WPS I9 v2
POLICY RESEARCH WORKING PAPER 1902
When Economic Reform Household surveydaii
income Inequality incre,Clasn
is Faster than Statistical in post-reform rurlra
Reform but this may re lect
used to process car[a Fathe:
than the real efre(t of
Measuring and Explaining Inequality structural chancies on
* T 1 r>1 * ~~~~~~~~~~~~~rural economy.
in Rural China
1\Martiz kRz'alliomz
Shblobua Cheni
The World Bank
Development Research Group
March 1998
I POLICY RESEARCH WORKING PAPER 1902
Summary findings
Official tabulations from household survey data suggest allowances are made for regional cost-of-living
rising income inequality in post-reform rural China, a differences.
trend of public concern. But the structural changes in The data revisions also suggest somewhat different
China's rural economy have not been properly reflected explanations for rising inequality. Nonfarm income was
in the methods used to process raw survey data. secondary to grain production. While access to farm land
Using micro data for four provinces, Ravallion and was relatively equal, higher returns to land over time
Chen find that two-thirds of the conventionally were inequality-increasing. But holding other factors
measured increase in inequality in 1985-90 vanishes constant, lower returns to physical capital reduced
when market-based valuation methods are used and inequality over time, as did private transfers.
This paper - a product of the Development Research Group - is part of a larger effort in the group to improve data on
poverty and inequality in developing countries. The study was funded by the Bank's Research Support Budget under the
research project "Dynamics of Poverty in Rural China." Copies of this paper are available free from the World Bank, 1818
H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-632, telephone 202-473-3902, fax 202-
522-1153, Internet address psader@worldbank.org. March 1998. (38 pages)
The Policy Researcb Working Paper Sedbes disseminates the fiydReags of work in progress to encourage the exchange of ideas aborCt
development issufes. An objective of the series is to get the findings otit quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordinigly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Execaftive Directors, or the
|countries they represent.
Prod-uced by the Policy Research Dissemination Center
When Economic Reform is Faster than Statistical Reform:
Measuring and Explaining Income Inequality in Rural China
Martin Ravallion and Shaohua Chen
1 Introduction
There is a widespread view that the transition from a socialist economic system to a market
economy will entail rising inequality, and there is support for that view in recent compilations of
distributional data for the 1980s and '90s (Milanovic, 1996; Ravallion and Chen, 1997).
However, these compilations are typically based on the tabulations of distributional data (drawn
from household surveys) that are made available by official sources. While economic reforms
often have important implications for the methods used in measuring economic welfare and
inequality, government statistical agencies may not be adjusting as rapidly as one would like to the
structural changes going on in the economy. And users of the official data rarely probe into the
raw micro data underlying the distributional comparisons being made, either because of lack of
access to the data or lack of resources for doing so.
Could lags in reforming statistical methods entail substantial biases in assessments of how
inequality is changing during the transition? The structural changes going on are not necessarily
inequality-increasing. A common element of socialist economic planning was the suppression of
food-staple prices, to help finance industrialization.2 Through market liberalization, the transition
typically entails higher food staple prices. To the extent that food-staple producers are
concentrated among the poor, the transition will put downward pressure on inequality. If all
incomes were derived from market exchange then these effects should be seen quickly in official
data on distribution drawing on household surveys. However, a large share of income in poor
rural economies takes the form of direct consumption of own production. Valuations must be
2 This vvas often referred to as the "price scissors" and there is a large literature on the practice;
for a recent analysis and references see Sah and Stiglitz (1992).
2
imputed for this and other income sources which were not acquired through exchange. When
prices are controlled by administrative fiat, the same prices are naturally used for valuation. But
there can be no assurance that old administrative prices will be replaced by market prices as the
transition proceeds. Unless statistical agencies are quick enough to adapt to such changes, biases
can enter survey-based analyses of (among other things) income inequality.
The transition can have many other implications for measurement. The level of prices may
rise faster in some regions of the economy than others after reforms (reflecting nontraded goods,
or less than perfect spatial market integration, due for instance to poorly developed transportation).
If it were the initially better-off regions which saw higher growth and higher inflation (due to
higher aggregate demand locally) then assessments of income distribution which ignored
geographic differences in prices could overestimate the rate at which inequality was increasing.
There is no good a priori reason to assume that there will be a bias, or that (when there is)
it could go only one way. For example, the share of income from undervalued components may
be no different between the rich and poor, or the rate of inflation may be higher in poorer regions.
These are empirical questions, although they can be difficult to answer since they require access
to, and reprocessing of, the raw data underlying official tabulations.
This paper addresses these concerns in the context of post-reform rural China. Beginning
with Premier Deng's reforms in 1978, China's rural economy became market-oriented; prices were
freed and the farm-household replaced the commune as the decision-making unit. These reforms
brought about changes to data collection, including greater reliance on household surveys. The
scope and collection methods of such surveys improved significantly during the 1980s, starting
with the Rural Household Survey (RHS) introduced in 1984. This has been the main source of
3
data for distributional analysis on rural China.
Tabulations of results from the RHS in China's Statistical Yearbooks have suggested rising
income inequality since the mid-i 980s. This has been widely reported and attracted much
attention? However, there are reasons to be cautious in interpreting the available evidence on
income inequality in rural China. A number of potential problems have been identified in recent
literature, including the undervaluation of income in kind from the consumption of own-farm
products due to continuing reliance on planning prices for valuation purposes.4
We examine how the problems in official tabulations from the household survey data have
affected measurements of the overall level of inequality, and how it changes over time. We also
examine how these data problems impinge on explanations of the observed changes in overall
inequality.5 Suppose, for example, that we want to know if the rising income inequality in China
is due to the booming rural non-farm sector (including the famous Township and Village
Enterprises). Or we may want to see what role public and private transfers played. In principle,
the answers to such questions will depend on the method used to measure incomes at the
household level. For example, undervaluing income in kind from own production might lead one
to underestimate the contribution of this income component to rising income inequality, given that
3 See, for example, the front page article in The New York Times, December 27, 1995.
' Discussions of the problems can be found in World Bank (1992), Khan et al. (1993) and Chen
and Ravallion (1996).
