
Cornell University Press.
Kucera, David and Leanne Roncolato. 2012. “Structure Matters: Sectoral
Drivers of Development and the Labour Productivity–Employment
Relationship.” Geneva: International Labour Organization.
Lewis, W. Arthur. 1954. "Economic Development with Unlimited
Supplies of Labor," Manchester School of Economic and Social Studies
22 (2): 139-91.
McMillan, M. and D. Rodrik, 2011 ”Globalization, Structural Change
and Productivity Growth.” In Making Globalization Socially
Sustainable, edited by M. Bacchetta and M. Jansen, 49-84. Geneva:
World Trade Organization and ILO.
Memedovic, O. and L. Lapadre. 2010. Structural Change in the World
Economy: Main Features and Trends. Vienna: United Nations
Industrial Development Organization.
Ocampo, J. A., C. Rada. and L. Taylor. 2009. Growth and Policy in
Developing Countries: A Structuralist Approach. New York: Columbia
University Press.
Szirmai, Adam. 2012. "Industrialisation as an Engine of Growth inDeveloping Countries, 1950–2005." Structural Change and Economic
Dynamics 23 (4): 406-420.
215

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Timmer, M. and G. de Vries. 2009. “Structural Change and Growth
Accelerations in Asia and Latin America: A New Sectoral Data Set.”
Cliometrica 3 (2): 165-190.
UNECE. 2010. Measuring the Quality of Employment Report. UNECE Task
Force on the Measurement of Quality of Employment. Geneva:
UNECE.
World Bank. 2012. “Jobs and Productivity” World Development Report
2013, Chapter 3. Washington DC: World Bank.
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World Bank.

216

Chapter 8

The Elusive Quest for Inclusive Growthin Sub-Saharan Africa:
Regional Challenges and Policy Options

Bruno Losch

1� Introduction
Sub-Saharan Africa (SSA) exemplifies the issue of the quality of growth.
While the sub-continent has been under the spotlight for several years
for its strong recovery and dramatic growth rates, persistent poverty - inspite of real progress - and growing social and political risks highlightthe fragility and the uncompleted nature of the growth process. SSA is acase of economic growth without structural transformation,
productivity advances and effective human development.
The paper recapitulates first the reasons for new narratives about subSaharan Africa; it is a reminder that the fragility of current narratives isdue to poor analytical perspective and limited evidence. It then reviews
the main characteristics of the diverse African trajectories and theircommon challenges related to an incipient economic transition and astrong on-going demographic transition. The remarkable boom of thelabor force currently underway questions the absorption capacity of the
sub-Saharan countries’ economies within the highly competitive
context of globalization and under the constraints of climate change andresource depletion. The paper finally addresses possible options for an
inclusive and sustainable process of change and reaffirms theimportance of investing in development strategies.

2� Changing narratives and blind spots about growth in SSA
From the “hopeless continent” to a “rising Africa”?
In 2011 and 2013, within a period of fourteen months, The Economist
published two special issues devoted to Africa, under the titles, "Africa
Rising" and "Aspiring Africa," thereby apologizing for having run “The217

Chapter 8

Hopeless Continent" as a headline a decade earlier.1 Without ignoring
the difficulties still to be overcome and differences between countries,
this optimistic view was based on the sustained growth rate of theregion following the long recession of the 1980s and 1990s. Over the lastten years, average per capita income grew by 30 %, as the region resisted
the global crisis. Six of the ten fastest growing economies in the world
during the 2000s were in SSA (Angola, Chad, Ethiopia, Mozambique,
Niger, Rwanda), all above a 7% annual growth rate – meaning adoubling of the GDP within 10 years. This expansion is expected tocontinue for the next decade.
While this performance is, in part due to the price of raw materials, thiswas not the only factor, as some countries did also progress rapidly
without the benefit of resource extraction. This economic growth has
been accompanied by some improvement in other indicators of humandevelopment (such as secondary education, life expectancy) and theliberalization of political life marked by regular elections in many
countries. Other drivers of change are a strong urban growth, abooming young population, a rising middle class, and the rapid
adoption of mobile telephony, accompanied in some countries by arevolution of credit. These are all the ingredients of a "new Eldorado forinvestors": after admiring the growth of Asian dragons and tigers, andLatin American jaguars, an enthusiastic literature has been brimmingwith "Lions on the Move" and an "Emerging Africa" where "Poverty is
Falling... Much Faster Than [we] Think." 2However, sub-Saharan Africa’s realities also display otherwise mixed
pictures: while many conflicts came to an end during the last decade(Liberia, Sierra Leone) or more recently (Côte d'Ivoire), the fragility of the
Sahel has been highlighted by the war in Mali. Other "smoldering"
conflicts continue or have been developing in Guinea-Bissau, Nigeria,
CAR, the Horn of Africa and Kenya, DRC. Several countries facepolitical stalemates that paralyze progress in implementing changes(Madagascar, Zimbabwe), while others are “vitrificated” by “big men”:
presidential monarchies (Burkina, Cameroon, Chad, Congo, Uganda) oreven hereditary presidencies (Equatorial Guinea, Togo, Gabon). Even in
rapidly growing countries, the widening inequality leads to economic,
1. Respectively, December 3, 2011, March 2, 2013, and May 13, 2000.
2. Respectively in Le Monde, January 1, 2013, and titles of books published by the McKinsey
Global Institute (2010), Radelet (2010), and Sala-i-Martin and Pinkovskiy (2010).

218

The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Optionssocial and political tensions: in 2013, 39 of the 49 SSA countries are
situated in the last 50 places in the UNDP’s Human Development Index(UNDP 2013), and only four SSA countries are among the 80 countries"where it is better to be born" – apart from South Africa (which came53rd), they were in the last places.3 This situation is reflected in the
distress experienced by African youth, plagued by unemployment,
underemployment, and despondency (Solignac-Lecomte 2013).
A debate obfuscated by the GDP ideology
The overall discussion on African growth, rising middle classes and“emergence” is based on the GDP paradigm that says little about the real
wealth and health of an economy and its sustainability in the long run(Fioramonti 2014). GDP growth does not mean structural transformation
and welfare improvement for the majority of the population, i.e. avirtuous process of economic and social change.
In SSA, among the main issues contributing to the mystification of thedebate is the tragedy of African statistics (Devarajan 2013). The collapseof statistical systems (national accounts, sectorial data and evenpopulation censuses), their many biases (e.g. spatially blindinformation), and the importance of the informal economy, whichlargely escapes reporting, leads to poor and misleading numbers (Jerven2013), incorrect knowledge bases and high risks of inappropriate policies
targeting inadequate objectives.
Additionally, GDP statistics fail to report the depletion of energy,
minerals, forests, soil fertility (etc.), which are critical components ofsustainability. This perspective requires specific attention to the case of
SSA due to the importance of extractive resources and agriculture aswell as their contribution to GDP. The World Bank adjusted net savings
statistics show that African countries have been depleting their wealthover the last twenty years in an unsustainable way, with resource-richcountries being the main contributors to this negative trend (WorldBank 2011; AfDB et al. 2013; Aglietta in this volume).

3� Many national trajectories but common challenges
The challenges for Africa are very specific because its structural
3. The “where-to-be-born” index is prepared by the Economist Intelligence Unit. In 2013,
Angola, Kenya, and Nigeria are respectively ranked 77th, 79th and 80th.

219

Chapter 8transformation has been lagging in comparison to other regions of theworld. The process of structural transformation refers to the change inthe sectorial and spatial distribution of economic activities illustrated by
the evolutionary pathway followed by European economies (andcountries of European settlement) and replicated in several other partsof the world.
Based on statistical evidence (Johnston and Kilby 1975; Timmer 2009), a
stylized summary of this process and its main determinants shows thegradual transition from an agriculture-based economy to one based on
industry and then on services, in conjunction with a shift from rural tourban areas. This was made possible by the energy shift to fossil fuelsthat started at the end of the 18th century. The shift was indeed the causeof profound technological changes and impressive productivity gains,which led to wealth accumulation and then the transfer of labor andcapital from one sector to another. This process was accompanied by anincrease in income and demand and its diversification. It benefited fromthe demographic transition4 at the origin of an improved ratio between
the working and non-working population, i.e. less dependent and more
economically active people. In this process of change, agriculture played
an initial role and was the first driver of accumulation. Productivity
gains have been accompanied by a massive exit of workers fromagriculture, their migration to the cities, to other regions or to othercountries.
The evolutionary vision, which was formalized after the Second WorldWar (Rist 2003), is the source of mainstream thinking on development. It
is based on the idea of a step-by-step catching up (Rostow 1960) with themost advanced countries in terms of technical, economic and social
progress (generally and prosaically reduced to per-capita GDP); and it isreinforced by the similarities observed in the trajectories of some Latin
American and Asian countries.
An incipient economic transition
Compared to this reference pathway, sub-Saharan Africa has
experienced rapid changes characterized by huge process towards
urbanization and a rapid economic growth in the last decade; but it
4. The demographic transition is the result of improvements in health and lifestyles,
facilitated by mass education, which lead to the reduction in mortality and birth rates.

220

The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

remains, with South Asia, the poorest region in the world. The "African
lions" of McKinsey (2010) are deprived of 35% of their GDP when North
Africa is subtracted. Moreover, SSA only represents 45% of the total
wealth of the continent when South Africa (20% of the overall GDP) is
withdrawn. However, together, it comprises 75% of its population (seeMap 1).
Map 1� African countries weighted by population (2010) and GDPs (2009)

Source: Losch 2013b and Cirad Cartography Unit, based on World Population Prospects 2010 and World
Development Indicators
Note: In this cartogram, sizes in km2 are replaced by population and GDP values; colors correspondto UN regional groupings for Africa.

Current GDP per capita is very low: in 2010, 27 of the 49 countries in SSA
were in the low-income countries group according to the World Bank(<$1,025/person/year), including 13 countries with per capita income
under $500. Thirteen others were in the lower middle-income group(between $1,025 and $4,035), seven were in the upper middle income
group (between $4,035 and $12,475); and the oil-booming Equatorial
Guinea, with only 700,000 inhabitants, has been propelled into the highincome countries group (with $20,700/person/year, equivalent to South
Korea or Portugal).
Nevertheless, when looking at the major economic aggregates, SSA
structurally changed little over the last fifty years and remains
permanently marked by the weight of its primary sector and the
exploitation of its natural resources (Magrin 2013). Agriculture, mining
and energy account for over 50% of GDP for 17 countries, between 40
221Chapter 8

and 50% for nine countries and between 30 and 40% for nine others. The
manufacturing sector is extremely limited: only 18 countries have anindustrial added value exceeding 10% of GDP and seven countries reachthe threshold of 15%. These results show a deep structural inertia, where
only services and construction, driven by urban growth, havedeveloped. SSA is a region of urbanization without industrialization, avery specific situation in the economic history of the world.
This African exception is explained by the historical conditions of theintegration of the continent into the global economy (Grataloup 2007),with a late and restrictive colonial rule resulting in young and small
states. When they were only entering their twenties, and before they had
the opportunity to consolidate their institutions or to implement
modernization policies, these new countries were simultaneously
projected into the international competition of globalization andsubmitted to the sharp constraints of structural adjustment.
In comparison, several Asian countries, which had the same level ofrelative wealth fifty years ago but another historical background, grewsteadily and rapidly, despite pessimistic perspectives.5 Their growth
process was characterized by a sharp decrease of agriculture and thedevelopment of manufacturing (see Figure 1). Over the same period,
African growth was much lower, and especially marked by its volatility(see Table 1), which raises the question of the sustainability of the recent
growth trend (Devarajan and Fengler 2013), characterized by the
position of raw materials, construction and services, and the relative
weakness of investment (Ali and Dadush 2010).
Table 1� Regional dynamics of GDP per capita growth (1960-2007)
North Africa & Middle East
Sub-Saharan Africa
Latin America & Caribean
East Asia & PacificSouth Asia
Source: Arbache and Page 2007

5. See Myrdal’s "Asian Drama" (1968)

222%
per year
2,06
0,72
1,73
5,44
2,72

variation
coef.
1,68
3,10
1,38
0,76
0,99

The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

Figure 1� Evolution of GDP structure:
SSA versus East and South-East Asia (1965-2005)

Source: Losch et al. 2012 based on World Development Indicators

However, if the prices of mineral resources or agricultural products
have played a clear part in the recent trend, they do not tell the wholestory. Among the 49 SSA states many differences exist (highlighted
above), which relate to resource endowment, access (landlocked
countries), colonial legacies, post-independence history, institutions andgovernance. If many oil-exporters à-la-Equatorial Guinea are heavily
dependent on international markets and little diversified, somecountries, especially in East Africa (Ethiopia, Uganda, Rwanda), areprogressing rapidly without benefiting from extractive resources.
Processes of structural change are even more uneven. Reliable data are
missing but, when looking at reallocation of labor within or between
sectors (McMillan et al. 2013), a couple of countries show a progressive
shift due to the development of more diversified exports with higher
value and technological contents (Uganda, Tanzania, Rwanda, but also
Senegal or Nigeria - see Figure 2).

223

Chapter 8

Figure 2� Structural change in SSA:
Focus on labor movement in 19 countries (2000-2005)

Source: based on McMillan et al. (2013)

Nevertheless, these slight changes in the labor force structure of SSA do
not modify, in absolute terms, the remaining importance of agriculturein the economically active population. With the exception of South
Africa, where employment in agriculture is marginal, for reasons
related to the very specific history of the country, agriculture still
occupies 50-60% of the labor force in the vast majority of SSA. This rate
rises to 75% and higher in some countries (Sahel, East Africa). The broad
definition of agricultural employment,6 however, does not signify
exclusive occupation in agriculture: multiple activities are acharacteristic of rural households (Losch et al. 2012). Other sectors of
employment are mainly services (trade and transport first and,marginally, government, banking), construction and public works
boosted by urban growth, and handicrafts. Employment in
manufacturing remains extremely low - a few hundred thousand jobs inmost countries (and often less).

6. According to the FAO, the economically active population in agriculture corresponds tothe number of all employed and unemployed persons engaged in or seeking work inagriculture, hunting, fishing or forestry.

224

The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

A cross-sectional feature of this employment structure is theimportance of what is commonly called the informal economy orsometimes the popular economy. There is no standardized definition
and the notion of the informal is disputed, but the informal economy isgenerally defined in the negative, that is, by saying what it is not(Charmes 2011). The informal economy is businesses that are notdeclared or registered with the tax authorities, do not apply accounting
rules or economic and social labor standards (such as regulations related
to hiring, firing, minimum wage and working conditions).
This informal sector corresponds with the bulk of sub-Saharan African
economies because it includes agriculture, comprised almost exclusivelyof family farming,7 and also because urban employment happens
mainly through self-employment activities or small businesses (Beaujeu
et al. 2011). Thus the informal economy supposedly employs 75 to 95% of
the sub-continent's workers (Jütting and Laiglesia 2009), with the
exception of South Africa, where the rate would be only 50%. The
specificity of this informal economy is its great flexibility, which gives it
a strong resilience against hazards, a situation that is balanced by highrisks, low to very low levels of remuneration and underemployment(low number of hours worked per worker). These features explain thevery low quality of African growth: the high rates of the 2000s do nottranslate into inclusive forms of employment.
An on-going and delayed demographic transition
These low-transforming African economies are facing a uniquedemographic reality characterized by unprecedented growth and thelasting importance of their rural population. SSA is the last region of theworld to be engaged in the process of demographic transition and theprocess is far from complete: the population growth has been strong overthe past four decades (more than 2.5 % per year) and it has lasted longer
than originally projected due to continued high fertility rates in many
countries, leading the United Nations to revise their latest projections
upward in 20128 : in 2050, SSA’s population should reach a total of slightly
above 2 billion people, with the population continuing to grow until after
7. There is a limited business/corporate sector, mainly for agricultural exports (agroindustrial plantations, large mechanised farms). It represents little in terms of jobs and evenless in relative terms: in countries where such agricultural businesses are best established(in East and Southern Africa in particular), the numbers rarely exceed 100,000 jobs
compared to the millions of family farms.
8. By nearly 10% for the medium fertility variant.

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Chapter 8

2100. SSA will overtake China and have two and a half times more people
than Europe (a reversal of the relative weights of Europe and Africa in lessthan a century).
This population growth will also be accompanied by a change in the age
structure of the population, with strong growth of the labor force - andtherefore of the demand for jobs - and a gradual change in the activity ratio(active/inactive people). With one inactive for every active person in the
1980s and 1990s, this ratio was close to one, reaching a “record” in world
history. It was a major economic disadvantage for Africa, which was hit at
the same time by the impact of structural adjustment policies, and it led to asignificant increase in poverty (see Figure 5). Over the same period of time,
China had two active for every one inactive person (and has a 2.5 ratio
today), which is a radical difference in terms of productive capacity andpossible increase in individual wealth and living standards (see Figure 3).
The progressive improvement of the activity ratio in SSA will be a majoradvantage in terms of growth and the region will progressively reap its“demographic dividend.”9 However, the convergence with the rest of
the world in terms of fertility reduction is lagging (Guengant and May2013) and the ratio of two active for one inactive person should only bereached after 2050.10 Most importantly, this improvement in the activity
structure of the population will only play its leverage role if it iscombined with adequate public policies and a favorable economic andinstitutional environment (productive investment, capacity building,innovation and productivity enhancement). If not, the demographic
bonus (many workers) could turn into a "penalty" (many jobless), andresult in major social and political tensions.
The other feature characterizing African demographic changes is thespatial distribution of the population. The gradual shift of the global
population to the cities is a major phenomenon in world history, withthe tipping point reached at the end of the 2000s. SSA did not escape thisprocess with the urban population increasing tenfold since
independence. But the subcontinent is still mainly rural, with around
9. The demographic dividend constitutes a unique moment in the dynamic of a population.
After this window of opportunity (also named the “demographic bonus”) the ageing of the
population leads to a progressive decrease of the activity ratio – a process broadly at play in
China and Europe.
10. SSA is the only region in the world where the labor force will continue to significantly
increase after this date.

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The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

65% in 2010, and it will remain rural until the mid-2030s due to a slowing
down in the pace of urbanization – a consequence of limited labor
opportunities and low paid informal jobs. Above all, SSA is the only
region of the world, along with South Asia, where rural population willcontinue to grow – a consequence of today’s spatial distribution andstrong birth rates – and it is the only one where this growth willcontinue after 2050 (Figure. 4). With 300 million additional rural people
by that date, the sub-African rural population will increase by 57%.
Figure 3� Activity ratio by major countries and regions (1950 – 2050)

Source: Losch 2013a, based on World Population Prospects, 2010 Revision

Figure 4� Expected rural population by major countries and regions (2010 – 2050)

Source: Losch 2013a, based on World Urbanization Prospects, 2011 Revision

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Chapter 8

The “African Equation”
With a fourfold increase of the population over the last fifty years, theeconomies of the sub-continent were able to absorb a huge demographic
push. However, because of the recession of the 1990s and of an
insufficient and volatile economic growth process over the long run,living standards have been stagnating and massive persistent poverty
remains: on average most people (70%) remain below the threshold of $2
PPP per person per day, and 50% of the population are under the $1.25
poverty line – a major difference with China and also with India where
progress has been significant – notably in terms of extreme poverty
reduction (Figure 5).
Figure 5� Poverty headcount ratio (in $ PPP)

Source: World Development Indicators

The burning issue here is the continuing population growth and themassive change in scale: while SSA’s population increased by 560 million
people between 1970 and 2010, it will increase by 1.1 billion over the sametime period between 2010 and 2050 (and possibly more). This means adramatic “job challenge” for Africa (Bhorat and Naidoo 2013), which canbe more accurately described when considering the yearly cohort of newworkers entering the labor market.11 For a medium-sized country such as
Senegal, almost 300,000 young people reach working age every year, or 5
million over 15 years (two and a half times the population of a city like
Dakar today). Seen at the level of the whole of SSA, this means a yearlycohort of around 17 million, which will increase and add up to 330
million by 2025 (i.e. nearly the current population of the United States).
11. The annual cohort of workers entering the labor market, or reaching the age of finding
an income-generating activity, is 1/10 of the 15-24 year age group. This is a flow instead of achange in the total number of workers, which takes into account people moving from the15-64 age group to the supposedly inactive 65+ group (a disputable criteria in countries
without a formal labor market and generalized pension system – the case of SSA).

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The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

These are not projections: on a 15-year time period, these new workers
have already been born. Based on the existing distribution of populationand trends in migration to cities, two-thirds of these new workers arelikely to be in rural areas (Losch 2012).
These magnitudes in numbers allow an articulation of the African
equation: with their undiversified economic structure, where the weightof primary and especially agricultural activities is dominant, and where
the weakness of industrialization does not offer mass employment
alternatives, how will SSA economies absorb their booming labor force?
What are the possible and realistic absorption sectors?
Lessons from past transitions are especially instructive but they also
help to point out differences: the “moment in time” matters andreplication is not an option because economic, institutional, andgeopolitical environments have changed.12 For sub-Saharan Africa, thechallenge is to succeed in its structural transformation in the newinternational regime of a liberalized global economy. SSA also has tomanage new constraints related to struggles over resources and theimpact of climate change, as the region will be one of the most impacted.
It will have to manage these challenges without benefiting from thesame economic policy options that other countries before them did – aconsequence of new international regulations.13

4� Policy options for a sustainable and inclusive growth process
The question of the sectors of absorption of the new African workforce istherefore a real challenge for public policy, which must invest in public
goods, facilitate and support private initiatives and promote thedevelopment of economic activities. This employment challenge isrecognized by the international community, as evidenced by the recent
publication of numerous reference documents, such as the World Bank’s
12. For instance, the importance of European hegemony (colonization, imperialism) for
European transitions, including mass migration of European settlers, or importsubstitution policies in Latin America based on protectionism, or state-lead development inAsia.
13. Chang (2002) emphasizes the difference in status between countries according to theirhegemonic or subordinate position. In particular, he recalls how the richest countries now
wish to prevent others from applying the policies they had themselves implemented(especially those of protection and subsidies) and which they sometimes continue eventoday.

