Historic, archived document Do not assume content reflects current scientific knowledge, policies, or practices. ~~, United States “\. Department of ; Agriculture Forest Service Pacific Northwest Forest and Range Experiment Station Research Note PNW-432 October 1985 _ Abstract Assessing Impacts A Dynamic Simulation Model for Analyzing the Importance of Forest Resources in Alaska Wilbur R. Maki, Douglas Olson, and Con H Schallau PSW FOREST AND RANGE XPERIMENT STATION 7 >, = om ~ es 8 JAN ZY 986 } ‘ 4 STATION LIBRARY COPY | A dynamic simulation model has been adapted for use in Alaska. It provides a flexible tool for examining the economic consequences of alternative forest resource management policies. The model could be adapted for use elsewhere if an interindustry transaction table is available or can be developed. To demonstrate the model’s usefulness, the contribution of the pulp and paper and tourism in- dustries to Alaska’s economy is analyzed. A $105 million increase in final demand for goods and services provided by the tourism industry would compensate for the loss of employment and earnings resulting from the closure of Alaska’s two pulp- mills. Most of the loss would be confined to higher paying technical jobs in two remote locations; the increase in jobs would involve lower paying jobs located throughout the State. Keywords: Economic importance (forests), models, simulation, Alaska, manage- ment planning (forest). The livelihood of many Alaska residents is dependent on forest resources. Employees of the forest products industry are obviously dependent, but to varying degrees, employees in commercial salmon fishing, tourism, and some mineral- based industries are also influenced by forest resource management policies. Any plan involving changes in National Forest management policies should include an analysis of socio-economic impacts. For example, the Alaska National Interest Lands Conservation Act (ANILCA) requires that the USDA Forest Service prepare periodic assessments of management for the Tongass National Forest. These assessments must include an analysis of how timber management policies affect the employment, income, and population of southeast Alaskans. WILBUR MAKI is a professor, University of Minnesota, Depart- ment of Agricultural and Applied Economics, St. Paul, MN 55108. DOUGLAS OLSON is a research fellow, University of Minnesota, Department of Agriculture and Applied Economics, assigned to the Forestry Sciences Laboratory, Corvallis, OR 97331. CON SCHALLAU is project leader at the Pacific North- west Forest and Range Experiment Station, Forestry Sciences Laboratory, 3200 Jefferson Way, Corvallis, OR 97331. Analyzing Hypothetical Scenarios Scenario 1: Alaska’s Pulpwood Industry To perform the economic impact analyses, a dynamic simulation model (IPASS) was adapted for use in Alaska. This paper describes how it can be used to evaluate forest resource management situations in Alaska. / IPASS can help to answer many of the questions facing policy analysts: Questions such as who would be affected by the closure of wood processing mills in Alaska? . who would be affected by new investment in recreation and tourism facilities? and might the growth of the tourism industry counteract the decline in timber-based in- dustries? The following discussion will show how IPASS can be used to analyze the economic significance of three resource-related scenarios. The two pulpmills in southeast Alaska produce dissolving pulp. In 1977, production and export was roughly valued at $105 million. But increasing world-wide competi- tion, depressed markets, and the high cost of installing pollution abatement equip- ment threatens the operation of these mills. In this scenario, we assume the worst case—a complete shutdown of both mills with a permanent loss of $105 million in regional exports. Table 1 shows the im- pact of the mill shutdown on both employment and earnings, by year, in ag- gregated sectors of the economy.2 The effect on the pulp and paper industry is immediate and, also, is greater than for any other industry. The two other wood products sectors, however, are also adversely affected because they provide logs and mill residues to the pulpmills. For years 2 through 5, the service industries show the indirect impacts of the