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A preliminary Analysis of Impacts of Development of the Coastal Planning Districts HT 393 .S6 j47 1979 A preliminary Analysis of Impacts of Development on the Coastal Planning Districts U . SDEPARTMENT OF COMMERCE NOAA COASTAL SERVICES CENTER 2234 SOUTH HOBSON AVENUE CHARLESTON SC 29405-2413 property of CSC Library Patricia L. Jerman Governor's Office, Division of Natural Resources in conjunction with Division of Research and Statistics, South Carolina State Budget and Control Board August, 1979 Table of Contents Introduction Acknowledgements I. Purpose ..................................................... I 11. Study Design ................................................ 2 Model Choice4 ........................................... 3 Geographic Boundaries .................................. 4 Unit of Measurement ....... ; ............................ 6 Assumptions ............................................. 7 ................................................. SCOPE 11 Model ......................................... 8 Planning District Models ............................... 9 Impact Assessment Methodology .......................... 10 Infrastructure Methodology ....... 6 ..................... 12 Assessing the Accuracy of the Models ................... 13 Comparison with other Planning Districts and State Total ........... I........................ 6 ... 14 IV. Results ...................................................... 15 Baseline Forecasts ...................................... 19 Impact Assessment ............................ 21 Infrastructure ................................ :"*:*"::24 Education ......................................... 26 Health Care ....................................... 27 Law Enforcement and Fire Protection ............... 30 Households and Electric utilities ................. 33 Water and Sewer Usage ............................. 35 V. Using the Model: An Example ................................ 36 V1. Concluding Remarks ........................................... 41 VII. Future Directions ........................................... 43 Bibliography ............................................ -.044 Appendix A - Baseline and Scenario Forecasts, Economic Impacts ....................................... 46 Appendix B - Social Factors ............................. ... 81 Introduction The three econometric models which form the basis of this study were developed by the Division of Research and Statistics, State Budget and Control Board, under a Coastal Energy Impact Program (CEIP) grant administered by the Coasta I Counc.il. It is hoped that the study will provide guidance to planners and administrators who must prepare for the future development of the coastal zone. The preparation of this report was financed in part by a grant from the Department of Commerce. .(Contract Number NA-09-AA-D, CZO 25-A.) Acknowledgements Several members of the Division of Research and Statistical Services staff were involved with this project from the outset, and deserve much of the credit for its successful completion. In particular, thanks are due to Harry Miley, E. A. Laurent, and Lynn Paul. I am also grateful to Mark E. Tompkins, James M. Stepp, and Ann Baker for their valuable comments during the draft stages of the project. Special thanks are due to.Jeanette T. Johnson for typing and arranging the manuscript in her "spare time." I PURPOSE In the Coastal Zone Management Act of 1977, the General Assembly declared that the basic state policy with regard,to the coastal zone is: "to protect the quality of the coastal environment and to promote the economic and social improvement of the coastal zone and of all the people of the State." In keeping with this policy, the first goal of the South Carolina Coastal Council is: "Development of a management program that will achieve a rational balance between economic development and environmental conse vation of -n-atura i -mmu-rces 1-n "t-he cDa-sta I zor e --of -Sout-h Czroi i-n-a.-I I One of the Coastal Council's objectives is to develop a "comprehensive data base to aid in making rational decisions." To this end, the staff has worked closely with the Division of Research and Statistics of the State Budget and Control Board to acquire information regarding the effects of development on some economic and sociological aspects of t he coastal zone. They were aided in this effort by a grant from the Coastal Energy Impact Program, which was designed to "assist State and local communities as they expe rience the onshore and offshore impacts of coastal energy testing and to encourage them to cope with the impacts in a manner consistent with the State's developing coastal management program. ,2 Energy has become, and undoubtedly will remain, one of the principle factors affecting regional development. For that reason, it is vital to know not only the impact that energy developments will have on the coastal zone, but also the impact that coastal zone development will have on regional energy demands. -With that in mind, econometric models have been developed for each of the three planning districts in the coastal zone. The purpose of these models is twofold: they forecast levels of economic activity within each planning district and can be used to assess the impact of development of various kinds. In other words, the econometric models IS. C. Coastal Zone Management Program, Goals and Objectives, 1979. 2S. C. Intrastate Allocation Process, Coastal Energy Impact Program, April, 1979, p. 2. supply both a baseline forecast and, more importantly, a measure of how this forecast will change if new development occurs. Thus, the models can be used to forecast the effects of an energy-related development (such as an oil refinery) on various employment sectors in the area, and at the same time, projected growth in various employment sectors can be used in conjunction with other sources of information to estimate future energy needs. In either case, the results of the study will enable the Coastal Council to evaluate possible sites for energy develop- ments in a more rational fashion. The ef-f e-ct-s of dev-etopmen-t -an -re-gi-oli-a-i i-nf-ra7s-t-rvc-t-u-re -T_@ --al-so -be e-st ima-tad using the econometric model. Population projections generated by the econometric model can be used to estimate-the additional demands for public expenditures such as number of schools, number of law enforcement and fire-fighting personnel, etc., necessitated by new development. 11. STUDY DESIGN Regional growth resulting from new developments can be disaggregated into three components: direct, indirect, and induced effects of development.3 Direct effects are those arising from industry itself--500 new employees in a chemical manufacturing plant, for example. Indirect effects are those caused by the demand for goods and services created by those 500 employees and their families. Induced effects are caused by responses to the needs of the industry itself, as is the case when a chemical manufacturing plant attracts a scientific equipment company. Both indirect and induced effects generate additional demands in other sectors of the economy, thereby creating even more jobs and additional cash flow. If cultural or leisure activities are developed, or if the area's infrastructure is significantly 3Because regional growth analysis is a relatively new field of study, the terminology used tends to be confus'ing. Therefore, if the terms used herein are not consistent with those found elsewhere, please be patient. improved, new industries may find the location more attractive, increasing economic activity still more. Thus., it can be seen that regional growth feeds upon itself, and that growth in one sector of the economy will generate attendant growth in other sectors. It is this principle which makes impact analysis worthwhile; the direct effects of development are self-evident, but the indirect effects are less obvious, and their prediction is aided by a model of some sort. Model Choicei There are three commonly accepted models of regional analysis: economic base, inp ut--output, and econometric. Economic base models rely on the theory that a local economy can be divided into two producing sectors: One producing goods for sale outside the region (basic- sector) and one producing goods for sale within the region (nonbasic, or service sector). It is a quick and relatively inexpensive method of analysis, but one which is quite limited. 4 The second method, input-output analysis, is far more elaborate than the economic base model. It relies on the theory that all sectors of the economy are interdependent, and consequently allows for the detailed mapping of multiplier effect throughout the entire local economy. The principal drawbacks of the method are the time and resources required to complete it and the fact that once it is completed., the model is tied to the assumptions regard- ing technology and relative prices prevailing at the time. The econometric approach falls somewhere between the other two, both with regard to the time and resources necessary to develop it, and to the depth of analysis it yields. An additional advantage of econometric models is the fact that they are not tied to any one theory, ,but rely instead on observed relationships among sets of data. Because they are empirically, rather than theoretically based, the econometric models are more responsive to changing conditions. 5 4 Glickman, Norman J., Econometric Analysis of Regional Systems; Explorations in Model Building and Policy Analysis, Academic Press, New York, 19@7.. See pp. 20-27. 5 Ibid., - 38-39. pp 3 Econometric techniques rely on least squares regression analysis to determine relationships between two or more variables. The outcome of least squares analysis is an equation in which the left hand variable (the dependent variable, or the one to be expla,ined) is equal to the right hand variables (independent or explanatory variable) multiplied by some coefficient plus a constant plus some residual error. If values for the independent variables are known, values for the d.ependent variable can be determined, based on the relationship between the two as estimated by least squares regression analysis. Econometric approaches to regional ana,]_ysis are not wi.t,hout.pro.blems,. They offer a relatively simplistic explanation of regional phenomena and rely on regional data which have a number of limitations. The necessary time series data are often incomplete or available for only a few years. Regional data are also generally available only on an annual basis, resulting in fewer observations. (A larger number of data points would enable us to estimate with greater confidence.) Econometric models genera'lly treat regions as discreet and closed, thereby ignoring "leakage" and interrelationships across county planning district or state borders. Conclusions drawn from the model are tied not only to relationships which have existed in the past (and may not hold true into the future), but to national models as well (see Glickman for a more complete discussion). This last point is both detrimental and advantageous to regional analysis: it may result in erroneous comparisons, yet the regional economy does not operate in a vacuum and responds to many of the same factors as the state and national economies. Of course, the accuracy of the national model used is also a factor which must be considered.6 Geographic Boundaries: As noted above, this project utilizes models for each of the three planning districts which fall (completely or partially) within the State's coastal zone. 6 Ascher, William, Forecasting: An Appraisal for Policy Makers and Planners, Johns Hopkins University Press, Baltimore, 1978, pp. 65-92. 4 Section 3(B) of the.South Carolina Coastal Management Act of 1977 defines the coastal zone as: Hall coastal waters and submerged lands seaward to the State's jurisdictional limits and all lands and waters in the counties of the.State which contain any one or more of the critical areas. These counties are Beaufort, Berkeley, Charleston, Colleton, Dorchester, Horry, Jasper, and Georgetown." Because the models-employed here include entire planning districts, Williamsburg and Hampton counties are a part of the study without being a part of the coastal zone. T-hey-e -a-re -sravei-ai T-easv-n-s -foT -r-aTry-i-nq -out -t-he an-a] ys i-s on -the 'bas i s of planning districts rather than separate coastal counties or the coastal zone as a whole. In the first place, economic activity often transcends legal'and geo- graphical boundaries. While the coastal zone boundary was designated on the basis of natural features, planning district boundaries were determined primarily on the basis of social, economic, and commercial factors. Thus, a new development in Georgetown County would be as likely to affect Williamsburg County as Horry. The effects of development may vary for each county in the planning district. A plant located in one county may impose certain infrastructure costs on that county, in the form of additional demands on water and sewage treatment facilities, for example, while many of its employees may live in a neighboring county. The second county must absorb the increased costs for schools, fire protection and other services demanded by residents. Both counties may share the indirect benefits brought on by increased demands for goods and services. Therefore, aggregation of economic information to the planning district level is necessary in order to capture more of the effects produced by a new industrial or energy-related development. A second reason for aggregating counties into planning districts is that the Division of Research and Statistics is compiling similar econometric models for 5 each of the State's ten planning districts. Making the coastal zone models compatible with those for the rest of the state enables one to make useful comparisons between the various regions of the state. Local and regional growth will be constrained by the total growth predicted for the state; no one area of the state will show unreasonably depressed or inflated growth patterns, since all will be formulated on the-same basis. Such a design also enables each planning district to serve as a check on the others since the cumulative growth totals for all the planning districts will not exceed those of the state. (More will be said about this in the section dealing with the accuracy of the models.) Finally, aggregation of county data into planning districts is desirable from a statistical point of view. Forecasting generally becomes more accurate as the number of observations and size of the region studied increases. Thus, as we increase the observations by merging county data into planning districts, we increase the reliability of our forecasts; a tradeoff must be made between a relatively high degree of detail and a low degree of accuracy and less detail with greater confidence in the results. Unit of Measurement: Employment, rather than regional output or personal income, was chosen as the measure of impact for this study. Employment data are generally more useful for planners than overall output because employment is a more reliable indicator of total population change. Technological change may enable an industry to greatly increase its output whHe at the same time decreasing its number of employees. If changes in output were used as a measure of impact, these technological changes might go unnoticed by planners and lead to an inflated estimate of the number of people migrating into an area. Changes in population are vital to regional planners, since most costs (and a relatively largeproportion of revenues) at the local level are a function of population rather than total economic activity. (For example, as the number of residents increases, the number of policemen needed to 6 serve them will increase. We would expect this increase to be the same regardless of the residents' income. Employment data are generally easier to obtain than regional output data. This is especially true in the area of impact analysis, since a firm may not be able to accura tely estimate its output, but should have a fairly good idea of its employment needs before its plant is even built. Assumptions: A number of assumptions have, of necessity, been made in the course of develop- +n@g -the-moid-ei-s. Dn,e -af ttie most troubl esome i s 't-he 'i nabi i i-ty of the model s to a-11 ow for changes in income or cash outflow across planning district or state lines. In other words, the models assume that all economic and demographic effects of develop- ment will be distributed as they were in the years preceding the forecast period. In many cases, of course, residents of one planning district may be able to afford to travel to new areas in order to shop. This is particularly true in those areas of Planning District 10 which are close enough to Savannah to permit "shopping across state lines," particularly for luxury items. Many employees of one district may reside in newly developed suburbs located in another planning district or state, making the problem still more complex, since some level of service will have to be provided for these individuals in two different counties. Another assumption made in the course of model-building is that relationships between sectors of the economy which were found in the early to mid-seventies will hold true into the nineties. The models assume that conditions will remain the same, and that no major perturbations will occur. Unfortunately, we have no way of knowing how relationships have changed (or will change between now and 1990), or even the direction of change. In fact, we do not know with any degree of certainty that the relationships will change. Reliance upon state and national economic models forces us to accept another set of assumptions--those forecasts which are generated by the larger models. 7 For example, the national.forecast which was used as the basis of the state and local forecasts used here projects growth in the Gross National Product (GNP) to average approximately 5.8% from 1980 to 1990, general inflation to average approxi- mately 6.9%, and the unemployment rate to decline from 7.2% in 198o to 5.6% by 1985 and to 5.4% by 1990. During this period, the U. S. economy is assumed to be relatively stable, and to be approaching growth trends which converge on its potential long- term rate of growth.7 III. METHODOLOGY -The effects of poss'ib'le industrial or energy-re'lated developments on the various regions of the coastal zone are estimated using an econometric model devel- oped for each of the planning districts. The models are satellite models of the South Carolina Operations Planning and Evaluation model (SCOPE 11), although less detail is found in the satellite models due to data limitations. The planning districts included in the analysi s are Planning District 8: Waccamaw (Georgetown, Horry, and Williamsburg.counties); Planni.ng District 9: BCD (Berkeley, Charleston, and Dorchester counties); and Planning District 10: Lowcountry (Beaufort, Colleton, Hampton, and Jasper counties). As noted earlier, Williamsburg and Hampton cou-nties are not a part of the coastal zone. SCOPE 11 Model: The South Carolina Operations Planning and Evaluation 11 (SCOPE 11) Model is designed to forecast the performance of the major economic sectors in South Carolina. The forecasting models, a state model and a tax model, are maintained in a computer time-sharing arrangement with Data Resources, Incorporated (DRI), a Lexington, Massachusetts, firm, which is a leading consultant in the area of state forecasting models in the country. 