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02332 Coastal Zone COASTAL ZONE Information AUG 16 1974 INFORMATION CENTER Center ESTABLISHMENT OF OPERATIONAL GUIDELINES FOR TEXAS COASTAL ZONE MANAGEMENT Interim Report on Economics & Land Use May 1973 THE UNIVERSITY OF TEXAS AT AUSTIN HC 107 .T4 C822 1973 E. Gus Fruh Associate Professor of Civil Engineering Center for Research in Water Resources Project Director Other Co-Principa I Investigators: William L. Fisher, Bureau of Economic Geology Kingsley Haynes, LBJ School of Public Affairs Jared E. Hazleton, LBJ School of Public Affairs Joseph F. Malina, Jr. , Environmental Health Engineering Frank D. Masch, Civil Engineering Carl H. Oppenheimer, Marine Science Institute at Port Aransas Joe C. Moseley II, Texas Council on Marine-Related Affairs Project Staff: Michael J. Cullender, Center for Research in Water Resources Robert S. Kier, Bureau of Economic Geology James S. Sherman, -Environmental Health Engineering George W. Murfee, Center for Research in Water Resources Thomas Isensee, Marine Science Institute at Port Aransas Sandy Bryant, Senior Secretary, Center for Research in Water Resources Liaison Investigator with the Division of Planning Coordination, Office of the Governor of Texas: Joe B. Harris, Interagency Council on Natural Resources and the Environment ESTABLISHMENT OF OPERATIONAL GUIDELINES FOR TEXAS COASTAL ZONE MANAGEMENT Interim Report on ECONOMICS AND LAND USE Prepared by Kingsley E. Haynes, Assistant Professor of Geography & Public Affairs Jared E. Hazleton, Associate Professor of Public Affairs Staff: Tom Kleeman, Department of Economics Michael Ryan, Center for Cybernetic Studies Gerald White, Department of Geography for Research Applied to National Needs Program National Science Foundation Grant No. GI-3487OX and Division of Planning Coordination Office of the Governor of Texas Interagency Cooperation Contract No. IAC (72-73)-806 Coordinated through Division of Natural Resources and the Environment The University of Texas at Austin May 1973 Property of.CSO Libraz7 This is one in a series of six interim reports describing progress on this 0- research project during the year ended May 31, 1973. The six reports cover ty-@ the following areas: rq c4D, alp cll;:) Summary Resource Capability Units Economics & Land Use Estuarine Modeling N Water Needs & Residuals Management Biological Uses Criteria U - S . DEPARTMENT OF COMMERCE NOAA COASTAL SERVICES CENTER 2234 SOUTH HOBSON AVENUE CHARLESTON , SC 29405-2413 ACKNOWLEDGMENTS This research has been supported by the National Science Foundation, Research Applied to National Needs Program, through Grant GI-3487OX and by the Office of the Governor of Texas through Interagency Cooperation Contract IAC (72-73)-806. We would like to acknowledge the help and cooperation of a series of private and public organizations that have played a critical role in the completion of this interim report. They are: The City of Corpus Christi,. Data Processing Division and the Division of Long Range Planning Systems Research Service Coastal Bend Council of Governments The Population Research Center, The University of Texas at Austin Bureau of Business Research School of Architecture, The University of Texas at Austin Office of Information'Services, Office of the Governor of Texas Texas Water Development Board Lyndon B. Johnson School of Public Affairs, The University of Texas at Austin Specifically the following individuals gave of their time and their talent in aiding this portion of the study. Dr. David L. Huff Dr. Stanley Arbin.gast Mr. Mark Estes Mr. Ken Ramsay Dr. Herbert Grubb Mr. Jack Huffman Mr. Thomas F. Freeland Mr. Robert Rusk Mr. Arthur Hill Mr. Paul Woods Ms. Shirley Russell Ms. Susan Conway Mr. Spencer Rappold Dr. Dudley L. Poston, Jr. Ms. Dianne Glaser Mr. Kenneth Ferguson SUMMARY The overall goal of this project is to develop and test a methodology,-for assessing the economic and environmental impact of alternative public policies for management of Coastal Zone environmental resources. The objectives of this task force in fulfilling that goal were: (1) the development and evaluation of input/output and demographic models to project under varying assumptions the level of economic activity by industrial sector and the expected level and composition of population in the Coastal Bend Region; and (2) the development and assessment of a land use model to spatially allocate households and industry within the study region. The major steps necessary to accomplish these objectives were: (1) construction of an input-output model for the COG region; (2) projection of the input-output model to 1980 and to 1990; (3) development of demographic projections for the region for 1980 and 1990; (4) allocation of projected popu- lation increases to various types and locations of residences; and (5) projection of the distribution of retail and commercial establishments. The results to date of each of these steps are presented in this report, along with a discussion of the methods used, including the necessary assump- tions. These results can provide an estimate of the economic impact of various alternative plans for the region. In addition, they serve as input to the other task forces on the project team, providing them with a basis for evaluating the environmental impact of the projected changes in the level of households and economic activity, including the effects of increased water demand and waste discharges. TABLE OF CONTENTS ACKNOWLEDGMENTS SUMMARY TABLE OF CONTENTS LIST OF FIGURES iv LIST OF TABLES v CHAPTER I. Land Use/Economics Task Force Interim Report CHAPTER II. Development of an Input-Output Model and Economic Forecast for the Coastal Bend COG CHAPTER III. Development of a Demographic Forecast for the Coastal Bend COG Region, 1970-1990 APPENDIX A. Description of the Methodology Used in the Cohort Migration- Survival Projection A- I APPENDIX B. Demographic Features of the Coastal Bend COG, 1960-1970 B-1 CHAPTER IV. Manufacturing and Port Facilities IV-1 APPENDIX C. Location of Industrial Growth C-1 CHAPTER V. Qualitative Allocation of Residential Population V-1 CHAPTER VI. Analysis of Intra- Metropolitan Population Patterns VI-1 CHAPTER VII. Transportation VII-1 CHAPTER VIII. Retail Allocation VIII-1 CHAPTER IX., Hou sing IX_ I CHAPTER X. Environmenta I- Land Use Integration Procedure X-1 APPENDIX D. The Regional Impact of an Environmental Policy: Pollution Abatement on the Hudson, The Refuse Act of 1899 D-1 CHAPTER XI. Data Management - Information System XI-1 iii LIST OF FIGURES Page I-1. Coastal Bend Region I-2 II-1. Texas Input-Output Regions II-2 II-2. Major Sections in an Input-Output Model II-4 II-3. Hypothetical Transactions II-5 II-4. Hypothetical Direct Requirements II-7 II-5. Hypothetical Interindustry Coefficients II-8 IV-1. Corpus Christi Ship Channel IV-14 IV-2. Port of Corpus Christi--Inner Harbor Industrial District IV-15 V-1. Corpus Christi Region S.M.S.A. Census Tracts as of 1970 Census V-4 V-2. Corpus Christi Region Growth Areas 1980 V-6 V-3. Corpus Christi Region Growth Areas 1990 V-7 V-4. Population Density and Distance from the CBD V-9 V-5. Population Density V-10 VI-1. Population Growth, Nueces County and Coastal Bend Region 1920-1970 VI-2 VI-2. Corpus Christi Population Growth and Distribution 1960-1970 VI-10 VIII-1. Corpus Christi Region--Census Tracts as of 1970 Census VIII-2 IX-1. Mapping Procedure of Factor Scores IX-4 IX-2. Corpus Christi Region Residential Population, Socio-Economic Status IX-8 IX-3. Corpus Christi Region Residential Population, Life-Cycle IX-9 iv LIST OF FIGURES (CONT'D.) Page IX-4. Map of Corpus Christi Tenant Characteristics IX-10 IX-5. Example of Multi-Family Housing Matrix IX-11 IX-6. Graph Relating Rent Paid to Acres Per Rented Unit IX-11 IX-7. Graph Relating Housing Values (Single Family Dwellings) to Acres Per Unit IX-14 X-1. Example of Surface Water Distribution With Regard to Resource Units X-6 x-2. Waste Treatment Models X-8 LIST OF TABLES Page I-1. Land and Water Area in the Coastal Bend Region I-5 I-2. 1970 Census of Population for the State, Coastal Bend Region, Counties and Places of 1,000 Population or More I-6 II-1. Sectors of the Coastal Bend COG Regional Input-Output Model II-14 II-2. Processing Sector, 1967 II-20 II-3. 1980 Projected Final Demand II-29 II-4. Direct Requirements Table: Coastal Bend, 1967 II-30 II-5. Direct and Indirect Requirements Table: Coastal Bend, 1967 II-42 II-6. Projected Processing Sector, 1980 II-48 II-7. Total Output of Economic Sectors 1967 and 1980 II-55 III-1. Total Population in the Coastal Bend Region 1920-1970 III-2 III-2. Coastal Bend COG Populations Projections III-7 v LIST OF TABLES (CONT'D.) Page B. 1. Population and Percent Change 1960-1970 B-2 B. 2. Birth Rates 1960 and 1970 B-3 B. 3. Death Rates 1960 and 1970 B-4 B. 4. Vital Statistics--Coastal Bend Region B-5 B. 5. Migration 1960-1970 B-6 B. 6. Urban and Rural Residence 1960 and 1970 B-7 B. 7.1 Median Age 1960 and 1970 B-8 B. 8. Ethnic Composition- -1970 B-9 B. 9. Average Family Size and Net Change 1960-1970 B-10 B.10. Years of School Completed--Texas and the Coastal Bend Region 1960 and 1970 and Percent Change B-11 B. 11 . Population Distribution 1960 and 1970 B-12 B. 12. Population Density Coastal Bend Region 1960 and 1970 B- 13 IV-1. Characteristics of Selected Manufacturing Firms in the Corpus Christi S.M. S.A., 1972 IV-3 IV-2. Total Commerce at Leading U. S. Ports: 1970 IV- 12 IV-3. Commerce at Leading Texas Ports: 1961 and 1970 IV-13 V-1 . Basis of 1960-1970 Small Area S.M.S.A. Data Match V-2 VI-1. Inmigration and Outmigration for Nueces County from 1965 to 1970, by'Age Group VI-4 VI-2. Residence in 1965 of Population in Towns and Cities in Corpus Christi S.M.S.A. VI-S vi LIST OF TABLES (CONT-D.) Page VI-3. Population Change in Corpus Christi: 1960-1970 VI- 7 VI-4. Population of Corpus Christi by Census Tract: 1960 and 1970 VI- 9 VI-5. Population Movement Within Corpus Christi, by Census Tract VI-12 VI-6. Population Movement Within C Corpus Christi, by Area VI-13 VI-7. Ethnic Composition and Distribution of Corpus Christi's Population: 1970 VI-14 VI-8. Ethnic Composition of Corpus Christi's Population, by Census Tract: 1960 and 1970 VI-15 VI-9. Distribution of the Ethnic Population in Corpus Christi, by Census Tract: 1970 VI-16 VI-10. 1980 Population Projection for Corpus Christi Census Tract VI-18 VI-11. Migration VI-32 VIII- I . Percentage of Income After Taxes Spent on Retail Items and Selected Services VIII-4 VIII-2. Total Allocation of Customers and Expenditures from 38 Statistical Areas Using Alpha = 1.000 VIII-8 VIII-3. Total Allocation of Customers and Expenditures from 38 Statistical Areas Using Alpha = 2.000 VIII-9 VIII-4. Primary Retail Centers VIII-10 IX-1. Variables Utilized IX-2 IX-2. Factor Loadings IX-3 IX-3. Factor I - Socio-Economic Status IX-5 IX-4. Factor II - Life Cycle IX- 6 vii LIST OF TABLES (CONT'D.) Page IX-5. Matrix of Rental Categories by Census Tract 1960 and 1970 IX-12 IX- 6. Matrix of Rental Categories by Census Tract 1980 (Projected) IX-13 IX-7. matrix of Housing Categories (Single Family) by Census Tract 1960 and 1970 IX- 15 IX-8. Matrix of Housing Categories (Single Family) by Census Tract 1980 (Projected) IX-16 IX-9. Urban Ecology Variables by Tract IX-17 IX-10. Census Tract Residential Use: In Acres 1970 and 1980 IX-27 viii CHAPTER I LAND-USE/ECONOMICS TASK FORCE INTERIM REPORT The overall goal of the Texas Coastal Zone Management Project is.to develop and test a methodology for assessing the economic and environmental impact of alternative public policies for management of the Coastal Zone environmental resources. The objectives of this Task Force in fulfilling that goal are: (1) the development and evaluation of input/output and demographic models to project under varying assumptions the level of economic activity by industrial sector and the expected level and composition of population in the Coastal Bend Region; and (2) the development and assessment of a land use model to spatially allocate households and industry within the study region. From the information developed by this Task Force the environmental impact of location of the projected changes in levels of households and economic activity could be evaluated by other Task Forces concerned with resource capability units and biological uses. Also, the environmental impact from increased water demand and waste discharges could be estimated by other Task Forces based on data generated by this Task Force. Definition of Sub-Area The first task undertaken was to define the appropriate sub-area for study. The Texas Coastal Zone is comprised of a series of urban-port complexes, Port Arthur-Beaumont, Houston-Texas City- Galveston, Corpus Christi, and Brownsville, between which run long stretches of rural-agricultural environments. These urban-port interfaces are where much of the conflict between economic development and environmental maintenance has become acute. The Corpus Christi area provides a representative example of both settings at a scale that can be modelled within the time and resource capabilities of this group. The pressure to expand the port and it facilities, and to develop the tourist potential of the coast and the barrier islands (i.e. Mustang and Padre), and the associated patterns of urban growth are in direct conflict with attempts to preserve a scenic and unique coastal environment and the maintenance of unique marine related species of flora and fauna (i.e. whooping cranes) . Further, the maintenance and expansion of an irrigated agricultural environment in the immediate port hinterland poses a threat to the supply of fresh water inflows to the bays and estuaries that is critical to the existence of the K ene y c'rn R IS roe @vers lup eville 0 st ;ke4io Maj@i SAN PATR 10 --.-A s MCMULLEW LIVE OAK F-7 - - - -I.. c r JIM ILLS Sinton a s Pcns oer t--- Rort Aronsm San i lice pus Chr* I ;F@ L.. NUICES KirQsville T", .,,L!@UVA@ Falfurrias LsItOOKS north KENEDY - - - - - - scale 10 mi. COASTAL BEND REGION Figure I 1-2 present marine environment. Given the representativeness of the area, and in addition, its proximity to Austin, the availability of dataf and its general economic and physical characteristics, it was decided to focus the study on the Corpus Christi area. Since it was decided that the study area should be county contiguous and should include two or more counties, a number of alternative configurations were considered, including the Corpus Christi SMSA, alternative groupings of counties, and the Coastal Bend Council of Governments Region (COG). It was decided to define the study area as the Coastal Bend COG, a thirteen county region. The principal advantage of using the COG as the study area is that it represents a governmental unit with planning responsibility. The principal disadvantage of using the COG is its large size and the fact that some of the counties contained in the COG are located 60 to 80 miles inland from the coast. This disadvantage can be overcome by the use of a spatially hierarchical model employing three levels of resolution: the Coastal Regions, the Council of Governments Area and the Corpus Christi Standard Metropolitan Statistical Area. The remainder of this chapter con- tains a map of the Coastal Bend COG and some descriptive information about the region. The Coastal Bend Region The Coastal Bend Region is located on the broad gulf coastal plain of Texas and includes thirteen South Texas counties which encompass a land area of approximately 7,838,000 acres. The Region, shown in Figure I-1 is distinguished by low-lying tidelands along the gulf coast. The surface rises gently toward the inland counties and culminates in rolling hill country. 'Coastal counties include Aransas, Nueces, Kenedy, Kleberg, San Patricio and Refugio, all of which are separated from the Gulf of Mexico by continuous barrier islands. Between the coastal counties and the barrier islands lie numerous shallow bays, tidal flats and estuaries. The inland counties of the Coastal Bend include Bee, Brooks, Duval, Jim Wells, Karnes, Live Oak and McMullen. These gentle to rolling hill counties are used principally for cattle range land. The Coastal Bend Region includes portions of the drainage area of the Nueces, Frio, Aransas and San Antonio Rivers. The predominant drainage basin for the Region is the Nueces River basin, which pro- vides a major regional resource--water. Principal users of this valuable resource are the City of Corpus Christi, Nueces County, San Patricio County, its several municipalities, and the City of Alice in Jim Wells County. Even though the Nueces River is presently a significant source of area water supply, the Texas Water Development 1-3 Board forecasts that anticipated water needs will exceed the entire available supply from the Nueces River basin before the year 2000. A breakdown of land and water area in the CoaFtal Bend Region by county is given in Table I-1. The climate of the Coastal Bend is intermediate between the conditions in the humid subtropical region to the northeast along the Texas coast and those of the semi-arid region to the west and southwest. The significant features of the climate are moderate temperatures, cooling sea breezes in the coastal counties and variable rainfall. Peak rainfall months are from May to September with winter months having the least rainfall. Seasonal tropical storms occur during the summer months with a major hurricane occurring an average of once every ten years. The 1970 population of the Coastal Bend Region as shown in Table 1-2 was 433,822 persons. Of this total, 77.5 percent of the population resided in the coastal counties of Aransas, Kenedyi Kleberg, Nueces, Refugio and San Patricio. During the 1960-70 census period coastal counties increased in population as shown in Table 1-2 with the exception of Refugio County, which lost 13.5 percent in population, and Kenedy County, which lost 23.3 percent. The population of the coastal counties numbers 336,394 persons with 284,832 of these residing within the Corpus Christi SMSA (Nueces San Patricio Counties). The growth of the Corpus Christi SMSA has been moderate during the past decade (12.2 percent); however, this condition is not likely to prevail if present plans for economic stimulation are implemented. Most of the present sources of economic activity in the Region are related to productive use of the land including development of oil and gas resources. Mineral production (oil and gas primarily) accounts for about one-half of the Region's total income. Agricultural enterprise, including crop production and cattleranching, accounts for 12.0 percent of regional income but only 10.3 percent of employment. Other economic sectors affecting the present structure of the Region's economy are the fishing industry and tourism. Immediate growth of the Region's economy will be closely related to expansion of the manufacturing sector, particularily chemicals and the basic metals. Growth opportunities in both of these industries will be related to expansion of foreign imports through the Port of Corpus Christi. 1-4 Table I-1 LAND AND WATER AREA IN THE COASTAL BEND REGION Land Area) Total Land Area2 J Water Area Salinate Acres- Area Sq. Miles Acres Sq. Miles Acres Fresh Acres Coastal Bend Region Counties Aransas 271 173,440 276 176,640 57 81,920 Bee 842 .538,880 842 538,880 600 - Brooks 904 578,560 904 578,560 250 Duval 1,814 1,160,960 1,814 1,160,960 75 Jim wells 845 540,8M 846 541,440 1,8304 Karnes 758 485,120 757.8 484,992 2,)50 - Kenedy I.394 892,160 1,393.8 892,032 50 20,363 1--A Kleberg 851 -54,640 850.9 544,576 75 72, W 1 4 C, Live Oak 1,055 675,200 1,051 672,640 2,738 McMullen 1,159 741,760 1,157 740,480 1,175 - Refugio 774 495,360 774 495,488 132 18,246 SMSA COUNTIES Nueces 845 540,800 838 536,320 99 156,160 Son Patricia 6136 470,596 680 435,200 4,3724 TOTALS 12,198 7, 83 7, 8 76 12,185 7,798,208 13,603 349,554 Source: 1. City-County Data Book; includes land subject t@o inundation by water; .11 streams and estuaries, lakes, reservoirs, ponds less thor 40 acres. 2. Soil Conservation Service; includes morshes, stTeoms and water areas less than 40 acres. 3. Soil Conservation Service; includes marshes, s@reorns and water areas less than 40 acres . 4. Coastal Bend Regional Planning Commisbion Special Survey of all salt water areas, and fresh water arecn larger than 40 acres. Table 1-2 1970 CENSUS OF POPULATION FOR THE STATE; COASTAL BEND REGION, COUNTIES AND PLACE5 OF 1,000 POPULATION OR MORE Population or-rd Population and 1960 1970 Percentage Change 1960-70 1960 1970 Percentage Change 1960-70 Texas 9,579,677 11,196,730 + 11,6711,053 + 16.9 K le6erg 30,052 33,166 + 3,114 + 10.4 K ingsvi I le 25,297 28,711 + 3,414 + 13.5 Coastal Bend Region 419,778 433,822 + 14,044 + 3.1.4 Live Oak 7,846 6,697 - 1,149 - 14.6 Counties: George West 1,878 2,022 + 144 + 7.7 Three Rivers 1,932 1,761 - 171 - 8.9 Aransas 7,006 8,902 + 1,896 + 27.1 Aransas Pass 935 726 - 209 - 22.4 McMullen 1,116 1,095 - 21 - .).9 Rockport 2,989 3,879 + 890 + 29.8 Nueces County 221,573 237,544 + 15,971 + 7.2 Bee 23,755 22,737 - 1,018 - 4.3 Agua Dulce 867 742 - 125 - 14.4 Beeville 13,811 13,506 - 305 - 2.2 Bishop 3,722 3,466 - 256 - 6.9 Corpus Christi 167,690 204,57-5 + 36,835 + 22,0 01 Driscoll 669 626 - 43 - 6.4 Brooks 8,609 8,005 - 604 - 7.0 Port Aransas 824 1,218 + 394 + 47.8 Falfurrias 6,515 6,355 - 160 - 2.5 Robstown 10,266 11,217 951 + 9.3 Duval 13,398 11,722 - 1,676 - 12.5 Refugio 10,975 9,494 - 1,481 - 13.5 Son Diego 3,746 3,759 + 13 + 0.3 Austwel 1 287 284 - 3 - 1.0 Refugio 4,944 4,340 - 604 - 12.2 Jim Wells 34,548 33,032 - 1,516 - 4.4 Woodsboro 2,081 1,839 - 242 - 11.6 Alice 20,861 20,121 - 740 - 3.5 Orange Grove 1,109 1,075 - 34 - 3.1 Son Patricia 45,021 47,288 - 2,267 5.0 Premont 3,049 3,282 + 233 + 7.6 Aransas Pass 6,021 5,087 - 934 15.5 Son Diego 605 731 + 126 * 20.8 Gregory. 1,970 2,246 - 276 14.0 Ingleside 3,022 3,763 741 + 24.5 Moth is 6,075 5,351 724 - I I' 9 Karnes 14,995- 13,462 - 1,533 - 10.2 Odern 2,088 2,130 42 + 2.0 Fal Is City 462 442 - 20 - 4.3 Portland 2,538 7,302 4,764 +187.7 Karnes City 2,693 2,926 + 233 + 8.7 Sinton 6,008 5,563 445 - 7.4 Kenedy 4,301 4,156 - 145 - 3.4 Taft 3,463 3,274 189 - 5.5 Runge 1,036 ],)47 + III + 10. 7 Kenedy 884 678 206 23-3 Source: Bureau of the Census Bulletin, PC fV -4,5 U. S. Department of Commerce, Januar'-' '971 CRAPTER II DEVELOPMENT OF AN INPUT-OUTPUT MODEL AND ECONOMIC FORECAST FOR THE COASTAL BEND COG To provide a basis for making projections of economic activity within the thirteen county Coastal Bend COG, it was necessary to: @1) construct an input-output model for the COG from data contained in the State Input-Output Study; and (2) project the input-output model for the COG to some future date. Construction of an Input-Output Model for the COG Region The State Study1 In 1971, the Office of the Governor of the State of Texas completed an interindustry study of the'structure of the Texas economy. For study purposes, Texas was divided into nine regions, as shown in Figure II-1. A separate input-output model was constructed for the economy of each of the regions and for the State for 1967. The input-output models were.estimated from a combination of survey data obtained from a sample of Texas manufacturing and business establishments and secondary data obtained from state agency files and publications of the United States Bureau of the Census and other federal agencies. Individual factory and business establishments of the economy were classified according to the major product or service produced. Establishments which produced identical or quite similar products or services were grouped into homogeneous sectors. Establishments in multiproduct lines of production were classi- fied according to the major product and the establishment's entire activities were included in the section into which the major product is placed. The Standard Industrial Classification system (SIC codes) in use At the national level. The single exception to the sectoring concept was for agricultural sectors which were defined along activity or enterprise lines of economic endeavor rather than along establishment lines. 1. Transactions Table Transactions in a regional economy may be characterized in two manners. First, the transactions may be measured as the dollar 1See Herbert W Grubb, The Structure of the Texas Economy, Two.Volumes (Austin, Texas: OFfice of the Governor, Office of Information Services, March, 1973). TEXAS IINPUT-OUTPUT REGIONS IDA. ,oc.... o IDA--- I.., I- DAUA AA.-I I CJ. 1,-4 (D ... ... w.. "AGAA, ton m.f. cc__. .AAIAIAI 1-1 A Fa -01 IA. Iol -.11 111. -Io A I-I muDm -0.1111 oo A--- A.Ion W- UVA. V1110- 0011A. Legend D..." 1 A Upper Rio Grande 2. High Plains 3. Low Rolling Plains Inn", Coastal Bend COG 4o North Central 7 _- 5. Northeast 6. South Central 0 20 40 60 80 IGO 7. Lower Rio Grande 8. Houston 9. Southeast CA.I.- value of inputs (purchases that are required from each producer to produce the total output of an industry). The purchase of inputs by industry i from industry j to produce its output is also a sale by industry j to industry i. The transactions are classified as transactions by processors, final demand transactions, final payments transactions and final demand-payments transactions .(Figure 11-2). Transactions by processors are included in the upper left hand section of Figure 11-2. Industries that are classified as processors will produce goods and/or services to sell to other industries and/or final demand industries such as agriculture, manufacturing, services, trade and mining are included in this section. Final demand transactions are included in the upper right hand section of figure 11-2. It is the autonomous sector that determines the level of activity that will take place within a given processing sector, i.e. processors process in order to fulfill Final Demand requirements. Final demand consists of the following sectors. Households; Federal, State, and Local Govern- ment; Capital Formation; Net changes in Inventories; and Exports. Inputs may be purchased from non-processing sectors in the region such as government. In addition, the industry may purchase inputs from industries outside the region. These purchases of inputs are included in the final payments transactions which are included in the lower left hand section of Figure 11-2. Final demand-payments transactions are included in the lower right hand section of Figure 11-2. The purchase of goods and/or services outside the region by households would be an example. Hypothetical Example: Wm. H. Miernyk's The Elements of Input-Output Analysis offers a concise and clear example of how an Input-Output model functions. The hypothetical example shows in Figures 11-3, 11-4 and 11-5 come from Miernyk's above mentioned book. The Hypothetical Transactions Table consists of a Processing Sector (with six separate industries), Fiscal Demand (with five separate sectors), Final Payments_(with five separate sectors), Total Gross Output and Total Gross Outlays (Total Gross Output will always be balanced by Total Gross Outlays for each element in the Processing Sector). Purchases of inputs by sector A from all other sectors? including Final Payments, can be determined by reading down the column for Industry A. Figure 11-3 shows that for Industry A to product a total output of 64 units it must purchase 10 units from other members of Industry A (intra-industry purchases, 5 units from B, 7 from C, 11 from D, 4 from E, and 2 from Industry F). In addition it must make purchases totalling 25 11-3 \ Output Input Purchasing sectors Processing Final demand Selling sectors Final payments Final demand Final pay- ments F-la-ure 11-2, Major sections in an input-output model Inpu@F H-4 Purchasing sectors Output Processing Total output Input Final Demand Agricul- Manufac- Trade ture turing Agriculture 21 0 2 62 85 @4 Ma nu f ac t ur ing1 5 20 15 41 0 En Pq Trade 30 1 0 5 36 -4 -4 Final 33 35 14 0 82 Payments Total inputs 85 41 36 82 244 Figure 11-.3. Hypothetical transactions (thousand dollars) \ Outpu@, -Input,@ [A g@ Man U-5 units from all of the components of Final Payments. The distribution of Industry A's output is found by reading across the row for Industry A. A quick check on the hypothe- tical Transactions Table reveals that A produced 10 units for intra-industry use, 15 for B, 1 for C, 2 for D, 5 for E, and 6 for F. The total of Industry A's sales to each sector of Final Demand was 25 units. The example shows that each sector's total output is equal to the total of all of its inputs. Additionally it shows that the purchases of sector i from sector j are equal to the sales of sector j to sector i; in the example: A 5 units from R and R sells 5 units to A. 2. Direct Requirements Table The value of the input requirements by sector j to produce one dollar of output is called the direct requirements. The direct requirements are the interindustry requirements to produce one dollar's worth of output by sector j. That is, the direct,requirements by sector j are the required purchases of inputs from each selling sector i to produce one dollar of output by sector j. The direct requirements for a processing sector j comprise a list of inputs that are required by sector j from other sectors to produce its product (Figure 11-4). Hypothetical Example: The Direct Requirements Table or Miernyk's input or technical coefficients table measures, "the amounts of inputs required from each industry to produce one dollar's worth of output of a given industry." In Miernyk's model the Direct Requirements coefficients are found by: (1) adjusting gross output by subtracting inventory depletions from it; (2) the next step is to divide the new output figure for each sector into each entry in its column. .In the example, the adjusted gross output for Industry A is equal to 63 (told gross output minus gross inventory depletion). To compute the coefficients for column 1, each entry in this column is divided by 63 (shown by the entries of column 1 of Table 11-4). In!addition there are indirect effects in each sector resulting from the increased output of a given sector. If, for example, Industry A were to experience an increase in Final Demand it would require increased inputs from the remaining sectors. In order for B to increase its output to A it will require additional inputs from C, D, etc. For C to meet B's requirement it must have additional inputs from the other sector, etc. Obviously the iterative process can be quite lengthy. H pothetical---Example: Miernyk's Direct and Indirect Requirements show in Figure II-b is -easily obtainable with the aid of high speed electronic computers. The technique is to Direct Requirements H-6 Output Processing sectors Input Agriculture Manufactur- Trade ing Agriculture 0.25 0 0.06 to Manufactur-. 0.01 0.12 0.56 ing 0 0 4J @4 P "4 ,_4 Trade 0.35 0.02 0 w Final 0.39 0.86 0.38 payments Total 1.00 1.00 1.00 F-i-12-re 11-4. Hypothetical direct requirements \ Outp In@ 11-7 Output Pr@cessing sectors Input Agriculture Manufactur- Trade ing Agriculture 1.364 0.002 0.077 Process- ing Manufacturing 0.329 1.157 0.662 sectors Trade 0.489 0.028 1.043 Total 2.182 1.187 1.782 Figure 11-5. Hypothetical interindustry coefficients \ Outp@@, Inp@ 11-8 Table from the Identity Matrix and then computing the transpored inverse matrix. Interindustry Coefficients=(I-A)T where I=the identity matrix with 1 occupying each element on the diagonal of a square matrix and every other element occupied by 0. A=the processing sector (or Leontief A matrix) (Table 1.1-5 contains a slight rounding error) In describing the new table Miernyk states, "what does Table 11-5 show? In Table 11-4 we saw that each dollar's worth of produc- tion in industry A required 16 C of intraindustry transactions. But it will be recalled that there were direct purchases only. Table 11-5 shows that total intraindustry transactions will rise an additional 22 cents to 2 total of 38 cents for each dollar's worth of industry A's products delivered to the final demand sector. This is because when industry A's output rises it must by more from B, C, and the others in the table. When B sells more to A it must buy more-from A, C, etc. The same holds true for all the economy. Thus Table 11-5 shows the total dollar production directly and indirectly required from the industry at the top . for each dollar of delivery to final demand by the industry at the left. Deriviation of Regional Model There are two methods available to accomplish this; the first would be to construct our own model from basis data, using the same procedures as followed for Region 7. This alternative was rejected, as the 4-6 man/month manpower requirement was to great. The Alternative method used was a mathematical technique known as the Location Quotients Method. Conceptually, an input-output model can be constructed for any portion of a regional model, or the state model. The procedure to be followed is as follows: 1. Determine the sectors which are to be represented in the new model of the sub-regioh. i.e., some sectors in the region may not transact business within the sub-region, or it may be desired to disaggregate some regional sectors to focus upon important variables within the subregion. 2. obtain estimates of the total output model of each of-the sectors thatare to be represented in the sub-regional input-output model, using secondary data. H-9 3. Obtain a complete input-output model of the next higher level that contains the desired sub-region. 4. If the regional.model and subregional model do not match as to sectors then the difference between the two must be reconciled by aggregating or disaggregating the regional model. 5. Using the Location Quotient program construct the new transactions table for the sub-region. A Location Quotient for each sector is calculated as follows: LQ(i) 'zi/5 i=i.ndex of sectors Xi/X where Si=output for sector i in the sub-model 5=total subregional output=Egi Xi=output for sector i in the regional model X--the total output for the region The Location Quotient measures the degree of "representation" that a sector of the sub-region has in the region. If the Location Quotient is exactly 1, then that sector of the sub-region produces at the same proportion as the sector for the region. If the L.Q. is greater than 1, then the industry in the subregion produces more than it's proportionate share, and the excess must be exported out of the sub-region. If the L.Q. is less than 1, the industry in the subregion does not produce enough to satisfy the.needs of the subregion, and some amount must be "imported" to make up the deficit. Restating, the criteria used for constructing the Direct requirements matrix is: If the L.Q. is greater than or equal to 1, then the coefficient from that sector's row of the region direct requirements table is used directly in the sub-region table, and the excess sales for that sector are put in export. If the L.Q. is less than 1; then the technical coefficients of that sector's row are proportionately reduced by the L.Q. Since there is an excess demand for the sector's output, the deficit must be made up in the import sector. Once the coefficients for each sector have been determined, based on the L.Q.'s, then the final adjust- ments. are made to imports and exports to balance the table. Using the method of Location Quotients, an input-output model was computed for the Coastal Bend COG by disaggregating the Region 7 Model of the State Input-Output Study. The 71 sectors in the Region 7 model were reduced to 45 processing sectors as shown in Table II-1. In addition, sectors 46 to 52, were included as final payments sectors. H-10 Projecting the Tnput-Output Model To 1980 The 1980 projections of Final Demand were derived from a static model which assumed rates of change compatible with those projected for the U.S. (All values in the projectedimodel are stated in terms of 1967 dollars). The rates of increase to 1980 came from the Dept. of Labor's projected average annual rate of change from 1965 to 1980 in each industrial sector'. This method assumed that the portion of sales of each sector2in the C.O.G. would grow at the same rate as for the nation. Of some significance in the figures for Fed. Def. Expenditures is the U. S. Dept. of Labor's projection that defense expenditures in 1980 will be approximately $8 billion above the 1965 level of $50.1 billion. This projection was based on the assumption that Defense Expenditures would return to levels existing prior to the Viet Nam buildup. While the most recent federal budget is not consistent with this forecast this would seem to be, none-the-less, a basically sound projection. The appropriate U. S. Dept. of Labor tables are contained in Appendix B, while the 1980 input-output model tables (Transactions, Direct Requirements, and Direct, Indirect are given in Appendix C). Thus, to increase the output of Agriculture by $1,364, the total requirement is $2,182 for all processing sectors. Alterna- tively, to increase the output of Agriculture by $1.364, all of the processing sectors were required to produce $2.182 of output. To produce one dollar of output by Agriculture would require $1.60 ($2.182 divided by $1.364) of output by all processing sectors. This number, $1.60, is called an output multiplier. The output multiplier estimates the total requirements of all processing sectors per dollar of output by Agriculture. To produce $1,000 of output for final demand by Agriculture would require $1,364 of output by Agriculture with an associated economic activity of $2,182 ($1,364 times $1.60). This is the same amount of economic activity that was estimated by the final demand multiplier. The Lower Rio Grande Regional Model3 The region is the basic functional unit in the Texas Input-Output Model. It was at this level that the basic work of data collection 2A similar procedure was followed by W. E. Mullendore in projecting the statewide final demand for 1980. 3See Joe C. Murrell, Jr., Donald B. Eeenens, and Robert N. McMichael, An Input-Output Model of the Lower Rio Grande Region of Texas (Austli-n, Texas; Office o_f__t_Fe__Governor, Division of Planning Co@T_r_dination, April, 1972). H-11 and analysis was accomplished, resulting in a basic transactions table including all sectors operative in the region. These region- al models were then aggregated into the State model. As mentioned under the state section the regional models were estimated from a combination of survey and published data. The following infor- mation was collected: 1. Dollar value of sales and purchases of the establishment for the calendar year 1967; 2. Breakdown of sales and purchases by destination of source; 3. Standard Industrial Classification, at the 4 digit level, of purchases and sales. A majority of the data was collected from individual estalbishments within the region, but it was categorized and reported only as aggregated industry totals. The regional input-output model which contained twelve of the thirteen counties in the study area was the Region Seven, Lower Rio Grande Region Model. This Region includes nineteen count- ies located in the southernmost tip of Texas (see Figure II-1). The remaining county in the study area, Karnes, was contained in the Region Six Model (see Figure II-1). Development of the Input-Output Model for the Coastal Bend COG In order to develop an input-output model for the Coastal Bend COG, a method was needed for disaggregating the Region'7 Model. There are some inconsistencies in the projections, however, these are being corrected and the new projections will be available in September 1973. While the above mentioned method is admittedly static the main thrust of this early effort was to experiment with and test the applicability of the methodology for the purposes of this project. During the summer the next step will be to refine the 1-0 model by testing different methods of projecting Final Demand. Among them will be such techniques as: 1. Consumption trends consistent with national growth rates in percapita household consumption and projected population changes. 2. The use of time series analysis of personal income data for the state and region. JI-12 3. The use of U. .S. Dept. of Labor income elasties figures. 