loss of personal income, loss of population, and the overall reduction in economic activity caused by the mill closures. Table 1 also shows how the various occupations were affected by the closure of the two pulpmills. Industrial technicians, who account for the largest proportion of the pulp and paper employees, experience the greatest and most lasting impact. The pulpmills account for most of the basic jobs in the communities where they are located. Consequently, the mill closures would undoubtedly cause many in- dividuals to move elsewhere—in the State or otherwise—because of the lack of reemployment opportunities. Pulpmill workers have traditionally received above- average wages; consequently, former pulpmill employees choosing to remain somewhere in Alaska would undoubtedly have to be retrained or accept lower wages. YA brief description of the IPASS model is provided in Appen- dix 1. For a more complete explanation of the IPASS system see, Olson, Doug; Schallau, Con; and Maki; Wilbur. IPASS: an interactive policy analysis simulation system. Gen. Tech. Rep. PNW-170’Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Sta- tion; 1984. 70'p. 2/ Appendix 3 provides.a list of the 75 sectors in the Alaska model. Data for 75 sectors were derived and then were ag- gregated for the purpose of this paper. Table 1—Impact on the Alaska economy’ caused by closure of two pulpmills Industry Agriculture, forestry, and fisheries Mining Construction Manufacturing: Logging Sawmil1s Pulp and paper mills Transportation, communications, and utilities Trade Finance, insurance, and real estate Services Government Total Agriculture, forestry, and fisheries Mining Construction Manufacturing: Logging Sawmills Pulp and paper mills Transportation, communications, and utilities Trade Finance, insurance, and real estate Services Government Total Managers Professional Technical Service Industrial technicians Clerical Sales Farm Year of simulation 1 2 3 4 5 JOBS LOST OR GAINED, BY SECTOR “11 11 -12 =] -8 =i 3 -5 -4 -4 -14 -59 -82 -52 -42 -1,578 -1,646 -1,319 -1,068 ~99] -460 -518 -342 -164 -138 -48 -56 -46 -4) -4) -1,065 -1,056 -917 -843 -793 -9) -136 -156 -136 -123 -30 -153 -315 331 -417 =13 -69 -14 = -15 =2i -102 -172 -156 -187 -18 -30 -46 -59 -51 -1,778 “2,208 -2,122 -1,891 -1,905 EARNINGS LOST OR GAINED (THOUSAND DOLLARS) -24) -263 -223 -105 -138 234 -112 -170 -155 -130 -501 -2,156 -3,000 -1,900 -1,526 -36,879 -38,440 -30,764 -24,900 -23,079 -10,753 V2, Uileae7 981): '=39826 -3,222 -1,025 -1,179 -965 -868 -867 -25,012 -24,817 -21,537 -19,805 -18,646 =aeaS =3 M0) =3,923 0/3268 -2,932 -605 =2,763 ~ -4,805 -3,780 -5,545 -196 -1,052 -205 -1,172 -1,129 -495 750 62,681 25442 -2,815 -244 -440 -654 -862 -848 -41,305 -50,147 -46,426 -38,584 -38,143 EMPLOYMENT, LOST OR GAINED BY OCCUPATION -18 -125 -128 -122 -130 -9) =121 -130 =121 -125 -18 -28 -42 -39 -42 -81 -117 =n “117 -195 =,327 =1,494 -1,289 -1,050 -1,002 -164 -269 -279 -298 -314 =17 -54 -77 -81 -95 =] -1 -3 =3 ~3 \/The impact is derived by subtracting the baseline data (that is, simulation of historical data) from the impact scenario data. employment or earnings. A minus sign indicates a loss of Scenario 2: Changes in Tourism Scenario 3: Will growth in tourism offset a decline in pulp production? In this scenario, we assume that promotion of Alaska tourism will increase the sale of goods and services produced in Alaska by $105 million.2/ What impact will this have on employment and earnings? To answer this question we used national averages for tourism-related expenditures to derive estimates of tourism expen- ditures by industry. Table 2 shows that increased tourism would greatly stimulate employment and earnings in the service, trade,.and transportation industries. All occupational categories would also grow. Scenario 3 is a combination of scenarios 1 and 2. This scenario examines the ex- tent to which an increase in annual tourism expenditures of $105 million compen- sates for a coincidental decrease of $105 million in exports resulting from a closure of the two pulpmills. Table 3 shows the impact of this scenario on employment, earnings, and employ- ment by occupation. After the third year, an increase in tourism can more than compensate for the loss of total employment and earnings resulting from closure of the two pulpmills. A $105 million increase in demand for goods and services provided by the tourism industry would eventually compensate for the loss of two pulpmills in terms of total employment and earnings. The employees losing work as a result of the mill closures would not, however, necessarily be people employed in the tourism in- dustry. An examination of the changes, industry by industry, indicates that there are “gainers” and there are “‘losers.’”’ The wood products industry loses a large number of its employees and earnings, but the service and trade sectors gain. Employment by occupation also varies: for example, the employment for industrial technicians declined while service employment increased (fig. 1). +The value of expenditures by tourists would exceed the net economic contribution to Alaska’s economy. Many of the items purchased by tourists, and the services provided, rely heavily on imports. Total tourism expenditures would consequently have to exceed $105 million. “/The Research and Analysis section, Alaska Department of Labor, provided unpublished tourism survey data showing ex- penditures by nonresident tourists. These data were converted to expenditure classes in the Bureau of Economic Analysis’ “National Income Product Account’’ (NIPA) that were identified as “tourism” related. The distribution of tourist dollars among Alaska industries was derived from the NIPA expenditure classes. Table 2—Impact on the Alaska economy’ of increased tourism expenditures Year of simulation Industry 1 2 3 4 5 JOBS LOST OR GAINED, BY SECTOR Agriculture, forestry, and fisheries 3 22 23 28 25 Mining 0 23 25 26 24 Construction 3 213 82 716 36 Manufacturing ] 94 94 100 89 Logging 0 1 0 1 0 Sawmills 0 1] 1 0 0 Pulp and paper mills 0 0 0 0 0 Transportation, communications, 652 970 945 928 893 and utilities Trade 101 540 920 845 982 Finance, insurance, and real estate lee 42 nD 93 105 Services WA), 770 153 884 858 Government 27 48 30 714 80 Total 929 Diese 2,947 3,056 3,090 EARNINGS LOST OR GAINED (THOUSAND DOLLARS) Agriculture, forestry, and fisheries 58 347 358 412 370 Mining 0 894 934 990 903 Construction 100 7,848 3,003 2,809 1,283 Manufacturing 22 1,739 1,642 1,766 SSH Logging 0 29 0 16 6 Sawmills 0 25 13 8 3 Pulp and paper mills 0 0) 0) 0 0 Transportation, communications, and utilities HaROw2 19,532 19,304 18,627 17,796 Trade 1,406 7,267 11,477 8,220 10,911 Finance, insurance, and real estate 320 635 1,142 1,436 1,589 Services Ono 0 9,240 8,936 10,533 10,053 Government 417 Daley 428 1,098 1,196 Total 17,700 48,220 AI ees 45,891 45,654 EMPLOYMENT, LOSS OR GAIN BY OCCUPATION Managers 115 271 296 295 308 Professional 29 114 126 151 150 Technical 1s 88 9] 105 104 Service 49 615 670 838 7152 Industrial technicians 528 1,031 977 933 940 Clerical 154 496 590 599 637 Sales 4] 103 194 127 191 Farm 0 4 5 8 7 1/The impact is derived by subtracting the baseline data (that is, simulation of historical data) from the impact scenario data. A minus sign indicates a loss of employment or earnings. Table 3—Impact on the Alaska economy’ caused by the coincidental closure of two pulpmills and increased tourism trade Industry Agriculture, forestry, and fisheries Mining Construction Manufacturing: Logging Sawmills Pulp and paper mills Transportation, communications, and utilities Trade Finance, insurance, and real estate Services Government Total Agriculture, forestry, and fisheries Mining Construction Manufacturing Logging Sawmills Pulp and paper mills Transportation, communications, and utilities Trade Finance, insurance, and real estate Services Government Total Managers Professional Technical Service Industrial technicians Clerical Sales Farm Year of simulation 1 hye, 3 4 5 JOBS LOST OR GAINED, BY SECTOR -8 10 12 21 16 =] 20 20 22 20 -11 150 =) 3] = 5 -1,576 -1,552 -1,224 -969 -903 -460 -517 -34) -163 -138 -48 -54 -45 -40 —4] -1,065 -1,056 -917 -843 -194 562 833 788 193 768 718 439 55] 535 513 8 2 2 24 28 102 672 615 686 676 V7 9 =) 16 22 -829 583 742 1,160 1,134 EARNINGS LOST OR GAINED (THOUSAND DOLLARS) -180 73 144 306 224 -30 710 7164 835 771 -381 5,526 260 lls. -188 -36,854 -36,701 -29,099 =239 193) =2))', 53K! =O Ve =20yeks} =) ,905 -3,818 =8 5222 -1,025 =|) 5199 -952 -860 -863 -25,012 -24,817 A593 SUG RS. = Gh eh 10,959 16,341 55375 Sais 14,830 885 5,168 5,965 4,103 4,116 128 26 35 375 419 1,860 7,556 6,652 7,556 7,402 283 157 -210 248 337 -23,332 -1,083 -634 7,400 6,975 EMPLOYMENT, LOSS OR GAIN BY OCCUPATION 40 156 155 17] 7 -60 -5 =| 23 23 =8} 58 5] 64 63 -30 50] 498 658 557 -193 -452 =329 -114 -15 =] 256 267 300 309 26 65 100 52 82 =I 3 3 5 4 1/The impact is derived by subtracting the baseline data (that is, simulation of historical data) from the impact scenario data loss of employment or earnings. A minus sign indicates a £74 Services Ea] Industry technicians Number of jobs lost or gained 1 2 3 4 5 Years of simulation Figure 1.