WPS4182
The Welfare Effects of a Large Depreciation:
The Case of Egypt, 200005
Aart Kraay
The World Bank
Abstract: The Egyptian pound depreciated sharply between 2000 and 2005, declining
by 26 percent in nominal tradeweighted terms. This paper investigates the effect of the
large depreciation on household welfare operating through exchange rateinduced
changes in consumer prices. I estimate exchange rate passthrough regressions using
disaggregated monthly consumer price indices to isolate the impact of the exchange rate
changes on consumer prices. I then use householdlevel data from the 2000 and 2005
Egyptian household surveys to quantify the welfare effects of these consumer price
changes at the household level. The average welfare loss due to exchange rate
induced price increases was equivalent to 7.4 percent of initial expenditure. Stronger
estimated exchange rate passthrough for food items imply that this effect
disproportionately impacted poorer households.
World Bank Policy Research Working Paper 4182, April 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to
encourage the exchange of ideas about development issues. An objective of the series is to get
the findings out quickly, even if the presentations are less than fully polished. The papers carry
the names of the authors and should be cited accordingly. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the view of the World Bank, its Executive Directors, or the countries they represent.
Policy Research Working Papers are available online at http://econ.worldbank.org.
__________________________________________
1818 H Street N.W, Washington, DC, 20433, akraay@worldbank.org,
http://econ.worldbank.org/staff/akraay. This paper was prepared as background for the 2007
Egypt Poverty Assessment. I would like to thank without implication Sherine ElShawarby, Heba
ElLaithy, Faika ElRefaie, Francisco Ferreira, Jed Friedman, Michael Lokshin, Martin Ravallion,
Luis Serven, and conference participants at the Egyptian Center for Economic Studies for helpful
discussions.
1. Introduction
Between 2000 and 2005 Egypt experienced a large nominal depreciation of the
Egyptian pound, much of it concentrated around a sharp devaluation in early 2003. The
objective of this paper is to assess the welfare implications of the large changes in
consumer prices that accompanied this movement in the exchange rate. To address
this issue I first need to isolate the component of observed price changes during this
period that are due to the depreciation. I do this by estimating disaggregated exchange
rate passthrough regressions, using monthly consumer price index (CPI) data over the
period July 2000 through June 2005, for 8 regions in Egypt, disaggregated into 20
different goods and services.1 The fitted values from these regressions provide
estimates of the effect of the depreciation on 160 different price indices. Disaggregation
of exchange rate passthrough to this level is important, as there is considerable
heterogeneity across commodities in the response of domestic consumer prices to the
exchange rate. In particular, I find that on average, exchange rate passthrough was
greater for food items than for nonfood items, and even within food items varied
considerably. Regional variation in passthrough is also present, but is not as large as
across consumption items.
I then bring the estimated price changes due the depreciation for each of these
160 different price indices to the household survey for Egypt, to investigate their welfare
effects. I empirically construct estimates of the compensating variation associated with
these price changes for each household. In particular, I estimate how much higher (or
lower) each household's total expenditure would have to be in order to attain the pre
depreciation level of utility at postdepreciation prices.2 This compensating variation
consists of two parts. The first is the change in the cost of households' initial
1The regions are dictated by the disaggregation available in the CPI data, and are Cairo,
Alexandria, Canal, Border, Upper and Lower Urban, and Upper and Lower Rural. The commodity
disaggregation is dictated by overlap between expenditure categories in the household survey
and the CPI data.
2See Friedman and Levinsohn (2002) for a similar exercise investigating the welfare effects of
relative price changes following in Indonesia during the East Asian crisis of 1997. The main
difference with this paper is that they do not estimate exchange rate passthrough to consumer
prices, but, reasonably enough in the case of the enormous depreciation of the rupiah, assume
that all of observed price changes were due to the depreciation. Ferreira et. al. (2004) study the
distributional consequences of a large depreciation in Brazil, using a sectorallydisaggregated
macro model to quantify the effects of the depreciation on wages and prices, and then linking this
to a household survey.
1
consumption bundles as a result of depreciationinduced price changes. The second
captures changes in household behavior in response to these price changes. A modest
methodological contribution of this paper is to show how these substitution effects can
be estimated easily given the (pseudo) panel dimension of the data that I have for
Egypt. I find that most of the compensating variation is captured by the direct effect,
which averages 7.4 percent of initial expenditure, and is statistically significantly
(although quantitatively modestly) higher in poorer households. I find that there is a
great deal of heterogeneity across households in the estimated size of the welfare effect
of the depreciation. Most of this heterogeneity is due to differences in consumption
patterns across households. A policy implication of this heterogeneity is that it would be
difficult to accurately target any kind of subsidy program to offset the effects of the
depreciation.
Three major qualifications regarding these results should be kept in mind. First,
a significant limitation of this paper is that I am only able to study the welfare effects of
depreciationinduced changes in consumer prices. The depreciation is likely to have
had heterogenous effects on the incomes of different households as well. With
imperfect labour mobility, for example, it is plausible that households employed in
exporting sectors would have seen increases in earnings, while household employed in
importcompeting industries would have seen declines in earnings, as a result of the
depreciation. Unfortunately, however, the Egyptian household survey data that I use
provide only very limited information on the economic sector of employment, and so I
cannot investigate these kind of effects, and their distributional consequences, in any
detail.3
A second limitation is the fairly coarse level of aggregation at which I am able to
estimate the exchangerate induced component of price changes. As discussed further
below, by working at this coarse level of aggregation, I am likely to be underestimating
the scope that households have for adjusting their expenditure patterns in response to
price changes. This in turn means that I am likely to be overestimating the adverse
3See Ravallion and Chen (2004) for an effort to look at the effects of relative price changes on
household consumptions and incomes, in the case of China, and Ferreira et. al. (2004) for the
case of Brazil.
2
welfare effect of the depreciation, which could be substantially smaller than what is
reported here.
Third, I stress that I am looking at the welfare effects of depreciationinduced
changes in consumer prices over a fairly short period with a fairly large depreciation, and
this time horizon drives the finding of significant welfare losses. However, looking at
exchange rate changes over other horizons would naturally lead to different conclusions.
