A joint Initiative of Ludwig-Maximilians-Universität and Ifo Institute for Economic Research

Globalisation, Inequality andWell-Being

CESifo Conference Centre, Munich8-9 November 2002

Poor Results and Poorer Policy: AComparative Analysis of Estimates of

Global Inequality and Poverty

Surjit S. Bhalla

CESifoPoschingerstr. 5, 81679 Munich, Germany

Phone: +49 (89) 9224-1410 - Fax: +49 (89) 9224-1409E-mail: office@CESifo.de

Internet: http://www.cesifo.de

Preliminary and Incomplete DraftComments Welcome

Poor Results and Poorer Policy: A Comparative Analysis of Estimates of GlobalInequality and Poverty

By

Surjit S. Bhalla *

Nov. 6, 2002

*Managing Director, Oxus Research and Investments, New Delhi, IndiaE-mail:ssbhalla@vsnl.com .

Paper prepared for the CESifo conference on Globalization, Inequality and Well-Being,Munich, Germany, Nov. 8-9, 2002. I would like to thank Nabhojit Basu for excellentresearch assistance.

This is strictly a first draft, it is in parts incomplete, and its results are preliminary.Comments, suggestions, correction of mistakes is therefore particularly welcome.

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Introduction

The turn of the century brought about considerable amount of stock taking on the part of

policy makers and multilateral agencies. The boom decades of the eighties and nineties

had just ended. Led by the United States, the rich countries had enjoyed an

unprecedented increase in prosperity and wealth – the longest, most sustainable post-

war expansion. During these twenty years, per capita GDP in the US had increased by

more than 50 percent. However, while the industrialized countries had enjoyed a boom,

several developing countries – particularly in Africa and Latin America – had been

buffeted by one crisis after another. Over in Asia, the currency crisis was still fresh in the

minds of most people. While political freedom had increased in the former Soviet Union,

the citizens were not so lucky with income growth; indeed, per capita incomes had

declined by about 25 percent, and inequality had worsened (implying a much larger fall

in the incomes of the bottom half of the population). So it did not seem to be a happy

ending for the world, just a happy twenty years for the Western rich.

Faced with these facts, the UN and associated multilateral aid agencies met at the

Millennium summit in September 2000, and made a declaration: “We will spare no effort

to free our fellow men, women, and children from the abject and dehumanizing

conditions of extreme poverty, to which more than a billion of them are currently

subjected” United Nations Millennium Declaration. The Summit also set targets for the

world community to achieve in fifteen years – these targets are the popular Millennium

Development Goals (MDG). The most recognizable of the targets is the one for

reduction in the proportion of people living in extreme poverty - from 29 percent in 1990

to 15 percent in 2015. The latest World Bank poverty estimates indicated that in 1998

poverty had declined by only 4 percentage points to 24 percent in eleven years since

1987. So the MDG goal setters naturally felt that there had been too little poverty

reduction, and that the development process was not, or more accurately had not been,

“pro-poor”; and the process was definitely not suggestive of any more ambitious target

than the “high” poverty level of 15 percent in 2015.

If so much growth in the developed world had been observed (and the ubiquitous stock

market boom, and Nasdaq quotes, were a constant reminder), and if poverty had not

declined by much, then it must have been the case that world inequality had

deteriorated, and worsened by a significantly large amount. For evidence, there was the

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recently published UN Human Development Report, 1999. The Report is unequivocal in

its statement about worsening world inequality:

“Inequality between countries has also increased. The income gap between the fifth ofthe world’s people living in the richest countries and the fifth in the poorest was 74 to 1 in1997, up from 60 to 1 in 1990 and 30 to 1 in 1960. In the nineteenth century, too,inequality grew rapidly during the last three decades, in an era of rapid globalintegration: the income gap between the top and bottom countries increased from 3 to 1in 1820 to 7 in 1870 and 11 to 1 in 1913”. (p.3, HDR(1999))

These three results – high growth, worsening global inequality and slow reduction in

world poverty – are most likely responsible for the discourse, and policy response, of the

world community to the deleterious effects of globalization. The MDG goals, the

prospects for meeting them etc. are already a rich research industry. Demands for extra

aid to meet these goals have also been made by the World Bank – it has asked for a

doubling of aid from the present $ 60 billion a year to a prospective $ 120 billion a year.

As noted above, per capita incomes in the rich countries doubled – there is little reason

not to double aid as well. Especially given the fact that the poor of the world have not

shared equally in the increased prosperity.

Extra aid forms only part of the “reform” package for the poor countries. Research is

underway, either financed or undertaken by the multilateral agencies themselves, to

design a new approach to development, an approach that would try and ensure that

equitable, pro-poor growth takes place. That such an approach was necessary, and

feasible, was articulated only a few months before the Millennium Summit Meeting in the

World Bank’s World Development Report – Attacking Poverty. This report does not deny

that growth is important; but it emphasizes that a growth only or “growth is sufficient”

strategy was wrong – as had been abundantly proven by the findings contained in it and

the findings of the UN Human Development Report1. The Report goes on to argue that

growth was one of three objectives; the other two were voice and empowerment for the

poor. And one method of achieving these other objectives was via asset and income

redistribution; data and analysis was presented to show that a given amount of growth

1 Each of these documents takes over a year to complete, involves hundreds of researchers, and is a flagshipforum for the policy advocacy of the respective organizations. In other words, these documents are notmere research outputs; instead, the reports are written to substantiate existing policy, or provide a rationalefor new policy.

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led to greater poverty reduction in more equal economies2. Summarizing, the new

Washington-New York consensus was that it was better, much better, to go for quality

rather than quantity – it was the quality of growth that really mattered.3

At the time of the MDG declaration, the in-house reports of the World Bank and UN were

almost the only voice on world trends in poverty and world inequality. But other

estimates were available. Schultz(1998) had indicated that world inequality had flattened

out at a high level i.e. there was no evidence to suggest that inequality was worsening.

Bourguignon-Morrisson(June 1999) supported the lack of inequality trend finding, but

their analysis ended in 1992. Milanovic(Oct. 1999), in contrast to both, and co-

incidentally a World Bank staff member, stated that inequality had not only worsened,

but worsened by a significantly large amount in just five years, 1988 to 1993. Unlike the

UN, he did not use the per capita US dollar calculations as his basis, but rather used the

“correct” PPP calculations. Even if everything was right with this paper (and most was

not), Milanovic’s calculations were in stark contrast to both Schultz and Bourguignon-

Morrisson, and policy makers should have paused to at least evaluate the relative merits

of the calculations. The worsening inequality conclusion should at best have been

tentative.

In June 2000, Bhalla presented estimates of both world inequality and world poverty for

the period, 1975 – 1997. Conclusions on inequality and poverty were strongly divergent

from the above received wisdom. World inequality had not only not stayed constant, but

instead had improved considerably; world poverty, rather than declining by only 4

percentage points, had declined by over 14 percentage points, to a level of 20.5 percent

in 1997. Improved estimates were recently published in Bhalla, Imagine there’s no

country: poverty, inequality and growth in the era of globalization (hereafter referred to

as Imagine). The conclusions are the same, almost identical, to the initial results

reported in Bhalla(2000b). The “new” reported poverty level for 1997 is 16.7 percent,

rather than the 20.5 percent reported earlier. The paper reported quintile shares for three

different distributions – world, developed economies and developing economies – and

for three separate years, 1977, 1987 and 1997. The equivalent world Gini in 1997,

according to the old method, was 67 ; today, the improved Imagine method number is

2 As shown later, this seemingly tautological result only holds when several restrictive conditions are met.3 See Thomas(2000) for a book with the same name.

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65.5. Additional new research on inequality and poverty, and not housed, or financed, by

the multilateral agencies, is beginning to surface. Since 2000, several pieces of research

have appeared, including those of Sala-I-Martin(2002a,2002b) who contends that far

from achieving the MDG goal in 2000, the world was already there in 1977! Reddy-

Pogge(2002) indicate that far from being an over-estimate, the World Bank poverty

numbers are too low!