—Change in employment resulting from coincidental closure of two pulpmills and increased tourism expenditures does not affect all occupations equally. 3,500 3,000 2,500 2,000 1,500 1,000 500 gained -500 -1,000 -1,500 i -2,000 : His 22323 -2,500 ater pulp mills Increase tourism -3,000 [| Combination -3,500 Number of jobs lost or 55,000 45,000 35,000 25,000 15,000 5,000 + -5,000 HEE: -15,000 -25,000 -35,000 -45,000 -55,000 Total earnings lost or gained (thousand dollars) Years of simulation Figure 2.—Changes in total employment and earnings resulting from : (1) the closure of two pulpmills; (2) an increase in tourism expenditures; and (3) a combination of (1) and (2). Summary Appendix 1 A Brief Explanation of the IPASS Model Figure 2 summarizes the change in employment and earnings associated with the three scenarios. The impact on employment and earnings caused by the closure of two pulpmills (Scenario 1) is immediate and negative throughout the simulation. Most of the impact is felt by employees in the industry technician category, and most of the loss in jobs is likely to be limited to the towns in which the mills are located. If tourism expenditures increase (Scenario 2), the impact is immediate and positive throughout the simulation with service occupations making the major gains. These gains in employment would probably be spread throughout Alaska. When the decrease in pulpmill activity coincides with increased sales by the tourism industry (Scenario 3), the negative impact in loss of earnings resulting from the former is greater than the positive gains from the latter until the fourth year of the simulation, at which time the net impact is positive. In terms of employment, the impact of increased tourism is greater than the loss of pulpmill activity after the first year of the simulation. This apparent anomaly is explained by the fact that earnings per worker in pulp and paper is much higher than earnings per worker in tourism. Although a $105 million increase in demand for goods and services provided by the tourism industry would compensate for the loss of employment and earnings resulting from the closure of Alaska’s two pulpmills, worker displacement must be kept in mind. Most of the loss would be confined to higher paying, technical jobs in two remote locations, and the increase in jobs would involve lower paying jobs located throughout the State. IPASS measures change over time.—The IPASS model provides analysts with a flexible, interactive technique for simulating how a particular economy will react to changes in both supply and demand associated with policy alternatives. The IPASS system is composed of eight basic elements or “‘modules’’ (fig. 3). Unlike the traditional interindustry model, IPASS introduces the element of time. The dot- ted lines indicate how each of the modules are linked recursively for use in measuring changes over several time periods. The eight IPASS modules deal with both demand-side and supply-side factors that affect a region’s growth and development. The investment module calculates the investment needed to expand capacity in order to produce more goods and services. This module is connected to the final demand module. The latter forecasts changes in final demand; for example, change in exports. The produc- tion module is a Leontief inverse that performs the conventional multiplier calcula- tions of the individual industry impacts of changes in the demand for a region’s in- dustrial output. This module also responds to the production constraints emanating from the demand side via the final demand module and the supply side via the in- vestment and labor force modules. The employment module updates model