4. Projected capital formation per employee ratio. 5. Projected government expenditures (all levels) per employee ratios. 6. The use of other secondary sources of data for projecting and testing. Once Final Demand had been projected, the complete'1980 Transactions Table was developed using methods consistent with those @escribed by Miernyk in his discussion of static model building, in The Elements of Input-Output Analysis. Since Miernyk's.disucssion of the methodology is precise and straightforward the following description was taken from pages 35 and 36 of the above mentioned book. The computational steps for projecting a transactions table are as follows: 1. (Step 1, the computation of adjusted final demand, was modified somewhat in this case). 2. Multiply each row of the table of direct and indirect coefficients by the adjusted final demand figure for that row. The result will be another table the same size (as the table of direct and indirect requirements). 3. Sum the columns of the matrix obtained in step 2 to obtain new adjusted total gross outputs for each industry. Transfer the row that is thus obtained to the bottom of the table of direct coef f icients. 4. Multiply each column entry in the table of direct coefficients by the adjusted total gross output at the bottom of the column. The result is the processing sector of the projected transactions table. 5. To obtain total gross output figures add the appropriate inventory adjustment to the adjusted total gross outputs found in step 3. 6. Insert the original projected final demand figures as a column of the projected processing sector, and insert the total gross output figures obtained in step 5 as a column to the right of final demand. The result is the projected transactions table. 11-13 TABLE II-1 Sectors of the Coastal Bend COG Regional Input-Output Model Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 1 Irrigated Crops 1,2,3 0112'-0313,0122-,0123,0 9 2 Dry-Farmed Crops 4,5,6 0212,0413,0219,0141 3 Range and Feedlot Livestock Production 7 0235,0315,0316 4 Dairy,Poultry,&Eggs 8 013210133,0134 5 Agricultural Supply 9 5962r5969 6 Ginning 10 0712 7 Agricultural Services 11 0713,0714,07154,0719,0 2 0723,0729,0731,0741 8 Fisheries. 12 0912,0913,0914,0919,0 S 9 Mining:Crude Petroleum, Natural Gas, and Services 13 0311,1321,1381,1382,138S 10 Construction 14,15,16 1311,1511,1611,162l,itC 11 Meat Products 17 201112013 12 Dairy Manufacturing 18 2021,2022,2023,2024,2kE 13 Canned,Preserved,Pickled, Dried,and Frozen Food 19 2035120361203712038 14 Other Food and Kindred Products 20 2041,2043,2044,2045i,2t( 2042F2051112052112061,206@ 206312069,2071,2072,2 9 2092r2O93,2094,,2095,219i, 20970,20984,2099,2121 15 Beverages 21 208212084120860,2089 11-14 TABLE II-1 (cont'd.) Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 16 Textile Mill Products, Furnishings,&Apparel 22 2211,2221,2231,2241,2251 2253,2256,2259,2261,2262 2269,2271,2272,2279,2281 22841,2291,2293,2294,2295 22971,22981,2299,2311,2321 2322,2323,2327,2328,2329 2331,2335,2336,2337,2339 2341,2342,2351,2352,2361 2363,2369,2371,2381.12384 2385,2386,2387,2389,2391 2392r2393,2394,2395,2396 2397f2399 17 Wood Furniture&Other Wood & Paper Products 23 2431,2432,2433,2441,2442 2443f24451,2491,2499,2511 251212515,2519,2521,2543 2591,2599 2641,2642,2643,2645,264( 2647,2649 2651,2652,2653,2654,265-1 18 Newspapers,Publishing& Printing 24 2711 2721,2731,2741 2732JI2751,2752,2753 2761 2711,2782,2789,2791,279 279412799 19 Chemicals,Drugs& Related Products 25 28121,28122,28123,28124 28132p28133?28134 28182JI28183f28185128191 28192f28193,28194,28195 28196,28197,28198,28199 2879,2871,2872,2879,285 2871,2891,2892,2893,289 2899 20 Petroleum,Refining& Products 26 2911 2951,2952,2992,2999 21 Clay,Cut Stone,& Shell Products 27 3221,3229,3231 3251,3253,3253,3255,325 3261,3262,3269 328lf3291,3292,3295,325 32971,3299,3274,3275,323 3293 H-15 TABLEII-1 (cont'd.) Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 22 Cement&Concrete Products 28 3271,3272,3273,3241 23 Primary Metals,Foundairs &Forging 29 3321,3322,3323 3331,3332,3333,3339,3 1 3334 3362,3369,3391,3392,3399 24 Fabricated Steel & Other Metal Products 30 3441 3443 3444,3446,3449 3471,3479 3494,3498 3481,3491,3492,3493113 9 25 Machinery&Processing Equipment 31 3522.13531,3537 3532,3533 3511,3519 3551,3552,3553,3559,3 4 3555 1 3561,3562,3564,3566 3567,3569 3581,3582,3586,3589f3 1 S @26 Electrical&Electronic Equipment 32 3611,3612,3613,3621,3 : 36231,36241364113642,312 364413629 3651,3661,3662,3671,3 3673,3674,3679 3691,3693,3694,3652,3KS 27 Transportation Equipment 33 37l3r3715137141,3711 3731,3732 1 3741,3742,3791,3751,379S 28 Other Manufacturing 34 3011 3079 3111,3121,3131,3141 , 3-14, 3151,3161,3171,3172,3Fc 3841,3842,3843 3851,3861,3871,3831,3941 3942,3949 1 3941,3942,3949 3911,3913,3914,3931,3951 3952f3953,3955,3961,3 6" 3963,3964,3991,3982 , 318' 3984f3987,3993,3994,399! 3999 11-16 1 TABLEII-1 (cont'd.) Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 29 Highway Motor Freight Passenger Service, & warehousing 35 4131,4132 4213,4231 4212F4214,4224 4221 4222,4223 4224,4226 30 Water Transportation 36 4411,4421,4441,4452 4453,4454,4459,4463 4464?4469 31 Air Transportation 37 4511,4521,4582,4583 32 Other Transportation 38 4011,4013,4021,4041 4612,4613,4619 4111,4119,4121,4140,415C 4141,4142,4151,4171,4172 4742,4782,4783,4784,478S 4721 33 Communications 39 4811,4821 4832,4833 4899 34 Gas Services (public & private) 40 4922f4923,4932 9149,4249,9349 35 Electric Services (public&private) 41 4911,4931 9151,9241,9351 36 Water&Sanitary Service Systems(public & private) 42 9102,9202,9302 4941,4952,4953,4959,496 37 Wholesale Groceries & Related Products 44 5041,5042,5043,5044,504 5046,5047,5048,5049 38 Wholesale Livestock 46 5 054,4731 11-17 TABLE II-1 (cont'd.) Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 39 Wholesale Trade-Other 43r450,47,48,49 5012,501315014 5052,5053,5059 50810,5082,5084F5O85 5083,5088,5087 5092 5022,5028,5029,5033#1514 5036,5037,5039,5063,5064 5065,5072,5074,5077,5 1 5093,5094,5095,5096,517 5098,5099 40 Retail Food Stores 54 5411,5421,5431,5441,5 1 5462,5499 41 Automobile Dealers,, Repair Shops,&Gasoline Service Stations 55,56 5511,7549,5521,5531,7532 7534,7535,7538,7539,7 2 554 42 All Other Retail Trade 50,51,52,53 57,58,5.9 60 5211 5252 5221F5231,5241,5251 5311,5331,5399 5411,5421,5431,5441 5451,5462,5499 5611,562lf5631,5641,5 1 5699 57l2p5713,5714,5715,501S 572215723r5733 5812,5813 5321 5912,5921,5932,5933,504@ 5943,5952,5953,5591P-51P@ 55994,5971,5582,5983,5984 5992,159930,5994,5996,5f 5999 115995 F5341,5351 43 Banking,Insurance,Real Estate,&Finance 61,62 60,61 631,6411 62164,65,66,67 44 Education (public & private 70 8211 @8221,8222 11-18 8231,8241,8242,8299 TABLE II-1 (cont'd.) Standard Sector Sector Region 7 Industrial Number Name Sector Number Classification 45 Services-Other 63,64,65,66, 67,68,69,71 7011,7021,7041,7031,703@ 7211,7212,7213,7214,721! 7216,7217,7218,7231,7247 7251,7261,7271,7299 7311,7312,7313,7319 7331,7332,7339 7361 7813,7814,7815,7821,739@ 7221 7391,8921 7341,7342,7349,7351,739: 7393,7394,7396,7397 73, 7398,7309 7816,7817,7818,7832,783: 7911,7929,7932,7933,794- 7942,7943,7945,7946 7947,7948,7949 7512,7513,7519,7523,7521 7622,7623,7629 7631,7641,7692,7694,7691. 8011,8021,8031,8041 8061,8071,8072 8092,8099 8111 8911 8931 8411,8421,8611,862i,863 8641,8651,8661,8671,869, 8811 46 Households 47 Federal Government 48 State Government 49 Local Government 50 Depreciation 51 Imports 52 Residual 11-19 Table 11-2 PROCESSING SECTOR, 1967 2 '4 @119041250 61,161250 0,000000 0.000000 01000000 2 09000000 59011250 775,523250 1965.20250 01000000 3 0*000000 0,000000 5179o675250 0.000000 0,000000 4 0*000000 01000000 0.000000 0.000000 0,000000 S 1240464250 4206453250 45.983250 13.522250 0,000000 6 157,681250 43991LO41000 0,000000 0.000000 0,000000 7 638s178000 1581.347250 863.273000 4111.616250 0,000000 8 06000000 01000000 0,000000 0,000000 0,000000 9 04000000 0,000000 0,000000 0.000000 0,000000 .10 97.178250 333.853250 330,521250 138.536000 0,000000 It 00000000 01000000 0,000000 0.000000 0,000000 12 0,000000 0,000000 0,000000 00000000 13 0.000000 01000000 0,000000 0.000000 0,000000 14 00000000 0.000000 2318.331250 2401.591250 01000000 Is 00000000 0.000000 01000000 0.000000 0,000000 16 . 0942250 2.963250 0,000000 0.000000 '094250 17 4vO52250 40.149opto 234,7952150 14.962250 1,943250 is 0,000000 0.000000 .305000 0,000000 3,22625-10 19 920.189000 3201.386250 420,747250 119.834250 0,000000 20 243,395250 1636.336250 2.907250 226,263000 137000 21 04000000 0,000000 0,000000 0-.000000 '004250 22 0,000000 0,000000 01000000 0,000000 0,000000 23 .10677250 70,059250 01000000 01000000 01000000 24 16g822250 53.950000 39,U20250 17.537250 0,000000 2S 145,378000 10389211250 3s315000 23,161250 0,000000 26 3,072250 20,220250 1.160250 1.077250 001250 27 00000000 00000000 0*000000 0.000000 000009i0o 28 60g127250 82,455250 9.470250 14.110000 254250 29 09000002 0,000000 675,200250 533.364250 017000 30 01000000 0,000000 00000000 0.000000 oooooqoo 31 00000000 01000000 01000000 0,000000 00000000 3e 0*000000 0.000000 00000000 0,000000 o020250 33 256878250 90,888000 42.227000 37.477@50 l4e824250 34 0,000000 0.000000 5,990250 13,BS4250 2,268250 35 28g699000 100,793250 50,640250 24,452000 21,267000 36 00000000 01000000 0.000000 0.000000 41039250 37 04000000 0,000000 0,000000 0.000000 06000000 38 00000000 0.000000 616.621250 62.640250 00000900 39 396,042250 1433.969250 740.126000 1133.932250 249,71,4250 40 0,000000 01000000 01000000 09*000000 01000000 41 240g435250 1198.485250 191.864250 418,441250 2,081250 42 130j821250 664.259000 236.923250 106.081000 00000000 43 8904634000 3072.986250 2144.365250 1015,288250 37s485000 44 217,S70250 755.241250 306.414000 313.764250 5,345250 45 110839250. 46,112250 19,041250 84,034250 71,272250 11-20 Table 11-2 (cont'd.) 6 7 a 9 0,000000 133250 1.800250 002250- 0,000000 0,000000 Is209250 0.000000 00000000 01000000, 0.000000 9017250 S45,483250 0,00000.0 0,000000 0,000000 281,783000 01000000 0,000000 09000000 00000000 01000000 0.000000 0,000000 04000000 86000000 01000000 01000000 09000000 06000000 06000000 00000000 0.000000 0,000000 0,000000 00000000 00000000 307.446250 0,000000 0,000000 .00000000 3,443250 0.000000 51579,572250 04000000 199,360250 0,000000 0.000000 l102.02S250 2122.79.3250 0,000000 tt43250 0.000000 06000000 ID.V1130000 049250 0.000000 00000000 09000000 0,000000 012000 1954000 9001000 04000000 09000000 27,663250 874.616250 0,000000 01000000 00000000 5*399250 0,000000 06000000 0,000000 270250 oO30250 .4.978250 0,000000 2,946250 065250 495.421000 0.000000 132.530250 2352.193250 19333000 058250 0,000000 19,768250 162*143000 0,000000 1148,149250 30,211250 1139,276250 1147,377250 1491519250 222,167250 642,122000 4950,671000 1894,445250 09000000 0044000 .039250 122250 310,121250 08000000 064250 0.000000 00000000 7983,602000 OP000000 o225250 01000000 00000000 38o227250 0041250 219250 84.468250 36,982250 2577,732250 0060250 449553000 ?23250 249,710250 235,401250 poolooo s301250 21,185000 88,658000 71,180000 60703250 9027250 1673.534250 0.000000 2377,847250 9002250 e722250 .138250 296250 27,730250 21,439250 5.016000 6.758250 .1685.742250 392e693250 0,000000 00000000 0.000000. 14245,616250 02000000 0.000000 30,535250 0.000000 904s253000 01000000 0,000000 otooilooo 0,000000 389340250 3ol78000 11;499000 424711250 65,341000 1655,157250 879,771250 20e817250 5,113250 8.416250 379,072250 58,542250 108,845250 259148000 35.695250 1597o7O6OOO 359,346250 164250 8,871250 19.430250 23,922250 42,120000 08000000 9016250 0,000000 00000000 0.000000 01.000000 0,000000 0.000000 04000000 00000000 l6o751000 S29309250 853,067000 1860j202250 7346o434250 00000000 0903000 628.231250 .68q250 01000000 17*372250 3,154250 18.662250 753,612000 2298,570250 0,000000 168,881250 0.000000 58.748250 130.876250 $44745250 44,265250 12.654250 IS70.925250 4018,217000 16,192250 8m521000. 33,752000 34239902250 366,026250 36st30000 309sJ68250 346.00250 4802,884000 2413s497250 11-21 Table 11-2 (cont'd.) 11 12 13 14 a S 1 *052447 211709 35.590862 596.027531 1.246580 2 0,000000 0.000000 1,666031 2S38.125571 0,000000 3 3184,727262 1,520586 7,855302 143,475274 a5.749074 4 220,548746 5143.220129 2,146398 58.682058 2QJ51603 s 00000000 01000000 09000000 1 0.000000 01000000 6 00000000 0.000000 0,000000 219.835428 0,000000 7 0,000000 0.000000 01000000 0.000000 o"000000 a 09000000 0.000000 743,991931 .780582 0,000000 9 02000000 0,000000 0,000000 0.000000 0,000000 to 08000000 0,000000 Is294087 391.532571 0,000000 11 464144477 0.000000 761898 6.738629 0,,000000 12 09000000 92,695641 01000000 893784 0.000000 13 00000000 323940 s132312 2,344833 003159 14 00000000 45,150810 3,530163 4765.781769 2.018930 Is 01000000 0,000000 00000000 12.627211 2,274800 16 0,000000 00000FOO 01000000 8,651235 01000000 17 6003445 59454507 11.470366 600,4218-42 .30049,0 18 '16555to 1.684925 4,716563 12.683298 15,857661 19 09000000 0.000000 774762 .131726 11086238 20 10,168277 33,t5SIIO .026830 49,982454 079367 21 9003063 *001822 0,000000 .142532 2,865514 22 0,000000 0.000000 0,000000 .311307 01000000 23 00000000 0,000000 00000000. 866513 0.000000 24 0,000000 0.000000 01000000 9.153442 0,000000 25 00000000 004737 0.000000 .,056087 0,000000 26 001914 gOI1660 01000000 1,163412 003159 27 0,000000 0,000000 0,000000 .078213 0,000000 28 9037134 6,285309 .248820 35,484316 0,,000000 29 .165764 0.000000 1,668972 136,454668 25.630102 30 01000000 .0,000000 0,000000 0,000000 0.000000 31 00000000 0,000000 0,000000 01000000 0,000000 32 o388953 0,000000 0,000000 966.656253 0,000000 33 32.092033 16.269508 6,295121 632.056248 91,,769500 34 6,871368 0,000000 5,750068 290.154690 16,548275 35 379532775 0.000000 23mI72646 1538,869604 .74,033480 36 3.390319 0.000000 4.359320 83.494476 19.760873 37 00000000 10437506 259680330 316,139268 15,150463 38 00000000 0.000000 0,000000 0,000000 0.000000 39 56.476604 22,723164 2,690716 244.443568 13.542585 40 09000000 0.000000 0,000000 0.000000- 06000000 41 99459281 48.949178 448759 26,885052 34,329309 42 364634 0.000000 s135620 6'.372780 246789 43 09000000 0.000000 9033813 3.050290 0,000000 44 9.S78340 10.477215 5,604525 333.408292 25,010959 45 680581604 9'sI53762 4o094695 958,460400 120,,077952 Table 11-2 (cont'd.) 16 29 20 9030250 0061259 o"080000 9019250 00000000 29000000 gOO5250 004250 0588250 0,000000 80600000 gOO1250 0.000000 007b256 0,000000 66000000 o022000 018250 0579000 01000000 opooooop 09000000 o"006000 0,000000 0,000000 0010250 00000000 0.000000 9003250 0,000000 opooooopl 00000000 0,000000 0,000000 0,000000 096000mo 0,000000 0,000000 003250 otoooooo 60600000 0,000000 .028250 9082,250250105605.335250 042250 28407250 1.433000 3852,437000 2290693250 SOS90000 v019250 .006250 9508250 0,000000 890000.000 0006250 1001250 o093250 0,000000 6,000000 9062000 0,000000 *027250 04000000 90008000 420467250 055250 25s162000 0.000000 .9 P966000 *155250 186000 2,290250 01000000 01078000 s370250 442250 1,555250 0,000000 9221250 64,179000 40,587250 504,720250 8,238250 09872SO 9,73025,0 81.994250 l4o524000 16,002000 80000000 31,845250 1.012000 32-79,360250 5355.810250 0101250 9168250 1,493250 935o714250 415,483250 8:080000 *017000 .004@50 273250 9312250 89000000 09000000 012250 0.000000 4,174000 86808000 102250 1031000 .528,859000 0,000000 6,000000 10428250 58s223250 166.959250 *119250 80000000 9020250 116250 331g300250 1041250, 6601000 *020000 .530250 9590250 9068250 00000000 052250 040000 4,893000 0,000000 0009250 689250 1,066250 422,366250 878000 g684250 59,249250 78.062250 1327oO95250 184v476250 SPOO0000 09000000 .4.591250 923,143250 578,339250 P023250 1 502000 .024000 163.338000 0,000000 29586000 45:9642SO 29.358250 4323,748250 391.812250 0897250 629372250 55,281250 488,287250 45,875000 s177250 3,693250 9.070250 5450.093250 1474s940250 0598250 33,580000 61.320000 7778.847000 834,987250 9063000 2e482250 4.260250 32.488250 11895572SO 04000000 .0082SO .006250 20557250 0,000000 00000000 09000000 0.000000 00000000 0.000000 173250 150544250 56.080250 13859911250 396,581000 0:000000 s002000 .001000 0020000 0,000000 P0232SPI 3,5252SO 16.7S62SO. S14.547250 0,000000 oOO1250 14sIO6250 2,303250 140886250 6o384250 9006000 9772250 .477250 BISoOO9250 00000000 205250 13,392000 35.253000 386.871000 158s644250 :201250 329147250 186m.197250 1455,946250 763lOOO250 11-23 Table 11-2 (cont'd.) 21 22 23 298 25 .04000000 010000-00 00000000 0,000000 0572250 2 9002250 08000100 1001230 0121250 3 00000000 0,600000 0,000000 .00000000 oOOQa5O 4 - 9012250 00000000 002250 1022000 5 .04000000 0,000000 01000000 01000000 0,000soo 6 00000000 0,000000 09000000 00000000 00000000 7 -- 00000000 0,000000 0,000000 01000000 01000000 8 .0.000000 01000000 0,000000 0,000000 00000000 9 005250 0,000000 0,000000 0,000000 0051250 g137000 11,359250 0,000000 2,131250 16,910250 014250 0.000000 06000000 002000 o099250 12 mOO4250 0,000000 02000000 001250 0039000 13 .0,000000 01000000 00000000 0.000000 oOO4250 14 031250 0000.0000 .257.728250 015250 o122259) Is 0006000 0.000000 01000000 048250 0459250 16 0.000000 0.000000 o461250 1001000 o002000 17. 6,726250 18.388250 34,713250 2.989250 0446250 18 *179250 13.178250 42,365000 895250 0959250 19 2.179250 0,000e0o 20.623250 1,016250 2oI70250 20 39409000 963000 09000000 o293000 60716000 21 o127250 89153250 9378250 039250 0070250 22 1,335250 317,036250 0.000000 0.000000 549250 23 g059250 59.754250 3,026250 o026250 410832250 24 -*455000 0,000000 459000 1.848250 25,013000 25 9055250 0.000000 0,000000 7,851000 10o604250 26 g1212SO s002000 6,189250 071250 4,,699250 27 0023250 0.000000 01000000 005250 2.405250 28 4057000 0.000000 27,830250 036250 10610000 29 14.586250 612,144250 110,204250 0284000 80610250 30 -OcOO0000 0,000000 0,000000 0"000000 64"183250 31 eO37250 0,000000 01000000 cOS9250 6,011250 32 '26,536250 280,304250 1653.968000 2.594250 5,270-0100 33 3sI18000 78,073250 273,793250 31,958250 34,698250 34 142334250 18.180000 351,2502SO 5,039000 5@651250 3S 169454250 0,000000 1710.388250 22.209250 15,381250 36 *266250 8.448250 89,860000 2,411250 20837000 37 g114000 0,000000 08000000 @003250 ,060250 38 00000000 0.000000 01000000 0,000000 06000000 39 40172250 73,239250 2864,018250 69,732000 66o85925O 40 002250 0.000000 00000000 0.000000 00000000 41 q387250 78,957250 44,520250 4.949250 2,745250 42 4096000 3.777000 16440250 10.619250 4,33400n 43 565250 .0.000000 1266.425000 552250 1,929250 44 g865250 19,156250 226,116250 19,047000 11,021250 45 4,403250 66,340250 -1689.415250 9.307250 35,,203250. 11-24 Table 11-2 (cont'd.) 26 27 28 29 30 0601290 00000000 1002250 09000000 0,000000 oS.10250 0,000000 14,736250 0.000060 0.000000 90000000 00000000 00000000 00000000 00000000 00000000 o00325-0 09000000 000000M^A, 00000000 01000000 60000000 00000000 0.000000 S0000000 0,000000 00000000 0.000000 0400000o 0,000000 01000000 01000000 09000000 0,000cor) 80060000 04000000 00000000 00000000 09000000 0967250 00000000 o002000 00000000 00000000 10357000 o330250 l4o487250 167003250 71,988250 00000000 oOO5250 00000000 01000030 00000000 0001250 09000000 0.000000 06000000 00000000 01-000000 02000000 00000000 9025250 09000000 014000 o303250 04000000 -.04st2so 00000000 165250 00000000 0,00000o 9882000 9197250 015250 0,000000 0,0000no 9643250 1,13,2725,0 111714250 8,435250 8 , 0422@-',C' 9307250 o157000 e270000 1,513000 3,46325t'i 120846250 02000000 16,341250 00000000 3640@@,o o662000 0007250 9635250 249.498250 546,40125.-,J' 0029250 o006250 00*2250 0111250 0001250 00000000 11250000 00000000 35,32S-2 29047250 00000000 *990250 09000000 71,807000 5;933000 13033250 2,307250 09000000 0,000000 2952250 lo869000 99438250 001250 389250 9?365250 4o834250 099000 2005000 922250 9003250 00000000 050250 00000000 0,000000 39506000 190459250 5,496250 .064250 0,000000 1,607250 01000000 4,937250 02000000 11,471250 0119250 oi0ooooo 00000000 0.000000 4297,34400,li 19.588259) 09000000 370000 00000000 00000000 4oI46000 00000000 77.291250 00000000 22,694250 6o470250 6vII8250 18.071250 8049013250 600881250 p709250 39962000 5,081250 185,565250 19098250 4.0008250 17,426250 41.423000 308s757000 330054000 253000 2sT14250 1,868250 14,350250 3090250 10001250 0 000000 003250 0,000000 06000000 61000000 0:000000 0.000000 06000000 0,000000 6,630250 1339131250 18.264250 653,454250 1500.1402@-O 69000000 00000000 01000000. 00000000 00000000 P.31,1250 10438009 1,662000 09000000 Iv908250 o279000 3,4402SO 2,343250 00000000 239911000 o444250 0,000000 744250 7609996250 8859468250 10680250 2035250 4.693250 Si,940000 22,530250 11084250 1400871250 12,679000 676*907250 299*763250 11-25 Table H-2 (cont'd.) 31 32 33 54 1 -09000000 00000000 00000000 60000900 0,000000 2 0,000000 01000000 0,000000 o"000000 o"000000 3 @01000000 0.000000 0.000000 01000000 00000000 4 01000000 0,000000 0,000000 .0.000000 0,000000 5 00000000 0,000000 0.000000 0.000000 00000000 6 0,000000 0.000000 0,000000 0.000000 0,000000 7 0.000000 0,000000 0.000000 00000000 8 0.000000 0*000000 0.000000 0,000000 9 .,0,000000 10591.619250 0,000000t8S551.076250 0,000000 10 -,2o725250 3010969250 11,411250 3680367250 80,184250 It 09000000 08000000 o"600000 0,000000 12 .08000000 0,000000 01000000 0.000000 00000000 13 .0,000000 0.00oppe 00000000 0"000000 0,000000 14 0.000000 19.687250 00000000 0.000000 0000owl Is 0,000000 0.000000 0.000000 0,000000 00000000 16 0,000000 0.000000 0.000000 o243250 "109250 17 o413250 5.362000 .867250 .039000 3og42250 .18 lg038250 19.317250 304.511250 lt,,995250 56,496000 19 .0.000000 .266250 0,000000 1.201250 1283250 20 SB79099000 103,596250 239097000 288,677250 50,442250 21 - 001250 2.073000 .002250 .0.000000 0013250 22 .0,000000 0.000000 0.000000 0.000000 1,774000 23 08000000 29.813250 0,000000 0,000000 o365250 24 0,000000 5,537250 0,000000 0.000000 1090250 25 9006250 56,564250 029250 54,019000 o103250 26 22.290000 32.832250 .354250 839000 27 OsOO0000 4.244250 29230000 - .172250 o209250 28 .004000 17,360250 6.658250 .001250 132250 29 -0,000000 10,842250 29*554250 168,,821000 58,217250 30 01000000 0,000000 0,000000 0,000000 0,000000 31 672,995250 6.607000 25,996250 0-.000000 1411000 32 00000000 91.915250 23.617000 58.976250 126,865250 33 31.600250 25,556250 125,656250 686,706250 236,310250 34 944250 53.273250 26,597250 3397.598250 5340,995250 3S Is863000 97.633000 234,273250 20t.706000 o"000000 36- 247250 4,640250 24.639000 41.959250 92,,385000 37 0,000000 ofoopipoo 0.000000 0,,000000 0,,000000 38 0,000000 0,000000 0,000000 0,0000oo- 00000000 39 42,542250 63.359250 3,991250 78.914250 31,917250 40 0.000000 0*000000 0,000000 0.000000 0,000000 41 0,000000 0.000000 0.000000 197.665250 182,,657250 42'..' 0.000000 0.000000 7,837250 902000 5,426250. 43 459437250 664044250 205,556250 1583.118250 327oOMOO 44 51,751000 79.597000 497.732000 3612.233250 820,477250 45 84t930250 64gl56250 176,427250 168le829250 aglogj4250 11-126 - Table 11-2 (cont'd.) 36 37 .38 39 40 04000000 0,000000 0.000000 00000000 04600000 .00000000 00000000 01000000 060000-00 01000000 00000000 08000000 01000000 09000000 0 000000 06000000 0*000000 01000000 09000000 0:000000 00000000 0,000000 0.000000 0,000000 00000000 89000000 0,000000 01000000 09000000 00000000 00000000 00000000 01000000 09000000 00000000 08000000 01000000 01000000 00000000 09000000 0,000000 0,000000 0,000000 01000000 09000000 344,744250 00000000 20,507250 06000000 09000000. OP000000 0,000000 06000000 09000000 08000000 04060000 04000000 0,000000. 00000000 0,000000 00000000 3*566250 0,000000 9001250 06000000 8*900000 12*658250 0*000000 00000000 1,059250 04000000 09000000 01000000 09000000 04000000 0070250 00000000 01000000 *236250 04000000 50374250 4021250 .009250 89674250 5*305250. Is695000 160401000 001250 28,176000 '513s350250 690@727250 01000000 0*000000 00000000 01000000 190352250 o285250 0.000000- 9747250 06000000 5*0333250 01000000 01000000 s002250 09000000 900862000 00000000 0,000000 0,000000 00000000 0351250 04000000 01,000000 00000000 00000000 o6752SO 00000000 01000000 00000000 06000000 9802250 02000000 .01000000 1*599250 04000000 3005zooo 09000000 01000000 2,063000 01000.0.00 0@000000 .579342250 0,000000 8*273250 06000000 .29579250 1,905250 001000 .11*082250 9002000 00000000 11,756000 0,000000 119s532250 *8.21250 66000000 00000000 01000000 0,600000 00000000 00000000 00000000 0,000000 127,450000 00000000 00000000 417250 0.000000 23.539250 00000000 17s419250 355,352250 9.265250 2382,181250 179,100250 04000000 31g474250 646250 814,141250 107*184250 435o334250 3089299000 7.446250 1499,566000 10389525000 4649273000 35,721250 .968000 283*305250 1519124250 OP000000 00000000 01000000 06000000 09000000 0,000000 0,000000 0.000-000 00000000 06000000 -.1110.302250 44,585250 4.199250 355,246250 04@00000 08000000 0*000000 04000000 00000000 04000000 00000000 11,334250 2.514250. 135.979250 15,097250 0@000000 Is661000 0,000000 145,383000 OsOO0000 18g442250 97,292250 83,803250 2957,335250 2369143250 00000000 52tI91250 13.450000 433,584250 233*309000 3sI31250 567sOO1250 38,096250 2675*531250 548*262250 11-27 Table 11-2 (cont'd.) 41 42 43 - 44 45 94000000 0.000000 01000000 20367250 11,568250 00000000 0.000000 04000000 0,0010000 9865250 3 09000000 00000000 OV000000 15,365250 98o916250 4 00000000 0.000000 04000000 3,975250 16,894000 00000000 09000000 0,000000 00000000 01000000 09000000 0,000000 00000000 0,000000 *004250 7 09000000 0.000000 09000000 0,000000 00000000 a 09000000 0.000000 09000000 0.000000 749944250 9 06000000 0.000000 00000000 00000000 9372250 10 08000000 0,000000 968*063250 416.675000 776,748000 @I 08000000 7,147250 02000000 406,391250 90751250 12 -.00000000 89,055250 OmOO0000 1307.815250 28,619250 13 00000000 1,059250 00000000 62.746250 9,803250 14 0,000000 1075.276000. 015250 832.615000 32s842000 is 00000000 .84,269250 150017250 536137250 16g644250 16 09000000 9004250 0,000000 088250 o211250 17 s074250 17,22525a 3,086000 208,217250 749297250 18 648a277250 2077,845000 801,242250 151*958000 SIS0,821000 19 0,000000 0.000000 0,00JO-000 12.360250 169o828250 20 04000000 38,862250 00000000 6*041250 20s332250 21 00000000 003250 0073250 098250 0125250 22 04000000 0,000000 0,000000 0,000000 00000000 23 0*000000 0.1000000 00000000 0,000000 *793000 24 340602SO 0,000000 0008250 5S3000 44545250 25 0*000000 .123250 0047000 6,427250 41753250 26 1 9005000 0663000 0058250 219'378250 9,416250 .27 06000000 0.000000 00000000 846250 035000 28 sOO1250 058250 027250 1911S,7000 24,018250 29 4510535250 182.846250 0,000000 362.478250 785,984250 30 0,000000 0,000000 06000000 0.000000 01000000 31 09000000 132.557250 117,921250 36,599250 167.963250 32 09000000 - 11,901000 41951000 7,979250 410s963000 33 1777,999250 1078,739250 1529,876250 327,100000 1974.844250 34 97,172000 123,660250 156,784250 230,606250 322,273250 35 943,522250 1535,203250 711,790250 1036.043250 2244.002250 36 154m717250 263.872000 95o75OOOO 135,618250 629.476,000 37 04000000 0,000000 06000000 28,844000 479,959250 38 06000000 01000000 0,000000 01000000 00000000 39 00000000 249,655250 210.843250 533o866250 1213,474250 40 00000000 0,000000 01000000 04000000 619,594250 41 760880250 28.857250 37,369250 1928S250 651t701000 42 00000000 11,080250 400,647250 4,987250 121,469250 43 1628,191000 4097,753000 6321,166000 542,822000 20,637250 44 193.819250 381.520250 720f683250 15,131250 726,777250 45 1528o,097250 1893.311250 4842.086.250 279,935250 39499982000 11-28 -4 3D -fl Z> 0 C", --;) -D -') Co 20 CD 05 -4 M -4D 8 C@ P@ :-*t M M C@ @4 A) ID 0 C) n- 4) C) 0 M A V M W W M C@ 4D 40 C@ 0 40 %0 C@ Q 40 Q 40 C N C, 0 0 M 4 0 OD 4 CDP 4D in CP r- -- r- C) c@ -V,c:o M M 10 aD lr@ C- r-- a a 0 a * * a a 0 0 0 9 a * 0 # * 0 0 0 0 0 * 0 a 0 0 0 0 0 0 0 0 0 0 & a a & 0 0 a o comc, C>OQ a am aQc) 0 coQc%jcac2,c) 4 04.4 cc rqit--Qr-r@ m-pook a co 64 ocvQ cio %a v4cp- 4J M M oam owv"W@qc rl- 4 1,-, M -P@ qc V" tv 0- fnn) M* wmo@Nm 4 * u r- cp N o, Q 0 @c C), 2, MM o a 4 4) 0 '. M Ok f- 1*4 0@ P- M 4cp v v lh w ff) C4 jpl %n 7- 0 0 r- Z) 0% M '0 CP M V 0 ID C;l in t- -4 M 7k N in WMMM-MWCP 4p- -r:t: a*% ;;M*r4;czczz I; CUZ I; czp!.;rf a;,; c4;zg 0 a M - V 4= a, M ft M to V W, - 40 --4,0 C) '"'V r- '" C> CP, zmo-ar-Mmm Mfk: M 4 cc fMT 0@,Vv-op wo"@",ndl I"', N c CP C> In M am MNPI" @44 M 01 -W& 4 4 F- lp %- V 71 --4. n a - 0 w n n --q n 'm -> n 'M r" --4 "M 10 V .41 M tv @* 7k * .#I,- w ..4 tr, "n ru F.- Ic Q CD Ir. M lcm"mm zo Q c, CD, C), Q ck 10 Q - C), C@, C@ a 0 0 Q C> 00 -0, IZ Fl- 0 N M 0 M,Q co *- Q M a, 0 Cp to 'I Q C> C> :@ ---> Z r> IV ru -'M Z:l c) :> o lo =@ - m cz =p D CO CDP (=# r ch C> co 4 m 4 Q N M In C> fu C-- 40 C> C> 0 C- V a Q 0 0 C5, N Co C5 r 0 * 0 a 0 0 q * 0 0 0 0 0 C;C), QQ coo, mc@ 0 c, Q4 c, 4 omm co 00, a C> fl- -4 QM IV Co cv In z H =o Co o@ 4 Ol @4= f*- 4D 0 0 M 1-6 0% (ZP M M J) 0' (V Q M M 1@' 0' 1' r- Co co F- cp a Q (M 3% cu rT4 36 op C@ @ C) 0 oh M (M P- 4t --s * v lc:> in M tv 3D JO 1) (Z), D C) '> (1-0 -* C5, C) MC --'3 'D An D in 4M C. Q CDP C), C) w 0 C, 44 o, 4 c m m co in in un in 4 f" 40 No W iD OD W OD 40 Q W 0 V M -4 M W * 0 a 0 0 0 0 0 0 9 0 0 0 0 * & 0 0 0 0 a 0 & 0 0 00 0 0 9 0 * 0 0 0 *. .0 0 * Q a @f 01 c, Q C) a N 0 #,- r- (71 a -9 f- 0 M Q m w 4 w 4 r- Cp I- C) 00 C%# 40 Co 0@ cp C21 r- m en E-4 4J cr) 1* 0 0 M c a, F- CW) fl- tv M 0 r- 4 r-4 U u) V-4 I OD C) 0 Qczo CDP C:00@* (=Ooo ok 'o C> rj W) C@ M). 0 %P M Mc> CC (%j C20 a 0 C:p 0 a 't'o tn 0 M a CP W 0 e4 CO C5 0 M M 43D -4 a (V M 0,0 t%- W u.- tn a N 0 co c, f-- Ct) 0@ pl- ID f" M UN OV V M IM -4 7b M to co V ly@ %0 a CIO ru -4 V PJ -.;o in %D 14 0 4 w cl Ln op al (M a, co co M OD C o 44 u% cp e-"j, -v JD oo <ZP 10 'J@ C- C@ -* '-' =@ 40 Ch C) CP (U V CD -4 "Y C3 0% 40 M @4 CU -4 C) Cl -4 CD 0 @n 4 A M tP (jk oC a 40 4=0 C> 4D CZO CM Cl 9104 04 C@ C> 4 40 -4 CE C*' 9=@ a -* fn 4=0 C@ M V@- C30 CD @4 M <5 * ft C@ C, OD 4=0 C5 Co V 0 Q) o 0 0 * e 0 0 o o a 0 a 0 * 0 0 e 0 0 0 a 0 0 o 0 0 0 a 0 e 0 0 0 a 0 0 * * 0.0 0 0 a r) * -* %a Co Co C3, a C> C, 4D Co CD 'M P- C> CDO OD M Q C> Ok C@ AJ 0 0 OD in 0 @-1 tO 4 CC 4D 0 Q Q P@ Q Co io 4 QD U ro rl@ le f@ 9) C., -4 M I-Ir co IV 40 M p- CP M 4D P- M M <> M fn 4% c (v ev) %C M f@ Oh- (Y T, c kn 4 4 4 o V zp co M V P 0 vo, r" M c- to cl C@ 0-- 0% & M M C w 4,0 C> w M ok a F., tv 00 0 F.. Q) g-- tn m in 1:> m 0 CC a Q a W 4 co C:o 1- 4 0 1- 4,e in Cp Cr. W 00 M IV Cp r-- %Q CC OD NO M %ON %D a; r; In 0 %a a -4 a, ir M4 :D Log, r- @Mc- = 7- nc C* @- r.- No m --I t- 'M m 4 :3 P.- Is 4 C, 4 w 'D --t (v tr t- x C> Ln w IV (V 7 w Cr Ln 1 q Mj IM .0 4 0 0 4 iz@ * x 'n 'r ID -4 "> --'> v fu F-- 10 10 N 10 on led ub-11 Ili 11 CL.- [email protected]. Z, Ic -4 kn L.) t- m -4 V. @0.. K ff C) it '0 -4 r, -o -c, r- 7., r 2, @7 'VI C) r, ryl tl@ C.*_ vt rr 7 ') --4 "r 1'@ @v .-0 r, '.) @: z X X L7 Ll. C, T ;v r V, -4 . Ir. I rLm -4 r' v) r- v C*, C, C, C) CL C, C.- lc@ *cl C, L7 f, r- c 4@@ k;.- C-N r C. 0 C.*; vl@ %11 .. %lw li %.n r) C. In 41 w N In C. V. C-. f WC- C. k@ C, cc, -4 C, 0 C, N Q C, Q C L) C, C.@ 4 C.) 0 C* 0 C. ci Ici 1:71 10 00 C. C@ 0c) 41 C) 0 41 - " 00 1A, 0 c. P, 0 Op, C@ .0 cc NO 41 0. 1 0v C, 0 C 0 C, c.@ 10 aC; (I C.) C) Cj Cd 1-3 C, ll@ C- (P to C 4, C, 'D C, -4 %,R I Ir- w@D C, tx. 01 0 %.'It @O -4 C, ?v -j 1 60 1rm 41 CD -4 C N, 64' C) Li C) N C. 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OD z i.A N Lo '0 1 01 Table H-6 Projected Processing Sector, 1980 Transactions Table for Coastal Bend C.O.G. 2 3 4 244854360 157,008484 010000?0 0.000000 0,000000 2 09000000 151.745881 1598,029775 3313.858531, 00000000 3 0.000000 0,000000 10673,14qO95 0.000000 0,000000 4 00000000 0.000000 0,000000 0,000000 0,000000 5 280,174737 1079,355431 94*752288 22.801505 01000000 6 354,947728 1128,004347 0.000000 0.000000 0,000000 7 14364567956 4059.513733 1778.845390 699,134730 0,000000 9 00000000 0.000000 O'empe 00000000 O.ViOOVOO 10 218,7S2699 857.042533 681,066362 233.602347 0,000000 It 0,000000 0.000000 0.000000 0,000000 00000000 12 0.000000 0.000000 0,000000 0,000000 0,000000 13 01000000 0.000000 0.000000 0,000000 9J,000000 14 0,000000 0,000000 4777,113214 4049.614192 01000100 15 0,000000 0.000000 01000000 0.000000 01000000 16 2,121048 7,607029 0.000000 00000000 057751 17 9,121801 103.067a43 483.815325 25,22q664 111907is 18 0,000;)Oo 0.000900 .628478 0.000000 19976966 19 2071,387655 8218.353q69 866.484495 202,067058 .09000000 20 547.893874 4200.677289 5,990629 384.901920 083946 21 0,000000. 0.000e-021 0.000000 .,002604 22 0,000000 0.0moo Otmoeo 0.010000 O'CO0000 23 3,775567 179,850749 Oivozooo 0.000000 0*000001 24 37,867657 138911963111 St.-22_8664 29.571684 0.0m7op 25 327o252548 2665.216528 6.830832 39,054qq2 0,000000 26 6g915776 5J.907880 2.409336 1,816482 000766 27 0,000000 0.000000 0,000000 0.000000 e"0000'00 28 135,349198 211,672612 19,514233 23,792582 155790 29 0,000000 0,000VOO 1391,305939 899,370131 10101317 30 0.000000 0,000000 0.000000 0.000000 0,000000 31 - 08000000 09000000 - 0,000000 0.000000 00000000 32 0,000000 0.00meoe 0,000090 0,000000 4012408 33 58,253128 233.320723 87.012225 63,194935 9oO83472. 34 0,000000 0,000000 J2.343405 23.361331 777113 35 64.602766 258.748724 104,348422 41.231482 13,031229 36 00000000 0.000000 01000000 0,000000 2,475027 37 0.000000 0.000POO 0,000000 0.000000 0,000000 38 0,000000 0,000000 1270.599066 105.625320 01000000 39 891,509274 368l,t76202 1525.090815 1912.060653 153*029315 40 0,000000 0.000000 0,000000 0.000000 06000000 41 54lt230778 3076l659685 395,352150 705,58US26 1.@75274 42 294.484636. 1705.234qO8 488,199945 178.876036 0.000000 43 2004.857994 7888.735308 4419.641888 1712.000619 22,968712 44 480,760614 1938.797583 631,391381 529.P75945 3,275270 45 26,650695 119.916116 39,236070 141.700338 43,671650 11-48 Table 11-6 (cont'd.) 7 9 a $084167 2,236023 9006814 0,000000 0:000009 4763620 01000000 01000000 0*000000 0.000000 4010896 677,524324 01000000 09000000 0,000000 177,987597 0,000000 09000000 0.000000 0.000000 @,meoo 01000000 08000000 00000000 09000000 01000000 0.000000 01000000 00000000 0.000000 0109JO000 01000000 090,00000 0,000000 0.000000 0,000000 381,867478 0,000000 0,000000 0,000000 2,174921 0.000000156209,138004 0,000000 130,S39944 O.Omoo 0,000000 3337,492089 3675sI63491 00000000 090484 0.000000 09002000 0,000000 .00000000 403JI09 0,000000 09000000 0,000000 0,000000 007580 1.184928 s003029 0,000000 0,000000 17,473430 1086.328102 0,000000 0,000000 0,000000 3,410424 0.000000 0,000000 0,000000 0176958 019107 6,183298 00000000 5,100803 oO42T25 312$931559 01000000 401,368899 4072,320635 o87284i 6036793 0,000000 59,868300 280,716002 0.91000op) 725,225888 37,524263 3450,307034 198b,433848 90507121 140.331443 797,555698 14993,145847 3279,827660 0,000000 .027793 048751 @,370235 536,90@762 9040583 01000000 0,000000 13821s903395 00000000 142279 01000"1100 01000030 669162325 10270te 138489 104,914851 112.001033 4462,793281. ,039451 28.141802 .277290 756t249445 407,547025 0 000655 190284 26,313095 268,501446 1239232980 .4.0389250 M7212 2018.634241 OlOO0000 4116g735061 ;001473 0456208 171715 o897195 48.009010 14*038298 3,168345 8.394J69 5710e985154 679,864558 00000000 0,000000 0.000000 43142.960239 0,000000 OsOO0000 19@287522 0,000000 2738t537283 0*000000 0.000000 0.000000 0-.0viO000 1160113747 5*502029 7?529479 26sq78465 81.157610 5012,656678. 1523.136169 13g631015 30229773 10,453SO9 11489023274 101,353412 71s271243 159884678 44,335734 4838,665118 622,131344 .107550 5,60 - 3505 24,133586 729448721 72s921791 0*000000 010264 0,000000 0,000 000 00000000 09000000 0,000000 0.000000 0,000000 0,000000 10,968458 33,041020 1059,562585 5633,637064 12718,783113 O'emop 570378 780.302517 2,087399 0,000000 110375249 1,992375 23,179682 2282,319837 39799483854 0,000000 106.673461. 0.000000 177e9iq535 226,584297 55*490702 27,960045 15,717370 4757,559407 6956,685214 10,602592 5,382270 41.922096 1036q9314745 633l696339 23,657716 195,285429 429.866227, 145545.571761 4178*455428 11-49 Table 11-6 (cont'd.) 17 19 20 $04677S 4061905 0.000000 038055 0,000000 14162890 0,000000 g004221 00314S GOON0000 'Oems 0,00000 0 150736 01000000 69010000 s017687 11013506 16144604 04000000 60000000 0,000000 01000000 00000000 0,000000 00158so 00000000 0.000000 9006425 02000000 0,000000 06000000 0.000000 01000000 01000000 8,6000000 0.000600 0.000000 006425 08000000 00000000 0,000000 020907 179S49362538227674ll82573 0065331 1*935289 1,060502 7615m739343 495619489-3 0.000000 .015476 .004625 1,004741 0,000000 0,000000 6005025 000925 184342 09000000 .0,000000 OM608 0.000000 053870 0,000000 06000000 34st41197 040888 499741821 04POO000 0,000000 41248t2 137651 4,527SiO 01000000 ,120611 o2976S9 031267 3,074516 01000000 034521VJ 519596180 .30,036892 997,762680 l7e760815 9011211 7,822555 60.686445 28,711955 34o498657 00VIO0000 250601727 748938 6482,845242 JIS46,573076 ,001933 j135263 1,105091 1849,778680 895,738924 0,000000 013667 003145 540178 9673179 0.000000 00000000 *009066 0,000000 89998712 0.000000 082203 .022942 1045.481676 otoki0ooo 01000000 1*148230 43,OSE503 33o,os55,n *257091 o0emoo 016280 .086.032_ 654,935135 31753955 ,001546 .016079 392415 1,166843 *J47140 0.000000 0042006 *029602 9o672790 0,000000 014303 *554117 .789086 634o960122 14892877 ,130275 479632948 57.770540 2623,48S214 3979711719 80000000 00000000 3,397788 1824,927538 1246,839619 .035951 1,207521 017161 3.22.896814 0,000000 3,998719 36,952581 21,726788 8547,457014 844,706695 1,387413 500143658 40.9il294 965.276894 98.901756 *274081 2,969158 6,712505 10774,08-6529 3179,818661 o925071 26g996365 45,380315 15377,713157 1800,146168 ,097417 1,995584 3.152829 64*224812 255,597171 0,000000 006633 004625 5,055332 0,000000 0,000000 0.000000 01000000 0,000000 267896 12,496672 41,502600 2739,756376 854,9-87627 0.000000 001608 .000740 .9039537 0,000000 9035951 2,834096 12.400586 1017,189310 34000000 eoot933 119340604 1,704537 29,428074 13,763783 ,009278 o620844 35302 1611,161457 06000000 9317377 10.766388 26.089,.@41 764,790883 342.020598 0311192 256844518 137*796,639 28789205961 16449949640 11-50 Table 11- 6 (contd.) 21 22 23 24 25 1 00000000 O'e0oppe 0.000000 0,000000 0545039 2 9000263 0.000000 0,000000 000769 0115484 3 01000000 01000000 0,000000 0.000000 .004048 4 tOO1432 0.000000 0.000000 .001384 102oq54 5 00000000 0,000000 0.000000 0.000000 0,000000. 6 0.000000 P1.000000 0.000000 0,000000 0.000000 7 06000000 01000000 0.000000 0,000000 01000000 01000000 01000000 0,000000 0.000000 0,000000 9 000614 0,000000 00000000 0.000000 0/4,8813 to g016011 7.122783 0,000000 1.310919 16:106141 11 j001665 0,000000 04000000 .001230 .094531 12 .000497 0.000000 0.000000 000769 037145 13 00000000 01000000 010000PO 0,000000 004048 14 0003652 0.000000 441,374533 .009380 g116437 is 0000701 0.000000 0,000000 .029678 e437412, 16 0,000000 0,000000 *789917 .000615 4001905 17 v786105 11.530295 59ta48448 1.838670 426935 18 9020949 8.263381 72,552513 550663 o913636 19 0254692 0,000000 35.318508 625089 2,067051 20 398414 .603846 00000000 180223 6,396643 21 9014872. 5,112470 .647775 .024142 066910 22 0156052 198.796601 0,0003em 0.000CLIel 52313P 23 g006925 37.468718 5,182628 .016146 39,843A60 24 053176 0.000COZ .786064 I,J36647 23,823592 25 g006457 0.000000 0.000;300 4.829103 10*100001 26 014171 .001254 10,599449 .043825 4,475793 .27 g002717 0.000000 010,00000 .003229 2.290877 28 9006662 0.000000. 47o660913 .022297 1,723932 29 19704712 383.843161 188,731152 174687 8.3qI309 30 89000000 0.000000 01000000 0.000000 61,131234 31 004353 0.000000 00000000 ..036444 5,725405 32- 3t101323 175.763914 28329515855 11595709 5,019403 33 o364404 48.q55590 468,886775 19,657328 33,048293 34 1.675261 11.399713 601,536367 3.099.459 5,382524 35 11923028 0.000000 2929.138796 13.660777 14,649847 36 0031117 5.297449 153,890447 1,483145 2,702096 37 s013323 0.000000 01000000 001999 j057385 38 O,OaOOOO 0,000000 04000000 0,000000 01000000 39 9487616 45.924445 4904.796889 42,891735 63,679q86 40 sOO0263 0.000000. 