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Income Inequality in the Developing World

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Should income inequality be of concern in developing countries? New data reveal less income inequality in the developing world than 30 years ago. However, this is due to falling inequality between countries. Average inequality within developing countries has been slowly rising, though staying fairly flat since 2000. As a rule, higher rates of growth in average incomes have not put upward pressure on inequality within countries. Growth has generally helped reduce the incidence of absolute poverty, but less so in more unequal countries. High inequality also threatens to stall future progress against poverty by attenuating growth prospects. Perceptions of rising absolute gaps in living standards between the rich and the poor in growing economies are also consistent with the evidence.
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Income Inequality in the Developing World
Martin Ravallion
1
Abstract: Should income inequality be of concern in developing countries? New data reveal less
income inequality in the developing world than 30 years ago. However, this is due to falling
inequality between countries. Average inequality within developing countries has been slowly
rising, though staying fairly flat since 2000. As a rule, higher rates of growth in average incomes
have not put upward pressure on inequality within countries. Growth has generally helped reduce
the incidence of absolute poverty, but less so in more unequal countries. High inequality also
threatens to stall future progress against poverty by attenuating growth prospects. Perceptions of
rising absolute gaps in living standards between the rich and the poor in growing economies are
also consistent with the evidence.
Development economics emerged as a sub-discipline of economics in the 1950s and its
initial focus was on economic growth, with inequality as a secondary concern. The prevailing
orthodoxy for many decades was that a period of rising inequality was to be expected in growing
poor countries. Rising inequality was seen to be more-or-less inevitable and not something to
worry about, particularly if the incidence of poverty was falling. Another commonly held view
was that policy efforts to reduce inequality were likely to impede growth and (hence) poverty
reduction.
The existence of high inequality within many developing countries, side-by-side with
persistent poverty, started to attract attention in the early 1970s. Nonetheless, through the 1980s
and well into the 1990s, the mainstream view in development economics was still that high
and/or rising inequality in poor countries was a far less important concern than assuring
sufficient growth, which was the key to poverty reduction. The policy message for the
developing world was clear: you can’t expect to have both lower poverty and less inequality
1
Science, forthcoming. Department of Economics, Georgetown University, Washington DC, and National Bureau
of Economic Research, USA. Correspondence: mr1185@georgetown.edu.
2
while you remain poor, and if you choose to give poverty reduction highest priority then focus
on growth.
Other objections could still be raised to high income inequality. The classical utilitarian
formulation—whereby social welfare is judged by the sum of “utilities,” assuming diminishing
marginal utility of incomepointed to social welfare losses from high inequality at a given
mean. But that did not persuade those who believed that there was a trade-off between equity and
growth. A moral defense could also be mounted for the view that inequality is not an important
issue for a growing developing country by appeal to John Rawls’s (1971) “difference principle
that (subject to assuring liberty and equal opportunity) higher inequality can be justified as long
as it benefits the worst off group in society.
The period since 2000 has seen a deeper and more widespread questioning of this
longstanding view of “pro-poor inequality. New concerns have emerged about the instrumental
importance of equity to other valued goals, including poverty reduction and human development
more broadly. It appears more likely today that high inequality will be seen as a threat to future
development than as an inevitable and unimportant consequence of past progress. The long-
standing idea of a significant “growth-equity trade-off” has come to be seriously questioned.
This paper reports new estimates of the levels and changes in income inequality measures
for the developing world. The new estimates take us up to 2010, embracing the period of higher
growth rates in the developing world since the turn of the millennium. In the light of these new
data, the paper revisits the arguments and evidence pertaining to past and ongoing debates on
inequality in developing countries and the trade-offs with growth and poverty reduction.
Income Inequality Measures
To measure inequality in the developing world as a whole one ignores country borders
pooling all residents and measuring inequality amongst them. This overall measure will naturally
depend on the inequality between countries as well as that within them. Thus its evolution over
time will depend on whether poorer countries are seeing lower growth rates as well as the things
happening within countrieseconomic changes and policiesthat impact on inequality.
If we are comparing country or regional performance then we will want to isolate the
within-country component of inequality, as distinct from that between countries. While there are
many inequality measures, not all of them allow a clean separation of the “between” and
3
“within” components. For example, such a decomposition is not generally possible for the Gini
indexa popular inequality index based on the average absolute difference between all random
pairs of incomes normalized by the mean. (The exception is when the distributions of different
countries do not share any common support, which is unlikely.)