7Data Resources, Incorporated, June, 197.9. National Forecast. 8 Data Resources su,pplies a foreca.st-of the national market conditions which basi- cally determine the level of activi ty for the manufacturing sector of the economy in the State. The level of nonmanufacturing activity in the economy is essentially @determined by demands originating within the state. By estimating the level of personal income and combining this with demands in the manufacturing sector, the level of activity in the nonmanufacturing sector can be modeled. Hence, the interaction between the state and the national economies determines the level of manufacturing activity in the state and this interaction among sectors within the State determines the level of nonmanufacturing activity. The SCOPE 11 Model disaggregates the economy of South Carolina to a degree that enables manufacturing and manufacturing demands to be determined more accurately. This is essential since the economy of South Carolina, especially the manufacturing economy, is significantly different in composition from the national economy. By estimating the nondurable and durable sectors separately, the model can forecast a more precise picture of the South Carolina economy. The forecasted levels of state economic activity are then used to estimate General Fund Revenues in the State. The SCOPE 11 Model consists of 89 interdependent equations--46 behavioral equations and 43 identities. The model forecasts employment in the principal sectors of South Carolina's economy including the 2-digit SIC manufacturing industries personal income disaggregated into its components, population broken down by age and vital statistics, and several other major economic indicators in the state. These include prices, wages, deposits in savings and loans,-a manufacturing pro- duction index, value of residential construction and retail sales.8 Planning District Models: Each of the planning district submodels is simultaneous and contains eight equations and one identity. The planning district models are less dis- aggregated than the SCOPE 11 model due to data limitations. In addition, data are recorded on an annual basis, rather than a quarterly basis. The planning district 8Cindy Stribling, Division of Research and Statistics, In-house publication, I;nr i rin 1 Q7Q Q models disaggregate employment into Manufacturing; Contract Construction; Finance, Insurance,_Real Estate; Transportation, Communication, and Public Utilities; Service; Government; and Trade. ln,addition, a value for all nonagricultural employment is obtained by adding the values for all the employment sectors. Population and (real) personal income are also included. The results of the modeling activity are a series of forecasts for the years 1978 to 1990. There is a Baseline forecast showing the levels of economic activity which may be ex- pected if no new industries or energy-related facilities are built in the coastal zone-, and a series of impact forecasts which show what changes may be .,ex _pgr (in employment patterns) if new development occurs in the coastal zone. Impact Assessment Methodology: Impact forecasts were generated by increasing the number of employees in the appropriate sector for a given scenario. The model was then used to generate a new set of forecasts, which provide estimates of the additional effects arising from that development scenario. For the baseline forecast, no impact values were added. To measure impacts, the anticipated level of employment, beginning with a low number in the year the plant construction was completed and increasing gradually until the total projected employment needs were reflected in the variable. In all cases, an increase in construction employment would precede employment in the sector under consideration. In all cases, hypothetical construction was completed within three years and the construction impact variable was returned to zero. Impact variables for all sectors are as follows: Manufacturing = MFG Contract construction = CONSTRUCTION Transportation, Communication and Public Utilities = UTILITY Trade = TRADE 10 Finance, I Insurance and Real Estate = FINANCE Service = SERVICE Government (Federal, State, and Local) = GOVERNMENT Population = POP (Real) Personal Income = INCOME As an example, suppose that an oil refinery (defined as a manufacturing plant) is proposed for thecoastal zone. Refinery construction will require 1,000 people during peak periods. Construction is to begin in 1980 and will take approximately 3 years. During the last year of construction work, some production employees will be hired, with full operating employment leveling off at 300 by 1984. Values for the two impact variables of concern will be as follows: MFG CONSTRUCTION 1978 0 0 1979 0 0 198o 0 500 1981 0 1,000 1982 100 300 1983 300 0 1984 300 0 1985 300 0 1986 300 0 1986 300 0 1987 300 0 1988 300 0 1989 300 0 1989 300 0 These values will be added to the forecasts for the proper years. In addition, because the model is simultaneous, indirect effects in various sectors will be reflected in the final forecasts. Therefore, even though the impact constants for all other sectors remain 0, there will be some increase in employment and income in other areas of the economy. The impact on each sector may be determined by subtracting the level of employ- ment in the Baseline forecast from the respective level in the impact forecast for any given year. For example, in Table 2, Appendix A, 300 manufacturing employees have been added to Planning District 8. The impact of this addition on the service sector in 1990 can be determined by subtracting 22.17 (Baseline employment, Table A-]) from 22.20 (impact employment, Table A-2). The additional manufacturing employees will generate a need for 30 new service employees. The total nonagricultural employment impact may be determined by subtracting Baseline from impact values in each of.the employment sectors and summing them. A general multiplier may then be generated by dividing the total (nonagricultural) employment impact by the initial direct employment, in this case 300. The resulting multipli er is 1.97. (See Table 31, Appendix A.) A simple formula for generating employment multipliers is: Change in total employment Multiplier Change in direct employment lnfrastructure@Methodology: Like many of the other terms used in regional growth analysis, "infrastructure" is a word which means different things to different people. As used here, it will represent structures which provide services of one sort or another. In almost all cases, the term will refer to those structures or organizations which provide public services at the local level, such as fire and police departments, schools, and so on. Exceptions are "physicians" and "hospital beds,'' which were included in the analysis 12 even though the services they provide are not generally considered ''public." Infrastructure needs were determined from histor-ical data.. When available, time series data were used; however, several of the infrastructure equations are based on data for only one year. (The years considered are noted in the text.) Because of severe data limitations, the effects of development on area infrastructure were not determtned by modeling, but were estimated by simple ratios instead. Each estimation is based on a ratio of the amount of service provided to the size of the population served. (In other words, three policemen may be required for every 1,000 new residents of a county.) In some cases, only one ratio is given for an entire planning district. However, when data at the county level were available, a separate ratio was developed for each county, as well as for the planning district as a whole. While county ratios cannot be used directly with the model (since population data generated by the model is not disaggregated to the county level), they can be used to give planners a more accurate idea of trends in individual counties. The estimate obtained using planning district data can be modified depending upon whether the planning district average is higher or lower than the figure for the specific county under study. Assessing the Accuracy of the Models: The regression equations used in each of the models were selected from a 9 number of possible equations on the basis of various-statistical indicators.. The statistic to which.the most weight was attached was the R 2 value, which measures the amount of change in the left hand (dependent) variable which can be explained by changes in the-right hand (independent, or explanatory) variable. If the R2 is equal to 1.0, all of the variance in the dependent variable can be explained by variance in the independent variable. Summary of the R 2 values for each of the planning districts follows: 9See McLagan, Donald L., A Non-Econometrician's Guide to Econometrics, Business Economics, May, 1973, pp. 38-45, for further information. 13 2 Planning District 8: 81.8% Of the R values were .95 100% of the R values were .90 Planning District 9: 63.6% of the R2 values were .95 81.8% of the R2 values were .90 90.9% of the R2 values were .80 100. % of the R2 values were .6o 2 Planning District 10: 45.5% of the R2 values were .90 81.8% of the R2 values were .85 100% of the R values were .65 Another important factor used to select the "best" equations was a comparison between the actual historical values for the dependent variable and the "calculated" values, or those values which would have been predicted had the equation been used to est'imate historica"I va-lues. In part-icu'lar, if an equation is sensitive to changes in trends, there is a reasonable chance that it will perform well in the future. Table 111-1 below, compares the average percent error for the years 1970-1977 for each dependent variable in the three planning districts. As can be seen, some equations are significantly better predictors than others. Table 111-1 Variable P. D. 8 P. D. 9 P. D. 10 Manufacturing Employment 2.30 3.24 1.36' Construction Employment 3.71 1.50 5.95 Transportation, Communication, and Utilities Employment 2.24 .99 4.71 Trade Employment 1.74 .88 .83 Finance, Insurance, and Real Estate Employment 1.86 2.38 7.20 Service Employment .96 1.51 4.14 Government Employment .47 1.24 3.56 Population .6o .20 1.82 Deflated (real) Personal Income 1.15 1.24 2.28 Average 1.6 1.6 3.5 Comparison with other Planning Districts and State Total: As noted above, similar models were developed for each of the planning districts in the State. The values forecast by these models were summed and compared with the State total, which had been forecast independently. The two were very close, indi- cating that the values forecast by the various planning district models are at least 14 reasonable, if not exact. (Other possible explanation.s are that the State model and the planni.ng district models err in the same direction, or that planning district ,model errors cancel each other out. We prefer the more optimistic approach, but cannot discount the others entirely.) Of course, it must be remembered that even equations which perfectly reflect relationships between variables will.not produce accurate forecasts if the exogenous variables are not predicted accurately. For this ' the SCOPE (State) and DRI (National) models must be relied upon. There is no quick way of assessing the relative accuracy of the SCOPE model; the DRI model, on the other hand, hasbeen 10 exposed to extensive error analysis. DRI's average absolute error of quarterly current-dollar (not corrected for inflation),- GNP forecasts were approximately 3 billion (1958 dollars) if the forecast was made late in the quarte r, and approxi-. mately 4.5 billion if the forecast was made early in the quarter. These figures compare favorably with many similar models; only two were more accurate than DRI's late-quarter forecasts. When GNP was corrected for inflation, DRI's model performed somewhat better for late-quarter forecasts, and somewhat worse for early-quarter forecasts. Four other models proved to be more accurate than DRI's late-quarter forecast for real GNP. (it must be remembered that even if the DRI model is relatively accurate, we have no measure of how accurate SCOPE, its sub- model, is. This is significant, since most of the exogenous variables used in the planning district models are state variables.) IV. RESULTS Before proceeding with a discussion of the results of the study, it is necessary to define more fully the variable abbreviations used in the forecasts: o' EM' Manufacturing Employment EC* Contract Construction Employment ER* Transportation, Communication, and Public Ut.ilities Employment ET* Wholesale and Retail Trade Employment Finance, Insurance, and Real Estate Employment IOAscher, pp. 73-84. 15 ESV* Service employment EG* Government employment EEA* Nonagricultural employment (this is a total of the preceding sectors.) N* Population YPD* Deflated (real) Personal Income (YP PC) I LAG Denotes a one-year lag in the variable *May be followed by no suffix, indicating a U. S. variable, or by 11SCY" "D8,11 "D.9," or "DIO,''' indicating values for South Carolina, Planning District 8, Planning District 9, or Planning District 10, respectively. Tables IV-1 and IV-2 present the equations used in each of the three models. Table IV-] lists the implicit equations, while Table IV-2 lists the complete equations as used to solve the models. A Baseline forecast was generated'for each of the planning districts. Once these initial values were established, a number of different scenarios were intro- duced. It is hoped that the range of scenarios included here will enable the planner or local official to guage--albeit roughly--the effect of most new developments in the area. The scenarios are as follows: Scenario I - Baseline. Present conditions prevail until 1990. Scenario 2 - A manufacturing plant employing 300 people moves into an area. Construction begins in 1980, and 700 workers are employed during peak construction periods (1981). Construction tapers off in 1982, as the first production workers are hired. Full employment is reached in 1983. (This example is typical of a small refinery.) Scenario 3 - Construction for a manufacturing plant which will employ 500 workers begins in 1980. 500 construction workers will be employed at peak con- struction (1981). Full employment will be reached in 1983. Scenario 4 - Construction begins in 1980 for a plant employing 700 manufacturing employees at full capacity (1983). 1,000 construction workers will be employed during peak construction times (1981). Scenario 5 - Construction begins in 1980 for a 1,000-employee manufacturing plant. 1,000 construction workers will be needed during peak construction periods in 1981. Production will begin gradually, starting in 1982 and reaching full capacity in 1984. 16 Table IV-1 IMPLICIT EQUATIONS Dependent Variables --Independent Variables (Berkeley-Charleston Planning District 8 (Waccamaw) Planning District 9 Dorchester) Planning District 10 (Low Country) EMD8 + ECD8 + ERD8 + EMD9 + ECD9 + ERD9 + EFIRD9 + ESVD9 + EMDIO + ECDIO + ERDIO + EFIRDIO + EEA EFIRD8 + ESVD8 + EGD8 EGD9 ESVDIO + EGDIO EM YPDSC, ND8 YPDSC EMSC EC ECSC YPDSC, ND9 ECSC ER EFIRM, ND8 YPDSC, ND9 YPDDIO ET EFIR EFIRSC, YPDD8 YPDSC, ND9 YPDDIO ESV ESV, YPDD8 __ESVSC, ND9 YPDDIO EG EGSC, N08 EGSC, ND9 ILAG EGSC, NDIO N EEAD8 _YPDD9 EEADIO YPD EEAD8 EEAD9 EEADIO TABLE AV-2 EXPLICIT EQUATIONS MODEL 8 . (Waccamaw Region - Planning District 8 EMD8W*i .4218 + (1 3397 x YPIDSC) + MFG ECD8(--i.i337 + (59,444 x ECSC)*+ CONST YPDD0(-38*867 + (10,457 x EEADG) + -INCOME NDB+.95808 + (1310.4 x EEADS) + POP ETD(4(--17.767 + (15.633 'x ETSC) + ( 4,00016336 x ND(3) + 'TRADE -EFIRD84- , 16106 (3.8272 x EFIRSC) + ( *0024377 x YPUD(3) + F I NANCC E9VD8(---1i#575 + (1.2467 x ESV) + (,0049989 x YPDDS) + SERVICE EGDW--7.i894 + (33.491 x EGSC) + ('0000585is x NDS) + Gov ERDS*.-669847 + (.7085*7 x EFIRD8) + (,.0000078821 x ND8) + UTILITY ZZADJU +-f-MDS + E C.D.8 + -E-R-9 8 ETi)S + EFIRVO. + SvD 8 + E OD 8 MODEL 9' (Berkeley-Charlest-on-Dorchoster Region - Planning District 9) EMD9 4-.2564 + (1.4957 x YPDSC) + MFG YF,DDY+--49#9i6 + (12,332 x EEADY) + XNCOME ETD94--5.9543 + (165.1 x irrsc) + (.00085212 x YPDD9) + TRADE. ND9(-207i 00 + (i 16 , 17 x YPDD9) +' POP ECD94--5.765 + '(1+1189 x ' YPDSC) f- (.0000013464 x ND9) + CONST ERD9*-'- - 11927 + (.19356 X Y F'D S C ) + (.000013464 x ND9) + ' uTILITY EFIRD9(--.63943 + .(o46348 x YPDSC) + (.0000014049 x ND9) + FINANCE ESVD9+.-34.664 + (33.489 x ESVSC) + (,00012647 x ND9) + SLRVICE EGD9'1.--1.7458 + (904,304 x EGSC) + (.000061W6 x Nl),?) + UOV EEAD?*-EMD9 + ECD9 + E,RD9 + ETD9 + EFIRD9 + r@SVD9 + EGD9 MODEL 10 (Lowcountry Region - Planning District 10) EMDiO(-i .9617 + (10.736 x EMSC) + MFG ECDIO(--1&4947 + (53.439 x ECSC) + f'oms,r -NDi0*-768ti9 + (1359.8 x EEAD10) t POP YPDDiO*-30.576 + (f4.jj',-! x EEADi0) + INCOME ERDio+.-i.3373 + (0001285 X YPDDi 0) + (,,10327 x F_T + LJT.[L.c ry ETDiO(--6-2724 + (#56978 x ET) + (.000014416 ND10) + TRADE EFIR004---i-968 + (-001512 x YPDDiO) + FINANCE ESVDi04--2-23i5 + (,,0144!>" x YPDDi0) f. _-'@KVXCE EGDIO+--'.5?94 + (36.881 x ( i LOU EGSC) ) + (-000010489 x NDio) + GUk EEADi0(-EMD10 + ECI)i o + ERDiO 4- ETDj(i + EFIRDio + ESVD10 + EGDiO Note: 1 s a computer symbol reprosenti.ng 18 Scenario 6 A utility generating station will be built, beginning in 1980. 500 construction workers will be needed during peak construction activity in 1981. By 1982, construction will taper off and full production capacity (200 workers) 11 will be reached. (This represents a typical 500 megawatt genera.ting facility.) Scenario 7 - A larger utility facility, employing 300 workers, will be built, beginning in 198.0. 