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Approaches to Estimating the Economic Impacts of Tourism; Some Examples Daniel J. Stynes
Updated January 1999

Introduction
The purpose of this bulletin is to present examples of different approaches to estimating the economic impacts of tourism. In a previous bulletin (Stynes 1997), I summarize economic impact concepts and methods as they apply to tourism. Here we apply the methods to illustrative cases in order to demonstrate some practical approaches. Three specific examples are presented. These represent a range of alternatives for estimating the economic impacts of visitor spending. The techniques covered range from methods based largely on judgement, to methods that utilize secondary spending data and published multipliers, to the use of visitor surveys and input-output models. A third bulletin in this series discusses survey methods for measuring visitor spending and includes sample spending instruments. While the construction and operation of tourist facilities also has economic impacts, we will restrict our attention here to the impacts of visitor spending. Review of Basic Approach and Levels of Analysis The economic impact of visitor spending is typically estimated by some variation of the following simple equation: Economic Impact of Tourist Spending = Number of Tourists * Average Spending per Visitor * Multiplier This equation suggests three distinct steps and corresponding measurements or models: (1) Estimate the change in the number and types of tourists to the region (2) Estimate average levels of spending (often within specific market segments) of tourists in the local area. (3) Apply the change in spending to a regional economic model or set of multipliers to determine the secondary effects. The three steps and corresponding information typically involve distinct methods, models and information sources. Each component of the equation may be estimated via expert judgement, from secondary sources, through primary data collection, by means of a model, or through some combination of these methods. Table 1 from the earlier report summarizes the alternatives and is repeated here for the readers convenience. The approaches for each step may be mixed in a given study. For example, the number of tourists may be estimated using judgement, spending via a visitor survey, and multipliers from a published secondary source. Or, an input-output model may be applied to spending estimates derived from tourist spending averages and visitation numbers taken from secondary sources. As one moves from judgement to secondary data to primary data and formal models, the methods become more complex and the time and expense of the study increases. The added cost is hopefully associated with estimates that are both more accurate and more detailed, although this isn’t always the case. In some cases good judgement or existing data may be more accurate than a new visitor survey, particularly if the survey has a low response rate, small sample size, and measurement and sampling procedures that do not guarantee a representative sample or reliable measurements. Methods based on judgement typically yield highly aggregate estimates, while estimates derived from formal models may estimate spending within several categories and impacts within as many as 500 distinct

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economic sectors. Which method is preferred depends on the intended uses of the results, the accuracy and detail that are required, and the time, money and expertise available.

Table 1. Approaches to Tourism Economic Impact Assessment
Level Tourism Activity Spending Multipliers

1Judgem ent 2

Expert judgement to estimate tourism activity

Expert judgement or an “engineering approach” 1

Expert judgement to estimate multipliers

Existing tourism counts for the area or total estimates from a similar area or facility Estimate tourism activity by segment or revise estimates by segment from another area Visitor survey to estimate number of tourists by segment or a demand model

Use or adjust spending averages from studies of a similar area/market Adjust spending that is disaggregated within particular spending categories & segments Survey random sample of visitors to estimate average spending by segment & spending category

Use or adjust aggregate tourism spending multipliers from a similar region/study Use sector-specific multipliers from published sources

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4Primary data

Use an input-output model of the region’s economy

Three examples introduced in the previous bulletin are presented here in more complete detail. First we reintroduce them: • The National Park Service’s “Money Generation Model “(USDI, National Park Service 1990.) is a simple fill-in-form for generating economic impacts. It is an example of a simple approach that relies largely on judgement and available secondary data in a highly aggregate form. While an extremely simple approach, it captures the essential elements of an economic impact analysis. The number of visits, average spending per visitor and an aggregate sales multiplier are entered on a simple worksheet to generate estimates of the direct and total sales effects of visitor spending. Sales effects are converted to income and jobs using ratios of income to sales and jobs to sales. Tax effects of visitor spending can also be estimated by applying local tax rates to sales estimates. With sound judgement in choosing the parameters, the MGM model can yield reasonable ballpark estimates of economic impacts at minimal cost. This approach, however, provides little detail on spending categories or which sectors of the economy benefit from either direct or secondary effects. The aggregate nature of the approach also makes it difficult to adjust recommended spending rates or multipliers to different applications. The example described here is taken from a study of the local economic impacts of Mammoth Cave National Park in Kentucky (Stynes and Rutz 1995). The Bureau of Economic Analysis’s (BEA) RIMS II user handbook (USDC, BEA 1992) illustrates how to apply published multipliers to estimate economic impacts. This approach starts with visitor spending (from a survey or secondary sources) divided into a number of spending categories and makes use of sector-specific multipliers to estimate the direct and total sales, income and employment effects. Multipliers from the BEA’s RIMS II model are used to estimate secondary effects. Multipliers are reported for 39 sectors for each state in the second edition of their report (USDC 1992). The BEA method illustrates the proper use of margins to account for retail purchases of

•

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In an engineering approach, one estimates the costs of producing a “trip” by itemizing typical costs for each input - e.g., a typical overnight visitor party of 4 staying two nights will incur $50 per night for motel room, $20 per person per day for meals, $10 for half tank of gas, and $50 for souvenirs = total of $320 per party per trip.

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goods and makes use of disaggregate sector-specific multipliers for each state. Multipliers for sub-state regions are not as readily available, but can be acquired from BEA or other sources. Secondary effects cannot be disaggregated to individual sectors using the BEA approach. The BEA approach is illustrated using a hypothetical increase in tourism to the State of Michigan. • The MI-REC/IMPLAN System. Stynes and Propst (1992, 1996) have developed a fairly complete micro-computer-based system for estimating economic impacts of recreation and tourism. The system combines spreadsheets for estimating spending with the IMPLAN input-output modeling system. IMPLAN uses county level data to estimate input-output models for regions down to a county level. IMPLAN generates a complete set of economic accounts within up to 528 sectors for the region, including multipliers and trade flows. MI-REC spreadsheets estimate visitor spending within up to 33 spending categories based on the number and types of visitors attracted to an area. Spending is then bridged to the IMPLAN model sectors to estimate direct, indirect and induced effects in terms of sales, income and employment. Users may estimate spending via visitor surveys or use the MI-REC database of spending profiles, compiled from previous studies. The system also includes price indices to update spending data to a current year. The MI-REC /IMPLAN system is applied here to estimate the statewide economic impacts of tourism to Michigan in 1990.

