The distribution of purchasing power in Pakistan, 1985-86.
de Kruijk, Hans
I. INTRODUCTION
Suppose that you and I earn the average income of Pakistan and that
we are looking at a parade in which the whole population of Pakistan
takes part. It is a spectacular parade because the size of all marchers
is proportionate to their income. We have the average height. Anyone who
earns more than the average is taller than we are; anyone who earns less
than the average is smaller. The procession is organized in such a way
that the smallest walks in front and the tallest in the rear. The parade
moves at uniform speed and its duration is one hour exactly. What do we
see? What is the size of the marchers in front and in the rear? Who
passes by during the first ten minutes, the second ten minutes, etc.?
Where do they live, in which province, in urban or in rural areas? How
long does it take before we see people of our own length? Who are they?
Section 4 presents the Pakistani Parade and tries to answer these
questions. (1)
But before being able to form the queue by putting everybody in its
correct position, we need to discuss the way of estimating the size of
each person, and therefore, the choice of the concepts of income and
income units (Section 3), the data base, the choice between using
grouped data versus tape data, and the related type of computer required
(PC versus mainframe) for these calculations (Section 2).
Actually, the contribution of this paper is twofold. First, the
presentation of the Pakistani Parade, and secondly, after having made
adjustments for economies of scale of households earlier by using
equivalence scales [Kruijk (1987)], this paper standardizes these
adjusted household incomes further by correcting for differences in
average price levels between provinces and between urban and rural areas
as announced but not executed in Kruijk (1986). After these adjustments,
the term 'distribution of income' is no longer a proper
description of what finally is ranked. Accordingly, the title of the
present paper is now called 'The distribution of purchasing power in Pakistan, 1985-86'.
2. THE DATA BASE
Although not ideal, the Household Income and Expenditure Survey
(HIES) is the best data source on household incomes in the country. It
presents data on household composition, sources and levels of household
income, structures and levels of household expenditures. Most likely,
the Pakistani HIES is--like all other HIESs in the world--incomplete and
bad at the top. The rich are not easily accessible to investigators and
their extent of under-representation is not known. In a situation where
data are consistently incomplete, there is no other option than being
pragmatic. Unfortunately, it is not always possible to check and
supplement HIES data by data from other sources like income tax data
from the Central Board of Revenue (as the late Javaid Azfar had done in
the 1970s).
Further, to raise the sample size to 'actual' population
figures, population trends are extrapolated by applying annual compound
growth rates (of the in-between Census period of September 1972--March
1981)at the most disaggregated level available in published form, i.e.
population figures of urban and rural areas by province (eight regions).
This paper makes use of published data in the form of grouped data
and not of unpublished individual income data from computer tapes.
Undoubtedly, inequality calculated on the basis of grouped data is an
under-estimation of true inequality because inequality within brackets is neglected. However, as reported earlier on the basis of data of other
countries, the extent of under-estimation is insignificant in cases
where the number of income groups is more than ten [Kruijk (1986)]. In
the Pakistan case with twelve income groups, the level of Theft
coefficients calculated on the basis of published grouped data appears
to be consistently (overall Pakistan, urban and rural separate) about
0.002 points lower than on the basis of tape data for the year 1984-85
at a level of about 0.270, which is an under-estimation of about one
percent only [Government of Pakistan (1989)]. This implies that
published data can be used, and--more important--that a (super)mini or
mainframe computer which can handle large data tapes is not required. A
personal computer is adequate enough, which makes this kind of analysis
accessible to a much wider group of researchers. In fact, all
calculations presented in this paper are constructed in an ordinary
spreadsheet.
3. CONCEPTS OF INCOME AND INCOME UNITS
Choice of the Income Unit
The intention of forming a parade is to get a picture of the
distribution of welfare in the country. If the size of each marcher
would be based on his or her individual income like in Pen's parade
for the Netherlands and for England, [see Pen (1971) and The Economist (1987)] it happens that some wives and children, who supplement the
family income, are reduced to pygmies and have to walk in the front of
the parade, whereas the husband/father, who is the main income earner of
the family, might be taller than average. This situation is not
acceptable in our parade. It is more satisfying to organize the parade
in such a way that everybody joins in the parade and that members of the
same household have equal size and walk hand in hand alongside. In that
case, it seems more appropriate to consider household incomes instead of
individual incomes as the basis for everybody's size, but
unfortunately it is not as easy as that. Measuring welfare on the basis
of income per household ignores the reality that households differ in
size and composition. A one-person household earning Rs 1000 per month
is much better off, of course, than a six-person household with Rs 1000
per month. An often used remedy to adjust for household size is simply
to deflate household income by household size, so that income is
expressed in per capita terms. However, such an approach is equally
unacceptable because in that case it is believed that a single person
with Rs 1000 is as well off as a two-person household with Rs 2000, and
a six-person household with Rs 6000, which obviously is not so.
Economies of scale in household consumption cannot be denied.