The mean-log deviation (MLD) offers an elegant solution. This is given by the
(appropriately weighted) mean across households of the log of the ratio of the overall mean
income to household income per person. When all incomes are equal MLD=0; the higher the
inequality the higher the MLD. Like the Gini index, MLD is a relative inequality measure, in that
it depends on ratios of incomes to the mean; this is implied by the scale invariance axiom of
inequality measurement, which says that when all incomes are multiplied by a constant the
inequality measure does not change. (We will return to this axiom, which can be questioned.)
Also, both measures satisfy the standard “transfer axiom” for inequality measurement, namely
that a small transfer to someone with a lower income will reduce inequality. However, unlike the
Gini index, MLD is exactly decomposable by population sub-groups.
Inequality is measured using household surveys. The estimates presented here are based
on around 900 surveys spanning 1980-2010 and 130 countries. The calculations have been done
on an updated version of the dataset used by Chen and Ravallion (2010) for measuring poverty
and (when relevant) the same methods have been used here. For over half the surveys, household
consumption expenditure (including imputed values for consumption from own-production) was
used rather than income, although for brevity the word “income” is used for both. For a given
economy, income inequality measures are expected to be somewhat higher than for consumption
given the scope for smoothing consumption in the presence of income shocks.
While household surveys are the only source of data on inequality across households,
there are two sources of data on the growth rates in average household consumption or income.
The same surveys used to measure inequality comprise one source, while the other is the
household consumption component of domestic absorption in the national accounts. There is no
clear way of ranking these measures; in some developing countries the measures based on
national accounts are considered quite unreliable, while elsewhere there are serious concerns
about the survey-based measures.
It is acknowledged that household surveys may well underestimate the extent of
inequality, notably through either the rich under-reporting their incomes or through selective
4
compliance in the randomized assignments of the survey instrument, whereby richer households
are less likely to participate. (The latter problem does not imply that inequality will be
underestimated; see Korinek et al. 2006, who nonetheless find that selective compliance leads to
an underestimation of inequality in the U.S.) While there are methods that can be used to address
these concerns, they have so far tended to be confined to research studies, and mostly in rich
countries. No corrections for these data problems have been made in the primary data used here.
The overall MLD for the developing world in 2010 is estimated to be 0.578. To help
interpret this number, imagine a distribution with three incomes (1, 2, x); for this distribution to
have MLD=0.578, x would need to be 12.73, i.e., the richest of three people would need to have
over 12 times that of the poorest and 6 times that of the middle-income.
Fig. 1: Inequality in the developing world
Inequality has fallen over this 30 year period. For the earliest year that the data permit an
estimate, 1981, MLD was 0.651. (This is equivalent to x=14.79 in the distribution (1, 2, x).)
Figure 1 plots MLD for the developing world as a whole and its between-country component.
(The supplementary notes give detailed estimates.) We see that the overall decrease came with
ups and downs, and an increase over 2005-10. The variance over time is largely accountable to
inequality between countries. Over the period as a whole, the between-country component has
.0
.1
.2
.3
.4
.5
.6
.7
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
Inequality in the developing world (MLD)
Total
Between
country
Within
country
5
fallen while the within-country component has risen. The latter accounted for 31% of total
inequality in the developing world in 1981, but 47% in 2010. Strikingly, however, this pattern
has reversed since 2000, with inequality rising between countries but falling on average within
countries. Something different seems to be happening since the turn of the millennium. The next
section will consider the role played by the higher growth rates since then.
Fig. 2: Evolution of average inequality within countries
Figure 2 plots the within-country component by region. Latin America and the Caribbean
(LAC) have persistently had the highest average inequality of any region. (The mean difference
between LAC and other regions falls only slightly when one controls for income surveys, which
have been more popular in LAC; see the Addendum.) Inequality in LAC was rising until around
2000, but has fallen noticeably since then. Eastern Europe and Central Asia (EECA) saw a sharp
rise in average inequality in the 1990s but has seen generally falling inequality since then. Sub-
Saharan Africa (SSA) has the second-highest average inequality, though with no clear trend.
South Asia has been a region of low inequality, though rising somewhat since the early 1990s.
East Asia started out as the region with the lowest inequality, but has seen a steady rise. The
Middle-East and North Africa (MENA) region has seen steadily falling average inequality. The
next section will take a closer look at the pattern of changes over time.
