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THE IMPACT OF REMANUFACTURINGIN THE ECONOMY

by

G. FERRER*and

R. U. AYRES**

9 7/52/TM

This working paper was published in the context of INSEAD's Centre for the Management of EnvironmentalResources, an R&D partnership sponsored by Ciba-Geigy, Danfoss, Otto Group and Royal Dutch/Shell andSandoz AG.

* Phd Candidate at INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France.

** Sandoz Professor of Management and the Environment at INSEAD, Boulevard de Constance, 77305Fontainebleau Cedex, France.

A working paper in the INSEAD Working Paper Series is intended as a means whereby a faculty researcher'sthoughts and findings may be communicated to interested readers. The paper should be consideredpreliminary in nature and may require revision.

Printed at INSEAD, Fontainebleau, France.

Last saved 12 May, 1997

The Impact of Remanufacturing in the Economy

Geraldo Ferrer' and Robert U. Ayres2

Abstract

The 43-sector aggregation of the French input-output model is used to evaluate the impact thatremanufacturing may have on the economy. Very few durable goods are recovered at the end oftheir useful lives. In this paper, we evaluate several hypothetical scenarios whereremanufacturing becomes the usual practice. It would have direct impacts on the demand forseveral inputs. We adapt the inter-industry input-output framework so as to take intoconsideration these changes. The analysis assumes that the remanufacturing sector substitutesusual inputs such as raw materials and semi-finished goods for labor and transport services. Inaddition it is assumed that the final demand for all goods is unchanged. As a result, we find thatremanufacturing reduces the total output from all sectors while increasing the value of laborregarding the demanded goods.Keywords: remanufacturing, product recovery, inter-industry flows, input-output model, France

1. Introduction

The production of durable goods represents an important contribution to the GNP of all

developed countries. It employs large amounts of human resources, raw materials and energy.

However, most of the resources consumed are not renewable. The minerals that provide most

of the raw materials and energy in the production of durable goods have been continually

depleted. Moreover, many durable products are disposed in landfills at the end of their useful

lives. The landfill space has been decreasing and the price charged by the landfills still in

operation have increased very fast.

It has been suggested that remanufacturing is one approach to deal with the used durable

goods at the time of disposal, because of two first-order effects. In the production side,

remanufacturing reduces the demand for raw materials since part of the production is assured

by the recovery of used goods: it is the resource conservation effect. In the disposal side,

remanufacturing provides an alternative stream for used durable goods at the end of their

useful lives, thus reducing the amount of material being landfilled given that significant

fractions of the durable goods are reused: it is the emissions reduction effect.

Today, there are two major constraints precluding the expansion of remanufacturing

activities. The existing infra-structure is not suitable for the return flow, and most products

arriving at the end of the first useful life are not designed for recovery. This situation may

change. Manufacturers are more conscious of their responsibility regarding product disposal;

Ph.D. Student, INSEAD, Blvd. Constance, F-77305 Fontainebleau, France. E-mail: [email protected] Sandoz Professor of Environment and Management, INSEAD, Blvd. Constance, F-77305Fontainebleau, France. E-mail: [email protected]

meanwhile, changes in legislation may require manufacturers (or importers) to take back the

carcasses of end-of-life automobiles, electronic goods and other durable products for

responsible disposal. Consequently, when the industry adopts efficient methods of product

recovery, one may see a substantial increase in the remanufacturing activity, even if regulation

fails to require it. Since remanufacturing affects the level of consumption of several inputs

(raw materials, labor and energy), it will have significant impact on the economy once it

becomes a generalized practice.

The remanufacturing success stories available today (photocopiers, disposable cameras)

represent a tiny fraction of economic activity. Thus, we cannot be certain about their second-

order effects if remanufacturing becomes a general practice. For example, intuition says that

the reduction in the consumption of raw materials will reduce the demand from utility suppliers

and machinery manufacturers. Likewise, we expect that employment will be reduced in the

same industries. But it is hard to guess what direction total employment will take, since

remanufacturing is also a labor intensive activity.' If remanufactured products secure

significant market shares, these questions have to be dealt carefully, in order to assure that

remanufacturing does not have a harmful impact in the economy.

In this paper, we develop a model for evaluating the economic impact of a generalized

product recovery and remanufacturing activity. We examine the structure of the inter-industry

input-output model to identify the changes incurred once we incorporate the remanufactured

dual of the original industries. The model gives insights on the changes in demand for labor,

energy and raw materials. It is structured around the input-output table for the French

economy in 1991.

The matrix of inter-industry relations is augmented, to incorporate the remanufactured

sector, and adapted, to recognize the different demands in labor, energy, raw materials, and

inputs from the other industries. It is assumed that the physical demand for the product being

remanufactured is constant. In addition, it is assumed that the remanufactured product is sold

at a price lower than that of a new good. Consequently, because of the unit price reduction,

the monetary demand (total sales) is reduced. This drives the first-order effect of

implementing remanufacturing. However, the higher-order effects are harder to estimate, but

the model adaptation in this paper allows the identification of their impact.

Key questions are: what would it happen if durable goods, such as automobiles, trucks,

televisions and personal computers, were substantially remanufactured? How would it affect

the demand for the traditional inputs in these industries? How would the massive presence of

3 The argument is quite straightforward: primary production (usually "mass production") is much moreable to capture economies of scale than secondary production (or remanufacturing). Due to heterogeneousinputs, small batches is the typical production rule. Also, disassembly is inherently more labor intensive, andless amenable to automation than is assembly.

Remanufacturing Impact - 2

remanufactured goods affect the demand for raw materials? Some of the inputs, like energy

and semi-finished goods, are expected to decline but labor utilization might increase. The main

contribution of this paper is to generate the methodology that enables answering these

questions. In order to decide on the implementation of take-back regulations, policy makers

and industrial lobbies need to understand the consequences of such decisions in the whole

economy

2. The input-output methodology

The input-output model was first developed by Leontief (1936) as an analytical framework

describing the interdependence of the different industrial sectors in the economy. It is also

known as the inter-industry analysis or Leontief model. The model requires the preparation of

an economy-wide table, generally compiled by the statistical agency of the national

government. Each entry in the table corresponds to the sales from a given industrial sector to

another industrial sector.

We use the following table as an illustration' s . It is a highly aggregated representation of

the US National Input-Output Table in 1977. In this simplified form, we identify a 7x7 matrix

corresponding to the monetary transactions between the seven sectors represented in the table.

For example, we observe that in 1977, the sales from the mining to the manufacturing sector

equaled US$77,704x106 and the sales from the manufacturing to the -mining sector equaled

US$7,425x106. Given the level of aggregation, we cannot specify what fraction of mineral

goods purchases corresponded to petroleum or aluminum. Likewise, we do not know what

fraction of manufactured goods purchases occurred in the automobile or in the electronic

industries.

Interindustry Transactions (xl 06 US$)

Sectors 1 2 3 4 5 6 7 Final TotalDemands Outputs

1. Agriculture 31930 29 934 63714 733 4745 172 27405 129663

2. Mining 275 5566 2397 77704 372 19794 1217 -29295 78031

3. Construction 1383 2923 303 8706 7182 32058 4971 206809 264334

4. Manufacturing 26198 7425 98337 517408 30714 93117 3301 578483 1354983

5. Trade & 7463 1726 28970 88262 30795 27283 2274 323867 510639Transportation

6. Services 13056 9875 20934 101615 90507 188910 6226 648710 1079832

7. Other 355 292 844 10375 4050 9521 492 220583 246512

4 Source: Miller and Blair (1985), page 425.


On the right of the Interindustry Transaction matrix, there are two column vectors: the

Final Demands column represents the demands from external sectors such as government,

consumers and export. For example, we notice that, in 1977, the total services provided to (or

demanded by) consumers, government and export added to US$ 648,710x10 6. The Total

Outputs column contains the sum of all elements in each row. For example, the total output

from the agriculture sector, including sales to all other sectors, consumers, government and

export totaled US $129,663 x106.

The input-output model has been extensively used to evaluate the impact of structural

changes in industrial practices (for example, increased consumption of a given commodity due

to government incentives) and the externalities generated from chronic malfunctions in the

economy (for example, the effect of energy shortages, or repeated strikes). Others have

evaluated the dynamic changes in a given economy using the input-output tables of successive

years. In what follows, we show the basic mathematics used in this type of evaluation.

2.1 Using input-output tables

Consider an input-output table as just described. Let Z be the nxn matrix of interindustry

transactions, Y the column vector of final demands and X the column vector of total outputs.

For any sector i, by construction,

Xi 7-- Za ± Zu + ... + Zin ± Yi

This expression corresponds to the sum of all sales made by sector i in the economy.

Each Zii element in the right-hand side correspond to the interindustry sales from sector i to

sector j. Considering the similar equations for all n sectors in the economy, we construct the

following set of equations:

X1 = Zil + Z12 + ... + Zln + Y1

X2 = Z21 + Z22 + ... + Z2n + Y2

Xi = 41 + 42 + ... ± 411 + Yi

Xi, = Zni + Zn2 ± ... + Z. + Yi,

Consider the jth element of each equation. They make up the jth column in the Z matrix:


4 =

Zii

Z2j

Z..ij

Zn-

_ .1 _

This column represents the payments made by sector j to each sector in the economy.

(In particular, the element Zii represents the payments within the sector.) Let A i; be the ratio

between each element Zii and the total output of the sector, Xj . That is

Aii = Zii / Xi

Repeating this operation for each element Zii one can obtain a matrix A, represented as

follows:— — _• • •

Al„ Z11 • • •Z1n 1/X, 0 • • • 0 —

A2n Z21 Z2„ 0 1/X2 0. . - .• •• . . .

An2 — Ann_ _Zn1 Zn2 • • •Z„„ _ ... 0 0 • • • 1/X.....

—

Z11 /X1 Z/2/X2 ••• Zin l X„-

A=Z21 /XIZ22 /X2 Z2„/ X„

• • •

_Zni l Xi Zn2 /X2 •-• Z„„/X„_

Matrix A is called the matrix of technical coefficients. One fundamental assumption in

the input-output model is that the production function of all industries is linear. That is, if the

total output of a given sector is increased by a certain amount, its consumption from (or

payments to) all other sectors is increased in the same proportion'. This implies that the

technical coefficients is the fixed set of parameters under which the economy operates. It is an

assumption of the model that the technical coefficients do not change unless there is some

change in the technology employed in one or more of the sectors6.

We notice that each element Ai; in matrix A corresponds to the necessary input from

sector i into sector j for one monetary unit of total output from sector j. For example, in the

case of the US National Input Output Table in 1977, the technical coefficients are given in the

following matrix, obtained according to the matrix operation just described:

5 The linearity in the input-output model is in great contrast with the Cobb-Douglas productionfunction, as discussed by Burress (1994).6 In practice technological changes do occur in most sectors every period. One should expect to finddifferent matrices of technical coefficients each year, reflecting these changes.


All

A= A21

_Anl

Sectors

Technical Coefficients: matrix A

1 2 3 4 5 6 7

1. Agriculture 0.2463 0.0004 0.0035 0.0470 0.0014 0.0044 0.0007

2. Mining 0.0021 0.0713 0.0091 0.0573 0.0007 0.0183 0.0049

3. Construction 0.0107 0.0375 0.0011 0.0064 0.0141 0.0297 0.0202

4. Manufacturing 0.2020 0.0952 0.3720 0.3819 0.0601 0.0862 0.0134

5. Trade & Transportation 0.0576 0.0221 0.1096 0.0651 0.0603 0.0253 0.0092

6. Services 0.1007 0.1266 0.0792 0.0750 0.1772 0.1749 0.0253

7. Other 0.0027 0.0037 0.0032 0.0077 0.0079 0.0088 0.0020

Looking at the third column, one could say that each $1 of output from the Construction

sector requires approximately 37 cents from the Manufacturing sector and 11 cents from the

Trade & Transportation sector, among other inputs.

Since the technical coefficients are the parameters that represent the structure of the

economy, we may want to rewrite the total output expression in terms of the elements of A:

X1 = AJAX' + Al2X2 + ... + AinXn + Y1

X2 = A21X1 + A22X2 + ... + AliXn + Y2

Xi = AilX1 + Ai2X2 + ... + AinX, + Yi

Xn = Aid(' + An2X2 + • . • + AnnXn + Y.

