ABSTRACT AN ANALYSIS OF APPARENT MALADJUSTMENTS IN LOCAL LABOR MARKETS OF THE UNITED STATES By John Wayne Nixon The main objective of this study was to develop an empirical index for measuring and examining the serious- ness of maladjustments in local labor markets of the U.S. in 1959, by type of residence area, with respect to (l) magnitude of the labor maladjustment, (2) variation in labor maladjustments among residence sectors of a commun- ity, (3) variability of labor maladjustments among commun- ities and regions, and (A) the relative importance of variables influencing per capita earnings of size of labor maladjustment. Empirical results of the study are based on a national multiple regression equation (fitted by zero-one dummy variable techniques) which statistically relates individual earnings with several socio—economic variables believed to influence the earnings capacity of persons. County labor force data from the 1960 Census of POpulation were substi- tuted into the national estimating equation to obtain estimates of average potential earnings per person in the total, urban, rural nonfarm, rural farm, and total nonfarm sectors of each community (county) in the United States. John Wayne Nixon These estimates were compared with actual per capita earnings in the same sectors as a measure of the extent of labor maladjustment or economic underemployment in local labor markets in 1959 (termed an earnings gap). These methods were deve10ped in Chapter II and III, with the major results reported in Chapters IV and V. Variations in size of actual per capita earnings among counties and county residence areas of each major region were found to be much greater than for potential per capita earnings, and in all residence areas, the distribu- tion of counties by size of per capita earnings potential either coincided or lay to the right of the actual distri- bution. In every major region per capita farm earnings in most communities were significantly lower than in the other four sectors analyzed. Actual and potential per capita earnings were lowest among Southern communities, for the labor force of each residence area analyzed. The South, with 45 per cent of all counties in the U.S., had an extremely depressing effect on the national distributions. In all four regions both actual and potential earnings were found to be highest in the urban sector and lowest in the rural farm sector. It also appears that the larger the urban sector in a community, the greater its influence on per capita earnings of the total labor force in the community. John Wayne Nixon Severe labor maladjustments were found in every major region and division of the U.S., but the South contained by far the greatest prOportion of counties with.serious eco- nomic underemployment (an earnings gap exceeding $700 per person) of labor. The incidence of severe labor maladjust- ments was greatest in the farm sector and least in the urban sector. The rural farm labor force in two-thirds of the Southern communities was severely maladjusted in 1959, compared to “0 per cent or less in the other major regions. Definite patterns of geographic concentration were detected among communities for which severe labor malad- justments were computed. These areas are delineated graph- ically in the text. There appears to be a striking parallel between the incidence of poverty and economic underemployment of labor among communities of the U.S. This is evident from the close correlation found between communities having severe labor maladjustments and those designated by recent studies as the nation's chronic poverty areas. Such areas are characterized by relatively high proportions of the labor force in agriculture and the low educational attain- ment and age categories, as well as a very small, non-viable industrial sector. An additional striking parallel drawn from the study shows apparent labor maladjustments in each area of resi- dence (particularly the farm sector) to be positively related to distance of a county from an industrial-urban center, and John Wayne Nixon in most cases, the larger the industrial complex, the further out was its effect extented. Actual farm earnings, and hence the farm earnings gap, was significantly affected by off-farm employment Opportunities in most communities, although the labor force in those areas characterized by a