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Example 1. The Money Generation Model
The Money Generation Model (MGM) was developed by the National Park Service (NPS) to generate quick and inexpensive estimates of the economic impact of National Park visitor spending on the region’s economy. In it’s simplest form, the MGM relies on agency records for estimates of visits, American Automobile Association (AAA) estimates of per person per day lodging and meals expenses to estimate spending, and judgement or available sources for multipliers. A pretty good aggregate estimate of impacts can be obtained with this simple method if one has accurate visitation data, spending data that adequately represent the visitors, and multipliers for the local region. The default values suggested in the MGM manual are unlikely to provide accurate estimates for most applications. The MGM estimates direct and total sales , the income and employment effects of this spending, and state and local government tax revenues. The MGM worksheet (with some minor modifications) is shown in Table 2, with entries completed for an application to Mammoth Cave National Park in Kentucky. All spending and visits is for 1993 and multipliers are for a three county region around the park. Notes explaining the application to Mammoth Cave and other tips for using the MGM approach are provided below by line number on the worksheet. The worksheet is divided into three sections. Section A. Direct and total sales effects of visitor spending. Line 1. Only visitors from outside the local region (in this case a three county area around the park) are counted as “new dollars” for an impact analysis. Park visitor counts entered on line 2 are reduced to include only nonlocal visitors using the percentage of visitors from outside the local area. Line 2. The number of park visits for a given year are entered on line 2. One must be sure visit estimates are accurate and in units consistent with the spending average entered on line 3. The NPS estimates visits to the park from axle counts on access roads. Vehicle counts are expanded to person visits using a party size factor and adjusted somewhat for re-entries and commercial traffic. In assessing the park’s contribution to tourism in the area, person visits to the park are not as important as party trips to the area. The NPS's report of 2.4 million visitors to Mammoth Cave in 1993 was converted to 430,000 party trips to the area by adjusting for multiple entries to the park (many visitors stay in motels outside the park and may make multiple visits during their stay in the area) and dividing by the average party size per vehicle for each season. In 1993 there were an estimated 430,000 party trips to the area involving a visit to the park, 93% of these trips were from outside the local area. Line 3. Spending averages are from exit interviews and a mailback survey of a random sample of park visitors throughout the year (422 returns). In this case, spending was itemized in 21 categories covering all spending during the visitor’s stay in the three county region around the park and spending profiles were developed for six visitor segments. Only a single aggregate visitor spending average is used on the MGM worksheet. Visitor spending inside the park for cave tours and camping are not counted here, as they were covered in a separate analysis of impacts of the park’s operational expenses in the area. If local spending data do not exist, the MGM manual suggests using the American Automobile Association recommended average per person per day cost of lodging and meals as an initial estimate of spending. Notice that this approach wouldn’t cover several other kinds of spending by visitors and the average motel rate doesn't apply very well to campers or day users. The AAA spending figures will generally not represent national park visitor spending very well. Line 4. Multiplying the entries on the first three lines yields total visitor spending of $51 million. This figure includes all spending by non-local visitors in the three county region around the park. Line 5a. The MGM model doesn’t explicitly handle the capture rate, so we have added an entry to reduce visitor spending to a measure of local final demand. Only 72% of visitor spending shows up as direct effects in the region, after deducting the producer prices of imported items bought by visitors. This deduction would not be necessary if only restaurant and lodging expenses were included as all of this spending is captured. The spending survey measured purchases of “local crafts” to better estimate the portion of manufactured items bought by visitors that were produced locally. Most other goods purchased by tourists, such as gasoline and t-shirts, are not made locally and only the retail margins on these items are captured. Line 5b. The sales multiplier of 1.93 comes from an input-output model for a three county region around the park. The multiplier includes both indirect and induced effects (Type III). The sales multiplier is only slightly below the MGM suggested default of 2.0, although the effective tourist spending multiplier after taking into account the capture rate is only 1.4 (1.93 * 72%).

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Line 6. Total sales impacts are calculated by multiplying visitor spending by the sales multiplier, while also adjusting for the percentage of spending captured as final demand in the local area.

Table 2. Sample Application of Money Generation Model to Mammoth Cave National Park, Kentucky, 1994.

Money Generation Model (Revised Worksheet)
Economic Benefits from Park Visitor Expenditures,

A. Sales Benefits from Tourism: Dollar value of goods and services purchased in the local area 1 2 3 4 Enter estimated non-local percent of park use Enter annual visits to park (party trips entered here) (Visits to park were converted to party trips to the area) Enter average expenditures per unit of use (average spending per party trip from visitor survey) Calculate Total Visitor Spending (1) x (2) x (3) in millions 93% 430,000 $ 127.40 $ 51 72% 1.93 $ 71

5a. Estimate the capture rate 5b. Enter Sales Multiplier (Type II) 6 Calculate Total Sales Effects (4) x (5a) x (5b) in millions B. Tax Revenue Benefits from Tourism 1 2 3 4 5 6 7 Enter Total Sales from A.6 above in millions Enter combined state and local retail sales tax rate Calculate sales tax collections from tourism in millions Enter sales to income ratio (range .20-.60, average 30%) Enter combined state and local income tax rate Calculate income tax revenue (1) x (4) x (5) in millions Compute total tax revenue (3) + (6) in millions

$ 71 5% $ 3.54 30% 5% $ 1.06 $ 4.60

C. Income and Job Benefits from Tourism 1 2 3 4 5 a. Enter Total Sales from A.6 above (in millions) Estimate job to sales ratio (range is 10-50 jobs per million in sales, average =30) Estimate income to sales ratio a Compute total employment effects (1) x (2) Compute total income effects (1) x (3) in millions a $ 71 32 51 % 2,265 $ 36.1

Income effects are not computed in the original MGM Worksheet, but are added here.