Rearranging the terms, we obtain the expressions of the final demand Y in terms of the

total output X and the technical coefficient matrix A:

(1 - A11)X1 - Al2X2 - ... - AliXi - ... - AinXn = Y1

- A21X1 + (1 - A22)X2 - ... - A2iXi - - A2nXti = Y2

- AiiXi - Ai2X2 - ... + (1 - Aii)Xi - • .. - AiriXn = Yi

- AnIXI - An2X2 - ... - AniXi - +(1 - Ann)Xn = Yn

Rewriting these expression in matrix algebra notation, this becomes simply

(I - A) X= Y


In general, the matrix (I - A) is nonsingular, that is, its determinant is not zero. Hence,

to evaluate the total outputs in the economy X for a given final demand Y, assuming a stable

technology (that is, a constant matrix of technical coefficients A), it suffices to solve the

system of equations by inverting (I - A):

x = (I - A)-' Y

Returning to the example of the US National Input Output Table in 1977, the matrix of

total requirements, (I - Ay' equals:

Sectors

Total Requirements: matrix (I - A)-1

1 2 3 4 5 6 7

1. Agriculture 1.3608 0.0170 0.0491 0.1097 0.0139 0.0213 0.0042

2. Mining 0.0404 1.0963 0.0567 0.1119 0.0163 0.0389 0.0092

3. Construction 0.0289 0.0500 1.0172 0.0254 0.0250 0.0415 0.0224

4. Manufacturing 0.5162 0.2391 0.6841 1.7307 0.1637 0.2191 0.0456

5. Trade & Transportation 0.1303 0.0549 0.1762 0.1384 1.0867 0.0562 0.0172

6. Services 0.2503 0.2088 0.2127 0.2206 0.2553 1.2570 0.0426

7. Other 0.0112 0.0084 0.0121 0.0171 0.0123 0.0136 1.0030

That matrix says, for example, that in order to satisfy $1 of final demand of its products,

the Agriculture sector produces goods worth $1.36 (hence, 36 cents are consumed in inter-

industry transactions), the mining sector contributes with 1.7 cents of output and the

manufacturing sector contributes with an output worth 11 cents, among others. Until recently,

the main practical difficulty dealing with input-output tables was to produce this matrix, given

the complexity of performing large matrix operations. One simple way to overcome this

problem is to use the expansion

g - Ay' = 1+ A + A2 + ...

Nowadays, typical spreadsheet programs can invert matrices of up to 50 dimensions very

quickly. A specialized FORTRAN code might invert much larger matrices using minimal

resources. In this paper, we limit the size of the problem using a 43-sector aggregation of the

economy.

The remaining difficulty using input-output tables is to adapt the tables generally made

available by official sources into one that contains the sectors that we want to analyze. This

adaptation requires two sets of operations: sector aggregation and sector disaggregation,

which are now discussed.

7 Often, the matrix (I - A) is called the Leontief matrix (L), and its inverse is called the Leontief inversematrix (e).


The augmented matrix is one of the many (n+1)x(n+1) matrices satisfying the set of

equations above. It is defined as the one matrix where sectors n and n+1 require the same

inputs, controlling market shares of p and q, respectively. Hence, it can be fully specified by

the following operation:

H =

-H11H21

:H n1

H_ n+1,1

H12 ...

H22

— •

•H

-H1,n+1

Hn+1,n+1 _

_10

_O

0 ••1

00

•

——

-00•

p'q _-

- A

'n"A 21

Am

•••

Ant •

AinA2„

- • Ann

1O

0

01

0

– •

...

0O

1

0O

1

(2) )

The auxiliary matrices pre- and post-multiplying matrix A are the same appearing in Eq.

(1), operating in the opposite order. The operation can be easily generalized to more complex

cases.

The augmented matrix assumes that sectors n and n+1 are essentially the same. The

disaggregated matrix, however, must reflect the different inputs and outputs of these sectors.

This differentiation is obtained with the distinguishing matrix. Once we perform all the matrix

operations in (1) and (2), we can identify the parameters that characterize the distinguishing

matrix D. Let i < n and j < n designate any of the (n-1)x(n-1) elements in matrix A that is

identical to the corresponding elements in the larger matrix B. Since, D = B - H, we obtain

By = Hu = Ay

Dv =0,V i <n, j <n (3)

Now, the nth and the (n+l)th rows in the H matrix assume that the market share across

all columns is the same. This assumption must be relaxed with the corresponding rows in the

D matrix.

{Bni + B „Li = An'

pAni = Hnj

qAni = Hn+lj

Dni = —Dn+ij (4)

Also, the nth and the (n+l)th columns in H are identical, assuming that both sectors use

the same technology. The corresponding column in matrix D relaxes this assumption.

{

pBin + q13, ,„+1 = AinHin = Hi,n+1 = Ain

Din = q(Bin–A.,1)

Dim+1 = 1#1,n+1 — Bi,n;(5)


Finally, the four elements in the lower-right corner of H incorporate both assumptions

mentioned above. The corresponding elements in D relaxes them as follows.

{

p(13„„+B+1,„)+q(13„,„+1 + Bn+i,„, i ) = Ann

Hnn = Hn,n+1 = Min

Hn+1,n = Hn+1,n+1 =nn

D„„+ D„+1,„ = q(Bnn — Bn,n+i ) + q(Bn+i,n — Bn+1,n+1)

n,n+1+ Dn+1,n+1 P(Bn,n+1 Bnn) + POn+1,n+1 BI1+1,n)D

Dn,n+1 +Dn+1,n = "Om Dn+1,n+1)

Notice that, since each 111„ = H1, ,,+ 1 , than each Bin - B,,,,+1 = Din - An+1. Hence, the

distinguishing matrix D is completely defined with n - 1 parameters to differentiate the last two

rows, n - 1 parameters to differentiate the last two columns, and 3 parameters to differentiate

the four elements in the lower right corner.

The row-differentiating parameters, call them si, indicate how much sector j departs from

the average in its demand from sector n. They satisfy Eq. (4). The column-differentiating

parameters, call them d1, reflect the difference between sectors n and n + 1 in their demand for

inputs from sector i. They satisfy Eq. (5). The last three parameters, s„, d,,, and e satisfy Eq.

(6). They serve the functions of row- and column-differentiating- parameters with one

additional degree of freedom given by e, allowing full differentiation between the sectors.

Hence, the distinguishing matrix becomes:

Dw =

0

0

S1

— s

0

0

S2

— s2

• • •

•• •

Sn

— Sn

qd1qd2

q(dnq(dn —

— pdipd2

s„ — p(d n + e)

— s — p(d n —

(7)

The development of the distinguishing matrix, due to Wolsky, allows for the

disaggregation of two sectors previously aggregated in the input output tables.9 We use this

methodology as a first-step to introduce the remanufacturing dual of the original

manufacturing industries in the input-output matrix, as follows.

3. Comparing remanufacturing and manufacturing activities.

Remanufacturing is the process where a used durable good is disassembled at the module or at

the component level, to repair or substitute components and modules that are worn out or

obsolete. In addition, the whole equipment is refurbished and critical modules are overhauled

or substituted. A machine of today is built on yesterday's base, receiving all the enhancements,

(6)

Remanufacturing Impact -10

expected life and warranty of a new machine. One interesting advantage that often occurs with

remanufactured goods is that most bugs that eventually existed in the original design are

eliminated. On the other hand, it is clear that components subjected to stress will have

accumulated some fatigue – microscopic fractures that gradually compromise the performance

of the component. However, the reduction in the expected life of non-critical components

often does not affect the expected life of the whole. It is the slack in expected life of valuable

components that makes remanufacturing technically and economically viable.

For most products, the remanufacturing process has many similarities with the original

manufacturing process. It also has some important differences. For the purpose of this study,

we are interested in the difference between the inputs and the outputs of corresponding

industries. For example, what is the difference between the inputs of tire manufacturing plants

and tire retreading plants? How do their outputs differ? The answer to this type of question is

in the distinguishing matrix, specific for the input-output analysis when a new sector is

introduced. The requirements in the Wolsky distinguishing matrix change significantly in our

problem. While Wolsky developed a method for disaggregating two industries operating

independently in the same sector, we would like to adapt it to analyze the introduction of the

remanufacturing dual of an existing manufacturing industry, absorbing part of the market for

all-new products. The distinguishing matrix that accounts for the differences between the all-

new sector and its remanufacturing dual does not have to satisfy Eq. (5) because the two

industries do not use the same technology. Hence, the distinguishing matrix to incorporate the

remanufacturing industry simplifies considerably, taking the form:

0 0 •• • 0 di

0 0 0 d2

D=s1 S2 sn

—s1 –s2 • • • –s,

Notice that, as in the Wolsky distinguishing matrix, this one is fully defined by 2n+1

parameters. In order to maintain the general equilibrium aspect of the original model we make

the following assumptions:

1. We assume that the final demand for the product (in physical units) is not changed and

that the remanufactured product is sold at a price lower than the all-new. Consequently,

the monetary demand is reduced accordingly. Let Y. be the final demand from the

original sector, in monetary units, before the introduction of remanufacturing. Also, let 5

< 1 be the ratio between the unit price of the remanufactured and the all-new product.

The new demand from the original manufacturing sector becomes p*Yn and the demand

9 Wolsky (1984) also provided useful bounds for the parameters in the distinguishing matrix. Given the

(8)


from the remanufacturing sector becomes q*Y n. Since q = 5*(1-p), it follows that p + q

< 1.

2. The final demand from all other sectors in the industry is unchanged. The changes in

their total outputs come from the new inter-industry flows.

3. For each monetary unit of outputs from the remanufacturing sector, there is the same

total amount of inputs as in the all-new sector. This assumption implies that both sectors

operate with the same profit margin. Eq. (9) translates this assumption:

n+1

E di = —d labor

(9)i=1

where dia is the difference between the inputs that each sector receive from the labor

sector. The rationale of this assumption is that the original manufacturing and the

Remanufacturing sector are competing in the same market operated by different firms.

Hence, in order to coexist in a general equilibrium model, one should expect that both of

them are equally profitable.

4. Given the complexity in establishing appropriate reverse logistics, the transportation

services sector represents a larger fraction of the inputs to the remanufacturing sector

than the fraction of inputs to the original manufacturing sector.

5. Remanufactured products have as good a performance as all-new products. Hence, if

they cost less, "utilitarian" sectors would choose to buy from the remanufactured sector

rather than the original manufacturing sector, as long as they are available (e.g., tires

purchased from the agricultural sector would come from the tire retreading industry,

rather than from the new tires industry). On the other hand, if the good is a direct input

of an all-new product (e.g., tires as a production input in automobile manufacturing), or

if it is critical for its performance, that sector will only buy the all-new version. This is

obtained from the sj parameters. They represent the deviations from the mean demand

for that output made by each sector. It is simple to verify that these parameters are

bound by the relations:

maxt— pAni ,-1 + gilni } 5 s i � mintgAni ,1— pAni ) (10)

Eq. (10) ensures that all elements in the new technical coefficients matrix are non-

negative. If sj equals its lower bound, sector j has no inputs from the all-new sector. Likewise,

if sj equals its upper bound, the sector has no inputs from the remanufacturing sector. The

main difference between the matrices Dw and D is that, in our distinguishing matrix, even the

column corresponding to original manufacturing sector has n-1 elements equal to zero. Hence,

nature of our problem, they are not needed here.


all changes in the economy are due to the transfer of part of the demand from the original

manufacturing sector to the remanufacturing sector. The impact of this transfer is driven by

the lower final cost, lower direct input from the raw material and energy sectors, and higher

direct demand from the labor sector, characteristics of the remanufacturing operation. The

following table reflects the proposed structural changes in the technical coefficient matrix, as

well as in the row corresponding to the labor sector and in the column corresponding to the

final demand.

Demand Sectors

Supply Sectors Final Other Sectors Original Remanufacturing Final Demand

Assembly Manufacturing

Raw material no change no change no change less than original no change

and energy manufacturing

Transport no change no change no change larger than original no change

services manufacturing

Other sectors no change no change no change same as original

manufacturing

no change

Original no change partly lost to no change larger than original partly lost to

manufacturing remanufacturing manufacturing remfg.

Remanufacturing none partly gained none small partly gained

from original

manufacturing

from remfg.

Labor no change no change no change larger than original

manufacturing

We observe the effect of these changes using the input-output tables for the French

economy in 1991 (INSEE 1995). The original table, containing 99 sectors, was aggregated

into 43 sectors. The aggregation criterion was such that any distortion in the analysis would be

minimal. The aggregated sectors had, generally, very low inputs into the manufacturing

sectors. Moreover, the aggregation allows illustrating the model using a spreadsheet software.

