Est i mat i on Techni que of  I nt ernat i onalI nput - Out put  Model  by Non- survey Met hod
著者Okamot o Nobuhi ro,  Sano Takao,  I nomat a Sat oshi
権利Copyri ght s 日本貿易振興機構（ジェトロ）アジア経済研究所 / I nst i t ut e of  Devel opi ngEconomi es,  J apan Ext ernal  Trade Organi zat i on( I DE- J ETRO)  ht t p: //www. i de. go. j p
j ournal  orpubl i cat i on t i t l e
I DE Di scussi on Paper
vol ume28
year2005- 05- 01
URLht t p: //hdl . handl e. net /2344/330
INSTITUTE OF DEVELOPING ECONOMIES 
Discussion Papers are preliminary materials circulated  to stimulate discussions and critical comments 
        
DISCUSSION PAPER No. 28 
 Estimation Technique of International Input-Output Model by Non-survey Method  Nobuhiro Okamoto1), Takao Sano2) and  Satoshi Inomata3)
May 2005 
            
Abstract  The Asian International Input-Output (IO) Table that is compiled by Institute of Developing Economies –JETRO (IDE), was constructed in Isard type form. Thus, it required a lot of time to publish. In order to avoid this time-lag problem and establish a more simple compilation technique, this paper concentrates on verifying the possibility of using the Chenery-Moses type estimation technique. If possible, applying the Chenery-Moses instead of the Isard type would be effective for both impact and linkage analysis (except for some countries such as Malaysia and Singapore and some primary sectors. Using Chenery-Moses estimation method, production of the Asian International IO table can be reduced by two years. And more, this method might have the possibilities to be applied for updating exercise of Asian IO table. 
Keywords: International Input-Output, non-survey, Chenery-Moses, compilation JEL classification: C67, D57   
1) Institute of Developing Economies-JETRO, 3-2-2, Wakaba, Mihamaku, Chibashi, Chiba, Japan 
2) Gifu Shotoku Gakuen University, 1-38, Nakauzura, Gifushi, Gifu, Japan 
3) Institute of Developing Economies-JETRO, 3-2-2, Wakaba, Mihamaku, Chibashi, Chiba, Japan 
              
The Institute of Developing Economies (IDE) is a semigovernmental, 
nonpartisan, nonprofit research institute, founded in 1958. The Institute 
merged with the Japan External Trade Organization (JETRO) on July 1, 1998.  
The Institute conducts basic and comprehensive studies on economic and 
related affairs in all developing countries and regions, including Asia, Middle 
East, Africa, Latin America, Oceania, and East Europe.  
The views expressed in this publication are those of the author(s).  Publication does 
not imply endorsement by the Institute of Developing Economies of any of the views 
expressed. 
INSTITUTE OF DEVELOPING ECONOMIES (IDE), JETRO 
3-2-2, WAKABA, MIHAMA-KU, CHIBA-SHI 
CHIBA 261-8545, JAPAN  
©2005 by Institute of Developing Economies, JETRO 
1  Introduction 
The Institute of Developing Economies – JETRO (IDE) has compiled the Asian 
International Input-Output Tables for the years, 1975, 1985, 1990 and 1995 (Institute of 
Developing Economies 1982,1992,1998,2001). It is now compiling the table for the year 
2000 in the form of non-competitive import or so called Isard type (Isard 1951). This 
shows the commodity of transaction by country of origin. 
Compilation of the Asian International IO tables needs a lot of time, work, and 
personnel. In the compilation procedure (Tamamura 1994), there are many estimation 
steps such as updating or estimating country’s national table, conducting the survey of 
import commodity inputs, compiling an import matrix, dividing it by country of origin, 
linking each economy’s table, and conducting a number of balancing exercises by IO 
specialists. The survey and primary data provided by each statistical organization in 
each country is especially important in the construction of the international IO table. 
Such construction work is done after the collection and evaluation of results of survey 
and primary data. Since this important information is included in the table, the Asian 
international IO table has been regarded as a survey based table so far.  
When survey based tables are planed to be constructed, a lot of data must be 
collected such as production outputs and intermediate inputs. Further more, the survey 
must be conducted to capture purchase and sales of imported commodities. Although 
the ideal is to conduct such a detailed survey, there is generally not enough time and 
money to allow this kind of data gathering. Even if this kind of survey can be conducted, 
the data of inputs from sectors (countries) for production and those of outputs to sectors 
(other countries) may be inconsistent. Row-wise and column –wise information must be 
reconciled for constructing input-output data; this adjustment must be faced. 
One of the most frequent criticisms of the Asian international IO table may be the 
“time-lag problem.” As seen earlier, a lot of estimation work goes into the construction of 
the Asian international IO table. Thus, six or seven years have passed after the 
reference year when the table is published. The user of this table may do analysis based 
on the assumption of a constant input coefficient and that the data are updated when 
they want to apply the table to an understanding of current economic issues such as the 
economic integration of the East Asia or the environmental impact of China on other 
countries. 
Okamoto and Arakawa (2003) tested the stability of the input coefficient. They 
concluded that the input coefficient was relatively stable but that the input coefficient of 
the economy with a large relation to the international market and primary sectors were 
not so stable. They also mentioned that the RAS method was useful in updating the 
international IO table. Okuda (2003) also proposed the entropy method for updating the 
interregional IO table of Japan. Takagawa and Okada (2004) collected exogenous data 
and trade statistics; they then updated the Asian international IO table by using the 
RAS method in consideration of trade data. 
The RAS method is commonly used for updating1. The entropy method is also one of 
the RAS family. However, these techniques require more information than other 
methodologies. For example, intermediate demand (total output minus final demand) 
and intermediate input (total input minus value added) must be considered. Regional or 
international economists usually cannot collect these exogenous data by country and by 
sector. In such cases, other estimation technique under time and data constraints muste 
be considered. 
Based on the above, the Chenery-Moses (column) model (Chenery 1953; Moses 
1955) is proposed in the paper for updating or estimating the international IO table. 
Additionally, accuracy of its technique is assessed and clarified. 
This paper consists of four parts: (1) the compilation procedure of the Asian 
International IO table, (2) the Chenery-Mose model and a proposed estimation 
technique, (3) the result of the estimation and a discussion of model accuracy, and (4) 
final conclusions and discussion on the future works. 
 2. Compilation Procedure of the Asian International Input-Output Table in IDE 
The compilation procedure of the Asian international IO table in IDE has four steps 
as follows (a detailed explanation is provided in the Annex): 
Step 1: Preparation of a national input-output table in non-competitive import type 
format. 
First, national input-output tables are prepared for the same referential year. 