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Section B. Tax Revenues. State and local taxes are computed by applying sales and income tax rates to the visitor spending data. The MGM worksheet applies sales tax rates to total sales and an income tax rate to total income in order to estimate tax collections. A better approach is to disaggregate purchases to specific items and apply corresponding tax rates to taxable items. For example, a room use tax would be applied only to the cost of lodging, gasoline taxes only to fuel, and sales taxes only to taxable goods. Many items bought by tourists as well as indirect and induced sales may not be taxed or be taxed at different rates. There are similar problems with estimating income taxes using such an aggregate approach. An accurate estimate of tax effects requires a more detailed itemization of spending within categories that match those to which taxes are applied.

Section C. Income and employment effects are estimated by applying job to sales and income to sales ratios to total sales. Income effects are not calculated explicitly in the original MGM worksheet, but are buried in the tax calculations in section B. I have income effects to the impacts section here, as income is a much better impact measure than jobs and can be computed just as easily. The most commonly used measure of contribution to gross domestic product is value added, which is primarily the income effects (plus indirect business taxes). Line 2. A job to sales ratio captures the number of jobs required to produce a given amount of sales, usually expressed in jobs per million dollars in sales. These ratios vary considerably from industry to industry. Tourism businesses and retail trade sectors typically have high job to sales ratios (40-50 jobs per million dollars in sales), other services moderate rates, and manufacturing typically has lower rates (less than 25 jobs per million in sales). The appropriate rate for tourism spending applications is probably near the MGM's suggested average of 30 jobs per million in direct sales, as this ratio reflects jobs associated with a mix of direct, indirect, and induced sales. Using an input-output model for Mammoth cave, we were able to accurately estimate this ratio. The ratio for Mammoth Cave visitor spending was 32 jobs per million in sales. Line 3. The income to sales ratio entered on line 3 captures the total income effects per dollar of total sales. The income figure used here includes wage and salary income and proprietor’s income, rents and profits. Income to sales ratios vary across industries in a similar fashion as the job ratios. Tourism businesses may convert 50-60% of sales directly to income, while ratios for manufacturing can be much lower (20-40%). Based on the sectors receiving direct, indirect and induced effects of tourism spending, the income ratio associated with tourism spending generally falls between 45% and 55%. For Mammoth Cave, the income ratio was 51%. Readers familiar with multipliers will note that the job and income ratios used in the MGM worksheet are slightly different than job and income multipliers normally used by regional economists. The MGM worksheet estimates total sales first, and then converts total sales to total jobs and income. More traditional multipliers (Keynesian) would convert from direct sales to total income and jobs without the intermediate step. The multipliers (from an IMPLAN model) in this case are .98 for income and 62 total jobs per million in direct sales. These multipliers should be applied to tourist spending that has been adjusted for the capture rate. Equivalently, the multipliers themselves may be adjusted by the capture rate to yield corresponding “tourist spending” multipliers (.71 for income and 44 for jobs per million sales). These adjusted multipliers can be multiplied directly times tourist spending to yield total income and jobs accruing to the region’s economy. This example illustrates how easily multipliers can be confused and multiplied by the wrong base. We are able to illustrate all of these relationships here because a complete input-output modeling approach was carried out to establish the correct multipliers and ratios for this example. Table 3 summarizes the direct and total effects for Mammoth Cave and the various multipliers and ratios that can be derived. Table 3. Economic Impact of Mammoth Cave Visitor Spending, 1993 Economic Measure Sales Income Jobs Direct effect ($millions) 36.5 18.6 1,544 Ratio Multiplier 1.93 1.94 1.47 Total effect ($millions) 71 36.1 2,265

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For practice, readers are urged to compute the various multipliers and ratios themselves and apply them to the spending figures. The multiplier calculations are shown below: Multiplier Formulas Visitor Spending = $51 million Capture Rate = Direct sales / Visitor spending ( 72%= $36.5 / $51), Direct sales effects = Visitor spending X capture rate ($36.5 = $51 * 72%) Ratio Multipliers (Table 3)- Multiply these by the direct effects column to get total effects. Sales multiplier = total sales/direct sales (1.93= $71 / $36.5) Income multiplier = total income/direct income (1.94 = $36.1 / $18.6) Job multiplier = total jobs/direct jobs (1.47 = 2,265 / 1,544) Keynesian Multipliers (Response coefficients). Multiply these by direct sales to get total effects. Income multiplier = total income/direct sales ($36.1 / $36.5 = .98) Job multiplier = total jobs/direct sales (2,265 / $36.5 = 62 jobs per million in sales) Tourist spending multipliers – Multiply these by total tourist spending to get total effects Income multiplier = total income/tourist spending = $36.1 / $51 = .71 Job multiplier = total jobs/tourist spending = 2,265 / $51 = 44 jobs / million in spending Check on spending multipliers Spending multiplier = Keynesian multiplier * capture rate Income: .71 = .98 * 72% Jobs : 44 = 62 * 72%