For example, the depreciation in the tradeweighted nominal exchange rate between
2000 and 2005 was preceded by an even larger tradeweighted nominal appreciation in
the previous five years between 1995 and 2000. In fact, for the entire period between
1995 and 2005, the tradeweighted nominal exchange rate appreciated by about 20
percent. If the pattern of exchange rate passthrough to disaggregated consumer prices
was similar during this earlier period, then one can interpret the welfare losses sustained
between 2000 and 2005 as just a partial reversal of the welfare gains experienced
during the appreciation between 1995 and 2000.
2. The Depreciation and Consumer Price Changes
Figure 1 shows the evolution of the nominal exchange rate and consumer price
index in Egypt between 1995 and 2005. The pound was fixed against the US dollar
between 1995 and 1999, followed by a moderate depreciation during 2000 and 2001. In
2002 the pound was again fixed against the US dollar, but during 2003 it depreciated
sharply by 31 percent against the dollar, and by 41 percent in tradeweighted terms.4
The consumer price index increased by 6.2 percent during 2003 and by another 10.8
percent during 2004. As shown in Table 1, the tradeweighted exchange rate
4The tradeweighted exchange rate index used here is constructed using data from Egypt's 25
largest trading partners in 2000. I use fixed weights based on these countries' shares in Egypt's
imports in 2000. During this period there were some exchange controls in place and the parallel
exchange visavis the US dollar diverged significantly from the official rate (in levels). This raises
the question of whether it is more appropriate to use the parallel market rate. For the analysis
that follows, what matters is the exchange rate at which importers actually transact. If they have
access to foreign currency at official (parallel) rates then the official (parallel) rates are
appropriate. Absent information on this, and absent data on parallel rates visavis all trading
partners, I use the tradeweighted official rates. However, in unreported results I obtain very
similar estimates of passthrough using the parallel market rate visavis the US dollar. This
because although the two series diverge somewhat in levels, in differences they track each other
quite closely over the period I consider.
3
depreciated cumulatively by 26.2 percent between 2000 and 2005, and the exchange
rate visavis the dollar depreciated by 52.2, while consumer prices rose by 27.6.
The key question I address in this section is the effect of the large depreciation
during 2003 on disaggregated consumer prices. Table 2 reports the cumulative growth
rate between July 2000 and June 2005 of the disaggregated components of the
consumer price index that I have for Egypt.5 A quick glance at this table reveals that
price changes have varied considerably across expenditure items, and to a lesser extent
across regions. Most striking is the behavior of food prices, which increased faster than
the overall consumer price index. Taking a simple average across regions, overall
consumer prices increased by 28 percent, but food prices increased by 38 percent,
implying a 10 percent increase in the relative price of food. In the remainder of this
section I investigate in detail the contribution of the depreciation of the Egyptian pound to
these absolute and relative price changes.
2.1 Empirical Framework
As shown in Table 2, I have monthly data on the consumer price index
disaggregated into 31 goods and services, for 8 regions in Egypt. Because of difficulties
in mapping the expenditure items in the CPI to the household survey, I will work with a
somewhat more aggregated set of 20 of these expenditure items that correspond to
expenditure categories in the household survey. I model the consumer price of item i in
region r in month t as follows:
(1) Pirt = Pirt
( ) ( )
N ir Pirt
T 1ir
where PN denotes the price of the nontraded component and PT denotes the price of the
traded component of that item. To simplify notation, we can think of the nontraded
component as capturing both purely nontraded goods within this item, as well as non
traded distribution costs associated with the traded goods. Accordingly we can think of
PT as the price of imported goods "on the dock" in Egypt. Concretely, one of our
disaggregated items is fruit. PT would therefore be the price of imported fruit "on the
5We would like to thank the staff of CAPMAS for kindly assembling this dataset.
4
dock", while PN is a price index of nontraded fruit as well as the distribution costs
associated with both kinds of fruit.
Following the large empirical literature on exchange rate passthrough, I model
the logarithm of this import price as a linear function of the log exchange rate and a
measure of foreign marginal costs of production:6
(2) lnPirt =0 + 1 (L)lnEt + 2 (L)lnCt +uirt
T
ir ir ir
where E is the exchange rate, C is a proxy for foreign marginal costs, and u is an error
term that I assume is independent of the exchange rate. I do not have any direct
measure of foreign marginal costs of production disaggregated by product. I therefore
simply introduce an aggregate foreign cost variable, which is a tradeweighted average
of the monthly producer price index in Egypt's five largest trading partners for which this
data exist.7 Note that I allow the extent of foreign cost pressures on export prices to vary
by product and region. 1(L) and 2(L) are polynomials in the lag operator, so that I allow
current and lagged values of the exchange rate and foreign costs to affect import prices
in order to capture slow adjustment.
Taking log differences of (1) and using (2) gives the growth rate of the consumer
price as a function of the growth rate of the exchange rate:
(3) lnPirt = ir lnPirt +(1ir)(0 + 1 (L)lnEt + 2 (L)lnCt + uirt)
N
ir ir ir
The effect of current and lagged changes in the exchange rate on consumer prices is
given by (1 ir ) 1 (L) , and this is the key parameter of interest for this section. It is
ir
important to note that the sensitivity of consumer prices to the exchange rate is likely to
be substantially smaller than the sensitivity of border prices to the exchange rate. This is
because consumer prices contain a substantial nontraded component, both in the form
6 See for example Campa and Goldberg (2005) for a justification of this particular specification.
Burstein, Eichenbaum and Rebelo (2005) document the importance of nontraded components of
traded goods prices and their role in real exchange rate fluctuations.
7 These are the United States, Germany, Italy, Great Britain, and Japan. Saudi Arabia and
France are among Egypt's top 5 sources of imports in 2000 but do not report monthly producer
price indices.
5
of nontraded items themselves, as well as distribution costs. I do not have direct
information on the size of these distribution margins in the case of Egypt, although in
principle these can be extracted from inputoutput tables for Egypt. In industrial
countries, these distribution margins are typically quite substantial, averaging 3050
percent of the prices paid by consumers (Campa and Goldberg (2006)).
Unfortunately, however, I cannot simply estimate Equation (3) econometrically
since I do not directly observe the price of the nontraded component of each good, PN.
I also cannot ignore this term and treat it as part of the error term in a regression since
movements in the nontraded component of goods prices might be spuriously correlated
with movements in the exchange rate. In particular, during much of the period of interest
there were acrosstheboard increases in nominal prices in Egypt together with a
depreciation in the exchange rate, and at least part of these price increases were likely
driven by purely domestic factors.