While the Japanese table is compiled in the year with its last digit being either 5 or 0 
(1995 or 2000, for example), the Chinese table is compiled in years where the last digit 
is 2 or 7 (such as 1997 or 2002). Thus, tables of some countries relative to the same 
referential year must be extrapolated. Second, tables are produced in the form of the 
non-competitive import type. If some tables do not have an import matrix, then with the 
                                                  
1 However RAS method has been criticized. For example, see Polenske (1997). 
collaborating organization/agency in each country, a sample survey is conducted to 
collect effective information about the input/output structure of imported goods. This is 
done to single out the import matrix. 
Step 2: Making of import matrices by countries of origin 
First, data on duties and the import commodity tax are collected, and these 
amounts are removed from the import matrix. Then, the vector for duties and the 
import commodity tax is compiled. Second, the country of origin’s import shares for each 
commodity is computed, and the import matrix of goods is split by the country of origin 
using import share. Finally, a reconciliation of import matrices by country of origin can 
be reconciled if more reliable information on the input structure of imported goods by 
country of origin can be obtained. Since there is no information on country of origin for 
service trades, the import of services is added to the import from the rest of the world. 
Step 3: Conversion of import matrices into CIF and the producer’s price 
So far, all import matrices are valued at CIF, net of duties and import commodity 
taxes. Import matrices from the member countries should be made FOB by removing 
international freight and insurance from each country of origin. All the matrices of 
international freight and insurance are aggregated column-wise into a single row vector, 
and this is placed below the matrices from the member countries. The data on 
international freight and insurance is provided by the collaborating organization/agency 
in each country. When there is no such data, estimation may be based on the existing 
information. 
After import matrices are converted from CIF to FOB, they can be further 
converted into producer’s price. Rates of domestic transportation costs and trade 
margins (TTMs) on the export of the countries of origin are computed. Then, TTMs are 
removed from import matrices country by country. The TTM matrices thus derived are 
aggregated in a column-wise direction country by country in order to obtain TTM 
vectors (rows) for the countries of origin. Finally, the derived TTM vectors are added to 
the corresponding sectors of import matrices as imports of trade and transportation 
services. 
Step 4: Linking and Balancing 
Through the above steps, all components, including domestic transaction or import 
matrices by country of origin, can be prepared and made ready for linking. After due 
aggregation into the Asian IO sector classification, all the parts are linked within the 
Asian IO framework. It should be noted that export vectors to member countries are not 
used, in order to avoid double-counting with corresponding import matrices. 
In principal, figures are balanced in a column-wise direction since they are expanded 
from a single country’s data. The row-wise direction, however, is not balanced since the 
export data of one country is replaced by the import data of its trade partner. Row-wise 
discrepancies occur primarily because of the data inconsistency among the trade 
statistics of the countries concerned. In balancing work, sectors with the error ratios of 
over 5% are checked. Assuming that these errors come from the misclassification of 
items at the HS coding level, “bad” trade figures moved from one sector to another. This 
is based on the coding correspondence of the export country. If the error is due to other 
reasons, the primary data source may be checked and revised to reduce the discrepancy 
using the opinion of experts. 
Past experience in this compilation work indicates that step 1 and step 2 take 
almost four years. Step 3 and 4 are executed in 1 or 2 years. Thus, this kind of 
survey-based table requires 5 or 6 years to complete. 
 3. A Proposed Methodology of an International Input-Output Model 
3.1 Discussion on model 
The Asian international IO table is compiled using the non-competitive import or 
Isard type. This is sometimes called the “interregional IO model.” For estimating the 
Isard type international IO table, a lot of information is required concerning 
distribution or input structure of imported goods by country of origin. Unlike the 
interregional model, the multi-regional model has also been developed. It requires less 
information than the interregional model and needs only the national IO table in 
competitive import type and the trade coefficient. In addition, separation of a country’s 
technical structure from its trade structure allows updates to model to be made more 
easily. Among the various kinds of multi-regional model, Hioki (2003) compared (1) a 
column coefficient model, (2) a row coefficient model, (3) a gravity model and (4) a linear 
programming model, concluded that the column coefficient model (Chenery-Moses 
model) is best.  
The reasons are: (1) non-negativity of Leontief inverse matrix is secured only in the 
column coefficient model. It is possible that negative entries might happen in a Leontief 
inverse matrix when we utilize the latter two models. This is especially the case of the 
row coefficient model, sometimes even leading to negative projection of outputs 
(Polenske 1980, Bon 1984, Toyomane 1988 etc). (2) Performance of the column 
coefficient model is known to be fairly good. Polenske (1970) used sets of Japanese 
interregional input-output table to check the accuracy of these three models. From the 
comparison between the estimated outputs and interregional trade flows by the models 
and the real data listed in the table, she showed that the accuracy of the row coefficient 
model is considerably worse than those of the other two models, and that the column 
coefficient model is almost as accurate as the Leontief-Strout gravity model. In 
Polenske’s MRIO model for the United States, 44regions-78sectors tables of several 
years were estimated based on the regional technical structure and the interregional 
trade structure in 1963. In the work, although they used the Leontief-Strout gravity 
model at first, they could not get the convergence solution and finally changed to use the 
column coefficient model (Polenske 1980). (3) The amount of data necessary for the 
estimation of these 3 models is almost indifferent. (Hioki 2003, pp.68-69) 
In the case of international trade, it is easy to get trade data, so the gravity model 
does not need to be considered. As Hioki (2003) showed in empirical research, the 
column model is better than the row model (see Polenske 1970). 
3.2 Estimation Model 
It is generally impossible for a general economist to construct the international 
input-output table for Asia using a full-survey base or to collect the exogenous data 
required for RAS updating because this work needs a large amount of time, funds, and 
manpower. These are usually far beyond the capacity of a general economist. It is easier 
to use the existing IO table for each country along with trade statistics. Even though a 
country’s IO table has been provided a few years later, trade statistics have usually been 
available after one year. 
Consider the column model of the multi-regional IO model. Assume that there are 
two countries, country one and country two in Asia. Further assume that the country’s 
value added, final demand, total output, technical coefficient, and trade coefficient are 
given. The column input-output model can be represented as follows (Miller and Blair 
1985, pp.69-85): 
⎥ = ⎡⎦
⎥ + ⎡⎦
⎥ + ⎡⎦
CCCC       (1) 
Where 
X R= Total output in country R 
F R = Final Demand in country R 
ER= Export to the Rest of the World of country R 
RA = Technical coefficient matrix of country R 
Cˆ RS= Trade coefficient matrix from country R to country S. (a diagonal matrix of 
coefficients). 
(R, S = 1 and 2 respectively) 
In this model, import from the rest of the world is not considered and will be treated it 
later. 
The elements of the trade coefficient matrix, denoted by , show the proportion 
of goods  used in S that comes from country R. Trade coefficients are derived from the 
transactions going from R to S divided by the total imports of S. This is defined as: 
RSiiRSt
tc   (n=1,2)                   
where  is the amount of goods  moved from country R to country S. With this as a 
background, it is assumed that each sector in the country purchases commodities and 
services from the other country in the same ratio.  
it RSi
   From equation (1), we can derive the followings: 
⎥ + ⎡⎦
⎥ = ⎡⎦
In this model, the trade coefficient plays an important role on the impact of each 
country’s final demand.  