However, it is not easy to suggest a proper equivalence scale. Since the
one I proposed earlier has been rejected by Kemal (1987), the old
proposal is replaced, herewith, by a new suggestion for an equivalence
scale which has been applied in this paper and which has the advantage
of being very simple: the head of the household is one (as always), but
now any additional person counts for 0.5. This scale implies that a
one-person household with Rs 1000 has the same level of welfare as a
two-person household with Rs 1500, a three-person household with Rs
2000, etc. The result of using this scale to adjust for economies of
scale in the household is presented in Table 2.
Choice of the Income Concept
Although standardizing household incomes by taking the size of the
household into account is an improvement, it is still not sufficient for
estimating the size of each person. Generally speaking, the cost of
living in rural areas is lower than in the urban areas. In other words,
one rupee has more purchasing power in rural areas than in urban areas.
Welfare estimates should take these price differences into account. The
HIES presents detailed data on family expenditures (in rupees) and on
shopping-baskets (in units) disaggregated into provinces and urban and
rural areas (eight regions). On the basis of these figures, it is not
difficult to calculate prices of the various items per region. Table 1
present these prices as well as a (weighted) average family
shopping-basket which is constructed on the basis of regional
shopping-baskets. With this information it is feasible to estimate
regional price indexes with the qualification that these are based on
food items only.
After having estimated these regional price indexes all (household
size adjusted) family incomes are first divided by their index before
determining the size of all marchers. The effect of this price
adjustment for the level of average income in each region is indicated
in Table 2. It is clear that the sequence of e.g. all residents of urban
areas in Punjab in file parade changes with respect to residents of
other areas, but not with respect to each other, because all (adjusted)
household incomes of urban Punjab are divided by the same amount i.e.
1.037. Both adjustments (for household size and for regional prices
differences) can be summarized in the following formula:
Y [stan.sub.i, r] = [Y.sub.i] x 1 + 0.5([[bar.S] - 1])/1. + 0.5
([S.sub.i] - 1) x 100/[p.sub.r]
in which:
Y [stan.sub.i,r] = Standardized income of household i in region r;
[Y.sub.i] = Income of household i;
[bar.S] = Average household size ([bar.S] = 6.34 in 1985-86);
[S.sub.i] = Size of household i; and
[P.sub.r] = Price index of region r.
Table 2 shows that the standardization mentioned above have a
substantial impact on the ranking of average monthly household incomes
of the eight regions. According to unstandardized HIES data, Punjab
ranks last in urban areas and third in rural areas, which is not in line
with the popular perception of its being the most prosperous province
[see also Naseem (1986)]. After standardization the picture becomes much
more in line with commonsense. Now Punjab ranks second in urban areas
after Sind which is dominated by cosmopolitan Karachi; in rural areas
Punjab has now the highest average income, while Baluchistan and NWFP are the poorest and not the richest provinces in rural areas as
suggested by unadjusted figures.
4. THE PAKISTANI PARADE
After having discussed the way in which everybody's size has
been determined, it is high time to watch the parade. Graph 1
facilitates to visualize the cortege a bit, each bar illustrates one
minute of the parade. Table 3 shows the Pakistani Parade in figures.
Each major group of Tables 3 and 4 represents ten minutes. Table 4 gives
an indication of the over-representation of the eight regions (the
shaded segments) in the six groups of ten minutes In this paper only one
attribute has been selected, viz. the eight regions of the country. Of
course, it is possible-and also desirable--to extend the number of
characteristics like occupation, working status, age, level of
education, etc. of the over-represented parties in each group.
What do we see? In the first seconds, we see very tiny gnomes pass
by, the size of a match-stick. (2) But this does not go on for long, the
average length during the very first minute is already more than two
feet (see Graph 1 and Table 3). During the first ten minutes an
over-represented group from rural Baluchistan, rural Punjab and urban
NWFP passes by (see Table 4). The impact of standardization on each
position in the queue is illustrated once more by Table 3. The average
size of the marchers going by after exactly ten minutes (NWFP-urban) is
not (according to unstandardized incomes) twice as high as those of the
first minute (Baluchistan-urban), but they are less than fifty percent
taller. They are still dwarfs of about 3.5 feet. During the second
period of ten minute, the size of the marchers does not increase much.
After twenty minutes people are about four feet. In this group residents
from rural areas of NWFP and Baluchistan are highly over-represented. As
far as townsmen are concerned, Punjab and Baluchistan are
over-represented. Twenty minutes is a long time to keep seeing small
people passing by who barely reach to our midriff.
[GRAPHIC 1 OMITTED]
Looking in the direction of the approaching parade to see what we
can expect next, we still cannot observe normal people. We keep seeing
dwarfs. After half an hour the size of the marchers is less than 4.7
feet. During the last ten minutes, we have seen many Sindhi's. In
fact, this is the first group where Sindhis--both from rural and from
urban areas are over-represented. It takes exactly forty minutes before
we can see people of our own size arriving (average standardized
household income is 1900 rupees per month in 1985-86). They mainly
belong to the rich of the countryside. The group of 'normal'
people is fairly small. After fifty minutes the size of the marchers has
increased to over 6.5 feet. More than fifty percent of the richest group
are city-dwellers, while the overall percentage of urban population is
about thirty percent only. Urban areas of all the four provinces are
over-represented in the richest group. The average size of marchers
passing by in the last few minutes increases rapidly. Average size in
the last minute is nearly twenty feet. During the last few seconds, we
see people like tower flats. The highest household income reported in
1984-85 HIES corresponds with a size of about five hundred feet. This
giant family has an income of Rs 244,000 per month. (3)
5. CONCLUSIONS
First, the impact of standardization is substantial. Nominal incomes have to be adjusted for household size, economies of scale
within households, and last but not least for regional price
differences.
