2014-15
IMPACTS OF EAST-JAPAN’S DISASTER
ON PRODUCTION OF A SMALL－MEDIUM CARDBOARD MANUFACTURER
IN FUKUSHIMA
Hideki Murakami Akiko Yanagida

Chapter XX
IMPACTS OF EAST-JAPAN’S DISASTER ON PRODUCTION OF A SMALL－MEDIUM CARDBOARD MANUFACTURER IN FUKUSHIMA
Hideki Murakami and Akiko Yanagida
Abstract This paper analyzed the impact of the earthquake, tsunami, andexplosion-disaster at the Fukushima nuclear power plant in March 2011 on asmall-medium cardboard manufacturer’s production and transportation systems.
We first estimated the Cobb-Douglas production function to determine the changepre- and post-disaster using time-series monthly data from January 2010–
December 2012 (35 months); we found few structural changes to the input ratio in
the company’s production system. We then performed statistical analyses toinvestigate changes in the cargo flow originating at the firm and shipped todevastated/non-devastated destinations. The results indicated that the disaster
affected cargo flow only to the coastal area devastated by the tsunami orcontaminated by nuclear radioactivity. Since the demand for cardboard is a “derived
demand” of agriculture and relatively light industries, this finding suggests thatthese industries were not unduly impacted by the disasters, and that the disaster’s
effects may be overemphasized.
Keywords East-Japan earthquake, small-medium firm, production function

XX.1.

Introduction

East Japan experienced a historical disaster in March of 2011. The Asahi
newspaper reported in 2011 that about sixteen thousand people were dead andthree thousand are still missing.
In addition to these human disasters, many industries and infrastructures weredamaged.

These disasters are classified into three categories depending on thecause of the damage. The first is the earthquake itself, the second is the tsunamithat caused the greatest damage along the coast, and the third is the radioactive
disaster generated by the explosion of the nuclear electric plants. Damaged
infrastructure was restored in accordance with its importance and necessity, andMail to:H.Murakami
Kobe University, Graduate School of Business; Tohoku Kogyoe-mail: hidekim@panda.kobe-u.ac.jp

1

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Impact of East-Japan’s disaster on production on small-medium manufacturer

individual industry has also been revived.

However, people are still forbidden fromentering or staying in the areas contaminated by radioactivity. While these areas
comprise only a part of the Fukushima prefecture, it seems that many people inother areas of Japan and outside in other countries still have misconceptions about
the nuclear contamination. The contamination spread only to limited areas in the
Fukushima prefecture, and economic activities were quickly restored immediately
following the earthquake.
There have been many studies and reports on issues related to the Fukushimadisaster (Kushida, 2012; Holt et al., 2012;, Conferences Proceedings of the STSForum on the 2011 Fukushima/East Japan Disaster, UC Berkeley, May 11-14,
2013 ).

However, all of them have dealt with the disaster itself, for example,

scientific analyses of radioactive contamination, or time-series reports about
political and institutional responses. Few studies have conducted economic analyses,
since many businesses in Fukushima closed down operations, and there wasinsufficient data for a rigid analysis using economics or statistics.
This paper is the first to do an economic analysis highlighting a small-medium
manufacturer of cardboard in the Fukushima area. The purpose of this study was toinvestigate whether the production activities of a manufacturer in the Fukushima
prefecture was changed after the disaster by estimating (1) the production function
and change in the ratio of input share, and (2) regional-area specific logistics
activities of the firm by time-series analyses. The methodology combined
microeconomics and econometrics, using 35 months of data. In the next section, we
demonstrate the impact of East-Japan’s disaster on regional small-medium
enterprises and discuss the remedies enacted by the Japanese public sector, such as
national and regional government to alleviate the effects on companies. In Section 3,
we present an overview of the demographic issues and the damage to Fukushima
Prefecture. Section 4 presents the empirical models and dataset. In Section 5 weshow and discuss the empirical results, followed by future perspectives in Section 6.