  4. Empirical Study 
4.1 Data and methodology 
As discussed above, a multi-regional IO model can be constructed on the 
assumption that only the technical coefficient, trade coefficient and final demand are 
available. Considering the circumstances of the general economist’s research, it is easier 
to get these kinds of data. However, there are some difficulties in estimating the trade 
coefficient. A methodology for estimating the trade coefficient must be established. Here, 
it is assumed that only the input-output table (competitive import type) and total trade 
(import) statistics by country of origin are available. In the trade statistics, only one 
total is used, not commodity. Although the estimate might be better estimates if trade 
statistics by commodity were used, the bad condition of data must be considered. Model 
accuracy may be verified under the worst data limitation. Import statistics are used 
because this is regarded as more reliable than export data. Import data is carefully 
collected for custom duties in each country. 
The following steps are made to estimate the trade coefficient. First, a trade matrix 
is made by using total import data. Second, a self-sufficient ratio is estimated using 
each country’s IO table by commodity or sector. Finally, the trade (column) coefficient of 
each commodity or sector is calculated according to the self-sufficient ratio of commodity. 
A detail procedure may be shown in Figure 1. 
Two points must be mentioned regarding estimation of the trade coefficient. First, 
Singapore does not have a figure for imports from Indonesia, so the figure for export to 
Singapore from Indonesia is used as an alternative. Second, except for the trade and 
transport sector, it was assumed that no transaction would be occurred in the 
non-commodity sectors of countries2. For estimating trade and transport sector, a total 
commodity self-sufficient ratio was calculated for the country, then a weighted average 
of commodity imports data by country of origin was used for estimating trade 
coefficients of this sector. Here, it was implicitly assumed that trade and transport 
transactions among countries would increase with the increase of the commodity 
transactions. 
After estimating the trade coefficient, the international input coefficient A~  can be 
estimated from the equation (1) as follows: 
⎥ = ⎡⎦
CCCCAAAA 
In this paper, only the coefficient matrix is estimated. After obtaining the data of the 
international input coefficient matrix, the Leontief multiplier can be calculated. In the 
input-output model, impact analysis or linkage analysis is the most commonly used 
approach. The Leontief multiplier is derived from the input coefficient. It plays a more 
important role in model accuracy. 
                                                  
2 When we compile Asian table, we also make no international transaction in non-commodity sectors among countries because of lack of service trade direction data. 
Figure 1  Estimating Trade Coefficient (Case of 3 countries)  
       
 Step 1 Compiling trade matrix    
       
 Import data     
   A B C   
 A sAmABmAC  
 B mBAsBmBC  
 C mCAmCBsC  
 ROW mRAmRBmRC  
 Total m0Am0Bm0C  
       
 Note: Singapore's total export consists of domestic exports and re- exports. 
       
 Step 2 Estimating the Self- sufficient ratio (Diagonal cell)  
       
 s = 1- (imports/intermediate and final demand)   
 :::estimated from IO data     
       
 Step 3 Calculation of column trade coefficient   
       
 column coeffient calculation    
   A B C   
 A sAmAB/m0B*(1- sB) mAC/m0C*(1- sC)   
 B mBA/m0A*(1- sA) sBmBC/m0C*(1- sC)   
 C mCA/m0A*(1- sA) mCB/m0B*(1- sB) sC  
 ROW mRA/m0A*(1- sA) mRB/m0B*(1- sB) mRC/m0C*(1- sC)   
 Total 1 1 1   
 Trade coefficient      
   A B C   
 A cAAcABcAC  
 B cBAcBBcBC  
 C cCAcCBcCC  
 ROW cRAcRBcRC  
 Total 1 1 1   
To verify the model accuracy, several comparison methods are used. For impact 
analysis, the Leontief-Carter method (Leontief 1953; Carter 1970) is applied as in 
Okamoto and Arakawa (2003). The total output is calculated by using the estimated 
international input coefficient multiplied by original final demand. This is then 
compared it with original output. Two error indices, the Overall Percentage Error (OPE) 
and Correlation Coefficient (CC), are used. 
For linkage analysis, the estimated input coefficient is compared with the original. 
Many kinds of comparison methods have been developed, and Lahr (2001) discusses the 
characteristics of each. Since each index has its own character, all indices are used as 
follows: 
【Standardized total percentage error (STPE) 】 
∑ ∑∑ ∑−
jiijija
aa0100 
【Mean Absolute Difference (MAD) 】 
aajiijij∑ ∑−0100 
【Index of inequality (Theil’s U)】 
⎡−∑ ∑∑ ∑
jiijij
【Root mean squared error (RSME) 】 
aaijij
5.00 2∑ ∑−
【Weighted absolute difference (WAD) 】 
()∑ ∑∑ ∑
jiijij
jiijijijij
Lahr (2001) recommends the WAD. Considering holistic accuracy (Jensen 1980), the 
original error index is proposed as follows: 
【Weighted total percentage error (WTPE) 】 
∑ ∑∑ ∑•−
jiijijija
aaa0100 
4.2 Result 
In conducting empirical research, the international input coefficient was estimated 
with 10 countries and 24 sectors for the year 1995. First, input-output tables are 
collected from ten countries using competitive import type with 24 sectors for the year 
1995. These are re-compiled from the Asian IO table, and then various kinds of trade 
coefficients are estimated by using 1995 trade data3 as follows: 
Case 1. Total import data 
Case 2. Total import data adjusted with international freight and insurance 
Case 3. Import data by sectors 
Case 4. Import data by sectors adjusted with international freight and 
insurance  
As seen in the previous section, under usual circumstances, it is easy for an 
economist to get total import statistics from the IMF or OECD. Case 1 shows the worst 
data condition. Since import data is valued at CIF prices, including international 
freight and insurance, trade among countries may be overestimated. Therefore total 
import data must be adjusted by the formulae CIF/(1+r), where r is international freight 
and insurance rate; r is assumed to be 0.06 according to experience in compilation of the 
Asian international IO table. This is seen in Case 2.  
The international transaction of primary goods seems to be different from that of 
manufacturing goods. Resources of agriculture, forestry, fishery and mining are located 
in relatively unbalanced fashion in Asian countries. This may lead to some bias or 
errors in estimation when only total import data is used. Thus, in Case 3 the import 
figures by sector (only commodity) are collected. In Case 4, the trade coefficient is 
estimated from import figures by sector and adjusted with international freight and 
insurance. 
4.3 Total Output 
Table 1 reports the overall percentage error (OPE) of the model in the four cases. 
The original output is compared with the estimated output derived from the estimated 
Leontief multiplier. This is calculated from the international input coefficient, with the 
original final demand of each country. 