To address this problem I assume that the growth rate of the nontraded
component of the price of each item in each region consists of a common component
and an idiosyncratic component that is orthogonal to movements in the exchange rate:
(4) lnPirt =lnPrt + virt
N N
I assume further that I can approximate the common component of nontraded
goods prices with a simple average of a few items in the consumer price index that
appear to be primarily nontraded on a priori grounds. These are Domestic Services,
and Restaurant and Hotel Services.8 These two assumptions (of a common component
in nontraded goods prices, approximated by these two particular prices) are clearly
strong ones and open to debate. However, it is not clear what good alternatives might
be available. Although the results that follow are based on this assumption, I have tried
three alternatives, and found that the estimates of passthrough are not very different.
One possibility is to try to model explicitly purely domestic sources of inflation, for
example by including measures of growth in the money supply in the regression. I
8 Other clearly largelynontraded items are rent and education. However prices of these items
are tightly controlled in Egypt and movements in them are unlikely to properly reflect movements
in overall nontraded goods prices.
6
experimented with this but found it difficult to obtain reasonable estimates of the effect of
money growth on disaggregated consumer prices. Another possibility is to simply allow
for a time trend in the regression for each good, to capture the upward trend in domestic
prices during the period. A third possibility is to simply ignore the domesticallyinduced
changes in nontraded goods prices and drop them from the regressions.
In any case, denoting the growth rate of the simple average of these items in
each region as lnPrt , I obtain the following empirical specification:
^ N
(5) lnPirt = 0 +1 (L)lnEt + 2 (L)lnCt + 3 lnP^rt + eirt N
ir ir ir ir
where 0 = (1 ir )0 is the intercept; 1 (L) = (1 ir )1 (L) captures the effect of
ir ir ir ir
the exchange rate on consumer prices; 2 (L) = (1 ir )2 (L) captures the effect of
ir ir
foreign costs on consumer prices; 3 = ir captures the contribution of changes in non
ir
traded goods prices; and eirt = ir virt + (1 ir )uirt is the error term. Since this
composite error term is by assumption uncorrelated with the righthandside variables, I
can estimate Equation (5) by ordinary least squares. In practice, I measure all growth
rates as monthly observations on quarterly log differences, and I allow for 3, 6, and 9
month lags of these growth rates in the estimation. Since I have monthly data from July
2000 through July 2005 this gives me 60 monthly data points on which to estimate this
specification for each item and region.
2.2. Results
I first calculate the longrun passthrough coefficient as the sum of the
coefficients on the current and lagged exchange rate variables, i.e. ^1 (1) , for each of
ir
the 160 productregion combinations for which I have data. Figure 2 provides a visual
summary of the passthrough estimates, and Table 3 reports some summary statistics.
In the top panel of the graph I report passthrough estimates for some aggregate
categories, and in the bottom panel I report estimates for disaggregated food items. I
organize the passthrough estimates by product, and use boxplots to show the
distribution across the 8 regions of our estimates of passthrough for each product. In
7
Figure 3 I generate the same boxplots by product category, but now reporting the t
statistics associated with the test of the hypothesis that the longrun passthrough
coefficient is zero. There are several striking features of these two figures and table:
· Estimates of the longrun impact of the exchange rate on consumer prices are
quite substantial for many products. The median longrun estimated pass
through effect was 19 percent, indicating that 19 percent of the movement in the
tradeweighted exchange rate was reflected in consumer prices. Many of the
estimated passthrough coefficients are much higher, with the 75th percentile
equal to 47 percent passthrough.
· Estimates of passthrough vary substantially across products. The most notable
difference is between food and nonfood items, with much higher estimates of
passthrough for food items. In particular, pooling all regions, the median
estimate of passthrough for food items is 0.43, while for nonfood items it is only
0.07 (see the second column of Table 3). In the top panel of Figure 2, the pass
through estimates for an aggregate price index for food, beverages, and tobacco
are clearly much higher than for other nonfood categories shown. This is true
also for many individual food products as shown in the bottom panel of Figure 2.
· Estimates of passthrough are in most cases very statistically significant. This
can be seen in Figure 3 which reports the distribution of tstatistics associated
with the null hypothesis that the longrun estimated passthrough effect is zero.
For almost all food items, and for some nonfood items, these tstatistics are
quite large indicating highly significant estimates. For several nonfood items,
however, estimated passthrough effects are not significantly different from zero,
and in some cases are even negative (peculiarly, for entertainment, the
estimates are significantly negative). As these negative passthrough estimates
are difficult to interpret, I will set them to zero in the subsequent analysis of
welfare effects.
Unfortunately, there are few studies of exchange rate passthrough to
disaggregated consumer prices in developing countries to which we can compare these
results. Campa and Goldberg (2006) study a sample of 21 OECD countries and
document that the median (across countries) passthrough of the exchange rate to the
consumer price index is 17 percent, which is quite similar to the median (across goods)
8
passthrough estimates reported here for Egypt. Choudri and Hakura (2001) estimate
exchange rate passthrough to the aggregate CPI in a sample of 71 developed and
developing countries. They find an average longrun passthrough of 35 percent in a
group of moderateinflation countries including Egypt, and 24 percent for Egypt itself,
which is slightly higher than the median (across commodities) estimate reported above.
Frankel, Parsley and Wei (2005) report a substantially higher estimate of passthrough
of 42 percent for a set of eight very specific branded commodities, pooling data from a
sample of 76 developed and developing countries. However, several of the commodities
they consider are food, alcohol and tobacco products, and in the case of Egypt I find
substantially higher passthrough for such commodities.
The estimated change in consumer prices due to the depreciation can be
obtained by multiplying the passthrough estimates by the observed change in the
exchange rate. Between 2000 and 2005, the tradeweighted exchange rate that I use
depreciated by 26.2 percent (see last column of Table 1). I therefore multiply the
estimates of passthrough reported in Figure 2 by 26.2 percent to obtain the estimated
change in consumer prices due to the depreciation. To take a specific example, the
estimate of passthrough for Meat and Poultry in Cairo is 0.43, implying that the
depreciation increased the price of this product category in Cairo by 11 percent. The
actual change in the price of Meat and Poultry in Cairo was 43 percent, so that roughly
onequarter of the observed increase in the price of this item in Cairo was due to the
depreciation.
