Policy Research Working Paper 8841
Uncertainty in Ex-Ante Poverty
and Income Distribution
Insights from Output Growth and Natural
Resource Country Typologies
Fabian Mendez Ramos
Development Economics
Development Research Group
May 2019
Policy Research Working Paper 8841
Abstract
This paper studies future poverty, inequality, and shared are expected to achieve poverty levels below 3 percent by
prosperity outcomes using a panel data set with 150 coun- 2030. Global and country aggregations show a decrease in
tries over 1980–2014. The findings suggest that global income inequality by 2030; though, significant downside
extreme poverty will decrease in absolute and relative terms risks could increase wealth inequality in high- and low-out-
in the period 2015–2030. However, absolute poverty is put growth economies by 2030. Substantial uncertainty,
likely to increase by 2030 in resource-output oriented as measured by the variability of the simulated outcomes,
countries and economies with low rates of output per exists on shared prosperity gaps across the studied country
capita growth. Countries with high growth rates of output typologies.
This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the
World Bank to provide open access to its research and make a contribution to development policy discussions around the
world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may
be contacted at fmendezramos@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Uncertainty in Ex-Ante Poverty and Income Distribution
By Fabian Mendez Ramos1
JEL Classification: E170, I3, O4, O11, Q0
Keywords: Poverty, economic growth, shared prosperity, income inequality, natural resources, uncertainty.
1
Fabian Mendez Ramos–The World Bank, Development Research Group (DECRG), email:
fmendezramos@worldbank.org. The views expressed here are the authors’ and do not reflect those of the World Bank,
its Executive Directors, or the countries they represent. I am grateful to Aart Kraay and Espen Beer Prydz for sharing
preliminary data and Stata codes and for answering numerous questions, and to Norman V. Loayza for guidance,
multiple discussions and feedback. I am also grateful to the Global Monitoring Report 2015/2016 team for useful
inputs at the beginning of this research, with especial thanks to Philip Schellekens, Ayhan Kose, Jos Verbeek, Eugenia
Suarez Moran, Vandana Chandra, Hiroko Maeda and Christian Eigen-Zucchi. I am also thankful to Ha Minh Nguyen,
Dorina Georgieva, Cesar Cancho, Emmanuel Chavez, Elke D. Groh, Ramon Padilla Perez, Jesus Antonio Lopez
Cabrera, participants of the Seminar in the Economic Commission for Latin America and the Caribbean in Mexico
City, and particiants of the World Bank DECRG Seminar Series in Kuala Lumpur, Malaysia, for helpful comments
and suggestions at different stages of the paper.
Page 1 of 45
I. INTRODUCTION
This paper follows a strand of the literature that debates that sustained economic growth is the
primary source of poverty alleviation (World Bank 2016, and World Bank; International Monetary
Fund 2016, and World Bank 2018). In addition, this research focuses on potential future pathways
in wealth inequality based on the idea that, in multiple countries, economic growth has played a
vital role in the lower end of the income distribution diminishing poverty rates (Dollar, Kleineberg,
and Kraay 2016) and in increasing wealth at the higher endespecially for the very
rich(Campos‐Vazquez, Chavez, and Esquivel 2017). Also, our study aims to highlight the recent
historical role of both the natural resource sector and the speed of economic growth to predict
future changes in poverty and income distribution.
The contribution of this paper to the literature on poverty and income distribution is
threefold. First, our Monte Carlo simulation procedure offers an alternative method of estimating
the dynamics of the income distribution in a simple setup, expanding the work of World Bank
(2014) by modeling of the randomness of the growth rate of the Gini coefficient and its interaction
with mean income growth. At the country level, the simulations assume independent movements
of the growth rates of both average income and inequality according to historical and country
category benchmarks. The second contribution of this paper is that it provides estimates of the
certainty of future poverty and some aspects of the income distribution by 2030 for some country
categories. The standard deviation of the Monte Carlo simulated projections accounts for the
uncertainty of the poverty headcount, inequality and shared prosperity measures. The third
contribution of this study intersects the discussion on the position of natural resource-rich countries
regarding their effects on long-term growth (Havranek, Horvath, and Zeynalov 2016) versus the
position of more-diversified economies, and the significant role that the speed of economic growth
across countries plays in poverty alleviation (Ravallion 2013), income inequality (Ravallion 2018),
and changes in shared prosperity measures (Dollar, Kleineberg, and Kraay 2014).
Uncertainty in poverty projections is a topic that has been long debated in the literature. In
many cases, researchers study the uncertainty of ex-ante poverty outcomes via scenario analysis.
For instance, Ravallion (2013) finds that alternative scenarios could lead to very different poverty
reduction outcomes. A “pessimistic” scenario could see 1,000 million people lifted out of poverty
Page 2 of 45
over the period 2012–2062, while a more “optimistic” state could achieve the same results but in
a period ending between 2025 and 2030. Lakner, Negre, and Prydz (2014) acknowledge
uncertainty in the speed of change for different percentiles of the income distribution. These
authors use scenario analysis to test how meaningful the difference of growth rates between the
income growth of the bottom 40 percent (B40) and the mean of the income distribution is for future
world poverty attainment. Chapter 4 of World Bank (2014) simulates potential outcomes for
income growth and its effects on the poverty headcount under a variety of economic growth
contexts and distributional considerations highlighting significant uncertainty in future income
poverty attainments. More recently, World Bank (2018) discusses poverty projections up to 2030
and tests its premises—of country future growth rates and the nature of this growth—to stress that
there is some degree of uncertainty in poverty outcome attainments.
Our paper approximates growth rates in the mean income and the Gini coefficient by
combining household—income and expenditure—survey data from 1980 to 2015 with
macroeconomic information from 1970 to 2015 and using country typologies. The country
classifications denote different sets of interconnected features—institutions—related to production
methods, such as government and political systems, regulation, human capital formation, trade
structure, and innovation, among others. In this paper, two embedded institutional
characteristics—resource-output orientation and GDP per capita growth information—are
exploited to derive future poverty and income distribution outcomes by country and country
aggregations. Despite the arbitrary selection of the country typologies, these classifications aim to
show that there is heterogeneity in poverty and income distribution changes beyond global
aggregated outcomes. Our selected country typologies are two among several others that could be
used to understand the commonalities of environments in which firms operate. However, to our
knowledge, no other study considers these two specific types of country categorizations to explain
future movements in poverty rates, inequality, or shared prosperity measures.
We denote resource-output and non-resource-output oriented economies as ROC and
NROC, respectively. 2 While natural resource-related activities present opportunities for economic
growth and poverty reduction, they also involve risks such as commodity-export dependency and
2
Throughout this paper, we use ROC economies, ROC countries, or simply ROCs interchangeably. The same rule
applies for NROC economies.
Page 3 of 45
reduced economic diversification. There is a segment of researchers that argue that countries with
oil or another type of natural resource wealth have failed to grow more rapidly than those without,
denoting this phenomenon as the natural resource curse (Frankel 2010). On the contrary, some
literature debates that resource-rich countries face a curse of concentration—of export revenues—
rather than a curse of natural resource abundance per se (Lederman and Maloney 2007, and
Lederman and Maloney 2012). Research also shows that natural resource countries are also more
prone to have lower quality institutions, suffer from deindustrialization and have a higher
likelihood of civil war, Dutch disease and macroeconomic volatility (van der Ploeg 2011).
Some of the literature claims that there is an adverse association between macroeconomic
volatility and economic growth across countries. Hnatkovska and Loayza (2003) identify the
adverse causality effect from volatility to growth, which is notably worse in poor and
institutionally underdeveloped countries and in economies that are unable to put countercyclical
fiscal policies into practice. van der Ploeg (2011) asserts that the volatility of resource windfalls
hurts economic growth, especially in countries with less advanced systems and institutions. In this
regard, periods that involve high commodity prices of natural resources could likely lead to an
increase in productive activities in countries with good institutions, whereas economies with weak
institutions might devote more resources to rent-seeking behaviors (Bulte and Damania 2008, and
Crafts and O’Rourke 2014).
Based on concerns related to the downside risks associated with the volatility of resource
rents and output and considering our predictions of the poverty headcount, resource-based
countries might continue reforms that strengthen their institutions, including those that involve
risk management procedures that hedge for more stable economic performances. These institutions
could foster the study and implementation of fiscalprobably countercyclicalrules (Devarajan,
Dissou, Go, and Robinson 2015, and Galego Mendes and Pennings 2017), commodity price
hedging strategies (Frankel 2017), diversification strategies for the economy (Lederman and
Maloney 2012), and other actions. Also, economies could design plans and policies that would
provide confidence, stability, and more transparent use of resource rents.
Page 4 of 45
We denote low-, medium-, and high-output growth countries with LOGC, MOGC, and
HOGC, respectively, using GDP per capita growth over the period 1970–2014. 3 The identification
of economies via the growth rate of output per capita over a recent 45-year period aims at capturing
similar macroeconomic performance as well as understanding the implications for future changes
in poverty and the income distribution. For instance, the average high-output growth observed in
some countries could help us identify institutional arrangements that may be valuable for poverty
alleviation or vice versa; low-output growth country characteristics suggest some poor practices
implemented in corporate and governmental structures.
In 2013, the World Bank set two main goals to guide its work: end extreme poverty by
2030 and boost shared prosperity. These two goals were aligned to support the Millennium
Development Goals (MDGs), which were the predecessors of the current Sustainable
Development Goals (SDGs) established by the United Nations (Cruz, Foster, Quillin, and
Schellekens 2015). The World Bank’s twin goals specifically target reducing extreme poverty in
the world to less than 3 percent by 2030 and promoting income growth for the bottom 40 percent
of the population in each country (World Bank 2014). In this context, one of our main results
indicates that alleviating extreme poverty below the 3 percent target across the world economies
by 2030 is an outcome with a very low probabilitybelow two percent in all our simulations.
Our results suggest that depending on the historical growth rates used in the projection
exercises, world poverty outcomes in 2030 could vary over a broad range. For instance, our more
positive result of poverty headcount at the global level indicates a median value of 4.6 percent with
a standard deviation of 0.48, whereas the more pessimistic result shows a median outcome of 8.9
percent with a 0.93 standard deviation. 4 Our 2030 world predictions confirm the results of World
Bank (2014) 5 and World Bank (2018), indicating that recent historical country performances in
terms of income growth and changes in income inequality and commodity prices are insufficient
to reach a poverty headcount below 3 percent. World Bank (2014) suggests that countries will
have to depart from their historical experiences in terms of both economic growth and
distributional effects and policies to reduce poverty faster and achieve the 2030 target. This
3
In this paper we interchangeably use LOGC economies, LOGC countries, or simply LOGCs. The same rule applies
for MOGC and HOGC economies.
4
The 4.6 and 8.9 poverty rate estimates imply that approximately 370 million and 720 million people live in extreme
poverty conditionssubsisting with less than $1.90 purchase power parity (PPP) 2011 U.S. dollars per dayby 2030.
5
Note that World Bank (2014) uses a poverty line of $1.25 PPP 2005 PPP U.S. dollars per day.
Page 5 of 45
statement aligns with the discussion in World Bank (2018), which adds that the world’s poorest
countries must grow at a rate that surpasses their historical episodes to reach the 3 percent target.
Besides, our aggregated results indicate that global income inequality will most likely
decrease between 0.7 and 1.9 Gini points (on the Gini scale of 0-100) in the period 2015–2030. 6
From this perspective, these results are conservative but follow a decrease that is similar to that in
the latest historical patterns, as discussed in Anand and Segal (2015), Lakner and Milanovic
(2016), and World Bank (2016). Our results on the reduction in global inequality by 2030 also
appear to be in the same direction as those predicted in Hellebrandt and Mauro (2015), where an
average decline in the Gini coefficient of global inequality is estimated with a magnitude of 4 Gini
points in the period 2013–2035. Our estimates of the global Gini coefficient show substantial
uncertainty and downside risks that involve outcomes that imply an increase in the level of
inequality in the 2015–2030 period.
