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Cullen has been accepted towards fulfillment of the requirements for Dom degree in W WWW M Date M41111 /?%O O 5 / Major professor 0-7639 LIBRAR y Michigan State { fl-;\\\\ 5 all; ‘31. ‘T”; ' .4 W mversi ty OVERDUE FINES: 25¢ per day per item RETURNING LIBRARY MATERIALS; Place in book return to remove charge from circulation records © 1980 BRADLEY THOMAS CULLEN All Rights Reserved WOOD PRODUCTS PLANTS IN NORTHWESTERN CALIFORNIA: CHANGES IN LOCATION AND SIZE By Bradley T. Cullen A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Geography l980 ABSTRACT WOOD PRODUCTS PLANTS IN NORTHWESTERN CALIFORNIA: CHANGES IN LOCATION AND SIZE By Bradley T. Cullen This research describes the processes which have changed the pattern of wood products plants in Northwestern California; projects the future arrangement of the wood products industry; and provides information about the nature and factors influencing the spatial and structural pattern of movement. The determination of structural and spatial changes in the wood products industry required the compilation of a comprehensive list of the location and structure (size) of plants for the years l966 and 1976. Complete structural information was obtained for 398 plants, and complete spatial information for 466 plants. Two Markov chains were calculated. In the first chain each state denotes a subregion; and in the second chain each state represents a size interval. The spatial fixed probability vector indicates that the number of plants operating in the North Coast will decline from about a third to a sixth of the entire population. The proportion of all plants located in the Northern Interior is also expected to decline, but by only slightly over 25 percent. Conversely, a larger proportion of the industry is expected to be located in the Sacramento Area, particularly in the Sacramento- Westside area. Several scenarios, with different assumptions about the growth of the study area's wood products industry, can be developed to explain Bradley T. Cullen the projected proportional changes in the regional distribution pattern. Most of the available information indicates that the overall plant population will probably decline. Several production and marketing factors will negatively affect the wood products industry of Northwestern California. As a result of increasednmchanization,favorable freight rates, and lower wages, producers located in the South are increasingly able to compete for customers in traditional western market areas. Potential consequences include production cut backs and plant closures. Further, in the North- western California timber regions, much of the accessible old growth timber has been harvested or preserved, hardwoods of little commercial value have succeeded in harvested areas, and for some companies secondary growth has not yet reached the level needed for sustained yield rotation. The situation in the study area has been exacerbated by: l) the intrusion of other land uses; 2) California's strict environmental and safety regulations;auui3) periodic shortages of skilled labor. Between l966 and 1976, employment was concentrating in fewer, but larger plants. Large plants had a higher survival rate in this period because they: l) cut production by using a larger percentage of their residue; 2) often insulate themselves from local variations in demand by marketing their products throughout the country and abroad; 3) have increased production and lowered unit costs by utilizing the latest equipment; 4) can average together the high priced bid timber and lower cost logs from their private lands; and 5) spend a lower percentage of their total costs on transportation. Bradley T. Cullen The study provides an example of how the arrangement of an industry can be analyzed. The fixed probability vectors identify what spatial and structural movement is occurring, and a review of the factors of production indicate that much of the change is occurring in response to variations in the factors of production. In other words, the research presents a means of evaluating changes in the locational distribution of an industry. ACKNOWLEDGMENTS Hallowed custom requires authors to accept responsibility for their mistakes, while passing to others the credit for their successes. While completing this dissertation, I have come to respect this prac- tice. During the entire dissertation process, advice and help from fellow students, faculty and especially my committee have been in- valuable. Nearly every page of the study has benefited from the geographic knowledge and editorial judgment of Dr. Lawrence M. Sommers, my major professor, and Drs. Robert I. Wittick, Bruce Wm. Pigozzi, and Ian Matley. It is also a pleasure to acknowledge my gratitude to the Depart- ment of Geography at Michigan State University, which has supported my studies over the past five years. I would further like to thank Terry Westover for typing my dissertation, as well as those who con- structed my maps and graphs. My special thanks to the wood products producers of Northwestern California for their cooperation in the data collection process. Finally, I wish to thank my parents, Ralph and Adwina Cullen, without whose support and encouragement this document would never have been completed. Thank you one and all. ii TABLE OF CONTENTS LIST OF TABLES ......................... V LIST OF FIGURES ......................... vii CHAPTER l: INTRODUCTION ...................... l Study Area .......................... 2 Historical Perspective .................... 8 Site and Situation ...................... ll Statement of Hypothesis ................... 20 Industrial Organization ................... 22 Review of Literature: Industrial Location Theory ....... 23 Data ............................. 27 Organization of the Research ................. 3l CHAPTER 2: PROJECTED STRUCTURAL AND SPATIAL DISTRIBUTION PATTERN . . 34 Wood Products Firms: Spatial Mobility ............ 40 Wood Products Firms: Structural Mobility .......... 48 Structural and Spatial Relationship ............. 54 Summary ....... . . .................. 57 CHAPTER 3: FACTORS OF PRODUCTION ................. 61 The Importance of Each Factor of Production . ........ 66 CHAPTER 4: FACTORS OF PRODUCTION: RAW MATERIAL SUPPLY ....... 70 Overview of Supply ...................... 70 Access to Material Supply: North Coast ........ . . . . 79 Access to Material Supply: Sacramento Area . . . . ...... 86 Access to Material Supply: Northern Interior ........ 92 Wilderness Areas: Roadless Area Review and Evaluation II (RARE II) ..................... 95 Summary ........................... 96 CHAPTER 5: OTHER FACTORS INFLUENCING THE STRUCTURE AND SPATIAL DISTRIBUTION OF WOOD PRODUCTS PLANTS ............ -99 Cost of Factors of Production ................ 99 Optimal Size of a Primary Wood Products Plant ....... lOO Demand for Wood Products .................. lOS Labor ........................... 112 Transportation ....................... ll9 Environmental Regulations and Restrictions ......... l23 Summary .......................... l25 CHAPTER 6: SUMMARY AND CONCLUSION ................ l26 APPENDIX A: SECONDARY SOURCES ..... ~ ............. l35 APPENDIX B: QUESTIONNAIRE AND COVER LETTER ............ 137 APPENDIX C: THE IMPORTANCE OF EACH FACTOR OF PRODUCTION ..... l4O APPENDIX D: MATRIX OPERATIONS .................. l47 LIST OF REFERENCES ........................ l49 iv l-l. 2-l. 2-3. 2-4. 2-6. 2-7. 2-9. 2-lO. 2-ll. 2-12. 2-13. 2-l4. . LIST OF TABLES Community Dependency on the Wood Products Industry Northwestern California ................... 14 Example of a Tally Matrix .................. 37 Example of a Transitional Probability Matrix ........ 37 Successive Values of p(n) .................. 39 Tally Matrix for Wood Products Plants Northwestern California l966-l976 .............. 4l Spatial Probability Matrix for Wood Products Plants Northwestern California l966-l976 .............. 45 Vector of Present Spatial Distribution of Plants ...... 46 Fixed Probability Vector .................. 46 Spatial Matrix of Mean First Passage Times Northwestern California ................... 47 Spatial Matrix of the Variance of First Passage Times Northwestern California ................... 49 Structural Tally Matrix for Wood Products Plants Northwestern California ................... 51 Structural Probability Matrix for Wood Products Plants Northwestern California l966-l976 .............. 52 Vector of the Present Structural Distribution of Plants . . . 53 Fixed Probability Vector .................. 53 Structural Matrix of Mean First Passage Times Northwestern California ................... 55 . Structural Matrix of the Variance of First Passage Times Northwestern California ................... 56 . Relationship Between Size and Location of Wood Products Plants 1976 Northwestern California ................ 58 V 3-l. 3-2. 4-1. 4-2. 4-3. 4-4. 4-5. 4-6. 4-7. 4-8. 4-9. 4-10. 5-1. 5-2. 5-3. 5-4. 6-1. . Percentage of Firms Planning to Increase or Decrease Employment ............ . . . . . . . . . 59 The Average Ranking of Factors Important in the Present Location Decisions of the Study Area's Wood Products Producers . . . . 66 The Main Reason for the Closure of Wood Products Plants Percentages by Spatial and Structural Subdivision Northwestern California . . . ........... . . . - . . 68 Average Per Acre Costs for Management Operation California ........... . . . . . . . . . . ..... 75 Softwood Log Exports Northern California and the Ports of Eureka and Sacramento . . 78 Commercial Forest Land By Ownership, North Coast Region . . . 8l Alternative Projections of Softwood Saw Timber Output from Private Lands in California's North Coast ........ . . 81 Projections of Sawtimber Output from Private Lands in the Humboldt-Del Norte and Mendocino-Sonoma Subregions . . . . . . 83 Commerical Forest Land and Sawtimber Volume by Ownership Sacramento Area ............. . . . . . . . . . . 87 Timber Sales on Plumas National Forest l976 Sacramento Area ......... . . . . . . . . . . . 89 The Growth and Harvest of Timber in the Sacramento Area . . . 9l Commercial Forest Land by Ownership, Northern Interior . . . 93 Potential Impact of Roadless Areas, Northern Interior . . . . 97 A Comparison of the Percentage of Total Cost Spent on Selected Factors of Production by Location and Size . . . . . lOl Percent of Total Output for Firms in Various Size Classes Northwestern California, l966 and l976 . ......... . lO3 Principal Market for California's Primary Wood Products l968, 1972, and l976 (in percentages) ......... . . . l08 Value Added and Capital Expenditures, Lumber and Wood Products Industry, United States, l967 ....... . . . . . . . . . ll7 Attractiveness Index, Wood Products Areas of Northwestern California . . . . . . . . . . . . . . . . . . . l3O vi l-l. l-2. l-3. l-4. l-5. l-6. l-7. l-8. 3-l. 4-l. 4-2. 5-1. 5-2. 5-3. 5-4. 5-6. 