Secondly, the parade is a more fascinating and appealing way of
looking at the distribution of income than other inequality indicators
like Gini or Theft coefficients. The parade is mainly a parade of dwarfs
with a few giants only. It takes forty 'minutes before average
income passes by.
Thirdly, the parade has the attractive property that the
participants can be identified which is extremely useful for policy
preparation and for analyzing changes in the relative positions of
household groups over time. Distribution indicators like Gini and Theft
coefficients are not affected by a mutual exchange of households in the
distribution. In other words, if two households exchange positions in
the queue, the distribution itself will remain the same. However,
exchange of positions is important information for understanding
determinants of the distribution and for analyzing the impact of
distribution policies. As mentioned earlier, in this paper only one
attribute has been selected, viz. regions. Of course, it is possible to
extend the number of characteristics like occupation, working status,
age, education, source of income, etc.
Finally, although these kind of exercises do not give great
precision, it is a challenge to keep trying to be as accurate as
possible and to see what can be done with the available data.
Comments on "The Distribution of Purchasing Power in Pakistan,
1985-86"
Kruijk has an interesting paper. It is a snapshot of
Pakistan's income distribution/purchasing power at one point in
time, 1985-86. Jan Pen described the typical income distribution in a
market economy as a parade of countless dwarfs and a few giants in 1971.
Kruijk uses the same device.
Estimation
He uses HIES grouped data. The basic unit used for comparison is
the household. To make different family sizes equivalent, household
income is divided by a deflated family Size. Dividing income by family
size alone does not make per capita incomes comparable. Economies of
scale in household consumption accrue to larger families. So dividing by
family size reduces income for large households disproportionately.
Kruijk alternatively uses 1 for head of household, and 0.5 for each
family member. I suggest alternatively 1 for adults and 0.5 for
children.
Results
The most important aspect of this paper is that it does a regional
comparison of purchasing power. Given the debate on regional access to
resources, this kind of analysis is needed. He uses purchasing
power/household to rank provinces. All urban areas have the highest
rank, followed by rural areas in the following order.
Urban : Sind, Punjab, Baluchistan, NWFP.
Rural : Punjab, Sind, Baluchistan, NWFP.
So the two most prosperous provinces are Punjab and Sind. The
result also shows a clear urban bias.
He ranks provinces within each income group. The sample is divided
into 6 income groups. For a non-biased distribution, provincial
distribution within each income group should be same as provincial
distribution of the population c.f. rural Punjab has 39 percent of total
population. So within each income group rural Punjab should have a 39
percent share. But actually in the poorest group rural Punjab has a 52
percent share so it is over-represented. So rural Punjab is both the
most prosperous and has the largest percentage of the poor. This
polarisation of income is a very interesting result, a product of the
high rate of growth and the particular pattern of growth in rural
Punjab.. However this is not pointed out by Kruijk.
In fact this is the fundamental shortcoming of this paper, that it
presents a set of interesting statistics but makes no attempt to analyse them. There is no attempt to establish a regional pattern to this income
distribution. There is no attempt to explain a priori why a particular
regional pattern should hold. No hypotheses about growth of income
across provinces. There is no attempt to explain a fortiori why a
regional pattern does hold--no testing of these hypotheses and a causal explanation of the prevalent phenomena.
I do see a regional pattern. And I can venture some hypotheses to
explain this pattern.
(a) The Punjab has had high growth and structural change in
agriculture. Therefore polarization of income is expected in
agriculture. It has had high growth in manufacturing. Growth in
manufacturing in itself does not lead to polarization, but to a skewed
distribution towards the upper end of the scale. Table 4 shows that the
Punjab is over-represented in the poorest and two richest income groups.
Rural Punjab is over represented in the poorest and richest groups. This
supports polarization of rural income. Urban Punjab is over represented
in the richest income group. This supports skewness of manufacturing
income.
(b) Sind has not had any structural change in agriculture, so no
rural polarization is expected. Sindh has had high growth in
manufacturing. This should lead to a skewed distribution of urban
income. This should also lead to a skewed distribution of total income
for Sindh. Table 4 shows that Sind is over-represented in the richest
income group. And this is due to urban Sind. Rural Sind shows no
polarization.
(c) NWFP and Baluchistan have had neither structural change in
agriculture nor significant growth in manufacturing. So neither
polarization nor skewness of income is expected in each province. Table
4 shows that both provinces are not over-represented in the poorest and
richest income groups.
So the statistics presented have a tot of potential. But they are
just dead figures without analysis and causal explanations.