Our simulation outcomes also show that NROCsor more-diversified economiesand
countries with fast annual output growth rates are likely to reduce extreme poverty to below 3
percent by 2030. In contrast, the results for income inequality in these more-diversified economies
and high-output growth countries involve substantial uncertainty. These estimates predict
substantial downside risks in pushing for increasing inequality over the period 2015–2030 in these
countries, especially in HOGCs. The results also indicate that ROCs and LOGCs are quite likely
to observe a decline in relative extreme poverty and income inequality across the period 2015–
2030; they are, however, facing downside risks associated with increasing extreme poverty in
absolute terms. MOGCs are the only economies that show positive results in terms of poverty and
inequality attainments: declining relative and absolute poverty paths and diminishing income
inequality by 2030.
Furthermore, the results regarding the shared prosperity gaps show considerable
heterogeneity between and within country classifications in the period 2015–2030. Measured by
the standard deviations of simulated outcomes, all the results indicate that there is significant
uncertainty in shared prosperity gaps; HOGCs and LOGCs show higher levels of variability. The
overall results highlight the importance of fostering economic growth; however, they underline the
6
Our estimates are population-weighted averages of country Gini coefficients.
Page 6 of 45
relevance of designing effective social spending and fairer taxation mechanisms to tackle potential
increasing income inequality. 7,8
The rest of the paper is organized as follows. Section II describes the modeling details and
the assumptions used to predict poverty rates, inequality, and shared prosperity measures. Section
III discusses the exact definitions and few features of the country typologies. We discuss data and
descriptive statistics by country typologies in section IV. Section V outlines our simulation
procedure whereas Section VI summarizes the predicted results. Finally, we make some
concluding observations in Section VII.
II. MODEL
The selection of the functional form of the income distribution is essential for constructing reliable
projections. Note that the parameterization of the distribution of income could take a variety of
well-studied functional forms (Chotikapanich 2008, and Cowell and Flachaire 2015). The shape
of the distribution of income could have implications regarding the flexibility that is needed to
accommodate the dynamics of the projected parameters. For instance, in general, a three-or four-
parameter distribution is preferred and has more flexibility to incorporate the characteristics of
skewness or the tails of the income distribution than one that considers only two parameters.
Despite the large variety of functional forms, the most critical binding constraints for selecting the
best parametric distribution to fit income are the availability and consistency of the sample
statistics. 9
In addition to the difficulty of choosing the functional form of the income distribution,
there are other potential concerns when projecting income, such as modeling and data
measurement errors. For instance, if we use an econometric model to predict changes in the mean
or specific percentiles of the income distribution, our modeling errors could be related to the
correct specification, omitted variable bias, and reverse causality. Besides, data measurement
7
van der Weide and Milanovic (2018) empirically show that high levels in inequality reduce income growth among
the poor but increase the income growth of the rich.
8
The trends of our simulated outcomes remain consistent when using various sample periods, country typology
threshold definitions, and substituting the growth rates of per capita consumption with GDP per capita to proxy
missing household mean income observations.
9
For instance, Ravallion (2015) highlights that the literature has identified three relevant parameters that depict income
growth: inequality, poverty, and the size of the middle class. This type of information is, however, not available in
some countries or across several time periods.
Page 7 of 45
errors, caused by either sampling or non-sampling issues, are a problem that could arise when
projecting future income growth. Given that income statistics and poverty measures depend on
Census and household survey data, mistakes made when collecting this information might skew
the recovered statistics, for example, Gini coefficients and income averages.
Another significant concern to model the dynamics of the income distribution is related to
the causality between inequality and economic growth. When we think about the forces interacting
between wealth inequality and economic growth, we might be hesitant to make conclusions
regarding the magnitudes and even directions. In the specific case of wealth inequality affecting
growth, there is a strand of the economic literature highlighting that inequality is suitable for
incentives and therefore for growth in market-oriented economies, whereas another strand argues
that inequality has a direct adverse effect on economic growth (Aghion, Caroli, and Garcia-
Penalosa 1999). 10
Recent empirical research finds that the presence of weak instruments, using a system
generalized method of moments (GMM) procedure, is in detriment of robust conclusions about
the effect of inequality on growth in either direction (Kraay 2015). Likewise, by making use of
robust GMM estimations, Ferreira, Lakner, Lugo, and Özler (2018) find no significant result of
total income inequality affecting economic growth. Lastly, Marrero and Serven (2018) develop a
model where the impact of inequality on growth can be positive or negative, as it combines two
types of effects—indirect and direct—that could be mutually opposing. Poverty is used to identify
the indirect impact of inequality on growth. The effect of inequality on the aggregate investment
of non-poor individuals can explain the direct outcome. The authors find that the sign and
immediate impact of inequality on growth are fragile; this impact can take a positive or negative
sign depending on the specific model used and the econometric approach employed. The authors
also find that the indirect effect of inequality and growthvia povertyis negative and significant
at highbut not extremely highpoverty rates, whereas it is nonsignificant at low poverty rates.
10
Aghion, Caroli, and Garcia-Penalosa (1999) discuss that there are at least three reasons why inequality might have
a direct adverse impact on growth in economies with heterogeneous wealth or human capital endowments and
imperfect capital markets: i) inequality reduces investment opportunities; ii) inequality deteriorates borrowers’
incentives; and iii) inequality generates macroeconomic volatility. In contrast, the same authors discuss three views
of why inequality can be growth-enhancing: i) Kaldor’s hypothesis that the marginal propensity of the rich to save is
higher than that of the poor; ii) investment indivisibilities for setting new industries: in the absence of a broad and
well-functioning market for shares, wealth needs to be sufficiently concentrated to be able to cover large sunk costs;
and iii) incentive and moral hazard considerations: the trade-off between output efficiency and (wage) equality.
Page 8 of 45
The following subsections describe the processes behind the estimation of future poverty,
inequality, and shared prosperity measures. Subsection A explains our assumptions regarding the
income distribution and the details of the econometric method used to project income growth.
Subsection B provides a detailed description of our assumptions to model the Gini coefficient
dynamics. We present our definitions of relative income inequality and shared prosperity gaps in
Subsection C.
A. Income Distribution and Mean Income Growth
In this study, , denotes the income of the population in country at period . The income variable
, is transformed via natural logarithms, such that , ≡ ln , . A conservative and common
assumption is to consider that , is distributed as a normal random variable �, ,
2
,
�. The
previous consideration implies that the income of the people in country at period should follow
a lognormal probability distribution function, such as , ~ log �, ,
2
,
�. 11,12
To provide the dynamic framework for the income distribution, we focus on modeling the
mean and variance parameters of the lognormal assumption. Then, we construct a model that
predicts the growth of both the mean income and the Gini coefficient. 13 The combination of
independent randomly generated patterns of average income growth and changes in inequality
allows us to quantify the uncertainty associated with the overall change in the income distribution.
Growth in the mean of the income distribution is assumed to follow a basic linear
econometric specification where global and idiosyncratic factors play a role. Growth in the real
mean income in country , , 14,15 follows the stochastic pattern shown in Equation (1). This
stochastic process consists of four components, three of which involve random elements. The first
component is an idiosyncratic fixed effect factor, , which measures the long-run trajectory of
11
Cowell and Flachaire (2015) discuss that the lognormal distribution is useful to fit few economic processes. These
authors note that there are important theoretical weaknesses in a process that involves adjusting the upper tails of more
broadly-based income distributions.
12
Lopez and Servén (2006) provide empirical evidence indicating that income can be effectively proxied by a
lognormal distribution.
13
As mentioned in the introduction, our dynamic framework expands on World Bank (2014).
14
Both income and expenditure household survey information were retrieved from World Bank (2019). Consumption
and GDP data were retrieved from PWT 9.0 (Feenstra, Robert Inklaar, and Marcel P. Timmer 2015).
15
If household survey’s information of mean income or mean expenditure is not reported in a specific period, the
missing observations are inputed using the growth rates of per capita consumption or GDP per capita.
Page 9 of 45
the mean income growth for country ∈ ℎ , such that ℎ is a subset of the complete set of
countries in the world, . The subindex ℎ is used to denote country groups or typologies. In this
paper, six country groups are utilized such that ℎ ∈ = { all, NROCs, ROCs, HOGCs, MOGCs,
LOGCs }. 16 The second component in Equation (1) is a factor affecting global income per capita
growth, ; this is a semi-stochastic compound factor consisting of a country-specific parameter,
, which weights the response of country to shocks in global income per capita growth, . The
third component is also a global and semi-stochastic compound factor, ; this component
consists of one country-specific parameter, , and one random variable that accounts for global
real commodity prices, . Finally, the fourth component, , , is an idiosyncratic stochastic error
factor.
, = + �������
+ + , , for all ∈ ℎ , (1)
where the expected value of the error term in Equation (1) is assumed to be zero, �, � = 0, for
all countries and periods . The expected values of global income per capita growth and real
commodity prices are assumed to be constant and can be proxied by their sample means, [ ] ≡
and [ ] ≡ , respectively. In addition, the variance of the idiosyncratic error term in
Equation (1) is assumed to follow a homoscedastic process: �, � =
2
, for all . The same
assumption is made for the variance statistics of the global factors in Equation (1). Homoscedastic
processes are attached to global income per capita growth and real commodity prices; both are
respectively proxied by their sample variances: [ ] =
2
and [ ] =
2
, for all .
Furthermore, the covariance between the idiosyncratic error term and the global factors is
assumed to be null, which implies that �, � = 0 and �, � = 0, for any country in period
. Finally, the global factors and are associated, however, we assume that this concurrent
association is almost zero, implying a contemporaneous covariance stationary process: [ , ]
16
Table 1 provides the definitions of the country groups in .
Page 10 of 45
= , (0) ≈ 0, for all . 17 Under the above discussed assumptions, we can estimate Equation (1)
country by country using OLS (Wooldridge 2001). 18
In addition to the above-stated econometric and statistical considerations, the coming up
parametric distributions are assumed to complete the description of the random components
affecting mean income growth at the country level:
2
, ~�0,
�. (2)
2
~� , �. (3)
2
~� , , , �, s.t., = 0, = ∞+ , (4)
where, (∙) denotes a normal distribution and (∙) represents a univariate truncated normal
distribution with mean , variance
2
, and lower and upper bounds , and , respectively.
B. Dynamics of Relative Inequality
We assume that the logarithm of the Gini coefficient, ln ,ℎ, , follows a random walk process
limited by—recent historical—Gini index thresholds. Thus, the exponential growth rate of the Gini
coefficient, ,ℎ, , is assumed to follow a random normal behavior bounded by thresholds of
historical Gini coefficient levels. Equations (5) and (6) summarize these assumptions. In this
regard, the thresholds of the Gini coefficient play a conservative modeling role; these boundaries
indicate that countries are not able to attain inequality levels beyond those observed in recent
history. 19
17
Under these econometric considerations, the expected variance of income growth for all periods can be estimated
by �, � ≡
2
,
2
≈ 2
[ ] + [ ] + �, � + 2 [ , ].
18
Under the explained assumptions, this panel data structure represents a seemingly unrelated regression (SUR)
model. Wooldridge (2001) highlights that when the SUR model does not place cross equation restrictions on the
coefficients, the separated OLS estimation of Equation (1)—country to country—corresponds to using the system
OLS estimator.
19
In the most conservative case, the neutral income distribution assumption could be implemented by holding the Gini
coefficient constant across the simulated periods: ,ℎ, = 0, for all .