capital intensive, commercialized agriculture appeared well-adjusted in 1959. Overall, the West and Northeast were found relatively free of labor maladjustment problems in 1959, while hundreds of communities in the other four-fifths of the nation were plagued with very large earnings gaps in all residence areas. Industrial mix and educational attainment were found to be the most important determinants of per capita earnings potential, especially in communities of the South and North Central regions. The empirical results indicate that severe labor maladjustments and acute poverty in many communities result mainly from low-paying nonfarm employment Opportun- ities and lack of emphasis on public education. Unfortunately, a thorough eXploration of the many policy implications of these results was outside the sc0pe of this one study. It appears evident, however, that given the high complementary between the incidence of acute labor maladjustments and poverty among communities of the United States, and the causes of these conditions, that public policies and specific remedial measures dealing with poverty John Wayne Nixon problems, if effective, should also help alleviate severe labor maladjustments in local labor markets. It also seems eminent that priorities in most communities must be given to improvement of educational facilities and development of job opportunities consistent with the characteristics of the local labor force. Employment and mobility opportunities outside the community should be continuously reappraised in light of business fluctuations in the total economy. AN ANALYSIS OF APPARENT MALADJUSTMENTS IN LOCAL LABOR MARKETS OF THE UNITED STATES By John Wayne Nixon A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1969 ACKNOWLEDGMENTS The author gratefully acknowledges the invaluable advice, guidance, and constructive criticisms of Dr. Dale E. Hathaway, Chairman of the Thesis Committee, whose encouragement throughout my graduate program and during the course of this study are sincerely appreciated. Further, the author would like to express his appre— ciation to other members of the thesis committee, Dr. James T. Bonnen, and Dr. Harold Riley, for reviewing the manu- script and offering many helpful suggestions. Appreciation is also extended to Dr. L. V. Mandersheid for his valuable assistance with the methodology used in this study, and to a number of fellow graduate students who contributed, perhaps unknowingly, to the organization and content of this thesis. In particular, I wish to thank Dr. L. L. Boger and the Department of Agricultural Economics for providing financial assistance for this study and my entire graduate program. Several others also deserve special credit, including many Agricultural Economics personnel in the Michigan State University Computer Center who assisted with the programming and analysis of the data, and the departmental secretaries for typing a preliminary draft of the thesis. ii Finally, the author expresses his deepest appreciation to his wife, Marilyn, and children, David and Melanie, for their unselfish display of tolerance, inspiration, and continuous encouragement throughout this study and, indeed, during the whole of my graduate work. It is to them that this manuscript is dedicated. Any errors or omissions in this study are, of course, the responsibility of the writer. iii TABLE OF CONTENTS ACKNOWLEDGMENTS LIST OF TABLES LIST OF FIGURES LIST OF APPENDICES Chapter I. INTRODUCTION Nature and Importance of the Problem Importance of the Study . . Empirical Evidence of the Problem Objectives . . . . . . Previous Work Procedure II. CONCEPTS AND THEORETICAL CONSIDERATIONS PERTAINING TO HUMAN RESOURCE RETURNS IN LOCAL LABOR MARKETS . . Concepts and Definitions Local Economy The Community . . Local Labor Markets Earnings Earnings Capacity Labor Force Residence Areas Census Places Standard MetrOpolitan Statistical Areas Labor Maladjustment . Comparability of Labor Labor as a Factor . . . Theoretical Considerations Introduction . Labor Market Equilibrium iv Page ii viii xii ll 14 16 22 Chapter Page III. METHODOLOGICAL PROCEDURES, DATA SOURCES, AND ESTIMATING TECHNIQUES . . . . . . . 63 Introduction . . . . . 63 Earnings Capacity Estimation Process . . . 65 Population and Sample . . . . 66 AID Method of Variable Selectivity . . . 67 The Earnings Capacity Equation . . . . 