5 There have been a number of studies attempting to throw light on the causes of inequality in
China since reforms began in the late 1970s. Decompositions have been done along various dimensions
(geographic and by income source) and at various levels of spatial aggregation (some by county, some by
village, some household) and for differing time dimensions (some using single cross-sectional surveys,
some including comparisons over time). Contributions include Knight and Song (1993), Rozelle (1994)
and Howes and Hussain (1994).
4
its progressive undervaluation over time would probably lead one to conclude (incorrectly) that
this income component is becoming less covariate with total income. It is an empirical question
just how robust explanations of rising inequality are to these data problems.
We address these issues using a large household-level data set for rural China spanning the
period 1985-90. The region we study embraces booming Guangdong on the coast (the province
surrounding Hong Kong) and the far less prosperous, and more economicly stagnant, inland
provinces of Guangxi, Yunnan and Guizhou. Having access to the micro data means that we can
attempt to correct the main concerns about existing distributional data. After making corrections
to the processing, we are able to use the survey to address a number of questions about the
proximate causes of the observed changes in income inequality.
The following section summarizes the theoretical results we will be using from the
literature on inequality measurement. Section 3 then looks at the theoretical implications of
undervaluing an income component for measures of inequality and their decomposition. In
section 4 we describe our data, while section 5 gives our overall results on income inequality, with
and without our corrections to the data processing. We then turn in section 6 to the task of
explaining the observed changes in inequality. Our conclusions are summarized in section 7.
2 Inequality measurement and decomposition methods
A measure of inequality can be written in generic form:
I = I(y 1/A Y2/R . ...YNIR )(1
where y, is the i'th person's income in a population of size N, and 1 is mean income. We assume
5
that this measure is continuous, symmetric (swapping incomes does not change the measure),
normalized such that inequality is zero when all persons have the same income, and that the
measure satisfies the "transfer axiom" such that a transfer from rich to poor reduces inequality.
For some sorts of distributional comparisons we may not need to know any more about the
measure of inequality. For example, if the Lorenz curve (giving, on the vertical axis, the share of
total income held by the poorest x% of the population) for distribution A is everywhere above that
of B then all inequality measures in the above class of measures will show higher inequality in B
than A (Atkinson, 1970).
In our empirical work we will focus on two special cases of the above class of measures.
The first is the well-known Gini index (G), given by the (household-size weighted) mean absolute
deviation between all pairs of per capita household incomes. The second is a member of the
Generalized Entropy class of additively decomposable measures, namely the average log deviation
of incomes from their mean:6
LD = - log(i /Y1) (2)
N
We will also be interested in explaining inequality and its changes over time. There are
potentially many ways of decomposing a change in inequality by income source. Here we follow
a strand of the theoretical literature which has constrained the choices by postulating certain
6 If N stood instead for the number of households then household-size weights would appear in
this formula. All statistics in this paper which are based on the household-level data have been
household-size weighted.
6
axioms that are deemed desirable for any decomposition. (We only summarize the basic results
that will be needed for the empirical work later.)
Let total income (per person) be divided into m categories, such that, for the i'th
household:
m
Yi kE Yik (3)
k= 1
If these components were uncorrelated with each other, and one measured inequality by the
squared coefficient of variation (CV), then the natural decomposition would be to measure the
contribution of each income component to inequality by its squared CV. However, in practice
different income sources are correlated to varying extents. And there are many other inequality
measures that one might want to consider besides squared CV. How then should one apportion
total inequality between components?
A powerful result proved by Shorrocks (1982) shows that a modified version of the
squared-CV decomposition (allowing for non-zero correlations) can also be defended as a
decomposition method for a wide range of inequality measures. For the class of inequality
measures described above,7 Shorrocks shows that the proportion of total inequality contributed by
the k'th income source is given by:
cov(yV,y) rks
Ck = 4=k
k var(y) s
' In fact Shorrocks proves the following result for an even larger class of measures; see his paper
for full details.
7
where rk is the correlation coefficient with total income and Sk and s are the standard deviation of
the k'th income component and of total income respectively. Note that Ck sums to one over all k
and is simply the ordinary least squares regression coefficient of Yk ony. The decomposition based
on (4) is independent of the precise measure of inequality used (within the aforementioned class of
measures).
Notice that the contribution of any income component to total inequality depends on both
the variance of that component (relative to the variance in total income) and its correlation
coefficient with total income. So the fact that some income component contributes a lot to total
inequality does not necessarily mean that it is itself very unequally distributed; it may instead be
highly correlated with total income, yet quite equally distributed. Similarly, a highly unequally
distributed income component may contribute little to total inequality because it is roughly
uncorrelated with total income, or it may be inequality-reducing because of a negative correlation
with total income.
The above result holds for a decomposition of the level of inequality. What about changes
in inequality over time? Building on the Shorrocks' decomposition, we follow Jenkins (1995) and
Fields (1996) in calculating the contribution of the k'th income source to the change in total
inequality between dates 1 and 2 by:
k2I2 kl I(5)
12 -II
I2 1,
which sums to one. Notice that (unlike the levels decomposition) this decomposition will depend
on the specific inequality measure used. We will compare results for the Gini index with those for
8
the average log deviation given by (2).
One can also ask how much of the level of inequality or its change over time is due to
some variable determining income through a stochastic process. To do so, replace equation (3)
with a regression model for income:
m
Y= 3kik (6)
where xik is the k'th asset (xi. can be taken to be an error term, with pm=l). Following Fields
(1996), the contribution of the k'th explanatory variable to total inequality is given by:
Pkcov(Xk, Y)
k var(y)
This is simply the product of the partial regression coefficient of income on schooling (holding all
other variables constant) with that total regression coefficient of schooling on income (holding
nothing else constant). The contributions of each asset to the changes over time can then be
determined using equation (5). The precise decomposition will naturally depend on the regression
specification in (6). This should be borne in mind when interpreting the results.
3 Effects of valuation errors on measured inequality and its decomposition
It is known that inequality measures can be highly sensitive to measurement errors; a few
bad observations can have a large impact on measured inequality (Cowbell and Victoria-Fester,
1996). Here we are concerned with a particular structure of measurement error, arising from
9
undervaluation of an income component, as discussed in the introduction. We cannot find a
treatment of this case in the literature, so we offer some observations, to help interpret the
empirical results later. We examine effects of undervaluation on the level of inequality, the factor
decomposition of inequality, and on the decomposition of changes in inequality over time.
