Chapter 5. Development of Social Inventory Database using Asian International Input-Output Table
5.3 Results
5.4.2 Sensitivity analysis
5.4.2.1 Assumption change effect on fatal injury intensity of Japan
Estimation of fatal intensity in the manufacturing sector of Japan based on the disaggregated original data by employment share of each sector (S0) is averaging 2.56×10-6 cases/1000 US$, and that of estimated using the fatality rate of USA (S1) is averaging 2.63×10-6 cases/1000 US$. The difference of the estimation between the two scenarios showed 2.7%. The results of the sensitivity analysis done for two scenarios effect on the fatal intensity are shown in Figure 5-15. The result presented as percentages relative to the base case scenario (S0). The assumption change based on (S1) was the significant effect in 8 sectors of total 49 sectors including the milled grain and flour, fish products, clothing products, leather and leather products, timber, wooden furniture, cement and cement products, and other non-metallic mineral products. These sectors show some significant differences between Japan and USA.
While the rest 41 sectors, a similarity situation is observed: insignificant effects on the fatal intensity of changes of assumption as S1. Since the fatal rate of these sectors in Japan is similar to the USA, the impact of S1 on the 41 sectors are not so significant in this analysis. It is recommended that the estimate of the fatal accident of each sector by disaggregated original data based on the employment share of each sector can make it possible to provide reliable data. Due to we do not have sufficient information to distribute the fatal cases across all economic sectors in each country.
121
Figure 5-15. Sensitivity analysis of the fatal occupational intensity for Japan.
5.4.2.2 Assumption change effect on fatal injury intensity of Indonesia
In order to evaluate the impact of changes of calculation assumption on the fatal injury in the manufacturing sector of Indonesia, taking into consideration the sensitivity analysis were carried out as following:
Base case scenario (S0): the proportion of workers with fatal injuries of each sector in 2005 would be as same as the statistical data in the year 2009, and disaggregate original data by employment share of each sector based on assumption that the proportion of workers with injuries would be same as for sub-sector under major sector.
Scenario 1 (S1):estimation the missing data in the manufacturing sector of Indonesia by using the fatal rate of the manufacturing sector in the Philippines.
Scenario 2 (S2):estimation the missing data in the manufacturing sector of Indonesia by using the fatal rate of the manufacturing sector of Indonesia in the year 1997.
The results of the sensitivity analysis done for three scenarios effect on the fatal intensity of Indonesia are presented in Figure 5-16. This result displayed as percentages relative to the base case scenario (S0). The average difference of the estimation between the S0/S1 and S0/S2
0%
20%
40%
60%
80%
100%
120%
140%
160%
Milled grain & flour Fish products
Slaughtering, meat, &…
Other food products Beverage
Tobacco Spinning
Weaving and dyeing Knitting
Wearing apparel Other made-up textile…
Leather and leather…
Timber Wooden furniture Other wooden products Pulp and paper Printing and publishing Synthetic resins and fiber Basic industrial chemicals Chemical fertilizers and…
Drugs and medicine Other chemical products Refined petroleum & its…
Plastic products Tires & tubes Other rubber products
Cement & cement…
Glass & glass products Other non-metallic…
Iron & steel Non-ferrous metal Metal products Boilers, engines & turbines
General machinery Metal working machinery Specialaized machinery
Heavy electrical…
Television sets,…
Electronic computing…
Semiconductors &…
Other electronics &…
Household electrical…
Lighting fixtures,…
Motor vehicles Motor cycles
Shipbuilding
Other transport…Precision machinesOther manufacturing…
SENSITIVITY ANALYSIS - MANUFACTURING SECTORS - JAPAN
Japan-S0 Japan-S1
122
scenarios showed 1.1% and 3.2%, respectively. It can be verified that among the studied variables, the one which presents the most significant impact on the fatal injury of Indonesia is the calculation assumptions. The assumption change based on (S1) was the significant effect in 9 sectors of total 49 sectors, such as the other food products, spinning, weaving and dyeing, etc. While, the assumption change based on (S2) was the significant effect in 17 sectors of total 49 sectors, such as the other food products, tobacco, spinning, weaving and dyeing, other non-metallic mineral products, household electrical equipment, etc. It is recommended that the estimate of the fatal accident of each sector based on the S0 can make it possible to provide reliable data at this time. Due to we do not have sufficient information to estimate the fatal cases across all economic sectors in Indonesia.
Figure 5-16. Sensitivity analysis of the fatal occupational intensity for Indonesia.
5.4.2.3 Fatal rate change effect on fatal injury intensity of China
In order to evaluate the impact of changes of the fatality rate on the fatal intensity in the manufacturing sector of China, taking into consideration the sensitivity analysis was carried out as follows:
0%
20%
40%
60%
80%
100%
120%
140%
160%
SENSITIVITY ANALYSIS - MANUFACTURING SECTORS - INDONESIA
Indonesia-S0 Indonesia-S1 Indonesia-S2
Other wood products Weaving and dyeing
Knitting Other food products
Other non-metallic mineral products
Wearing apparel Household electrical equip.
Tobacco Spinning
123
Base case scenario (S0): the fatality rate of each industrial sector in 2005 of China gathered from the National Profile Report on Occupational Safety and Health in China (ILO, 2012).