Moazam Mahmood
Pakistan Institute of Development Economics, Islamabad.
REFERENCES
Kemal, A. R. (1987) Some Comments on Sources of Income Inequality
in Pakistan. The Pakistan Development Review 26 : 4.
Kruijk, Hans de (1986) Inequality in the Four Provinces of
Pakistan. The Pakistan Development Review 25 : 4.
Kruijk, Hans de (1987) Sources of Income Inequality in Pakistan.
The Pakistan Development Review 26 : 4.
Naseem, S. M. (1986) Comments on Inequality in the Four Provinces
of Pakistan. The Pakistan Development Review 25 : 4.
Pakistan, Government of (1989) Some Developments of Income
Inequality in Pakistan during the Period 1979-1986-87. Islamabad:
Federal Bureau of Statistics. (A Summary Report to the Economic
Coordination Committee of the Cabinet)
Pen, Jan (1971) Income Distribution. Middlesex, England: Penguin Books. The Economist (1987) From Rags to Riches in 60 Minutes 28-29.
(1) The idea of organizing such a parade has been put forward by
the dutch economist Prof. Jan Pen (1971).
(2) The lowest monthly household income reported is Rs 30, which
corresponds with a size of less than two inches (this figure together
with the highest income reported are the only two figures that are
derived from tape data; since the tape of 1985-86 is not operational
yet, these extreme figures are from the tape of 1984-85, as mentioned
earlier all other calculation are based on grouped data and made on a
personal computer).
(3) Commenting upon an earlier draft of this paper, Pen points to
the non-appearance of real giants in this parade: 500 feet is not much
compared to e.g. the Duke of Westminster who is about 20 miles [see The
Economist (1987)]. Apparently, in Pakistan, these giants do not report
to the HIES.
HANS de KRUIJK, The author is associated with the Centre for
Development Planning, Faculty of Economics, Erasmus University,
Rotterdam.
Table 1
Estimation of a Regional Price Index on the
Basis of Food Prices, 1985-86
Price per Region (in Rupees per Unit)
Price
per
Region
(in
Rupees
per
Unit)
Punjab
Shopping-basket Rural
Wheat and Wheat Flower (KG) 10.96 2.1
Rice and Rice Flower (KG) 1.28 5.1
Gram-whole (KG) 0.04 8.5
Gram-split (KG) 0.20 8.0
Mash (KG) 0.09 11.6
Moong (KG) 0.10 10.4
Masoor (KG) 0.06 15.8
All Other Pulses (KG) 0.03 7.0
Milk (Fresh & Boiled) (LT) 6.67 3.3
MilkPacked (Tetra, etc.) (LT) 0.03 5.0
Milk (Dry & Condensed) (KG) 0.03 6.0
Butter (KG) 0.06 36.0
Ghee (Desi) (KG) 0.10 36.8
Yogurt (KG) 0.18 3.8
Vegetable Ghee (KG) 0.58 14.0
Mustard Oil (KG) 0.03 10.0
Mutton (KG) 0.12 25.3
Beaf (KG) 0.42 12.0
Fish Fresh (KG) 0.06 12.0
Chickens (KG) 0.06 22.8
Eggs (NO) 1.54 1.0
Banana (NO) 2.94 0.4
Moosmi, Malta, Keeno (ft) 2.40 0.4
Mango (KG) 0.04 9.0
Apple (KG) 0.05 9.7
Mellon (KG) 0.06 3.5
Other Fruit = +Grapes (KG) 0.12 6.7
Potatoes (KG) 0.78 2.4
Tomatoes (KG) 0.27 4.8
Onion (KG) 0.70 2.4
All other Vegetables (KG) 1.62 3.0
Salt (KG) 0.24 1.4
Chillies (KG) 0.00 15.9
Refined Sugar (KG) 0.79 9.1
Sugar (Desi) (KG) 0.04 6.2
Gur and Shakar (KG) 0.39 4.6
Tea (Black / Green) (KG) 0.06 64.3
Bill per Region (Rupees) 112.2
Price Index 100.00 93.7
Price per Region
(in Rupees per Unit)
Sindh Sindh NWFP
Shopping-basket Rural Urban Rural
Wheat and Wheat Flower 2.3 2.6 2.4