Let us first consider the effect of the valuation error on the level of measured inequality, as
this is the easiest case. The revaluation can be thought of as a negative income tax. Let the
average rate of revaluation (analogous to the average tax rate) be defined as the increase in
imputed value as a proportion of original income. Following results from the literature on tax
progressivity (see, for example, Pfingsten, 1988), the correction for undervaluation will lead to
lower (higher) measured inequality if the average rate of revaluation falls (rises) as income
increases.
What about the effect on the factor decomposition of inequality? Recall that the share of
inequality attributed to a given income component is the regression coefficient of that component
on total income (equation 4). Both the regressor and regressand are underestimated (by the same
amount). There will be two sources of bias in the regression coefficient; the first is the usual
attenuation bias due to miss-measurement of the regressor, while the second is the bias due to the
fact that the same error contaminates the regressand. These two biases will work in opposite
directions and so one cannot say on a priori grounds what effect this will have on the regression
coefficient. Intuitively, the lower the regression coefficient, the less important will be the second
source of bias. So one expects undervaluation to lead to underestimation of the contribution to
inequality when that contribution is sufficiently low.
We can derive a very simple sufficient condition for signing the effect when the k'th
10
income component is undervalued by a constant proportion, such that the revaluation yields:
Yk = (1 + a)yk (8)
for a>0. We assume that 1 > ck> 0, although this can be relaxed; the following result holds for
1 +1/a> c > -(1 + a2vd/(2a)wherevk -var(ydlvar(y). On revaluing the undervalued
component, its contribution to total inequality becomes:
COV(Yk'Y) ( +a)(Ck + avd
kV var(y ) +a vk + 2ac
From (9) it is readily verified that c** > ck if and only if
k k
(2ck - I)ck
k I + a(l -ck)
So a sufficient condition for the undervaluation to underestimate the contribution to inequality is
that the undervalued component of income accounts for less than one half of inequality.
The effect of undervaluation on a factor's contribution to changes in inequality over time
(yk given by equation 5) is more complicated, since it will clearly also depend on how the factor
decomposition evolves. We confine attention to the case of empirical interest later in which
inequality is increasing (with or without revaluation) and the undervalued income component's
11
contribution to inequality is underestimated. Let * denote the contribution of the k'th income
component to rising inequality. It is readily verified that:
* (kl Ck;) + (C -k2)I2],I* + [(Ck; - ckYl) + (ck2 - k;)2]22
Tk 7yk (I2 - I1)(I2* -I,) (11)
If the factor decomposition does not change over time (c c * and c =c ) then clearly
k2 ki k2 Ckl)te lal
* = ct -- C*] = c - c* < ;the undervaluation ofthe k'th income component also leads to
Yk-'k = ki .Cki =k2 k2
an underestimation of its contribution to rising inequality. However, the outcome is ambiguous
when the factor decomposition is changing over time. From (11), the sign of - will also
depend on the "cross-terms", c ck2 and ck2 c at least one of which must be positive.! A
sufficient condition for ye > yk is that:
c -c I c
k2 kI < 2 Ckl kI (12)
c * - cII * -c|
k2 k2 k k2l
However, it is entirely possible for revaluation to diminish the contribution of the undervalued
income component to rising inequality, even when revaluation yields higher inequality at any one
s The cross terms cannot both be negative, for then (c ckl) + (c2 - ck2) < 0 - a
contradiction.
12
date. Suppose, for example, that with its undervaluation the measured contribution to inequality of
the k'th income component does not change over time (ck2 =Cki ), but with the revaluation its
contribution is found to fall over time (Ck* < ck i). Then * > y if and only if I2*/I* >
(Ckl -CkY)(Ck; ck2)
4 Data
The data are the household-level data from the Rural Household Survey (RHS) done by
China's State Statistics Bureau (SSB). Our sample covers 9,500 rural households in Guangxi,
Yunnan, Guizhou and Guangdong. The survey and steps we have taken in data processing are
described in detail in Chen and Ravallion (1996). The RHS is a high quality survey in many
respects, including both sampling methods and the care taken to minimize nonsampling errors
through close supervision and regular visits to the sampled households. There are, however,
problems in the methods used in processing the data after its collection, leading up to the
tabulations found in the Statistical Yearbook for China. Chen and Ravallion (1996) review the
main concerns about these data. We attempt to resolve the main problems by reprocessing the
primary data for 1985-90.
An important concern about the official data is that they continued to rely on old planning
prices for the valuation of income-in-kind from consumption of own-farm production. These
prices were below market prices (and also below government procurement prices). This
undervalued a large component of income - notably non-marketed home production of grain - and
13
at a rising rate over time (Chen and Ravallion, 1996). The standard definitions indicate that, for
our data set, an average of 21% of income came from grain production, of which 80% was the
imputed value of consumption from own production. Other components of farm income appear
also to have been undervalued, but this is a less worrying since the shares of income involved are
smaller (22% of income came from non-grain farm output, but only 10% of this was from own
consumption). Another problem is that the incomes used in past work have not included imputed
rents for housing and durables. Past work has also ignored spatial differences in the cost of living.
To deal with these problems, we have revalued grain-income in kind at median local
(county-level) selling prices for grain, as determined from the primary household-level data.9 The
administrative prices conventionally used by SSB for valuation were 72% of median selling price
in 1985, and this had fallen to 48% by 1990. We have also imputed rents for housing and
consumer durables, based on the asset valuations available in the primary survey data; we used
five percent of the recorded dwelling value for housing and 10 percent for durables (Chen and
Ravallion, 1996). And we have constructed new province-level spatial and inter-temporal cost of
living indices. The spatial cost of living adjustment is based on poverty lines aiming to measure
the local cost in 1988 of the same standard of living everywhere, based on a common bundle of
foods and an allowance for non-food spending consistent with spending behavior at the food
poverty line. The inter-temporal price indices are based on the rural CPI, though we have changed
the weights to accord with consumption behavior of the poorest 30% of the population. Full details
on both the poverty lines and the intertemporal cost-of-living deflators can be found in Chen and
9 Similar data are unavailable for revaluing other components of income in kind from own-farm
production, although (as noted above) these appear to be minor.
14
Ravallion (1996).'°
To assess the contribution of these data adjustments, we give results for each of the three
income definitions: The first is SSB's "net income" measure direct from the data files. We call
this "original income." The second incorporates our revaluation of grain-income from own
production, and imputed rents. The third uses our new deflator as well.