0,000000 0.000000 0,000opo 41 0045258 49.509900 76.243503 3.0441254 2.614709 42 e011220 2g368356 2,466511 6.531837 44127911 43 066061 0.000000 2168.826054 339686 1,8375it 44 g101123 12.011867 387,237155 11.715696 10,497172 .45 gSI4613 41.598449 2893,221320 5*724834 33a529279 Table 11-6 (cont'd 31 32 33 34 315 0,000000 0,000000 0.000000 01000000 01000000 2 0.000000 0.000000 0,000000 01?00000 0,000000 3 00000000 - 0.000000 0,000o0o 0.000000 0 0000100 4 0*000000 01000000 0,000000 0,P1009100 0:000000 5 09000000 0,000PAO 00000000 0.000000 0,000pop 6 0,000000 0.000000 00000000 0.000000 0,0003m 7 0,000000 0.000000 09000000 0,000000 0,000000 a 0,000000 0,000000 0.000000 0.000000 0.000000 9 01000000 10617,909500 0.000000360628.6315464 0.000000 to 3j207825 302.718790 12.078599 715.941839 75,458330 11 0,000000 0.000000 Oloeoooo 0.000000 00000000 12 0,000000 0.000000 0,000000 0,000000 Osoilvoovl 13 00000000 0,000000 0,000000 0,000000 0,000000 14 0,000000 19,736117 0.010000 0.000000 0100,0000 Is 0,000000 0.000000 0,000000 0,000000 06000000 16 0,000000 0,000000 01000000 472770 g1028 'II 17 g486426 5.375309 917968 .075799 2.957t7,5i 18 1,222099 19,365199 322,319581 23.313423 53,166224 19 0.000000 266911 09000000 2.334695 266556 20 691,059881 103.8533q4 24.447751 561,059978 47,46q272 21 cOO1471 2.078146 .002382 0.000010 01246c? 22 0,000000 0,000000 0,09kales 0.000000 1,669444 23 01000000 29.887252 0.000091 0.000000 343723 24 09000000 50550994 P1,030OLeo 01060000 0084931 25 007357 56,700652 030961 104,988872 0971,65.. 26 026190 22.345328 34,752335 688504 .769551 27 00000000 4.254765 2*360414 334777 0196917 .28 004708 17,403341 7,047636 .002429 124,455 29 0,000000 i0,869J62 31,282632 326.112820 54978-6 30 0.000000 0.000000 0,000000 0.000000 31 792,166257 6,623400 27s516555 0,000000 3 6.6 7,7 6'@'@ 32- 0.000000 92.143400 24.998162 114.623558 A19,388033: 33 37095882 25.60685 133,00483q 1334,651045.'@ 222,382536 34 IvIIIA54 53,405484 28,152702 6603.,411,7.-4-8,2 5026.206299 35 2sI92892 247,973909 391?.026611 0,0oovipip 36 291032 14,852264 26,079930 'A 1 .5 5 010 q I .86,93998q 37 0,000000 0.0000eO 0-9., 0,01000 00000000 01000000 38 01000000 0,000000 0"1000000 0.000000 00000000 39 50,075442 63.SI,6519 153.374148 30.036103 40 0,000000 0.0001?00 '--0,000000 0.000000 0,000000 41 09000000 0,000000 0,000000 384,173193 171,891750 42 0.000000 0.000900 8.295586 1,753086 5,106436 43 53t483O76 66.208183 217,577526 3076.876652 307,745070 44 60.914837 79.794574 526,840206 7020.572310 772,119751 45. 990969321 64,315497 186,745013 3268o726864 180,603t59 11-52 Table 11-6 (cont'd.) 36 37 38 .39 40 0.000000 0,000000 0.000000, 0,000000 O'Ci0opop 0.000000 01000000 01000000 0,000000 O,POOQIOO 0.000000 0.000000 00000000 04000000 0.000000 O,OQIOOOO 0.000000 00000000 04000000 00000000 01VIO0000 0.000000 .01000000 0,000000 00000000 06000000 0,000000 0,000000 0,000000 0,000000 0.000000 0,000000 06000000 0,000000 6,000oviv, 0.000000 0.000000 0,000000 0,000000 0,00000P 0,000000 0.000000 0.000000 0.000000 320,633093 0.000000 3,666165 i 0,000000 04000000 6,000000 0,000000 0,000000 @0.0000QJO 0,000000 0.000000 00000000 0,000000 00000000 0,000000 6*000000 5,434071 0,000000 0003016 otmooo 0,000000 19.288001 0.000000 00p,00000. 14491572 0,000000 34000000 0,000000 0 000000 O,om;oo ,06S337 01000000 0.000000 :570096 01000020 4,998379 032380 .0016S4 20,931889 7l470534 1,576453 24.99101S .000223 67,096514 722e868967 64,850578 0.000000 0,000000 0,000000 0,000000 17,998768 312750 0,000000 JjB03J94 04000000 4,960246 00000000 01000030 1 1005429 0,000000 $4,501179 0,000000 0,000000 09000000 0,000000, .326684 0,000000 0.000000 olecogoo 01000000 0814036 09003003 0,000000- 09003000 asnozoo. 0746141 0.000000 0.000000 3.659161 0.000000 ?,'838S45 0,000000 0,000000 4 978239 0*000000 6000000 87.375222 0.000000 19:964233 0.000900 2o398859 2,903124 000179 26.742649 qO028J6 04000000 17,913198 0.000000 . 288,444044 IlIS6435 evooom 06000000 01000000 01000000 00000000 00000000 04000000 0,000000 307,550418 01000000 0,000000 g635784 0,000000 56g8O2716 0,0.00000 16,2OOq6t 541.467794 1,656387 5748,456955 252,198207 0,00000e 47,958871 115533 1964,609507 150,q30419 404,887296 469,770430 1,331197 3618,612397 14 62,388485 431,802091 54,430235 .173053 683,'645728 212,804086 0.000000 0,000000 0,000000 0,000000 0,000000 0,000000 09000000 0.000000 09000000 0,000000 1030517853 67.936750 750717 857,247020 04000000 0.000000 0.000e0o 01000000 10,000000 0,000000 0,000000 17,270557 .449483 328,132406 214259040 00000000 21530948 0,000000 350,824656 08300000 17*152413 148,249012 IIA.981851 7136.364786 332,522731 0,000000 79.526388 2,404512 1046,284953 328,531711 2s912253 863j, 967840 6,810623 @64S6s341734 772tO29947 11-53. Table 11-6 (cont'd.) 41 .42 43 44 415 1 0,000000 0,000000 0,00000M 3,635764 26,581545 2 0,000000 0.000000 0,000SPO 01000000 4..qB8195 3' 0,000000 0.000000 0,000000 23.SqSB68 227,29249q 4. 00000000 0.000000 0,000000 6.105426 38,619501 5 00000000 1 0.00APOO 01000000 0.000000 0,000200 6 0,000000 01000POO 0.000000 0.000000 .009766 7 00000000 0.000000 00000000 0.0000no 010000no 8 09000000 09000000 01000101 0.000000 172,2O8q7n 9 09000000 0.000000 0,000000 0.000000 855366 to otcome 0.000000 2194.817736 639.95432A 1784.U3072 11 01000000 12.303587 0.000000 624.159931 22,406692 12 0,000000 153.303576 0.000000 2008.620699 65,762105 13 0,000000 1,823439 04000000 96,369435 22.526179 14 00000000 1851.026819 *034575 1278,779800 75,U6r5?57 15 0,000000 145,064748 342.163445 81,611359 38,24S619 16 09000000 007316 0,000000 .135540 485416 17 125521 29,308005 6,996658 31q,792477 170,722279 18 1095o922830 3576.892651 1816.596902 233.386164 1288,670030 19 09000000 0.000000 0,000000 18,983609 39Ot236057 20 09000000 66.899165 0,000000 9.278512 46,72@)V-07 21 0,000000 Oos5qs 166074 .150898 2878q3 22 00000000 0.000030 O.P00000 0.000000 23 01000000 0.00em 0,000POO 0.00@-m 1,822J77 24 50173400 0.000000 SoIB705 .849330 10,444201 215 0,000000 '212168 sIO6560 9.8.'1354 10,q22150 26 008453 1.141317 9132066 32.833992 21,636920 27 04000000 0.000000 00000000 1,299721. 0080424 28 002113 100274 .061782 29.422464 S5,189800 29 763,327402 314.759478 0.000000 556.715726 18eb,056383 30 0.000000 0.000000 09000000 0.000000 O.emoo 31- 0,000000 228,189809 267,354071 56.211312 385.950609 32 0.000000 2004868198 11,225034 12,255018 944,322166 33 3005,735541 1856.988608 34689574525 502,379698 45,37.851824 34 164.270786 212,874127 355.465258 354.178839 7400528401 35 1595.039121 2642,765568 1613.789042 t591.217045 5156.33M533 36 261,551931 454,2D0724 217.086847 208.290601 1446.427390 37 0.000000 0.000000 OtOO0000 44,300336 1102.863660 38 00000000 0.000000 0.000000 0.000000 0,010MOO 39 0.000000 429,7673c,3 4780029204 8194943643 2788.35@747 40 OvOO0000 0.000000 0.000000 .04000000 1423.720832 41 1296967265 49.676124 84,724518 1,973964 1497,496611 42 0,000000 19,074020 908,357662 7.659716 279,115408 43 2752,082351 7054.050032 14331.509070 833,698418 47,420845 4@1- 327,654474 656.765533 1633,951479, 23.239477 1670.908898 AIS.'. 2583o272301 3259.228237 10978.101668 429.941261 9076,378061 11-54 Table 11-7 TOTAL OUTPUT OF ECONOMIC SECTORS 1967 & 1980 Total Output 1967 Projected Total Output 1980 Sector (Thousands of Dollars (Thousands of Dollars) 1 11402.000 25666.437 2 39175.000 100567.078 3 33760.002 69565.287 4 13153.999 22180.561 5 1494.286 915.618 6 1926.000 1261.137 7 7390.735 4668.346 8 28080.008 34877.134 9 700286.858 2120824.247 10 184170.995 @318852.785 11 4922.666 7538.133 12 10177.906 14834.787 13 2183.333 3209.805 14 52,043 .627 107117.520 15 10376.965 16 134.197 207.516 17 8116.384 6525.107 18 9440.177 6986.278 19 159658.824 315623.606 20 231932.227 50021.910 21 356.998 41.724 22 13380.839 8390.417 23 96243.999 164823.423 24 4852.081 2984.491 25 5981.865 @5697.426 26 1186.001 1388.477 27 4471.012 230.156 28 2443.794 250'0.430 29 15175.825 14142.559 30 21120.000 2198.780 31 7121.213 8382.211 32 16912.965 16954.952 33 31017.083 32831.020 34 299801.497 582680.569 35 40490.906 38104.448 36 6840.443 6362.032 37 22861.855 34835.746 38 762.453 136.307 39 117498.504 283536.412 40 68959.016 97103.947 41 77271.641 130628.924 42 109983.055 189329.619 43 104920.949 237879.468 44 75549.551 116033.520 45 132698.294 304917.836 11-55 CHAPTER III DEVELOPMENT OF A DEMOGRAPHIC FORECAST FOR THE COASTAL BEND COG REGION, 1970-1990 Prior to 1960, the thirteen counties in the Coastal Bend COG experienced steady increases in population, although the rate of increase per decade continuously fell between 1920 and 1960. During the most recent decade, the region continued to experience a declining rate of increase in population. Between 1960 and 1970, the United States and Texas grew at respective rates four and five times that of the region. of the thirteen counties in the region, only four.increased.in population. Table III-1 summarizes the growth in population in the region between 1920 and 1970. The Recent Past A breakdown of the major components of regional population1 change between 1960 and 1970, as shown in Appendix A, revealed: (1) The birth rate (births per 1,000 population) decreased by 23.3 percent while the death rate (deaths per 1,000 population) increased by 13.6 percent; (2) Net migration accounted for a loss of over 62,000.persons, almost 15 percent of the 1970 population; and (3) The shift of population from rural to urban places continued with 5 percent fewer persons living in rural areas of the region in 1970. Examination of the age, sex, ethnic composition, family size, and education characteristics of the region's. population over. this period indicated that: (1) The number of persons 65 years or older increased by almost 2 percent while the number of persons 20 years or younger decreased by nearly 4 percent; (2) While the median age for the State of Texas dropped.0.6 years, the median age was increasing for every county in the Coastal Bend COG; (3) In 1960, the ratio of males per 100 females was 99.9, or almost a one-to-one ratio, but by 1970, the ratio had dropped to 97.3, A decrease of 2.6 males for every 100 females; iThe observations in this section and the compilation of data in Appendix-A were taken from Population of the Coastal Bend Regiont areport prepared by the Coastal Bend Council of Governments, (September, 1972). TABLE III-1 TOTAL POPULATION IN THE COASTAL BEND REGION* 1920 - 1970 Year Population Population Change Percent Change 1920 1041884 1930 1791569 + 74,685 + 71.2 1940 2431496 + 63,927 + 35.6 1950 3381684 + 93,188 + 27.7 1960 419,778 + 831094 + 19.8 1970 4331822 + 14,044 + 3.3 *All figures reflect totals for the thirteen counties presently in the Coastal Bend State Planning Region. 111-2 (4) In 1970, the white population was.divided almost equally between Anglosand persons of Spanish heritage, Negroes accounted for 3.9 percent of the region's population, and other races accounted for le ss than 1 percent; (5) Average family size decreased from 4.15 to,3.88 during, the decade, both figures representing an average family size larger than those for-the State; and (6) The educational attainment level in the region-is increasing, although the region still lags behind the state in the increase in the percent,of persons completing'four years or more of college. The density and distribution patterns reflected a continued intra-regional rural to urban population shift. The regional population density increased by 1.3 persons per square mile while the region's share of total state population decreased by nearly six-tenths oi one percent. Development of Demographic Projections To provide a basis for analyzing expected changes@in population in the study region, it was necessary to develop a set of demographic projections for the area. Three demographic projections were evaluated. The first was developed by the Population Research Center at the University of Texas at Austin, utilizing the ratio/ correlation-multiple*regression method. A second set of population projections was developed by the Coastal Bend COG using the standard cohort-survival method.. A-third set also was developed by the Population Research Center at the University of-Texas at Austin, utilizing the cohort-migration-survival method. In this section, some general comments on demographic projections are presented, followed by a description of the three population projection methods, the results of these three projections, and a general evaluation of the projections. Population Projections in General Before,describing the methodology employed in the three sets of population projections made for the Coastal Bend COG it is appropriate to make a few comments concerning the general nature of population projections. Populations projections should be thought of as statements about the future size of populations based on certain assumptions. Population forecasts, on the other hand, may be thought of as the most likely population projection for an area at some-time in the Eture,'and require a substantial amount of judgment., Ratio-Correlation/Multiple Regression Method Small area population projections have proven difficult to 111-3 make directly or by extrapolation (arithmetically or geometrically) but projections for larger areas, such as a state, have proven more accurate in the past. The ratio-correlation/multiple regression method makes use of this fact in projecting regional population. This method involves mathematically relating changes in several indicator 2series to population changes by a multiple regression. equation. More specifically, a multiple regression equation is derived to express the relationship between the,,.dependent variable, the change in an area's share of the population of the state, and a set of independent variables, the change in an areals share of the total for the state for several symptomatic s,eries. In making its population projection for Texas counties, the Population Research Center at the University of Texas at Austin used five symptomatic indicators: births, deaths, auto registrations, voter registrations, and school enrollments. The dependent variable (Xo) in the regression equation re- presents the ratio of a county's share of the total state population in 1970 to its share in 1960, that is, Percent of total state population in county i, 1970 Pt 1971 Percent of total state population in county i, 1960 Pi Y96i Pt The independent variables (Xl,X2,-.-.X5) are expressed in a corresponding manner; for example if X, equals Births, Bi Percent of total state births in county i, 1970 -,IrT 1970 Percent of total state bi tbs in county i, 1960 1 Xj B 1960 or in general, X? where i=county j=symptomatic indicator X i X 2 This method has been described by Robert C. Schmidt and Albert H. Crosetti, "Accuracy of Ratio-Correlation Method for Estimating Postcensal Population," Land Economics, (August, 1954) pp. 279-80; and David Goldberg, Allan Feldt, and J. William Smit, "Estimates of Population Change in Michigan, 1950-60," Michigan Population Studies, No. 1, 1960, pp. 36-39. 111-4 The regression equation thus appears: 1970 i X0 PPT a+b,,Xl+b2X2+b3X3+b4X4+b5X5 P 1960. ( -Pii The resulting coefficients, a,bl,b2,b3,b4,b5, are then .applied to the estimated values of the five symptomatic indicators for each future period to obtain an estimate of the county's share of the state population for each future period. The estimated values of the symptomatic indicators are themselves obtained by extrapolation from the trends established over the base period, 1948, to 1960. Using the Bureau of the Census projections of state population for each future period, the per- centage share of the county is then applied to the estimated state total to obtain an estimate of the county's population for each future period. Thus, the basic assumptions made in this procedure are that the past relationship of the symptomatic indicators to the county's share of total state population will remain unchanged, the U. S. Census projections forthe state-will prove accurate, and the estimates of the future values of the symptomatic indicators from past trends will prove accurate. The Cohort-Survival Method The cohort-survival method of projecting population, often termed the Hamilton-Perry method, is based on the assumption that the vital rates of a region, natality, mortality, and migration, experienced in the most recent period are likely to remain relatively constant over the next few periods.3 Essentially, this. method involves dividing the population into cohorts, i.e. popu- lationgroups that enter on some stage of the life cycle simultan- eously. Commonly, five year cohorts are employed in making projections. Thus, the population is divided into those individuals age 0 to 4 years, 5 to 9 years, etc. The population'is then adjusted to its ages on the first estimate date, i.e. the individual cohorts are "aged", i.e. moved forward for the number of years of the first estimate. This would.give what the population.would be if there had been*no change,due to births, deaths, and migration. Each 3see Henry S. Shryock, Jacob S. Siegel, and Associates, The Methods and Materials of Demography, Volume 2 (Washington, D.C.: The U.S. Department of Commerce, Bureau of the Census, 1971), pp. 735-40. 111-5 cohort is then multiplied times a survival coefficient which reflects both mortality and migration, i.e. a person leaves the cohort either through death or out-migration, and enters only through in-migration. one final step is required. Since at the time of the first estimate, there will be no individuals in the first two cohorts, because these cohorts did not exist in the base period, (i.e. if the base period is 1970 and the first estimate is 1980, in 1980, the 0 to 4 years and 5 to 9 years cohorts will have advanced to 10 to 14 years and 15-19 years respectively; at this point, there will be no individuals in the 0 to 4 years and 5 to 9 years cohorts) it will be necessary to add in to the population for the first estimate date the number of individuals entering the population via birth over the projection period. This final step will result in a new set of age cohorts for the first estimate dates. The basic disadvantage of this method is its failure to separate net migration and mortality. In areas where migration is a significant determinant of population change, the method is of limited usefulness. Cohort Migration-Survival Component Method The third method for projecting regional population, the cohort migration-survival component method, overcomes the principal objection to the cohort-survival method by employing a separate estimate of net migration after removing the effects 4 of mortality through the employment of life table survival rates. In using this method, the Population Research Center at the Univer- sity of Texas at Austin projected to 1980 and 1990 the-male and female populations separately by age group. The complete methodology employed in this projection is given in the Appendix to this chapter. Coastal Bend COG Population Projections The population projections for the Coastal Bend COG using each of the three methods discussed above are given in Table 111.-2. It can be seen that the three projections vary widely by county, but that the projections for the Corpus Christi SMSA and the entire Coastal Bend COG Region are reasonably close. In general, the ratio-correlation/multiple regression method gives the largest pro- jection, while the cohort migration-survival component method gives the lowest projection. Because the latter method is based on assumptions which appear to be most reasonable, and because it includes migration as a separate component in the analysis, this projection has been adopted as the "most likely" for use ' in estimating the future labor force and in the spatial allocation models. 4 Shryock, Siegel, and Associates, op. cit., pp. 797-799. 111-6 TABLE 111-2 COASTAL BEND COG POPULATIONS PROJECTIONS Population Estimate Projected, Area 1970 1971 1980 1990 County: 13,600 .17,900 Aransas .8,902 9,900 10,937 12,990 1OP210 111267 24,500 29,300 Bee 22,737 23,300 221516 211411 23,360 251073 9,300 9F4OO Brooks 8,005 8,500 7,338- 61679 7,587 70,344 121200 10,500 Duval 11,722 12,200 10,148 8,698 111074 11F227 351900 401800 Jim Wells 33,032 32,700 31,668 30,1004 27r296 11,227 12,700 91300 Karnes 13,,462 13,500 11,870 1OP340 12,867 12,998 700 600 Kenedy 678 1,000 516 390 612 612 38,400 46,000 Kleberg 33,000 38,049 41F237 37,394 42,618 6F300 5.1500 Live Oak 6,697 6,700 6,003 5,050 6,054 5F720 800 500 McMullen 1,095 1,500 1,069 1,043 1,136 1,209 274,300 307f300 Nueces 237,544 253,000 2601741 280,721 263,551 298,141 111-7 TABLE 111-2 (cont'd.) Population Estimate Projected Area 1970 1971 1980 1990 9,600 9,100 Refugio 90,494 8,700 81,286 7,163 6,625 4,272 46,000 44'.900 San Patricio 47,288 47,800 49,199. 51,029. 46,754 43,862 484 300 531,100 Coastal Bend Region 433,822 451,800 458,340 476,755 454,520 483?518 320,300 3521,200 Corpus Christi SMSA 284,832 300,800 309,940 331,750 310,305 342,003 Notes: a - 1970 Census b - Popul ation Research Center Estimate, rounded-to nearest 100. c - Three projections are given:@ (1) PRC Estimate -*Ratio/Correlation - Multiple Regression Method (2) COG Estimate Cohort Survival Method (3) Population Research Center Projection Cohort Migration - Survival Method 111-8 Coupling the Input-Output Projection With the Population Projection To test whether the economic projections obtained from the Input-Output model were consistent with the population projections, it was necessary to develop a mechanism to couple the two sets of projections. It was decided to provide this coupling through employment. The labor force required for fu'1- fillment of the input-output model projections was computed by applying sector employment multipliers derived from secondary data. In each sector, each million dollars of output was assumed to require a certain labor input. Then, using the age-sex breakdown of the population projected to 1980, average participation rates were applied to yield labor force estimates for the COG for 1980. The projected labor force was then compared with the projected employ- ment requirements derived from the input-output model. These projections were sufficiently close to be judged consistent. The increase in population projected to 1980 is about 90 percent and the employment increase estimated from the input-output model is 92 percent. 111-9 APPENDIX A DESCRIPTION OF THE METHODOLOGY USED IN THE COHORT MIGRATION-SURVIVAL PROJECTION In this Appendix, the methodology used in the cohort migration-survival projection is described. Assumptions Two sets of assumptions may be specified: those inherent in all population projections, i.e. the Basic Assumptions; and those underlying this specific projection model, i.e. the Model- Specific Assumptions. Basic Assumptions 1. It is assumed that the counties under investigation will not be affected by-any natural disaster. 2. it is assumed that the counties under investigation will not be subject to unpredictable economic fluctuations, depressions booms, etc. A-1 Model-SRecific Assumptions 1. It is assumed that rates of fertility current in Texas counties between 1960 and 1970 will continue through 1980, and the mortality rates of males and females of Texas counties will approximate that of the United States in 1960. 2. The areal unit of,analysis is the Texas county in 1970. Accordingly, it is assumed that no boundary changes will occur in the composition of each county during the projection period. 3. Net-migration is considered separately in this model. Since migration is the major component in subnational projections, the assumptions about migration are the most important. Therefore assumptions are incorporated concerning the previous migration change of each age-sex group in each county, modified.according @o the recent trends in each county's net migration. These assumptions are explained in more detail in the methodology section below. Methodology The basic method employed in this projection model is a variation of the "Cohort Migration - Survival Component" method. This basic method is preferred over the simple "cohort survival" approach since, as will be noted below, the survival coefficients account separately for net migration and mortality, 2 and thus will tend to yield a more accurate population projection. Furthermore the use of a refined Cohort Migration-Survival Component method is favored since a) the assumptions dealing with net migration are thought to be the most realistic, and b) the population categories employed are the best possible given the available census data. Since the Cohort Mig ration - Survival Component method, by definition, projects actual cohorts, a certain degree of realism 3 is introduced not found in some of the other projection procedures; by employing cohorts, i.e., age-sex-specific population categories ' the order of demographic events in real life is closely paralleled. Another feature of the Cohort migration - Survival Component method is the introduction of net migration as a separate component, i.e., independent.of fertility and mortality.,- This component is a composite measure of in-'and out-migration. It is considered as the major demographic component in projections at the county level, lHenry S. Shryock, Jacob S. Siegel and Associates, The Methods and Materials of Demography, (U. S. Department of Commerce, 1971F-, pp. 797-799. 2 Ibid., p. 797. 3See for example the statement 'on the ratio-correlation projectiog technique in Dudley L. Poston, Jr., and Benjamin S. Bradshaw, Populatiol Projections For Texas Counties, 1975-1990 (Austin.: Population Research Center, The University of Texas at Austin), May, 1972. A-2 and the two migration streams, when aggregated, measureltheir combined effect on populations in the future, i.e., the projection period. Having discussed some of the basic features of the Cohort Migration-Survival Component projection model, it is appropriate at this point to turn to a consideration of the methodological specifics involved. 1. As already noted, this method requires separating mortality from net migration in the survival coefficients. Accordingly, a standard set of survival rates was employed for the age-set population categories. (Survival rates are nothing more than the complement of mortality rates.) The standard set of survival 4 rates was taken from a United States life table for the year 1966. The obvious assumption here is-that mortality by age and sex for the United States closely approximate those for the Texas counties. 2. The use of the projection modelprojects to 1980 the male and female populations separately by age group . Interpolation and extrapolation then occur back to 1973 and forward to 1985. (These details are delineated in a separate,section below). In any event the projection procedure provides an ongoing method surviving cohorts as they pass through life. 3. A specific consideration in calculating net migration is the use of numerical net migration totals, rather than net migration rates. This decision in favor of absolute migration totals prevents the domination of migration in populous counties and provides for a slower population growth effect due to net migration. 4. In those counties with small populations with a history of repeated out migration, the net migration component will frequently override the fertility and mortality components, with the result that the county could "disappear" sometime during the projection period unless the continual negative net migrations were modified with a constraining assumption. Accordingly it is assumed in the Projection.model that any age-sex cohort in a single decade will not experience a decline of more than half.its population through negative net migration. This constraining assumption is not that unrealistic; indeed since 1950 no age-sex cohort in a Texas county has lost more than half its population in a decade through net out-migration. Steps in the Development of the Projections The actual steps followed in the preparation and computation of the projections are described below. 4 Nathan Keyfitz, Population: Facts and Methods of Demography, (San Francisco: W. H. Freeman, 1971), p. 557. A-3 Step 1: With respect to@the organization of the data inputs for each county, the male and female populations were organized separately into age groups 0-4, 5-9, 10-14,....60-64, and 65+. These data were made available for each of the three decennial census periods of 1950, 1960, and 1970. Step 2: The,1950 age-sex cohorts were survived to 1960 applying the "Standard" life table survival rates already discussed. This computation thus produced "expected 1960 totals" for each of the age-sex cohorts. These would be the numbers in each cohort that would have survived,from 1950 to 1960 if only mortality were .involved. Step 3: Net migration in the county was assumed to be the differences between the "expected 1950 totals" (as calculated in step 2) and the actual 1960 totals as recorded by the census enumerations. Thus at the termination of this step, "estimated 1950-1960 net migration totals" were produced for each of the age-sex cohorts in each of the Texas counties. Step 4: The next concern was the projection of the 1970 population cohorts from the 1960 actual base populations as enumerated by the census. From these actual 1960 cohort data, the "standard" set of life table survival rates were employed and the 1960 cohorts were survived to 1970. Next the "estimated 1950-1960 net migration totals (as calculated in step 3) were employed and five different projections for the 1970 age-sex cohorts were computedl ,assigning variable weights to the "estimated 1950-1960 net migration totals." The weights used were 60%, 80%, 100%, 120%, and 140%. A weight of 100% merely employs the."estimated 1950-1960 net migration totals" as given. The obvious assumption here is that the 1960-1970 net migration for each of the age-sex cohorts is the same as the 1950-1960 net migration (as calculated in step 3). on the other hand, a weight of 60% takes only 60% of the "estimated 1950-1960 net migration totals" in the development of 1960-1970 ,net migration totals, the assumption here being that 1960-197O.net, migration was only 60%.of that in the 1950-1960 period. As already noted, fiveseparate projections were made for each of the age-sex cohorts, each projection dependent upon the assignment of a different weight to the "estimated 1950-1960 net migration totals." Step 5: After computing the five separate 1970 projections for ia--chage-sex cohort, each projection was summed over the age-sex cohort categories producing five 1970 population projections for the county being investigated. Of the five 1970 population projections, that projection the closest numerically to the "actual 1970 census population" of the county was judged to be the "best" projec- tion. Accordingly, the net migration "weight" employed in the "best" 1970 projection was favored over the other four, and this "best weight" was used in all further steps of the projection procedures with respect to the estimation of the net migration component. A-4 Step 6: in this final step of the actual projection procedures, the 19-80 projections were developed. Employing the life table survival rates discussed above, the 1970 age-sex cohorts were survived to 1980. The amount of 1970-1980 net migration for each age-sex cohort was then calculated. An "estimated 1960- 1970 net migration" for each cohort was first calculated in the same manner as the 1950-1960 estimate of net migration was computed (see step 3). These numerical totals for each cohort were next multiplied by the net migration "best weight" (see step 5). The resulting product, i.e., the "estimated 1970-1980 net migration-" component was then added to its respective 1980 "survived" age-sex cohort. Step 7: The completion of the preceding step, made available 1980.grojections for each of the age-sex cohorts delineated earlier (see Step 1), with the obvious exceptions of the male and. female 0-4 and 5-9 year old cohorts. Since these four cohorts were not alive in 1970, they could not have been survived to 1980 as was possible with those cohorts alive in 1970. In any.event, the .problem was now one of "creating" these four new cohorts and placing them into the 1980 population. This objective was accom- plished in the following manner: a) The number of males in the age groups 0-4 and 5-9, and the number of females in the age groups 0-4 and 5-9, per 1000 women in the age group 15-49 in 1970, e.g., 1970 ratio for males 0-4=# males 0-4 in 1970/#women 15-49 in 1970, were determined. b) From the above equations, four ratios were produced: one each for.males and females in age groups 0-4 and 5-9,. Each ratio 'was then taken one at a time and multiplied,by the # of women 15-49 in 1980; thus using the "male 0-4" ratio, the result would be the # 3-f-males in the age category 0-4 in 1980. c) The assumptions in this operation focus on the fertility component. It is assumed that the ratio of males and females in the age categories 0-4 and 5-9, per 1000 women between the ages of 15-49, in 1970 will be the same in 1980, and in each of the other projection years. This final step provided projected age-sex cohort totals for each of the 254 Texas counties for the year 1980. The projections for individual years 1970 to 1973i and 1981 to 1985 were computed through linear interpolation and extrapolation. Similarly the age categories employed in the college-age projections were developed subsequent to the completion of Step 7 above through interpolation and addition. A-5 APPENDIX B DEMOGRAPHIC FEATURES OF THE COASTALBEND COG, 1960-1970 B.1 Population and Percent Change, 1960-1970 B.2 Birth Rates, 1960-1970 B.3 Death Rates, 1960-1970 BA Vital Statistics-Coastal Bend Region B.5 Migration, 1960-1970 B..6 Urban and Rural Residence, 1960-1970 B.7 Median Age, 1960-1970 B.8. Ethnic Composition, 1970 B.9 Average Family Size and Net Change B.10 Years of School Completed 1960-1970 and Percent Change B.11 Population Distribution, 1960-1970 B.12 Population Density Coastal Bend Region, 1960-1970 B-1 TABLE B.1 POPULATION AND PERCENT CHANGE 1960-1970 .1960 1970 Population Percent Area Popolation Population Change Change United States 180,684,000 203,184,722 +22,500,722 +12.5 Texas 9,579,677 11,196,730 + 1,671,053 +16.9 Coastal Bend Region.. 419,778 433,822 + 14,044 + 3.3 SMSA 266.594 284,832 + 18,238 + 6.8 Metropolitan Counties 71,606 75,000 + 3,494 + 4.9 Non-Metropolitan Counties 81,578 73,890 7,688 - 9.4 County Aransas 7,006 8,902 11896 +27.1 Bee 23,755 22,737 - 1 1018 - 4.3 Brooks 8,609 8,005 - 04 - 7.0 Duval 13,398 11,722 - 1/676 -12.5 Jim Wells 34,548 33,032 1,516 -4.4 Karnes 14,995 13,462 1,533 -10.2 Kenedy 884 678 206 -23.3 Kleberg 30,052 33,166 + 31114 +10.4 Live Oak 7,846 6,697 1,149 -14.6 McMullen 1,116 1,095 21 -,1.9 Nueces 221,573 237,544 + 15,971 + 7.2 Refvgio 10,975 9,494 - 11481 -13.5 Son Patr;cio 45,0121 4.7,228 + 2,267 + 5.0 .SCkJRCE: 1960 and 1970 Census. B-2 TABLE B.2 BIRTH RATkS 1960 and 1970 1960 1 19602 1960 Birth Rate 1970 1 19702 1970 Birth Rote Percent Change Area -Population Births Per 1000 Population Population Births Per 1000 Population 1960-1970 United State$3 180,684,000 4,258,000 23.7 203,184,772 3,718,000 1.8.3 -22.8 Texas 9,579,677 246,737 25.6 11,196,730 230,624 20.6 -)9.5 Coastal Bond Region 419,778 12,418 29.6 - P3,822 9,85 2 22.7 -23.3 County Aransas 7,006 23.6 8,902 153 17.2 -27.0 Bee 23,755 723 30.4 22,737 578 25.4 -16.5 Brooks 8,609 248 28.8 8,005 164 20.5 -28.8 Duval 13,398 376 28.1 11,722 246 21.0 -28.8 Jim Wells 34,548 979 28.3 33,032 757 22.9 -18.4 Karnes 14,995 413 27.5 13,462 233 17.3 -37.1 Kenedy 884 27 30.5 678 1) 16.2 -46.9 Moberg 30,052 11002 33.3 33,166 887 26.7 -19'.8 Live Oak 7,846 181 23.1 6,697 713 16.9 -26.8 McMullen 1,116 22 19.7 1,095 11 10.0 -49.2 Nueces 221,573 6,597 29.8 237,544 5,526 23.3 -21.8 Refugio 10,975 296 27.0 9,494 110 11.6 -57.0 .Son Patricio 45,021 1,389, 30.9 47,288 1,063 22.5 -27.2 SOURCESs 11960 and 1970 Census. 2T&xas State Department of Health, Vital Statistics, 1960 and IM. TABLF B. 3 DEATH RATES 1960 and 1970 1960 1960 1960 Death Rate 1970 1 1970 2 1970 Death Rate Percent Change Area Population Deaths Per 1000 Population Population Deaths Per 1000 Population 1960-1970 United States3 180,684,000 1,712,000 9.5 203,184,772 1,921,000 9.5 - Texas 9,579,677 77,231 8.0 11,196,730 94,287 8.4 +5.0 Coastal Bend Region 419,778 2,791 6.6 433,822 3,264 7.5 +13.6 County Aransas 7,006 51 7.3 8,902 96 10.8 +48.0 Bee 23,755 172 7.2 22,737 180 7.9 + 9.7 Brooks 8,609 76 8.8 8,005 77 9.6 + 9.1 Duval. 13,398 110 8.2 11,722 117 10.0 +21.9 Jim Wells 34,548 224 6.5 33,032 259 7.8 +20.0 Karnes 14,995 159 10.6 13,462 181 13.4 +28.0 Kenedy N4 3 3.4 678 4 5.9 +73.5 Kleberg 30,052 173 5.8 33,166 187 5.6 - 3.5 L.ive Oak 7,846 49 6.2 6,697 72 10.8 +74.2. McMullen 1,116 10 9.0 1,095 7 6.4 -28.9 Nueces 221,573 1,365 6.2 237,544 1,692 7.1 +14.5 Refugio 10,975 .80 7.3 9,494 61 6.4 -12.3 Son Patricio 45,021 319 7.1 47,.288 331 7.0 1.4 SOURCES': 11960 and 1970 Census 2Tekds State Department of Health, Vital Statistics, 1960 and 1970. 3BUreau of the Ce'nsus, Statistical Abstract of the U. Si., 1971. TA13LE B. 4 VITAL STATIST,ICS COASTAL BEND REGION Total Births % of Total Deaths % of Total Births % of Total Deaths % of County 1960-1970 Region 1960-1'070 Region 1970-1971 Region 1970-1971 Region Aransas 1,452 1.4 763 2.6 153 1.6 96 2.9 Bee 6,103 5.7 1,757 5.9 578 5.9 180 5.5 Brooks 2,262 2.1 615 2.1 164 1.7 77 2.4 Duval 3,093 2.9 1,051 3.6 246 2.5 117 3.6 Jim Wells 8,380 7.9 2,366 8.0 757 7.7 259 7.9 Karnes 3,186 2.9 1,530 5.2 233 2.4 181 5.6 03 )8 4 .1 I Kenedy 166 .2 .1 11 .1 (n K leberg 8,445 7.9 1,843 6.3 887 9.0 187 5.7 Live Oak 1,418 1.3 613 2.1 113 1.2 72 2.2 McMullen 180 .2 98 .3 11 .1 7 .2 Nueces 57,683 54.2 14,702 49.7 5,526 56.1 1,692 51.8 Refugio 2,293 2.2 876 2,9 110 1.1 61 1.9 Son Patricio 11,830 11.1 3,305 11.2 1,063 10.8 331 10.1 TOTALS 106,491 100.0 219,562 100.0 9,852 100.0 3,264 100.0 SOURCE. lexas Sttitt, bepartment of Health, Vital Statistics,.. 1960-1971. TABLE B.5 MIGRATION 1960 - 1970 Population Net Migration 1960 1970 Change Births Deaths Net Total as a % of 1960 Area Population Population 1960-1970 1960-1970* 1960-1970* Migration Population Texas 9,579,677 11,196,730 +1,617,053 2,244,631 844,196 +216,618 + 2.3 Coastal Send Region 419,778 433,822 + 14,044 105,764 29,669 - 62,051 -14.6 266,594 284,832 + 18,238 69,118 18,073 - 32,807 -12.3 SMSA Metropolitan Counties 71,606 75,000 + 3,494 18,159 5,004 - 9,661 -13.5 Non-Metropolitan Counties 81,578 73,890 7,688 18,487 6,592 - 19,583 -24.0 *Figvres hove 6een adjusted for compatability with April I Census dates. SOURCES: 1960 and 1970 Census Texas State Department of Health, Vital Statistics, 1960-1970 TABLF@ B, 6 URBAN AND RURAL RESIDENCIE* 1960 and 1970 1960 1970 Rural to Urban Shift Urban Rural Urban Rural 1960-1970 0 gof %a' % of % of Area Number Total Number Toto; Number Total Number Total Number Percent Texas 7,187,470 75 2,392,207 25 8,920,946 so 2,275,784 20 1,M,476 + 5 Coastal Bend Region 316,061 75 103,717 25 348,902 so 84,920 20 32,841- + 5 SMSA 223,591 84 43,003 16 253,606 89 31,226 11 30,015 +5 Metropolitan Counties 53,736 75 17,870 25 57,350 76 17,650 24 3,614 + I Non-Metropolitan Counfies 38,734 47 42,844 53 37,846 51 36,044 49 - am + 4 County Aransas 3,924 56 3,082 44 4,605 52 4,297 48 681 4 Be& 13,811 58 9,944 42 13,506. 59 9,231 41 - 305 + I Brooks 6,515 76 2,1094 24 6,355 79 1,650 21 - 160 +3 Duvol 61470 48 6,928 52 6,563 56 5,159 44 93 + 6 Jim Wells 241515 71 10,033 29 24,134 73 8,898 27 381 +2 Karnes 6,994 47 8,001 53 7,082 53 6,380 47 88 +6 Kenedy - - 884 100 - - 678 100 - Kleberg 25,297 84 4,755 16 @28,711 87 4,455 13 3,414 +3 Live Oak - - 7,846 100 - 6,697 100 - - MCMVI I an - 1,116 100 - - 1,095 100 - Nueces 196,462 89 25,111 11 223,266 94 14,278 6 26,80.4 + 5 Refugio 4,944 45 6,M1 55 4,340 46 5,154 54 - 604 + I Son Potricio 27,129 60 17,892 40 30,340 64 16,948 36 3,211 + .4 SOURCE: 1960 and 1970 Census. 'The urban population comprises all persons livinq in urbanized areas and in places of 2,500 inhabitants or more outside urbanized areas (Bureau of the Census definition). "Even tl@iouoh many countl&s had fewer urban residents 'in 1970 [email protected] in 1960, due to extenslive ourmigration, urban residence as a percent of total population increased during the decode. In Aransas Cn-t@ - .- tk-A -ia. -7d.