Which of the many competing estimates is right, or at least more right than wrong – that

is the question for academics, and policy makers. And indeed, that is the question that I

will attempt to answer in this paper. Given that development policy is at least influenced

by estimates of world poverty and inequality, it is incumbent on all of us to at least get

“the facts right”. This paper, an extension of Imagine…, provides different pieces of

evidence so that the reader can decide for herself what happened to world growth, world

inequality and world poverty, and whether the three sets of evidence are consistent with

each other.

The evidence presented leaves little doubt that something is drastically wrong with the

official World Bank figures on world poverty. The levels, and trend, indicated by these

data are not consistent with the World Bank’s own evidence on growth and poverty in

developing countries. The calculations of poverty involve manipulation of data pertaining

to both means and distributions for several countries and several years. One can, and

does, get lost in the trees. It is possible to state the inconsistent given all the “confusion”.

But the means forest is not that difficult to traverse. Indeed, it is a very straight road. If

there was only one country, the mean growth rate between any two points is the

simplest calculation. If there are two or more countries, the computation is marginally

more complex in that the respective means have to be weighted by the respective

populations. What is disturbing is our tentative conclusion that the mean growth rate of

consumption between 1987 and 1998, presented in the later, final version of Chen-

Ravallion’s paper “How did the world’s poorest fare in the 1990s”, the paper that

contains the World Bank estimates of world poverty trends since 1987, cannot be

reproduced. The authors state: “imagine if all household consumptions in all countries

grew at the growth rate in the population-weighted survey mean cross our entire data

set, namely 0.90 % per capita per year between 1987 and 1998” (p.16). No such survey

mean growth is reported in the earlier version of the paper. The mean growth rate

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obtained by us using World Bank data only, as posted on the website

www.worldbank.org/research/povertymonitor, and reproduced in Table ?? and Annex

Table 1, is less than half that reported by the World Bank, or 0.4 percent per annum

overall, or 4.5 percent in the aggregate rather than 10.4 percent.

But this is not the only statistic that is obviously wrong. The World Bank publishes its

poverty figures for individual countries and for different years in its publication World

Development Indicators. This is an annual publication and to date it has been impossible

to compile the poverty figures for all the countries for a single common year. Hence, it is

impossible to use only the World Bank published data on poverty to see if the numbers

actually add up. However, the Bank has published in the above document poverty

figures for East Asia, and East Asia excluding China, for several years between 1987

and 1999. From this, the China figures can obviously be derived and are reported in

Table ??. These numbers suggest that the head count ratio of poverty in China was 27.9

percent in 1987, that poverty increased to 31.7 percent in 1990 and then dipped to 29.4

percent in 1993. During this six year time-period, inequality was worsening and it is

certainly possible that poverty increased by 1.5 percent. It is possible but not plausible

given per capita growth rate of at least 30 percent over this time-period, and a decline in

the share of the bottom 20 and bottom 40 percent of less than 20 percent. In other

words, the mean consumption of the poor must have increased by at least 10 percent,

and therefore poverty increase is a statistical impossibility. Further, these same World

Bank figures for China suggest poverty levels stagnant at 17 percent for 1996, 1998 and

1999. Neither household surveys nor national accounts data suggest a per capita

consumption growth rate of less than 10 percent for these three years, nor do survey

data suggest any worsening in the distribution of income. Hence, stagnant poverty levels

in the face of net growth of 10 percent is an impossibility given the distribution figures

reported by the World Bank on its website.

In Imagine… Bhalla presents and analyzes the different poverty estimates published for

that other large and poor country, India. The World Bank poverty figures are shown to be

particularly off, and off by about 10 to 20 percent, or between 100 and 200 million. The

book also documents the fact that the widely available official figure for mean survey

consumption in 1993-94 for India is about 8 percent higher than the implied World Bank

figure for the same year! The reason implied is because curiously the World Bank does

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not report on its web-site the mean survey consumption for this important benchmark

year, but publishes the results for the surveys for 1992 and 1994!

Further, the World Bank mean per capita consumption figure reported for India for 1997

implies that Indians were all dead in 1950, if not in 1960. Projecting backwards from the

World Bank survey consumption in 1997 (using growth rates yielded by the national

accounts, the same method as used by the Bank for its projections, see section 2) one

obtains the mean consumption of an Indian of 11.4 cents a day in 1950 (1993 PPP) and

18 cents a day in 1960. “To reiterate, these are estimates of the mean consumption of

the entire population, not the poor population, at constant 1993 prices. History (at least

since 1950) has yet to record such low levels for any economy. The lowest consumption

level (NA data) for any country in 1950 was 44 cents a day for China. For 1960, the

lowest average consumption level was in Tanzania, at 60 cents a day” (Imagine…,

p.100).

Whether taken at an individual country basis for two countries India and China, or at an

aggregate level for all countries, the World Bank data that is possible to reproduce

cannot be reproduced. It is possible that some of my calculations for the World Bank

data are incorrect, though cross-checks to date have not yielded any error. The revised

draft of this paper will contain the triple and quadruple rechecked estimates. In the

interests of helping find some of the possible errors, I have reproduced the mean

consumption data used for deriving the mean consumption estimates for 1987 and 1998.

(Annexure Table 1). It is unlikely, however, for the entire different set of reproduction

calculations (for India, for China, for the different regions, etc.) are in error. However,

given the shocking nature of these findings, I am quite willing to accept that possibility.

If the official World Bank statistics on poverty are “wrong” and/or their computations

cannot be reproduced, then important policy questions are raised. Most importantly,

“who regulates the regulator?” And second, how did it happen? Bhalla(2002b) tentatively

explores the hypothesis that this happened because of the monopoly nature of the World

Bank in the funding of research, and in its monopoly of data on poverty. Monopolies get

sloppy, they always do – just ask IBM about PCs or ask Microsoft about surfing the

internet. This monopoly is now ending, or started ending with wide availability of

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computers and the advent of the internet. What took an army of researchers to compute

just a decade ago, and lots of money, can now be done by a small outfit in New Delhi.

Section 2 discusses the data and the bricks used by every estimator of inequality and

poverty. This discussion helps identify the sources (assumptions) behind each of the

different estimates, assumptions that are likely to drive the results. Section 3 examines

the methods for computing poverty.

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Section 2: The bricks and mortar of analysis of inequality

There is a lot of hype about the complicated procedures required to estimate world

inequality and world poverty. While the steps may be many, the procedure is simply one

of counting and accounting. This is why Bhalla(2002d) calls his method of calculating

inequality and poverty as the “Simple Accounting Procedure” or SAP. What is not

recognized is that most if not all of the different estimates use mostly the same data, and

the same procedures, to estimate means and distributions. Most recently,

Milanovic(2002) pretends that his use of the data is “different” and, by implication, his

estimates of global inequality are more accurate. This version of the reality was met by a

sharp response from Sala-I-Martin (2002c). Analogously, Ravallion(2002) contends that

Imagine… uses questionable procedures to generate means and distributions for years

when surveys do not exist. That Ravallion, and the World Bank, use exactly the same

procedures, is documented below and the reader is also referred to Sala-I-Martin’s

response to Milanovic mentioned above.

All inequality and poverty estimates require data on the distribution of income4 and its

mean. There is only one source for the distribution, and that is obviously from household

surveys. But there are two sources for the mean – that obtained from national accounts

data (NA) and that obtained from surveys. Data for income are proxied in the national

accounts by GDP per capita, and for consumption, by private final consumption

expenditure. At any particular point in time, the survey mean will diverge from the NA

mean. This is to be expected, since definitions differ e.g. the NA mean is GDP per

capita, the survey mean is personal income per capita5. There is a closer

correspondence in the consumption measures; the two sources differ in coverage of the

institutional and NGO population (the NA includes them, surveys exclude).

While levels differ at a point in time, there is little reason for the growth rates to be

different. And indeed, until the mid-eighties, the survey and NA growth rates observed in

most parts of the world were very similar. This parallel movement in the means became

4 Most poverty estimates require data on the distribution of consumption; as “short-hand” notation, incomewill be used to denote both income and consumption.5 For a few countries (e.g. US) NA data on personal incomes are easily available; for several countries, thisis not the case. For consistency reasons, most analysts prefer to use GDP per capita data even whenpersonal income data are available.