Summarizing the Results The results may be briefly summarized as follows. Visits by 430,000 parties to Mammoth Cave National Park in 1993 (from outside the local area) resulted in $51 million dollars in spending in the local area. An average party spent $127 in the area during their stay, not including spending inside the park. Seventy-two percent of the spending was captured by the local economy (a three county area around the park) as local final demand. Each dollar of direct sales added another 93 cents in secondary effects (mostly induced effects), yielding a total sales effect of $71 million. Including these multiplier effects, visitor spending added $36 million in income to the regional economy and supported over 2,000 jobs. Like many tourism impact studies, the Mammoth Cave example doesn’t explicitly identify an action being evaluated. The study measures economic activity associated with visits to a given park. The results assume any trip to the area involving a visit to the park, should be counted as a park impact. This implicitly assumes that none of the spending by park visitors would have occurred in the absence of the park. Some visitors and spending would likely not be lost if the park were closed as some visitors would still come for other attractions in the area or simply as a rest stop along interstate 65. One can apply the MGM model framework to evaluate a specific policy action by only including the change in visitors and spending due to a proposed action. This example illustrates how a quick estimate of economic impact can be generated using the MGM worksheet. For this example, we had the advantage of a more complete study that included both a visitor spending survey and a local input-output model. These provided the appropriate estimates of the key MGM worksheet variables - spending averages, multipliers, and income and employment ratios. The survey also provided information to adjust park visit figures to a party trip basis and to omit some of the double counting associated with multiple park visits on a given trip.

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Example 2. Using “Off-the-Shelf” Multipliers -- the BEA approach.
The U.S. Department of Commerce, Bureau of Economic Analysis has an ongoing program of regional economic analysis. As part of this effort they maintain an input-output modeling system called RIMS II. Clients may obtain customized multipliers for various sub-regions of the United States from the RIMS II model. In a user handbook (USDC, BEA, 1992) BEA has published a set of multipliers for 39 designated industry groups for each of the 50 states. The BEA multipliers for Michigan are shown in Table 4. These are Type II multipliers including indirect and induced effects. The use of these BEA multipliers in tourism impact estimation can be illustrated via a simple example.

EXAMPLE: Assume a promotional program or some other action generates an additional 20,000 visitor (party) days to the state of Michigan, say 10,000 day users and 10,000 overnight stays in motels. The resulting spending and economic impacts may be computed in a series of steps illustrated in Tables 5a-5e. 1. Estimate total visitor spending. Table 5a begins with a spending profile for each segment with spending itemized into 6 categories. Spending must be divided into categories to apply sector-specific multipliers. In the example, we assume a typical day user spends $50 per party per day, an overnight visitor spends $100 per party per day. The spending estimates could be based on judgement, previous studies, or a new survey. Notice how breaking visitors into even two segments helps to estimate the spending profiles and also permits the evaluation of marketing strategies that may differentially attract day users or overnight visitors. Multiplying the number of visitor days for each segment by their average spending and adding the two segment's contributions yields a total spending of $1.5 million. Table 5a. Computation of Total Visitor Spending Visitor Segment
Day Users Overnight Visitors Total

Number of Visitors (party days) Average spending per visitor Lodging Restaurant Groceries Gas & Oil Recreation Other Total Total Spending a Lodging Restaurant Groceries Gas & Oil Recreation Other Total

10,000

10,000

20,000

$0 $20 $10 $5 $5 $10 $50

$25 $30 $10 $5 $5 $25 $100

$13 $25 $10 $5 $5 $18 $75

200,000 100,000 50,000 50,000 100,000 500,000

250,000 300,000 100,000 50,000 50,000 250,000 1,000,000

250,000 500,000 200,000 100,000 100,000 350,000 1,500,000

a. Multiply spending per party by number of visitor parties, making sure visits and spending are in comparable units.

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Table 4. Total Multipliers, by Industry Aggregation --for Output, Earnings, & Employment- Michigan Industry Aggregation Final demand multipliers Output Earnings Jobs
1.7833 1.3907 1.0000 1.4477 2.1005 2.1913 2.1249 1.8095 1.8086 1.9003 2.0230 2.0890 1.8646 2.0897 2.1002 2.0487 2.1519 2.3508 2.1657 2.1782 2.4770 2.1356 1.9835 2.0648 1.8959 1.5725 1.5810 1.7576 1.9514 1.9620 2.2135 1.2046 1.8605 1.8953 1.9725 1.9395 2.0127 2.0881 1.0659 0.4433 0.1655 0.0000 0.1922 0.6123 0.7068 0.7839 0.3463 0.4338 0.5207 0.4627 0.6313 0.3814 0.5349 0.6155 0.5704 0.6099 0.7035 0.7215 0.6437 0.5631 0.6068 0.6288 0.5597 0.7181 0.4777 0.2116 0.6537 0.7817 0.7142 0.8286 0.0793 0.5745 0.7571 0.8518 0.5800 0.9067 0.7281 0.3317 35.1000 9.7000 0.0000 7.4000 23.1000 30.2000 31.7000 5.6000 17.2000 19.6000 17.3000 27.2000 13.5000 23.1000 25.1000 21.4000 21.7000 26.3000 26.1000 24.7000 19.3000 22.2000 25.1000 24.5000 28.8000 18.1000 7.5000 24.2000 48.7000 31.4000 33.8000 3.7000 38.3000 50.0000 36.5000 49.9000 39.2000 42.0000 17.5000 ……

Direct effect multipliers Earnings Jobs
2.0227 4.3015 0.0000 1.9540 2.0412 2.2620 1.8160 2.7893 2.1632 2.0166 2.4461 1.9957 2.6099 2.1968 2.2339 2.1583 2.3001 2.4326 2.0200 2.2883 3.8872 2.3016 1.9363 2.3398 1.6852 1.6155 2.5793 1.5886 1.6056 1.8422 2.0020 5.4688 1.8490 1.5949 1.6153 1.8537 1.5110 1.8223 1.5571 2.8744 0.0000 2.4041 2.4836 2.4017 2.0795 3.5207 2.4775 2.5570 2.9950 2.1020 3.6920 2.1780 2.4543 2.6170 2.9811 2.8572 2.5140 2.6060 6.4247 2.8092 2.1917 2.3571 1.9438 2.0005 3.6638 2.0117 1.4420 2.0065 2.3495 4.6981 1.5260 1.3903 1.7696 1.3398 1.6294 1.5856 .....................