.0
.1
.2
.3
.4
.5
.6
.7
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
Developing world as a whole
East Asia
Eastern Europe and Central Asia
Middle East and North Africa
South Asia
Population-weighted mean log deviation
Latin America and the Caribbean
Sub-Saharan Africa
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Inequality and Growth across Countries
As is well known, the developing world has seen substantially higher growth rates since
2000. Have the processes of stronger economic growth in developing countries, and the
acceleration of growth seen since the turn of the century, put upward pressure on inequality?
The recent signs of rising inequality in the developing world as a whole have coincided
with the higher growth rates since 2000 (Figure 3). However, when we un-pack the relationship
(exploiting the properties of the MLD) we find two opposing forces: the higher growth rates
have come with higher between-country inequality but lower average within-country inequality
(Figure 4). Across the developing world as a whole, and taking 3-year periods back to 1981, the
periods of higher growth have come with higher between-country inequality (r=0.70), while
inequality within developing countries has been falling with higher growth (r=-0.63). But the
latter relationship is clearly rather flat; going from zero growth rate to a 5% annual rate is
associated with a move from an expected rise in MLD of 0.005 (about one third of the standard
deviation in the annualized change in MLD) to no change on average. Growth in average
household income appears to be close to inequality-neutral on average.
These are aggregates across countries. I also assembled a data set of the longest spells
between two household surveys, and I obtained complete data for about 100 countries. The
median year of the first survey is 1989 and it is 2008 for the latest survey. The (un-weighted)
mean MLD is unchanged at 0.318. (Population-weighted values are slightly lower.) However,
the variance has fallen, from a standard deviation of 0.195 to 0.171 for the latest year. Inequality
fell over time in 11 of the 13 countries that had MLD greater than 0.576the aggregate value
(including the between country component) in 1990. The annualized change in MLD is
negatively correlated with its initial value (r=-0.33, n=122) and the same is true for the Gini
index (r=-0.55; n=122). This pattern of inequality convergence is consistent with other evidence
(Bénabou, 1996; Ravallion, 2003). While measurement errors exaggerate the signs of
convergence, it remains robust to the use of an econometric estimator that addresses this concern
(Ravallion, 2003).
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Fig. 3: Growth in average income and changes in inequality for the developing world as a
whole
Note: The year labels refer to the end year of three-year periods (two years for 2010)
Fig. 4: Growth rates and changes in inequality between versus within countries
-.024
-.020
-.016
-.012
-.008
-.004
.000
.004
.008
.012
.016
-1 0 1 2 3 4 5 6
Growth rate in household income per capita (% per annum)
Change in inequality in the developing world (MLD)
2010
2008
2005
2002
1987
1996
1984
1993
1990
1999
r=0.57
-.03
-.02
-.01
.00
.01
.02
-1 0 1 2 3 4 5 6
Growth rate in household income per capita (% per annum)
Change in within-country inequality (annualized)
Change in between-country inequality (annualized)
Change in inequality (MLD)
r=-0.63
r=0.70
8
These data suggest a small negative correlation between changes in inequality and growth
rates amongst developing countries. The correlation coefficient between the growth rate
(annualized log difference) in the survey mean and the annualized change in MLD is -0.24,
which is significant at the 5% level. Using growth rates from the national accounts instead of
surveys the correlation drops to -0.04. Using the Gini index rather than MLD gives similar
results. Nor did I find evidence of nonlinearity in the relationship; regressing the change in
inequality on both the growth rate and the annualized change in the squared log mean, neither
coefficient was significant. (See the supplementary notes for details.) So, again, inequality
increases about as often as it falls during spells of growth. This finding is consistent with past
evidence using cross-country comparisons (as surveyed in Ferreira and Ravallion, 2009).
It is not then surprising that there is a strong negative correlation between growth rates
and changes in absolute poverty. Across the regional averages used above, the correlation
coefficient is -0.75 between growth rates in mean household consumption or income and the
annualized changes in the log of the poverty rate for a poverty line of $1.25 a day at purchasing
power parity. Across countries the corresponding correlation coefficient is -0.85. This is
consistent with a large body of cross-country evidence back to around 1990, as reviewed by
Ferreira and Ravallion (2009).
There are a number of caveats that should be noted about this negative correlation. There
are measurement errors to consider. There are some rather extreme values in some of the country
data that may well reflect such errors. However, the correlation is still high at -0.70 (n=88) if one
trims extreme values (dropping absolute log differences per year over 0.5). The correlation is
also robust to the use of an econometric correction for correlated measurement errors between
the poverty measures and the survey means (Addendum). The correlation is lower if one uses
growth rates from national accounts instead of those from surveys. (The correlation between
annualized log differences in the $1.25 a day poverty rate and the growth rates in private
consumption per capita from national accounts is -0.56.) Some of the things that are included in
the national accounts do not get passed on quickly (or at all) to household living standards. Also,
survey dates do not cover entire years as do national accounts. Another caveat is that the
correlation tends to be much weaker using relative poverty measures, whereby the poverty line
rises with average income (consistently with the cross-country relationship between national
poverty lines and average income); see Chen and Ravallion (2013).