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particularly haphazard over the last fifteen years – and particularly hazardous for

calculations of inequality and poverty. The survey means are capturing less and less of

the national accounts, and this is happening both in the developed economies (e.g. the

USA) and developing economies, e.g. India. In population weighted terms, the mean

consumption captured by the surveys is about 10 percent lower in the nineties than in

the mid-eighties. Why this is happening is a major research undertaking; a likely cause is

the wider choice of consumption items (which do not make it to the interview list of

questions) and the increasing opportunity cost of time (people do not have time for the

typical 5 to 6 hour interview – they have other work to do).

Regardless of the reasons, the fact that survey data are capturing 10 percent less today

means that the growth of consumption is under-estimated by 10 percent. Which means

that both the trend decline in poverty as well as the level, is about 5 to 6 percentage

points lower today than conventionally assumed. Which means that even the World

Bank poverty estimate of 23 percent in 1999 is likely closer to 17-18 percent. Which

means that about the time the Millennium declaration was being made, the goal of 15

percent poverty in 2015 was already reached, and done so not by somebody else’s

calculations, but as revealed by a correct application of World Bank’s own data.

What if survey data not available for the year of interest:

All estimates require a transformation of the data from the year for which survey data are

available to the year for which computation is made. For example, if there was one

survey in a hypothetical country called Surmeth, in 1970 and the next in 1990, and one

needed to compute the poverty estimate for 1987, what does one do? One needs both

the mean and the distribution for that year. And all analysts use virtually identical

methods for both. The distribution is assumed to be the same as in 1980 on the grounds

that there is no other information. And mean growth is taken from the national accounts;

again, there is no other information.

This updating procedure makes a mockery of the World Bank’s “only survey” data

approach. In the US, a country whose practice the World Bank is following, there is a

household survey every year. So no updating with NA is required in the US. But most

developing countries do not have this luxury, especially since monies are required to

fight poverty “in the name of the many poor”. For such countries, use of the NA data for

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deriving growth for the many more intervening than survey years has two implications: (i)

the resulting time-series on mean consumption will be considerably closer to the NA

series in terms of growth; and (ii) this series will have up and down spikes in the survey

year. The question to be addressed by all is whether the mal-adjusted survey

consumption series is in any way preferable to an objectively adjusted NA series.

The above adjustment procedure, common for intervening years, also applies to years

prior to the first survey, and years after the last survey. Surmeth had its last survey in

1990, and yet we need to make an estimate of poverty for 2000. what should one, what

can one, do? No new estimate of distribution is available, so one takes the last such

estimate available – the one in 1990. What about the mean: no new survey estimate is

available, so if one believes in adjusted NA a la Bhalla(2002d), one takes the mean as

85 percent of the NA mean6. If it was the old World Bank method (i.e. the one used for

twenty years prior to 1989), or the method used by the Indian government prior to 1996

(i.e. the prevailing method before publication of the Expert Committee Report on the

measurement of poverty, EGGO(1993)), one would take the actual NA mean without

adjustments. If it were the World Bank today, then one takes the survey mean of 1990

and adjusts it upward according to the per capita consumption growth (between 1990

and 2000) as measured by the national accounts.

Note the large role of NA in the new World Bank “only household survey” based method

of computing the mean consumption, and therefore poverty, in 2000. Assume the NA

mean was 100 in 1990, and the survey mean in that same year was 70. Now assume

the per capita growth rate during the nineties was 3 percent per annum, or a cumulative

increase of 35 percent over 10 years. The survey based method would assume that per

capita consumption in 2000 was 70*1.35 or 94.5. In terms of composition, NA

adjustment is about 20 percent of the survey mean in 2000. So the claim that the survey

based method somehow does not include large elements of NA adjustments is plain

false. All analysts use NA growth rate to update. This simple necessity makes it difficult

to accept the survey purists claim that use of NA means is unwarranted.

What if one needed to project the poverty level backwards to 1950, as done by

Bhalla(2002d), and Bourguignon-Morrisson who indeed project backwards all the way to

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1820? Exactly the same method as projecting forwards – there is, conceptually, zero

difference. Thus, ravallion’s claim that “…..” is false. An analyst, or the World Bank, can

choose not to project backwards, but it should realize that it has no intellectual basis for

not doing it. Note that the classic, and the first paper on world poverty published in 1979

was by three World Bank economists Ahluwalia, Carter and Chenery. They projected

mean per capita income both backwards to 1960 and forward to 2000, and used

identical methods for doing both.

What if no survey data are available

For some, and for especially poor countries like Afghanistan, North Korea (and now after

twenty years of war, devastation and sanctions, Iraq) no survey data may be available.

The “purist” new World Bank method would dictate that one calculate poverty on the

basis of available data, and then impute the resulting poverty ratio (24 percent in 1998)

to the population of these countries i.e. the ratio stays the same as computed, but the

number of poor gets adjusted upward by 24 percent or ?? million.

There is another inexact method of computing poverty in countries without a national

survey, and is the method adopted by Bhalla. The distribution for these countries is

assumed to be the corresponding regional distribution. This assumption yields the

number of poor in the above three countries as ?? million, or almost twice?? The amount

assumed by the other inexact method.

What if no NA or survey means are available?

This is a problematical situation, but fortunately, due to the pioneering efforts of

Maddisson(2001), it is the case that population and mean income estimates are

available for every country in the world for the post World War II period.7 Sala-I-martin

(2002a,b) does not include the countries of the former Soviet Union in his estimates of

global inequality trends for the period 1970-2000. Mr. Martin excludes these countries on

the grounds that the Summers-Heston 1985 PPP data does not have estimates for these

countries. Since data, and methods, of incorporating these countries exist, it is

6 The next section explains the rationale for the large downward adjustment to the mean.7 These estimates are based on the ?? PPP method, with 1990 as the base. Using the method of chain-linking, 1990 incomes are adjusted to 1993 PPP prices by inflating the Maddisson estimates by ??.analogously, if PPP 1993 data are not available (for countries like ?? and ??), but 1985 data are available,

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somewhat surprising that Sala-i-Martin does not include them. Milanovic(2002) is

strongly critical of this omission and believes that Martin’s results on a declining trend in

inequality (identical to those reached by Bhalla(2000/, and 2002?) would not have been

possible if the data for the formerly SU countries had been included. As discussed in

section ??, milanovic is incorrect in his surmise – the declining trend in world inequality

remains very much intact and is indeed completely unaffected by data on soviet Union

republics.

How detailed the data on distribution?

In the pioneering effort of constructing a world inequality distribution, Berry et. al.

(1981,1983) use the data on quintile distributions for each country. For example, if the

mean income for 1970 is known for India, say u, as well as the share in the distribution

of each quintile, say x, then the mean income of any quintile is 5xu. This mean is then

assumed to be the income of every individual residing in that quintile. This is obviously

an approximation, and equally obviously somewhat incorrect. Schultz(1998) computes

inequality estimates for all the years 1960-1989, and imputes the aggregate mean

income u to all the residents in a country. The resulting inequality estimates are very,

very different. Berry et. al. obtain a Gini of 66.2 while Schultz’s estimate is a

considerably more equal Gini level of around 55 (See Table 1). This 17 percentage

difference in the Gini is extremely large; simulations suggest (see Imagine, p. 79-80) that

this difference is equivalent to the relative incomes of the people in the West being half

as high in Schultz (compared to the relative income implied in the Berry et. al

calculation). Clearly, therefore, the detail of individual country distribution is very critical

to the overall assessment of inequality, and its trend.

Several authors, post Schultz, incorporate quintile adjustments into the data – see

Korzeniewicz-Moran(1997) , Bourguignon-Morrisson, Milanovic and Sala-I-Martin. Their

results are also similar. The only “outlier” is Milanovic who alone amongst all the

analysts computing world distributions uses survey means for income, rather than

national account means.

then the latter are adjusted upwards by a factor of ??, which is the ratio of nominal per-capita incomes in1985 for all countries for which both sets of data are available.