- lner-,ad - it r@&.!td in a dacreasinn, oerc@nt of torai cnuntv ocoulation, - TABLE B.7 MEDIAN AGE 1960 and 1970 Median Age Change Area 1960 1970 1960-1970 Texas 27.0 26.4 - .6 Coastal Bend Region 22.6 24.1 +1.5 County Aransas 27.3 34.1 +6.8 Bee 22.3 23.7 +1.4 Brooks 22.3 24.6 +2.3 Duval 24.1 26.3 +2.2 Jim Wells 21.6 24.1 +2.5 Karnes 25.5 28.9 +3.4 Kenedy 20.6 22.9 +2.3 Kleberg 21.9 22.6 + .7 Live Oak 24.2 30.7 +6.5 McMullen 29.4 31.9 +2.5 Nueces 22.8 24.1 +1.3 Re fugio 24.4 27.9 +3.5 Son Patricio 20.3 23.2 +2.9 SOURCE: 1970 Census. B-8 TA13LE 'B. 8 ETHNIC -COWPOSITION 1970 White Less Total White Spanish Heritage Spanish Heritage Negro Other Races Area Population R-umber Percent Number Percent Number Percent Number Perce;t Number Percent Texas 11,196,730 9,696,569 86.6 7,636,898 68.2 2,059,671 18.4 1,419,677 12.7 $0,484 0.7 Coastal Bend Region 433,822 413,123 95..2 209,M 48.4 203,130 46.8 16,752 3.9 3,947 0.9 SMSA 284,832 269,623 94.7 142,849 50.2 126,774 44.5 12,241 4.3 2,968 1.0 Metropolitan Counties 75,100 72,239 96.2 34,182 45.5 38,057 50.7 2,294 3.1 567 0.8 Non-Metropolitan Counties 73,890 71,261 96.4 32,962 44.6 38,299 51.8 2,217 3.0 412 0.6 County w Aransas 8,902 8,437 94.8 6,065 68.1 2,372 26.6 411 4.6 54 0.6 CD Bee 22,737 21,891 96.3 12,999 57.2 8,892 39.1 616 2.7 230 .1.0 Brooks 8,005 7,872 98.3 1,4473 18.4 6,399 79.9 108 1.3 25 0.3 Duval 11,722 11,662 99.5 1,757 15.0 9,905 84.5 27 0.2 33 0.3 Jim Wells 33,032 32,472 98.3 11,347 34.4 21,125 64.0 40.9 1.2 151 0.5 Karnes 131,462 12,976 .96.4 7,461 55.4 5,515 41.0 438 3.3 48 0.4 Kenedy 678 678 100.0 146 21.5 532 78.5 0.0 .0 0 0.0 Kleberg 33,166 31,330 94.5 16,770 50.6 14,560 43.9 1,474 4.4 362 1.1 Live Oak 6,697 6,587 98.4 3,884 58.0 2,703 40.4 94 1.4 16 0.5 McMullen 1,095 1,079 98.5 336 30.7 743 67.9 7 0.6 9 0.8 Nueces 237,544 223,773 94.2 120,230 50.6 103,543 43.6 11,096 4.7 2,675 1.. 1 Refugio 9,494 8,516 89.7 4,906 51.7 3,610 38. 0 927 9.8 51 0.5 Son Patricio 47,288 45,850 97.0 22,619 47.8 231231 49.1 1,145 2.4 293 0.6 SOURCE: 1970 Census TABLE B.9 AVERAGE FAMILY SIZE AND NET CHANGE 1960--1970 1960 1970 Net Change In Average Family Size Total Total Persons Av@,Qge Total Total Persons Average Area Families In Families* Family Size Families In Families Family Size 1960-1970 Texas 2,368,412 8,801,300 3.72 2,809,850 10,120,127 3.60 12 Coastal Bend Region 95,379 395,874 4.15 103,391 401,602 3.88 -.27 .SMSA 60,643 252,350 4.16 6@,082 266,006 3.90 -.26 Metropolitan Counties 16,098 66,453 4.13 17,535 67,237 3.83 -.30 Non-Metropolitan Counties 18,638 77,071 4.14 17,774 68"359 3.85 -.29 J Counties Aransas 1,793 6,545 3.65 2,450 8,254 3.37 -.28 Bee 5,338 21,633 4.05 5,392 20,270 3.76 -.29 C) Brooks 1,980 8,377 4.23 1,920 7,631 3.97 -.26 Duval 3,026 12,932 4.27 2,799 11,196 4.00 -.27 Jim Wells 7,747 33,390 4.31 7,770 31,479 4.05 26 Karnes 3,429 14,333 4.18 3,214 12,530 3.90 -.28 Kenedy 173 813 4.70 137 632 4.61 -.09 Kleberg 6,558 26,518 4.04 7,315 27,504 3.76 -.28 Live Oak 1,814 7,510 4.14 11688 6,212 3.68 -.46 McMullen 290 1,043 3.60 284 1,041 3.67 +.07 Nueces 50,805 208,980 4.11 57,072 220,977 3.87 -.24 Refugio 2,588 10,430 4.03 2,340 8,847 3.78 Son Patricia 9,838 43,370 .4.41 11,010 45,029 4.11 SOURCE: 1960 and 1970 Census. *Includes head of family (male or femaPe@,owife o"ead, c i of ea and 10"" rell"w's of a Mon M No M M M M M M M M M-M TABL.E B. 10 OOL COMPLETEW Texas and the Coastal Bend Region 1960 and 1970 and Percent Change 1960 1970 Years of Texas Coastal Bend Region Texas Coastal Bend Region Percent Change 1960-1970 lon School Completed Number PercWn-t Number Percent Number Percent Number Percent Texas Coastal Bend No School Years Completed 204,045 4.1 20,834. 10.6 176,676 3.0 14,925 7J - 1.1 -3.5 Elementary: 1-4 Years 468,181 9.3 27,495 14.0 6.3 21,888 10.4 -3.0 3.6 5-8 Years 1,381,937 27.5 48,229 24.6 1,217,670 20.9 43,047 20.5 6.6 4.1 High School: to 1. 1-3 Years 986,842 19.6 30,801 15.7 1,302,223 22.4 38,795 18.5 + 2.8 + 2.8 4 Years 1,095,017 21.8 37,867 19.3 1,458,297 25.1 50,232 23.9 +3.3 +4.6 College: 1-3 Years 491,090 9.8 16,M 8.6 665,746 1).4 21,966 10.5 + 1.6 + 1.9 4 Years or More 403,447 8.0 14,316 7.3 632,476 10.9 19,034 9.1 +2.9 + 1.8 Total Persons 25 Years and -Over 5,030,559 100.0 196,434 100.0 5,817,155 100.0 209,887 100.0 SOURCE: 1960 and 1970 Census *Persons 25 Years or Older. @TABLE B.11 POPULATION DISTRIBUTION 1960 and 1970 Texas as a Percent of United States Coastal Bend Region as a Percent of Texas Counties and Groups of Counties as a Percent of Coastal Bend Region Area 1960 1970 Change 1960-1970 Texas 5.34 5.50 + 0.16 Coastal Bend Region 4.38 3.87 - 0.51 SMSA 63.51 65.66 + 2.15 Metropolitan Counties 17.06 17.30 + 0.24 Non-Metropolitan Counties 19.43 17.03 2.40 County Aransas, 1.67 2.05 + 0.38 Bee 5.66 5.24 - 0.42 Brooks 2.05 1.85 - 0.20 Duval 3.19 2.70 - 0.49 Jim Wells 8.23 7.61 - 0.62 Karnes 3.57 3.10 - 0.47 Kenedy 0.21 0.16 - 0.05 Kleberg 7.16 7.62 + 0.46 Live Oak 1.87 1.54 - 0.33 McMullen 0.27 0.25 - 0.02 Nueces 52.78 54.76 + 1.98 Refugio 2.61 2.19' 0.42 Son Patricia 10.72 10.90 + 0.is SOURCE: 1960 and 1970 Census B-12 TABLE B.12 POPULATION DENSITY COASTAL BEND REGION 196O.and 1970 1960 1970 Area in 1960 Population 1970 Population Change County Square Miles I Population2 Density3 Population4 -Density3 1960-1970 Aransas 270 7,006 25.9 8,902 32.9 +7.0 Bee 842 23,755 28.2 22,737 27.0 - 1.2 Brooks 904 8,609 9.5 8,005 8.9 - 0.6 Duval 1,814 13,398 7.4 11,722 6.5 -0.9 Jim Wells 846 34,548 40.8 33,032 39.0 - 1.8 Karnes 757 14,995 19.8 13,462 17.8. -2.0 Kenedy 1,407 884 0.6 678 0.5 - 0.1 Kleberg 850 30,052 35.3 33,166 39.0 +3.7 Live Oak 1,051 7,8@6 7.5 61697 6.4 - 1.1 McMullen 1,157 1,116 1.0 1f095 0.9 - 0.1 Nueces 838 221,573 264.3 237,544 283.4 +19.1 Refugic, 771 10,975 14.2 9,494 12.3 - 1.9 San Patricia, 680 45,021 66.2 -47,288 69.6 +3.4 TOTALS 121187 419,778 34.3 4331822 35.6 1.3 11972-73 Texas Almanac SOURCES: 21960 Census 3Persons Per Square Mile 41970 Census B-13 CHAPTER IV MANUFACTURING AND PORT FACILITIES In 1970, there were 1,791 persons employed in manufacturing activities within the Corpus Christi metropolitan area, representing- 11.2 percent of total employment. Although there was little or no growth in terms of employment in the area in the sixties, it now appears that manufacturing will offer increasing job opportuni- ties in the seventies. Industrial Development The major employers are high wage, low labor intensive industries (i.e., labor costs represents a small part of total cost), refineries, chemical plants, and metal plants. These industries display an affinity toward waterfront locations. New companies locating in the area include DuPont's new freon -plant, the first major industry to locate in Corpus Christi since ARADMAC in 1961; Levi Strauss and Robstown Manufacturing Companies, which located there because of an available female labor supply; and IHC Holland-LeTourneau Marine Corporation, builder of offshore drilling rigs and barges. Holland-LeTourneau employs approximately 700 persons; total employment should reach 1,500'by 1977. Table IV-1 shows'the linkages that exist within the manufactur- ing sector of the local economy, and-between this sector and other areas. For example, the San Patricio Reduction Plant supplies the Sherwin Alumina Plant with alumina, and receives raw bauxite from Jamaica and Haiti. Manufacturing industries support many local businesses other than manufacturing firms. For example, IHC Holland-Le Tourneau is served locally by Alamo Express, Union Barge Company, City Machine Shop, Western Auto, Xerox, IBM, and Texas Office Supplies. The linkages that local manufacturers have with areas outside the'local economy are particularly relevant when considering new industries that might locate near Corpus Christi within the next ten years. Inputs which are now produced elsewhere may be produced here, and products which are manufactured locally and shipped to other regions for further processing may be further processed locally in the future. These linkages have been slow to develop in Corpus Christi for several reasons: (1) lack of local raw materials (e.g., bauxite, zinc, newsprint, timber); (2) lack of local'markets; (3) distance from major distribution and transpor- tation centers. Within the next de cade, industrial development resulting from IV-1 backward linkages will be more likely to occur than development stemming from forward linkages. For example, when DuPont begins operation it will utilize nitrogen and oxygen shipped from Houston. Big Three Industries is planning a new $9 million plant near Ingleside which, by 1975, will supply these inputs locally. Other possible developments in the seventies include a new lime plant, new refinery capacity, a soft drink canning plant, and varied wholesalers and suppliers Ce.g., welding equipment and barge equipment) The Port of Corpus Christi The Port of Corpus Christi is located immediately adjacent to the downtown business district. In 1970, it was the thirteenth largest port in the United States and the second largest in Texas. The port is operated by the Nueces County Navigation District. It functions as well-protected, fully equiped maritime harbor, and also as an inland harbor with access through the sheltered waters of the Intercoastal Canal to ports on the Mississippi River System and its linking tributaries and canals. The port provides. essential low-cost transportation for cotton, grain, and petroleum products. Besides direct employment of longshbre- men, etc., port-related work includes truckers, rail carriers, weighers, testers, samplers, ship chandlers, and others who repair and clean ships. The port has seen continuous improvement and each new improvement has in some way benefited the area economy. The 90th Congress has authorized $20 million for port improvements, the most significant contribution for improvements to be made by the government since the port was opened. Completion of the enlargement and deepening program will make Corpus Christi the deepest port on the Gulf of Mexico. It will then accomodate many vessels that cannot presently enter or that can enter only with partiall cargoes. On April 24, 1972, the Nueces County Navigation District presented its plan for a deep water port at Harbor Island (72 ft. depth) to the U. S. Army Corps of Engineers. Corpus Christi is in contention with other coast cities for such a port. A port of this size is needed to accomodate the very large tankers carrying foreign crude oil to the United States. If the port is approved, it will make Corpus Christi one of the foremost port areas in the country. It is estimated that with the completion of such a project, petroleum tonnage alone would double in the first five years. Major industrial growth could also be expected. IV-2 TABLE IV-1 CHARACTERISTICS OF SELECTED MANUFACTURING FIRMS IN THE CORPUS CHRISTI SMSA, 1972 Year of Average** Original sic Initial Home Current Wage Labor.** Location Code Industry Operation Office Employment Rate intensity Factors 20 FOOD AND KINDRED PRODUCTS 2024 + Ice Cream & 2026 Fluid Milk M L Borden Dairy Products 1929 Houston Market 2036 Fresh or Frozen Packaged Fish & Sea Food VL if < Coastal Freezing Plant 1950 Aransas Pass Raw material 2042 + Prepared Animal Feeds & Wet 2 04 6 Corn Milling M-H L CPC International 1947 Englewood Cliffs, 370 (1) Source of grain sorghum New Jersey (2) Water transportation 2051 Bread and Other Bakery Products M M Rainbo Baking Company 1930 Dallas Market 2086 Bottled and Canned Soft Drinks L VL American Bottling Company 1904 Corpus Christi Market Royal Crown-Dr. Popper Bottling Company 1@65 Corpus Christi Market 23 APPAREL 2327 Trousers VL R Robstown Manufacturing'Company 1968 Dallas 475 Labor Supply @Levi Strauss and Company 1970* San Francisco Labor Supply 24 LU1-1BER AND WOOD PRODUCTS 2431 Millwork H Vauter Door Company 1946 Corpus Christi Historically evolved from- TABLE IV-1 (Continued) Percent of Production Production Total Outside Inside Output Sold Region Using Region Using Outside Product Product sic Coastal as as Code Industry Inputs* Outputs Bend Region Input Input 20 FOOD AND KINDRED PRODUCTS 2024 + lee Cream & 2026 Fluid Milk Borden Dairy Products Raw milk Processed milk and ice cream 2036 Fresh or Frozen Packaged Fish & Sea Food Coastal Freezing Plant Shrimp Shrimp Ab 2042 + Prepared Animal Feeds & Wet 2046 Corn Milling CPC International Yellow dent* and Dextrose, starch, 85 Foods, textiles, Aluminum, liestock feeds, waxy Cora* edible oil, live- primary Metals, dairy products stock feeds beverages, paper 2051 Bread and Other Bakery Products Rainbo Baking Company Flour*, sugar,. Batrs.-y products, shortening*, except speciality yeast*, wappers* items* 2086 Bottled and Canned Soft Drinks American Bottling Company Liquid sugar* and Coca-Cola concentrates* Royal Crotm-Dr. Pepper Liquid sugar* and Soft drinks Bottling dompany concentrates*; 23 APPAREL Trousers Robstown Manufacturing Company Fabric* Men's slacks 99 Levi Strauss and Company Fabric* Men's slacks 24 LIR.WER AND WOOD PRODUCTS 24fin ftork Comm te'"" M MWberJ.WeeAWWoocW PI nlastic*. 21uc* laminated doors TABLE IV-1 Continued) Year of Average** Original sic Initial Home Current Wage Labor** Location Code Industry Operation Office Employnicnt Rate Intensity Factors 27 PRINTINC AND PUBLISHING 2711 Newspapers Caller-Times Publishing Company 1886 San Antonio 390 Ma rket 28 CHEMICALS AND ALLIED PRODUCTS 2812 Alkalies and Chlorine L PPG Industries 1932 Pittsburgh 667 (1) Fort, (2) proximity to salt, oyster shell, limestone supplies, and natural gas 2813 Industrial Cases VL .Big Three Industries 1975 Houston Market (DuPont) 2818 Industrial Or*ganic Chemicals,N.E.C. VH VL DuPont 1973 Wilmington, (1) Reasonably close to raw Delaware materials, (2) favorable construction costs, (3) availibility of water and rail transportation, (4) favorable attitude of @local government to,,.,ard business, (5) attractive neighboring ccununiLf 5 Celanese Chemical Plastics Proximity to fuel gas and Company 1945 New York 815 plus raw materials 115 contiZactor TABLE IV-1 (Continued) Percent of Production Production Total Outside Inside Output Sold Region Using Region Using Outside Product Product sic Coastal as as Code Industry InpL%, Outputs Bend Region Input Input 27 PRINTING AND PUBLISHING 2711 Newspapers Caller-Times Publishing Company Newsprint* Newspapers 5 28 CHall CALS AND ALLIED PRODUCTS 2812 Alkalies and Chlorine PPG Industries Limestonc*,potash*, Chlorine, alkalies, 95 Glass, papers, Petroleum, freon chrome ore*, chrome chemiclas phosphates, plastics, electricity and leather, petro- naiural gas chemicals 2813 Industrial'Gases Freon products Big Three Industries Air Oxygen, nitrogen, argon 2818 Industrial Organic Chemitals,N.E.C. DuPont Chemical raw Carbon tetrachloride 99 Refrigeration and materials* and freon products air conditioning systems, aerosols, cleaning agents., foam blowing. Celanese Chemical & Plastics Company Liquified More than 50 100 Plastics, paints Petroleum gases, industrial organic fibers, building fuel gas chemicals & special- materials, packaging (methane), zied engineering materials, toys, auxiliary plast@cs engineering parts, chemicals* and touch brushes, drapes, M gloagipM M MteeriMheclon M M M M supplic TABLE IV-1 Year of Average** original sic Initial Eome Current Wage Labor** Location Code Industry Operation office Employment Rate Internsity Factors 2819 Industrial Inorganic Chemicals, N.E.C. H L Sherwin Alumina Plant 1953 Richmond, 850 (1) Fort, (2) available and Virginia inexpensive fuel and water supply, (3) labor supply, (4) plant site elevation relatively h1gh at sea frontage, (5) climate 2895 Carbon Black H L Ashland Chemical Company 1939 Columbus, Ohio (1) Availability and cost of natural gas 29 PETROLEUM REFINING AND REIATED PRODUCTS 2911 Petroleum Refining VH VL Chanplin Petroleum Company 1937 Fort Worth 295 (1) Readily available supply of crude oil for conversion Southwestern Oil and Refining into energy producing Cowpany 1936 Corpus Christi 240 products,(2) witer and pipeline transportation Coastal States Petrochemicals Company 1936 Corpus Christi 324 Suntide Refining Company 1953 500, 32 STONE,CLAY, GLASS, AND CONCR ETE PRODUCTS Ready-Mix Concrete M M South Texas Materials Company 1932 Corpus Christi Market -Gulf Concrete Company 1963 Corpus Christi Counts.Concrete Company 1961 Corpus Christi .,TABLE IV-1 (Continued) Percent of Production Production Total Outside Inside Output Sold Region Using Region Using Outside Product Product sic Coastal as as Code Industry Inputs* Outputs Bend Region Input Input 2819 Industrial Inorganic Chemicals, N.E.C. Sherwin Alumina Plant Bauxite*, caustic Metallurgical 85 Aluminum ingot, Aluminum ingot soda*, starch, grade aluminum aluminum sheeting, lime*, fuel labor oxile(alumina) beverages cans, Reynolds wrap 2895 Carbon Black Ashland Chemicals Company Oil*, natural, Carbon black 100 Rubber tires and gas, electric other rubber power products co 29 PETROLEUM REFINING AND RELATED PRODUC`rS 2811 Petroleum Refining Champlin Petroleum Company Crude Petroleum*$' Motor fuels, jet .95-99 Synthetic fibers, Petrochemicals chemicals* turbine fuels, waxes, solvents, Southwestern Oil and Refining home heating fuels, plastics, Company furnace oils, pharmaceuticals petrochenicals pesticides, Coastal States Petrochemical fertilizersi Company pigments and dyer, synthetic Suntide Refining Company rubber, and hundreds of other products 32 STONE, CLAY, CLASS, ALTD.CONCRETE PRODUCTS 3273 Ready-Mix Concrete South Texas Materials Company Oyster. Cement Construction and shell, varied otheruses Gulf Concrete Company sand, gravel ts " ete ny . M M M , M M 17rntev Cpmonr rnnnatAnn -TABLE M-1 CContinuedl Year. of Average** Original sic Initial Home Current Wage Labor** Location code Industry Operation office Employment Rate Intensity Factors 33 PRU@%RY 11EETALS 3333 Primary Smelting & Refining of Zinc M 14 American Smelting and Refining Company 1941 New York 780 (1) Port and rail facilities, (2) ample gas, water, and electrical power, (3) availability of labor (4) proximity to Mexico for import of raw materials (5) climate LD 3334 Primary Production of Aluminum VH L San Patricio Reduction Plant (Reynolds Metals Company) 1952 Richmond, 920 (1) Nearness to alunina Virginia supply,'(2) proxinity to gas fuel 34 FABRICATED M1-:TALS 3441 @abricatcd Structural Steel M M Western Steel Company 1946 Corpus Christi Market,including marine Coastal Iron Works 1945 Corpus Christi and industrial repairs Gulf Iron Works 1944 Corpus Christi 3443 Fabricated Plate Work Hunt Tool Company 1964 Houston Market 3.5 MACHINERY, EXCEPT ELECTRICAL 3522 Farm Machinery & Equipment ii M E. L. Caldwell Sons 1916 Corpus Christi Rome grown company 3533 Oil Field Machinery it M IHC Holland-LeTourneau Marine Corporation 1971 Kilgore, Texas .400 (1) Trainable work force (2) access to Gulf with no TABLE IV- I (Continued) Percent of Production Production Total Outside Inside Output Sold Region Using Region Usin,- Outside Product Product sic Coastal as as Code Industry Inputs* Outputs Bend Region Input Input 33 PRI-MY METALS 3333 Primary Smelting & Refining of Zinc American Smelting and Refining Company, Zinc sulphide* and Special high grade 99-100 Automobiles,bardware Typewriter parts,,. Zinc ores* zinc, die cast and bousehold auto parts alloy metals, appliances, other sulfuric acid, metals,, cadmium 14 3334 Primiry Production of Aluminum San Patricio Reduction Plant C) (Reynolds Yetals Company) Carbon*, alumina, Primary Aluminum 99-100 Sheeting, beverage Typewriter*parts, die gas ingot cans, Reynolds wrap castings for traffic signal lights, parts for automobile manufacturers, water tanks, guard rails 34 FABRICATED METALS 3441 Fabricated Structural Steel Western Steel Company Structural stecl*j Fabricated 15-20 Petroleum and Coastal Iron Works reinforcing structural and petrochemicals, Gulf Iron Works steel* reinforcing steel, transportation, pressure vessels,. construction tubing bundles 3443 Fabricated Plate Work- Hunt Tool Company Shell and tube heat exchangers 35 MACHINERY, EXCEPT ELECTRICAL @3522 Farm Machinery & Equipment E. L. Caldewell & Sons Steel* Farm equipment 43 Food & fiber Food & fiber 3533 oil Field Machinery " 11olffigaLeT ine r0775ratow. Mff.Aftrill" MtrilNp and barRe rips and barstes operations TABLE IV-1 (Continued) *Inputs with an asterisk originated at least partially from outside the Coastal Bend Region. **%'L (very low), L (low), It (moderate), H (high), VH (very high). See text for.furthe'r explanation of these symbols. means zero or negligible. ***Signifies less than 200 employees. SOURCE: Average Wage Rate and Labor Internsity information is from Growth and Labor Characteristics of Manufacturinp,_IndLi,.;triols, Economic Development Administration, U.S. Department of Commerce. Other Information is mostly from the CRP Industrial Questionnaire (Summer, 1972). TABLE IV-2 TOTAL COMMERCE AT LEADING U.S. PORTS: 1970 PORT TONS New York 174,008,108 New Orleans 123,674,208 Houston 64,654,263 Norfolk 53,544,337 Philadelphia 521224,396 Baltimore 51,084,394 Chicago 481254,387 Baton Rouge 45f535,281 Duluth 42,758,965 Toledo 31F932,493 ,Detroit 311241,263 Tampa 311356,522 Corpus Christi 30,544,712 SOURCE: Waterborne Commerce of the United States, Calender Year 1930: Part 5, National gummaries, U.S. Army Corps of Engineers, Vicksburg, Mississippi. IV-12 TABLE IV-3 COMMERCE AT LEADING TEXAS PORTS: 1961 and 1970 TONS PERCENT PORT CHANGE 1961 1970 11961 - 1970 Houston 560,474,299 64,654,263 14.5 Corpus Christi 26,760,121 301544,712 14.1 Beaumont 28,735,935 30,480,706 6.1 Port Arthur 25,579,302 22,671,406 -11.4 Texas City 16,418,556 17,097,411 4.1 SOURCE: Waterborne Commerce of the United States, Calender Year 1970: Part 5, National Summaries, U.S. Army Corps of Engineers, Vicksburg, Mississippi. IV-13 ARANSAS IREQ ESTED Is] PASS U ESTOWN ,CHANNEL AND BASIN AD IT PORTLAND LA\,Q UICN TA Ou Ep.1-T T, INGLESIDE J-1 y -5 itwELL FULTOP0 F S Am wed" Elf c@ RINCON CANAL ;.-@ o "i C) 2 IF X IT IT, A .TULE LAKE BASIN 7 ST.., C. OLA BASJN@t CHEMICAL BASIN C." um..17 7 [email protected] U. A 0. CORPUS CHRISTI SHIP D FEET AVERY POINT GREDGE It A., DqEDGZO D"Tm - $0 FEET at BASIN CC TU DASWO ATIOTH -440 FEET TAG. BA ,f, ZED Ol". - .5 Itt, AWWAmZt* ATIT. - 000 FEET Comus C"I"3TI uppto HARBOR "AABOR ORIDGE VERTICAL UFT 401-DOE y 'a 44 CORPU R ;Tl l f? c 0 R p U.'s. f2 NAVAL A R CORPUS CHRISTI SHIP CHAW STATION PORT OF CORPUS CHRISTI, TEXAS L A G N A DUANE' ORR - DIRECTOR OF INDUSTRIAL DEVELOPMENT If 1, AND PORT [email protected] A A D R E 5jW%Me.!MAAM-AAAA6wAAMAAAMMe'_!!!@ SCAI,E m STATUTE MILIS FIGURE IV-1 CORPUS CHRISTI SHIP CHANNEL FoRr OF CORPUS CHRIsrl INNER HARBOR INDUSMAL 01srRIcr mvf"s covmrr &AmAvo Disrairr "warg IN U E GE S 0 A V 4- --- -------- C I T Y Of R @-S -T )2 FIGURE IV-2 PORT "OF. CORPUS CHRISTI--INNER HARBOR INDUSTRIAL DISTRICT I I I I I I I I I APPENDIX C I LOCATION OF INDUSTRIAL GROWTH I I I I I I I I C-1 I LOCATION OF INDUSTRIAL GROWTR Because of the high spatial concentration of industry in the COG, it was decided that little could be gained from a re- gional model of industrial location. The greater part of the heavy industry in the COG is concentrated in the Corpus Christi area. In order to ascertain the likely future spatial growth patterns of industrial firms located in the study area, a series of personal interviews were held with plant executives in the Coastal Bend COG region. The.information obtained from these inter- views provides an estimate not only of the future increase in plant capacity for firms in the region, but also the most likely locations of this additional capacity. The interviews are summarized below. Industrial Development El Paso Natural Gas Co.-plans to have a new facility for the reprocessing of crude oil and liquified natural gas in operation by 1977. The plant will be located on the North shore of Corpus Christi Bay at a cost of $500 million, and initial employment is estimated at 200-300 with an annual payroll of about $3.5 million. Coastal States Gas Producing Co.-plans the construc tion of a $30 m'1111on plant near the exi'9TIng coastal states Petrochemical Co. for the production of substitute natural gas (SNG). Completion date is set for 1974. DuPont-presently constructing a freon plant on the North shore of Nueces Bay. Started in March, 1972, the plant will be built in stages and is scheduled for startup in fall 1973. DuPont has purchased about 1500 acres, but will only use about half the land for present development. The first stage will employ about 300 persons, but no detailed plans for subsequent stages are available. Big Three Industries, Inc.-plans to build a $9 million plant near Ingleside to furnish oxy;Ten and nitrogen to the DuPont plant . Production will begin about Fall, 1975. IHC Holland-LeTourneau Co.-presently building a construction yard near Ingleside for the pr@Tduction of "jack-up drilling platforms." When full-scale production begins this year, the firm hopes to be building about three platforms a year, at a cost of 10 to 15 million dollars each. Employment will be about 200, mostly welders. C-2 American Petrofina-recently purchased 250 acres of land along the Aransas Ship ChanF-el, adjacent to the proposed Harbor Island Superport. The company will undoubtedly develop the land if the port improvements are made, and will most likely exert considerable lobby pressure to get the project approved. Public Utilities Central Power and Light-presently constructing a new generating station which will provide an additional 325,000 kilowatts by 1974. The Barney M. Davis Power Station will run on natural gas and utilize.a 1,150 acre cooling lake. The plant is located just South of Corpus Christi on the King Ranch. Water Supply-all projections for the Corpus Christi area show that a larger supply of water will be needed by about 1980. The Texas Water Rights Commission has designated the City of Corpus Christi and the Nueces River Authority as co-sponsors for the Choke Canyon reservoir project. At present the co-sponsors are working on the complete proposal, including environmental impact statements, to be submitted for congressional approval next January., Officials from South Texas say they will urge for immediate con- struction of the project. Cost estimates for construction by 1976 are as high as $.80 million. However, no estimates on the length of time needed for construction have been made available. Master Plan for Water Lines-Bob Schneider, Director of Utilities, has said that the city will soon require more planning with regard to water lines, and hopes to develop a master plan for water line extension. The city already has a master plan,for.sewers. Padre Island Sewage Treatment Plant-The city is trying to get funds from EPA to build a new treatment plant to replace two temporary package treatment plants already on the island. The plant will be built as,soon as possible and will serve as the city's tool for limiting development by Padre Island Investment Corporation. (i.e. capac ity will be limited). Environmental Protection Reynolds Metals co.-plans to install $6 million worth of air pollution equipment o:;@e__r the next year in order to upgrade their older facilities. Sun oil Co.-plans to build a $2.7 million waste treatment facility at tFe-ir Suntide refinery. The project is to be funded by the sale of revenue bonds to be issued by the Nueces County Navigation District and paid off by Sun Oil. City Sewage Control- The highest organic loading of the inner Harbor now comes from the city's sewage treatment plant. Part C-3 of the bond issues approved in December will be used to upgrade the effluent substantially. Beeville Sewage Control-Beeville voters approved a bond election to rehabilitate a sewage treatment plant and provide for another. Kingsville Sewage Control-Previously served by septic tanks which empty into Baffin Bay, the city now has under construction a collection system and sewage treatment plant. Other South Texas Cities-George West, Alice, Rockport, Port Aransas, Mathis, Aransas Pass, Refugio, Trivoli, orange Grove, Edroy, and Skidmore have all either improved their sewage treatment systems in the last year or are planning to do so in 1973. Increased effluent control should upgrade the quality of the bay waters in the next few years. Residential and Commercial Development Cochera Corporation- planning a 91 acre mobile home development along Cayo del Oso north of Padre Island Drive and west of Flour Bluff Drive. Padre Island Investment Corporation- recently applied for a permit to dredge the entirety of Packery Channel East of Mustang Island Highway in connection with a plan to convert the surrounding area to a hotel and multifamily residential develop- ment. Dredging is not planned to start for two or three years, which seems to give a time-frame for the rest of the development. General Construction-In an article appearing in the Corpus Christi Caller, Bill B*Cobb said, "To replace the boom ii@ -housing expeMTH'cedin the Corpus Christi area ... will be the expansion planned by commercial and industrial construction ... DuPont... Coastal States...Big Three Industries. In the commercial field... Mission Shopping Center ... Handy Dan, Best Products, Commerce I Commercial Center, several HEB outlets...and a new Sears and HEB Center." The author put little confidence in the housing market for 1973. Another author predicted a sharp rise in the townhouse and condominium styles of living, at the expense of single-family unit construction, especially FHA 235 homes. Transportation Expansion of Corpus Christi International Airport-Voters re- cent17 approved a @500,000 bond issue for the acquisition of land adjacent to the airport for future expansion. Enough land will be bought in the coming months to make the present location adequate for another 50 or 60 years, according to the director of the operation. C-4 Port Eaansion-Many proposals have been made in this area, but all seem a long way off. The Maritime Administration of the U.S. Department of Commerce has recommended the construction of an offshore facility capable of handling 500,000-ton tankers by the year 1982. No definite decisions have been made yet regarding such a port. The Nueces County Navigation District has offered a different plan for port development. The so-called Harbor Island Plan would be built in three stages: first, deepening of the channel to Harbor Island to a depth of 72 feet, thus permitting ships up to 275,000 tons to unload approximately 8 miles away from the present port. Phases 2 and 3 consist of deepening inland channels to facilitate movement from Harbor Island to the present docking areas. Local officials do not see the two possibilities as mutually exclusive. The Harbor Island proposal, however, has no provisions or policies for either division of costs or environmental protection. These deficiencies and their resolutions would seem to place the Harbor Island Plan at least 3-5 years in the future, with the possibility of a deep-channel project taking up to 10 years. Regardless of the outcome of a superport decision, Port offi- cials are presently planning to deepen the existing channels from 40 to 45 feet. President Nixon's 1972 budget included $4,7 million to begin this project, which should be completed in the near future. Rincon Industrial Park-The City of Corpus Christi is promoting the development of this area extensively. Current plans are to widen two existing channels from 150 to 250 feet each, as well as to widen access to each. So far only one firm has located here, but future development seems inevitable. The City has not given a timetable for development, but the extent of the promotion efforts might indicate the city was looking at approximately 3 years. Texas Highway__Department-plans to build a new bridge across the Packery Channel to Padre Island (in conjunction with.proposed resort and residential development). Overall Highway Construction-Delays in Federal appropriations for new highway construction have seriously hampered completion of district projects now in progress and could eliminate some of the planned ones. The outcome will be based on the priority given to the Urban Mass Transit Program by Congress. Local Land-Use Policies and ordinances Mobile Home Restrictions-The Corpus Christi City Council recently voted to require 6-u-ir-lding permits for mobile homes. Also, mobile home lots must now be at least 6000 sq. ft. Other restrictions C-5 are being considered, such as a slab foundation requirement, and it seems inevitable that the trend will be to further control and possibly discourage mobile home development. General Development Control through Utility Restriction- The city of Corpus Christi seems to be following a trend toward stronger land-use control by limiting access to available utilities Padre Island Investment Corp. is being controlled by the limitations of sewer facilities*, and fringe area development is being supervised through annexation and water distribution policy. Also, the city hopes to use limited water distribution to limit peripheral develop- ment to the proposed DuPont plant. (The city contracted with DuPont- for a maximum of 28 million gallons per day,and any peripheral industries or expansion by DuPont will have to share this supply.) National Steel recently purchased a large tract of land adjacent to DuPont, and th-e city may also limit development here by rationing the water supply. Education and Recreation Texas A&I University-opening a new branch this September with an anticipated enroll nt of 700 to 800. The school hopes to expand to around 2000 in a few years. The city was recently granted authority to issue $1.5 million in bonds to purchase 35 acres of land and the buildings at the University of Corpus Christi's Ward Campus. Tourism-Due to expanding hotel accommodations, beach restoration projects, and liquor by the drink, the Corpus Christi Area Tourist Bureau is expecting the tourist industry to grow rapidly in the coming years. They are planning an intensified advertising campaign for 1973. Miscellaneous Corpus Christi's Relationship to the COG-In December, Mayor Price Johnson of Refugio said that the City of Corpus Christi will probably need to be given more representation to keep it in the Coastal Bend Council of Governments. Naval Air Training Base-In an analysis of cuts in Defense spending the Corpus Chri-stH-Caller said that the Naval Air Base will "probably not" be affected. Public Works P-rojects-Over the next 3-5 years the following projects will be completed (lumped together because they were all approved in the same bond election): $3.6 million for sanitary sewer improvements $3.5 million for beach restoration C-6 Additions to the police building, museum, and two new fire stations $4.2 million for street improvements $3.2 million for drainage improvements Voters approved $4.9 million for an auditorium that would be a part of an $11 million convention center, but the latter was not approved. The center is still in question, but if it does not pass another vote it is unlikely that either the auditorium or the convention center will be built in the next few years. C-7 CHAPTER V QUALITATIVE ALLOCATION OF RESIDENTIAL POPULATION The regional base for the present modelling effort is outlined in the S.M.S.A. map of the Corpus Christi Region (Figure V-1). Where appropriate, data will be graphically displayed in this format. The matching of tracts for 1960 with tracts and or portions of counties for 1970 is presented in Table V-1. Qualitative Assessment First, an attempt was made to identify those geographical areas in and around the Corpus Christi region which are most likely to accommodate the industrial and residential growth anticipated over the next ten years. For a number of reasons, industrial growth is much easier to anticipate geographically than is resi- dential growth. First, industrial locations are usually centralized due to zoning laws, proximity to natural resources, and transporta- tion availability. Second, the number of industrial locations is usually fewer than the number of single-family residences which accompany them. And third, decisions concerning industrial location are usually well-planned and made years in advance of actual location. As an example, development of the north shore of Corpus Christi Bay by El Paso Natural Gas Co., DuPont, Coastal States Gas Co., and others will not be completed until 1977, yet there is no uncertainty as to where they intend to locate. No such definitive statements can be made about the *residential growth which will occur as a result of these plants. For these reasons, a set of models are utilized to deal with residential growth. A qualitative and subjective methodology was the basis for a preliminary examination. Personal observation of current events was combined with the opinions of knowledgeable city residents and the forecasts of Bell Telephone Co., the Corpus Christi Dept. of Utilities, the Coastal Bend Council of Governments, the Corpus Christi Planning Dept., realtors and land developers. What follows is more or less a "greatest common denominator" of fact and opinion. The results have been mapped with an appropriate discussion associa- ted with each node of activity. 1980 Growth Areas - Qualitative Assessment The future development ofthe Coastal Bend region can be divided into five distinct geographical categories: South Corpus Christi, West Corpus Christi, North Corpus Christi Bay, Padre and Mustang Islands, and Outlying or Peripheral areas. V-1 TABLE V-.-l BASIS OF 1960-1970 SMALL'AREA S.M.S.A. DATA MATCH Corpus Christi City Tracts 1960 1970 1960 1970 1960 - 1970 1960 - 1970 1960 1970 1 *11 11 *21 - 21 *52- 29 Ingleside - 103 2 - 2 *12 12 *22 - 22 *53- 30,31,32 Gregory - 105 3 - 3 *13 13 *23 23 54 - 5-4 Portland - 106 4 - 4 *14 - 14 *24 24, 34, 33 56 - 56 Taft - 108 5 - 5 *15 - 15 *25 25 57 - 57 Sinton - 110 6 - 6 *16 - 16 *26 26 58 - 58 Odem - ill 7 - 7 *17 - 17 *27 27 59 - 59 Mathis - 113 8 - 8 *18 - 18 28(Bay) 28 60 - 60 Remainder of San Patricio Co. -104 -107 -109 -112 9 - 9 *19 - 19 *50, -50,35,36,37 Aransas Pass -102 *10 -10 *20 - 20 *51 51 The letters accompanying the discussion of geographical categories refer to the appropriate areas on the map in Figure V-2. A. South Corpus Christi: it is estimated that between 50 and 75 percent of all presen-t-To-using construction is occuring in this area and that it will continue to accommodate about half of all residential growth until about 1980. Most of the present develop- ment is occuring east of Oso Creek in Census Tract 30, but both sides of the creek should be developed.in 5 to 7 years to include Census Tracts 31, 32, and 33. The number of people this area could accommodate is questionable because there may be a pattern toward increased high density living but it is estimated that an addition- al 10,000 people will locate here by 1980. B. and C. West Corpus Christi: This area includes the vicinity of the Corpus Christi Airport aKd--t--he Bay area northwest of the city known as North Beach. In 1972, the voters approved a bond issue for the restoration of the beaches in the North Beach area and it is estimated that approximately 2,500 people will move to this area by 1980, many of the new residents being of retirement age. Much of the residential growth in the rest of West Corpus Christi will accompany new industry in the area and will locate in Census Tracts 36, 8, and 17. In addition, the most westerly portion of the city, Tract 37, will also develop in the next 5-7 years. The major limitations to growth in this area are in Tracts 8 and 35. Tract 8 includes a large section of land to be reserved for the airport expansion recently passed by the voters. Tract 35 will not develop for a few more years because of oil fields and drainage problems, although the planned improvement of Highway 44 might spur development in this area. Estimates as to how fast this area will grow in comparison to the Southern areas vary widely. It seems most probable that growth in this region will lag b6hind the southern-expansion for the rest of the decade. Population increase of about 5000 (excluding the North Beach area) seems reasonable by the year 1980. D. North Corpus Christi Bay: The area north of Corpus Christi,Bay is wiEhin the extraterritorial jurisdiction of Corpus Christi but not within the city itself. The two basic growth areas in North Bay are Ingleside and Portland, with Ingleside attracting more of the industrial growth and Portland serving as a residential growth center. It is estimated that this area might accommodate asmuch as 25 percent of the Coastal Bend's total growth in the next 5-7 years. A reasonable esti mate of population growth would be about 1000 persons per year until about 1980 for both Portland and Ingleside combined. E. Padre-Mustang Islands-. These two Islands have the potential to develop in one or two ways; as a second home and resort community, or as a retirement community. The lack of hospitals, shopping centers, and other services demanded by older persons will prohibit growth V-3 CORPUS CHRISTI REGION S. M. S. A. 113 SIATHIS SINTOPI-ODES TAFT we 112 ol 74 08 104 lot GREGORY-POR R:.