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Chapter 8World Development Report (WDR) 2013 focusing on employment (WorldBank 2012) or the African Economic Outlook 2012 (AfdB et al. 2012).14 The
challenge is clearly to identify the most strategic policy options capableof transforming this workforce potential, and the possible demographic
dividend,15 into a veritable engine of growth and development (i.e. anengine of economic and social inclusion).
Reengaging in development strategy design
In order to meet the challenge of employment and inclusiveness, theimperative is to reinvest in development strategies, which articulate
long-term visions and sectorial approaches based on a realistic analysisof the structural situation of every national economy, regional grouping
and their international environment. Regional institutions and some
African governments (like Ethiopia, Rwanda, Kenya) are increasingly
reengaging in such a perspective, given that it had been obscured by thelimitations of structural adjustment and market-only policies.
The need to address structural change and to implement transformative
policies through willing developmental states is becoming a “credo”
and “structural transformation” a buzzword. The African Union
Commission engaged in the drafting of the Africa Vision 2063 with atransformative agenda as a guideline; the 2012 World Economic Forum
for Africa focused on Shaping Africa’s Transformation; the African
Development Bank’s new 10-year strategy puts Africa’s transformation
at the center; the Economic Commission for Africa (ECA) titled its 2011
Economic Report on Africa, Governing Development in Africa: The Role of
the State in Economic Transformation (UNECA 2011); and the African
Center for Economic Transformation (ACET), an Accra-based thinktank, is now publishing an African Transformation Report and has
proposed an African Transformation Index (ACET 2014).
The common objective is undoubtedly to support and strengthen theprocess of change towards more diversified economies, with higher
added value and qualified jobs – a prerequisite for improved living
standards. It is clear however that this diversification is a gamble in an
14. See also the ILO report on employment trends for youth (ILO 2012), the report of theAfrica Commission on the potential of African youth (Africa Commission 2009), the work of
the FAO on the employment of rural youth (van der Geest 2010) and the special report on
African youth employment (Filmer and Fox 2014).
15. It represents a limited window of opportunity because the dividend is only "distributed"
over a few decades, until the ageing population reverses the active / inactive ratio.

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Regional Challenges and Policy Options

increasingly integrated global economy. Globalization offers many newopportunities in terms of access to new markets. It also facilitates accessto knowledge and technical progress, which the richest countries today
did not have when they engaged in their transition. But globalization
also means constraints, because of rising asymmetries in productivity
and competitiveness.16 Local firms must compete with foreigncompanies - especially those from large emerging countries like China on a “stormy open field” (Birdsall 2006) characterized by the instabilityof the world economic environment; and the challenges are rising, notonly in international markets but also in domestic markets throughimports.
There is however a dead angle in this discussion on competition: if theexisting asymmetries affect only the overall economic situation of acountry, they also impact the local dimension of structural change, sinceinternational competition weakens local links between agriculture,industry and urbanization, which have contributed greatly to economictransitions in the past (UNRISD 2010). Easier access to imports has
notably modified urbanization patterns because cities (especially large
cities) often resort to imports rather than make use of local economic
resources. There is therefore a spatial dimension in structuraltransformation and an issue of reconnecting places and urban and rural
areas. Most of the efforts of policy makers are to identify the best
possible sectors for diversification and the ones with a potential forcompetition, but a realistic assessment is most often missing about thepriorities with regard to the job challenge. In order to define an adequate
time sequence, public policies have to be based on regional and sectorial
distributions of activities and people.
Sectorial priorities
The large majority of African economies is still defined by the weight ofthe primary sector (agriculture and mining), the population is still
predominantly rural, and its activity structure is characterized by theoverwhelming importance of the informal economy both in agriculture
and the large range of urban activities (household enterprise sector).
And yet, the debate is raging, with extremely contrasting points of view
between proponents of industrialization and the strengthening of urban
dynamics on the one hand, and proponents of "agriculture first" on the
16. In the case of SSA, the difference in average productivity with other developing
countries is 1 to 5, and 1 to 100 with OECD countries (UNCTAD 2006).

231

Chapter 8other hand. This debate is also blurred by aid agencies, which sometimes
adopt contradictory positions, such as the World Bank, which developed adetailed argument on the central role of agriculture in development in its
WDR 2008 (World Bank 2007), and then focused on the prominent role ofthe process of agglomeration and economic density brought about by
urbanization in its WDR 2009 (World Bank 2008).
What are the major arguments? As far as the "industrialists" areconcerned, only manufacturing can meet the scale of the challenges
facing Africa: agricultural productivity is too low and the expected
progress too slow to allow for a rapid escape from poverty; the solution
for the future of the rural poor lies in the cities.17 The major arguments
refer to the change in the international economic environment thatwould offer new opportunities for industrialization: an improved
business climate in many countries, the gradual increase in
manufacturing costs in Asia due to higher wages (especially in China),
and the prospects offered by task-based production rather than the
manufacture of end products (UNIDO 2008). This new type ofindustrialization, or light manufacturing (Dinh et al. 2012), is aconsequence of the development of outsourcing and intra-firm tradethat characterizes globalization. It is more accessible to late developers
to the extent that it requires less capital and technical and managerial
skills, and remains doable in a more fragile economic and institutional
environment (AfDB et al. 2014). This approach could also develop in theservice sector, based on the potential offered by new information andcommunication technologies, where outsourcing is developing quickly.
There are undeniable areas of diversification and opportunities for SSA:
its growing workforce and the increased costs of production
experienced by its main competitors in the developing world will
gradually strengthen its competitiveness; and it is not unrealistic, inabsolute terms, to imagine a future Africa being the "factory of theworld", that could take the place of China. Using this comparativeadvantage approach, ACET (2013) has already identified priority sectors:
agroprocessing, the garment industry and component assembly. But it isimportant to take into account the necessary timeframe for an effective
industrial development with regard to the current structural situation,
lessons from the past, and the massive demand for jobs. There has notbeen significant industrialization in SSA over the last fifty years despite
17. Several writings by Paul Collier illustrate this vision (Collier 2008, 2009).

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Regional Challenges and Policy Options

formidable urbanization.18 Examples of industrial free trade zones haveproduced mixed results and, most importantly, they have only helped
create, depending on the country, tens of thousands of jobs whereas
hundreds of thousands of jobs or millions are required annually. Itmeans that the possible new comparative cost advantages, which willarise very progressively, are insufficient.19 Heavy investments areneeded in infrastructure, training and support for businesses; and evenwith such investments, it will be impossible to create millions ofindustrial jobs each year in the near future to meet the demand forjobs.20 These reminders show the importance of prioritizing support forexisting activities, which are mostly those of the informal urban andrural sectors. This does not mean ignoring the necessary process ofindustrialization: governments must improve the business climate inorder to facilitate private investment, but they must also deal with theoverwhelming number of household enterprises.
Regarding the informal urban sector, there is a certain shift in the viewof policy makers, with greater consideration of the potential for
modernization (Fox and Sekkel Gaal 2008, Beaujeu et al. 2011). If there isan informal (sponge-like) buffer-type sector characterized by very low
productivity,21 there is also an informal sector with a great potential for
modernization based on significant production factors and strong
innovation dynamics (Ranis and Stewart 1999). This sector, which is amajor provider of urban employment, should be supported.
This discussion of the rural informal sector side, which includes the
majority of the active population in most African countries, brings us to
the second side of the debate on the sectorial priorities for action, ideas
championed by the "pro-agriculture" group. The first argument is aboutthe "basic arithmetic" of large numbers (Headey, Bezemer and Hazell
2010) – even with another decade of growth as good as or even better
18. Yet this urban growth offered all the economic benefits of density touted by the WDR2009 (World Bank 2009).
19. Competitiveness cannot be reduced solely to costs; it includes product quality andproduction capacity (the volume of supply).
20. In the case of China, the "township and villages enterprises" policy, which was the
backbone of rural industrialisation, is an interesting yardstick. Between the 1960s, when
the policy was initiated, and the 1990s a maximum of 135 million jobs were created(Vendryes 2012).
21. This buffer sector absorbs surplus labor, especially related to rural depopulation. It
represents many "odd jobs" and incomes can sometimes be lower than in rural areas.

233Chapter 8than the past one, structural transformation and the change in
employment structure will be slow (Fox et al. 2013); and absolute
number of workers in agriculture will not shrink but grow and continue
to challenge the rural economy.
The driving force of agriculture, its intersectoral effects, its role in rural
poverty reduction and rural diversification are basics of the literature on
economic development (Johnston and Mellor 1961, Johnston and Kilby
1975), and on African development in particular (Delgado et al. 1998,
Diao et al. 2007). Improving agricultural performance was a major factor
in explaining the rapid progress achieved in East and South East Asia
(World Bank 2007). Several recent studies have confirmed thecomparative potential of agricultural growth with respect to urban
development: Dorosh and Thurlow (2012) have shown, on the basis of
growth models applied to Ethiopia and Uganda, that even if cities arestill the unquestionable source of growth and structural change in thelong term, it is actually agricultural activities that are likely to have thefastest impact on poverty reduction.
The real strategic challenge today is to identify the right development
model for agriculture in Africa. Because of a generally weak
performance in the past (Benoit-Cattin and Dorin 2012), there is verysignificant room for improvement. However, new investments by new
players promoting large-scale farming22 have reopened the old “small
vs. large scale” debate about the relative merits of different sizes andtypes of farms (Losch and Fréguin Gresh-2013). This risky discussion,
which could result in adopting inappropriate modernization pathways,
tends to obscure a central issue: the need to increase production, while
creating employment in agriculture, as well as in upstream anddownstream activities, by strengthening value chains and improving
the incomes of farmers - which are the levers of rural demand andeconomic diversification (Losch et al. 2012).
In this context, and in order to secure the maximum impact, the primarytarget of public policy in this area should be the broad masses of farmers
and family farms. They offer a great potential for the integration of
22. These new investments were triggered by the fear of shortages of agricultural products,
revived since the crisis of agricultural prices in 2008-2009, and by the new needs for biofuel.
They are favored by the (debatable) reputation for available land on the continent and thewillingness of many governments to attract foreign capital (Cotula et al. 2009).

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The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

young people (Brooks et al. 2013), provided that attention is paid toimproving working conditions in order to make farming more attractive(Proctor and Lucchesi 2012; Sumberg et al. 2012). Agriculturaldevelopment in other parts of the world demonstrated the truedevelopment capacity of family farms when they received the necessary
support (Bélières et al. 2014). A recent comparison between African,
Brazilian and Thai agriculture, whose successes are undisputed despite
very different contexts, has shown that African producers arecompetitive at the farm-gate level, and would be competitive oninternational markets if recurring obstacles in supply and marketingwere removed (World Bank 2009).
Territorial priorities
Beyond sectorial discussions, there is a need to take into account the
spatial realities of the continent. African rural areas have profoundlychanged in recent decades due to demographic growth and increasing
densification of population. They have also changed as a result of urban
development itself that occurred not only through growth in numbers
but also in terms of spatial dimensions: new urban areas have emerged
as a result of the progressive development of rural towns, and this ongoing process is dramatically reshaping the territorial structure of many
regions of the subcontinent.
The Africapolis work (Denis and Moriconi-Ebrard 2012) highlights alarge emerging megapolis in the Gulf of Guinea, rooted in Nigeria (themost populous country), and developing many offshoots along the West
African coast and the Sudanese region, as well as demographic hotspots
like the Nairobi – Kampala corridor, the northern part of the Great Lakes
region, the Ethiopian highlands and, of course, the South African
conurbation. In these regions, the vast majority of the rural population is
located within 50 km of a city of at least 50,000 inhabitants.
It is obvious that in this type of new configuration, where transportation
infrastructure facilitate mobility and access to markets, the springs of
economic dynamism and diversification opportunities areincommensurate when compared to more remote areas. This "newemerging rurality" (Losch, Magrin and Imbernon 2013) is completely
invisible in the official statistical systems. However, beyond theseanalytical difficulties, another reality is emerging: poverty often
persists, including in these highly populated and connected rural areas
235Chapter 8(Losch et al. 2012). This raises the question of the quality of urbanization(i.e., the type of services and infrastructure provided by the city, theurban assets, without which urbanization cannot really play its driving
and catalyst role for economic development) and rural diversification.
The weakness of urban assets in secondary cities appears as a common
feature of African urbanization. The urban framework is most often
asymmetrical and features one big city – generally the capital city –provided with significant amounts of equipment and services on one side,and the many lagging regional cities and local towns, missing basic
infrastructure, on the other side. Recent works on the relationshipbetween type of urbanization, poverty and inequality have highlighted
the importance of intermediate urbanization and the economic functions
it provides (Christiaensen and Todo 2009; Christiaensen, de Weerdt andTodo 2013). In Africa, this “missing middle” is a major obstacle for an
inclusive growth process: it accelerates concentration and spatial
exclusion, and contributes to the growing costs of metropolization(Paulais 2012). This situation provides strong arguments for strengthening
the development of intermediate cities and this territorial priority could
contribute to the overall process of structural transformation.
Breaking the rural-urban divide and decompartementalizing public
policies
One of the major problems of development policies today is their highly
segmented conception. Sectorial policies as well as urban policies andrural policies are siloed and this stove-piping results in a major obstacle
to a spatially inclusive growth model. Beyond the need to develop
regional integration leveraged by major infrastructure, which in thelong term will also help to open up economic areas and to enhancedomestic and continental trade, it is a proactive approach to territorial
development that will produce the initiatives and innovations needed toboost economic and social development.
As reminded by Rodrik (2013), in the new phase of the world economy,
East Asian style growth rates will be difficult to sustain by Asian
countries themselves and to attain by other developing countries, andgrowth will depend first “on what happens at home”. In sub-SaharanAfrica, a proactive approach to territorial development means
consolidating urban-rural linkages, and providing the network of
medium-sized cities with the urban assets needed to foster local
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The Elusive Quest for Inclusive Growth in Sub-Saharan Africa:
Regional Challenges and Policy Options

dynamics and to facilitate their connection to metropolitan areas.
Agriculture will have to play its role, taking advantage of the potentialof the African domestic market and of external opportunities, since
growth in agricultural incomes will reinforce rural demand that drives
economic diversification at the regional level.
Therefore, public policies for structural transformation should combine
territorial development tools and support for promising economic
sectors, the construction of infrastructure, policy dialogue between
actors at the local level, and the reinforcement of human capital. This
integrated approach rooted in renewed development strategies should
be a major attribute for consolidating the quality of growth.

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Chapter 9

What are the Macroeconomic Impacts of
Natural Disasters? - The Impacts of
Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in JapanGo Shimada

1� Introduction
Typhoon Haiyan, one of the strongest storms ever recorded, swept
across the central Philippines with gusts of up to 200mph (320km/h) on
November 8, 2013. It has been estimated that the cost of reconstruction
will reach almost US$6 billion. Japan also suffered huge earthquake on
March 11, 2011, the Great East Japan Earthquake, the fourth largest inrecorded history. The earthquake caused a major tsunami on a scale that
occurs only once every few hundred years, claiming around 20,000
lives. As the following figure shows, in the last two decades there has
been an upward trend in the number of disasters.1
Figure 1� Regional Distribution of the Number of Natural Disasters

Source: Author’s calculation (2014) based on the data by the EM-DAT/CREDS.

1. The EM-DAT database constructed and maintained by the Centre for Research on the
Epidemiology of Disasters (CRED 2010). The EM-DAT database is global, and contains
natural disaster data (e.g., geophysical, meteorological and climatological natural
disasters) from 1900 to the present.

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As the frequency of disasters increases rapidly, the need to build social
resilience becomes more and more important.
So far, there has been widespread debate over the long-term economic
and macroeconomic impacts of natural disasters (Skidmore and Toya
2002). Economic analysis of natural disasters has only just started. In thepast, only a small number of papers attempted empirical analysis, but
the number has been growing over the last few years. There is noconsensus as to whether natural disasters have positive or negative
impacts. There is a strong need for more empirical studies.
As we will see in detail in the next section, previous literature has failed tocapture the heterogeneous characteristics of natural disasters. Most
studies use the number of disasters occurring across countries as an
explanatory variable. Considering the nature of most disasters, theirdirect impacts are local rather than national. Hence, for empirical study, it
seems more appropriate to use disaggregated data to capture theheterogeneous nature of disasters. For example, in the case of Japan,
prefectural data on disasters is available. Utilizing these data, we would
be able to capture a better picture of the macroeconomic impacts.
Furthermore, most studies analyse the correlation between economic
growth and the number of natural disasters. Since natural disasters havedifferent effects depending on various conditions (e.g. the impact ofearthquakes is different depending on their magnitude), it seems more
appropriate to use data such as the total amount of damage expressed inmonetary terms and the number of victims (including both dead andinjured), rather than the number of disasters, to capture the real impacts.
To tackle these issues, this paper investigates the impacts of natural
disasters on the growth rate of gross prefectural domestic product,utilizing the 47 prefectural governments’ unbalanced panel data forJapan for twenty years from 1975 to 1995.

2� The macro-economic impacts of natural disasters
in previous research
There is an on-going debate, as we will see, on whether disasters havepositive or negative macroeconomic impacts. Some analysts have foundthat natural disasters are detrimental to economic growth, but others
have found them to be a form of “Schumpeterian creative destruction.”244

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

There is a need for more empirical study, and this paper aims tocontribute to this debate.
Disasters can be classified into three categories, according to the Center
for Research on Epidemiology of Disasters (CRED, 2010): natural
disasters, technological disasters (e.g. industrial accidents), and manmade disasters (e.g. war, financial crises). This paper focuses only on
natural disasters. Macroeconomic impacts can be different dependingon the time frame (short term or long term). This section reviews
existing studies that classify these two frameworks. Many past studies
have used cross-country panel data, which is available from EM-DAT.
However, there are very few papers that examine the impacts on a
specific country (e.g. Noy and Vu 2010, on Vietnam; Rasmussen 2004, on
several Caribbean islands). This paper is an attempt to contribute
further to the discussion.
2�1 Short-and middle-term impacts of disasters
The analyses of short- and middle-term impacts vary. The field ofstudies on the economic impacts of disasters started with the short-term
effects on the economy. The growth model approach to natural disasters
was first introduced by Dacy and Kunreuther (1969). They found that
Gross Domestic Product (GDP) tends to increase immediately after anatural disaster. This analysis was supported by empirical studies byAlbala-Bertrand (1993a; 1993b). They developed an analytical model ofdisasters and response and collected data on disasters (28 disasters in 26
countries during 1969–79). Using before–after statistical analysis,
Albala-Bertrand found that the following variables increase: GDP,
capital formation, twin deficits, and agricultural and construction
output. He concluded that capital loss is unlikely to have a profoundeffect on growth and that a very moderate response expenditure may besufficient to prevent the growth rate of output from falling.2
Chaveriat (2000) and Hochrainer (2009), however, found a mixed
picture. Chaveriat found a pattern of GDP decreasing in the year of thedisaster, followed by growth over the subsequent two years. The growth
2. He found no long-run effects in developing countries. His finding was that in developing
countries aggregate negative effects lasted only two years. Hence, he concluded thatnatural disaster effects are primarily a “problem of development,” but essentially not a“problem for development.” Tol and Leek (1999) also found positive impacts on GDP in the
short term following a natural disaster, explaining that the disaster destroys the capitalstock and increases the flow of new production.

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results from the high investment in fixed capital. The paper also argued
that the short-term negative impacts depended on the scale of thedisasters (e.g. the loss-to-GDP ratio). Hochrainer studied thecounterfactual versus the observed GDP. He also examined the disaster
impacts of factors such as vulnerability, hazard, and exposure of assets.
He found that in the medium term (up to five years) natural disasters
often lead to negative consequences. As these empirical studies show,
views on the short- and middle-term impacts vary.
2�2 Long-term economic growth
Natural disasters can have long-term effects through various causal
relations. Those causal relations include destruction of schools, thecrowding out effect of reconstruction expenditure on private investment,
worsening fiscal balance leading to inflation, and environmental damage
to agriculture, fishing, and forestry (Rasmussen 2004).
Skidmore and Toya (2002) extended the short-term analysis to long-term
economic impacts by examining the causal linkage among disasters.
They counted the frequency of natural disasters from 1960-1990 acrosscountries and pursued an empirical investigation.3 Their regression
found that climatic disasters have positive and statistically significant
impacts on the growth of TFP (Total Factor Productivity). On the other
hand, geological disasters are generally statistically insignificant.
The findings of Sawada, Bhattcharyay and Kotera (2011) are in line with
Skidmore and Toya (2002); that is, that disasters have positive effects on
economic growth, especially climatic disasters. They quantitatively
assessed and compared various natural and man-made disaster impacts
using 189 cross-country panel data from between 1968 and 2001. The
empirical findings were as follows. First, in the short term all disasters
had negative impacts on GDP per capita. This is particularly true ofclimatological disasters, conflicts and financial crises. Second, in thelong term natural disasters had very strong positive impacts on thegrowth of GDP per capita. Sawada, Bhattcharyay and Kotera (2011)
3. They have three hypotheses. First, they stated that disaster risks could have both positive
and negative ambiguous impacts. They argued that the impact could be negative by
lowering the expectation on the rate of return on physical capital, but would also lead toincreased investment to meet the needs of disaster management. Second, regarding humancapital, they followed the endogenous growth theory (Lucas 1988; Azariadis and Drazen
1990). They argued that a low expected rate of return on physical capital could shift to ahuman capital increase, then to a higher rate of economic growth.