The 43 sectors are disclosed in Appendix 1.

We consider the creation of three remanufacturing industries: one for tires, other for

computers and another for automobiles. We start with the assumption that in the present

economy, there is no product recovery of any of these products. Although some recovery

already exists in all three industries, they are not discriminated in the national accounts,

reflecting the small economic significance of each of them. So we believe that the distortion


imposed by this assumption is acceptable. For the sake of simplicity, we assume that the

market absorbs all of the output from the remanufacturing industries. Producers of recycled

paper knows that this is not always true. The stochastic aspect of input availability and

demand for outputs have a significant influence over the economics of any product recovery

activity. However, since our interest is in understanding the impact of successful

remanufacturing sectors in the economy, we believe that we can obtain significant insights if

we concentrate in the basic model, without uncertainty.

4. The impact of remanufacturing tires, computers and automobiles in theFrench economy

In what follows we disaggregate the 43x43 input-output table of the French economy to

examine the impact of remanufacturing tires, computers and automobiles. The individual

impacts are driven by the parameters defining the respective distinguishing matrix. Hence, we

examine a number of scenarios, corresponding to different levels of market share, and different

levels of price discount offered by the remanufactured products. In all scenarios, the four

assumptions described in the previous section are strictly observed. The following table

describes these scenarios:

Remanufacturing Sector Parameters

Symbols Levels

Market share secured by the original manufacturing sector. P

0.4 and 0.6

(previous market share = 1)

Relative price of the remanufactured good. 5 0.5 and 0.7

(price of all-new good = 1)

Labor input in Remanufacturing sector. X. 1.2 and 1.5

(Labor sector input in original manufacturing = 1)

The meaning of these parameters follow. If the market share secured by the all new-

product since the introduction of remanufacturing is p, then the final demand for the products

from the original manufacturing sector will be multiplied by p. Moreover, in the augmented

matrix, the row corresponding to the inputs from original manufacturing to all other sectors

have all elements multiplied by the same factor p. (The actual technical coefficients are

adjusted by the parameters s; in the distinguishing matrix, as described in the previous section.)

Since the market share of the remanufactured good is 1-p (as a fraction of the physical

market), but each product is sold at the discount price 8 < 1, than the actual sales from the

remanufacturing sector equal to 8*(1-p) times the original sales by the original manufacturing

sector. Therefore, in the augmented matrix, the row corresponding to the inputs from the

remanufacturing sector is obtained by multiplying each element of the original row by 8*(1-p).


Although the physical number of goods sold is maintained, the monetary value of the sales

secured by the two sectors combined is less than the original sales level.

The relationship between remanufacturing and labor-intensity is complex. We cannot

explore it in the depth it deserves in the present paper. However, some insight into the

problem may be gained from the basic scale relationship for manufacturing cost, viz.

Ct = Cf CvN

where Ct is total cost, Cf is fixed cost and Cv is variable cost per unit, while N is the

number of units. For a single unit, the total cost is the same as the fixed cost, whereas for a

long run in mass production, the unit cost falls toward the variable cost. In a typical mass

production case (such as automobile engines) the fixed cost of a plant, including design and

engineering, would be about $1 billion, and the plant would produce about 10 million units

over its lifetime, for a fixed cost of $100 per unit and a variable cost of perhaps $2000 per unit.

It follows that the first unit costs about 500,000 times as much as the 10 millionth unit. On the

other hand, when design and engineering are excluded, the cost differential is much lower: the

cost of making all the parts of a unique prototype engine in a batch-type machine shop is

estimated to be around 200 times the variable cost of a mass produced version.

Taking the latter relationship as typical, we have an approximate relationship for unit

cost vs. batch size as follows:

unit cost = 1 + 200/N

The key point here is that it is roughly ten times more expensive to manufacture (or

repair) a single unit than a batch of 10; and increasing the batch size to 1000 cuts unit costs

again by another factor of twenty. Beyond that point, the advantages of increasing batch size

are negligible. However, another implication of the above analysis is that the cost

disadvantages of remanufacturing are much lower in the case of products that were originally

customized.

Obviously remanufacturing is inherently much less able to capture large scale advantages

of standardized mass production than primary manufacturing. A remanufacturing operation

necessarily deals with a number of different models and ages, each of which involves different

set-ups and tools. The heterogeneous input stream must clearly be sorted into batches before

further operations are undertaken, and since very small batches are uneconomic, a significant

amount of storage space may be needed as batches accumulate to critical size. On the other

hand, as remanufacturing becomes more the rule than the exception, production runs will

increase. Developments in computer-integrated manufacturing and programmable automation

have reduced the disadvantage of small batches, and this trend can be expected to continue

[Ayres et al 1990]. Also, as customers become more sophisticated, and mass-customization


becomes the order of the day (and the added cost of customization decreases), the less is the

cost differential between new and remanufactured products. Finally, successive models can be

designed to minimize differences in disassembly, setup and processing costs. Thus, the scale-

related penalties of remanufacturing is expected to reduce in the course of time.

Most of these added costs (of processing small batches) are labor costs. Thus, as

remanufacturing increases, direct employment increases. Clearly, these added costs impose a

limit on the economic feasibility of remanufacturing. Although environmental benefits may be

significant (see below), the primary concern for a firm must be its own cost structure. It can

justify remanufacturing only if other cost reductions compensate for the higher labor

requirements in the remanufacturing operation.

All things considered, we have limited our analysis to two hypothetical cases: (a) where

labor costs increases by 20% (X = 1.2) and (b) where labor costs increases by 50% (2 = 1.5).

It means that, if the labor increase in the remanufacturing sector is X, than each monetary unit

of output from this sector required labor inputs equivalent to those required by the original

manufacturing sector multiplied by X . Since the inputs from the transport sector are also

increased, and the total inputs from all sectors are the same as in the original manufacturing

sector, there will be a substantial decrease in some of the other inputs. (This trade-off

characterizes the remanufacturing process, where physical inputs such as raw materials or

semi-finished goods are traded against labor time in recovering the used goods.)

One may argue that the suggested price reductions are excessive. A value of 5 = 0.7

implies that the remanufactured good is sold for a 30% discount over the price of the all-new

good. However, the objective here is to identify directions of change, using the original

economy as the benchmark. We do not believe that any of these scenarios is the best

representative of what might happen if remanufacturing is a generalized activity in the

economy. Nonetheless, the combination of the proposed scenarios offer a reasonable coverage

of potential scenarios, under the assumptions stated in the previous section.

4.1 The impact of expanding the tire retreading sector

Tire retreading is a healthy business in many countries. It commands nearly 50% of the

commercial tires replacement business in the US and Europe (Ferrer 1997). However, its

penetration in the passenger car market has reduced steadily. In the input-output tables of the

French economy, this business is pooled in the Tire and Rubber sector (BDS 52). Here, we

disregard the penetration that retreaded tires already have, assume that the data on the tire and

rubber industry refer exclusively to new tires, to measure the likely impact of a first time

introduction of retreaded tires.


In our 43-sector aggregation, the Tire and Rubber sector accounts for a total output of

55.69x109 FF in 1991. It is the 36 th sector in this economy, measured by its total output, or

the 30th sector, measured by its total inputs from the other sectors in the inter-industry matrix.

The impact of introducing retreaded tires depends on its total output in the economy. For

example, if the sector is split, the original manufacturing sector secures one-half of its original

market, and the remanufactured product is marketed at a price 8 = 0.7, the total output of each

sector would be around 28x10 9 FF and 20x109 FF, that is, the 39th and the 42m sector,

respectively. Hence, we should not expect a large impact.

We have performed the calculations corresponding to the introduction of a Tire

Retreading sector in the economy. We disaggregate the 43-sector matrix to introduce a 44th

sector, Tire Retreading. The augmented matrix is produced according to the method described

in Section 2.2 and the distinguishing matrix takes the form developed in Section 3. The

following table summarizes the impact of these changes. All assumptions described in Section

3 are strictly observed.

Tire Retreading Sector Parameters

p=S=

Sectors =

0.40.51.5

0.40.51.2

0.40.71.5

0.40.71.2

0.60.51.5

0.60.5'1.2

0.60.71.5

0.60.71.2

New tire and rubber + Tire retreacfingindustries

-30.0% -29.3% -17.9% -17.1% -17.9% -19.3% -11.9% -11.2%

Iron and steel industry -0.828% -0.815% -0.780% -0.763% -0.661% -0.649% -0.629% -0.614%

Production of other minerals -0.608% -0.577% -0.523% -0.483% -0.467% -0.437% -0.410% -0.375%

Chemical and pharmaceutical industry -0.559% -0.456% -0.503% -0.372% -0.437% -0.342% -0.400% -0.286%

Coal mining and coke production -0.434% -0.408% -0.397% -0.363% -0.342% -0.318% -0.317% -0.288%

Electricity, gas and water utilities -0.223% -0.199% -0.186% -0.156% -0.169% -0.147% -0.144% -0.118%

Plastic industry -0.216% -0.205% -0.142% -0.128% -0.149% -0.139% -0.100% -0.087%

Foundry and metalworking -0.171% -0.163% -0.156% -0.145% -0.135% -0.127% -0.125% -0.115%

Private teaching and research services -0.201% -0.187% -0.139% -0.121% -0.141% -0.128% -0.100% -0.084%

Industrial equipment production -0.172% -0.145% -0.153% -0.118% -0.134% -0.108% -0.121% -0.091%

Oil and gas production -0.150% -0.126% -0.130% -0.098% -0.116% -0.093% -0.102% -0.074%

Labor -0.126% -0.151% -0.045% -0.077% -0.072% -0.095% -0.018% -0.046%

Petroleum refining -0.102% -0.076% -0.087% -0.054% -0.078% -0.054% -0.068% -0.040%

Transport services -0.069% 0.008% -0.047% 0.051% -0.048% 0.023% -0.033% 0.052%

Total Output -0.196% -0.189% -0.125% -0.117% -0.134% -0.127% -0.087% -0.079%

The percentage values in this table correspond the changes in the total output of the

individual sectors due to the introduction of the Tire Retreading sector in the economy. The

direct impact is a function of the parameters selected in that particular scenario:


direct change in Original + Remanufacturing = 1 - 5 * (1-p)

We observe that the impact in the first row (the sum of the sectors being analyzed) is driven by

the direct change. As expected, the impact in the other sectors of the economy is very small.

However, the sectors that observed the largest changes were not necessarily the largest direct

suppliers. Of all 25 sectors with direct inputs in the Tire and Rubber industry, the largest

change occurred in the Iron and Steel (4th direct input), Production of Other Minerals (19th),

Chemical and Pharmaceutical (largest source of inputs), Coal and Coke Production (25 th) and

Electricity, Gas and Water Utilities (5 th) sectors. It is also surprising that, in all scenarios, there

was a general reduction in the total output of most sectors, even in the two sectors favored by

the retreading process: Labor and Transport Services. Before we discuss this further, we

introduce the remanufacturing process in the automobile and in the computer sectors.

4.2 The impact of creating an automobile remanufacturing sector

It is well known that the automobile sector (BDS 311) has a great influence over the general

economy, because it draws large inputs from many sectors. It includes automobiles, truck,

motorcycles, mopeds and bicycles10 . In our 43-sector aggregation of the French economy, 14

of them correspond to sectors manufacturing durable goods. The total output from the

automobile sector is the 8 th of all sectors and the first among durable goods sectors. In fact,

the automobile industry receives inputs from 30 sectors. Hence, remanufacturing automobiles

must have a strong first-order effect in each of these sectors and significant higher-order

effects on most other sectors. For this reason, this analysis is likely to be a good representative

of the impact of remanufacturing in the economy. However, the marketing limitation

precluding such venture are very large, and not in the scope of this study.

This time, we disaggregate the 43-sector input-output matrix to introduce as the 44th

sector the Automobile Remanufacturing industry. The following table shows the changes in

the output of selected sectors, under the assumptions stated in Section 3.

10

This aggregation is the same as in the original 99-sector table.