Page 11 of 45
,ℎ, iff ln ,ℎ,−1 + ,ℎ, ∈ [ℎ , ℎ ] (5)
⎧
ℎ − ln ,ℎ,−1 iff ln ,ℎ,−1 + ,ℎ, < ℎ
,ℎ, = ,
⎨ℎ − ln ,ℎ,−1 iff ln ,ℎ,−1 + ,ℎ, > ℎ
⎩ 0 otherwise
2
,ℎ, ~ � , �, (6)
ℎ ℎ
where and
2
denote the mean and variance of the Gini coefficient growth, respectively,
ℎ ℎ
and ℎ and ℎ respectively symbolize the lower and upper bounds of the Gini coefficients per
country typology—in logarithmic terms. The coefficients ℎ and ℎ can be proxied by the first
and largest order statistics of recorded Gini coefficients, respectively. In addition, note that the
thresholds ℎ and ℎ and the random behavior of the growth rates of the Gini coefficient, ,ℎ, ,
vary by country typology ℎ. 20,21
Our econometric considerations do not model an explicit association between growth in
the Gini coefficient and growth in the mean income. The degree of association of the Gini
coefficient growth rate with the average annual growth rate of per capita consumption, GDP per
capita, and mean income—measured through Pearson’s correlation coefficients—is almost null
across country classifications. 22 Thus, we argue that there is not enough information yet to provide
evidence of covariance between growth in the Gini coefficients and growth in the mean income.
20
One moderate alternative of the modeling of Equation (5) would not let ln ,ℎ, reach the thresholds ℎ and ℎ .
Thus, Equation (5) could be estimated following this specification:
iff ln ,ℎ,−1 + ,ℎ, ∈ [ℎ , ℎ ]
,ℎ, = � ,ℎ, .
0 otherwise
21
One conservative alternative to Equation (6) involves modeling the rate of change in the Gini coefficient following
a truncated normal distribution: ,ℎ, ~ � , 2
, 0 , 1 � , where, (∙) represents a univariate truncated
ℎ ℎ
normal distribution with mean , variance
2
ℎ
, and lower and upper bounds 0 , and 0 , respectively.
ℎ
22
We account for four important results regarding Pearson’s correlation coefficients pooling our original 1980–2014
observations by the studied country classifications. First, the significant correlation coefficients of the average growth
rate of the Gini coefficient and the average growth rate of per capita consumption vary between -0.077 and -0.048
across the studied country categories. Second, in the case of the growth rate of the Gini coefficient and the growth rate
of GDP per capita, all the correlation coefficients are found nonsignificant across the country classifications whereas
they vary close to zero in magnitude: from -0.68 to 0.067. Third, few significant correlation coefficients between the
Gini coefficient growth rate and the growth rate of the mean income are found across the country typologies: 0.14
pooling all country observations, 0.181 for NROC economies, 0.16 for MOGCs, and 0.4 for LOGCs. Fourth, all the
correlation coefficients of the growth rate of the Gini coefficient and the growth rate of the mean income become
almost null and/or statistically nonsignificant across the study typologies when we complete our 1980–2014 panel
data set using per capita consumption growth rates—or GDP per capita growth rates in the alternative case.
Page 12 of 45
Hence, our model—and subsequent simulations—assumes there is no covariance between these
two variables: �, , ,ℎ, � ≈ 0.
C. Aggregations
We aggregate country-specific Gini coefficients to explain the average outcomes across country
classifications, ℎ. Our aggregated Gini coefficient, ℎ, , is a population-weighted average of
country Gini coefficients,
ℎ, =
1
∑∀∈ℎ , , , for all country classifications, ℎ, (7)
ℎ,
where the total population of the specific country typology ℎ in period is denoted by ℎ, , and
the population in country at period is represented by , .
We also estimate the aggregated shared prosperity measures by country classifications. The
shared prosperity gap requires subtracting the growth rate of the mean, or any percentile of the
income distribution, from the 40th percentile of the income distribution. Specifically, the
comparison of the growth rates between the B40 of the income distribution and the statistic ,
across countries is estimated via the following weighted gap:
40−,
ℎ, =
1
∑∀∈ℎ , �40 − �, (8)
ℎ,
, ,
for all country classifications, ℎ, and the mean and percentile of income, , =�, ,
,
�. Note
th
40 denotes the annual growth rate of the 40 percentile of the distribution of
that , , while the
,
corresponding rate for , is denoted by .
,
III. COUNTRY TYPOLOGIES
Two typologies are used to describe economies with large output shares of natural resource
activities—extractive sectors—and to denote the importance of economic growth (Table 1).
Although arbitrarily selected, our country typologies aim to capture substantial deviations from
the results with global scope. In specific, our typologies look at deviations in terms of future
Page 13 of 45
poverty attainments and income distribution changes to underline the heterogeneity of countries
in our sample. The first classification includes countries with historically substantial natural
resource rents, as a share of gross domestic product. 23 ROC economies are those above the 90th
percentile of country observations of resource rents in the period 1970–2015. In contrast, NROC—
or more-diversified—economies are those with smaller shares of natural resources. The second
typology comprises economies in three sub-categories based on rates of growth of output per capita
in recent decades. We define thresholds for GDP per capita growth rates using cross-country
sample statistics between 1970 and 2014. These selected thresholds are the 30th and 70th percentiles
of pooling all our country GDP per capita growth rates in the period 1970–2014. 24
TABLE 1. COUNTRY CATEGORIES
Category Descriptor Definition Country examples
ROCs Resource output- � ≥ p90
Algeria, Cameroon, Myanmar, and Venezuela.
R70−15
oriented countries
NROCs Non-resource output- � < p90
Argentina, Finland, Japan, and Vietnam
R70−15
oriented countries
LOGCs Low-output growth � ≤ p30
y
Haiti, Madagascar, Niger, and Zimbabwe
countries
70−15
MOGCs Middle-output growth y70−15 <
p30 � ≤ p70
y70−15 Belgium, Ecuador, Nigeria, and Pakistan
countries
HOGCs High-output growth � > p70
y
Bulgaria, Chile, China, and South Korea
countries
70−15
Source: Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. Note: R � denotes the
median value of resource rents as a percentage of GDP over the period 1970–2015 pooling all country information. The median
cross-country value of GDP per capita growth—in percent—over the period 1970–2014 is represented by � . GDP per capita data
in purchasing power parity (PPP) 2011 U.S. dollars. In practice, the selected percentiles of output per capita are rounded to 0 and
4, i.e., p30 70
y70−15 ≈ 0, and py70−15 ≈ 4. The 90 percentile of the natural resource rents as a percentage of GDP is rounded to 7,
th
90
pR70−15 ≈ 7. For robustness purposes, the estimates presented in the results section are tested varying the thresholds that define
ROCs, NROCs, LOGCs, MOGCs, and HOGCs, i.e., varying p90 30 70
R70−15 , py70−15 and py70−15 .
By combining both typologies, Figure 1 summarizes the median behavior of output per
capita growth and natural resource rent shares across countries over the period 1970–2014 and
1970–2015. Figure 1 shows what some authors have already argued: some countries might look
cursed, while others might appear to be blessed by having significant shares of natural resource
23
This paper follows the definition of natural resource rents used in World Bank (2017): natural resource rents are the
sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. For instance, natural gas
rents are the difference between the value of natural gas production at regional prices and total production costs.
24
The thresholds that define these two country typologies are varied to test the robustness of our simulated outcomes
as described in Subsection IV of the results (Section VI).
Page 14 of 45
output or exports (Crafts and O’Rourke 2014). In Figure 1 we also observe two different poverty
headcount performances across countries. First, most countries with abundant natural resource
rents in the period 1970–2015 have issues with high extreme poverty headcounts. Second,
countries with current high poverty headcounts are those for which the median annual growth rates
of GDP per capita in the period 1970–2014 are below the 4 percent threshold, or in other words,
those countries that could not attain high-output growth rates in a more consistent form in the study
period.
FIGURE 1. GDP PER CAPITA GROWTH, NATURAL RESOURCE RENTS, AND POVERTY
Source: Penn World Table 9.0, World Development Indicators, PovcalNet, United Nations, and the author’s estimates. Note: GDP
per capita data in purchasing power parity (PPP) 2011 U.S. dollars. PovcalNet poverty headcount observations using the
international poverty line of $1.90/day in 2011 PPP U.S. dollars. The sample used in this figure includes 148 countries, each with
more than 44 annual observations of GDP per capita over the period 1970–2014. Table 1 provides details of the thresholds used in
this figure.
Page 15 of 45
Definitions used in Table 1 split Figure 1 into six country classifications. First, the
Democratic Republic of Congo faced negative growth—a median estimate between 1970 and 2014
observations—while having significant resource rents, which in this context indicates it is a cursed
resource rent economy. 25 Second, countries with a small share of resource rents also experienced
negative output growth. Labeled as cursed non-resource rent economies, Haiti and Djibouti are
examples of this negative growth and significant resource rent performance. Third, countries with
substantial resource rents such as Chile had median rates higher than 4 percent over the period
1970–2015 and are tagged as blessed resource-based countries. Fourth, countries with unimportant
natural resource rents—such as Belarus and the Republic of Korea—still perform with high
median GDP per capita rates which in these circumstances are denoted as blessed non-resource
rent countries. Fifth, quasi-blessed resource rent economies performed with moderate median
growth rates and significant resource rents, such as Cameroon and the Islamic Republic of Iran.
Sixth, there is a set of countries with reasonable median growth rates and small resource rents;
these are called quasi-blessed non-resource rent economies, e.g., Bolivia and Kenya. 26
IV. DATA
The projections of future poverty and income distribution changes are performed based on sample
statistics derived from information of mean income, Gini coefficient, population, per capita
consumption or GDP per capita, commodity prices, and purchasing power parity (PPP) prices. We
present some descriptive statistics of these variables by country typologies for the period 1980–
2014 in Table B 2 in Appendix B.
For the first country typology, the pooled country information indicates that more-
diversified economies—NROCs—have, on average, a much larger mean income than resource-
output oriented economies. For our second typology, MOGCs have, on average, a higher mean
income than HOGC and LOGC economies; the LOGC economies have, on average, the lowest
mean income during the study period.
25
According to Sachs and Warner (2001), the natural resource curse implies that countries with considerable natural
resource wealth tend to grow at a slower pace than resource-poor economies.
26
To denote the strength of the relationship pointed out in Figure 1, we highlight in Figure D 1, Appendix D some
stylized facts of GDP per capita growth and natural resource rents across decades.
Page 16 of 45
In terms of income inequality, on the one hand, ROCs present, on average, higher values
in the Gini index than NROCsa difference of more than 2 percent. On the other hand, LOGCs,
on average, have higher total income inequality, while HOGCs present the lowest. There is a
substantial difference of 7 percent between the average Gini coefficient of LOGC and HOGC
economies.
V. SIMULATION
Our simulation procedure permits to make a dynamic assessment of income distribution by
country. The Monte Carlo simulation method combines multiple paths of mean income growth
with a variety of inequality trajectories to predict changes in income distribution. The practical
exercises described in the sections below use household survey data of incomes and expenditures
in the period 1980–2015. 27,28 Thus, given the scarcity of microdata for the study period, per capita
consumption growth is initially used to construct synthetic observations of mean income between
spells. As an alternative, GDP per capita growth was tested and used instead of per capita
consumption growth.
The Monte Carlo simulation procedure consists of deriving simulated paths of the mean
income and the Gini coefficient. To recover these paths, at each period = {2015, 2016, … , },
number of random draws are generated to complete the simulated sequences. These sequences
consist of global mean income growth {{2015 }
=1 , … , { }=1 }, global real commodity prices
{{2015 }
=1 , … , { }=1 }, idiosyncratic shocks in the mean-income growth
��,2015 �=1 , … , �, �=1 �, and idiosyncratic shocks in the growth of the Gini coefficient
��,2015 �=1 , … , �, �=1 �. Each draw is generated using mean and variance sample statistics for
a specific period.