%SAS @A TLAND INGELSME CORPUS CHRIST, W5 '06 03 z 102 Poo* as 36 A 30 1 -1, as boto 6 1,3 28 12 0 '.M 21 17 tl,- 2 20 51 09 @3 24 '@S OWOP 4/ 27 33 32 29 54 30 DRISCOLL CORPUS C.R., so SOUTH m of mmp 11011MLES CENSUS TRACTS AS OF 1970 CENSUS - @P--L 12 Figure V-1 V-4 as a retirement community for at least five years. At present the majority of development on the Islands is being done on the north end of Padre Island. The city hopes to limit the long-run growth of the areas south of presently planned development by limiting the capacity of the new sewage treatment plant to a level sufficient only for the immediate area. It is generally agreed that Padre Island will develop prior to Mustang Island to the north. Additionally, most of the development on Padre Island will probably be low-density, whereas predictions for Mustang Island are for more condominium and hotel construction. From all indica- tions there should be about 3000 families or about 10,000 people living on both islands by 1980. F. Outlying Areas: Little population growth is anticipated in these areas during :Ehe current decade. 1990 Growth Areas - Qualitative Assessment In the decade 1980-1990 development in the Coastal Bend Region will continue in South Corpus Christi, West Corpus Christi, North Corpus Christi Bay, Padre and Mustang Islands, and commence in several outlying areas. (See Figure V-3) A. South Corpus Christi: Development in this area should continue past 19-80 but at a slower pace. Expansion should continue to the south to the King Ranch and to the southwest to the Chapman Ranch which present absolute barriers to further growth. An additional estimated 7500 people should move into this area between 1980 and 1990. B. and C. West Corpus Christi: North Beach will continue to develop with an expected completi y 1985 and an additional 2,500 people. The rest of West Corpus Christi should continue as in the last decade with an additional 5,000 people moving into the areas indicated by 1990. D. North Corpus Christi: Portland and Ingleside will continue growing with an annual inE-rease of 1000 or more. It has been estimated that portland will expand to engulf Gregory to the north by the year 2000. E. Padre-Mustang Islands: Continued population.-growth on these islands depends on the provision of sufficient services to handle increased population. F. Outlying Areas: There are three pockets of potential growth in this decade Tn these outlying areas. In Aransas County, Aransas Pass has the potential to become a resort or retirement area but this will probably have to wait until Padre and Mustang Islands are extensively developed. The city of Edroy may grow provided it can get water from the proposed Choke Canyon Reservoir to be built V-5 CORPUS CHRISTI REGION GROWTH AREAS 1980 TAFT to? Tort f" va 104 101 ""ofty-PORTL406D m AftsAs PA s $ - 1110CMDE wy costpos cmasm) WEST (;a D 11110-# C it c r. T. .7 1511 is 1 &SHOP .24 *26 fil 3,1 21 L 29 A -sout" a. CENSUS TRACTS AS OF 1970 CENSUS Figure V-2 0 V- 6 CORPUS CHRISTI REGION GROWTH AREAS 1990 F rK MATMS SINTON-ODEN 109 TAFT lt2 IOT Fd Edroy *04 101 ORE OORV- PORTLAND RANSAS PASS. INOWLSIDE co"UscHms" WEST D DI F 5- 0 bela- 2 6 14' 07 1 3 1012 E 13 - at 39 19 20 23 24 @e 27 29 84 to DM:.aLL CORPUS "S"Op 0 CHNSTSOUTH 61 16hp E 0 IDINLES CENSUS TRACTS AS OF 1970 CENSUS Figure V-3 f Do _T V-7 around 1985. Finally, Mathis has some potential for inexpensive summer home development, possibly of the mobile home variety. Graphs of the shift in popul.ation density between 1960 and 1970 demonstrate only a mild expansion of the city but a sharp adjustment of the density curve to. the right..(See Figure v-4) This implies a continuing low density central city but rising density in the adjacent land. This increasingly homogenous density distribution is likely to be exaggerated in the future with continued expansion of residential populations in the periphery. (See Figure V-5) V-8 'P6PUL`A7rI0N-.,:btNSftY, AND persons DISTANCE FROM THE CBD vigure V-4 per acre 60 18 14. 70 12 10 8.0 4. Ike 2, 00 miles 0 !52 :54 !@-6 v -9 Sliifon 107 .,Taft /--j 101 POPULATION DENSITY *rn RANSAS @A PASS - INGELSIDE 4-gory 106 renses 102 pas a *36o J@- 87 Robstown 00350 56 0 0 28 51 PERSONS 0 29 PER ACRE 54 0 0 0 a 03(p CORPUS 00 maw. DRISCOLL 0 CHRISTI 0 u Ott 2.0-8.1 so SOUTH -13.7 8.2 V. 13.8+ SOURCE: 1970 CENSUS OF POPULATION, Figure V-5 @A A Z.-.- 1/2 V-10 CHAPTER VI ANALYSIS OF INTRA-METROPOLITAN POPULATION PATTERNS Differing national, state, regional, and county growth rates together with internal migration have led to changes in the present distribution of population. In every decade since at least 1920 the percent of Americans living in Texas has increased, and now stands at 5.5 percent. The percent of the Texas population re- siding in the Coastal Bend Region increased 'until it reached 4.4 percent in 1950 where the figure remained through 1960. However, in 1970 only 3.19 percent of all Texans lived in the Coastal Bend Region. Within the region, population has been increasingly con- centrating in Nueces County, which now accounts for 54.8 percent of total region population, Nueces County Migrants Nueces County experienced a net outmigration of 28,130 people in the 1960's, representing 12.7 percent of the 1960 population. People moved into Nueces County, but a far greater number moved .out. Much of the out-of-state net migration is accounted for by California and Florida due to the concentration of military personnel at NAS and ARADMA in Nueces County. Fifty-six percent of the people moving into Nueces County came from another residence in Texas, while 62 percent of the outmigrants relocated in another part of Texas. Houston accounts for most of the net outmigration from Nueces County. From 1965 to 1970, 6,034 more people moved from Nueces County to Houston than vice verse. The Dallas-Ft. Worth area and Austin also attracted many area residents. Nueces County experienced a net immigration from the Rio Grande Valley and South Texas. These migration trends can be explained in large part by the economic differential between Houston and Corpus Christi, and between Corpus Christi and the South Texas Rio Grande Valley. Houston has a low unemployment rate, and offers a higher standard of living, while also offering a wider variety of jobs from which to choose. According to the 1970--Census of Population, the male unemploy.- ment rate in 1970 was 2.6 percent in Houston, 3.4 percent Jn Corpus Christi, 6.4 percent in Brownsville, 41.8 percent in VI_1 Z== NVOL-fi,93 S' t,.*.o as t1i -7/ 3and P. ogidn ....... ..... . 5 0 ............. ............. ........... ... ...................... ....................... ............ . ................. .................. ........... .......... ... ....... 5yo of U. S. .......... ............. .............. ..................... -,-I oiv - % of T-@'X.4 C., @.@j @a 14 @Dl C-1 102 1 S - 10 VD-To IVY-1-CE.0, COWITY 5- 4. 2 Lt AS 56' OFREGIOM P.101041 AS 7EZA'S rj 0.0 31, U. 8 Figure 171-1 VI-2 Harlingen, and 4.6 percent in McAllen. Per capita income in 1969 was $3r395 in Houston, $2,646 in Corpus Christi, $1,487 in Brownsville, $1,909 in Rarlingen,,and $2,030 in McAllen. Table VI-1 shows migration into and out of Nueces County by age group. From 1965 to 1970 there has been a net outmigration for every age group, including those of retirement age. People under fifty-five account for A larger part of the total popula- tion and are more mobile than those over fifty-five. Therefore, these people account for the major share of net outmigration. Nevertheless, almost 1,000 more people aged fifty-f.iv,e and over moved out of Nueces County than moved in. There was also a net outmigration from the northern coastal Bend Region of 10,158 persons between 1965 to 1970. There has been a net outmigration in every age group up to age fifty-five. However, there was a net inmigration of 666 persons into the north Coastal Bend Region who were over fifty-five. Urbanization Population has increasingly been concentrating in towns and cities of the counties comprising the Coastal Bend Region, even in those counties losing population. This is due to increases in the number of people residing in these towns and cities, decreases in the rural population, and annexation. Forty-seven percent of the population in the region resides in the city of Corpus Christi, compared with 44 percent in 1960. Eighty-four percent of the population in Nueces County resided in Corpus Christi in 1960, compared with 86 percent in 1970. These figures take into account Corpus Christi's growth from an- nexation. The fastest growing city in the region is Portland, northside of Corpus Christi Bay, which grew by 188 percent in the sixties and had a population of 7,302 in 1970. Residential Mobility Table VI-2 describes the mobility of residents in the towns and cities in the Coastal Bend and the population movement between towns and cities. The Robstown County Subdivision (Robstown, North San Pedro, and South San Pedro) and Taft display the most stability in their existing populations. Over seventy percent of the population in these places lived at the same residence in 1965. Portland, be- ing the fastest growing town in the area, displayed the least population stability. VI-3 Table, YI-f-I INHICHATION AND OUTMIGRATION FOR NUECES COUINTY FROM 1965 TO 1.970, BY AGE. GROUP Total Population Percent of Inmigration* 1965-1970 Outmi ratJ.on* 1965-1970 5 Years Old and Over, 1970 Population Percent of Percent: of 5 Years Old Total To ta 1. Net Number Percent and over, 1960 Number Iumigration Number Outmigrat ion Migra t ion TOTAL 214,739 100.0 100.0 37,175 100.0 .49,419 100.0 -12,244 Age Group: 5-9 27,260 12.7 15.3 4,713 12.7 6,867 13.9 -2,154 10-14-- 27,338 12.7 13.1 3,808 10.2. 5,431 11.0 -1,623 15-19 24,984 11.6 9.3 3,772 10.1 5,148 10,4. -1,376 20-24 20,031 9.3 7.4 6,723 18.1 7,795 15.8 -1,072 25-29 15,486 7.2 7.6 4,842 13.0 6,528 13.2 -1,686 30-34 13,23.7 6.2 8.4 3,330 9.0 4,339 8.8 -1,009 35-39 13,355 6.2 8.4 2,544 6.8 3,437 7.0 -893 40-44 14,371 6.7 6.7 2,249 6.0 2,764 5.6 -515 45-54 25,186 11.7 11.5 2,626 7.1 3,579 7.2 -953 55-G5 18,579 8.7 6.9 1,421 3.8 2,007 4.1 -586 65 and over 14,932 7.0 5.4 1,147 3.1 1,524 3.1 -377 *Innigrants moved into the county from elsewhere in the United States between 1965 and 1970 and were still living there in 1970. Outmigrants lived in the county in 1965 and moved out to some other area in the United States where thby lived in 1970. Migrants within the. county arc excluded. SOURCE U.S. Bureau of the Census. 1970 Census Of P02ulation.PC(2)-2E. Migration Betw@cn State Econonilc Areas. TABLE VIr2 RESIDENCE T11 1965 OF POPULATION IN TOWNS AND CITIES IN CORPUS CHRISTI SMSA Place Wumber Of Percent Of Reported Residence In 1965 Of Persons 5 Years Old And Over In 1970 Persons 5 Population 5 Years Old Years Old And Outside Of Corpus Christi Outside Of SESA Abronl And Over, Over Whose Corpws Christi 1@lat Within SM@,A - I- 1970 Residence In Wumber Per Numbcr PerceTit iFull er Perc6t Number P(,.rcent 1970 Was The Same As In 1965 Corpus Christi 185,172 48.6 44,807 52.5 4,306 5.0 33.894 39.7 2,313 2.7 Fob5town, North San Pedro, And Scuth San Pedro 14,632 71.8 136 4.2 2,541 79.0 Solt. 15.7 36 1.1 Bisbor., 3,159 62.5 55 5.3 407 39.3' 573 55.4 Fort Afrim7r-'3 1,234 46.3 120 20.2 275 46.4 iss 31.1 10 1.7 r'n PorL].:nd 6,51,8 36.1 991 27.4 670 18.81 50.0 138 3.8 A7an:;ac varL; 4,607 50.8 97 4.8 1,557 77.7 344 17.2 7 .3 sinton 5,048 54.3 .24 1.1 1,575 75.0 459 21.9 1; 2 2.0 Yathis 4,940 66.6 62 3.8 1,017 62.7 537 33.1 .7 4 Taft (including Taft Southweat) 4,877 72.4 16 1.3 953 76.8 273 21.9 .3,313 59.7 142 12.6 507 45.0 473 4-2.4 2,151 57.1 52 6.3 603 72.5 177 21.3 Od(= 1,687 49.9 3.6 540 65.0 244 29.4 17 2.01. SOURCE: U.S. Bureau Of The Consus. Of the people in Corpus Christi in 1970 who moved between 1965 to 1970, 52.5 percent moved from another residence in Corpus'Clixisti, and 42.4 percent moved from outside the metropolitan area U.e., Nueces and San Patricio Counties) or abroad. Only five percent moved from another residence within the metro- politan area. Of the persons in Portland in 1970 who moved between 1960 to 1970, 27.4 percent moved from Corpus Christi, 18.8 percent moved 'from another city besides Corpus Christi within the metropolitan area, and 53.8 percent moved there from another place outside the metropolitan area or abroad. Population Distribution; The City of Corpus Christi The 1970 U. S.-Census of Population shows the population of Corpus Christi increasing from 167,690 in 1960 to 204,525 in 1970, a 22 percent increase. However, as Table VI-3 indicates, much of this growth was due to annexation. City boundaries increased significantly from 1960 to 1970. In 1960, the city was bounded roughly by Corpus Christi Bay, the Corpus Christi Industrial Zone, Cayo Del Oso, and what is now Padre Island Drive. In the intervening years the boundary was extended to Saratoga Blvd., Calallen, and Flour Bluff. Population growth from 1960 to 1970 within the 1960 city limits was small, amounting to only a .8 percent increase. The most realistic measure of population growth in Corpus Christi is the increase in population from 1960 to 1970 within the land area which now defines the city. Using this measure there were 18,112 more people in Corpus Christi in 1970 than in 1960, representing a 9.7 percent gain. Although population grew only slightly from 1960 to 1970 within the land area defined by the 1960 city limits, there was a very substantial shift in population within this area toward South Padre Island Drive. Further, most of the growth in the 1960's in the areas eventually annexed by the city was also along South Padre Island Drive. The section of town which lost population in the sixties includes downtown and a very large surrounding area: the area between Ocean Drive and Staples to Everhart; out the Crosstown Expressway to Horne Rd.; out Agnes (Hwy 44) to Baldwin; the area between Leopard, Corn Products Rd. and the Port of Corpus Christi and North Beach. The population in this area declined by 16.5 percent from 1960 to 1970. In 1960, 59 percent of Corpus Christi's population lived in this section of town, compared with only 44.9 percent in 1970. Every one of the eighteen census tracts comprising this section lost population from 1960 to 1970. V1-6 TAn'.M-'VI - 3 POPULATION CjLkNj,-E 1,T CCR2"S CFLI.ISIII: v u 1960-1970 Po,)ulation Area 1960 1970 Percent Change City Limits as Existe d in 1960 and 1970 167,690 204,525 22.0 Within, 1960 City Limits 167,-690 168,951 .8 Within 1970 City Limits 186,413 2.04,525 9.7 -Ind lious ing, Source: 'U. S. B-ureau of the Census, C,-2nsus of Po-Qulation L Census-Tracts, Final Report Pi-IC (1) - 51, 1.972, and Final Report PHIC (1) - 33, 1952; 1970 Ceznsu@s_ o-f Housizvz, Block Statistics, Report HC (3) -228, 1971. The 1970 population within the 1960 city 1,4-Mits, and the 1960 po-pulation within -the 1970 city limits, were arz-ivad at through' the ma Inipula- tion of census 'crack and block data. VI-7 The area of greatest population loss was the corridor around Leopard, Agnes, and Port. The area of greatest population gain within the 1960 city limits was Around Weber, South Padre Island Drive, Airline, and the Padre-Staples Mall. The rapid increase in the population between Padre Island Drive and Saratoga also concentrated between Weber and Airline in new developments. Table VI-4 presents the total population for each census tract in 1960 and 1970, showing the changes in population resulting from boundary changes due to annexation. Intra-Urban Mobility The fact that population in one area of town decreased dramatic- ally does not necessarily mean that there was a population movement within the city from the declining area to the growing area. All available evidence indicates that this was not the case. Instead, it appears that in the 1960's a significant portion of the out- migrants from Nueces County were from the Central Area of Corpus Christi. A significant portion of the new residents in the growing Southeast Area are inmigrants from outside of'Corpus Christi. Population mobility figures from the 1970 U. S. Census.of Population show that most of the people moving to Corpus Christi from 1965 to 1970 moved here from outside the immediate metro- politan area. Of these, 58 percent located in the Southeast Area of the city, 18 percent located in Flour Bluff, and 10.5 percent located in the Northwest Area. Only 13.5 percent of the inmigrants to Corpus Christi located in the CentralArea of town, even though this Area accounted for 44.8 percent of the total city (as defined in 1970) population in 1960. These mobility figures do not show movement within Corpus Christi when a household moves from one residence in town to another. This information was obtained from the CRP Community Information Survey and from special population movement surveys conducted by the CRP. The results are set forth in Tables VI-5, and VI-6. Very little population movement was found to occur between the Central and the Southeast parts of town. In Table VI-5 current residence (summer, 1972) is shown along the top of the table, and residence two years previously is shown along the left side of the table. For example, of the forty households interviewed in census tract 1, eighteen resided in the same dwelling two years ago, four of them for over twenty years while eight households moved into their current dwelling place from census tract 9 within the last two years. Ten households interviewed resided outside of Corpus Christi two years ago. A visual glance at Table VI-5 shows a grouping of large numbers (5,7,8,etc.) that forms a diagonal extending from the upper VI-8 TABLt VI-4 POPULMON OF CORPUS'Cl,'RISTI, BY CENSUS Tl@,NCT: 19GO AN.D 1970 PoT)ulation Census 1970 in Tract 1960 1970 1960 Area@'c 1 1,631 831 2 1,113 495 2.99 217 471 731 4 -3) 4 4 4,981 31178 5 2,834 2,725 6 9,028 8,656 7 3,834 3,758 8 700 830 792 9 10,173. 6,593 10 8,350 5,538 11 5,857 3,555 12 6,599 5,724 13 5,291 5,131 14 5,764 5,287 15 6,652 6,298 16 15,512 12,397 17 6,117 3,126 7.%174 18 7,370 8,797 8 1-2006 19 9,4319 11,625 11,593 20 8,806 8,616 21 7,442 7,271 22 5, 92 9 6)193 23 5,181 8,M), 6$742 24 9,426 8,482 12,924 25 .5,151 4,873 26 5,472 9,094 27 8,047 13,328 14,467 29 n.- - 5,214 30 n.a. 3,981 31 n.a. 3,018 32 n.a. 2,781, 33 n.a. 4,224 .34 n.a. 7,84-3 35 n.a. 2.)184 36 n.a. 6,939 37 n.a. 1,494 *Estimated using block statistics from the U.S. Bureau of the-Census. SOURCE: U.S. Bureau of the Census. VI-9 CORPUS. CHRISTI LEGEND @ 1970 CITY &$MIT$ 0 F U L A T 1 0 N @ 1960 COTT LIMITS fVAWO difffffgOt f"A 1910) SICTION ROUNDARY frure differfelf from city lintusl GROWTH and DISTRIBUTION 1960-1970 RATIO 11,960 ronutAllow fs Of tomi 1"Opaggo, in 19?0 0,00) 1970 POPUIATION J% *IF Intel IV?0 pap.1 ASIA OF 1960 CITY 1101111 DRIAIEST SOSS 1-13,67071 ARIA OF 1960 CITY WITH 0112ATISS GAIN (013,5401 maingp ##no&# 4fole 91 U.S. BUREAU .9 to. 9911151" C) -09:zI, At Alga- 4 to 8,103 10,396 S7 74 9,3 2, 13,4)"@c 6.0)0 11,691 .7) COMMUMITY WERCIVAIL PROGRAM Oerr. or URVAlf 09VELOPOINT ,c4lv of CORPUS Cu*tsrl left corner of the table to--the lower right corner of the table. Move away from the diagonal and the numbers become more scattered as well as smaller. This means that for those who move from one dwelling unit to another in town, the tendency is to re- locate in the immediate vicinity and not to move from one area of the city to another. For example, of the ten households who moved into a new dwelling in census tract 12 within the last two years, five moved from another dwelling in the same tract. Two moved there from adjacent tract 18. Two moved there from adjacent tract 8. Table VI-6 summarizes and emphasizes the conclusions of Table VI-5. Of the 520 households interviewed in the Southeast Area of town, only 14 (2.7%) reported a residence two years ago in the Central Area of town. Ethnic Composition of Population Of the 204,525 persons in Corpus Christi, 53.1 percent are Anglo, 40.6 percent are Mexican American, and 5.1 percent are Black. Eighty-nine percent of all Blacks and 63.8 percent of all Mexican Americans live in the Central Area of town. Only 6.3 percent of all Anglos live in the Central Area. Forty-two percent of all the Blacks in Corpus Christi live in Census Tracts 4 and 5. This is the area between Carrizo, Leopard, Broadway, and Nueces Bay Blvd. Enough information exists for this area to say something about the mobility of the Black population in Corpus Christi. The percent of the population in Tract Five which is black increased from 24.7 percent in 1960 to 74.2 percent in 1970. Eighty-three percent of the Black movers moved into a dwelling in the same tract or from another dwelling in Corpus-Christi most probably from Tract Four since it was the largest Black neighborhood in 1960. Further, 71.5 percent of those moving into Tract Four between 1965 and 1970 came from another town within the metropolitan area, representing 11 percent of all immigrants into Corpus Christi from another town within the SMSA. Thus, it appears that most Blacks migrating into Corpus -Christi locate in Tracts Four and Five, the predominantly Black neighborhoods. Moreover, there appears to be little movement of Blacks out of these census tracts to other areas of town. Population Projection by Age Groups for Small Areas The areal base used for the projection of population is the census tracts within the.City of Corpus Christi. Later projections VI-11 Table VI-5 POPULAT ION MOVENENT WITHIN CORPUS CHUM, BY CENSUIS Total c., Two Tract years Ago 1 2 '3 4 5 6 2. 13 It. 1&]@I@L9 20 21 22 23 21; 25 -26-2-7 2-8 2-9 30 -31-22- 3-3 34 -4.99yrs. 5 5 7 3 5 C 8 17 Ifl 6 1 35 ji 371__ 2 9 Sf k3& 0 18 _Q __@_ 5, 4 -- 0 0 -10- .10 E 27" Same @L -L @-R 5-9 1 6 6 6 9 96 5 3 7 8 4 7 91 6 7 L@r-s - . - -- -- -- - -- -- - __-3 -- -.- 12 7 6 9 8 14 7 7 13 12 2 _11 1@ 11 1 I -9.99"Y:f.. _2 __L .1-2 1.1 _5_ _5 8 6 6 1 15 0 12 7 11 1 cc 9 811 11 5 11 11 5 10 2 8 1 6 2 13 91 2t@ 4 3 7 10 3 79 3 13 11 16 6 8 7 1, 7 9 2 7 2 1 1,17 Tract 2- 1 4 5- .-6 1 1. 4. 7 3 8 13 1 2 2 2 2 < 15 1 4 1 -5 1 2 1 1 1 1 1 1 3 J.- 1 -D- ?,S T T 2C- 26 1 -3 -27 1 1 -2 .-l --I 28 2T' 30 3 L 4 3 ? - _@. _? _ __ _ _ . 7 33 34 31 2 36 2 L I 37 Other Town n Coastal Bend 3 1 1 1 Re Y" Ago\ 121, L25 @2 9 4 L7 @111 1-2 2 7 2 Other Town in Texas outside Coastal bend 4 3 5 1 3 3 1 1 2 2 1 11 1, 1 4 1 3 1 1 1 2 4; 2 Outbide'Texas 5 -T2@2 and Abroad 5 3 5 1 3 1 7 1 2 1, 6 1 1 1 2 -2, 3 2 11 3 511 3 1 77 40 il.) 40 7=tv v,,,d go 4 0 J 4 Q 40 R.110 1,0 PON 40 r,',c 0 t, M.40 TABLE VI-6 POPULATION MOVEMENT 14IT11IN CORPUS CHRISTI, BY AREA Current Residence Total Area Central Flour Bluff* Northwest Southeast 4. 98 2 21. 14. 535 al Centr, (2.5%) (8.8%) (2.7' .53 3 56 Flour 33luff-,", (66.3%) --- (.6%) 188 2 Nor thwes L (.7%) (78.3%) (.4%) 0 0) 12 6 6 4.51 475 Southenst (2.1%) (7.5%) (2.5%) (86.7%) Vj W 46 19 25 50 140 Out of Town (8.2% (23.8%) (10.4%) (9.6%) Total Ilouseholds 560 80 240 520 1,40 0 Interviewed (100.0%) (100.0%) (100.0%) (100.0%) *YxclucIcs Naval Air Station. SOURCE: CRP Household Survey. TABLE V1-7 THE ET-1-12"'IC MIPOSITTOX ANM DISTRIBUTION OF TT J_ I COR? S CL11R_ST_'S P0?U!,ATIOS,,*: 1970 our Co r0t, @6 C ,itral f NN o r th 10 s t Southeast Cri -].So c. B! Total Population 70,298 12,213 23,791 93,223 204,525 Percent of Total City Population 34.4 6.0 11.6 48.0 1001 Analo Population 6,850 9,986 162565 75,247 108,61 Percent of Total Area Population 9.7 81.8 69.6 76.6 53.1 Percent of Total City Anglo* Population 6.3 9.3 15.2 69.3 100 Mexican American Population- 53,005 1,429 6,709 21,904 83,03 Percent of Total Area .1 Population 75.4 11.7 28.2 22.3 40.6 Percent of Total City Mexican American Population 63.8 1.7 8.1 26.4 100.0 Blacic Population 9,410 444, 439 233 10,52 Percent of Total Area Population 13.4 3.6 1.3 .2 5,J Percent of Total City Black Population 89.4 4.2 4.2 2.2 100, *Area figures may not add to CorDus Cl,-Irjs@i total because of roundinrr. The ce sus divides all Dersons as bein- '*@.,'hite," "Negro," or "Other." In this study the "W-hita" population is divided into ",kn-lo," and "Maxican A_=rica-n.,, "'Mexica-a Americans" ar defined as those persons of Spanish lav.,-, a.-a together with ".0ther persons of Spanisi surnal'.--e." -Persons of Spanish languaga co:-.-Iprise persons of Spanish niother tongue an , 1@ 1-1 all other persons in families in which the head or wife re?orted Spanish as his or 'other tongue is defined as the language spoken in the pers n' her mother ton-ue. 1,1 0 6 C) C, home w1hen he was a child. The data on =.6ther tongue may not reflect a person's current language skills. SOURCE: U.S. Bureau of the Census. VI-14 Table VI"8 THE '7 C Co-j,:?, OST 11-1 ioN OF CORPUS CHRISTI Is 1.2i PO-2ULkTIGX', BY CENSUS TR-kCT: 1960 AN-D, 1970* Percent Black and, Census P 0 1- c L B 7. Li@--rican Mexican American 1960 0-0 Tract 9 0 1970 1960 1970 15 8 30.3 15.8 -00.3 1) 1.9 1.4 27.3 53' . 7 .29.2 55.1 -) 1. 1 - 24 .2 26.1 24.2 15. 0 4 72.4 76.1 -2 0 . 3 2 1' . 4 92.7 97.5 5 24.7 74. 2 31.7 20.5 56.4 94.7 6 .5 4.7 14.0 41.4 14.5 46.1 7 .1 7.4 24.4 7.4 24.5 8 - .4 8.4 33.2 8.4 33.6 9 .2 .1 87.3 94. 9 87.5 95. 1 0 10 4.4 3.2 81.4 88.'3 85.8. 910@5 11 7.9 6.5 79.8 85.7 87.7 92.2 12 1.4 1.0 35 . 8 61. 1 37.2 62.1 13 - .3 46.1 .78.2 46.1 7S.5 14 .3 1) 5.0 12 . 2 5.3 12.4 .15 - .1 49.8 81.6 49.8 81.7 16 li.s 11.5 81.4 86.1 931.2 97.6 17 21.8 26.2 74.7 74.0 96.5 100.0 is 8.2 9.6 80.6 8A.8 @88.8 .94.4 19 - .3 1.4.9 54.4 14.Y 54.7 20 - .5.7 38.5 5.7 38 8-. 21 .4 .5 3.8 13.5 4.2 14:0 22 - 4.S 23.2 4.8 23.3 @23 6.2 29.1 6.2 29.2 24 i 4.4 17.8 4.5 17.,9 25 .2 .1 00.4 4.5 3.6 4'.6 26 .1 i 3.1 17.2 3.2 17.3 .27 - .2 1.8 11.3 1.8. 11.5 29 n.a. 7.6 M. --- 7.1 n.a. 14.7 ra. a. 30 n.a. n.a. 19.6* 20.7 31 n.a. i n.a. 9.111 il. a. 9.5 32 n.a. 1.9 r.. a. 17.0 n.a. 18.9 33 n.a. n.a. 22.2 n.a. 22.3 34 n.a. .1 n. a. 7.2 n. a.. 7.3 35 n.a. .1 n. zz. 50.8 n.a. 50o9 36 n.a. -.4 n.a. 10.4 n.a. 10.8 37 n.a. - n.a. 10.2 'a a. 10.2 *The [email protected] @[email protected] population is dif.'eren-L-ly in 1960 and 1970, rendering the figures noncomparable. See Table for the 1970 definition. Nexican P-nericans in 1960 are t..ose people w--IL-,,- a Sp--mish surname. For tracts with very large d@--ff:ersnces@in the perce-nt 11jazwo-an 1960 and 1970, however, these w-M -anora lly @:':!flect mn- er-11-,nic composition of the opulation. SOURCE: U.S. 3-,ire--u of the Census. VI-15 TABLE VI-9 J T- \ T T () @r, lN CCi PUS 'bl' U-NSUIS TRA,"@: 1970* ?--rceat of Of Lotal Nntm-.Le 0@- I Nu. o@ lexican Census of Liacks Americans Tract i-11 Ci Ly in CiLy .3 2 .4 3 .1 1 A 23.1 .8 5 1-9.3 .7 6 3.9 4 7 1:1 8 - .4 9 . 1 7.4 io 1.7 5.9 11 2.2 3.7 12 .5 4.3 13 .2 41+. 8 14 J- .8 15 i 6.2 16 13.7 1-9.9 17 ?0. - - 7.3 i8 8.0 9.0 ,g .3 7.6 20 .2 4.o 21 .3 1.2 22 1.7 23 2. 9 24 1.8 25 .3 -)6 1.9 27 .3 1.9 29 3.8 .4 0 .4 .9 31 - .3 32 .5 6 -3) 3 _3 4 .7 35 1.3 36 .3 .9 37 C- @3 Trcct 2.03 "Craws o' S T al) 1 -2 14 o:.- d af i-.1 t i o ---, o 1,: -z: c -z n c a S -3 C U.S. Of --l-le Cc--Isus. VI-16 will be generated for S.M.S.A. tracts and the sub-county areas. These small area projections (Table VI-10, VI-11) will then be aggregated and matched to county projections developed earlier in this report. The basic methodology employed in this model is a variation of the "Cohort Migration-Survival Component" method described in detail in Chapter III. Comments concerning the preference for .'and assumption of this approach are equally applicable to this situation. Alternative approaches are being evaluated but at the moment this orientation is the most flexible since it will allow us to enter the model later to adjust intra-urban migration assump- tions to accommodate (1) past trends, (2) new distribution of jobs related to retailing and commercial activities.and (3) trans- portation constraints. These adjustments, however, have not been applied to the,present projections. Adjustments in the present model from the formulation presented in Chapter III include: (1) The application of actual birth and death rates to each tract rather than the general survival ratio. This we feel is more realistic when tract data is employed since variation in these rates are substantial between the white, brown, and black populations of this city. Further- more, since racial segregation is pronounced these differences are reflected at the census tract level. (2) The application of a unitary weight to.migra- tion. This allows us to assess the direct impact of this projection process and it will be at this level that in the future we will attempt to cor- rect for existing information on inter-urban mo- bility rates. (3) The application of a different minimum out-migration constraint,to census tracts than was applied to county data. Since the maximum of any single census tract during the 1960-1970 was -55%, this set the maximum for all tracts during the 70-80 and 80-90 periods. On similar grounds a maximum gain in any tract was set at 80% of its previous population. These constraints are more critical in the tract projection than in county projections since the probability of "zeroing out" a tract is much higher due to the smaller magnitudes of population that are being dealt with. (4) Finally it was necessary to utilize ten year rather than five year projection cohorts in order to create ,reasonably sized groups. VI-17 Table VI-10 1980 POPULATION PROJECTION FOR 1980 POPULATION PROJECTION FOR CORPUS CHRTSTI CENSUS TAT CORPUS CHRTSTI CENSUS TRT TOTAL MALE FEmALF TOTAL MALE F E M -ALF ow 9 51 19 3:? O@ 9 29 16 14 10-19 42 1b. 27 10-19 26 14 1? 20-29 44 2*0 24 20-29 2s 13 1,@ 30-39 43 25 2r'l 30-39 24 12 1;) 40-49 34 20 14 40.49 17 9 a so-59 48 26 22 50-59 31 18 13 60-69 64 34 3n 60-69 44 26 117 70+ 102 56 46 76+ 5c; 35 21 TOTAL 42q p TOTAL 256 143 14 P 14 co 1980 POPULATION PROJECTION FOP 1980 POPULATION PROJECTIOlm FnP CHRTSTI CENSUS TRT CORPUS CHRISTI CENSUS TRT 4 CORPUS TOTAL MALE FFmALF TOTAL MALE FFmALF 0- 9 3o 11 2 .0- 9 426 p07 10-19 34- 7 2P 10-19 332 1,61 71 20-29 6P 29 3q 20-29 29-1 146 .147 30-39 39 18 21 30-39 189 88 101 40-49 21 12 c') 40-49 133 55 7A 50-59 26 13 IP SOW59 139 56 81 ko-69 2o. 10 jn 60-69 15o 63 - 31 2,f-, 70+ 196 70+ .57 TOTAL 29F; 00 16r, TOTAL 1860 qbo lno@ MTAMVIJM M. mo (cont'd.) 1960 POPULATION PROJECTION FOR 1980 POPULATION PROJECTION FOP CORPUS CHRISTI CENSUS TRT CORPUS CHRISTI CENSUS TRT . TOTAL MALE FEMALE TOTAL MALE FEMALE of. 9 522 p76 245 00. 9 1569 826 741 10-19 486 249 237 10-19 1300 660 639 20-29 455 191 263 20-29 1268 614 655 30-39 244 ill j33 30-39 1188 604 584 142 j66 40-49 678 353 -425 40a.49 301 - - 50-59 296 138 157 50-59 641 ?88 353 60-69 170 85 89; 60-69 716 '307 409 764 136 64 7? 704 879 40 47n TOTAL 2609 1257 135i TOTAL 8238 4()59 4i7s 1980 POPULATION PROJECTION FOR 1980 POPULATION PROJECTION FOP CORPUS CHRISTI CENSUS TRT 7 CORPUS CHRISTI CENSUS TRT TOTAL MBLE FEMALE TOTAL MALE FF_mALF 0@ 9 696 350 346 OW 9 187 94 94 io-19 458 215 24-4 10-19 83 37 46 20-29 147 0 9,4 20-29 628 334 ?99 - 30-39 464 246 30w39 178 85 91 22S .40-49 95 43 5P 46-49 311 149 i6- SO-59 122 47 75 SO-59 358 158 200 60-69 336 151 186 60769 114 54 60 78+ 337 154 -.Te-4 To+ 119 71 4F% TOTAL 35W 1751 IA3A, TOTAL In415 484 C;61 TABLE VI-10 (cont'd.) 1980 POPULATION PROJECTION FOR 198.0 POPULATION PROJECTION FOR CORPUS CHRTSTI CENSUS TPT CORPUS CHRTSTI CENSUS TRT TOTAI_ MALE FFMALF TOTAL MALE FEMALF 0- 9 861 436 429, 0. 9 725 347 '47c) 10-19 6 8!5 347 33Q 10-19 566 270 29f, 20-29 72c; --A 7 P-0-29 526 248 27s ,30-39 310 1.46 169 30-39 397 189 20A 40-49 232 0 T33 40-49 283 136 i5l 50-59 213 94 IJQ 50-59 317 13@ 187 60.69 216 joi III 60-69 345 176 i6n 94 70* 722 -42,A 70+ 351 160 i9l TOTAL 351n 1E,96 1.919 TOTAL 3968 1940 2629 1980 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 11 1980 POPULATTON PROJECTION FOR TOTAL MALE FEMALF CORPUS CHRrSTI CENSUS TRT 1 TOTAL MALE FEMALF 0- 9 402 203 19q 10-19 332 168 1 64 0- 9 864 432 43p 20-29 383 184 ?o n 70-19 574 226 34.8 30-39 191 89 lop ;?,0-29 1067 55j C.; I C, 40-49 146 0 81 30-39 641 311 324 SO-59 157 68 89 40-49 264 136 i29 6o-69 14Q 64 8c; 50-59 271 130 141 76+ 252 119 3 -3 60-69 262 lib I-5;) TOTAL 2013 956 in,54 7()+ 634 ?28 406 TOTAL 457f3 2131 2447 TABLE VI-10 (contd.) @1980 POPULATION PROJECTION FOR 1980 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 114 CORPUS CHRISTI CENSUS TRT 14 TOTAL MALE FEMALF TOTAL MALE FEM- ALE 0. 9 1122 579 v-; 4 p olm 9 620 291 32q 10-19 1006 Soo 0 f, 10-19 599 274 324 20 29 928 441 48i 26-29 sso *49 loi 30:39 600 283 317 30-39 7S6 345 413 40"49 389 lu i96 40-49 438 206 233 50-59 341 132 209 50-59 324 14@ i7q 600069 241' 91 60-69 485 ISO 110 393 145 4 70+ 1020 349 671- ,.TOTAL 502o 2371 2649 TOTAL 479? 2036 2756 1980 POPULATION PROJECTIONFOR 1980 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT I F; CORPUS CHRISTI CENSUS TR7 TOTAL MALE FFMALF TOTAL MALE FEMaLF 0. 9 1546 793 75-1 OW 9 1776 883 A94 10.19 1082 S47 534 .1304 64S 0556 20-29 1019 47i %4q 2,0-29 1445 716 729 199 P514 .30-39 8913 37@ c. 30-39 457 522 40-49 331' 12l 7 0 9 .40-49 573 24i 337 99 19P 50-59 854 356 49S 50-59 292 60-69 294 79 215' 60-69 797 367 .42q 76+ 695 91 70* -02 4 @835 394 441 370 TOTAL 848p 41500 TOTAL 5715 2S11 4 3982 TABLE VI-10 (cont'd.) 1980 POPULATION PROJECTION FOR 1980 POPULATION PROjECTION FOR CORPUS CHRISTI CENSUS TRT 17 CORPUS CHRISTI CENSUS TAT . TOTAL MALE FEMALF TOTAL MALE FEMA-LE oft 9 3052 1521 .0- 9 39on 1995 1905 @531 - 10-19 21,42 Irla 1124 10-19 2708 1362 1346 20-29 2191 1021 117o 2OW29 2847 1358 1@89 30-39 1272 5 6 9 30-39 2143 1 r)47 I n9c'; 40-49 1003 441 SS6 40-49 1097 523 574 50-59 992 486 C; 0 c; SO-59 903 374 524 606 -;27 >7Q 477 60-69 60-69 sop 33i 75+ 512 ?36 ;,74 70"+ 104n 365 67c; TOTAL 11769 5626 61,41 TOTAL 15445 7.156 A@qn 1980 POPULATION PROJECTION FOR 1980 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 1A CORPUS CHRISTI CENSUS TAT 2;, TOTAi- MALE FEMALE TOTAt MALE FEMALE 04. 9 231 1 1165 i-146 9 1563 758 A 0 C; 10.19 989 qoo@ 10-19 141o 735 67s 1895 20-29 1373 696 677 20-29 2486 1226 1?6i ' 30-39 1197 558 AS39 30-39 987 456 9;31 ' 4()-49 548 221 '-426 40-49 979 439 5 4 0 130-59 584 p17 367 50-59 113o s5l 60-69 677 236 44i 320 322 - 60-69 177 7 1 7()* 97p, 3719 594, 7()* 392 TOTAL 8322 3799 45214 TOTAL j0824 5323 5c; 0 1 @LEMO (cont'd.) 1980 POPULATION PROjECTIOWFOP 1980 POPULATION PROJEC.I.ION FOP CORPUS CHRISTI CENSUS TRT 21 CORPUS CHRISTI CENSUS TP 2i TOTAt- HALE FEmALP TOTAL MALE FEMAL;-r 0- 9 937 454 481 0. 9 2801 1425 i37A low19 79p 374 417 10-19 2149 Ifi4o 110Q 20-29 95p 43@ 52, 20-29 235c; 1121) 1;?27 30-39 1237 627 6in 30-39 1850 910 94,) 40-49 551 276 P71 40-49 1359 OS 6 0;97 50-59 599 ;129 3 7 50-59 1149 r, 6 60-69 820@ 3 09 17 60-69 683 146 137 70+ 1146 406 74ri 7()+ 32(% 14i 177 TOTAL 7()40 3109 TOTAL 622j 6441 393p 12667 1980 POPULATION PROJECTION FOP 1980 POPULATTON PROJECTION FOP CORPUS CHRISTI CENSUS TRT 2;? CORPUS CHRrSTI CENSUS TRT 24 TOTAt.. MALE FFM4LF TOTAi- MALE FEMALF 9 1138 -591 S47 0- 9 146- 768 10-19 78? 39P 384 10-19 8 2 1 431 20-29 1124 527 597 20-29 11307 s;70 q34 30-39 1011 40 r, I A 30-39 935 456 .484 40-49 494 P.31 ?6? 40-49 619 PE14 33@5 50-59 669 p 50-59 927 455 47;) @85 384 - 6o-69 636 ?92 344 60-69 576 ?,90 9 OL, 764 486 7-19 ?67 70-+ ?17 76 141 TOTAL 6341 3,n 2 31in TOTAL 7364 3624 174!, TABLE VI- 10 (cont'd.) 1980 POPULATION PROJECTION FOP 1980 POPULATION PROJECTION FOR CORPUS CHRTSTI CENSUS TRT 2 CORPUS CHRISTI CENSUS TRT 27 TOTAL MALE FFMtLF T 0 T A MALE FF-MALF7 0- 9 477 745 P3? 0- 9 3635 lr426 171n. 10-19 530 P93 P37 10-19 3422 1783 lf,4n 20-29 500 P63 ;03 -7 20-29 4112 2146 2-166 30-39 565 p6b P919 10-39 2596 IiO9 IPL47 40m49 491 ?26 ;)6c; 40-49 2503 1187 111(, 50-59 541 p45 p9,c, 4 1- ' 60-69 Sol 219 psp 50-59 2611 110 ; 0. 7 0+ 60-69 1527 8ok 72c; 7 646 p76 3711-1 704 952 358 494 TOTAL 4251, 203 P TOTAL pl45q 10815 1.n-,r-44 1980 POPULATION PROJECTION FOR 1980 POPULATTON PROJECTION FOP CORPUS CHRTSTI CENSUS TAT 20L CORPUS CHRISTI CENSUS TRT 2q TOTA(,. MaLE: FEmALF,, TOTAL M A L E FFmaLF 0@ 9 227(,. 1146 11?Q 0.0 9 146-1 733 72JR 10-19 1868 gol, q67 10-19 1151 744 407 20-29 2558 1221 20-29 1631 Ir,82 c;4q 30-39 1935 966 96Q 30-39 1243 A-46 !;97 40-49 1664 P09 A54 40-49 434 241 19,1 50-59 1775 A64 Q11 - 50-59 104 63 41 60-69 1263 (,li (,5;,, 60-69 29 14 76+ 861 354 C;06 - 70+ 4 1 TOTAL 14198 6s@72 712 f, TOTAL' 6057 3725 P13P TABLE VI-10 (contd.) 1990 POPULATION PROJECTION FO R 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT N CORPUS CHRISTI CE SUS TRT TOTAL MALE FEMALF TOTAL MALE FEMiLE om 9 27 10 17 0@ 9 27 9 17 10-19 23 14 io-19 3n 2 s 20-29 19 20-29 46 12 35 30-39 20 30-39 31 ij is 40-49 19 9 40-49 18 10- 50-59 is 9 6 so-59 9 4 60-69 22 12 1@ 60-69 12 6 A 76+ 75 40 34- 76+ 35 is 16- TOTAL 22a 1.01 T14 TOTAL 208 77 131 11990 POPULATION PROJECTION FOR 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 4 CORPUS CHRISTI CENSUS TRT TOTAL MALE FEMA ILE TOTAL MALE FEMALE 0- 9 246 119 0- 9 14 7 6 1.0-19 192 93 9 q IOM19 13 7 6 20-29 149 72 .77 20-29 12 6 5 30-.39 132 66 66 36-39 13 7 40'.49 85 40 46 40-49 11 F sq.59 6o 25 35 59-59 8 4 4 60,.69 63 25 37 60-69 14 8 6 76+ 156 66 9() To + 4 27 17 TOTAL 1083 sob c;76 TOTAL 129. 72 57 TABLE VI-10 (cont'd.) 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 090 POPULATION PROJECTION FOR TOTAL MALE FEMALF CORPUS CHRISTI CENSUS TRT 7 OW 9 5@3 ;)83 75 .i TOTAL MALE, FEMALr 10-19 467 P39 729 0- 9 548 ?,76 p7? 20-29 321 137 184 10-19 355 164 ig? 30-39 39n 156 P34 20-29 335 141 1-94 40-49 209 @95 119 30-39 534 319 50-59 249 119 13n 40-49 324 165 15o 60-69 171 82 .8q 50-59 .223 86 ).37 76+ 143 72 71 60-69 246, 89 i57 TOTAL 2484 1181 1103 7-04 667 p83 184 TOTAL 3232 1523 710 1990 POPULATTON PROJFCTION FOR 1990 POPULATION PROJECTION FOR, CORPUS CHRISTI CENSUS TRT 6 CORPUS CHRISTI CENSUS TRT TOTAL MALE FEMALF TOTAL MALE FEMALF 0- 9 1445 760 A84 0- 9 207 1.03 1014 10-19 1290 655 A3r, 10-19 133 62 71 2.0-29 957 463 49c; 20-29 125 41 81 30m39 1229 f., 2 8 0,01 30-39 156 55 40-49 95P 494 457 40-49 206 92 i14 50-59 345 198 f47 so-59 124 51 Ti 60-69 1?88 129 1-50 60-69 109 36 71 704 1280 50 73.3 76+ 281 147 134 ,TOTAL 7786 3876 3911 TOTAL 1343 C;90 75;; TABLE VI-10 (cont'd.) 1990 POPULATION PROJECTION FOR 1990 POPULATION PRO'JECTION FOR CORPUS CHRISTI CENSUS TAT CORPUS CHRISTI CENSIJS@TRT TOTAL MALE FEMALE TOTAL MALE FEMA-LF om 9 480 243. ?37' 0- 9 219 111 ioA 106019 387 196 i9l 10-19 lal 91 96 20-29 308 156 Ts? 20-29 149 76 74 30-39 328 161 i61 30-39 173 8@ 91) 40-49 .179 86 94 40-49 86 40 46 50-59 12s 59 6q 50-59 66 28 37 60-69 143 5� 84 @60-69 71 31 46 704 390 151 P-39 7o+ 180 82 98 TOTAL 2343 IjIb i?26 TOTAL 1124 542 9583 1990 POPULATION PROJECTION FOR 1990 POPULATTON PROJECTION FOR CORPUS CHRISTI CENSUS TAT I@ CORPUS CHRISTI CENSUS TAT .TOTAL MALE FEMALE TOTAL MALE FEMALE 0. 9 41 Ft 200 ?Ip q 625 313 311 156 i76 10-19 326 10-19 458 194 263 20-29 255 1_22 133 2- 3 30-39 237 112 12S 00.29 722 .56 364 30-39 51o P-78 232 40-49 139 63 7 F, 46--49 328 156 .171 50-59 104 45 6(-l 50-59 6i 5p 60-69 .96 42 6Ow69 122 58 64 70+ 735 274 -46f 76+ 465 5@ TOTAL 2311 InO 129P TOTAL 3349 i77o .TABLE VI-10 (cont-d.) 1990 POPULATION PROJECTION FOP 1990 POPULATTON PROJ;-CTION FOR CORPUS CHRISTI CENSUS TRT CORPUS CHRISTI CENSUS fRT 1-4 TOTAL MALE FEMALF TOTAL MALE FEMALF ow 9 1131 F)84 547 0- 9 1327 680 f,47 - 10-19 851 42� 422 100-19 1043 519 921 - 20-29 795 -.499 395 20-29 619 ?89 13n 30-39 757 361 19f, 30-39 750 '456 399 40--49 450 p 40-49 206 8t) Tlo@ 05 p4c; 50-59 149 5S 94 50-59 199 gi 107 60-69 182 45 i37 60-69 154 59 94 -+ 0+ 70 93P 737 7 43o 1.27 -%ol - 194 TOTAL 4958 2346 TOTAL 5014 2132 2981 co 1990 POPULATION PROJECTION FOR 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT 14 CORPUS CHRTSTI CENSUS TRT 10; TOTAL MALE FEMALF TOTAL MALE FEMALP 0- 9 535 ?51 784 0- 9 1027 9510 917 10-19 599 ;>74 124 10-19 799 '491 40? 20-29 461. 