Agriculture, forestry, and fisheries:
Agricultural products and ag., for., fishery services Forestry and fishery products

Mining:
Coal mining Crude petroleum and natural gas Miscellaneous mining

Construction:
New. construction Maintenance and repair construction

Manufacturing:
Food and kindred products and tobacco Textile mill products Apparel Paper and allied products Printing and publishing Chemicals and petroleum refining Rubber and leather products Lumber and wood products and furniture Stone, clay, and glass products Primary metal industries Fabricated metal products Machinery, except electrical Electric and electronic equipment Motor vehicles and equipment Transportation equipment exc. motor veh. Instruments and related products Miscellaneous manufacturing industries

Transportation and public utilities:*
Transportation Communication Electric, gas, water, and sanitary services

Wholesale and retail trade:
Wholesale trade Retail trade

Finance, insurance, and real estate:
Finance Insurance Real estate

Services:
Hotels and lodging places & amusements Personal services Business services Eating and drinking places Health services Miscellaneous services Households

1. Each entry in column 1 represents the total dollar change in output that occurs in all row industries for each additional dollar of output delivered to final demand by the industry corresponding to each entry. 2. Each entry in column 2 represents the total dollar change in earnings of households employed by all row industries for each additional dollar of output delivered to final demand by the industry corresponding to each entry. 3. Each entry in column 3 represents the total dollar change in number of jobs in all row industries for each additional dollar of output delivered to final demand by the industry corresponding to each entry. 4. Each entry in column 4 represents the total dollar change in earnings of households employed by all row industries for each additional dollar of earnings paid directly to households employed by the industry corresponding to each entry. 5. Each entry in column 5 represents the total change in number of jobs in all row industries for each additional job in the industry corresponding to each entry. SOURCE: USDC, BEA (1992).

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2.

Extract margins on retail purchases of goods. Table 5b reports the retail margins for each spending category and divides the producer price of the item between local producers and imports. Services like lodging and meals are all produced locally and there are no margins on services. In this example, we assume 10% of tourist purchases of groceries goes to the grocery store (the retail margin), and 10% of the remaining cost of the groceries accrues to Michigan producers. The other 80% immediately leaks out of the region to cover the costs of groceries produced outside Michigan. For simplicity we have ignored wholesale and transportation margins in these calculations. They can be handled in the same way as retail margins, if the wholesaler or shipper is located within the study region. Table 5b. Retail margins, local production and imports by sector Sector Lodging Restaurant Groceries Gas & Oil Recreation Other Retail Margina 0% 0% 10% 10% 0% 40% Local Production 100% 100% 10% 0% 100% 10% Imports 0% 0% 80% 90% 0% 50%

a. For illustrative purposes, we assume no wholesale or transportation margins. These would be handled similarly.

3.

Compute direct sales effects. Direct sales effects of tourist spending are estimated in Table 5c by applying the margins in Table 5b to total spending within each sector in Table 5a. The retail margins are accumulated across all goods and then entered as local production in the retail trade sector. $1.5 million in tourist spending yields $1.075 million in direct sales effects within Michigan -- $425,000 of the spending leaks out immediately to cover tourist purchases of imported items. The ratio $1.075/$1.5 is called the capture rate. In this case 72% of tourist spending is captured by the local region as direct sales. Table 5c. Computation of Direct Sales Effects - Local Final Demand Sector Lodging Restaurant Groceries Gas & Oil Recreation Other Retail Total Visitor Spending 250,000 500,000 200,000 100,000 100,000 350,000 1,500,000 Retail Margina 20,000 10,000 140,000 170,000b
Local Production

Importsc 160,000 90,000 175,000 425,000

250,000 500,000 20,000 100,000 35,000 170,000b 1,075,000

a. Multiply margins in Table 5b times corresponding total spending by sector in Table 5a. b. Retail margins are accumulated across goods that tourists purchase and entered in Retail trade sector as local production. c. Imported goods do not show up as direct effects for the region. The $425,000 in imports are deducted from visitor spending to yield local production or final demand. The capture rate = total local production/visitor spending =1,075,000/1,500,000 = 72%.

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4. Assemble multipliers by sector for the region. Multipliers in Table 5d come directly from the BEA table for Michigan (Table 4). The multipliers from Table 4 have been rounded here for simplicity. One must find a BEA sector that matches the spending category. Table 5d. Multipliers for State of Michigan a Sector Lodging Restaurant Groceries Gas & Oil Recreation Manufacturing Retail Final demand multipliers Sales Earnings 1.86 1.94 1.81 1.86 2.09 2.07 1.95 0.57 0.58 0.35 0.38 0.73 0.56 0.78 Jobs 38.30 49.90 15.60 13.50 42.00 24.50 48.70 Direct effect multipliers Earnings Jobs 1.85 1.85 2.79 2.61 1.82 2.34 1.61 1.53 1.34 3.52 3.69 1.59 2.36 1.44

a. From RIMS II published multipliers for State of Michigan 1992, see Table 4.

5.