760 construction workers will be needed during peak periods. By 1982, construction employment will taper off and peak full-time employment will be reached. Scenario 8 Gross Trade Employment 12 will be increased by 300 new jobs. Construction, beginning in 1980 will employ 500 workers at its peak. In 1982, construction will be completed, and some full-time workerswill be hired. Full capacity will be reached in 1983. Scenario 9 - Construction will begin in 1980 for a facility employing 1,500 "tradesmen."' -"Construct-ion wil"I peak i-n 1981, wit@h 1 000 workers, and -tapeT off in 1982 as trade employees are hired. Full capacity will not be reached until 1985. (This is typical of a large shopping center, such as Columbia MaIlI3 or Myrtle Beach Mail.) Scenario 10 - Construction begins in 198o for a government facility which will employ 900 people at full capacity. Construction will require 700 workers at its peak, and will be complete in 1982. Full Twployment will not be reached until 1984. (This represents a county hospital.) BASELINE FORECASTS The baseline (Scenario 1) projections for each of the planning districts are shown in Appendix A. The values are listed in terms of thousands of employees . (EEA - EG), millions of dollars (YPD) and actual numbers of residents N. (See pages 7-8 for a discussion of relevant assumptions.) Waccamaw Region: Between 1978 and 1990, it is projected that approximately 45,96o new jobs will be created in the Waccamaw region. This represents an increase of 80 .7 percent, considerably higher than the 51 percent growth estimated for the State as a whole,during the same period. Service, manufacturing, and trade are IlDr. Glen Rhyne, Research Economist Public Service Commission, personal communication. 12Competition for customers created by a new shopping center may reduce the need for employees in existing facilities. Therefore, the numbers of trade employees added to planning district economies by scenarios 8 and 9 represent the gross number of employees added, rather than the net addition once competition is accounted for. It is, of course, possible that a large influx of new residents could permit the opening of a new shopping center without a loss of trade employees in existing establishments. 13COlumbia Mall Administrative Offices, personal communication. 141-exington County Hospital Personnel Office, personal communication. 19 expected to be the fastest growing sectors, with increases of 92.6, 89.8 and 84.9 percent respectively. In each case, the planning district leads the corresponding State projection by a considerable margin. In spite of the rapid growth in employ- ment, the District's personal income is projected to lag behind that of the State, showing a 70. 4 percent increase, as opposed to the State's projected 82.4 percent increase. Population, on the other hand, is expected to increase by 35.3 percent by the year 1990. This is higher than the comparable State figure (18 percent), but reasonable in light of the many new jobs anticipated. Berkeley-Charleston-Dorchester Region: The model projects that overall nonagricultural employment in this planning district will.increase by 66.1 percent. This is a slower rate of growth than that projected for Planning District 8, but is still higher than that projected for the State as a whole. The projected increase represents the creation of approximately 86,500 new jobs by 1990. As is the case in the Waccamaw Region, the service and manufacturing sectors in the model show large gains, relative to both the State and the other sectors of the planning district. Finance, insurance, and real estate employment and construction employment also show large gains in the B-C-D Region. (Construction employment shows a 136.6 percent increase over the twelve-year period, which is undoubtedly attributable to the structure of the model. However, since our primary concern is with impact estimation and not baseline fore- casting, the unusually high growth rate should have little bearing on relative differences between impact scenarios.) Population in Planning District 9 is projected to increase by 31.9 percent over the twelve-year period. Although this is a slower rate of increase than that projected for the Waccamaw Region, it is still greater than the projected State average. AS is the case in the Waccamaw Region, B-C-D's personal income is expected to increase at a slower rate than the State's (68.2 percent versus 82.4 percent for the State as a whole). Lowcountry Region: Unlike Planning Districts 8 and 9, the Lowcountry Regional model projects slower growth, overall, than is anticipated for the State. Nonagricultural 20 employment is only projected to increase by 46.9 percent (as opposed to 51 percent for the State). The largest increase is expected to be in the finance, insurance, and real estate sector, which the model shows will grow by approximately 78.9 percent, versus a projected 51.9 percent in the corresponding state sector. Transportation, communication, and public utility employment is,the only other sector in the District which is expected to grow at a rate faster than that of the State. Population growth is expected to be very close to that of the State (16.9 percent versus 18 percent for the State), but personal income is expected to grow only 43.9 percent, as opposed to 'tiie St-ate Is S2.4 -percent --pvoJected @growtsh- IMPACT ASSESSMENT The projected consequences of scenarios 2 through 10 are shown in Appendix A. The likely impacts, expressed numerically, can be readily determined by subtracting the values after the impact from those of the corresponding year of the baseline forecast. For example, the effect of an additional 900,government workers on the total nonagricultural employment in 1990 in Planning District 8 can be determined by subtracting the baseline value (Table A-]) from the Scenario 10 values (Table A-10). 104-33 -102.89 174 Thus, the numerical impact is 1,440 new jobs. If the same calculation is made for the year 1985, the total number of jobs is even larger--1,490. (Bear in mind that the projections are just that, and should not be viewed as factual.) 84.22 -82-73 I -LF . 9 This is probably a result of the secondary effects of construction employment- even though the construction workers are no longer employed, the sectors which benefitted from high construction employment may still show an increase in employees. The projected effects of an impact on specific sectors can be determined in the same way, by subtracting the sector's baseline value from its value in the year under 21 study. This is a useful exercise, since impacts may affect some sectors far more than others. A more convenient way of comparing the effects of various impacts is to examine multipliers. A multiplier is a ratio of the number of people directly employed to the number of new jobs which are ultimately created. For example, a multiplier of 1.5 indicates that for a given scenario in a given planning district, there will be 1.5 jobs created for each job which is a direct result of the new industry. If the industry employs 100 people, 150 jobs will be created in the planning district. Tab'les A-_31 through A-33 show the dlifference'between-base14ine and -impact scenarios, as well as the nonagricultural employment multipliers for each-of the scenarios. As can be seen, the multipliers for Planning District 8 are higher than those for either of the other two districts, with Planning District 9 having the lowest values of all. It would seem that the B-C-D area, the most heavily developed of the regions discussed here, would have the highest multipliers. Generally, in a well-developed economy, manufacturing industries develop a number of linkages with related firms which supply materials, component parts, and other "factors of production." Service industries spring up to serve the expanding manufacturing base, as do shops, banks, etc. A dollar increase in the manufacturing sector will be passed along to many other sectors within the region. This may well explain the high multiplier projected for the Waccamaw COG region, where manufacturing accounted for approximately 25% of the total nonagricultural emp.loyment in 1978. In contrast, manufacturing in the.Berkel.e-y- Charleston-Dorchester region accounted for only 14% of the total nonagricultural employment in 1978. Service, on the other hand, accounted for approximately 15% of the total. Because the service industries in the Berkeley-Charleston-Dorchester area are geared toward the peak tourist seasons, they are able to accommodate a fairly large increase in demand before reaching a threshhold, beyond which expansion must occur. Thus, the B-C-D area has a lower multiplier as a result of a more elastic local economy. In other words, the District 9 economy is resilient 22 enough to absorb a large number of new (direct) employees before additional (secondary) employees are needed to serve them. Because planning districts 8 and 10 depend to a greater degree an manufacturing, and at the same time have less well-developed service and trade economies, the impacts described here will have a greater effect than in the B-C-D region. Location of a manufacturing plant employing 700 people in rural Jasper County would necessitate the opening of a new restaurant, at the very least, and would probably provide the impetus for a number of more far-reaching developments. The Waccamaw Region may have the -Iflghest multlpl'iers -because, w-h-ile -it -is st-ill relatlvely undeveloped, it has the potential to become a more commercialized area by virtue of existing linkages. Georgetown is already a manufacturing center of some consequence, with port facilities, rail lines, and so forth. Adding to such a base would be easier than making a fresh start in an area without a strong manufacturing base. In some scenarios, the multipliers are lower than they might otherwise be relative to the rest of the impacts, since full employment is not reached until one, or even two, years later than in the majority of cases. Because full employment has "been in effect" for one or two years less than it has in the other scenarios, the impact on other employment sectors will be somewhat less. This is true for scenarios 5 (1,000 manufacturing workers), 9 (1,500 trade employees, with full employment reached two years later than the standard), and 10 (900 government employees). Another way of looking at the problem is to say that the 1990 impact multipliers for scenarios 5 and 10 are the equivalent of 1989 multipliers in the rest of the scenarios. For Scenario 9, the equivalent year would be 1988. Construction impacts have been determined separately from general employment impacts, because their effects are relatively short-lived. Construction impacts may pose special planning problems precisely because they are both temporary and involve a large number of workers. A small town may have difficulty accommodating 1,000 is new workers, even if very few of them move into the municipality. (Even fast food restaurant owners may quake under the onslaught of the new lunch time crowd.) If the 23 additional workers necessitate changes in local service, trade, or other employment patterns, the effect of removing the workers should be considered as well. Of course, construction workers will be replaced by permanent full-time emp loyees, who will be more l1kely to move into the area. The resulting increase in total population (as opposed to labor force) may fill the gap left by the larger number of construction employees. However, if the construction work force is primarily made up of commuters, and the full-time work force is drawn from people who already live in the area (as developers often suggest will be the case), the construction impact may be significant. Construction multipliers for each of the planning districts are shown in Table A-34. INFRASTRUCTURE: As noted earlier, infrastructure is used here to mean those structures or organi- zations which provide public services at the local level. The effects of industrial impacts on local infrastructure needs are more difficult to project than their effects on various employment sectors. There are a number of reasons for this, the most obvious being the lack of data at the local level. In some cases, reporting is not uniform from county to county, or even from year to year within the same county. In other cases, the necessary information has simply never been collected, due to a lack of funds, man- power, or both. Because the estimating ratios are based on figures for one year, or an average of two or three years, they are less reliable than they might be if more ex- tensive time-series data were available. Another very important factor making infrastructure needs estimation difficult is the uneven nature of public service growth. Because public service expenditures generally either lag or precede population growth, it is difficult to assess the accuracy of public service-to-population ratios based on historical data. Historical relationships may reflect several years of inadequate service, followed by a growth spurt, or, conversely, may reflect increased service levels in anticipation of an increased population. In using the results presented here, one should be aware of the recent trends in the area under study. If service levels have been inadequate, ratios should be somewhat higher than those reported here. 24 While there are many difficulties associated with estimating increased public personnel needs, there are infinitely more associated with general public expenditures. Capital expenditures are made in a step-wise fashion, generally following a period of inadequate capacity and preceding a short period of excess capacity. For example, a new school will probably not be built until classrooms are extremely crowded and all other avenues of expansion have been exhausted. Once built, the school should be large enough to accommodate an increasing number of pupils for several years to come. The decision about when to build the new school will depend as much, or perhaps more, on political and financial factors as it does on the number of pupils to be served. The latter can be estimated with some hope of success, but few are brave '(or foolish) enough to attempt to predict the,outcome of the former. - Still another factor leading to inaccurate estimations is the changing expectations of the local population. Greater disposable incomes and more prosperous lifestyles may lead residents to demand higher service levels in some areas. New services may also be demanded; a wealthier populace might expect public marinas or docks from which to launch their pleasure boats. A change in the age or racial distribution of the population may alter the emphasis on public service. For example, a shift toward a more mature population might bring about a shift in concerns from education to health care. In particular, new residents, accustomed to a higher standard of living, may require services heretofore not provided in the area. New residents may have some effects on the local infrastructure which differ from patterns established by "old" residents. For example, an influx of "new" residents will have a more severe impact on water and sewer supply systems than the same number of "old" residents, since new lines will have to be constructed. Therefore, the esti- mates which follow may be low, because they are based on ratios between the existing or "old" population and the levels of service pro vided to them.15 15 Hite, James C., and James M. Stepp, "Estimates of State and Local Benefits of New Metal Fabrication Plant Industries at Port Victoria Site", Special Report, Department of Agricultural Economics and Rural Sociology, Clemson University, Clemson, S.C. March 1, 1973. 25 For all these reasons, the estimates which follow must be viewed as rough approximations and evaluated accordingly. Education: Table IV-3 shows the estimated number of additional pupils and schools needed for each 1,000 person increase in population. Figures are based on the 1976-77 school year. Since 1970,, there has been a steady downward trend in the number of students enrolled in public schools relative to the size of the general population, and it is logical to assume that this trend will continue at some level into the future. Private schoo I enro I I ment a l,so dec I i nod dur 1 ng tb Vs .,period,., bm-t a,t. a m.",h -s.]-Qwer rate Because it is impossible to predict when the decline in the school age populations will taper off, figures from the most recently tabulated year will be used; one should be aware, however, that they may be high and should try to obtain the most recent trend information possible for the specific area under study. For example, if class- room size has historica.lly been smaller in one municipality than in the county as a whole, more schools may be needed to accommodate an influx of students there than in other areas of the county. (it must also be remembered that the ratios presented here assume that historical levels of service are desirable.) Another important variable is the rural/urban nature of the county; schools in a rural area may be smaller and more widely dispersed, resulting in fewer students per school. A new industrial development might cause a large population concentration in one area of the country, making it feasible to build larger schools, with more students. 26 Table IV-3 EDUCATION Increase 1,000 new residents Location Public Schools Private Schools Students Schools Students Schools Planning District 8 Average 220 .44 10 .05 Georgetown 24o .49 20 14 Horry 210 .39 10 .05 Williamsburg 240 1.55 20 .08 Planning District 9 Average 220 .33 20 .08 Berkeley 280 .37 10 o6 Charleston 190 .30 30 .11 Dorchester 240 .33 20 .10 -P1anning District - 10 -Avereage 193O .41 20 .09 Beaufort 160 .33 20 o6 Colleton 210 .56 30 .17 Hampton 240 .53 20 .11 Jasper 230 .29 30 .13 Detailed data tables which show past trends are found in Tables 3 to 7, Appendix B. Health Care Table IV-4 shows the number of extra physicians needed for each increase of 10,000 in the general population, if the current level of physician availability is to remain constant. These figures have been obtained by averaging the corre- sponding figures over the years 1976, 1977, and 1978. No clear trend is evident, as can be seen from the primary data presented in Appendix B, Table 8. It must be remembered that people may travel farther to obtain the care of a physician than they will to obtain education, police protection, etc. Therefore, a low physician-to-population ratio in one county may be quite reasonable in light of a high ratio in a neighboring county. This is particularly true of Berkeley and Dorchester counties, relative to Charleston County, which has a disproportionately high number of physicians. 