Although the simulation exercises are designed to be comprehensive and to project
multiple and simultaneous performances of factors affecting the countries’ mean income growth,
our model is simple by design and includes some caveats. Four core elements considered when we
27
Data were retrieved on February 22, 2019 from PovcalNet (World Bank 2019).
28
A priori, given the embedded historical information in the period 1980–2015, country-specific simulations are
expected to be much closer to the idea of inequality convergence; this means that inequality tends to decrease in
countries with high inequality and increase in countries with low inequality (Benabou 1996, and Ravallion 2003).
Page 17 of 45
analyze the results of the Monte Carlo simulations. First, there is the potential issue of
misspecification of the econometric model, Equation (1). The econometric specification of mean
income growth could be affected by omitted factors: global and country-specific components.
Additionally, some of the factors affecting mean income growth might not respond to a linear
form.
Second, the probability distribution assumptions may affect the variables used to predict
the future behavior of the country mean income growth. Global growth and commodity prices
could entail more complicated distributions with longer tails. In the same vein, the idiosyncratic
error terms could have a long-tailed distribution or skewed parameterization instead of a normal
distribution arrangement. Thus, our projections of mean income growth may not capture tail
behaviors. In the same vein, given a lack of observations for the Gini coefficient across countries
over the study period 1980–2014, the bounded random walk stochastic process assumption that
models the logarithm of the Gini coefficient could entail a very restrictive specification.
Third, country-level statistics are based on household data that incorporate multiple sources
of errors. For instance, collected household observations could include sampling and non-sampling
measurement errors. Although the number of household surveys has expanded in countries around
the world, the frequency and quality of the collected information vary greatly, which may cause
problems related to the consistency and comparability of the data between and within countries
(World Bank 2014, Chapter 5). Fourth, demographic trends that account for changes in the total
population might have some degree of uncertainty, especially in terms of the materialization of a
significant factor such as war, massive migration, or an epidemic (United Nations 2017).
VI. RESULTS
This section presents our simulated outcomes of poverty and changes in the income distribution
for the period 2015–2030. All our results are analyzed at the global level and by the country
typologies introduced in Table 1. Subsections A and B discuss poverty headcount and relative
income inequality estimates, respectively. In Subsection C, we analyze the speeds of the B40
percent relative to other percentile growth rates of the income distributionshared prosperity
Page 18 of 45
gaps. 29 In Subsection D, we present the robustness checks. We use the robustness exercises to test
for factors that can lead to heterogeneity in the simulation results: the sample period, the thresholds
that define the country typologies, and alternative measures for mean income growth rates.
A. Poverty Headcount
Our results show that the poverty headcount projections vary substantially depending on the
selection of the base period of the simulations (Figure 2). 30,31 These predicted median estimates of
global poverty headcount for 2030 are 8.8, 8.0, 6.1, and 6.6 percent; these are the simulation
outcomes based on country-samples for the periods 1980–2014, 1990–2014, 2000–2014, and
2005–2014, respectively. Our global poverty estimates show that a substantial decline in global
poverty is very likely by 2030, both in relative and absolute terms (Figure 2 and Figure 3). When
comparing the simulated median values, global poverty headcount is predicted to decline between
3 and 5 percentor, in other words, between 80 million and 300 million peoplein the 2015–
2030 period. These median estimates, however, are insufficient to reach the global target of 3
percent for extreme poverty headcount by 2030. Our empirical distribution of simulated outcomes
suggests that the global 3 percent poverty target is reachable in 2030 with a probability of less than
2 percent (see Figure A 1, Panel A, Appendix A).
While the reduction in world poverty in the last 35 years is a significant achievement, its
continued eradication in the following decades is expected to remain a substantial concern. If we
consider the recent historical observations of income growth, inequality dynamics, and commodity
prices to be useful information to predict the future performance of the income distribution, then
our expectations for reducing poverty below the 3 percent threshold by 2030 should be very
optimistic. To accelerate growth across the income distribution, we can explore alternative
29
The outcomes reported in Subsections A, B and C are based on per capita consumption growth rates, which were
used to complement the country observations for household survey mean income or expenditures present in PovcalNet.
30
The poverty estimates use the $1.90 international poverty threshold in purchasing power parity (PPP) 2011
international U.S. dollars per day.
31
The detailed list of countries used in the simulations is provided in Table B 1 in Appendix B.
Page 19 of 45
mechanisms to fuel growth across nations and monitor countries where poverty rates are predicted
to be a significant problem. 32
FIGURE 2. POVERTY HEADCOUNT RATES
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note:
Per capita consumption growth rates are used to complete the panel of average income/expenditures in the household survey
observations. Projected country population weights the estimates. A total of 500 simulations are performed per country and
forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014,
and 2005–2014. The standard deviation corresponds to that associated with poverty headcount simulated outcomes in 2030.
The uncertainty around our 2030 predicted estimates of global povertymeasured by the
standard deviation of the simulated pathsindicates conservative values with magnitudes of 0.9,
1.0, 0.9, and 0.6 percentage points for the same four sample periods, 1980–2014, 1990–2014,
2000–2014, and 2005–2014, respectively. The positive skewness of the poverty headcount
simulationsusing the 1980–2014, 1990–2014 and 2000–2014 sample periodsimplies more
32
Based on the standard variance decompositions of Equation (1) presented in Figure C 2 in Appendix C, we presume
that mechanisms that foster idiosyncratic country characteristics are key for boosting mean income/expenditure
growth.
Page 20 of 45
substantial downside than upside risks. In contrast, the simulated outcomes of poverty headcount
for 2005–2014 are slightly skewed to the upside. 33
FIGURE 3. ABSOLUTE EXTREME POVERTY
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note:
Per capita consumption growth rates are used to complete the panel of average income/expenditures in the household survey
observations. Projected country population weights the estimates. A total of 500 simulations are performed per country and
forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014,
and 2005–2014. The standard deviation depicts that associated with the poverty headcount simulations in percentage as presented
in Figure 2. “M” in the X and Y axes denotes million.
ROCs and NROCs. The simulated results indicate that poverty headcount rates will most likely
continue declining in both ROCs and NROCs to 2030. In 2015, the estimated rates of poverty
headcount in ROCs and NROCs were approximately 22 and 8 percent, respectively. By 2030,
ROCs and NROCs will likely achieve average poverty headcounts of 20 and 3 percent,
respectively (see Figure 2 and Figure A 2 in Appendix A). In absolute terms, NROCs could lift
between 225 million and 290 million people out of extreme poverty in the period 2015–2030. In
contrast, there is a significant likelihood that ROCs will see an increase in the number of people
33
For the poverty headcount rates, a negative skewness value indicates that the distribution is skewed to the upside.
Page 21 of 45
living in extreme poverty by between 20 million and 70 million over the same projected period
(Figure 3).
The set of simulations focused on ROCs and NROCs predicts 2030 poverty headcount
results with uncertainty—the standard deviation of simulated outcomes—at 2.9 and 0.6 percent,
respectively. By 2030, on average and across the four studied sample periods, the ratio of this
uncertainty between ROCs and NROCs is approximately 5 to 1. This comparison of the size of the
uncertainty between ROCs and NROCs shows how difficult it is to predict the poverty headcount
performance of resource-output oriented economies; this is in line with the recent historical
variability of economic growth in ROCs and NROCs (Figure D 2 in Appendix D). 34
LOGCs, MOGCs, and HOGCs. The estimates of the 2015 poverty headcount in LOGCs, MOGCs,
and HOGCs was approximately 50, 13, and 4 percent, respectively. The predictions of poverty in
LOGCs in 2030 are quite susceptible to the sample period used to generate the Monte Carlo
simulations (Figure 2). Using the 1980–2014 and 1990–2014 sample periods, the LOGCs are
expected to decrease their poverty headcount by 2 and 4 percentage points, respectively, in the
period 2015–2030. Using better performance periods of growth (2000–2014 and 2005–2014),
LOGCs expect, in terms of the median values, to reduce their initial 2015 poverty headcount rates
by more than 20 percentage points in the 2015–2030 horizon. In contrast, extreme poverty in
absolute terms in LOGC economies over the horizon 2015–2030 is predicted to either increase by
more than 30 million under the 1980–2014 and 1990–2014 sample periods or to decrease on
average more than 9 million people under the 2000–2014 and 2005–2014 base periods. The
simulations for the MOGC economies show more consistent results of poverty headcount hovering
around median values between 7 and 9 percent by 2030. These poverty rates correspond to a
decline in extreme poverty by 200 million and 100 million people between 2015 and 2030,
respectively. HOGCs, in contrast, expect to decrease their poverty headcount rates, in terms of the
median simulated outcomes, to levels of approximately 1 percent in 2030, lifting around of 70
million people out of extreme poverty conditions in the same 2015–2030 horizon.
Likewise, the uncertainty associated with the poverty headcount predictions for 2030 varies
significantly depending on the country-output growth subcategory. Regardless of the study period,
34
Among the four studied sample periods, the information embedded in the period 2005–2014 (global pre- and post-
financial crisis period) leads to simulated outcomes of the poverty headcount with less dispersionuncertaintyfor
both country typologies, ROCs and NROCs.
Page 22 of 45
the predictions indicate that LOGCs have higher embedded uncertainty in poverty outcome
attainments, followed by the HOGCs and MOGCs (see Figure 2 and Figure A 4 in Appendix A).
The average standard deviations of the poverty predictions for 2030 are 9.4, 0.9, and 1.3 percent
for the LOGCs, MOGCs, and HOGCs, respectively; this outcome shows the degree of difficulty
predicting poverty rates in LOGCs.
B. Income Inequality
Our main result suggests that global income inequality will decline over the period 2015–2030
(Figure 4). Our simulated aggregate of the global Gini coefficient fluctuates from a median of 38.9
in the Gini scale of 0–100 in 2015 to a median of 37.5 by 2030 across the four study periods.
Specifically, the results for the population-weighted average indicate reductions in global income
inequality between 0.7 and 1.9 Gini points in the period 2015–2030. Panel B of Figure A 1 in
Appendix A presents the estimated trend of this global aggregate of the Gini coefficient since
1980. Our model also captures substantial uncertainty in the projected global Gini coefficient
outcomes. This attached uncertainty hovers between 1.6 and 2.1 Gini points in 2030.
ROCs and NROCs. Note that in the period 1990–2014, the ROC economies experienced a
decrease in inequality by more than 2 Gini coefficient points. Similarly, our estimates for the ROCs
reveal a downward trend in our simulated projections over the 2015–2030 horizon. Overall, these
results show that income inequality will decrease in ROCs, in terms of the median values, between
2.4 and 4.5 Gini coefficient points in the period 2015–2030. We expect the same median pattern
in NROCs, but the magnitude of the decline in inequality could be smaller: between 0.3 and 2.0
Gini points over the 2015–2030 horizon (see Figure 4 and Figure A 3 in Appendix A). The results
of the uncertainty attached to the projected paths of the aggregated Gini coefficient have similar
magnitudesapproximately 2.2 Gini pointsfor ROCs and NROCs by 2030. The simulations
indicate that by 2030, there are significant downside risks in NROCs, such that these economies
are expected to see increased income inequality (see Figure 4 and Figure A 3 in Appendix A).