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While “pure”, the Milanovic assumption does not pass various smell tests of consistency

and reality. For example, Milanovic pools in consumption and income distributions, and

the respective means, for all countries. The former assumption (income distribution is

the same as consumption distribution when the other is not available, and vice-versa) is

reasonable and within most spirits of estimation , but the latter assumption has no

justification. Surely, the simple expedient of adjusting the consumption mean upwards by

the inverse of one minus the consumption ratio would give more plausible results for the

mean income in the economy. Since everyone in the economy does not save at the

same rate, this simple adjustment is incorrect; but it may be considerably more accurate

than if this assumption was not made. With his assumption of using whatever distribution

is available, Milanovic is forced to conclude that Korea was richer than England and

Sweden in 1993, that India was substantially poorer than Ethiopia in 1993.

The different steps involved in estimation of income or consumption distribution is as

follows;

(1) Assemble data on distributions for each country for as many years as possible. This

kind of exercise first started with a World bank effort in the mid-seventies (Jain(1975)

and has been supplemented in recent years by the Deininger-Squire (World Bank) data

base (1995). The UN sponsored WIDER institute in Helsinki extended the Deininger-

Squire data set; together these two data sets have over 2000 distributions. However,

usable distributions are less than a 1000. The difference is due to repetition, overlap

and slight difference in definitions. Further, both sets of distributions share a curious

problem – the share in income (or consumption) of a lower quintile is higher than the

share of the higher quintile i.e. a definitional impossibility. Surprisingly, this error occurs

in even the most “clean” of all the distributions – the “accept” quality data set contained

in Deininger-Squire

Table 1 documents the results obtained according to the various studies, and Table 2

contains the results according to different definitions etc. and based on the data

contained in Bhalla(2002d). The first table is ordered according to the date of the

circulated draft, and it is seen that Bourguignon-Morrisson were a significant departure

from all previous studies. The “innovation” was not only in terms of coverage (1820-

1992) but also in using different survey data for the same country over time if such data

15

existed.8 The method was one of adding up the distributions for the different countries

(regions for Bourguignon-Morrisson) to obtain a world Lorenz curve for each year. The

8 Note that the original study on this topic was by Berry-Bourguignon-Morrisson in 1981 and 1983. Thesepapers used the same distribution for each country from 1970 onwards.

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Table 1: World Individual Income Distribution - Some EstimatesMoran Schultz B-M Milanovic Bhalla Bhalla Sala-I-Martin Sala-I-Martin

(1) (2) 1997 1998 1999 1999 2000 2002 2002 2002

1960 65.8* 54.7 63.5 66.41970 66.2 57.5 65 68.6 65.7 63.31977 74.41980 68.2 55.3 65.7 68.5 66.2 63.81987 54.8 62.5 69.4 67.3 65.0 62.61990 74.0 55.2* 65.9 67.5 65.4 63.01992/3 65.7 67.0 63.9 61.51997 67.0 65.5 63.3 60.92000

Source: 1. Bourguignon, Francois and Morrisson, Christian "Inequality among world citizens: 1820 -1992", June 1999; final version 20022. Chen, Shaohua and Ravallion, Martin "How did the world's poorest fare in the 1990s?",Review of Income and Wealth, Sep 20013. Sala-i-Martin, Xavier "The world distribution of income (Estimated from individual countrydistributions)", NBER Working Paper, May 20024. Bhalla, Surjit S, "Imagine There’s No Country: Poverty, Inequality, and Growth in the Era ofGlobalization", IIE, Sept 20025. Bhalla, Surjit S, "Trends in world poverty - Ideology and Research", mimeo presented in IMF6. Milanovic, Branko "True World Income Distribution, 1988 and 1993: First calculation basedon household surveys alone", Oct 19997. Korzeniewicz R.P. and Moran T.P. "World-Economic Trends in the distribution of Income,1965-1992", Jan 1997

17

Table 2: World Distribution (Gini) of Income - Some Simulations 1950 1970 1980 1988 1990 1993 1998 2000

WorldNA-Percentile dataPPP $, 1993 base 68.5 68.6 68.5 67.2 67.5 67.0 65.5 65.1Current US$ 76.8 77.3 80.8 80.4 81.6 80.5 80.7Survey-Percentile dataPPP $, 1993 base 71.7 72.8 73.8 72.8 72.4 70.4 64.8 64.5Current US$ 75.3 75.4 79.1 79.2 79.9 80.0 80.2NA-Quintile dataPPP $, 1993 base 67.6 67.6 67.5 66.0 66.2 65.4 63.7 63.3Current US$ 75.8 76.3 80.3 79.7 80.8 79.8 80.1Survey-Quintile dataPPP $, 1993 base 70.8 72.0 73.2 72.1 71.5 69.1 63.0 62.7Current US$ 74.2 74.3 78.5 78.4 79.0 79.1 79.5

Developing WorldNA-Percentile data, PPP $, '93 base 59.1 60.6 62.4 57.3 56.7 57.2 55.4 55.2Industrialized WorldNA-Percentile data, PPP $, '93 base 47.5 39.6 36.4 37.7 37.7 37.1 37.5 37.9

Notes: 1. Above simulations are all based on the Simple Accounting Procedure, (SAP) data set containedin Bhalla, "Imagine There’s No Country: Poverty, Inequality, and Growth in the Era ofGlobalization", IIE, Sept 20022. NA refers to per capita PPP according to National Accounts data; survey data refers to meanincomes as reported in household surveys.3. Distributions are formed from readily available quintile data; these have also been converted intopercentiles following a procedure outlined by Kakwani (1980). See Bhalla (2002d) chapter 8 andAppendix II for details.4. Distributions are reported for two definitions of per capita income - PPP (1993 base) and in

current US dollars.

18

authors also report on using a kernel curve to derive the percentile distribution for the

world Lorenz curve, a procedure also followed by Sala-I-Martin. This quintile to

percentile distribution is an approximation, but is nevertheless necessary to construct

inequality indices and poverty levels.

The comprehensive and trend-setting Bourguignon-Morrisson study was soon followed

by Milanovic and Bhalla. Milanovic’s departure is to use only survey data for both the

means and the distribution, and this departure is taken to an extreme in that a

consumption distribution, or even its mean, is not transformed to conform to an income

mean. In other words, if only a consumption distribution was available, it was assumed

that that mean was the mean of the income distribution. Bhalla, following Bourguignon-

Morrisson and all other authors before who had worked on world income distribution,

uses national accounts means, transforms a consumption distribution into an income

distribution if the latter was not available9, and extends quintile distributions into

percentile distributions. Over the last three years, three separate methods have been

experimented with; first, a simple interpolation between quintile means, using inter-

percentile growth relationship from several unit record distributions for consumption in

India, and income in Malaysia, and third, using the Kakwani(1980) method of generating

percentile distributions from limited, grouped data (e.g. quintile data). The Kakwani

method yielded the best results (in terms of errors for each percentile computed against

the benchmark unit record data). However, his method did not always yield theoretically

correct computations e.g. the sum of the shares for each percentile did not add up to

unity. A filtering procedure was developed to make the Kakwani model predictions

“conform” to theory.

In the end, the filtered Kakwani method was so accurate that it was able to “clean” the

distribution data as published in Deininger-Squire and WIDER. Simple consistency

problems present in these data were described above, and Atkinson-Brandolini describe

in detail about the problems with several seemingly “correct” distributions present in

these data sets. In Bhalla(2002d), 10 pairs of distributions are documented to show how

9 There are 27 country year observations for which both an income and consumption distribution wasavailable. By regressing each quintile share of consumption on income (and vice-versa), eight“transformation” equations are obtained (the fifth decile absorbs all the “errors” and is computedresidually). These equations allow one to compute income inequality and poverty estimates in a consistent

19

the Gini coefficients are not consistent with the underlying reported data. This

consistency check was a major input into the selection of “one distribution per country

per year” from the many reported in the published data sets. Thus, it is likely that among

all the studies pertaining to computation of world income distribution, the Bhalla study

worries the most about “cleaning” the data.

The results in Tables 1 and 2 yield the following conclusions:

(1) Studies based on either one mean per country distribution or one mean per

quintile per country distribution tend to severely under-estimate the level of

inequality at any point in time.