246

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japanargued that this counterintuitive positive growth effect was a result of
the “Schumpeterian” creative destruction process.
Contrary to the findings of Skidmore and Toya (2002) and Sawada,
Bhattcharyay and Kotera (2011), the results of the research by Cuaresma etal. (2008) showed a different picture. They argued that the view expressedby Skidmore and Toya on “Schumpeterian” creative destruction isdifferent from that of Schumpeter himself (1950). Schumpeter’s view on
creative destruction stressed the importance of “competition” in aperfectly functioning market as an engine for technological progress, but
Skidmore and Toya use the same term as more literal interpretation only
for technological replacement after a disaster. The paper tested thevalidity of the Schumpeterian view expressed by Skidmore and Toya bymeans of a gravity equation to examine the correlation between transfer
of technology and disasters in developing countries in the long term.
Cuaresma Hlouskova, and Obersteiner (2008) found that disasters arenegatively correlated to the adoption of new technology from abroad, andonly countries with a higher level of development benefit from theintroduction of technology after disasters.
Similarly, Noy (2009) found that 1) the amount of property damage
caused by disasters is a negative determinant of GDP growth and 2)
there is no correlation between the number of victims (killed or affected)
and growth of GDP. He studied the determinants of macroeconomic
output decline, using a linear regression model approach, and foundthat countries with the following factors are resilient to initial disastershocks and further worsening of the macroeconomy. The factors hediscussed are 1) higher rate of literacy, 2) better institutions, 3) better per
capita income, 4) higher degree of openness to trade, and 5) higher levelsof government spending.
The other empirical study that argues that natural disasters have negative
impacts on economic growth in the long term is Benson and Clay (2003),
while World Bank (2003) and Rasmussen (2004) found that natural
disasters have no significant impact on economic growth. Rasmussen
studied several Caribbean islands. He found that developing countries
tend to be affected the most by natural disasters. Small island states have ahigh frequency of natural disasters. The paper identified a median
reduction of the growth rate of 2.2 percentage points in the year of theevent, but found that the long-term effect of natural disasters was
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indeterminate.4
From this review of previous literature, we see that there is noconsensus as to the macroeconomic impacts of disasters. There is astrong need for more empirical studies on the consequences.
Accumulating this knowledge will certainly contribute to policy
planning for recovery after a disaster. One of the common problems
with previous literature is the treatment of data. Almost all of theprevious literature uses the EM-DAT database constructed andmaintained by the Centre for Research on the Epidemiology of Disasters(CRED).5 The EM-DAT database is global, and contains natural disasterdata from 1900 to the present. It seems, however, that past literature has
failed to capture the heterogeneous characteristics of natural disasters.
Most studies use the number of disasters in a country as an explanatory
variable. Considering the nature of a disaster, its direct impacts are local
rather than national. For example, Okinawa is far to the south of theJapanese mainland and is prone to experience more hurricanes thanTokyo. The case is similar for Hawaii and the USA. Hence, for empirical
study, it seems to be more appropriate to use disaggregated data tocapture the heterogeneous nature of disasters. For example, in the caseof Japan, prefectural data on disasters is available. Utilizing these data,
we are able to capture a better picture of the impacts.
Furthermore, most studies, like that of Skidmore and Toya (2002),
analyse the correlation between the “number” of natural disasters andeconomic growth. Again, natural disasters have different effects
depending on various conditions (e.g. an earthquake’s magnitude).
Therefore, rather than the number of disasters, it seems more
appropriate to use data such as the total amount of damage and thenumber of victims to capture the real impacts, because the number ofpeople affected indicates the direct impacts of the disaster.6

4. Rasmussen (2004) provides a box reviewing studies on the macroeconomic implicationsof natural disasters such as 1) an immediate decrease in economic output; 2) a worsening of
external balance; 3) deterioration in fiscal balances; and 4) poverty increase.
5. According to the CRED homepage, the database is compiled from various sources such as
UN agencies, NGOs, insurance companies, research institutions, and press agencies.
6. Noy (2009) disaggregated the EM-DAT data by region. He found that island countries areon average twice as vulnerable to disasters as other countries.

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What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

3� Initial evidence on disasters and economic growth
Before going into detail, this paper will present an initial analysis using
a simple correlation between disasters and long-term economic growth
for the 47 prefectures of Japan using the same analytical framework as
Skidmore and Toya (2002) (Figure 2). The vertical axis shows the averageannual per capita growth rate over the 1970–98 period. The horizontal
axis measures the likelihood of a natural disaster. Skidmore and Toyapresented the relationship between the total number of disasters andper capita GDP growth. As discussed above, instead of the number ofdisasters, in this paper the natural log of the number of victims wasused as a better indicator to grasp the impact of natural disasters.7
This regression line shows a statistically significant negative correlation
between the number of victims and economic growth. The coefficient is-0.069. This seems to be very small, but the absolute value of thecoefficient is still greater than that of Skidmore and Toya (2002), which is
0.0033. On the basis of this number they argued that disasters havepositive impacts. Naturally, the impacts of a natural disaster on
economic growth are small, but this estimate is statistically robust.
Figure 2� Per capita prefectural income growth and disaster

Source: Author’s calculation.

7. This paper uses absolute figures rather than relative figures. The previous literature uses
both. This is because absolute figures sometimes capture the real impact of a natural
disaster better. Furthermore, past studies, such as Skidmore and Toya (2002), examined theimpact using both relative and absolute figures, and found the same results each time.

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4� Data
For more detailed empirical analysis, this paper used the variables listed
in Table 1. The definitions and data sources are also listed in Table 2. As
discussed in the literature review section, this paper uses prefectural
disaster data. The database is unbalanced panel data, covering all 47
Japanese prefectures for twenty years from 1975–1995. The maximum
amount of total damage is huge because of the Great Hanshin Awaji
Earthquake in 1995.
On the other hand, there is no prefectural data available on the numberof disasters to actually hit a prefecture classified into geophysicaldisasters, meteorological disasters, and hydrological disasters.
Therefore, unlike other past studies, this paper will not compare theimpacts of each class of disaster. Furthermore, past studies
differentiated between rich and poor countries, but in the case of Japanthe gap between prefectures is small, and in many cases people caneasily move from one prefecture to another. Therefore, this paper will
not classify prefectures into income groups.
Table 1: Descriptive Statistics
Variable Obs

GPDP 1128
Pgex 1360
Pgexrcv 1360
Tot_damage 1340

Mean

7,560,000,000,000
656,000,000
8365957
373,000,000,000

Std. Dev.

10,900,000,000,000
761,000,000
8,859,131
4,220,000,000,000

Min667,000,000,000
46,600,000
19,0001,000,000

Max

86,100,000,000,000
7,030,000,000
124,000,000
137,000,000,000,000

Source: Author’s calculation.

Table 2: Definitions and sources of variables
Variable

Description

SourceGPDP_rGrowth of gross prefectural
domestic product (at current price)

Pgex_rGrowth of prefectural government
expenditure

Privtcapstx_r

Growth of prefectural private capital
Takero Doi (2002)
stock

Tot_damage

Total amount of prefectural damage
in Japanese Yen

Source: Author’s calculation.

250

Cabinet Office, Government of Japan
Ministry of Internal Affairs andCommunication(Chihou Zaisei Nenpou)

White paper by the Fire Defense
Agency (each year)

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

5� Methodology
In order to set the stage for the analysis, this section presents ananalytical framework for empirical analysis, which modifies the modelof Noy (2009) and Noy and Vu (2010).
where Yi,t is the annual GDI (Gross Domestic Income) growth rate. i is aprefectural index to capture prefecture-specific effects, and t is the timeindex. Disi,t -1 is the measure for disaster magnitude, estimated by theamount of direct damage. Since disaster affects the following year, thisis the disaster lag variable.
is control lagged variables (such as
growth of prefectural government expenditure and growth ofprefectural private capital stock). This model includes a GDI growth lag
following Islam (1995).
Islam (1995) also stated that a time span of just one year is too shortbecause the short-term business cycle may influence the estimation
results over such brief spans, so he proposed five-year time intervals.
This is because his study focused on convergence. Unlike literature on
convergence, the impacts of external shocks such as disasters differ year
by year, especially during the first several years. Hence, instead of fiveyear time intervals, this paper employs annual data.
The lagged dependent variable might correlate with the error term. If
this is the case, the conventional panel data analysis methods (poolingcross-sections across time, fixed effects, and random effects) are notconsistent. These estimators are consistent only when all regressors are
not correlated to the error term. In order to correct for the bias arisingfrom the presence of a lagged dependent variable, this paper also
employs the Prais-Winsten estimation, PCSE (panel-corrected standard
error), and the system General Method of Moments (GMM) estimator(Noy and Vu 2010; Roodman 2003). The Prais-Winsten estimation is amethod of multiple linear regression with AR(1) and exogenous
explanatory variables. The Prais-Winsten standard errors account forserial correlation; the OLS standard errors do not. The PCSE (panelcorrected standard error) handles the issue of cross-section
heteroskedasticity (Beck and Katz 2004). The presence ofheteroskedasticity makes the OLS standard errors inconsistent. PCSE
improves on OLS standard errors with respect to panel
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heteroskedasticity, but not other issues. The system GMM is used totackle other possible biases by endogeneity and omitted variables in
addition to the bias. Arellano and Bond (1991) first established the“difference-GMM” estimator for dynamic panels (Roodman 2003).
Arellano and Bond’s estimation starts by transforming all regressors, bydifferencing, and uses the GMM. The method regards lagged dependent
variables as not exogenous and predetermined. A problem with theoriginal Arellano–Bond difference-GMM estimator is that if there is an
issue of a random walk of endogenous variables, the estimation becomesa biased coefficient estimation.
To tackle the above problem, Blundell and Bond (1998) articulated an
improvement on augmented difference GMM by Arrelano and Bover(1995), adding more assumptions that the first difference of instrument
variables are uncorrelated with the fixed effects, allowing more
instruments to be introduced and making them exogenous to the fixed
effects. The augmented estimator is called “system GMM.” Thecommand xtabond2 implements both estimations by Stata. The majoradvantage of the system GMM estimation, compared with thediﬀerence GMM, is that this approach effectively controls for
autocorrelation and heteroskedasticity.
The system GMM estimation corrects for omitted variable bias byeliminating fixed effects through first-differencing, and for endogeneity
bias using lagged endogenous regressors as effective instruments. In
our system of GMM estimation, the lagged dependent variable isconsidered to be endogenous. This paper employs one-step estimationand implements the Hansen test to verify whether the instruments
really satisfy the orthogonality condition (uncorrelated with the errorterm), and also implements the AR(1) and AR(2) test for autocorrelation.

6� Estimation results: The impacts on economic growth
The results are presented in Tables 3, 4, 5, 6, 7, 8 and 9. Each table shows
the results from a different time lag of tot_damage, starting from 1 year to
20 years. As Table 3 shows, the F-test result (Prob>F=0.6189) indicatesthat the pooling model is more appropriate than the fixed effects
estimation. Considering this, the Breusch and Pagan test and theHausman test were implemented. The Breusch and Pagan test result(Prob > chibar2 =1.0000) indicates that the pooling regression model is252

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

more appropriate than the random-effects model. The Hausman test
result (Prob>chi2 = 0.0000) means the fixed effects model is better than
the random effects model. These three tests confirm that the pooling is
the most suitable.
According to pooling, random effect, and fixed effect estimates, theresults became significantly negative in years 3, 5, 6, 9, 10, 11 and 15. In
these years, all three estimations returned the same results. In addition,
fixed effect estimation returned statistically negative results in years 12
and 14. In sum, the conventional panel data analyses show negative
impacts of natural disasters not just in the short term but in the longterm as well.
The results of the Prais-Winsten estimation agreed, finding statisticallynegative results in years 1, 3, 4, 5, 6, 7. 8. 9, 10, and 11.8 The negative
impacts of natural disasters further were confirmed by the PCSE
estimation. All estimated results became significantly negative. Due tothe unbalanced nature of the panel, results were estimated until the 16year lag. The results of the system GMM confirmed the impacts. The
results of the Hansen test, AR(1) and AR(2) imply that, in most cases, theinstruments are orthogonal to the error term and the error term is not
autocorrelated in the system GMM estimation. The system GMM results
became negative and consistent all through the years.

7� Conclusion
This paper analysed the economic impacts of natural disasters by
utilizing the 47 prefectural panel data of Japan for twenty years. Whatcan we conclude from the empirical findings above? The initial
empirical study of “average annual per capita growth rate over the 1970–
98 period” and “natural log of the number of victims” showed a negativeand statistically significant relationship. In the following detailed study,
this paper employed the conventional panel data analyses (pooling,
fixed effects, and random effects), Prais-Winsten and PCSE and thesystem GMM.
Unlike several previous studies, which found positive long-term effects
of natural disasters, this paper found that the impacts of natural
8. The Prais-Winsten estimation did not estimate in year 16 because convergence was notachieved.

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disasters measured by total value of damage on economic growth arerobustly negative according to our analyses. This study indicates thatthe impacts from natural disasters are long lasting. This conclusion is
concurrent with what happened to the city of Kobe, one of major cities ofJapan, after the earthquake in 1995 (Shimada 2014 and forthcoming). The
economic gap between Kobe and the rest of Japan widened until 2003,
and then after 2004, the economic trend in Kobe equalled that of the restof Japan, but the city still has not totally ‘filled the gap’. As the impacts
are long lasting, it seems necessary to consider proactive recovery
policies, not only short-term but also long-term.
As we showed, most previous literature used cross-country data of thenumber of natural disasters, and failed to capture the heterogeneous
nature. As this study showed, it seems to be more appropriate to use
disaggregated data. The findings of this paper are specific to Japan. In
the future, more analysis using this kind of disaggregated data will benecessary from other regions and countries especially in developing
countries where natural disasters hit harder than in developed
countries. Further, it will be desirable to control other factors, which
effect on economic growth other than natural disasters.

254

* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 3� Estimation result 1

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

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256

* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 4� Estimation result 2

Chapter 9

* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 5� Estimation result 3

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

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* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 6� Estimation result 4

Chapter 9

* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 7� Estimation result 5

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

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Source: Author’s calculation.

Table 8� Estimation result 6

Chapter 9

* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s calculation.

Table 9� Estimation result 7

What are the Macroeconomic Impacts of Natural Disasters?
- The Impacts of Natural Disasters on the Growth Rate of
Gross Prefectural Domestic Product in Japan

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264

Part Ⅳ
Multiple Paths towards
Quality GrowthChapter 10

Industrial Transformation andQuality of Growth
Akio Hosono

1� Introduction
The emphasis and priorities in international aid policy have changed
significantly since the end of the Cold War. Even within the last two
decades, dominant aid policy has shifted from macro-oriented
structural adjustment to poverty reduction with concrete social sector
targets, and subsequently to a search for a new source of growth (Ohno
2013, 1). As the World Development Report 2013 (World Bank 2012, 87)reminds us, the conventional wisdom was then to focus on growth andassume that improved living standards will follow. This is the main
tenet behind “growth strategies”, “growth diagnostics”, and “binding
constraints analysis,” all of which aim to identify and remove obstacles
to economic growth. (87)More recently, increasingly greater attention is being paid to “quality” of
economic growth, in terms of its relations with jobs, the environment,
learning, accumulation of skills and capabilities, innovation, and so on.
Quality of growth was featured in Vinod et al. (2000), which exploredissues of faster and better growth. In Asia and the Pacific region, APEC
leaders gathered in Yokohama in 2010, and agreed on an “APEC Growth
Strategy.”1 The strategy stressed that “the quality of growth” needs to be
improved, so it will be more balanced, inclusive, sustainable, innovative,
and secure. Ten months later, the World Economic Forum’s Summer Davosin Asia held its annual meeting focusing on “Mastering Quality Growth,”
where sustainability, inclusion, fairness, balance, and technology and1. “The APEC (Asia Pacific Economic Cooperation) Leaders’ Growth Strategy” was agreed
on Nov. 14th, 2010. It is referred to here as the “APEC Growth Strategy.” APEC comprises 55%
of real global GDP, 44% of global trade, and 40% of the global population. The APEC Growth
Strategy mentions that APEC senior officials should report to leaders in 2015, for theirreview, on APEC’s progress in promoting APEC Growth Strategy (APEC 2010, 12).
Regarding the 22nd. APEC Economic Leaders’ Declaration in 2014, see Section 2 of thispaper.

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innovation were highlighted (World Economic Forum 2011, 3).
On the other hand, in recent policy debates on growth and development,
including the post-2015 discussions, structural transformation has beenfeatured much more. The Report of the High-Level Panel of Eminent
Persons on the post-2015 Development Agenda is titled “A New Global
Partnership: Eradicate Poverty and Transform Economies throughSustainable Development” (hereinafter referred to as “The Report of
HLP on the post-2015 Agenda”). The panel concluded that the post-2015
agenda needs to be driven by five big transformative shifts,2 including acall for the transformation of economies for jobs and inclusive growth.
Likewise, focusing on economic transformation, the latest report ofAsian Development Bank (ADB 2013a), mentions that development isdistinct from aggregate growth, which can occur without significant
transformation, as has happened in some oil-rich economies. This reporthighlights key components of structural transformation.3
Therefore, there now seems to be more of a consensus on the importanceof quality of growth on the one hand and structural/industrial
transformation on the other. However, we need to recognize that there isconsiderable diversity between countries in the agendas in regard toboth the quality of growth and transformation. In the former agenda,
countries that depend a lot on natural capital 4, the focus of their qualityof growth agenda could be sustainability. On the other hand, countries
that are exposed to higher risk of natural disasters have to focus onresilience and human security. Some low income countries are strongly
concerned with under-nutrition, which persists in spite of considerablyhigh rates of growth as has happened in Sub-Sahara African countries.
There is also diversity in the transformation agenda. Challenges facing
countries in terms of structural transformation are different as they movealong the development path and changes in endowment. They could havedifferent foci on infrastructure, human resource development,
technological innovation and so on. In some countries, industrial
2.The five big transformative shifts are: “Leave no one behind”, “Put sustainable
development at the core”, “Transform economies for jobs and inclusive growth”, “Build
peace and effective, open and accountable public institutions”, and “Forge a new global
partnership” (High-Level Panel of Eminent Persons on the Post-2015 Agenda 2013, 7-12).
3. The five components are reallocation of factors of production; diversification, upgrading,and deepening of the production and export baskets; use of new production methods andprocesses and different inputs; urbanization; and social changes (ADB 2013a, 3-5).
4. Regarding natural capital, see Section 5.

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Industrial Transformation and Quality of Growth

challenges are shaped by special circumstances affecting particulargroups, such as resource-rich countries, land-locked countries, small
countries, countries located close to large consumer markets, and so on(World Bank 2012, 190). Thus, a typological approach could be useful toaddress these diversities. JICA and JBIC (2008) distinguish, first of all,resource-rich countries and resource-poor countries. The World Bank
(2012) identifies eight categories of “job challenges”, including resourcerich countries, urbanizing countries and conflict-affected countries.
Obviously, individual countries’ growth strategies need to address theirown transformation agenda as well as quality of growth agenda.
The main objective of this paper is to obtain insights into the “quality ofgrowth” in terms of the above-mentioned attributes, namely, balanced,
inclusive, sustainable, innovative, and secure aspects of growth, as wellas the relationship with “structural transformation,” drawing
principally on selected outstanding cases of what we term “industrial
development”5 that have resulted in a remarkable economictransformation and growth in a country or in regions of a country. These
cases presented here are the automobile industry in Thailand, thetransformation of the “Cerrado” in Brazil from barren lands to a sourceof high-productivity agriculture, the garment industry in Bangladesh,and salmon farming in Chile.
The next section will discuss key issues and the analytical perspective of
quality of growth and economic/industrial/structural transformation.
Section 3 will review briefly the process of outstanding industrial
development of the four cases. Then sections 4, 5, 6, 7 and 8 will discussinterrelationships, synergies and trade-offs among transformation andeach of five attributes of quality of growth drawing on experiences ofthe outstanding cases. The final section (Section 9) presents someconcluding remarks.

2� Key issues and analytical perspective
The above discussion implies that the major challenge developing
countries face is to transform their economies to achieve high-quality
growth.

5. As regards the definition of industry, see the last paragraph of this section.

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Key issues of high “quality growth”
Concerns about quality of growth have been increasing especially in
Asia Pacific region. The leaders of APEC gathered in 2009 to chart a new
growth paradigm for the Asia-Pacific region and agreed to formulate acomprehensive long-term growth strategy (APEC 2009, 1; APEC 2010, 2).
As mentioned above, APEC leaders agreed on the APEC Growth
Strategy in 2010, which highlighted “five growth attributes” andaffirmed that, “APEC aims to achieve balanced, inclusive, sustainable,
innovative, and secure growth” (2). The report of the “World Economic
Forum Annual Meeting of the New Champions 2011: Mastering Quality
Growth” (Summer Davos in Asia) stated that the Chinese premier’s
comprehensive plans underscored the many dimensions of the quest for
quality growth, a concept with which the participants enthusiasticallyembraced. These included sustainability, inclusion, fairness, balance,
and technology and innovation (World Economic Forum 2011, 3).
Many of the Asia Pacific countries also emphasize the importance ofquality of growth. The new generation of Chinese leaders and thegovernment set out an ambitious and comprehensive agenda forstructural reforms in the “Decision by the Third Plenum Session of theChinese Communist Party” held in 2013 in which, in addition torebalancing the economy, other goals such as mitigating social
inequality, protecting the environment and addressing climate change,
addressing rural urban divide, and improving the quality of growth, areplaced high in the policy agenda (Wang, Wang and Wang 2014, xi). In
Japan, the Annual Report on the Japanese Economy and Public Finance 2012
featured growth with “quality” (Japan. Cabinet Office 2012, 226). Someother countries of Asia including Malaysia, Thailand, India, Bhutan, andVietnam, have been introducing a similar concept both explicitly andimplicitly. For example, Malaysia launched a “New Economic Model”, aframework of both inclusive and sustainable growth in 2012 (UNESCAP 2013, 8). Recently, in 2014, the Japanese Government posted adraft of the new “Development Cooperation Charter” to replace the“Official Development Assistance (ODA) Charter” for public comment.
It highlights one of the most important challenges of development:“‘high quality growth’ and poverty reduction through this growth,” inwhich inclusiveness, sustainability and resilience are stressed.
Most recently, the 22nd APEC Economic Leaders’ Declaration: Beijing
Agenda for an Integrated, Innovative and Interconnected Asia-Pacific270

Industrial Transformation and Quality of Growth(APEC 2014, 7) stated that, “We agree to strengthen macroeconomic
policy coordination with a view to forging policy synergy, and creatinga sound policy environment for the robust, sustainable, balanced andinclusive economic growth in the region.” The declaration emphasized
especially innovative growth: “We recognize innovation as animportant lever for economic growth and structural reform” (7).
In terms of enhancing the analytical perspective of quality of growth, areport published by Economic and Social Commission for Asia and thePacific (UN-ESCAP), titled Shifting from Quantity to Quality: Growth with
Equality, Efficiency, Sustainability and Dynamism (UN-ESCAP 2013),
deserves special attention. The report presents a holistic framework,
distinguishing three dimensions of quality of growth: environmental,social and economic. It highlights five key determinants of quality ofgrowth: inclusiveness in relation to environmental, social and economic
benefits, efficiency and productivity in the use of natural, human andmanufactured capital, structural transformation that promotes socialand economic values, balanced investment in all forms of capital, andlimits in the economic, social and environmental spheres (UN-ESCAP
2013, 7).
While all the above-mentioned documents emphasize the quality ofgrowth, their foci are not necessarily the same. However, they
commonly give a high priority to inclusiveness and sustainability. In
addition, both APEC and Summer Davos documents refer to balanced
growth and innovative growth. On the other hand, some aspects of
secure growth of the APEC Growth Strategy, such as food security andfood safety, infectious disease preparedness, control of noncommunicable diseases and strengthening health systems, are includedin the concept of inclusive growth of other documents. Likewise, while
resilience constitutes an attribute of quality growth in the Japanese
Development Cooperation Charter, maximizing business andcommunity resilience is included in the secure growth concept in thecase of the APEC Growth Strategy.
As such, in summary we could safely say that the cited documents
coincide in the importance of inclusiveness and sustainability as highpriority attributes of growth. They draw on outcomes of discussion onthese attributes over a decade as reviewed in the following section.
Innovative growth and balanced growth are attracting attention more271

Chapter 10

recently and have been included in the APEC Growth Strategy and theSummer Davos documents. APEC’s Beijing Agenda emphasized
innovative growth especially. Secured growth and resilience are
explicitly indicated in the APEC Growth Strategy and in Japan’s
Development Cooperation Charter respectively, while they are includedin the concept of other attributes of growth in other documents. We
could conclude that, so far, APEC Growth Strategy could be consideredas the most comprehensive and widely agreed framework for quality ofgrowth. Accordingly this paper uses this framework as the principle
reference for the quality of growth and focuses mainly on two aspects,
inclusiveness and sustainability, paying due attention to other aspects ofquality of growth as well.
Relationship between quality of growth and transformation
To start with, we summarize some of the basic aspects of economic/
industrial/structural transformation, on which there has been growing
consensus in recent years. First, we understand that transformation is
crucial and creates a virtuous circle of growth and furthertransformation, as the ADB’s report (ADB 2013a) envisages. The reportargues that when structural transformation creates a virtuous circle, it
leads to higher growth and higher income per capita, and these induce
further changes in the structure of the economy. This implies that atransformation agenda features basically in the drivers of growth, while
a quality of growth agenda focuses on the desired attributes of growth,which appear to be related to both means/process and ends of growth,
as discussed later.
Secondly, there is no standard model for transformation. As mentioned
above, the transformation agenda differs among countries: for example,
countries with a very high proportion of the population living in rural
areas, early-industrializing countries, urbanizing countries, countries
that need transformation from a labor-intensive to a knowledgeintensive economy, overcoming the middle-income trap. Figure 1
roughly illustrates the diverse transformation agenda with examples ofsome selected countries. Hence, from this viewpoint, there is no “onesize fits all”-type standard model for transformation, although thecommon denominator of transformation is industrial structural changeof an economy that generates growth.