LOGCs, MOGCs, and HOGCs. By 2030, the estimated Gini coefficient for LOGCs, MOGCs, and
HOGCs is predicted to vary across a broad spectrum of values (Figure 4). The aggregated Gini
coefficient for LOGCs and MOGCs could decrease up to 5.9 and 2.5 Gini points, respectively, in
terms of the median values, over the 2015–2030 horizon. On the opposite direction, the estimation
Page 23 of 45
based on the period 1990–2014 shows that the Gini coefficient could increase by 0.1 Gini points
in LOGC economies over the same horizon. Unevenly, HOGCs show three simulation outcomes
where the Gini coefficient could decrease between 3.3 and 0.9 Gini points in the period 2015–
2030. In contrast, the simulation based on 2005–2014 indicates an increase in income inequality
in HOGCs by 0.5 Gini points (see Figure A 5 in Appendix A).
FIGURE 4. GINI COEFFICIENT
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note:
Per capita consumption growth rates are used to complete the panel of average income/expenditures in the household survey
observations. Projected country population weights the estimates. A total of 500 simulations are performed per country and
forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014,
and 2005–2014. The standard deviation depicts that associated with the aggregated Gini coefficient which ranges from 0–100.
Besides, the uncertainty in the estimated Gini coefficient is smaller in MOGCs than in
LOGCs and HOGCs, with HOGC economies showing the largest dispersion of simulated
outcomes. The uncertainty attached to the predicted Gini coefficient aggregations has an average
standard deviation of 3.2, 1.6 and 7.3 Gini points in LOGCs, MOGCs, and HOGCs, respectively
(Figure 4).
Page 24 of 45
C. Shared Prosperity Gaps
The simulations of the global estimates of shared prosperity gaps between the B40 and the mean
50 60 70 80
and
,
,
,
,
,
, and
,
percentiles, show that the average and median simulated
outcomesestimated by Equation (8)are all positive in magnitude over the period 2016–2030. 35
Figure 5 shows the specific results of the B40–median shared prosperity gap. For instance, the
median outcomes generated by using the 1980–2014 base period suggest that at the global level
the B40 might be growing 2.9 percentage points faster than the median of the income distribution
by 2030.
ROCs and NROCs. For this country typology, we distinguish two main patterns in the set of
simulations of shared prosperity gaps. The first patternbased on median outcomesindicates
that the B40 is expected to grow at faster rates, on average, than higher percentiles of the income
distribution across both country classifications (ROCs and NROCs) by 2030 and throughout the
period 2015–2030 (Figure 5). However, a second pattern shows that the outcomes of shared
prosperity measures in both types of economies (ROCs and NROCs) present a more positive
standard deviation, or uncertainty, as the difference between percentiles increases. 36
LOGCs, MOGCs, and HOGCs. Our simulated shared prosperity gaps for LOGCs, MOGCs, and
HOGCs show uneven patterns. We identify four main results that shape our shared prosperity
measures. The first main pattern indicates that, in terms of the median values, the shared prosperity
50
gaps in MOGCs increase as the difference between the B40 and the studied percentiles,
,
,
60 70 80
,
,
,
,
,
, also increases. Note that the shared prosperity gaps in the MOGC simulations
always have a positive sign. The second major outcome indicates that in terms of the median
values, the absolute value of the shared prosperity gaps in LOGCs and HOGCs increases as the
difference between the B40 and the studied percentiles expands; this pattern means that depending
50 60 70 80
40− 40− 40− 40−
40−
35
Note that the studied shared prosperity gaps are: ℎ , , ℎ, ,
, ℎ, ,
, ℎ, ,
, and ℎ, ,
.
Additional statistics of these simulated shared prosperity gaps are available upon request.
36
Based on our selected shared prosperity gaps, this second pattern means that the gap in the growth rate between the
80
B40 and the 80th percentile, ,
, has the most uncertainty, whereas the gap that compares the B40 with the median,
50
, , has the least amount of uncertainty.
Page 25 of 45
on the period used to generate the simulations, LOGCs’ and HOGCs’ shared prosperity gaps could
be either positive or negative.
The third leading result indicates that across the three output growth country classifications,
the standard deviation of the simulated shared prosperity gaps increases as the B40 moves away
from closest percentiles of the income distribution. As in the case of the ROCs, NROCs, and global
outcomes, this result indicates that our model is predicting noisieror more uncertainoutcomes
of shared prosperity gaps as the studied percentile moves further from the B40. Finally, the fourth
important outcome shows that MOGCs and HOGCs have the lowest and highest
uncertaintydispersionon their predicted shared prosperity gaps across the three output growth
country classifications, respectively (Figure 5).
FIGURE 5. B40 – MEDIAN - SHARED PROSPERITY GAP
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note:
Per capita consumption growth rates are used to complete the panel of average income/expenditures in the household survey
observations. Projected country population weights the estimates. A total of 500 simulations are performed per country and
forecasted period. The simulations use 4 sample periods to test the robustness of the results: 1980–2014, 1990–2014, 2000–2014,
and 2005–2014. The standard deviation depicts that associated with the B40–median shared prosperity gap simulations as a
percentage.
Page 26 of 45
D. Robustness Checks
We test for three main dimensions that may impact our simulation outcomes: the study periods,
the country typology definitions, and the mean income growth rates. First, as reported in the
previous subsections, we vary the base period of the simulations to check the robustness of our
predictions. Despite differences in outcomes, the results for extreme poverty, the Gini coefficient,
and the shared prosperity measures in ROCs, NROCs, and MOGCs vary conservatively across the
studied sample periods. Regarding extreme poverty in relative terms, the main discrepancies are
in the simulated results for the LOGCs, with levels ranging from 25 to 50 percent by 2030 (Figure
2). In terms of absolute extreme poverty, the main variation in the outcomes in 2030, in terms of
median values, can be observed in the global—all countries—classification, with results ranging
from 490 million to 725 million people across the study periods (Figure 3). Regarding the Gini
coefficient estimates, the HOGCs and LOGCs show dispersed effects such that their estimated
median values for 2030 hover between 37 and 40, and 36 and 42, respectively, across the four
studied sample periods (Figure 4). Regarding the shared prosperity gaps, the main differences in
the 2030 median predictions within country typologies are found in HOGCs and LOGCs (Figure
5).
Second, we redefine the country typologies in Table 1 by varying the thresholds that define
these classifications. Our new simulations add and subtract 0.3 percentages points to the
boundaries described in Table 1 for both natural resource rent and output growth country
classifications. The change in the boundaries shown in Table 1 confirms that countries near the
threshold definitions are not driving the results, as we can observe in the estimates for extreme
poverty and the Gini coefficient (see Figure A 6 and Figure A 7 in Appendix A). These changes
in the boundaries lead to a higher degree of certainty in the outcomes for extreme poverty and the
Gini coefficients discussed in the previous subsections and depicted in Figure 2–Figure 4. Despite
similar magnitudes in median values and standard deviations, the simulated shared prosperity gaps
differ within country typologies in terms of direction when comparing the results presented in
Figure 5 with those in Figure A 6, Panel D and Figure A 7, Panel D, Appendix A.
Third, as an additional robustness check of the simulated outcomes, given the scarcity of
country microdata, instead of using the growth rate of household expenditures per capita to
construct the synthetic observations of mean income/expenditures between spells, the model is
Page 27 of 45
tested using the GDP per capita growth rate. We re-estimate our simulations for the study periods
and country classifications incorporating GDP per capita growth information (the results are
summarized in Figure A 8 in Appendix A). These new results are consistent with those reported
in previous sections with similar magnitudes and directions. The main difference is that extreme
poverty is reduced by a more significant proportion in 2030 when using GDP per capita growth
rates (see Figure A 8, Panel A and B, Appendix A) compared to the outcomes obtained using
consumption growth rates (see Figure 2 and Figure 3).
VII. CONCLUSIONS
This research contributes to the discussion on future economic growth, poverty, inequality, and
shared prosperity measures. Our analysis exploits two country typologies to show heterogeneity
of predictions and uncertainty in poverty outcomes and income distribution conditions. The first
typology splits the economies by the size of their natural resource sectors. The second country
classification relies on the speed of the output per capita growth. Although our typologies are
arbitrary, our results and robustness checks suggest that these studied country classifications matter
significantly in poverty eradication and income distribution attainments, possibly capturing and
indicating institutional quality. The primary finding of this paper shows a significant level of
uncertainty in future poverty and income distribution achievements at the global level and by
country classifications. One important caveat of the article is that our predictions are based on
recent historical performances. This recorded history used in our simulations suggests that
countries performing poorly—in terms of future poverty reduction and shared prosperity
attainments—should emphasize efforts to implement policies that boost economic growth and
provide social safety nets as a hedging mechanism, especially in the most impoverished and
unprotected sectors of their societies.
We summarize the three main results of this study. The first significant insight is related to
the predicted poverty headcount. The results indicate that despite the continuous decrease in
relative and absolute poverty, alleviating extreme poverty below the 3 percent threshold by 2030
is very optimistic at the global level and involves considerable uncertainty. The simulations
generated by combining the information from the 2000–2014 sample period with GDP per capita
growth rateswhich were used to complement the household survey mean income
Page 28 of 45
observationsprovide the most positive results for decreasing extreme poverty by 2030: median
value of 4.6 percent or 371 million people in relative and absolute terms, respectively. In contrast,
the simulations derived by combining data from the 1980–2014 base period with growth rates of
per capita consumption provide the worst predictions for global poverty reduction by 2030: median
value of 8.9 percent or 724 million people in relative and absolute terms, respectively.
Non-resource-output oriented economies, however, are predicted to achieve and even
surpass the 3 percent poverty target by 2030. Although extreme poverty is expected to diminish in
relative terms, in several simulation exercises, resource-output oriented economies could see an
increase in absolute poverty over the period 2015–2030. Note that there is significant variability
in the predicted future poverty outcomes for resource-output oriented countries, implying that
given recent historical economic episodes, it is hardly possible to predict precise estimates of
poverty rates in these economies. Moreover, poverty is expected to decline in the period 2015–
2030 in relative terms in low-output, middle-output, and high-output growth country categories.
Nevertheless, poverty in low-output growth economies is expected to increase in absolute terms.
The results show that the high dispersion—low precision—in the simulation outcomes denotes
some inability to predict poverty outcomes in countries classified as low-output growth economies.
The model simulations predict that high-output growth economies will quickly alleviate poverty
below the 3 percent level before 2030. Noticeably, the simulations show a low degree of
uncertainty in the expected poverty outcomes of countries with high-output growth rates.
The second main result indicates that changes in relative income inequality are predicted
to be on the positive side, meaning that in general, the Gini coefficients across the studied country
classifications are predicted to decrease on average over the period 2015–2030. Across this 2015–
2030 horizon, our estimates of the Gini coefficient at the global level are expected to decline
between 0.7 and 1.9 Gini points (in the Gini scale of 0–100). The simulation outcomes suggest
some degree of uncertainty such that non-resource-output oriented countries and low-output and
high-output growth countries could either decrease or increase their income inequality levels
across the period 2015–2030.
The third significant result indicates that the B40 of the income distribution is, on average,
predicted to grow faster than the mean and higher percentiles over the 2015–2030 horizon and
across the studied country aggregations. There are a few simulations, however, with median values
Page 29 of 45
indicating that the B40 could grow at lower speeds than the higher percentiles; see the cases for
the resource-output oriented, low-output and high-output growth countries. Notably, there is an
extensive range of uncertainty in all simulated shared prosperity gaps. The larger the distance
between the bottom 40 and upper-income percentiles, the more positive the magnitude of the
standard deviation of the simulated outcomes; these results hold across the studied country
categories.
In comparison with point predictions and perfect-foresight methods, our proposed
approach considers both the outcome precision of a multiplicity of scenarios and the uncertainty—
standard deviation of simulated outcomes—embedded in the predictive fan chart generated for
each situation. This multiplicity of scenario results and the predictive fan chart and associated
uncertainty provide strong incentives to hedge risks in poverty and income inequality declining
via the constant evaluation and revamping of income re-distributive policies; the potential new
plans should be flexible and easy to adjust to rapidly changing economic conditions.