27 Table VI-5 presents the number of new hospital beds required to maintain present levels of service for each increase of 1,000 people. The figures are based primarily on 1978 service levels, since these were the only figures available at the time of publication. A complete table is found in Appendix B, Table 9. Table IV-6 shows the number of outpatient and public health centers which will be required to serve each additional 1,000 residents in a planning district. Note that in some cases the present number of facilities is lower than the prescribed national standard. Figures are for 1977 only. More complete data are found in Appendix B, Table 10. Table IV-4 NEW PHYSICIANS PER 1,000 NEW RESIDENTS Georgetown .60 Horry .64 W-illi,amsburg .29 Planning District 8 .56 Berkeley .11 Charleston .90 Dorchester .27 Planning District 9 .66 Beaufort .64 Colleton .45 Jasper .34 Hampton .44 Planning District 10 .47 28 Table IV-5 NEW HOSPITAL BEDS PER 1,000 NEW RESIDENTS Georgetown 3.3 Horry 4.3 Williamsburg 2.1 Planning District 8 3.6 Berkeley --- Charleston 6.32 Dorchester --- Planning District 9 4.3 Beaufort 3.2 Colleton 4.6 Hampton 4.o Jasper 2.2 Planning District 10 3.5 Table IV-6 NEW OUTPATIENT AND PUBLIC HEALTH CENTERS REQUIRED PER 1,000 NEW RESIDENTS Planning District 8 1.6 Planning District 9 1.1 Planning District 10 2.8 29 Law Enforcement and Fire Protection Law enforcement data is reported for planning districts only, since the information available was not extensive enough to justify a county-by-county breakdown. Municipal figures have been obtained by averaging yearly ratios from 1974 to 1978. County data is only available for the years 1977-.and 1978; the figures presented here are an average of those two years. The total number of law enforcement personnel for each planning district is also based on an average of 1977 and 1978 figures. It is important to note that the source of municipal data changes from the State Law Enforcement Division (1977-1978) to the Federal Bureau of Investigation (years preceding 1977). The ratios obtained appear to be consistent from one source to another, and as a consequence, it is assumed that reporting methods remain constant. Given this assumption, the number of law enforcement personnel required per 1,000 residents decreases steadily in Planning Districts 9 and 10 from 1974 to 1977. In 1978, the ratio begins to increase again, indicating that the downward trend may be changing. (However, this could be an aberation, or simply a function of a change in reporting or recording methods from one year to the next.) Data for 1977 and 1978 includes a breakdown of civilian employees and sworn officers. The ratio of civilian personnel to sworn officers has been included to aid planners who must anticipate salary requirements, benefits, etc. The ratio presented in Table IV-7 is an average of the ratios in 1977 and 1978. Table IV-7 displays the number of additional law enforcement personnel needed to serve an increase of 1,000 residents at the county and municipal levels. More detailed data is found in Appendix B, Table 11. The figures presented in Table IV-7 have been extrapolated from the original data. The percentage of municipalities (or counties) reporting was calculated for each year, and the ''missing percentage" was supplied based on the number of law enforcement personnel reported. No effort was made to generate more accurate 30 estimates based on the population of the counties and municipalities failing to report.. It was felt that the value of the information to be gained did not warrant the amount of time which such a calculation would require. Table IV-7 LAW ENFORCEMENT PERSONNEL Muncipal County Number Civilian: Number Civilian: per 1,000* Sworn Officers per 1,000* Sworn Officers Planning District 8 1.47 1:9 .53 1:2.85 Planning District 9 1.58 1:2.75 .75 1:2.75 Planning,District 10 .96 1:4 .92 1:2.25 *Residents Fire protection data was available for 1978 only. Paid and volunteer personnel were treated as a unit, since there are no paid firefighters at all in many of the smaller incorporated areas. Therefore, in utilizing these results, it is important to ascertain the present balance between paid and volunteer firefighters in the municipality under study. Obviously, an all-volunteer fire department will be less costly than one which is staffed on a full-time basis. However, an all- volunteer department may no longer be effective if an area's population increases sharply. Table IV-8 shows the number of fire protection personnel which will be needed to maintain present levels of service if the population increases by 1,000. These figures are inaccurate to some degree, since approximately 100 fire departments throughout the state do not belo.ng to the State Firemen's Association, from which these figures were obtained. The location and size of these fire departments is 31 unknown; presumably some of them are located within the coastal zone, making the figures listed here lower than they might otherwise be. Table IV-8 FIRE PROTECTION PERSONNEL Paid and Number Locatlon 'Volunteer F-iremen -Populat'ion per -1 @10010 Georgetown 128 4o,300 3.2 Horry 147 95,400 1.5 Williamsburg 51 36,700 1.4 Planning District 8 326 172,400 1.9 Berkeley 254 78,ooo 3.3 Charleston 574 265,000 2.2 Dorchester 130 51,600 2.5 Planning District 9 958 394,500 2.4 Beaufort 125 6o,goo 2.9 Colleton 70 30,700 2.3 Hampton 62 17,000 3.6 Jasper 22 14,000 1.6 Planning District 10 279 122,600 2.3 Source: State Firemen's Association, Statistician's Report, January 1, 1978. Grady C. Hill, Statistician. 32 HOUSEHOLDS AND ELECTRIC UTILITIES Table IV-9 shows the number of new electricity hookups necessitated by an increase of 1,000 in the area's population. This information is useful in and of itself, particularly if it indicates a possible strain on existing generating capabilities. However, it is also useful as a proxy for the number of dwelling units - and, con- sequently, number:of households - which can be expected as a result of the increase in population. This should help planners to anticipate a housing shortage, in the event that one is likely. There are some inaccuracies inherent in estimating households this way. Some older multiple-family units are serviced by only one electricity hookup, thereby lowering the estimate of households per unit of population. Conversely, many farms and other business/residences have several hookups, resulting in an artificially high estimate of households. The latter is a more common problem in the coastal counties. In some rural areas, not all households are serviced by public utilities, making the number of households higher than the number of hookups. The most recent tally of county households was done in 1970 as a part of the U. S. Census. Using electricity hookup data from 1970, the ratio of hookups to households was determined. This same ratio can be used to correct inter-census year utility data. (Unfortunately, there will be no way to assess the accuracy of this ratio beyond the year 1970 until the 1980 census data is released.) The data from which Table IV-9 was drawn are found in Appendix B, Table 12. The ratios presented here are an average of 1976 and 1977 figures. 33 Table IV-9 HOUSEHOLD AND ELECTRICITY HOOKUPS Hookups per 1,000 Households per Location Residents 1,000 Residents Georgetown 360 289 Horry 425 330 Williamsburg 360 286 Planning District 8 396 315 Berkeley 345 323 Charleston 310 306 Dorchester 310 292 Planning District 9 318 300 Beaufort 215 246 Colleton 375 327 Hampton 250 321 Jasper 195 301 Planning District 10 256 312 3.4 WATER AND SEWER USAGE Data on water and sewer usage for the three planning districts is limited- and somewhat inconsistent. Therefore, only general "rules of thumb'' will be used to estimate increased needs in this area. The Division of Water Supply of the State Department of Health and Environmental Control, which must certify new or ex- panding water supply systems, uses the following estimates to determine adequacy of supply: 100 gallons/day/person (residential and industrial use) -50 @g-a-ll;om-s/-,day/-pe-r-so-n*-(-res-ideh-tia-I -use on-ly) Therefore, a 1,000-person increase in population would result in roughly a 100,000-gallon per day increase in water used. (50,000 gallons per day if only residential Use is considered.) Obviously, this amount will vary greatly, depending upon the area under study, to say nothing of the nature of the associated industrial development. In rural areas, nearly all water is supplied by private wells. This has been particularly true of Berkeley, Hampton, and Williamsburg counties in the past. A sudden influx of people could make construction of a new water supply system necessary. Some industries use considerably more water than others; this fact should be taken into account when estimating the impact of development on an area's water supply. The Department of Health and Environmental Control's Division of Domestic Waste Water also employes "general guidelines" to estimate sewage use. The Guidelines for unit Contributory Loadings to Waste Water Treatment Facilities list the following rates of use for common wastewater - generating facilities- Residence - 4 persons 100 gallons/day/person School (cafeteria, gym, showers) ZU gallons/day/person Hotel (no restaurant) 20 gallons/day/bedroom Apartment (2 bedroom, 3 persons) 100 gallons/day/person Office (no restaurant) 25 gallons/day/person The complete listing may be found in Appendix B, Table 13. "Discrepancies between these two figures may be explained by amounts of water which filter into the system from the water table. In addition, wastewater estimates tend to be high as a safeguard against contamination of the receiving body of water. A ratio which was developed for another planning district may also be used, with reservations. The Water and Sewer Study, Lower Savannah Region showed a need for an additional .175 million gallons/day capacity for each 1,000 new residents. However, it is reasonable to assume that water use varies from planning district to planning district and there is no way of determining the magnitude or direction of the difference. V. USING THE MODEL: AN EXAMPLE A small town in Planning District 10 has been proposed as the site for a new industrial development. Residents of the town, curious about the effects of the plant on the area's economy, hope to use this report to obtain an approximate idea of the impact. The first step is to contact the firm's main office or its consultants, the State Development Board, and any other sources available to them, in order to find out as much as possible about the size and nature of the development proposed. In particular, they should try to obtain accurate estimates of the number and types of employees required both during construction and operation of the plant. For the purposes of the example, assume that the proposed development will employ approximately the same number of people as projected for Scenario 4 (see page 16). In most cases, proposals will be close enough to one or another of the suggested scenarios to obtain an approximate impact forecast. (If a development is proposed which differs significantly from the scenarios presented here, contact the S. C. Coastal Council to discuss the possibility of preparing an impact scenario which will represent the new development.) Since we are dealing with Planning District 10, turn to Table 21 of the Appendix A, which shows projected growth in the area without added impacts. Now turn to Table A-4 (Appendix A) to see the effects of impact Scenario 4. Total (nonagricultural) employ- ment in the district will be increased by 1,135 people by 1985. '(Remember that the numbers in these tables are in thousands.) The nonmanufacturing sectors, by and large, will be affected more than the manufacturing sector. 36 While this information is only an estimation of possible effects, it may be useful to anyone debating the wisdom of opening a new store, for example. The new business generated by developments in the manufacturing sector may be sufficient to justify additional investment in the nonmaufacturing sector. By 1990, the actual numbers of employees caused, directly or indirectly, by the new plant will have increased very slightly, to 1,140. Once again, the primary impact will be in the manufacturing sector, which will gain the original 700 employees, the finance, insurance, and real estate sector, which is expected to gain 142 employees, and the service sector, which will increase by 231 new employees. Turning to Table A-33, we can see that the multiplier for Scenario 4 in Planning District 10 is 1.62 for the year 1990. This means that by 1990, each employee added to the manufacturing sector will result in 1.62 new employees overall. Said another way, total nonagricultural employment will increase by 162 for every 100 employees added to the manufacturing sector. (This should also hold true in 1985, since the actual numbers of employees differed so slightly from the' 1990 totals.) As notedearlier, construction employment is analyzed separately from long-term employment. If we return to Table 34 in Appendix A, we can see that in 1981, the year in which construction employment peaks, 1,630 new employees will be added to the total of nonagricultural employees in the District. Because there are 1,000 employees added as a direct result of the impact, we can divide the number of total employees by the number of "direct" employees, to obtain a multiplier of 1.63. In other words, for every 100 employees added to the construction sector, 163 employees will be added to the total of District (nonagricultural) employees. The influx of construction workers will place demands on other sectors of the economy, but these demands may be short-lived. For example, 330 new service employees will be needed to accommodate the additional construction employees in 1981. By 1982, when con- struction is tapering off and full operating employment has not yet been reached, only 250 service employees will be needed. By 1983, when full operating capacity 37 has been reached, and construction work has ceased, the numbers of additional service employees will have fallen still further, to 230, where they will remain until at least 1990. Thus, planners should beware of encouraging significant in- creases in any of the employment sectors on the basis of demands created by con- struction employment alone. In this example, if large-scale, permanent changes were made in 1981, there would be a surplus of 100 workers in the service sector by 1985. Obviously, employment patterns are more complex than this; however, the example.should illustrate the dangers of failing to discriminate between short and long--t-erm emptoy-ment. Infrastructure needs may cause the greatest concern to those evaluating a proposed development, since increased needs may result in increased public expendi- tures. As noted in the methodology section, infrastructure needs are determined on the basis of population. Comparing Tables A-21 and A-24 once again, we find that the proposed impact will increase the District's projected population by 1,535 in the year 199.0. This figure must be multiplied by the appropriate factor from Table 2, Appendix B, in order to make it conform to the most'recent official State estimates. When we multiply 1,535 by.1.042, the conversion factor for 1990, we obtain the corrected population increase of 1,599. This figure will be used to determine infrastructure needs using Tables 111-3 through 111-9 in the "Results" section. As is evident from Table IV-3, page 26, each additional 1,000 residents will result in approximately 190 new public school students, 20 additional private school students, .41 public schools and .09 private schools. Multiplying each of these figures by the expected increase in population results in the following: Additional public school students = 190 x 1.6 = 304 students Additional public schools = .41 x 1.6 = .66 schools Additional private school students = 20 x 1.6 = 32 students Additional private schools = .09 x 1.6 = .14 schools More accurate estimates can be obtained by examining the supporting tables in Appendix B which show recent trends for each of the counties in the planning district. 38 Obviously, an individual county may behave quite differently from the weighted average of all the counties in the planning district. The actual need for a new school will depend largely on how crowded existing schools are, the age distribution of the new students and the financial situation of the school district involved. Health care needs are displayed in Tables IV-4, 5, and 6. The following results are likely from the impacts of Scenario 4- Additional physicians = .47 x 1.6 = .75 Additional hospital beds = 3.5 x 1.6 = 5.6 Additional outpatient/public health facilities = 2.8 x 1.6 = 4.48 ,(Note that some planning districts already fall below national standards.) Again, the exact effects will depend on trends in the county under study, the availability of health care in nearby counties or planning districts, and the degree to which health care needs have been met in the past. (The years on which these figures are based may have been years in which a large number of physicians moved to the area, causing a slight surplus. On the other hand, the period studied may have been one of relative scarcity, meaning that the numbers shown here are lower than the actual numbers which will be needed to adequately serve the new population. Scenario 4 will have the following impacts on law enforcement and fire pro- tection personnel needs: Additional municipal law enforcement personnel = .96 x 1.6 = 1.47 (ratio of civilian to sworn officers = 1:4) Additional county law enforcement personnel = .92 x 1.6 = 1.39 (ratio of civilian to sworn officers = 1:2.25) Additional fire protection personnel = 2.3 x 1.6 = 3.68 When the total numbers involved are relatively small, as they are here, the ratio of civilian to sworn officers is probably not important. However, in the case of a larger development, or several concurrent developments, it would be well to note the likely distribution of civilian and sworn personnel, since the pay scales are presumably different. As noted earlier, volunteer and paid firemen were not separated, due to the fact that many rural areas are served only by volunteer firefighting personnel. In 39 this instance, the specific location under study dictates the amount of public expense involved--if the area is served strictly by volunteers, the cost will be considerably less than if the personnel are full-time employees. Table 9 on page N displays the average number of households and utility hookups which may be expected for each increase of 1,000 In the population. When the average figures are multiplied by the expected population increase, the following figures are obtained: Additional utility hookups = 256 x 1.6 = 410 Additional households = 312 x 1.6 = 499 (The discrepancy between the two numbers may be explained by the fact that many older multiple-family units have only one utility hookup.) Water and sewer usage statistics are presented on page 35. Using DHEC guidelines, we can see that Scenario 4 will create the need for an additional 160,000 gallons of water per day for residential and industrial use. (80,000 gallons for residential use only.) There is no quick way of estimating sewage use on a per person basis, unless the Lower Savannah Planning District figure of .175 million gallons per day capacity. is used. (This is the equivalent of 175,000 gallons.) In that case, Scenario 4 will require a .28 million (280,000) gallon/day/capacity increase over what would normally be required. A better sense of what will be needed may .come from the Guidelines used by DHEC to determine waste water facility loading capabilities. If households are used as a...proxy-for residences, and all residences are assumed to - house four people, residential sewage use may be determined as follows: 499 households x (100 gallons/day/person) x 4 = 199,600 gallons/day. However, the number of assumptions necessitated by the latter method makes its reliability as doubtful as that of the former method. 