XX.2. Government’s response to the disaster and descriptive statistics of revival ofsmall-medium firms in Tohoku afterwards
In this section we survey the Japanese government’s actions to relieve the effects of the disasteron small-medium firms in the Tohoku district. According to Japan’s Small-Medium Enterprise
Agency, the following financial plans were announced to assist all small-medium companies in
the immediate aftermath of the disaster on March 13th 2011. Actions included granting loans ashigh as eighty million yen without collateral, extending the period of redemption from seven to3

H.Murakami and A.Yanagidanine years, lowering the bid rate by 0.9%, and offering indirect subsidies from national
government to small-medium companies via local government.1 These financial remedies havebeen revised and updated, and may now be granted to those who restart their businesses either
in their original location, or where they had to move due to the high density of radioactivity.
Having received this aid from national and local governments, small-medium companies haveshown slight signs of recovery according to the statistics officially published by theOrganization for Small & Medium Enterprises and Regional Innovation, Japan. Using thesestatistics, we analyzed whether and to what extent the small-medium companies of Tohoku had
been revived as of 2013.
Fig.XX.1 shows the time-series changes in the number of small-medium manufacturers in the
pre- and post-disaster period. The data are from the second quarter (April-June) of each year.

Fig.XX.1 Time-series changes in the number of small-medium manufacturers in Tohoku
Note: the vertical axis shows the number of manufacturers and on the horizontal axis, zero represents 1994 and 20,
2013.

According to Fig.XX.1, the number of small-medium manufacturers showed a downward trend
in each business cycle. “Series” refers to data that were actually observed. These manufacturers
produced high-quality, high-price goods for export from Narita worldwide, especially to Eastand Southeast Asia. As can be seen in the figure, the recession caused by the 1998 currency
crisis in Asia strongly affected export activities (time unit 6). Another bottom was apparently
1Information from the home page of the Small Medium Enterprise Agency accessed 3rd April,2014. http://www.chusho.meti.go.jp/keiei/antei/2011/110313TohokuGekijinShitei.htm

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Impact of East-Japan’s disaster on production on small-medium manufacturer

caused by the Tohoku disaster in 2011 (time unit 17). Fig.XX.1 reveals that the drop in thenumber of firms in the year following the disaster was almost in line with the lower bound of
the 95% confidence interval. The curve then began to climb in 2011-12 (time units 17-18), andindeed the recovery has since accelerated from (2012-13; time units 19-20), but is still muchsmaller than the forecasted value for these twenty years.2

Fig.XX.2 Time series changes in the number of small-medium wholesalers and retailers in Tohoku
Note: the vertical axis shows the number of manufacturers; on the horizontal axis, zero represents 1994, and 20,
2013.

Fig. XX.2 shows the time trend for wholesalers and retailers.3 The curve looks similar to thatfor manufacturers, but the degree and speed of the recovery were smaller. This may be because
national and local governments offered smaller subsidies and other financial assistance toindustries that did not require a lump-sum investment for their recovery, and because theamount of merchandise and agricultural products decreased significantly, especially in the areas
devastated by the tsunami and contaminated by radioactivity from the damaged nuclear power
plant.

The time-series analysis of the change in the number of manufacturer follows AR(2). We
obtained the following result by the Box-Jenkins method. The constant is 75.20 with t=1.44,
the parameter of t-1 is -1.41 with t=12.87, that of t-2 is -0.563 with t=5.00, and adjusted
R-square is 0.833.
3 The time-series analysis of the change in the number of wholesalers and retailers follows
AR(1). We used the same method as for manufacturers, and obtained the result that theconstant is 43.48 with t=0.65, the parameter of t-1 is -0.927 with t=8.84, and adjusted
R-square is 0.703.
2

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H.Murakami and A.Yanagida

XX.3. Outlook for Fukushima, the disaster, and the surveyed firms
Fukushima Prefecture is the third largest prefecture in Japan in terms of width4, and has apopulation of about two million.

Fig. XX.3 Fukushima Prefecture
Note: Blue square denotes Aizu Wakamatsu, the triangle Koriyama, the circle Fukushima-city, and the pentagon
Iwaki (Onahama port is in Iwaki city). Area “A” is Aizu, “B” is Naka-dori, and “C” is Hama-dori.

Generally, the prefecture is divided into three parts based on demographic and governmental
factors.5 The first is the “Hama-dori” (coastal corridor), the second the “Naka-dori” (central
corridor), and the third the “Aizu” district.