REFERENCES
Aghion, P., E. Caroli, and C. Garcia-Penalosa. 1999. "Inequality and Economic Growth: The Perspective of
the New Growth Theories." Journal of Economic Literature 37 (4): 1615-1660.
Anand, S., and P. Segal. 2015. "The Global Distribution of Income." In Handbook of Income Distribution
edited by A. B. Atkinson, and F. Bourguignon, 937-979. Amsterdam, The Netherlands: Elsevier.
Benabou, R. 1996. "Inequality and Growth." NBER Macroeconomics Annual 11: 11-74.
Bulte, E., and R. Damania. 2008. "Resources for Sale: Corruption, Democracy and the Natural Resource
Curse." The BE Journal of Economic Analysis & Policy 8 (1).
Campos‐Vazquez, R. M., E. Chavez, and G. Esquivel. 2017. "Growth Is (Really) Good for the (Really) Rich."
The World Economy 40 (12): 2639-2675.
Chotikapanich, D. 2008. Modeling Income Distributions and Lorenz Curves. New York: Springer Science &
Business Media.
Cowell, F. A., and E. Flachaire. 2015. "Statistical Methods for Distributional Analysis." In Handbook of
Income Distribution Volume 2A edited by A. B. Atkinson, and F. Bourguignon. Amsterdam, The
Netherlands: North-Holland.
Crafts, N., and K. H. O’Rourke. 2014. "Twentieth Century Growth." In Handbook of Economic Growth
edited by P. Aghion, and S. N. Durlauf, 263-346. Amsterdam Elsevier.
Cruz, M., J. E. Foster, B. Quillin, and P. Schellekens. 2015. "Ending Extreme Poverty and Sharing Prosperity:
Progress and Policies." Policy Research Note, W. Bank, Washington, DC.
Devarajan, S., Y. Dissou, D. S. Go, and S. Robinson. 2015. "Budget Rules and Resource Booms and Busts: A
Dynamic Stochastic General Equilibrium Analysis." The World Bank Economic Review 31 (1): 71-
96.
Dollar, D., T. Kleineberg, and A. Kraay. 2014. "Growth, Inequality, and Social Welfare: Cross-Country
Evidence." World Bank Policy Research Working Paper (6842).
Page 30 of 45
----------. 2016. "Growth Still is Good for the Poor." European Economic Review 81: 68-85.
Feenstra, R. C., Robert Inklaar, and Marcel P. Timmer. 2015. "The Next Generation of the Penn World
Table." American Economic Review 105 (10): 3150-3182.
Ferreira, F. H. G., C. Lakner, M. A. Lugo, and B. Özler. 2018. "Inequality of Opportunity and Economic
Growth: How Much Can Cross‐Country Regressions Really Tell Us?" Review of Income and Wealth.
Frankel, J. 2017. "How to Cope with Volatile Commodity Export Prices: Four Proposals." 335, H. University,
Cambridge, MA.
Frankel, J. A. 2010. "The Natural Resource Curse: A Survey." National Bureau of Economic Research.
Galego Mendes, A., and S. M. Pennings. 2017. "Consumption Smoothing and Shock Persistence: Optimal
Simple Fiscal Rules for Commodity Exporters." Policy Research Working Paper Series WPS/8035,
W. Bank, Washington, DC.
Havranek, T., R. Horvath, and A. Zeynalov. 2016. "Natural Resources and Economic Growth: A Meta-
Analysis." World Development 88: 134-151.
Hellebrandt, T., and P. Mauro. 2015. "The Future of Worldwide Income Distribution." LIS Working Paper
Series/635, P. I. f. I. Economics, Luxembourg.
Hnatkovska, V., and N. Loayza. 2003. "Volatility and Growth." Policy Research Working Paper Series
WPS/3184, W. Bank, Washington, DC.
Kraay, A. 2015. "Weak Instruments in Growth Regressions: Implications for Recent Cross-Country
Evidence on Inequality and Growth." Policy Research Working Paper WPS/7494, W. Bank,
Washington, DC.
Lakner, C., and B. Milanovic. 2016. "Global Income Distribution: From the Fall of the Berlin Wall to the
Great Recession." The World Bank Economic Review 30 (2): 203-232.
Lakner, C., M. Negre, and E. B. Prydz. 2014. "Twinning the Goals: How Can Promoting Shared Prosperity
Help to Reduce Global Poverty?" World Bank Policy Research Working Paper Series WPS 7106,
World Bank, Washington, DC.
Lederman, D., and W. Maloney. 2012. Does What You Export Matter?: In Search of Empirical Guidance for
Industrial Policies. World Bank Publications.
Lederman, D., and W. F. Maloney. 2007. Natural Resources, Neither Curse nor Destiny. World Bank
Publications.
Lopez, H., and L. Servén. 2006. "A Normal Relationship? Poverty, Growth, and Inequality." World Bank
Policy Research Working Paper Series.
Marrero, G. A., and L. Serven. 2018. "Growth, Inequality, and Poverty: A Robust Relationship?" Policy
Research Working Paper WPS/8578, W. B. Group, Washington DC.
Ravallion, M. 2003. "Inequality Convergence." Economics Letters 80 (3): 351-356.
----------. 2013. "How Long Will It Take to Lift One Billion People out of Poverty?" The World Bank Research
Observer 28 (2): 139-158.
----------. 2015. "The Idea of Antipoverty Policy." In Handbook of Income Distribution, 1967-2061.
Amsterdam: Elsevier.
----------. 2018. "Inequality and Globalization: A Review Essay." Journal of Economic Literature 56 (2): 620-
42.
Sachs, J. D., and A. M. Warner. 2001. "The Curse of Natural Resources." European Economic Review 45 (4):
827-838.
United Nations. 2017. "World Population Prospects: The 2017 Revision." New York: United Nations.
van der Ploeg, F. 2011. "Natural Resources: Curse or Blessing?" Journal of Economic Literature 49 (2): 366-
420.
van der Weide, R., and B. Milanovic. 2018. "Inequality is Bad for Growth of the Poor (but Not for That of
the Rich)." The World Bank Economic Review: lhy023-lhy023.
Wooldridge, J. M. 2001. Econometric Analysis of Cross Section and Panel Data. MIT press.
Page 31 of 45
World Bank. 2014. "A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts,
Data, and the Twin Goals." Washington, DC: World Bank.
----------. 2016. "Poverty and Shared Prosperity 2016: Taking on Inequality." Washington, DC: World Bank.
----------. 2017. "World Development Indicators." Accessed in December 2017.
----------. 2018. "Poverty and Shared Prosperity Report 2018: Completing the Poverty Puzzle." Washington,
DC: World Bank.
----------. 2019. "PovcalNet." Accessed on February 22, 2019.
World Bank; International Monetary Fund. 2016. "Global Monitoring Report 2015/2016: Development
Goals in an Era of Demographic Change." Washington, DC: World Bank.
APPENDIX A: GENERAL FIGURES
FIGURE A 1. PREDICTED GLOBAL POVERTY HEADCOUNT AND GINI COEFFICIENT
Panel A. Poverty headcount Panel B. Gini coefficient
(Base period: 1980–2014) (Base period: 1980–2014)
(weighted by population)
75 70
Poverty headcount
Gini index proxied
(All economies)
60
(All economies)
(percentage)
57.5
45
45
30
15 32.5
0
20
1980 1990 2000 2010 2020 2030 2040
1980 1990 2000 2010 2020 2030 2040
Years
Years
Poverty headcount outcomes derived from 500 random draws per year in each of the 150 Aggregated Gini index stats derived from 500 random draws per year in each of the 150
selected countries. The 2015 and 2030 corresponding results are: i) median = 11.42 and 8.84; selected countries. The 2015 and 2030 corresponding results are: i) median = 38.92 and 38.21;
ii) mean = 11.45 and 8.89; iii) skewness = 0.16 and 0.38; iv) standard deviation = 0.52 and ii) mean = 38.93 and 38.23; iii) skewness = 0.23 and 0.16; iv) standard deviation = 0.55 and
0.93. Estimated number of persons living in extreme poverty corresponding to the median 1.99.
headcount = 811 and 724 million people.
96% confidence interval 80% confidence interval
40% confidence interval Median
Poverty headcount of 3 percent Year = 2030
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s
estimates.
Page 32 of 45
FIGURE A 2. PREDICTED POVERTY HEADCOUNT BY COUNTRY RESOURCE ORIENTATION
Panel A. ROCs Panel B. NROCs
(Base period: 1980–2014) (Base period: 1980–2014)
75 75
(NROC economies)
Poverty headcount
Poverty headcount
(ROC economies)
60 60
(percentage)
(percentage)
45 45
30 30
15 15
0 0
1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040
Years Years
Poverty headcount outcomes derived from 500 random draws per year in each of the 150 Poverty headcount outcomes derived from 500 random draws per year in each of the 150
selected countries. The 2015 and 2030 corresponding results are: i) median = 22.73 and 20.68; selected countries. The 2015 and 2030 corresponding results are: i) median = 8.03 and 3.53;
ii) mean = 22.75 and 20.49; iii) skewness = -0.07 and -0.37; iv) standard deviation = 0.93 and ii) mean = 8.04 and 3.69; iii) skewness = 0.12 and 2.26; iv) standard deviation = 0.56 and 0.89.
3.14. Estimated number of persons living in extreme poverty corresponding to the median Estimated number of persons living in extreme poverty corresponding to the median headcount
headcount = 369 and 438 million people. = 440 and 215 million people.
96% confidence interval 80% confidence interval
40% confidence interval Median
Poverty headcount of 3 percent Year = 2030
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s
estimates.
FIGURE A 3. PREDICTED GINI COEFFICIENT BY COUNTRY RESOURCE ORIENTATION
Panel A. ROCs Panel B. NROCs
(Base period: 1980–2014) (Base period: 1980–2014)
(weighted by population)
(weighted by population)
70 70
(NROC economies)
(ROC economies)
Gini index proxied
Gini index proxied
57.5 57.5
45 45
32.5 32.5
20 20
1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040
Years Years
Aggregated Gini index stats derived from 500 random draws per year in each of the 46 selected Aggregated Gini index stats derived from 500 random draws per year in each of the 104
countries. The 2015 and 2030 corresponding results are: i) median = 39.22 and 34.68; ii) mean selected countries. The 2015 and 2030 corresponding results are: i) median = 38.78 and 38.48;
= 39.20 and 34.87; iii) skewness = 0.04 and 0.43; iv) standard deviation = 0.70 and 2.33. ii) mean = 38.81 and 38.62; iii) skewness = 0.25 and 0.25; iv) standard deviation = 0.61 and
2.45.
96% confidence interval 80% confidence interval
40% confidence interval Median
Poverty headcount of 3 percent Year = 2030
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s
estimates.
Page 33 of 45
FIGURE A 4. PREDICTED POVERTY HEADCOUNT BY INCOME GROWTH COUNTRY TYPOLOGY
Panel A. LOGCs Panel B. MOGCs Panel C. HOGCs
(Based period: 1980–2014) (Based period: 1980–2014) (Based period: 1980–2014)
75 75 75
)
)
)
60 60 60
(percentage)
(percentage)
(percentage)
45 45 45
30 30 30
15 15 15
0 0 0
(
(
(
1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040
Years Years Years
Poverty headcount outcomes derived from 500 random draws per year in each of the Poverty headcount outcomes derived from 500 random draws per year in each of the Poverty headcount outcomes derived from 500 random draws per year in each of the
150 selected countries. The 2015 and 2030 corresponding results are: i) median = 50.44 150 selected countries. The 2015 and 2030 corresponding results are: i) median = 12.87 150 selected countries. The 2015 and 2030 corresponding results are: i) median = 4.5
and 48.41; ii) mean = 50.32 and 47.86; iii) skewness = -0.24 and -0.04; iv) standard and 9.12; ii) mean = 12.89 and 9.15; iii) skewness = 0.04 and 0.33; iv) standard and 0.85; ii) mean = 4.65 and 1.48; iii) skewness = 0.99 and 1.76; iv) standard deviation
deviation = 2.55 and 9.87. Estimated number of persons living in extreme poverty deviation = 0.55 and 1.07. Estimated number of persons living in extreme poverty = 1.30 and 1.59. Estimated number of persons living in extreme poverty corresponding
corresponding to the median headcount = 87 and 127 million people. corresponding to the median headcount = 635 and 531 million people. to the median headcount = 90 and 18 million people.