40 VI. CONCLUDING REMARKS At this juncture, it is important to reiterate the cautions which have been voiced throughout this paper. Modeling is not an exact science; while some models are more accurate than others, none is completely reliable. A number of factors limit this model's accuracy. In the first place, there is a serious lack of adequate data at the planning district level. This not only leaves gaps in our knowledge, but reduces the confidence with which we may forecast because our forecasts are based. on fewer observations. Because the planning district models are based on both the SCOPE and DRI (national) models, inaccuracies in either of the latter are likely to be incorporated into the former. We do not know the direction or magnitude of such error, so it is entirely possible that the effects on the planning district models are minimal. (It is also possible that several errors have canceled each other out.) However, the possibility of a serious error cannot be discounted. Time horizons pose problems as well. In some cases, the length of time covered by the forecasts is not sufficient to uncover the full impact of new development. Unfortunately, econometric models perform best when they are used to forecast very short periods into the future. Ascher notes that: "Prediction of short-term economic trends depends on the capacity to understand the intricacies of the existing economic structure...In long-range economic forecasting, these short-term fluctuations need not be accounted for so carefully. What is most crucial for the long-term is anticipation of changes (or lack of changes) in economic structure.ii 16 Thus, we are using a method which may be ideal for short-term forecasting to predict long-term change, thereby risking the possibility of error increasing dramatically as the length of time increases. However, as Ascher points out, econometric models are very useful tools for policy analysis, in that they are valuable for comparative purposes.17 Even though the baseline forecast may be 16 Ascher, p. 85 17 Ibid, PP. 83-84 41 inaccurate, the relative effects of two or more courses of action will remain constant. There are also problems within the models themselves which may reduce accuracy. As noted in the text, some equations are considerably more accurate than others. The simultaneous nature of the models may also be troublesome, in that measurement errors may be multiplied. There is no evidence that simultaneous models are more accurate than others. 18 The assumptions inherent in the models have been discussed in detail on pages 7 and 8. However, the fact that historic conditions may not prevail in coming years is so important that it bears further discussion. There has been another period of high gasoline prices and high levels of inflation within the historical segment included in the models. This might lead us to conclude that if the model reflected the 1973 economic downturn, it also should have a reasonable chance of predicting future down- turns. However, every change in the economy is unique in at least some major respects, meaning that this model may be very restricted in the .types of recessions (or periods of increased prosperity) it is able to project. There is an additional problem inherent In estimating infrastructure needs, in that a static relationship (based on one year's data) is used to predict a constantly changing relationship. As noted in the text, population grows fairly gradually, and at a relatively even rate. Infrastructure needs, on the other hand, tend to grow in stepwise fashion, building up to a threshhold before action is taken. There is virtually no way of knowing at what stage of the "threshhold building process" the infrastructure data was acquired. 18 Armstrong, J. Scott, Long-Range Forecasting: From Crystal Ball to Computer, Wiley, N. Y., 1978. P. 179. 42 VII. FUTURE DIRECTIONS At present, the mo del includes only economic forecasts and estimates of some public personnel needs. It does not include estimates of capital expenditures, or a number of other possible public expenditures necessitated by development. It also does not include estimates of revenues generated by new development. Each of these areas could, and should, be explored further, -even though the number of possible contingencies makes the exercise resemble augury rather than forecasting. Another major subject needing elaboration is the specific economic climate of each county and municipality. There are a number of factors which may be applicable to only a few areas, yet which have profound effects on the economics of the specific areas in question. A list of questions should be developed which will help reveal these special conditions to planners and decision-makers. 43 BIBLIOGRAPHY Armstrong, J. Scott, Long-Range Forecastial: From Crystal Ball to Computer, Wiley, New York, 1978. Ascher, William, Forecasting; An Appraisal for Policy-Makers and Planners, Johns Hopkins University Press, Baltimore, 1978. Coastal Energy Impact Program, S. C. Intrastate Allocation Process, April, 1979. Columbia Mall Administrative Office, personal communication. Data Resources Incorporated, (National Forecast) June, 1979. Department of Health and Environmental Control, Bureau of Health Licensing and Certification. Licensed Hospital Beds, 1978. Department of Health and Environmental Control, Division of Domestic Wastewater, Guidelines for Unit Contributory Loadings to Wastewater Treatment Facilities, 1972. Department of Health and Environmental Control, Division of Water Supply, personal communication. Department of Health and Environmental Control, Office of State Health Planning and Development, State Health Plan, 1977. Division of Research and Statistics, Population Data (in-house, unpublished. Prepared by David Frontz.) Division of Research and Statistics, In-house publication, Spring, 1979. (Prepared by Cindy Stribling.) Division of Research and Statistical Services, S. C. Statistical Abstract, 1974, 1975, 1976, 1977, 1978. Division of Research and Statistics, Utility data (in-house, unpublished. Prepared by Thomas Evans.) Federal Bureau of Investigation, Uniform Crime Reports, 1976, 1975, 1974. Glickman, Norman J., Econometric Analysis of Regional Systems; Explorations in -Model Building and Policy Analysis; Academic Press, N. Y., 1979. Hite, James C., and James M. Stepp, "Title," Special Report, Department of Agricultural Economics and Rural Sociology, Clemson University, Clemson, S. C. March 1, 1973. 44 Lexington County Hospital, Personnel Office, personal communication. McHogan, Donald L., A Non-econometrician's Guide to Econometrics, Business Economics. May, 1973, PP. 38-45. Public Service Commission, Division of Research, (Dr. Glen Rhyne) personal communication. South Carolina Coastal Council, Coastal Zone Management Program, 1979. South Carolina Coastal Management Act of 1977. South Carolina State Firemen's Association, Statisticians' Report, January q1, 1978. Grady C. Hill, Statistician. South Carolina Law Enforcement Division, Uniform Crime Reprots, 1977, 1978. 45 APPENDIX A 0 Baseline and Scenario Forecasts Economic Impacts G 46 Table A I Planning District 8, Scena.rio I (Baseline Forecast) (Numbers are in thousands) 1 7 C3 1979 1981 1982 1 983 190 4 1 1? 85 N 'i'Ej . 0 1 -F 3 3 I- Y.3 I a 9. 3@1892 6 1 A4 t5 9 521 C.') 0 9 59. 1672*7 -7 3 2 7 2 Ic 8 14 e.)94 79 5 1 46 1 296 16 .75 19 3 1 . "'6"""0 0 31") 0 5 20. 2 1 . 63 4 4 4 1- -3 . 55401; a 05, .1 3. @")i 30 4 .09143 4 . YFID'2`4 .-01 C14 E I*p 0 3. 40901 3 4 6..y 413 I . Y S 3 2. 04244 1 12 6) 6 2. 2 2 c 1 33 G 09 4 6 0 22' 2 0 2 '3 Y 95 03 "1 @ 3) 6 1 11 13. 9'-.1;62 1 Al. 9 6 15. Al 91 11") 1 6. 41469 1 4 3 7 17 1 8. 6 1 '8-; 8 i 9. 6 1-1 f I R I @3 8 i 132 1 . 9.3 9 0 1) 0 4 7 4 2, A 12 18 2 "113 47 21 . 3" 6 10 2 . 4--i'37 2 "83 30 I 1 1-5. 0 6 1 12.33096 13. 14022 14 . 17794 15 V >50i 16. 1 .1101 1 1. 1 1'35 e. i 6"@O F.608 iv. 11931 10 . 3,2 7 9 1 1 0 S, 1.3 A 6 0 10 . 97-1110 1 1 14,02"1 12 11 . 91'@ @4 i 1 2. 60A 13 1 2 -."J 'S I 0 1" G 1 70 2 63 @ '13 4 14 1 '.,3 4 2 1 ':,7 1 7642G. 99-138 1 ti 15 -,) 3 @ 5 Al 7 A 8,`.)2' 5 . 4 9'*@,) 0 191634 . 7350*15 19-11339 . 2 -Dil 00-3 1 ".99, tic) Y F, D P" 0 m 6 3,1 . I 1 6 5 8 . 25 1 15 ")'22 . 2:?,.105 -722.71799 -f 60. 71 1334 0 0 3 . 5 6 4 9 3 3 00) 2 9 6 9 3 1 906 19 0 7 1 9 18 8 A 909 V90 (9 0 12 0 9 1 1 2 6 0 95. 164113 99. 10969 0". 0 92 4 6 1) U 22. 33 95 24. 1 "907 -1.5918 2 6 ES' I 1 7. 9 0 A 2 @i 6 @ 0 AlAfZ'@ @j 11 9 -'-' 4 0 73 8 4. .5,66 5 - 0 4 *3'@ :1 F 1AM 9 Lf: MI 3 . 1 6 0 7 :,i . 22 -1 3.33247 41 7 '2 @'S . L i 10 4 6 24.7,0349 IJ 1,499 G 69 9 0 8 2.81112 Y i 9 1 3 . 0."', 4 9 a -i . i ':.*, 6 0 19 0 -3 1 1 9 . 87,209 6 C) J 2 1 .4 .2 6, 6 1. G 1) 0 1 9 1 7 -.19 1-51 10 1 a i i S. @.,A 4 12 A. 9 9 9 9 -1 Al 7 5 12 . 0 3 6 6 5 i 1',' 1 2 2';-.; 4 2 4 . 3 4 6 37 2-3 03 -., i . 4 9 10 0 0 1.) 94""'. 6,1"Ai 990 . 16302 1032 . 02992 A073.20624 i i 12 8- A) -1 fS Table A - 2 Planning District 8. Scenario 2 (Numbers are in thousands) 1978 1979 1980 19al 1932 1903 1 Y84 i sm;@.; MFG 1") 0 0 00000 .0"DOOO .000o.') .20000 .30000 .30000 3 0 0 C 0 ?4.'.:"l 01 3000o .00000 00000 0 0 0.) 0 00;.)00 0 0 LEAD8 56. 99-121 59.41401 6 2 . 132 57 66. 694 1 70. 0,.-,890 '73.82856 7 10 . S 7 0 B EI 18 29 79 0 C KD8 14. @)9479 15 .'25 7 4 6 15 . 7 1296 16. 75793 17.96270 1 Y. 1813015 0 . 55 4' 5 5 2 1 . 93444 CAIDO .3. C ,)901 3. 40"',48 E.15 4 0 5 4.413054 4.1 il 38 4 .09 14 '3 4.37022 4 5'8 1 4 a i . 9 *..' 3 9 9 2.04709 12601 2.26227 2.36396 2.47852 2.61943 7 '; 9 0 7 E f IM 13.394 70 14 . 1 4. 64034 15. 74415 16. 60973 17.57572 1 I.J. -.59(44 i 9. 940 OD E F I RD 8 1 3 1 .94@i23 2.01673 2. 14225 2, 23670 2 . 34 3 4 `2 1 7 2, 60 - 46 E 9 V D8 i i ,'1493 12.33953 13.16431 14.2-.W59 15.20344 A 6. 131550 1 1 1 @@iioi 18 , 1 4 1 E G 1) CI 10. 13137 10.34048 10.61766 11.06744 li.49o98 i 'W'! . 0 16 -32 1 2. 65 46 6 13. 32996 G i'.`,',A69.96325 A73639.2GO29 177045. 31999 i(1304b. 91479 iU7459.44648 192479.13961 1 11*10703. 186.3.1! 204886 . 34 70 F, D' 6 34. 6 6 9 9-1 659. 96508 607. 14131 -1".3 5 .0 5 0 0 3 770.24610 0 10 . 3 03 213 85 9. 9 '112 3 9 0 9. 3 1 1 9'(J 1986 1987 19138 IY89 i ly, 9 C) M F'G .30"DOO .30000 .3000o 0 00 0 .30000 C 0 N.1; 1 0 0 0 fl) 0 .00000 0 0 0 0 0 J0000 0 0 0 0 EEAD8 87.,.)'. ).122 91 .73019 95 . -14 6 6 8 99.69700 103. 40440 EMD8 2 3. 2 33 9': '- 24.47987 5 . 9 18 26.98511 20.20423 E. C, Da 4.716*18 4 - 806 5 4 4 . B S 7-3 8 4.9-7j36 5, 0,13,72 CRDS 8 @ -.3 7 '1 11 3.00806 1 -1628 -@@24231 3 @ 35335. 1 2'2 0 3 "1 2. 0: 71 3. 0 06 9 3 23.96542 24.88094 F, I I il) 8 2. 71860 2. 82895 2 . 9 3 6 3 . 04 *.':)' 4 4 3.14495 -1., v L); @ 19 1 0".) @i "1 1 9 . 9 -,) 0 65 20. 71326 .21 .48t@73 2' 2 . 20 4 8 1 E G 1) 0 14.00501 14.6'li,12 15.33639 15.99963 16.65239 2-10590.11504 15 9 3 4 . 54 0 1 * 1 221197.09656 226372.94696 2 3 13 3 f, . 3 5 0 4 1 ),PDWI 9 -9 11 . 8-113 9 B 997 @ 47 f.52 1039.4.276 i080.77(I.W7, i 1210. 3 76o B Table' A - Planning District 8, Scenario 3 (Numbers are in thousands) i 97B 1979 191.30 19431 iY82 i IY83 1 912 4 191131.1* M F G 000") A -@O 0 0 0 000-10 .50000 50000 0 0 0 C C) WIt, I A 0000c) 21 0 0 0 A k,) 00 0 A k,)0000 00000 0 -3 0 0 0 5!J 9 59. 42587 62. 05962 66. 4 46" 71i '70.00409 7 4. 1 8t) 10 7 1:1 . 9,3 li 8 0 8 3 . 6'. 1 8 2 0 14 A 6 9 47 9 15.25746 1 71296 -5 _:- 16. 7'@793 1 B. 0 1270 19. 30805 20. 7' 4' 5 22. 13444 4. 3- F. C Da 3, 4,")9,:)1 3. 46'., 48 A 7 0 4.2805 4 4 .061,38 4.0914 ',).'!2 4 A Ii a 18 4 ER108 I A 97-18 A 2.04 i94 1.12795 2 . 2513 8 2.36576 2.489Y'3 2, i3007 2 .7 70' 4 LI'DO 13 A -4 9 2 14.02712 14.65493 5. *71858 16.62332 1 *F. 66 176 1 G. 846-D9 @20 .0 27 i's F F, I I @, 1) G I A 8 7 6 -3 6 1 .94399 2 A 0 1 B4 7 2. 13 92 0 21 A 2 3 s 3 1 2 .3': ,-2 -`8 2.4'B2A4 2 - 6 10 76 1 16 4 3 12. 34110 1 3.1 i837 14 A 2 3 3 1-5.20676 16. 1 6.- 1 17.i6YO7 18-17252 10.13357 11). 342713 1 6,21-139 1 1 .0158,28 1 1 .49585 12.04".64 12.685t;6 13.36092 17 05 0 7 A B 6 4 173679.06336 iTN34.62385 102892.3':,,381 187' 42. 62"'.'62 19 3 0 05 - Be') -75 6 19923i.i')993 2 4 1 4 4 2,.) 6 634,96985 6 6 0 A 2 -F 85 6 607.85395 733.80060 .170 190985 Bi Al. 50653 8 4 A 1 (14 4 5 9 13. 53'PD6 193 6 1987 1 9R8 1939 1 1/90 11 F G) 5 0 0 0 0 0 C) -,') o .1500-00 A 0 () 0 11-1,0000 A C 0 N @,i -,.@0000 00000 0000o .0000o A 00c)(A) 0 A is 6 92 . 09 A 15 9 6. 1 D796 i O.D. 050") 9 1 03.846*3o Er.11) B 23.43395 24 - c,79,37 25. 93918 27. i8f;i 1 28.40423 C 1) 1.1 4. 71 18 4. 80,'@54 Al. 88738 4 .975,36 5 .0-1372 E N, 1) 8 2.W.0'13 3 . 0 19 S 1 3. 1378 2 3 . 2 5 3 3 7 3.36494 F. T 1) a A J 15 2 2 . 1 13 5 2, 2 1-) A 0 1 i i 9 1 2 4 .135 2 15 7 24.960'26 E F I RD 8 2 . 7239-2 2 A Fj:*,; 9 2 9 2.94.162 3. 05.33 0 2 3. 1 553f; ESYDIJ 19. () (13 8,:P a 19 A 9129 a 5 2 0 . -7 3450 21.50701 22. 226175 ECDO 14.03604 14. 70252 5.36-755 16,03085 @6.68-367 NIA3 2M20.46353 216465.95748 221729.56634 226906.4"1 74 231869.84-177 959-06116 i00-7i@24 1043.1-'2187 loal.0334. 1124.6038 Table A Planning District Scenario 4 (Numbers are in thousands) 1978 1979 1 9M) i 9N 1982 1903 1 @,'S 4 MFG 00000 00000 0 0 0 0 0 0 29000 .70000 10000 00000 50000 i .00000 5000.) .00000 00000 E E DO 7 . 0 1; 33 9. 42 6 13 6 4 -77 13 67.13989 7 0 . 4 2 2 6 3 74.47519 19 . 212 58 9 B.;. Y4530 F M 1) 8 14 69-1-19 15 - 25' 7 4 6 IS.-M296 1 6 "15 7 9 *3 113.01270 19.50805 20.95-455 22.3-3444 ECDO 40YOI 3. 4 65 4 8 4 . D "; 40 5 4 4 4 .3 6 13 8 4.09143 4 . 3'10 2'2 AB 113 4 ER,ba 1 9 7 4 82 2.04796 2.136-35 2. -) 7270 2. 3 742 4 2.49616 21 . 637 10 7 @6 7 7 E ID13 13.40105 14.02726 A4.7ig3i i t; . 18,2217 5 16. 6 8 72 5 17.700173 14:1. 89 30 7 20-0706 EF IR'08 1 .87608 1 .94401 02601 2. 15161 2 . 24'J93 2.35837 2.48003 2. 61635 EE9DO i 1 .51646 12.34113 13. 18385 14. 25870 1 5.2'22:i7 16. 16798 i'l . 1805 4 113. 1 U39'c@ EGMI I -'I. 13 2 10.34282 10. 64559 1 1 .095',')0 11 .5 1075 12.06447 12.70238 13. 37775 i7o,108.35445 173679.90342 17 ?522.58017 183530.04069 187933,986'72 193293.39679 19 9 10 . 7 1 2 0'-'; 70 3 06 634.97628 660. 2-113526 690. 94985 738.88942 774.032Y4 BA6.80AO6 866.479A5 915.82@97 A986 1987 1988 1939 1990 IIFG 110@,WO .70000 7 00%) 0 .70000 .,70000 C ONNI 00000 00000 0000 0 .10000 00000 LEA00 CO.; . 2-1 9 G 9 7 92.3 1027 Y6.39509 100 . 34 5 104. 133-(3 0 '23 . 6 -12 3 9 5 '24 .87987 26.i391B '@!7.305i i 28.6042"'l- ECDO 4.716?8 4 - B 065 4 1.813730 4 . 9 -.*":13 6 5 . 0 4 3 72 E 1-1.1) B 2-90546 3.13,25 C3 0 3.14-105 75.2-'010 3.37117 EA D8 21.1'; 'S65 22.16051 23.14090 24.09957 2ti. 01 t,26 E F I F"j) a 2. 7 3,15 1 a4-188 2 . 9.";3 2 1 @i.05941 3, 1 6CO4 E SVDj 19. 099,i5 A9-94133 -0. 7459 -7 21 . 5 18 49 21 2 . "13 76 1 EGDG 14.0528-1 14.71935 i-5,3043B 16.04768 16.70051 N D a 211400.07039 2116753.513724 222017.:?11367 227194. 12627 232157.54358- YPIAM 9 1 - Tp 4r.) 2 7 iN.M.0i352 i046.0t734 1126,93719 Table A Planning District 8, Scenario 5 (Numbers are in thousands) 19 ifj 1979 1960 19ai il/82 i 'A.33 1984 1985 MFG 0 0 0 0 000)00 000 000oO 0 0 0 0 1 00000 1 .0000C, D.) C: L) (A.T. 1 (10 C) 1") 00000 5 0 C) 0 0 1 .00000 00000 .00000 0 () 0 0 F. .5 7 . 0 () 5 8, 7 59 4 2- 6 17 62.54'-.)90 @,.'37.24909 4 . '-:14 10 3 79,69019 '8 4 4 0 9,., 0 E M D 13 14 . 69 4 -.19 15.25746 15.71296 1 6.7579.3 12. 062 -10 19.33805 21.2545!@ 22. i3444 E L, I) a 3 - A090i 3.46548 1 . 0 '5 4 0 5 4 . 7'..'s @)` , 4 4.36138 4.09143 4 , -37022 4 . 5-C I i I 14 I . 9 7 4 B3 ".04796 2.14106 2 . 2 Y 1 2, 38(138 2 .493721 .640'85 2.782's E 108 3. 41""107 14. 027-26 14.753iB 15. 1:18 164 16 . 73 35 Ve. 69031 18.98i6B 20. 1 62@-;-, F F I FA) 3 1 .3,1600 1 . 9,14 0 1 2 .0,5024 2.151:362 . '25 14 4- 2.35618 2.49858 2 6 2,,., 9 0 164 7 12.34114 1 3.1925 1 14.27316 1).23367 16 . i 634C.1 17, 20'..18 10 , OPS ) 3 EGFJB 10 13 3 63 10.34283 10.65830 1 1 . I i i@,)69 . -i 1 5 3;33 1 12. 05"787 1 '2 7 3, 4 13 13.40950 14 D 0 1 05 0 G 4 8 13 9 173690.03576 177739.72715 18 3890 5 .1 CY i a 18 82117 19 0`3 1931EI0.63234 20006 1 15608 206245.469,19 1) D18 63 4. 9 -17 3 0 660. 286-32 6 9 13' 26 8 7 4 1 7 6 6 '28 15 9 M 0 07 0. 8 07 8 2 920. 1`5860 19(96 1987 1988 1989 1990 MFG 0 0 0 i.0000o i .0"'DOO A .010000 1.00ooo 00000 0 () 0 "1" 3 0 0 0 EE61M 8 (J . 7 6 3'. -.1 92.84258 9 6. W-5 9 39 100 . 8 1 () 0 104.59774 F ell) B @2 3 . 9 3 3 9 5 2'5.17987 26.439iB 27.6131. 1. 11 8 . 9 0 4 23 ECDB 4. -1678 4 - 806 4 4 . 00738 4.9' ''S --i 6 5,043'12 '@i 722 - . 0 3 -,7 E I'D 8 56 3. 15'; 20 3 .27186 3. 3 132 1; 2 E fDFj 2 1 . 2 4 4 2 61 2 '2 . '2'4 9 12 2 3 . * 2"295 2 24. 1 G;:J 18 -15.10387 F I f-@, @) 8 2 . 74 5, CA 6 2,85544 2,963@7 3.06196 311- ';,150 E S., V 1) ,1 19.12139 19.96296 2 0. 767@5 i 2i.5401-i 22.25925 GD8 i4.01.J462 14.75109 1). 416 12 16.07943 16.'1'3225 NDS 2119 5 0 . 5 113 6 6 -,@17296.039i:i '2 2'25 t; 9 . 6 7 4 1 1 '22 ""736. 5*2520 232700.00587 965. 60'501 1008. -.i4229 iO5O.3A6i3 1091.65792 il3i.26604 Table A Planning District18, Scenario 6 (Numbers are in thousands) 1 9 1979 19EJO 1981 1 Ya i 1 ?8,4 1 9 5 0 ID 0 0 0 1") 0 200-DO 2,' D (A)f I '.'1 0 0 1") 0 0 0 0 IW) 0 0 2 0 0 F E i". b;3 9 3 0 93 4 8 9 61 9 -1-6 22 6 635 1 9 .,9. 8".1792 713 6 0 13 2 _3 4 -.7 C 9 2 4 44 FAN-1 A 11. 69479 125746 1 12 9 6 16 -15 7@y` 3 1 6 2' 7 C) 1 18. a80015 C C b G 0 1 6548 3 1-5405 4'. "00 4 Al 06138 1. 0 q'i I -.i 'J 212 31':4 D 2 9 "@'95 3 04'? A 4 2190 2 . 25 2. 74 2 69 '.@) 4 4. 5'@ J "'.1 1 9 4 92 8 DO 13 3t) 1 1 1 I.S. 1 46099's 15. 667,13 5 2 7 17 15 IM 0 6 8. 66967 E f RT 8 1 .87 132 1 .9-3905 2.01311 2 1 -3.'.-; 1 i 19 . '0 "') 6'0'."' 2 30 2 1 2.33447 2 , Alt)3 14 21 1 '; 9 1 e., I I -D67i 133096 13. 1' 739 1 4-11).) 85 11110, 14 1 6, i 1898 1 Y. 13073 1 G' . I 3'@33 7 k.n 10.'1 j 931 0- 32 ?91 10 1 6 0'@ 6 77 11 0 3 917, 1.'x 4*1147 1 1 . 9,.,,2 5 9 12 1 6.2 9 3 1 3 3 ") 3 19 n D i-:S A 0' 631 014,;.) A 4 1 4 2' - 00'. 7 1 7 6 85 9 7 -1 k? [-.3 1 7 92 410 7 7 1 t. j 1216 1 ") 4 7 2 0 192 0 65 , 0 12 6 3 19 2 2 69. 9 3 s 14 2104 4 3 . 9 ;A 7 - d 6..'; 0 -'5 1 1 658. 2111 5 6 05 1 c.*,!--; 6 e.) 7 7 :7) 13 0 ID 767. 58557 C., 06 .',,190.55 5 5 13 0 7 9 0 9 6 1926 198-6 19BU 1 989 1 990 UHL I ry 1 0 0 0 2 I'D 0 oo '20 () 0 0 2 -,) 0 0 0 0 13 0 UCJW*1 .")0000 00 00 0 0 C) 0 0 -'1-000 Jd E* E 1-" D a 84 0 0 9 3 9 1.48292 95 . 4 9 45 1 99. 44k) Of) 0 3. 2 2 279 I E M D 8 2 1 9."3 9 5 21 4 . 17 9 8 25 439 1 8 2 66 8 5 1 1 7. 