                                                  
3 Trade statistics comes from Noda ed. (2003). They are compiled from the data of 
United Nations and OECD. 
Table 1. OPE and CC by country       
  OPE CC 
  Case 1 Case 2Case 3 Case 4 Case 1Case 2Case 3 Case 4
Indonesia 0.63% - 0.05%1.95%1.20%0.9988 0.9962 0.9986  0.9987 
Malaysia 6.38% 3.90%5.22%2.83%0.9780 0.9780 0.9949  0.9957 
Philippine 3.16% 2.40%1.81%1.14%0.9923 0.9923 0.9899  0.9899 
Singapore 11.98% 9.40%9.84%7.40%0.9859 0.9859 0.9974  0.9982 
Thailand 1.60% 0.77%0.71%- 0.05%0.9955 0.9955 0.9976  0.9976 
China 5.27% 4.61%4.66%4.03%0.9988 0.9988 0.9961  0.9964 
Taiwan 8.85% 7.46%- 0.02%- 0.78%0.9977 0.9977 0.9987  0.9985 
Korea 2.56% 1.89%2.65%1.98%0.9989 0.9989 0.9995  0.9995 
J apan 1.01% 0.72%0.82%0.55%0.9999 0.9999 0.9999  0.9999 
US - 0.88% - 1.03%- 1.07%- 1.20%0.9998 0.9998 0.9998  0.9998 
Overall/Average 0.66% 0.35%0.30%0.01%0.9945 0.9943 0.9972  0.9974 
This table shows that OPEs of all countries tend to decrease with increases in 
availability of trade data and international freight and insurance. In Case 1, this is 
0.66%, and it decrease to 0.35% in Case 2, to 0.30% in Case 3, and to 0.01% in Case 4. At 
the country level, Singapore, Malaysia, and China have relatively large OPE in all cases. 
Singapore and Malaysia are some of the most open economies, and their re-exports also 
should be higher than other regions. Thus, the trade coefficient might be relatively 
difficult to estimate by using only import data. This also implies that the trade 
structure by sector may be different from the whole trade direction in this region. China 
has implemented an open door policy for two decades and has had remarkably very high 
economic development by exporting to other countries. Hong Kong played an important 
role in China’s exports as a trade port. Thus, trade statistics may be problematic 
relative to other countries such as Japan and the USA. Using only import data may not 
give good fit to the estimates. Taiwan also has a relatively large OPE in both Case 1 and 
Case 2. However, this OPE becomes almost 0% in Case 3 and Case 4.  
The correlation coefficient (CC) is also shown in Table 1. In Case 1 and Case 2, this 
is estimated from total import data only. Here, the CCs of Malaysia and Singapore are 
relatively low, the same as the tendency of OPE. On the other hand, the CCs of China 
and Taiwan are high. It seems that the trade structure of Malaysia and Singapore may 
be different from the direction of total imports. Further, China and Taiwan may have 
been totally over or under estimated by sector. Case 3 and Case 4 indicate that total 
output by country may be better estimated if import data by sector is used. 
Table 2. Percentage error of Total Output by Sector and Economy
Case 1
 IndonesiaMalaysiaPhilippinesSingaporeThailandChinaTaiwanKoreaJ apanUSA
Paddy1.3%- 1.1%1.1%na- 8.2%3.0%- 0.4%4.2%4.1%naOther agricultural products3.0%46.1%6.7%750.3%4.6%5.0%39.2%17.8%15.7%- 6.9%Livestock2.0%6.9%1.4%162.9%- 4.5%3.6%3.0%3.5%5.7%0.8%Forestry- 10.7%- 54.4%21.5%na48.2%12.6%158.4%45.1%22.1%- 3.1%Fishery2.4%15.9%- 2.2%75.7%2.2%2.5%9.1%0.3%4.5%4.6%Crude petroleum and natural gas  - 35.1%10.6%737.0%na229.1%54.1% 1047.6%na 1626.4%20.2%Other mining- 6.1%180.0%11.0%1495.0%26.4%9.0%81.5%27.7%17.4%10.6%Food, beverage and natural gas1.2%- 4.8%1.0%41.3%- 8.8%4.2%0.3%4.2%4.6%0.9%textile, leather and such products 12.7%67.2%32.3%164.4%5.2%8.1%19.0%3.7%1.2%- 5.3%Timber and wooden products- 25.9%- 48.6%9.0%120.8%22.9%10.1%55.8%21.3%6.0%0.7%Pulp, paper and printing 4.9%58.7%45.0%49.9%13.2%12.9%21.4%8.8%3.8%- 0.6%Chemical products26.7%69.5%41.5%42.5%50.6%11.5%33.9%7.4%4.3%0.1%Petroleum and petro products- 4.8%24.9%2.1%- 53.4%13.0%10.2%29.4%2.7%6.3%1.1%Rubber products- 11.1%- 15.6%0.6%133.4%- 34.1%11.0%29.3%1.4%1.2%- 3.7%Non- metallic mineral products6.4%7.7%13.7%34.1%- 0.5%1.6%3.8%0.6%1.3%- 0.8%Metal products27.4%64.2%9.2%102.0%44.7%4.7%8.7%- 0.7%2.9%3.8%Machinery19.7%- 10.4%29.3%- 9.5%- 3.5%2.4%4.0%- 7.1%0.6%0.0%Transport equipment10.1%49.4%72.8%54.6%11.7%9.6%23.5%8.5%0.0%- 0.2%Other manufacturing products19.9%30.4%48.4%50.9%10.6%7.8%8.8%- 0.3%- 1.3%- 2.4%Electricity, gas, and water supply2.5%6.9%0.3%7.1%2.8%5.5%6.7%1.5%1.0%- 0.4%Construction0.0%- 0.1%- 0.2%1.0%0.0%0.3%0.8%0.1%0.1%- 0.1%Trade and transport- 4.2%- 3.2%- 16.8%2.4%- 9.9%1.7%- 1.2%2.7%- 2.2%- 6.9%Service0.5%- 2.6%- 3.2%5.3%- 1.4%5.1%5.6%1.8%0.7%- 0.3%Public administration0.0%0.9%0.0%1.1%0.0%0.0%0.0%0.0%0.0%0.0%Overall error0.6%6.4%3.2%12.0%1.6%5.3%8.8%2.6%1.0%- 0.9%
Case 4
 IndonesiaMalaysiaPhilippinesSingaporeThailandChinaTaiwanKoreaJ apanUSA
Paddy0.4%- 4.5%1.3%na- 3.8%3.0%- 3.8%1.7%3.0%naOther agricultural products1.4%2.4%8.5%36.2%1.1%4.8%17.1%12.3%8.9%- 2.1%Livestock0.7%7.8%1.0%15.9%- 3.0%5.1%- 2.3%0.1%2.2%0.8%Forestry12.5%14.8%- 9.9%na21.8%5.5%43.0%7.7%10.9%- 1.9%Fishery5.5%0.6%- 1.1%2.1%25.5%1.0%8.9%- 1.4%1.0% - 10.8%Crude petroleum and natural gas  9.5%11.6%65.0%na6.9%6.9%2.0%na3.3%- 1.0%Other mining3.8%- 2.9%28.0%79.4%0.6%5.0%- 9.4%- 3.1%- 0.5%0.4%Food, beverage and natural gas0.4%- 7.1%1.3%5.1%- 3.6%3.7%- 2.5%1.7%3.4%1.2%textile, leather and such products17.8%24.9%42.0%25.9%6.1%17.4%6.3%15.5%- 4.9%- 9.4%Timber and wooden products16.5%- 0.9%8.3%21.9%20.3%7.5%36.7%6.5%1.0%- 0.9%Pulp, paper and printing 2.2%3.9%9.2%19.0%- 9.7%4.7%- 3.5%3.0%0.8%0.1%Chemical products7.0%0.3%12.1%13.6%17.6%8.5%- 11.6%8.2%1.9%0.5%Petroleum and petro products- 0.8%17.8%- 3.2%34.9%1.2%4.3%0.4%6.1%2.5%- 1.0%Rubber products17.0%11.7%- 8.9%31.9%27.0%3.9%5.8%- 1.4%2.0%- 5.5%Non- metallic mineral products1.3%- 2.5%4.4%- 1.9%0.5%1.9%- 3.4%- 0.4%1.5%- 1.9%Metal products4.9%0.1%- 4.9%19.7%9.1%2.9%- 4.6%2.6%2.1%0.3%Machinery- 20.6%7.2%56.1%10.1%2.4%- 2.1%- 2.0%- 3.2%3.6%0.8%Transport equipment- 0.3%7.6%14.5%- 6.7%2.7%- 0.4%5.2%4.0%1.9%0.2%Other manufacturing products3.0%19.2%34.5%22.4%12.4%13.4%0.3%- 4.4%- 2.1%- 2.1%Electricity, gas, and water supply0.9%1.1%- 0.8%6.7%0.2%3.5%- 1.9%1.6%0.4%- 0.8%Construction0.0%- 0.5%- 0.2%0.5%0.0%0.2%0.2%0.1%0.0%- 0.2%Trade and transport- 5.5%- 6.2%- 18.2%- 1.0%- 10.7%0.3%- 7.9%2.0%- 2.5%- 7.1%Service0.6%- 4.0%- 3.8%3.6%- 2.7%4.4%2.8%1.6%0.5%- 0.3%Public administration0.0%0.0%0.0%0.4%0.0%0.0%0.0%0.0%0.0%0.0%Overall error1.2%2.8%1.1%7.4%- 0.1%4.0%- 0.8%2.0%0.5%- 1.2%