More systematically, I calculate the ratio of the exchangerate induced change in
each price to the actual observed price change for each of the 20 goods in eight regions
in Egypt, and summarize these ratios using boxplots in Figure 4, while the last column of
Table 3 reports summary statistics. For the median consumption item, 19 percent of the
observed growth in nominal prices can be attributed to the depreciation, with an
interquartile range from 6 percent to 34 percent. Since the estimated passthrough
coefficients are substantially bigger for food than for nonfood items, our estimates of the
exchange rateinduced price changes are also much bigger for food items, where the
median is 31 percent, as opposed to 10 percent for nonfood items.
9
These higher rates of passthrough for food items give a first indication of the
distributional consequences of the depreciation. Since poorer households devote a
greater share of expenditure to food items, the price changes associated with the
depreciation would have had a larger effect on them. In the next section of the paper I
turn documenting in more detail the welfare effects of these price changes.
3. Estimating the Welfare Impact of Exchange Rate Induced Price Changes
The next step is to take the estimates of the changes in prices induced by
movements in the exchange rate and calculate their welfare effects.
3.1 Empirical Framework
I use the compensating variation as a standard measure of welfare effects of
price changes. In particular, let e(u,p) denote the expenditure function, i.e.
(6) e(p,u*) min p'x s.t. u(x) > u*
where p is an nx1 vector of prices, x is an nx1 vector of quantities demanded, u(x) is a
wellbehaved utility function, and u* is a reference level of utility. Let p0 denote the
reference prices prevailing in 2000, the time of the initial household survey, and let
p p1 p0 denote the vector of price changes that were caused by the depreciation
between 2000 and 2005, that I have isolated empirically in the previous section. The
compensating variation measures the change in expenditure that would be required in
order for households to achieve their predepreciation utility u* at the postdepreciation
set of prices, p1:
(7) cv =e(p1,u*)e(p0,u*)
I will empirically approximate the compensating variation using a secondorder Taylor
expansion of the expenditure function around the initial period prices:9
9 This approach is also taken by Levinsohn and Friedman (2002). An alternative is Vartia (1983),
who shows how to get the exact comparison of utility in time 0 and time 1, by numerically
10
(8) cv p' ep (p,u*) + 1 p 2ep(p,u*) p
'
2 p'
where the matrices of first and second derivatives of the expenditure function are
evaluated at p0. Using Shephard's Lemma and the fact that compensated and ordinary
demands are equal at the initial optimal allocation, I can write this approximation to the
compensating variation as a share of initial expenditure e0 as:
cv p'x0 1
(9) + p'hp (p,u*) p
e0 e0 2e0 '
where h(p,u*) is the Hicksian or compensated demand function.
The interpretation of this expression is straightforward. The first term captures
direct effect of price changes, which is just the change in the cost of purchasing the
initial consumption bundle, x0, expressed as a share of initial total expenditure, e0. In
particular I can write the direct effect of the price changes in proportional terms as:
p'x0 pi
(10) = wi
e0 i pi
where wi = pi xi
0 0 is the share of good i in initial total spending and pi is the
e0 pi
proportional change in the price of good i. Thus, the direct effect of the price changes,
as a share of initial expenditure, is just a weighted average of the growth rate of the
prices of each good, with weights equal to the initial expenditure shares.
Considering only this direct effect would overstate the welfare effect of the price
changes because it does not take into account how households change their spending
patterns in response to price changes. If households can substitute away from goods
integrating demand functions. The disadvantage of this approach is that it relies on parametric
estimates of the entire demand system which are difficult to implement empirically.
11
that become relatively more expensive, then the direct effect of the price changes will
exaggerate the welfare effects since it assumes no such substitution is possible.
Estimating these substitution effects is therefore important, although substantially more
involved. One direct approach is to econometrically estimate a demand system over the
n consumption goods, using data from the household survey, and retrieve from this an
h
estimate of the matrix of price derivatives of the compensated demand function, .
p'
Doing so however requires data on goods prices at the household level. In the case of
the Egyptian household survey, I have some information on unit values for individual
consumption items. However, at the more aggregated level at which the exchange rate
passthrough estimates are calculated, these unit values become very difficult to
interpret.10
In this paper I take a different and computationally much simpler approach that
exploits the fact that I have two household surveys for Egypt, for 2000 and 2005. The
basic idea is to use information on observed changes in expenditure shares over this
period to back out estimates of the substitution effects. The key simplification of this
approach is that it obviates the need to estimate an entire demand system, but instead
requires only estimates of the expenditure elasticities for each consumption item. As
long as prices faced by individual households are orthogonal to total expenditure, these
elasticities can be estimated by simple regressions of expenditure shares on total
expenditure alone.
To implement this idea, I first need to relate observed changes over time in
quantities demanded to the substitution effects of interest. Taking a firstorder
approximation to changes in the observed ordinary demand function I have:
(11) x(p,e) x(p,e)p * +x(p,e)e
p' e
where x(p,e) is the ordinary demand function; x(p,e) are the changes in quantities
demanded between 2000 and 2005; and p* is the vector of overall price changes
10Friedman and Levinsohn (2002) implement an approach originally due to Deaton (1988, 1990)
who shows how to estimate demand systems when only unit value data are available.
12
between 2000 and 2005. Note that p* refers to overall price changes during this
period, while p above refers only to the depreciationinduced component of price
changes. Next I can use the Slutsky equation, which express the observable elasticities
of the ordinary demand function in terms of the unobserveable elasticities of the
compensated demand function, i.e.
(12) x(p,e)= h(p,u) x(pe,e) x(p,e)'
p' p'
Substituting Equation (12) into Equation (11) and rearranging results in:
(13) h(p,u)p * x(p,e) x(p,e)x (p,e)'p
p' e
Suppose momentarily that we were interested in evaluating the welfare effects of the full
set of price changes between 2000 and 2005, i.e. p*, as opposed to simply those price
changes induced by the depreciation, i.e. p. Then I could simply premultiply Equation
(13) by p*' and I would have the substitution component of the compensating variation
on the lefthandside, expressed in terms of observables on the righthand side. In
particular, on the righthand side of Equation (13) I have observed changes in quantities
demanded, x, and the derivatives of demand with respect to total expenditure, x
, that
e
can readily and easily be estimated from available data on expenditure shares and total
expenditure at the household level.