parameters that influence labor productivi- ty, while the labor force module calculates the supply of labor by occupation | classes. The population module uses migration and cohort survival rates, as well as age-specific birth rates, to estimate year-to-year changes in a region’s popula- tion. Components of value added, including personal income, are calculated by the primary inputs module. Appendix 2 Assembling and Calibrating the Alaska IPASS Data Base Final demands Regional output = — Fs = == == (ee? Final demands [Final demands|{ investment | Year T =| Regional output —— —— S| - = | Employment + [ tavortorce fo | Population | | Population | Primary inputs — i i 4 pies poe 7 | Production [ Production |] ee Jf tnvesiment ]—— —! Year [+1 Figure 3.—IPASS is a dynamic, recursive system. Estimates for year T are influenced by transactions during the current as well as previous years. Investments for year T, for instance, are a function of regional output and primary inputs for year T-1. Ideally, all data for a particular IPASS model would be unique to the geographical area to be analyzed (see Appendix 3 for industry classification used for Alaska). For Alaska published data sources for some of the economic indicators and model parameters are lacking, however, and conducting a survey to obtain this informa- tion would be too costly and time consuming. For the Alaska model, we have, therefore, augmented Alaska published sources with data for the United States. Population and labor force participation, for example, are specifically for the State of Alaska. Capital-output ratios, however, are based on national ratios and trends. The USDA Forest Service software system, IMPLAN,’ was used to develop a synthetic input-output (I/O). Because the IMPLAN system uses direct coefficients from the national I/O model, coefficients for the Alaska IPASS model were modified to reflect Alaska’s economy. YUnpublished report, 1982, ‘‘IMPLAN User’s Manual,” Land Management Planning, U.S. Department of Agriculture, Forest Service, Fort Collins, Colorado. 10 An important feature of the IPASS simulation system is the ease with which the user can examine the sensitivity of forecasts based, in part, on nonlocal sources. By introducing a range of values for a parameter, for example, the user can deter- mine how much a particular economic indicator would be affected by a change in the underlying assumptions. Calibrating the Alaska IPASS data base.—Parameters and rate-of-change variables were adjusted so that the 1977 to 1982 baseline simulation corresponded to historical trends of value added, employment, earnings, and population for Alaska. Economic impact analyses will be the principal uses of IPASS; conse- quently, the change of a particular indicator is a more important consideration than its absolute level. During calibration, we were mainly interested in simulating the historical levels for various indicators. The calibration can be viewed as an on- going activity since the model can be easily recalibrated as new information becomes available. Tables 4 and 5 compare the calibrated baseline simulation of selected employment and earning indicators with historical 1977-1982 data. With few exceptions, the IPASS estimates corresponded closely (that is, + 10 percent) with the historical data. In general, the more annual fluctuations exhibited by an industry (for exam- ple, the construction and mining sectors), the larger the deviation between simulated baseline estimates and actual levels. Table 4—Percentage of difference between the baseline simulation by IPASS and Alaska historical employment by industry Year Industry 1977 1978 1979 1980 1981 1982 =--=---+-+- +--+ Perens = ===> == >>> 25 5 Agriculture, forestry, ' and fisheries 2.24 -20.69 -8.72 -1.79 2s 1.38 Mining -5.4] 13.05 25.64 15.74 -1.34 0.15 Construction 0.00 1.9 31.30 34.53 18.29 -4.29 Manufacturing 2.08 -5.67 -1.36 -11.84 -8.81 Je Se Transportation, communications, and utilities 2.28 -4.02 —5.17 -3.58 -5.714 -3.15 Trade -0.88 4.72 6.50 12.81 7.58 2.82 Finance, insurance, and real estate -0.15 -4.93 -2.27 4.64 3.69 -0.58 Services -0.07 -0.97 -0.90 -3.11 -1.46 -11.69 Government 0.34 -4.03 -5.05 -1.04 .09 -0.30 All employment 0.22 -2.27 -0.2] 2.33 0.04 -1.46 Table 5—Percentage of difference between baseline