Of these three districts, Naka-dori is the mostthriving, with two large cities—Fukushima city and Koriyama city. Koriyama, the center ofbusiness activities, is larger than Fukushima in terms of population. Fukushima Airport islocated 20 minutes from Koriyama, and east-west and north-south bound highways cross in thecity. The Hama-dori region, meanwhile, used to be prosperous due to coal mining, and four
nuclear power plants are located on the central coast in this area. The international container
seaport along Onahama city is connected to Koriyama by a highway and national road No. 49.
The Aizu area is far from the pacific coastal area and is full of historical areas called “small
Kyoto”, where there are many famous temples. Overall, Fukushima prefecture ranks high interms of producing high-value products like semi-conductors, which are exported from Narita
Airport, as well as farm products such as vegetables, almost all kinds of fruit, and high-quality
Japanese rice and rice wine (sake).

In a sense, Fukushima is better than any other prefecture interms of agricultural products.

4
5

For more detail, please access the prefectural official website (accessed 29th March, 2013).
The three prefectures were merged into their current status in 1876. See Fig. 5.

6Impact of East-Japan’s disaster on production on small-medium manufacturer

However, the radioactivity contaminated the land and agricultural products around the power
plants and the areas northeast of them. These include the cities and towns along the Pacific coastand the inland areas between the coast and Naka-dori. As of now, it is still forbidden to enter orstay in several towns, and the previous inhabitants are only allowed to pass through to monitor
their properties and prevent theft. In terms of traffic routes, national trunk route No. 6, which
originates in Tokyo and passes along Hama-dori (coastal aisle), has been shut down around thepower plants so that all traffic must use route No. 4 that passes Naka-dori.
The data used in this study was obtained from Tohoku Kogyo Co., Ltd, which mainly provides
cardboard and wrapping services. The company was founded in 1972 and is located in
Koriyama city. As of April 2012, it employed about 100 people, and its current capital stock is
0.8 million USD (assuming one USD = 100 yen).

It obtained ISO9001 in January 2005.

XX.4. Model for Estimating Structural Change in Production
We investigated the effect of the East Japan disaster on the Tohoku Kogyo Co.’s production
structure. In our analysis, we introduced the “post-disaster dummy variable” both on theconstant term and slopes of input variables, as well as other “shock dummy variables”.

Theproduction function of a general form is defined as Eq.(1):(1)
where Q is the amount of final goods,,

are units of input (in-process goods, labor,

inputs [extra hours in addition to fixed hours per month], and utilization of electric power), IIP
is indices of industrial production of the Fukushima Prefecture, and Dt is a group of dummy
variables: t=2011.3 and 2011.4, and D2011.3 and D2011.4 are the dummy variables for the disastermonth and the next month; post disaster is a dummy variable that takes 1 for the post-disasterperiod, and time is a variable to remove the time trend. Among the input variables, “in-process(intermediate) goods” (IPG) have different characteristics from other inputs. Since IPG also
require labor, energy inputs, and so on, we had to estimate a production function of IPG
simultaneously with the production function of final goods. The IPG function worked asfollows. When estimating this system of production function, we needed to impose the1

restriction that0, following microeconomic theory.

log IPG

log

log

log
log FG

β
β

β

β
β

. +

.

log
.

β

β

β

time+e(2)log. +β time+u

(3)

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H.Murakami and A.Yanagida

A description of the variables used in Eq.(2) and (3) is given in Table.XX.1; “e” and “u” areterms of random disturbance.
Table XX.1. Description of variables
Variables
IPG

EnergyIIPTransportation
DDist
D2011.3
D2011.4

Explanations
The square meters of containerboards produced in one month. These are cut folded and assembled intoboxes.
The square meters of final goods (boxes dispatched to customers). Not all the IPGs are assembled intoboxes. Defective goods are disposed of, and some containerboard is kept in stock to match supply todemand.
Extra working hours added to regular working hours. The data are aggregate for all employees in one
monthly totals.
Monthly consumption of electric power (kw)
Indices of industrial production of Fukushima Prefecture (average=100)
The number of cargo trucks engaged in carrying final goods
The 3.11 disaster dummy variable that takes 1 for 2011.5 – 2012.12 and 0 for the rest )The dummy variable that takes 1 for March 11, 2011 (to see the month of disaster)
The dummy variable that takes 1 for April 11, 2011 (to see the next month’s effect)