5—7. LIST OF FIGURES Study Area -- California's North Coast, Sacramento Area, and Northern Interior .................. 3 Subregions of the Study Area ................. 5 Production of California Timber Operators l947-l976 .......................... 10 Distribution of Tree Types ................. l2 Population Distribution -- Northwestern California, 1970. . . l6 The Locational Distribution of Major Highways and Railroads -- Northwestern California, l976 ................ l7 Distribution of Wood Products Plants, l976 ......... 28 Distribution of Wood Products Plants, l966 ......... 29 Decision-Making Model .................... 63 Timber Production Planning Model .............. 72 Estimated Long Run Supply for Timber, California ...... 74 A Comparison of Housing Demand and the Relative Price of Softwood Lumber ........ . . . . . . . ...... 106 Plant Size and Extent of Market Northwestern California, l976 ........... . . . . llO Population Change by County —— Northwestern California, l96O to l97O . . . . .................... lll Components of Labor Productivity in Sawmills and Planning Mills, l958-l975 ..................... ll3 Projected Employment Changes by Occupational Group Wood Products Industry, l970-l980 ............. ll6 States Which Have Not Adopted 80,000 Pound Truck Payload l977 . . .. ......................... l22 vii CHAPTER I INTRODUCTION The wood products industry has played a significant role in the economic growth of California. The California gold rush was triggered by the discovery of gold at the state's first interior water-powered sawmill. Subsequently, the timber and lumber industry expanded in order to meet the construction and energy needs of the mines and mining communities. Concurrently, lumber first from Marin County and the San Francisco Peninsula, and then from Mendocino County was utilized in building and rebuilding the city of San Francisco. During the nineteenth century, wood products were produced to meet local needs. Expansion to a national market awaited the decline of the Great Lakes States' timber supply; the construction of the transcontinental railroad; and the opening of the Panama Canal (Zivnusha l965, p.35). Growing demand for western wood products, combined with increased accessibility, and the arrival of steam power, precipitated the construction of large scale sawmills. Even though some of these mills are still in operation, the industrial pattern has continued to evolve. This study concentrates on the locational arrangement of wood products plants1 in Northwestern California. The objectives of the 1This study deals with wood products plants which fall into the following Standard Industrial Classifications: 2420-sawmills and 2 research are to: (1) determine what structural2 and spatial changes occured in the wood products industry between 1966 and l976; (2) project the spatial and structural arrangement of the wood products industry; (3) analyze the factors of production which influence the locational arrangement of the wood products industry; and (4) use the factors of production to evaluate changes in the locational distribution of wood products plants. Markov chains are used to extrapolate the trends that seemed evident between l966 and l976. The traditional procedure of asking the decision maker to identify the factors of production felt to be important in the choice of location was used by the author to analyze the evolution of the industrial pattern. The questions asked are neither unique,3 nor are the attempts aimed at their amalgamation. What distinguishes this research is that it utilizes a probabilistic model (Markov chains) to deal with decisions that change the locational arrangement of an industry's activities: extensions, retractions, closures, relocations, and initial location decisions. Study Area The study area consists of several regions of California: the North Coast, Sacramento Area, and Northern Interior (Figure l-l). planing mills; 2430-millwork, veneer, plywood, and prefabricated structural wood products; 2440-wooden containers; and 2490-miscellaneous wood products. ZStructure refers to the size of an industrial plant. That is, the number of employees engaged at an industrial plant at a given point in time. 3Examples of the questions being asked are: What motivates an industrial move or a change in industrial structure?; Why was the specific relocation site chosen?; What will the industrial structure and spatial distribution pattern be like in the future? 2A -:_:-: o lo 20 so co so SCALI NORTHERN INTERIOR NORTH COAST SACRAMENTO AREA Figure l-l. Study Area —- California's North Coast, Sacramento Area, And Northern Interior. With a few exceptions,4 this regionalization corresponds with the physical subdivisions identified by the U.S. Forest Service (Bolsinger 1976, Wall 1978, Oswald 1978). The current economy of the North Coast is dominated by the wood products industry. Until World War II redwood lumber products pre- dominated, but after the war Douglas fir became important and the region's production skyrocketed. During this boom period, small operators moved into the area to satisfy the postwar housing market, but many became inactive within a few years. And as Lantis (1970, p.479) observed, processing plants are becoming increasingly peripheral rather than central to the logging areas. This trend reflects the migrating nature of logging, the inertia of long established plants, as well as the establishment of new plants and the relocation of existing plants. The North Coast was divided into two subregions (Figure l-2): Humboldt-Del Norte and Mendocino—Sonoma. These two subregions are at different stages of economic development. Since the early l960's, diminishing harvest and mechanization have reduced the wood products industry's contribution to the economy of the Humboldt-Del Norte subregion. Although agriculture and fishing traditionally have been important, they are not growth industries. Tourism is one of the few sectors of the subregion's economy that has growth potential. Even though the primeval redwood groves attract thousands of visitors, the seasonal nature of the tourist industry limits its potential contribution to the subregion's economy. It is thus not surprising that the economy 4The extreme northeastern part of the state was included by the U.S. Forest Service in the Northern Interior. 1213' '33' , Yroh ‘ r “we“ NORTHERN Nuloul Nth "‘"' SHASTA lurch. - .Ioddln' HUIBOLDT .""°"°' DEL NORTE .1“ sum -..- EASTSIDE ‘°°‘ .Oulncy Curb .Chuo {zybmflb .vailo .Ion Ova. """' SACRAMENT . EASTSIDE “:7""' SIERRA or 705. cu DOC'NO Ploconnllo SONOMA ’ Sou-nun. 0 ’9..." N om was 1213' Figure l-2. Subregions Of The Study Area. of the Humboldt-Del Norte subregion is depressed. The economic problems have resulted both because the population has outstripped the economic base, and because so many individuals are willing to trade economic prosperity for environmental amenities. For the Mendocino-Sonoma subregion, the period of declining timber harvest has passed -- the annual cut can be maintained at current levels (Oswald l972, p.28). Even though the northern coastal towns are small and stagnant, their southern counterparts (Ukiah and Santa Rosa) are prosperous trade centers. The southern third of the subregion lies within the San Francisco Bay Area's hinterland, and benefits from urban overspill. Like the North Coast, the Northern Interior was divided into two subregions (Figure l-2): Northern and Shasta. The Shasta subregion has Redding as its service center and wood processing as its economic mainstay. Major firms include U.S. Plywood, Diamond International, and Kimberly-Clark, each of whom have private holdings in the surrounding mountains which guarantee production (Lantis l970, P.358). Much of the timber needed by Shasta's wood products industry is supplied by the Northern subregion (Howard I974). This subregion encompasses the southern fifth of the Cascade Mountain Range, which extends naturally through Oregon, Washington, and into British Columbia. For the purposes of this study, the boundary was placed_at the California- Oregon state line. Besides lumbering, the only constant elements of the economy are ranching and recreation. The economy of the Sierra subregion generally mirrors the Northern - subregion. But economically the Sierra subregion is tied to the Sacramento Valley, and contains the Sacramento Area's only extensive supply of commercial timber. The remainder of the Sacramento Area was divided into two subregions (Figure l-2): Eastside and Sacramento-Westside. The Sacramento-Westside is dominated by the study area's largest city: Sacramento. The city's functions include government, military bases, commerce, manufacturing, retail and wholesale trade. For the next several years, tertiary and quaternary economic activities in the Sacramento SMSA will grow faster than secondary economic activities (CaliforniafisEmployment.Development Department 1976, p.6). California's Employment Development Department (l976, p.8) predicts that most industries, including those producing wood products, will expand only modestly. Outside the Sacramento urbanized area agricultural production and processing constitute the major economic activities. But the farms on the Westside are only modestly prosperous. Since the area lies within the rain shadow of the Coastal Ranges, large tracts of land are limited to livestock ranching and dry farming. The only exceptional agricultural areas are found in the southern portion of the subregion. The Eastside consists of a series of fertile agricultural districts on alluvial fans. This area is better watered than the Westside and thus more productive. Most Eastside communities function as agricultural trade centers with no appreciable industry. There are only a handful of area towns (Red Bluff, Chico, Oroville, Marysville, Yuba City) presently engaged in wood processing, though the industry was more dispersed in past decades. Historical Perspective5 The wood products industry has been migratory in nature; continually pursuing the dwindling supply of old growth timber. A cyclical pattern has occurred in several sectors of the country: (l) production for the local market expands; (2) the area improves its accessibility to the national market; (3) the area's timber industry expands, as it becomes economically feasible to harvest large tracts of old growth timber; and (4) economic decline occurs, as the accessible old growth timber is harvested, and new, more lucrative tracts are found elesewhere. Even though the cyclical pattern can apply throughout the country, the specifics vary from one region to another. For example, on the Eastern Seaboard both the growth and decline of the industry occurred gradually, because different species achieved economic prominence at different times. In contrast, the Great Lakes States' wood products industry grew rapidly in the last half of the nineteenth