(2) All studies based on PPP data (whether 1985 or 1993 base) show that inequality

declined post 197010. Only Milanovic’s study shows a deterioration, but his

assumptions are also the most questionable - not adjusting survey means by

national accounts, and assuming that expenditure levels are representative of

income levels. In other words, the ratio of incomes in the US to that in India, is

the ratio of incomes in the US to consumption in India! not mixing expenditure

and income data, but assuming survey means are correct, results in a very sharp

decline in world inequality in the nineties (see Table 2).

(3) The dollar per capita GDP calculations of Gini inequality are about 25 percent

higher than the corresponding PPP calculations, using identical data for

distributions. This implies that mean incomes in the West, and therefore

purchasing power, is more than double (about 125 percent) higher than that

suggested by PPP calculations.

fashion. The average consumption Gini is about ?? points lower than the average income Gini, a numbernot that different than the one suggested by both Berry et. al. and Deininger-Squire.10 The interesting exception here is the Bourguignon-Morrisson study which shows that inequality stayedconstant at a Gini level of ??. One explanation for this divergence could be the pooling of all the countriesinto 33 regions that the authors have to do in order to project backwards till 1820. If some of the poorcountries that have been pooled show faster growth than the average (e.g. Bangladesh, Viet-Nam etc.) thenthe Bourguignon-Morrisson method will be biased against showing an improvement.

20

Section 3: Methods of calculating the head-count ratio of poverty

Developing country analysts have tended to use NA means in their computation of

poverty. The first estimate of world poverty was made by three World Bank economists –

Ahluwalia, Carter and Chenery in 1979. They used national account means. The first

domestic poverty line (in the post-War period) originated in India; the Indian government

used NA means. Latin American economists (see Altimir(1981)) used NA means.

Indeed, until the late eighties, only US analysts estimated their poverty purely on the

basis of household survey data.11 The World Bank started using survey means with the

publication of the World Development Report of 1990, and the Indian government soon

followed this change in practice. As noted by both Bhalla(2000a) and Deaton(2001) no

satisfactory reason was ever given by the Indian government for this major change in

procedure – a “new” procedure which had well known, and automatic reasons to reveal

more poverty than previously indicated.

If distribution of consumption is available (obtained in an analogous fashion to income

distribution) then only one important step remains before determining the magnitude of

poverty and its trend. The step pertains to the specification of the poverty line. The

popular consumption less than one PPP $ a day, 1985 prices, is used by most

researchers, but important differences obtain.

Origins of the international poverty line of a dollar a day:The origin of the international $ a day poverty line are in the Indian poverty line of 1962!

The Indian Planning Commission defined a line in terms of per capita consumption. The

first international poverty study was published by three World Bank authors in 1979;

entitled Growth and Poverty in Developing Countries, this pioneering Ahluwalia-Carter-

Chenery study used the survey consumption line in Indian rupees and transformed it into

a national accounts PPP (then Kravis $) income line. This transformation was achieved

via use of the income level corresponding to the 45th percentile person in India, the

specific percentile having been obtained by noting that based on a consumption

11 A little commented upon result is that US poverty rates in 1968 and 2001 are virtually identical at around13 percent of the population. Inequality there has worsened, with the share of the first quintile declining by15 percent. Personal incomes in the US 1968-2001 has increased

21

distribution, 45 percent of Indians were poor. The Indian poverty line adopted as an

international poverty line was almost exactly equal to one PPP $ a day, 1985 prices.

But Ahluwalia-Carter-Chenery did not use one dollar a day as their poverty line; they

used $ 1.25 a day. The 25 percent increase occurred because the authors noted that in

the seventies, Indian household consumption surveys were only capturing about 80

percent of national accounts consumption. This transformation allowed them to convert a

survey based line into a 25 percent higher NA based line. Further, needing an income

based line12, the authors raised the poverty line by a further 43 percent to account for an

average saving rate of 30 percent that was observed in their sample of developing

countries. In other words, the $200 Kravis dollars per year, 1970 prices, is equivalent to

$ 653 a year, 1985 prices, or $ 1.79 a day. Summarizing, the NA income based line of $

1.79 a day is “equal” to a $ 1.25 a day, NA consumption, is equal to $ a day, survey

consumption, is equal to Rs. 15 and Rs. 22.50 per capita per month, 1960 prices, in

rural and urban areas of India, respectively – and is equal to the $ a day survey

consumption line offered by Ravallion et. al. in 1991.

The reason for emphasizing the origins of the poverty line is because inter-temporal

comparisons of poverty are critically dependent on the poverty line that is chosen. Over

time, the base period for various income series also change; some researchers (e.g.

Sala-I-Martin) prefer to use an income poverty definition rather than consumption

poverty. There should be little reason to dispute the method of transformations; lately,

however, two controversies have arisen and both have an effect on our understanding of

what happened to world poverty during the recent period of high growth.

The new 1993 based PPP series appeared in 1999/2000, and Chen-Ravallion

(2000,2001) have two different versions of the paper entitled “How did the world’s

poorest fare in the 1990s?”. The authors convert the $ a day, 1985 base poverty line into

an “equivalent” poverty line of $1.08 a day, 1993 prices. Obviously, relative prices

change in the transformation, just as they do with consumer price indices when the base

changes every decade or so. When the base changes in consumer prices, past inflation

12 Ahluwalia-Carter-Chenery recognized that consumption was preferable to income as a predictor ofpoverty; however data availability considerations (at that time most developing country surveys wereconducted in Latin America and these were all income surveys) forced them to specify an income povertyline.

22

is not changed, though future inflation is based on the new set of relative prices.

Average world inflation during the two years was close to 30 percent, and not 8 percent

as implied by the Chen-Ravallion updating. Further, US GDP deflator (the numeraire

for the PPP calculations) increased by 27.5 percent during the eight year period; US CPI

increased by an even higher 34 percent! So there seems to be little basis for reducing

international inflation to only 8 percent. Bhalla(2002d) explores this question in detail,

including the behavior of food prices, an important consumption item of the poor. For the

poorest eight countries (the ones which constitute the basis for the poverty line

computations of Chen-Ravallion) there was very little difference between food and non-

food inflation.

There is one further method by which the two PPP series (1985 and 1993) can be linked

– by observing the difference between the two nominal PPP income estimates for the

world in 1985. The difference is 28.7 percent suggesting that in 1985, one $ a day (1985

base) was equal to $1.29, 1993 prices.

Given the overwhelming evidence and rationale for choosing 30 percent as the rate of

international and poor country inflation during 1985 to 1993, it is curious that the World

Bank authors of world poverty estimates choose to use 8 percent as their estimate of

world inflation. This obviously biases downward any point estimate for poverty, ceteris

paribus and goes against the conjecture in Bhalla(2000b) that it is in the interests of the

World Bank to actually show more poverty than actually exists. How the choice of a

lower poverty line may actually result in higher point estimates for the last half of the

nineties is a subject examined below.

Reddy-Pogge(2002) severely criticize the World Bank for the $1.08 poverty line, and

also contend that it is too low. Their theoretical rationale is sound – the “common”

international basket of goods is not the basket of the poor. If inflation of the goods

consumed by the poor has risen faster than the international inflation of the world’s

“middle class”, then an inflator higher than 30 percent is needed to correctly assess the

purchasing power of the poor. The criticism rests on the empirical reality; the bias can

also go the other way. There is an indirect method of assessing the validity of this

critique – that method rests in noting what the change in real incomes has been of the

poor countries in local currency, and PPP terms. As it happens, developing countries

23

growth rate averaged 0.5 percent per annum lower in the last twenty years; if the poverty

line was fixed according to local consumption patterns, then this ten percent

understatement of consumption levels means an understatement of the decline in

poverty by about 5 percentage points over the last twenty years. In conclusion, Reddy-

Pogge are right about their theoretical concerns, but in reality, use of inappropriate PPP

deflators has biased poverty counts upwards.