9
An important caveat is that the fact that we do not see rising inequality on average in
growing economies does not imply that inequality can safely be ignored. The rest of this paper
points to a number of reasons why inequality should be considered a central development issue.
Inequality Matters to Progress against Poverty
Does higher inequality in poor countries permit faster progress against poverty through
economic growth? While most development economists would probably have answered “yes”
even 10 years ago, recent development theories and evidence are more suggestive of a negative
answer.
The performance of developing countries against poverty is quite diverse. Inequality
comes back into the story in efforts to explain this diverse performance. We have learnt that
inequality plays three important roles in influencing the pace of progress against poverty. First,
changes in inequality during the growth process have implications for how much that growth
impacts on poverty. Using the country level data set discussed in the last section, I find that
amongst growing developing countries in terms of mean household income based on the surveys,
those experiencing falling inequality see the $1.25 a day poverty rate falling at a median rate of
about 1.30% points per year versus a median fall of only 0.42 % points per year for countries
with rising inequality. Either way, poverty incidence tends to fall, but at very different rates.
Second, initial inequality reduces the growth elasticity of poverty reductionthe
responsiveness of poverty measures to growth in mean income. This is intuitive: the more
unequal the original distribution, the smaller the share of the growth accruing to the poor, and the
lower the poverty reduction arising from that growth; this was demonstrated empirically by
Ravallion (2007). The converse holds too: in more unequal societies the poor tend to be more
protected from aggregate economic contractions.
Third, even if inequality does not rise during a period of economic growth, a high initial
level of inequality can mean less growth and (hence) less progress against absolute poverty. In
the 1990s, we started to see various theoretical arguments that high levels of inequality stifled
investment, innovation and reform. Here I shall only provide a sketch of the arguments that have
been made; Voitchovsky (2009) and Ravallion (2014) survey the arguments and evidence in the
literature on how the initial level of inequality influences the subsequent growth rate.
10
An influential strand of this literature points to the implications of borrowing constraints
associated with asymmetric information and the inability to write binding enforceable contracts.
Credit market failure that disproportionately affects poor people leaves unexploited opportunities
for their investment in physical and human capital, and it is assumed that there are diminishing
returns to capital, such that poor people have higher marginal products of capital. (This idea can
be extended to also embrace technical innovation, assuming that everyone gets new ideas, but
that the poor are more constrained in responding.) Then higher current inequality implies lower
future mean wealth at a given value of current mean wealth.
Other sources of economic distortions can create costly inequalities. This can happen if a
relatively privileged sub-group is able to restrict entry to economic opportunities (including jobs)
and thus set the returns to those activities above the market clearing level. Labor-market failures
in the form of persistent unemployment can also have lasting adverse consequences for both
equity and efficiency. Human capital is developed in part by working; thus longer spells of
unemployment create a de-skilling that makes it harder to get a job. Unemployment can also be
associated with psychological distress and depression. This psychological scarring may also
make it harder to get a job.
Another class of models is based on the idea that high inequality restricts efficiency-
enhancing cooperation, such that key public goods are underprovided, including legally-secure
property rights, or desirable economic and political reforms are blocked. High initial inequality
can also induce costly political economy responses.
Some of the literature has pointed to other aspects of the initial distribution of income
besides inequality. It has been argued that a larger “middle-class” helps assure a more diversified
economy (especially though greater demand for consumer goods) and that the middle class also
tends to be a stronger political force for pro-growth economic reforms. Others have argued that
higher current wealth poverty” (of which access to land is a key factor) impedes growth, such as
through access to credit.
A strand of this new empirical literature on economic growth has tested for inequality as
an initial condition and the results have generally supported the view that higher initial inequality
impedes growth. And the effect is quantitatively large, as well as statistically significant; two
recent examples are Berg et al. (2012) and Herzer and Vollmer (2012).
11
We have seen that the literature has pointed to various aspects of initial distribution
inequality, the size of the middle-class or povertythat matter to progress against poverty.