century, and its decline was just as precipitous. It was in the Great Lakes States that the "cut out and get out“ philosophy reached its apex. So by the end of the first decade of the twentieth century, the center of the industry had shifted to the South. This area experienced a very rapid increase in production, but due both to the extent of the forest and the rapid rate of secondary timber growth, the South was able to temper its 5The information presented in this section has been taken from several other sources (Zivnusha l965, Greenbalgh l974, for example), and only a brief summary will be given below. decline. As a result, the area has maintained a smaller, yet economically viable wood products industry, based mainly on secondary growth. Since the mid-l920's, the West has dominated the wood products industry. But production has not been uniform throughout. The western boom began in the state of Washington in the l920's, and has since slowly migrated southward along the Pacific Coast. Evolution of California's Wood Products Industry Even though the wood products industry has a long history in California, it did not experience rapid growth until after World War II. The frustrated demand for housing during the Great Depression, and the constrictions placed on construction during World War II contributed to the post war housing boom. Since California was a focus of the boom, as well as one of the few areas in the United States with a large supply of old growth timber, its wood products industry expanded rapidly. As Figure l-3 shows, peak production for the state as a whole was reached in l955, and since the l950's the trend in production has been downward, with only modest increases in l962, l968, and l972 (California Division of Forestry l977, p.l). Besides timber harvesting per se, competition from other land uses has contributed to the decline. In the past, the demand for farming and grazing land resulted in the conversion of large tracts of timberland. More recently, urban California's water, power, and recreational needs have resulted in the transmutation of timberland. Still forty-two million acres of California's lOO million acres of land area are forested (Oswald l970, p.5), but only 16.8 million acres can be classified as commercial forest land (Western Wood Products Association 2 £2 10 onmp P d .29 595250 5:85“; So c2220 £50an 5 22m Hoocaow Emo> mom? comp mmmw @375va $9880 895... £58.30 u_o actuated ommp .m-P weaned nvm— was meoe Uouue ll l977, p.2). Over seventy-five percent of this commercial forest land is located in the study area (Oswald l970, p.5). The forested land of the study area is composed of several forest types. In the Sacramento Area and Northern Interior, the non-commercial forests consist mainly of digger pine and broadleaf woodlands, while commercial forests contain tracts of ponderosa pine (Figure l-4), Jeffrey pine (Figure l-4), and mixed stands of pine, Douglas fir, and true fir. In contrast, most of California's Douglas fir (Figure l-4) and redwood forests (Figure l-4) are found on the North Coast. The location of commercial forests, as well as transportation facilities have strongly influenced the distribution of wood products plants. Site and Situation If a plant is located where there are extensive tracts of old growth timber (with heavy volume), then economies of scale would favor one large plant. But discontinuous tracts of cut-over or secondary growth would increase the cost of collecting and concentrating logs. Such a situation might favor several small, dispersed plants. Manufacturing savings resulting from economies of scale can be offset by the additional transportation costs incurred when a plant is not juxtaposed to the logging site. The transport network provides a framework around which spatial and structural forces operate. But besides good transportation facilities, the entrepreneurs interviewed indicated that they desired a location in or near a community that could provide the company's employees with housing and the company with needed services. The day of the "company town” is gone, even though there are still a large number of communities Pinus ponderosa Plnus jeffreyl (Ponderou pine) (Jeffrey pine) - snub. non nun 2 mus Acnoss I STAND, LISI THAN 1 MILII ACROSS Pseudotsuga menziesii Sequoia sempervirens (Douglas W) (Rodi-cod) Figure l-4. Distribution Of Tree Types. l3 within the study area largely supported by wood products firms (Table l-l). Further, when a sawmill is combinedvfitfiianother type of wood processing facility (i.e. particle board plant), "the availability of water and a strategic location for purchasing and concentrating residues from other mills may become major considerations" (Zivnusha l965, p.23). For secondary firms, a location near a population center with a vibrant housing industry is a major consideration. All of these factors influence the distribution and structure of the wood products industry. Therefore, it is necessary to generally understand the site and situation characteristics of the study area. As Figure l-5 shows, the population of the Humboldt—Del Norte subregion is concentrated along the coast, particularly around Crescent City, Eureka, and Arcata. Away from the coast, the subregion is mountainous and rugged. There are very few sites in the interior of the subregion suitable for building a large primary wood products6 plant. Flat land is at a premium, and as Figure l-6 shows, the northern half of the subregion is devoid of major transportation routes. Therefore, most of the subregion's wood products firms were originally located near the coast, where water transportation was available. The site of these plants was enhanced when the railroad was extended north to Arcata, and a major highway was built along the Northern California Coast. Although the wood products industry in the Mendocino—Sonoma subregion also began along the coast, by the mid-l800's mills were 6Primary producers obtain their raw materials from primary forms of economic production (i.e. forestry), while the raw materials of secondary producers come from primary producers. 14 TABLE l-l COMMUNITY DEPENDENCY ON THE WOOD PRODUCTS INDUSTRY NORTHWESTERN CALIFORNIA l976 Forest products Community Number of share of total plants* basic employment McCloud l 100 Adia l 80 Bieber l lOO Little Valley l 100 Burney 2 90 Central Valley l 90 Chester l 95 Greenville l lOO Crescent Mills l 100 Quincy 2 95 Sloat l lOO Loyalton l lOO Comptonville l lOO Truckee l 25 Grass Valley 4 25 Marysville 2 l5 Foresthill 2 75 Jackson l 90 North Fork l 75 Dinuba l 20 Happy Camp 2 92 Yreka 3 89 Weed l 80 Mt. Shasta 2 6O Hoopa 2 100 Arcata 12 9O Salyer 2 95 Burnt Ranch l lOO Weaverville l lOO Hyampom l lOO Hayfork l 70 Rio Dell 2 9O Dinsmore l 90 Anderson 7 85 Wildwood l lOO Garberville l 50 15 TABLE l-l (cont'd.) Forest products Community Number of share of total plants* basic employment Red Bluff 4 7O Covelo l 75 Potter Valley l 60 SOURCE: G. Bendix, "Timber Sales Bidding Procedures," Statement for U.S. Senate Subcommittee on Public Lands and Resources, Committee on Energy and Natural Resources, First Session, No. 95—55, (1977), p. lO9. * Refers only to primary wood products plants. l6 ’ o Oo' .. O .0 . O O , i ..5 o v. .. o ' 0-8.? o . 0 ~ .t’.r . . . 2m: .00 . o . . O o... o ’Q...‘ o. . a: ‘. 4 .t . O .0 l ‘ . . . o o o . o . o . . . ‘ . ’ r {0. 1.”. o . . . .o.. 2 ..OO. :0 O . O ' ° '1" ' 3'3 ' . ‘. . : tx O . .‘. .‘Q. o . 9. C o 7 .1. ‘5 ' . . “Q; 0 "“3. °.:. '° ' .' \ § .s '4 3 c .v . . o e 00 . d o ‘0 .0 “ $.. . .. . .35...‘ z .e O .. ! ~. 0. e. o 0‘ . .Q . b"? ' a. . ° ’ 5:}? . .:- o . "o; : .2 ’...o- . .. . o .. O i .'~ y NF). .5 ‘ .‘fiofo .. . y... a. o. . “ POWLAT'ON 1970 no no. Figure l-5. Population Distribution -- Northwestern California, l970. l8 established in the interior. Even though the interior valleys are small and discontinuous (except in the southern portion of the subregion), the North Pacific Coast Railroad linked the sawmills with markets in the San Francisco Bay Area. As Figure l-6 indicates, subsequent highway development has penetrated the interior, and provides good access between the San Francisco Bay Area and plants located near the cities of Laytonville, Covelo, Ukiah, and Willits.' Like the interior of the Humboldt—Del Norte subregion, the western half of the Northern subregion is rugged and transportation facilities are primitive. The Shasta Corridor is the only major transportation artery, which includes the rail connection between Portland and the Sacramento Valley, U.S. 99, and Interstate 5. Consequently, most of the population and economic activity in the subregion are located in the corridor, or along the transverse highways that link the eastern half of the subregion with Interstate 5. The Redding complex (the largest urban area in the northern Sacramento Valley) is situated at the junction of Interstate 5 and California 299. Because the urban area is surrounded on three sides by forested areas, logs can be trucked to mills located along the rail lines and highways. With flat land, teritary activities, and water the area has become a major wood processing center. Similarly, several cities in the Eastside subregion have major lumber operations. Red Bluff's location at the junction of Interstate 5, California 99, and California 36 provides producers with good access to the forests of the Sierra subregion, as well as the markets of Central California. Oroville also has a prime site, since it is located at T9 the mouth of the Feather River Canyon, which is paralleled by both California 70 and the Western Pacific Railroad. Although further from sources of supply, a similar transportation advantage exists for Yuba City and Marysville. Even a location near these last two cities is preferable to a forest site location, because they are of sufficient size to meet the service needs of wood products producers: housing, amenities for laborers, adequate building sites, transportation facilities,anuiaccess to sources of both supply and demand. The city of Sacramento is connected to the San Francisco Bay Area and the Midwest by two transcontinental railroads: the Western Pacific and Southern Pacific. The Southern Pacific Valley Line also provides a link between the city and both Southern California and the Pacific Northwest. Further, Sacramento is located at the junction of the north-south and east-west routes on California's largest rivers, and where two major east-west highways (Interstate 80 and U.S. 50) bisect the north-south routes of Interstate 5 and California 99. Thus, the city is an important transportation node. Local producers therefore have easy access to regional supply and demand points, and to the larger national market. Since the local market is expanding, secondary wood products producers have been attracted to the area. There are only a handful of communities in the Sierra subregion that are large enough to