We use household income per person. This does not allow for economies of scale in
household consumption. It is often argued that scale economies are small in low-income
countries, because the share of income devoted to collectively consumed goods within the
household tends to be small, although this assumption is questioned by Lanjouw and Ravallion
(1995). We will consider the implications for some of our main results of allowing for scale
economies, and examine how this is affected by the other data revisions.
All our inequality measures, and other statistics, assume equality within the household (in
terms of income per person, or income per equivalent single person), and are household size
weighted. The Gini indices were calculated by numerical integration using the trapezoidal rule) of
the empirical (household-level) Lorenz curve.
5 Results on the overall level of inequality
Figure 1 plots the proportionate change in income after all out data revisions against the
log of original (unadjusted) income for 1985 and 1990. The fitted line was obtained by locally-
'0 A remaining limitation of these price indices is that the same deflators are used for all income
groups in a given province and year. Depending on how much budget shares vary by income level, and
how much relative prices change over time, this limitation could also have bearing on both the level of
measured inequality and its evolution over time.
15
weighted smoothing (using the "KSM" program in STATA). The figure also gives the fitted
values for the increase in income attributable to grain revaluation alone, as well as the remainder
due to other changes noted above. (The scatter of data points is for the total income increase due
to data revisions.)
The proportionate change due to our data revisions tends to decrease as income increases,
indicating that inequality falls after the changes. It is clear from Figure 1 that the revaluation of
grain income in kind accounts for the bulk of the change, although the other changes are also
inequality reducing on their own. The revaluation rates tend to be higher in 1990 than 1985,
largely reflecting the rising divergence between market prices and planning prices.
Table 1 gives our estimates of two measures of income inequality over time. Both
measures show rising inequality over the period for all three definitions of income. The
magnitude of the increase is markedly less when one combines the new valuation methods with
the new cost of living deflator. This can be seen more clearly from Figure 2. The adjustments to
the data entail lower inequality, and a lower rate of increase in inequality.
The finding of lower inequality when our revisions are made to the income data is robust to
the choice of inequality measure. This can be seen in Figure 3 and 4, which give the Lorenz
curves before and after the data revisions, for 1985 and 1990 respectively. Figures 5 and 6 give
the Lorenz curves for 1985 and 1990, based on the original and revised incomes (using the new
valuation method, and new deflator). There is Lorenz dominance in both cases, so the conclusion
that inequality increased is also robust to the inequality measure used. With the revisions to the
primary income data, however, the two Lorenz curves have clearly converged considerably.
Figure 7 allow us to examine the effect on the inequality comparisons of introducing an
16
allowance for scale economies. Instead of income per capita we use income divided by n 0where
n is household size and 0 is a parameter between 0 and 1 interpretable as (minus one times) the
elasticity of the cost of living with respect to household size. The conclusion that the Gini index
of income inequality rose over the period is robust to the choice of 0. So is the conclusion that the
inequality is lower after making our revisions to the raw data, at any given value of 0.
6 Inequality decompositions
Our aim here is not to attempt an exhaustive explanation of inequality in rural China, but
rather to test sensitivity to the measurement problems. For this purpose, we decompose income
into the 14 sources in Table 2. These are largely self-explanatory. The category "joint costs"
allows for costs which we cannot apportion between factor income components. Table 3 gives the
average shares of income attributed to these 14 sources. As expected, the revaluation of grain-
income in kind entails a sizable increase in the share of income attributed to this component. On
average, 21% of SSB's income measure is attributed to grain, while this rises to 31% on revaluing
at average local selling prices. The new income component for imputed rents accounts for about
7% of income on average.
Tables 4 gives the source decomposition of the levels of inequality at the beginning and
end of the period for the three income definitions. When compared to the original incomes, the
new valuation methods entail a sizable increase in the share of inequality attributed to grain
incomes, from 6% to 14% in 1990. Notice that, while the new valuation methods indicate lower
inequality (Table 3), they also indicate that grain income is more covariate with total income, and
17
hence it is found to account for a higher share of the (lower) level of inequality. These two
findings are consistent. On the one hand, grain income from own production accounts for a larger
share of the incomes of the poor, and this is why its revaluation tends to reduce measured
inequality. On the other hand, better-off rural households tend to have higher incomes from grain
production (even though the share of income from this source is lower). Since the undervaluation
is in the output price, it acts like a constant proportionate mark-down of this component as in
section 3, where it was shown that the undervaluation of grain income will then lead to an
underestimation of the share of inequality attributed to this component of income as long as that
share is less than 0.5, as is the case here (see the figures for grain in Table 4).
Table 4 also gives the shares of the measured increase in both inequality measures which
are attributed to each component of income. Over the period, the revaluation of own-grain
consumption entails a large increase in the share of rising inequality which is attributed this
component. (It is readily verified from Tables 1 and 4 that for grain the second inequality in (12)
holds for the Gini index but the first does not, although the difference is small; both inequalities in
(12) hold for grain when using the log deviation.) The usual income definition used in data for
China suggests that income from collectives (including TVEs) was the most important single
factor in the increase in overall inequality (Table 4). Our definition points instead to grain income.
Using our revised incomes (both revaluing grain income and using the new deflator) our
results indicate that 104% of the increase in the Gini index over the period 1985-90 can be
attributed to grain income; 61% was attributable to income from collectives (including TVEs).
Smaller positive contributions to rising inequality came from self-employment in industry and
construction (42%), labor earnings (36%) and services (32%). Against these positive contributions
18
to rising inequality, there were large inequality-decreasing effects from private transfers (-131% of
the increase in inequality) and other farm income (-65%).
Turning to the decomposition in terms of assets, we postulate that incomes are determined
by the variables given in Table 5. The dependent variable is income per person, in constant prices.
"Fixed productive assets" comprise the survey valuations of all immobile productive farm assets,
expressed in constant prices using the same deflator as the dependent variable, and normalized by
household size. "Labor force per capita" is the number of able-bodied workers per capita in the
household. The variables "hilly area" and "mountainous area" are dummy variables for the
geographic area in which the household lives, and the omitted dummy variable is that for
households living on the plains. "Cultivated land," "hilly land" and "fishpond" land are all areas
of land owned or contracted per person in the household. The education variables are all dummy
variables for the highest level of education reached by the workforce in the household; the omitted
dummy variable is for a household in which all members are illiterate. We also include household
size as an independent variable, to allow for possible scale economies.