Rice and Rice Flower 3.0 4.4 4.9
Gram-whole 8.0 8.5 7.0
Gram-split 8.5 6.0 8.3
Mash 10.5 10.7 10.2
Moong 9.9 9.9 10.0
Masoor 14.8 13.6 5.5
All Other Pulses 7.1 6.8 8.4
Milk (Fresh & Boiled) 3.4 5.1 4.8
MilkPacked (Tetra, etc.) 4.0 9.0 5.8
Milk (Dry & Condensed) 4.8 28.7 17.0
Butter 30.4 37.0 64.0
Ghee (Desi) 30.5 27.0 48.5
Yogurt 5.2 7.1 6.4
Vegetable Ghee 14.0 13.9 14.3
Mustard Oil 15.5 14.0 17.0
Mutton 29.3 29.0 12.0
Beaf 14.1 15.7 12.0
Fish Fresh 17.1 14.6 14.0
Chickens 22.0 17.9 25.8
Eggs 0.9 0.8 0.9
Banana 0.3 0.3 0.4
Moosmi, Malta, Keeno 0.4 0.5 0.4
Mango 7.0 7.0 3.0
Apple 9.7 9.3 11.3
Mellon 2.6 3.0 2.0
Other Fruit = +Grapes 5.5 6.7 8.2
Potatoes 2.8 2.5 2.7
Tomatoes 3.0 3.4 4.1
Onion 2.1 2.1 2.9
All other Vegetables 3.5 3.8 3.2
Salt 1.1 1.3 1.2
Chillies 16.1 16.4 15.7
Refined Sugar 8.7 8.7 9.4
Sugar (Desi) 7.5 7.0 7.9
Gur and Shakar 5.0 4.0 5.6
Tea (Black / Green) 63.0 68.5 59.1
Bill per Region (Rupees) 112.4 130.1 128.9
Price Index 93.8 108.6 107.6
Price per Region
(in Rupees per Unit)
Balu- Balu-
NWFP chistan chistan
Shopping-basket Urban Rural Urban
Wheat and Wheat Flower 2.6 2.5 2.3
Rice and Rice Flower 6.1 4.0 6.4
Gram-whole 9.0 6.0 12.0
Gram-split 7.9 9.0 9.7
Mash 6.6 10.9 11.9
Moong 4.8 11.6 10.9
Masoor 12.5 14.7 15.0
All Other Pulses 9.3 8.7 9.0
Milk (Fresh & Boiled) 4.9 4.5 4.9
MilkPacked (Tetra, etc.) 4.8 4.0 9.3
Milk (Dry & Condensed) 11.0 14.2 44.0
Butter 50.0 50.0 50.0
Ghee (Desi) 56.0 37.3 43.7
Yogurt 5.7 5.7 5.0
Vegetable Ghee 13.8 14.9 15.5
Mustard Oil 12.5 15.1 18.3
Mutton 16.0 28.1 28.3
Beaf 11.9 13.5 13.9
Fish Fresh 16.0 15.2 14.9
Chickens 25.3 22.8 22.8
Eggs 1.0 0.9 1.0
Banana 0.4 0.4 0.4
Moosmi, Malta, Keeno 0.4 0.6 0.5
Mango 7.0 8.8 2.4
Apple 11.7 7.4 9.4
Mellon 3.0 3.4 4.1
Other Fruit = +Grapes 7.3 17.7 16.5
Potatoes 2.4 3.2 3.0
Tomatoes 3.6 4.4 4.3
Onion 2.5 2.6 2.5
All other Vegetables 3.9 3.4 4.5
Salt 1.2 1.3 1:3
Chillies 16.3 10.4 17.9
Refined Sugar 9.2 9.3 9.1
Sugar (Desi) 6.5 5.8 5.5
Gur and Shakar 5.2 5.5 5.0
Tea (Black / Green) 75.7 66.4 75.0
Bill per Region (Rupees) 132.7 129.1 137.5
Price Index 110.8 107.8 114.8
Table 2
Average Income per Household per Region, Adjusted for Differences
in Household Size and Average Price Level per Region
Income per Income per
Household Household Average
Published Calculated Household
in HIES from HIES Size
Region (Rs) (Rs)
1. Sindh-urban 2884 2828 7.10
2. Punjab-urban 2333 2295 6.56
3. Baluchistan-urban 2519 2428 6.47
4. NWFP-urban 2334 2445 6.83
5. Punjab-rural 1618 1622 6.07
6. Sindh-rural 1599 1645 6.31
7. Baluchistan-rural 1621 1638 5.59
8. NWFP-rural 1777 1789 6.65
Household Purchasing
Income per Regional Power per
Standard Price Standard
Household Index Household
Region (Rs) (Rs)
1. Sindh-urban 2563 108.60 2360
2. Punjab-urban 2228 103.70 2149
3. Baluchistan-urban 2386 114.80 2078
4. NWFP-urban 2292 110.80 2069
5. Punjab-rural 1684 93.70 1797
6. Sindh-rural 1652 93.80 1761
7. Baluchistan-rural 1824 107.80 1692
8. NWFP-rural 1717 107.60 1595
Table 3
The Pakistani Parade in Figures (each Major
Group is Ten Minutes)
No. of
Income Household Household
Region Class (Millions) Size
Group I
Balochistan-urban 600-700 0.00 4.30
NWFP-urban < 600 0.01 2.81
Punjab-rural < 600 0.50 3.06