904'23 F C' F, 0 Al 1 7 1 c"-.,* 13 4 - 9 0 6f, 4 Al 4 97'I,S6 4:.,; 7 2 RD8 7 -l"S 6 3 . 19 75 2 3 3 15 40 35 4.-- 1 Ef 3 1 13 . "1" 4 1 7 9 o4'... 2 8 21 .947ne '12 . 924cJ6 23, 88--) el -14 . 79378 E Ff 1) t:j - -5 1 %)-i 4 6 21 a 19 4 9 2 . 927,0 3.03335 3 . 1 -.11) 4 5 7 19 -048313 19 B8926 2 0 1 6 93) 2 2 2 1 . 46'. 0 S 22 . 10352 1: G 1) a 133 1 9 'F 7 1; -:1 14 64297 15.30699 1 5 -969"30 16. 621 17 2 10 A 20. 32 2 A 7 :.!15440. 31 42t; 22069A.68081 2215054.624"7 230801 . 768 610 .1 G 6 ili6. 1310 5 111") 0 0 3 993,59744 1035. 46354 0 7 6. 6,.i 9' Table A Planning District 8, Scenario 7 (Numbers are in thousands) i I.J 19,-..,9 1900 19ei i Y82 1983 19 4 1905 I y 0 0 0 .00000 0"1 0 (K) 3 0 000 301JOO 0 0 30000 C CIN S 1 00000 0 0 0 0 1.) 3 0 0 0 0 i 0 0 0 0 .30000 00000 0 0 0 0 0 0 0 0 E F. A D8 5'6 .99 42 1 '59. 41401 62. 1 B461 6 6 . 70 58 8 7 0. 21 136 73 8 5 0 2 9 78. 60002 83. 31 C347 E W18 i A .69479 15.257, 46 15.'11296 16. 75".793 17. 7 6'.:.@ 70 18.138805 2 0 . @ 4 5 5 21 .63444 E C 1) 8 3. 4090 1 3.46548 3.85405 4.40054 4. 16130 4 , 0 9 14 3 4, 37022 4 5Wi 04 E R D 43 1 .97399 2.04709 2 . 12973 2.26311 2.66906 2.78007 2,92094 3. 06055 E I D3 13. 39476 14.02072 14.66(341 15. 7>047 16.65421 17. 58744 1 V . -1-112 19.951 8@ E F I RID a i . 6 -.., 5 3 3 1.943213 2 . 02 0 0 7 2.14300 2.24199 2.34393 2. 47353 -2 .60178 E .1-1, 41, 1) @ I li.5149," 5 12.33953 13.17166 14.24113 15.21,130 16.13836 17. 150".19 10.15411 E G V) 8 10. 1-5137 10.34048 10.62772 11.06971 11.50692. 12.02W2 1 .2. 65875 1 3.33-393 8 70 6 9 . 9 6 32-1 5 1-1"3639.00029 17 7 -1 17 . 121 33 2 183 0 87 . '-9 5 0 4 187731.69096 19'25' ,SO . 89 183 198773.00194 21 0 4 9'.) -1 . 12 26 4 - 6 9 F, 0 0: 6 3 4 .,-.) - 9 9 21 659.96588 68B."A 230 7 35. 35*0 70 Tt2.41068 at 0. 87507 86-0. 52834 09,8')3*12 1'906 19B7 19aa 1989 1990 LJ I i t- f. r), Z0000 .30000 .30000 .30")00 13 0 0 .0 0 CONTI 0 0 -*_jO 0 0 ".) 0 0 0 00000 0 0 0 0 0 -D 0 0 0 E EAD8 87 .6712!@ 9 1 .74971 95.116571 99.7 1553 10 3 . 55 0224 7 M F. 8 22.93395 24 . i '1`9 EI 7 25.43918 26.68@)ii 2 1.90423 E CD 8 4. @I 678 4 . 806 S 4 88738 4.97536 5.04:172 E. R'D 8 3.1' 1917 3.j0945 .5 . 4 2 4 3.54364 3.65465 .1 22.03723 23.01719 E11)(3 . 03-183 23.97542 24.a9061@ E F IRD8 "1.719,39 2,03021 2,9*31348 3.0,1463 3 . 1,161 1 E-VIAM 19.069-76 19.91122 2 0 . 7 15 76 21 .48Bi 7 221.20719 GD8 1 11 . OOG08 14.67519 15.3,1006 16.00321 16.655:38 NDCI 210656. 22535 2115998. 98558 22 1'-:159.90204 226434.13949 231394.99658 ff,DKI 955.35654 997.Y9iBO 1039.97394 1081.26439 1120.85206 Table A - Planning District 8, Scenario 8 (Numbers are in thousands) 1 9 78 1979 1981 1 918 19 -8 3 1 984 I RADF 100000 000C)") 0 0 0 0 0 0 0 200, 0 0 2,0000 Clo 0 0 0 C. o "d o C. 6 9 9 4.2 1 15 9. 4 14 1 62".04492 6 IS . 4 2 61 57 0 6 9. _76 6 2 7 73 . B2427 .70 1@74 1 3 9 '24 .7 E. M 1) a 1 4 .,.)9,1-19 15 . "'; 746 15 . 71 "96 14). '15793 i 7. '7,.) 2 @ 0 10 . E38805 20 2 5 -4 '.5 21 6 4 C D a 3 . 4 () 9 0 1 3. 4654H 3. 4 . "? 8 0 t.-) 4 4 . 0 6 1 3 0 4 . 09143 4 . 3 le 0 2 '2. 4 a A t -1 E R, 1) a I , 9 -1,*39 9 2.0,1709 2 . 12690 2.25743 2-359,18 2 . 4 7192 1 2 9: 61 WA 394-10 14. 0'-,07 14. 6 4 7i ')0 15 . 16.6.;*596 1 0 7 2) 4 0 -:,722 2 Co 23 7 B F F 11:;:)) 8 1 F. i 75 3 3 1,94323 0 1 -;":- 2 2 . 1 .71 -.19 0 2.23267 2.34226 2,A i 915 2 . I," i i 1 5 149.3 1 "1 . 3 -,5 9 '-; 3 13. 16 643 14.23( ,')68 1 1 ,, 5 19 16. i3493 1 7. i,4737 1 B. I E GDa 10. 13137 10.3404@ 1 6 . 6 -20 0 5 1 1 . 0", 4 37 1 1 .4-.@089 12. Oi5j?9 1 2. 6' 3'*.':! 1 N 1'112 1 6 9 . 9 632 5 1 71.5 3 9 . 0 0 02 1? 17 0 OcS . 0 G3 6 2 18 2 ('75 2 .515,")4 187 6 "e 7 15 1924 64 . 9 'j, 0 0 1 19 a 6 0 7. 1 '@) 0 1 D 1) u @1.3 4 .14) 6 9 92 6'.) 9 9 r., 58 E9 6 13 7 . 4 6 66 C) 7 3 '2 6 -." 3 0 7 6 a . .-) 9 0 10 . i 9037 a t; 9 a @i ",at, C;, 9 . I 1 -13 j- 1906 1910 19813 i989 1 990 I R i'l 14:. 3 0, -"10 0 .30000 .30000 300.10 300"DO C: 0 N.V1 J, 4`23 f; 73 9 6 9 51 9 68 95' 1 1 03. 1764j E M 1) U ?33 1? 5 1 19,97 2 S . 4 3 9 113 2 6. 6(', 5 11 27. Y04'13 71) "IJ 4.E -f0651 4. 887-513 4.9,7536 5 . 04 372 2. a "i 1 21 -8 3 . "? 4 1 1 3. 3' 279 D8 1 .3 1 I...J 7 9 2 3 2 3 2 0 23.30316 24.26130 5 - 1 -166 2. 718'22 2 . 82 fff-p 3 ... 1 9 3 8 1 3.04296 '3. 14444 EYVI;1i 19o'.)6633 19. 90 -(,ao -") - 7 1 3 4 21.40475 2 2 . 203 76 1 4 . 0 03 'S 5 14.61M6 5. 33504 15. 994:11 a 1 6 .."5086 OD"I f; 0 . 3'24 0 3 2 15913.083 76 221 174 . 00022) 2226 3 4 0. 2 3 - 6 7 23i:iO9.09477 4, P 1) Da 9 A I . @j "' j 0 997.30630 1039."8245 030. 57@189 11--'0.16656 Table A 9 Planning District 8, Scenario 9 (Numbers/are in thousands) 1 970 1 9 79 1 980 981 1 982 1 b -@i i 'D 4 1 9 5 0 0 CI 0 0 0 0 0 0 0 0 00 C I,i i'l 'of 0 0 1') 0 3 0 0 0 1 7 0 -10 1,) 00" (W Cf@' W I 's 0 2 1 5 9 0 11) -11 3 2 1.) 9 6 4 3 66. 676bO '?0 . 1 2 6 3 74 1 9 . 2, 3 19 84 22 A 15 E. ri D U i 4 . Al 1' 9 1 25@4c.) 15. 71296 16.75 `93 1 6 2) -@* 0 VIB, a@:@ wo'. 2 0 . 2': 4'@'; ':I 6.'@ 4 4 4 f C 1) 13 3 @ 4 0 V1 --I 1 3. 4 6 5 413 8 11 A 0 5 4 . 4' 65 -1 4.16-133 4 .09143 4 F fl: 1) 1C. 1 1 '97529 2' . 3 -." 5 6 "1343 2 , 2 & 1 .173 2. 3 6:3 5 1 ?2 1 4 7 CI 4 2 1 64406 82A i E I I)@ 1 -13'. 10907 i3.94GS4 1 4. 62085 15. 734-1`9 16 1 6 0 6 3 5 1 '? 1j'e 5 7 1 U . 9 11 1-1 t; S '? 0 . i 1 9 E F 11:6) a i . 8 "' 7 C) 3 i .93467 '.'.01441 21 . 14 1 1 3 6 9 34 2'. 5 2 .49,128 2. 6211 42 15, 20262 16. 13554 i'i. 19336 E YVD8 i 1 5 1842 1 32196 1 i 1 5 14 1 2.3 F.31") - 18. 1 Y-137 A 13049 10.31473 10 & 1 1") 6 a i i . 06-109 11 . 7 11 19 _7 7 1 121 5 1 6f.-'17 3. 6'2 1 18 A A . 29,! 99 0 1) U I _7 f; 4 5 72 7 1 -13 199. 06501 176925.901 17 1 'U'29 9 1 . 5 9 0 4 0 18 7 4 3 8. "1" 114 19,'4 Ilk) 07 113 1991,339. 9581 e.. '2 05 9 6 3 . 35 -," (8 3 '56.45456 06. 1 889B 7 3 4 . 92' 8 63' .3 61B A 3 '7 7 1") . C E169, OA1,266 I Pf) NJ I A3095 Ic I 10 . :i 1 7 'i 9 0 4 3 1 9 U 19 87 19oa 1918 9 1990 G G tj 1? 6 "10 0 .900"DO .90000 .90000 .90000 C 0 N 0 1) 1') 0 0 1") C-I --d 0 ''30000 1 00000 8 0 92.624A6 9 6. 6 25 27 1 -DO. 56046 0 4 . 13) 3 !17,; 7 L pi b G 21" 9 .3 -3 ro, 5 '24 . 17 9 0 7 25 . 4 3 9 18 2 6 . 605 1 1 2 9 0 42,3 UDS 4 . 7 1 e, A . 206 54 4 . 8 0 7 3 8 4.9-.`536 043-12 F F, D 18 90939 i . -TAI. 9 0 9 3 . 1462 1 35. 2611 6 3 1 3 11 16 EIDU 21 A10 9 22. 18525 3. V*i Ili a 4. 10*152 25 . () 152 2 f F 1, RO 0 2. ?3848 2 1 0 4 7 33 2, 95515 3 . 060 36 15 1 16094 E.EVD8 19 . 10 .1109 19. 9 473 7 0 9 9 5 2 1 1 57 4 3 22.23760 E.GD8 14, 96432 15 .6 282 1 1 6.29021 1 6.950'13 1 1. 6004", f4ba 2 A 16 12. " 1329 16905. 8'21 j 0 2221 16.0-721? 2 2 12 4 2. *? 9 -2 15 232157,32453 r ,f)"DIN) 96:2.98533 1005.22246 1046. Bi 257 1087.'-.11'744 !1-26.93544 Table A 10 Planning District 8, Scenario 10 (Numbers are in thousands) I 97H i S, ?9 1 980 1 981 1 1/ G 2 98.5 4 I ["AIA j 000.,)0 00000 ()0.,).")0 0 800-30 0 C, 0 WAS' 1 0 0 0 0 0 1 .000i)O 00 00 1") 0 0 E E A W IN..) 9 '59 .,12'-)G7 62. 4-1998 6 -11 1 18 950 7 0 . .1. i@ 15 9 2. -i,l - L) 4169 'S 7 1 8'@. i Y 2 1 "A 14 . 4 -."9 15 . 2 4 6 1 1296 1 6 . '."@793 1 76 2 7 0 tj . 08,005 25,15 1 . 631 -1 -14 '0" E C 1) 3, . 4 4 '43 0 5 4 . 78094 4 . 361 38 4 .091 43 G, A S 4 _10 @8 E R, 1) H i . 9 1 -118 1 2.04794 136' *'6 24 2 . 33091 2 . 6 It 3 W E ID 1.3 13 . A 14 . 0 2 -,,' 121 14. @19 .5 1 1;. 31195 1 1 7 . 2,3 75 113, 49 06 7 01% 1 1.10 M E F I (,:1) 8 1 . Ij 1606 1 .94399 0 '.*6 2 0 2. 1 " -179 21 2 S 1 3,5622 21 . 4 9 0 (11 8 12 . -.34 1 10 13 . 184'2" 11 . 3 1 1 2,.,; 4 6 16, 7 A I . 1061)8 .1 E 6 1) 10 133'.) 7 10.34278 j ')-')-1614 1 1 8 1 1 5-36 "0 1 11 05 1 J i @'. 711 1 1 'i 6 NOR 0 G e.'@ 4 1 "'36-i9.-,)63-36 1 7 7 5 3.2 . 0 0 74 4 1 83693 @'.,068 [email protected] 19,*5 1 K! . '8 ,') 7 19 9 6 , 0 @ 1 2, -11 7 0 3 o I 6,) -1 . '71 L, 90 1) oo 0 . 2 7 85 6 691 4 0 . 1 3 9 76.4901.1 01".YiO.56 8,`@ 7 .6 9 -i 0 1 X.- 1987 1 YEW i Y89 i Y90 7 PAK 0 0 1 .50000 1 5 0 0 1 0 0 1 .";0000 L.411 V 1 0 0 0 0 00O.")o ID 0 0 C) 0 0 0 000 0 E @-, D., Y @, .1 @, 82 9 3 6 121 7, 6 4 19 3 5 :-`;@ 6 1 OS TW26 E 11D8 1? 33 9 5 ."? 4 1 Y 1) 19 7 25 -39 1 a 2 6. 61,A L@ J 1 7. 904',? 3 E C 1) 1.3 4 806's ,4 4 00 i'33 4 9 3'." .5. 04@'i 12 E RI)b 93 3 3. 0 t) 7 7 7 3. 1760 1 3 '-19'. 10 7 5 10 3 13 I-II)IJ 22.',19(' '66 3. 9 0 1 " 1 -1 . 8 1--,3 1 Y 0 AJQ'@ 6 26. .187 31-)a 98191 3 "Mlif i i 189 F.1 1:.')) 8 - . i,4, , 19. 1 11260 0 . 0 -1) 0 1 Ej 20 . 8 0A 43 2 2 A 7@34 21 2 9 5 GDO 1,1.13921 1 '1. 80,;@68 1 S. 47,171 16,.1340 1 6 7,D603 2 1 8 B's . 3 82. 's 0 1 B223 . 0 6' 4 2 2 3 4 9 4 b `@',4 0 '220669 . 370'PO 3 6 3 2) 6 0' 8 4 1 92 7 10 15 78 63 4 @18997 101/9 1 0 V.,5 1 138. 707AU Table A 11 Planning District 9, Scenario I (Baseline Foreca5t) (Numbers are in thousands) I y G Y 9 Y "10 1 981 191), 3 1" Wy )-")@931 26 1 3A o!j V, to 13 7 . .,'0'2 1 1 44 . I 14()? A ".) .9639 Y 4 5 1 1 6 43;,-@ 4 7 9 7.186 19 . -i,4 -,, @1 0 2 0 . 5 i . ','i 4 a 22 . Y3 12 1 "4 . @111 -183 (y 696-38 2 @i 9 . 9 6 -718 9 0 . 0 6 4 1 9 Y PD9 A1 9 8 3 -1 1 1' .4 17 8 0 (@ A 5 6 lu . 2 7 7 7 03 Y 0 . y '-@ 4 1 21 _3 30. '1 1, L I W@ 76 13 3 1 05 6 3 3 6'@ 6 13 3 "Ttl I, k"? i 'i 1, G Ei I kw/ 82 -4 5 6 '13 -1 -1`3 1 11 G) 62 221)29 6 L 1 .4 6 . 'y '2 1-, @@3 2 4 i9 I 141@' 21-1 - q I", I"? J, 20 . 20. '4o,18 .24400 --23. 0 t f G 112 . i S92 42 . cm-,"A.19 43.49 42 4 4 .1-3 619-1 4 AS i (111) 9 8 8 e .I Y'23 3 39,12 19 . I 1--161 3 9 8 0 8 . " 4 1 4 6 7 . 2(32 8 6 4 1 -1 8 -4 i@ 3 1) 0 2 'q 4 1 -1 i 4'- 4': YI/ .-y P 0 OY 1 6 A, .7, 0 A 6 1 0 . 0I A e., 4 9 . 3:10 5 9 A 729 . 94 132 65 Y'd 6 1? .58 14 @>j@ I Y,@ Y6 YU7 1983 '1989 Y 9 0 'c@ 3 " i 9 1 Y3 . 62"191? 2011 .10 121) 21 4.5),l 1 1 Zi L 2. i . A'IIA"'i 2 0 . R, ; 9? , '6 19 L IA) 9 OL 3 Y 1 261 '36 1 3-11,30 1 fj . 3 2 9 . q I b@ 3 1- 5 10 . Q 2 652 1 3 -S 5 1 A 10 . o'Y`4 6 2 1 . 3 Z9 WY Al -'.'.) ' A 3 0 1 4 040 4 Ij 3 I, I--: 19 a 1 11-4,'-1 i4 8 . 8"'03 7 Y . 112 "S 9 9 . 789A14 -,'@'6 ?6 10 31 . ("S', 4 3 1 . I 1 3 i I Q 9 0 . 184 i16" 1: 6 Wk 05 -i 13, -3 21 .1 , 01 "A 6 S N DY 1`0 1 -i @ 12.2 2 1 . 12 Y 3 1Y 0 WD 6 . 3 @i' o '0 '/3 'e 3 8 1@ 9 930 8 0 F'11 DY -123 1 . 33666 ")0 13,1 2')37. ."hY@14 K2 . 1@ 11231-, Table A 12 Planning Di.strict 9, Scenario 2 (Numbers are in thousands) 19 72 1979 1980 1 981 j Y82 i 9@3 1 9 G 4 M 1- 6 0 0 0 0 0 0 00000 .00000 20000 3 0 00 0 D.. 0 00000 L ON. 0 0 0 0000%) 3-,)000 .70000 30 @)6000 E. L A D9 A 30. 9,5052 A 34. 65094 13 8 . 9 4 144. 9,1 i 3 6 151 . `5 54 8 t@ i'Al. 82945 1 6_`. '@":056 i -i . -Xi.." - E M D 9 13.2,1967 A G. 077u6 19. 33' -A0 @10. 55305 2 1 B7 402' 3 . 2 3 121 2.4 . Ij683 2,.). ?,10 C CD9 U.21 i B95 B. 69600 9. 3t-5-90 10. 66 07 1 1 1 1 '107, 4 1 1 7 7476 12. 93 i3 I 1 4. 10 36-1 -100 9 E R 1) 9 5 05 7.51?026 7 . 72 80 1 8.00,/68 B . 20 3 47 4 335 W *3 .6. '1352,06 E i D9 3 0. 3 10 31 .55695 3 2' . 2 t., 0 4 4 3 "i . 6 3 13 0 3'@ . 23 3(3 71 .573 4 0 . I C F I f:.1) 9 5 6 5. 68473 5.840913 6 . 224 1 3 6.'A351 7 6 . 9 892 4 4 1 7 . Y'76 Q I ELY09 22,32027 23.89393 2 5 6.-) 0 7 . 5 92 ICJ i i Oa 20.06838 2 0 . 7 7'27t) 00 EUD9 41._16710 12.17661 42.67646 4 3 , 53 42 2 4 . 5 6 i 2 2 4S . El 2 9 7 1 4 _1 . 3 5 02 7 -19. 0 0..'@ 9 6 i4t,9 3,3 88 7 3 . 6 18 39 4 2 1 0 73 10 390963. 61:3494 4 B 6 70 , 3 4 9 4 182 23. 503 10 42872 3. 0295 3 44 15 t;,-, . 9 85 415,4's 3 @@ 4 9 'i 1, D D? 15 64 . -F2 i " 7 16 10 . 7 0 0 4 7 165 1 .5*76f.17 1 7 :35 . i K`5 3 11.-117 . 3 6 6 112 19 0 1 . 7`14 4 1 2 0 1 12 . 18 B 2 7 .32 . 2 4 1 (..5 6 1926 1987 1938 1989 1 1/90 MI-6 .30000 30000 .30004) 30-DOO 0()o C G NS, 1 . ".) k" ") () 1") () () .0 0 . 00000 0 ") 0 0 0 00000 C LADY i G 5 i 6 9 4 193. 91322 i 202. 10434 10 .06296 2 17 . """ 3 9 9 8 E vi D 1? '2 7 - 74023 219 . 1 -5 92" 3 @iO.545 10 3 1 .93619 _3 .5 . 29 7 2 7 C 1) 1? 4 1 6. 26'? " '2 1 7. 3 21,.@ i@ 5 1 U . 39 j 5 a 19.41868 E 1--,- 1) 9 Y 0 9 9 10. o 3005 1 0 . 3.) 6 0 6 5 10 . 7 -")'? 17 1 1 . i -.3) 6 F ID9 4.':.! . _205 4 il 43.762-16 4 5. 12033 -16. 39017 4 1 .58566 E. F I RD 9 R . i 1 0. 8 9 0 *14 9.3427*6 9.7G98i 10. 227 13 E .,t 11.19 3 1 16 *184,11 33. 53067 35 . 36586 3 7 , i ': '2- YU "'i U. a G 210 E G09 50. 60496 5 2. 35 9 31 54 . ") 3 191 5 S . 6 9 92 6 S F.351 70 OV9 Al 6 72 7 1 . 4 B9 14 179003.73538 4907 19. 4 1301 50" 1'20. Y 2233 5 13190. 66'@ 60 '.,-,39.15 1553 2341.25622 `A4 i . 11700 2539.56204 6 34 . 35'1 2 1 Table A - 13 Planning District 9., Scenario 3' (Numbers are in thousands) 1978 1979 1 980 1981 19 0 2 1 Y93 1'184 1 9 C 15 F G 00000 .00000 00000 .00000 - '2 5 0 0 0 .500..)0 5 0 00 0 0 0.1 0 ) 0 .00000 -.001..) .5000o !00;).,) 0 0 0 0 0 ") 0 *1) o N. X) 5 1 143 1: k ADI/ 130.93045 134 . I`15flB6 13 ".91 001-*@ i 4 4 . 7'f i i 'A 543 74 6a EMD9 12. 24" ,@ C.1) 7 16. 87-0 86 A9. 32164 0 20.55305 21 . Y'24 6 J'.@ . 4 3 121 24.956"13 457' 1 -10 1-.Cf)9 B. "i L195 13.69600 9 . 28291 10. 4c 880 1 A . 0.- OY 9 i 1 .775-35 1 2. 93,390 1 4. 10421 F, f., 1) 9 7 4 5 7.59826 7.72014 U. 01064 8. "1859" 8 . 5 9 33 6 .1.96352 9.341 e., . ati f, ") 9 3c".. 71 696 E *1 f)9 30. 376 10 31 ."5695 -12. 2605 1 '.13. 63183 '3 2 3 4 8 6 11 () 6 16 1*'9 F @ I kl)) 9 5 . 40 2 5 6 5 . 6 8 4 -13 S. OM900 6 . 2, -: 4 2 4 6 . "; 0' 14 6. 13 6 7 0 ", 3 "i, . 9 3 E. @: 4) 1.11/ 9 11 2' 0 . 0 6 8 -13 2 0. 'I-A 0 2 2 3 21 9 "1 Ij ',.!3.9-1 --, -.) 123 62 0 613 27, 6484i 29. F' )-7 - 117 . 3-7 -.", i 3 ,@i 2 EGD9 0 .7@* 700 42.17@.')65 4 2 -,F707 4 3. 5,-.1 P -.,)o 411.57446 -15 6 5 6 ",'6 49 .03@') N D9 380073. 34'320 394214. 69921 3 9 8 1? 7 3 . 6 8-2 5 6 4 B7 4 2. 1 4 11 -a -? i 9 -., 1 4-29 16 3. 82 19 1 4 4 1993. 06 48 13 4.5 5.,.? 4'). 71 IB " 5 1 4 A R 1; 0 1 6 10 . 69 72"i 1651.66-293 17 3!) A1.3 1 '0 18 95 75 2 3 1911 5 111 a9 2021 .9 ."c,46 2 1 i ID3 10 9 a I 91B6 1987 19138 1989 11/90 In F G 5 0 0 110000 0 0 0 0 .50000 .50000 COW71 000.30 00000 00000 . 000f.;O I () 0 C) 0 0 E E, A W) I B 6 . 02 98 6 194.27521 202, 39-130 210@35594 i G. 03119 7 Ei-11 ,)9 217 . 9 -1 G 2 3 29.J 3923 3 0. 'i 4 5 1 a @Q.i36i9 -,,i 3 . 4 9 -('2 7 1 CD9 1 2 0 `@ -3 3 1 6 . 216 2t i 1 17.3 ,5024 18-38617 A 9.4 192 1 HAW 9. IsYb92 10.03597 io.31459 I (). 708 11 1 1 . 033 ..)q L TD9 A) 2 . 2. t'-j 13 6 7 113 . -76 5 2 9 4':,. 12356 46.40021 4 7 . 5',:.i 0 9 0 EV IR))9 1.3. 44 -,; 12 8. 89 136 9,343.i7 9.79i ' )43 1 0 . 22 7 ".15 I- E 1) PY 3 1 . 41 6 33.1) .9 4 3 7 35. 4,1157 37.20870 3 8 . 9 -3 78 3 5. -72 6 1 4 57 . 3 7 OC-1, FGD9 5 0 . i '@i 3 .386 i a 54.0t,079 141)9 A167 h 1 .78529 479524,13807 4 91 159. 9 13 35 '0-15,61 .5iOldO 513631 .32:126 A F, 1) DS, 2'.-'4.5 -S 6 5 6 3 2 3 45. 4 7 2 4 '24 4 f; - 08 0 6 2543, 35466 2638. 6444" Table A 14 Planning District 9, Scenario 4 (Numbers are in thousands) 1979 191(30 1 Ya 1 1982 i V83 1 I G 0 0 "'P Q 0 00 0 00000 .)0".) "'O.A00 0 0 1) 0 0 0 1 0 C000 0 0 0 0 00000 0 o L 01 W,/ 1 3 13 4 0') 1 3zi 3,?'a 0 1 4 _3 9 0 1 1 9.334 i i 3 -1 L PID9 18, 37 78/1 19 .3 0 6 110 2 53 0 2 63 12 1 3 6 y -i @4 ECD9 a. 21 89@5 8 69e.@Ocl 95 83 4 3 10.96966 6 1 1 77.,91 w 1. R, 1) 9 9 '11@12 6 .',@336 11") 19,211 B."9167 9 c,9 0 Y 3 -'1 6@@, k ID9 . 3C. . -i 6 10 3 1 " 'S 6 9 3 2 . 6335 33. 6S 650 3`1 . -13 19 1 3 6. z55832 3 0 . 9 c, 73 - @ I- I I.D9 4 0 2 S 5 6 94 4 6 . 2'-";, 13 6.' B602 1. f 1) 9 99",)4 4 19. 37104 2, 0 8 33 03 2:3 22. 4 0 9'.) 2 .2. 3 1? '1,' 0 9 II.'j 6 .' 2 Y 6 E ODY 4 1 8 7 0 8 112 .1 766t@ 42, 4 _3 . 5 7,4':, 4 0 ") 3 y 4 tj . 88 1 18 4Y . W, 6 0 -1 300JO F,3, 3 817 "39 4,2 A 4 . 6,,"( 4 3 9 9 3 6 1 . o") 6 5 1 @4 9 3 7 01 . 9 2 4 1 8,0.3'w'. "o6,26 11 -1 Al 4 c.,), 0 I PDDY 5 16 10. @:'@9695 A 65 4 . 9 9 7'., 6 1 1 . 2. 10 1 8 `2 . I Oj 2 19 1') - 1 00 16 DO 2', . ':'.3 @I 1 3 ".,'Y y I 9@J6 1987 1 98a 1 9?0 I[I f 6 70-DOO 0 0 0 o . 1 0000 L 0 N L* 1 0 6 0 0 00000 .000M 0 0 -D () 1") E E A 1) 1) 186. 3 110 1 Y 4 . t,,'24 5 2 0 _2 . 4 -.10. 6131 3 2 1 a A 1102@! L ril)9 i I,@3 29 . " 3 9 23 30. 9,11 -; I a 32 . 3 @) @) I '? 33. c,9 )""-.),;9 1 ,. 26,33 1 i. 33C.Biji 1 9. Al'i 983 E CI)v L R D9 -")'2.-19 10 4 15- 4 10. 1!, 0, it '3'6@ I 1 31) E fD9 42 29 17 0 4, 1_.'69 S " 4 j126 5, 9 4 6 '10 4 4 )'.i L F L F, 1) 9 8.4, 1,431 8 . 10 9 19 4 Y34'396 9 '1 I'i 10 3 1 . 72 6 4 Al 7 `6 3 5 -173,8 f; 3 098 313 . 9 90 1 1 E L, F) Y 5 0 . 7,,)5 2. Al S 4 . 011 10 1 7 LD 13 6 S' 11 c')d 12 1 -4 199 37. 1) 391A3 49 15 7 3. 3'.113 7 9 `@02974 . 9'19 )0 5 1 404AI . 74 "/37 22-4e, . 92-11 4 1113. 5 a 3 448. 16 25, 116 . 9 1 .34 0 6 4 2. 203 2 1 Table A - 15 Planning District 9, Scenario 5 (Numbers are in thousands) B 1 9 1`9 90 0 1901 1 VI-52 0 -_i I Y'L -;4 G 00000 0 3 1,,) 0 0 .1"') 0 JI) 00 0o 0 0 0 0 E I- DY 3 3 0 4 4 1 34. 6':@aO'@ 13 8 . 3 7 4 45,448 @6 1 2. 0,1116 9. @33 I C"'cl B I C", 0 .,SO Y @197 10 8 -,` 7. @i 6 A 9 121") 4 0 2 0 . -5 -i 2,@. . 43 i ?1 '4 1 .9 7--,07 i I . 3-1 109 1 1 .775*6y A I F@ 10 2 A a Y 1; 8 . 6960B 1) i3 6 A").9 31 7 . 13`,:@)9 02 291 G.29A91 8 6 ` 4 8 , 9 9,3,-A I 0 . 3 i 6 1 31 . 95 -1'--; . 2 39 -.113 9 . 1`2 A 0 5 E11 . -4 7 -6 E F 1 5 5 6 3,2 . 2 6 @i 63 @3 1 4 F 1 1'.1119 .40'.`56 5 6 B 4 ." 3 84977 6 `25'j 2 6 ':,&'@36 9 v 01.11 @48i '29 9 v 8 4 9 3 1104 20 061:13,3 A 3? 2 '44,1" 6 4 6013 9 9, 1 G8 42 .1 7665 121 .)BO 4 3.!:>9,(4 i 8 1 41 1 ID k") 6 41' a A IE19 'I i 436 16 1, 9 3,8 "-A, 1 -5 3 1 6 0 a3'@421 4 .6619`1 9 9 15 2 4 1 .2 5 4 0 9 6'.. 0 4 iY"', 13 - I Bi 6 -V? 9A 15 9 4 4 0 f, I @ DY Ei.! 2 16 1 6 9 6 Y3 i c,';6. 42OB-i 1 74 3 5r`2 9 1 4 0 Y i i Y 1 3 7(14601 3 9 2, 6 1989 C. J 0 C, 0 1 1 00000 1 B L L @, DY 2 -i A 05 1 94. 9794 1 20 3 . 10 A 0 1 1 .06014 J 3@1`1 9 E M D? -28 . 4 -113.'3 !9. H 3923 31 .'.45AB 32.6- :i3.99e2.1, E- C 1) 9 1 j '; 3 A 8 . 32747 19 . 4 8 4, 3 1 6 .,26 4 1 1 17., I Al ') ' F 11:4)k? 9 . 709Y5 I 0.38@6)i 10. 7' 1 1 3 1 1 .)4.4)32 42 . 2 9 t@ i 7 1 -3 . 7, .1 2 3 9 A 5 . A 30 n6 4 6 . 40 0 4 7 . 5 Y', 9 9 1-1- 11-:1) 9 G . A 4 5 0 8 8 9 21 -1-2 1/ . 344"13 9 . Y 1 9 1 0 . 229 1 1 EV W? ot; 3 33 . 71 4 3': tiA-394 3 7 . @5 I Q 7 19 . 602 0 1 6, 783 1: j ? . 43 J-70 E C; D9 0 . Y 52 . 4 4 52 5 5 . et:15 I if I.ID9 Y . 3 0 4 Y 480491.73390 4 92 12 7. t@ i If 9 503--,29. 1 "tA 7 4')? G . 9 4 TA I IA) D9 '-'2'-.; A . 6 9 4 6 8 2353,37638 2453 . 5380B 2551 .60396 2646. Y73 @G Table A 16 Planning District 9, Scenario 6 (Numbers are in thousands) A 9 R 1979 8 1 19 3 YO-1 0., U r I L f I Y 0 0 .00000 0 0 0 0 0 1") 2 -' D0 0 2 0,1') 0 C 2 0060 j.' E LAD9 1 73, 9,3 0 5 1 -4 -e . 9495 2 144. 74 0 4 1 AI . 472 2 3 1 -j C's . -,.10 3 1 67 . i '178 14 1 7 1? 26 "IS E M LI 9 18 . 2496-1 10 . 87786 19 . 3'36 4 0 55705 2 1 . 6740 2 2 2 . 9-3 12 1 2, 4 . 4 j 3 9?"1"40 4 . 1 3-57 8. 2 1139' 9 . '20,205 1 C) 4 6-87 D I i - Q 20 8 6 1 1 1 9 3',';; 2 7 F CD9 5 8.69608 -1 -19 L I'D 9 Y I " , 1. F: F: F9 11,1105 -1.59826 -1 . 72760 8 . 0 OY6 2 0 - 11045a 7 .3 3 9 1 1) 9 30 3-1610 31 .5,5695 32.26021 33. 631 '27 3 2,) 4 1 1 -'s6 .85184 38. 71 "50 A'j 7' 97 a% Ef If."D9 5 4 5 . 684773 !j . 64894 6. 224 13 6 j 85 0 98920 4, .9 -) i 9. 3-.,108 '20 .0683B 20. 76090 22 . 3 19 -71 -23 - 90434 25 6 i i B 2 7 . '.; (31't: 19 6 9 . 4 9 1. @ 0 5 L. L. 1) 9 4 1 . 78-7 10 42.1-.'-667 4 74 @) 0 4 3 - 5`3 9 4 44.*6824 4 5 .'82'@'85 A 7 .,i4b,12 il 9. 002 1 0 OD9 30,88 a 394 2 1 `0 . C)73 1 398933. 4 8 6 6 5 . 4 1 6 --1 a -., o 9 , 1 4 2,9 6 9 3 . 3 8 -,.,; 0 2 4 4 15'22 . 48 4 3 5 4 4 7 1") @-. 9 1? 9 0 Y F, D 1) 1 4 . i " 7 16 10 . 