175 p8o, 20-29 587 pq@ 30-39 - ;)9r, 52n 234 p8r, 30-39 821 416 40c; 40-49 683 306 17c;' 40-49 404 176 ?3r, 50-59 21,1 1,06 lor, 14q 60M69 50-59 25s 106 146 65 60-69 437 160 ;)76 76+ 1132 140 TOTAL 428A 1754 79P 70+ 142A 63@ 79c; P9,31 TOTAL 5761- 2687 In 7c; (cont'd.) 1990 POPULATIO14 PROJECTION FOR 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TAT 17 CORPUS CHRISTI CENSUS ,TAT IQ TOTAL MALE FEMALE TOTAL MALE FEMALF 0. 9 4436 2211 2- 0@ 9 5376 2750 2626 10-19 3248 IS69 1679 10.19 4008 2o2l 198i 20-29 2046 80 -115,1 20-29 3256 1540 1716 30-39 2886 136@ 1524 30-39 3419 1694 IT20- 40e-49 1541 7o3 A3A 40.49 1983 951 103? 50-59 12b3 557 706 50-59 845 '348 497 6 -69 576 60-69 788 264 52c; 0 1209 633 - T 764@ -2215 764 1451 0 1463 718 745 TOTAL 18092 8589 9sol TOTAL 21891 10343 1154q 1990 POPULATION PROJECTION FOR 1990 POPULATION PROJECTION FOR CORPUS CHRISTI CENSUS TRT ip CORPU-S CHR-ISTI CENSuS TRT TOTAL MALE FEMALE TOTAL MALE FEmA-LF 0. 9 2802 1413 f-A96 0@ 9 1483 720 764 10-19 2214 1150 ,ln64 10-19 1458 -756 20-29 56 30-39 1524 7 " -768 20-29 1083 S44 53q 2878 1439 1439 30-39 1286 63S 40 49 908 411 497 40-49 855 342 ) I 50:59 1034 456 57p 50-59 249 99 is n-- 60-69 1243 50 @6o 60-69 263 06 76+ -1241 598 643 76+ 14,84 sog q7r TOTAL 13843 6806 7637 TOTAL 8160 3705 4455 TA13LE VI-10 (cont'd.) 1990 POPULATION PR .OJECTION FOR 1990 POPULATION PROjECTION FOR CORPUS CHRTSTI CENSUS TRT ?i CORPUS CHRISTI CENSUS'TRT 2,1 TOTAL MALE FEMALF TOTAL MALE FEMA-LF 749 363 387 0. 9 4014 2042 1977 10-19 746 352 394 10-19 3219 1584 1@35 20-29 593 ?46 '447 ZO-29 2762 1.-406 146? 30-39 994 498 497 30-39 317b 1576 40-49 40-49 2097 11?43 ln54 1011 527 484 50.59 1550 749 Pon 50-59 297 141 156 6OW69 1299 626 A 7 7 60-69 380 103 777 764 1132 55@ 70+ 1872 611 IP-61 - TOTAL 664,-% 2841 TOTAL 19249 9473 977m 1990 POPULATiON PROjFCTION FOR 1990 POPULATrON PROJFCTION FOR CORPUS CHRISTI CENSUS TRT 2? CORPUS CHRISTI CENSUS TRT 24 TOTAL MALE FEMALF TOTAJ- MALE FF-MALF 0- 9 1019 529 490 0. 9 117A 618 6 10-19 76_,; 374 '38Q 10-19 65S _'I 4 6 Ili 20-29 59,14 ?64 32q 20-29 599 257 341' 30-39 1249 624 62c; 30-39 1661 P45 Alr, 40-49 421 ;oo3 plQ 40m49 746 361 '38A 50.59 351. 131 P2m 50-59 338 lzo ;?Iq 60-69 741 345 19r@ 60-69 556 216 3461 76# 679 P93 187 76+ 1188 529 055Q TOTAL 6282 3045 3;?37 TOTAL 6459 3C.10 344p TABLE VI-10 (cont'd.) j990 POPULATION PROJECTION FOR 1990-POPULATION PROJECTION FOR CORPUS CHRTSTI CENSUS TRT 2c; CORPUS CHRTSTr CENSUS TPT 27 TOTAL MALE FEMALF TOTAL MALE FEm4LF- 040 9 370 ]90 13n .0. 9 495@-j 2624 P329 10-19 36) 206 iss 10-19 4567 2384 20-29 236 13@ i07 PO-29 4299 2192 ?f?A 30**39 694 356 339 30-39 5397 2739 ?TO4 40.49 511 pse PSI 40-49 3234 1(,2@ 50-59 261 107 1,54 50-59 3159 isio 60-69 164q 60-*69 286 lid j75 2932 1456 76* 1145 490 654 76+ 2699 1286 148P J413 TOTAL 3866 1849 2617 TOTAL 31?36 15421 1990 POPULATION PROJECTION FOR 1990 POPULATION PROjFCTION FOR CORPUS CHRISTI CENSUS TRT 29 CORPUS CHRISTI CENSUS TRI 2f, A TOTAL MALE FEMALE ,rOTAL MALE FEMALE - 0-0 9 184t) 923 917 ow 9 3089 IS56 10-119 149A 916 586 10-19 2676 1306 20-29 1946 IP66 6 a 1141 02A - 2OW29 2466 30--39 931 481 @44 3 .39 3456 1707 174q 40-49 1117 78i 336 0 IP-35 jp47 40-49 2462 1r)77 1149 50059 365 @194 i7i 50-59 ?226 1(j40 1167 60-69 103 64 3q 60-69 2207@ 1154 1417 76+ 35 14 17 76+ 2571 0218 TOTAL 78395 4,652 3T8i TOTAL 21173 Table VI-11 MIGRATION ---- ------ J--- - ----- Age t 0 9 10 19 20 29 30 39 'Group 19 - 1990 1980 _T9_90 1�60 1990 M IF Trac M F m F M m F IM F 151 13 1 22 381- 121 211@ - 28 35 101 17i 33 - 2 8; -.131- 16!- 2 9 -20 9 & 7 18 15 1 20 15 - 1 5@1 7. 7 9 12 -.1. 7 6 4 61 - 4 - 12 5; - 5" - 5 24 - 281: - 18 -24 16 -12 - 11, - 12 T-4" -221 -2351-128, -135 -201 -2021- -99: -106 -121, -139!1 - go -911- 751-107 - 541 - 6Y' 0 -7-72 -481 -__72' - 48 -143---.-8,2L-.-jqj - 60 - 62 - 0 - 62 i 34- 35 311 - 35 *--i-W-N& -223i -278! -2221 - 611 -1541 - 601 - -27-0, J-j- 6.-271 -199@ 8 3; -198'-182'-198 --T 4 1-246 ---ft4i 1 -130 :-1181 106 --i:f5 7 249 -216@ !-213 -111@ 9 6@ 4@ -fg; 23- 18 24 221 - 11..- 23 - 12 8 --64 '5C- 65@ - 55 -2921 -284 nol--'248 --Ab -127 - 39 9 -517 -504@ -553 -5341-2321 226 -2821- 94 -229 1 10 -4--0-4-- -'-2---3-- 4--@ 256 -371. -@@1-6 -18@11 -20-0: -210 -253@ -168 r--14'8 976---- - 115'- 7Yj 11 -2ff:' mi IV-:127 - 2 6 1': ---2 8- 4 -1071-1051; -1211 -145 -117 -128 - 90 -118 - 57- 65 - 12 -371!-@249 -3121-243 901- 49 93--- 43 -377 4111 -369 -371 -221 -211 -216 2b6 -851-- 1-9-61 -f6-f -166-- -- - 3 - 15 7 6-1-1 -S -I -18-5-i---195 162,-167 -125 -125 -126 -125 -1131! 55- - 281 - 31' -- 28@ 14 - 321 -T - I - 3111-- 5.3 -7. - 60 - 11 60 -472 -364, 307 -361 -304 -211, -25-4*-----2-0--8- - --f4- ---f 15 -516 -477 -5111 -251 @!-200 -2'@2 6T @11- -3-85[am4M -38 -- -58 --43 7 1-350--465 -251 - 16 961 -973, 8@-596 @a631.--108L --Z-43Z! 41-408 -@-48 3 61 --4 2 -1191- 81 -130 - @-l 9 17 -527 -4301 -566 -470 -510! 5, - 45: - 881-1711 - 4@9@! -376@- 97@ -1401 18 -310 -3-7721 -322 -384 -483i -367, 110 -153 -161 -168 -:4-65 - 174 j-615 3911---201@'-- 66': - 711--21f,- 0 -:@51-i-ZVI-4---5-9-6 -4-3-51'--'4'1-8- 19 -574, - -541 - 4-2@ -298 -234i i-220 -110 -222 -1121 -WOU@ -235@ -218 5 91 20 -162; -!-@5i -1.64; -f!6- -i5ll --i4b ------- I --- Ji '-'*139 -1301-160 -;- -1-0- -6- 16 0"! -10 5 I- - f54* '171 --1521 21 2911- 68 301 671 176 I -- 293, --------- - @2 -227 -176, -108 -1751 8 30 47 -19S -199 -194 -198 2J, ffi 135 46' 71 1Y -ii6ll 8 135 59 1 139j 2 71-130 -231 42@ - 68 -4W-451 -399 2921 -6- 99 -1 2'-11-4--rii -415 4 24 23. -@64 ------ -213 -31 57 19 25" -48! -861- 48@ 8 -326 -310i -1721-140 82 93. 82i 92 18i 92 26 -7a 68 3 51; 1@6[ 601-165 231 242 -145 671- 76 85 27 242 176 172 185 .272 - 2515 L 283 204; 2761 2f7i 47' 242 362[ 257 16 -- -49 @_:@81+ 3 284i 4 2! -94of 2!801-3061-1 5 34 - 25 -357 - b�I 1571 1 1 -1235 26 7*''*-3 - @--188__I -61 2281- 0- 258 299; 199, 1-6 981 140@-Ef@ p @@Ou IT'raci I -LD C@3 VI-32 TABLE VI- I I (cont'd.) MIGRATION (COntinued) Age 40 - 49 50 - 59 60 - 69 --,,Q7=1LL;p 1980 1990 1980 1990 19 0 1990 Tr: M I F M F M 1 F M F M F M F - 38i ........ 31i - 13 9 491- 43.-- 17!- 141- 80'- 65 - 49 58 #2 - 231- 171 - 5 4 331; - 21 - lli - @6 j5 2i i 1 7 - 44 - #3 - 181- 171 - 7 6 14! ---- 13 - 81- 8 - 42 - 35 25 22 - 78 -1131 - --32@ 48 8T -119 - 35,,- 51 -114 -155 91 12-" #5 - 41 - 41 - 5-0 -7 3 - - -9 6-- 1-0- -8-'- --9- 6 ---1-0-8- --Z-9 6 7@71 U@ #6 -198 -217----l@@-7 -217 -296 -284 -193 -237 -255 -253 -255 -2,53 #7 - 90 - 53 89 - 52 --97 --79 - 96 - 77 - 76 - 52 - 73 - 48 #8 7 4 ; - - . . - 3 - 24i 30 25 - 31 - 1 12,- 22 #9 -194 -198 87 -103 -168--211 87 -125 -239 -164 -225---nl 67 f-54 80 --1721 147 -174 -149 426 4 53 - 91i - - - -- 97 - 0 - 121 44 - 57 -152 -489 --H @ .-139- -130 -168 - 97 -164@ -214 --94 -102 -263 -350 -244 -341 ---#13 -134 -121-134 -121 -1451-168 - 95 -157 -149 -161 -149 -162 #14 -110 -161 - 53 -140 -14911-135 - 87 -108,217 -236 -216 -235 164 -173 - 89.-154 -1231- 92 - 73 91 -141 -107 - 139 -102 7 #16 -281 -260 -161 -221 -275i --MO - #17 - 59 - 60 - 7,1 74 - 9-511 - 63 -107 - 76 - 60 - 17 --7 5 -.: -3 f #18 - 93 - 99 104 -107:- 66 -113 - 72 - 25 - 30 - 30 - 36 #19 -333 -252 '-'-S4C4- -263 -224 -164 232 -175 -1441- 50 -159 - 7 - -151 -256 1 54 iS -1941 --@-211 -195 -213' M '1601 141 --1-591--1-4-'0----*'-'---------*--'- -- 194f@-125 -145 -125 -:165 ---r6-3 -TO FO' #-2 2 -1411 - 78 --1411 --l-i -- 1-0.-6' -423 - 44 - 33 - 42 - 31 - 51- 23 511- If -3F4!'- 34 - 32 - 32 #24 -181 -149 -1811-149 -155 -124 --B-4-1-12 -110i- 83 -109 - 82 -f2-5 -127 -121 -128!-122 185-131 --14@4 -132 - 24i - 22 - 24 - 23 #26 99 116 106 -124 - 4' 65 4. 74 - 1-7- ----4- 27 #27 182 194- 191 - 4@' @5. 5 341- 7; 54 0 63 #29 - 77 -124 - 83 - 16-- 14 - 16 - 21 - 1 5 - 3 .1 6 #F3_12 58 - 3 76 141-129 -1141 -114 -101 -232i -127:-2201-114 8 6 1 6 8 VI-33 We turn at this point to a delineation of the actual steps followed in the preparation and computation of the projections. Ste 1: With respect to the organization of the data inputs for eac tract, the male and female populations were organized separ- ately into age groups 0-9, 10-19, 20-29,....60-69 and 70+. These data were made available for each of the decennial census periods of 1960, and 1970. Step 2: The 1960 age-sex cohorts were survived to 1970 applying the actual tract specific birth and death rates already discussed. This computation thus produced "expected 1970 totals" for each of the age-sex cohorts. These would be the numbers in each cohort that would have survived from 1960-1970 if only mortality were involved. Step 3: Net m igration in the tract was assumed to be the difference 5-etween the "expected 1970 totals" (as calculated in step 2) and the actual 1970 totals as recorded by the census enumerations. Thus at-the termination of this step, "estimated 1960-1970 net migration totals" were produced for each of age-sex cohorts in each of the .tracts. Step 4: The next concern was the projection of the 1970 population cohorts from the 1960 actual base populations as enumerated by the census. From these actual 1960 cohort data, we advanced 1970 cohorts to 1980. We next employed the "estimated 1960-1970 net migration totals (as calculated in step 3) and computed different projections for the 1970 age-sex cohorts, assigning a fixed weight to the "esti mated 1960-1970 net migration totals." A weight of (1) 100% merely employs the "estimated 1960-1970 net migration totals" as given. The obvious assumption here is that the 1970-1980 net migra- tion for each of the age-sex cohorts is the'same as the 1960-1970 net migration (as calculated in.step 3). on the other hand, a weight of (6) 60% takes only 60% of the "estimated 1960-1970 net migration totals"-in the development of 1970-1980 migration totals, the assumption here being that 1970-1980 net migration was only 60% of-that in the 1960-1970 period. As already noted, other projections will be made for each of the age-sex cohorts, each projec@- tion dependent. upon the assignment of a different weight to the "estimated 1960-1970 net migration totals." Step 5: In this next step of the actual projection procedures, the IM-projections were developed. Employing the birth and death rates discussed above, the 1970 age-sex cohorts were survived to 1980. The amount of 1970-1980 net migration for each age-sex cohort was then calculated. Step 6: Upon completion of the preceding step, we had made available 1980 projections for each of the age-sex cohorts delineated earlier (see Step 1), with the obvious exceptions of the male and female 0-9 year cohorts. Since these two cohorts were not alive in 1970, we could not have survived them to 1980 as was possible with those VI-34 cohorts ali ve in 1970. In any event, the problem is now one of "creating" these new cohorts and placing them into the 1980 population. This objective was accomplished in the following manner: a) determin e the number of males in the age groups 0-9 and the number of females in the age groups 0-9 per 1000 women in the age group 15-49 in 1970, e.g., 1970 ratio for males 0-9=#males 0-91in 1970/# women 15-49 in 1970. b) from the above equations, two ratios were produced: one each for males and females in age groups 0-9. Each ratio was then taken one at a time and multiplied bythe # of women 15-49 in 1980; thus if we were using the "male 0-9" ratio, the result would b7--t-he of males in the age category 0-9 in 1980. C) the assumptions in this operation focus on the fertility component. We assume that the ratio of males and females in the age categories 0-9 per 1000 women between the ages of 15-49, in 1970 will be the same in 1980, and in each of the other projection years. VI-35 CHAPTER VII TRANSPORTATION A central issue in most urban and regional transportation studies is the prediction of flows between locations or traffic zones. While aggregate interactions or "interzonal transfers" are often easily derived from overall population or employment figures.the individuals flows between pairs of zones is a more difficult prob- lem. The "gravity model" is the simplest and most common formulation of this problem. Basically it assumes that the flow between zones is proportional to the product of the populations size or "poten@ial" of each zone and inversely related to the distance between them. The wide application of the gravity model is stimulated,by its simplicity and its empirical validity and not by the theoretical basis of the model. Although there have been many attempts to increase the credibility of its formulation2 as well as to extend its application to new Areas, the theoretical literature remains largely unsatisfactory. Furthermore there has been a rising cry concerning the dangerous use of yh analogies from the phy- sical sciences in social forecasting. Formulation The purpose of this portion of the study is to utilize concepts in information theory as originally outlined by Wilson4 to 1 See review by Isard, W. Methods of Regional Analysis (Cambridge) Mass.: 'M.I.T. Press, 1960) Chapter 9. 2A continuous issue of this type proliferates the various Regional Science.journals (Papers of the Regional Science Association, TTe_ Journal of Regional Science., Proceedings of t European and Asian Congresses of Regional @Tciencel. 3Wilson, A. G. "Notes on Some Concepts in 'Social Physics' Papers, Regional Science Association Vol. 22 1969) pp. 159-193 and lson A. G. "Use of Analogies in Geography" Geographical Analysis, Vol. 1 (1969) pp. 225-233. 4Wilson, A. G. "The Use of Entropy Maximizing Models in the Theory of Trip Distribution, model Split, and Route Split" Journal of Transport Economics and Policy Vol. 3 (1969) pp. 108-126 and Working Note Vol. 195 (1970) Center for Environmental Studies, London, U. K. VII-1 characterize the gravity formulation. It has been demonstrated by Charnes, Raike and Bettinger5 that if such a formation can be specified,then the duality properties of certain non-linear programming problems follow from the Kuhn-Tucker theorem. Theorem MIN f(x) Subject to: hk(x)=O, k(=l,...., K) gj(x)>-O,j=l,....,j Where x=(xl,....xn) is an n-vector and where f(x), g(X) for j=l,.... and hk(x) for k =1,....K are real functions, twice differentiable. f must be convex and each gj must be concave with each hk having linear affinity. If there is a vector x for which simultaneously one has all gj(x)>O or if there are no gj constraints, then then for a vector x to be an optimal solution to the above problem, it is necessary and sufficient that uj and wk exist as multipliers for which J K @f(x)/@xi = E uj @gi(x)/@xi E wk @hk(x)/@xi j=1 k=1 all i=l,....n and uj>0 where j=l,...... J and either uj=O,gj(x)>O or uj>O,gj(x)=O (i.e. ujgj(x)=O) or uj=O,gj(x)=0 holds for j=l,....J. Application Since such a problem is solvable it is clear that the Charnes et. al.translation of the "gravity model" into an information theory framework has allowed the determination of unique solutions for particular cases. It also provides us with an expected basis for the determination of directions of change under different transformations. This model is being operationalized and applied to the Corpus Christi area for the purpose of specifying interzonal flows. Two major questions are then addressed given these characteristics. What are the constraint characteristics of the existing transport network for population distributions projected for 1980 and 1990? How will such constraints be reflected in those expected distributions? 5charnes, A., W. M. Raike, C. 0. Bettinger "An Extremal and Information-Theoretic Characterization of Some Interzonal Transfer Models" Socio-Economic Planning Science Vol. 6 (1972) pp. 531-537. VII-2 (i.e. how will they retard or enhance growth of specific areas?) These questions are first asked in a status quo framework and then in a framework of highway projections of the Texas Highway Dept. for the same area. Finally the interzonal flows (Tij) are also utilized in the retail location model to be described in Chapter VIII. We feel that the development, operationalization and utilization of this format is not only a useful and vital addition to planning tools but is also an important theoretical con tribution. VII-3 CHAPTER VIII RETAIL ALLOCATION The allocation of new retail activities to space is dependent upon the distribution of consumers and their associated buying power and the distribution of existing centers which service the consuming public. The nature of the firm's market area, therefore, determines the entry characteristics of new retailing. Specification Perhaps the best way of formulating this is in probabilistic terms as given by Gambini, Huff and Jenks*. The expected number of consumers at any point i selecting retail establishment j is determined by Eij=PijCi where P.. is the probability of a given consumer at i selecting j Ci is the number of consumers at i The total number of consumers from all locations selecting firm j is the sum of all locations m Tj='.E E The line of market equilibrium between any two firms k and h is given@-as P,j= AjlDija n Z AjlDij.4 j=l n such that Z Pij=li and, O<P ij <1. j=l Where P.. is the probability of a consumer located at i selecting a retaillfirm j A is the attraction of firm j VIII-1 CORPUS CHRISTI REGION @Illl I lilt, 111 11111 11 1 A-A L I _Lj I I 1- 7 1 1 M0q014 61 EN L TAT C.@ ? j I't I I M.M., 'Al I IMILS E mi IS4 wIf I I W P 3 I h .T UL IT r F ITZ '@z 7 4, 3C C DR V I DU' to if 3% ff V. 10 ts so re p ve 0 ca CENSUS TRACTS AS OF 1970 CENSUS Figure VIII-1 VIII-2 Dij is the distance from a consumer at origin i to destination j a is a constant that reflects the effect of distance n is the set of retail establishments from which the consumer chooses. The equilibrium poi nt between firms kand h is determined by' Ak/Dika Ah/D.iha n E A3/D13 A3/D 13U j=1 or more simply Dik @k) 1/a Dih Ah Operationalization In order to operationalize this model for assignment of retail activities at the tract level within the City of Corpus Christi, it was necessary to determine the distribution of consumer buying power by tract. Each tract was specified on an x, y coordinate according to its centroid. The number of families in that tract were multiplied by the retail expenditures levels to which their median income was related (see Table VIII-1). Alternative retail centers were specified by x, y coordinates and size of center (attraction index). The coefficient to which distance was raised was specified at 1 and 2 in alternate runs of the model. Once the transportation model is complete (Chapter VII) a coefficient for distance determined from that model will be introduced. Allocation of commercial space will parallel this retail allocation approach but with weaker constraints on travel distance and as an inverse relationship with distance from the C.B.D. This commercial index will also be evaluated with direct travel times specified through the transportation analysis. The model compares each retail establishment with the attraction index (sq. ft.) of alternative retail locations and the weighted distance the retail location is from the adjusted buying power units (Census tracts weighted by retail expenditures rates and population). This produces an evaluation of each existing center-in terms: sales per sq. ft. and market share. When alternative sites are postulated for each tract then each tract is in turn evaluated as a possible location for new retailing. By knowing the amount of total retailing supported in cities of various sizes it is possible VIII-3 TABLE VIII-1 PERCENTAGE OF INCOME AFTER TAXES SPENT ON RETAIL ITEMS AND SELECTED SERVICES Average income and expenditures Money income after taxes $3,000 $4,000 $5,000 $6,000 $7,500 $10,000 to to to to to to Items $3,999 $4,999 $5,999 $7,499 $9,999 $14,999 Average money income after taxes and other money receipts. 3580.57 4608.94 5559.27 6729.63 8713.01 11,753.39 Expendi tures: Food prepared at home, 771.94 858.93 997.65 1067.72 1274.29 1410.67 Food away from home, in home city, total 126.73 167.11 @02.14 250.44 360.62 415.06 Alcoholic beverages 40.27 41.99 44.08 69.00 80.84 149.06 Tobacco 82.59 88.83 106.37 116.02 125.51 141.76 Housing rented dwelling, repairs 4.14 1.21 3.04 1.91 3.49 .74 owned dwelling, repairs and replacements 64.10 63.31 74.60 95.13 129.44 145.08 Fuel coal and coke 8.34 7.53 4.89 3.75 4.48 3.34 wood, sawdust, pressed wood, logs, etc. 1.24 .87 2.19 .42 .97 3.27 kerosene 5.45 9.41 7.20 5.72 2.64 4.08 fuel oil 5.22 7.28 8.11 10.86 20.42 29.18 other solid and petroleum fuels .29 .39 1.42 1.54 1.38 1.90 Water softening service .19 -- Ice .67 1.74 1.39 1.38 1.64 2.45 Food freezer rentals .06 .05 .15 .19 .19 .10 @TABLE VIII-1 Average income and expenditures - Money income after taxes $3,000 $4,000 $5,000 $6,000 $7,500 $10,000 to to to to to to Items- -$3,,999 -$4,999 $5,999 $7,499 $9,999 $14,999 Household operations laundry supplies 27.14 31.72 34.53 36.81 41.00 44.73 cleaning supplies 15.03 15.17 18.71 22.25 20.27 24.77 household paper2@-supplies 17.39 19.62 22.42 26.37 29.25 38.30 laundry and cleaning, sent out 37.25 46.16 -45.75 50.26 56.52 69.21 domestic service 34.09 42.06 48.07 55.44 118.04 306.34 day nursery care 4.40 12.45 12.97 14.33 13.76 6.03 repairs of furniture and equipment 8.69 6.11 10.09 9.35 26.46 46.64 freight, express, and storage 11.22 6.05 6.45 7.28 4.27 13.12 House furnishings and equipment, total. 210.15 218.75 334.53 336.06 449.83 608.65 Clothing, clothing materials and services 373.73 469-93 576.27 644.39 835.43 1214.19 Transportation, automobile purchase 196.04 293.40 409.57 419.86 503.08 598.22 Transportation, automobile operation gasoline 132.40 176.95 235.04 264.39 273.03 302.56 motor oil 9.10 11.46 16.62 19.57 16.38 lubrication, washing, etc. .9.60 14.59 17.09 23.10 21.29 24.97 tires and tubes 24.35 33773 33.21 42.83 44.00 56.63 batteries and other equipment 10.38 13.53 10.04 17.39 16.12 19.09 operating expenses not allocated 12.14 9.21 9.04 12.57 19.61 68.38 repairs and parts 36.52 34.94 52.32 62.21 77.61 102.48 Transportation, public, in home city 26.94 26.13 22.09 20.65 22.87 32.57 Medical.care, direct expenses, total 195.79 198.71 254.96 293.14 336.27 458.59 Personal care, total 133.*96 146.21 185.81 192.06 234.31 306.41 Recreation television 35.12 44.00 46.09 44.89 54.21 68.24 TABLE VIII-1 Average income and expenditures Money income after taxes $3,000 $4,000 $5,000 $6,000 $7,500 $10,000 $15,000 to to to to to to to $3,999 $4, $5,999 $7,499 $9,999 _$14,999 $35,000 radio 5.61 6.78 8.10 11.92 14.90 14.04 phonographs, tape recorders, etc. 8.85 11.04 13.76 19.35 31.34 38.66 musical instruments 14.45 7.23 3.12 11.83 28.83 38.70 spectator admissions, total 15.47 22.39 23.09 30.93 34.09 50.27 pets (purchase and care) 12.94 12.20 15.18 28.87 30.46 55.70 toys and play equipment 12.89 16.64 23.32 24.28 30.40 28.52 Reading, total 35.88 36.42 39.10 50.01 61.10 100.94 Education, books, supplies, and equipment 5.76 6.99 8.89 12.71 19.83 32.29 Total expenditures 2784.36 3239.25 3989.46 4429.18 5470.26 7092.31 10600.00 Percentage of money income after taxes and other money receipts .7776 .7028 .7176 .6582 .6278 .6034 5436.00 Source: Bureau of Labor Statistics, "Family Expenditures, Income, and Savings, by Income Class, Family Size, and Region: All Urban Families and Single Consumers South, 1960-61. to determine a rate of expansion of new retailing and then allocate it as new retail acreage per tract without specifying the precise site. Application Clearly from Tables VIII-2 and VIII-3 locations 16 and 6 are the most under- served areas of the city in terms of retail capacity vs. sales. However certain locations e.g. 28 are extremely sensitive to the correct specification of the ' distance coefficient (Alpha). The low population growth rate projected for the 1980 implies a slow increase in retail acreage. The projected increase is only about forty acres, most of which would be allocated to tract 14 (20 acres) (I.D. unit-16) and tracts 18, 24, and 25 (6.3 acres each) (I.D. units 26, 26, and 9 respectively). Note, this low increase in retailing reflects only in- creased sales within the City of Corpus Christi that results from city growth alone. It does not reflect increased sales due to SMSA growth or the fact that Corpus Christi is the primary retail center for high order goods purchased within the Coastal Bend Region. A retailing model for the S.M. S.A. has now been determined and will be implemented. Clearly this model can be refined to the enumeration district and perhaps block level while specifying the distance parameter more ac@- curately through the transportation model. The allocation process will specify retail and commercial acreage prior to the allocation of housing since it is felt that this is a realistic reflection of the market mechanism priorities. VIII-7 Table VII1,2 TOTAL ALLOCATION OF CUSTOmERS AND EXPENDITURES rROM 38 STATISTICAL AREAS USING Alpha i*000 LOCATION NUMBER OF ANWAL SALES PER MARKET SIZE CUSTOMERS SALFS SQ* FT, SHARE 1 100000 1581o4l 9443980*30 94*44 *03 2 40000 641*08 3232665.94 815*82 001 3 100000 1931,44 90-844*3.56 96.84 o04 4 T5000 1104.89 5713167,62 76917 002 5 75000 1061.11 649867100 86965 902 6 100000 1738976 11068529,63 116*69 *03 7 100000 1125,23 6767648,06 67.68 -02 a 75000 1082o66 5190919,09 69o2l -02 9 100000 1156905 710159-"7943 Ti,02 002 10 60000 831987 5618251.11 83964 002 11 -55000 789o98 4238598,80 77'*07 902 12 60000 773,84 4764472.99 79o4l 002 13 175000 2685,78 13933299962 79962 005 14 621ooo 6794,08 407524n-1*97 65962 613 is 28oooo 3853*14 232839i5,39 83,16 008 16 25000 677,91 5757973,86 236932 001 17 125000 1979,51 10330721,87 82o65 *04 is 90000 1705,07 857TS52,33 95931 *03 20 150000 2360,43 11786621916 78958 '05 21 60000 821s04 4873335,42 81'*22 *02 22 25ooo 397.39 1983042,95 79932 -01 23 110000 1409,02 8650449ol4 78964 *03 24 22000 294973 1777696*96 86.80 -01 25 75ooo 1015,15 6291944,54 83989 o02 26 60000 550,13 3414527907 56,91 061 27 100000 1486,83 7136648,67 7i*37 *03 28 100000 1467969 838777-0105 83,88 -03 29 160000 1118080 67089i2.87 67@09 s02 30 100000 1082912 6486104,40 64 002 4986 31 88800 853o89 $118240,62 63*98 *02 32 60000 826,61 S0925,6*31 $4932 902 33 500000 513805 2522138708 56944 010 VIII-8 Table VIII-3 TOTAL ALLOCATION OF C1JSTOmEPS AND EXPFNr)TTURES FROM 38 STATISTICAL AREAS USING Alpha 2,000 LOCATION NUMBER OF ANNUAL SALES PEP MARKET SIZE CUSTOMERS SALES SQo FT, SHARE ----- ------ 1 100000 2145,79 14043631934 140,44 904 2. 4000(1 673,53 3133398.40 78,33 .01 3 1uoooo 2983,79 1227o432,62 122.70 006 4 7500o 907.82 435n996.70- 5@3.(.)j 02 r 1038.2,9 6747592,49 89,c)7 102 75000 6 loooou 2417,01 16431.115.2o 164,31 105 7 160000 763.67 4723989,79 47.24 02 15000 1391.03 5763592.47 76.A5 03 100000 19U8,57 12594213.52. 125.94 .64 16 boooo 651.89 4076891.12 .67*95 101 11 55600 673.78 3489402.16 63944 101 I? 60ooo 932.39 6201941.72 103.37 .02 13 175000 3410,24 16450984.70 94.01 107 14 621000 4!@09,61 27732290,68 44.66 109 1c; 280000 29d9,47 18693094,41 66.76 006 16 25000 1211*36 11968568.76 478974 o02 17 1250oo 2218.46 1144n285.89 91052 005 118 90000 2846*95 14214408,58 157.94 906 26 1500oo 2351,38 1OP27845.2$; 72.19 005 2i 60000 6o3.53, 5052512063 84.21. .02 2P. 250oo 3d9,00@ 1745899.51 69,P4 001 23 110000 1036.16 6601369.63 6,,)ooi s02 24 2200o 199,04 1227357,86 55#79 000 e5 75000 692,84 4511708,46 60.16 001 26 60000 1481,09 10136113,78 168*94 *03 27 1uoooo 1673*55 7052357,13 7,e).r,2 o03 ?q 100000 1736,60 10237267,13 102937 *03 2Q 100000 7,26o87 4459P78,48 44.59 001 3n 160000 70.62 4481860.33 44.82 001 3i 80000 591,41 3655052.27 45.69 .01 32 bOO00 732,40 4718635,32 78.64' .01 3'@ 500000 3459,68 14620489.82 29o24 *07 viii-9 TABLE VIII-4 PRIMARY RETAIL CENTERS City of Corpus Christi Size (sq. ft.) Mission Shopping Center 175,000 Padre Staples Mall 621,000 Parkdale Plaza Shopping Center 280,000 Pope Plaza Shopping Center 25,000 Port Ayers (strip) 125,000 Saxet Shopping Center 90,000 Shell Shopping Center 50,000 6 pt. Shopping Center 150,000 South Park Shopping Center 60,000 Suburban Shopping Center 25,000 Town & Country Shopping Center (strip) 110,000 United Savings Shopping Center 22,000 Village Shopping Center 75,000 Westwood Shopping Center 60,000 Woodlawn Plaza 100,000 Downtown CBD 450,000 Alenda Shopping Strip 10 0,000 Arcadia Village 40,000 Ayers Shopping Center 100,000 Bell-Aire Shopping Center 75,000 Carmel Shopping Center 75,000 Country Club Plaza 1001000 Cullen Mall 100,000 Gaslight Square 75,000 Gulfway 1001000 Hamlines Shopping Center 60,000 Lamar Park Shopping Center 55,000 Meadowbrook Shopping Center 60,000 Shopper World 100 000 K-Mart 100,000 Kroeger 100,000 Handy Dan 80,000 Attonta 60,000 Remainder of S.M. S.A. Port Aransas 36,300 Portland 10,51000 Taft 1321664 Aransas Pass 6461085 Sinton 50,000 Mathis 25,000 Bishop 25,000 Odem 15,000 Ingleside 20,000 VIII-10 CHAPTER IX HOUSING Urban Ecology Prior to allocating housing it was necessary to cap ure the neighborhood character or spatial ecology of the region. The purpose of this process was to understand the.distribution of population by residential characteristics. If.an allocation mechanism is to recognize the critical role of income, occupation and education affinity in the demand for housingit is important that these variables can be linked to the organization of population in residential space. For this reason the first step in allocating housing was to perform an urban factorial ecology on the City of Corpus Christi and the S.M.S.A. Variables utilized included income, employment, education, dependency, crowding and demographic characteristics of the population in each census tract,(Table IX-1). Three basic factors were identified from each of the princi- pal components analysis. These results were varimax rotated for maximum clarity. The factor loadings for both the City and the S.M.S.A. were very similar in terms of their level and ordering (Table IX-2). Since three factors were rotated,it was decided to interpret only the first two since the final factor often becomes a depository of unexplained residuals. The disaggregated distribution of variables by tract are found in the Table IX-9. However, the two basic factors that were identified can be charac- terized as a Socio-Economic Status Factor (I) and a Life-Cycle Factor (II). The socio-economic factor has positive loadings on education (6), Income (1) (3), high skill occupations (18), and negative loadings on crowded dwellings (12), low income (5) and low skill occupations (15). The life-cycle factor is positively related to large families (11), children (10), family units (2) and owner occupied dwellings (4) and is negatively related to.older populations (13) and unemployed males (7). These findings imply that socio-economic status is one factor in the-spatial organization of neighborhoods while the stage in the life cycle (i.e. child rearing or retirement) is another character- istic of neighborhoods. the first factor explains 35% of the common For discussion of this approach see B. J. L. Berry (ed) Factorial Ecology, special supplement of Economic Geography, (Clark Univ, Worcester, Mass., 1972). IX-1 Tab le IX-1 VARIABLES UTILIZED 1. Median Family Income 2. Percent of Households with Married Heads 3. Percent of Families with Income over $7,000 4. Percent of Dwellings Owner Occupied 5. Percent of Families with Income under $2000 .6. Median School Years Completed 7. Percent of Males Unemployed 8. Percent of Population Who Moved inside the S.M.S.A. 9. Percent of Population Who Moved ihto the S.M.S.A. 10. Percent of Population Under 19 11. Population per Household 12. Percent of Dwellings with over 1.01 persons per room 13. Percent of Population over 65 14. Percent of Females in the Labor Force. IX-2 TABLE IX-2 FACTOR LOADINGS S.M.S.A. CITY FACTOR I FACTOR I 6 99246 6 .9088 1 08924 1 8855 3 .8794 3 8818 12 M,8471 5 W.8525 18 .8222 15 mo8479 5 " 8010 18 8210 Is _:7b87 12 W,7815 17 -,5730 17 M,66?7 9 5305 14 4427 FACTOR 2 13 -0,8732 FACTOR 2 19.1 .8652 13 W,9076 it 833i 11 9045 2 815? 2 .8908 4 722q to 1,7848 7 wq5891 4 .6342 16 5571 7 5195 16 4274 IX-3 variance while the second factor explains 25%. Each tract is then scored independently (Tables IX-.3, IX-4) on each factor and these scores are mapped. By combining these two independent measures it is possible to locate a tract in two dimensional space (ie. Xl,X2,X3,X4) and to determine the factor distance between the tracts. Each tract therefore can be evaluated in terms of their distance apart in physical space (linear road distance) and their distance apart in factor space (Figure 1). The distance in the Socio-Economic Status (35%) Factor X Factor Distance Distance X4 X2 +Life Cycle (25%) Factor Distance X 3 Figure IX-I case of factor space must be weighted by the level of importance (explained variance or eigenvalue) of each factor. Not only does this exercise provide insight into the residential clustering habits of urbanites in Corpus Christi and its S.M.S.A. but it pro- vides a mechanism for determining tract similarities. This mechan- ism is utilized in the housing allocation model to allocate housing to new tracts after first preference tracts are filled. Housing Allocation Prior to allocation the housing market is partioned into two sub-groups. The first is the apartment and multi-family dwelling unit market and the second is the single family housing market. IX-4 Table IX-3 FACTOR I-SOCIO-ECONOMIC STATUS Tract Score 1 3236 2 :7084 3 3503 4 -1.5498 5 - .6819 6 .5627 7 .4204 8 *1853 9 -1 *6232 10 -1.4809 11 -1.4831 12 .0043 13, .3692 14 1.3387 15 -1.1529 16 -1.1712 17 -1.5818 18 - .6956 19 .3044 20 .8247 21 1.6279 22 1.0968 23 1.1299 24 1.3540 25 2.1239 26 1.3838 27 1.4062 29 .1847 31 .8371 32 .8762 33 7389 34 1.5652 36 .9781 37 1.1651 51 7238 54 .7922 56 6025 57 -2*.2133 59 - .6675 101 .6966 103 .6306 .104 - .6567 105 -1.3781 106 1.0794 1107 - .5323 108 - .6327 ill - .6299 112 - .7926 113 -1.5947 D(- 5 Table IX-4 FACTOR II-LIFE CYCLE Tract Score 2 -2.7912 3 -3.0084 4 -1.3621 5 - .3950 6 - .3259 7 .0683 8 - .6578 9 .2457 10 - .5909 11 - .6638 12 -1.4794 13 .0519 14 -1.2685 15 .0406 16 .8679 17 1.1755 18 1.6505 19 .5822 20 - .2944 21 - .8271 22 .3594 23 .6460 24 .8881 25 .5346 26 .1648 27 .7337 29 1.8310 30 .4652 31 .5137 32 .8937 33 .5087 34 1.1315 35 .4365 36 .6432 37 .5521 50 .7701 51 -2.4778 54 .2699 56 .4312 57 .5281 58 .0693 59 .2763 60 .1187 61 .3046 101 .4128 102 .3323 103 .2686 104 .5726 105 .9125 IX-6 FACTOR II-LIFE CYCLE (Continued) Tract Score 106 .7740 107 .4139 108 .4701 109 .1332 110 - .3025 ill * 1912 112 -1.1059 113 .5383 IX-7 CORPUS CHRISTI REGION RESIDENTIAL POPULATION SOCIO-ECONOMIC STATUS FACTOR I 1.5 0.5 . ...... ...... . ............. 0.5 SWON-0010 ......... Xx ..2. $moo. . ..... ...... . + ................. . .............. . . . . . .x........... .......... 4a .......... CORPUS ST X. C?4 *ISTI OIE ----- ....... .0 7 e.. bet*- :k 26 12 .................. .................... ............... ...........X .......... 30 ORISCaLL so ....... NOMMES CENSUS TRACTS AS OF 1970 CENSUS Figure IX-2 0 Mf* IX-8 CORPUS CHRISTI REGION RESIDENTIAL, POPULATION LIFE-CYCLE FACTOR11 I ............. ..... j* ..... ****"P-*'-*i*".*.*.-*...*'**..-.-.-.* ........ . . . . ..................... . .......... . ................ i...... N. ................ I ....... ............ ....... .......... .............. ............... .... 0.5 SWON-00" NN V TAFT in to? + Mir.: 1:3 M ft -:%:::* ......... . .. MANGAS PASS - WMLSW colwUS cNaw" MOT '10 Pass, of 0 :36.* 36 6 26 % e w9mop 64 30. DOSCOLL CORPU%Mwl so SOUTH 31 1011 left, CENSUS TRACTS AS OF 1973 CENSUS Figure IX-3 IX-9 Si@fon 107 It cl@7;@a- //--j ml TENANT CHACTERISTICS RANSAS PASS - INGELSIDE 1, knory 05 me \102 si J 0 36 67 Robstown 35 7 26 r 21 51 If 2 33 29 84 30 OWNER CORPUS OCCUPIED DJUSCOLL CHMSTI SOUTH so 31 MIXED T"ACTS RENTER TRACTS souRCE,1970 CENSUS OF POPULATION Figure IX-4 @2 14 /-P.. s DC-10 Apartments and multi-family dwellings are allocated first on the premise that they can out-bid single family housing for the site they prefer. A matrix of rent paid by apartments and multi-family units per tract is developed. (FigurelX-5). Figure IX-5 1 2 3 --- 6 Rent Categories Tracts 2 X n x=No. of Rental Units in tract 2 paying rent in rent category three ($60-$80) in time (tl) For the City of Corpus Christi such a matrix is given for 1960 and 1970 in Table IX-5. The change in each category provides the markov transition probability2 in the creation of a new distribution for 1980 under the constraint that (1) no category becomes negative @ie. it can only go to zero) and (2) the total increase in units is within the range of increase expected for 1980 (ie. we match the .total expected number of units per population with the number of units generated). The new 1980 distribution is presented in Table IX-6. Although the aggregate number of units is constrained to remain within that level demanded by the 1980 population the number of units,assigned to each tract is not limited to the amount of acreage Figure IX-6 Rent$ Y=a+bx Reftt=-185.7+2571.4x Acres per Rented Unit 2W.A.V. Clark "Markov Chain Analysis in Geography: An Application to the Movement of Rental Housing Areas" Annals of the Association of American Geographers (June, 1965), pp. 351-359. IX-11 Table IX-5 Matrix of Rental C4zegoi-ies By Cen6u4 Tract 1960 & 1970 60 MATRIX 70 MATRIX TRACT 1 112 214 @98 17 0 13 TRACT 1 76 109 88 20 16 1 TRACT 2 66 211 51 0 4 4 TRACT. 2 30 Its 69 9 3 1 TRACT 3 49 161 50 26 0 35 TRACT 3 6 60 39 20 66 35 TRACT 4 513 647 121 0 0 0 TRACT 4 305 406 121 6 2 0 TRACT 5 12 113 184 46 11 0 TRACT 5 .22 151 170 21 1 0 TRACT 6 35 257 283 153 18 0 TRACT 6 37 186 314 151 147 22 TRACT 7 20 137 175 53 4 0 TRACT 7 25 66 129 87 42 19 TRACT 8 15 42 49 0 0 0 TRACT a 12 22 20 It 13 1 TRACT 9 333 597 87 8 0 0 TRACT 9 224 383 103 4 2 0 TRACT 10 484 599 169 20 W 0 TRACT 10 326 457 113 19 2 2 TRACT it 452 528 85 12 0 0 TRACT It 280 367 99 12 6 2 TRACT 12 213 729 617 209 52 20 TRACT 12 113 481 606 298 108 81 TRACT 13 57 249 231 67 0 0 TRACT 13 26 199 284 66 35 0 TRACT 14 17 75 217 160 202 21 TRACT 14 19 71 166 215 195 S4 TRACT 15 517 224 124 117 48 0 TRACT 15 485 272 203 42 6 a TRACT 16 220 410 122 16 0 0 TRACT 16 138 396 132 17 0 la TRACT 17 130 80 20 0 0 0 TRACT 17 113 265 108 10 s 2 TRACT 18 0 18 53 14 4 0 TRACT 18 4 56 132 22 12 0 TRACT 19 5 73 209 220 91 0 TRACT 19 7 98 260 243 292 4 TRACT 20 51 149 305 208 90 4 TRACT 20 28 141 226 274 169 12 TRACT 21 4 25 250 294 188 32 TRACT 21 9 41 296 339 334 136 TRACT 22 8 12 59 77 78 9 TRACT 22 3 12 68 99 184 34 TRACT 23 0 12 63 66 84 8 TRACT 23 9 9 65 91 236 203 TRACT 24 12 50 111 57 140 20 TRACT 24 21 66 126 @123 472 410 TRACT 25 0 0 4 4 25 26 TRACT 2S 1 2 2 0 28 Joe TRACT 26 0 13 65 139 96 8 TRACT 26 4 6 80 142 293 423 TRACT 27 a 12 15 72 134 15 TRACT 27 3 12 28 102 374 431 TRACT 50 82 121 115 51 31 4 TRACT 50 58 137 176 95 126 46 TRACT 51 8 31 19 0 4 0 TRACT 51 44 14 25 13 13 6 TRACT 52 a 239 174 182 109 0 TRACT 52 2 28 6 3 178 18 TRACT 53 73 263 145 37 12 8 TRACT 53 64 198 241 151 308 115 TR'ACT 54 42 54 73 7 16 0 TRACT 54 27 21 20 32 33 TRACT 56 299 173 Ito 64 16 0 TRACT S6 336 191 128 33 23 TRACT 57 274 8 4 0 0 0 TRACT S7 142 33 6 1 0 0 TRACT 58 27 39 28 16 4 0 TRACT 58 32 27 24 7 6 1 TRACT 59 71 26 37 21 4 a TRACT 59 53 22 2 0 1 0 TRACT 60 1@ 23 30 9 0 0 TRACT 60 30 12 8 0 1 0 TRACT 61 26 34 58 32 38 0 TRACT 61 26 40 63 38 12 7 Table IX-6 Matrix of Rental Categories By Census Tract 1980 (Projected) 80 MATRIX TRACT 1 57 3 LA 82 2 F' 18 0 TRACT 2 20 14 80 to 3 0 TRACT 3 3 0 34 19 112 35 TRACT 4 161 184 121 6 2 0 TRACT 5 53 971 0 0 0 0 TRACT 6 38 67 402 148 318 26 TRACT 7 31 0 0 228 118 36 TRACT 8 11 6 a 15 19 1 TRACT 9 145 118 108 4 2 0 TRACT 10 180 273 95 19 2 2 TRACT 11 125 177 103 12 6 2 TRACT 12 39 0 562 471 148 113 TRACT 13 0 0 2793 55 239 0 TRACT 14 20 61 fo 637 146 118 TRACT 15 0 865 932 0 0 TRACT 16 5 331 17 0* TRACT 17 106 445 143 IP 5 2 TRACT 18 4 72 208 23 13 0 TRACT 19 7 106 303 261 484 4 TRACT 20 13 115 0 695 479 14 TRACT 21 9 43 334 381 469 175 TRACT 22 3 12 72 113 308 39 TRACT 23 9 9 65 97 330 307 TRACT 24 21 67 128 133 661 603 7RACT 25 1 2 2 0 29 200 TRACT 26 4 6 82 143 385 703 TRACT 27 3 12 29 106 . 