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Industrial Transformation and Quality of Growth

Figure 1� Diverse economic transformation agenda(Selected cases)

Source: Prepared by the author, GNI per capita (2012) from World Development Indicators database,World Bank.

Thirdly, it appears that structural transformation is closely related tochanges of endowments or assets and changes in comparativeadvantage, as well as higher efficiency of economy, innovation andtechnological progress. As Vinod et al. (2000) illustrate with the use of aframework of asset accumulation, growth, and welfare, “investments inphysical, human and natural capital, together with many policy
reforms, contribute to technological progress and the growth of totalfactor productivity (TFP), thereby boosting growth” (xxvi).
The nexus between changing endowments or asset accumulation andtransformation could be more clearly explained by changing or dynamic
comparative advantage. According to Noman and Stiglitz (2012), the“old” policies focused on improving economic efficiency within a static
framework, “but the essence of development is dynamic. What matters,
for instance, is not comparative advantage as of today, but dynamic
comparative advantage” (7). Lin (2012), likewise, discusses ‘changing
comparative advantage,’ arguing that “the more effective route for theirlearning and development is to exploit the advantages of backwardness
and upgrade and diversify into new industries according to thechanging comparative advantages determined by the changes in theirendowment structure” (73).
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Among key components of structural transformation identified by ADB(2013a), are reallocation of factors of production, diversification,
upgrading, and deepening of the production and export baskets, and useof new production methods and processes and different inputs. These are
intimately related to the changing or dynamic comparative advantage.
As such, quality of growth needs to be discussed in the context oftransformation, because it is a driver of growth and could be related todifferent attributes of growth. As discussed above, the most widelyagreed on and one of the most comprehensive definitions of quality ofgrowth appears to be that of the APEC Growth Strategy, namely, toimprove quality of growth focusing on five desired attributes for
economic growth. In the action plan of the Growth Strategy, structuralreform, human resource and entrepreneurship development, green
growth, knowledge-based economy and human security are mentioned.
Figure 2 roughly illustrates diverse quality of growth agenda with someselected examples of concrete actions to address the agenda.
Figure 2� Diverse “quality of growth” agenda (selected cases):
Examples of concrete actions to achieve growthwith different desired attributes

Source: Prepared by the author, based on APEC Growth Strategy (2010)

Therefore, the central theme could be to analyze the relationshipbetween transformation and quality of growth, bearing in mind a274

Industrial Transformation and Quality of Growth

distinct transformation agenda with a specific industrial structure. This
analytical approach could elucidate aspects that might be overlooked or
be invisible when economic growth is discussed at a macro level, whereindustries are treated collectively at an aggregate level. The quality ofgrowth agenda and industrial transformation agenda could be different
among countries due to different combinations of industries orindustrial structures they have or intend to have.
We use the term ‘industry’ very broadly to refer not only to the
manufacturing sector but also agro-business, modern agriculture,
aquaculture, transport, logistics, tourism and any other activities that
produce goods and services that are new in the country and that requiresignificant human (and social), physical, natural (and environmental)
capital as well as accumulation of knowledge and capabilities.

3� Outstanding cases of industrial development andtransformation6
This section consists of very brief thumbnail sketches of four
outstanding cases of industrial transformation. More details in terms of
the impact of transformation on quality of growth will be provided inlater sections of this paper. These sketches stress the positive aspects ofthe cases, while some challenges will be mentioned later.
Thailand automobile industry
In 1995, Thailand’s annual automobile exports were less than half abillion US dollars, well below exports from India and Malaysia. In 2008,
exports approached 28 billion US dollars, making Thailand the largest
automobile exporter in the ASEAN region, and by 2012, Thailand was
the seventh largest exporter in the world. Production of 1 million cars
was achieved in 2005 and 2.5 million cars in 2012.
It is estimated there were about 690 first-tier parts makers, 30 percent ofthem Thai majority joint venture companies, and 23 percent of thempure Thai companies, and 1,700 second- and third-tier parts makers,
most of them locally owned small and medium enterprises (SMEs),
supporting the automobile industry in Thailand in 2010 (Natsuda and6. Analysis of these cases is based on another of the author’s papers (Hosono 2013). See thepaper for details of these cases. In addition to these cases, the author also drew on nine casestudies included in JICA/JBIC (2008).

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Thoburn 2011, 8). At present, the automobile industry is the principal
engine for growth in Thailand’s economy. “The Detroit of Asia” envisaged
once by the Thai government is now a reality and an “automobile belt”
has been established from Ayutthaya to the Eastern Seaboard. As
Athukorala and Kohpaiboon (2011) point out, “the automobile industry
has been the target of industrial development in many countries as adriver of growth – a source of employment, technological expertise, and astimulus to other sectors through backward linkages…. But only ahandful of developing countries have managed to develop aninternationally competitive automobile industry.” 7
Development of an automobile industry requires skilled labor andsupporting industries to provide up to 20,000 to 30,000 parts andcomponents. Supporting industries and automobile assembly plants areclosely related and provide externalities to each other. Furthermore, thedevelopment of supporting industries for automobile industries takes
years because they need a prolonged process of accumulation ofknowledge and capabilities, especially in the formation of humanresources and learning about technology.
Brazil’s Cerrado agriculture and agro-industrial value chain
Starting from the mid-1970s, the tropical savanna of Brazil, called the
Cerrado, transformed itself into one of the world’s most productive graingrowing regions in just a quarter of a century, realizing modern uplandfarming in a tropical region for the first time in human history (Hosono,
Magno Campos da Rocha and Hongo, forthcoming). This remarkabletransformation has become known throughout the world as the ‘Cerrado
Miracle’(The Economist, 2010). Today, Brazil is one of world’s major grainproducing countries, and in 2012 exported the world’s largest volume ofsoybeans. Dr. Norman E. Borlaug, who received the Nobel Peace Prize forhis work related to the Green Revolution, rated the development ofagriculture in the Cerrado as one of the great achievements of agriculturalscience in the 20th century. This agricultural transformation not only
increased the production of competitive commodities such as soybeans,corn, coffee, sugar, and cotton, but it also enabled the development of foodvalue chains both inside and outside the Cerrado region. While the7. It goes without saying that the automobile is a complex product, consisting of a large
number of parts and components that involve different production processes and factor
proportions. Many of these parts and components are manufactured by independent
suppliers in other industries such as textiles, glass, plastic, electronics, rubber products, aswell as steel and other metals (Athukorala and Kohpaiboon 2011, 1) .

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production of broiler chicken and pork had been growing steadily in the
1990s, this growth accelerated at the end of the decade and resulted in a
sharp increase in meat exports.
Bangladesh garment industry
In 1981, ten years after Bangladesh achieved independence, raw jute andjute goods were its major exports, corresponding to 68 percent of totalexports. In 2011, garments and textiles constituted 85 percent of totalexports, of which 76 percent corresponded to garments. These
industries’ business entities amounted to 50 percent of all
manufacturing establishments in the country (UNCTAD 2012, 11).
Today, the garment industry has 5,000-6,000 factories with 7-8 million
workers using the assembly-line method of production. The wages of
the workers in these industries are around 35 percent higher than thenational average (11). Exports as a percentage of GDP tripled between
1990 and 2010, with much of the increase in the thriving ready-madegarment industry, which is highly intensive in female labor (WB 2012,197). This Bangladeshi success story is remarkable because, as a recent
World Bank study highlighted, “the country was often held out in thedevelopment literature as a hopeless case” (197).
Chile’s salmon industry
Aquaculture has been growing globally, and made up 49.4 percent of the
global fish harvest in 2012, compared with 16.4 percent in 1990, in what
is called the “blue revolution,” to draw a comparison with agriculture’s“green revolution” (“Aquaculture Satisfies the Demand for Fish” 2014;
OECD 2008, 85). One of the most impressive cases of the blue revolution
is Chile’s salmon farming and processing industry. Salmon did not exist
in Chile four decades ago.
Now, Chile is one of the world’s top salmon-exporting countries, ranked
on a par with Norway. It is no exaggeration to describe this as a“miracle.” Moreover, Chile is a resource-rich country highly dependent
on copper exports. In 2011, exports of mineral ores and their refined
products corresponded to more than 60 percent of total exports, 52
percent of which are copper ore and refined copper. Creating a new
industry in a highly mineral resource-rich country is considered to bedifficult due to Dutch disease and other factors.

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Enabling factors of transformation
The four cases show how distinctive critical factors for industrial
transformation identified by several recent studies interact in practice.
Learning and accumulation of knowledge and capabilities are essential.
Its process is gradual, incremental and, generally, path-dependent.
However, the process is critical for changing the endowments to attain
dynamic comparative advantage. In most of the cases, the governmentor public institutions facilitated the process. In all the cases, the constantimprovement of the capabilities of those involved in the new industries
was crucial.
Change of endowments is also attained by infrastructure construction
and technological innovation. They often trigger or accelerate industrial
development and transformation. In these cases, effective institutions
accomplished the role of facilitator or catalyzer of transformation. Firstof all, many of them had been created for specific purposes and had along-term vision and sense of mission. Second, most such institutions
regarded public-private interaction, consultation or coordination to beof the highest priority. Third, most of these institutions adapted flexibly
to changes in the global market and phases of industrial development.
These findings generally confirm the conclusion of a report of JICA/JBIC(2008) regarding decisive factors of economic growth found in the Asianexperience. They are the mid- to long-term vision for development andstrategies, flexibility in responding to a changing environment,
government’s close ties with the private sector, and harnessing the private
sector’s capacity to the maximum. The report also highlights thefollowing as growth-driving functions: development of infrastructure,human resource development and upgrade of the credit market. (11; 17-18)

4� Transformation and “inclusive aspects” of quality of growth
Key issues and analytical perspective
In recent years, “inclusive development” has attracted increasing
attention in the international community. In 2007, the World Bank’s
president declared the contribution to inclusive and sustainableglobalization to be the vision of the World Bank group. The Japan
International Cooperation Agency (JICA) incorporated the term“inclusive” into its vision in 2008. A year later, the Asian Development
Bank (ADB) positioned inclusive growth as one of the three agenda
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Industrial Transformation and Quality of Growth

items in its long-term strategic framework of “Strategy 2020” (ADB 2009).
As was mentioned, the APEC Growth Strategy was agreed in 2010 and“inclusive growth” was featured in the document as one of the fivedesired attributes for growth8. The close relation between transformationand inclusive growth was also highlighted in the Report of HLP on the
post-2015 Agenda, which emphasizes the importance of transforming
economies for jobs and inclusive growth as one the five high-prioritytransformative shifts.
The definition of inclusive growth in recent literature explicitly or
implicitly explains the intrinsic relationship between transformation,and jobs and inclusive growth. In several studies, such as the ADB’s“Framework of Inclusive Growth Indicators (FIGI)”, inclusive
development is understood to include, among others things, threefundamental components (or pillars): development of productive jobs,
equal access to economic opportunities by expanding human capacities,
and social protection.9
The first component refers to the creation of jobs, which in terms oftransformation means a change of industrial structures through thecreation of new industries, diversification of industries, and up-gradingof industries with subsequent impacts on the creation of jobs or increaseof demand in the labor market, particularly through good jobs. The
second component, on the other hand, is related to the capacity ofworkers to be able to respond to new opportunities for jobs. Assuring
equal access to opportunities by education, training and skill means
improvement of the capacity to respond to demands in the job market.
The third component is to strengthen the safety net to protect thechronically poor, or those who are not yet able to participate in theprocess (see Figure 3).

8. As for definition of and recent discussion on inclusive growth, see Kozuka (2014).
9. This definition is based on ADB’s Framework of Inclusive Growth Indicators (FIGI) (ADB
2013b), which is similar to the definition of Ali et al. (2007) and Zhuang and Ali (2010). FIGI
asserts that the outcomes of inclusive growth are achieved through the three policy pillarsof (i) sustained economic growth and development of productive jobs and economic
opportunities, (ii) social inclusion to ensure equal access to economic opportunity by
expanding human capacities, and (iii) social safety nets to protect the chronically poor andto address the risks and vulnerabilities of the population. (3)279Chapter 10

Figure 3: Relationship between transformation and inclusive growthNote: This figure roughly illustrates the relationship between the main components of “inclusive
growth.” Words in italic are added by the author.
Source: Author. Based on discussion of main text, Hosono (2014) and ADB (2013: p.4).

The Report of HLP on the post-2015 Agenda coincides with this view. It
considers the creation of opportunities for good and decent jobs andsecure livelihoods as the first priority in transforming economies for jobs
and inclusive growth. The report also mentions that people need theeducation, training and skills to be successful in the job market andrespond to demands by business for more workers (8-9). These two
aspects precisely correspond to the first two components of the ADB’s
FIGI. They are, so to speak, the two main wheels of “inclusive growth”
that should be well articulated to become the driving force of the growth.
However the matching of these two components, creation of employment
opportunities and enhancement of capacity of people to respond to theopportunities, is not always easy. FIGI’s second pillar, “social inclusion toensure equal access to economic opportunity,” includes the directive“access and inputs to education and health.” This is a fundamental aspectof the pillar, because basic education is essential for human capital toenhance its capacity; however, in order for people to respond to changing
demand in the process of industrial transformation, advanced capacity
development is required and knowledge and capabilities need to beembodied in individuals and organizations. As Cimoli, Dosi and Stiglitz
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Industrial Transformation and Quality of Growth(2009, 2) put it, “a ‘great transformation’ entails a major process ofaccumulation of knowledge and capabilities … Certainly, part of suchcapabilities builds on education and formally acquired skills. However, atleast equally importantly, capabilities have to do with the problemsolving knowledge embodied in organizations — concerning, forexample, production technologies, marketing, labor relations, as well as‘dynamic capabilities’ of search and learning”.
In order to reflect this aspect, which is essential for the two pillars ofinclusive growth to become well articulated or well matched, “capacity
development and institution building to respond to opportunities(through learning, accumulation of knowledge and capabilities)” hasbeen added by the author (in italics) into the second pillar of the Figure 3.
Findings from case studies
In the four cases presented in Section 3, new industries that emerged
from scratch created jobs and enabled people to participate in thegrowth. In this process, it was crucial there were people who werecapable of responding to the new opportunities created. Through thisprocess, transformation with inclusive growth took place.
In the case of the Bangladeshi textile and apparel industry, the changes
in rural society in this country have been profound and are related
closely to the massive mobilization of female workers by the garment
industry located mainly in two big cities: Dhaka and Chittagong.
Generally speaking, urbanizing countries like Bangladesh are endowedwith abundant unskilled labor and these countries’ integration into theworld economy can lead to the development of light manufacturingindustries (World Bank 2012, 197).
Several factors interacted in order for this change to take place.
Modernization of agriculture, based on technology adoption, enabledfarmers to shift from low-yield, single crop, deep-water rice to double
cropping of short maturity, high-yield rice, as well as the well-known
rapid spread of microfinance and construction of rural infrastructure,
were among major factors that changed the rural society of Bangladesh(World Bank 2012, 197). More specifically, rural roads, irrigation, market
facilities and other rural infrastructure, micro-credit, school educationand so forth, provided by NGOs, central and local governments anddonors, all together enabled the remarkable agricultural and rural
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development of Bangladesh over the last three decades. In this process,the rural development programs of the government and donors were
implemented effectively by the Local Government Engineering
Department (LGED), which played a critical role in the provision of rural
infrastructure.10 Micro-credit and related services were also effectively
extended by NGOs including BRAC and Grameen Bank. The Jamuna
Multipurpose Bridge, inaugurated in 1998, as the largest construction
project in Bangladesh history, has been a major channel for integrating
the lagging western region of the country with the leading easternregion, enabling cheaper transportation of gas, electricity andtelecommunications, and enhancing the labor mobility of the westernregion (Hossein et al. 2012, 11).
This whole process enhanced the mobility and readiness of lowopportunity-cost labor in rural Bangladesh and changed gradually, but
steadily, the endowments of the country.11 The mobilization of this labor
was triggered by the Desh-Daewoo garment project. As Rhee (1990)
stated, “development is a dynamic process in which self-generating
mechanisms may emerge once action is initiated…. To start on the path ofdevelopment in an outward-oriented direction, a first spark must be
created.” That spark was the collaborative effort of a domestic catalyst(Desh) that mobilized the necessary local resources and a foreign catalyst(Daewoo). It was a process in which the synergy between creation of jobs
by development of a new garment industry and enhancement of capacityof people to respond to new opportunities took place.
In the case of the Thai automobile industry, engineers and skilled/semiskilled labor was crucial to making the industry viable, competitive andinclusive. Development of the car assembly industry by foreign direct
10. The role of LGED in rural development cannot be overemphasized. LGED is one of thelargest public sector organizations in Bangladesh, with a staff exceeding 10,000 and adevelopment budget accounting for 14% (FY2009-10) of the total development budget of thegovernment. For details of LGED, see Fujita (2011).
11. We should remember that a pessimistic appraisal was common in regard to the role ofwomen in the labor market in Bangladesh. This caused pessimism about the country’s
growth, due to, among others, the fact that most East Asian countries had the advantage of ahigh initial female labor force participation rate at the start of the growth process (WorldBank 2012 and other studies). As Hossain et al. (2012, 29) emphasized, none of thepredictions were able to anticipate that women would offer the secret ingredients of successthat was achieved in Bangladesh, in areas from exports to schooling to microcredit use. The
dramatic nature of the increase in female participation in the growth of ready-madegarment (RMG) workers is a case in point.

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Industrial Transformation and Quality of Growthinvestment created employment opportunities in Bangkok and later in
the newly developed Eastern Seaboard. As in the case of Bangladesh,
this was enabled by a series of infrastructure projects. To make the Thai
automobile industries competitive and at the same time inclusive, small
and medium enterprises (SMEs) were essential and they wereencouraged by several policy measures.
In order to develop supporting industries (SMEs) for the Thai
automobile industry, one of the specific and very effective policies is
considered to be the implementation of the LCR (local contents
requirement) system. In order to comply with the LCR, automobile
assembly companies in Thailand had to increase the local content ofcomponents produced by themselves, to ask their component suppliersin their countries of origin to invest in Thailand, or to support local Thaifirms to produce components of a required quality standard.12
Based on extensive field research, Yamashita (2004) concluded that, “theprocess of adaptation to the LCR enabled the accumulation of a very wide
range of automobile parts industries and formation a pool of skilled
technicians and engineers, both of which are indispensable for thedevelopment of the automobile industry” (5).13 Through this process,assembly companies have offered continuous technological support to local
supporting industries. In addition to these specific aspects, it should also behighlighted that Thailand has been making efforts for years to strengthen
education in engineering and training related to industrial skills.
In the case of the Cerrado development, employment opportunities
were created first by the new agricultural development of soybeans, andother grains enabled by technological and institutional innovations aswell as infrastructure development. However, massive employment
opportunities have been further created by the expansion of the value
chains around Cerrado agriculture, composed of agricultural andlivestock processing activities.
Engineers and skilled workers as well as technology for agricultural andlivestock processing had been accumulating over a long period in the
12. The LCR encouraged car assemblers to produce parts locally by themselves or topurchase them from local companies. This was not easy because supporting industries inThailand did not exist. Assembling companies had to start the process of localization fromscratch. Following this, the LCR was raised incrementally through 1994 up to 60 percent for
pick-up trucks with gasoline engines and 72 percent for those with diesel engines.
13. Yamashita (2004, 5). Translation is by the author.