simulation by IPASS and Alaska historical earnings by industry Industry Agriculture, forestry, and fisheries Mining Construction Manufacturing Transportation, communications, and utilities Trade Finance, insurance, and real estate Services Government All employment 1977 1978 Year 1979 1980 G50 SN 4 SiR OOM ciemoe 64.55 61.57 -11.64 -14.98 G23 9.65 16.48 24.02 30 ibe uy S390 2. Iy 0.67 1.66 Use) dba) 1981 11 Appendix 3 ‘Table 6—Comparison among IPASS, Bureau of Labor Statistics, and Bureau of Economic Analysis input-ouput model sectoring schemes and the Standard Industrial Classification code Bureau of Bureau of IPASS Labor Economic Standard Industrial sector Statistics Analysis D Classification number Industry (154 sectors) (466 sectors) (1972 edition) 1 Dairy and poultry 1 1,2 pt.01,pt.02 2 Meat animals 2 3 pt.01,pt.02 3 Feed, food grain 4 5 pt.01,pt.02 4 Other crops 355 4,6-10 pt.01,pt.02 5 Agricultural services pt.7 pt.12 0254 ,07(exc.074) 6 Forest products and services pt.6,pt.7 pt.1],pt.12 081-085 7 Fish products and services pt.6,pt.7 pt.11,pt.12 091-092 ,097 8 Gold and silver mining pt.10 17-18 1041 ,1044 9 Other metal ore mining 8,pt.10 13-16,19,21-23 10(exc. 1031 ,1044,1081) 10 Metal mining services pt.10 20 1081 i Coal mining in} 24-25 111, pt.112,1211,pt.1214 12 Natural gas and petroleum 12 26-28 1311,1321,pt.138 13 Stone, gravel, and clay 13 29-43 141-145, pt.148,149 14 Chemicals and fertilizers 14 44-50 147 15 New construction 152 51 pt.15,pt.16,pt.17,pt.108,pt.1112,pt.1213 pt.138,pt.148 16 Maintenance and repair 15 52 pt.15,pt.16,pt.17,pt.138 V7 Ordnance and related 16-17 53-58 348 , 3761 3795 18 Meat products 18 59-02 201 iF) Dairy products 19 63-57 202 20 Canned, cured seafood pt.27 68 2091 21 Fresh, frozen seafood pt.27 73 2092 22 Other canned, preserved food 20 69-72,74 203 23 Bakery products 22 82-83 205 24 Beverages 25-26 88-92 208 25 Animal, marine fats, and oils pt.27 97 2093 26 Other food and tobacco 21 ,23,24,pt27,28 75-81 ,84-87 ,93-96 ,98-106 204 ,206-207 ,209( exc. 2091-2093) ,21 27 Textile goods 29-31 107-120 22(exc.225) 28 Apparel and fabrics 32-34 121-135 225 ,23(exc.239) ,39996 29 Logging 35 136 2411 30 Sawmills 36 137-139 2421 ,2422,2429 31 Other wood products 37-38 140-149 , 388 243-245 249 32 Furniture and fixtures 39-40 150-162 25 33 Pulp and paper mills pt.41 163 251-262 34 Other paper and allied pt.41-42 164-175 263-266 35 Printing and publishing 43-45 170-190 27 36 Chemical and allied 46-53 191-210 28(exc.28195) 37 Petroleum and refining 54 211-213 29 38 Rubber products 55-57 214-219 30 39 Leather products 58-59 220-228 31 40 Stone, clay, and glass 60-64 229-253 32 41 Primary metals 65-69 254-275 33 42 Fabricated metals 70-70 276-303 34 43 Nonelectrical machinery 77-87 304-345 35 44 Electrical machinery 88-96 346-375 36 45 Snip and boat 99 383-384 373 46 Other transportation 97,98, 100-102 376-382 , 385-387 ,389 37(exc.373) 47 Scientific instruments 103-107 390-399 38 48 Miscellaneous manufacturing 108-110 400-419 39 49 Railroad mW 429 40,474, pt.4789 50 Local transit 112 42) pt.41 51 Truck transportation 13 422 42,pt.4789 52 Water transportation 114 423 44 53 Air transportation 115 424 45 54 Pipeline 116 425 46 55 Transportation services W7 426 47(exc.474, pt.4789) 56 Communications 118-119 427-428 48 57 Electrical utilities 120 429 pt.491 ,pt.493 58 Gas utilities 121 430 492 ,pt.493 59 Water and sanitation 122 43) 494-497 ,pt.493 60 Wholesale trade 123 432 50,51(exc.Mfgrs. Sales Off.) 61 Retail trade 125 433 52-57 59,7396 ,8042 62 Finance and insurance 126-128 434-438 60-64( exc. pt.613) ,67 63 Real estate 129-130 439-440 65,66,pt.1531 64 Hotels and lodging 131 44] 70(exc. Eating & Drinking) 65 Personal services 132-133 442-443 72,762-764,pt.7699 66 Business services 134-136 444-446 73(exc. 7395) ,769(exc. 7699) ,81 ,89(exc.8922) 67 Eating and drinking 124 447 58,pt.70 68 Auto repair 137 448 75 69 Motion pictures and recreation 138-139 449,450 78,79 70 Health services 140, pt.141 451-453 ,456-457 80(exc.8042) ,074 7 Education and nonprofit pt.141-144 454-455 82-84, 86,8922 72 Federal enterprises 145-146 458-461 4311 ,pt.491,pt.613 73 State and local enterprises 147-148 462-464 pt.41,pt.491 - 74 Scrap 151 466 75 Administrative government a arrrrrrrnneannnennrnnranEEEEESEREEEEEEEEEEEEETEETNEREIO De 12