Automobile Remanufacturing Sector Parameters

p= 0.4 0.4 0.4 0.4 0.6 0.6 0.6 0.68= 0.5 0.5 0.7 0.7 0.5 0.5 0.7 0.7

Sectors X = 1.5 1.2 1.5 1.2 1.5 1.2 1.5 1.2Automobile, motorcycle and bicycleproduction + remanufacturing

-32.1% -31.4% -19.0% -18.0% -21.7% -21.2% -12.8% -12.1%

Foundry and metal-working -8.265% -8.091% -7.475% -7.226% -5.531% -5.416% -4.952% -4.787%

lire and rubber industry -8.600% -8.418% -5.130% -4.858% -5.813% -5.688% -3.449% -3.270%

Iron and steel industry -7.239% -6.680% -6.418% -5.621% 4847% 4478% 4254% -3.731%

Coal mining and coke production -3.101% -2.882% -2.735% -2.423% -2.077% -1.932% -1.813% -1.608%

Glass industry -3.003% -2.934% -1.807% -1.707% -2.028% -1.982% -1.214% -1.149%

Plastic industry -2.816% -2.752% -1.756% -1.664% -1.901% -1.858% -1.178% -1.117%

Extraction and metallurgy of nonferrousmetals

-2.341% -2.266% -1.815% -1.708% -1.573% -1.523% -1.208% -1.138%

Machine tool industry -2.135% -2.089% -1.449% -1.383% -1.439% -1.408% -0.969% -0.925%

Production of other minerals -1.541% -1.462% -1.285% -1.172% -1.034% -0.981% -0.853% -0.779%

Electrical equipment industry -1.108% -1.080% -0.742% -0.702% -0.747% -0.728% -0.497% -0.470%

Chemical and pharmaceutical industry -0.989% -0.923% -0.797% -0.703% -0.664% -0.620% -0.530% -0.468%

Labor -1.025% -1.130% -0.511% -0.660% -0.694% -0.763% -0.347% -0.444%

Instrument and precision materialindustry

-0.980% -0.959% -0.604% -0.573% -0.661%, -0.647% -0.406% -0.385%

Electronic professional equipmentindustry

-0.801% -0.782% -0.502% -0.475% -0.541% -0.528% -0.337% -0.319%

Oil and gas production -0.665% -0.618% -0.539% -0.472% -0.446% -0.415% -0.358% -0.314%

Transport services -0.540% -0.515% -0.147% -0.111% -0.368% -0.351% -0.104% -0.080%

Total Output -1.930% -1.906% -1.222% -1.186% -1.302% -1.286% -0.819% -0.796%

Given the size of the automobile industry and its large interaction with many other

sectors, the impact is quite significant in several of them. Generally this change takes the form

of output reduction, even in the Labor and the Transport Services sectors. The greater impact

occurs at the direct suppliers of the auto industry, such as in the Foundry and Metalworking

sector (largest supplier), Tire and Rubber sector (third largest), the Iron and Steel sector (4th),

Coal and Coke Production (28 th), Glass Industry (11 th) and Plastic (5 th). Clearly, the indirect

effect plays an important role for some raw materials whose consumption is reduced due to the

remanufacturing process. Again, the total output from all sectors is reduced, in any scenario,

when remanufacturing is introduced.


4.3 The impact of expanding the computer remanufacturing sector

The computer manufacturing sector (BDS 27) includes portable computers, mainframes and

peripherals, as well as some office equipment." In this 43-sector aggregation, it receives

inputs from 24 sectors. Since computer manufacturing is a very international business, most

computers actually consumed in France are manufactured abroad. For this reason, it is just the

28th largest sector in this aggregation, the 7th among the 14 sectors that manufacture durable

goods. However, since the input-output table only refers to the inter-industry transactions

occurring within a region - in this case, France - an eventual remanufacturing industry is likely

to process (or produce) more units than were originally manufactured within the country. We

do not deal with this possibility now.12

We disaggregate the 43-sector table to introduce Computer Remanufacturing as the 44th

sector in the economy. This table shows the changes in the output of the other sectors

assuming that just the domestically produced computers are remanufactured.

Computer Remanufacturing Sector Parametersp =5 =

Sectors =

0.40.51.5

0.40.51.2

0.40.71.5

0.40.71.2

0.60.51.5

0.60.51.2

0.60.71.5

0.60.71.2

Office and computer equipment + -31.5% -31.3% -18.9% -18.6% -21.2% -21.0% -12.6% -12.4%Computer remanufacturing industriesElectronic professional equipmentindustry

-1.629% -1.552% -1.525% -1.421% -1.184% -1.123% -1.108% -1.029%

Plastic industry -1.276% -1.268% -1.245% -1.234% -0.936% -0.929% -0.910% -0.901%Postal, telecommunication andbusiness services

-0.518% -0.391% -0.419% -0.248% -0.366% -0.265% -0.298% -0.168%

Private teaching and research services -0.326% -0.315% -0.304% -0.290% -0.237% -0.228% -0.221% -0.211%Glass industry -0.295% -0.290% -0.283% -0.275% -0.216% -0.211% -0.206% -0.201%Labor -0.241% -0.270% -0.107% -0.146% -0.156% -0.179% -0.066% -0.095%Foundry and metalworking -0.159% -0.151% -0.147% -0.137% -0.115% -0.109% -0.107% -0.099%Transport services -0.244% -0.168% -0.133% -0.031% -0.162% -0.102% -0.087% -0.009%Chemical and pharmaceutical industry -0.115% -0.112% -0.108% -0.104% • -0.084% -0.081% -0.078% -0.075%Extraction and metallurgy of nonferrousmetals

-0.108% -0.103% -0.095% -0.087% -0.078% -0.073% -0.068% -0.062%

Electricity, gas and water utilities -0.122% -0.116% -0.085% -0.076% -0.084% -0.079% -0.058% -0.052%Total Output -0.380% -0.369% -0.244% -0.229% -0.258% -0.249% -0.166% -0.154%

Since the sector is relatively small, the impact of establishing a large Computer

Remanufacturing sector in France is not very large. The most affected sectors are among its

11 The photocopy industry is part of the Instrument and Precision Material sector (BDS 34).12 One could deal with this phenomenon with the appropriate reduction of the Import sector in themodel, to account for the substitution of some of the inputs from this source by the remanufactured unitsproduced in the country.


direct suppliers, such as the Electronic Professional Equipment sector (responsible for the 3"1

largest share of inputs to Office and Computer Equipment sector), Plastic (6 th largest), Postal,

Telecommunication and Business Services (1 d), Private Teaching and Research Services (136)

and Glass (21 d) sectors. The raw material sectors do not suffer a significant impact given that

they do not make up a significant share of the value of the original good.

5. Discussion

Even though the impact of introducing a Computer Remanufacturing sector is not very large,

we notice that the changes in output were always negative, just as with the two other

remanufacturing examples. It becomes clear that remanufacturing diminishes the demand not

only from raw materials and energy sectors, but even from its direct beneficiaries and other

seemingly distant sectors. This has some implications:

First, the reduction in raw material demand shows that remanufacturing can reduce

pollution and emissions — if fewer materials enter the system, fewer have to leave.

Second, even the sectors whose direct impact from remanufacturing is positive (Labor

and Transport Services) have total negative impact. That clarifies the debate whether

remanufacturing — a labor intensive activity — fosters the creation of jobs or not. It does not.

From these results, one may hastily conclude that remanufacturing dampens business. It

reduces GNP, by contracting the demand from all inputs in the economy, labor included.

However, many would argue that GNP (or GNP per capita) is not a good proxy for welfare.

Even in this context, where we compare the status of the economy under two scenarios, with

or without remanufacturing, we argue that measuring the changes in the output from all

sectors gives an imperfect view of the impact of the proposed changes.

We propose an alternative measure to evaluate these changes. Recall the first and

second assumptions in our analysis. The Final Demand from all sectors is the same as before

the introduction of remanufacturing. The only exception is the disaggregated section, for

which we maintained the size of the physical demand. In order to maintain the same physid

demand, the monetary demand was adjusted by the price reduction adopted by the

remanufacturing sector. Hence, a better measure of the changes in welfare is to compare the

total inputs from the Labor sector with the sum of values in the Final Demand column, before

and after the introduction of remanufacturing. The following table shows the results13

13

Scenario shown: p = 0.6, 8 = 0.7, A. = 1.5


Scenarios Labor Output Final Demand Ratio(x106 FF) (x106 FF)

Relative change inlabor

43 Sectors (no remanufacturing)

Tire Retreading

Automobile Remanufacturing

Computer Remanufacturing

3,531,792 8,220,857 42.96%

3,531,142 8,217,723 42.97%

3,519,553 8,161,460 43.12%

3,529,469 8,210,312 42.99%

+0.01%

+0.16%

+0.03%

The table shows that, from a labor point of view, remanufacturing does have positive

effect. The larger the sector where remanufacturing takes place, the greater is the reduction in

Labor Output and in Final Demand. However, the monetary reduction in final demand is

relatively greater, thus increasing the relative value of labor regarding the amount of goods

consumed. This results from the price reduction provided by the remanufacturing sector.

Although the total flow into the payment of salaries is reduced, the same amount of goods is

delivered at a lower price, increasing the relative value of labor. Hence, although

remanufacturing does reduce GNP, it increases the relative labor output.

This research has examined the effect of a potential development of large

remanufacturing sectors in the economy would have in the rest of the economy. Assuming that

the remanufacturing activity has direct negative effective in the energy and raw material

output, as well as a positive effective in the labor and transport services output, it is observed

that a generalized remanufacturing activity would reduce the output from all sectors in the

economy. The greater the sector where remanufacturing occurs, the larger is the reduction in

the output. It appears that the aggregated changes in labor and pollution due to

remanufacturing are favorable. Further research is necessary to identify the qualitative aspects

of these changes. It is important to compare the quality of the labor required, as well as the

quality of the polution and emissions in the remanufacturing scenario and in the original

manufacturing actiuvity.

Appendix 1: Input-output tables (43x43), France 1991 (x 106 FF)

This appendix contains some of the intermediate steps necessary to obtain the results in

Section 4. These tables result from the aggregation undertaken on the 98x98 data provided by

the INSEE, as described in Table 1 and Table 2. The main inter-industry exchange data

appears in Table 3 through Table 8. The new inputs and outputs corresponding to the

introduction of a tire retreading industry appear in Table 9 and Table 10. The new inputs and

outputs corresponding to the introduction of a computer remanufacturing industry are shown

in Table 11 and Table 12. Finally, the new inputs and outputs corresponding to the

development of the automobile remanufacturing industry appear in Table 13 and Table 14.

Additional details can be obtained directly from the authors.


Table 1: Purchasing sector aggregation from the original 98x98 table.

43-Sector Aggregation Nr. of Sectors inthe Aggregation

Codes 43-Sector Aggregation Nr. of Sectors inthe Aggregation

Codes

BDS01-03

BDSO41-2

BDS051-2

BDS053

BDS06-8

BDS09-1

BDS12-3

BDS14

BDS15

BDS16

BDS17-9

BDS20-1

BDS22

BDS23

BDS24

BDS25

BDS26

BDS27

BDS28

BDS291

BDS292

BDS30

Agriculture, forestry andfishingCoal mining and cokeproductionOil and gas production

Petroleum refining

Electricity, gas and waterutilitiesIron and steel industry

Extraction and metallurgy ofnonferrous metalsProduction of other minerals

Construction material andceramics industryGlass industry

Chemical andpharmaceutical industryFoundry and metal-working

Production of agriculturalmachinesMachine tool industry

Industrial equipmentproductionPublic works and metalworking equipment industryArmament industry

Office and computerequipment industryElectrical equipmentindustryElectronic professionalequipment industryElectronic home productsindustryHome appliance industry

3

2

2

1

3

3

2

1

1

1

4

2

1

1

1

1

1

1

1

1

1

1

BDS311 Automobile, motorcycle andbicycle industry

BDS312, 32-3 Rail equipment. shipyardsand airplanes _

BDS34 Instrument and precisionmaterial industry

BDS35-2 Food, beverage and tobaccoindustry

BDS43-9 Textile, leather and woodindustry

BDS50 Paper and pulp industry

BDS51

BDS52

BDS53

BDS54

BDS55

BDS56

BDS65

BDS66

BDS67

BDS68-4

BDS75-9

BDS57-4,BDS80-1BDS82-3 Private teaching and

research servicesBDS84 Private health services

BDS85-98 Other services

Press

Tire and rubber industry

Plastic industry

Other industries

Civil engineering andconstructionProduct recovery services

Automobile repair and sales

Other repair services

Hotels and restaurants

Transport services

Postal, telecommunicationand business servicesSales and Rentals


PDS01-3 Agricultural, forestry and fishing 3products

PDSO4 Coal and coke 2

PDS05

PDS053

PDS06-8

PDS09-1

PDS12-3

PDS14

PDS15

PDS16

PDS17-9

PDS20-1

PDS22

PDS23

PDS24

PDS25

PDS26

PDS27

PDS28

PDS291

PDS292

PDS30

Petroleum and natural gas

Refined petroleum products

Electricity, gas and water

Iron ore, iron and steel products

Nonferrous minerals, metals andsemi-finished productsOther minerals

Construction material andceramicsGlass products

Chemical and pharmaceuticalproductsFoundry and metalworkingproductsAgricultural machines

Machine tools

Industrial equipment

Public works and metal workingequipmentArmaments

Office and computer equipment

Electrical equipment

Electronic professional equipment

Electronic home products

Home appliances

2

1

3

3

2

1

1

1

4

2

1

1

1

1

1

1

1

1

1

1

Table 2: Input products aggregation from the original 98x98 table.