Compute multiplier effects. Economic impacts in terms of sales, income and jobs are calculated in Table 5e by multiplying the direct sales effects (local production from Table 5c) by the appropriate multiplier in Table 5d. Sector-specific multipliers are applied to the changes in direct sales (final demand) in the first column to yield total sales effects (direct plus indirect plus induced). Income and employment multipliers are applied to the direct sales to estimate total income and job effects. Notice that the RIMS II income and job multipliers are different than those used in the MGM worksheet. The RIMS II multipliers should be multiplied by direct sales within the corresponding sector, while the multipliers used in the MGM worksheet were multiplied by total sales, including secondary sales effects. This illustrates the importance of understanding sometimes subtle differences in how multipliers are defined. It is important to read the footnotes in Table 4 to understand what the RIMS II multipliers should be multiplied by and what they mean. Table 5e. Tourism Spending Impacts ($MM) Sector Lodging Restaurant Groceries Gas & Oil Recreation Manufacturing Retail Total Direct Sales ($MM) 0.25 0.50 0.02 0.00 0.10 0.04 0.17 1.08 Sales ($MM) 0.47 0.97 0.04 0.00 0.21 0.07 0.33 2.08 Total effects Income ($MM) 0.14 0.29 0.01 0.00 0.07 0.02 0.13 0.66 Jobs 9.30 24.20 0.41 0.00 4.29 0.99 8.33 47.52

Notice that the BEA approach provides a bit more detail in terms of which sectors contribute to the direct and total effects of tourism spending. The method also forces the analyst to explicitly handle margining of goods that are purchased by tourists and determine what percent come from local producers. The sector-specific multipliers will adjust the estimates of secondary effects to fit the kinds of spending under consideration. One should not apply the statewide multipliers for Michigan to another state or to smaller regions within Michigan. With a little effort and expense, one can obtain multipliers for local regions (e.g., they may be obtained from the BEA or other organizations that develop and maintain input-output models such as the Minnesota Implan Group (MIG)). BEA includes other examples of the use of published multipliers in the first (1985) and third edition (1997) of their regional multiplier handbook. A tourism application is included in the first edition (USDC, BEA, 1985).

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Summarizing the results Twenty thousand additional visitors (half day users and half overnight) spend $1.5 million in Michigan. About a million dollars is captured by the Michigan economy (72% capture rate) as final demand or direct sales. Only retail and wholesale margins are captured on purchases of goods not manufactured in Michigan. Including indirect and induced effects the tourist spending results in a total impact of $2.08 million in sales (sales multiplier of about 2), yielding $660,000 in income, and supporting about 48 jobs. Restaurants account for $.29 million of the $.66 million in income, followed by lodging establishments ($.14 million) and retail trade ($.13 million). These results may be divided by 20 to put them on a "per thousand visitor day" basis., i.e. each additional 1,000 tourist party nights results in $75,000 in spending (at $75 per party day) and a total economic impact on the state of $104,000 in sales, $33,000 in income and 2.4 jobs.

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Example 3. Using an Input- Output Modeling System; The IMPLAN/MI-REC System
More complete economic impact analyses typically use an input-output (I-O) model of the region’s economy to estimate secondary effects. These models provide more sectoral detail in the multipliers and results and give the user some control over model assumptions and calculation methods. Some familiarity with input-output models and the modeling software is needed to use these models and properly interpret the results. An inexpensive and quite accessible economic impact estimation system tailored to recreation and tourism applications is the MI-REC/IMPLAN system (Stynes & Propst 1996). This micro-computer based system combines a spreadsheet program for estimating tourist spending with the IMPLAN input-output modeling system. The system is quite flexible allowing varying levels of aggregation and segmentation to suit the application and available data. Users estimate spending in a spreadsheet program by entering the number of visitors within designated segments and a spending profile for each segment. Users may select from several available spending data sets from recent surveys, adjust these to suit the application, or enter their own spending data from a local survey. The MI-REC program bridges the spending to a local input-output model estimated with IMPLAN. IMPLAN is a regional economic modeling system that generates local I-O models with up to 528 sectors by adjusting the national I-O table to a local region. Recent data on sales, income and employment for each county within each of the 528 sectors are used to adjust the model to the local area. IMPLAN produces a variety of reports describing the regional economic structure, trade flows and multipliers. It also includes an impact estimation routine that estimates direct, indirect, induced, and total impacts of changes in final demand. MI-REC imports the impact reports produced by IMPLAN to a spreadsheet and automatically generates summary reports. Tables 6a-6c show sample MI-REC reports for estimates of statewide tourism impacts in Michigan. The example estimates the impacts of all tourist spending at destinations in Michigan in 1990. Tourism was defined as all trips of 100 miles or more or overnight to Michigan. The spending figures do not include en route spending or airfares. 1. Spending and Visits. Two basic inputs to the MI-REC system are estimates of the number of visitors and a per visitor spending profile. The system encourages estimates of visits and spending for a set of market segments. To capture spending of tourists to Michigan, visitors are divided into 10 segments defined by residency and lodging type. Table 6a reports the distribution of visitors across the 10 segments. Visits and spending are both in terms of party nights. Table 6b reports average spending within 8 spending categories for the 10 market segments. Spending profiles were compiled from a number of sources, in some cases using judgement or an engineering approach to make the estimates. MI-REC includes spending profiles for a number of recreation and tourism market segments that can be adjusted to a particular application. Users may also enter their own spending averages. Total visitor spending is computed in Table 6c by multiplying the spending profile by number of visits segment by segment. Table 6a. Number of Visitors to Michigan by Segment
SEGMENT Michigan residents Motel (MOTEL) Camping (CAMP) Seasonal home (SEAS) Day user (DAY) Visit Friends & relatives (F&R) Out of state Visitors Motel (MOTEL) Camping (CAMP) Seasonal home (SEAS) Day user (DAY) Party days/nights (millions) 13.1 2.5 13.1 9.4 11.9 3.1 1.2 1.8 1.2 5.0 62.6 Percent 21% 4% 21% 15% 19% 0% 5% 2% 3% 2% 8% 100%

Total

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Table 6b. Average spending per party day/night at destination Segments defined by residency and lodging type Michigan residents Out-of-state visitors CAMP SEAS DAY F&R MOTEL CAMP SEAS DAY