Balochistan-rural 600-700 0.05 3.94
Punjab-urban < 600 0.08 2.84
Balochistan-urban 700-800 0.00 4.38
NWFP-urban 600-700 0.01 3.68
NWFP-rural 700-800 0.08 4.40
NWFP-rural < 600 0.08 2.42
NWFP-urban 600-700 0.06 3.70
Punjab-urban 600-700 0.06 3.89
NWFP-urban 700-800 0.01 4.04
Balochistan-rural < 600 0.04 2.45
Punjab-rural 600-700 0.31 4.04
NWFP-rural 800-1000 0.23 5.07
NWFP-rural 800-1000 0.02 4.83
Punjab-urban 700-800 0.07 4.17
Balochistan-urban 700-800 0.06 3.89
Sindh-rural < 600 0.07 2.64
Sindh-rural 600-700 0.06 3.71
Punjab-rural 700=800 0.36 4.39
Balochistan-rural 800-1000 0.14 4.52
Sindh-rural 700-800 0.07 4.32
Sindh-urban < 600 0.01 2.04
Sindh-urban 700-800 0.02 3.56
Punjab-urban 800-1000 0.21 4.62
Punjab-rural 800-1000 0.85 5.00
Sindh-urban 800-1000 0.07 4.30
Sindh-rural 800-1000 0.26 4.93
NWFP-urban 1000-1500 0.06 5.96
Average
Group 2
NWFP-rural 1000-1500 0.59 5.96
Balochistan-rural 1000-1500 0.30 5.56
Punjab-urban 1000-1500 0.60 5.88
Balochistan-urban 800-1000 0.01 3.60
Balochistan-urban 1000-1500 0.03 4.96
NWFP-rural 1500-2000 0.38 7.46
Punjab-rural 1000-1500 0.75 5.92
Average
Group 3
Punjab-rural 1000-1500 1.00 5.92
Sindh-rural 1000-1500 0.63 5.80
NWFP-urban 1500-2000 0.06 7.20
Sindh-urban 1000-1500 0.26 5.03
Balochistan-urban 1500-2000 0.03 6.23
Balochistan-rural 1500-2000 0.13 6.29
Sindh-urban 1500-2000 0.30 6.58
Punjab-urban 1500-2000 0.45 6.58
Average
Group 4
Sindh-rural 1500-2000 0.42 7.27
NWFP-rural 2000-2500 0.21 8.13
Sindh-urban 600-700 0.01 1.76
NWFP-urban 2000-2500 0.03 7.67
Punjab-rural 1500-2000 1.11 6.92
NWFP-urban 2500-3000 0.02 9.13
Balochistan-rural 4000-4500 0.01 14.46
Sindh-urban 2000-2500 0.21 7.33
Balochistan-rural 2500-3000 0.03 8.85
NWFP-rural 2500-3000 0.10 8.93
Average
Group 5
Punjab-urban 2000-2500 0.29 7.36
Balochistan-urban 2000-2500 0.02 6.70
NWFP-rural 3000-3500 0.07 10.07
Balochistan-rural 2000-2500 0.09 6.52
Sindh-rural 2000-2500 0.19 7.54
Sindh-rural 2500-3000 0.10 9.34
Punjab-rural 2000-2500 0.58 7.39
Balochistan-rural 3000-3500 0.02 9.48
Sindh-urban 2500-3000 0.15 7.59
NWFP-urban 3000-3500 0.01 8.75
Balochistan-urban 2500-3000 0.01 7.00
Balochistan-rural 3500-4000 0.01 10.72
Punjab-urban 2500-3000 0.18 7.69
Punjab-rural 2500-3000 0.29 8.42
Balochistan-urban 3000-3500 0.01 8.26
Average
Group 6
Sindh-rural 3000-3500 0.05 9.95
Sindh-urban 3000-3500 0.10 8.37
NWFP-rural 3500-4000 0.04 9.84
Balochistan-urban 3500-4000 0.00 8.92
NWFP-urban 3500-4000 0.01 9.12
Punjab-rural 3000-3500 0.17 8.84
Punjab-urban 3000-3500 0.13 7.75
NWFP-rural 4000-4500 0.02 9.70
Punjab-urban 3500-4000 0.10 8.34
Sindh-urban 3500-4000 0.06 7.88
Punjab-rural 3500-4000 0.11 9.16
NWFP-urban 4000-4500 0.01 8.75
Sindh-urban 4000-4500 0.04 9.04
Sindh-rural 3500-4000 0.03 8.91
Balochistan-urban 4000-4500 0.01 7.67
Sindh-rural 4000-4500 0.02 9.47
Punjab-rural 4000-4500 0.07 9.11
Punjab-urban 4000-4500 0.07 7.82
Balochistan-urban >4500 0.01 10.07
NWFP-rural >4500 0.07 11.35
Sindh-urban >4500 0.20 9.44
Sindh-rural >4500 0.04 10.34
Punjab-urban >4500 0.22 8.73
NWFP-urban >4500 0.03 8.06
Punjab-rural >4500 0.18 9.27
Balochistan-rural >4500 0.04 7.29
Average
Average/Total 15.4 6.34
Cumulative Income per
Population Population Household