503 689 TRACT 50 52 146 222 113 17 *1 54 TRACT 51 .74 10 28 16 15 7 TRACT 52 2 16 4 2 204 19 TRACT 53 63 174 284 183 477 138 TRACT 54 20 9 2 4 t, 43 0 TRACT 56 580 258 173 13 26 2 TRACT 57 0 41 6 1 0 0 TRACT 58 41 8 18 3 7 1 TRACT 59 42 21 1 0 0 0 TRACT 60 59 6 0 0 1 0 TRACT 61 26 160 220 152 0 31 IX-13 in that tract. The result is that overassignment occurs. In order to determine the level of overassignment an equation relating rent paid and land consumption was calculated. The land consumption based on the median rent for each category was multiplied by the number of units in the category to determine the amount of acreage consumed by new units. These could then be summed for each tract and compared with the land vacant in that tract in the initial period. Of the 38 tracts considered.eight had been overassigned (tracts 5, 13, 14, 15, 20, 22, 23, 26). If the loses, tear-down or conversion in these tracts were less than the acreage needed for new units and space was not availablerdwelling unit acreage was reassigned. Acreage was reassigned based on the factor similarity measures and spatial continguity. After the most similar and near tracts are filled the next nearest (physical distance) and most similar (factor distance) tract is allocated any remaining units by acreage. A similar process occurs in the allocation of single family units. First a matrix of tracts by housing value (six categories) is constructed for 1960 and 1970 (Table IX-7). Again we develop markov transition probabilities for each tract category and project a 1980 distribution (Table IX-8). The equation relating housing value to land consumption is then identified for the median housing value Figure IX-7 Housing $ Value Y=a+bx House Value=10,000 + 30,000x Acres per Housing Unit in each category,and equivalent land consumption value are determined. By multiplying the land consumption value for a particular category by the number of units in that category and summing across the tract, total acreage assigndd to that tract is determined. This acreage is compared to available (vacant) acreage in that tract plus tear- down ratio. If overassignment has occurred then the overassigned acres are distributed to near-by (linear distance) and similar (factor distance) tracts until all overassignments are allocated. This allocation process determines the amount of acreage in DC-14 Table IX-7 i,-,atrix of Eousing Catec-ories (Single Family) by Census Tract 19 6 1, 7 3 60 MATRIX 70 MATRIX TRACT 1 19 to 8 2 1 5 TRACT 1 4 14 1 1 1 TRACT 2 9 15 1 1 0 4 TRACT 2 1 6 2 0 0 3 TRACT 3 4 3 2 2 2 8 TRACT 3 1 2 1 1 1 1 TRACT 4 48 121 39 10 3 2 TRACT 4 26 60 19 10 2 6 TRACT 5 31 281 89 7 1 0 TRACT 5 37 253 104 17 3 1 t34 38 47 TRACT 6 68 724 700 171 41 37 TRACT 6 75 828 745 208 45 24 TRACT 7 31 426 156 24 7 9 TRACT 7 40 291 TRACT 8 3 35 14 3 0 to TRACT 8 to 31 13 0 5 TRACT 9 620 305 16 1 1 0 TRACT 9 326 384 37 9 3 TRACT 10 276 271 3'9 6 2 0 TRACT to t92 249 63 16 0 TRACT it 144 146 4b it 5 6 TRACT it 81 116 18 10 3 2 TRACT 12 50 244 90 31 19 31 TRACT 12 39 17-8 90 25 7 14 TRACT 13 56 679 93 12 1 0 TRACT 13 41 523 192 15 0 I@ 547 269 123 189 TRACT 14 8 117 504 336 122 220 TRACT 14 2 172 3 1 TRACT Is 6 552 156 5 0 0 TRACT Is 10 446 too 10 TRACT 16 1050 1098 77 7 0 1 TRACT 16 627 1105 305 33 6 4 TRACT 17 501 362 a 2 1 0 TRACT 17 301 651 172 46 13 3 TRACT 18 321 1202 109 1 0 0 TRACT 18 41 1073 413 111 8 6 TRACT 19 87 1303 496 10 2 1 TRACT 19 49 1162 791 56 10 4 TRACT 20 60 '183 913 143 13 4 TRACT 20 37 644 991 188 24 6 TRACT 21 3 141, 705 382 143 200 TRACT 21 1 126 606 390 199 267 TRACT 22 18 358 675 211 95 22 TRACT 22 6 281 650 283 123 66 TRACT 23 12 379 606 62 1 2 TRACT 23 8 271 659 268 194 151 TRACT 24 105 457 941 465 85 5 TRACT 2.4 49 303 961 1010 950 821 TRACT 25 1 2 121 279 311 529 TRACT 25 0 3 108 247 345 609 TRACT 26 16 434 550 44 12 41 TRACT 26 2 392 612 175 212 479 TRACT 27 8 373 833 274 140 too TRACT 27 3 233 730 458 363 905 TRACT 50 324 480 259 174 58 64 TRACT 50 227 494 313 484 298 112 TRACT 51 42 77 26 0 4 9 TRACT 51 27 63 41 43 26 .35 TRACT 53 283 271 85 12 12 9 TRACT 53 154 286 319 413 135 72 TRACT 54 112 89 37 17 0 0 TRACT 54 84 45 21 3 5 14 TRACT 56 723 527 186 88 48 32 TRACT 56 642 684 301 141 50 73 TRACT 57 962 57 4 4 0 0 TRACT 57 642 198 21 1 1 0 TRACT 58 101 84 16 4 8 0 TRACT 58 86 73 81 94 47 35 TRACT 59 146 15 4 0 0 (6 TRACT 59 135 53 to 7 3 1 TRACT 60 76 28 18. 0 0 0 TRACT 60* 58 37 34 19. 4 13 TRACT 61 247 261 168 39 13 13 TRACT 61 14'6 219 209 80 35 51 TRACT 0 -0 IR 0 -.10 00 go "0 TRACT NO 00 0 *0 00 *0 Table IX-8 Matrix of Housing Categories (Single Family) By Census Tract 1980 CProjected) 80 MATRIX TRACT 1 2 16 0 0 1 0 TRACT 2 0 3 2 0 0 3 TRACT 3 0 2 0 0 0 0 TRACT 4 20 23 is to 2 6 TRACT 5 74 0 364 45 4 1 TRACT 6 64 126 450 221 42 34 TRACT 7 53 0 594 79 39 12 TRACT 8 24 6 10 a 10 Pi I TRACT 9 0 549 41 9 3 0 TRACT 10 0 175 83 18 0 0 TRACT it 41 89 14 10 3 2 TRACT 12 35 73 90 24 6 12 TRACT 13 32 0 467 16 .0 1 TRACT 14 18 0 0 4838 98 1584 TRACT is 11 0 243 11 3 1 TRACT 16 0 1156 760 39 6 4 TRACT 17 108 1254 262 52 13 3 TRACT 18 0 0 7021 7S4 11 8 TRACT 19 38 215 2140 71 10 4 TRACT 20 4 0 3964, 513 34 6 TRACT 21 0 0 0 598 942 1460 TRACT 22 4 0 108 962 238 163 TRACT 23 8 211 730 381 271 197 TRACT 24 48 280 970 1280 1354 115o TRACT 2S 0 3 88 132 515 1315 TRACT 26 2 371 661 205 267 750 TRACT 27 3 196 645 553 455 1647 TRACT 50 188 S06 343 748 424 121 TRACT 51 22 52 49 67 33 47 TRACT 53 126 292 425 647 158 78 TRACT 54 56 21 17 2 5 16 TRACT 56 461 1058 422 167 so 83 TRACT 57 0 368 23 0 1 a TRACT 58 80 69 107 136 56 41 TRACT 59 106 92 23 8 3 1 TRACT 60 34 45 47 27 4 17 TRACT 61 0 0 8778 3360 805 1989 TRACT PO 0 0 0 0 0 0 IX-16 Table IX-9 ux-1-u; ECOL@DG'Z VAR1;,b7,LL BY Til:-CT TRACT NO, I TRACT NO, 3 TRACT NO NED, FAMILY INCOME b5b7,999 NED, FAMILY INCOME S4990090 NED, FIMILAY INCOME 4897,096 PCT HH W HARR, HEADS 24 S95 PCT MH W HARR, HEADS 22,430 PCT MM W MAiIR, HEADS S1,506 PCI FANS W INC@37000 37529 PCT FAMS W INCZ57000 28,4 1 PCT FAMS W INCIS7000 31,832 PCT DWELLS OWNER OCC 12,727 PCT DWELLS OWNER OCC 3,718 PCT DWELLS OWNER OCC 45 680 PCT FAMS W INCtS2000 16,523 PCT FAMS W INC462000 13,664 PCT FAMS W INCvS20@9 11:26i NED, SCHOOL YEARS 9,500 NED, SCHOOL YEARS 12,000 NED, SCHOOL YEARS 9,900 PCT MALES UNEMPLOYED 7,34S PCT MALES UNEMPLOYED 8,108 PCT MALES UNEMPLOYKD 1,314 PC MOVED INSIDE SMSA 29,122 PC MOVED INSIDE SMSA 24 194 PC mOVED INSIDE SMSA 30,710, PC MOVED INTO SMSA 25,993 PC MOVED INTO SMSA 8:S2S PC MOVED INTO SMSA 3,927 PCT POP %19 YRS OLD 23,10s PCT POP 519 YRS OLD 18,894 PCT POP 519 YRS OLD 41,904 POP PER HOUSEHOLD 1,079 POP PER HOUSEHOLD 18352 POP PER HOUSEHOLD 2,736 RCT OWLS W kI 01 ORM 7 792 PCT OWLS W X1,01 ORM 5,919 PCT OWLS W 11,01 ORM 13,755 PCT POP k6S YAS OLD 19:495 PCT POP t65 YRS OLD 19956S PCT POP t65 YRS OLD 7,266 PCT FEMS IN LAB FORS 27,372 PCT FEMS IN LAB FORS 71,724 PCT FEMS IN LAB FORS 60,952 PCT LABS IN LAB FORS 5,86t PCT LABS IN LAB FORS 0,000 PET LABS IN LAB FORS S,405 NO MIND HEMS IN TRCT 831,000 NO MIND HEMS IN TRCT 434 0 NO MIND HEMS IN TRCT 2725,000 5)PCT PVT HHWORK IN 2,J98 PCT PVT HHWORK IN LF 0:00:0 PCT PVT HHWORK IN LF 19,764 1,PCT PROAMGR IN LF LF 34,066 PCT PRO^MGR IN LF 15's?o PCT PROAMGR IN LF 8.90 -'@ PCT CRFTAOPS IN LF 31,700 PCT CRFT^OPS. IN LF 25,628 PCT CRFTAOPS IN LF 16,723 TRACT NO, 2 TRACT NO, 4 TRACT NO, 6 NED, FAMILY INCOME 389S,000 NED, FAMILY INCOME 3510,000 PCT HM W HARR, HEADS 30,592 PCT HH W HARR, HEADS 25,193 NED$ FAMILY INCOME 8033 000 PCT HM W HARR, HEADS 68:SSI, PCT FAMS W INC?$7000 23,478 PCT FAMS W INCt$7000 IS,429 PCT FAMS W INCZ57000 57,833 PCT DWELLS OWNER OCC 6,250 PCT DWELLS OWNER OCC 11,747 PCT DWELLS OWNER OCC 64,001 PCT FAMS W INC<$2000 26,957 PCT FANS W INC<S2000 22,143 PCT FAMS W INC452000 7,596 NED, SCHOOL YEARS 8,600 NED, SCHOOL YEARS 90100 NED, SCHOOL YEARS 11,300 PCT MALES UNEMPLOYED 22,388 PCT MALES UNEMPLOYED 20210 PCT MALES UNEMPLOYED 3,640 PC MOVED INSIDE SMSA 2S,4SS PC MOVED INSIDE smsA 20,201 PC MOVED INSIDE SMSA 28,535 PC MOVED INIO SMSA to 101 PC MOVED INTO SMSA '818 PC MOVED INTO SMSA 10,270 PCT POP 519 YRS OLD 24:444 PCT POP 519 YRS OLD 43,738 PCT POP $19 YRS OLD 36 437 POP PER HOUSEHOLD 1,628 POP PER HOUSEHOLD 2056 POP PER HOUSEHOLD 2:896 PCT OWLS W Z1,01 ORM 6,224 PCT OWLS W Z1,01 ORM 120210 PCT OWLS W Z1,01 ORM 10,572 PCT POP Z65 YRS OLD 16 768 PCT POP Z65 YRS OLD 91283 PCT POP 165 YRS OLD 8,410 PCT FEMS IN LAB FDRS 33:7SO PCT FEMS IN LAB FORS 50,890 PCT FEMS IN LAB FORS 36,721 PCT LABS IN LAB FORS IS,278 RCT LABS IN LAB FORS 11,810 PCT LABS IN LAB FOR$ S,590 NO MINO HEMS IN TRCT 495 000 NO MIND HEMS IN TRCT 3178.000 NO MIND HEMS IN TRCT 8656,000 PCT PVT NHWORK IN LF 2:315 PCT PVT HHWORK IN LF 22%571 PCT PVT HHWORK IN LF t,648 PCT PROAMGR IN LF 12,037 PCT PROAMGR IN LF 36619 PCT PRO^MGR IN LF 19,388 PCT CRFT^OPS IN LF 24,537 PCT CRFTAOPS IN LF 21,524 PCT CRFTAOPS IN L@ 31,539 Table IX-9 (cont'd.) TRACT 40, 7 TRACT NO, 9 TRACT NO$ MED, FAMILY INCOME 7851,000 NED, FAMILY INCOME 4062,000 NED, FAMILY INCOME 4017,00% PCT HH W MARR, HEADS 72,28s PCT HH W MARR, HEADS 60,311 pCT Hm w MARR, HEADS 44,96Z, PCT FANS W INC?S7000 609261 PCT FAMS W INCt$7000 20,320 PCT FANS W INCk$7000 190543 PCT DWELLS OWNER OCC 62,723 PCT DWELLS OWNER OCC 42,911 PCT DWELLS OWNER OCC 20,914 PCT FAMS W INC452000 5.416 PCT FAMS W INC452000 22,825 PCT FAMS W INCAS2000 19 924 MEO, SCHOOL YEARS 110136 NED, SCHOOL YEARS 5,200 NED, SCHOOL YEARS 6:200 PCT MALES UNEMPLOYED 3,909 PCT MALES UNEMPLOYED 5 9S? PCT MALES UNEMPLOYED 2'asq PC MOVED INSIDE SMSA 131784 PC MOVED INSIDE SMSA 23:SO7 PC MOVED INSIDE SMSA 30 830 PC MOVED INTO SMSA 236816 PC MOVED INTO SMSA 2,213 PC MOVED INTO SMSA 2:9S4 PCT POP S19 YRS OLD 39,649 PCT POP S19 YRS OLD 47,620 PCT POP S19 YRS OLD 44,7Z6 Pop PER HOUSEHOLD 3.045 POP PER HOUSEHOLD 3,544 POP PER HOUSEHOLD 3,010 PCT OWLS W k1#01 oRM t2,318 PCT OWLS W t1,01 ORM 33,942 PCI DW45 W tl,81 ORM 23,291 PCT POP ?65 YRS OLD 6067 PCT POP k65 YRS OLD 6,123 PCT POP Z65 YRS OLD lo,239 PCT FENS IN LAS FORS 33,306 PCT FENS IN LAB FOR$ 2S,223 PCT FEMS IN LAB FORS 29,402 PCT LAOS IN LAB FORS 5,34 PCT LASS IN LAB FORS 15,2SS PCT LABS IN LAB FORS t2'621 NO MIND MEMS IN TRCT 3758,00: NF MIND HEMS IN TRCT 6598 000 No MIND MEMS IN TRCT 3555,000 PCT PVT HHWORK IN LF 9585 P T PVT HMWORK IN LF 3:050 PCT PVT HHWORK IN LF 6,699 PCT PROAMGR IN LF 12@802 PCT PROAMGR IN LF 11,672 PCT PROAMGR IN LF 8,252 eCT CRFTAOPS IN LF 36,942 PCT CRFTAOPS IN LF 26,041 PCT CRFTAOPS IN LF 24,854 rRACT NO, 8 TRACT NO, to TRACT NOO 12 NED, FAMILY INCOME 6048,000 NED, FAMILY INCOME 4008,000 NED FAMILY INCOME 5365,000 PCT HH W HARR, HEADS 67,477 PCT HH W MARR, HEADS 48,34 11 PCT@HH W HARR HEADS 43,856 PCT FAMS W INCkS7000 29v461 PCT FANS W INC?S7000 23,077 PCT FAMS W INItS7000 33,047 PCT DWELLS OWNER OCC 58 663 PCT DWELLS OWNER OCC 32,324 PCT DWELLS OWNER OCC 18,624 PCT FAMS W INC4$2000 6:639 PCT FAMS W INC<S2000 22 479 PCT FAMS W INC4S2000 14,807 NED, SCHOOL YEARS 10,300 NED, SCHOOL YEARS 6:400 MED, SCHOOL YEARS 10,600 PCT MALES UNEMPLOYED 0,000 PCT MALES UNEMPLOYED 7,207 PCT MALES UNEMPLOYED 1,181 PC MOVED INSIDE SMSA 32,892 PC MOVED INSIDE SMSA 27,068 PC MOVED INSIDE SMSA 26,642 PC MOVED INTO SMSA 9,277 PC MOVED INTO SMSA 3,9S4 PC MOVED INTO SMSA 17,208 PCT POP 519 YRS OLD 30#960 PCT POP 519 YRS OLD 43,843 PCy Pop 519 YRS OLD 32,687 Pop PER HOUSEHOLD 2,523 POP PER HOUSEHOLD 3,225 POP PER HOUSEHOLD 2 ,212 PCT OWLS W tt,01 ORM 99119 PCT OWLS W Il.01 ORM 25,510 PCT DW45 W tlgOt ORM 10,896 PCT POP t65 YRS OLD 8,434 PCY POP ?:65 YRS OLD 12,20Y PCT POP t65 YRS OLD 13,976 PCT FENS IN LAB FORS 38oO@O PCT FEMS IN LAB FOR$ 34,080 PCT FEMS IN LAB FORS 42,01 PCT LABS IN LAB FORS 3s509 PCT LABS IN LAB,FORS 14,141 PCT LABS IN LAB FORS 8, '439 No MIND HEMS IN TRCT 830,1000 NO MIND MEMS IN TRCT 5538,000 NO MIND HEMS IN TRCT 5724,000 PCT PVT HHWORK IN LF 4,096 PCT PVT HHWORK IN LF 6,912 PCT PVT HHWORK IN LF 2 40S PCT PROAMGR IN LF 20g466 PCI PROAMGR IN LF 7,483 PCT PROAMGR IN LF 19:983 PCT CRFTAOPS IN LF 33q918 PCT CRFTAOPS IN LF 29,6t3 PCT CRFTAOPS IN LF 22,868 Table IX-9 TRACT NO, 13 TRACT ND, Is MED, FAMILY INCOME 6091,000 rRACT NO, 17 PCI H" W MARR, HEADS b3,677 MFD,- FAMILY INCOME O@e MED, FAMILY INCOME 4949,00tk PCT FAMS W INCt$7000 43,75S PCT WN W HARR, HEADS 56:7SS PCT HN W MARR, HEADS 69'ssp PCT DWELLS OWNER OCC 52'338 PCT FAMS W INCtS7000 3@g578 PCT FAMS W INCkST000 27,654 PCT FAMS W INC<$2000 6:969 PCT DWELLS OWNER OCC 36,932 PCT DWELLS OWNER OCC 63,740 MED, SCHOOL YEARS 9,100 PCT FAMS W INC<S2080 19-312 PCT FAMS W INC<52000 14,630 PCT MALES UNEMPLOYED 2,6t4 @ARS MED, SCHOOL YEARS 7,200 OF'JUPW LOYED Nis PC MOVED INSIDE SMSA 25,SSI a PCT MALES UNEMPLOYED t,93S PC MOVED INSIDE SMSA 33 344 PC MOVED INSIDE SMSA t9,838, PC MOVED INTO SMSA 8,809 PC MOVED INTO SMSA 7:209 PCT POP $19 YRS OLD 43,072 PCT POP 519 YRS OLD so PC MOVED INTO SMSA 6,37S POP PER HOUSEHOLD 3 267 :127 PCT POP SJ9 YRS OLD 55 329 PCT OWLS W t1,01 ORM 20:243 POP PER HOUSEHOLD 3 487 POP PER HOUSEHOLD 4:22t PCT OWLS W kt,8t ORM 229370 PCT OWLS W Zt,01 ORM 34,026 PCT POP t65 YRS OLD 7 699 PCT POP Z65 YRS OLD 9 178 PCT POP ?65 YRS OLD 3,569 PCT FEMS IN LAS FOR$ 37:st4 PCT FEMS IN LAS FORS 32:STS PCT FEMS IN LAS FORS 32,744 PCT LAOS IN LAB FORS 4,8ST PCT LABS IN LAS FORS 10,536 ptT LABS IN LAB FORS t6,055 NO MIND MEMS IN TRCT S131,008 NO MIND MEMS IN TRCT 6298@009 NO MIND HEMS IN TRCT 8126,000 PCT PVT HHWORK IN LF 2,629 PCT PVT HHWORK IN LF S%299 PCT PVT 'AHNORK IN LF 9,934 PCI PROAMGR IN LF 12 b29 PCT PROAMGR IN LF 11,035 PCT PROAMGR IN LF 8,239 PCT CRFTAOPS IN LF 33:429 PCT CRFTAOPS IN LF 27,307 PCT CRFTAOPS IN LF 27,825, TRACT NO, 16 rRACT NO, t8 MED, FAMILY INCOME 5547,090 MEDI FAMILY INCOME 7160,000 PCT MH W MARR, HEADS ?OgU3 PCT HH W MARR. HEADS 80,66i PCT FAMS W INCk3jogo 34,786 PCT FAMS W INCtS7000 52,220 TRACT NO, t4 PCT DWELLS OWNER OCC 699271 PCT DWELLS OWNER OCC MED, FAMILY INCOME 97S8,000 PCT FAMS W INC<32000 171202 PCT FAMS W INCg$2000 7,13$ PJT HH W MARR H$ASS 56 MEDs SCHOOL YEARS ?,too P T FAMS W INtas 0 0 63:6614 MED, SCHOOL YEARS 9,169, PCT MALES UNEMPLOYED 2,626 PCT MALES UNEMPLOYED 2,213 PCT DWELLS OWNER OCC 6t,034 PC MOVED INSIDE SMSA 20,S78 PCT FAMS W INC4$2000 5,533 PC MOVED INSIDE SMSA 15's2s PC MOVED INTO SMSA 3g292 PC MOVED INTO SMSA 4,gtt MED, SCHOOL YEARS 12'soo PCT POP 519 YRS OLD 49 577 PCT POP St9 YR9 OLD 53,655 PCT MAkES UNEMPLOYED 760 POP PER HOUSEHOLD 4 03S PC m POP PER HOUSEHOLD 4 S72 Ov 0 INSIDE SMSA 14'.999 Ptl DWLS W Z1,81 ORM 329bt7 PCT OWLS W ?1,01 *RM 35:49q PC MOVED INTO SMSA 16,28S PCT POP t65 YRS OLD 5,493 PCT POP Z65 YRS OLD 2 262 PCT POP St9 YRS OLD 24,645 PCT FEMS IN LAB FORS 36g3RI PCT FEMS IN LAB FORS 35:30 POP PER HOUSEHOLD 2.297 PCT LABS IN LAB FORS 11072 RCT LABS IN LAB FORS 9,547 PCT OWLS W ?1,01 ORM 3,17t NO MIND mEms IN TRCT 12397,000 NO MIND MEMS IN TRCT 8797,000 PCT POP Z65 YRS OLD 17,496 PCT FEMS IN LAS FORS 44,610 PCT PVT HHWORK IN LF 8,399 PCT PVT HHWORK IN LF 3,332 PCT LABS IN LAB FORS 1,707 PCT PRO^MGR IN LF to.18i NO MIND MEMS IN TRCT 5287,008 PCT CRFT^OPS IN LF 33,996 PCT PVT HHWORK IN LF 583 PCT PROAMGR IN LF 8,176 PCT PROAMGR IN LF 38,010 PCT CRFTAOPS IN LF 28,219 PCT CRFTAOPS IN LF 13,072 Table IX-9 (cont'd.) TRACT NO, 19 TRACT NO, 2t TRACT N09 23 10664,000 NED, FAMILY INCOME 10873,0 0 1 NED, FAMILY INCOME NED, FAMILY INCOME 8065,000 PCT HH W HARR, HEADS 60,89, PCT MM W HARR* HEADS 7S,473 PCT MH W HARP, HEADS TS,fdSS PCI FANS N INCIS7000 7S,415 PCT FANS W INCZ$7000 82,721 PCT FAMS W INCZ37000 61,611 PCT DWELLS OWNER OCC 54192q PCT DWELLS OWNER OCC 63,351 PCT DWELLS OWNER OCC 66,4a8 PCT FANS W INCOS2000 21231 PCT FAMS W INC<62000 t1659 PCT FAMS W INCCS2000 3,359 NED, SCHOOL YEARS 12,900 NED, SCHOOL YEARS 12,500 NED, SCHOOL YEARS 11,300 PCT MALES UNEMPLOYED 842 RCT MALES UNEMPLOYED 21413 PCT MALES UNEMPLOYED 2169S PC MOVED INSIDE SMSA 22 954 PC MOVED INSIDE SMSA 30,491 PC MOVED INSIDE SMSA 30,237 PC MOVED INTO SMSA 20:094 PC MOVED INTO SMSA 20 592 PC MOVED INTO SMSA I 1 303 PCT POP S19 YRS OLD 28,901 pCy pop s19 YRS OLD 45:467 PCT POP S19 YRS OLD 45,Sbb POP PER HOUSEHOLD a,397 pop PER HOUSEHOLD 31292 POP PER HOUSEHOLD 3,620 PCT OWLS W tlgOl oRM 16.15S ;FT Rg@IkWSI@AJIOCRM OAJ PCT OWLS W t1@01 #RM 9, 94ft 5,462 6 D 11:1 PCT POP k65 YRS OLD 2,264 PCT POP ?65 YRS OLD PCT FENS IN LAB FORS 51,440 PCT FEMS IN LAB FORS 49,136 PCT LASS IN LAB FORS 1,263 PCT L405 IN LAB FORS 2,844 NO MIND MEMS.IN TRCT 7271,060 No MIND mEms IN TRCT BIT3,000 PCT PVT HHWORK IN LF 061 PCT PVT HHWORK IN LF 1464 PCT PROAMGR IN LF 40,086 PeT PROAMGR IN LF 31,777 PCT FEMS IN LAB FORS 40,235 PCI CRFTACPS IN LF J3,544 pel CRFIAOPS IN LF ZS,255 PCT LABS IN LAB FOR$ 4,373 r<NO MIND HEMS IN TRCT 11625,000 24 PC T PVT HHWORK IN LF 1,546 TRACT NO, 22 TRACT N09 (@:'PCT PRO^MGR IN LF 16,816 NED, FAMILY INCOME 10204,000 miDs FAMILY INCOME 11617,000 PCT CRFTAOPS IN LF 30,901 PCT HH W HARR, HEADS 79,140 pey HH 0 MARRq HEADS 84,3SO Rob pCT FANS W INCZ67000 880990 PCT FAMS W INCt$7000 74 7S*633 PCT DWELLS OWNER OCC 74:738 PCT DWELLS OWNER OCC rRACT. NO, 20 PCT FAMS W tNC<82000 z,619 PCT FANS W INCA$2000 1,416 NED, FAMILY INCOME 8385,000 NED, SCHOOL YEARS 12,400 NED, SCHOOL YEARS PCT HH W MARR, HEADS 67,S83 PCT MALES UNEMPLOYED 3,403 PCT MALES UNEMPLOYED 1,609 PCT FAMS W INCt$7000 63 981 PC MOVED INSIDE SMSA 23,861 PC MOVED INSIDE SMSA 2S,784 PCT DWELLS OWNER OCC 6S:S38 PC MOVED INTO SMSA 14,862 PC MOVED INTO SMSA 19,972 PCT FAMS W INC02000 6,18S pty pop sJ9 YRS OLD 44 199 NED, SCHOOL YEARS 12,300 pop' PER HOUSEHOLD 3:s2I PCT MALES UNEMPLOYED 3,083 PCT OWLS W kt,01 ORM 8,012 PC MOVED INSIDE SMSA 29,944 pCT POP Z65 YRS OLD 2,440 PC MOVED INTO SMSA 12,036 'PCT POP S19 YRS OLD 39,245 PCT FEmS IN LAB FORS 47,39S PCT POP 919 YRS OLD 36,9SS POP PER HOUSEHOLD 3,209 PCT LABS IN LAB FORS 20962 POP PER HOUSEHOLD 2,888 PCT MiLS 4 ti,01 ORM 6,604 No MIND MEMS IN TRCT 8482,000 PCT OWLS W ki 01 ORM 74643 PCT POP t65 YRS OLD 4,524 pCT PVT HMWORK IN LF 0440 PCT POP 2!65 YAS OLD 8,1377 PCT FEMS IN LAB FORS SI,632 pCT PRO^NGR IN LF 310642 PCT FEMS IN LAB FORS 43,966 22,493 PCT LABS IN LAB FORS 3,799 PCT LABS IN LAB FORS 2,229 PCT CRVTAOPS IN LP NO MIND HEMS IN TRCT 8616,000 NO MIND MEMS IN TRCT 6123,000 PCT PVT HHWORK IN LF J'jas PCT PVT HHWORK IN LF 1,806 PCT PROAMGR IN LF 2S,971 PCT PRO^MGR IN LF 27,325 PCT CRFTAOPS IN Lf 22,482 PCT CRFT^OPS IN LF 24,020 Table IX-9 (cont@d-) TRACT,110, 25 TRACT NO, 27 MED, FAMILY INCOME 17400,099 MED@ FAMILY INCOME 122t3s099 TRACT NO, JU PCT HN W MARR, HEADS 83,837 PCT MH W MARR, HEADS 79 815 MED, FAMILY INCOME b130,000 PCT FAMS W INCkS7000 92,038 PCT FAMS W INCk37800 84:251 PCT HM W HARR, HEADS 59,042 PCT DWELLS OWNER OCC 86,no PCT DWELLS OWNER OCC 69 954 PCT FAMS W INCk$7000 45,000 PCI FAMS W INC4$2000 2,264 PCT FAMS W iNC<62000 1:930 PCT DWELLS OWNER OCC 39,620 MED, SCHOOL YEARS 12,80V PCT FAMS W INCt$2000 5,818 PCT MALES UNEMPLOYED 1042 MED. SCHOOL YEARS 11,500, PC MOVED INSIDE SMSA 23,279 PCT MALES UNEMPLOYED 3,732 PC MOVED INTO SMSA 23 250 PC MOVED INSIDE SMSA 13,740 MED, SCHOOL YEARS 14,306 PCT POP St9 YRS OLD 43:463 PC MOVED INTO SMSA 37,478 PCT MALES UNEMPLOYED 1,21S POP PER HOUSEHOLD 3,359 PCT POP 519 YRS OLD 38 709 PC MOVED INSIDE SMSA 18,880 PCT OWLS W k1,81 oRM 6,110 POP PER HOUSEHOLD 2:384, PC MOVED INTO SMSA 16g3lb PCT POP k65 YRS OLD 2,994 RCT OWLS W t1,01 ORM 11,796 PCT POP 519 YRS OLD 35,789 PCT FEMS IN LAB FORS 42,599 PCT POP Z65 YRS OLD 5 149 POP PER HOUSEHOLD 3,282 PCT LABS IN LAB FORS I'S91 PCT FEMS IN LAB FORS 31:5991: PCT OWLS W al,81 ORM 2,431 NO MIND HEMS IN TRCT 13828,000 PCT LABS IN LAB FORS 3,150' PCT POP Z65 YRS OLD 8,SS7 PCT PVT HHWORK IN LF 530 NO MIND MEMS IN TRCT 3981,000 PCT FEMS IN @,kB FORS 37,739 PCT PROAMGR IN LF 40,739 PCT PVT HHWORK IN LF 0,000 PCT LABS IN LAB FORS 3,429 PCT CRFTAOPS IN LF 17,19R PCT PRO^MGR IN LF 8,561 -NO MIND MEMS IN TRCT 4873,000 PCT CRFTAOPS IN LF 36,713 PCT PVT HHWORK IN LF 768 PCT PROAMGR IN LF 55,681tRACT NO, 29 PCT CRFT^OPS IN LF 6,448 MED, FAMILY INCOME 6878,000 TRACT NO, 31 MED, FAMILY INCOME 104 06:011101 PCT HH W HARR, HEADS 723 PCT FAMS W INCa$7000 72 36, TRACT NO, 26 MED, FAMILY INCOME 11653,000 PCT DWELLS OWNER OCC 711481 HEADS 95:133 PCT FAMS W INC<S2000 5:21, PCT HH W MARR, HEADS PCT HH W HARR, 12 40 PCT FAMS W INCk$7000 8@1569 PCT FAMS W INCk$7000 47 719 MED, SCHOOL YEARS PCT DWELLS OWNER OCC 59,418 PCT DWELLS OWNER OCC 348 PCT MALES UNEMPLOYED 3 18, PCT FAMS N INC4$2000 2,121 PCT FAMS W INC<52000 a,444 PC MOVED INSIDE SMSA MED, SCHOOL YEARS 12,700 MED, SCHOOL YEARS 12,500 PC MOVED INTO SMSA 31 PCT MALES UNEMPLOYED 2,W PCT MALES UNEMPLOYED 154 PCT POP S19 YRS OLD 44:33 PC MOVED INSIDE SMSA 23,367 PC MOVED INSIDE SMSA 4,622 POP PER HOUSEHOLD 3,08 PC MOVED INTO SMSA 20,662 PC MOVED INTO SMSA 69,160 PCT OWLS W Z1,01 oRM I I , 56 74 5@ PCT POP 519 YRS OLD 39,059 PCT POP 519 YRS OLD 40,238 PCT POP ZbS YRS OLD 3,645,. POP PER HOUSEHOLD 6,042 30,011' POP PER HOUSEHOLD 3s005 PCT FEMS IN LAB FORS PCT OWLS W t1,01 ORM 5,486 PCT OWLS W k1#01 ORM 10,429 RCT LABS IN LAB FORS 4,539 PCT POP ;!bS YRS OLD 44993 PCT POP t65 YRS OLD 058 NO MIND MEMS IN TRCT 3018,000 PCT FEMS IN LAB FORS 40 85ij PCT FEMS IN LAB FORS 37o306 PCT PVT MHWORK IN LF 0,000 PCT LABS IN LAB FORS 2:446 PCT LABS IN LAB FORS 5g7O2 PCT PROAMGR IN LF 31,479 NO MIND HEMS IN TRCT 9094,0@0 NO MINO HEMS IN TRCT 5214,000 PCT CRFTAOPS IN LF 2.7,233 PCT PVT HHWORK IN LF 598 PCT PVT HHWORK IN LF 4,386 PC7 PROAMGR IN LF 4o,690 PCT PROAMGR IN LF 14,912 PCT CRFT@%OPS IN LF 19,211 PCT CRFTAQPS IN LF 0, Table IX-9 (cont'd.) TRACT NO, 34 NED, FAMILY INCOME 13S54,000 TRACT NO, 36 PCT HK W HARR, HEADS 64,633 NED, FAMILY INCOME 10398,000- ,KACT NO, 3 de PCT FAMS W INCt$7000 92,13s PCT HM W HARR, HEADS 81 302': NED, FAMILY INCOME 9663,000 PCT DWELLS OWNER OCC 72,620 PCT FANS W INCt$7000 75:617! PCT HN W HARR, HEADS 82,843 PCT FAMS W INC462000 2,911 PCT DWELLS OWNER OCC 76,848,, PCT FAMS W INCt$7000 73#120 NED, SCHOOL YEARS 13,000 PCT FANS W INCtS2000 3, qso@ PCT DWELLS OWNER OCC 64,461 PCT MALES UNEMPLOYED 1,308 NED, SCHOOL YEARS 12 200 PCT FAMS W INC4$2000 1,2S3 PC MOVED INSIDE SMSA 28,n-4 PCT MALES UNEMPLOYED 1:013 NED, SCHOOL YEARS 12 6@9 PC MOVED INTO SMSA 38,072 PC MOVED INSIDE SMSA 24,526 PCT MALES UNEMPLOYED 3:318 PCT POP 519 YRS OLD 4S,378 PC MOVED INTO SMSA is 216 PC MOVED INSIDE SMSA 18p894 POP PER HOUSEHOLD 3,SSS PCT POP 519 YRS OLD 43:090 PC MOVED INTO SMSA 40,481 PCT OWLS W t1,01 ORM 4 060 POP PER HOUSEHOLD 3,396 PCT POP $19 YRS OLD 43,7SO PCT POP t65 YRS OLD 2:346 PCT OWLS W tt,01 oRM 10,328 POP PER HOUSEHOLD 3t412 PCT FENS IN LAB FDRS 40 S30 PCT POP a65 YRS OLD 5,11b PCT OWLS W t1s@1 ORM 7,966 PCT LABS IN LAB FORS I:stg P CT FENS IN LAB FORS 38,348 PCT POP t65 YRS OLD 2,335 PCT LABS IN LAB FORS 3,249 PCT FENS IN LAB FORS 32,349 NO MIND HEMS IN TRCT 6939,000 RCT LABS IN LAB FORS 2,949 PCT PVT HHWORK IN LF 560 NO NINO HEMS IN TRCT 2784,000 PCT PROAMGR IN LF 24,496 PCT PVT HHWORK IN LF 2,949 PCT CRFTAOPS IN LF 33,159 PCT PROAMGR IN LF 289aOS NO MIND HEMS IN TRCT 7843,000 PCT CRFTAOPS IN LF 2Sv513 PCT PVT HHWORK IN LF 328 TRACT NO, 37 PCT PROAMGR IN LF 47:600 NED, FAMILY INCOME 10578,000 PCT CRFTAOPS IN LF 14,161 I'RACT NO, 33 PCT HN W HARR, HEADS 78,2st NED, FAMILY INCOME 10289,060 PCT FANS W INC257000 88,611 PCT HH W MARR, HEADS 720948 TRACT NO, 35 PCT DWELLS OWNER OCC 75,658 PCT FAMS W INCZ$7000 710926 NED, FAMILY INCOME 71S6,000 PCT FANS W INC4$2000 1,667 PCT DWELLS OWNER OCC 62,994 PCT HM W HARR, HEADS 70,462 NED, SCHOOL YEARS 12,200 PCT MALES UNEMPLOYED 1,304. PCT FAMS W INC<82000 5,131 PCT FAMS W INCZ$7000 49,333 PC MOVED INSIDE SMSA 26,6W MED, SCHOOL YEARS 12,200 PCT DWELLS OWNER OCC 62,462 PC MOVED INTO SMSA 18,329 PCT MALES UNEMPLOYED 2,322 PCT FANS W INC02000 6:476 PCT POP 919 YRS OLD 43,539 PC MOVED INSIDE SMSA 39,347 NED, SCHOOL YEARS 9 300 POP PER HOUSEHOLD 3,366 PC MOVED INTO SMSA 20.478 PCT MALES UNEMPLOYED 1,778 PCT POP 519 YRS OLD 45#455 PC MOVED INSIDE SMSA 26 23 POP PER HOUSEHOLD 3,210 PC MOVED INTO SMSA 15:9064 PCT OWLS w 41 01 oRM 13 298 PCT POP St9 YRS OLD 44,S51 PCT POP Z65 YAS OLD 4:498 POP PER HOUSEHOLD 3,360 PCT FEMS IN LAB FORS 35,011 PCT OWLS W ?1,0t ORM 210.385 PCY OWLS W ?1,01 ORM 8,270 PCT LABS IN LAB FORS 30776 PCT POP t65 YRS OLD 4 1712 RCT POP t65 YRS OLD 5,540 NO MIND MEMS IN TRCT 4224,000 PCT FEMS IN LAB FORS 33:;jj PCT FEMS IN LAB FORS 39,546 PCT PVT HHWORK IN LF 713 PCT LABS IN LAB FORS 9 606 PSTMJABSMINSLAB FORS -6,97? PCT PROANGR IN LF 24,234 NO MIND MEMS IN TRtT 2184:090 N NO N IN TRCT 1424,000 PCT CRFTAOPS IN LF 30,364 PCT PVY MHWORK IN LF 4 736 PCT PVT HNNORK IN LF '99-@ PCT PROAMGR IN LF 10:149 PCT PROAMGR IN LF 31,894 PCT CRPTAOPS IN LF 34,912 PCT CRFTAOPS IN LF 27,u@9 Table DC-9 (cont'd.) TRACT NO, S7 TRACT NO, 54 NED, FAMILY INCOME 3930,000 NED, FAMILY INCOME 61360000 PCT HM W HARR, HEADS 59,204 PCT HH W HARR, HEADS 619820 PCT FAMS W INCtS7000 13,810 TRACT NO, so PCT FANS W INCIS7000 39,933 PCT DWELLS OWNER OCC 64,748 MED, FAMILY INCOME 7379,000 PCT DWELLS OWNER OCC 35 461 PCT FAMS W INC4$2000 20,381 PCT HH W HARR, HEADS 64,789 PCT FANS W INCA82000 6:489 MED. SCHOOL YEARS 3,100 PCT FAMS W INCt$7000 58,036 NED, SCHOOL YEARS 7 : 9:0 PCT MALES UNEMPLOYED ii,011 PCT DWELLS OWNER OCC 66,S49 PCT MALES UNEMPLOYED 3 0 8 PC MOVED INSIDE SMSA 14,820 PCT FAMS W INCtS2000 14,286 PC MOVED INTO SMSA 2,647 NED, SCHOOL YEARS 8,600 PCT POP $19 YRS OLD 52,684 .PCT MALES UNEMPLOYED 1,911 POP PER HOUSEHOLD 3,760 PC MOVED INSIDE SMSA 604 PCT OWLS W k1*01 ORM 38,948 PC MOVED INTO SMSA 3:122 PC MOVED INSIDE SMSA 211109 PCT POP &65 YRS OLD 5,671 PCT POP 519 YRS OLD 47,633 PC MOVED INTO SMSA q'jsq PCT FENS IN LAB FORS 18,585 POP PER HOUSEHOLD 3,496 PCT POP 519 YRS OLD 48,444 PCT LABS IN LAB FORS 18,77@ PCT OWLS W t1,81 ORM 26 956 Pop PER HOUSEHOLD 3,304 4 330 NO MIND MEMS IN TRCT 5290,000 PCT POP Z65 YRS OLD PCT DWLS W al.01 ORM 21,277 PCT PVT HHWORK IN LF 3,319 PCT FEMS.IN LAB FORS 37,176 PCT POP a65 YRS OLD 5,295 PCT PROAMGR IN LF 7,074 PCT LABS IN LAB FORS 3,540 PCT FENS IN LAB FORS 27 735 NO MIND MEMS IN TRCT 993, 00 PCT LABS IN LAB FOR$ 7:264 PCI CRVIAOPS IN LF 41,834 PVT HHWORK IN LF ':85 NO MIND MEMS IN TRCT 2795,000 @ PCT PROAMGR IN LF 44,248 PCT PVT HHWORK IN LF 4,479TRACT NO, so PCT CRFTAOPS IN LF 12,979 PCT PROAMGR IN LF 12:833 NED. FAMILY INCOME 7906,000 RCT CRFTAOPS IN LF 19 976 PCT HH W HARR, HEADS 62,489 PCT FAMS W INCZ57000 58,693 TRACT NO, 51 NED, FAMILY INCOME 7029@000 TRACT NO, 56 PCT MH W HARR, HEADS 22,r547 NED, FAMILY INCOME 6616,000 PCT FAMS W INCk$7000 48 049 PCT HH W HARR, HEADS 66,S'39 PCT DWELLS OWNER OCC 21:401 PCT FAMS W INCt$7000 44,413 PCT DWELLS OWNER OCC 52,339 PCT FAMS W INCIS2000 0,000 PCT DWELLS OWNER OCC 64,300 PCT FAMS W INC4$2000 9,935 NED, SCHOOL YEARS it 900 PCT FAMS W INC452000 11,865 MEDI SCHOOL YEARS 11,100 PCT MALES UNEMPLOYED 4 :16 0 NED, SCHOOL YEARS 7,900 PCT MALES UNEMPLOYED 0,000 PC MOVED INSIDE SMSA 29,216 RCT MALES UNEMPLOYED 48167 PC MOVED INSIDE SMSA 28,634 PC MOVED INTO SMSA 13,9" PC MOVED INSIDE SMSA 16,876 PC MOVED INTO SMSA 11,110 PCT pop $19 YRS 04D 28g772 PC MOVED INTO SMSA 3,24S PCT POP .S19 YRS OLD 44,503 POP PER HOUSEHOLD 040 PCT POP S19 YRS OLD 46,688 POP PER HOUSEHOLD 2,842 PCT OWLS W t1,01 ORM 4,161 POP PER HOUSEHOLD 39621 PCT DWLS W k1,01 ORM 13,063 PCT POP k65 YRS OLD 14,349 PCT OWLS W ?1 01 oRM 26,307 PCT POP k65 YRS OLD 6,832 PCT FENS IN LAB FORS S4,S99 PCT POP t65 YAS OLD 7,694 PCT FEMS IN LAB FORS 27,043 PCT LABS IN LAB FORS, 4,StO RCT FEMS IN LAB FORS 32,138 NO MIND HEMS IN TRCT 1352,000 PCT LABS IN LAB FORS 8 790 PCT LABS IN LAB FORS 2,854 0 NO MIND MEMS IN TRCT 3220,000 PCT PVT HMWORK IN LF 2,259 NO MIND MEMS IN TRCT 11217,000 PCT PROAMOR IN LF 32,831 PCT PVT HHWORK IN LF 2,848 PCT PVT HHWORK IN LF 1,903 PCT CRFT^OPS IN LF 12,t99 PCT PROAMGR IN LF 19,449 PCT PRC^MGR IN LF 15,319 PCT CRFTAOPS IN LF 300995 PCT CRFTADPS IN LF 30,542 Table IX-9 (cont'd.) 'RACY NO, lea NED, FAMILY INCOME 6830,000 TRACT NO, S9 pCT Hm w HARR, HEADS 62,797 NED, FAMILY INCOME 571a,060 TRACT NO, 61 PCT FANS W INCtS7000 50,000 PCT HM W MARR, HEADS 6S,826 NED, FAMILY INCOME 8392,001 PCT DWELLS OWNER OCC 58,699 PCT FANS w INCZ57000 41,328 PCY HM W MARR, HEADS 72,W PCT FAMS W INC462000 7,176 PCT DWELLS OWNER OCC 45,518 PCT FAMS W INCtS7000 63,471 MED, SCHOOL YEARS 10,600 PCT FANS W INC412000 9,963 PCT DWELLS OWNER OCC 71'saa PCT MALES UNEMPLOYED 4,629 NED, SCHOOL YEARS 8000 PCT FANS W INC4$2000 S,64S PC MOVED INSIDE SMSA 32,514 PCT MALES UNEMPLOYED 780 NED, SCHOOL YEARS 11,002 PC MOVED INTO SMSA 6,762 PC MOVED INSIDE SMSA 19,644 PCT MALES UNEMPLOYED asi PCT pop s19 YRS OLD 41,006 PC MOVED INTO SMSA 10,127 PC MOVED INSIDE SMSA 13,321 pop PER HOUSEHOLD 2,828 PCT POP $19 YRS OLD 464576 PC MOVED INTO SMSA 16,53i 42,32.5 PCT OWLS W >1 at ORM 14 230 91 POP PER HOUSEHOLD 3,416 PCT POP $19 YRS OLD PC ypop Z65 YAS OLD b9l PCT OWLS W ki 01 ORM 21:148 Pop PER HOUSEHOLD 3 zle K T FENS IN LAB FORS 30,486 PCT POP k65 YAS OLD 6 150 PCT OWLS W Rls0l ORM 13:SS6 PtT LASS IN LAB FORS 8,105 PCT FENS IN LAB FORS 21 341 PCT POP ?65 YRS OLD 7,992 NO MIND HEMS IN TRCT 5087,000 PCT LABS IN LAB FOR$ 9:746 PCT FENS IN LAB FORS 29,392 RCT PVT HHWORK IN LF 536 NO MINO MEMS IN TRCT 2439,900 PCT LABS IN LAB FORS 5,119 PCT PVT HHWORK IN LF 3,531 NO MINO HEMS IN TRCT 3466,000 r< PCT PROAMGR IN LF 2,834 t 10,593 PCT PVT HHWORK IN LF PCT CRFTAOPS IN LF 23,729 PCT PROAMGR IN LF 20g'364 26,103 PCT PROAMGR IN LF PCT CRFT^OPS IN LF 31cTIS .PCT CRFTAOPS IN LF 31,943 TRACT NO, 60 NED, FAMILY INCOME 587S,000 TRACT NO, lot TRACT NOm 103 PCT MH 0 mARRj HEADS 64,493 NED, FAMILY INCOME 8100,000 NED, FAMILY INCOME 9595,000 PCT FANS W INCtS7000 50,984 PCT HM W HARR, HEADS 61,756 PCT HM W HARR, HEADS 71.525 PCT DWELLS OWNER OCC 42,029 PCT FANS W INCit$7000 61,053 PCT FANS W INCtS7000 70,494 PCT FAMS W INC<S2000 5,709 PCT DWELLS OWNER OCC 60,482 PCT DWELLS OWNER OCC 67 060 MEDg SCHOOL YEARS 8,400 PCT FANS W INC<$2000 5*684 PCT FANS W INCA52000 6:330 PCT MALES UNEMPLOYED 2,408 NED, SCHOOL YEARS 12,100 NED, SCHOOL YEARS 11,200 PC MOVED INSIDE SMSA 24,789 PCT MALES UNFMPLOVED 1,220 PCT MALES UNEMPLOYED 3,513 PC MOVED INTO SMSA 3,649 PC MOVED INSIDE SMSA 34,083 PC MOVED INSIDE SMSA 17,247 PCT POP 519 YRS OLD 46,061 PC MOVED INTO SMSA 18,397 PC MOVED INTO SMSA 12,703 POP PER HOUSEHOLD 3,257 PCT Pop s19 YRS OLD 39,96s PCT POP s19 YRS OLD 442,865 PCT DW@%W541 01 ORM 19 7;0 POP PER HOUSEHOLD 2,056 POP PER HOUSEHOLD 3,170 PCT PO 6 YAS OLD 8:6 8 PCT OWLS W ?1,0t ORM 8:499 PCT OWLS W Z1.01 ORM 14,406 PCT FENS IN LAB FORS 23,925 PCT POP Z65 YRS OLD 8 131 PCT POP Z6S YRS OLD b,458 PCT LABS IN LAB FORS 4,615 PCT FEMS IN LAB FORS 33 .002 PCT FEMS IN LAB FORS 35,153 NO MIND MEMS IN TRCT 2247 000 PST,JABS,IN,LAB FORS 6 000 PCT LABS IN LAO FORS 4,686 PCT PVT HHWORK IN LF 4:056 N NO N IN TRCT 1734:000 NO MIND MEMS IN TRCT 3763,000 PCT PROANGR IN LF 10,070 PCT PVT HHWORK IN LF 1000 PCT PVT HHWORK IN LF .,788 PCT CRFTADPS IN LF 24,7S5 PCT PROAMGR IN LF 26 833 PCT PROAMGR IN LF 22,38R@ PCY CRFTAOPS IN LF 37 333 PCT CRFYAOPS IN LF 42,639 M ElRbIPW9M M M M M M M M (cont'd.) RACT NOg 104 ..TRACT NO$ 108 - MED, FAMILY INCOME 57980000 MED@ FAMILY INCOME 5750,000 106 RCT HN w MARRO HEADS 659481 PCT HH W MARR, HEADS 68,647 MILY INCOME 112548000. PCT FAMS W INCt$7000 43g907 PCT FAMS W I.NC@$7000 35,19bb W MARRO HEADS 799282 PCT DWELLS OWNER OCC 642163 PCT DWELLS OWNER OCC 4109t4 tS W INCW000 820308 PCT FAMS W INC<$2000 13,483 PCT FAMS W INC@c$200V 79895 @LLS OWNER OCC 751368 MEDI SCHOOL YEARS 81900 MED, SCHOOL YEARS 9i600 4S.W INC<$2000 39048 PCT MALES UNEMPLOYED 11530 OCT MALES UNEMPLOYED 11969, :HM YEARS 128600 pC MOVED INSIDE SMSA 189377 PC MOVED INSIDE SMSA lSe754 -ES UNEMPLOYED 4g,?18 PC MOVED INTO S@SA 64916 ED INSIDE SMSA 228857 PC MOVED INTO SMSA 50151 PCT POP 519 YPS OLD 499376 ED INTO SMSA 24g?47 POP PER HOUSEHOLD 3*436 P $19 YRS OLD 459946 RCT OWLS W k1l0l oRM 20,11-11W-7 R HOUSEHOLD 3g3b2 PCT POO a65 YRS OLD 3g650 LS W tIvOl ORM 76090 PCT POP 519 YRS OLD 48.226 PCT FEMS IN LAB FORS 31.690 P Z65 YRS OLD 3v6S7 POP PER HOUSEHOLD 39491 PCT LABS IN LAB FORS 1 305,57 MS IN LAB FORS 42g74i oRM 2,51099 NO MINO MEMS IN TRCT 10419000 SS IN LAB FORS 31727 PCT DWLS W t1901 PCT-PVT HHWORK IN LF 3,9S3 0 MEMS IN TRCT 7302tOOe PET pop a65 YRS OSD 38: 019 T HHWORK IN LF 11781 PIT FEMS IN LAB F RS 2 5S9 PCT PROAMGR IN LF 13 043 99032 ,qPCT CRFTAOPS IN LF 31:2RS O^MGR IN LF 34g9bt PCT LABS IN LAB FORS 5300s000 1 2196S5 NO MINO MEMS IN TRCT FTAOPS IN LF PCT PVT HHWORK IN LF 5s393 PCT PROAMGR IN LF 189843 RACT NO$ 105 PCT CRFT^OPS IN LF 23l392 MED9 FAMILY INCOME 55289000 107 PCT HH W MARRO HEADS 70j714 'AMILY INCOME 62940000 PCT FAMS W INC217000 410324 1 W MARRO HEADS 66g414 RACT Now 109 PCT DWELLS OWNER OCC 65,357 %MS W INCt$7000 42gO29 PCT FAMS W INC9$2000 94132 oEL48 OWNER OCC 274273 MED, FAMILY INCOME 690210(did MEDg SCHOOL YEARS 51400 4MS W INC<$2000 15g942 PCT HH W MARRO HEADS 60j623 PCT MALES UNEMPLOYED 5,156 SCHOOL YEARS 8$500 PCT FAMS W INCt$7000 529567 PC MOVED INSIDE SMSA 29jJ63 ALES UNEMPLOYED lbseoe PCT DWELLS OWNER OCC 49*965 PC MOVED INTO SMSA 7t881 VED INSIDE SMSA 9,577 PCT FAMS W INC452000 124423 PCT POP sig YRS OLD s3107? VED INTO SMSA 3g113 MED, SCHOOL YEARS 89400 Pop PER HOUSEHOLD 41@11 OP 519 YRS OLD 48@843 PCT MALES UNEMPLOYED l4b87 OCT OWLS W ?1901 ORM 340821' ER HOUSEHOLD 3gl64 PC MOVED INSIDE SMSA 22 597 OCT POP ?65 YRS OLO 4,274 WLS W alsOl ORM 190697 PC MOVED INTO SMSA 9:434 OP ?65 YRS OLD Sg666 PCT pop s19 YRS OLD 489001 EMS IN LAB.FORS 31j231 POP PER HOUSEHOLD 39081 ASS IN LAB FORS 29849 PCT OWLS W ?1*01 ORM 20,830 NO MEMS IN TRCT 1253j000 PCT POP z65 YRS OLD 69671 OCT FEMS IN LAB FORS 27gf641 VT HHWORK IN LF 51413 PCT FEMS IN LAB FORS 31a384 PCT LABS IN LAB FOPS 605st PCT LABS IN LAB FORS 259746* 'ROAMGR IN LF 19 373 44529000 NO MIND MEMS IN TRCT RFTAOPS IN LF 11:966 NO MINO MEMS IN TRCT 29736 2246g0@0- PCT PVT HHWORK IN.LF PCT PVT HHWORK IN LF 51808 PCT PRO^MGR IN LF 121455 PCT PRO^MGR IN LF 124559 PCT CRFTAOPS IN LF 260494 Table IX-9 (cont'd.) TRACT NO$ 6709,000 TRACT N09 112 MEDI FAMILY INCOME 59,115 MED9 FAMILY INCOML 40431000 PCT HH W MARRO HEADS 48j614 PCT HH w MARRO HEADS 43g387 PCT FAMS W INCt$7000 57,1490 PCT FAMS W INCtS7000 269667 PCT DWELLS OWNER OCC 12 360 PCT DWELLS OWNER OCC 4189bP) PCT FAMS W INC<$2000 I PCT FAMS W INC<S2000 259686 MEDI SCHOOL YEARS 99100 PCT MALES UNEMPLOYED 41470 MEDs SCHOOL YEARS 80400 PC MOVED INSIDE SMSA 28l743 PCT MALES UNEMPLOYED 49276 PC MOVED INTO SMSA 8 251 PC MOVED INSIDE SMSA 154053 PCT pop 519 YRS OLD 42:243 PC MOVED INTO SMSA 60873 pop PER HOUSEHOLD 2g9bs PCT POP A19 YRS OLD 409997 1727SI POP PER HOUSEHOLD 19966 PCT OWLS W Zidi ORM 96168 PCT DWLS W tiqfdl ORM 150033 PCT pop Z65 YRS OLD /41g?44 PCT POP k65 YRS OLD 99293 PCT FEMS IN LAB FORS 111407 PCT FEMS IN LAB FORS 289246 PCT LASS IN LAB FORS NO MINO MFMS IN TRCT 5563sO@O PCT LABS IN LAB FORS S9833 PCT PVT HHWORK IN LF 2 663 NO MINO MEMS IN TRCT 20661000 04101 PCT PVT HHWORK IN LF 30333 PCT PROAMGR IN LF 2 9 PCT PROAMGR IN LF IS4417 PCT CRFT^OPS IN LF 21sigb PCT CRFTAOPS IN LF 220500 TRACT NO# III MEDI FAMILY INCOME 6256000 TRACT Not 113 PCT HM W MARRO HEADS 64j006 MFDO FAMILY INCOME 4532j0@0 PCT FAMS W INCZ$7000 4?m566 PCT HH W MARRO HEADS 594762 PCT DWELLS OWNER OCC 709266 PCT FAMS W INCa$7000 284465 PCT FAMS W INC<$2000 104183 RCT DWELLS OWNER OCC 589041 MED, SCHOOL YEARS 8s300 PCT FAMS W INC<S2000 229SI2 MEDv SCHOOL YEARS 5,1200 PCT MALES UNEMPLOYED 196S2 PC MOVED INSIDE SMSA 20s164 PCT MALES UNEMPLOYED 20817 PC MOVED INTO SMSA 109036 pC moVE0 INSIDE SMSA 26u7bl PCT POP 519 YRS OLD 526607 PC MOVED INTO SMSA 1.10455 POP PER HOUSEHOLD 30541 pCy pop 519 YRS OLD 440960 PCT DWLS W Z1901 ORM 32e363 30333 PCT PUP t65 YRS OLD 79419 pop PER HOUSEHOLD 21,263 PCT FEMS IN LAB FORS 23,616 PCY DWLS W Z@ioOi ORM 8g967 PCT LASS IN LAB POP$ 100910 pCy pop Z65 YRS OLD 360iA, NO MINO MEMS IN TRCT 53510000 PCT FEMS IN LAB FOPS 62772 PC7 PVT HHWORK 1N LF 30540 PCY LABS IN LAB FOR$ 213090m PCT PROAMGR IN Lr. 12, Bib a NO MIND MEMS IN TRCT 6934e PCT CAFTACPS IN 0 16(,064 pCy PVT mHWORK IN LF 11023@ PCT PROAmGR IN LF 34,15k P"CRF'rAoPS,.6 LF m- @M@mmmmm Table IX-10 ACRES Vacant Apartments and Single Family Vacant Tratt In Use 1970 1970 Multi-Family, 1980 Homes, 1980 1980 1 88.8 101.7 0.2 0.0 101.5 2 472.1 70.0 1.1 69.8 0.0 3 168.8 12.5 12.5 0.0 0.0 4 188.2 41.9 0.0 41.9 0.0 5 165.2 24.4 24.4 0.0 0.0 6 698.6 47.4 29.9 17.5 0.0 17 2,490.4 262.4 28.3 234.7 0.0 8 12,627.8 2,245.2 46.7 2,198.' 5 0.0 1 9 251.8 40.0 40.0 0.0 0.0 0 234.0 24.7 24.7 0..0 0.0 il 391.0 39.7 37.8 1.9 0.0 2 229.7 43.4 43.4 0.0 0.0 3 244.9 13.4 13.4 0.0 0.0 4 482.2 14.7 0.0 0.0 0.0 5 297.4 18.4 18.4 0.0 0.0 6 472.4 70.3 70.3 0.0 0.0 2,597.6* 259.7 19.7 240.0 0.0 18 1,540.3 205.1 70.0 135.1 0.0 9 1,519.1 .145.6 77.1 68.5 0.0 10 567.9 10.6 10.6 0.0 0.0 21 702.7 66.0 43.3 22.7 0.0 2 461.9 11.4 11.4 0.0 0.0 3 1?613.7 60.2 33.7 26.5 0.0 f4 760.4 38.1 38.1 0.0 0.0 5 494.2 21.4 5.3 0.0 6 748.9 29.6 29.6 0.0 0.0 07 1,807.6 522.9 54.6 468.3 0.0 30 1 4,061.0 6,959.0 31.6 6,927.4 0.0 2 33 1,555.1 513.2 513.2 0.0 4 776.0 317.2 21.2 296.0 0,0 5 @_6 23,000.0 2,401.8 13.7 2,387.1 0.0 37 0 L 10,000.0 90,000.0 2.7 12,252.6 77,745;7 1,963.1 645.0 30.4 346.5 268.1 7 327.2 109.0 0.7 100.0 8.3 ti 1,177.9 392.0 42.6 349.4 0.0 IX-27 each tract which is likely to become used for retail and commercial purposes (,see Chapter VIII),for apartments and multi-family dwellings and for single family dwellings. Incorporated in the housing determination is the area needed for the supply of support facilities such as utilities and f6eder roads but not the supply of trunk lines. Housing determination is carried out for each of six rental categories and six housing value categories. This summer the program for areal determination of housing will be computerized so that it may be re-run under differing conditions (i.e. zoning, density, etc.) Implicit in the present approach is that zoning is simply a temporally constant lag function on the market mechanism. IX-28 CHAPTER X ENVIRONMENTAL-LAND USE INTEGRATION PROCEDURE It is anticipated that land use management policies will be implemented by legislation based upon the concept of "critical environmental units."l The definitions of "environmental units" and "resource capability units" are being developed for both land and marine areas by the Texas Land Office and the Bureau of Economic Geology of the University of Texas. These units comprise broadly defined geological structures for which admissible types and intensities of use will be established based upon both surface and subsurface geological considerations. It will also be necessary to include the consideration of biologic assemblages that are coinci- dent or interactively dependent on these surficial conditions. The development of these units and the establishment of associated use criteria will provide a direct means of incorporating environmental zoning considerations into the land use planning process. The development of these criteria and their application in the planning process is the primary focus of this study. A secondary focal point deals with the fundamental character of ports. Port cities have been of major importance in the economic development of the coastal zone. Ports as transportation nodes and as industrial and residential centers affect bays, estuaries and littoral environments. The influence of port cities on marine environments may manifest itself through depletion of fresh water inflows, canalization, waste inflows and recreational demands. Construction on littoral areas often conflicts with conservation policies derived from both political and environmental considera- tion. Multi-Level Models In order to evaluate the impact of land use management poli- cies on the type, location and intensity of industrial and resi- dential activity in the coastal zone, a series of coupled linear programming models are developed. The overall model has a multi-level structure with the variables at each level being coupled to those at the next by appropriate constraints. Thi@ model draws upon the work of Charnes, Cooper, Niehaus et. al. on manpower planning developed in the research program of 1All pending federal legislation on land use,the most publi- cized of which is The Jackson Bill, refers to the need for protec- tion and maintenance of "critical environments." 