In order to clarify the estimation errors of output by sector level and by country, the 
percentage error of total output by countries is checked and shown in Table 2. It can be 
seen that the primary sector, especially crude oil and other mining, does not show good 
results when total import data is used. Errors of total output by sector in Malaysia and 
Singapore show that the trade structure by sector is different from the total import data. 
Therefore, this does not provide such good estimates at the sector level. On the other 
hand, outputs of China and Taiwan are totally overestimated at the sector level, so, the 
CC index becomes high as OPE is high. 
Table 2 shows the percentage error by sector (Case 4). On the whole, estimated 
results improved by sector level. Singapore, however, showed small improvement at this 
level. 
4.4 International Input Coefficient 
In input-output analysis, the input coefficient plays a key role in calculating the 
Leontief inverse matrix. Its accuracy determines the reliability of the input-output 
model. 
    Table 3 shows error indices of the international input coefficient. From this table, it 
can be seen that every index has the same tendency. So emphasis is placed on the STPE 
and WTPE. For both indices, Case 1 and Case 2 show these to be relatively large, but in 
Case 3 and Case 4, they are rather small. Using import data by sector may produce 
better results than using total import data. In Case 2 and Case 4, such improves the 
result. It is very important to adjust trade data by the international freight and 
insurance ratio. The WTPE in Case 4 is somewhat larger than in Case 3. This implies 
that using the same freight and insurance ratio may influence relatively important cells. 
As a whole, this information shows us that the Chenery-Moses model is effective even if 
total import data is used. 
Table 3. Error indices of the international input coefficient 
   Case 1Case 2Case 3Case 4  
 STPE 30.3562 29.6680 25.4097 24.9337  
 MAD 0.0585 0.0572 0.0490 0.0480  
 Theil's U 0.2810 0.2780 0.2459 0.2449  
 RSME 0.0000 0.0000 0.0000 0.0000  
 WAD 0.0149 0.0149 0.0142 0.0142  
 WTPE 1.4685 1.4725 1.3947 1.3973  
5. Conclusion 
Okamoto and Arakawa (2003) have questioned the stability of the input coefficient 
of the Asian International IO table and arguer for the use of the Leontief-Carter method 
to quantify the stability of the input coefficient. Compared with their result, the model 
estimated by the Chenery-Moses type seems to give us better estimates than in using 
the previous or old table. It can also be concluded that this estimation method would be 
useful for an economist. In short,  
(1) Applying the Chenery-Moses instead of the Isard type appears to be effective in both 
impact and linkage analysis. Even total import data is only used, the estimation 
may be better. 
(2) As in the conclusions of Okamoto and Arakawa (2003), economies with large 
relations to the international market, such as Singapore and Malaysia may not 
provide better estimates. 
(3) In the case of the Asian IO model, the structure of the primary sector may provide 
worse results. 
(4) Gathering more data (here gathering trade data by sector), makes a contribution to 
the higher accuracy of the international IO model. 
(5) In the Asian IO, the 24 sector classification includes many primary sectors (7 
sectors), and service sector is highly aggregated. This affects model accuracy 
because only total trade statistics is used. The model might give better results if 
sector classification is changed with the inclusion of more manufacturing sectors. 
With experience in the compilation of the Asian international IO table, it appears 
that at least two years is necessary for estimating the import matrix by country of origin 
for each country, linking these matrices, and balancing. Based on empirical work 
presented here, more than two years can be saved getting the Asian international IO 
model when the Chenery-Moses estimation method is used. This method may also be 
applied in updating the Asian IO model. Trade data is revised annually, so the trade 
coefficient can be updated every year. If the technical coefficient is assumed to be stable 
in each country, a new Asian international IO model estimated annually by using 
updated trade coefficient4.  
In estimating the international input-output model, the worst situation was 
considered in which no survey can be conducted, and only national IO table and trade 
statistics can be obtained. Considering this worst situation, the worst limitation of this 
                                                  
4 The problem of how to update the self-sufficient ratio of each country remains. Trade statistics do not provide the information on the transaction volume in each country. 
methodology can be understood. If even a, partial and crucial, survey can be conducted, 
or superior data from some agency can be obtained, it is obvious that the accuracy of the 
estimated model will be better. 