MOTEL

F&R

TOTAL

SPENDING WITHIN 30 MILES OF Destination Lodging Restaurant Auto Boat Fish Groceries Recreation Other Total 35.00 25.00 8.50 0.05 0.05 10.00 3.00 18.40 100.00 6.00 10.00 6.00 0.00 0.00 15.00 3.00 15.00 55.00 0.00 9.00 7.50 2.00 0.00 16.00 2.00 13.50 50.00 0.00 15.00 5.00 0.00 0.00 10.00 9.00 11.00 50.00 0.00 10.00 5.00 0.00 0.00 10.00 0.00 25.00 50.00 35.00 35.00 9.50 0.05 0.05 10.00 3.00 27.40 120.00 10.00 15.00 7.00 0.00 0.00 20.00 3.00 10.00 65.00 0.00 9.00 7.50 2.00 0.00 16.00 2.00 13.50 50.00 0.00 15.00 7.00 0.00 0.00 10.00 9.00 11.00 52.00 0.00 10.00 5.00 0.00 0.00 10.00 0.00 25.00 50.00 9.54 15.11 6.68 0.49 0.01 11.84 2.97 17.89 64.54

Table 6c. Total spending by Visitors ($000's) at Destinations Segments defined by residency and lodging type Michigan residents Out-of-state visitors CAMP SEAS DAY F&R MOTEL CAMP SEAS

MOTEL

DAY

F&R

TOTAL

SPENDING WITHIN 30 MILES OF Destination Lodging Restaurant Auto Boat Fish Groceries Recreation Other Total Pct 460,638 329,027 111,869 658 658 131,611 39,483 242,164 1,316,107 32.5% 15,041 25,069 15,041 0 0 37,603 7,521 37,603 137,878 3.4% 0 118,450 98,708 26,322 0 210,577 26,322 177,674 658,054 16.3% 0 141,011 47,004 0 0 94,008 84,607 103,408 470,038 11.6% 0 119,076 59,538 0 0 119,076 0 297,691 595,382 14.7% 109,676 109,676 29,769 157 157 31,336 9,401 85,860 376,031 9.3% 12,534 18,802 8,774 0 0 25,069 3,760 12,534 81,473 2.0% 0 16,921 14,101 3,760 0 30,082 3,760 25,382 94,008 2.3% 0 18,802 8,774 0 0 12,534 11,281 13,788 65,179 1.6% 0 50,137 25,069 0 0 50,137 0 125,344 250,687 6.2% 597,889 946,970 418,647 30,897 815 742,034 186,135 1,121,449 4,044,836 100.0% 14

2.

I-O models. An input-output model for the local region is estimated using the IMPLAN system. IMPLAN can estimate models for any county or grouping of counties. In this case a model was estimated for the state of Michigan. MI-REC bridges spending in up to 33 categories to the IMPLAN model sectors. Table 6d shows sample IMPLAN impact reports for the direct and total effects. Total effects are the sum of direct, indirect, and induced effects (IMPLAN produces separate tables for indirect and induced effects, not shown here). The aggregated reports shown here group economic sectors into 20 broad categories, while highlighting individual tourism sectors like lodging, eating and drinking establishments, and amusements. Six measures of economic activity covering sales, income, value added and jobs are reported for each sector (see footnotes at the bottom of the table for definitions). Impact reports. Table 6e summarizes the economic impacts of tourism in Michigan. In this example, $4.045 billion in tourist spending in Michigan resulted in $2.7 billion in direct spending effects (67% capture rate), $1.5 billion in direct income and supported about 95,000 direct jobs. The reported sales, income and employment multipliers are all ratio type multipliers capturing both indirect and induced types of secondary effects. IMPLAN produces Type III multipliers. Total sales, income and job effects may be obtained by applying the multipliers to the corresponding direct effects. MI-REC also computes an “effective spending multiplier” which is simply the sales multiplier times the capture rate. This multiplier may be multiplied by tourism spending to yield total sales impacts. Table 6e. Economic Impacts of Tourism Spending in Michigan, 1990 - Summary Economic measure Output/Sales ($MM) Total Income ($MM) Jobs Direct Effects 2,709 1,475 94,442 Multiplier 2.29 2.27 1.65 4,045 67% 1.53 Total Effects 6,200 3,348 155,839

3.

Total Visitor Spending ($millions) Capture rate Effective spending multiplier
4.

Sectoral detail. Table 6f breaks the income effects down into seven sector groupings. Sales and jobs may be reported in a similar fashion. Notice how the use of an I-O model permits impacts to be traced to particular sectors. The direct effects accrue largely to tourism and retail sectors, while secondary effects accrue largely to services and retail trade. The majority of secondary effects are induced, as the Type I sales multiplier for tourism spending tends to fall in the 1.1 to 1.3 range. Table 6f. Income Effects of Tourism Spending in Michigan, 1990 Sector Group Manufacturing Trans. & Services Recreation Hotel Eat & Drink Retail and Wholesale trade Government Total
a.

INCOME EFFECTS ($MM) DIRECT SECONDARY TOTAL MI 1990a 95 37 123 334 432 449 6 1,475 264 1,046 33 16 45 402 66 1,873 358 1,083 157 350 478 851 72 3,348 80,884 40,973 323 637 3,183 19,826 21,756 167,582

PCT 0.4% 2.6% 48.4% 54.8% 15.0% 4.3% 0.3% 2.0%

MI 1990 is the total income reported by all establishments in each sector in Michigan in 1990.