provide the housing and services needed to support a wood products firm. Even though two major railroads (Western Pacific and Southern Pacific) and several major highways (Interstate 80, California 50, and California 70) provide access to market areas, the potential timbershed available to sawmills is 20 limited by topography: steep slopes, canyons, and a paucity of low passes. Winter snows often block transportation routes, forcing mills to close for the season. Since the subregion's population is small and dispersed, few secondary wood products plants have located in the area (an exception would be firms engaged in firewood production). Therefore, the few suitable location sites in the Sierra subregion cannot easily compete for establishments with their counterparts in the Sacramento-Westside, Eastside, or Shasta subregions. Statement of Hypothesis As the arrangement of an industry evolves, plants concentrate in those areas and size categories with the greatest comparative advantage for the production of the goods in question (Smith l97l, p.51l). In theory, when the average number of plants entering a size category or region in a given time period equals the average number leaving it, then a state of equilibrium exists. But implicit in this equilibrium model are assumptions that do not hold in the real world. Locational inertia prevents instantaneous adjustment to marginal changes in costs and return (Richardson 1969, p.39l). Inertia is often regarded as evidence of some imperfection in the economic system, a delay in making desirable responses to a new equilibrium position (Townroe l974, pp.270-29l). Further, the factors of production are not as mobile as is assumed in location theory (Isard l969), and the assumptions of perfect competition, perfect knowledge, and economic rationality are untenable. 21 Industrial movement may also take the form of disequilibrating movements. Thus, in a dualistic system, such as that described by Myrdal (l957), disequilibrium is not met by balancing forces, but by a set of cumulative changes which reinforce regional and structural differentials (Sant l974, p.4). This differs from the equilibrium model, in which movement of capital and labor in response to disparities leads directly to equilization. In the cumulative model, the areas of profitability continue to hold their advantage, at least over the short- run (less than ten years). Therefore, it is hypothesized that the decisions of existing firms to relocate, expand, or retract facilities, the location of new plants and the closure of existing plants take place in response to variations in the factors of production, assuming changes in demand are held constant. Over the long-run (over seventy-five years) the cumulative decisions of the entrepreneurs might approximate a stable state, but the data are not available to realistically predict the composition of such a state. Past tendencies can be extrapolated, however, and over the short-run the present industrial environment of wood products plants in the study area can be used to evaluate the projections. That is, regional and relative changes in the factors of production can be compared with the extrapolations to determine if they are reliable. In the study area, the author anticipates that production will concentrate in fewer but larger firms, and that the Sacramento-Westside and Shasta subregions will attract additional wood products plants. In other words, the wood products industry will become more agglomerated and oligopolistic. 22 Industrial Organization Portions of the wood products industry still fit the mold of the nineteenth or early twentieth century ”free market“ economy. A study done by Mead (l966, pp.97-l34) on the Douglas fir lumber industry concluded that the lumber industry was unconcentrated, that there were few barriers to the entry of new firms, that product differentiation is difficult, and that the market determines the price of lumber. Irland (l976a, pp.22-23) concurred with Mead; only one-eighth of the 420 four-digit manufacturing industries in the United States had concentration ratios (percentage of production controlled by the eight largest firms in the industry) equal to or lower than lumber in l970. Compared to other modern manufacturing industries, the capital required to start a new sawmill is relatively small. Since anyone can bid on public timber, material supply is technically not a barrier to entry, though ownership of private timberland can improve the competitive position of a firm. However, a more recent study done by the President's Council on Wage and Price Stability (l977, p.5) found that lumber production was becoming more oligopolistic. So even though the lumber industry is still characterized by small, competitive firms, there is an increasing tendency for production to concentrate in fewer, but larger establishments. In contrast to the lumber industry, a few firms dominate the softwood plywood industry both nationally and on the Pacific Coast (Irland l976b, p.40). Thus it might be postulated that large plywood firms would be able to influence the price of their products and protect their market. Irland (l976b, p.40) asserts, however, that 23 plywood, a concentrated industry, is as competitive as the lumber industry. The large initial investment needed'UJestablish a new plant precludes many potential entrepreneurs from breaking into the industry, however. For a plywood plant to be competitive, it must be relatively large (employing over one hundred persons). Many secondary wood products firms produce specialized products that are easily differentiated. Their reputation and specialization of the local industry guarantees survival.8 Other producers, such as those who manufacture particle boards, limit contracting for the available material supply, protecting their relative position of importance in the industry. Even though the wood products industry is becoming more concentrated and perhaps less competitive, traditional location theory still has some utility when analyzing its locational arrangement, since, according to Hamilton (1974, p.5): The main lines of industrial location analysis were appropriate to the ‘thme when, and to the region where, small firms with one, usually single product, plant were economically (and not only numerically) dominant, technologies and business organization were small-scale and simple, and location decisions were made essentially in response to relatively simple economic, social, political, and spatial environments external to the manufacturers. Review of Literature: Industrial Location Theory Geographers, economists, and others have been concerned with industrial location theory. In general, industrial location theory has had its roots in micro-economics: the economists' theory of the firm. 8Five percent of the surveyed firms spend over ten percent of their total costs on marketing and advertising. All were small secondary producers. 24 According to Townroe (1969, p.15), this development has been based on "the central twin postulates of that theory which states that the decision maker of the production unit has two primary goals: maximizing receipts and minimizing costs.” The minimum cost model of Alfred Weber (1929) was the first industrial location model to gain wide acceptance. But in attempting to introduce more reality to location theory than his predecessors, tbsch (l954) rejected the least-cost location approach of Weber and his followers, and the alternative of selecting the location at which revenue is the greatest. He felt the right approach was to seek that place where revenue exceeds costs by the greatest amount: the place of maximum profit. But since the early 1950's profit maximizing models of location have been criticized, because they fail to allow for informational differences and trade-offs that are made between monetary and psychic incomes (Katona and Morgan 1951, Eversely 1965). In turn, traditional location models have emphasized transportation costs. But these costs have been downgraded since the 1950's because the composition of the manufacturing sector has changed radically as lighter industries have expanded; the material inputs have improved in quality or purity, and are used more efficiently; substitution of material inputs has reduced transportation constraints on activity location; and transportation technology has been developed and dramaticly improved (Norcliffe 1975, pp.22-23). Still transportation costs are an important factor in the location of many industries which utilize large quantities of raw materials and have a high material index (Norcliffe 1975, p.23). Most wood products firms fit into this latter category. 25 Most of these studies were concerned with the reaction of individual firms. In this research, however, industrial movement is seen as a form of resource allocation, with much broader questions: changes in locational values must be analyzed in a wider context of regional factors of supply and cost; and the distribution of firms and industries is seen as a function of regional variations in comparative and absolute advantage (Sant 1975, p.2). The movement of either of the two main factors, labor and capital, is the usual response to long-term disequilibrium. Since the main concern is with capital redistribution, labor movement will not be discussed directly.9 Research in industrial movement has followed a diversity of approaches. Many studies have compared the relative importance of factors between two or more spatially separate areas. Keeble (1968), for example, identified such factors as availability of labor, labor costs, access to market, and governmental incentives as being of major importance in industrial location. Further, Griffin (1956), in a study of New York, shows that though market forces are significant, low rents and vacant factories also can play a decisive role. Similarly, Holt (Smith 1971, p.39) in a survey conducted in 1964, demonstrated that fixed capital equipment could attract the relatively mobile factors of financial capital and enterprise: perpetuating existing industrial location patterns. In addition, analyses of the movement of manufacturing, especially in the United Kingdom, have focused on governmental inter- vention (Beacham and Osborn l970, Keeble l972, Sant 1975). 9For a detailed review of the labor movement see Richardson (1969). 