96% confidence interval 80% confidence interval
40% confidence interval Median
Poverty headcount of 3 percent Year = 2030
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates.
FIGURE A 5. PREDICTED GINI COEFFICIENTS BY INCOME GROWTH COUNTRY TYPOLOGY
Panel A. LOGCs Panel B. MOGCs Panel C. HOGCs
(Base period: 1980–2014) (Base period: 1980–2014) (Base period: 1980–2014)
(weighted by population)
(weighted by population)
(weighted by population)
70 70 70
)
)
)
57.5 57.5 57.5
45 45 45
32.5 32.5 32.5
(
20
(
20 20
(
1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040
Years Years Years
Aggregated Gini index stats derived from 500 random draws per year in each of the 8 Aggregated Gini index stats derived from 500 random draws per year in each of the Aggregated Gini index stats derived from 500 random draws per year in each of the 25
selected countries. The 2015 and 2030 corresponding results are: i) median = 42.08 and 117 selected countries. The 2015 and 2030 corresponding results are: i) median = 38.09 selected countries. The 2015 and 2030 corresponding results are: i) median = 40.81 and
38.30; ii) mean = 42.12 and 38.88; iii) skewness = 0.15 and 0.85; iv) standard deviation and 37.08; ii) mean = 38.11 and 37.20; iii) skewness = 0.15 and 0.41; iv) standard 39.66; ii) mean = 40.90 and 41.11; iii) skewness = 0.22 and 0.36; iv) standard deviation
= 1.23 and 3.73. deviation = 0.44 and 1.69. = 2.27 and 7.90.
96% confidence interval 80% confidence interval
40% confidence interval Median
Poverty headcount of 3 percent Year = 2030
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates.
Page 34 of 45
FIGURE A 6. SIMULATIONS VARYING THRESHOLD DEFINITIONS OF COUNTRY TYPOLOGIES (TABLE 1) BY MINUS 0.3 PERCENT
Panel A. Poverty headcount Panel B. Absolute extreme poverty
Panel C. Gini coefficient Panel D. B40 – median shared prosperity gap
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note: Per capita consumption growth rates are used to
complete the panel of average income/expenditure household survey observations. Projected country population weights the estimates. Five hundred simulations are performed
per country and forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014, and 2005–2014.
Page 35 of 45
FIGURE A 7. SIMULATIONS VARYING THRESHOLD DEFINITIONS OF COUNTRY TYPOLOGIES (TABLE 1) BY PLUS 0.3 PERCENT
Panel A. Poverty headcount Panel B. Absolute extreme poverty
Panel C. Gini coefficient Panel D. B40 – median shared prosperity gap
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note: Per capita consumption growth rates are used to
complete the panel of average income/expenditure household survey observations. Projected country population weights the estimates. Five hundred simulations are performed
per country and forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014, and 2005–2014.
Page 36 of 45
FIGURE A 8. SIMULATIONS BASED ON GDP PER CAPITA GROWTH RATES
Panel A. Poverty headcount Panel B. Absolute extreme poverty
Panel C. Gini coefficient Panel D. B40 – median shared prosperity gap
Source: World Development Indicators, PovcalNet, United Nations, Penn World Table 9.0, and the author’s calculations. Note: GDP per capita growth rates are used to complete
the panel of average income/expenditure household survey observations. Projected country population weights the estimates. Five hundred simulations are performed per country
and forecasted period. The simulations use 4 sample periods to test for the robustness of results: 1980–2014, 1990–2014, 2000–2014, and 2005–2014.
Page 37 of 45
APPENDIX B: GENERAL TABLES
TABLE B 1. LIST OF COUNTRIES
(1) (2) (3) (4) (5) (6)
ID Country Income Output Growth Resource PovcalNet Number of
Country Category Category Category Surveys
1 Albania UMC MOGC NROC 5
2 Algeria UMC MOGC ROC 3
3 Angola LMC MOGC ROC 2
4 Argentina HIC MOGC NROC 27
5 Armenia UMC HOGC NROC 18
6 Australia HIC MOGC NROC 8
7 Austria HIC MOGC NROC 13
8 Azerbaijan UMC HOGC ROC 6
9 Bangladesh LMC MOGC NROC 9
10 Belarus UMC HOGC NROC 21
11 Belgium HIC MOGC NROC 13
12 Belize UMC MOGC NROC 7
13 Benin LIC MOGC ROC 3
14 Bhutan LMC HOGC NROC 4
15 Bolivia LMC MOGC NROC 19
16 Bosnia and UMC HOGC NROC 5
Herzegovina
17 Botswana UMC HOGC NROC 4
18 Brazil UMC MOGC NROC 31
19 Bulgaria UMC HOGC NROC 16
20 Burkina Faso LIC MOGC ROC 5
21 Burundi LIC MOGC ROC 4
22 Cabo Verde LMC MOGC NROC 2
23 Cameroon LMC MOGC ROC 4
24 Canada HIC MOGC NROC 11
25 Central African LIC MOGC ROC 3
Republic
26 Chad LIC MOGC ROC 2
27 Chile HIC HOGC ROC 13
28 China UMC HOGC NROC 16
29 Colombia UMC MOGC NROC 18
30 Comoros LIC MOGC NROC 2
31 Congo, Dem. Rep. LIC LOGC ROC 2
32 Congo, Rep. LMC MOGC ROC 2
33 Costa Rica UMC MOGC NROC 30
34 Croatia HIC MOGC NROC 14
35 Cyprus HIC MOGC NROC 12
36 Czech Republic HIC MOGC NROC 14
37 Côte d'Ivoire LMC LOGC NROC 10
38 Denmark HIC MOGC NROC 13
39 Djibouti LMC LOGC NROC 3
40 Dominican Republic UMC MOGC NROC 22
41 Ecuador UMC MOGC ROC 20
42 Egypt, Arab Rep. LMC MOGC ROC 8
43 El Salvador LMC MOGC NROC 23
44 Estonia HIC HOGC NROC 19
45 Ethiopia LIC MOGC ROC 6
46 Fiji UMC MOGC NROC 3
47 Finland HIC MOGC NROC 13
48 France HIC MOGC NROC 13
49 Gabon UMC MOGC ROC 2
Page 38 of 45
(1) (2) (3) (4) (5) (6)
ID Country Income Output Growth Resource PovcalNet Number of
Country Category Category Category Surveys
50 Gambia, The LIC LOGC NROC 4
51 Georgia LMC HOGC NROC 21
52 Germany HIC MOGC NROC 11
53 Ghana LMC MOGC ROC 6
54 Greece HIC MOGC NROC 13
55 Guatemala UMC MOGC NROC 6
56 Guinea LIC MOGC ROC 5
57 Guinea-Bissau LIC MOGC ROC 4
58 Haiti LIC LOGC NROC 2
59 Honduras LMC MOGC NROC 28
60 Hungary HIC MOGC NROC 21
61 Iceland HIC MOGC NROC 12
62 India LMC MOGC NROC 6
63 Indonesia LMC HOGC ROC 25
64 Iran, Islamic Rep. UMC MOGC ROC 9
65 Iraq UMC MOGC ROC 2
66 Ireland HIC MOGC NROC 13
67 Israel HIC MOGC NROC 8
68 Italy HIC MOGC NROC 13
69 Jamaica UMC MOGC NROC 7
70 Japan HIC MOGC NROC 1
71 Jordan UMC MOGC NROC 7
72 Kazakhstan UMC MOGC ROC 17
73 Kenya LMC MOGC NROC 5
74 Korea, Rep. HIC HOGC NROC 4
75 Kyrgyz Republic LMC MOGC NROC 18
76 Lao PDR LMC HOGC ROC 5
77 Latvia HIC HOGC NROC 19
78 Lebanon UMC MOGC NROC 1
79 Lesotho LMC MOGC NROC 4
80 Liberia LIC MOGC ROC 2
81 Lithuania HIC HOGC NROC 20
82 Luxembourg HIC MOGC NROC 13
83 Macedonia, FYR UMC MOGC NROC 14
84 Madagascar LIC LOGC NROC 7
85 Malawi LIC MOGC ROC 3
86 Malaysia UMC HOGC ROC 12
87 Maldives UMC HOGC NROC 2
88 Mali LIC MOGC NROC 4
89 Malta HIC MOGC NROC 10
90 Mauritania LMC MOGC ROC 7
91 Mauritius UMC MOGC NROC 2
92 Mexico UMC MOGC NROC 16
93 Moldova LMC MOGC NROC 20
94 Mongolia LMC MOGC ROC 9
95 Montenegro UMC MOGC NROC 10
96 Morocco LMC MOGC NROC 6
97 Mozambique LIC MOGC ROC 4
98 Myanmar LMC HOGC ROC 1
99 Namibia UMC MOGC NROC 4
100 Nepal LIC MOGC NROC 4
101 Netherlands HIC MOGC NROC 12
102 Nicaragua LMC MOGC NROC 6
103 Niger LIC LOGC NROC 6
104 Nigeria LMC MOGC ROC 5
105 Norway HIC MOGC NROC 13
106 Pakistan LMC MOGC NROC 12
107 Panama HIC MOGC NROC 23
Page 39 of 45
(1) (2) (3) (4) (5) (6)
ID Country Income Output Growth Resource PovcalNet Number of
Country Category Category Category Surveys
108 Paraguay UMC MOGC NROC 20
109 Peru UMC MOGC NROC 22
110 Philippines LMC MOGC NROC 11
111 Poland HIC MOGC NROC 24
112 Portugal HIC MOGC NROC 13
113 Romania UMC HOGC NROC 22
114 Russian Federation UMC MOGC ROC 21
115 Rwanda LIC MOGC ROC 5
116 Senegal LIC MOGC NROC 5
117 Serbia UMC MOGC NROC 13
118 Seychelles HIC HOGC NROC 3
119 Sierra Leone LIC MOGC ROC 3
120 Slovak Republic HIC HOGC NROC 13
121 Slovenia HIC MOGC NROC 16
122 South Africa UMC MOGC NROC 7
123 Spain HIC MOGC NROC 13
124 Sri Lanka LMC MOGC NROC 8
125 St. Lucia UMC MOGC NROC 1
126 Suriname UMC MOGC ROC 1
127 Swaziland LMC MOGC NROC 3
128 Sweden HIC MOGC NROC 13
129 Switzerland HIC MOGC NROC 10
130 Syrian Arab Republic LIC MOGC ROC 1
131 São Tomé and LMC MOGC NROC 2
Principe
132 Tajikistan LIC HOGC NROC 6
133 Tanzania LIC MOGC ROC 4
134 Thailand UMC MOGC NROC 21
135 Togo LIC MOGC ROC 3
136 Trinidad and Tobago HIC MOGC ROC 2
137 Tunisia LMC MOGC NROC 6
138 Turkey UMC MOGC NROC 17
139 Turkmenistan UMC HOGC ROC 1
140 Uganda LIC MOGC ROC 9
141 Ukraine LMC MOGC NROC 20
142 United Kingdom HIC MOGC NROC 12
143 United States HIC MOGC NROC 10
144 Uruguay HIC MOGC NROC 24
145 Uzbekistan LMC MOGC ROC 4
146 Venezuela, RB UMC MOGC ROC 13
147 Vietnam LMC HOGC NROC 10
148 Yemen, Rep. LIC MOGC ROC 3
149 Zambia LMC MOGC ROC 9
150 Zimbabwe LIC LOGC NROC 1
Source: Penn World Table 9.0, World Development Indicators, PovcalNet, United Nations, and the author’s calculations. Note:
ROCs denotes resource-output oriented countries. NROCs symbolizes non-resource-output oriented countries. LOGCs stands for
low-output growth countries. MOGCs depicts middle-output growth countries. HOGCs refers to high-output growth countries.