For both theoretical and measurement reasons, a consumption based poverty

assessment is preferred, and this has become standard practice when analysis veers to

poverty measurement in the developing countries. In the US, however, the practice is to

measure poverty in terms of income; perhaps this is the reason why Sala-I-Martin

chooses to compute income poverty estimates for the developing world. This is not so

problematical as Martin’s apparent misuse of the poverty line. He bases his poverty

estimates on “the conventional definitions of absolute poverty: less than one dollar per

day” (p. 17). But he goes on to argue that the consumption poverty line should be higher

than an income based line, when in fact it should be lower: “Since, for poor economies

more than 50 % of GDP is consumed… this means that the consumption poverty line

should be located somewhere between our one-dollar and two-dollar lines” (note 24,

page 17, Sala-I-Martin(2002a)).

Sala-I-Martin’s results on income poverty will surprise most analysts – he finds that world

poverty had declined to less than 10 percent in the mid-eighties, and that its level in

1998 was around 7 percent. India in 1990 is shown to have a poverty level of only 10

percent, and 21 percent in 1970 and 17 percent in 1980. Above, the Ahluwalia-Carter-

Chenery income poverty line was discussed and it was contended that conceptually they

also used the $ a day line. However, their poverty estimate for India in 1975 was 45

percent, more than 25 percentage points higher than Sala-I-Martin’s estimate.

There are several differences in the estimates of poverty. Authors differ on the definition

of the poverty line (all have consumption other than Martin who uses an income poverty

line), on whether 1985 PPP or 1993 PPP base are used (Bourguignon-Morrisson and

Martin employ the 1985 base while Bhalla and the World Bank use the 1993 base), on

whether consumption or income PPP estimates are used. World Bank publications are

the only ones using consumption PPP estimates; these estimates are “internal” to World

24

Bank staff; and only after pressure from outside researchers were these made available

on the web in mid 2002. Bhalla(2002d?) discusses the peculiarities of these

consumption PPP estimates and how they differ from the official and conventional PPP

exchange rates. One striking result – these consumption PPP exchange rates suggest

consumption levels in South Asia about 20 percent lower than official PPP exchange

rates.

Section 4 – Can the World Bank poverty results be reproduced?

This is discussed in the introduction – will be filled in later

25

Table 3: World Poverty at $ a day, Head Count ratios (1950-2000)Year Population World Bank B-M Bhalla Bhalla Sala-I-Martin Sala-I-Martin

(Millions) Income Consumption

WorldDeveloping

World 2000 1999 2000 2002 2002 2002

1950 2691 1937 76.1 63.21960 3018 2155 61.6 52.51970 3673 2723 48.0 46.4 23.2 42.41980 4427 3403 41.0 47.3 43.5 17.0 36.31987 4987 3922 28.3 34.4 29.9 11.2 27.31990 5248 4164 29.0 32.4 25.4 10.8 26.11993 5496 4396 28.2 29.6 29.0 22.8 9.5 23.51998 5890 4766 24.0 20.5(1997) 16.2 8.3 19.81999 5970 4842 23.0 14.22000 6061 4928 13.1

Source: 1. B-M - Bourguignon, Francois and Morrisson, Christian "Inequality among world citizens:1820 - 1992", Feb 20012. Chen, Shaohua and Ravallion, Martin "How did the world's poorest fare in the 1990s?",Review of Income and Wealth, Sep 20013. Sala-i-Martin, Xavier "The world distribution of income (Estimated from individual countrydistributions)", NBER Working Paper, May 20024. Bhalla, Surjit S, "Imagine There’s No Country: Poverty, Inequality, and Growth in the Era ofGlobalization"5. Bhalla, Surjit S, "Trends in world poverty - Ideology and Research", mimeo presented in IMF2000

Notes: 1. The poverty figures reported by B-M and Sala-i-Martin in their respective papers have beenconverted to developing world populations to be consistent with the other estimates.

26

Table 4: World Bank Data onlyConsumption Growth Total Population HCR

1987 1998 change 1987 1998 1987 1998

China 58.58 89.18 3.82 52.24 1084 1242 27.9 17.1East Asia 61.3 89.0 3.39 45.27 1371 1590 26.6 15.3East Asia excl. China 71.5 88.4 1.94 23.76 287 348South Asia 43.1 49.3 1.21 14.23 1034 1279 44.9 40.0Asia 53.5 71.3 2.61 33.32 2405 2869Sub-Saharan Africa 84.3 81.1 -0.35 -3.77 256 341 46.6 46.3Asia+SSA 56.4 72.3 2.26 28.17 2661 3210Latin America 189.4 201.4 0.56 6.31 360 438 15.3 15.6Asia+SSA+LA 72.3 87.9 1.77 21.49 3021 3648MENA 147.7 166.0 1.06 12.36 158 196 4.3 2.0Asia+SSA+LA+MENA 76.1 91.8 1.71 20.75 3179 3844Eastern Europe 254.8 161.5 -4.15 -36.62 360 366 0.2 5.1

NIW 94.2 97.9 0.35 3.91 3539 4210 28.3 24.0

Notes: 1. The above means are computed from the consumption data posted on the World Bank website"www.worldbank.org/research/povmonitor"2. The data posted on the website does not report the household surveys for India for 1993-94 and1998, and does not report any household survey for Nigeria.3. Growth reported is in log terms, while total change is simple percentage change.

27

Table 5 : Growth, Poverty Reduction and Inequality – selected countriesLog Change HCR SDE HCR SDE Gini

In consumption (%) (National Poverty Line) ($1.3 poverty line)

India1962 1.84 31.7 0.71 35.4 0.69 32.01967 0.42 38.7 0.76 42.6 0.77 31.31972 -1.68 33.3 0.73 37.1 0.76 30.21977 0.30 37.5 0.75 41.3 0.74 30.91982 3.58 35.3 0.75 39.1 0.76 32.41987 2.88 25.0 0.67 28.5 0.70 32.81992 3.38 14.5 0.54 17.5 0.60 31.61997 3.78 7.9 0.36 9.9 0.41 34.4China1962 -2.25 72.5 0.65 92.9 0.25 29.51967 3.80 60.9 0.75 87.6 0.41 29.51972 1.89 52.0 0.68 82.2 0.49 29.51977 -1.04 56.8 0.71 85.2 0.45 29.51982 8.27 44.6 0.81 77.0 0.59 27.81987 7.12 22.1 0.50 48.2 0.71 33.31992 6.40 12.4 0.34 35.2 0.76 38.21997 8.90 1.3 0.26 12.3 0.38 40.5Brazil1962 3.03 86.5 0.20 27.6 0.32 49.71967 3.08 84.5 0.22 24.6 0.31 49.71972 9.20 73.8 0.33 14.8 0.23 60.11977 3.02 66.4 0.37 9.7 0.22 62.41982 -0.65 64.7 0.35 8.7 0.22 56.91987 -1.40 64.5 0.37 8.6 0.20 60.41992 0.45 68.6 0.35 11.0 0.23 62.81997 1.58 65.1 0.38 8.8 0.22 55.4

Notes: 1. Income Ginis have been reported for China & Brazil and consumption gini for India.2. SDE refers to the shape of distribution elasticity e.g. an SDE of 0.22 means that if distribution of consumption stays constant, a 10percent change in consumption will result in a 2.2 percentage point decline in the head-count ratio.

Table 6: Reproduction of World Poverty using only World Bank data – all countries1985 1987 1990 1993 1996 1998 1999 2000 Change/Growth

1987-98Distribution (% Share)Quintile1 4.64 4.54 4.75 5.04 5.17 5.06 5.16 5.14 10.84Quintile2 7.46 7.35 7.65 8.18 8.54 8.38 8.51 8.51 13.11Quintile3 10.8 10.93 11.06 12.05 12.7 12.45 12.61 12.65 13.02Quintile4 17.2 18.1 17.61 19.04 19.81 19.73 19.88 19.99 8.62Quintile5 59.9 59.08 58.95 55.69 53.78 54.38 53.84 53.72 -8.29Shape of Distribution Elasticity$1.08 Poverty Line 0.53 0.45 0.52 0.48 0.49 0.49 0.43 0.42 7.41$1.50 Poverty Line 0.55 0.57 0.59 0.53 0.52 0.52 0.52 0.51 -8.59Consumption '93 pricesWorld Bank website data 2.85 3.08 3.06 2.94 3.09 3.22 3.29 3.44 4.45Ravallion 10.4

HCR$1.08 Poverty Line 31.22 28.1 26.13 25.09 20.87 19.88 18.01 16.17 -8.22$1.50 Poverty Line 41.93 37.27 36.31 34.43 29.45 28.48 26.48 24.44 -8.79

Data as reported on World Bank website and/or publicationsGrowth in consumption 1987-98HCR ($1.08 poverty line, 1993 prices) 28.3 29.0 28.2 24.5 24.0 22.6

Notes: 1. The above means are computed from the consumption data posted on the World Bank website "www.worldbank.org/research/povmonitor"2. The data posted on the website does not report the household surveys for India for 1993-94 and 1998, and does not report any household survey forNigeria.