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southeastern agricultural zone, and there was also access to an extensive
labor force from all over Brazil. By accessing this labor force, including
engineers and workers, the Cerrado region managed to build acompetitive agricultural, livestock, and processing value chain, which
has enabled high-value-added processed food to be produced andemployment expanded in this region.
There are many organizations participating in the value chain. For
example, in the western Bahia, one of the typical Cerrado agricultural
regions, the Western Bahian Technology and Research Center (CPTO)
coordinated with a branch office of Brazilian Agricultural Research
Corporation (Embrapa) on testing, research, and technologicaldevelopment, and the resulting technologies were provided to
producers through regional producer support organizations such as theAgricultural Cooperative of Western Bahia (COOPROESTE).
This process produced a massive migration to inland states from all overBrazil, in particular, from southeastern and northeastern states, andcontributed to inclusive development with expanded employment.These
cases provide evidence regarding the necessity of advanced capacity
development in order for people to respond to changing demand in theprocess of industrial transformation. In this regard, the APEC Growth
Strategy indicates concrete actions to promote inclusive growth, such as
promotion of SMEs, MEs and entrepreneurship development,
promotion of more inclusive access to finance and financial services tofacilitate access to finance for SMEs, MEs, women entrepreneurs, andvulnerable groups, as well as promotion of job creation, human resource
development, and active labor market policies.14 These actions are
considered to be effective based on the above case studies.
5� Transformation and “sustainability aspect” of quality of growth
Key issues and analytical perspective
Transformation and sustainability is becoming one of the central themesof the development agenda. Generally, emphasis is put on the necessityof transforming the actual economic structure into a more sustainable or
14. Other actions for inclusive development included the APEC Growth Strategy areenhancements of social resilience and social welfare through means such as improvingsocial safety nets and supporting vulnerable groups, creation of new economic
opportunities for women, elderly, and vulnerable groups, and the promotion of tourism.

284

Industrial Transformation and Quality of Growthgreen one. “Green Growth” is a part of the “Action Plan for the APEC
Growth Strategy” (APEC 2010, 10-11). Five years before this strategy was
agreed on by APEC leaders in 2010, “green growth” was adopted at theFifth Ministerial Conference on Environment and Development in Asiaand the Pacific (MCED) as a strategy for achieving sustainable
development and for achieving Millennium Development Goals 1(poverty reduction) and 7 (environment sustainability) (UN-ESCAP
2013, 26). The green growth approach adopted by the MCED sought toharmonize economic growth with environmental sustainability, while
improving the eco-efficiency of economic growth and enhancing thesynergies between environment and economy. As such, green growth isa concept that was coined in the Asia and the Pacific region in 2005.
Later, an OECD-DAC report, “Natural Resources and Pro-Poor Growth:
the Economics and Politics,” focused on the economic dimensions of
natural resource management and intended to encourage decision
makers to recognize the contribution of natural resources to pro-poor
growth and the importance of policies encouraging the sustainable
management of these resources (OECD 2008, 26). Furthermore, a more
recent OECD report titled Towards Green Growth provided the following
definition of green growth: “Green growth means fostering economic
growth and development, while ensuring that natural assets continue toprovide the resources and environmental services on which our wellbeing relies (OECD 2011, 4).
Finally at the worldwide level, the Rio+20 Conference in 2012 featured
green growth. Its outcome document, The Future We Want stated that, “weexpress our determination to address the themes of the conference, namely
a green economy in the context of sustainable development and poverty
reduction, and the institutional framework for sustainable development”(United Nations Conference on Sustainable Development 2012, 2) .
The concept of green growth has also been endorsed by United Nations
Environment Program (UNEP). Its recent document titled Towards a
Green Economy (UNEP 2013) expounds the aims of transforming society
into one that can grow economically while increasing environmental
quality and social inclusiveness. UNEP defines a green economy as one
that results in “improved human well-being and social equity, while
significantly reducing environmental risks and ecological scarcities”(UNEP 2010, quoted in UNEP 2013, 9). The UN-ESCAP document285

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proposes a “system change for quality of growth: turning tradeoffs
between the three dimensions of sustainable development, intosynergies” (2013, 16). In this document the three dimensions of growthqualities are ecological quality, economic quality and social quality.
Through this process since the MCED meeting of UN-ESCAP in 2005,
the concept of green growth, has been increasingly mainstreamed ininternational debates on sustainable and inclusive development and istherefore closely related to the quality of growth, as the APEC Growth
Strategy indicates. As in any process of development, althoughsustainability and environmental aspects should be fully taken into
account as the above documents discuss in a general framework, special
attention to these aspects is needed in cases of transformation in which“natural capital” is the essential endowment that enables thetransformation.
While natural capital assets are not created by human activity, their
quality and capacity to yield goods and services, and therefore theirvalue as productive inputs, are affected by it (OECD 2008, 30).
Understanding the synergies between the three dimensions of thegrowth qualities is normally the most difficult in the cases in which
natural capital plays the crucial part of transformation. Analysis of thesecases could give us a clue towards an effective approach to quality
growth with sustainability and inclusiveness.
Findings from case studies
Two of this paper’s outstanding cases, Cerrado agriculture and Chile’s
salmon aquaculture, are cases in which human activity, particularly
technological innovation or adaptation increased the value of natural
capital: Cerrado land and southern Chile’s seashore. A remarkabletransformation of a vast region of Cerrado and southern Chile took place
utilizing natural capital, which was not necessarily used before as an
input for production of goods and services. However, special attention
has been needed to address the risk of sustainability due to the possible
degradation of or other consequences to the natural capital.
Furthermore, it is necessary to bear in mind the potential value ofnatural capital. For example, the Cerrado has become known as thesavanna zone with the world’s richest biodiversity (Brazil. Ministry of
the Environment 2010). Plants in the Cerrado cope with the unique286

Industrial Transformation and Quality of Growthstresses of extreme nutrient shortages, high soil acidity, and highaluminum saturation, and are believed to have evolved to protect
themselves against damage from ants and wildfires, making them avaluable genetic resource.
In the case of the Cerrado agriculture, from the outset, active
environmental conservation measures were implemented. As well as
adhering strictly to the 20% legal reserve (50% in Tocantins and otherstates) in individual farms, to prevent the legal reserve areas frombecoming a haphazard patchwork of points, efforts were advanced tocreate joint strengths of reserve land through a “condominium” model,
as well as the formation of micro-corridors of reserve land made up ofindividual reserves. Moreover, measures to preserve agriculturalenvironments have been actively promoted, such as the introduction ofcontour cropping, crop rotation, and no-till farming (direct planting).
The government of Brazil conducted a series of broad and varied
initiatives aimed at environmental conservation. In the Cerrado region,
the government especially pursued a balance between Cerrado
agricultural development policies and environmental conservation
policies. This could be considered as a pioneering initiative forsustainable transformation of unused land into fertile agricultural land.
In 1998, Brazil enacted the Environmental Crimes Law, and in 2000 theForest Code was amended to impose stricter legal reserve percentages
on landowners and to enable the trading of reserve land with the land ofother forested landowners. Also in 2000, the National System of Nature
Conservation Units (SNUC) was established. This system was designed
to organize categories for native reserves and both protect and restore
the biodiversity found in their ecosystems.
The Environmental Conservation Expansion Program, which is meant
to efficiently manage expansive areas of privately owned land using
satellite imaging technology, was launched through Presidential
Directive 7029 at the end of 2009, and the government also decided to
introduce the Rural Environmental Registry (CAR, Cadastro Ambiental
Rural, in Portuguese).15 In October 2012, the Ministry of Environment
15. A registry that uses a GIS to determine the borders of each farm, as well as the legal
reserve and preservation districts in each part of owned land. Upon the request of a farm,expert contractors prepare digitized drawings of the land usage status inside each farmarea. These electronic data are incorporated into Integrated Environmental Monitoringand Licensing databases operated by state government environmental agencies.

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issued a decree that established the obligation of registering all A registrythat uses a GIS to determine the borders of each farm, as well as the legal
reserve and preservation districts in each part of owned land. Upon therequest of a farm, expert contractors prepare digitized drawings of theland usage status inside each farm area. These electronic data areincorporated into Integrated Environmental Monitoring and Licensing
databases operated by state agricultural land in Brazil in accordance with
CAR within a year (or in two years if authorized by the President). As of
July 2013, 25 states and the Federal District (DF) had agreed to participate
with CAR.16 The National System of CAR (SiCAR; Sistema National deCadastro Ambiental Rural, in Portuguese) was established by Presidential
Directive in October 2012. Moreover, Brazil’s environment conservation
policy was further strengthened by Law 12651 and Law 12727(amendment to Law 12651), enforced in 2012.
In spite of the impressive increase of agricultural production in Cerrado
in the last 3 to 4 decades, land used by “Cerrado agriculture” has notincreased as fast as the rate of production growth. This is due to theremarkable improvement of yield per hectare. According to theBrazilian Institute of Geography and Statistics (IBGE) farm census,
61.36% of the growth of agricultural production (soybeans, rice, edible
beans, corn, cotton and coffee) in the Cerrado during the period
between 1970 and 2006 was provided by yield growth, while the rest,
38.64%, was due to expansion of the planted area.
As a result of the development of Cerrado agriculture, the areas used forcrops amounted to 21.6 million hectares in 2002, occupying 10.5% of thetotal Cerrado biome, which is estimated at 204.7 million hectares,
according to an analysis based on LANDSAT satellite images.17 The
same analysis showed that about 26% of Cerrado, 54.2 million hectares,
was occupied by “improved pasture” (or cultivated pasture) in 2002.
Hence, the sum of cropland and improved pasture, which could beconsidered as total “farm land” for agriculture and livestock production,amounted to 75.7 million hectares, equivalent to 37% of the total of
16. According to the announcement of the Minister of the Environment on July 4th, 2013.
17. This is the result of research to map land use in Cerrado at a scale of 1:250,000 using themethodology of Landsat image segmentation. The research was supported by the Project ofConservation and Sustainable Utilization of Brazilian Biological Diversity of the Ministryof Environment, World Bank Global Environment Facility and IBGE, among others. The
results were published in 2008. (Sano et al. 2008)288

Industrial Transformation and Quality of Growth

Cerrado.18
In the case of Chilean salmon farming, it was crucial to establish aregulatory framework for many aspects of the salmon industry,
especially environment and quality standards for the aquacultureindustry. In other words, what was needed was so-called “institutional
infrastructure,” including laws, regulations, and environment andquality standards. Mr. Mitsuo Sakai, who participated in the Japan Chile
Technical Cooperation (PTTC) for salmon farming in 1980s noted that,“by starting technology transfer activities concerning feed development
and fish disease control early, the PTTC project forestalled the problems
the farming industry faced later, including concerns about the spread of
salmonid bacterial kidney disease (BKD), and the paucity of feed for thefeeding culture business that had traditionally used living feed rather
than fish meal.” He concluded by saying, “These technology transfer
activities anticipated technical problems that would arise in the earlystages of the development of the Chilean salmon industry, and thus
devised precautionary measures, including the development ofnecessary technologies” (Sakai and Ishida 2002).
However, the Chilean salmon industry experienced a sanitary crisis
caused by Infectious Salmon Anemia (ISA) in 2007. This experiencedemonstrated how natural resource-based activities such as salmon sea
farming need to be supported not only by advanced production
technology, but also by scientific knowledge on the local environment sothat appropriate local regulatory institutions to manage the use ofcommon resources can be established. This case also demonstrates that
progress in production capabilities may not be sufficient to ensure longterm sustainability in natural resource-based industries in which
natural capital plays an important role. Institutional change took place
as a reaction to the sanitary crisis in 2007 in Chile: the Aquaculture Law
to modify the existing General Law of Fishery and Aquaculture enacted
in 2010 was an important milestone. This modification granted moreauthority to the government to ensure sustainable management ofaquaculture.

18. As there are urban areas in the Cerrado, which amount to 0.4%, and reforested areas
corresponding to 1.5%, the total areas with some kind of land use were equivalent to 39% of
Cerrado’s total land. The remainder, 61%, had no changes caused by human activities, andwere considered to be “natural Cerrado”.

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As discussed above, the quality of growth extends to green growthconcepts. UN-ESCAP (2013, 27) argues that, “Policies and investmentsthat promote green growth seek to improve the “eco-efficiency” ofgrowth, which involves minimizing resources use and negativeenvironment impacts per unit of benefit generated by the economy….
Green growth is a pre-requisite for building a green economy
characterized by substantially increased investments in economic
activities that build on and enhance the earth’s natural capital or reduce
ecological scarcities and environmental risks...” The above-mentioned
cases are examples of the efforts to promote green growth defined asabove.

6� Transformation and the “innovative aspect” ofquality of growth
Key issues and analytical perspective
Noman and Stiglitz (2012, 7) emphasize that “long-term success rests onsocieties’ “learning” – new technologies, new ways of doing business,
new ways of managing the economy, new ways of dealing with othercountries.” Related to this notion of a “learning society” is the view of
Cimoli, Dosi, and Stiglitz (2009, 2) that great industrial transformation“entails a major process of accumulation of knowledge and capabilities,
at the level of both individuals and organizations.” A learning society
and learning economy are featured in Stiglitz and Greewald (2014). They
contend that, “most of the increases in standard of living are, as Solowsuggested, a result of increases in productivity—learning how to do
things better. And if it is true that productivity is the result of learningand that productivity increases (learning) are endogenous, then a focal
point of policy ought to be increasing learning within the economy; thatis, increase the ability and the incentives to learn, and learning tolearn...” (5-6). The innovative aspect of quality of growth precisely deals
with learning and accumulation of knowledge and capabilities. From
this point of view, quality of growth is improved if growth isaccompanied by an endogenous process of learning and innovation thatwould produce further growth with transformation.
As was cited above, the ADB report (2013a, 5) argues that whenstructural transformation creates a virtuous circle, it leads to higher
growth and higher income per capita, and these induce further changes
in the structure of the economy. In this virtuous circle, capabilities and290Industrial Transformation and Quality of Growthlearning matter. The report explains that countries’ structuraltransformation patterns differ in both direction and pace due to, alongwith demand and supply factors and demographic and geographic
variables, such factors as (1) good organizational capabilities thatencompass all the tacit knowledge necessary to produce a good ordeliver a service; and (2) specific policies and actions (e.g. those thatpertain to education and the technological learning needed to competeinternationally); institutions (that have developed historically andfacilitate or retard structural transformation); and politics, which often
work jointly to determine the direction and pace of structuraltransformation.
The APEC leaders’ Beijing Agenda for an Integrated, Innovative andInterconnected Asia-Pacific (APEC 2014, 7) not only emphasizes theimportance of innovative growth but also its intrinsic relationship withtransformation. The Beijing Agenda affirms that, “We realize that theprospects for the shared prosperity of APEC will depend on innovative
development, economic reform and growth in the region, which arecomplementary and mutually reinforcing. We recognize that the AsiaPacific region is at a crucial stage of economic transformation. We arecommitted to accelerating the pace of reform and innovation, andexploring new growth areas with the goal of bolstering the position of
the Asia-Pacific as an engine of world economic growth.” (7) 19
Currently, in terms of innovative growth, industries of which main
resource is natural capital have much more difficulty investing inknowledge and technological breakthrough than other productive
sectors such as manufacturing and services due to the high degree ofsite specificity caused by the biological nature of this type of production
and due to the normally large number of stakeholders (farmers andfishers) involved. In addition to the above, there is the general difficulty
appropriating the benefits from knowledge generation. These conditions
create higher barriers to investment in knowledge and technology underthe market mechanism in general, and especially for activities based onnatural capital such as agriculture and fishery. The case of the Cerrado
19. Regarding this aspect, the APEC Growth Strategy stated four years ago: “The adoptionof policies that foster an enabling environment of innovative growth will be increasingly
crucial for future prosperity. Technology breakthrough and information andcommunication technologies (ICTs) play a significant role as a primary driver of economic
growth, and innovation in new products and services can enhance progress on criticalglobal issues...” (APEC 2010, 6-7).

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agriculture and agro-industry value chain discussed below could be
relevant from this perspective of the innovative aspects of quality ofgrowth.
Findings from the case study on the Cerrado agriculture and agroindustrial value chain
The importance of the innovative aspect of quality of growth could beunderstood most clearly through the contribution of the Brazilian
Agricultural Research Corporation (Embrapa) to the continuous
development of Cerrado agriculture and its value chain. This solid andhighly effective institution was essential to continuously achieve thetechnological innovations required for Cerrado agriculture. A strong“national innovation system (NIS)” in the area of agriculture has beendeveloped around the institution of Embrapa in the case of Brazil.20
From the perspective of the innovation system, Embrapa could beconsidered one of the most important hubs or coordinators of the
agricultural sector innovation system and a part of NIS. Embrapa
established and led the “National System of Agricultural Research”(Brazil. Ministry of Agriculture, Livestock and Supply and JICA 2002, 26
of Part 4). This system has “an excellent structure and aims at thetechnological development and its diffusion in an efficient and effective
manner.” The system was designed for collaboration and exchange oftechnical information among more than 400 organizations over thewhole country, with the participation of state research companies,
university research organizations, agricultural research departments inprivate companies, and others.

7� Transformation and “secure growth”, including“human security”, and resilience aspects of quality of growth
Key issues
It is unnecessary to say that, in the transformation process, efforts
should be made to minimize natural and human risks to growth.
Preparedness or resilience for emergencies, natural disasters, infectious
diseases need to be enhanced. Nutrition, food security and food safety
should be essential. In other words, human security should be realized.
The APEC Growth Strategy included secure growth as an attribute of
20. Regarding the concept of the national system of innovation, see Malerba and Nelson(2012).

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the quality of growth and stated: “We seek to protect the region’s
citizens’ economic and physical well-being and to provide the secureenvironment necessary for economic activity.” This strategy refers toresilience in relation to preparedness for emergencies and natural
disasters: “APEC will identify gaps in disaster risk reduction approaches
in the region and develop practical mechanisms to maximize businessand community resilience...” (APEC 2010, 9). Japan’s Development
Cooperation Charter explicitly includes resilience as one of theattributes of high quality growth. It emphasizes that, in the concept ofhigh quality growth, inclusiveness, sustainability should be combinedwith resilience enabling people to cope with and recover from diverse
types of shocks including economic crisis and natural disasters.
Findings from case studies
Secure growth and resilience are the most recent foci of discussion
related to quality of growth and are closely related to other attributes
such as inclusiveness and sustainability. Further discussion is needed tosee if secure growth and resilience can be sufficiently addressed byinclusiveness and sustainability. From the perspective of industrial
transformation, the cases of Bangladesh garment industry and Chile’s
salmon farming appear to be relevant. The case studies could provide
some insight into the above-mentioned question.
The case of the Bangladesh apparel industry elucidates the secure aspectof industrial transformation. An apparel factory fire in November 2011that killed 117 workers and the collapse of a building housing several
factories located in the outskirts of Dhaka in April 2012, causing the tragic
death of 1,129 workers and injuries to about 2,500 workers, brought theoccupational security issue to the world’s attention. A new study by theInternational Labour Organization Research Department (ILO 2013)
warns that unless a comprehensive set of labor market and social policies
are introduced, Bangladesh will be unable to maintain its economic
momentum and improve living standards in a sustainable way.
In the case of the Chilean salmon industry, the development of this new
industry might have succeeded in creating ‘productive employment’, or
employment that yields sufficient returns to labor to permit workers andtheir dependents to live at the level of consumption above the poverty
line (ILO 2009). However, a part of this ‘productive employment’ might
not be always considered as ‘decent work,’ or work that provides
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security (health, pensions, security against job loss), equity (equity ofconditions and opportunities for all the workers) and adequacies(appropriate working hours, absence of coercion) (ILO 2012). This islargely due to the lack of well-established labor legislation, including alabor code capable of adequately regulating labor relations at thecompany level in Chile. For instance, employment without contract andsocial security for employees was the standard practice in some smallscale salmon farming firms in Chile.
These cases show the necessity of providing the secure environment foreconomic activity, which is an attribute of quality of growth not fully
addressed by inclusiveness and sustainability of growth.

8� Synergy, trade-offs and other relationships
among attributes of quality of growth and transformation
Both APEC and Summer Davos documents refer to balanced growth.
The APEC Growth Strategy states: “We seek growth across and within
our economies through macroeconomic policies and structural reforms
that will gradually unwind imbalances and raise potential output”(APEC 2010, 2). The Summer Davos 2011 document affirms that,“Imbalances in national economies and the global economy are
ingredients of crisis and have to be addressed to mitigate the risks fromvolatility and uncertainty” (World Economic Forum 2011, 3).
As fiscal balance is an important part of balanced growth, this obviously
would limit the capacity of government to increase expenditure to
improve quality of growth in terms of some of the attributes. For
example, conditional cash transfer (CCT), which effectively contributes
to the access of poor people to education and health, has to be carefully
carried out within fiscal constraints. The cost of Bolsa Familia, which
has been a successful CCT in Brazil that resulted in significant
improvement in education and in the decrease of the mortality rate ofchildren under five years of age, was around 0.5% of GDP. Brazil, as a
middle-income country, has been able to afford this cost. The benefits of
Bolsa Familia have been really remarkable compared with its cost.
However, some low-income countries with serious budget constraints
could not afford CCT on the same scale as Brazil.
In this regard, the analysis of Lopez et al. (2008) on fiscal policies for294Industrial Transformation and Quality of Growthbetter results in terms of quality of growth seems highly relevant.
“Fiscal policy is important for allocating resources to maintain a balance
between the three key assets of society: human capital, physical capital,and natural capital, which are critical for the quantity and quality ofgrowth....Fiscal policy is therefore a powerful instrument, capable ofaffecting the orientation of asset accumulation and economic growth ina dramatic way” (p. 17).
From this perspective, a holistic approach to quality of growth whilebearing in mind its different attributes together appears to be essential,
because there are instances in which transformation and quality ofgrowth could be achieved together and could produce synergies, forexample, between inclusiveness, sustainability, and innovative virtuous
process, as mentioned in Sections 4, 5 and 6, especially from a long-term
perspective. Furthermore, while in the short term, inclusive growth could
need external resources to assure people of equal access to opportunities
through, among other things, education and health, the necessary
resources could be endogenously generated in the medium and longterms by transformation with inclusive growth, due to increases in tax
and other revenues derived from expansion of industrial activities or
economic transformation. On the other hand, as the investment in three
types of capital, human, natural and manufactured capital, is the key fortransformation and quality of growth (UN-ESCAP 2013, 7), quality ofsuch investment could be crucial for quality of growth. Japan’s
Development Cooperation Charter considers it important to catalyze
private sector investments to contribute to inclusiveness, sustainability,
resilience, and capacity development in order that such investments
facilitate “high quality growth.”
Not all attributes and transformations can be achieved simultaneously
or in the short term. Synergies, trade-offs, priorities and sequences could
be important for both transformation and quality of growth agendas.
Not only financial resources, but also effective institutions forachievement of high-quality growth are essential. Therefore, acomprehensive and holistic approach could be necessary for “quality ofgrowth” and “transformation” discussion. In this regard, both the
APEC Growth Strategy and UN-ESCAP document (2013) provided suchapproach. The former document states that APEC members cannot
continue with “growth as usual” and “the quality of growth” needs tobe improved in terms of the five attributes (APEC 2010, 1-2). The latter
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provided a “framework for quality of growth by building a virtuous
cycle of investment in people and nature—a growth path which is moreclosely aligned with sustainable development” (UN-ESCAP 2013, 30).