References 
1. Bon, R. (1984) "Comparative Stability Analysis of Multiregional Input-Output 
Models: Column, Row, and Leontief-Strout Gravity Coefficient Models," Quarterly 
Journal of E onomics, 99(4),pp.791-815. c
2. Carter, A.P. (1970) Structural Change in the American Economy, Cambridge: 
Harvard University Press  
3. Chenery, H.B. (1953), ‘Regional Analysis, in H.B. Chenery, P.G. Clark and V.C. 
Pinna (eds), The Structu e and Growth of the Italian E onomy (US Mutual Security 
Agency, Rome), 91-129 
4. Hioki, S. (2003) Hioki, S. (2003a) ‘Chugoku Tachiikikan Sangyorenkan Moderu 
niokeru Moderu Sentaku to Chiikikan Koueki Suikei,’ (The Model Selection and the 
Estimation of Interregional Trade Flows in the Multi-regional Input-Output Model 
for China), in Okamoto, N.(ed) (2003), pp. 21-48 
5. Institute of Developing Economies (1982) International Input-Outpu  Table for 
ASEAN Countries, 1975, I.D.E. Statistical Data Series No.39. Tokyo: Institute of 
Developing Economies  
6. Institute of Developing Economies (1992) Asian  International  Input-Outpu   
Table，1985, I.D.E. Statistical Data Series No.65. Tokyo: Institute of Developing 
Economies 
7. Institute of Developing Economies (1998) Asian  International  Input-Outpu   
Table，1990, I.D.E. Statistical Data Series No.81. Tokyo: Institute of Developing 
Economies 
8. Institute of Developing Economies (2001) Asian  International  Input-Outpu   
Table，1995, I.D.E. Statistical Data Series No.82. Tokyo: Institute of Developing 
Economies 
9. Isard, W. (1951), ‘Interregional and Regional Input-Output Analysis: A model of a 
Space Economy’, Review of E onomics and Statistics, 33, 318-28  
10. Jensen, R.C. (1980) ‘The concept of Accuracy in regional input-output models’, 
International Regional Science Review, 5, pp. 139-54 
11. Lahr, M.L. (2001), ‘A Strategy for Producing Hybrid Regional Input-Output Tables’, 
in Lahr, M.L. and E. Dietzenbacher eds, Input-Output Analysis: Frontiers and 
Extensions, Palgrave 
12. Leontief, W. et al. (1953) Studies in the Structu e of the American E onomy, New 
York: Oxford University Press 
13. Miller, R and P.D. Blair (1985) Input-Output Analysis: Foundations and Extensions, 
Prentice-Hall, Englewood Cliffs, NJ. 
14. Moses, L.N. (1955) ‘The Stability of Interregional Trading Patterns and 
Input-Output Analysis, American Economic Review, 45, 803-32 
15. Noda, Y. ed. (2003) World T ade Matrix Revised: by Asian International 
Input-Output Table 24 Sectors, Statistical Data Series No.84 Chiba: Institute of 
Developing Economies-JETRO 
16. Okamoto(ed) (2003) Chugoku no Chiikikan Sangyoko---Chiikikan Sangyo Renkan 
Bunseki---(Ⅱ), (Interregional Industrial Structure in China---Interregional 
Inout-Output Analysis (Ⅱ)), Asian Input-Output Series No. 63, Chiba: Institute of 
Developing Economies-JETRO. 
17. Okamoto, N. and S. Arakawa (2003) ‘A Note on the Stability of Asian International 
Input-Output Tables’ in Nakamura and Arakawa eds, Kokusai Sangyo Renkan – 
Ajia Syokoku no Sangyo Renkan Kozo (II) (International Input-Output Analysis:
Inter-industrial structure in Asian Region (II)), Asian Input-Output Series No.62, 
Chiba: Institute of Developing Economies-JETRO.  
18. Okuda, T. (2003) ‘An Updating Method of Inter-regional Input-Output Tables 
Utilizing Entropy Optimization,’ Paper presented at 17th ARSC conference. (in 
Japanese) 
19. Polenske, K.R. (1970) "An Empirical Test of Interregional Input-Output Models : 
Estimation of 1963 Japanese Production." American Economic Review Vol.60, No.1, 
pp.76-82. 
20. Polenske, K.R. (1980) The U.S. Multiregional Input-Outpu  Accounts and Model. 
Lexington Books. 
21. Polenske, K.R., (1997) ‘Current Uses of the RAS Technique: A Critical Review’, in A. 
Simonovits and A.E. Steenge (eds), Prices, Growth and Cycles (Macmillan, London), 
58-88 
22. Takagawa, I. and T. Okada (2004) ‘Input-Output Study in the Asia-Pacific Region: 
An Analysis Based on a New Method for Updating Input Coefficients,’ Bank of 
Japan Working Paper Series 04-J-6 (in Japanese) 
23. Tamamura, C. (1994) ‘Conceptual Framework and Compilation Procedure of 
Bilateral I-O table,’ in Sano, T. and Tamamura, C. (1994) 
Kokusai-sangyorenkan-hyo n  riyo t  sakusei (V) (The Usage and Compilation of 
International Input-Output Tables (V)), Asian International IO series No. 34, 
Institute of Developing Economies: Tokyo  
24. Toyomane,N. (1988) Multiregional Input-Output Models in Long-Run Simulation. 
Kluwer Academic Publishers. 
Appendix Table:  Comparison of estimated and original output by country in Case 1
Indonesia
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
Malaysia
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
Philippines
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
Singapore
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
Thailand
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
120,000,000
140,000,000
160,000,000
180,000,000
200,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
Taiwan
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
120,000,000
140,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
50,000,000
100,000,000
150,000,000
200,000,000
250,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
J apan
500,000,000
1,000,000,000
1,500,000,000
2,000,000,000
2,500,000,000
3,000,000,000
3,500,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
500,000,000
1,000,000,000
1,500,000,000
2,000,000,000
2,500,000,000
3,000,000,000
3,500,000,000
4,000,000,000
4,500,000,000
5,000,000,000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24Sector
1000 US$
Original
Estimated
AnnexCompilation Procedure of Asian International Input- Output Table
Asian International Input- Output Table
<STEP 1>  Compute duties and import  commodity taxes ratios.
001002
<STEP 2>  Remove duties and  import commodity taxes.
<STEP 3>  Aggregate the duties and  import commodity taxes  matrix into a single row  vector.
<STEP 4>  Compute country- of-  origin's import shares  for each commodity.
<STEP 5>  Split the import matrix  of goods into 11 countries  of origin plus the Rest  of the World, using  the  import shares derived in  the STEP 4.
<STEP 6>  Assemble the parts thus derived, i.e.,   ① goods import matrices from 11countries of origin,  ② the import matrix from the Rest of the World  embracing import of services from all countries,  ③ and the vector of Duties & Import commodity taxes.