These distributional measures tend to be correlated with each other, yet very few studies have
tested encompassing models that try to see if one measure is more important than another. In
one exception, Ravallion (2012) finds that high poverty at a given initial mean matters more to
rates of growth in mean consumption in developing countries than inequality, or measures of the
middle class or polarization. This does not imply that inequality is unimportant, but rather it tells
us that inequality matters to growth in poor countries mainly via its bearing on the extent of
initial poverty.
So the arguments and evidence from modern development economics do not suggest that
we should expect any significant aggregate trade-off between progress against absolute poverty
and progress in reducing inequality. Indeed, the evidence suggests that falling inequality tends to
come with falling poverty incidence (Ravallion, 2005).
Differing Concepts of “Inequality
The value judgments made in measuring inequality carry weight for the position one
takes on whether economic growth tends to be inequality increasing or not (Ravallion, 2004). So
far, this paper has followed the long-standing practice of relying on relative inequality measures,
defined in terms of ratios of incomes or consumptions. By contrast, “absolute inequality”
depends on the absolute differences in levels of living. If the distribution of income amongst two
people changes from ($1,000, $10,000) to ($2,000, $20,000) then relative inequality is
unchanged, yet the absolute gap between the “rich” and “poor” has doubled.
Perceptions on the ground that “inequality is rising” appear often to be referring to the
absolute concept. Amiel and Cowell (1999) report experiments to identify which concept of
inequality is held by people. They found that 40% of the university students they surveyed (in
the UK and Israel) thought about inequality in absolute rather than relative terms. (Harrison and
Seidl, 1994, report similar findings for a large sample of German university students.) For the
purpose of this paper I fielded a similar (confidential) survey to my class of undergraduates at
Georgetown University. From the 130 responses (out of 150 students), the class was roughly
evenly split between those who thought about inequality in relative terms and those who thought
about it in absolute terms.
12
It is not that one concept is “right” and one “wrong.” They simply reflect different value
judgments. The relative inequality concept is implied by the scale invariance axiom, while the
absolute concept is implied by an alternative axiom called translation invariance. The former
axiom has dominated practice in the measurement of inequality by economists and statisticians
but the “axiom is hardly a self-evident truth.
In this light, let us return to the longstanding debates on growth and equity. Finding that
the share of income going to the poor does not change on average with growth does not mean
that “growth raises the incomes (of the poor) by about as much as it raises the incomes of
everybody else,as claimed by the Economist magazine (May 27, 2000, p.94). Given existing
inequality, the absolute income gains to the rich from inequality-neutral growth will of course be
greater than the gains to the poor. For example, for the richest decile in India, the income gain
from aggregate growth will be about four times higher than the gain to the poorest quintile; it
will be 15-20 times higher in Brazil or South Africa.
The empirical finding in the literature that growth tends to be inequality-neutral within
developing countries will carry little weight for those concerned about absolute inequality, who
prefer translation invariance to scale invariance. One expects an absolute measure to rise with
growth, and fall with contraction. I confirmed this on the aforementioned country-level data set.
Changes in the absolute Gini index show a significant positive correlation with growth rates in
either survey means (r=0.51, r=123) or consumption per capita from the national accounts
(r=0.75, n=114). Figure 5 plots the relationships.
It may well be that past and ongoing debates about the distribution of the gains from
growth in the developing world rest in no small measure on this (rarely discussed) conceptual
difference in how inequality is defined. Unlike those who see inequality as relative, those who
view it in absolute terms will expect to see a trade-off between reducing inequality and reducing
poverty. That does not mean that any policy that is good for one is necessarily bad for the other,
or that it is impossible to have both; the correlation is just thata correlation. However, it does
help us understand why some growth-promoting and poverty-reducing policy reforms may well
come in for serious criticism and even be blocked by a non-negligible number of observers
concerned about widening gaps in living standards between the rich and the poor. How policy
makers deal with that critique may matter greatly to progress against poverty.
13
Fig. 5: The concept of inequality matters to assessing whether inequality rises in growing
developing economies
Note: Absolute Gini indices scaled by the mean of initial and final years.
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(eds) National Bureau of Economic Research Macroeconomics Annual,
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Berg, Andrew, Jonathan D. Ostry and Jeromin Zettelmeyer, 2012, “What Makes Growth
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14
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Financialization could increase income inequality by producing a very high income of financiers and by doing harm to wages, investment, and thus aggregate demand in the real economy. This study investigates the effect of financialization and financial rent on inequality in developing and emerging market economies (DEEs) in the 1980-2017 period. Employing the dynamic system GMM estimator and using a new measurement, we find that financialization measured by financial rent and more asset concentration increase income inequality in DEEs. Among other financialization indicators, bank income before tax, stock trading value, market capitalization ratio, and oversized financial system are associated with income inequality. We also find that trade openness, education, and union density reduce inequality, while foreign direct investment, the unemployment rate, and GDP growth increase it in DEEs.