Table 5 gives the regression coefficients for 1985 and 1990, for both SSB's original
incomes and our adjusted incomes (revaluing grain and using the new deflator). The signs are
unsurprising, and almost all coefficients are significant. By both measures, the income gain from
higher fixed productive assets fell over the period 1985-90, while the returns to land and schooling
(except college) rose.
Table 6 gives the simple correlation coefficients between each explanatory variable in the
regressions and total income per capita. These will help in interpreting the inequality
decompositions in Table 7 (analogous to Table 4).
19
A large share of the measured inequality at one date is attributable to the regression
residual (Table 7); the values of R2 in Table 6 are not unusually low for household-level cross-
sectional regressions of this sort, but the unexplained component of the variance in incomes still
accounts for 70-80% or more of the level of inequality. The residuals also account (positively) for
a share of the change in inequality over time.
In terms of the asset decomposition, the biggest quantitative difference between the two
income measures is in the estimated contribution of fixed farm assets to the change in inequality.
Both measures suggest that this was inequality-reducing over the period. This is largely
attributable to this factor's declining regression coefficient; the proportionate drops in the P
coefficient on fixed productive assets in the income regressions (Table 5) are roughly the same as
the drops in the shares of inequality (Table 7). Thus, the key factor appears to have been the lower
"rate of return" to farm assets in 1990 than 1985. One might conjecture that wider access to
capital in rural China during the 1980s helped reduce its returns. The reason why SSB's original
incomes appear to have underestimated the (inequality-reducing) contribution of wider access to
physical capital is that SSB's income measures underestimated the rate of return to these assets in
the base period. This is undoubtedly due to the undervaluation of grain income, leading to an
underestimation of the marginal product of the farm capital stock.
Living in a mountainous area (relative to the plains, where farm land tends to be of better
quality) was an important factor in explaining the level of inequality and an important source of
rising inequality over time. Access to cultivated land was of negligible consequence for the level
of inequality, but our adjustments to SSB's original incomes suggest that access to farm land was
a more important source of higher inequality over time than one would have otherwise thought.
20
This too is attributable in part to the increase in returns to land indicated by our corrections to the
primary data. (Notice the large increase between 1985 and 1990 in the regression coefficient on
cultivated land in Table 5, when based on our revised incomes; by contrast the original incomes
indicate a small drop.) With our data revisions the correlation between land and income also
increased (Table 6), adding further to its contribution to inequality. The revaluation of grain
income in kind is clearly the main factor here too.
Both income measures (with and without our corrections) indicate that living in the
mountains versus the plains was an important source of inequality, and a very important factor in
the increase in inequality. Indeed, the distribution of households between mountainous areas and
the plains accounts for 52% of the increase in the Gini index using our adjusted incomes (33%
using SSB's original incomes.) Although fishponds only accounted for less than 2% of the level
of inequality in 1990, they accounted for a sizable share of the increase in inequality, reflecting
both a higher ,B in 1990 than 1985 (Table 5) and a higher correlation with total income (Table 6).
The importance of the geographic variables to how distribution evolves over time is
consistent with the results of Jalan and Ravallion (1997) on these data. They found that rates of
consumption growth over time at the farm-household level are strongly influenced by geographic
variables, controlling for household characteristics. This can be interpreted as a "geographic
poverty trap" arising from the combined effect of credit market failure and an adverse effect of
mountainous terrain and other geographic variables on the productivity of private investment.
Primary education was inequality-reducing, while other levels of education had the
opposite effect, although the contribution was small in all cases (negligible in the case of college).
Recall that we find increases in the O's for the (non-college) education variables in Table 5. Lack
21
of schooling beyond primary is negatively correlated with income (Table 6), so the higher returns
put downward pressure on inequality. By contrast, the large increase in the returns to high school
education put upward pressure on inequality, although this effect was dampened by an
improvement in the distribution of high school education; the correlation coefficient fell slightly
(Table 6) and the standard deviation also fell (by 7%).) In terms of the impact on inequality, a
more equal distribution of secondary schooling helped compensate for its higher rate of return.
7 Conclusions
Tabulations of the distribution of rural incomes provided in the Statistical Yearbooks for
China suggest a large increase in inequality after the mid-1980s. However, there are a number of
concerns about the data underlying these numbers, as well as the level of their aggregation. While
China's rural economy has been going through a structural transition, the processing methods used
in the available survey data have not fully reflected those changes. Income in kind from the
consumption of farm products has been systematically undervalued in official sources, due to a
large and rising divergence in the 1980s between the prices used in official valuations and actual
selling prices. Another concern is that existing data sources have ignored spatial differences in the
cost of living, and how these have changed over time. Before we can be confident that there is in
fact rising income inequality, these concems should be addressed. Thankfully, one can go a long
way toward fixing the main problems if one has access to the raw data from China's Rural
" Recall that the share of inequality attributed to any income determinant is the product of three
things: the partial regression coefficient of income on that determinant, the simple correlation coefficient
with income, and the ratio of the standard deviation of that determinant relative to the standard deviation
of income.
22
Household Survey, which appear to be of good quality by international standards.
We find that about two thirds of the proportionate increase in measured income inequality
in rural southern China between 1985 and 1990 vanishes once one revalues own-grain production
at average local selling prices, imputes rents for housing and consumer durables, and allows for
inter-provincial cost of living differences. After making these changes in the measured incomes at
household level, instead of the 16% increase in the Gini index of income inequality between 1985
and 1990 implied by past data, we find a 6% increase; instead of a 36% increase in the average
proportionate deviation from the mean, we find a 12% increase.
The undervaluation of income in kind from foodgrain production in the official data is the
main source of bias in past inequality measures. This component of income tends to account for a
higher share of the incomes of the poor, so its undervaluation leads to an overestimation of the
level of inequality. Furthermore, the prices used by the provincial statistics offices diverged
progressively over time from market prices, with the result that the undervaluation also leads to an
overestimation of the rate of increase in inequality.
What accounts for the measured increase in inequality not accountable to these data
problems? The explanation depends on the income definition used. The revaluation of income in
kind from gain production indicates that a much larger share of the (albeit smaller) increase in
rural inequality was due to grain income than past data would have suggested. The income
definition used in past work suggests that differing fortunes in grain production account for 15%
of the rise in the Gini index; on revaluing grain income-in-kind at actual selling prices we find that
this income component accounted for 58% of the increase in the Gini index. Private transfers
were the strongest inequality-reducing factor amongst the income components we have measured.
