Input-Output Model and its Application
3.7 Applications of impact studies
3.7.4 Impact studies literature
Table 3-15 Final demand column vector .
Change in FD delta Y 1 Agriculture, forestry, fishing, and hunting 0
2 Mining 0
3 Utilities 0
4 Construction 100.00
5 Manufacturing 0
6 Wholesale trade 0
7 Retail trade 0
8 Transportation of warehousing 0
9 Information 0
10 Finance, insurance, real estate, rental, and leasing 0
11 Professional and business services 0
12 Educational services, health care, and social assistance 0 13 Arts, entertainment, recreation, accommodation, and food services 0
14 Other services, expect government 0
15 Government 0
sizes of the total output appear to be similar, the distribution of indirect shock looks different over the two total outputs.
In Table 3-17 , the same amounts of direct (initial) shocks are given to two different indus- trial sectors. Policy 1 is to assume an increase in final demand for the construction sector for
$100 million, and policy 2 is to assume increase in final demand for the information sector for $100 million. Do you see different patterns of distributions of indirect impacts over two policies? If you are the manufacturing sector, which one do you prefer to see assuming you would be happier with higher numbers? How about the case that you are working in the pro- fessional and business services sector? What you see is the magnitude of interdependencies among the different industrial sectors. No industrial sectors exist in isolation, even though some people may only be interested in learning the particular industry of their concern. I-O modeling can show you such intricate interdependencies among industrial sectors.
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demonstrated with the I-O model that the employment multiplier effect of the steel industry is well over a one-to-one ratio in relation to the regional economy.
The I-O/SAM model is versatile and can accommodate various types of impact analyses.
Ahlert (2001) applied an I-O model to a contemporary topic to discuss how the soccer World Cup in 2006 in Germany would affect the economy, considering different financing arrange- ments for extending the public sports infrastructure, calculated at the national accounts level, to show the positive influences on income and employment. Another example of a contempo- rary study on economic impact using the I-O/SAM model is a working paper estimating the effect of increasing childcare on a local economy ( Shockley and Ebeling, 2002 ). Shockley and Ebeling used the I-O model to simulate how much a local economy will benefit by increasing the capacity for childcare, assuming that working mothers with children in childcare would not be able to hold jobs without the formal care. The I-O framework is also used to estimate regional economic impacts for converting corn to ethanol ( English et al., 2002 ). Ethanol has been added to gasoline in some US states for some time, but this paper focuses on the eco- nomic impact on corn farmers if local government decides to invest in an ethanol production facility that purchases corn from farmers.
Hughes (1994) tried to draw researchers ’ attention to a common mistake in using an I-O-based model. He was concerned with abuse and over-quotation of multipliers from one study to another. He said that employment multipliers are the least reliable of all multipliers,
Table 3-16 Change in total output in response to the change in final demand .
Chg in FD delta Y Result Tot OPT delta X 1 Agriculture, forestry, fishing, and hunting 0 2.06
2 Mining 0 2.64
3 Utilities 0 1.33
4 Construction 100.00 100.47
5 Manufacturing 0 39.36
6 Wholesale trade 0 5.50
7 Retail trade 0 5.57
8 Transportation and warehousing 0 4.24
9 Information 0 3.00
10 Finance, insurance, real estate, rental, and leasing
0 8.51
11 Professional and business services 0 17.51
12 Educational services, health care, and social assistance
0 0.09
13 Arts, entertainment, recreation, accommodation, and food services
0 0.95
14 Other services, except government 0 2.12
15 Government 0 0.40
100.00 193.78
Table 3-17 Comparative display of two different shocks . Policy 1 Chg in FD delta Y
Policy 1 Result Tot OPT delta X
Policy 2 Chg in FD delta Y
Policy 2 result Tot OPT delta X 1 Agriculture, forestry, fishing,
and hunting
0 2.06 0 0.96
2 Mining 0 2.64 0 1.01
3 Utilities 0 1.33 0 1.29
4 Construction 100.00 100.47 0 0.78
5 Manufacturing 0 39.36 0 17.39
6 Wholesale trade 0 5.50 0 3.06
7 Retail trade 0 5.57 0 0.33
8 Transportation and warehousing
0 4.24 0 2.67
9 Information 0 3.00 100.00 125.55
10 Finance, insurance, real estate, rental, and leasing
0 8.51 0 12.46
11 Professional and business services
0 17.51 0 21.77
12 Educational services, health care, and social assistance
0 0.09 0 0.39
13 Arts, entertainment, recreation, accommodation, and food services
0 0.95 0 2.58
14 Other services, except government
0 2.12 0 2.45
15 Government 0 0.40 0 0.84
100.00 193.78 100.00 193.51
because of the assumption that increased employment will result from an increase in a lin- ear form, and the assumption of the existence of unemployed, mobile, and substitutable resources. Hughes pointed out that increased output might be met through increased utili- zation of existing capacity (including labor) or a less-than-proportionate increase in employ- ment. He concluded that all that is required for better analysis is consistency in use, a greater understanding of the concepts, and better data collection plus modification of assumptions.