26 Another approach has been to ask producers to identify those factors which have influenced their location decisions. In a classic example, Mueller and Morgan (1961) asked Michigan manufacturers to rank the factors important in the location, relocation, and expansion of industrial plants; the dominant factor cited by producers was labor costs. In addition, where possible, they studied plant histories, and learned that personal reasons, opportunity, and chance were important in the location of new firms. A third approach has involved relating industrial movement to the business cycle, with the discovery that a buoyant economy leads to more movement (Lever l972a, Sant 1975). Most models for forecasting changes in industrial activity have involved aggregate methods for quantitatively describing urban-industrial relationships. Of these models, input-output analysis has been widely used in estimating regional inter-industry flow patterns (Richardson 1975). But the input-output family of models is generally aspatial, static, and costly to utilize. Economic base models are more suitable for small area analyses (Tiebout 1962), because they are less costly. But the basic/nonbasic ratio is a very crude device. In recent years, simulation models, which utilize a probabilistic approach to stress the sub-optimal nature of man's decisions, have been gaining ground (Pred 1969). Of the stochastic models available, Markov chains seem to have the greatest potential for extrapolating changes in the locational arrangement of an industry (Hamilton 1967, Collins 1975). For example, Lever (1972b) applied the Markov chain model to the process of industrial movement at the intra-urban scale; Clark (1965) used it to evaluate movement of rental housing; Collins (1975) applied the model to industrial movement at the intra-regional scale and to changes in 27 industrial structure (Collins 1973); and Brown (1970) described its general applicabilities to movement research. But in most cases, the probability of moving from one location to another was based on past tendencies, dealing with either spatial or structural movement, but seldom both. Further, most failed to consider ''why" the movement occurred. If the underlying reasons are considered in evaluating the transition probabilities, then a more accurate projection can be achieved. Data Before the structural and spatial changes can be described, it is necessary to compile a comprehensive list of firm names. Several sources were utilized in gathering information: telephone directories, street directories, directories of forest products industries, state and local industrial directories, local Chambers of Commerce professional directories, and personal reconnaissance (Appendix A). The eventual list contains the names of 512 plants which were operating in 1976 (Figure 1-7), 1966, (Figure 1-8) or in both years. Those plants opened after 1966, but closed before 1976 were not identified. Many of the sources given in Appendix A were also utilized in 10 in the wood products determining structural and spatial changes industry. But where secondary sources fell short, and this was the rule rather than the exception, the needed data were obtained through 10Structural data refers to the maximum number of people a wood products plant employed at any one time in either 1966 or 1976. Spatial data refers to the location and ownership of a plant in 1966 and 1976. 28 '0 ° 0 O O O O O O o. . 0.3.? O. ’10. . o.- o.' .. .' O. . C .0 O. . . .o:..o O . . ...Oo . .0. 0:. O 0;. . O .0 .0 O C o 0.. 0'... . O O. o o ’ °. . . o O O .- ...o 0.0 :.. O O .3 . ... . O O o. . . o... '. f. . o O 0.3.... a--: l .. O .. 0. . . O .. . . . EMPLOYEES oi-zo . 21-100 0 ‘IOI OR MORE Figure 1-7. Distribution Of Wood Products Plants, 1976. 29 fig .2 \ ° . 3 i . [.o. o. -!3 ' :- .. 53: ° ., 2??! o ’. EMPLOYEES ° 3 ° 1 " 20 0 21-100 0 10! 0| MOI! Figure 1-8. Distribution Of Wood O .O .O O O .20 o 0'4- 0.. 0 O. O. z... 0 O '0 o 0. O .. o . O O O .. O '0'... O.. 0.0.. .: I. 0 o... o 9. .O.I . O. O . o O Products Plants, 1966. 3O telephone conversations and personal interviews. In all, complete structural information was obtained for 398 plants. Where only production data11 were available, they were converted to employment. By averaging the secondary information (for fifty-two plants) on the number of people needed to produce a million board feet of lumber, a conversion rate for sawmills of 8.8 forest products jobs per million board feet was established. The rate is slightly lower than the 9.2 forest products jobs per million board feet used by Greenaces (Humboldt County 1977, p. 35), but very close to the 8.7 ratio calculated by McKillop (Humboldt County 1977, p.35). Complete spatial information was obtained on 466 plants. The discrepancy between the spatial and structural informational totals exists because data sources for plants that closed during the time span often were not avialable, and several firms refused to release the needed information. But based on previous studies in which Markov chains were used,12 sufficient data were obtained for using the technique. The data mentioned above tell "what” occurred, but not ”why“ it occurred. To access "why” structural and spatial changes eventuated, required questioning industry entrepreneurs. A questionnaire nProduction data refers to the number of board feet of lumber produced by a firm each day and/or each year. 12Mansfield's (1967) conclusions were based on several 6X6 and 7X7 tally matrices all of which represented less than sixty firms; Preston and Bell (1961) utilized 6X6 matrices with less than thirty—five firms; and Archer and McGuire's (1965) 7X7 matrices contained data on 334 firms. 31 (Appendix B) was mailed to the 398 plants for which both structural and spatial data were available. Ninety-two (92) or twenty-four percent of the questionnaires were returned. A comparison between the regional distribution of the returned questionnaires and the population from which they were drawn shows that the Humboldt-Del Norte subregion is slightly over represented, while the Eastside is slightly under represented.13 Structurally, the sample is slightly biased in favor of larger plants. But the overall variations are so small that corrective measures were considered unnecessary. Further information was obtained from personal interviews with about forty non—responding firm managers, owners, local officials, and regional specialists. The interview information was used as a check to determine if those producers who returned the questionnaire were representative of the population. It was concluded that the answers were representative of all types of producers. Secondary information sources were also widely consulted: government reports, professional journals, industry publications, and so on. Organization of the Research In Chapter Two, the principal focus is on the predicted spatial and structural distribution patterns. The chapter opens with a brief explanation of Markov chains, and is followed by a Markovian analysis of spatial and structural changes in the wood products industry. The 13Twenty-three percent of the ninety-two questionnaires returned came from the Humboldt-Del Norte subregion, while only eighteen percent of the plants are located in that subregion. Conversely, only eight percent were returned from the.Eastside subregion, while the subregion contains twelve percent of the plants. 32 stress is on what the future distribution patterns would be like if the present trends were to continue to be appropriate. Chapter Three evaluates those factors which are important in the location decisions of wood products producers. Since most producers identified "access to material supply" as the most important factor, Chapter Four focuses on the timber supply and the variables which affect it. In Chapter Five, several other factors which influence the locational arrangement of the industry's activities are analyzedzeconomies of scale, access to market, government regulations, labor availability and cost, technological innovations, and transportation costs. Chapter Six integrates the information presented earlier. By evaluating the factors important in the locational arrangement of the wood products industry, a better understanding of the present spatial and structural trends and Unalikelihood of their perpetuation is achieved.14 14Throughout the remainder of this research, a plant refers to a company's total facilities at a specific location, while a firm refers to all the facilities owned and operated by a company. PLEASE NOTE: Page '33 is lacking in number only. No text is missing. Filmed as received.“ UNIVERSITY MICROFILMS . CHAPTER 2 PROJECTED STRUCTURAL AND SPATIAL DISTRIBUTION PATTERN Industrial location and relocation are partially stochastic processes (Lever l972b, p.22), and of the models available, Markov chains seem to be the most suitable for describing and predicting industrial location patterns. If an examination of the arrangement of industrial eStablishments is to be made in 1976, it seems reasonable to assume that the pattern is a function of the state in 1975, plus a change component which may be defined as a set of probabilities (Harvey 1967, p. 577). The locational arrangement of an industry, such as the wood products industry, is not dependent upon all previous states, as would be assumed in a classical deterministic model. But there is some dependency. So a purely random model, in which "the state of the system at any instance or point in time or space is wholly independent of its state at any other instant or point and is completely specified by the underlying fixed probabilities” (Collins 1972, p.7), is also inappropriate. Markov chain models occupy an intermediate position between the classical deterministic and purely random models, referred to by Collins (1972, p.7) as a position of partial dependency. This position of partial dependency approximates the processes involved in the differential growth of an industry (Harvey 1967; Collins 1973, 1975: 34 35 Lever l972b).1 Inherent in the Markov chain model is a sequence of stages with the following properties (Kemeny, Snell, and Thompson 1966, p.195): 1. A finite number of possibilities for the outcome of each stage; 2. The outcome of any stage depends upon the results of only the immediately preceding stage;' 3. A given number exists which represents the transition probabilities of the outcome for any stage; 4. If the initial state is given, it is possible to calculate the probabilities of a sequence of stages; 5. Transition occurs at discrete time intervals, and transition probabilities throughout the predictive period are stationary; 6. The probabilities for all individual components of each state are uniform.2 The Markov process can be represented as a sequence of matrix operations of the form: (n) (n+1) P x p = p where P is a transition matrix, and p is a vector of conditions at time n. 1 Further, the model facilitates the first aim of this research: to describe and project the future distribution pattern of wood products plants in Northwestern California. If the objective of the study was to perform sampling experiments on a model of a real situation, the Monte- Carlo simulation would have been appropriate; and if the concern was with inter-industry flow patterns, then Input-Output would have been used. But the Markov chain model seemed to befit the problem at hand. 2According to Collins (1972, p.26), "there is no theoretical or empirical evidence to suggest any correlation between the length of time a plant remains in a location and the likelihood of its relocating.” 