9� Concluding remarks
This paper was intended to discuss and gain insights into theinterrelationship between transformation and attributes of quality ofgrowth. Based on this discussion, the following points should behighlighted.
First, transformation and quality of growth need to be considered in aholistic manner as a comprehensive target to be achieved. The APEC
Growth Strategy could be regarded as a pioneering initiative with aholistic framework to attain such target. For the transformation agenda,
the quality of growth with attributes such as being balanced, inclusive,
sustainable, innovative and secure, should be fully taken into account,bearing in mind the interrelationship, synergy and trade-offs among them.
Second, as the transformation agenda is different among countries of
distinctive characteristics such as those with a high proportion ofsubsistence agriculture, urbanizing economies, early industrializing
economies, those in the middle-income trap, and so on, measures totransform their economies and attain desired attributes of quality ofgrowth could be different, and there is no standard model of growthstrategy to address challenges of transformation with growth withimproved quality.
Third, it might be more realistic to design measures to attain the desired
attributes of growth quality alongside development of specificindustries, with which structural transformation is taking place,bearing in mind the specific transformation and quality of growth
agendas.
Fourth, on the other hand, the interrelationship among attributes ofquality of growth and industrial transformation is complex and,
therefore, a holistic and comprehensive approach is needed with due
attention to macro-economic balance and stability, fiscal balance,synergy, trade-offs and so on. The discussion on quality of growth andindustrial transformation deserves a further in-depth analysis.
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The Governance of Natural Resource Wealth:Some Political Economy Considerations on
Enhancing Social Investment1
Andrés Mejía Acosta1� Introduction
The presence of non-renewable natural resource revenues (oil, mineraland gas) represents both an opportunity and a challenge to provide thefunds necessary to lift poor countries out of the poverty trap. Whenadequately invested, revenues accrued from taxation of the extractive
industries (EI), royalties, contracts and licensing fees have tremendous
potential to boost economic activity, increase employment and improve
investment on development sensitive sectors (education, nutrition,
health). But the extraction and allocation of natural resource revenues
can also make countries more vulnerable to prevailing problems
associated with resource curse, including Dutch disease, slower
economic growth, triggering of violent conflict, corruption, rent seeking
and political instability (Ahmad and Singh 2003; Auty 2004; Bauer 2013;
Karl 1997; Ross 2007).
It has been well argued that the strength and quality institutionalarrangements can play an important role in mitigating the resource
curse (Ross 2007). Institutions can safeguard the effectiveness andimpact of social investments by increasing, for example, theaccountability and transparency in the use of natural resource revenues,
allocating revenues into high return investments, allowing for greater
voice and participation in decision making and imposing sanctions on
corrupt behavior (Mejía Acosta 2012).
The governance of natural resource revenues can be improved (at least)
at three different levels. At the extraction stage, governments make1. This paper builds on previous work done by the author on “Extractive Industries, revenue
allocation and local politics” (co-authored with Javier Arellano Yanguas) (UNRISD 2014).

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strategic decisions regarding when and how to obtain taxes and rents
from extractive companies, whether this is done through concessions or
negotiating licensing fees, the share of state participation in theextraction, production and distribution, and whether revenues are
independently managed by state owned companies or whether theyaccrue directly to the country’s budget. Secondly, governments decide
whether to save, spend or further invest these revenues. Revenues could
be put into stabilization funds to protect the economy from the volatilityof commodity prices, ensure a smooth flow of revenues and avoid
macroeconomic mismanagement. Natural resource revenues could also
be saved for future consumption, or redistributed according tobudgetary allocations, specific territories where the extractive industry
is located, transferred to local governments or devolved directly tocitizens. Thirdly, governments can strategically decide how to spendthese resources by prioritizing specific types of high returninvestments, infrastructure or enhancing human capital through skills
formation.
This paper argues that political institutions, and in particular the choiceof fiscal rules governing the allocation of natural resource revenues, canhave a significant impact on the quality of growth. So far, the policy andscholarly debates about revenue allocation have not necessarily linked
revenues to types of investments, partly because of the availability andreliability of data but also due to difficulty of measuring impact at theexpenditure level. This paper offers a contribution to that debate bydiscussing in greater detail and, with the help of some selected
countries for which data is available, the political decisions governing
the allocation of natural resource revenues at the subnational level. This
paper does not venture into an evaluation or review of the development
impact of different spending modalities. Rather, it discusses the politicalchoice behind distribution modalities, between central and subnational
governments and across subnational governments, to illustrate some of
the inherent distributive trade-offs and some of their consequences forgovernment spending.
The paper proceeds as follows. The first section explores some political
economy considerations to explain how different actors and specificintraregional bargains can be instrumental in the adoption of different
allocation formulas. The next section briefly discusses the potential
impact of allocation modalities to enhance the impact of social spending302

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

and maximize the quality of growth. The last part provides a conclusionand develops an agenda for future research, including some of theremaining empirical and methodological challenges necessary toprovide an understanding of the meaning and measurement of social
investment and its relationship to the quality of growth.

2� Managing natural resource revenues effectively
The commodities boom of the last decade, has given countries that arerich in non-renewable natural resources a windfall of revenues that has
exceeded all fiscal expectations and has made a significant contribution
to boosting growth. But the pressing question for governments,
policymakers and scholars is how to turn the commodity bonanza into astable source of growth that delivers on socially desirable outcomes. In
other words, how can the commodities boom contribute to enhancing
the quality of growth? From the perspective of managing publicfinances, it is key to establish, ex ante, what is the main expectation for
managing natural resource revenues, whether in the creation of longterm savings, investment or direct spending, fairness in the allocation offunds, macroeconomic stability, strong local ownership, or other factors.
To that extent, there are multiple instruments that could help to smooth
future revenues from volatile fluctuations, maximize the accrual of
fiscal revenues, generate savings and investment mechanisms,
guarantee the transparency and accountability of management, and thedesign of distribution or allocation mechanisms that privilege social
investment to benefit the population. In practice, these mechanisms canbe combined with –and sometimes contradict- one another: for example,
accumulating revenues into a well-protected stabilization or savings
fund may fuel citizen concerns about the need for immediate spending;similarly, deciding to invest current revenues in education will trigger
debates about the long term benefits of investment on infrastructure or
human capital.
From a political economy perspective, these critical questions represent
dilemmas of redistribution and therefore pose more fundamental
questions of political cooperation, feasibility and commitment towards
adopting, implementing and sustaining one or more mechanisms. For
example, the increased volume of investment for the provision of public
services such as health and education may not be a sufficient condition
for success if social investment lacks transparency in the administration
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of such funds or there is no strategic vision that is linked with
development objectives (Mejía Acosta 2012, Bennett 2002).
The following chart illustrates how there are three interlinked stages in
the processes of translating natural resource wealth into long-termsocial investment. The design, implementation and sustainability ofeach mechanism will depend less on the technical features of each
allocation formula and more on the political bargains and distributionagreed between the main stakeholders.
Figure 1� Translating natural resource wealth into quality growthThis paper will mostly focus on the intermediate stage of the process, thefiscal management, and will discuss some of empirical and methodologicalchallenges to understanding the quality of investment. Exploring thesestages requires addressing two related questions. The first issue is todetermine who has a claim over ownership of the resources or, put in otherterms, to what extent do political actors outside the central government
have the leverage to demand a share or benefit from the wealth accruing
from the extractives sector. In most cases, the executive at the national levelhas the primacy over decision-making, but the degree to which other actors
have a credible claim and the distributional mechanisms can vary a great
deal (Arellano and Mejía Acosta 2014). In some cases, the shared“ownership” may be embedded in specific constitutional rules, but in othercases, ownership and allocation is subject to a continuous and repeatedpolitical bargaining between different stakeholders.
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The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

The second question refers to the mechanisms set in place to promote atransparent and accountable management of extractives revenues. The
main concern is that extracting and allocating natural resource revenues
almost inevitably leads to enhanced opportunities for rent seeking andpolitical capture (Auty 2004; Haysom and Kane 2009). In general, thespace for mismanagement of funds is inversely proportional to thepresence of legal frameworks, independent judiciaries and the existenceof effective checks and balances. Transparency and accountability
mechanisms, including the reporting, auditing and monitoring themanagement of revenues will enhance legitimacy and could contribute toan effective investment of natural resource rents (Bennett 2002). In
practice however, most of the transparency and accountability initiatives
are located and strengthened at the national level, but there is significant
variation regarding the extend and effectiveness to which these initiatives
can encourage better governance at the local level (Mejía Acosta 2013).
In this section we examine four different instruments used to manage
revenues from the extractive industries (to spend, save or invest):
stabilization funds, saving funds, revenue sharing formulas and direct
cash transfers. We explore how these instruments work, what the mainarguments for the adoption of such mechanisms are, who the mainstakeholders supporting such instruments are, and what are the main
expectations in terms of performance.
Stabilization funds
Sovereign wealth funds (SWF) are government-owned resources
accrued from pensions, privatization income, investment revenue ofstate owned enterprises, natural resource revenues, and so on. A
common feature in the case of the extractives sector is that revenues
tend to fluctuate widely depending on international commodity prices
thus creating the potential to destabilize the country’s macroeconomic
goals. Stabilization funds are one of the instruments designed to
manage these fiscal flows. Stabilization SWFs are created to build up
fiscal reserves when commodity prices are high and draw reserves
down when prices are low. The adoption of stabilization funds is themost classic mechanism adopted by resource rich countries to dampen
the fiscal and economic impact of boom and bust cycles while restoring
countercyclical spending. Stabilization funds can effectively contributeto long term fiscal planning if there are clear and enforceable rules toensure contributions into the fund during boom times and constraint305

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over the conditions under which these funds can be withdrawn or usedduring bust periods. Stabilization funds such as Chile’s Economic andSocial Stabilization Fund (ESSF) are usually considered most effective
for managing mining revenues (Havro and Santiso 2011).2
The case of Chile illustrates that the effective adoption and managementof a stabilization fund needs to be developed in the context of a soundfiscal policy with an overall budget strategy and clear fund targets. The
formulation can be aided by a MTEF to help limit the expenditure ofshort run expenditure responses to rapidly changing resource revenue(Davis et al. 2003). From a political perspective, a well-managedstabilization fund tends to work better in a context of consolidated state
institutions, preferably where there are greater constrains on thediscretionary use of executive power (Bagattini 2011). Greater politicalparty competition, an independent civil service and a well-educatedcivil society are other factors contributing to the success of suchstabilization funds. Citizens can be actively involved in the monitoringand enforcement of transparency and accountability mechanisms
including regular reporting, auditing and press releases. Conversely,
stabilization funds are most likely to be undermined in a context ofincreased fiscal uncertainty, in a context where the executive power
concentrates discretionary decision-making power, or when existing
rules may change depending on political circumstances.
Savings funds
Savings funds constitute an alternative mechanism for dampening the
fiscal and economic impact of volatile commodity prices, with a focus on
saving those revenues for future generations. According to Dumas(2011), there are three factors that are critical for the success of saving
funds: a) to disassociate the decision on how much should be saved fromwhat to be saved, b) to create a separate account to directly deposit all
natural resources revenues and to ensure proper transparency andgovernance principles to account for those deposits, c) to control andminimize the discretion for determining the level of transfers anddisbursements out of the fund.

2. The ESSF receives fiscal surpluses, which are above 1% of GDP. Its investment strategy isintended to diversify assets in the fund, putting 15% of the portfolio into variable income
assets, 20% in corporate fixed income papers and gradually adjust liquid assets (SWF
Institute 2014).

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The Government Pension Fund of Norway is an example of a savings
fund. Originally created in the late sixties, it had two components: The
Government Pension Fund - Global (formerly known as The
Government Petroleum Fund) was established as a fiscal tool toencourage counter cyclical spending in the nineties. The Government
Pension Fund - Norway was initially established as The National
Insurance Scheme Fund in 1967. While the GPF Global derives its
financial backing from strategic investments from the surplus wealthproduced by Norwegian oil income, the GPF derives its income frompension contributions. It is calculated that the total value of the GPF
Global is around $893bn USD, making it the biggest sovereign wealthfund worldwide today.3
The GPF Global aims to smooth the path of spending out of volatile oilrevenues whereas the GPF Norway seeks to accumulate long-termsavings from the oil revenues to cope with rising pensions and related
expenditures on an aging population. All revenues accrue to central
government and the funds are integrated into the budget process that iscontrolled by the Ministry of Finance (Havro and Santiso). The
centralized control of the fund allows the government to absorb fiscal
fluctuations and ensure a better distribution of funds to minimize interregional disparities. Another element of success is the well-developed
structure of its institutions, capable of adopting and implementing good
fiscal policies, coupled with a professionalized and independent staff tomanage these policies.
Despite the existence of good governance principles, the Norwegian
government has not been exempt from considerable political pressure tospend. Although the Norwegian parliament has the authority to allocate
oil revenues into the budget within an estimated 4% of the petroleum
earnings, Norwegian MPs have been under increasing public pressure
to increase government spending beyond the 4% action rule (Havro andSantiso 2011).
Revenue sharing formulas
The adoption of revenue sharing formulas is predominantly driven by
the need to distribute natural resource wealth between the central
government and resource rich territories. This formula emerges as aconvenient way to transfer fiscal resources to sub-national governments
3. Sovereign Wealth Fund Institute. http://www.swfinstitute.org/fund-rankings/

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to promote a more equal redistribution of wealth. While the principle ofredistribution is generally uncontested, the actual form of the
distribution has been subject to intense debate. Some of the pressing
questions regarding the allocation of intergovernmental transfers debate
over the exact location of extraction points, the territorial ownership ofsuch resources, and the merits of potential beneficiaries. At the end of theday, central governments centralize tax collection activities and theyretain considerable decision-making capacity over the share of revenues,
allocation criteria and other conditionalities imposed on potential
beneficiaries. Thus, central governments are key actors in the rents
allocation game. Some of the distributional criteria include:
Fairness: In this scenario, the central government ensures that resources
are allocated according to the districts’ own levels of production. This
criterion suggests that many non-producing districts would not receive
allocations or may be seriously marginalized from distribution. There
are several difficulties stemming from this direct type of distribution.
One problem is that it highlights and deepens horizontal inequalities
across districts and regions: those districts that generate more revenue
will continue to receive greater transfers, potentially creating a problemof “regional resource curse.” An associated problem can emerge if larger
transfers from the central government reduce the need to extract non-oil
taxation, thus creating greater dependency from the national
government and potentially volatile natural resource revenues. One last
pitfall, in cases where revenues are transferred directly to autonomous
territorial units, is that this reduces the incentives to ensure transparent
expenditure management. If regional or local governments feel they are“entitled” to receiving specific allocations, they would feel lesscompelled to account for those transfers back to the central government.
Equality: Under this distribution criterion, central governments would
favor a compensation formula to address existing inequalities. Revenue
sharing formulas can be adapted to allocate rents proportional to theexisting population, to equalize and improve the provision of public
services between provinces, to proportionally reduce poverty rates or
any other socioeconomic indicators, or to balance expenditure patterns
between the national and regional levels. Another potential use forrevenue sharing formulas is to compensate provinces, districts andindigenous people for the exploitation of natural resources andassociated environmental damage (Haysom and Kane 2009). An
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The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

immediate problem associated with this type of revenue sharing is that
it is difficult to determine the relevant jurisdiction entitled to receive theresources. If a particular local district or municipality is home for amine, should the neighboring districts also benefit or should the regionin which the district is located?
Revenue sharing formulas increase the sense of ownership for therecipient localities, but this does not necessarily translate into stronger
regional and local governments. Regions are more likely to claim
autonomy where political parties or regional movements are stronger.
They are more likely to be accountable to the needs and demands oftheir own population rather than the conditionalities imposed by thecentral government.
Direct Cash Transfers
The mechanism of a cash transfer involves a direct transaction of natural
resource revenues from the central government to individual citizens in
the form of direct regular payments. The underlying assumption is thatnatural resource wealth increases the available fiscal space to financedevelopment goals, it encourages greater levels of social expenditureand enables new social policy initiatives (Hinojosa 2012). The
underlying logic behind the “oil to cash” argument is that a directtransfer will enhance the “social contract” between individuals and oilrich states. The resource revenues would have a direct income benefit forthe poorest segments of society as it increases individual purchasing
power (Moss 2011). Direct cash transfers can also increase individuals’
capacity to pay taxes and thus enhance incentives for greater
government accountability. With a personal stake in the government’s
budget, the citizens could hold governments accountable for theprovision of goods and services (Gillies 2010). Cash transfers it is argued,
would also create a demand for increased budget transparency andaccountability in the management of public finances (Tsalik 2003).
The evidence supporting this argument is mixed. Evaluating the only
case in which this mechanism has been adopted, Michael Ross argues
that Alaska is a prime example of inefficient spending: “the distributionof petrodollars to individuals has substituted for a broad based tax
system, a personal income tax, and even a sales tax” (Ross 2007, 273). The
results are fairly negative, including chronic budget deficits, unfinishedpublic works projects, lower productivity and spending patterns that309

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privilege consumption over investment (Ross 2007). In the context ofdire public finances, Alaska has also threatened to abandon theadministration of other public services like school systems or healthcare. Ross argues that the Alaska example raises additional concerns forthe applicability or desirability of direct cash transfers in countries
where there is less rule of law, less educated populations and less citizen
engagement (Ross 2007). The main concern is that, in the absence ofcredible democratic institutions, direct cash transfers further fueled rent
seeking and clientelisic behavior that existed prior to the organizedtransfer of resources.
Inevitably, any cash transfer mechanism will have to be implemented
alongside centrally-controlled revenue smoothing mechanisms andsignificant central government investment on to improve the service
delivery infrastructure (schools, hospitals). Other relevant factors inensuring effective distribution include whether these funds arecentrally administered or not, whether subnational districts have thebureaucratic and technical capacity to spend wisely, or whether local
authorities are politically aligned with the government coalition in thecapital city, to mention a few factors (Gonzales 2014).
The next section offers a comparative perspective to understandinstitutional variation in the design of revenue sharing formulas acrossseveral countries.

3� The politics of redistributing extractive revenues
across different regions
This chapter claims that the choice of distributional formulas, the actual
share of natural resource revenues distributed and the political affinities
between central and subnational governments are contributing factorsthat enhance (or undermine) the quality of government investment.
Further, it is plausible to assume, although not explicitly tested, that theefficiency, transparency and targeting of government spending shouldhave a direct impact on the quality of growth triggered by the
extractives sector.
The existing literature on intergovernmental transfers from center to theperiphery identifies a basic trade-off between fairness and equity (Birdand Smart 2002; Schroeder and Smoke 2002). The notion of fairness
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should tend to privilege a distribution that rewards each region’s fiscal
efforts in generating their own revenues, financing their own
expenditure and responsibly managing their own debt commitments.
This principle however, may go against a (re)distributive logic, thatdemands greater government investment in more depressed areas
where revenue collection is insufficient, spending needs are high andgrowth rates are stagnating. Thus, the key challenge for policymakers is
to ensure a basic level of subsidiarity while minimizing rewards forpoor performers or fiscal laziness.
Following the work by Arellano and Mejia Acosta (2014), the next section
discusses some of the existing criteria used to transfer natural resource
revenues from the central to subnational governments (vertical
distribution) as well as the formulas used to redistribute wealthbetween producing and non producing territories (horizontal
distribution).
3�1 Vertical distribution of revenues from the EI
From a technical perspective, the purpose of (vertical) fiscal transfers is
to ensure that the revenues and expenditures assigned to each territorial
level are approximately balanced and match their administrative
responsibilities (Bird and Smart 2002, 900). According to this criterion,
the share of fiscal transfers plus local level revenues should match therevenue needed by subnational governments to fund the public services
they are responsible for (Schoeder and Smoke 2002).
From a political perspective, national executives (presidents or prime
ministers) are likely to prefer to centralize revenues to maintain fiscal
discipline, to centralize policy planning and to minimize liabilities(such as the growth of subnational debt). National executives are likely
to agree to decentralize if they need to secure the support of regional/
local elites in their governing coalition, if they need to build up
legislative majorities, and/or if they need to build a broader base ofelectoral support with citizens (Gonzalez 2014).
The presence of a commodities bonanza is likely to exacerbate this
distributive dilemma. Voters and local governments are likely todemand a larger share of fiscal transfers from the national government,
especially if the government benefits from windfall rents without thecorresponding fiscal effort to generate those revenues. The rents
311

Chapter 11

obtained from the extraction of mineral or oil exports, may also generatea sense of entitlement among the population if citizens feel this is part oftheir national wealth, and in some cases, this “right” has been enshrinedin the constitutions of the countries (Ahmad and Mottu 2003; Ross 2007)
The decentralization literature acknowledges that greater fiscal
transfers are likely to happen when the money follows pre-existinglevels of administrative and political decentralization (Falleti 2010,
O’Neil 2005). In other words, the relative strength of regional and local
opposition political groups is likely to increase the pressure for effective
fiscal decentralization. These groups in turn may choose to bargain withthe central government regarding the “appropriate” level of public
services as well as the “matching revenues” needed to sustain them.
Table 1 illustrates some of the findings reported by Arellano Yanguas
and Mejia Acosta (2014) regarding the vertical and horizontal
distribution of revenues in ten resource rich countries (oil and mining).
The Table summarizes the distribution of EI-revenues for countries forwhich we found reliable and comparative data. The chosen sample is by
no means representative of regional or income distribution patterns.
Nevertheless it offers a rich variation of institutional mechanisms,
showing how revenues have been distributed to regional, state and local
government levels, the type of revenue exploited and the date of the lastreform (Arellano and Mejia Acosta 2014). The selected countries are
Bolivia, Brazil, Colombia, Ghana, Ecuador, Indonesia, Mexico, Nigeria,
Papua New Guinea and Peru. The data shows significant variations
between a) countries with low levels of decentralization if subnational
governments receive less than 10% of state revenues (Ecuador, Ghana
and Papua New Guinea); b) medium if subnational governments receive
between 10% and 50% (Colombia, Mexico and Indonesia); and c) high if
subnational governments receive more than 50% of EI-revenues (Bolivia,
Brazil, Peru and Nigeria).4

4. These data do not include reports on the distribution of profits from state-owned oil andmining companies, generally managed by national governments, which may further
reinforce a centralist bias.