23
We also estimated a decomposition of inequality in terms of land and (physical and
human) assets. This suggests that the differences in income between those living in mountainous
rural areas and those on the plains have been an important source of rising inequality, while
diminishing returns entailed that the distribution of farm assets was inequality-reducing, once our
corrections are made to measured incomes. Higher returns over time to good quality agricultural
land (including whether one lives in the plains or the mountains) were inequality-increasing, even
though the distribution of land was of little consequence to the level of inequality at any one date.
There are still problems in the data that we have not been able to deal with here, and at
present it is only possible for us to perform these calculations for rural areas of four provinces.
Nor have we addressed two other potential sources of rising inequality nationally, namely
inequality between urban and rural areas, and inequality within urban areas. There are a number
of as yet unresolved issues here, not least of which is allowing for differences in the cost of living
between urban and rural areas (adjusting for inflation over time using separate urban and rural
consumer price indices does not incorporate the spatial difference at any one point in time). A
further problem is obtaining a definition of income which is comparable between urban and rural
areas of China; the rural and urban household surveys for China are largely independent and there
appear to be a number of inconsistencies. Reprocessing of the raw survey data for both the urban
and rural household surveys for all provinces could go a long way toward dealing with these
issues.
24
References
Atkinson, A. B. (1970). 'On the measurement of inequality'.Journal of Economic Theory,
Vol. 2, pp. 244-263.
Chen, S. and Ravallion, M.(1996). 'Data in transition: Assessing rural living standards in
southern China'. China Economic Review, Vol. 7, pp. 23-56.
Cowell, F. A. and Victoria-Feser, M. (1996). 'Robustness properties of inequality
measures'. Econometrica, Vol. 64, pp. 77-101.
Fields, G. S. (1996). 'Accounting for Differences in Income Inequality'. Mimeo, Cornell
University.
Howes, S. and Hussain, A. (1994). 'Regional Growth and Inequality in Rural China'. STICERD
Discussion Paper 11, London: London School of Economics.
Jalan, Jyotsna and Martin Ravallion (1997). 'Spatial Poverty Traps?', Policy Research Working
Paper 1862, World Bank, Washington DC.
Jenkins, S. P. (1995). 'Accounting for Inequality Trends: Decomposition Analysis for the UK,
1971-86'. Economica, Vol. 62, pp. 29-64.
Khan, A. R., Griffin, K., Riskin, C. and Zhao, R. (1993).'Sources of Income
Inequality in Post-Reform China'. China Economic Review, Vol. 4, pp. 19-35.
Knight, J. and Song, L. (1993). 'The Spatial Contribution to Income Inequality in Rural China'.
Cambridge Journal of Economics, Vol. 17, pp. 1 95-213.
Lanjouw, P. and Ravallion, M. (1995). 'Poverty and household size'. Economic Journal,
Vol. 105, pp. 1415-1434.
Milanovic, B. (1996). 'Poverty and inequality in transition economies: What has actually
25
happened' in Bart Kaminski (ed.) Economic Transition in Russia and the New States of
Eurasia, New York: M.E. Sharpe.
Pfingsten, A. (1 988). 'Progressive Taxation and Redistributive Taxation: Different Labels for the
Same Product?'. Social Choice and Welfare, Vol.5.
Ravallion, M. and Chen, S. (1997). 'What can new survey data tell us about recent changes in
distribution and poverty?'. World Bank Economic Review, Vol. 11. pp. 357-382.
Rozelle, S. (1994). 'Rural Industrialization and Increasing Inequality: Emerging Patterns in
China's Reforming Economy'. Journal of Comparative Economics, Vol. 19. pp. 362-391.
Sah, R. and Stiglitz, J. E. (1992).. 'Peasants Versus City-Dwellers'. Taxation and the Burden of
Economic Development. Oxford: Oxford University Press.
Shorrocks, A. F. (1982). 'Inequality Decomposition by Factor Components'. Econometrica,
Vol. 50. pp. 193-211.
World Bank, (1992). 'China: Statistical System in Transition'. World Bank, Washington DC.
26
Table 1: Income inequality measures for southern China
Inequality 1985 1986 1987 1988 1989 1990
measure (%)
1. Original measure Gini 29.11 30.12 31.04 32.96 33.75 33.90
of household net
income, as used in
the Statistical Year- Log deviation 13.97 14.92 15.82 17.84 18.82 18.96
book for China
...............................................................................................................................................................
2. With new Gini 27.47 28.75 29.46 30.57 31.10 30.88
valuation methods
(for income from
own-grain production Log deviation 12.38 13.55 14.16 15.26 15.85 15.50
and imputed rents) ... ...
and imputed rents) ~~~~................................. ...................................... ...........................................................................................................