36 The Markov process for regular chains is illustrated below using Lever's(J972b)example. Table 2-1 represents the transition locations of a sample industrial population in a four zone system (1959-1969). The elements along the main diagonal indicate the number of plants remaining in their original state. Thus, 118 of the 149 plants located in Zone 1 in 1959 could still be found in that zone in 1969. Conversely, the elements off the diagonal indicate the number of plants witnessing a change in state. For example, thirteen plants changed from Zone 1 to Zone 2, four plants changed from Zone 1 to Zone 3, and so on. Those plants that either moved into the study area or were initially established there after 1959 are included in the bottom row X. The right hand column X represents those plants that were Operating in 1959, but subsequently either went out of business or moved beyond the bounds of the study area. Element XX in the lower right hand corner of the matrix acts as a reservoir; a source of potentialentrantsinto the system and a pool for liquidated plants (Collins 1972, pp. 29-30). Although the exact size of the reservoir is arbitrary, it must be of sufficient size to cover births and deaths for several generations. Lever chose to have a reservoir of 906 plants. From the tally matrix (Table 2-1), it is possible to construct a transition matrix (Table 2-2). The conversion involves presenting each element of the tally matrix as a proportion of the total number of plants in each row. For example, the 118 plants in Zone 1 that maintained their location represented 0.56 (56 percent) of the total number of plants located in Zone 1 in 1959; the thirteen plants that moved to Zone 2 is 0.06 (6 percent) of the total; the four plants that moved to Zone 3 is 37 TABLE 2-1 EXAMPLE OF A TALLY MATRIX TO: Zone 1 1 Zone 2 : Zone 3 Zone 4 E X Zone 1 118 g 14 E 4 1 14 ' 63 E Zone 2 g 6 33 8 _ 6 20 Lb Zone 3 1 1 : 1 ' 68 5 i 24 Zone 4 L 2 f 0 5 3 43 : 17 x f 17 24 17 36 906 SOURCE: W.F. Lever (1972), "The intra-urban movement of manufacturing: a Markov approach," Institute of British Geographers, Transactions, p. 30. TABLE 2-2 EXAMPLE OF A TRANSITIONAL PROBABILITY MATRIX TO: Zone 1 g Zone 2 ? Zone 3 5 Zone 4 I X Zone 1 0.56 i 0.06 5 0.02 0.07 0.29 gZone 2 0.08 0.41 0.11 . 0.08 0.27 asZone 3 3 0.01 : 0.01 _ 0.69 0.05 0.24 Zone 4 0.03 1 0.00 g 0.05 0.04 0.26 x 0.02 i 0.02 I 0.02 0.04 0.90 SOURCE: W.F. Lever (1972), ”The intra-urban movement of manufacturing: a Markov approach," Institute of British Geographers, Transactions, p. 30. 38 0.02 (two percent) of the total; and so on. Each row of the matrix sums to 1.0 (100 percent). The distribution of plants in 1959 was: 212, 73, 95, and 65 in each of the zones respectively, with 1000 actual or potential plants in row X. Thus, the initial probability vector or proportional distribution of plants is: p(0) = .147, .050, .068, .043, .692 The distribution in 1969 is derived by multiplying the initial probability vector by the transition matrix P. The resulting distribution is: p(]) = .100, .046, .072, .073, .708 Thus, ten percent of the 1447 actual and potential plants would be expected to be in Zone 1, 4.6 percent in Zone 2, and so on. The next step is to multiply the first generation vector by the transition matrix P, and the routine is continued until the system reaches equilibrium (Table 2-3). The proportion of plants in X can be disregarded, since the concern is only with those plants in existence (Collins 1972, p.31). Therefore, Lever (l972b) summed the proportions in Zones 1, 2, 3, and 4, and converted (0) the proportions to percentages. For example, the snmi of p , Zones 1 through 4, was .308, and the percentage representation for each zone is forty-eight percent, sixteen percent, twenty-two percent, and fourteen percent, respectively. When equilibrium is reached in time period 8 (p(8)) the proportion of plants located in Zone 1 will decrease from forty-eight percent to eighteen percent, the share of plants in Zone 2 also will decrease slightly from sixteen percent to twelve percent, 39 TABLE 2-3 SUCCESSIVE VALUES OF (0) 9 Zone l E Zone 2 E Zone 3 1 Zone 4 i X p(0) 0.147 2 0.050 E 0.068 ; 0.043 ; 0.692 0(1) 0.100 I 0.046 g 0.072 ' 0.073 i 0.078 p(2) E 0.077 E 0 043 i 0.075 g 0.090 E 0.715 p(3) 1 0.064 i 0.040 t 0.078 i 0.100 t 0.718 0(4) 1 0.057 ‘ 0.038 I 0 078 5 0.105 i 0.721 p(5) 1 0.054 0.037 0.079 ' 0.109 3 0 721 p(6) : 0.051 . 0.035 4 0.080 a 0.113 f 0.722 p(7) E 0.059 ' 0.034 3 0.080 g 0.113 i 0.723 p(8) i 0.049 i 0.034 0.080 ' 0.115 § 0.722 SOURCE: W.F. Lever (1972), ”The intra-urban movement of manufacturing: a Markov approach,” Institute of'British Geographers, Transactions, p.33. 40 Zone 3's share will increase from twenty-two percent to twenty-nine percent, and Zone 4's share will increase from fourteen percent to 3 The predicted proportions cannot, however, be forty-one percent. converted to an actual numerical distribution of plants, because the size of the reservoir can influence the predicted total number of plants even though the proportions in each zone will be constant. To describe and predict structural and spatial changes in the wood products industry, two Markov chains were calculated. In the first chain, each state denotes a subregion; and in the second chain, each state represents a size interval. Wood Products Firms: Spatial Mobility The question being asked in this section is: to what extent is the future spatial arrangement of an industry affected by its present distribution? But in order to answer this question, it is first necessary to construct a tally matrix, which represents the location transition of all wood products plants in the study area for which data are available (Table 2-4). The most striking aspect of the tally matrix is the number of plants in state X; the state in which row elements indicate the number 3The same results can be achieved through matrix multiplication: PxP=P(2) P x P(2) = p(3), and so on. The computer program utilized irI this study used this method (Marble 1967). This procedure is widely used because it yields further descriptive measures (i.e. matrix of mean first passage time.) FROM: TALLY MATRIX FOR WOOD PRODUCTS PLANTS 41 TABLE 2-4 NORTHWESTERN CALIFORNIA: l966—1976 TO: 1 E ! Subregion l 2 3 g 4 i 5 6 7 X Sacramento- 45 O l E l E l l 3 2 Westside ! i Humboldt- 1 1 50 6 3 ‘ 4 3 1 25 Del Norte : 3 Mendocino- 0 g 4 38 g 1 i 2 0 ,0 17 Sonoma 1 ; i I 1 ‘ n 1 Eastside 1 = 0 1 30 g 3 0 E 2 8 r 1 1 ' 1 Shasta 1 f 1 0 1 0 § 20 f 0 E 0 14 5 i 1 Northern 1 1 0 : 0 4 i 18 I 0 14 j . 1 i Sierra 0 0 1 i 0 1 E 0 1 32 15 Birth- 39 10 ' 8 12 13 3 ‘ 9 999 Deaths 42 of new plants established, and column elements show the number of existing plants that went out of business. Also included as births are firms that existed outside the study area, but relocated their facilities, established another: plants, or acquired an existing plant in the study area. Deaths included plants that relocated facilities, or, where it was possible to detect, established anothen.pflant outside the study area. In addition, acquired plants were classes as deaths, tending to slightly inflate the value of column elements.4 There were three alternatives: (1) to ignore acquired plants, since even though a movement of financial capital occurred, the distribution of fixed capital equipment remained constant; (2) to chart the movement of inter-regional financial capital, but ignore the financial capital withdrawn from the system when a plant is externally acquired; or (3) to chart both acquisitions that occur inter- and intra-regionally. Since the concern of this research is only with inter-regional movement of capital, alternative three was rejected. And because an area that has a comparative advantage will attract both financial and fixed capital equipment, inter-regional movement of financial capital had to be considered. But in order to account for all the capital in the system, it was necessary to class acquired plants as deaths.5 4From this point on the words births and deaths cover the changes in the locational arrangement of the industry mentioned above. 5A reservoir of 999 plants was included in element XX. Different reservoir sizes were tested, but the results were not significantly altered (as long as the reservoir was large, over 600 plants). 43 If deaths and births are ignored for the moment, nearly eighty percent of the plants maintained their established location. Further, only 2.56 percent of the entrepreneurs who returned the questionnaire indicated that they planned to move their facilities (Appendix 8, question 4). This is to be expected, for once capital is committed to the physical plant, it is practically immobile, and thus tends to perpetuate the existing industrial location pattern (Smith 1971, p.39). As described earlier, the elements off the diagonal indicate the number of plants witnessing a change in state. Even though relocation of fixed capital equipment has occurred, much of the charted mobility resulted from the relocation of financial capital. Examples include the establishment of a second plant or the acquisition of existing plants in another subregion. As the tally matrix (Table 2-4) clearly reveals, major outflows of capital occurred from the Humboldt-Del Norte and to a lesser extent the Mendocino-Sonoma subregion, while the Shasta subregion was a major recipient of the capital flow. Distributional changes, therefore, resulted both from the relocation of capital, and a differential birth-death rate. For example, forty-three new plants were established, relocated, or acquired in the Sacramento- Westside subregion, while only two existing plants closed and seven relocated or acquired facilities out of the subregion: a net gain of thirty-four plants. At the other extreme, the Humboldt-Del Norte subregion suffered a net loss of twenty-seven plants. However, the concern is not with the actual distribution of plants, but with the arrangement of the industry. That is, what proportion of all plants are in each subregion? 