... Monetary poverty has nevertheless declined only min imal ly (38.6 to 35.7 per cent) between 1991 and 2007, with the absolute number of people living in poverty actually rising to 3.3 million (Arndt et al. 2016, based on Household Budget Survey consumption figures that exclude expenditures on durable goods). Furthermore, as regards inequality-well recognized as impeding future economic growth and progress towards poverty reduction (e.g., Ravallion 2014), and now prioritized as one of the seventeen Sustainable Development Goals to guide development over the next fifteen years-the picture is again mixed. Tanzanian society is showing widening wealth inequality between regions, gender, and the rural-urban divide (Maliti 2016, using DHS wealth data 2004, and both increasing and (latterly) decreasing horizontal inequality in education (Hassine andZeufack 2015, Maliti 2016, using HBS andDHS data respectively). ...
Book
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Tracking change in assets access and ownership in longitudinal research is difficult. Assets are rarely assigned to individuals. Their benefit and management are spread across domestic units which morph over time. And this dynamism means that any claim about changing prosperity must also include other important claims about how prosperity should be measured and the stability of the social units which experience that prosperity. The chapter reviews the challenges of using assets to understand poverty dynamics, and tracking the domestic units that own and manage assets. It argues that changing asset ownership can be tracked, but who owns them and how their benefits are distributed—and how those distributions change—remains key.
... However, this topic has a special relevance in economic studies, where income inequality has been extensively investigated (see, for example, refs. [12,13]). The most popular statistic used to measure inequality is the Gini index. ...
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The problem of missing data is a common feature in any study, and a single imputation method is often applied to deal with this problem. The first contribution of this paper is to analyse the empirical performance of some traditional single imputation methods when they are applied to the estimation of the Gini index, a popular measure of inequality used in many studies. Various methods for constructing confidence intervals for the Gini index are also empirically evaluated. We consider several empirical measures to analyse the performance of estimators and confidence intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely large biases under certain non-response mechanisms, and this problem gets noticeably worse as the proportion of missing data increases. For a large correlation coefficient between the target and auxiliary variables, the regression imputation method may notably mitigate this bias problem, yielding appropriate mean square errors. We also find that confidence intervals have poor coverage rates when the probability of data being missing is not uniform, and that the regression imputation method substantially improves the handling of this problem as the correlation coefficient increases.
... Over the past 25 years, trade flows have nearly quadrupled. At the same time, within-country income inequality, especially in developed countries, has been worsening rapidly (Ravallion, 2014;Nolan et al., 2019). This has led to renewed concerns that international trade is responsible for rising inequality, leading to scepticisms towards globalization. ...
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There have been concerns that international trade is responsible for rising inequality. However, existing empirical studies provide no consensus on this matter. This article studies the effect of trade on income inequality by applying meta-regression analysis on 40 years of empirical studies. We discover that the disagreement in the literature can be explained by differences in the development levels of the countries chosen by the studies and whether the endogeneity of trade is controlled for. When endogeneity is addressed, we find that trade may reduce income inequality in middle- and high-income countries, but has no statistically significant effects in low-income countries. Therefore, concerns that trade leads to more inequality could be overstated. Our work sheds light on how certain features in an empirical model of inequality on trade could influence the analysis itself, which provides some guidance on empirical design for future research.