29

Table 7: Reproduction of World Poverty using only World Bank data excluding Eastern Europe1985 1987 1990 1993 1996 1998 1999 2000 Change/Growth

1987-98Distribution (% Share)Quintile1 5.54 5.45 5.63 5.41 5.42 5.26 5.36 5.33 -3.55Quintile2 8.68 8.53 8.86 8.57 8.76 8.51 8.66 8.65 -0.23Quintile3 12.15 12.15 12.37 12.29 12.77 12.39 12.55 12.59 1.96Quintile4 17.89 18.79 18.16 18.64 19.56 19.17 19.34 19.47 2.00Quintile5 55.74 55.08 54.99 55.08 53.49 54.68 54.08 53.97 -0.73Shape of Distribution Elasticity$1.08 Poverty Line 0.60 0.57 0.60 0.52 0.47 0.48 0.47 0.45 -16.89$1.50 Poverty Line 0.62 0.65 0.62 0.58 0.54 0.52 0.55 0.54 -21.48Consumption '93 pricesWorld Bank website data 2.33 2.50 2.51 2.68 2.89 3.02 3.09 3.23 18.90Ravallion 10.4HCR$1.08 Poverty Line 34.2 31.1 28.5 27.2 22.2 21.4 19.4 17.4 -9.75$1.50 Poverty Line 45.5 41.2 40.2 37.4 32.0 31.1 29.0 26.4 -10.09

Data as reported on World Bank website and/or publicationsGrowth in consumption 1987-98HCR ($1.08 poverty line, 1993 prices)

Notes: 1. The above means are computed from the consumption data posted on the World Bank website "www.worldbank.org/research/povmonitor"2. The data posted on the website does not report the household surveys for India for 1993-94 and 1998, and does not report any household survey forNigeria.

30

Table 8: China: Income, Inequality & Poverty, 1987-1998 1987 1998 Change/Growth 1987-98

Survey Capture 91.4 82.2 -10.6

Distribution (% Share)Quintile1 6.9 5.9 -15.7Quintile2 11.1 10.2 -8.5Quintile1+2 18.0 16.1 -11.2Quintile3 15.9 15.1 -5.2Quintile1+2+3 33.9 31.2 -8.3Quintile4 28.4 22.2 -24.6Quintile5 37.6 46.6 21.5National AccountsConsumption '93 official PPP, per capita per monthLocal Currency 78.2 175.6 80.8PPP $ 1.91 3.95 72.7Shape of Distribution Elasticity$1.50 Poverty Line 0.55 0.39HCR$1.50 Poverty Line 47.5 10.0 -37.5Household Survey DataConsumption '93 pricesLocal Currency 71.5 144.3 70.2PPP $ 1.75 3.25 62.1Shape of Distribution Elasticity$1.50 Poverty Line 0.68 0.39HCR$1.50 Poverty Line 44.46 11.98 -32.5Household Survey Data, World BankConsumption '93 consumption PPP, per capita per monthLocal Currency 58.58 89.18 42.0Shape of Distribution Elasticity$1.08 Poverty Line 0.47 0.34HCR$1.08 Poverty Line 28.0 17.2 -10.8

Notes: 1. The above means are computed from the consumption data posted on the World Bank websitewww.worldbank.org/research/povmonitor

31

Table 9 : Head Count Poverty Ratios, World Bank, 1987-1999Head Count Ratio # of Poor (in millions) Population

1987 1990 1993 1996 1998 1999 1987 1990 1993 1996 1998 1999 1999

East Asia 26.6 27.6 25.2 14.9 15.3 14.1 417.5 452.5 431.9 265.1 278.3 260.0 1843.2

China 27.9 31.7 29.4 17.2 17.1 17.1 303.4 360.5 348.4 210.1 213.2 214.0 1253.1

South Asia 44.9 44.0 42.4 42.3 40.0 36.9 474.4 495.1 505.1 531.7 522.0 490.0 1327.3

Sub-Saharan Africa 46.6 47.7 49.7 48.5 46.3 46.6 217.2 242.3 273.3 289.0 290.9 300.0 644.0

Latin America & Caribbean 15.3 16.8 15.3 15.6 15.6 15.4 63.7 73.8 70.8 76.0 78.2 77.0 498.4

Middle East & North Africa 4.3 2.4 1.9 1.8 2.0 1.9 9.3 5.7 5.0 5.0 5.6 7.0 367.2

Eastern Europe & Central Asia 0.2 1.6 4.0 5.1 5.1 4.8 1.1 7.1 18.3 23.8 24.0 17.0 356.1

World 28.3 29.0 28.2 24.5 24.0 22.6 1183.2 1276.4 1304.3 1190.6 1198.9 1150.0 5083.0World excluding China 28.5 28.1 27.7 27.0 26.2 24.4 879.8 915.9 955.9 980.5 985.7 936.0 3829.9World excl EE & China 33.3 32.4 31.4 30.2 29.2 26.5 878.7 908.8 937.6 956.7 961.7 919.0 3473.8

Source: 1. World Development Report 2000-01, and other World Bank publications; Chen-Ravallion (2001).Notes: 1. Poverty Line is $1.08 at 1993 PPP prices.

32

Annexure Table I – Detailed data1987 1998 Annualized growth

Country year1 cons1 annual growthtill cons87 cons87 pop87 year2 cons2 annual growth

till cons98 cons98 pop98 in cons. 1987-98

East AsiaChina 1985 1.65 7.9 1.93 1084 1998 2.93 0.00 2.93 1242.2 3.8Indonesia 1987 1.83 1.83 169 1998 2.01 0.00 2.01 203.7 0.9Cambodia 1997 6.25 2.3 4.97 8.3 1997 6.25 0.05 6.22 11.5 2.0Philippines 1985 2.47 1.4 2.54 56.9 1997 3.62 0.26 3.53 72.8 3.0Thailand 1981 2.64 4.2 3.40 52.8 1998 4.57 0.00 4.57 59.8 2.7

1.77 2.01 1371 2.93 2.93 1590 3.4Eastern EuropeBulgaria 1989 10.36 4.0 9.55 9 1995 5.39 1.60 4.74 8.3 -6.4Belarus 1988 6.68 3.5 6.45 10.1 1998 6.40 0.00 6.40 10.1 -0.1Czech Republic 1988 7.73 2.3 7.56 10.3 1993 6.78 -1.76 7.54 10.3 0.0Estonia 1988 7.40 2.1 7.25 1.6 1995 4.92 -2.92 6.21 1.4 -1.4Hungary 1989 6.96 0.7 6.86 10.5 1993 5.17 -2.78 6.11 10.1 -1.1Kazakhastan 1988 6.43 4.9 6.12 15.9 1996 5.35 -0.34 5.52 15.1 -1.0Kyrgyz Rep. 1988 5.94 10.8 5.33 4.2 1997 5.46 0.03 5.44 4.8 0.2Lithuania 1988 12.55 12.3 11.11 3.6 1996 5.63 -1.35 6.36 3.7 -5.1Latvia 1988 13.41 4.4 12.84 2.7 1998 5.96 0.00 5.96 2.4 -7.0Moldova 1988 10.68 0.7 10.60 4.3 1992 3.49 9.92 2.13 4.3 -14.6Mongolia 1995 2.58 -2.6 3.18 2 1995 2.58 -0.78 2.75 2.4 -1.3Poland 1987 7.10 7.10 37.7 1993 5.30 -4.73 7.04 38.7 -0.1Romania 1989 6.28 -3.7 6.76 22.9 1994 3.29 -0.08 3.30 22.5 -6.5Russia 1988 9.40 1.7 9.23 145.9 1998 5.70 0.00 5.70 146.9 -4.4Slovak Republic 1987 7.62 7.62 5.3 1993 8.26 -4.36 10.73 5.4 3.1Turkmenistan 1988 3.67 8.0 3.39 3.4 1993 2.30 8.93 1.34 4.9 -8.4Ukraine 1988 10.19 2.1 9.98 51.3 1996 3.95 0.38 3.82 50.3 -8.7Uzbekistan 1988 6.72 5.9 6.34 19 1993 3.82 1.18 3.56 24.1 -5.2