44 An analysis of the transition matrix (Table 2-5) can follow two routes: (1) consider at each stage the total population, and predict the fraction of the population which will be in each subregion; and (2) study a single plant, whose history is the outcome of a Markov chain with a transition probability matrix such as the one shown in Table 2—5. Since this section focuses on industrial movement as a form of resource allocation, emphasis is placed on the locational arrangement of the entire industry. The 1976 distribution of plants is displayed in Table 2-6. The largest concentrations of plants are found in the Sacramento- Westside and Humboldt-Del Norte subregions, while the Northern subregion has a paucity of plants. When equilibrium is reached, the fixed probability vector (Table 2-7), shows the population clustering in the Sacramento-Westside subregion, with a slight expansion of the Sierra subregion's population. Conversely, the industry is contracting on the North Coast and to a lesser extent in the Northern, Eastside, and Shasta subregions. To get an indication of the relative stability or fluidity of plant locations, it is useful to examine the matrix of mean first passage times (Table 2-8). Elements in this matrix represent the mean number of time periods (in this case ten year intervals) needed to move from one given state to another for the first time. For example, the mean time to go from the Sacramento-Westside subregion to the Humboldt- Del Norte subregion is nearly 113 decades, while it would take thirty—two decades to go from the Humboldt-Del Norte subregion to the Sacramento- Westside. 45 TABLE 2-5 SPATIAL PROBABILITY MATRIX FOR WOOD PRODUCTS PLANTS NORTHWESTERN CALIFORNIA: l966-1976 TO: ‘ g Subregion ‘ 0 FR Sacramento- 1 .833 :.000 ;.018 .018 f.018 (.018 ...055 L .037 Westside ! 1 ‘ Humboldt- 2 .011 '.538 1.065 1.032 1.043 .032 1.011 .269 Del Norte , ' Mendocino- 3 ’ .000 §.065 ;.513 :.016 .032 ,.000 E .000 .275 Sonoma 1 Eastside 4 .022 .000 .022 :.667 1.068 .000 :.044 .178 Shasta 5 .028 .028 .000 .000 .556 .000 1.000 .389 Northern 6 I .026 3.026 .000 :.000 ;.105 .474 .000 ..368 Sierra 7 1 .000 .000 ’.020 .000 .020 '.000 .653 .306 Births- x Q .037 ;.009 ..007 ,.011 '.012 3.003 .008 .914 Deaths 1 . . 46 TABLE 2-6 VECTOR OF THE PRESENT SPATIAL DISTRIBUTION OF PLANTS mecmvm ccmcpcoz somagm mcwmpmmm mEocom -ocwooucmz mpcoz Foo auvponE:I metapmaz -oocmEmcomm Actual Number 66 55 47 48 25 47 88 of Firms Fractional .175 .146 .125 .128 .066 .125 .234 Representation TABLE 2-7 FIXED PROBABILITY VECTOR mccmwm ccmspcoz mummsm onwmummm meocom -ocwooocmz mpcoz Poo -puPonE:: anampmaz -opcmEmLomm Fractional .060 .084 .102 .114 .030 .138 .471 Representation 47 ccmgpgoz u o mpmpm mEocomuocwooucmz u m oumom mesaao-m;paam n w apaom sumagm u m spasm asaoz _ao-pe_an=I u N spasm mcgmwm n u mumpm wuvmpmmm n a mpmpm wuvmpmmzuoucmsmcomm n — mumpm om.F Fm.mm ow.¢w_ m¢.nm nn.¢w mo.mm mm.woP mm.om mumpm mm.m mm.—m mm.nwp mn.mm mm.mw Fm.mw mo.o—F am.mm mpmwm m_.m mo._o mn.mm w¢.m¢ on.mw Fv.mm ¢N.¢o— NP.Fm mpmum mo.m mm.oo um.mw_ mm.om mo.ow Fm.mm mo.¢o_ No._m mumpm mw.¢ op.¢m mm.me w¢.w¢ m¢.mm om.wm mm.o_F mm.mm mumum no.m mo.mo om.mm~ oo.¢m mm.mw No.mm wo.mm oo.mm wumum ow.m w—.oo cm.¢- Fv.mm or.—w NF.Fw ¢—.om om.mm wpwpm mN.m no.m¢ mm.—mp Rm.¢m oo.mw om.©w mm.m__ om.o mpmpm w apapm N apapm o abapm m aoaom a spasm m apapm N apaom _ spasm mIH no meH mzh no meH Fmopppgu w 0 wt m.mp umx Fo.no m—m we OPP QMF m—mpOH as am am Sam. 3.3 No_ m.mm s.ww N.WM spaposspspcp a: em 8% am :as mpapop Lopgopcp ccogpgoz pmaou :pgoz oop< opcoaacoom coppooop oNpm _ pcoaxoposm . smoopooo op capo ” s.m_ _.s m.m N.o_ N.s N.N assess: s.mm N.NN a.mm . m.mp N.aa N.Nm oz N.NN N.Np m.oN N.o_ _.o_ m.om msp M . pcosxo—osm W _ omoogocp op capo Loppopcp pmoou ccogpcoz :pgoz opcoEoLoom msgpa mo coppooop swoop opopooscopcp pposm mELpa mo oNpm D . Hzmz>04m2m mmEvaluation Of er”””fi ,,,.Alternative P011C1€Smu mesa—a :moz p ugmvcmum to seesaz k cowmeOA >2 mcopoma LowgmucH stagecoz ommoo gotoz mox< oucmEmcomm Logo; Lewgwch ccmgpcoz pmaou :pcoz moc< oucwEmLomm Loam; Lovcmch :Logucoz pmmoo sucoz mwg< opcmEmcomm cowampsommcmce cowuoznoca to cowoma nopom_mm mump m onHosooma no machuov magma 00._ _P020 NA.0 mm 0.0 A00000_000 00_-_Nv 50000: 0s._ 00cm; 00?; 0..0_ mm 0.0? Amamsoeaem 0Nn00 Fussm cowomugoamc0c» cowosnegpmwu cowpmv>ov mpcmpa u venucmpm to Lonsaz 00m: cowpoznoca mo Lopoma umpoo_mm mem 00 mgouomm mn.— mms< opcmsmgomm «N.N Fm om.m LowgwocH stagecoz 0m. Lowcmch cgwgpLoz mo.m om oo.m pmmou cpcoz “m NA._ 00000 escoz 00.0 00 00.0 00c< opcmsmcosm mm mcvuwxcmz N0. 00g< oucwsmcomm om.0_ Fm mP.w¢ Lowcmch :Locpcoz wk. LowgmucH ccogucoz m0 mpc0_a u camccmpm mo Longsz scmoz cowpmooo >0 mcopoma cowposvoca mo Lopoma umuumpom A0_00000 _-0 momqe .moc0owwwcmwm 00 00>0P —o. 0;“ 00 0000000 0:0 000 00000000 no: 003 0000000000 0: mo 0000:0000; __:c 00000 .2000030000 00 0000000 uopowpwm 0:0 00 0:000 pmoo P0pOp we 0000000000 000 :o 0900 F020000 00 0005 0000000 0 0.0 000002 00.0 00 0.0 00000 00. _P000 00.0 00 0.0 000002 00. 00000 00.0 00 0.0 F_0sm 9.059.082 000002 00.00 00 _.00 00000 00.0 __000 00.0_ 00 0.00 00000: 00. 00000 00.0_ 00 0.00 _F000 , - m 20:920.; __.0 000002 00.0 00 0.0 00000 .m 0.0 F_0sm 00.0 00 0.0 00000: m 00; 00000 00. N 00 0.0 :000 «$300. 0N._ E0000: 00.0_ 00 0.00 00000 00. 00000 00.00 00 0.00 000002 00., 00000 0003 00.0_ 00 0.00 P_0sm Loam; 00000000000000 000000000 0000_0 p 0000:0pm 00 000502 000.00: 0Nwm 00 m0ouo0a 0000000000 we 000000 vmpom_0m 00.00000 _-0 00000 102 The ”survival technique,” as used here, involves comparing the percentage of total annual output produced by plants in production (number of board feet of lumber produced annually) intervals of various sizes. ‘A plant's annual output in 1966 represents predicted daily production for a 220 day year,2 while 1976 figures denote actual annual output. Table 5-2 reveals that plants producing less than sixty MMBF annually have lost part of their market share, while plants producing more than sixty MMBF show both a relative and absolute increase in output. Although a precise indication of the optimum size plant cannot be discerned from the information available, it can be assumed that lumber mills producing between 60-100 MMBF annually are near the optimum size. Traditionally, it has been postulated that the size of a sawmill was limited by available technology. According to Zivnuska (1965, p.34): The rate of production is controlled by the capacity of the headsaw, with resaw, edgers, and trimmers being related to headsaw output. As the capacity of the particular headsaw is exceeded, the basic response is the installation of an entire additional "site," consisting of a second headsaw and its accompanying equipment. The advantage of such multiple battery type of organization at a single site tends to be quickly overcome by transportation advantages in establishing the additional "side" as a separate mill with shorter log haul distances. But the direct positive relationship between the size of a plant and the utilization of new technology weaken the traditional argument. For example, large plants utilize a substantial proportion of their residue, so transportation costs are not as critical in the location of plants as they were in the past (Howard 1974, p.16). That is, material that once was waste now has value, so the importance of weight loss has 2Where possible, predicted annual output was compared with actual annual output, and the variation was generally less than five percent. 103 TABLE 5-2 PERCENT OF TOTAL OUTPUT FOR FIRMS IN VARIOUS SIZE CLASSES NORTHWESTERN CALIFORNIA, 1966 AND 1976 | 1966 1 1976 - 0 1 8129 Class 1 No. of Annual Percent of No of Annual Percent of 1 Firms Output Total Output Firms Output Total Output 1 0-10 23 138.1 .036 : 5 38.7 .011 10-20 49 768.2 .203 l 5 84.6 .024 20-40 42 1359.6 .359 l 36 1094.2 .306 40-60 9 856.7 .226 1 14 680.8 .191 60-80 2 194.6 .051 l 11 743.9 .208 80-100 0 0.0 O l 4 372.5 .104 over 100 4 470.8 .124 2 5 555.5 .156 SOURCE: Miller Freeman Publications, Inc., Directory of the Forest Products Industry, Portland, Oregon: For years 1967 through 1977. 104 been reduced for larger plants. By making use of a greater percentage of their residue, large plants gain an advantage over their smaller competitors who still dispose of materials such as sawdust, bark, and small and irregularly shaped pieces of lumber. As shall be shown in a subsequent section, the advantage of large plants is enhanced because they are more capital intensive, and more likely to employ the latest equipment. There is also some indirect evidence that multi-plant firms are more efficient than single-plant firms. Mergers and acquisitions are increasingly common in the wood products industry. ”Economic pressures encourage such transactions, as corporations of all sizes seek to diversify and enlarge, and smaller companies look for an infusion of capital" (Forest Industries 1970, p.30). Between 1966 and 1976, for example, Masonite Corporation acquired four established firms in the study area; Louisiana Pacific, a firm that broke off from Georgia Pacific in the late 1960's, now runs twenty-three wood products plants in California; DeGeorgia Corporation obtained both R.F. Nikkel Lumber and Vita Bark Inc. in 1970; Eel River Lumber bought out Halvorsen Lumber Products; Arcata National Corporation is purchasing Simonson Lumber Company, and so forth. Thus multi-plant companies are producing a greater percentage of the total annual output. There is also evidence that many primary and some secondary wood products manufacturers are diversifying (Forest Industries 1970, p. 31), and becoming more integrated. Some of the larger firms in the study area (i.e. Louisiana Pacific, Georgia Pacific, Diamond International, Arcata National, and so forth) own and Operate tree farms, secondary wood products manufacturers, 105 wholesale and retail establishments, as well as sawmills. Even though there are still many single product, single plant, owner operated firms, the large multi-plant, vertically integrated companies dominate in terms of production and sales. These large companies are also better able to adjust to fluctuations and changes in demand. According to the Humboldt County Overall Economic Development Plan (Humbo1dt County 1977), the brightest prospects for expansion in the wood products industry (at least in the North Coast) appears to be in residue based industries such as hardboard and particle board. Demand for Wood Products The single major consumer of wood products is the residential construction industry. According to the Forest Service (1977, pp.l43- 144) between one third and one half of all softwood plywood and lumber goes into housing. For a typical $24,301.00 house in 1976, $2,035.59 or about one-fifth of the hard costs3 were spent on lumber, $1,381.92 for millwork (approximately one-tenth of the hard costs), and an additional $414.31 was spent for wood roofing materials (Crow's Digest, Nov. 1977, p.28). Not surprisingly, the price of softwood lumber (and thus indirectly demand) is directly related to changes in housebuilding activity -- specifically to the construction of single family dwellings (Figure 5-1). Even though there are substitutes for and between wood products, Mead (1966, p.44) asserts that the demand is still relatively inelastic. Thus, futunehousebuildingactivity could strongly influence spatial and structural changes in the area's wood products industry. 3Hard costs refer to the cost of such items as materials and labor. Financing costs, permits, taxes, and so on are not considered hard costs. 