Codes 43-Sector Aggregation Nr. of Sectors in Codes 43-Sector Aggregation Nr. of Sectors inthe Aggregation the AggregationPDS311 Automobiles, motorcycles and 1

bicyclesPDS312-3 Trains, ships, airplanes and 3

assimilated productsPDS34 Instruments and precision 1

materialPDS35-2 Food, beverages and tobacco 10

PDS43-9 Textile, leather and wood 10products

PDS50 Paper and pulp 1

PDS51 Printed material 1

PDS52 Tires and other rubber products 1

PDS53 Plastic products 1

PDS54 Other industrial products 1

PDS55 Civil engineering and 1construction products

PDS56 Recovered products 1

PDS65 Automobile repair-and sales 1

PDS66 Other repairs 1

PDS67 Hotels and restaurants 1

PDS68-4 Transport services 7

PDS75-9 Postal, telecomm and business 2services

PDS80-1 Rentals 3

PDS82-3 Private teaching and research 1services

PDS84 Private health services 1

PDS85-98 Other services 12

R10 Labor


Table 3: Purchases of all products and labor by sectors BDS01 to BDS15

BDS01-03 BDSO41-2 BDS051-2 BDS053 BDS06-8 BDS09-1 BDS12-3 BDS14 BDS15P0801-3 94011 74 0 0 0 1 38 0 0PDSO4 0 3527 0 0 5057 5424 259 17 594PDS05 302 53 20922 63108 260 692 346 166 951PDS053 13008 84 8 2019 3022 541 735 175 2471PDS06-8 3913 528 94 1689 20584 4412 4945 329 2846PDS09-1 554 31 0 266 0 34698 21 1 250P0812-3 0 27 0 0 14338 3296 38103 0 585PDS14 884 0 0 0 73 948 177 0 893PDS15 366 0 0 191 103 996 0 0 20290PDS16 909 0 0 0 0 0 0 0 0PDS17-9 35815 291 0 6062 162 2817 980 179 1230PDS20-1 504 509 162 2177 1148 863 167 24 408PDS22 8004 0 0 0 0 0 0 0 0PDS23 32 0 0 0 0 0 0 0 0PDS24 19 0 201 874 835 563 685 0 155PDS25 0 457 0 0 56 546 304 1122 851PDS26 0 0 0 0 0 0 0 0 0PDS27 0 10 25 0 37 162 0 0 0PDS28 85 11 94 1569 2179 1006 231 18 224PDS291 0 77 0 0 1449 242 54 0 0PDS292 0 0 0 0 0 0 0 0 0PDS30 0 0 0 0 0 0 0 0 0PDS311 550 0 21 76 110 59 101 55 998P08312-3 242 0 0 0 0 0 0 0 0PDS34 0 0 1 0 33 0 0 0 0P0835-2 46108 88 0 0 0 0 0 0 0P0843-9 2478 277 0 0 60 248 37 81 358PDS50 14 9 1 119 222 22 122 64 632PDS51 149 24 0 93 771 34 24 3 67PDS52 517 0 0 303 0 34 0 28 148PDS53 1193 0 0 736 0 0 238 326 898PDS54 396 78 0 0 2 2 18 3 37PDS55 1547 166 2118 3242 5916 216 81 31 234PDS56 0 0 0 0 0 5152 2626 0 0PDS65 678 15 58 0 120 124 95 174 2532PDS66 0 0 0 0 112 128 70 0 110PDS67 310 16 136 189 23 89 53 15 118P0S68-4 2826 243 1610 4702 2057 3527 1238 309 10606P0S75-9 3851 1317 2750 18873 16013 3018 1507 42 6636P0880-1 2006 410 1250 610 6793 328 221 36 495P0S82-3 297 50 0 1159 1100 690 429 5 236PDS84 2805 0 0 0 0 0 0 0 0P0885-98 4297 85 158 1671 4792 1647 756 202 2551Total Inter-industry inputs

228670 8457 29609 109728 87427 72525 54661 3405 58404

R10 35985 4622 9477 6547 48389 18434 13294 2478 24876Total inputs 264655 13079 39086 116275 135816 90959 67955 5883 83280



BDS16 BDS17-9 BDS20-1 BDS22 BDS23 BDS24 BDS25 BDS26 BDS27PDS01-3 0 463 0 79 0 0 0 0PDSO4 0 340 231 2 1 8 1 0 0PDS05 533 3255 544 26 40 146 41 26 32PDS053 522 17061 1802 158 163 714 241 13 87P0806-8 1049 6998 3137 246 348 1544 380 141 499POS09-1 0 0 23579 882 523 8461 2078 355 0PDS12-3 146 3300 10785 158 466 2568 553 54 0FOS14 466 2416 518 0 0 0 0 0 0PDS15 334 199 2137 0 0 627 0 0 0PDS16 0 3620 22 0 0 313 1 15 127PDS17-9 1867 79369 7286 219 293 1530 503 186 144FDS20-1 912 8008 44760 3245 2571 23086 5697 368 359PDS22 0 0 0 971 0 0 0 0 0PDS23 0 0 3024 272 3941 6231 844 428 0PDS24 235 2738 1072 1147 130 2962 877 1555 0PDS25 0 0 917 0 0 342 0 424 0PDS26 0 0 0 0 0 0 0 1683 0PDS27 0 253 231 0 1614 189 0 25 9652PDS28 144 525 3458 424 482 4054 1199 0 0PDS291 0 553 951 160 294 3137 1038 3018 4931PDS292 0 0 0 0 0 0 0 0 260PDS30 0 0 0 0 0 0 0 0 0FOS311 329 348 529 19 57 262 24 110 68P0S312-3 0 0 0 0 0 0 0 0 0PDS34 0 0 441 202 447 3808 498 147 0PDS35-2 0 12022 0 0 0 0 0 0 0PDS43-9 73 2389 1657 280 103 559 206 465 663PDS50 1846 7130 825 109 0 335 0 55 405PDS51 427 3807 383 48 16 90 39 21 702PDS52 50 1426 484 868 47 653 370 138 263PDS53 559 7303 3093 335 0 846 329 0 2433PDS54 113 0 0 0 0 0 0 153 0F0855 71 702 329 20 18 177 52 112 314PDS56 0 146 601 0 0 0 0 0 0PDS65 97 610 559 42 48 276 41 23 316F0866 0 554 276 0 17 134 42 19 213PDS67 46 351 368 21 36 168 26 6 293PDS68-4 1210 13890 7049 294 363 2608 695 67 2614P0875-9 2224 29101 11877 2248 5061 28578 10337 3327 10568PDS80-1 318 2748 454 277 145 912 308 103 850P0S82-3 295 8105 553 2 58 1058 49 128 344PDS84 0 0 0 0 0 0 0 0 0PDS85-98 802 7033 4943 257 309 2628 714 643 2543Totallnter-industry inputs

14668 226763 138875 13011 17591 99004 27183 13808 38680

R10 10288 72980 84056 5067 8368 47408 11820 4293 19112Total inputs 24956 299743 222931 18078 25959 146412 39003 18101 57792



BDS28 BDS291 BDS292 BDS30 BDS311 BDS312, 32-3 BDS34 BDS35-2 BDS43-9

PDS01-3 0 0 0 0 0 0 0 218771 14128

PDSO4 0 0 0 4 43 1 0 245 28

PDS05 192 99 14 50 461 166 37 2034 588

PDS053 457 442 41 165 1810 478 -192 3949 2305

PDS06-8 1249 954 68 351 2873 1083 475 8379 5081

PDS09-1 2021 0 0 1030 12293 1663 1633 0 431

PDS12-3 8617 3032 0 0 2315 1348 898 491 0

PDS14 63 0 0 0 0 0 0 294 0

PDS15 0 0 0 0 0 330 0 0 0

PDS16 786 2005 115 103 4007 229 586 3496 553

PDS17-9 3203 182 0 347 5038 899 269 2511 6988

PDS20-1 15131 6819 490 3536 42921 7924 1943 7085 5654

PDS22 0 0 0 0 0 0 0 0 0

PDS23 515 0 0 0 2296 1805 0 0 1616

PDS24 310 0 0 259 2102 1611 0 2003 3264

PDS25 0 0 0 0 0 0 0 0 0

PDS26 0 0 0 0 0 0 0 0 0

PDS27 0 0 0 0 676 0 0 0 0

PDS28 4459 2578 190 349 3569 1093 75 51 0

PDS291 2473 20824 4441 805 3365 23803 297 0 0

PDS292 0 0 0 0 0 0 0 0 0

PDS30 0 0 0 0 0 0 0 0 0

PDS311 113 142 37 0 70078 30 31 333 251

PDS312-3 0 0 0 0 0 34008 0 0 0

PDS34 394 1775 0 321 2378 1367 3352 0 0

PDS35-2 0 0 0 0 0 0 0 87021 3325

PDS43-9 717 1399 528 112 6181 1747 386 4291 95870

PDS50 177 173 209 249 22 30 762 9716 3428

PDS51 84 155 368 266 253 77 74 3859 587

PDS52 775 741 337 349 14565 276 0 0 894

PDS53 9249 4326 2225 2006 9572 780 1408 13276 7110

PDS54 0 0 0 0 0 2 0 402 373

PDS55 175 159 53 82 584 372 55 364 347

PDS56 0 0 0 0 0 0 0 0 1200

PDS65 225 166 63 36 533 163 76 772 597

PDS66 54 86 0 59 115 66 46 374 226

PDS67 90 162 27 21 374 77 76 524 384

PDS68-4 4910 3044 756 799 4954 1420 610 6693 4961

P0S75-9 9024 18273 2234 3737 30448 16340 4978 50804 26673

P0880-1 792 317 180 144 1710 455 519 5441 1207

P0S82-3 833 3176 63 0 4106 8331 174 925 675

PDS84 0 0 0 0 0 0 0 0 0

PD885-98 2080 2182 307 491 5551 1628 685 5016 5493

Total Inter-

industry inputs

69168 73211 12746 15671 235193 109602 19637 439120 194237

R10 35166 52610 2451 6334 61190 44537 14350 92804 86258

Total inputs 104334 125821 15197 22005 296383 154139 33987 531924 280495


Table 6: Purchases of all products and labor by sectors BDS50 to BDS65 and BDS80-1