7 0 0 4 7 65 1 . 31 -178 1 735 . k039i B 1 18 . 0 7 :; a 19 () 7 . 4 9,2 .3,2 1 9 2 (-.) i G 2 i 9 7 9 y F, h I u 1) 18 . '2 1 2'29 . 2 4 9 3 1 239 - 39423 5 9 . a k.) 1, 0 6 4 2 7 9 1 'G'5 2 9 300. 53150 26. B 1: 9 4 1 0 - 4 17 "POm: 1 1)9 1' 72 . 30583 i660.909io 1 7 4 2. . 4 " 6 2 1907. 09Z5 3 2 Q 6 3 -3 7 95 2 2 3!.'; . 2 2115 '.-'A 4 5 0' 0' 1907 19613 19,89 1 990 U T I L FY .20000 '10060 20000 0 0 1") 0 CI 0 00"WO O."WOO 63 0'@ 1 193.875134 2.0 1 .99-192 209 1 95,653 2 1 0 B 3 5 5' EMI), 'Y@ 44 C-3 2 3 nil) .839213 0 . 2,1 `- i B -.i 1 63 6 19 9 9 7 2 ECDY 1 "0 1 @ 6 26-2 113 1 7. 3';'9 6 1 18 5 4 19. 4 1 E RD 9 9 c196513 10. '22964 10.56824 10 . 9 1 6 1 1 . 2 2 9'> F 1 D9 4 2 c 2 A) 75 . 7 a 4 4 5 . 4 '-" C, I 1 4 6. 39' 0 14 7 . 5 4 4 E F I RO 9 8 4 4 -3 0 6 8.89069 9 . 34-1-7 1 9. 7@;977 io. 2 '"0 9 E@'Ajiw 6 -., .16 3 33. 5342-1 35 .3 620 1 3 1 14 9 13 38. 8'i-825 r E GD9 68310 5 2. " 35 7,15 5 5 5 . 6@@ @ 4 0 57.34992 tqD9 4 -1 2 4 1 . 0 4 '-16 3 4 905 3 . ':.I G 1--j 8 7 A19()LOO. 96650 '02090.47583 513160.21909 ITI)DY 2239.31344 2340.9'@414 2441.1'.492 2539,29996 2634.50913 YF'MFOD9 379.7i3so 403.97300 427.8-;343 451.29,110 474.03784 YFIOMFGD9 --'B73.10316 3068.64509 3261.20262 3449.90051 3633.14443 OEX Eb@i:.'E ALLVARO i i i i Table A - 17 Planning District 9, Scenario 7 (Numbers are in thousands) i 9**.,.,t3 1979 980 1981 1 982 1 YB3 1984 1 Y G)',* U I I L i TY 0 0 0 0 W,)00o 0 C) 0 .00000 .30000 .1,0000 i"Ij 0 0 o 3 .0 .1. 0000 S "1 0 0 0 0 0 0 00000 0 0 0 11) Ef E 1) D" 130 .93,',)44 134. 65385 138 .()9,)3-.,' 1 45.,")2234 15 1 7 5'3') 4 1 @, 0 . 8 9 43 7 1 "1 7 a 9 9 33 1 7 1 1.5 3 4 1) f MD9 I @'i . 2' 4 9 6 7 18.07786 19. *38640 2 0 . 5 5 3017p 2 1 . 6 74 82 22.93121 21, 4' ,:,6,83 2 y 9 1., 4 -') E C D 9 8 . .2 .1 "3 95 8. 69608 9. 3@*.1751 " 10 . 6 67?2 2 1 1 Ql41 i . -,, -I, -, i ., 1 2. 9-3,104 14 . i i. 1,12" 8 - - 1 3021 8159009 18 1 a 9 A 15 13 9. 4 9 2 Y F F, 1) 9 1, .44505 7.59826 7. T. 8. 01-484 E I f,9 3 7 6 10 31 .55695 32.26163 33.63412 35 . "' 3 71 1 3"). 35 432 38. 7 17 i,., 4 0,6i 914 4 8 25 6 5.6121173 '3 4 9 1 6 . 2 4,:) 7 6 @ 'A0 5 .1 j 6 6.905,67 430,78 7.Y7631 DY 19. 37104 20. 06)033 '20.79338 22. 3@,880 -23. 95606 6 0 3 9 3 2 1 6,-!@ 159 703 42.17665 42. 68642 4 Q 4'. , ITGD9 3 . 5 5-1 @ 3 44.593 19 -1 84 CHO 4 e . 303, A 6 4 9 . 1310 , D OD9 10 9 7 3 . 3 15 8 1 394214.66160 399126.82775 40YO54. 0' 87' 418714.82107 4'.'?90@i .43@iB5 4,12096. -!Fi".184 -1': 3 2 6 9 1 S 4. 17 1 ',. 12 2 10 . 6 9 6 9 2 1652.98122' 1730. 435t@6 1'321 .59612 19iO.4-'D23i 211) 2 8 6 9 ".1 B 1 0 6 1918*7 1988 19@9 it/90 UI 11-j@ TY 3 C) 0 0 0 30000 3 0 0 0 0 C, C, 0 0 .30000 C, 0 N."I"i 0 .00000 .3 0 0 0 0 0 0 0 0 .00000 E EH' Dy 18."; . 85 72 0 1 94. 1 0'2@i6 2 0 2,4 66 10 . i 'ci'.3 2 9 '2 17. 9103 3 EMD9 7.4,18"3 2 @.i . @ 9 2 3 30.24518 3 1 619 -12.99727 E CD9 6' 16.',6"1 17.33042 18 116 35 1 9. 41945 -,0 E R, P 9 9. 9 9 `@ 6 7 10 . --i 3 7 12 10.67633 1 1 .009135 11.33504 E 11,09 42. 2Wk@2 A 3. 76624 o 5. 1 241i 1 46.40116 4 1. 511985 EFI[-!D9 8.89154 9. 3.1356 9 . 79061 10;2279-3 E,',V W) 3 A . -F 5 5 3 3 . 6 1 Q -1-6 35. -1,3 "1"? 6 :i 7. 2',, 'j 09 3 8, 9 5A) 2 2 EGDY f, 0 . 7 19 7 3 52.39409 5 4 . 0,-'@ 6 7 0 55. 7.i-W5 51.30657 ND9 467041.36512 4 7 9 6 5 3. 7 " 8 6' Z 491289.1,i3@2 502691 . 120011 513760.93874 2446.32417 I -DDY 2244.ilBI06 2346.16-277 2544.47035 Table A 18 Planning District 9, Scenario 8 (Numbers are in thousands) 19 10 19 79 1 9fJO 1 9111 1 9 a-'-, I vib 3 1 984 1 B 5 1 0 0 0 0 0 1() 0.") 0 300-DO 0 5 C CIN A 0 0 0 0 I-J id () C. ") 0 0 F. E f; D", 09 3 '),15 134. 65006 13 941832 14 4 . 73 0 14 15i . 3 4 () 8 4 150 . 85 2 1 1 1 67 80 1 -.1, 7 . 0 5'..) 9 1 F MD9 G .2,1967 1 a . B C @ 6 9 . 3 t 3 -'@ 4 0 0 . 5 15 5 6 -.,, 4 8 21 2 93 12 1 2 4 . 4'.6 G 3 25 . 9 9 @'A 095 9 . '2 0, -2 113 5 10. 4.' 869 1 1.20 @5 F CD9 696-18 6 1 _7 7 4 9 0 1 '2 . 9,33 1 10 3 6 7 . 5 9 3'.? 6 6 5 a ti .,58,13,88 1* 33 .9 i E F: 1) 9 .00947 U . 91 Y 3ll ---I .0 1 s A t,@ 15 39 . 0 1 452 40.9 15 "1" F Fb9 3 1 31 .5,jo95 6 -3 1 19 "1 939 0 E F 19 -1 G) 5 6 69473 84B93 6- 224 A i e, .80 7 6 . 90 7 . 4 80 17 9 @6 i 19 . 3 .'104 2 0 6 2 2 . 3 i 13 3 E 16 1 1 6, 4 0 2 20. 06833 19 'S 8 E U. 4 1 . 7 0 7 "') 8 42. 17665 A l 4 " 6 4 '53 3'Al. 9 44 . 559 16 4' . 83626 4 7 . 35 6 8 3 4 9 . 0 1 o 5::., 0 P9 3 @I 8 ii 7 -3 . 4 G 2 0 394'.-'14.69921 913 9 2 5 a -1 18 4 0 8 6'*.; S . 09 8'a B 4 1 I'J i f; 7 . i "i 0 4 2B 8 3 1 . 20AB4 4 4 16 6 0 . '3 7 -1 '4"A 9 10 , 10 14 9 72 5 1 65 1 . 26 o.) 14 i 7 3 5 . 0 19 11.3 1 9 f; 4 1 Y P I # 1'.9 1 5'A 1610. 69 19 8 2 '."' 0 9 . i 1 2 6 2 13-1 .3 . I F -1" 8 'i PrIF-1, D9 2 1 G' . '2 12 229 . 2493 1 3 9 . -3 9 4':.. 3 '25 9 . 8 Of','O 6 2 *0 A 8 5':.'9 30"s 53 1 SO 326 . @1'8 9 4 1 354 i 0@487 ILLj t.j@ I" 17 4 4' 6 2 A 907 . 9 353 3 .3 7 9 2': 3 4 4 5 O'S 05 26 6 6 ;2') 15 A 5 3," 5 9 3 660.9-3910 1 5 S 22 15 I 91@6 1987 1908 1989 1990 0 30000 3 0 0 0 0 3 () 0 . 30 0 0 0 0 0") 13 0 D C' 0 0 0 '.) 0 0 11) 0 ')6 C 121 7 1 1 2 10. 0,3':: 74 2 A 7 8 12 1 B 1:. 1 9 A) 'j () 5 E 1,11)9 -14 0 ' 3 28 . Ill 3921-15 _3 4 5 A 3 3 1 . 63 6 i 9 .j 99 I? 1 6. 26236 1 7 32979 10 . 3135 7 2 19 . 4 1 0,81-5 9 OS 150 YFO i 1 10.7.0363 1 101-1 8 0 1 E I'D9 4'."' 5`0623 14 . 0 6'213 5 AlS. 4-A 1 4 6 -'@97 77 4-7 U1 3 -,!, 4 6 - `6 1? 9 7 G 99 6 - E F I FID 9 13 .44 3' 0. 109039 3429 1 10. 2'-1 ?28 E. E V 1)", 3 1 9-1 0 9 -3 3 . 5 5 2 3 1 3':, 3 195 -0 :5 -.7.16 663 38.89576 E G, 0 9 0 . 6 9 15 3 S 2 . :i 6.5 0 G 4 .."13 16*4 4 9 5 . 7 0 L.5 0 4 i 7 . 35 8 3 7 ND9 4 6 7 3 9 . 16 02, 3 4 79 191 .52 10 0 490027.29628 502228. 89374 5 13298 . 70'@55 yF,DDII -1240.50244 '-.-'3 4'..! . 1 .3 4 0 5 2442.34567 2'540.49147 '@,635.78i23 YPMFOD9 3 79. '113-50 403.97360 4 27 - El 7343 451.29410 4 74. 04 Y FNMF G D9 2873. 113316 3068.64509 3261.20262 3449.90051 3633.14443 Table A 19 Planning District 9, Scenario 9 (Numbers are in thousands) 1 9.1,13 1 979 1920 1 981 1 Y82 Y83 i Y04 Of 0 00 0 0 0 0 ,-,1 .860"') 0 0 0 2C)"100 f 0 134 6""EJO', 13 91' 1 -.1 4 6 W, 63 6 11 @,9 1086 1 "'.0 131718cv 8, -!,19@.,7 1IG.8- 'i@@ 6 19 0 3 5 2 1 6 7A '13 2 93 1 :11 9 '14,005 7 5 9 B'2 6 7 A 0 1 0'@.) e') . . I. 2 9W-j 0 G 5 9 9 0 9 7 19 3 03 31 3 2 @2 9 3 F 6 3 12. 1 9 F I 1.:)) 9 6 5 684 73 5 04096 6 2 4 '2 3 65 all 6 9 9 3 04 2 0 0 6 0 3 3 025 23 (?61,) 15 8 11 1 Y u 32i: 90 2'@ 67@ G D 9 41 38 --i 1.,2 4 2' 1, 7. 66 5 4 2 S 4 4 ?.5 4 6 -11", 8844' 1 4 0 4 0 11'@ 3'! H -7.3 11 3 9 A 2 14 6 6 16 0 39 8 4 _3 90 3 4 3 4 1 a i .3 B f. 6 4 1 '8 @'4 1 9107 1 42Y6'. 1 .3 2 6 1 -14 2 ? 6 8 1 1 4 J, 10 -'I "I S22 69619" 1 17 3 19 7 1'_j '21 .91';39 1 y 1 '4 'W) 1 2 2,,1 6 Y i'i 6 19 'j@ 7 1 988 A 989 1 ly Y 0 1 0 0 0 'j 1I . 50000 1 . 50000 15000 0 0 0 0 0 0 0 L L 1) 8 7 . 3,13 W,' 19 S H'J 0 4 7 1 o94 2 1 1 @)i@@95 2 19 . 39c" E ,I W? .2.13 '13 3 9 2 3 0 . 2,1 1 a 31 6 36 1 9 Y 9 7 7 1-( D9 3 "S 1 6 . 26 -I "I i 1 7 . _33 2 4 1 a . 36 I:j 17 1 9 . 4 2 1 U E I-J)'? 9 . 10 . 0 9 ,'1 1 0 . 39 @'11:;9 10 . 128 12 1 1 . 01 3 0 L T b", 43 9, v n 45. 276 1 Y 46 - 63'146 17. 1h I i 1 11 Y . 0 9'9 [I ]PI) 9 El' . 8 1 8.09344 9 . 3 4 5 4 6 9 . 5 2 10. 2"!9n4' E VVD ? I 1 3 3 - -1 '22 3 6 0 9' "? 3 7 . ' iY6 6 t) 3 9 . )78 8 0 -!,) 0 52. 4 -eL,06 1 -4 9 5 5 . 8 A 6 It'? 5 1. 116931 _'4 Al 9 19 9 2,4 9 3 '18 10 10 . 2H H 4 3 A 6 0 @3`@'3 51-D4047.6 ?905 1 ? .4 98') j "'2S 6 A 013 2.3, -1 8,10 13 2 48 00 1 EM '25 S 6 1 4 7 ? 1 26t, 1 A 3 _5 4 ........ . .. Table A - 20 Planning District 9, Scenario 10 (Numbers are in thousands) 19 L i 1 9 -79 1 980 1 981 iY132 1983 1 Y04 1 985 i@', s.) @'j "j 0 0 0 0 .90, J- .d v 0 0 0 C 0 t 1 0 0 0 0 E C;; 09 1 'i 9 3 0 -1 -1 1.34.65Wj5 138.0710716 1 45. 01'@',001 151 .76757 1 @j9. i ',15,Q 160.,.')8"69 18 8 E hf)9 19. 2-1967 18.87-.736 19. 73c.A0 2 0 . 5.'. 3 -.) 5 "1 . 67402, 2'2. 93 1 21 2 4 5 6 IS 3 2" . 99 140 E CP9 B.2 1 1095 a. 69608 9. -i's 3@),I 10. 06908 il .12146 1 1 , 7-15*75 12. 9 352,@ i i Ot@Y2 E RD9 4 5 -) S 7.59826 7.1,2947 a . 0 1 3, 13.29066 8.59,738 8 , 9 ""' 6 Ff 1 9 . 350 3 7 ON E'r b 3 1") . 3 7 6 10 31 .556?5 32.26123 3 3 . 6'., 3, -, 4 2 3 7 42 3 6. 85 *1'4 B 3 C', . 7 2 4 1 4 0. 6 2 7': E 17 1 [-3) 9 5.49256 5.68473 '@.34914 6.22456 6 . 5 1), 2 6.99028 7 . -18202 7. 97,':tl 1 F. V I'll, 19 37 104 20.06033 20.78643 2 2 . 3 9 6 14 G 25-1' 1.144 2 !. 11327 ? 9. 9 1 k 0 F G I A i a 0 8 42. 17665, -12' . 6 G 3 0 6 113 1 1 B 4 @I . O,"ai 2 0 4 6 . 3 -7;1 '18 118, . 37 .14 1; C', . 0 @'2 4 8 1.109 3 2 '1*313 -,",3 . 3 i 1: 1 394214 . 66160 399071 .822' 6 40139-18.4266".13 4 1 18-75 1 12 3 3 1'-:'9 4 4 195 9 4 4 :29 *19 . 82 4 G A 45 65 11 . (35, 'A PLOW/ I to 4 . "1822 1610.69,W2 i z) 5 2 . 5 0 7 7,3 1 73 7 . 5'2 48 9 1 G 2 i 9,.) 44 1 19 14 . 112 'S 4 -11 O@j I il 953 4 8 i Y B 1987 1 9 6 C13 1989 1990 G 0 4) 9C)OO0 YoO..')O 9 0 0 I'D 0 .90000 0 w@ 1 00000 .00000 00000 .00000 0 0 F: C 1" IYI 9 ".164 19,1.94299 203.o6509 21l . 0 2 3 21 Es. 15076 EMD9 -2 1 -18 2 3 28. 83923 30. 24 f; 18 3 1. 63619 3 2. 9 9-127 C 1) 1? 15 . i -") 4 1 6. 26 4 5 21 17 . -.33191; 18. 313 @88 i 9.42091B E P D 9 9 1 3 0 6 10. 3Y 167 1 "1.; 11? 1 1 .0'- 0 Yi CA D9 42. 2Y-.198 43 . 7 6 0 4 5 . 1 3 -2 8 7 46.411 91. 1 4 7. 91L1,10 .) S .3,15'16 9. 79 22 1 E FT R 1) 9 1 C) 2 9 5.@ E ' OJ DY 30 8 9-16 4 3 3. 75,10 5 3 5 5 8'. 1 0 5 :37. 3691 N 3 9. 0 9 8 3 1 .GD9 15 1613 9;2 5 5.3. 36360 5 5 -3 6 2"1 5 6. 7035 6 "Cl. 3t;609 7 ND9 A68900.66346 400793.0"695 49242U. Bi 206 503G30. 4 1030 t; 14900. 23 ?OU YFIDD9 4 . 2 8 B.2 3 22 3 @, 5 . 1? 6 9 9 3 2,156. 131 64 2554.277,51 2649.56733 Table A 21 Planning District 10, Scenario I (Baseline Forecast) (Numbers*are in thousands) 9 7 G 1 9 2 9 3-3. 5 1 137 3 99046 5 A 8 ii o (Y.'? '3 6 42 A 0 "1 9 11,9 9 7 E: I o 4,.*.,. 6 . '22 6 4 8 3 9`1 A 3 6' @499 Y 0 1 1 12 i 1 3, 9 '0 1-3 3 10 '3 zi 2 3 . C)A", I 0,290 '-,2 i4 iS 2 6 6 . 5 0 6 6 . j S 4 6,4B 29,1,, 7 8 71 6 6 14 18 0 9 o7 9 4 1 '2. 3 o 0 30 G 2'c; :1 Rio 0 2 4 "-'94 L I I Q 4 Q 3 '2 S 0 9 6 3 38,2 1 1 13 8 . 29 7 0 2 9 0 3 8 9 '.75399 9 .';1 44 10 6 fq 1) 1 0 1 -3 3'.5 12 2 3 @3 1 6'0 1 1 75 10, 1 2 4 13 0 9 1 "1 9 'i 12 9 9 y 13 1 0 34 1 'a i 0 .1 3 2 i I V, 0 C) 1 4185 20 4 7 1 @Y6 A 5 -i 9 9 541 1 1; 9 0 Y 1 4 9 10 0 1907 110018 19 09 A 990 3 1@ 6: 4 3 .2.", 2 Ci' .'.10 1 10 i W...; 7 3 9 3 9 -A, I Q 3 3 1 9 01 9 1 9 2 f: 17 -4 V*1 1:;: 1) A 0 43 od 4 80 2 EV D 1 0 7 3 7 6 1 6 1 10 S 9 "'S 1 0 3 0 A; I i .02v 7 1 N D 10 13 4'@ 55 . 71 il -73 1 3 6 2 95 . 8 9'8 1 3 72 . 6 1 17 1 i 9 6 171 . 3 6 1 4 1 i 2 9 4 22 @'F' A) 1 0 8 629 . ."P,4(.)9,. 647 . i o 060 6 -"'1 5 020 4 6u 5 3 4 2' 6 Y 7 9'@?,J: Table A 22 Planning District 10, Scenari, 2 (Numbers are in thousands) I Q iO 1 979 1 980 "'83 00" 1: G 0 -D (1., 0 0 0 0 0 0 00-V)0 000 006., 0 3 15 1 13 4 4 i 6 '3 6 1-5 0 6 3 Y 2;(: 4 893 ON 1: M( 6.226-113 4 . 22i 6 . 4 3 6 . 7 3 92, 1 Y Y ()0 FC 1) 1 o 2 . I-Au 9 1 1 2 . 6759c.30 950 3 90 2 6'2.K 10 3 295 G 3 "0259 31 41 S,.: 4" -16 2 E 1.: 1) A o 2, 1? 2 1 . 368' f 0 9 -.111) t? 5': e* 3 4 1 6 4 9 5 F I f) I o 6.8-30 7 k) 15'9 6 1. 31672 7 5`,":",.)6 8) 8 C) -:) 2 2 10(') A J J., F, 11-:1) 10 2 47294 3,) 3 C@ .15631 999 4 7 3 3 3 '.'? 25 -1 L S i) b .1 C) 4 @3. i U 6 5 . 0 3 2' 0 '2 2 03 2 4 4 5 9691 32. Y1 H 6 6 0 C GI) i o 8 G,,., i 7 G) -7 2 7 M a 1266 8 9 2 S -3 0 Y 0 4 ",.,1 el IB 9 4 00 9 9 1 3 5 9 3 6 9 0 @3 3 2 33 1 68 7 1 1 3 "S G '1 5 1 f." 9 9 12 6 2 4 7 E.1 i .; 0 9 1 -17196. 0641 1 1 2E-362-1.,19500 1 9 9 9 . 6 t.; i I i4 FIDW 0 502 . e@9896 5 1 0 6 42 .! 12 16 0 ff,'@ 2 '15 5 1 *,93 1 9 -1 1 @3 5 1 25'4 198 6 1987 1928 1989 1 9 MF G 3 0 0 0 CON.,: 1 0 .1. 2 9 '2 S, @9 -1 S* 2,5 4 c.) 6 - @56 4 6 4 73 9 E fp 0 15 S 13 1 9.2 9 .7. 75 6@ 851? 74 9 ', 3., ".) 9 64. . 8411.@46 .9 0 14 Y 9 '.*:.,. 3 E RD I o 1 0,1429 1 9 2 " -30 4 1 4 06 1:!'? L I i)w 6 .2.1 .)99-1 1/ 026 13 9 2 1 9 - 31862'@ 9 S 3 2 2 1 E. F.1 F, 1) 10 4 01 eS22 4 1754 6 4 33129 4 47006 I,) 1 0 6 Y,@ "a 7 21 -F74 7 4,z,9 19 7 7 1 '@ 2) 5 7 94 702 F CA) I o 1 21'208 i (). 60615 1 1 1 1 .42356 1 1 8-3661 0 1352113 .48068 1 369f@3. 66471 139,630.42742 140271.24331 14i8I*6.7M6 f; . 8 6 7 2 7 653-92690 671.32833 688.35672 704.39558 Table A 23 Planning District 10, Scenario 3 (Numbers are in thousands) 9 n F (5 3 1 S 7 3 6 13 2 3 ii Q, Y 3 -4 ";.,. .*; I? iti 1 3 1: T 1) ci C@. @i 384' 0 1 '2 60 i,c@6 3 1 -1 7, ct 2 14 3 4 U IV 9 3 3 it 9 33 o i 1 -2 73 4 o 2', Y E 9 3 6 Y7 8 0 1 989 a): @0 id 0 4 1,.-! 9 D I 3 U-1 4 6 k 1) 10 C'4' 1, 1 i I Y I? Ba i, 0 Ljt -3-.@ 5 F F 1, 1-:1) 10 1 w 3 F ."1 7 1 1 4 3 '2. it A@ 0 2 2', 4 3 1 5 Lo D I k) 1 0 2 16 10 0") -12 13 H 0 1 A c"..' 1 -/9 1 3 1 -i I@ 2. 1 --490 'n B -@3::l 5 1 1) 1) ?09 22 A 19 6 Al C) i4 1 H A 3 8, 4@., .1 09 Y q it /i Table A 24 Planning District 10, Scenario 4 (Numbers are in thousands) 1 979 Y 2 3 3 -112 I fI I" 1 2 2 6 4t: ';i 7 3 I -j 2 S 3 -4 11 t3 0 4 ;@' '-, , ), ) !@ i . @. 5@ -1, - .,", 4, L F I i-A, i C) -4 @Y'14 7 (Y 1, 9 4,, 4 Q 3 2.1.1 0 2 id 17 7? 7 4.:, @J 9 3 2 Y 4 3 9 0 A 2 24 1 2 6 iK@ 7 2 1 4 4 .2-'.)2..,;9 1 _2 t@ '1 5 1 63 4 1 3 18 A 13 0 1 'i i c, 19 twi 7 ")C 0 C', 0 0 1 0 14 .97 Q-A 1 3B 3 D 0 1@ Y .2 Y 3 1" 9 3 o 68 L !-'T 2 Z 9 9.3 400 9 2 i. 3/1 3 -2 6 1 9 3 3 Qo 3442o E 6 b @3 5 A 3 i@ 3 0 6,3 7 9 0 -4,18,-, 8 A I I Af 8 2 6 Y3 -('3 Y 6 "y' 0 @,3 00 2. o , 4 69 7 .4 1".) -14 -4 Y 3 -'d Table A 25 Planning District 10, Scenario 5 (Numbers are in thousands) 9 @,;LJ 9 79 9EW Ysi Y "S 0 .1". J." 0 j k.) C: A D.) ODOO" 1.-- 1.) 1 0 3 3 5 1 13 7 :1 -1 00089 3 6 8 37 '.:)-'.;16'12 0 1 -2 963 E. M 4 2 1,71 'j T? S 4 3 11 Y 9 0 9 "r., 6 E C I) t 0 5 8 9 11 2 6,i9j13 @-i 19 0 3. 92310 4(1?5 1-13 2 2 5 9 3 4 2 -3, 2 8 2 1 B 6 1 1 6 12 i 2 2 i p 1 .3 245 '1 W?229 3 2 UE @ 91 I S F 1. 9 4 2 32, 3 0 2 -19 79 6 3 06, 194 '3 A 5 1 9 3 2 S 7 12 5 9 5 1 i 12 L, 41. -NBO'86 5 03250 19 4 G 5 7'. 1 9 1 '3 1 1 3 -1 ',,. 9 6 2 .89,; 8 -18 6 1 7 8 772 9 '1 t j i 1 72 6 8 9 3 ".) 2. 9 1") 1") ";,;a 9 Al 2. 4..-. 4 9 9 '0 6;..*, sO 2@', 7C,3 @,@.'@033 12 2 3 3 1 8 e2 1 124 A 26 il 2 9 3 3 () 0 15 2 V'O C', --p5' 7 1 i 2153 -3 8 6 1 34 2 f.. 1.) 10 5 3 14 7 1 t@'02 69096 8 S 4 9 7 90 55 9 -4 ,wf 0 ZI 5 8 0 60 E' 3 4 1 1987 1 Y 10 B 1989 Y 9 0 M J7 6 1 0 1 ... 0 0 "i 0 0 100000 C 4 4 1 2 0 5 if 4 1 C, 5 1 6 t@ 8 7 4*1 8 71 iYi 4 9 c, 2 9 3-s929 0 '.,",9 `4 J:I 9 F C 1) o 7i W S -16 3 1 0 14 3 9 3 E, f'*-. f@ i o 1 &884 6 1 9 -1 ':.1. 7 7 1 99498 0,@s@ s.) 2) Q 9 Q 2 4 E- if) w 61 -'S 3 2 Co 9 0 4 t. i C I b 5, 3 7 4 1 9 +.t"'J38 9 63 o 2 E. F I 1 ".1 0 -i Y 19 4 16 A 98 A 32A 22 4 4 0 5 4 CA 1 1; 1 E. @'V b 0 7 A c i 4 7 .1,14-790 7 6 917@3 15 7 9-1541 El 21 0 5;,*p 9 E Ci D i o 10 '2 2'@:. @ 17 0 . 6 2224 1 1 0') 9 -3 .) i 1 .4.594)6 1 L-3'j 4,S 6 (41)10 1 36741] .26e'09 [email protected]'::A9i 140165@21,162 1 4 1806 . 0*@:,05 1 143'1-37.66139 5 1 19 11,;,:@ 9 6 z - 69. 85491 6 37 2 5 6 3 5 10 4 . '28,4 7 3 -.22.25559 Table A 26 Planning District 10, Scenario 6 (Numbers are in thousands) 1 9 7 tl 1 9 .79 1 0 1 VS-1 U rl 1. A F Y 0 0 0 2 2 2 C) 9 4 3 i 6 - (J 4 5 2 3 ti:i S 4 i 6 1 i 42 6 . 2 6 i4 18 6 2 @3 IS 3 9 4 1: C, 1" 1 o 9 M 2. 9 1 1? k, 3 4,2 o 0 3 @13 -13 -4 Y" 2 1 3 .8 8 4 1 2 1 1 A.? 1 24 4 1 322 7, wu 7 3 1 0 _,j 4 11 5 G 9 11 11) 'a 4 6 0. 2 V@39 2 E 1 11 RA 1 2 2 . 6 3.'@@ 3 0 2 7,31::;@'19 Q, E 4 78CI06 0 2 '3 3 . 4 `.'? 9 3 3 6 4'. y ': "21 93,') 25 6 "91 4,3 2 1 0 3 25 0 1 114 F G 1) 1 o 7 *129 7 0 i 036 8 920 70 0,113 U 9 2 3 E 9 19 0 0 1 i 2 0 7,D3 9 _13,i 1 "'23 9 1 6 8 '1, 2 1 2 3,16 8 2 AJ 3 09 3 0 2i) 6 12 S 1? 7 i: z:-(O -9 12 8 4 1) 23 9'7 6 1 3 7 8 01 32 13 1 3 2 i 43 5 @':;S 2 7 0 7 4 5 5 2 2 4 -11 5 0 2 4)9 0 9 6 51319_,@ I 3 8 _,`1 2 5 2 4 '2 0 S 4 9 j 99 6 1 13 1907 988 1 '? Cc), 9 11.190 y 0 0 0 F, E 3 1; 6 Al 121 '31 A.'; 361 38 Emb 10 7 7 7) -1 7 45 6' 69 3 iz, 4 Ili -13 414 's 9 1 G 14 9k@ 1_@ 3 8 F, R, 1) 3 a 2 1 - ':,).2 i 2 . 1 1 S-31c: 2 3 9 . 0,703".7 3 4 1) 4 24 3 @:@3 1") 13 3. 9Yk; 39 4 - i ", ai,,, 4 .15 E k?'23?") 10406 3631 7 9 1,e) 14 EGf) I .v 0 211) 97 0 Is 0 3 13 5- 1. 01 0 421 26 1 B.'i 113 1 N D 1 -1994 22'.@@* 3 6 1 3,, 7 3 4 4 0 93t/ 13 0 4 1 1 i2 A 0 11005 1 9B799 14159' 1 f, f 3 65 1 65 14 7 6 6 9 o"90 9 12 a 702. 1 2C)i Table A 27 Planning District 10, Scenario (Numbers are in thousands) 1 9 7 (3 19 1906 U Il L 1, P1, "'10000 2 6 3 2 3 i 5 13 7 34 4 7 2 36.3@1915* 5 2 3 2'@ 5 312. 1 6 3 L pi b I o 6 . 1-; 14" 6 . '22640 6 . 2 2 893 3 95 - 13 6 6 . 6 '13,419 9 6, 9017' 9@ 3 2 5: 9 3 -C@3 B9 i A 2 . 6 3 32 3 . 0 19 15 0 3 . 623 2 2 1 . 3 IS Ir. j G i . 4 15 0 3 1 5, -1 0 9 AIT.-, 1 1 9@'-, 9 7 2 1 15 2 -3 6 7(1 9, 1: 1) 1 o 6. 83845 -A 59 6 E F !.1 -'0 10 Al 4 21 `@@ 3 0 2 . 9 9 4 7 1 10 "1 S 3 2 -25' 4 -.7 6 E Y YL, i 0 11. 5 . 2'.'? 8 321 6 .2': 51 710't 35 15 51 6 3 6 -12 4 6 6 8, 48 2 2 9 7 6 9 -.1. 1@ 3 0 9 Aj-q@j ':'A) i)')g H b 2 7 0 S9 03 3 1 1-)381 .68721 1 23687.51699 81309 1 2. ',,1 A 5 . 3 i 942 i 1 49-:')*08 1 5 7 1 33 1 4 8'@ . "34 '50 2 .,,:) 9 0 9 5 1 4i, . 2 o O's 42 . I b 0 `5"'1 . 9 -.`9 0 5 1 7 1) '3 1; 9,71 . 13 5 3 9 Al 1 2 6 927 19213 1 '419 19 9 0 Pf "C-OoO 3 -D 0 0 3 0 0 0 (1." T 0 JQ @K 4 6 . *7 3,.) 4 -4 7@'@ 9 6 4 4 . 2 EV3 8 9 Al 5 2 -3 4 6 E PID 7 . 3-) 929 7 . 145 G 7 . 5 1? -.1 6 5, 9 E C. 1) 6 .,4 `j Al -1 (., 9121 3 1 99 3 4 0 1 8 -2310 2 . 3 6 13 99 7 - 9 , 21 1,20 9 . 31G36,.:.'.1., (? '.4@ 38 2 1 E ri) i C. f` IF:)) 1 0 3 . 1@ 9 5 4. Cli 6'12 1.1 .,5-16 4 . 33129 4 . 4 7,8 0 6 E 9 51 13 7 . 7 . 4 9 7 7 1 '-'; 2 5- 1-7 0' 2 6 0 6 15 11 . 0 21 1 1 . 4 2 3'--6 3,.) 6 A N 0 1 13"213. @420C,2 1 36953. 66471 1 -18630 . 4 274 2 1 -10271 . 2433 1 141816 . 71 1 16 YFT 1) 10 6 7-27 o53i 92690 671 68 8 . 3,; 0 7 2 "1, 0 4 . -i 9 5 5 8 Table A - 28 Planning District 10, Scenario 8 (Numbers are in thousands) 19-12 1 9 9 1 9 2 0 1 Y G 2 C 0 00 0 "600 3-3 "i A 37 3 4 3 1 1*4 6 3 3 11:3 3 ",A 6'..' 8 B 1 '2 Of 6 6 3'.. 6 2 2?'.) 118 2 9 @3 39, 43 S 3 6 9 18 3. 42 ii 0 '3 26259 @;3@,12 6 3 8 8 .919. I . A 9 1 24 i 55 i 49 6 2 A 10 3 (S ".3 2 0634 1 f I I', I o 6 L33 1.14 1; 0 2i: 3 i (),16 13.: ;,1, 9,3 EI -fl-A) i 2 9" 102 3 0 18 3 . 'S 4 7 4 0 3, 5. 5 ..-5 R C) S Ii 03 9' el 4 5, f A f, Ii, 6 A S 1.5 4 7 a. 77-1:)97 ICI I -j "16 a . 9,2 0 Y 9 24489 9 . 2 1 ? . 4 3,2 i . -4 f) 1 2 0 1 C, 3 03 3 121 2, i B 1 6 i G 9 3 -"-'-'4 6 6 3 4 G 1 6 9' 8 13 0 9 9 9 A 3 1? :'A3 3B 2 48 1 116? S 6 7 'S !V3 5 6 1 S - i 4 u." 2 10 q 7 1 5 90 9 6 5 -13, 9 5 9 1G. 6 1987 1988 1989 199".) 3,3000 3") 0.") 0 3 0 3 0 9 C-@ 4 44 223[39 f; - '.i A 6 6 2."(396 2, i -3 '19") 9 55 7 4 7 @r; 0 9 3 0 4-) 4 6 3 9 10 14 3 RD i o i @12 9 92 230 1 .9 111, 1 2 - 0231 11 6. 29 F. T f.) 1 9 1 699 7 9 3 6 ?3 y . 5 1 `12 B 9 6811, 25 9 8 3,. j 2 1 3 B"IC 95 4 01 6@?2 1 711,46 3.31 29 -1 "'806 E -1:*41,I)l 0 6, 9'@O 19 7 2 1 4 4 69 -j q 'e 7 V5 25 9 A ;* 0 2 Gb I o 2 '7,03 5 1 0 1 1 @42356 1 1 0 *,") 6 6 1 13 21 3 14, 6 8 136953. 66471 1 i 13 6 3-j . 4 2 -1'4 2 1 4 1 - ""13 3 1 I4ifjI6,'-.'A 1 16 0 63' 06 727 65 3 . 92 6 9 0 6 7 1 - -4'--'B -13 6EI8. 35672 -104.39558 Table A 29 Planning District 10, Scenario 9 (Numbers are in thousands) 1 9 0' 2 1924 I IA: 0 0 0 0 11) io* 00 G Oo 00 O"D 0 E., Ii i C, 3 9 3 3 -I 13 7 3 4 t:j 0 1]19 36 57 1 3 0 -4 6 9 6 9 2 z, 4 B 6 2 2 @.I 1? @-z -1 03 9 4 3 6 ""10 6B499 6 901 71(@ 7 ")'*@U *26 I- C 1) 10 'j, @9 i A 2633k@;'23 -3 @C4 9 E 0 1 u 3 A S '2 A4 9 8 E, T D 10 3 2 7 9 02 E F I F: 4 2 9 4 .