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Table 6d. Sample IMPLAN aggregated impact reports, Michigan tourism 1990 Direct Effects
Sector Number
1 28 48 58 108 125 210 215 299 433 443 447 448 454 456 463 477 483 488 510

Industry

Final Demand (MM$)
16.43 3.54 0.00 115.08 23.57 51.75 32.21 1.33 1.94 11.49 34.18 114.89 556.21 936.17 0.00 588.96 30.19 25.51 152.54 13.21 2,709

TIO (MM$)
16.43 3.54 0.00 115.08 23.57 51.75 32.21 1.33 1.94 11.49 34.18 114.89 556.21 936.17 0.00 588.96 30.19 25.51 152.54 13.21 2,709

Employee Property Income. Income (MM$) (MM$)
0.57 0.23 0.00 23.15 8.83 13.59 1.36 0.40 0.45 4.33 16.52 72.80 280.48 349.69 0.00 283.55 5.97 10.14 59.62 3.03 1,135 1.80 1.53 0.00 22.89 2.63 11.93 4.48 0.20 0.61 2.01 3.30 16.13 79.33 82.63 0.00 50.17 4.53 3.48 50.14 2.92 341

Total PoW Income (MM$)
2.37 1.76 0.00 46.04 11.46 25.52 5.84 0.60 1.06 6.34 19.82 88.93 359.80 432.32 0.00 333.71 10.50 13.62 109.76 5.95 1,475

Value Added (MM$)
2.59 2.79 0.00 47.48 11.57 25.92 6.75 0.61 1.07 6.68 20.36 110.47 437.16 484.70 0.00 393.94 10.99 16.49 112.59 5.96 1,698

Employment (Number of Jobs)
234 13 714 392 542 23 7 13 136 769 1,953 22,790 36,860 21,018 349 468 8,064 96 94,442

AG, FOR & FISH MINING CONSTRUCTION FOOD PROCESSING APPAREL MANUFACTURING Petroleum Refining AUTO PARTS & ACCESS SPORTING GOODS TRANSP. & COMMUNIC OTHER SERVICES Wholesale Trade RETAIL Eating & Drinking F.I.R.E Hotels And Lodging Places AUTO SERVICES OTHER AMUSEMENTS Amusement And Recreation GOV'T & OTHER Total

Total Effects
1 28 48 58 108 125 210 215 299 433 443 447 448 454 456 463 477 483 488 510 AG, FOR & FISH MINING CONSTRUCTION FOOD PROCESSING APPAREL MANUFACTURING Petroleum Refining AUTO PARTS & ACCESS SPORTING GOODS TRANSP. & COMMUNIC OTHER SERVICES Wholesale Trade RETAIL Eating & Drinking F.I.R.E Hotels And Lodging Places AUTO SERVICES OTHER AMUSEMENTS Amusement & Recreation GOV'T & OTHER Total 26.68 5.57 0.00 198.86 30.41 218.41 45.69 7.14 3.15 106.31 846.51 205.30 1017.97 1030.37 617.67 613.69 55.77 55.61 166.79 69.43 5,321 108.80 11.73 90.87 281.22 30.97 345.36 50.38 21.15 3.77 170.43 948.50 243.20 1022.23 1034.29 795.18 617.04 59.76 76.86 166.89 121.62 6,200 8.89 0.60 46.50 47.99 11.46 82.59 2.13 6.33 0.89 55.76 423.24 154.10 524.15 386.34 131.00 297.06 12.01 29.94 65.24 55.91 2,342 17.25 5.01 10.30 45.57 3.35 58.04 7.01 3.15 1.15 53.54 83.01 34.15 138.60 91.29 314.80 52.56 9.55 6.54 54.86 16.36 1,006 26.14 5.61 56.79 93.57 14.81 140.63 9.14 9.48 2.04 109.30 506.26 188.25 662.75 477.63 445.80 349.62 21.56 36.48 120.09 72.27 3,348 27.54 7.90 57.37 96.64 14.95 143.31 10.56 9.65 2.06 115.67 519.38 233.84 806.29 535.50 570.29 412.72 22.58 41.75 123.19 72.33 3,824 1,672 31 1,714 1,486 516 2,259 36 117 26 1,578 19,376 4,135 40,047 40,724 6,258 22,020 717 1,867 8,823 2,437 155,839

a. Economic measures reported by column are defined as follows: Final demand = sales to final consumers (tourists, households, or government) TIO = Total industrial output = sales to final demand plus intermediate sales between production sectors Employee Income = all wage and salary income Property Income = Proprietor's income, rents and profits Total Income by POW (place of work) = sum of employee compensation and property income reported by place of work Value Added = Total income plus indirect business taxes Employment = number of jobs (not full time equivalents)

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5.

Control Totals. Another advantage of the I-O model is the ability to compare impact estimates with the observed levels of economic activity in each sector. The last two columns of Table 6f report the total income reported by each sector in Michigan in 1990 and the percentage of this total that tourism spending impacts represent. The tourism spending included in this application accounted for about 55% of income in the hotel sector in 1990. This percentage is reasonable, as only room costs were allocated to the hotel sector. Room revenues generally account for about 60% of hotel receipts (and income). The control totals provided by the I-O model therefore help to validate the estimates. We’ve encountered many situations where inflated estimates of either visitors or spending yield impact estimates that exceed the actual sales or jobs in the region. The hotel sector is a good one for checking the validity of impact estimates as it is a reasonably clean “tourism sector”. The percentages also provide useful measures of the relative impact of tourism on the region

REFERENCES

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Stynes, D.J. and Propst, D.B. 1992. A system for estimating local economic impacts of recreation and tourism. In. Measuring tourism impacts at the community level. S. Reiling (Ed). Maine Agr. Expmt. Sta. Misc. Report #374. Stynes, D.J. and Propst, D.B. 1996. MI-REC manual Version 3.0. East Lansing, MI: Department of Park, Recreation and Tourism Resources, Michigan State University. Stynes, D.J. and Rutz, E.A. 1995. Regional economic impacts of Mammoth Cave National Park. East Lansing, MI: Department of Park, Recreation and Tourism Resources, Michigan State University. U.S. Dept. of Commerce, Bureau of Economic Analysis. 1992. Regional multipliers: A user handbook for regional input-output modeling system (RIMS II). Second edition. Washington, D.C.; U.S. Gov’t Printing Office. U.S. Dept. of Interior, National Park Service. (1990). The Money Generation Model. Denver, CO: Office of Social Science, Socio-Economic Studies Division. U.S. Travel Data Center. 1997. Impact of travel on state economies, 1995. Washington, D.C.

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