BDS50 BDS51 BDS52 BDS53 BDS54 BDS55 BDS56 BDS57-4,80-1 BDS65P0801-3 2105 0 703 0 15 0 0 0 0PDSO4 103 0 22 4 1 16 1 65 10PDS05 761 106 152 206 56 73 15 1392 77PDS053 963 476 354 603 275 10088 332 14005 2275PDS06-8 3293 826 887 1716 365 3135 76 11968 1316PDS09-1 0 0 1461 0 0 11426 0 0 0FI1S12-3 70 513 0 185 2488 3537 0 0 0PDS14 260 0 92 0 0 58 0 0 63PDS15 219 0 0 0 375 91201 0 0 85PDS16 0 0 0 973 161 6623 0 2472 91P0817-9 4637 2769 4717 29262 1260 8471 0 160 1905PDS20-1 0 72 555 431 2101 20704 392 1229 1798F11922 0 0 0 0 0 0 0 0 0PDS23 0 0 0 0 0 140 0 0 839FT/S24 256 563 690 2018 544 23818 0 136 2827PDS25 0 0 0 0 0 5303 0 133 629PDS26 0 0 0 0 0 0 0 0 0PDS27 0 193 0 0 0 94 0 982 0F11928 378 0 61 95 110 29581 0 192 1744PDS291 0 0 103 152 0 0 0 0 1038PDS292 0 3343 0 0 0 0 0 0 0PDS30 0 0 0 0 0 4954 0 0 226F08311 31 233 0 23 96 1332 1503 2889 3385PDS312-3 0 0 0 0 496 0 - 0 802 0PDS34 0 63 0 0 207 58 0 31 390PDS35-2 1472 0 0 0 40 0 0 0 0P0843-9 1039 615 2844 880 3004 32191 0 3628 853PDS50 42504 22205 284 169 1112 1817 0 4052 95PDS51 155 28285 101 152 495 718 0 9763 433PDS52 0 0 72 84 114 1787 0 152 186F1/553 563 1275 703 12408 4741 10309 0 3265 109F1/954 0 0 0 0 29 1591 0 212 73PDS55 69 45 63 76 35 402 0 7302 364PDS56 3107 0 0 0 0 0 20973 0 0PDS65 86 154 61 76 62 1139 282 2468 3544PDS66 0 63 0 0 34 55 0 421 60F0867 55 112 35 51 53 847 116 16728 661PDS68-4 2934 16175 308 380 1655 22958 294 74339 2123P0875-9 2961 19945 3666 13920 5414 132092 276 83474 20209PDS80-1 424 590 170 288 238 12529 72 28188 3661P0882-3 256 222 442 28 33 993 0 2094 213F1/S84 0 0 0 0 0 0 0 0 0PDS85-98 1694 1183 495 793 630 24970 507 31317 5406Total Inter-industry inputs

70395 100026 19041 64973 26239 465010 24839 303859 56688

R10 21365 51398 13941 23819 15237 234563 2976 63918 4662Total inputs 91760 151424 32982 88792 41476 699573 27815 367777 61350


Table 7: Purchases of all products and labor by sectors BDS66 to BDS98, except BDS80-

1BDS66 BDS67 BDS68-4 BDS75-9 BDS82-3 BDS84 BDS85-98

PDS01-3 0 15588 0 2 0 336 8051PDSO4 1 15 20 30 1 0 617PDS05 1 675 196 214 41 332 -5256PDS053 113 3003 38959 4461 480 429 17303FDS06-8 50 4805 5712 2726 182 154 23392PDS09-1 0 0 465 0 0 0 0FIS12-3 49 0 0 0 0 0 1295FUS14 0 0 0 0 0 0 613PDS15 0 3622 0 0 0 0 2284FIIS16 0 975 0 0 75 88 31FINS17-9 0 194 384 3141 0 3297 33180PDS20-1 308 3120 901 7090 106 263 1937PDS22 0 0 0 0 0 0 0PDS23 0 0 0 0 0 0 0PDS24 0 923 0 0 0 0 1286PDS25 0 0 1095 0 0 0 2341PDS26 0 0 0 0 0 0 9080PDS27 0 300 0 14482 114 400 582PDS28 0 92 165 9003 77 136 3218PDS291 0 0 773 6397 112 50 18660PDS292 546 100 0 678 0 100 102PDS30 2610 82 0 0 0 217 12PDS311 47 184 8219 1809 904 978 7135PDS312-3 0 0 8600 0 0 0 36932FT/S34 0 0 0 694 0 1101 841PDS35-2 0 39240 465 0 0 2386 25013PDS43-9 1303 2156 914 453 0 5802 16579PDS50 0 14 920 12743 768 80 6660PDS51 0 1088 734 49408 5197 12336 29092PDS52 0 0 3272 375 0 269 0PDS53 0 0 0 0 0 61 36FT/S54 68 91 22 2500 488 127 12232PDS55 32 394 887 1160 43 476 57053PDS56 0 0 0 0 0 0 0PDS65 78 623 3371 5992 775 3017 3054PDS66 0 608 23 0 0 34 476PDS67 425 5141 1865 13600 182 888 8324PDS68-4 500 733 55943 28262 207 1494 18110PDS75-9 515 14099 26302 168452 3302 2099 178522PDS80-1 190 2967 10581 34480 443 831 31064PDS82-3 0 200 1049 3545 366 0 26358PDS84 0 0 0 0 0 0 2328PDS85-98 327 2640 11962 30887 363 3585 704602Total Inter-industryimuts

7163 103672 183799 402584 14226 41366 1293651

RIO 97395 156960 419170 375940 14500 49295 1163459Total inputs 104558 260632 602969 778524 28726 90661 2457110


Table 8: Total sales of each product and labor.

Total Interindustrypurchases

Final demand Total output

PDS0143 354365 276668 631033

PDSO4 16688 3228 19916

PDS05 104637 31109 135746

PDS053 146782 204156 350938

PDS06-8 134798 127189 261987

PDS09-1 104122 45828 149950

PDS1243 99217 34558 133775

PDS14 7818 3105 10923

PDS15 123359 15564 138923

PDS16 28376 18221 46597

PDS17-9 251747 326490 578237

PDSZ1-1 227478 99165 326643

PDS22 8975 27508 36483

PDS23 21983 30874 52857

PDS24 56658 185465 242123

PDS25 14520 51449 65969

PDS26 10763 9770 20533

PDS27 30021 87873 117894

PDS28 72919 91287 164206

PDS291 99197 101574 200771

PDS292 5129 52989 58118

PDS30 8101 53533 61634

PDS311 103499 494978 598477

PDS312-3 81080 118748 199828

PDS34 18549 73185 91734

PDS35-2 217180 729130 946310

PDS43-9 193426 471996 665422

PDS50 120099 50986 171085

PDS51 150378 89406 239784

PDS52 29577 26113 55690

PDS53 101711 42902 144613

PDS54 19012 113862 132874

PDS55 85938 840869 926807

PDS56 33805 144 33949

PDS65 33221 72850 106071

PDS66 4475 15874 20349

PDS67 53082 291638 344720

PDS68-4 310470 167932 478402

PDS75-9 995087 258807 1253894

P0880-1 155745 560512 716257

PDS82-3 68640 34317 102957

PDS84 5133 234549 239682

PDS85-98 880825 1654456 2535281

Total Inter-industry inputs

5588585 8220857 13809442

RIO 3531792 3531792

Total inputs 9120377 8220857 17341234


Table 9: Changes in sale to largest customers of PDS52 (Tire and Other Rubber Products) in the presence of Retreaded Tires in the

market.

BDS311 BDS68-4 BDS55 BDS17-9 BDS43-9 BDS22 BDS28 BDS291 BDS24 BDS01-03 BDS20-1 BDS75-9 Total Inter- Finalindustry sales demand

Totaloutput

Scenario 0: French economy in 1991Tires and other rubberproducts

14565 3272 1787 1426 894 868 775 741 653 517 484 375 29577 26113 55690

Fraction of outputs inclient industry

0.0243 0.0068 0.0019 0.0025 0.0013 0.0238 0.0047 0.0037 0.0027 0.0008 0.0015 0.0003

Scenario 1 X . 1.5 8 . 0.5 1 - p = 0.6Tires and other rubberproducts

14564 654 357 569 357 868 310 296 652 103 193 150 20909 10445 31355


0.0243 0.0014 0.0004 0.0010 0.0005 0.0238 0.0019 0.0015 0.0027 0.0002 0.0006 0.0001

Retread tires 0 1308 715 426 268 0 232 222 0 207 145 112 4322 7834 12155Fraction of outputs inclient industry

0.0000 0.0027 0.0008 0.0007 0.0004 0.0000 0.0014 0.0011 0.0000 0.0003 0.0004 0.0001

Scenario 2 X . 1.5 8 . 0.7 1 - p = 0.6Tires and other rubberproducts

14565 654 357 569 357 868 310 296 652 103 193 150 20914 10445 31359


0.0243 0.0014 0.0004 0.0010 0.0005 0.0238 0.0019 0.0015 0.0027 0.0002 0.0006 0.0001


0.0000 0.0038 0.0011 0.0010 0.0006 0.0000 0.0020 0.0016 0.0000 0.0005 0.0006 0.0001

Scenario 3 X, = 1.5 8 . 0.5 1 - p = 0.4Tires and other rubberproducts

14565 981 536 854 536 868 465 445 653 155 290 225 22870 15668 38538


0.0243 0.0021 0.0006 0.0015 0.0008 0.0238 0.0028 0.0022 0.0027 0.0002 0.0009 0.0002


0.0000 0.0024 0.0007 0.0005 0.0003 0.0000 0.0009 0.0007 0.0000 0.0003 0.0003 0.0001



Totaloutput

Scenario 4 X = 1.5 8=0.7 1 - p = 0.4Tires and other rubberproductsFraction of outputs inclient industryRetread tiresFraction of outputs inclient industry

14565

0.0243

0

0.0000

981

0.0021

1603

0.0034

536

0.0006

876

0.0009

854

0.0015

399

0.0007

536 868

0.0008 0.0238

250 0

0.0004 0.0000

465

0.0028

217

0.0013

445

0.0022

207

0.0010

653

0.0027

0

0.0000

155

0.0002

253

0.0004

290

0.0009

135

0.0004

225

0.0002

105

0.0001

22875

4685

15668

7312

38543

11997

Scenario 5 = 1.2 8 . 0.5 1 - p = 0.6Tires and other rubberproductsFraction of outputs inclient industryRetread tiresFraction of outputs inclient industry

14565

0.0243

0

0.0000

654

0.0014

13080.0027

357

0.0004

715

0.0008

569

0.0010

427

0.0007

357 868

0.0005 0.0238

268 0

0.0004 0.0000

310

0.0019

232

0.0014

296

0.0015

222

0.0011

653

0.0027

0

0.0000

103

0.0002

207

0.0003

193

0.0006

145

0.0004

150

0.0001

112

0.0001

20910

4322

10445

7834

31356

12156

Scenario 6 X . 1.2 8 = 0.7 1 - p = 0.6Tires and other rubberproductsFraction of outputs inclient industryRetread tiresFraction of outputs inclient industry

14565

0.0243

0

0.0000

654

0.0014

1832

0.0038

357

0.0004

1001

0.0011

570

0.0010.

598

0.0010

357 868

0.0005 0.0238

375 0

0.0006 0.0000

310

0.0019

325

0.0020

296

0.0015

311

0.0016

653

0.0027

0

0.0000

103

0.0002

289

0.0005

193

0.0006

203

0.0006

150

0.0001

157

0.0001

20916

6055

10445

10967

31361

17023

Scenario 7 X = 1.2 8=0.5 1 - p = 0.4Tires and other rubberproductsFraction of outputs inclient industryRetread tiresFraction of outputs inclient industry

14565

0.0243

0

0.0000

981

0.0021

1145

0.0024

536

0.0006

625

0.0007

854

0.0015

285

0.0005

536 868

0.0008 0.0238

179 0

0.0003 0.0000

465

0.0028

155

0.0009

445

0.0022

148

0.0007

653

0.0027

0

0.0000

155

0.0002

181

0.0003

290

0.0009

97

0.0003

225

0.0002

75

0.0001

22871

3346

15668

5223

38539

8568



Totaloutput

Scenario 8 X, . 1.2 8 . 0.7 1 - p = 0.4Tires and other rubberproductsFraction of outputs inclient industryRetread tiresFraction of outputs inclient industry

14565

0.0243

00.0000

981

0.0021

16030.0034

536

0.0006

8760.0009

855

0.0015

3990.0007

536

0.0008

2500.0004

868

0.0238

00.0000

465

0.0028

2170.0013

445

0.0022

2070.0010

653

0.0027

00.0000

155

0.0002

2530.0004

290

0.0009

1350.0004

225

0.0002

1050.0001

22877

4686

15668

7312

38544

11997


Table 10: Changes in purchases from largest suppliers by BDS52 (Tire and Rubber Industry) in the presence of a Tire Retreading

Industry.