Region (Millions) (Millions) (Rs)
Group I
Balochistan-urban 0.02 0.02 486
NWFP-urban 0.02 0.02 486
Punjab-rural 1.52 1.55 470
Balochistan-rural 0.20 1.75 660
Punjab-urban 0.22 1.97 496
Balochistan-urban 0.01 1.98 773
NWFP-urban 0.03 2.02 650
NWFP-rural 34 2.35 750
NWFP-rural 0.20 2.56 476
NWFP-urban 0.24 2.80 655
Punjab-urban 0.24 3.04 667
NWFP-urban 0.03 306 758
Balochistan-rural 0.10 3.16 509
Punjab-rural 1.27 4.43 656
NWFP-rural 1.16 5.59 910
NWFP-rural 0.10 569 909
Punjab-urban 0.30 6.00 767
Balochistan-urban 0.23 6.23 757
Sindh-rural 0.20 6.42 505
Sindh-rural 0.23 6.65 656
Punjab-rural 1.56 8.22 755
Balochistan-rural 0.65 8.87 898
Sindh-rural 0.31 9.18 754
Sindh-urban 0.02 9.20 503
Sindh-urban 0.07 9.27 773
Punjab-urban 0.99 10.25 918
Punjab-rural 4.25 14.51 905
Sindh-urban 0.29 14.80 938
Sindh-rural L..9 16.09 914
NWFP-urban 0.38 16.47 1268
Average
Group 2
NWFP-rural 3.49 19.96 1242
Balochistan-rural 1.66 21.62 1228
Punjab-urban 3.54 25.16 1264
Balochistan-urban 0.04 25.20 936
Balochistan-urban 0.16 25.36 1239
NWFP-rural 2.81 28.16 1727
Punjab-rural 4.45 32.62 1242
Average
Group 3
Punjab-rural 5.92 38.54 1242
Sindh-rural 3.68 42.21 1227
NWFP-urban 0.46 42.68 1759
Sindh-urban 1.29 43.97 1301
Balochistan-urban 0.18 44.15 1757
Balochistan-rural 0.85 45.00 1690
Sindh-urban 1.95 46.95 1787
Punjab-urban 2.95 49.89 1734
Average
Group 4
Sindh-rural 3.06 52.96 1730
NWFP-rural 1.73 54.68 2217
Sindh-urban 0.01 54.69 678
NWFP-urban 0.26 54.95 1661
Punjab-rural 7.69 62.65 1729
NWFP-urban 0.19 62.83 2763
Balochistan-rural 0.10 62.93 4145
Sindh-urban 1.51 64.43 2280
Balochistan-rural 0.22 64.66 2703
NWFP-rural 0.91 65.57 2732
Average
Group 5
Punjab-urban 2.15 67.72 2268
Balochistan-urban 0.11 67.83 2345
NWFP-rural 0.70 68.53 3212
Balochistan-rural 0.59 69.13 2238
Sindh-rural 1.46 70.59 2226
Sindh-rural 0.95 71.54 2707
Punjab-rural 4.29 75.83 2231
Balochistan-rural 0.14 75.97 3264
Sindh-urban 1.16 77.13 2784
NWFP-urban 0.12 77.25 3223
Balochistan-urban 0.09 77.34 2766
Balochistan-rural 0.11 77.44 3861
Punjab-urban 1.40 78.85 2760
Punjab-rural 2.43 81.28 2713
Balochistan-urban 0.06 81.34 3314
Average
Group 6
Sindh-rural 0.50 81.84 3238
Sindh-urban 0.87 82.71 3272
NWFP-rural 0.41 83.12 3797
Balochistan-urban 0.04 83.17 3754
NWFP-urban 0.09 83.25 3789
Punjab-rural 1.46 84.71 3215
Punjab-urban 1.04 85.75 3256
NWFP-rural 0.18 85.93 4233
Punjab-urban 0.84 86.77 3737
Sindh-urban 0.44 87.22 3763
Punjab-rural 1.03 88.24 3730
NWFP-urban 0.07 88.31 4245
Sindh-urban 0.39 88.70 4315
Sindh-rural 0.24 88.94 3700
Balochistan-urban 0.05 88.99 4233
Sindh-rural 0.17 89.16 4240
Punjab-rural 0.63 89.79 4226
Punjab-urban 0.52 90.31 4212
Balochistan-urban 0.13 90.44 6940
NWFP-rural 0.78 91.22 7513
Sindh-urban 1.91 93.13 7081
Sindh-rural 0.45 93.57 6890
Punjab-urban 1.91 95.49 7040
NWFP-urban 0.28 95.76 7030
Punjab-rural 1.68 97.45 7113
Balochistan-rural 0.26 97.70 7962
Average
Average/Total 97.7 97.7 1900
Purchasing Standized
Power per Income
Standard Size In
Region Household Feet
Group I
Balochistan-urban 846 2.44
NWFP-urban 846 2.44
Punjab-rural 907 2.62
Balochistan-rural 910 2.63
Punjab-urban 915 2.64
Balochistan-urban 919 2.65
NWFP-urban 934 2.69
NWFP-rural 47 2.73
NWFP-rural 949 2.74