3. New valuation Gini 27.06 28.27 28.34 28.12 28.03 28.72
methods plus new
cost-of-living Log deviation 12.02 13.11 13.14 12.96 12.93 13.43
deflators
Table 2: Components of rural household income
1. Collective: Income from collective businesses (collective united accounting units, TVE,
economic union)
2. Grain: Grain income
3. Non-grain farm: Non-grain farming income
4. Animal: Income from animal husbandry
5. Other farm: Forestry, fishery, handicrafts and gathering & hunting
6. Industry: Income from industry and construction (small business or self employer)
7. Services: Income from transportation, commerce, restaurant, service and other (small business
or self employer)
8. Labor: Private sector labor earnings
9. State: Wages and pensions from state or collective own enterprises
10: Public transfers: Public transfers (government or village subsidies, bonuses and disaster
release finds)
11. Private transfers: Gifts or remittances from family members or relatives living outside the
village (in rural or urban areas) for more than six months per year (those living outside the village
for less than six months are counted as household members)
12. Other income: Other factor income
13. Reut: Imputed rent on housing and durable goods
14. Joint costs: Joint costs of production which cannot be apportioned (tax, contract fee and the
depreciation of fixed productive asset)
Table 3: Average income by source
Shares of 6-year mean income (%)
Original With new New valuation
incomes valuation methods plus
methods new deflator
Collective 3.01 2.37 2.26
Grain 21.12 31.11 31.48
Non-grain farm 22.27 17.58 17.61
Animal 17.82 14.03 14.09
Other farm 11.05 8.75 8.75
Industry 4.22 3.31 3.27
Services 7.31 5.75 5.68
Labor 8.83 6.91 6.78
State 2.43 1.92 1.93
Public transfers 2.74 2.16 2.15
Private transfers 2.97 2.36 2.30
Other income 1.40 1.10 1.09
Rent n.a. 6.72 6.68
Joint costs -5.17 -4.07 -4.06
100.00 100.00 100.00
Table 4: Factor decomposition of income inequality
Original incomes New valuation methods New valuations + new deflator
1985 1990 1985-90 1985 1990 1985-90 , 1985 1990 1985-90
Gini LD Gini LD Gini LD
Collective 7.20 12.57 45.20 27.60 6.43 10.23 40.81 25.29, 6.40 9.92 67.34 39.95
Grain 4.42 5.97 15.42 10.32 8.34 13.83 58.12 35.65s 8.77 14.25 103.55 60.95
Non-grain farn 17.31 19.74 34.52 26.55 15.46 16.48 24.71 20.53 15.51 16.63 34.91 26.19
Animal 12.37 11.16 3.83 7.78 11.04 9.84 0.12 5.05 11.04 10.33 -1.26 4.27
Other farm 14.52 9.77 -19.08 -3.52 11.97 7.69 -26.82 -9.31 11.70 7.25 -65.31 -30.70
Industry 4.84 8.41 30.10 18.40 4.27 6.75 26.71 16.58, 4.39 6.58 42.32 25.27
Services 12.30 14.30 26.46 19.90 10.42 11.50 20.14 15.75 10.57 11.80 31.93 22.33
Labor 7.15 10.10 28.02 18.36 6.32 8.67 27.62 18.00 6.22 7.93 35.82 22.51
State 3.43 3.29 2.44 2.89 3.03 2.70 0.05 1.40 3.10 2.99 1.25 2.08
Public transfers 5.20 3.77 -4.88 -0.21 4.51 3.19 -7.42 -2.04 4.43 3.20 -16.76 -7.24
Private transfers 13.14 3.86 -52.53 -22.12 11.86 3.35 -65.24 -30.44 11.63 3.40 -130.80 -66.78
Other income 2.42 2.76 4.84 3.72 2.05 2.29 4.23 3.24 2.09 2.25 4.86 3.62
Rent n..a. n..a. n..a. n..a. 8.18 8.42 10.34 9.36 8.07 8.28 11.76 10.10
Joint costs -4.27 -5.69 -14.32 -9.67 -3.89 -4.94 -13.36 -9.09 -3.90 -4.80 -19.60 -12.54
100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
Note: The figures under "1985" and "1990" give the factor decomposition of the level of inequality. The figures under 1985-90 give the
decomposition of the change in inequality using the Gini index and mean log deviation (LD).
Table 5: Regressions for real income per capita
Variable Original income With new valuation methods and
new deflator
. 1985 1990 1985 1990
Intercept 333.02 420.33 401.91 463.38
(23.52) (23.99) (27.60) (29.82)
Fixed productive assets per capita 0.32 0.18 0.36 0.21
(24.36) (17.01) (26.35) (19.47)
Household size -14.96 -21.30 -16.48 -24.08
(-12.33) (-14.38) (-13.21) (-18.33)
Household labor force per capita 155.69 126.55 181.50 147.89
(able to work, if notworking) (12.98) (9.91) (14.71) (13.05)
Hilly area (dummy variable for -74.48 -101.67 -93.30 -89.485
the locality of the household) (-12.22) (-13.42) (-14.88) (-13.32)
Mountainous area (dummy -144.39 -201.73 -175.19 -191.74
variable, as above) (-24.48) (-29.43) (-28.88) (-31.57)
Owned cultivated land area per 0.14 0.13 0.17 0.33
capita (6.90) (3 77) (8.41) (10.89)
Area of hilly land per capita -o.o 1 -0.002 -0.004 0.01
(-1.45) (-0.15) (-0.92) (0.80)
Area of fishpond land per capita 0.08 1.27 0.07 0.89
(2.96) (14.40) (2.53) (11.40)
Highest education level is 38.00 50.90 36.64 49.56
... primary school (3.87) (3.93) (3.63) (4.32)
... middle school 78.19 106.90 76.48 97.38
(7.93) (8.30) (7.55) (8.53)
... high school 117.57 171.63 115.55 148.20
(10.75) (12.13) (10.27) (11.81)
... technical school 133.40 216.59 121.10 193.13
(4.89) (7.55) (4.32) (7.59)
... college 226.61 253.68 213.28 203.78
(3.43) (4.43) (3.14) (4.02)
R 2 0.185 0.210 0.217 0.247
Note: Monetary values for 1990 are in 1985 prices
Table 6: Correlation coefficients with total income per capita
Original income New valuation
methods plus new
deflator
1985 1990 1985 1990
Productive assets 0.24 0.18 0.27 0.23
Household size -0.04 -0.05 -0.05 -0.05
Labor 0.20 0.15 0.21 0.19
Hilly area 0.05 0.08 0.04 0.09
Mountain -0.24 0.29 -0.27 -0.30
Cultivated land 0.08 0.05 0.09 0.13
Hilly land 0.00 -0.05 0.00 -0.03
Fishpond 0.06 0.16 0.05 0.13
Primary school -0.12 -0. 13 -0.12 -0.12
Middle school I 0.06 0.05 0.06 0.05
High school 0.13 0.13 0.13 0.10
Technical school 0.03 0.05 0.03 0.05
College 0.01 0.03 0.01 0.03
