Input-Output Model and its Application
3.7 Applications of impact studies
3.7.1 Steps for impact studies
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becomes so attractive, a general manger of a hotel in the US may switch to purchases from China instead of those from a domestic manufacturing industry in West Virginia. If the price of beef increases for some reason, you may find fewer beef dishes at the restaurant. Increase in prices would not cause a huge technical problem for the I-O modelers if the change in prices of goods and services are equally distributed across all of society. That is often not the case, such as the surge of crude oil prices. In the I-O world, the exact mixture of intermediate goods will remain fixed despite short-term price fluctuations.
3.6.4 Homogeneous sector output
If the sector produces more than one commodity (this will be discussed later), the proportion of such multiple commodities productions will remain the same as in the study period. If an auto- mobile manufacturing factory in the region (the industry) produces more than one model of cars (the commodities), and I-O table recorded industry by commodities, the relative portion of com- modities (small cars, mid-size cars, large cars, huge trucks, gigantic sport utility vehicles) will not be assumed to change even when the gasoline prices increase later. As an example of the hospitality industry, let us consider a full-service hotel. They have a rooms department and food and beverage department as a core department to generate their products, clean rooms, and tasty meals . The proportion of those products is not assumed to change, which means that if the proportion was 70% and 30%, then when the total revenue at the hotel increased from $100 mil- lion to $150 million, the sales of the two departments are assumed to increase from $70 million and $30 million to $105 million to $45 million as the relative ratio of proportions are fixed.
In general, relatively small changes to the economy in question would pose less of a prob- lem, and changes to relatively larger economic region would create fewer problems in the impact analysis using the I-O framework. Being aware of those structural limitations, mainly derived from linear-modeling techniques, you may as well present your arguments in perspective with- out misleading audiences and readers. It is evident that these are not the all limitations of your particular study, as there may be errors that are more specific and applicable to your research.
about the process later. Thus, we start by discussing the steps required to find an existing table for your research.
If you are interested in conducting an impact study of tourism, you want to make sure the regional economic data are available for use. Many nations in the world provide national data free of charge, and often make those data available for free download on their websites.
Available tables may include the interindustry transactions table, before standardization, or it may be in the Leontief inverse matrix format already.
As for the US, the BEA, US Commerce Department has a web page from which I-O data and valuable reports can be downloaded ( http://www.bea.gov/industry/index.htm ).
Eurostat at the European Commission shows I-O tables of 60 60 for over 25 nations in Europe. Some nations even offer I-O tables at regional levels. A case in point is Japan, which has more than 40 prefecture level I-O tables and more than 10 municipality level I-O tables in addition to national and multiregional tables. I-O data are often available free by e-mail, telephone, or links on a web page. The Japanese national I-O table is downloadable from the website of the Director-General of Policy Planning at Ministry of Internal Affairs and Communications ( http://www.stat.go.jp/english/data/io/index.htm ).
You may wonder how many nations in the world have national I-O data. According to data produced by Pan Pacific Association of I-O Studies in 2004, there were 83 nations in the world that create National I-O tables.
The reason for governments to make I-O data available is that it is useful for them to have the table for planning and analyses purposes; in addition, they could benefit from various aspects of discussions on policy analyses with taxpayers, students, and scholars. Despite the fact that data collection and compilation for I-O tables requires massive labor inputs of many government workers, I-O data tend to be underutilized, taking into consideration of the huge benefits that can be extracted from it.
3.7.1.2 Check availability of other relevant data – free sources
Once the government of the nation/region that you wish to study is identified to have the I-O data, it is more than likely that they have other important data such as labor, employment, income etc., which you will need to calculate various multipliers as we learned in section 3.5.1.
Creating the I-O table requires high levels of rigor and coordination among many different government offices, and existence of the I-O data implies that the government has certain lev- els of ability to collect important data. Once the data are collected, those governments tend to disseminate the details to taxpayers, students, and scholars free of charge. Recent advance- ments of the internet are without doubt helping governments to disseminate data.
3.7.1.3 Check availability of input-output tables – proprietary sources
Detailed I-O tables may be obtained from commercial and noncommercial proprietary organizations. In the US, two of the well-known vendors are RIMS and IMPLAN, which are also known as the software and data packages for impact modeling. As for IMPLAN, the data availability goes down to the county level, covering over 3000 counties in all states
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( http://www.implan.com/index.html ). Not all people are aware that the Minnesota IMPLAN Group (MIG) IMPLAN as a company sells not only the IMPLAN software but also IMPLAN data, which are updated annually at the county level. IMPLAN data already incorporates rel- evant data on employment, income, and taxes. (The US consists of 50 states and a federal district (the District of Columbia); most states have counties, with the exception of Louisiana, which has parishes (that are equivalent to counties). Some states have independent cities such as Baltimore in MD and St. Louis in MO. All together, there are approximately 3140 counties and comparable substate level regions in the US.)
3.7.1.4 Compose an appropriate final demand column vector
As long as you take A-matrix and subsequent Leontief inverse matrix as matrices with fixed coefficients, they are fixed. You do not tamper with them along the processes of calcula- tions. In that regard, defining an appropriate final demand column vector is where discretion enters into impact studies. This is the step in your impact studies that deserves extra atten- tion because the accuracy of representing the initial shock would be precisely reflected in the responding shocks as indirect impacts. You have to identify an appropriate column vector as collection of tourism-related initial shocks allocated to different industrial sectors. While the I-O table data would most likely be secondary data, identifying a final demand column vector for tourism as an industry faces the same challenge which motivated European econo- mists to start the TSA concepts as we see in later sections.