NWFP-urban 951 2.74
Punjab-urban 965 2.78
NWFP-urban 996 2.87
Balochistan-rural 1006 2.90
Punjab-rural 1019 2.94
NWFP-rural 1022 2.95
NWFP-rural 1033 2.98
Punjab-urban 1050 3.03
Balochistan-urban 1055 3.04
Sindh-rural 1086 3.13
Sindh-rural 1089 3.14
Punjab-rural 1097 3.17
Balochistan-rural 1109 3.20
Sindh-rural 1109 3.20
Sindh-urban 1118 3.22
Sindh-urban 1,145 3.30
Punjab-urban 1157 3.34
Punjab-rural 1182 3.41
Sindh-urban 1196 3.45
Sindh-rural 1.06 3.48
NWFP-urban 120 3.48
Average 1090 3.15
Group 2
NWFP-rural 1217 3.51
Balochistan-rural 1275 3.68
Punjab-urban 1300 3.75
Balochistan-urban 1301 3.75
Balochistan-urban 1329 3.83
NWFP-rural 1393 4.02
Punjab-rural 1407 4.06
Average 1325 3.82
Group 3
Punjab-rural 1407 4.06
Sindh-rural 1412 4.07
NWFP-urban 1421 4.10
Sindh-urban 1459 4.21
Balochistan-urban 1554 4.48
Balochistan-rural 1579 4.56
Sindh-urban 1594 4.60
Punjab-urban 1619 4.67
Average 1480 4.27
Group 4
Sindh-rural 1637 4.72
NWFP-rural 1657 4.78
Sindh-urban 1661 4.79
NWFP-urban 1702 4.91
Punjab-rural 1711 4.94
NWFP-urban 1808 5.22
Balochistan-rural 1826 5.27
Sindh-urban 1850 5.34
Balochistan-rural 1869 5.39
NWFP-rural 1877 5.41
Average 1717 4.95
Group 5
Punjab-urban 1920 5.54
Balochistan-urban 1947 5.62
NWFP-rural 1980 5.71
Balochistan-rural 2027 5.85
Sindh-rural 2039 5.88
Sindh-rural 2048 5.91
Punjab-rural 2084 6.01
Balochistan-rural 2122 6.12
Sindh-urban 2190 6.32
NWFP-urban 2191 6.32
Balochistan-urban 2211 6.38
Balochistan-rural 2244 6.47
Punjab-urban 2248 6.49
Punjab-rural 2257 6.51
Balochistan-urban 2289 6.60
Average 2101 6.06
Group 6
Sindh-rural 2314 6.67
Sindh-urban 2360 6.81
NWFP-rural 2389 6.89
Balochistan-urban 2420 6.98
NWFP-urban 2481 7.16
Punjab-rural 2560 7.39
Punjab-urban 2635 7.60
NWFP-rural 2699 7.79
Punjab-urban 2833 8.17
Sindh-urban 2864 8.26
Punjab-rural 2877 8.30
NWFP-urban 2885 8.32
Sindh-urban 2905 8.38
Sindh-rural 2921 8.43
Balochistan-urban 3122 9.01
Sindh-rural 3169 9.14
Punjab-rural 3276 9.45
Punjab-urban 3381 9.75
Balochistan-urban 4009 11.57
NWFP-rural 4150 11.97
Sindh-urban 4584 13.22
Sindh-rural 4754 13.71
Punjab-urban 5123 14.78
NWFP-urban 5142 14.83
Punjab-rural 5428 15.66
Balochistan-rural 6542 18.87
Average 3746 10.81
Average/Total 1900 5.50
Table 4
Estimated Population Share per Income Group,
Pakistan, 1985-86 (in percentages)
ALL GROUPS
Urban Rural Total
Punjab 16.6 39.0 55.6
Sindh 10.4 12.7 23.1
NWFP 2.0 13.2 15.2
Balochistan 0.9 5.2 6.1
Total 29.9 70.1 100
GROUP 1 (Poorest)
Urban Rural Total
Punjab 10.6 52.3 62.9
Sindh 2.3 12.3 14.6
NWFP 3.4 11.8 15.2
Balochistan 0.1 7.2 7.3
Total 16.5 83.6 100
GROUP 3
Urban Rural Total
Punjab 17.0 34.2 51.2
Sindh 18.0 21.3 40.1
NWFP 2.7 2.7
Balochistan 1.1 4.9 6.0
Total 39.6 60.4 100
GROUP 5
Urban Rural Total
Punjab 22.5 42.6 65.1
Sindh 7.3 15.3 22.6
NWFP 0.7 4.5 5.2
Balochistan 1.7 5.4 7.1
Total 32.2 67.8 100
GROUP 2
Urban Rural Total
Punjab 21.9 27.6 49.5
Sindh
NWFP 39.0 39.0
Balochistan 1.2 10.3 11.5
Total 23.1 76.9 100
GROUP 4
Urban Rural Total
Punjab 49.1 49.1
Sindh 9.7 19.6 29.3
NWFP 2.8 16.8 19.6
Balochista 2.0 2.0
Total 12.5 87.5 100
GROUP 6 (Richest)
Urban Rural Total
Punjab 26.3 29.3 55.6
Sindh 22.1 8.3 30.4
NWFP 2.6 8.4 11.0
Balochistan 1.4 1.6 3.0
Total 52.4 47.6 100