... 为代表的经济学者认为,贫困是指家庭收入低、难以满足人类生存对食物和 营养等基本商品和服务的需求。20 世纪 70-80 年代,Sen [10,24] 提出了能力贫困理论,认为 贫困是个体发展能力的不足。2001 年联合国经济、社会和文化权利委员会 [25] 将贫困定义 为"个体的资源、能力、机会、安全和权利受到持续或长期性剥夺,难以享受基本生活 福利和其他公民、文化、经济、政治和社会权利" 。从贫困概念的演化历程来看,贫困具 有极其丰富的内涵。首先, "贫困"从词意上看既有"贫"也有"困"之意。前者描述数 量,表示缺少、不足或匮乏之意;后者刻画状态,可理解为困境、窘境。 "贫困"可以简 单地理解为一种缺少或不足的状态。其次,贫困是一个以人为主体的概念,反贫困的出 发点、聚焦点和回归点均是"人" [26] 。因此,贫困描述了作为行为主体的"人"在某一 方面缺少或不足的状态。再次,从福利主义的视角看,人类的生存和发展有着不同层 次、不同类型的福利需求,例如对基本营养和社会包容性的需求。贫困则刻画了人们在 福利方面缺失或剥夺的状态,这种状态既有可能来自于主观上幸福感、满足感以及安全 感的缺失,也有可能来自于就业市场与政策、区位条件和发展环境等客观因素的限制。 最后,贫困自身是一个"相对"的概念。 "相对"可以理解为:任意一方面的缺少或不足 总是与一个标准相比较,否则难以表达和度量。综上,我们可以将"贫困"理解为:与 一定标准相比,人们所享受的各种福利处于劣势、缺少或不足的状态。 贫困具有多维性、区域性、动态性和复杂性特征 [4] 。贫困的多维性主要表现在福利的 多样性。贫困表征福利的缺失与剥夺,包括经济、社会和环境 3 个维度 [23] ,分别对应经济 福利、社会福利和环境福利的剥夺。经济福利剥夺主要包括收入低下、消费不足、经济 增长缓慢等,经济发展水平低下是经济贫困的具体表现。社会福利剥夺则体现在教育/医 疗不足、机会不平等、社会排斥等方面,导致社会贫困。环境福利的剥夺主要通过自然 地理环境的劣势来体现,包括资源禀赋不足、区位条件差、生态环境脆弱、生态系统服 务供给不足或生态保护、环境污染严重等,容易出现生态贫困。贫困的区域性是指贫困 人口的空间分布具有显著集聚性特征。已有研究证实,在不同地域类型、不同空间尺 度,贫困人口主要聚集在偏远山区和少数民族地区 [18,23,[27][28] 。贫困的动态性是指贫困人口 及其贫困程度随减贫投入的增加和扶贫力度的加大在空间上发生动态变化,贫困的动态 性还与贫困标准或贫困线的变化密切相关。贫困的多维性和动态性决定了其复杂性,贫 困问题是一个社会经济系统和生态环境系统耦合失调的复杂的系统性问题。 2.2 绝对贫困与相对贫困 从贫困测量的标准和方法来看,贫困包括绝对贫困和相对贫困。绝对贫困又称"极 端贫困" ,表征人们难以满足生理或物质上的最低需要,在测量标准上更强调"极小 值" 。相对贫困指个人或家庭的收入及其所拥有的资源能够满足其基本生活需要,但难以 达到社会平均水平 [29] ,在测量标准上更强调"平均值" 。绝对贫困与相对贫困的差别体现 在 5 个方面:① 绝对贫困测量方法简单、方便,而相对贫困标准不一,难以测量。绝对 贫困测量是通过人类生存发展的基本需求来划分贫困线。中国脱贫攻坚期间家庭人均收 入 2300 元 (2010 年购买力平价) 以及现阶段世界银行每人每天 1.9 美元的贫困标准,都 是绝对贫困线。相对贫困测量是从收入分配的视角,以他人为参照,与平均水平相比存 在的差距和不足。相对贫困测量起源于 20 世纪 60 年代,在 20 世纪末的欧洲逐渐流行, 常以平均值或中位数的固定比例 (如 40%、50%、60%) 作为相对贫困线 [30] 。② 绝对贫 困反映了一个家庭或地区在收入或经济方面的总量和水平,而相对贫困更多用于表征收 入分配与区域经济发展中的不平等程度。③ 绝对贫困更多用于中低收入国家或欠发达国 家,刻画的贫困程度更高;相对贫困更多用于发达国家,刻画的贫困程度较低。④ 绝对 贫困使用相对独立的、唯一确定的标准,很少随着时间变化;相对贫困则随着地方收入水 平和生活条件而变化 [31] 。⑤ 在减贫成效上,绝对贫困在政府和市场的共同作用下可以解 决甚至消除,而相对贫困则主要依靠政府在二次分配中通过宏观调控得到减缓、难以根除。 当然,绝对贫困与相对贫困并不是完全对立的概念,两者也存在一定程度的联系。 ① 本质上来说,贫困是一个相对的概念。相对贫困和绝对贫困本身就是"相对"的,并 不能完全区分,是相互包含的 [9] 。绝对贫困的标准不是一个唯一值,是"相对"的。即使 从人类生存发展的最低营养需求来测算,由于食品价格波动,绝对贫困线也会随着社会 经济阶段的差异有所变化。相对贫困自身就包含着绝对贫困,这是因为其测量标准更 高,最终识别出的相对贫困人口和地区涵盖了绝对贫困人口和地区。② 绝对贫困与相对 贫困并存。当物质资源匮乏时必然会出现绝对贫困;而物质资源充裕时,由于难以实现 绝对平均,相对贫困也不会消失 [31] 。绝对贫困与相对贫困的共存共生意味着,不能只关 注其中一个而舍弃另一个,两者需要联系起来综合考虑。 2.3 区域贫困与个体贫困 按照瞄准或减贫对象划分,贫困包括区域贫困和个体贫困 [4,5,21,32] 。个体贫困以微观 层面的个体或家庭为瞄准对象,关注个人福利或能力的缺失与不足,一直备受学界关 注。收入贫困 [33][34] 、能力贫困 [10] 、权力贫困 [24][25] 、个体多维贫困 [35][36][37] 等都是当前贫困研究中 较为成熟的概念,均属于个体贫困范畴。区域贫困则是以宏观层面的行政区、特殊地区 或区域为瞄准对象,从空间出发关注区域层面影响个体福利的各类因素,即个体福利背 后的"区域福利" ,例如区域自然本底、资源禀赋、市场环境、发展政策、交通建设等。 空间贫困陷阱 [16,38] 、城市贫困 [39] 和农村贫困 [40] 属于区域贫困的范畴。某种程度上来说,区 域贫困是一种长期性贫困,而个体贫困是暂时性贫困 [21,[40][41] 。暂时性贫困容易消除,长期 性贫困难以消除 [4] ...
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China's poverty alleviation practice has proved that geography has played an extremely important role in supporting anti-poverty targeting and poverty reduction decision-making. However, due to vague basic concepts, lagging basic theories, and an imperfect discipline system, "theoretical poverty" has become the biggest shortcoming restricting the innovation and development of poverty geography. Based on the analysis of the core concept of poverty, this paper systematically analyzed the nature, basic theory, research object, research content, and framework of poverty geography as a branch of the discipline, and put forward the frontier