Unfortunately, however, things are more complicated in this case since I want to
obtain an estimate of substitution effects in response to depreciationinduced price
changes, p' h(p,u)p, not substitution effects in response to overall price changes,
p'
p*'hp (p,u) p * . In order to make progress, I make one further, and nontrivial
'
13
h
assumption, that the Slutsky matrix is diagonal, i.e. that all compensated crossprice
p'
elasticities are zero. In this case, Equation (13) simplifies to:
hi pi pi * xi xi xi xj
(14) pi
hi pi xi  e e wj j xj
Given estimates of the expenditure elasticities xi xi
e e I can solve (14) for the
compensated ownprice elasticities hi pi
pi . Finally, I can substitute these into
hi
Equation (9) to obtain the following estimate of the substitution effect:
2
1 wi pi hi
1
(15) p'hp (p,u*) p
2e0
' 2 i hi pi pi pi
Clearly the assumption of a diagonal Slutsky matrix is a restrictive and
unappealing one. However, as I discuss later, we shall see that estimated substitution
effects for the full set of price changes p*, which do not require this restriction, are quite
similar in magnitude to the estimated substitution effects associated with the exchange
rate induced changes in consumer prices. This gives some comfort that this assumption
is not too misleading. Moreover, it is worth remembering that this restriction does not
imply that the crossprice elasticities of ordinary demands are zero. Rather, it restricts
the effect of changes in the price of good i on the quantity demanded of good j to
operate through the income effect of the change in the price of good j (i.e. the price
change multiplied by the initial spending share), multiplied by the income elasticity of
good j.
3.2 Results
I begin by reporting estimates of the direct effects of price changes, that I
summarize in Figure 5, graphing the estimated compensating variation on the vertical
14
axis against log total household expenditure on the horizontal axis. In particular, these
direct effects are calculated for each household as the sum across all expenditure items
of initial spending shares times the percentage change in the price of each item due to
the depreciation, setting negative passthrough estimates to zero. We shall see shortly
that our estimates of the substitution effect are generally quite small, and so it makes
sense to focus on the direct effects first. Several observations based on this graph:
· The estimated compensating variation is nontrivial for the vast majority of
households. The income loss associated with the direct effect of exchangerate
induced price changes for the median household is 7.4 percent of initial
expenditure. The 5th and 95th percentiles of the distribution of compensating
variations at the household level are 4.9 and 9.9 percent of initial expenditure,
respectively.
· The estimated compensating variation is significantly higher for poorer
households, although the magnitude of the effect is modest. A simple regression
of the compensating variation on log total expenditure gives a slope coefficient of
0.01. Since the logdifference in total expenditure between households at the
95th and 5th percentile of the expenditure distribution is about 2, this implies that
the estimated real income loss due to the depreciation is about two percentage
points of initial expenditure higher at the 5th percentile of the income distribution
than at the 95th percentile. Controlling for household characteristics (log age
and sex of household head and log household size) and regional effects raises
the slope coefficient to 0.016, implying a 3.2 percentage point difference in the
income effect of the depreciation between rich and poor households. This
adverse distributional effect of the depreciation is consistent with our finding that
passthrough for food items was higher than for nonfood items, coupled with the
observation that the share of food in total expenditure is higher for poorer
households.
· There is enormous heterogeneity across households in the size of the estimated
compensating variation. A simple regression of the compensating variation on
log total expenditure delivers an Rsquared of only 17 percent. Including
household characteristics and regional dummies raises this to 38 percent,
leaving the majority of the crosshousehold variation in the estimated
compensating variation unexplained. In the case of Indonesia, Friedman and
15
Levinsohn (2002) find even greater heterogeneity, with similar regressions
explaining only 11 (26) percent of the variation across rural (urban) households in
the estimated compensating variation.
Figure 6 disaggregates the direct effect of the price changes by rural and urban
households. To construct this figure I order households from poorest to richest within
rural and urban areas. I then construct a rolling average over 100 households of the
estimated compensating variation, and plot it against the percentile rank of the middle
household of each group in the entire combined rural and urban expenditure distribution.
Over most of the income distribution (and particularly from the 20th percentile on up) the
rural compensating variation is slightly higher than for urban households. This figure
also shows that the relationship between the compensating variation and income levels
is fairly flat over most of the range of the expenditure distribution, and tails off sharply for
the richest 10 percent or so of (mostly urban) households. It is also worth noting the
estimates of the compensating variation for rural households is likely to be overstated
relative to urban households. This is because rural household's net consumption of food
items is likely smaller than their gross consumption when compared with urban
households, and the depreciation in the exchange rate disproportionately increased food
prices.
Table 4 reports the mean and standard deviation of the compensating variation
by region and by quintile of the expenditure distribution. Regionally, the estimated
compensating variation ranges from a low of 6.7 percent in the Border region to a high
of 8.4 percent in Rural Lower Egypt. Within each region the estimated compensating
variation declines as we move to successively higher quintiles of the expenditure
distribution.
I next investigate further why there is so much heterogeneity across households
in the estimated direct effect of depreciationinduced price changes, with the help of a
simple decomposition exercise. Adding household subscripts h in Equation (10), I can
decompose the direct component of the compensating variation for each household as
follows:
16
ph'x0 h = wih
pih
e0h i pih
pi pih pi
(16) = wi

i pi + wi
i pih pi
+ (w  wi )pi + (w ) p ih pi
ih ih
i pi i  wi pih pi
where wi is the average across all households of the share of item i in total
consumption, and pi is the average across all households (effectively, across all
pi
regions since I don't have withinregion price variation) of depreciationinduced price
changes. The first term in this decomposition is the compensating variation for a
hypothetical household facing average price changes and having average expenditure
shares. The value of this is 7.5 percent which is (almost) the mean effect, and is the
same across households. The remaining terms vary across households and isolate the
different sources of crosshousehold variation in the estimated compensating variation.
The first of these captures crosshousehold variation due to crosshousehold differences
in price changes (since it holds the expenditure shares fixed for all households). The
second captures differences due to crosshousehold differences in expenditure shares,
keeping price changes constant, and the third term captures purely household and
pricespecific variation. The standard deviation across households of these three
components are 0.6 percent, 1.5 percent, and 0.3 percent. This suggests that cross
household differences in expenditure shares are the most important source of cross
household differences in the welfare effects of the depreciation, while price differences
(across regions, in our case) are less important, but still nontrivial.