  Then, convert these parts into AIO classfication,  using the NIO -  AIO converter.
001002
301302
GoodsImportCIF excludingDuties & Taxes
Duties & Taxes (vector))))
Duties &Import commodity taxes(matrix)
Duties & Taxes (vector)
Service Import
Service Import
Goods ImportCIF includingDuties & Imp. Com. taxes
SUMUP!
SUMUP!
GoodsImport(CIF)from ROW
Service Import
Goods Import (CIF)from INDONESIA
Goods Import (CIF)from MALAYSIA
Goods Import (CIF)from THE PHILIPPINES
Goods Import (CIF)from SINGAPORE
Goods Import (CIF)from CHINA
Goods Import (CIF)from TAIWAN
Goods Import (CIF)from KOREA
Goods Import (CIF)from J APAN
Goods Import (CIF)from THE USA
Goods Import (CIF)from HONG KONG
Goods Import (CIF)from EU
Goods Import (CIF)from THAILAND
Service Import
Goods Import (CIF)fromTHE REST OF THE WORLD(((
・・・・・・
Of course,there is noimport matrixfrom your own.
Duties &Comm. taxes
00National Tablenon-competitive
00Import vector(column: referringto import matrix)
10Duties andImp.Com.Taxes(column)
30Supplementaryinformation onimport demand
11Duties andImp.Com.Taxesratios vector (column)
<STEP 1:                >Computation ofDuties & Imp.Com.Taxes ratios
<STEP 2:              >Separation of Duties &Imp.Com.Taxes fromimport matrix
01National TablewithImport matrixCIF
Row totalsColumn totals
Macro- valuesand balancecheck
12Duties &Imp.Com.Taxesvector (row)
11Duties andImp.Com.Taxesmatrix
<STEP 3:              >Column- wise aggregationof Duties & Imp.Com.Taxesmatrix
<STEP 5:              >Demarcation of importmatrix into 11 countriesof origin plus ROW (as aresidual)
20Import vectorsby 11 countriesof origin
02National Tablewith 12 Importmatrices CIF
<STEP 4:              >Computation of country-of origin's import shares bycommodity
2111 Countries-of- origin importshares bycommodity
<STEP 6:              >Aggregation/Disaggregationof NIO to AIO classification
50Separationratios of CTs
40Code converterNIO to AIO
J OB 2
03AIO Table with12 Importmatrices CIF
13AIO Duties &Imp.Com.Taxesvector (row)
J OB 5
J OB 2: CONVERSION OF IMPORT MATRICIES into FOB
So far, all the import matrices are valuedat CIF, net of duties and import commoditytaxes
<STEP 1>  Import matrices from the member  countries should be made FOB,  by removing international freight   and insurance from each country  of origin.
<STEP 2>  All the matrices of international freight  and insurance are aggregated columnwise  into a single row vector, which is to be  placed below the import matrix from   the USA.
Import fromINDONESIA, CIF
Import fromMALAYSIA, CIF
Import frometc., CIF
International freightand insurance on importfrom INDONESIA(matrix)
ImportfromINDONESIA,FOB
Int. freight &insurancefrom INDO-NESIA
ImportfromMALAYSIA,FOB
Int. freight &insurancefrom MAL-AYSIA
Importfrometc.,FOB
Int. freight &insurancefrom etc.
International freightand insurance on importfrom MALAYSIA(matrix)
International freightand insurance on importfrom etc.(matrix)
Int. Freight & Ins. (vector))))
Import fromHong Kong,CIF
SUMUP!
SUMUP!
Import fromthe Rest of the World,CIF
Import fromthe EU,CIF
ImportfromUSA,FOB
Int. freight &insurancefromUSA
Import fromHong Kong,CIF
Import fromthe Rest of the World,CIF
Import fromthe EU,CIF
03AIO Table with12 Importmatrices CIF
60AIO InternationalFreight &Insurance ratios
<STEP 1:              >Separation of InternationalFreight & Insurancefrom import matrices
62AIO Int.Freight& Insurancevector (row)
61AIO Int.Freight &Insurancematrices
<STEP 2:             >Column- wise aggregationof Int.Freght & Insurancematrices
04AIO Table withImport matrices9 matrices = FOB3 matrices = CIF
J OB 3
J OB 5
J OB 3: CONVERSION OF IMPORT MATRICIES into producer's price
<STEP 1>  The ratios of domestic transportation costs and  trade margins (TTMs) on the export of  the countries of origin are computed.
<STEP 2>  TTMs are removed from import matrices,  country by country, using the ratios  computed in the STEP 1.
<STEP 3>  The TTM matrices  thus derived are  aggregated columnwise  country by country,  to obtain TTM vectors (rows)  for the countries of origin.
<STEP 4>  The TTM vectors thus derived  are added on the corresponding   sectors of import matrices,  being as imports of trade and  transportation services.
Columnwise aggregation bycountry into two row vectors.
DomesticTTMsINDONESIA's trade marginson its export  (matrix)
INDONESIA's domesitctransportation coston its export (matrix)
ImportfromINDONESIA,Prod. price
DomesticTTMsMALAYSIA's trade marginson its export  (matrix)
MALAYSIA's domesitctransportation coston its export (matrix)
ImportfromMALAYSIA,Prod. price
DomesticTTMsetc.
Importfrometc.,Prod. price
Import fromINDONESIA, Prod. price
Import fromMALAYSIA, Prod. price
Import frometc., Prod. price
INDONESIA's TTM
MALAYSIA's TTM
Import fromthe Rest of the World,CIF
Import fromthe EU,CIF
065066
065066
065066
04AIO Table withImport matrices9 matrices = FOB3 matrices = CIF
71AIO TTM ratiosof ctrys of originfor export
<STEP 2:              >Separation ofTTM matrices
73AIO TTM vectors(row)
72AIO TTMmatrices
<STEP 3:              >Column- wise aggregationof TTM matrices, country bycountry
05AIO Table withImport matriceswithout TTM
<STEP 4:              >Addition of TTM vectorson the import matrices,country by country
06AIO Table withImport matricesProducer's price
00Export vectorof country oforigin (referringto national table)
70TTM on exportof country oforigin
<STEP 1:             >Computation ofTTM ratios of countries oforigin for export
50Code converterNIO to AIO
J OB 5
50Separationratios of CTs
J OB 4: MAKING OF EXPORT VECTORS by countries of destination
<STEP 1>NIO 001
  TTM ratios for export are computed.NIO 002
<STEP 2>  Remove TTMs from the export vectors to  11 countries of destination, using the ratios  derived in STEP 1.  (The number of matrices to be removed is   subject to the definition of TTM.)
<STEP 3>  Aggregate each TTM matrix in a columnwise  direction to obtain a corresponding TTM vector.