PDS17-9 PDS75-9 PDS43-9 PDS09-1 PDS06-8 PDS01-3 PDS53 PDS24 PDS20-1 PDS85-98 PDS82-3 PDS053 R10Scenario 0 French economy in 1991Tire and rubber industry 4717 3666 2844 1461 887 703 703 690 555 495 442 354 13941Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503

Scenario 1 X 1.5 8. 0.5 1 -p= 0.6Tire and rubber industry 2656 2064 1601 823 499 396 396 388 312 279 249 199 7849Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503

Tire retreading 569 800 343 0 107 153 85 83 0 108 96 0 4564Fraction of tire retreadingsupplies

0.0468 0.0658 0.0282 0.0000 0.0088 0.0126 0.0070 0.0068 0.0000 0.0089 0.0079 0.0000 0.3755

Scenario 2 X =1.5 0.7 1 -p = 0.6Tire and rubber industry 2656 2064 1601 823 499 396 396 389 313 279 249 199 7850Fraction of tire and rubberindustry supplieslift icing

0.0847 0.0658

795 1120

0.0511 0.0262

479 0

0.0159 0.0126

150 215

0.0126

119

0.0124

116

0.0100

0

0.0089

151

0.0079

135

0.0064

0

0.2503

6391Fractroil of tire retreadingsupplies

0.0467 0.0658 0.0282 0.0000 0.0088 0.0126 0.0070 0.0068 0.0000 0.0089 0.0079 0.0000 0.3755

Scenario 3 X . 1.5 8 . 0.5 1 -p= 0.4Tire and rubber industry 3264 2537 1968 1011 614 486 486 477 384 343 306 245 9647Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0467 0.0658 0.0281 0.0000 0.0088 0.0126 0.0070 0.0068 0.0000 0.0089 0.0079 0.0000 0.3755


PDS17-9 PDS75-9 PDS43-9 PDS09-1 PDS06-8 PDS01-3 PDS53 PDS24 PDS20-1 PDS85-98 PDS82-3 PDS053 R10Scenario 4 X.1.5 5.0.7 1 - p = 0.4Tire and rubber industry 3265 2537 1968 1011 614 487 487 478 384 343 306 245 9649Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0466 0.0658 0.0281 0.0000 0.0088 0.0126 0.0069 0.0068 0.0000 0.0089 0.0079 0.0000 0.3755

Scenario 5 X = 1.2 5= 0.5 1 - p = 0.6Tire and rubber industry 2656 2064 1601 823 499 396 396 388 312 279 249 199 7849Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0828 0.0658 0.0499 0.0000 0.0156 0.0126 0.0123 0.0121 0.0000 0.0089 0.0079 0.0000 0.3004

Scenario 6 X, = 1.2 8 = 0.7 1 - p = 0.6Tire and rubber industry 2656 2064 1602 823 499 396 396 389 313 279 249 199 7851Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0827 0.0658 0.0499 0.0000 0.0156 0.0126 0.0123 0.0121 0.0000 0.0089 0.0079 0.0000 0.3004


PDS 17-9 PDS75-9 PDS43-9 PDS09-1 PDS06-8 PDS01-3 PDS53 PDS24 PDS20-1 PDS85-98 PDS82-3 PDS053 R10Scenario 7 X = 1.2 8 = 0.5 1 - p = 0.4Tire and rubber industry 3264 2537 1968 1011 614 486 486 477 384 343 306 245 9648Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0827 0.0658 0.0498 0.0000 0.0155 0.0126 0.0123 0.0121 0.0000 0.0089 0.0079 0.0000 0.3004

Scenario 8 X = 1.2 8 = 0.7 1 - p = 0.4Tire and rubber industry 3265 2537 1968 1011 614 487 487 478 384 343 306 245 9649Fraction of tire and rubberindustry supplies

0.0847 0.0658 0.0511 0.0262 0.0159 0.0126 0.0126 0.0124 0.0100 0.0089 0.0079 0.0064 0.2503


0.0826 0.0658 0.0498 0.0000 0.0155 0.0126 0.0123 0.0121 0.0000 0.0089 0.0079 0.0000 0.3004


Table 11: Changes in sale to largest customers of PDS27 (Office and Computer Equipment) in the presence of Remanufactured

Compueters in the market.

B0S75-9 BDS27 BDS23 BDS57-4,80-1 BDS311 BDS85-98 BDS84 BDS67 BDS17-9 BDS20-1 BDS51 BDS24 Total Inter- Final Totalindustry sales demand output

Scenario 0: French economy in 1991Office and computerequipment

14482 9652 1614 982 676 582 400 300 253 231 193 189 29365 87873 117238

Fraction of total outputs inclient industry

0.0115 0.0819 0.0305 0.0014 0.0011 0.0002 0.0017 0.0009 0.0004 0.0007 0.0008 0.0008

Scenario 1 X.= 1.5 8. 0.5 1 - p = 0.6Office and computerequipment

2885 3828 1130 687 473 116 80 60 177 92 77 132 11609 35149 46759


0.0023 0.0819 0.0214 0.0010 0.0008 0.0000 0.0003 0.0002 0.0003 0.0003 0.0003 0.0005

Remanufactured PCs 5770 0 242 147 101 233 160 120 38 69 58 28 7949 26362 34311Fraction of total outputs inclient industry

0.0046 0.0000 0.0046 0.0002 0.0002 0.0001 0.0007 0.0003 0.0001 0.0002 0.0002 0.0001

Scenario 2 X.= 1.5 S= 0.7 1 - p = 0.6Office and computerequipment

2889 3891 1130 687 473 116 80 60 177 92 77 132 12375 35149 47524


0.0023 0.0819 0.0214 0.0010 0.0008 0.0000 0.0003 0.0002 0.0003 0.0003 0.0003 0.0005


0.0065 0.0000 0.0064 0.0003 0.0002 0.0001 ' 0.0009 0.0005 0.0001 0.0003 0.0003 0.0002


BDS75-9 BDS27 BDS23 BDS57-4,80-1 BDS311 BDS85-98 BDS84 BDS67 BDS17-9 BDS20-1 BDS51 BDS24 Total Inter- Final Totalindustry sales demand output

Scenario 3 x, . 1.5 8 = 0.5 1- p = 0.4Office and computerequipmentFraction of total outputs inclient industryRemanufactured PCsFraction of total outputs inclient industry

4332

0.0035

50550.0040

5592

0.0819

00.0000

1291

0.0244

1610.0031

785

0.0011

980.0001

541

0.0009

680.0001

174

0.0001

2040.0001

120

0.0005

1400.0006

90

0.0003

1050.0003

202

0.0004

250.0000

138

0.0004

460.0001

116

0.0005

390.0002

151

0.0006

190.0001

15584

6446

52724

17575

68307

24020

Scenario 4 X, = 1.5 8 = 0.7 1- p = 0.4Office and computerequipmentFraction of total outputs inclient industryRemanufactured PCsFraction of total outputs inclient industry

4337

0.0035

70830.0057

5657

0.0819

00.0000

1291

0.0244

2260.0043

785

0.0011

1370.0002

541

0.0009

950.0002

174

0.0001

2850.0001

120

0.0005

1960.0008

90

0.0003

1470.0004

202

0.0004

350.0001

138

0.0004

650.0002

116

0.0005

540.0002

151

0.0006

260.0001

16378

9256

52724

24604

69102

33861

Scenario 5 X = 1.2 8 = 0.5 1- p = 0.6Office and computerequipmentFraction of total outputs inclient industryRemanufactured PCsFraction of total outputs inclient industry

2885

0.0023

57710.0046

3828

0.0819

00.0000

1130

0.0214

2420.0046

687

0.0010

1470.0002

473

0.0008

1010.0002

116

0.0000

2330.0001

80

0.0003

1600.0007

60

0.0002

1200.0003

177

0.0003

380.0001

92

0.0003

690.0002

77

0.0003

580.0002

132

0.0005

280.0001

11610

7950

35149

26362

46759

34312

Scenario 6 = 1.2 8 = 0.5 1- p = 0.4Office and computerequipmentFraction of total outputs inclient industryRemanufactured PCsFraction of total outputs inclient industry

4333

0.0035

50550.0040

5592

0.0819

00.0000

1291

0.0244

1610.0031

785

0.0011

980.0001

541

0.0009

680.0001

175

0.0001

2040.0001

120

0.0005

1400.0006

90

0.0003

1050.0003

202

0.0004

250.0000

138

0.0004

460.0001

116

0.0005

390.0002

151

0.0006

190.0001

15584

6446

52724

17575

68308

24021


BDS75-9 BDS27 BDS23 BDS57-4,80-1 BDS311 BDS85-98 BDS84 BDS67 BDS17-9 BDS20-1 BDS51 BDS24 Total Inter- Final Totalindustry sales demand output

Scenario 7 X. = 1.2 8 = 0.7 1- p = 0.6Office and computerequipment

2890 3891 1130 687 473 116 80 60 177 92 77 132 12376 35149 47525


0.0023 0.0819 0.0214 0.0010 0.0008 0.0000 0.0003 0.0002 0.0003 0.0003 0.0003 0.0005


0.0065 0.0000 0.0064 0.0003 0.0002 0.0001 0.0009 0.0005 0.0001 0.0003 0.0003 0.0002

Scenario 8 X = 1.2 8 = 0.7 1- p = 0.4Office and computerequipment

4337 5657 1291 785 541 175 120 90 202 138 116 151 16379 52724 69103


0.0035 0.0819 0.0244 0.0011 0.0009 0.0001 0.0005 0.0003 0.0004 0.0004 0.0005 0.0006


0.0057 0.0000 0.0043 0.0002 0.0002 0.0001 0.0008 0.0004 0.0001 0.0002 0.0002 0.0001


Table 12: Changes in purchases from largest suppliers by BDS27 (Office and Computer Equipment Industry) in the presence of

Computer Remanufacturing.

PDS75-9 PDS27 PDS291 PDS68-4 PDS85-98 PDS53 PDS80-1 PDS51 PDS43-9 PDS06-8 PDS50 PDS20-1 R10

Scenario 0: French economy in 1991Office and computerequipment industry

10568 9652 4931 2614 2543 2433 850 702 663 499 405 38680 19112

Fraction of computerindustry supplies

0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621

Scenario 1 A, =1.5 8 . 0.5 1- p = 0.6Computer and officeequipment industry

4191 3828 1956 1037 1009 965 337 278 263 198 161 142 7580


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621

Computerremanufacturing

3076 1685 363 951 187 179 247 52 193 37 118 0 8343

Fraction of computerremanufacturing supplies

0.0896 0.0491 0.0106 0.0277 0.0055 0.0052 0.0072 0.0015 0.0056 0.0011 0.0034 0.0000 0.2432

Scenario 2 X =1.5 8 = 0.7 1- p = 0.6Computer and officeequipment industry

4260 3891 1988 1054 1025 981 343 283 267 201 163 145 7704


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621


4351 2384 306 1345 158 151 350 44 273 31 167 0 11802


0.0896 0.0491 0.0063 0.0277 0.0033 0.0031 0.0072 0.0009 0.0056 0.0006 0.0034 ' 0.0000 0.2432


PDS75-9 PDS27 PDS291 PDS68-4 PDS85-98 PDS53 PDS80-1 PDS51 PDS43-9 PDS06-8 PDS50 PDS20-1 R10Scenario 3 X = 1.5 8 = 0.5 1 - p = 0.4Computer and officeequipment industry

6123 5592 2857 1515 1473 1410 492 407 384 289 235 208 11073


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621

Computerremanufactunng

2153 1770 83 666 43 41 173 12 135 8 83 0 5841

Fraction of computerremanufactunng supplies

0.0896 0.0737 0.0035 0.0277 0.0018 0.0017 0.0072 0.0005 0.0056 0.0004 0.0034 0.0000 0.2432

Scenario 4 X = 1.5 8 = 0.7 1- p = 0.4Computer and officeequipment industry

6194 5657 2890 1532 1491 1426 498 411 389 292 237 210 11202


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621


3035 2495 21 938 11 10 244 3 190 2 116 0 8234


0.0896 0.0737 0.0006 0.0277 0.0003 0.0003 0.0072 0.0001 0.0056 0.0001 0.0034 0.0000 0.2432

Scenario 5 X = 1.2 8 = 0.5 1 - p = 0.6Computer and officeequipment industry

4192 3828 1956 1037 1009 965 337 278 263 198 161 142 7580


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621

Computerremanufactunng

3076 1685 1086 951 560 536 247 155 193 110 118 0 6675


0.0896 0.0491 0.0317 0.0277 0.0163 0.0156 0.0072 0.0045 0.0056 0.0032 0.0034 0.0000 0.1945


PDS75-9 PDS27 PDS291 PDS68-4 PDS85-98 PDS53 PDS80-1 PDS51 PDS43-9 PDS06-8 PDS50 PDS20-1 R10Scenario 6 X = 1.2 8= 0.5 1 - p = 0.4Computer and officeequipment industry

6123 5592 2857 1515 1473 1410 492 407 384 289 235 208 11074


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621


2153 1770 590 666 304 291 173 84 135 60 83 0 4673


0.0896 0.0737 0.0246 0.0277 0.0127 0.0121 0.0072 0.0035 0.0056 0.0025 0.0034 0.0000 0.1945