areas of future research on poverty geography. The results show that firstly, poverty refers to the state of inferiority, lack, or insufficiency of various welfares enjoyed by people compared to a certain standard, which has multidimensional, regional, and dynamic characteristics. In terms of measurement standards, absolute poverty emphasizes the minimum value, and relative poverty emphasizes the average value. In terms of target objects, poverty can be divided into individual poverty and regional poverty. The former focuses on the lack and deficiency of individual welfare or capability, while the latter focuses on the regional welfare behind individual welfare from the perspective of space. Secondly, poverty geography is a discipline that studies the formation, distribution, geographic characteristics of poverty-stricken areas, their relationship with the environment, and anti-poverty measures. It takes the impoverished areal system (IAS) as the research object and the poverty-environment nexus as the research core. It has comprehensive, cross-cutting, and regional characteristics, focusing on the study of regional poverty. The basic theories of poverty geography include spatial poverty theory, regional poverty theory, multidimensional poverty theory, and sustainable development theory. Its research content and framework include three dimensions (economic, social, and environmental poverty), two elements (nature and human), two types of objects (regional poverty and individual poverty), and two standards (absolute poverty and relative poverty). Thirdly, there is an urgent need to strengthen the basic research of poverty geography in terms of the evolution of IAS, regional poverty measurement, relative poverty targeting, poverty dynamic monitoring and simulation prediction, urban poverty and rural poverty, poverty alleviation effectiveness evaluation, transformation and development and revitalization path of poor areas. In the situation that we face new challenges of poverty reduction and development at home and abroad, there is an urgent need to constantly innovate and develop the fundamental theory of poverty geography, promote the globalization of China's poverty research, and contribute to China's anti-poverty project to the eradication of global extreme poverty.
... Monetary poverty has nevertheless declined only min imal ly (38.6 to 35.7 per cent) between 1991 and 2007, with the absolute number of people living in poverty actually rising to 3.3 million (Arndt et al. 2016, based on Household Budget Survey consumption figures that exclude expenditures on durable goods). Furthermore, as regards inequality-well recognized as impeding future economic growth and progress towards poverty reduction (e.g., Ravallion 2014), and now prioritized as one of the seventeen Sustainable Development Goals to guide development over the next fifteen years-the picture is again mixed. Tanzanian society is showing widening wealth inequality between regions, gender, and the rural-urban divide (Maliti 2016, using DHS wealth data 2004, and both increasing and (latterly) decreasing horizontal inequality in education (Hassine andZeufack 2015, Maliti 2016, using HBS andDHS data respectively). ...
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