time

Time trend in logarithm

FG

Labor

When we estimate this production function, we need the pre-assumption that the firm operates
efficiently under circumstances of perfect competition. This assumption may not be true for ourcase if we estimate the production function of all (i.e., the aggregate) sections of a firm, but here
we could assume efficiency under perfect competition since every single activity of the
production-line division was strictly monitored by the production management division. If we
admit that the firm operates under imperfect competition, the estimation of production function
would still be useful, but the degree of homogeneity would be less than one.6
There is a tremendous variety of functional forms for production function according to a survey
by Griffin et al. (1987).7 Among them, the translog function is widely used for the estimation of
cost functions in the transportation industry (Caves et al., 1984; Fisher and Kamarshen, 2003).
Indeed, translog production function is flexible enough to approximate the form of function
precisely. The drawbacks of the translog function, on the other hand, are that it has so many
variables, including squared and cross terms.

Therefore, introducing “slope dummy variables”

into the translog would make the function very complex. To avoid this problem, we used a
simple Cobb-Douglas form for specifying the functional form of production function.
In estimating this system equation, we first carried out the regression of IPG function andobtained the estimate value of in-process goods,
standard error. We then let, by OLS with heteroskedasticity robust

be the instrument variable, and regressed FG on,

Transportation, time, and other dummy variables. In the estimation, we considered the existence

6
7

See Bruno (1978).
Griffin et al.(1987).

8
Impact of East-Japan’s disaster on production on small-medium manufacturerof heteroskedasticity, auto-correlation, and normality of random disturbances’ distributions. In
order to check the existence of concerns that might cause inefficiency, we carried out theBreusch-Pagan/Godfrey (BPG) test, Durbin Watson test, and Jarque-Bera normality test. Eq.(2)
had no problem with regard to these concerns, but in Eq.(3) we detected the existence of
auto-correlation by the Durbin-Watson statistic (it was 1.068). Therefore, we estimated Eq.(3)using the AR(1) model. In addition, we estimated the manufacturer’s cargo dispatchingactivities from Koriyama to Hama-dori and compared them to those in other regions. The
descriptive statistics are shown in Table XX.2.
Table XX.2. Descriptive Statistics of continuous variables
Variables

FG (squared meter)

IPG(squared meter)

Labor

Energy(kw/month)

IIP

Transp.

N

35

35

35

35

35

35

Average1058414.2

1152943.4

864.5

68818.9

86.6

390.7

S.D.

146449.3

173053.7

331.5

10766.1

8.4

76.7

Min

812604.7908908.8

111.545798.2

63.2

267

Max

1395830.3

1624970.1

1726.894372.0

103.2

593

Median1047587.01113479.1

862.6

68118.2

87.6

372

XX.5. Empirical Results
XX.5.1. Production function
First, we estimated the system of production functions. The production function of IPG isshown in Table.XX.3.
Table 3.

Production function of in-process goods (paper-sheets)

Variable Name
Labor input
Labor input*post disaster

Coefficient
0.238-0.180t-ratio
3.418-1.802p-value
0.002
0.083

Energy input

0.762

10.9500.000

Energy input*post disaster

0.180

1.802

0.083

Indices of Industrial Production (IIP)

0.658

3.909

0.001

IIP*post disaster-1.300-3.314

0.003

Post disaster dummy for constant

5.118

2.755

0.011

Dummy for March 2011

0.220

2.580

0.016

Dummy for April 2011

0.789

4.995

0.000

Time trend-0.040-1.695

0.102

CONSTANT

0.957

1.343

0.191

R-square adjusted=0.504, F(7,27)=5.326 (P-Value=0.001)
Durbin Watson Statistics=1.690
BPG test for heteroskedasticity: Chi-square=4.023 with d.f=10, P-Value=0.946
JARQUE-BERA NORMALITY TEST- CHI-SQUARE(2 DF)=1.384 P-VALUE= 0.501

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H.Murakami and A.Yanagida

Before the disaster, the input ratio8 of labor and energy was 24:76; after. it changed to 6:94.
This result is consistent with the fact that the firm dismissed part-time employees andreassigned a number of employees to other firms in the same group (keiretsu) after the disaster,and so production became much less labor-intensive. Post-disaster, it appeared that IIP nolonger affected the production of IPG (parameter=0.658-1.300=-0.642 with t-ratio=-1.842).
The parameters of the three “disaster dummy variables” were positive and statisticallysignificant. The dummies for March and April 2011 were significant because Tohoku Kogyo
increased its production on behalf of other firms that were seriously damaged and unable toproduce after the disaster. Despite inter-firm rivalry before disaster, businesses collaborated
with each other to restore the industry following the disaster.
The post-disaster dummy was also positive and significant. Tohoku Kogyo and the firms thatwere not significantly damaged increased production to respond to the increased demand for
cardboard for packing rescue-goods needed by people in the devastated regions and those who
escaped from places contaminated by radioactivity with the aid of the compensating funds from
Tokyo Denryoku (Tokyo Electric Power Company), which was responsible for the managementof the nuclear power plant. In other words, both firm’s budget and iso-quant curves shifted up,
and the input ratio also changed.