2Charnes, A., W. W. Cooper and R. J. Niehaus. "Studies in Manpower Planning," Office of Civilian Manpower Management., Depart- ment of the Navy, (Washington, D. C.: July, 1972). X-1 the U. S. Department of the Navy. originally these models were developed to aid in the managementf,projection and assignment of civilian and naval manpower. In this Approach the central set of models are multi-level# pulti-pexiod, goal programs which incor- porate input-output analysis in order to determine manpower requirements and utilize Markov processes to model manpower transitions, (e.g. geographical movements, skill changes and retirements of manpower). These transitions are linked to decision variables such as new hiring policies and involuntary retirements. An Environmental-Land Use Model The present study utilizes a similar hierarchical multi-level model with three divisions. At the top-level the aggregate allo- cation of population and industrial activity between areas within the coastal zone is determined. At the second level,housing and in- dustry are located spatially within each area and at the third level.water, waste treatment and demographic models are included. The independent models have been developed by others or are being de- veloped by team members. Therefore, the following outline is directed primarily toward elucidating the nature of the coupling mechanisms between these models of population, industrial output and land use within a multi-level multi-period context. The Coastwide - Inter COG Level At the highest level we consider the allocation of industry (including agriculture) between coastal Council of Government (COG) areas. A COG is an agency set up under state legislation to coordinate plans and initiate policies for groups of counties. COG's were set up to conform as far as possible to local trading areas by basing the division of counties upon core Standard Metropolitan Statistical Areas. COG's thus provide a means of incorporating policy making bodies into the models in a way which is coherent with economic considerations. For each COG an input-output tab le and final demand projections is determined for eac@ of a series of planning periods. We have for the rth COG in the tt planning period Xr(t) -@Lijxr (t) +Ee rs(t) -Ze @r t) +gjF+ (t) -g'P- (t) =dF (t) i s S where xJF(t) is the output of the ith economic sector in period t I I Li@is the input from sector j required to produce unit output in se tor i. e:Fs(t) esr(t) are dollar amounts of exports and imports respect- ively for COG r and area s which include coastwide, international X-2 and domestic trade. (t), g@-Ct) are deviations from the final demand goals df(t). Restrictions A trade reflecting port capacities and balance of payments considerations may be represented schematically as: ErL(t)<j, eIJt)<,ErU(t) is _S i is ErL W< E esr(t):@ErU(t) 2S SES i 2S rU rL where Eis (t), ElS(t) are upper and lower bounds on exports for a , ErL er and lower bounds subset of sectors in COG r and Eg ?Stare upp on inputs. The formalization of t is a he COG level and above draws heavily upon @he work of Isard et. al. in interregional linear programming. Intra COG Level At this level we consider the allocation of industrial and residential activity spatially within each COG. We have the coupling conditions: r x'@kr(t) = 0' where iris the set of land tracts in the rth COG. For subsets Kr of land tracts we have population projections pEK r(t) for the hth type of household. These subsets may correspond to cities, counties, or .other geographic or political units. We partition this population into labor force and non-labor force participating (e.g. retired people, second-home owners) components as follows: Pr r(t) Xr r(t)_,r hK -Ph hK h0t) = 0' where Xr r(t) is the amount of labor of type h in tract Kr in period t hK Phis the ratio of households to labor for type h. r r @hK W is the number of non-labor force participating households in tracts Kr of type h in period t. 3 Isard, W., et.al. Methods of Regional Analysis, (Cambridge, Mass.: 1960). X-3 We have relationships be labor and output given by labor coefficients Pih: E r x r -r E kre K pih ikr (t)-x hk (t)+g-r+r(t)-g -r = 0 i hk hk where g-r+r(t), g-r-r(t) are deviations from labor requirements of the hk hk type h in land tract Kr in period t. Land constraints take the following form: pr r r EB hkr hkr (t)+Eaikr xikr (t)< A kr h i where bh is the land required for a household of type h located in tract kri and aikr is the land requirement for a unit of output of sector 1 in tract kr. These coefficients reflect the zoning considerations specified for the environmental unit kr. Also E -pr (t) = pr r (t) kr Kr hkr hK That is, each subset of tracts Kr for which population projections are made must house that population. Water constraints are derived in a similar way to those on land and take the form: r r E E -e Phkr (t) + E E -f x (t) q- < (t) h kr EKr hkr i kr Ekr ikr ikr Kr where ehkr,fikr, are unit water requirements for households and industry respectively and q-Krt (t) is the available water to subset Kr of land tracts in period t. This will generally include water from both surface and artesian sources. Population, Water, and Waste Models The population within the coastal zone is coupled both spatially and temporally by means of Markov transition matrices to reflect the migration and aging respectively of the population.4 4Rogers, Andrei. Matrix Analysis Interregional Population Growth & Dist. (Berkley, California. Univ. of California Press, 1968). X-4 These couplings are made at a third level within the model structure to the variables P r (t) and.@' Ct) for non- labor force partici- k . hk pants and labor foM& participanEs respectively. Water and waste models are developed based u E on the6Ph. D. work (under Charnesf Logan, and Gotaas) of Heaney , Lynn and Deininger7 which are variations and specializatiohs,of the origi- nal (iinpublished) 1958 multi-page models of Charnes, Logan and Pipes.8 We may couple the variables q Ct) to a water distribution system in the following way: Kr r (t)+Zr r Let qX: (t) = W (t) where Z- (t) is surface water usage Kr Rr Kr Kr and Wr (t) is ground water availability in the set of tracts K r in Kr period t. For the surface water distribution system the tracts K_ may themselves correspond to environmental or resource capabilify units as illustrated in Figure X-l'. The quantities Zr(t) would then correspond to the net quantities of water @Kr withdrawn as shown in Figure X-2. Seasonal demand and supply variation, storage and pre- cipitation, and return flows are considered in detail in Heaney's work and need not be elaborated upon here. Once the type, intensity and location of industrial and residential activities are known,we turn to consideration of waste treatment models. Deininger's work provides an approach to the determination of the scale and location of treatment facilities in order to meet prescribed quality at minimum cost. Lynn considers the design and staged expansion of waste treatment plants over time in order to provide treatment for prescribed quantities of waste 5 Heaney, J. P. Mathematical Programming Model for Long Range River Basin Plannin@__@WIth Emphasis on the Colorado River Basin Unpublished Ph D. Dissertation (Northwestern University: 1968). 6 Lynn, W. R. Process Design and Financial Planning of Sewage Treatment Water. Unpublished Ph.D. Dissertation -FNorthwestern University; 1963). 7Deininger, R. A. Water Quality Management: The Planning of Economically Optimal Poilution Control sZstems. Unpublished Ph.D. Dissertation (NortHwestern University: 1965). 8Charnes, A., Logan and Pipes "Multi-Page Water and Waste Water Models" Systems Research Group, Northwestern University, Unpublished, Mimeograph, 1958. X-5 River EU . 21 ETMI EIJ12 EIU22 Reseqoir 1 2 Z4 Z31 z5 Z6 JZ7 E J 13 z T \ E'U EU24 E'U 23 10 Bay Figure X!--l EU 12 represents an environmental-or resource cap-ability of typ-e I--- in location 2 within the COG. 14 X-6 inputs at minimum cost including considerations of financing and service charges to the people served, Objective We seek to minimize a weighted sum of deviations from regional growth goals gr+(t), gr-i(t) subject to the constraints (1) - (8),(see Figure2). We also have goals of minimizing un- employment g-r+hkr and job vacancies g-rhkr on local basis. THE objective may then be represented schematically as: political and economic factors. Minimize Ecir+ (t)gr+i ()+Ecr-i (t)gr-i (t)+Ec-rthkr (t)+Ec-r-hkr (t)g-r-hkr(t) where the weights ck+r(t),c-ri(t), c-r+hkr (t),c-r-hkr (t) may reflect both political and economic factors. Summary and Extensions The model reported here is directed toward environmental policy appraisal. The multi-level multi-period structure which has been outlined was developed in response to the need to model the micro-level implications of macro-level zoning policy decisions. With this structure,varying degrees of detail and of emphasis may be incorporated in the overall modeling, In particular we wish to focus upon subareas within the coastal zone based on their ecologi- cal significance. For example, marsh and salt flat environmental units which are adjacent to bays and estuaries are major sources of nutrients for marine species. Subsets of environmental units may become increasingly important due, for example,to the growth of port cities. In view of this, more detailed research is being directed toward the development of a dynamic model for port oriented urban growth. X-7 r+ t) gr+(t) + r(t)g,(t) +,r + Minimize c i i r9r+r(t) + E' 9' hK hK hK hK.r(t) LEVEL I r ijxr r1t) + gr+(t) gr (t) i e e'@t) dr(t) Input x (t) L (t)+ i a i Output Trade Erl, (t) ,Z; e:@t) ErU (t) Restrictions is sr.S is (2) rL r E Mr. @e (t)'Eru (t) LEVEL 2 2S 3 i 2S. Spatial 1xr r.(t) 0 (3) Distribution i(t 3-, -Zr xik of Industry kr r Demographic pr r(t) Ph r r 0 (4) Relationships hk XhKr(t) UhK r(t) Employment Z 1; RrPihxikr(t) r (t) 9 r+r (t) r- 0 (5) I kre hKr hK hKr Spatial Distribution Pr (J pr r (t) of Households krckr hkr- hK 0 (7) Land r r r Restrictions a E b p (t) rA (6) 1 ikr ikr h hkr h kr kr Water r (t) +T"3- qTr(t) Z 0 (8) Restrictions F-fik ehk k r ik, r4k (t) r'Kr Wr CKr r LEVEL 3 s WATER WASTE TREATMENT MODELS DEMOGRAPHIC SUPPLY MODELS MODELS J FIGURE X-2 APPENDIX D THE REGIONAL IMPACT OF AN ENVIRONMENTAL POLICY: POLLUTION ABATEMENT ON THE HUDSON, THE REFUSE ACT OF 1899.* by Kingsley E. Haynes" Department of Geography & Lyndon.B. Johnson School of Public Affairs University of Texas, Austin and Fred Phillips. Department of Management Graduate School of Business University of Texas, Austin *The authors wish to acknowledge the, support of N.S.F.(RANN) Grant No. GI-3487OX "Establishment of Operational Guidelines for Environmental Management. **Dr. Haynes is also a Research Affiliate of the Population Research Center, University of Texas, Austin. D-1 ABSTRACT THE REGIONAL IMPACT OF AN ENVIRONMENTAL POLICY: POLLUTION ABATEMENT ON THE HUDSONr THE REFUSE ACT OF 1899, The decentralized nature of the Hudson River measures and the all-or-nothing character of the permit issuing proce- dure complicate the development of a symbolic model for the impact assessment of public policy. Even predictive state- ments of expected behavior of Hudson River f irms is obscured by the inability of these firms to negotiate with neighboring municipalities in an open and friendly fashion. By modifying Bohm's model of the."Theory of External Effects" and applying it to this si tuation we have attempted to develop some decision guideline s for the firm as well as a quantitative evaluation of the basic public policy issues of equity and efficiency. Despite the limitations of this symbolic approach it is pre- ferable to a numerical model since by the time comprehensive data is available,massive technological adjustments will have occurred commensurate with the enforcement of antipollution laws. Key Words: ENVIRONMENT, PUBLIC POLICY, DECISION MAKING, IMPACT ANALYSIS. D-2 The Hudson River begins in the Adirondack Mountains of upstate New York, and flows to New York Bay, sup orting 10.5 million people employed by riverbank industries .. Of these industries, 305 firms are considered polluters by the U. S. Army Corps of Engineers. Many of the employees of these firms belong to the powerful Hudson River Fishermen's Association, which has frequently brought suit against the major Hudson polluters, and finally, little more than a year ago, achieved the revival of the Refuse Act of 1899, in order to stop the Penn Central Pilroad from dumping oil into the Hudson at Harmon, N. Y. The Act has been widely applied since, and is administered by the Corps of Engineers. In essence, the Refuse Act of 1899 2l4r22 provides that no private firm may perform work in or discharge effluents into any navigable waterway in the United States without a permit from the Corps of Engineers. No permits are issued for the discharge of "harmful" effluents, harmfulness being d@termined by the Corps of Engineers "environmental impact" analysis . Further- more no "harmless" effluent can be discharged if it causes a harmful reaction with the permitted effluent of another firm up,or down- stream. Rejection of a firm's application for permit means that the firm must arrange for treatment of its effluent (with removal of all harmful waste), or effect some more basic change.in the activity of the firm. The Act provides for prosecution of noncomplying firmst public permit hearings, and advisory assistance to the firms from the Corps of Engineers22- The firm must also comply with any local and state discharge laws. Municipalities along the Hudson are currently subject to state laws requiring the construction of sewage treatment facilities. Adjacent municipalities are urged to build joint facilities, of sufficient capacity to enable local private firms to tie in. The state has issued bonds for the pu5pose of subsidizing these efforts, and cities are now doing the same As of October 1971, according to the Associated Press ...15 of the 16 major municipal and industrial polluters ... have tied into treatment plants com-. pleted under New York State's program... Some 150 of the Hudson's ... polluters in New York have built or tied into treatment facilities that are now operating. Some 110 others, including 47 municipalities, face a mid-1972 deadline for beginning plant construction.2 In this paper we wish to Ca) point out the ways in which the Hudson River situation lends itself to the development of policy guidelines, (b) review the Refuse Act as an antipollution measure, within the broad framework of a welfare economics and D-3 (c) from the same framework, offer some decision guidelines for a polluting firm located on the Hudson. Although we are concerned with a welfare economics formulation, it should be clear that there is little sense in pursuing Pareto optimality. Pareto optimality is destroyed by, among other things, externalities, monopolistic elements,- and interdependent utility functions between firms or consumers. All of these factors are present and have significant influence on the problem at hand. The negative externality of pollution of the Environment is of great magnitude. Monopolistic influences make nationwide antipollution measures nearly impossible to enforce. For example, a popular weekly reported it "learned that Attorney General ..... had promised some big-business men that his Department would never take them to court for failure to apply for permits to discharge effluents under the 1899 Refuse Act."16 The production functions, and hence the utility functions, of firms subject to the Refuse Act are interdependent in that, the issue of a permit to one firm is con- tingent upon the nature of other firms' dischargesl7. In a more general way, issue of a permit depends on the momentary water quality standards and the currently assessed assimilative capacity of the water, as well as on the nature 6f the effluent.* Samuelson20 holds that any external economy or diseconomy is suf6icient motivation for a conpensatory tax or subsidy program. Kneese disagrees, maintaining that centralized institutional measures can be more effective. Yet the current situation on the Hudson is similar to none of these. According to Bohm the Refuse Act would be a "too-strong policy measure"--extremely inefficient from the point of view of the theory of external effects,,lPt the recent special environmental issue of Fortune I presents clear evidence that businessmen welcome strong government policy measures--as long as the measures are applied as well to their competitors. This enables the firm to yield to popular sentiment by engaging in cleanup activities without placing itself at a disadvantage within the industry. Under these conditions, 85% of businessmen interviewed stated a willingness to comply with government antipollution poli- cies at great expense-to themselves8. From this point of view, the Refuse Act is a reasonable measure. Hazletonil brings up the point that we may, in trying to determine who should pay for pollution abatement measuresf overlook the most efficient ways of actually effecting abatement. This is question of "equity vs. efficiency." A strong policy measure such as Hassio presents a model of a centralized administration for a taxation-purification program, given a set of water quality standards. This is not applicable here, as the setting of standards and the treatment-affiliation decisions are made by separate and varied agencies. D-4 the Refuse Act could be quite effective in combating pollution. It may even be wise to drop theoretical equity calculations And ask people involved the extent to which they would support clean up.* In the case of the Hudson Area, voters have supported a number of antipollution bond issues to a'known extent, as well as a number of tax programs2. If we assume this result, plus the apparent attitude expressed above by businessmen, to be the limit of present public support (as well as a measure of the utility attached by the public to pollution abatement), we may work on the efficiency problem with that as a basis. -We will attempt to incorporate that philosophy into the theoretical outline below. Any theoretical statements we may make on abatement action, must be limited by the amount of information available, i.e. how much we actually know about the effects of pollution and about the public's desires with respect to environmental quality.19 The present level of such information in most regions is incredibly low, making it impossible to derive adequate utility functions and hence a major obstacle to even e theoretical modelling of environmental quality situationsq. As a consequence many see this as an indication that trial and error, heuristic, stopgap measures will prove most effective in pollution abatement for a long time to come. Bohml, however, states that,"...pollution is a subject of political concern, on the basis of which economic policy measures are actually being undertaken. Therefore, measure- ment must be seen as a difficult problem rather than as an insur- mountable obstacle .... In fact, the current situation on the Hudson offers concrete support for Bohm's statement. The enforcement of the 1899 Refuse Act, "inefficient" though the measure may be, has resulted in pioneering work in the techniques of gathering and organizing en- vironmental information. Much information leading to donvictions of or cleanup activities by Hudson polluters has been gathered by the Hudson River Fishermen's Association. The association makes constant efforts to determine public opinion concerning the state of the Hudson, and makes studies of the revenue lost locally due to polluted recreational facilities2. The testimonies of individuals at the public permit hearings pro- vided by the Refuse Act result in information on the behavior of firms in the area 4?22. Comprehensive information is required on the permit,application itself5, and federally enforced penalties are provided for the firm submitting false information2-2. Governmental agencies aid the'firm with the task of gathering accurate information. "Regional offices of the Environmental Protection Agency will provide technical advice as to the meaning and content of water quality standards and information about available technology for controlling pollution22." The Environmental Impact After outlining some of the medical and property-value benefits of pollution abatement, Sanford19 concludes that from the point of view of society, any future cost-benefit analysis will show that any cost of pollution abatement is justifiable. Statementl7, mentioned above, can attach dollar values to health and sanitation-oriented consequences of polIution in an Area. Title II of the Environmental Policy Act of 1969 "Will insure that presently unquantified environmental amenities and values may be given appro- priate consideration in decision making along with economic and technical considerations." Hasslo shows that the necessity of perfect information depends on the degree of centralization of the proposed abatement program. In this light, programs such as the extremely decentralized Hudson scheme may be ideal for pollution abatement during the,early stages of environmental information technology. What alternatives are open to a polluting firm under the Refuse Act, and what are the pertinent costs associated with each,> Should the firm decide, in compliance with the Act, to treat its effluent, it may (a) build its own treatment facility, (b) collabor- ate with other firms in the same area or whose effluents are of a similar nature on the construction of a common treatment plant, (c) negotiate for affiliation with an existing private treatment plant, or (d) contract with a nearby municipality for connection with an existing or planned municipal sewage treatment facility2. Each of these schemes has, in practice, involved a sunk cost to the firm for initial connection, and then a treatment price per unit of effluent, adding to the marginal cost of the product. With similar cost consequences, a municipality may choose to build a facility for itself alone, build one for itself and surrounding firms and neighbor- ing municipalities, or contract for connection to the facility of a nearby municipality. The firm may alternatively (p) discontinue the product whose manufacture leaves the polluting residue, (f) abandon the plant, or (g) shut down the plant long enough to change the equipment or the product or until the water standards are such that the proposed dischar eptable. To each of these alternatives is associated ,Ve is acc a cost2l, manifested in personnel changeover costs, zoning permit costs, costs of new equipment, and so forth. On th 'e other hand, the firm may decide to (h) relocate. For the case of relocation, we will allow the possibility of a firm relocating to an area where treatment of its effluent will be cheaper -- not to an area where they may pollute. The latter will be prohibited since it is a general goal among antipollution measures that the measures be enforced uniformly throughout a large region, so that there might be no geographical havens for polluters3. The firm may, by presenting the District Chief Engineer with an acceptable plan for eliminating an effluent in the near future (i) obtain a conditional permit22. Finally the firm may, choose Cj) to pay noncompliance fines, or (k) litigate, which has been quite effective in the short run. Both of these last measures, (j) (k), although costly, are commonly chosen by polluting industries. The consumer suffers the costs of administering the Refuse Actf D-6 as well as the effects of any company lay offs due to converted or discontinued operations. The utterly decentralized character of the Hudson River measures, as well as the all-or nothing nature of the permit-issuing procedure render difficulty any attempt at an interesting symbolic model of the situation. This, and the demonstrated inability of the Hudson River firms to negotiate with neighboring municipalities in a friendly fashion2 stymie the hope for either quantitative guidelines for public policy or predictive statements concerning the behavior of the firms. However, a modification of Bohm's ideas provides a basis for some decision guidelines for the firm, as well as for some quantitative statements concerning equity and efficiency. Let H be the set of Hudson River firms and municipalities. Assume single-product firms. Bohm's notation, modified, is used. Let PA=the pollution of firm or city A, measured according to the Environmental Impact Of QA, where QA=output of A's product per unit time. LA=consumer's loss of welfare from P A (extra health care costs, drops in property value, cost of traveling to alternative recreation spots and others3,19.) Then PA=FA(QA) F(0)=O, F1>0, F" 0. L f(0)=O, f'>0, f" 0. A=fA(PA) AB=cost to firm B resulting from PA. If A is upstream from B may be lost revenue to a fishing firm B, or cost to B of filtering water as input to a production process. AB=g AB(PA) g(0)=O, g' O, g" 0. The total value of negative external effects resulting from A's operation is hA(QA)=LA + Aj =f A (PA ) + gAj(PA) jeDA jeda h (0) =0 DA=set of all firms and municipalities downstream from A. The marginal external effect with respect to QA: D-7 The net benefit of A's operation at level QA is -ffA=PQA - C (QA) - h (QA) where C(Q) =total costs to a firm of output level A,, p=unit price of A's product At this point it seems reasonable for the water authority to require as a condition for the continued operation of firm X that there exist a Q* such that X (i) 7TX (Q*) > 0, and X (ii) X conforms to all provisions of the Refuse Act. For were there not a Q* so that n >0 legally, then X would be nothing but it X. X a pollution mill", and generally undesirable. More difficult to deal with is the ff of a municipality. Castleton, N. Y. (pop. 1700), in order to comply with New York law, needs a treatment plant before mid-1972 that will cost $1,000,000 more than the total assessed value of the village2. Back to this later--it is obviously a rele- vant problem, We have so far omitted the implicit subscript to: i.e. func- tions such as L = fA(PA) may change from day to day, depending on the buildup (failure to degrade) of yesterday's pollution in the water. We may say that if LA =fA(PA,t), then 3 LA >0, assuming consumers do not "get used to" the pollution. 3 t External effect theory tells usl that at this point a tax 'should be imposed on A at unit rate T (where T is a tax rate, not time), such that T =h'(@A) =p - MC(QA) where QA is the level of output A adjusts to after the imposition of the tax, and MC is the marginal cost at output level Q. The firm on the Hudson now chooses from the list of alternatives presented above, and picks the least-possible-cost measure, which costs C*(QA)' We are interested in the case where C* is not a one-shot cost, such as for plant abandonment, and where at least the variable component of C* is nonvanishing (thus discounting the possibility of, say, a fix.ed-fee treatment contract with a munici- pality). Thus C*(Q)>O C*I(Q)>O D-8 We now consider the choice on the part of firm A to connect with an available municipal plant or with a private plant. We recall that the price to a firm for connecting with a municipal facility may be unexpectedly low for several reasons: A city on the Hudson must remove 2nly 90% of municipal sewage wastes before discharging into the river , whereas a private firm in the area must remove 100%. Practically, this means a city must remove 100% of clearly identifiable industrial pollutant from contracted private waste sources, but only 90% of contracted industrial wastes that cannot be identified as municipal sewage. As well, municipal treat- ment facilities are largely financed by municipal and state bonds (which are free of federal tax, and which we will consider neither a benefit nor loss to consumers) and by tax revenues which we will assume to have been subsumed under E Lx. On the other hand, a differentiated and more expensive xCH facility may often be needed to process both sewage and chemical wastes of industry. one further assumption: It is legaT-11-n nearly all states (precedent: Bessemer, Alabama) for a city to float municipal byRds to build a manufactu'r- ing plant which it then leases to a firm . Legally we believe that the effluents of such a leasing firm may not without further contract be considered municipal waste. For the purposes of this paper, we will assume that this is true. We thus assume that muncipality M can offer to a firm A a link to its treatment plant at a rate C*'(Q@t)=T+r A A The city offers this rate using as a base the "fair" price T (which was determined above) incremented by.r@0, which takes the assets of the previous paragraph into account=to bring C*1 in A correspondence to the marginal costs of operatiyg the facility. The argument below is largely adapted from Bohm A adjusts its output to QA, so that MC(QA) + (T+r) =p The sign of hl'(Q,) is now relevant. We must assume h11 to be of the same sign'r-'over the entire domain of QA* First, the case of h">O: de MC hl t tax P h amount QA D-9 it is interesting to note here, that at level Q A, the treatment prive per unit time, if r=O, exceeds the total negative value of A's pollution during that time: total external effect =HC =TQ* =tax amount A Obviously if r<O i.e. the unit price of treatment less than the marginal external effect, it is advantageous for the firm to contract for waste disposal with M. In fact, if r< H(Q Q* A then the total value of daily pollution is less than the daily treatment price, and A enjoys what we may call a "polluter's surplus." There is then no question that it is profitable for A to contract with M. If r>0, though, A may wish to collaborate with other firms in the construction of a private treatment plant. Examininq the case of h"1<0, we obtain a surprising result. For r=O (and h' now a decreasing function), A tQ* A. i.e. A enjoys the "polluter's surplus" for r=O, and in fact for all r such that h (Q*) - Q* h' (Q*) < r < A A A Q* A although paying a marginal treatment rate, when r>O, great- er than the marginal value of pollutant. We must now recall the requirement that A (Q*0) >0. A Thus, except for the cases (i) (ii) D-10 or even in these. cases in the absence of a less-cost alternative? we see firm A will have considerable incentive to contract with. M. In reality, difficulty of negotiation weakens' t4is result some- what. . However, we have the consolation of having shown the Refuse Act scheme to be superior to the pure taxation scheme in one significant way. The introduction of the firm-independent price term r Cand the concomitant reduced necessity of exactly determining T) eliminates one problem pointed out by Bohm. For in the tax scheme, as soon as the complete determination of T by Q becomes evident to the firm, the firm will adjust Q to a level where marginal net revenue equals marginal c ost, so that d(,pQ-C(Q) -h(Q)Q)- =0, dQ i.e. P-h'(@) - h"(,O)Q =MC thus destroying optimality by setting output too <large or too small by a factor of @5h"(O), depending on hl'>O. Thus the term r encourages stability of whatever-optimality may have been obtained. In time, the firm will enjoy benefits stemming from its efforts at pollution control. The technological advances stimulated by strict antipollution legislation will have made available more efficient produc@; on equipment, some of which will make use of recycled wastes . Pollution control ."makes cities more liveable. And people who work in more liveable places don't have to be paid quite as much as those who work in less liveable places.1119 Progress has also been made in the generation of power from the combustion of sewage wastes, notably the financially sound operation of the municipal treatment-generation plant in Hempstead, New York. So farf we have assumed no adjustment by the firm of P, its product's unit price. This is unrealistic, as the effluent treatment price (and hence the firm's marginal cost) are dependent on the nature of the effluent, not on the nature of the product. Thus not only the prices of products will change, but the relative prices of products will change under legislation of the Refuse Act type, and will result in a period of chaotic price fluctuations-- probably without the traditional concomitant of higher employment.* Any wide-area assertion about the direction of prices would then depend on the validity of the assumption that the least-cost method of effluent treatment will be similar (at least in cost) for firms within the same industry. This would seem to be a necessary condition for stability of an industry, and a considerable amount of plant For discussion of the macroeconomics of pollution, see19 and Boulding, Kenneth, "Economics of Coming Spaceship Earth", in DeBell, Garrett, ed.), The Environmental Handbook, Ballantine, 1969. relocation may occur before it is achieved. Despite the limitations of symbolic models, a numerical model would not be appropriate1@t this time, because it would not account for technological change . By the time comprehensive numerical data is available, massive technological changes w:U1 have occurred due to the pressure of strict antipollution laws. A detailed report of thedecisions taken and costs incurred by the Hudson River firms during the first year of the new enforcement of the 1899 Refuse Act has not yet been compiled. Such a report can be expected soon since there is great legal and nationwide conservation interests in local applications of this Act. The Hudson River case will provide guidelines for future regional policies (To some extent it already has, in the cases of the Dela- ware Valley and the Miami River of Ohio), and, whether eventually judged either successful or infeasible, it will have contributed momentarialy to the methodology of environmental management policy- D-12 REFERENCES [1] Bohm, Peter, "Pollution, Purification and the Theory of external Effects"; Swedish Journal of Economics, June, 1970. [21 Boudreaux, Richard, "Hudson Pollution Rolls on Despite Promise of Cleanup"; Austin American Statesman, 10 October 1971, Associated Press. 133 Coalition to Tax Pollut ion,, "Turning the Tables on Polluters"; pamphlet, August 1971. [41 Dept. of the Army, Corps of Engineers, Permits for Work & Structures in, & for Discharges or Deposits into Navigable ters; 1971. E51 ItApplication to Discharge or Work in Navi- gaTl_e__V@ters and their Tributaries"; U.S. Govern- ment Printing Office, 197-1. [61 Davenport, John, "Industry Starts the Big Cleanup"; Fortune, February 1970 [71 Dept. of the Army, Corps of Engineers, A Procedure for Evaluating Environmental Impact; U.S. Government Printing Office, 19-7-67. E83 Diamond, Robert S. ' "What Business Thinks"; jortune, February, 1970. [91 Hagivik, George H., Decision-Making in Air Pollution Control; Praeger Publishers, New York, c.1970. [101 Hass, Jerome E., Decentralized Decision-Making: Non- linear Decomposition Algorithms and their Uses; Fh-D. dissertation., Carnegie-Mellon University, June, 1969. [ill Personal Communication with Dr. Jared Hazleton, Dept. of Economics, University of Texas, October 1971 and J.E.Hazleton "EfIients and Affluence" in Ecology and Economics ed, M.I. Goldman CEnglewood Cliffs, N.J.:. Prent ce Hall, 1972. [121 Henderson and QuandtV Microcconomic Theory; MoOraw-Hijl, New York, 1958. E133 Kneese., Allen V., and Blair T. Bower, Managing Water Quality: Economics, TechnoloZy, Institutions; hns Hopkinst Baltimore,- 1966. D-13 [14) Kneese, Allen V., "Water Quality Management by Regional Authorities in the Ruhr Area", in Goldman, Marshall I., Controlling Pollution; Prentice Hall, Englewood Cliffs, N.j . c.1967 . [15) "Research Goals and Progress Toward Them" in Jarret, Henry (ed.), Environmental Quality in a GroWing Economy; Johns Hopkins, Baltimore, 1968. [16] James Bishop Jr. in Newsweek, 4 October 1971. (17) Discussion with Mrs. Grace Paul, Institute for Advanced Environmental Studies, Austin, Texas, November 1971. [18) Personal Communication with Herbert Phillips, Technical Director, Associaton of Home Appliance Manufacturers, Chicago, Illinois, November 1971. [19) Rose, Sanford, "The Economics of Environmental Quality"; Fortune, February, 1970. [20) Samuelson, Paul A., Economics; McGraw-Hill, New York., 8th edition, 1970. [21) Spencer, Milton, Managerial Economics; Irwin, Homewood, Ill., 1968. (22) U.S. Army Corps of Engineers, "A Program for Clean Water"; information pamphlet on impact of 1899 Refuse Act on firms., U.S. Government Printing Office, 1971. D-14 CHAPTER XI DATA MANAGEMENT - INFORMATION SYSTEM SYSTEM 2000 is a general-purpose data base management system which operates on the University of Texas at Austin CDC 6600 computer. The basic system 2000, with special optional features, provides the basis for developing an information system tailored to the requirements of this study and the variety of possible users. Features.of the SYSTEM 2000 include a report writer, a user-oriented language providing on-line access to non-programmers, a procedural language interface for program- mer use, and sequential file processing. The University of Texas System 2000 This system provides the user with a comprehensive set of data base management capabilities. These include the ability to define new-data bases, modify the definition of existing data bases, and to retrieve and update values in these data bases. The components of data base definitions are data elements and repeating groups. Values are stored in data elements. Repeating groups descriS-ethe structure for storing multiple sets of data values and also provide a hierarchical linking mechanism. Values for each element and entry (record) may vary in length. The user may specify without restriction which elements in the data base are to be inverted and become key fieldsl and what hierarchical relationship an element will have with other elements in the data base. Data security is maintained by password control to each component and additional password control to the data base itself. The procedural language feature enables users to manipulate data in a data base from a procedural programming language such as COBOL, FORTRAN, or assembly language. This feature provides the mechani.sm for addressing any part of the data base of inter- est to the procedural program, to retrieve data in a sequence and ,format suitable for procedural processing, and to update the data base from the program. Interrelationships between two or more data bases can be established which permit network data structures to be defined. Data base qualification is performed by use of an internal file, facilitating the screening of data of interest. The sequential file feature makes possible the processing of a major part of the.data base from sequential media, such as magnetic tape. This option enables the processing for storage of census tapes for the region and enables the utilization of existing land- use information tapes. XI-1 Due to system 2000 application high flexibility, its availabil- ity on the U T system and the thorough documentation, it was chosen as the main information-management tool for the land-use part of the study. Our first step was to define a basic grid structure over the study area to which all data could be interrelated. This basic structure allows overlay-like procedures to be carried out by the computer, making mapping and other graphics a natural form of output. Preparation for formulating a base map of the study area, that could be generated by the computer at any desired scale, was begun. All county lines, towns and highways have been digitized on a magnetic tape.for input when the plotting routines are completed. Presently we are preparing to digitize the census tract boundaries and complete the plotting routines. This plotting data has been augmented by information dealing with land types such as: fresh water marsh, tidal flats, etc. An attribute structure has been implemented to allow all of the data to be placed in a system 2000 data base. The data base has been defined, built and loaded with a representative string of data. It has been successfully tested to see if rel- evant questions could be answered and to identify the complexity level of such questions. We are now in the process of finding out what other catagor- ies of data need to be introduced into the system and what kinds of questions need to be answered. We believe that this approach will be critical in the second stage of the project when various policy alternatives are being evaluated. The completion of this information-management system with graphs and tabular outputs together with simple allocation algorithums will be a vital tool for interaction between the various task forces. XI-2 COASTAL ZOM INFORMATH CENTER 0A7F- DUE G LORDINo. 2333 PRINTED IN US.A. AY 3 6668 14106 7019