312

Date of the last reform
Degree of decentralisation
National government andcentralised funds
Regional/state
governments
Local governments
Private landlords
Producing region/state
Producing localities
Localities in producing
regions
Total devolution
Region/state
Localities
Total formula-based

Type of revenue
transferredPeru(mining
and gas)

43%
―
12%
5%
38%

45%
21%
3%
45%
17%
4%
66%
―
―
―

37%
26%
28%
13%
―
41%
9%
13%
22%

55%
―
―
―

12%

45%

31%

37%

2004
High

1989
High

2007
High

13%
23%
18%
41%

―

18%
―
13%
―

36%

46%

1999
High

Nigeria(oil)

Colombia(oil since
2011)

Mexico(oil)
Total oilrevenue

38%

10%
38%

―

10%

48%

52%

―
17%
―
17%

―

―
―
―
―

17%

83%

15%
―
―
―

6%

12%
―
3%
6%

3%

85%

Ghana
(mining)

2%
―
―
―

―

1%
―
1%
1%

1%

98%

2010
Low

7%
―
―
―

―

2%
2%
5%
2%

5%

91%

1992-1999
Low

Total oilrevenue Royalties

Indonesia Ecuador
(oil)
(oil)

2011
1978
2004
Medium Medium Medium

Royalties Royalties
Royalties and Total oil Royalties Total oil
andand IDH participation
income taxes revenue
revenueBrazil**(oil and
gas)

5%
―
―
―

―

2%
2%
3%
2%

3%

93%

1998
Low

Royalties

Papua New
Guinea(oil and gas)

Taken from: Arellano Yanguas, Javier and Andrés Mejía Acosta. 2014. “Extractive Industries, Revenue Allocation and Local Politics” (UNRISD 2014).
*Some data on percentages reflect quantities for some specific years.
** In March 2013 the Brazilian parliament approved the reform of the criteria to distribute oil royalties more evenly across the country; however, the law is currently
under revision by the Constitutional Court.
Sources: Agustina, Dian, Ahmad, Nugroho and Siagianl (2012), Banful (2011), Departamento Nacional de Planeación-Colombia (2012), Energy Sector Management
Assistance Programme (2005), Illedare & Suberu (2012), Morgandi (2008).

Horizontal
distribution

VerticalDistribution

Devolution

Formulabased

Bolivia(oil andgas)

Table 1� Models of decentralisation of EI revenues in a group of selected countries*

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment313

Chapter 11

The data does not support the premise that greater fiscal decentralization
follows pre-existing administrative or political decentralization. Federal
countries,
withexisting
administrative
mandates
towards
decentralization of responsibilities, do not necessarily transfer greater
resources. Although Brazil and Nigeria nominally allocate more EI wealthto their regions, other federations like Mexico transfer less than 20% ofrevenues. Conversely, unitary countries (countries with formally
centralized governments) like Bolivia or Peru redistribute up to 55% ofrevenues to subnational units (Ahmad and Singh 2003, Miranda 2009,
Arellano and Mejia Acosta 2014). A closer look reveals that federal states
like Brazil, Nigeria and Mexico tend to channel most transfers to the statelevel government; state governments receive more than twice the share ofrevenues than local or municipal governments (45% to 21% in Brazil and36%% to 18% in Nigeria). The opposite is true in unitary countries like
Peru and Indonesia, where decentralization tends to benefit local levelgovernments at the expense of state level units. In Peru, 43% goes tomunicipalities compared to 12% to regions and in Indonesia, the ratio is
12% to 3% (Arellano and Mejia Acosta 2014).
While the formal territorial organization may not be a decisive factor in
the allocation, federal states would arguably be better equipped toeffectively process the administrative and fiscal demands of managing
natural resource revenues at the local level. These ratios suggest that “ifconfronted with the need to decentralize, the national executive prefers
to favor the local level as the targeted group, since mayors pose less of anelectoral and financial threat than governors” (Falleti 2010, 47). Even in
Bolivia where there was a more equitable revenue distribution acrossthe three tiers (37%, 37% and 26% respectively), the government
introduced changes to gradually shift transfers away from regional
prefecturas and towards financing cash transfer schemes formallyadministered by municipal governments as a way to defuse the growing
political opposition and increase their political leverage over local
governments (Arellano and Mejia Acosta 2014; Miranda 2009).
3�2 Horizontal distribution of revenues from the EI
A second relevant dimension is the nature of horizontal or interregional
distribution of EI revenues. As discussed earlier, the main policy
challenge from the central government is to reconcile a fair allocation oftransfers according to regions’ own fiscal efforts and more egalitarian
criteria to compensate those regions that need greater government
314

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

investment. Consequently, the design of interregional transfers could
promote a more progressive distribution if richer districts tend to
subsidize poor ones or they could reinforce existing income inequalities
between districts (Rodden 2006). In the case of the distribution of
extractive industry revenues, the main dichotomy is whether to allocate
EI revenues solely to territories that host extractive activities or promotea wider redistribution.
In a previous work, Arellano and Mejia Acosta (2014), we discussed
three types of mechanisms depending on their potential beneficiaries: a)devolution b) formula-based participation, and c) direct allocation fromthe central government. In practice, countries combine two or morecriteria when adopting reallocation formulas.
a) Devolution
The purpose of devolution is to transfer revenue, or a proportion of it, tojurisdictions associated with the extractive activity, either because theseare producing regions where the extractive income is generated in thefirst place or because they host some infrastructure for exploitation(mainly ports).5
This mechanism aims to compensate the producing regions for the
extracted benefit or the negative externalities (e.g. environmental) linked tothe extractive activity. The criterion of origin compensates for the mineral
extraction per se, and fiscal transfers seek to develop in principle othertypes of capital (human, physical, etc.) to enhance the developmental
potential of those territories. The second criterion seeks to compensate thenegative externalities associated with extraction in the producing orneighboring districts. In this sense, transfers take into accountenvironmental damage as well as the need to improve physicalinfrastructure (roads, the electrical grid, etc.) and to increase public services
in order to respond to the likely increase in population (Brosio 2003).
The allocation of transfers to producing regions may further increase
the inequality between producing and non-producing regions, maytranslate problems of revenue volatility to producing regions, and could
undermine the region’s own fiscal efforts to collect taxes given theabundance of central transfers.
5. According to Ahmad and Mottu (2003, 228), this mechanism is known as “derivation” but
we prefer to use the term “devolution”.

315Chapter 11b) Allocation from the central government
This is an intermediate scenario where central governments consolidate
the management to promote a more strategic investment of resources
and to minimize the fiscal liability of uncontrolled subnational
expenditure. These transfers could take the form of research anddevelopment or regional investment funds that are allocated on anannual basis from a central budgetary account, or revenues could beallocated through competitive investment grants aimed at supporting
specific types of projects.
There are two potential problems with centrally managed allocations.
First, competitive grants have the potential of reinforcing pre-existing
economic inequalities and power asymmetries between subnational
governments, especially if districts with greater technical orbureaucratic capabilities have a better chance to apply for and win
competitive grants. The other problem is that they may allow space fordiscretionary spending and protracted negotiations around theallocation and adequate purpose of such transfers (Arellano and Mejia
Acosta 2014).
c) Formula-based participation
The use of a pre-determined formula to distribute the revenue raised
nationally can bring certainty and equality in the redistribution of natural
resource revenues between producing and non-producing jurisdictions. The
adopted formulas can take into account the different needs andcharacteristics of each jurisdiction, the size of the population and territory,
pre-existing social and economic inequalities, and in some cases fiscal effort.
The adoption of distribution formulas underlines a basic paradox of fiscal
decentralization and interregional transfers. A redistribution of revenues
that compensates territories for the lack of infrastructure, weak tax
collection or high indebtedness levels may in fact reward poor fiscal
management and further undermine fiscal efforts. Conversely, the formula
may compromise central government transfers to relatively affluent andfiscally prudent districts (Bird and Smart 2002). Finally, formula based
redistributions tend to generate entitlements, which are fairly difficult toreverse or amend in the long run once a practice has been set in motion.
Elsewhere, we have shown significant variation in the existing
modalities for horizontal or interregional distribution (Arellano and
316

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

Mejia Acosta 2014). Table 1 illustrates that highly decentralized countries
like Bolivia and Nigeria tend to benefit both producing and nonproducing districts through devolution and formula based mechanisms.
Nigeria prioritizes the participation of all the subnational governments
through formula-based participation (41% of revenues) and devolves asmaller proportion (13%) to the producing states (Kâ Diongue, Giraud,
and Renouard 2011). Bolivia by contrast, has privileged devolution to
producing districts (41% of revenues) but has also allowed redistribution
to non producing states (22%) (Arellano and Mejia Acosta 2014).
In contrast, Brazil and Peru tend to only privilege devolution of revenues
to the producing region or states and localities in producing regions(66% and 55% of revenues respectively). In more recent times, Brazil has
moved towards the provision of essential infrastructure to support
extractive activities in neighboring jurisdictions: the ports from where
oil, gas and minerals are exported, and the territories crossed by roads,
pipelines, and railways. The Brazilian Congress even adopted
legislation in 2013 to redistribute oil revenues among all federal statesand has initiated legislation to allow the use of oil-related revenues forthe education sector. The initiative however has been blocked throughan appeal to the constitutional court by the three producing states thatwould benefit most from direct devolution of such revenues.
Colombia, a moderately decentralized country, has also adopted a
combination of devolution and formula-based mechanisms to distribute
oil royalties (and some mining revenue) across all subnational
jurisdictions (Rausch 2009). In Indonesia, oil-related transfers go
exclusively to the producing areas (provinces and districts), although
the central government discounts 50% of the value of these transfersfrom ordinary fiscal transfers (Morgandi 2008, 23-24) as an indirect way
to reallocate revenues to other needs. More recently, a percentage of the
EI-revenue has been given to the jurisdictions adjacent to the producing
ones but such transfers remain highly controversial because the benefits
and negative externalities do not always coincide with official legal andpolitical boundaries. In Mexico, EI-revenues are proportionally
distributed across the entire country through an allocation formula(Arellano and Mejia Acosta 2014). Finally, countries that are highly
centralized like Ecuador, Ghana and Papua New Guinea, tend to
devolve the small share of transfers to producing regions only.

317

Chapter 114� The politics of distributing EI revenues and their development
consequences
The distribution of EI revenues across subnational governments needs
to address two policy challenges. The first challenge is to determine theoptimal level of subnational transfers that allows sufficient subnational
autonomy to manage revenues without undermining centralizedplanning. The second dilemma is to identify who benefits from thosetransfers so that not only the producers of EI wealth are properly
compensated but so are their neighboring and less wealthy
jurisdictions. The previous sections discussing existing vertical andhorizontal distribution formulas illustrate great variation in the choiceof distribution formulas. The sections suggest that variation is notalways consistent with the amount or nature of resources but they rather
reflect the outcomes of specific political negotiations between nationaland subnational actors over time. This section explores the factors
influencing these political bargains.
4�1 Vertical distribution of EI revenues
From a political economy perspective, one of the main determinants ofvariation is the degree of political organization and mobilization thatsubnational governments have had vis-à-vis the central governments
over time. In countries where local elites have been traditionally strong(Bolivia, Brazil) and have achieved considerable political andadministrative autonomy, the central government has had tocompromise and allow for greater distribution of rents with subnational
governments. In Bolivia, the local exporting and mining elites organizedalong regional lines have played a significant role in the export economy(Laserna 2009). With the advent of the commodities boom, local demands
have put pressure on the central government to adopt a directhydrocarbons tax (IDH) in 2005 that distributes over 50% of mining rents
to subnational governments. In Brazil, state governors have long been acritical part of the governing coalition in the center, and even after the1998 constitution reduced the direct influence of state governors a goodlevel of decentralization was maintained. In both cases, the strong
presence of subnational politics explains a high degree of fiscal
decentralization that also included the distribution of EI revenues during
the commodities boom.
By contrast, the absence of an active political pressure from subnational
318

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

governments explains why dissimilar countries like federal Mexico andUnitary Ecuador are not very decentralized. In the case of Mexico, thesubnational pressures have been traditionally contained or managed by
the party in government for many decades. This is particularly true forthe management of EI rents that were maintained by the centrallycontrolled oil company PEMEX. The more recent political liberalizationof Mexican democracy since the late nineties has not been sufficient topush for greater decentralization in the management of oil revenues mostof which remain under control of the central government. In Ecuador,
subnational governments (provinces and municipalities) demanded
greater fiscal decentralization in the mid-nineties, but without a solidbase of political support, they obtained a mild response from the central
government allowing a transfer of 15% of central government spending
to be invested in regional governments (Mejia Acosta and Albornoz
2010). Furthermore, this mild allocation did not include the specifictransfer of EI revenues, so local governments could not benefit from theadvent of the commodities after 2004. Instead, the Correa administration,
inaugurated in 2006, found mechanisms to further centralize themanagement of fiscal and EI revenues (Basabe et al. 2010).
4�2 Horizontal distribution of EI revenues
The puzzle of distributing revenues across different jurisdictions isusually addressed through political bargaining between resource rich
districts, resource poor and central governments. All things being
equal, resource rich elites would have a preference to maximize direct
devolution of EI revenues to compensate for the value of resources
extracted or the negative externalities associated with extraction. A
greater share of revenues would also allow local elites greater
independence from central government influence on spending policies.
Resource poor districts however, would have a preference and demand
for greater (re)distribution of EI revenues considering that these districts
indirectly support extractive activities (and need better ports and roadinfrastructure for example) or making a claim for improved
redistribution to address pre-existing socio economic inequalities(Arellano Yanguas and Mejia Acosta 2014). From the perspective of thecentral government however, a broader redistribution (across all
districts) would be preferred if that contributes to consolidating its basesof electoral support (Gonzalez 2014). Likewise, a more targeted
devolution to producing districts only would be preferred if this helps toconsolidate the governing coalition at the center.
319

Chapter 11

The cases of Colombia, Peru and Brazil illustrate different scenarios ofpolitical bargaining. In the case of Peru, the local elite with the supportof mining companies has strongly bargained for a direct devolution ofrevenues from the central government in the form of a mining canon.
These transfers are distributed to producing regions, provinces andmunicipalities but not outside extractive jurisdictions (Orihuela 2012).
Benefiting regions have also resisted further government attempts togenerate saving funds or compensation funds that are transferred toother regions (Arellano Yanguas and Mejia Acosta 2014). Brazil provides
a similar illustrative case, where producing regions have resisted
further attempts by the central government to distribute the extractive
wealth to non-producing regions. As discussed, the government passed
legislation to distribute potential oil revenues to benefit education but
the redistribution has been challenged through a constitutional appeal
by three resource rich states.
Colombia provides an interesting example where the central government
defused some potential opposition of resource rich departments. It
launched a legislative proposal aimed at “spreading the jam across thewhole toast” when it came to distributing the wealth from the EI sector(Garcia Tapia 2011; Garcia Villegas and Espinosa Restrepo 2011). The
government obtained support from the majority of legislators who in turn,sought greater government investment on projects that would further
their chances of re-election. Not only were the representatives fromproducing districts in a relative minority but they were also interested indirect government investment, so the new legislation was approved inJuly 2011 (Arellano Yanguas and Mejia Acosta 2014). The reform
dramatically reduced the share of royalties received by subnational
governments (from 80% to 10%) but it also centralized the governments’decision-making ability, thereby giving the central government greater
say in the way Regional Compensation and Development funds were
invested (Arellano Yanguas and Mejia Acosta 2014).
4�3 Distributional consequences for enhancing the quality of growth
One of the key arguments advanced by this chapter is that the different
mechanisms of intergovernmental transfers and the underlying politicalbargains that sustain them are key to understanding the effectiveness ofgovernment spending. It is through these institutional configurations thatany windfall of natural resource revenues will have a positive impact on
the quality of growth. While the link between the choice of distributional
320

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

mechanisms and the underlying shape of the political bargain was
illustrated in the previous section, there is insufficient or inconsistentempirical evidence to test the second claim. Little is known about theactual impact of those transfers on fostering sustainable development
outcomes such as social spending or developing human capital.
This section will offer a brief discussion of pathways and factors that
may contribute to enhancing the positive impact of government
spending on social outcomes. While the empirical discussion herefeatures some Latin American cases for which there is detailed
knowledge on the quality of expenditure, generalizations to othercontexts must be made with caution. The selected countries all are not
low-income countries; they all display established democratic
institutions and fiscal rules, and allow for an active participation ofpolitical parties and organized civil society.
All things being equal, the allocation of EI revenues should promote
quality investments of funds when these are: a) fairly distributed acrossdifferent constituents without privileging a particularly powerful orwell organized group; b) allocated in consistent and predictable patterns
without being excessively vulnerable to economic or political cycles; c)
efficiently allocated so that they prioritize investment in high return
sectors or have a demonstrable long term impact; and d) are managed ina transparent and accountable manner (Hallerberg et al. 2009).
The scarce existing evidence seems to suggest that the promise ofdecentralized social spending does not necessarily ensure improved
outcomes; rather, it is the centralized planning, management andadministration of these funds that is more likely to create coordination,
monitoring and strategic investment. Empirical studies suggest that
despite the extraordinary amount of transfers to mining regions overthe last decade, social indicators have not improved by a comparable
proportion (Arellano Yanguas 2011, 2012, Loayza, Mier y Teran and
Rigolini 2013). This is partly due to the lack of managerial capacity at the
subnational level, the reduced political horizons that encourage shortterm investments and the lack of effective checks and balances thatallow for widespread rent seeking (Arellano Yanguas 2011, 2012).
At the other end of the spectrum we find a country like Ecuador that has
centralized the planning, management and allocation of EI rents in the321Chapter 11

hands of the executive and has produced visible improvement in therange of social indicators (Mejia Acosta and Albornoz 2010). The
improvements are most likely associated with the centralized
management of revenues from the commodities bonanza. Paradoxically,
this increased fiscal (re)centralization has come at the price of reducing
the political and administrative autonomy of local governments as well,in a way that local mayors would only increase their ability to gain reelection if and when they can show a clear association to the executive
branch. Further analysis is also needed to evaluate the transparency andsustainability of this type of investment when it is heavily centralized in
the hands of the executive branch. Far from making sweeping
generalizations, this paper argues that the formation of stable,transparent and inclusive coalitions should contribute to improving thequality of spending of natural resource revenues.

5� Conclusions
This chapter has offered a partial explanation and a testable claim about
the link between natural resource revenues and the quality of growth.
The partial explanation looks at the different nature of existing revenue
allocation mechanisms and the underlying political and institutional
configurations that sustain these policy choices. The chapter relies on
evidence from a selected range of cases to illustrate some of the policy
dilemmas and political trade-offs for allocating windfall revenues
between central and subnational governments. The testable claim is thatthese distributional mechanisms and the political bargains sustaining
them, should have an impact on the type and quality of government
investments over time. The preliminary and incomplete evidence in thisregard suggests that this is a promising agenda for future research.
The section below summarizes the most salient conclusions andpending themes.
1. The sole presence of political institutions or fiscal rules is not a
sufficient predictor of actual distribution patterns. The review
confirms the notion that fiscal decentralization alone is not a goodpredictor of increased devolution. Some of the countries withformally centralized (unitary) governments may actually concede
equal or greater fiscal autonomy to subnational territories than tothose with formal decentralized or federal structures. However, the322

The Governance of Natural Resource WealthSome Political Economy Considerations on Enhancing Social Investment

review suggests that the existing strength and political leverage of
local elites is a stronger predictor of greater decentralization. In
contrast, where local elites have limited political or economic
influence, the central government is more likely to recentralize theallocation of funds regardless of the fiscal formula.
2. The allocation of rents across producing and non-producing districts(horizontal association) is mildly associated with levels ofdecentralization. Highly centralized systems tend to adopt
devolution formulas benefiting producing districts only, whereas
formally decentralized systems tend to benefit both producing andnon-producing territories. Central governments are able to promotea more distributive (inclusive) allocation of natural resource
revenues when they can effectively mobilize non-producing districts
and/or defuse the power of producing districts (as illustrated in
Colombia). When elites in producing districts are strong andmobilized, it is likely that distribution formulas will continue toblock a broader distribution of wealth (as in the case of Peru).
3. In both cases of vertical and horizontal distribution, the chapter
shows a consistent pattern of path dependency that is consistent
with the existing literature: once distributional reforms have been
adopted, these become very hard to change over time unless there
is a dramatic realignment of the relevant political actors. Thesemajor political shifts can be produced after elections or as a
response to nationwide mobilizations for greater redistribution.
4. There is inconclusive evidence to claim which allocation formula ismore likely to enhance the efficiency of government investments.
Preliminary evidence discussed in this paper suggests that it is thecentralized management and allocation of funds that appears to be
associated with improved social outcome indicators andconversely, an extreme devolution to weak subnational units may
in fact reinforce rent seeking practices. In principle, this idea
should challenge the proposed benefits of fiscal decentralization
established in the literature. Looking ahead, more work is needed
to explore the mediating variables through which a more (de)centralized allocation and revenue management is likely to
improve government investment. Some pending themes include
the analysis of discretionary management of funds, the323

Chapter 11

effectiveness of transparency and accountability initiatives, andthe capacity of local governments to effectively administer central
government funding.
5. Finally, much more work is needed to systematically measure,analyze and compare the impact of natural resource revenues onsocial spending and how this links to quality of growth. One wayof approaching the question is to compare multiple cases based on
the funding modalities adopted at sector level (education, health,nutrition) and measure these against specific social indicators (e.g.
improved school enrolment or reduced stunting rates) over time.
This would be an empirical way to assess the contribution of thecommodity revenues on enhancing the quality of growth acrossthe developing world.