Nakajima (1994) used the I-O model to analyze the international impact of the Japanese construction industry. He discussed international I-O analysis as a useful analytical tool that can capture direct and indirect effects among industries in Japan and other countries.
He aggregated the number of sectors into 28 industries and complied four sets of bilateral international I-O models to examine the Leontief inverse matrices, output multipliers, value- added multipliers, income multipliers, and operating-surplus multipliers. Hayashi (1991) used the conceptual and mathematical framework of the I-O-based model to demonstrate
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how his proposed semi- I-O framework captures the direct, indirect, and induced effects of a large-scale infrastructure project on a regional economy.
3.7.4.1 Impact analysis for the tourism industry
Turning now to studies of economic impacts on the tourism industry, a paper by Fletcher (1989) is among the most quoted. Fletcher ’ s aim was to demonstrate the usefulness of I-O analysis in studying the economic impact of tourism. The paper ’ s conclusion states that I-O analysis is the most comprehensive method available for studying the economic impact of tourism, and that no other technique can offer the same flexibility and level of detail. He presents a theo- retical introduction to the I-O model, and quotes actual tourism multipliers for many nations.
Fletcher et al. (1981) quotes their previous research on the multiplier effect of the tourism indus- try on the economy of Gibraltar, saying that tourism generated the greatest marginal increase in both income and employment, even though final demand in Gibraltar was dominated by UK Ministry of Defense expenditures. Archer (1982) wrote a pioneering paper on the use of I-O models for tourism industry analysis in the early 1980s. He talked about analyzing different policy choices to compare each for its implications on income, wages, and employment, which would be valuable to policymakers and planners in the tourism industry.
Heng and Low (1990) utilized the I-O model to conduct a detailed analysis of the economic impact of tourism development on the economy of Singapore. They compared multipliers for the manufacturing industry, the export industry, and the tourism industry and concluded that tourism created three times more jobs per million dollars compared with total exports, and two times more as compared with manufacturing exports. The paper also pointed out that the tourism sector ’ s larger labor requirement may imply greater importation of low-skilled work- ers for the hotel, wholesale, and retail trade sectors and that automation and mechanization may be quite limited in a personalized, labor-intensive industry like tourism.
Briassoulis (1991) focused on methodological issues at greater length and took a more crit- ical view of the prevalent usage of the I-O model for tourism industry analysis. Briassoulis ’ s criticism of the I-O-based framework for tourism included the following points:
● the linear and additive I-O relationships assumed among economic sectors leave out inter- action effects;
● the constancy of the technical coefficients assumption has been shown to be unrealistic, even in the short term, because of capacity and supply constraints;
● the assumption of constant coefficients may not be accurate, as the early stages of tourism development of an area are characterized by dynamic, short-term changes, affecting unsta- ble technical coefficients;
● the broader possibility of substitution effects has for the most part not been dealt with;
● considerable interaction between the study region and the rest of the world (RoW) make the general equilibrium assumption implausible; and
● because of the vulnerability of the tourist industry to exogenous influences (economic, political, or social changes), it is also implausible to assume that the study region is in an equilibrium state.
Briassoulis also mentioned that the economic impacts that are assessed by I-O analysis rep- resent only part of the total economic value of tourism ’ s impacts. He suggested that, even if the application of I-O analysis is considered valid in a given situation, it should be remembered that it does not provide a complete account of the economic impacts of tourism. Caution over the economic impact studies of tourism-related events was raised in other papers, in terms of misuse and misinterpretation ( Crompton, 1995 ; Tyrrell and Johnston, 2001 ). Crompton identi- fied that most economic impact studies are commissioned to legitimize political position and pointed out 10 mischievous procedures; including local residents; inappropriate aggregation ’ including of time-switchers and casuals; abuse of multipliers; ignoring costs borne by the local community; ignoring opportunity costs; ignoring displacement costs; expanding the project scope; exaggerating visitation numbers; and inclusion of consumer surplus ( Crompton, 2006 ).
Fleisher and Freeman (1997) concluded that researchers using a single-region I-O model should take the downward bias into consideration otherwise they will not obtain an accu- rate estimate of the full economic impact of tourism. It is intriguing that the finding is based on their simulation of tourism sector on Israeli economy. Regional impact modeling to esti- mate the multiplier effects of tourism expenditures in the Washington DC area were well- documented with details in the paper by Frechtling and Horvath (1999) showing that tourism multipliers are relatively high for earnings and employment, but low for output, compared with other industrial sectors.
As for the topic of estimating visitor expenditure, a paper by Frechtling (2006) is very comprehensive to cover previous research over 30 years, identifying three important contexts of occasion, venue, and timeframe. This paper displays excellent details on issues of measure- ment of expenditures at a specific tourism event.