<STEP 4>  Assemble the parts thus derived  to obtain export vectors at producer's price,  with TTMs being export of domestic trade and  transportation services.
<STEP 5>  Derive the export vector to the Rest of  the World, by taking the difference between  the export vector of a national I- O (producer's  price) and the row- totals of the export vectors  obtained in Step 4 above.
  * The export vector of a national table  should be valued at producer's price.  Also, if direct sales to tourists and/or  any other international transaction that do   not pass the custom (e.g. service export)  are presented in separate vectors,  they should be added to the export vector  to the Rest of the World.
<STEP 6>  Convert these export vectors into   AIO classification using the NIO -  AIO converter.
HONG KONG
J APAN
TAIWAN
THAILAND
SINGAPORE
PHILIPPINES
MALAYSIA
INDONESIA
EXPORT VECTOR
Wholesale trade (matrix)
・・・ etc. (matrix)
Retail trade (matrix)
Water transport (matrix)
Road freight transport()
Railway freight transport (matrix)
Wholesale trade (vector)Retail trade (vector)Road freight transport (vector)Railway freight transport (vector)Water transport (vector)  ・・・etc. (vector)
HONG KONG
J APAN
TAIWAN
THAILAND
SINGAPORE
PHILIPPINES
MALAYSIA
INDONESIA
11 countries totals
EXPORT  TO ROW
NIO export vectors to 11 countries of destination,aggregated from the Foreign Trade Statistics (FOB).(Of course, there is no export vector to your own.)
(Row Totals)
Columnwise aggregation byeach TTM sector.
80Export vectorsby 11 ctrys ofdestination (FOB)
74TTM ratiosfor export
<STEP 2:              >Separation ofTTM matrices
76TTM vectors(row)sector by sector
75TTMmatrices
<STEP 3:              >Column- wise aggregationof TTM matrices,sector by sector
81Export vectorsby 11 destinationwithout TTM
<STEP 4:              >Addition of TTM vectorson the export vectors
82Export vectorsby 11 destinationProducer's price
00Export vector(s)(referring tonational table)
70TTMfor export
<STEP 1:              >Computation ofTTM ratios for export
82Export vectorsby 11 destinationProducer's price
00Export vector(s)(referring tonational table)
<STEP 5:              >Derivation of the exportvector to the ROW
83Export vectorto the ROWProducer's price
<STEP 6:              >Aggregation/Disaggregationof NIO to AIO classification
50Separationratios of CTs
40Code converterNIO to AIO
84AIO Export vectorsto EU, Hong Kong,and the ROWProducer's price
J OB 5
AIO Export vectorsto member countriesProducer's price
J OB 5: LINKING
Intermediate Demand (A)Final Demand (F)Export (L)
Indonesia
Malaysia
Philippines
Singapore
Thailand
Taiwan
U.S.A.
Indonesia
Malaysia
Philippines
Singapore
Thailand
Taiwan
U.S.A.
Export toHong Kong
Export toEU
Export toR.O.W.
tat st caDiscrepanc
TotalOutputs
code(AI)(AM)(AP)(AS)(AT)(AC)(AN)(AK)(AJ)(AU)(FI)(FM)(FP)(FS)(FT)(FC)(FN)(FK)(FJ)(FU)(LH)(LO)(LW)(QX)(XX)
Indonesia(AI)AIIAIMAIPAISAITAICAINAIKAIJAIUFIIFIMFIPFISFITFICFINFIKFIJFIULHILOILWIQIXI
Malaysia(AM) AMIAMM AMP AMS AMT AMC AMN AMK AMJ AMUFMIFMPFMSFMTFMCFMNFMKFMJFMU LHM LOM LWMQMXM
Philippines(AP)APIAPM APPAPSAPTAPCAPNAPKAPJAPUFPIFPMFPPFPSFPTFPCFPNFPKFPJFPULHPLOP LWPQPXP
Singapore(AS)ASIASM ASPASSASTASCASNASKASJASUFSIFSMFSPFSSFSTFSCFSNFSKFSJFSULHSLOS LWSQSXS
Thailand(AT)ATIATMATPATSATTATCATNATKATJATUFTIFTMFTPFTSFTTFTCFTNFTKFTJFTULHTLOT LWTQTXT
China(AC)ACIACM ACPACSACTACC ACN ACKACJACUFCIFCMFCPFCSFCTFCCFCNFCKFCJFCULHC LOC LWCQCXC
Taiwan(AN)ANIANM ANPANSANTANC ANN ANKANJANUFNIFNMFNPFNSFNTFNCFNNFNKFNJFNULHN LON LWNQNXN
Korea(AK)AKIAKM AKPAKSAKTAKC AKN AKKAKJAKUFKIFKMFKPFKSFKTFKCFKNFKKFKJFKULHK LOK LWKQKXK
Japan(AJ)AJIAJMAJPAJSAJTAJCAJNAJKAJJAJUFJIFJMFJPFJSFJTFJCFJNFJKFJJFJULHJLOJ LWJQJXJ
U.S.A.(AU)AUIAUM AUPAUSAUTAUC AUN AUKAUJAUUFUIFUMFUPFUSFUTFUCFUNFUKFUJFUULHU LOU LWUQUXU
International Freightand Insurance(BF) BAI BAM BAP BAS BAT BAC BAN BAK BAJ BAU BFI BFM BFP BFS BFT BFC BFN BFK BFJ BFU
Import from Hong Kong(CH) HAI HAM HAP HAS HAT HAC HAN HAK HAJ HAU HFI HFM HFP HFS HFT HFC HFN HFK HFJ HFU
Import from EU(CO) OAI OAM OAP OAS OAT OAC OAN OAK OAJ OAU OFI OFM OFP OFS OFT OFC OFN OFK OFJ OFU
Import from the R.O.W.(CW) WAI WAM WAP WAS WAT WAC WAN WAK WAJ WAU WFI WFM WFP WFS WFT WFC WFN WFK WFJ WFU
Duties and
Import commodity taxes(DT) DAI DAM DAP DAS DAT DAC DAN DAK DAJ DAU DFI DFM DFP DFS DFT DFC DFN DFK DFJ DFU
Value Added(VV)VIVMVPVSVTVCVNVKVJVU
Total Inputs(XX)XIXMXPXSXTXCXNXKXJXU
<STEP 1>
   So far, all the parts except the shadowed segments have been prepared and are ready for linking.
   The remaining parts are in fact directly transplanted from the corresponding parts of national tables, after due aggregation into AIO classification.
   The diagram shows an example of J apan's case, with arrows indicating the parts- correspondance between the AIO table and the J apan I- O table.
   The same treatment should be done for any other member countries. 
<STEP 2>
   After linking, all the rowwise statistical discrepancies due to the difference in data source are aggregated into a single column vector, QX.
* Note that the export vectors to the member countries are NOT used in the end, to avoid double- counting with the corresponding import matrices.
J apan I- O Table