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330

IndexA

ADB’s Framework of Inclusive
Growth Indicators (FIGI), 279
Africa, 3, 10, 75, 139, 172, 176, 177,
187–190, 214, 217, 219–226, 232,
234–235. See also Sub-SaharanAfrica (SSA)
Angola, 218
Burkina Faso, 218
Cameroon, 202, 218
CAR (Central African Republic), 218
Chad, 218
Congo, 218
Côte d'Ivoire, 218
DRC, 191, 218
East Africa, 223, 224
Equatorial Guinea, 218, 221, 223
Ethiopia, 171, 191, 202, 218, 223, 230,
234, 235
Gabon, 218
Ghana, 171, 312, 313, 317
Guinea Bissau, 61, 218
Horn of Africa, 218
Kenya, 171, 218, 230
Liberia, 191, 218
Madagascar, 218
Morocco, 202
Mozambique, 218
Niger, 218
Nigeria, 66, 67, 75, 129, 218, 223, 235,
312–314, 317
Rwanda, 218, 223, 230
Sahel, 218, 224
Senegal, 223
Sierra Leone, 218
South Africa, 219, 221, 224, 225, 235
Tanzania, 223

Togo, 218
Uganda, 218, 223, 234
Zambia, 202
Zimbabwe, 218
African Center for Economic
Transformation (ACET), 230, 232
African Economic Outlook 2012, 230
African Transformation Index, 230
African Union Commission, 230
Africa Vision 2063, 230
Aggregate net investment, 28
Aggregate wealth, 28
Agriculture, 10, 89, 165, 174–178,
187–188, 195, 201, 212, 220–225,
231–236, 237, 276, 281. See also
Cambodia, study on chronic
poverty in
Alaska, 309–310
Anglo-Saxon model of dispersedownership, 36–37
APEC Growth Strategy, 2, 267, 270–
272, 274, 279, 284, 292–294
Arrow’s impossibility theorem, 20
Asia, 2, 5, 58, 63, 67, 76–77, 202, 212,
222, 232, 234, 267, 270–271
Bangladesh, 269, 277, 281–282, 293
Bhutan, 202, 270
Brunei, 202
Cambodia, 9 (See also Cambodia,
study on chronic poverty in)
China, 2–3, 5, 8, 20–21, 34–35, 57–58,
63–65, 69, 77, 79, 162, 226, 228, 232
East Asia, poverty in, 76–77
India, 5, 35, 43, 59–61, 65–67, 70, 139,
162, 200, 202, 228, 270
Indonesia, 162, 312–314, 317
Japan, 4, 243, 270 (See also Natural331

disasters)
jobless growth in, 191
Korea, 202
Malaysia, 270
Maldives, 202
Nepal, 9, 202 (See also Disability andgrowth elasticity of poverty, in
Nepal, study on)
South Asia, poor people in, 73, 77,
79, 227
structural change across, 189–190,
202–203
Thailand, 270, 275–276, 282–283
Vietnam, 270
Asian Development Bank (ADB), 7,
268, 272, 278

B

Bangladesh, 281
garment industry in, 277, 281–282, 293
Between-sector growth, 190
Bhutan, 202, 270
Biofuel production, 175, 177
Bolivia, 312–314, 317, 318
Brazil, 66, 67, 162, 294, 312–314, 317, 318,
320
Brazil’s Cerrado agriculture andagro-industrial value chain,
276–277, 283–284, 286–289, 292
Broad-based growth, 139
Brunei, 202
Bureaucratic quality, 126
Business accounting, 39–41

C

Cambodia Socio-Economic Surveys(CSES), 86–87
Cambodia, study on chronic poverty
in, 85–86, 110–111
adjusted headcount ratio withdifferent cutoffs in rural area, 109,110
categories and characteristics from332

participatory poverty assessment,
95–96
consumption growth, 115
consumption poverty and chronic
poverty, 106–108
demographic characteristics ofchronic poor, 104–106, 105
economic activities and asset
ownership in rural area, 100
estimated chronic poverty
headcount in rural area, 101
estimated poverty indicators, 87–89
growth incidence curve, 89, 116
households in chronic poverty, 104
key factors for chronic poverty,
105–106
macroeconomic and humandevelopment indicators, 114
poverty rates in rural areas, 89–90
pro-poor growth and distribution,
86–90
qualitative information in, 93–97
regional distribution of chronicpoor, 105
sensitivity analysis, 108–110, 117–
119
socio-economic indicators,
comparison of, 103
Cameroon, 202, 218Cancùn Conference of Parties (COP16), 42
Carbon assets, 48
government guarantee on, 48–49
in monetary and financial systems,
50–52
Carbon certificates, 49
Carbon sequestration, 4, 176
Carbon tax, 6, 50, 174, 179
Causal time, 22–23
Cerrado Miracle, 276
Child undernutrition, 121. See also
Income growth and nutrition
status, relationship between,

Indexstudy onChile, 306
salmon industry in, 277, 289, 293–
294
China, 2–3, 5, 8, 20–21, 34–35, 57–58,
63–65, 69, 77, 79, 162, 226, 228, 232
inequality of incomes within, 69
poverty reduction in, 57–58, 63–65,
77
Chronic poverty, 91. See also
Cambodia, study on chronic
poverty in
Climate bonds, 51
Climate change, 4, 25
Cancùn Conference of Parties,
43–45
investments in energy efficiency
and low carbon technologies, needof, 42societies action against, reasons for,41–42
transition to low-carbon economy
and funding gap, 45–47
uncertainties of the costs from, 42
Climate finance, 45–47
Climate-smart agriculture (CSA), 176
Climatic disasters, 246
Colombia, 129, 312, 313, 317, 320, 323
Community resilience, 271, 293
Competition policies, 47
Comprehensive wealth, 31
for advanced and emerging market
countries, 33–36
Conditional cash transfer (CCT), 294
Consumption poverty, 1062009 Copenhagen Accord, 161
Corporate management, 27
Cost of Basic Needs (CBN) approach,141

D

Democratic accountability, 127
Demographic dividend, 226

Development, 1–3, 7, 32–35, 44, 137–
138, 187, 218, 220. See also
Millennium Development Goal
(MDG)
Devolution, for horizontal
distribution, 315
Direct cash transfers, 309–310
Disability and growth elasticity ofpoverty, in Nepal, study on, 138,
149–150
analysis of poverty, 141
caste-based classification of
population, 143
growth elasticity of poverty, 141–
142, 145–149, 156
poverty and inequality measures,
145, 155
sustainable and inclusive
development, need of, 146
Disasters, 245. See also Natural
disasters
Distribution formulas, horizontal,
315–316‘Distribution-neutral growth,’ 71Distributive Analysis Stata Package
version 2.2, 142
Dominican Republic, 202

E

East Asia, poverty in, 76–77
Economic and Social Commission for
Asia and the Pacific (UN-ESCAP),
271
Economic and Social Stabilization
Fund (ESSF), of Chile, 306
Economic Commission for Africa(ECA), 230
Economic development, traditionalmodels of, 187
Economic growth, 1–2, 5, 6, 187, 217,
218, 244, 246. See also Quality ofgrowthimpact of, on employment333outcomes, 187–188, 213–214
basic statistics on quality of
employment variables, 194
data sources for labor outcomesindicators, 193–195
economic growth and changes inlabor outcomes indicators, 203–204
evolution of labour outcome
indicators, 195–200
growth pattern and structuralchange, impact of, on laboroutcomes, 204–213
measuring structural change,
200–201
structural change and laboroutcomes, 188–192
structural change measures,
201–203
unemployment rates, OLS
estimates for, 209
vulnerable employment rates,
OLS estimates for, 211
youth unemployment rates, OLS
estimates for, 210
Ecuador, 312, 313, 317, 319, 321–322
Efficient market hypothesis, 19, 22
EM-DAT database, 245, 248
Emerging market economies (EMEs),
33
Emission rights market, 44
Employment quality, 192
Enterprise, 37
Environmental Conservation
Expansion Program, Brazil, 287
Environmental policies, weak, 44
Equal access to opportunities, 279
Equity markets, and social interest, 37
Ethiopia, 171, 191, 202, 218, 223, 230,
234, 235
Europe, 3, 4, 6–7, 37, 46, 73, 74, 200, 226
European market, 44
Extractive industries (EI), 301. See also
Natural resource revenues
334

F

2008 financial crisis, 2, 8, 29, 89, 195,
200
Firm accounting, 27
Fiscal decentralization, 312–314, 318
Focus group discussions (FGDs), 93
Food safety, 271, 292
Food security, 4, 159, 176, 271, 292
Formula-based mechanisms, forhorizontal distribution, 316
Fossil fuel subsidies, 173–174
Fractional response approach, 69–70
Fragile states, poverty in, 75
France, 25
Funding gap, 45–47
The Future We Want ( Rio+20
Conference, 2012), 285

G

Genuine saving, 31–32
Genuine wealth, 32
Geological disasters, 246
Ghana, 171, 312, 313, 317
Globalization, 231, 232
Governing Development in Africa: The
Role of the State in Economic
Transformation, 230
Government Pension Fund of
Norway, 307
Graduation effects, 60–61
Great East Japan Earthquake, 243
Green funds, 51–52
Green growth, 285–286, 290
Greenhouse gas (GHG) emissions, 4,
44, 159. See also Pro-poor lowcarbon growth
Green investments, handicaps to, 47
Gross Domestic Product (GDP), 1, 3, 5,
6, 122, 128–130, 202, 205, 219–223
for advanced and emerging market
countries, 33–36
impacts of disasters on, 245–247
invention of, 25

IndexGrowth elasticity of poverty, 139,
141–142, 145–149
Growth model approach, to natural
disasters, 245
Guinea Bissau, 61, 218
Gulf States, 195–196

H

Health-for-age scores (HAZ), 121. See
also Income growth and nutrition
status, relationship between,
study onHigh-Level Panel of UN Secretariat,
model of sustainability conditionby, 27–30, 27–31
Human security, 268, 274, 292–293

I

Implicit contract theory, 37
Inclusive growth, 3, 7, 278–284. See also
Sub-Saharan Africa (SSA)
Inclusiveness, definition of, 20–21
Inclusive wealth, and comprehensive
wealth, 32–33
Inclusive Wealth Index (IWI), 122, 128
Inclusive wealth indicator, 27, 29, 30
for advanced and emerging market
countries, 33–36
Income elasticity of poverty, 64
Income growth and nutrition status,
relationship between, study on,
121–123, 130–132
elasticity between height for age ofunder 5s and income, estimation of,
123–125, 132–133
nutrition-income relationship, roleof governance in, 125–128
quality of growth, approach for
exploration of, 122
stunting with respect to GDP per
capita and IWI per capita, 128–130
Income semi-elasticity of poverty, 64
India, 5, 35, 43, 59–61, 65–67, 70, 139,

162, 200, 202, 228, 270
poverty line, 61
poverty reduction in, 65–66
Indonesia, 162, 312–314, 317
Industrial and financial lobbies, 26
Industrial challenges, 268–269
Industrial development, 269
Industrial free trade zones, 233
Industrialization, 229, 231–233
Industrial transformation and qualityof growth, 267–269, 296
APEC Growth Strategy, 267, 270–
272, 274, 279, 284, 292–293, 294
case studies of industrial
transformation, 275
Bangladesh garment industry, 277,
281–282, 293
Brazil’s Cerrado agriculture andagro-industrial value chain, 276–
277, 283–284, 286–289, 292
Chile’s salmon industry, 277, 289,
293–294
enabling factors of transformation,
278
Thailand automobile industry,
275–276, 282–283
high quality of growth, key issuesof, 270–272
innovative aspect of quality ofgrowth, 290–292
relationship between, 272–275
synergies, trade-offs, priorities andsequences, 294–296
transformation and secure growth,292–294transformation and sustainability,
284–290transformation with inclusive
growth, 278–284
Inequalities, 19, 25, 79, 308
in developing world, 67–69
justification of, 21
Informal economy, 225
335

Informal employment, 193
Innovative growth, 290–292
Integral internal rate of return (IIRR),
40–41
International aid policy, 267
International Comparison Program(ICP), 60
International Country Risk Guide(ICRG) indicators, 126
International Energy Agency (IEA),
42, 161International Panel on Climate
Change (IPCC), 4, 31, 42
social cost of carbon, 48
5th Assessment Report (AR5) of, 162
International poverty line, 59
setting of, 59
updating of, 59–61

J

Japan Chile Technical Cooperation(PTTC), 289
Japan, economic impacts of natural
disasters on, study on, 244–261
Japanese Development Cooperation
Charter, 270, 271, 293, 295
Japan International Cooperation
Agency (JICA), 278
Job creation, 6, 279“Jobless” growth, 6, 191, 212

K

Kenya, 171, 218, 230
Knowledge services, 191
Kobe, Japan, 254
Korea, 202, 221
Kyoto Protocol, 4L

Labor market regulations index, 195
Labor outcomes indicators
data sources, 193–195
evolution of, 195–200
336

Latin America, 3, 63, 66, 79, 187, 189,
190, 212, 220, 312, 321
Bolivia, 312–314, 317, 318
Brazil, 66, 67, 162, 276–277, 283–284,
286–289, 292, 294, 312–314, 317, 318,
320
Chile, 277, 289, 293–294, 306
Colombia, 129, 312, 313, 317, 320, 323
Dominican Republic, 202
Ecuador, 312, 313, 317, 319, 321–322
Mexico, 312–314, 317, 319
Peru, 200, 312–314, 317, 320
structural change across, 189–190
Venezuela, 35, 129
Law and order, 126, 127
LCR (local contents requirement)
system, 283
Learning society, 290
Liberia, 191, 218
The Limits to growth, 3

M

Macro-micro consistency, 40
Malaysia, 270
Maldives, 202
Manufacturing and industrial sector,
growth in, 188–189
Market economy, distortions in, 19
Market failure, 6Mark-to-market accounting, 19
Mexico, 312–314, 317, 319
Millennium Development Goal
(MDG), 7, 57, 137, 138. See also
Poverty reduction
Ministerial Conference on
Environment and Developmentin Asia and the Pacific (MCED),
285, 286
Morocco, 202
Mozambique, 218

N

Nationally appropriate mitigation

Indexactions, (NAMAs), 44
National Social Protection Strategy(Cambodia), 111
National System of Nature
Conservation Units (SNUC),
Brazil, 287
Natural capital, 296
Natural disasters, 243–244. See also
Japan, economic impacts ofnatural disasters on, study on
local impact of, 244, 248
macroeconomic impacts of, 244–245
long-term effects, 246–248
previous literature on, review of,
244–248
short- and middle-term impacts,
245–246
regional distribution of number of,243
Natural resource revenues, 301–303,
322–324
distribution of EI revenues anddevelopment consequences
distributional consequences forenhancing quality of growth,
320–322
horizontal distribution of EI
revenues, 319–320
policy challenges and, 318
vertical distribution of EI
revenues, 318–319
effective management of, 303–305
direct cash transfers, 309–310
revenue sharing formulas, 307–309
saving funds, 306–307
stabilization funds, 305–306
extraction and allocation of, 301
governance of, improvement in,
301–302
political institutions, impact of, on
quality of growth, 302
politics of redistribution of, 310–311

horizontal distribution of EI
revenues, 313, 314–317
vertical distribution of EI
revenues, 311–314
translating natural resource wealthinto quality growth, 304
Nepal, 9, 202. See also Disability andgrowth elasticity of poverty, in
Nepal, study on
Nepal Living Standard Survey(NLSS), 138, 140“New Economic Model” (Malaysia),
270
Niger, 218
Nigeria, 66, 67, 75, 129, 218, 223, 235,
312–314, 317
Non-renewable natural resource
revenues, 301
Norway, 307
Nutrition and income, relationshipbetween, 121–123. See also Income
growth and nutrition status,
relationship between, study on
Nutrition programmes, need ofinvestment in, 121O

Oman, 202
Outsourcing, 232‘Overall inequality,’ 67P

Panel-corrected standard error(PCSE), 251
Papua New Guinea, 312, 313, 317
Participatory poverty assessments(PPA), 92, 93–97. See also
Cambodia, study on chronic
poverty inPeople with disabilities. See also
Disability and growth elasticity
of poverty, in Nepal, study onin developing countries, 139337

inclusive policies for, need of, 138
as marginalized groups, 137
poverty among, 139
Peru, 200, 312–314, 317, 320
Political stability, 126–127
Population growth, 5, 58, 63, 175, 213,
225–226, 228
Poverty line, international. See
International poverty line
Poverty reduction, 57–58, 77–79, 138
in Brazil, 66–67
in China, 57–58, 63–65, 77
consumption growth and, 62
in India, 65–66
international poverty lines, setting
and updating of, 59–61
in Nigeria, 66–67
poverty projections until 2030,
69–77, 83
in Sub-Saharan Africa, 67
World Bank poverty estimates byregion, 82
Price flexibility, 44
Primary goods, 20, 21
Pro-poor growth, 7, 71, 86, 139. See also
Cambodia, study on chronic
poverty in
Pro-poor low-carbon growth, 159–160,
178–180
decomposition of present GHG
emissionsby region, 162–163
by sector, 165–166
fossil fuel subsidies, elimination of,
173–174
GHG emission projections andtargets, 160–162
global GHG emissions in 2010 byregion, 162, 165
least developed countries (LDC)
emissions from mitigation
perspective, 163–164
low-carbon growth in LDCs,
338

reasons for, 166–170
low-carbon growth path, need oftransition to, 159–160
poverty implications of low-carbon
growthin agriculture and forestry sector,
174–178in energy sector, 170–173‘Pro-rich growth,’ 71Q

Quality of growth, 1–3, 267
APEC Growth Strategy, 267, 270–272
importance on, 267–268
inclusiveness and sustainability as
attributes of, 270–272
relationship with structuraltransformation, 269, 272–275 (Seealso Industrial transformation andquality of growth)
Quantitative easing (QE), 49, 50

R“Regional resource curse,” 308
The Report of the High-Level Panel ofEminent Persons on post-2015
Agenda, 268
Resilience, 7–8, 176, 178, 225, 268,
270–272, 292–293
Restraint of corruption, 127
Revenue sharing formulas, 307–309
Risk and uncertainty, distinction
between, 24
Rural Environmental Registry (CAR),
287–288
Rwanda, 218, 223, 230

S

Sahel, 218, 224
Saving funds, 306–307“Schumpeterian creative
destruction,” 244, 247
Senegal, 223

IndexServices sector, 191
Shadow prices, 28
estimation of, 30
Shaping Africa’s Transformation, 230
Shareholder value, 19, 36–37
Shocks, and growth, 7–8, 94, 247. See
also Natural disasters
Sierra Leone, 218
Small and medium enterprises(SMEs), 283
Social choice, 20
Social cost of carbon, 48
Social inequalities, 21
Social investments, 301, 303. See also
Natural resource revenues
Social marginal value, 26
Social protection, 279
Social responsibility, 41
Social value of investment, 39
Social values, evaluation of, 26
Social welfare approach, 25
Socio- Economic Surveys of
Cambodia (SESC), 86
South Africa, 219, 221, 224, 225, 235
Sovereign wealth funds (SWF), 305
Stabilization funds, 305–306
Stakeholder corporations
board of directors, 38–39
responsible shareholders in, needof, 39Structural change, concept of, 187. See
also Economic growth, impact of,on employment outcomes
Structural transformation, 268–269,
272–275. See also Economic
growthand changing endowments or asset
accumulation, 273
key components of, 274
no standard model for, 272, 273
and quality of growth, relationshipbetween, 274–275 (See also Industrial
transformation and quality ofgrowth)and virtuous circle of growth, 272
Structured poverty, 85
Stunting, in children, 121. See also
Income growth and nutrition
status, relationship between,
study onSub-Saharan Africa (SSA), 9, 58, 63,
66–71, 73, 75, 77, 79, 172, 176, 195,
212, 217, 268
activity ratio in, 226, 227
agriculture in, development model
for, 234–235
challenges for, 219–220, 228–229
incipient economic transition,
220–225strong on-going demographictransition, 225–227
employment structure in, 224–225
GDP statistics and, 218
policy options for inclusive andsustainable process of change,
229–230
consolidating urban-rural
linkages, 236–237
decompartementalizing public
policies, 236–237
reengaging in development
strategy design, 230–231
sectorial priorities, 231–235
territorial priorities, 235–236
poverty headcount ratio, 228
poverty in, 63, 67, 75, 77
rapid growth and changes in,
216–218
rural population in, growth in, 227
structural change in, 223, 224
urbanization without
industrialization in, 222
workers entering labor market in,
228–229
Sustainability, 21–25
and finance, 21–24
339

genuine saving concept, of World
Bank, 27, 31–32
strong condition of, by High-Level
Panel of UN Secretariat, 27–31
Sustainability of growth, 284–290
Sustainable livelihood approach(SLA), 92. See also Cambodia,
study on chronic poverty inTTanzania, 223
Thailand, 270
automobile industry in, 275–276,
282–283
Tipping points, crossing of, 31
Togo, 218
Total Factor Productivity (TFP), 246
Total national wealth, 28
Typhoon Haiyan, 243

U

Uganda, 218, 223, 234
Undernutrition, 121, 126, 175, 176, 268.
See also Income growth andnutrition status, relationshipbetween, study on
Unemployment, 7, 188, 193–197, 203,
206, 207, 212–214. See also
Economic growth, impact of, on
employment outcomes
United Nations Development
Programme (UNDP), 1
United Nations Environment
Program (UNEP), 285
Upfront investment cost, 45, 46
Urban framework, Africa, 233
Urbanization, 218, 220, 231–233, 236

V

Values, and beliefs, 23–24
Vietnam, 270
Vulnerable employment, 193

340

W

Within-sector productivity growth,
189, 191
World Bank, 1
on forms of capital, 32
genuine saving concept, 27, 31–32
on growth elasticity of poverty,
141–142
on inclusive growth, 7
on job challenges categories, 269
poverty reduction, 2013 declarationon, 57
World Development Report (WDR) 2013,
230, 267
World Economic Forum’s Summer
Davos, on “Mastering Quality
Growth,” 267–268, 270, 294
World War II, 25

Z

Zambia, 202
Zero poverty, 85, 131
Zero stunting targets, 131
Zimbabwe, 218

Growth is Dead, Long Live Growth

The Quality of Economic Growth and Why it Matters

JICA Research Institute January 2015

Lawrence Haddad
Hiroshi Kato
Nicolas Meisel

Growth is Dead,Long Live Growth
The Quality of Economic Growthand Why it Matters

JICA Research Institute

JICA Research InstituteISBN:978-4-86357-066-5

Edited by

Lawrence Haddad
Hiroshi Kato
Nicolas Meisel