4260 3891 1988 1054 1025 981 343 283 267 201 163 145 7704


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621


4351 2384 1330 1345 686 656 350 189 273 135 167 0 9442


0.0896 0.0491 0.0274 0.0277 0.0141 0.0135 0.0072 0.0039 0.0056 0.0028 0.0034 0.0000 0.1945


6194 5657 2890 1532 1491 1426 498 411 389 292 237 210 11202


0.0896 0.0819 0.0418 0.0222 0.0216 0.0206 0.0072 0.0060 0.0056 0.0042 0.0034 0.0030 0.1621


3035 2495 736 939 379 363 244 105 190 74 116 0 6587


0.0896 0.0737 0.0217 0.0277 0.0112 0.0107 0.0072 0.0031 0.0056 0.0022 0.0034 0.0000 0.1945


Table 13: Changes in sale to largest customers of PDS311 (Automobiles, Motorcycles and Bicycles) in the presence of Remanufactured

Automobiles in the market.

industry sales demand outputScenario 0: French economy in 1991Automobiles, motorcyclesand bicycles

70078 8219 7135 3385 2889 1809 1503 1332 998 978 904 550 103499 494978 598477


0.1171 0.0172 0.0028 0.0319 0.0040 0.0014 0.0443 0.0014 0.0072 0.0041 0.0088 0.0009

Scenario 1 X . 1.5 8 . 0.5 1 - p = 0.6Automobiles, motorcyclesand bicycles

30032 5713 1425 675 1153 357 577 533 398 196 356 220 58490 197991 256481


0.1171 0.0120 0.0006 0.0064 0.0016 0.0003 0.0177 0.0006 0.0029 0.0008 0.0035 0.0003

Remanufactured Autos 0 1224 2849 1349 865 715 433 399 299 391 267 165 15825 148493 164319Fraction of total outputs inclient industry

0.0000 0.0026 0.0011 0.0128 0.0012 0.0006 0.0133 0.0004 0.0022 0.0016 0.0026 0.0003

Scenario 2 x . 1.5 8 . 0.7 1 - p = 0.6Automobiles, motorcyclesand bicycles

30895 5729 1425 675 1154 359 579 533 398 196 358 220 65858 197991 263849


0.1171 0.0120 0.0006 0.0064 0.0016 0.0003 0.0177 0.0006 0.0029 0.0008 0.0035 0.0003

Remanufactured Autos 0 1719 3991 1891 1212 1005 608 559 418 548 376 231 25579 207891 233470Fraction of total outputs inclient industry

0.0000 0.0036 0.0016 0.0179 0.0017 0.0008 0.0186 0.0006 0.0030 0.0023 0.0037 0.0004

BDS311 BDS68-4 BDS85-98 BDS65 BDS57-4,80-1 BDS75-9 BDS56 BDS55 BDS15 BDS84 BDS82-3 BDS01-03 Total Inter- Final Total


BDS311 BDS68-4 BDS85-98 BDS65 BDS57-4,80-1 BDS75-9 BDS56 BDS55 BDS15 BDS84 BDS82-3 BDS01-03 Total Inter- Final Totalindustry sales demand output

Scenario 3 X = 1.5 8 = 0.5 1 - p = 0.4Automobiles, motorcyclesand bicyclesFraction of total outputs inclient industryRemanufactured AutosFraction of total outputs inclient industry

43760

0.1171

0

0.0000

6544

0.0137

818

0.0017

2138 1013

0.0008 0.0096

2494 1182

0.0010 0.0112

1731 538

0.0024 0.0004

577 628

0.0008 0.0005

877

0.0266

292

0.0089

799

0.0009

266

0.0003

598

0.0043

199

0.0014

293

0.0012

342

0.0014

537

0.0053

179

0.0018

330

0.0005

110

0.0002

76729

10384

296987

98996

373716

109380

Scenario 4 x = 1.5 8 . 0.7 1 - p = 0.4Automobiles, motorcyclesand bicyclesFraction of total outputs indient industryRemanufactured AutosFraction of total outputs inclient industry

44606

0.1171

0

0.0000

6556

0.0137

1147

0.0024

2139 1014

0.0008 0.0096

3494 1656

0.0014 0.0156

1732 540

0.0024 0.0004

808 882

0.0011 0.0007

879

0.0266

410

0.0124

799

0.0009

373

0.0004

598

0.0043

279

0.0020

293

0.0012

479

0.0020

539

0.0053

252

0.0025

330

0.0005

154

0.0002

83957

16032

296987

138594

380943

154626

Scenario 5 = 1.2 8= 0.5 1 - p = 0.6Automobiles, motorcyclesand bicyclesFraction of total outputs inclient industryRemanufactured AutosFraction of total outputs inclient industry

30033

0.1171

0

0.0000

5716

0.0120

1225

0.0026

1425 675

0.0006 0.0064

2849 1349

0.0011 0.0128

1153 358

0.0016 0.0003

865 715

0.0012 0.0006

578

0.0177

434

0.0133

533

0.0006

399

0.0004

398

0.0029

299

0.0022

196

0.0008

391

0.0016

356

0.0035

267

0.0026

220

0.0003

165

0.0003

58499

15830

197991

148493

256490

164324

Scenario 6 =1.2 8 = 0.5 1 - p = 0.4Automobiles, motorcyclesand bicyclesFraction of total outputs inclient industryRemanufactured AutosFraction of total outputs inclient industry

43761

0.1171

0

0.0000

6545

0.0137

818

0.0017

2138 1013

0.0008 0.0096

2495 1182

0.0010 0.0112

1731 538

0.0024 0.0004

577 628

0.0008 0.0005

878

0.0266

293

0.0089

799

0.0009

266

0.0003

598

0.0043

199

0.0014

293

0.0012

342

0.0014

537

0.0053

179

0.0018

330

0.0005

110

0.0002

' 76737

10386

296987

98996

373724

109382


BDS311 BDS68-4 BDS85-98 BDS65 BDS57-4,80-1 BDS75-9 BDS56 BDS55 BDS15 BDS84 BDS82-3 BDS01-03 Total Inter- Final Totalindustry sales demand output

Scenario 7 2, . 1.2 8 . 0.7 1 - p = 0.6Automobiles, motorcyclesand bicyclesFraction of total outputs inclient industryRemanufactured AutosFraction of total outputs inclient industry

30897

0.1171

00.0000

5732

0.0120

17200.0036

1426 676

0.0006 0.0064

3992 18910.0016 0.0179

1154 359

0.0016 0.0003

1212 10060.0017 0.0008

581

0.0177

6100.0186

533

0.0006

5590.0006

399

0.0029

4180.0030

196

0.0008

5480.0023

358

0.0035

3760.0037

220

0.0003

2310.0004

65871

25590

197991

207891

263862

233480

Scenario 8 X . 1.2 8 . 0.7 1 - p = 0.4Automobiles, motorcyclesand bicyclesFraction of total outputs inclient industryRemanufactured AutosFraction of total outputs inclient industry

44608

0.1171

00.0000

6558

0.0137

11480.0024

2139 1014

0.0008 0.0096

3494 16560.0014 0.0156

1732 540

0.0024 0.0004

808 8820.0011 0.0007

880

0.0266

4110.0124

799

0.0009

3730.0004

598

0.0043

2790.0020

293

0.0012

4790.0020

539

0.0053

2520.0025

330

0.0005

1540.0002

83969

16037

296987

138594

380955

154631


Table 14: Changes in purchases from largest suppliers by BDS311 (Automobile, Motorcycle and Bicycle Industry) in the presence of

Automobile Remanufacturing.

PDS311 PDS20-1 PDS75-9 PDS52 PDS09-1 PDS53 PDS43-9 PDS85-98 PDS17-9 PDS68-4 PDS82-3 PDS16 R10Scenario 0: French economy in 1991Automobile, motorcycleand bicycle industry

70078 42921 30448 14565 12293 9572 6181 5551 5038 4954 4106 4007 61190

Fraction of automobileindustry supplies

0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022

Scenario 1 X . 1.5 5 . 0.5 1 - p = 0.6Automobile, motorcycleand bicycle industry

30032 18394 13049 6242 5268 4102 2649 2379 2159 2123 1760 1717 26223


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022

Automobileremanufactunng

15393 8453 8360 3999 0 1885 1217 1524 992 1700 1127 789 25201

Fraction of automobileremanufactunng supplies

0.0937 0.0514 0.0509 0.0243 0.0000 0.0115 0.0074 0.0093 0.0060 0.0103 0.0069 0.0048 0.1534

Scenario 2 x . 1.5 6. 0.7 1 - p = 0.6Automobile, motorcycleand bicycle industry

30895 18922 13424 6421 5420 4220 2725 2447 2221 2184 1810 1767 26977


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022

Automobileremanufacturing

21870 10194 11878 5682 0 2273 1468 2165 1196 2416 1602 952 35806

Fraction of automobileremanufactunng supplies

0.0937 0.0437 0.0509 0.0243 0.0000 0.0097 0.0063 0.0093 0.0051 0.0103 0.0069 ' 0.0041 0.1534


PDS311 PDS20-1 PDS75-9 P0852 PDS09-1 PDS53 PDS43-9 PDS85-98 PDS17-9 PDS68-4 PDS82-3 PDS16 R10

Scenario 3 A, = 1.5 8 = 0.5 1- p = 0.4Automobile, motorcycleand bicycle industry

43760 26802 19013 9095 7676 5977 3860 3466 3146 3094 2564 2502 38210


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


15369 3499 5565 2662 0 780 504 1015 411 1132 750 327 16775

Fraction of automobileremanufacturing supplies

0.1405 0.0320 0.0509 0.0243 0.0000 0.0071 0.0046 0.0093 0.0038 0.0103 0.0069 0.0030 0.1534

Scenario 4 X = 1.5 8 = 0.7 1 -p= 0.4

Automobile, motorcycleand bicycle industry

44606 27320 19381 9271 7825 6093 3934 3533 3207 3153 2614 2551 38949


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


21727 4145 7867 3763 0 924 597 1434 487 1600 1061 387 23714


0.1405 0.0268 0.0509 0.0243 0.0000 0.0060 0.0039 0.0093 0.0031 0.0103 0.0069 0.0025 0.1534

Scenario 5 X, =1.2 8 = 0.5 1 - p = 0.6Automobile, motorcycleand bicycle industry

30033 18395 13049 6242 5268 4102 2649 2379 2159 2123 1760 1717 26224


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


15393 11243 8360 3999 0 2507 1619 1524 1320 1700 1127 1050 20161


0.0937 0.0684 0.0509 0.0243 0.0000 0.0153 0.0099 0.0093 0.0080 0.0103 0.0069 0.0064 0.1227


PDS311 PDS20-1 PDS75-9 PDS52 PDS09-1 PDS53 PDS43-9 PDS85-98 PDS17-9 PDS68-4 PDS82-3 PDS16 R10

Scenario 6 X=1.2 8 = 0.5 1 -p= 0.4

Automobile, motorcycleand bicycle industry

43761 26802 19014 9095 7676 5977 3860 3466 3146 3094 2564 2502 38211


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


15370 5357 5565 2662 0 1195 771 1015 629 1132 750 500 13420


0.1405 0.0490 0.0509 0.0243 0.0000 0.0109 0.0071 0.0093 0.0057 0.0103 0.0069 0.0046 0.1227

Scenario 7 X =1.2 8 = 0.7 1- p = 0.6Automobile, motorcycleand bicycle industry

30897 18923 13424 6422 5420 4220 2725 2447 2221 2184 1810 1767 26978


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


21871 14159 11878 5682 0 3158 2039 2166 1662 2416 1602 1322 28646


0.0937 0.0606 0.0509 0.0243 0.0000 0.0135 0.0087 0.0093 0.0071 0.0103 0.0069 0.0057 0.1227

Scenario 8 X, . 1.2 8 . 0.7 1 - p = 0.4Automobile, motorcycleand bicycle industry

44608 27321 19381 9271 7825 6093 3934 3533 3207 3153 2614 2551 38950


0.1171 0.0717 0.0509 0.0243 0.0205 0.0160 0.0103 0.0093 0.0084 0.0083 0.0069 0.0067 0.1022


21728 6771 7867 3763 0 1510 975 1434 795 1600 1061 632 18972


0.1405 0.0438 0.0509 0.0243 0.0000 0.0098 0.0063 0.0093 0.0051 0.0103 0.0069 0.0041 0.1227


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