I next examine the poverty impacts of these price changes. To do this, I begin
with the 2000 distribution of expenditure across households in Egypt. I then subtract
from each household the direct estimate of the compensating variation to arrive at a
counterfactual distribution of expenditure which reflects the losses due to the
subsequent depreciation. I then calculate the change in the headcount measure of
poverty between these two distributions, for Egypt as a whole, and by regions. The
results are summarized in Table 5. The first column provides the benchmark estimates
17
of the headcount for 2000, by region. The figures report the percent of households
falling below the householdspecific poverty lines calculated by ElLaithy, Lokshin, and
Banerji (2003). The second column uses the same poverty lines, but replaces the actual
distribution of expenditure with the counterfactual distribution reflecting the welfare
losses due to the depreciation, and the third column reports the difference between the
two. For Egypt as a whole, the estimated welfare effects of the depreciation can be
interpreted as raising the headcount measure of poverty by 5 percentage points. The
effects are lower in the major metropolitan centers of Egypt, with poverty increasing by
2 percent from a low base. Rural areas of Egypt saw the largest absolute increase in
the headcount of 6.4 and 6.7 percent in lower and upper Egypt respectively, but from a
much higher base. Not surprisingly, the ranking of poverty impacts across regions is
quite similar to the ranking of welfare effects across regions in Table 4. The final column
of Table 5 shows the actual headcounts by region in 2005 for reference. It is interesting
to note that the estimated poverty impacts of exchangerateinduced changes in
consumer prices are substantial when compared with the overall change in poverty
between 2000 and 2005.
I finally consider the role of substitution effects in response to the price changes
induced by the depreciation. In order to implement Equations (14) and (15) I need
information on changes over time on spending on each of the 20 expenditure items.
Although I have access to the 2000 and 2005 household surveys, unfortunately these
are not true panels but repeated crosssections. I therefore employ cohort techniques to
estimate the changes in spending shares, and from this the substitution effects. In
particular, for the 2000 and 2005 household survey I construct cohorts based on four
education categories, five age categories, and seven regional categories, for a total of
140 cohorts. For each cohort I calculate the average spending shares across the 20
expenditure items in the 2000 and 2005 surveys. Using the householdlevel variation
within each cohort in the 2000 survey, I also estimate cohortspecific income elasticities
for each expenditure share. Finally, I combine these ingredients with our estimates of
the depreciationinduced price changes, to estimate the substitution effect.
The results of this exercise are summarized in Figure 7, which plots the
estimated direct and substitution components of the compensating variation against log
total expenditure at the cohort level. The estimates of the direct effect at the cohort level
18
is quite similar to what I estimated at the household level, except that unsurprisingly
there is less variation given that I now have data only for 140 cohorts that by
construction are more homogenous in their spending shares than the underlying data.
The more interesting point is the comparison of the relative magnitudes of the direct and
substitution effects, with the latter much smaller (in absolute value) than the former. The
mean (across cohorts) substitution effect is just 0.2 percent of initial expenditure, as
compared with a mean (across cohort) direct effect of 6.6 percent of initial expenditure.
This suggests that substitution effects are small, and the bulk of the welfare effect of the
price changes is picked up by the direct effects that we have already discussed.
Clearly this estimate of the substitution effects is just an approximation, and one
might ask whether it is reasonable to find such small substitution effects. Two factors
suggest that these small estimates may not be too far from the truth. The first is simply
the fairly coarse level of aggregation at which data limitations force us to carry out the
analysis. Concretely, the scope households have for substituting between, say, food
and rent, is much smaller than it is for substituting between higher or lower quality in the
purchase of a particular food item. While such substitution undoubtedly occurs, it is not
something that we are going to be able to pick up at this level of aggregation. We also
note that Friedman and Levinsohn (2002), who use more finely disaggregated set of 155
food items and 64 nonfood items, find much larger substitution effects that offset on
average between onethird and onehalf of the direct effects. However, these authors
also argue that their estimates are probably an extreme upper bound on the magnitude
of the substitution effects.
One might nevertheless worry that assuming zero crosselasticiticies of
substitution is driving the results. Recall that this assumption was necessitated by the
fact that the quantity changes we observe in the panel are responses to the full set of
price changes observed between 2000 and 2005, and not just the depreciationinduced
price changes. I can however calculate the income and substitution effects associated
with the full set of observed price changes over this period, and then the calculation of
the latter will not require any assumptions about crosselasticities of substitution (recall
Equation (13) and the discussion immediately below). I have done this, and for this full
set of price changes, at this fairly coarse level of aggregation, I find that the substitution
effects are still very small relative to the direct effects. While these two calculations are
19
not strictly comparable because they refer to different sets of price changes, they do
suggest that the scope for substitution is lower at coarser levels of disaggregation.
4. Conclusions
This paper has empirically investigated the welfare effects of the large
depreciation in Egypt between 2000 and 2005 operating through exchangerate induced
changes in consumer prices. I find a significant, and very heterogenous across
products, degree of passthrough from the exchange rate to consumer prices. On
average, the welfare cost of these price changes was 7.4 percent of households' initial
expenditure. Since estimated passthrough for food items was significantly greater than
for nonfood items, the effects of the depreciation disproportionately affected poor
households.
One should however keep in mind three major caveats about these results. The
first is that I have looked only at the effects of the exchange rate working through
consumer prices. The depreciation is also likely to have had heterogenous impacts on
the earnings of households employed in different sectors, and these effects are not
capture for lack of (a) detailed information in the household survey of the sector of
employment of households, and (b) evidence on the effects of exchange rate changes
on wages across sectors in Egypt.
A second limitation is that data limitations have also forced me to work at a fairly
high level of aggregation. At this coarse level of aggregation, estimated substitution
effects in response to price changes are small, and so I am likely to be overestimating
the effects on household welfare. Consider for example the study of the Indonesian
depreciation of 1997 by Friedman and Levinsohn (2002). They worked with a much
more highly disaggregated set of expenditure items, and found that substitution effects
were roughly half the size of the direct effects. If similar substitution behavior occurred
for households in Egypt in response to the (much smaller) set of price changes, but was
missed at the coarse level of aggregation at which I have worked, then the adverse
welfare effects of the depreciation will be considerably overstated and could be much
smaller.