8.54 8.38 360 5.10 5.31 366 -4.1Latin America

33

Bolivia 1990 3.73 1.5 3.57 6.1 1990 3.73 -5.09 4.35 7.9 1.8Brazil 1985 6.46 3.7 6.96 140.4 1997 8.90 0.11 8.81 166 2.1Chile 1987 6.49 6.49 12.4 1994 8.28 -3.21 10.37 14.8 4.3Colombia 1988 10.60 2.0 10.39 33 1996 6.82 -0.04 6.85 40.8 -3.8

Annexure Table I (concl.)1987 1998 Annualized growth

Country year1 cons1 annual growthtill cons87 cons87 pop87 year2 cons2 annual growth

till cons98 cons98 pop98 in cons. 1987-98

Costa Rica 1986 3.34 1.7 3.40 2.8 1996 5.57 -1.04 6.11 3.7 5.3Dominican Republic 1989 5.68 -1.4 5.85 6.7 1996 7.98 -1.28 8.96 8 3.9Ecuador 1988 2.46 7.6 2.28 9.6 1995 2.93 0.05 2.91 12.2 2.2Guatemala 1987 2.18 2.18 8.1 1989 2.78 -6.32 3.15 10.8 3.3Honduras 1989 2.45 1.3 2.38 4.4 1996 2.31 -0.28 2.37 6.1 0.0Jamaica 1988 4.99 2.6 4.87 2.4 1996 4.11 0.41 3.96 2.6 -1.9Mexico 1984 3.98 -1.8 3.76 78.6 1995 4.39 -1.51 4.95 95.2 2.5Nicaragua 1993 1.79 -5.2 2.44 3.6 1993 1.79 -1.17 1.92 4.8 -2.2Panama 1989 6.52 -8.4 7.72 2.3 1997 6.20 -0.26 6.36 2.8 -1.8Peru 1985 8.70 6.5 9.90 20.3 1996 3.69 -0.28 3.78 24.8 -8.8Paraguay 1990 3.51 1.8 3.32 3.8 1995 5.61 0.55 5.37 5.2 4.4ElSalvador 1989 2.99 0.5 2.96 4.9 1996 3.33 -0.38 3.44 6 1.4Uruguay 1989 8.95 0.7 8.82 3 1989 8.95 -13.86 11.81 3.3 2.7Venezuela 1987 7.53 7.53 18 1996 4.37 -0.25 4.47 23.2 -4.7

6.03 6.23 360 6.39 6.62 438 0.6Middle East & North AfricaAlgeria 1988 5.55 -3.7 5.76 23.1 1995 5.19 -0.63 5.46 29.5 -0.5Egypt 1991 2.91 1.8 2.71 48.9 1995 2.52 -1.25 2.79 61.6 0.3Jordan 1987 8.84 8.84 2.8 1997 6.05 0.02 6.04 4.6 -3.5Morocco 1985 5.06 0.6 5.11 22.6 1990 6.96 -2.28 7.45 27.8 3.4Tunisia 1985 6.23 -0.3 6.19 7.7 1990 6.71 -7.43 8.38 9.3 2.8Turkey 1987 5.94 5.94 52.6 1994 5.60 -2.56 6.70 63.4 1.1

4.88 4.86 158 4.83 5.46 196 1.1South AsiaBangladesh 1985 1.73 1.6 1.79 102.2 1996 1.81 -0.76 1.94 126.6 0.7

34

India 1987 1.35 1.35 798.7 1997 1.48 -0.40 1.54 979.7 1.2Sri Lanka 1985 2.59 1.4 2.66 16.4 1995 2.90 -1.38 3.24 18.8 1.8Nepal 1985 1.46 0.9 1.48 16.9 1995 1.73 -0.74 1.83 22 1.9Pakistan 1987 1.35 1.35 100 1996 1.65 0.14 1.63 131.6 1.7

1.41 1.42 1034 1.56 1.62 1279 1.2Sub-Saharan AfricaBurundi 1992 1.70 0.3 1.67 5 1992 1.70 6.81 1.21 6.5 -2.9

35

Annexure Table II1987 1998 Annualized growth

Country year1 cons1 annual growthtill cons87 cons87 pop87 year2 cons2 annual growth

till cons98 cons98 pop98 in cons. 1987-98

Burkina Faso 1994 1.41 1.1 1.31 8.3 1994 1.41 -1.86 1.61 10.7 1.9Botswana 1985 2.88 6.3 3.27 1.2 1985 2.88 24.34 4.69 1.6 3.3Central African Rep. 1993 1.35 -2.9 1.61 2.8 1993 1.35 -0.66 1.40 3.6 -1.2Coted'Ivoire 1987 4.31 4.31 10.6 1995 2.80 -1.16 3.08 15.2 -3.1Ethiopia 1981 1.65 -1.5 1.51 46.1 1995 1.95 -0.74 2.07 61.3 2.8Ghana 1987 2.53 2.53 13.6 1997 0.84 -0.24 0.86 18.4 -9.8Gambia 1992 1.49 -0.3 1.51 0.8 1992 1.49 0.74 1.44 1.2 -0.5Kenya 1992 2.95 0.1 2.94 21.3 1994 2.42 -0.30 2.48 28.7 -1.6Lesotho 1986 3.35 1.5 3.40 1.6 1993 2.64 -1.68 2.91 2 -1.4Madagascar 1980 1.65 -3.4 1.30 10.7 1993 1.28 0.65 1.24 14.6 -0.5Mali 1989 2.52 3.5 2.35 7.8 1994 1.07 -1.37 1.17 10.3 -6.3Mozambique 1996 1.74 2.5 1.39 13.8 1996 1.74 -1.99 2.08 17 3.6Namibia 1993 6.60 -0.9 6.98 1.2 1993 6.60 -1.98 7.43 1.7 0.6Niger 1985 1.03 0.0 1.03 7 1997 1.13 -0.64 1.20 10.1 1.4Rwanda 1984 1.53 0.0 1.54 6.4 1984 1.53 -10.26 1.13 8.1 -2.8Senegal 1991 2.09 -0.8 2.17 6.7 1994 2.23 -1.36 2.45 9 1.1Sierra Leone 1989 1.61 1.6 1.56 3.7 1989 1.61 26.87 0.94 4.8 -4.6Tanzania 1991 2.18 0.6 2.12 23.2 1993 2.41 -0.44 2.47 32.1 1.4Uganda 1989 1.89 4.0 1.75 14.8 1992 1.77 -5.06 2.28 21 2.4South Africa 1993 7.07 -1.5 7.73 32.9 1993 7.07 -0.58 7.32 41.4 -0.5Zambia 1991 1.29 -1.9 1.39 7.1 1996 1.02 0.38 0.99 9.7 -3.1Zimbabwe 1990 3.02 3.1 2.75 9.3 1990 3.02 -1.40 3.15 12.2 1.2

2.77 2.77 256 2.54 2.67 341 -0.3All 3.00 3.10 3.12 3.22 0.3

Notes: 1. The above means are computed from the consumption data posted on the World Bank website"www.worldbank.org/research/povmonitor"

36

2. The data posted on the website does not report the household surveys for India for 1993-94 and 1998, and does not report anyhousehold survey for Nigeria.3. When a household survey was not conducted, the World Bank interpolates from the earlier survey mean to the year in question byusing national accounts growth rates in consumption. The growth figures reported in between the consumption years are these NA growthrates.

37

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