In addition, Tohoku Kogyo merged with another factory in

the adjacent region post-disaster. This means that if we had a “capital-stock variable” in our
production function, its parameter would have increased after the disaster. However, eachelement of a “capital-stock variable” takes the same value before disaster, changes once at thetime of disaster, and then takes the same value again. So, it could be expected that the variable
would have shown a result similar to the 0/1 binary variable, and the mixed effect of this and thecompensation from the Tokyo Denryoku appears in the parameter of the Post disaster dummy in
Table 3.

Considering the evidence of Tohoku Kogyo and the result of Fig.1 in section 2 thattells us that the number of firms decreased significantly after the disaster, it is inferred that
small-medium manufactures that survived the disaster increased production to compensate thosewhich damaged firms would have produced.
The production function of final goods is shown in Table.XX.4.

The “DDist” variable, which

was not statistically significant, was dropped to improve the efficiency.
Table XX.4. Production function of final goods
Variable Name

Coefficientt-ratiop-value

IPG (estimate)
IPG (estimate)*post disaster

0.783
0.001

8.837
0.424

0.000
0.672

8 If we use the standardized average of inputs, the input share ratio with negative signs is
the technical rate of substitution.

10
Impact of East-Japan’s disaster on production on small-medium manufacturer

Transportation
Transportation*post disaster
Dummy for March 2011
Time trend
CONSTANT
R-square adjusted
Estimation method

0.217
2.449
-0.001-0.424
0.102
2.429
0.031
2.374
1.527
2.230
0.907
Instrument variable

0.014
0.672
0.015
0.018
0.026

According to Table XX.4, the input ratio of IPG and Transportation had been about 78.3:21.7,
and was largely unchanged post-disaster (78.4:21.6), and the null hypotheses that β
and β

β

0 were rejected with asymptotic

.

β

0

=78.795 and 5.966, respectively.

Considering the results in Tables 3 and 4, the shares of exogenous inputs are shown in
Table.XX.5.
Table.XX.5. Comparison of input shares for the production of final goods
Pre-disasterPost-disaster

Labor
0.186
0.045

Energy0.597
0.739Transportation
0.217
0.216

The labor and the energy shares were computed by multiplying their parameters in the IPG
function by the parameter ofin Eq.(2). This computation makes sense as long as both IPG

and FG functions are properly estimated. Bruno (1978) stated that both depended on theassumption that the double deflated value-added (FG) production functions correctly measured
marginal productivities of primary factors as long as intermediate goods (IPG) relative to gross
output remain constant.9 Since our model followed the assumption of this proposition, we
consider the parameters to show the proper values.
Since our analysis focused on a mostly automated production line, the labor share wasextremely small. Post-disaster, the input share of transportation (trucks utilized) was almost
unchanged. The firm should have spontaneously reduced the input of logistics since it was
forbidden by the government from passing along a few local and thin-density contaminatedroutes, but there is little evidence it did so.

XX.5.2. Destination-specific analysis of disaster’s effect on the delivery of final goods
We will now investigate how the delivery of final goods was affected by the natural disasterusing statistical methods. According to Table 6, it appears that the amount of dispatched final
goods did not change from pre- to post-disaster.

This was supported by the estimate results ofTable 4 that the “DDist” variable was not statistically significant and could be omitted.
To turn to a destination-specific case, Fig.XX.4 depicts the change in traffic flow from
Koriyama-city to Hama-dori (Iwaki-city and Naraha-town).
9

See Bruno, op.cit.