20
Finally, as noted in the introduction, I have studied the welfare effects of
depreciationinduced changes in consumer prices over a fairly short period with a fairly
large depreciation, and this time horizon drives the finding of significant welfare losses.
It is important to note that the depreciation in the tradeweighted nominal exchange rate
between 2000 and 2005 was preceded by an even larger tradeweighted nominal
appreciation in the previous five years between 1995 and 2000, and that over the entire
period between 1995 and 2005, the tradeweighted nominal exchange rate appreciated
by about 20 percent. If the pattern of exchange rate passthrough to disaggregated
consumer prices was similar during this earlier period, then one can interpret the welfare
losses sustained between 2000 and 2005 as just a partial reversal of the welfare gains
experienced during the appreciation between 1995 and 2000.
21
References
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Reserve Bank of New York
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the World: A New Import for Developing Countries?". NBER Working Paper No.
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"Can the Distributional Impact of Macroeconomic Shocks be Predicted? A
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Freidman, Jed and James Levinsohn (2002). "The Distributional Impacts of Indonesia's
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Vartia, Yrjo (1983). "Efficient Methods of Measuring Welfare Change and Compensated
Income In Terms of Ordinary Demand Functions". Econometrica. 51(1):7998.
22
Table 1  Exchange Rates and Consumer Prices, 20002005
(Annual Change, December over December)
2000 2001 2002 2003 2004 2005 Cumulative
Trade Weighted Nominal Exchange Rate 3.0% 7.4% 0.1% 41.3% 4.4% 9.2% 26.2%
Nominal Exchange Rate 8.1% 16.6% 3.3% 31.2% 1.0% 7.9% 52.2%
Consumer Price Index 2.2% 2.4% 2.9% 6.2% 10.8% 3.1% 27.6%
23
Table 2  Disaggregated Price Change
(Cumulative Growth Rate, July 2000June 2005)
Log change in price index, 2005:6 over 2000:7
Lower Egypt Upper Egypt Lower Egypt Upper Egypt
Cairo Alex Canal Border Urban Urban Rural Rural
All Items 0.26 0.29 0.30 0.28 0.23 0.29 0.28 0.28
Food Beverage & Tobacco 0.38 0.39 0.41 0.37 0.41 0.39 0.36 0.34
Bread & Cereals 0.21 0.21 0.21 0.26 0.30 0.22 0.35 0.44
Meat & Pouitry 0.43 0.42 0.43 0.40 0.44 0.43 0.34 0.30
Fish 0.50 0.47 0.53 0.54 0.45 0.47 0.42 0.42
Milk & Cheese 0.38 0.41 0.40 0.41 0.45 0.44 0.49 0.46
Oil & Fats 0.43 0.40 0.42 0.40 0.45 0.42 0.39 0.36
Fruits 0.61 0.59 0.62 0.47 0.59 0.54 0.36 0.36
Vegetables 0.25 0.40 0.59 0.31 0.47 0.43 0.56 0.28
Pulses 0.34 0.33 0.42 0.32 0.35 0.37 0.31 0.34
Sugar & Sweets 0.38 0.37 0.17 0.44 0.40 0.41 0.32 0.34
Other Food Stuff 0.31 0.28 0.28 0.29 0.27 0.29 0.21 0.21
Beverages 0.23 0.24 0.35 0.23 0.25 0.26 0.23 0.23
Tobacco 0.31 0.31 0.42 0.30 0.31 0.30 0.28 0.28
Clothing & Footwear 0.22 0.24 0.23 0.21 0.27 0.27 0.24 0.27
Clothing 0.22 0.24 0.22 0.18 0.28 0.27 0.22 0.25
Fabrics 0.33 0.28 0.28 0.33 0.32 0.36 0.41 0.45
Footwear 0.21 0.25 0.29 0.31 0.25 0.26 0.21 0.22
Clothing manufacture 0.06 0.21 0.23 0.18 0.18 0.16 0.08 0.13
Rent, Power & Fuel 0.10 0.13 0.11 0.10 0.10 0.08 0.13 0.13
Rent & Water 0.11 0.15 0.13 0.11 0.11 0.12 0.13 0.14
Energy & Fuel 0.06 0.09 0.08 0.10 0.10 0.04 0.16 0.11
Furnture & Equipmet 0.26 0.25 0.28 0.21 0.22 0.25 0.21 0.21
Furnture 0.21 0.19 0.19 0.17 0.18 0.18 0.18 0.17
Maintenance Products 0.28 0.29 0.28 0.25 0.25 0.26 0.23 0.24
Domestic Services 0.29 0.26 0.73 0.22 0.23 0.26 0.19 0.19
Medical Care 0.12 0.19 0.21 0.22 0.18 0.11 0.18 0.16
Medical Products 0.16 0.16 0.16 0.17 0.16 0.01 0.16 0.16
Physician & Hospitals 0.08 0.22 0.26 0.27 0.19 0.20 0.20 0.16
Transport & Communication 0.30 0.29 0.34 0.33 0.37 0.48 0.30 0.29
Private Transportation 0.21 0.21 0.25 0.26 0.24 0.13 0.25 0.24
Purchased Transportation 0.17 0.14 0.23 0.24 0.24 0.41 0.24 0.24
Communication 0.55 0.60 0.63 0.62 0.67 0.43 0.64 0.62
Recreation & Education 0.14 0.17 0.14 0.19 0.15 0.10 0.11 0.16
Equipments 0.13 0.12 0.10 0.11 0.12 0.12 0.11 0.12
Entertainment & Cult. Serv 0.14 0.20 0.15 0.23 0.16 0.16 0.12 0.21
Education 0.15 0.06 0.15 0.19 0.14 0.17 0.10 0.10
Miscellaneous 0.19 0.18 0.20 0.21 0.21 0.21 0.17 0.17
Personal Care 0.15 0.14 0.18 0.18 0.19 0.18 0.14 0.14
Restaurants Hotels 0.26 0.26 0.28 0.27 0.26 0.27 0.25 0.25