Scenario 1 (S1):changes of +10% fatality rate of sub-sector in the manufacturing sector on the fatal intensity of China.
Scenario 2 (S2): changes of +20% fatality rate of sub-sector in the manufacturing sector on the fatal intensity of China.
The results of the sensitivity analysis done for three scenarios effect on the fatal intensity of China are presented in Figure 5-17. This result displayed as percentages relative to the base case scenario (S0). It can be verified that among the studied variables, the one which presents the most significant impact on the fatal intensity of China: variation of +20% fatality rate change causes the large changes in the fatal intensity in all the scenarios evaluated at the average value of 10.2%. Changes of +10% on the fatality rate also affect the fatal intensity of China, but with less intensity than the +20% fatality rate change.
Figure 5-17. Sensitivity analysis of the fatal occupational intensity for China.
0%
20%
40%
60%
80%
100%
120%
SENSITIVITY ANALYSIS - MANUFACTURING SECTORS - CHINA
China-S0 China-S1 (+10% fatal rate) China-S2 (+20% fatal rate)
Other wood products
Plastic products
Wood furniture Beverage
Other rubber products
Timber
Drugs and medicine Tobacco
Basic industrial chemicals
124
The results of sensitivity analysis of the fatal occupational intensity, the most important parameter effect on the fatal intensity is the changes of +20% fatality rate of sub-sector in the manufacturing sector of China. The next important parameter is the estimation the missing data in the manufacturing sector of Indonesia using the fatal rate of Indonesia in the year 1997, followed by the estimation the missing data in the manufacturing sector of Indonesia using the fatal rate of the Philippines, respectively.
5.4.2.4 Change in the type of database and country difference on the employment intensity and employment footprint
In order to evaluate the impact of changes of the type of database and country difference on the employment intensity of Thailand and the employment footprint of Thailand, Malaysia, China, Japan and USA, taking into consideration the sensitivity analysis were performed as follows:
Base case scenario (AIIO): evaluate the employment intensity and employment footprint using the AIIO table, included 10 countries and 76 sectors.
Scenario 1 (ADB MRIO):evaluate the employment intensity and employment footprint using the Asian Development Bank MRIO table (extended from with the World IO Database (WIOD) table), included 45 countries and 35 sectors.
The results of the sensitivity analysis done for two scenarios effect on the employment intensity of Thailand are shown in Figure 5-18. This result presented as percentages relative to the base case scenario (AIIO). It can be verified that the ADB MRIO presents the high significant impact on the employment intensity of Thailand in 6 sectors of total 34 sectors, including the (8) coke and refined petroleum products, (6) wood and products of wood, (4) textiles and textile products, (17) electricity, gas and water supply, (24) air transport, and (34) private households with employed persons. The ADB MRIO dataset has led to an investigation of the validity, comparability, uncertainty of the AIIO dataset. Due to different sectoral, country and temporal resolution, different databases are suitable for different analyses. AIIO database has usually only 10 countries which small region model and assuming that labor use of the rest of the world (RoW) are identical to those of Asian industries can introduce an error into the labor embodied in the commodities produced and international trade. On the other hand, the ADB MRIO database has more detailed which covered 45 countries and RoW, thus is better suited for analyzing in high resolution and reducing the error effect. For example, the coke and refined petroleum products sector in Thailand used the raw material (crude oil) from domestic
125
(15%) and imported from the middle-east (65%) and other countries included Asian countries (20%) (DEDE, 2006), the result showed that the ADB MRIO had higher accuracy than the AIIO database. Thus, the RoW in the ADB MRIO database has an influence on the employment intensity in the coke and refined petroleum products sector in Thailand about 2.75 times in comparison with the AIIO database. While, in the wood and products of wood sector was imports of logs and sawn timber from Malaysia, Myanmar, Laos, USA, New Zealand, EU, and other (Royal Forest Department, 2016). The RoW in the ADB MRIO database has an influence on the employment intensity in the wood and products of wood sector about 1.75 times when comparing the AIIO table.
Figure 5-18. Sensitivity analysis of the employment intensity for Thailand.
In addition, we can be analyzed the sensitivity effect of the type of database and country difference on the employment footprint of Thailand, Malaysia, China, Japan, and the USA as presented in Figure 5-19. This result showed the employment per capita of each country using the AIIO table in comparison with the ADB MRIO. It can be found that the ADB MRIO presents the high significant impact on the employment footprint of the USA due to the high contribution import from the rest of the world. Based on the ADB MRIO model, to satisfy the final demand of the USA, one American people require nine-tenths of one worker to support their lifestyle. This consists of domestic workers (54.9%) and foreign workers (China 17.7%, other Asian countries 4.4%, India 6.8%, and other RoW 16.2%). For one Japanese person needs
0%
50%
100%
150%
200%
250%
300%
Sensitivity Analysis - Employment Intensity of Thailand
AIIO ADB MRIO
Coke & Refined Petroleum products
Wood and Products of Wood Textiles and Textile Products
Electricity, Gas and Water Supply Air Transport
Private Households with Employed Persons
126
eight-tenths of one worker to sustain their standard of living, which comes from the domestic (58.3%) and foreign workforce (China 20.4%, other Asian countries 7.3%, India 2.5%, and other RoW 11.5%).
Figure 5-19. Sensitivity analysis of the employment footprint for Thailand, Malaysia, China, Japan, and the USA.