106 A Comparison of Housing Demand and the Relative Price of Softwood Lumber Weighted Housing Starts — -— Housing Starts - - -- Price of Lumber 250 d 225 .1 200 0 ‘E 8 175- 0) CL 150 - . 125 '- I, O O I \ / I I o 100 j T V T 1 j T 1970 1971 1972 1973 1974 1975 1976 1977 Year Source: Executive Office of the President Council. 1977. p. 10 Figure 5-1. 107 According to Marcin (1977, p.11), the demand for housing in the United States should remain strong throughout the 1980's, with construction concentrating in the West and South. The West's share of housing production in the year 2000 is projected to be about twenty-five percent overall (Marcin 1977, p.10). So if the economy continues to grow and inflation moderates, the demand for wood products should remain strong. But if, as authorities in the home building field predict (MIT—Harvard Joint Center for Urban Studies 1977), eighty percent of all households are denied the opportunity to purchase a home in the 1980's (and the middle class is denuded of its economic strength), then the wood products industry could go into general decline. Another factor which will influence demand is the proportion of total housing units accounted for by apartment dwellings, since they require considerably fewer wood products than one and two family houses. In 1960, multifamily units constituted twenty-one percent of the total housing units started, and single family units made up seventy-nine percent. By 1965, the proportion of multifamily starts had risen to thirty-five percent, and by 1973 it was up to forty-five percent (Duke and Huffstutler 1977, p.33). If this trend continues,4 the demand for wood products would have to be adjusted. That is, competition for markets would intensify,and some of the less efficient plants might be forced to close. As Table 5-3 shows, the principal regional markets for California's primary wood products have not dramatically changed since 1968. The 4The trend was reversed during the recession years of 1974-75, as multifamily housing was more severely affected than single family housing. 108 TABLE 5-3 PRINCIPAL MARKET FOR CALIFORNIA'S PRIMARY WOOD PRODUCTS: 1968, 1972, AND 1976 (in percentages) Market 1968 1972 1976 California 60.9 58.1 63.3 Other West 4.9 6.4 7.9 Midwest 16.2 14.5 12.7 Northeast 5.5 4.5 3.8 South Central 4.7 6.0 4.8 Southeast 4.8 8.7 5.3 Export 3.3 1.8 2.2 SOURCE: Western Wood Products Association, 1977, 1973, and 1969 Statistical Yearbook[s], Portland, Oregon: Western Wood Products Association Statistical Department, p.2. 109 western market is slightly more prominent in 1976; the importance of the Midwestern and Northeastern markets have declined; while the percen- tage of the total output sold in the other market areas has fluctuated. Even though comprehensive data are not available on secondary wood products firms, the survey (Appendix B, question 7) revealed that the principal market for secondary products was California. There is a direct relationship between the size of a plant and the extent of the market (Figure 5-2). Generally, small plants, employing less than twenty-six people, market their products locally. Concentrations of population and small plants are coincident (Figures 1-5 and 1-7). Since the Sacramento-Westside and Shasta subregions have a relatively large, expanding population (Figure 5-3), small plants will probably continue to concentrate in these two subregions. Firms of intermediate size market their products predominantly in the western United States, while the products of large firms, employing over 100 people, are sold throughout the country, and in a few cases, abroad. This does not necessarily imply that large firms also supply the local market. According to Chapman (1978), Arcata National Corporation, a large primary firm employing about 450 individuals, markets selective items in Europe, but prefers to sell mixed loads of lumber in the United States. Yet very little of Arcata's lumber is sold in California, because lumber prices are exceedingly variable. Instead, Arcata caters to the more stable, dependable south- western and midwestern markets. Even though location sites of intermediate and large plants are increasingly peripheral to the forest, access to material supply (not 110 Plant Size and Extent of Market Northwestern California 1976 100- D Local Market .California and Western U.S. .Nationwide and Abroad Percentage of All Firms in the Class Size 8 8 8 8 8 0‘ 8 8 l 1 1 l l L l I .A O I Small Firms Intermediate Firms Large Firms 1—25 26—100 Over 100 Source: Compiled by Author Figure 5-2. 111 PERCENT CHANGE o-i-ao) I - 1’ - 1. - 3° III :1- so Figure 5—3. Pogulation Change By County -- Northwestern California, 19 O to 1970 112 population centers) seems to characterize the spatial pattern of plants in these two size classes (Figure 1-6). But as reported in the section on "Site and Situation,” the choice of location depends not only on access to material supplies and markets, but also on whether needed services and labor are available. Law Other factors that can influence the spatial distribution and structure of an industry are the availability, cost, and productivity of labor. Even though the attributes of labor were not cited as important factors in the present location decision of most wood products plants, they are relevant to the industry's structure. The use of number of employees as a measure of industrial structure mandates an analysis of trends in labor. As Figure 5-4 shows, output per employee hour rose at an annual rate of 4.5 percent between 1958 and 1964, but since 1964 the increase has been comparatively slow and subject to large fluctuations.5 A Bureau of Labor Statistics (Dike and Huffstutler 1977, p.33) report asserts that the fluctuations relate to the variable demand for wood products (see Table 5-2), while productivity gains are attributable to technological improvements. Manpower requirements in sawmills have been reduced by the chipping headrig, which produces marketable chips; by automatic scaling and measuring equipment; by carbide saws and knives, which lengthen tool 5The data are for the wood products industry throughout the entire United States. 113 Components of Labor Productivity in Sawmills and Planning Mills 1958—1975 Index 1967= 100 1958 1960 1962 1964 1966 1968 1970 1972 19741975 Year Employee Hours I Output D Output per Employee Hour (Productivity) Source: Duke and Huffstutler. 1977. p.35 Figure 5-4. 114 life; by automatic grading systems; and by automatic kiln controls. For plywood, veneer, and millwork, new developments include automatic lathe chargers; improved curing and drying techniques; and labor extensive methods for feeding green veneer into the machines. Particle boards have benefitted from stronger glues, and a variety of new equipment is available to aid secondary wood products workers increase their productivity. Most of the technological improvements have been developed by machine suppliers, who benefit by making their own equipment obsolete as soon as possible (Irland l976a, p.23). Wood products firms per se spend only 0.4 percent of their net sales on developing new products and new processing machines and methods. Most of the research and development conducted by the wood products industry is performed by large firms (National Science Foundation 1975). And at least in the lumber industry, it is the large sawmills that have increased production and lowered costs by utilizing the latest equipment (Bureau of Labor Statistics 1974, p.15). Capital deepening,6 which resulted in sixty—one percent of the gains in productivity (Robinson 1973, p.52), is more important than technological change. If the percentage of total costs spent on labor is used as an indirect measure of capital deepening, then larger plants within the study area seem to be more capital intensive than their smaller counterparts. Small plants spend an average of 32.9 percent of their total costs on labor, while intermediate and large plants spend 28.1 and 6Capital deepening means that the industry is becoming more capital intensive; capital is being substituted for labor. 115 27.6 percent respectively. Even though there are wide variations in both the value added per production worker man-hour and capital expenditures per employee (Table 5-4), generally larger plants have a competitive advantage over the small plants. This may be one reason why trends in the structure of the wood products industry seem to favor the larger concerns. As mechanization and automation continue to influence the make-up of the labor force, production jobs will decline both relatively and absolutely, and nonproduction and skilled positions will increase (Figure 5-6) (Bureau of Labor Statistics 1974, p.19; Dike and Huffstutler 1977, p.34). For the plywood and veneer industry in the Pacific Northwest, LeHeron (1976, p.70) concluded: the best-practice and less productive mills are characterized by different output-growth impacts but possibly similar employment-growth impacts. . . . Best-practice operation reduced employment by modifying the process of manufacture while less productive mills carried out employment reductions in direct response to efficiency pressures. Assuming the situation is analogous for the remainder of the wood products industry, then overall employment levels will decrease while the demand for skilled workers increases. According to Irland (1975, p.226): Despite the publicity given log exports, interest rates, and zig- zagging public policy, industry managers have found that their problems are actually people problems, not "thing" problems. . . . The low wage sectors of the wood business have lost their labor pool to rural out-migration, high-wage rural industry, and, some say, to generous minimum wage and welfare programs ... . millmen have adapted through mechanization, but then they face another barrier -- a skill barrier. The knowledge needed for skilled wood products jobs often cannot be learned efficiently through training or vocational education (Irland 1975, p.226); experience is necessary. Therefore, occasional shortages of specialized machine operators could occur. The legislation which 116 Projected Employment Changes by Occupational Group Wood Products Industry 1970—1980 100- 801 704 q 0 6 q 0 5 ‘ d d d n. no no 4 mw 9. 1 .U -10. -20. 09.020 Ememd Source: Bureau of Labor Statistics. 1974, p. 19 Figure 5-6. 117 .m_.0 .00000 00 0000000000 .0.0 H0.0 0000000003 .000: 0000__00 .0000000000 80% :0 000000 amsomssé 0:8 002809 NdowmoNossomB .AvmmFV mowpmwpmpm 00000 mo 20003m “mumDOm m._ N._ 0 0._ 0.0 _ 100030_0 . 0:0 000:0> 0.0 00000000 0.: 0 0.: 00000000 :.0 _ 0_0: 00000_0 0:0 PFwEZ0m __. ___--.__1. __._. 00:0—0 09:000 0 p:0_0 00000>< 0:0000000 “0000 #:0P0 00000>< 0:0000000 00000 _ 00 00 _ 00 0e _ wcmwow$%w #moz pcwwuw$$w pmoz m pcmwow$%w pmoz pcmwuw$wm #mOZ ” Lepomm _ m >0am:0:H 00>0_050 000 000:.:02 0 000003 :000000000 000 0000< 00—0> 00:000:00xm F0pwa0u Romp .mmh<0m owHHz: >mhm2ozH mhuzoomm coo: oz< mmmzz0 mmmDHHozmmxm 0<0H0 0im m0m