Table 1 provides details of the definitions of the country classifications.
Page 40 of 45
TABLE B 2. DESCRIPTIVE STATISTICS
(1) (2) (3) (4) (5) (6)
Annual Mean Standard Minimum Maximum Coefficient of
observations deviation variation
All countries
GDP per capita, 2011 PPP U.S. dollars 4,892 11,423 12,465 162 84,362 1.1
GDP per capita, annual growth (percent) 4,892 1.7% 5.2% -57.5% 32.0% 3.1
Per capita consumption, 2011 PPP U.S. dollars 4,892 8,631 8,584 120 42,758 1.0
Per capita consumption, annual growth (percent) 4,892 1.6% 5.9% -26.3% 24.7% 3.7
Mean income, 2011 PPP U.S. dollars/month 1,383 533 498 23 2,218 0.9
Mean income, average annual growth (percent) 1,200 1.8% 8.8% -68.8% 58.0% 4.8
Gini index 1,339 39 10 16 66 0.2
Gini index, average annual growth (percent) 1,195 -0.3% 4.8% -74.2% 29.8% -18.4
ROCs
GDP per capita, 2011 PPP U.S. dollars 1,486 5,511 5,840 162 31,598 1.1
GDP per capita, annual growth (percent) 1,486 1.4% 6.0% -42.9% 30.2% 4.3
Per capita consumption, 2011 PPP U.S. dollars 1,486 3,741 3,674 120 22,488 1.0
Per capita consumption, annual growth (percent) 1,486 1.2% 7.3% -25.4% 23.5% 6.3
Mean income, 2011 PPP U.S. dollars/month 258 215 171 23 756 0.8
Mean income, average annual growth (percent) 197 2.2% 7.4% -26.2% 43.6% 3.3
Gini index 235 42 8 16 66 0.2
Gini index, average annual growth (percent) 193 -1.0% 7.0% -74.2% 22.2% -6.9
NROCs
GDP per capita, 2011 PPP U.S. dollars 3,406 14,003 13,653 727 84,362 1.0
GDP per capita, annual growth (percent) 3,406 1.8% 4.7% -57.5% 32.0% 2.7
Per capita consumption, 2011 PPP U.S. dollars 3,406 10,765 9,217 452 42,758 0.9
Per capita consumption, annual growth (percent) 3,406 1.8% 5.2% -26.3% 24.7% 2.9
Mean income, 2011 PPP U.S. dollars/month 1,125 606 519 35 2,218 0.9
Mean income, average annual growth (percent) 1,003 1.8% 9.1% -68.8% 58.0% 5.2
Gini index 1,104 39 10 21 65 0.3
Gini index, average annual growth (percent) 1,002 -0.1% 4.3% -21.9% 29.8% -35.8
LOGCs
GDP per capita, 2011 PPP U.S. dollars 271 1,681 676 555 3,693 0.4
GDP per capita, annual growth (percent) 271 -0.8% 4.6% -21.3% 13.2% -5.6
Per capita consumption, 2011 PPP U.S. dollars 271 1,462 574 511 3,043 0.4
Per capita consumption, annual growth (percent) 271 -0.4% 7.3% -21.9% 21.0% -18.4
Mean income, 2011 PPP U.S. dollars/month 32 102 59 23 267 0.6
Mean income, average annual growth (percent) 24 -2.2% 7.4% -23.5% 8.6% -3.3
Gini index 32 42 6 31 61 0.1
Gini index, average annual growth (percent) 24 -0.8% 5.7% -18.2% 10.3% -7.2
MOGCs
GDP per capita, 2011 PPP U.S. dollars 3,886 12,496 13,384 162 84,362 1.1
GDP per capita, annual growth (percent) 3,886 1.4% 4.6% -46.5% 30.2% 3.2
Per capita consumption, 2011 PPP U.S. dollars 3,886 9,409 9,114 120 42,758 1.0
Per capita consumption, annual growth (percent) 3,886 1.4% 5.6% -25.2% 23.6% 4.1
Mean income, 2011 PPP U.S. dollars/month 1,089 594 533 24 2,218 0.9
Mean income, average annual growth (percent) 966 1.6% 8.4% -68.8% 58.0% 5.3
Gini index 1,079 40 10 21 66 0.3
Gini index, average annual growth (percent) 966 -0.3% 4.0% -27.6% 22.2% -15.3
HOGCs
GDP per capita, 2011 PPP U.S. dollars 735 9,341 6,443 742 34,326 0.7
GDP per capita, annual growth (percent) 735 3.6% 7.1% -57.5% 32.0% 1.9
Per capita consumption, 2011 PPP U.S. dollars 735 7,160 5,182 452 22,146 0.7
Per capita consumption, annual growth (percent) 735 3.5% 6.8% -26.3% 24.7% 1.9
Mean income, 2011 PPP U.S. dollars/month 262 335 221 35 1,182 0.7
Mean income, average annual growth (percent) 210 3.5% 10.5% -42.4% 34.7% 3.0
Gini index 228 35 8 16 65 0.2
Gini index, average annual growth (percent) 205 -0.2% 7.5% -74.2% 29.8% -39.5
Source: Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates. Note: GDP
per capita estimates are in PPP 2011 U.S. dollars. Information over the period 1980–2014. Annual growth rates of the Gini index are calculated
as the average between spells.
Page 41 of 45
APPENDIX C: VARIANCE OF MEAN INCOME GROWTH
The standard deviation of mean income growth is presented in Figure C 1, Appendix C. This country-specific variability in ROCs is
generally higher than that faced by NROC economies (Figure C 1, Panel B, Appendix C). In terms of their median, LOGCs present
larger standard deviations of mean income growth than HOGCs and MOGCs, with MOGCs representing the economies with the smallest
median value (Figure C 1, Panel C, Appendix C). Furthermore, via a standard variance decomposition of the OLS estimates of Equation
(1), we identified three main features of the dispersion of the growth in the mean income (Figure C 2 in Appendix C). First, idiosyncratic
factors drive the variability in income growth faced by all countries and country typologies. 37 Second, global factors affecting income
growth appear to be less relevant in ROCs and LOGCs. Third, the covariance component between the growth of global income per
capita and global commodity prices is negligible, [ , ] ≈ 0, over the period 1980–2014 and across countries.
FIGURE C 1. VARIABILITY OF MEAN INCOME GROWTH
Panel A. All countries Panel B. Resource typology Panel C. Output growth typology
16 Annual growth (percentage) 16 16
Annual growth (percentage)
Annual growth (percentage)
12 12 12
8 8
8
4 4
4
0 0
0 NROC ROC HOGC LOGC MOGC
Source: Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. Note: Variability measured by the standard deviation of mean income
growth. Country regressions over the period 1980–2014. Similar results are obtained using the following periods instead: 1990–2014, 2000–2014, and 2005–2014. The total
number of countries used in this estimation is 150. The number of NROC and ROC economies is 104 and 46, respectively. The number of countries in the classification LOGCs,
MOGCs, and HOGCs are 8, 117, and 25, respectively.
37
Despite the inclusion of two global factors affecting mean income growth, the model in Equation (1) does not disaggregate the idiosyncratic components affecting
the country performance of growth of mean income. Besides, note that all the idiosyncratic random forces are captured via the error term in Equation (1). This
error term, however, could include omitted global components as well.
Page 42 of 45
FIGURE C 2. STANDARD VARIANCE DECOMPOSITION OF MEAN INCOME GROWTH
Based on Nonfuel Commodity Prices
Panel A. All countries Panel B. Resource typology Panel C. Output growth typology
Variance share (percentage)
Variance share (percentage)
100 100 100
Variance share (percentage)
80 80 80
60 60 60
40 40
40
20 20
20
0 0
0
-20 -20
-20 NROC ROC HOGC LOGC MOGC
Based on Fuel Commodity Prices
Panel A. All countries Panel B. Resource typology Panel C. Output growth typology
Variance share (percentage)
Variance share (percentage)
100 100 100
Variance share (percentage)
80 80 80
60 60 60
40 40
40
20 20
20
0 0
0
-20 -20
-20 NROC ROC HOGC LOGC MOGC
Idiosyncratic: variance share Global commodity price: variance share
Global mean income growth: variance share Global income growth–commodity price: covariance share
Source: Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. Note: Variability is measured by the standard deviation of mean income
growth. All panels consider country regression information over the period 1980–2014. Similar results are obtained using the following periods instead: 1990–2014, 2000–2014,
and 2005–2014. The total number of countries used in this estimation is 150. The number of NROC and ROC economies is 104 and 46, respectively. The number of countries in
the classifications LOGCs, MOGCs, and HOGCs are 8, 117, and 25, respectively.
Page 43 of 45
APPENDIX D: SOME STYLIZED FACTS OF THE RESOURCE TYPOLOGY
FIGURE D 1. GDP PER CAPITA GROWTH AND NATURAL RESOURCE RENTS VARYING ACROSS DECADES
Panel A. 1970–1979 median values Panel B. 1980–1989 median values
Panel C. 1990–1999 median values Panel D. 2000–2014 median values
Cursed resource rent economies Quasi-blessed resource rent economies
Blessed resource rent economies Cursed non-resource rent economies
Quasi-blessed non-resource rent economies Blessed non-resource rent economies
Linear fit: complete sample Linear fit: resource rent economies
Linear fit: non-resource rent economies
Source: Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. Note: GDP per capita
is in PPP 2011 U.S. dollars. The sample includes 148 countries, each with more than 44 annual observations of GDP per capita
over the period 1970–2014.
To determine the strength of the median values and trends presented in Figure 1, we check the
relationship between GDP per capita growth and natural resource rents over decades (Figure D 1
in Appendix D). In the 1970s, in all our sample of economies, we find a flat association between
output per capita growth and resource rents. However, this overall association—the blue dotted
Page 44 of 45
line—becomes negative in the 1980s and 1990s then reverts to a flattened association in the 2000s.
Over the decades, this association appears to be driven by the performance of resource-based
countries. ROCs show a positive associationthe green dotted linein the 1970s; this association
reverses to become negative in the subsequent decades. In contrast, in the NROCs, there is an
unchanging negative association between GDP per capita growth and natural resource rents across
the decadesthe red-dotted line.
FIGURE D 2. OUTPUT VARIABILITY AND NATURAL RESOURCE RENTS
Panel A. GDP per capita, Panel B. GDP per capita,
variability measure volatility measure
Cursed resource rent economies Quasi-blessed resource rent economies
Blessed resource rent economies Cursed non-resource rent economies
Quasi-blessed non-resource rent economies Blessed non-resource rent economies
Linear fit: complete sample Linear fit: resource rent economies
Linear fit: non-resource rent economies
Source: Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates. Note: GDP per capita
is in PPP 2011 U.S. dollars. The sample includes 148 countries, each with more than 44 annual observations of GDP per capita
over the period 1970–2014.
We also investigate the performance of the variability and volatility of GDP per capita
growth according to their level of resource rents across countries. Panels A and B of Figure D 2 in
Appendix D show that ROCs present higher variability and volatility of average output per capita
growth. In contrast, this association is almost negligible in NROC economies.
Page 45 of 45