Table 7: Decomposition by income determinants from Table 5
Original income . New valuation methods + new deflator
1985 1990 1985-90 1985 1990 1985-90
_____________ Gini LD Gini LD
Productive assets 5.86 3.45 -11.24 -3.32- 6.68 4.34 -33.82 -15.62
Household size 0.21 0.36 1.25 0.77. 0.25 0.63 6.81 3.86
Labor 2.42 1.41 -4.74 -1.42. 2.91 2.30 -7.61 -2.88
Hilly area -0.69 -1.39 -5.67 -3.36. -0.80 -1.49 -12.88 -7.45
Mountain 7.31 10.96 33.13 21.17 9.47 11.95 52.40 33.10
Cultivated land 0.53 0.19 -1.86 -0.75. 0.74 1.32 10.85 6.30
Hilly land 0.00 0.01 0.08 0. 04. 0.00 -0.02 -0.35 -0.20
Fishpond 0.12 2.17 14.67 7.93 0.09 1.40 22.79 12.59
Primary school -0.94 -1.17 -2.59 -1.82. -0.85 -1.13 -5.59 -3.46
Middle school 1.00 1.00 1.04 1.02- 0.92 1.05 3.15 2.15
High school 2.36 2.96 6.60 4.64. 2.20 2.28 3.60 2.97
Technical school 0.17 0.42 2.00 1.15 0.12 0.42 5.19 2.91
College 0.04 0.14 0.70 0.39- 0.03 0.11 1.34 0.75
Residual 81.61 79.49 66.65 73.58 78.23 76.84 54.13 64.97
100.00 100.00 100.00 100.00' 100.00 100.00 100.00 100.00
Figure 1: Incidence of Income revisions
Increase in income due to data revisions (%), 1985
150
100
Total
50
Other *
0
3 4 5 6 7 8
Income per person (log)
Increase in income due to data revisions (%), 1990
150 - Total
Grain onLy t \: . :
100
Other
0
3 4 5 6 7 8
Income per person (log)
Figure 2: Inequality measures for alternative income measures
Gini index of income inequality (%)
34 -
32 - incomes
32
30, methods
281
p~~~~~~~~ ~~~New valuation methods plus
26 _ ~~~~~~~~~~~new cost-of-living index
24-
22 -
20
1985 1986 1987 1988 1989 1990
Figure 3: Lorenz curves for original and revised income,1985
100.00
90.00
80.00
E
0
70.00
'; 60.00
' 50.00
c
, 40.00
30.00
original 85
1 20.00 ------revised 85
10.00
0.00-
0.00 20.00 40.00 60.00 80.00 100.00
The poorest p % of people
Figure 4: Lorenz curves for original and revised income, 1990,
100.00
90.00
* 80.00
E
° 70.00-
0) 60.00 ,,j;
4°50.00°~.
0
11 40.00-
30.00-
20.00 - - original 90
20.00
....revised 90
10.00
0.00
0.00 20.00 40.00 60.00 80.00 100.00
The poorest p % of people
Figure 5: Lorenz curves for 1985 and 1990 using original
income
100.00-
90.00 -
E 8000
E
0
70.00
' 60.00
g 50.00
40.00
*. 30.00
i0 original 85
i-20.00
-. - original 90
10.00 -
0.00-
0.00 20.00 40.00 60.00 80.00 100.00
The poorest p % of people
Figure 6: Lorenz curves for 1985 and 1990 using revised
income
100.00
90.00
* 80.00
E
0
e 70.00
0 60.00
50.00
e 40.00
0.30.00
20 00 revised 85
20.00 -.-revised 90
10.00
0.00
0.00 20.00 40.00 60.00 80.00 100.00
The poorest p % of people
Figure 7: Effect on Gini index of allowing for scale economies
Gini index of income inequality (xlOO)
40
35 t _Original incomes, 1990
l - Original~~~~ incomes, 1985l
30 - - - - - -t- - - - - -X
25 Revised incomes, 1990 Revised incomes, 1985
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Scale elasticitY
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS1883 Intersectoral Resource Allocation and Fumihide Takeuchi February 1998 K. Labrie
Its Impact on Economic Development Takehiko Hagino 31001
in the Philippines
WiPS1884 Fiscal Aspects of Evolving David E. Wildasin February 1998 C. Bernardo
Federations: Issues for Policy and 31148
Research
WPS1885 Aid, Taxation, and Development: Christopher S. Adam February 1998 K. Labrie
Analytical Perspectives on Aid Stephen A. O'Connell 31001
Effectiveness in Sub-Saharan Africa
WPS1886 Country Funds and Asymmetric Jeffrey A. Frankel February 1998 R. Martin
Information Sergio L. Schmukler 39065
V\PS1887 The Structure of Derivatives George Tsetsekos February 1998 P. Kokila
Exchanges: Lessons from Developed Panos Varangis 33716
and Emerging Markets
A!PS1888 What Do Doctors Want? Developing Kenneth M. Chomitz March 1998 T. Charvet
Incentives for Doctors to Serve in Gunawan Setiadi 87431
Indonesia's Rural and Remote Areas Azrul Azwar
Nusye Ismail
Widiyarti
WOIPS1889 Development Strategy Reconsidered: Toru Yanagihara March 1998 K. Labrie
Mexico, 1960-94 Yoshiaki Hisamatsu 31001
VIPS1 890 Market Development in the United Andrej Juris March 1998 S. Vivas
Kingdom's Natural Gas Industry 82809
WPS1891 The Housing Market in the Russian Alla K. Guzanova March 1998 S. Graig
Federation: Privatization and Its 33160
Implications for Market Development
WPS1892 The Role of Non-Bank Financial Dimitri Vittas March 1998 P. Sintim-Aboagye
Intermediaries (with Particular 38526
Reference to Egypt)
W!VPS1893 Regulatory Controversies of Private Dimitri Vittas March 1998 P. Sintim-Aboagye
Pension Funds 38526
5NPS1894 Applying a Simple Measure of Good Jeff Huther March 1998 S. Valle
Governance to the Debate on Fiscal 84493
Decentralization
WNPS1895 The Ernergence of Markets in the Andrej Juris March 1998 S. Vivas
Natural Gas Industry 82809
WPS1896 Congestion Pricing and Network Thomas-Olivier Nasser March 1998 S. Vivas
Expansion 82809
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS1897 Development of Natural Gas and Andrej Juris March 1998 S. Vivas
Pipeline Capacity Markets in the 82809
United States
WPS1898 Does Membership in a Regional Faezeh Foroutan March 1998 L. Tabada
Preferential Trade Arrangement Make 36896
a Country More or Less Protectionist?
WPS1899 Determinants of Emerging Market Hong G. Min March 1998 E. Oh
Bond Spread: Do Economic 33410
Fundamentals Matter?
WPS1900 Determinants of Commercial Asli DemirgOc-Kunt March 1998 P. Sintim-Aboagye
Bank Interest Margins and Harry Huizinga 37656
Profitability: Some International
Evidence
WPS1901 Reaching Poor Areas in a Federal Martin Ravallion March 1998 P. Sader
System 33902