There are several mistakes that students tend to make. One of the common mistakes asso- ciated with tourism impact analysis would be to put one single number in one sector that you believe is associated with tourism and leave all the other sectors with zero initial shocks (e.g. if you assume that additional tourists will come to the region and subsequently the final demand for tourism products increase of $100 million per year); some students tend to put all $100 million into one single sector, such as a hotel sector. While the hotel sector is certainly one of the relevant sectors associated with tourists, not all the expenditures of tourists are at within this sector (e.g. tourists may purchase souvenirs at local gift shop, dine at local restau- rants, participate in organized tours, and use local transportation).
Whenever possible or feasible, it would be advisable to consider prime data gathering of tourists ’ expenditures which will require you to design and collect enough sample data to represent the population. If there are secondary data specific for the region and specific to the type of tourists, they may be used to construct the final demand column vector to reflect the visitors ’ expenditures. When there is no other specific data, you may use national data or something comparable to your study region. In this case ensure that you state explicitly how you created the final demand column vector as there may be substantial regional deviation from the national average data, as shown in chapter 1. It is the final demand column vector that drives the change in total output and basically the whole impact studies that you con- duct. You may occasionally find some reports that do not disclose how the final demand col- umn vector was composed. You will have little clue on how they did it, thus it is challenging to verify the study.
3.7.1.5 Several cautions in composing final demand column vector
There are several additional cases where you have to be cautious about composing final demand column vector.
3.7.1.5.1 Defining the direct shock
When you try to estimate possible impacts of a new hotel, or a new amusement park, there would be two types of final demand column vectors due to the nature of the shocks. The first shock occurs from construction activities, and that is nonrecurring as it happens once during a project. After opening of the hotel or the amusement park, the second shock occurs from visitors ’ expenditures. This shock from operations is recurring, which means it happens every year once the operation starts. Thus, when you estimate an impact of a new project, you conduct two impact studies, one based on the final demand column vector representing the construction phase (the total impact from the construction), and the other based on the final demand column vector of visitors ’ expenditures (total impact from the operation) . 3.7.1.5.2 Location of the direct shock
It is desirable for you to measure the visitors ’ expenditures as a result of collection of primary data by way of surveys, questionnaires, etc. In case you wish to calculate regional impacts of a county or state, be very careful about the exact location of the occurrence of expenditures as tourists may spend outside of the study region. For example, if you are estimating the impact of the Japanese tourists ’ expenditure in the state of Nevada, their expenditures made in the state of California should not be counted as a part of final demand column vector in the study of Nevada. If European tourists purchased the Caribbean cruise ship tour organized by a travel agency in New York City, not all the amount that the tourists paid to the New York City travel agency would not be counted as a final demand column vector for the tourism economic impact analysis for Jamaica. Whatever the cruise ship company pays the Jamaican company for their short stay and the souvenirs that tourists purchased directly from Jamaican vendors would only constitute the final demand column vector for Jamaican economy.
3.7.1.5.3 Duration mismatch
As you see, the I-O data are based on annual flows in the economy, so the default setting for the duration is 1 year, which is the common duration for the income statements of firms.
Certain events that you want to capture may not match its duration, such as week-long festival, 3-day convention, etc. Whatever the shock you give will be the aggregate shock for the duration of the event, but the calculation results are presented based on the annual flow of data. This poses an interesting question. If duration of the event in question is shorter than 1 year by far, the default method of presenting the corresponding total output clearly poses a challenge. Because the result is shown on an annual basis, actual instant shock during the event may be more intense and the effect may diminish quickly after the completion of the event. If you think about the soccer World Cup or the Olympics, that would give you an idea.
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3.7.1.5.4 Visitors ’ definition and compliance
As we will learn in detail in Chapter 5, compliance with a definition of visitors will prevent certain portions of expenditures by local residents (i.e. nonvisitors) from being included in the initial shock.
For example, estimating an impact of a local popular concert in a metropolitan area will result in overestimation of total impacts if you include all the expenditures during the event, because a substantial portion of expenditures include those by local residents. In addition, this would occur with the development of large leisure-infrastructure projects such as museums, arenas, or sports facilities in the urban setting, as much, if not all, of the expected expendi- ture would be made by local residents. An example of estimating the economic impacts of huge tourism-related facilities in or near the urban center would be Tokyo Disneyland, which opened in 1983 and attracted 10 million visitors in the first year and 21 million visitors in 2001. Because Tokyo Disneyland is located at the Tokyo Bay, it is only 10 miles to the central part of Tokyo. But according to various definitions of who can be considered as visitors, more than half of the guests who go to Tokyo Disneyland would not be considered as visitors (i.e.
leisure travelers and nonleisure travelers who travel enough long distance). So the majority of expenditures would be considered to be made by the nonvisitors or locals who spend money as part of daily lives in usual environment. This is a thriving environment, but an impact study of tourists (i.e. visitors) would be smaller because of exclusion of those expenditures by nonvisitors (i.e. local residents).
When dealing with tourism-impact-related studies, defining the tourists or visitors to the study region is important to ensure accurate estimates of their expenditures and their sub- sequent impacts. This is another reason why hospitality and tourism students and scholars have to learn about TSA, which have been predominantly led by professional economists.
3.7.1.5.5 Producers ’ price base as default setting
I-O data are recorded at the producers ’ price, while tourism expenditures data may be recorded by purchasers ’ prices. This will be discussed later.