Mean 0.25 0.26 0.30 0.27 0.28 0.27 0.26 0.25
SD 0.13 0.12 0.16 0.12 0.13 0.13 0.13 0.11
24
Table 3  Summary Statistics on PassThrough Estimates
and Price Changes
Actual Price Change Estimated PassThrough Estimated Share of Price
2000:7  2005:6 Coefficient Change Due to Devaluation
Overall
25th Percentile 0.21 0.05 0.06
50th Percentile 0.28 0.19 0.19
75th Percentile 0.40 0.47 0.34
Food
25th Percentile 0.28 0.30 0.20
50th Percentile 0.37 0.43 0.31
75th Percentile 0.43 0.63 0.46
NonFood
25th Percentile 0.15 0.03 0.03
50th Percentile 0.21 0.07 0.10
75th Percentile 0.26 0.15 0.18
25
Table 4  Estimated Compensating Variation,
by Region and Quintile of Expenditure Distribution
Mean by Quintile of Expenditure Distribution
Mean Std.Dev. Lowest Second Third Fourth Highest
All Egypt 0.074 0.015 0.081 0.077 0.075 0.073 0.065
Metropolitan 0.072 0.016 0.083 0.077 0.073 0.069 0.055
Cairo 0.071 0.018 0.084 0.077 0.073 0.068 0.052
Alexandria 0.070 0.015 0.080 0.075 0.072 0.068 0.057
Canal 0.077 0.014 0.086 0.081 0.079 0.075 0.066
Border 0.067 0.013 0.072 0.068 0.068 0.066 0.062
Lower Egypt Urban 0.072 0.013 0.077 0.074 0.073 0.071 0.064
Upper Egypt Urban 0.072 0.015 0.079 0.075 0.074 0.071 0.061
Lower Egypt Rural 0.084 0.013 0.088 0.085 0.085 0.083 0.080
Upper Egypt Rural 0.070 0.010 0.073 0.071 0.070 0.069 0.066
26
Table 5  Poverty Impacts of DepreciationInduced Price Changes
Headcount Measure of Poverty (Percent of Households)
Counterfactual With
2000 Actual Devaluation Only Difference 2005 Actual
All Egypt 16.7 21.8 5.0 19.6
Metropolitan 5.1 7.1 2.0 5.7
Cairo 5.0 6.9 1.9 4.6
Alexandria 6.2 8.3 2.1 8.0
Canal 3.4 5.7 2.2 5.7
Border 9.9 12.5 2.6 14.5
Lower Egypt Urban 6.5 9.4 3.0 9.2
Upper Egypt Urban 19.3 24.0 4.7 18.6
Lower Egypt Rural 11.8 18.2 6.4 16.8
Upper Egypt Rural 34.2 40.8 6.7 39.1
27
Figure 1 Exchange Rates and Consumer Prices, 19952005
2
1.8
1.6
1.4
0)
10 1.2
1=
95: 1
19(
exd 0.8
In0.6
0.4
0.2
0
Jan95 May96 Sep97 Feb99 Jun00 Nov01 Mar03 Aug04 Dec05
TradeWeighted Nominal XR Nominal XR Consumer Price Index
28
Figure 2 Distribution of Estimated LongRun Exchange Rate
PassThrough to Consumer Prices
Aggregate Items
1
.5
0
5.
l
Al raewtooF&gnhi noitacu t
en tne noi
Ed mniatret ccoabo sleto
er
sTeg pmiuqE eraCl usoe leuF&
caid s&Htn ccoaboT atcniu
m
En ra M
ot ve
Cl BedooF e&rutniruF Me anllecsi owP&tn
Re rauasteR omC&tr
pos
anrT
Disaggregated Food Items
1
.8
.6
.4
.2
0
seg
ra
ve slaereC hs oc do
Fi ac stiu sebl
Fr yrtlu stee
obT eseeh staF&l ta
&Po
Be d& es at &Ckl Oi rFoeh
Ot geeV
earB agre Me Mi r&Swag
Su
evB
odoF
29
Figure 3 Distribution of Significance of Estimated LongRun Exchange Rate
PassThrough to Consumer Prices
Aggregate Items
15
10
5
0
5
0
1
l
Al arew
otoF& noitacu tn oc nte s l lset
Ed meniartet ac
obT
es pmiuqE reaCl
caid ouenlael ueF&
er Ho&s ccoaboT noitaic
un
m
ngih En agre M
ot
Cl evB
odoF e&rutniruF Me sci owP&tn ntaruats
Re moC&t
Re orpsnarT
Disaggregated Food Items
20
15
10
5
0
seg sale sh oc
Fi ac stiu yrtl
vera
Be erC&d Fr dooFr steew
obT eseeh staF&l
&Pou
es ate &Ckl Oi he
Ot r&S esblategeV
earB agre M Mi ga
Su
evB
odoF
30
Figure 4 Distribution of Share of Observed Price Changes 20002005
Due to the Exchange Rate Depreciation
Aggregate Items
.5
0
.5
1
l
Al arew
otoF& noitacu nte oc nte us leu
ac sleto
nmait eraCl ccoab noitaic
To
Ed un
nghito ertnE obTse pmuiqE caid r&Few s&Htn m
agre
Cl evB
odoF e&rutniruF Me eonallecsi
M &PotneR rauasteR moC&t
orpsnarT
Disaggregated Food Items
5
1.
1
.5
0
seg
ra
ve
Be salereC&d hs e
Fi ccoabo stiu yrlt es
Fr staF&l tsee esblat
earB sTeg ouP& Oi dooFreh ge
ate heC&lki Ot Ve
ra M M r&Swag
Su
ve
BedooF
31
Figure 5 Direct Effects of Price Changes on Welfare
(Compensating Variation Calculated as Percent Change in Total Expenditure Required
to Purchase Initial Consumption Basket)
5
.1
.1
ct
rei
cvd
5
.0
0
7 8 9 10 11
lnexptotal
32
Figure 6 Direct Effects of Price Changes on Welfare
(Compensating Variation Calculated as Percent Change in Total Expenditure Required
to Purchase Initial Consumption Basket, Rolling Average of 100 Households Ranked by
Total Expenditure)
0.09
)ylnOt 0.08
ffecEtcer 0.07
0.06
(Di
no 0.05
atiiraV 0.04 urban
rural
gnti 0.03
sanep 0.02
moC 0.01
0
0 0.2 0.4 0.6 0.8 1
Percentile Rank
33
Figure 7 Direct and Substitution Effects of Price Changes on Welfare
at the Cohort Level
8
.0
6
.0
4
.0
2
.0
0
8.5 9 9.5 10 10.5
lntotexpav00
direct subs
34