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H.Murakami and A.Yanagida

210
190
170
150
130
110
90
201001
201003
201005201007
201009201011
201101
201103
201105
201107
201109201111
201201
201203
201205
201207
201209201211

70

Fig.XX.4. Traffic flow of final goods to Hama-dori (Iwaki-city and Naraha-cho)Note: The dotted and bold lines are linear-approximations. The dotted line depicts the pre-disaster situation and thebold the post-disaster.

Usually, cargo traffic decreases in winter due to the off-peak demand for vegetables.
this fact, cargo traffic had been trending up when the disaster took place.

DespiteActually the IIP

elasticity of cargo traffic had been 0.854 (t statistics=3.133), but it became -0.146 andstatistically insignificant. This implies that before the disaster the traffic to Hama-dori wasincreasing despite the seasonal change in demand, but after the disaster the traffic had little to
do with the economic boom/recession index, i.e., IIP.
In addition, although the peak demand was in accordance with the peak of the output ofagriculture, the cyclical span was wider and deeper than before the pre-disaster. One of thereasons for this is that rescue and emergency goods were provided in large deliveries with atime lag in between. This implies that goods were not supplied in a timely fashion, i.e., when
they were really needed.
Finally, we investigated the traffic flow from Koriyama-city to Aizu Wakamatsu-city,
Kitakata-city, and Inawashiro-town pre- and post-disaster. These cities are located to the west of
Koriyama-city, within a four-hour drive of Hama-dori.10
Fig. XX.5 shows the traffic flow with a linear approximate line. There were seasonal cycles
based on the harvest of vegetables and fruits from early in summer to autumn, and a weak
upward trend over the observed time duration.

10

Time for using highways is partially included.

12
Impact of East-Japan’s disaster on production on small-medium manufacturer

170
150
130
110
90
70
201211

201209201207

201205

201203

201201

201111

201109201107

201105

201103

201101

201011

201009201007

201005201003

201001

50

Fig.XX.5. Traffic flow of final goods for the west (Aizu Wakamatsu, Kitakata, and Inawashiro)

The reason for this upward trend was that the firm relocated their fleet of trucks from Hama-dorito the Aizu Wakamatu district, where they could expect more stable demand. In order toinvestigate the character of traffic fluctuation further, we applied a brief time-series analysis.
If we assume that this fluctuation curve follows an AR(1) model, the parameter is 0.848 with t-value=5.908. The hypothesis that this parameter equals unity (i.e., the hypothesis of randomwalk) cannot be rejected (t-value=1.062) before the disaster. After the disaster, the parameterchanged to 0.619, and two hypotheses that the parameter equals zero and one are rejected witht-value=3.501 (reject at 1% level) and 2.155 (at 5% level). This implies that the post-disasterfluctuation was stationary. In the case of the entire time span, the parameter is 0.742 and bothhypotheses that the parameter equals zero and one are rejected at 1% and 5% levels(t-value=6.750 and 2.347, respectively).

This result is close to that of the post-disaster case. Insummary, the fluctuation followed an AR(1) process pre- and post-disaster for the entire
duration, and after the disaster, it was converging to stationary. Considering this result, ourconclusion is that the fashion of fluctuation has not changed over time. According to the firm’s
data, the volume of dispatched final goods for the northern area (Sendai-city, Fukushima-city
and Nihonmatsu-city etc.) and southern area (Shirakawa-city, Ishikawa-county andYabuki-town) has not substantially changed, either.

XX.6. Conclusion
This empirical study of a cardboard manufacturer, Tohoku Kogyo, highlighting the production
structure and direction-specific cargo flow before and after the disaster, concludes that the
production structure became energy-intensive but did not otherwise change in terms of the13

H.Murakami and A.Yanagidatransportation input. It is also inferred that Tohoku Kogyo increased its production on behalf ofthe firms that had been damaged by the disaster.

In terms of the direction-specific cargo flow,

only the flow to Hama-dori seems to have been affected. Since the demand for cardboard is a“derived demand”, mainly from the agriculture sector, this result suggests that the agriculture
and other industries that need this firm’s cardboard have also been recovering from the disaster.
In short, it appears that the degree of the disaster has been partially overemphasized beyond thereality: of course small-medium industry just started to revive only recently, especially, but it
appears that undamaged small-medium firms have been active and have tried to produce morethan the disaster occurred.
Since our analysis is limited to the case of a single manufacturer, our study can be regarded asa preliminary analysis only. Our next step will be to continue to research other manufacturersthat are recovering and prepare to disclose their evidence.

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