- ..) - , 41 32 "1 79 F9o 0 1-" 19 4 3 1i 99 24-.)712 3 @'I 16 3 B, 6 3 -1 '1.: J 9 4 892cf, E GI.) i Q 0472 97 C.9 2 0 0 `5 08 51 24' 45@ 2 @ i A! 03 t) 9 12 2 3 B 1 271 1 24 126 62762 1269-'1, 0 3 3.3 () IYI 12 9 0 6 6 7 1 1 20 9 1.-; i: 2 F f. F, 1 0 2B,4?i i9 26 913 9 6 4 9 0 6 i A 3 19137, 19,38 1 Y89 i v 9 1 .50000 0 0 0 "10 0 0 @,)000 j 4 '1 . 9') 2 -19 41 2 9 , 1 4 1 . 4 31 48 . 68 1 5 1 9 'Ll"' C:@ 3 c.) 7 . 'A*.. 7 . 3N 9 2 9 7 1,:@ 7"A8 7 . 9 1@ 4 7 9 E CD i o 'i . --,- . 0 1 0 84546 91,31 4 3 4 08 "S B E 1:11, 1) 10 1 0 9 9 3 1:1 1 .95739 '4Y -1 2 1 E. I'D 1 4 9 0 10 5 6 4 IIS 6 2 1 1 1") . 921 118 1 o 1039 1 F F 11 4. 1 0 Li 2 4.26609 '1 -,4 17 E. V f) i -,, .1 1 3' ),34 7 . 6 i 2 3 0 1 .9 8 C, 61 23 7 10 6 33 @4 .4" 1 A o i 1.'86 622 1 !,.1 . @j 4 4 4 6 1 391-,'134. 0 1 4,1902 . '3;. -.,Oy i 1 3 9 9 @ I." i 66. S Eli 698 "1 - 1 6 6') 1 9 7 3.1 c':.! 4 8 0 Table A 30 Planning District 10, Scenario 10 (Numbers are in thousands) 1 9 71"1 1 97Q 98 1".) 90 1 1 v1132 i 23 1 9 4 D C 000 G 0 0 N., A E C. ".0i Ci 3,2 ? 7o 2 9 33 3 4, 1176 2 6 3 C." 9 4 5 3 5 8 4 F. ri V I 1 142 6 21 2 6 4 13 6 2 2S 9 3 6 , 3 4 3 (34 i.119 OA ';..I C@ Y'8 91 1 2 i,.,3908 91 3 6 23 10 29ID .`18 2021 9 3 S 2 1 . S 60 B 0 1 4 1 (1 3 1 14 e 0 9 5 C., Q2 7 1 .630,u2 7 1: TO 6 . k) B 6 6 . @3384."- 7 G 7 . 5 8 1 U 1:5 C') 9 t*; U . 2375 1 <:I .1 9 2` . 6 3 3 0 5 3 1 ON 3 "12 3 1 1., -7 5 11.@ Y 1122 U 3 '118 135 3 410 9 6 1 'i 6 9 a. 8 . 8 i 26e., U 9-`@5.`I)-,.) 1? 3 Y 01 9 '@,l 5 4 9 1 3 5 003 12 1 . 6 0 7 2 -1 A 23,s 8 7 1 9 9 1 2 6 24 1 12 -11 S 3 1 9,4 1 -)g@,,@ i 13 2 .;i 1 4 1 00 171 .,@I 3 4 2 . S'.;-0 9 6 S42 W I 1 9 "D 193 7 1 989 1 9?() D 9 0 00 0 9 0 0 ID C V3 -IS 25642 zj C? 9;.-, ? 4 7 e.)5 -@ 5 9 E C 1) i o J cO .1 4 3 9 1 C), j .1 9 9",''. 1 3 4 1. @ 18 8 H -34 1 9 J) 3 S 1 9 92 0 15 2 04068 13`30 FTD o 9 0 4 9 2 S 9 11 2 y 5". 9'j 4 E FA 1'.11111 9 7.'.. -1 14 1 15 4 306110 4 4 2, 3 4 61"0'. -17 E f v i) I C, 4 0 6 4 1502 1, 6,6 6 -17 e 9 1 3 4 G D 10 1 1 1 2 "1 is 1 .11 1994 1 9 2.1 0 0 1 'w'. 33 i36 12 T-@'235 9 0 1 1 3 '13 2 6 9 i 9 IS 6 0 1 9 9 45 9 1 1 4 1 ':3 6 7 75 2 0 14 31 3 1 "17 C, DO i v "'19 1 @i36 667 5 @'948 6@ 8 9 1 9,2 7 0 21 0 1". 9 3 9 13 2 2 C1 F., F I I 5 7 T 0 3 0 7 1 "" 9 '10 4,1 C 0 ri tit. C VE P 0 2 0 tj 1 3 1 C F'LJ I I ME 68 . 3 90 Table A 31 Planning District 8 1990 Employment Impacts from Scenarios Beginning In 1982 Baseline Sector Employment Forecast Additional.Employees Caused by Each Scenario 2 3 5 6 7 8 9 10 300 Mfg. 500 Mfg. 700 Mfg. 1,000 Mfg. 206 UtIl. 300 Util. 300 Trade 1,500 900 Gov't. employees employees employees employees employees employees employees Trade Employees employees Manufacturing 27,900 300 500 700 .1,000 0 0 0 0 0 Construction 3,040 ---------------------------- No Construction Employment In 1910 ----------------------------------------- -------- Transportation, Com- munication, Utils. 3,330 20 30 4o 50 210 320 20 70 40 Trade 24,720 160 240 290 380 70 170 4,ro 2,030 290 Finance, InsuVance, Re6l Estate 3,310 10 20 30' 40 0 10 10 50 30 Service 22,170 30 50 6o -80 10 30 30 120 60 Government 16,600 50 80 too 130 20 50 50 180 1,000 Non-Agricultural a b c b Employment 102,890 590 960 1,230 1,710 330 61o 590 2,49o 1,44o -__Emp I oymen t Mu I t i p I i ers 1.97 1.92 1.99 1.71 1.60 2.03 1.90 1.66 1.60 Totals may not add due to rounding bPeak Impact I year later than normal cPeak impact 2 years later than normal 0 Table A - 32 Planning District 9 199.0 Employment Impacts from Scenarios Beginning In 1.982 Baseline -Sector Employment Forecast Additional Employees Caused by Each Scenario 2 3 5 6 7 8 9 10 300 Ofg. 500 Mfg. 700 Mfg. 1,000 Mfg. 200 Util. 360 Util. 300 Trade 1,500 900 Gov't. employees employees employees employees employees employees employees Trade Employees employees Manufacturing 33,00 .490 690 990 0 0 0 0 0 Construction 19,420 ---------------------------- No Construction Employment In 1990 ------------------------------------------------ Transportation, Com- munication, Utils. il,020 0 10 10 20 200 310 0 30 30 Trade 0 0 10 10 0 0 300 1'.510 10 Finance, Insurance, Real 10,230 0 0 0 0 0 0 0 0 0 14 Service 38,850 30 80 140 210 20 100 40 270 240 Government 57,340 10 30 6o 90 0 40 10 120 1,010 Non-Agricultural b c b Employmenta 217,430 360 650 .940 1,350 250 48o 380 1,930 1,320 Employment Multipliers 1.97 1.92 1.99 1.71 1.60 2.03 1.90 1.66 1.60 a Totals may not add due to rounding bPeak impact I year later than normal Peak impact 2 years later than normal ------ ----- .. ..... ----- -- Table A 33 Planning District 10 1990 Employment Impacts from Scenarios Begi nning In 1582 Baseline Sector Employment Forecast Additional Employees Caused by tach Scenario 2 3 4 5 6 7 8 9 10 30O.Mfg. 500 Mfg. 700 Mfg. 1,000 Mfg. 2QO Util. 360 Util. 300 Trade 1,500 9CO Gov't. employees employees employees employees employees employees employees Trade Employees employees Manufacturing 7,650 300 500 700 1,000 0 0 0 0 0 Construction 4,060 --------------------------------- No Construction Employment in 1990 ------------------------- ------------------- to Transportation, Com- munication, Utils. 2,060 0 10 20 30 200 300 10 40 30 Trade 9,530 0 10 20 30 10 10 310 1,550 30 Finance, Insurance, Real Estate 4,420 6o 100 140 220 40 60 60 330 200 ,Service 7,850 100 150 230 350 60 ]Do 100 520 220 Government 11,830 10 10 20 20 0 10 10 40 920 Non-Agricultural Employmenta 47,390' 490 810 1,140 1,68ob 330 48o 48o 2,49oC 1,510b Employment Multipliers 1.97 1.92 1.99 1.71 i.6o 2.03 1.90 1.66 1.60 aTotals may not add due to rounding bPeak Impact I year late'r than normal cPeak impact.2 years later than normal Table A 34 Construction Multipliers (Beginning 1980, ending 1982) Planning District 500 Construction Workers 700 Construction Workers 1,000 Construction Workers Non-Agricultural Employment Planning District 8 920 1,160 1,610 Multiplier 1.84 1.66. 1.61 Non-Agricultural Employment 00 Planning District 9 650 830 1,280 Multiplier 1.30 1.19 1.28 Non-Agricultural Employment Planning District 10 820 i,i4o 1,630 Multiplier 1.64 1.63 1.63 9 0 APPENDIX B . 0 81 Table B -1 TOTAL RESIDENT POPULATION 1970'- 1978 C 0 U N T Y P 0 P U L A T 1 0 N ---------------------------------------------------------------------------------------- ----------------------------------------- 1970 1971 1972 1973 1974 1975 1976 1977 1978 Georgetown 33,500 33,800 35,000 35,80o 36,500 37,600 38,200 39,300 40,300 Horry 69,992 74,300 77,600 79,900 82,300 84,600 88,goo 91,700 95,400 Williamsburg 34,243 34,200 34,500 34,300 34,4oo 34,400 35,900 36,300 36,700 Planning District 8 Total 137,735 142,300 147,100 150,000 153,200 156,600 163,000 167,000 172,400 00 Berkeley 56,199 57,200 58,8oo 60,000 61,100. 66,100 71,400 74.8oo 78,000 Charleston 247,561 248,ooo 252,300 252,4oo 20,300 260,000. 263,000 261,900 265,000 Dorchester 32,276 32,276 34,700 39,600 41,100 45,000 46,700 48,700 51,500 Planning District 9 Total 336,036 337,476 345,800 352,000 363,500 371,100 381,ioo 385,400 394,500 Beaufort 51,136 52,100 55,600 53,800 50,800 58,ooo 6o,ioo 59,400 60,900 Colleton 27,711 27,500 27,800 28,200 28,300 28,goo 29,500 30,200 30,700 Hampton 15,878 15,800 15,800 16,300 16,700 17,000 16,7oo 16,8oo 17,000 Jasper H,885 11,500 11,700 12,500 12,700 13,200 13,200 _j3_,700 14,000 Planning District 10 Total lo6,6io 106,900 110,900 110,800 108,500 117,100 119,500 120,100 122,600 Source: Division of Research and Statistical Services, June,'1979. Table B 2 POP61-ATION CONVERSION FACTORS Planning District 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 Waccamaw Planning District 8 1.037 1.03 1.03 1.026 1.02 1.01 1 .00 .997 .993 .989 .985 Berkeley, Charleston, Dorchester - Planning District 9 1.035 1.03 1.02 1.02 1.01 .993 .982 .972 .963 .955 .948 co Lowcountry Planning District 10 1.038 1.04 i.o46 1.047 1.044 1.042 l.o41 i.o42 1.043 1.045 1.047 Table B - 3 SCHOOL YM 1972-73 PUBLIC SCHOOLS PR TE SCHOOLS COUNTY NMBER ENROLIMENTI, STUDENT PER.SCHOOL NMB-ER ENROLLNEMP STUDENT PER SCHOOL Horry 37 19,399 S24 3 580 193 Georgetown 19 9,410 49S 6 1,074 179 Williamsburg 20 9,4S1 472 3 768 2S6 TOTAL 76 38,260 1,491 12 2,422 628 co -r.- Berkeley 23 19.1064 829 4 713 178 Charleston 80 57,23S 71S 31 8,306 268 Dorchester 15 .10,241 683 S 863 173 TOTAL 118 86,S40 2 ,2 27 40 9,882 619 Beaufort 19 10,056 S29 S 1,266 2S3 Colleton 17 6,966 410 4 827 207 Hampton 10 4,345 43S 1 229 229 Jasper -4 3,104 776 2 491 246 TOTAL so 24,471 2,1SO 12 2,813 93S 'Grades K-12 Source: S. C. Statistical Abstract, 1974 Table B 4 SCHOOL YFAR 1973-74 PUBLTC SqHOOLS PR CHOOLS 1. COUNTY NUMBER -ENROLLMENT' STUDENT PER SHCOOL, NLMFR ENROLLMENT* STUDENT PER SCHOOL Horry 39 193,244 493 3 s8s 19S Georgetown 20 9,162 4S8 6 1,053 176 Williamsburg 21 8,883 423 3 806 269 TOTAL 80 37,289 .1,374 12 2,444 640 Berkeley 23 18,961 824 6 743 124 Charleston 81 S4,893 678 28 8,044 287 Dorchester 16 10,443 653 5 891 178 TOTAL 120 84,297 2,15S 39 9,678 S89. Beaufort 20 9,778 489 6 1,296 216 Colleton 17 6,763 398 4 812 203 Hampton 10 4,068 407 1 320 320 Jasper 4 3,100 77S 2 467 234 TOTAL Sl 23,709 2,069 13 2,89S 973 *Grades K-12 Source: S. C. Statistical Abstract 1975. Table B - 5 SCHOOL YEAR 1974-75 PUBLIC SCHOOLS PRIVATE SCHOOLS COUNTY NUMBER ENROLLMENT STUDENT PER SCHOOL NUMBER ENROLLMENT STUDENT PER SHCOOL- Horry 38 18,972 499 4 612 1S3 Georgetown 20 9,143 457 6 8S6 143 Williamsburg 22 8,79S 400 3 775 2S8 TOTAL 80 36,910 1,3S6 13 2,243 SS4 00 Berkeley 26 19,7 12 758 S 69V 138 ON Charleston 82 S3,461 6S2 28 7,3S4 263 Dorchester 16 11,088 693 4 807 202 TOTAL 124 84,261 2,103 37 8,8S2 603 Beaufort 19 9,677 S09 6 1,20S 201 Colleton 17 6,633 390 3 8S2 284 Hampton 10 4,062 406 1 302 302 Jasper 4 3,048 762 2 476 238 TOTAL so 23,420 2,067 12 2,83S 1,02S *Grades K-12 Source: S. C. Statistical Abstract, 1976. Table B 6 SCHOOL YEAR 197S-76 PUBLIC SCHOOLS PRIVATE SCHOOLS COUNTY NUM13ER ENROLIMENT* STUDENT PER SCHOOL NLDIBER ENROLMENT. STUDENT PER SCHOOL Horry 38 18,819 49S 4 702 176 Georgetown 20 9,127 4S6 6 918 1S3 Williamsburg 22 8,669 394 3 797 26S TOTAL 80 36,61S 1,34S 13 2,417 S98 co Berkeley 28 20,SIS 733 S 708 142 Charleston 84 S2,26S 622 28 7,S77 271 Dorchester 16 11,43S 71S 4 810 202 TOTAL 128- 84,21S 2,070 37 9,09S 615 Colleton 17 6,44S 379 3 900 300 Beaufort 19 9,S62 S03 6 1,104 184 Jasper 4 3,080 770 2 474 237 Hampton 10 4,609 461 1 337 337 TOTAL so 30,141 2,113 12 2,81S I'OS8 *Grades K-12 Source: S. C. Statistical Abstract, 1977. Table B - 7 SCHOOL YEAR 1976-77 PUBLIC SCHOOLS PRTVATE SCHOOLS ENROLLMENT* STUDENT PER SCHOOL COUNTY NUMBER NUMBER EMOLIMNF* STUDENT PER SCHOOL liorry 35 190011 543 4 847 212 Georgetown 19 933S6 492 6 847 .141 Williamsburg -23- 8PS90 373 3 - 749 2SO Totals 77 36X7 1,408 13 2,443 603 Charleston 81 SO)998 630 29 7,604 262 Berkeley 28 21.1073 7S3 4 714 179 Dorchester 16 111798 737 4 807 202 Totals 12S 93,860 2,120 37 9,12S 643 Jasper 4 3piss 788 2 468 234 ocoo Colloton. 17 6,3S7 374 3 1,014 338 Hampton 9 4,049 4SO 2 3S9 180 Beaufort 19 9,315 490 6 1X4 182 Totals 49 22,876 2,102 13 2,93S 934 *Grades K-12 Source: S. C. Statistical Abstract, 1978. Table B - 8 PHYSICIANS (PRIVATE OFFICE) 1976 - 1978 Physicians/ Year Location Population Physicians 1,000 Residents 1976 Horry 88,900 57 .64 Georgetown 38,200 23 .60 Williamsburg 35,900 10 .27 Planning District 8 163,000 90 .55 Berkeley 71,400 7 .10 Charleston 263,000 218 .83 Dorchester 4.6,700 13 .28 Planning District 9 381,100 238 .62 Beaufort 60,100 34 .57 Colleton 29,500 13 .44 Jasper 13,200 4 .30 Bampwn J-6,7-00 .7 .4.2 Planning District 10 119,500 58 .43 1977 Horry 91,700 60 .65 Georgetown 39,300 U .59 10 ..27 Williamsburg 36,300 Planning District 8 167,300 93 .56 Berkeley 74,8oo 8 .11 Charleston 261,900 246 .94 Dorchester 48,700 13 .27 Planning District 9 385,400 267 .69 Beaufort 59,400 46 .77 Colleton 30,200 14 .46 Jasper 13,700 4 .29 Hampton 16,800 7 .42 Planning D-istrict 10 120,100 71 .49 1978 Horry 95,400 59 .62 Georgetown 40,300 25 .62 k Williamsburg 36,700 12 .33 Planning District 8 172,400 96 .56 Berkeley 78,ooo 9 .12 Charleston 265,000 244 .92 Dorchester 14 .27 51,500 Planning District 9 394,500 267 .68 Beaufort 6o,goo 36 .59 Colleton 30,700 14 .46 Jasper 14,000 6 .43 Hampton 17,000 8 .47 Planning District 10 122,600 64 .49 Source: S. C. Statistical Abstract, 1978 89 Table B - 9 HOSPITAL BEDS (1978)* County Number of Beds Population Georgetown 133 40,300 Horry 417 95,4oo Williamsburg 78 36,700 Planning District 8 Total 628 172,400 Berkell-ey --- 78,000 Charleston 1,687 265,000 Dorchester --- 51,500 Planning District 9 Total 1,687 394,500 Beaufort 195 6o,goo Colleton 142 30,700 Hampton 68 17,000 Jasper 31 14,000 Planning District 10 Total 436 122,600 *Includes some 1977 (non-licensed) figures from State Health Plan. Source: DHEC Licensing Division (1979). DHEC, Office of State Health Planning Development, State Health Plan (1979). 90 Table B 10 OUTPATIENT AND PUBLIC HEALTH CENTERS (1977) Public increase/ County Outpatient Health Total Population 1,000 Georgetown 2 Horry 5 Williamsburg 3 PJ-p.nning District 8 Total 10 *17 27 167,300 1.6 Berkeley *0 Charleston 13 Dorechester *1 Planning District 9 Total 14 +28 42 385,4oo 1.1 Beaufort 6 Colleton 2 Hampton 2 Jasper 4 Planning District 10 Total 14 *20 34 120,100 2.8 *1 facility below standard +2facilities below standard Source: DHEC, Office of State Health Planning Development, State Health Plan (1977) 91 lable B il Full-Time Law Enforcement Personnel 1974 1975 1976 1977 1978 Municipal MunicipTl Municipal Municipal County Municipal County Planning District 8 Tota: 196 229 208 259+ 89 292 97 Sworn Officers - - - 226 69 268 68 Civilian Officers 32.8 20 24 29 Civilian: Sworn ratio 1:7 1:3.5 l..II 1:2 Planning District 9 Total 680 518 405 406. 243 951 342 Sworn Officers - 297 181 690 246 Civilian Officers log 62 261 96 Civilian: Sworn ratio 1:3 1:3 1:2.5 1:2.5 Planning District 10 Total 119 115 96 94+ 108 132 115 Sworn Officers - - 78 78 96 81 Civilian Officers 17 30 36 34 Civilian: Sworn ratio 1:5 1:2.5 1:3 1;2 +Totals may not add due to rounding Source: (1977-1978) SLED, Uniform Crime Reporting Division (1974-1976) F.B.I., Uniform Crime Rjeports Table B - 12 UTILITY HOOKUPS AND HOUSEHOLDS (1976) 1970 Hookup:. Estimated Utility Estimated Utility Hookups Household Ratio Households Hookups/1,000 Households/1,000 Georgetown 13,654 .806 11,005 360 288 Horry 37,442 .777 29,092 420 327 Williamsburg 12,989 .787 10,222 360 285 Planning District 8 64,o85 .790 51,196 393 314 Berkeley 25,187 .939 23,651 350 331 Charleston 8o,318 .993 79,756 310 303 Dorchester 15,o6o .896 13,494 320 289 Planning District 9 120,565 .943 113,693. 316 298 kD Beaufort 12,529 1.157 14,496 210 241 Coileton 11,049 -873- 9,646 370 327 Hampton 4,150 1.294 5,370 250 322 Jasper 2,590 1.551 4,017 200 304 Planning District 10 30,318 1.219 36,958 254 309 Source: Division of Research and Statistical Services, 1978. Table 8 - 12 (continued) UTILITY HOOKUPS AND HOUSEHOLDS (1977) 1970 Hookup Estimated utility Estimated Utility Hookups Household Ratio -Households Hookups/1,000 Households/1,000 Georgetown 14,071 .806 11,341 36U 289 Horry 39,353 .777 30,577 430 333 Williamsburg 13,243 .787 10,422 360 287 Planning District 8 660667 .790 52,667 398 315 Berkeley 25,181 .939 23,645 340 316 Charleston 81,874 .993 81,301 310 310 Dorchester 15,961 .896 14,301 300 294 Planning District 9 123,016 .943 H.6,004 319 301 Beaufort 12,820 1.157 14,833 220 250 Colleton 11,325 .873 9,887 380 327 Hampton 4,145 1.294 5,364 250 319 Jasper 2,645 1.551 4,102 190 299 Planning District 10 30,935 .1.219 37,710 258 314 Source: Division of Research Statistical Services, 1978 rq.. ------ Table B - 13 SCPCA-WDd-4 GUIDELINES for UNIT CONTRIBUTORY LOADINGS to WASTEWATER TREATMENT FACILITIES R WATER POLLUTION CONTROL DIVISION ?Oolltition (:Z.ontrol 4;-,-4itt1,0ri1cy 1972 95 Table B - 13 (continued) SCPCA-WDG-4 SOUTH CAROLINA POLLUTION CONTROL AUTHORITY Water Pollution Control Division Guidelines for Unit Contributory Loadings to Wastewater Treatment Facilities The following are guidelines for the minimum design loadings for waste treatment facili- C) ties. These guidelines will be used by the South Carolina Pollution Control Authority in evaluating proposed facilities. Gallow Per Day Lbs. 5-Day BCD Type of Establishment Per Person Per Day Per Person Airport E'a c h Employee - 10 .06 Each Passenger 5 .02 A Bedroom 4 Persons Each 100 .17 partments - 0 - 2 Bedroom 3 Persons Each 100 .17 - 1 Bedroom 2 Persons Each 100 .17 - With Garbage Disposal Units 100 .23 Bars - Each Employee 10 .06 - Each Seat (Excluding Restaurant) 40 .01 Boardincr House - Resident 50 .10 Bowling Alley - Per Lane (No Restaurant) 125 .20 - Additional For Bars and Cocktail Lounges 3 .02 Camps - Resort (Luxury) 100 .17 - Summer - 50 .12 - Day (With Central Bathhouse) '3 5 .10 - Per Travel Trailer Site 175 .28 Churches - Per Seat 3 .02 Clinics - Per Staff 15 .0111) - Per Patient D .02 Country Club - Each Member- 50 .10 Factories - Each Employee (No Showers) 2 5 .06 1 - Each Employee (With Showers) -------------- - 00 .08 - Each Employee (With Kitchen Facilities) 40 .10 Fairgrounds - Average Attendance 5 .03 Food Service Operations Ordinary Restaurant (Not 24 Hours) (Per Seat) .,10 24-Hour Restaurant (Per Seat) 100 .1130 Curb Service (Drive-in) (Per Car Space) --------- 100 .20 Vending Machine Restaurant TO .12 96 Table B - 13 (continued) Gallons Per Day Lbs.5-Day BOD Type of Establishment Per Person Pei- Day Per Person Hospitals - Per Bed 200 . 31 0 - Per Resident Staff --- 100 .17 Hotels - Per Bedroom (No Restaurant) - ----- 100 .17 Institutions Per Resident - - ------ 100 .17 Laundries Self Service - Per Machine 400 .68 Mobile Homes - 3 Persons Each 100 17 Motels - Per Unit (No Restaurant) 100 .17 Nursing Homes Per Bed (No Laundry) 100 .17 Per Bed (With Laundry) ----------- 150 .20 Offices Per Person (No Restaurant) ------- - ------ - -- - -- - -- 25 .05 Picnic Parks - Average Attendance 10 .06 Residences - 4 Persons Each ----- - 100 .1-1 - With Garbage Disposal Units 100 .20" Rest Homes - Per Bed (No Laundry) 100 .17 - Per Bed (With Launa@y) 150 .20 Schools - Per Person (No Showers, Gym, Cafeteria) ----- 10 .04 - Per Person With Cafeteria (No Gym, Showers) 15 .05 - Per Person With Cafeteria, Gym & Showers 20 .06 Service Stations - Each Car Served 10 .06 - Each Car Washed 75 .03 - First Bay (Per Day) --- - --- - --- 1000 - Each Additional Bay (Per Day) 500 1.0 Shopping Centers - Per 1,000 Sq. Ft. Space (No Restaurant) 900 .40 Stadiums - Per Seat (No Restaurant) ------- ------------ 2 .008 Swimming Pools - Per Person (With Sanitary Facilities and Showers) ---------- 10 .04 0@ Theatres - Drive-In - Stall ------ - - - - ------- - -------- D .. 1.) - Indoor - Seat 5 .03 .@Jly major deviation from the above guidelines should be so noted and substantiated by the Engineer in the project report. C 97 I 3 6668 14106 4651