68 Predictability of Equation . . . . . . 7o Differences in Data . . . . . . . . . 78 County Data . . . . . . . . . . . 78 Source Program . . . . . . . . 79 Type of Residence Equations . . . . . . 80 Variables . . . . . . . . . 82 Base Populations . . . . . . . . . 82 Total Earnings . . . . . . . . . . 8U Constant Term . . . . . . . . . . 8a Type of Place . . . . . . . . . . 85 Age . . . . . . . . . . . . . 88 Female . . . . . . . . . . . . 89 Nonwhites . . . . . . . . . . . 89 Head of Family . . . . . . . . . . 90 Education . . . . . . . . . . . 9o Salaried . . . . . . . . . . . . 93 Self- -Employed . . . . . . . . . . 93 Industry . . . 93 Standard Metropolitan Statistical Areas . 94 Outside South . . . . . . . 97 Important Excluded Variables . . . . . . 97 Multicollinearity . . . . . . . . . 99 Basic Assumption . . . . . . . . . . 100 County Earnings Gaps . . . . . 101 Substitution of National Variables . . . . 102 Supplementary Programs . . . . . . . . IOA Ordering of Earnings Gaps . . . . . . 10M Distributions of Earnings Gaps . . . . 104 PlOts . . . . . . . . 105 Indexes . . . . . . . . . . . . 106 IV. COUNTY EARNINGS GAPS-—EMPIRICAL EVIDENCE OF LABOR MALADJUSTMENT IN 1960 . . . . . . 107 Introduction . . . . . . . . . . . 107 Terminology . . . . . . . . . . . 107 Organization . . . . . . . 108 Interpretation of Tables . . . . . . 109 Geographic Characteristics . . . . . . 113 Chapter V. Northeastern Region Actual Per Capita Earnings Estimated Per Capita Earnings Apparent Labor Maladjustments Total, All Residence Areas Urban Areas of Residence Rural Nonfarm Areas of Residence Rural Farm Areas of Residence Total Nonfarm Areas of Residence North Central Region Actual Per Capita Earnings Estimated Per Capita Earnings Apparent Labor Maladjustments Total, All Residence Areas Urban Areas of Residence Rural Nonfarm Areas of Residence Rural Farm Areas of Residence Total Nonfarm Areas of Residence Southern Region . Actual Per Capita Earnings Estimated Per Capita Earnings Apparent Labor Maladjustments Total, All Residence Areas Urban Areas of Residence . . Rural Nonfarm Areas of Residence Rural Farm Areas of Residence Total Nonfarm Areas of Residence Western Region Actual Per Capita Earnings Estimated Per Capita Earnings Apparent Labor Maladjustments Total, All Residence Areas Urban Areas of Residence . Rural Nonfarm Areas of Residence . Rural Farm Areas of Residence Total Nonfarm Areas of Residence Labor Maladjustments Among Regions Recapitulation . . . . RELATIVE IMPORTANCE OF VARIABLES AFFECTING LABOR MALADJUSTMENTS Introduction . . . . . . . . Age . . . . . . . . . . . . . Sex . . . . . . . . . . . . . Race . . . . . . . . . . . . . Education . . . . . . . . . . . Type of Earnings . . . . Industry of Employment . . . Summary . . . . . . vi Page 117 117 127 132 132 139 142 144 146 147 148 159 165 165 172 175 177 180 181 181 191 197 197 202 204 206 209 211 211 219 224 224 229 231 232 236 238 243 248 248 252 260 264 268 277 279 284 Chapter VI. SUMMARY AND CONCLUSIONS BIBLIOGRAP APPENDICES General Summary . . Summary of Statistical Correlations Some Implications Evaluations Further Study HY vii Page 286 286 295 308 315 319 323 328 Table 1. 4. 1 5 4.6 LIST OF TABLES Distribution of Counties by Per Capita Actual Earnings, Total, All Residence Areas, for Major Regions and Divisions of the U.S., 1959 . . . . . . . . . Overall Least Squares, Dummy Variable Multiple Regression Equation Depicting the Relation Between Average Per Capita Earnings and Several Socio—Economic Characteristics of the U.S. Population, 1960 Analysis of Variance for the Overall Regression . Variables Included in the Earnings Capacity Regression . . . . . Physical and Demographic Characteristics of Major Geographic Areas of the U.S., 1960 Distribution of Counties by Actual and Estimated Average Per Capita Earnings, for Each of Five Residence Areas, 1959, Northeast Region . . . . Distribution of Counties by Actual and Estimated Average Per Capita Earnings, for East of Five Residence Areas, 1959, United States . . . . . . . . . . Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five Residence Areas, 1959, Northeast Region . Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five Residence Areas, 1959, United States . . . . . . Distribution of Counties by Actual and Estimated Average Per Capita Earnings, for Each Of Five Residence Areas, 1959, North Central Region . . . . viii Page 12 71 73 74 114 119 120 133 135 149 Table 4.7 4.8 4.9 4.10 4.11 5.1 Distribution of Counties by Magnitude of Labor Maladjustments, for Each of Five County Residence Areas, 1959, North Central Region . . . Distribution of Counties by Actual and Estimated Average Per Capita Earnings, for Each of Five Residence Areas, 1959, Southern Region . . Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five Residence Areas, 1959, Southern Region Distribution of Counties by Actual and Estimated Average Per Capita Earnings, for Each of Five Residence Areas, 1959, Western Region . . Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five Residence Areas, 1959, Western Region . . Percentage Distribution of Persons in the U.S. Labor Force, 1960, by Area of Residence and Demographic Variables . . . . ix Page 166 183 199 212 225 253 LIST OF FIGURES Inter—Market Labor Movement Standard MetrOpolitan Statistical Areas, 1960 Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, Northeast and U. S. 1960 . Apparent Labor Maladjustments by Counties, Total Labor Force, 1959 . Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, North Central Region and U.S., 1959 Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, South and U. S. 1959 . . Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, West and U.S., 1959 . . . . Percentage Distribution of Counties by Magnitude of Labor Maladjustment in Five Residence Areas, U.S., and Major Regions, 1959 . . . . . . . . . . . . Relationship Between Apparent Labor Malad- justments in the Labor Force of the Rural Farm and Total Residence Areas of Each County in the United States, 1959 Relationship Between Apparent Labor Malad- justments in the Labor Force of the Rural Farm and Total Nonfarm Residence Areas of Each County in the United States, 1959 Relationship Between Apparent Labor Malad- justments in the Labor Force of the Rural Farm and Urban Residence Areas of Each County in the United States, 1959 Page 55 96 121 136 150 184 213 239 297 300 302 Figure Page 6.4 Relationship Between Apparent Labor Maladjustments in the Labor Force of the Rural Nonfarm and Total Residence Areas of Each County in the United States, 1959 305 6.5 Relationship Between Apparent Labor Maladjustments in the Labor Force of the Rural Nonfarm and Urban Residence Areas of Each County in the United States, 1959 . . . . . 306 xi Appendix A. LIST OF APPENDICES Allocation of Counties by Standard Metro— politan Statistical Areas . . . Distribution of Counties by Magnitude of "Actual" Per Capita Earnings, for the U.S., Regions, Divisions, and States, by Areas of Residence, 1959 . . . . . . Distribution of Counties by Magnitude of "Estimated" Per Capita Earnings, for the U. S. , Regions, Divisions, and States, by Areas of Residence, 1959 . Distribution of Counties by Magnitude of "Apparent Labor Maladjustment," for the U.S., Regions, Divisions, and States, by Areas of Residence, 1959 . Results of Simple Correlation Analysis xii Page 329 336 352 368 384 CHAPTER I INTRODUCTION Nature and Importance of the Problem This is a study of underemployed and underpaid labor resources in various sectors of local labor markets across the United States. It is thus an analysis of inequitable returns to human resources. It concerns the inability of persons scattered throughout our national society to produce income according to their earning potential, a situation which reduces the productive efficiency of the whole American economy. The reasons for such inefficiencies are varied, ranging from outright discrimination by color and sex in the job market, to a host of ingrained attitudes among individuals in the labor force, including their general lack of information about alternative opportunities. Retardation of community economic growth and devel— opment due to the underemployment or maladjustment of human resources too often has been neglected as an area of economic research. The economic structure of a com— munity is of vital concern to its people and consequently, progress in most local areas depends upon the ability of community leadership to strengthen and continuously alter that economic structure in the face of technological and economic change. Basic structural aSpects of a community must provide the fundamentals needed for the development of human satisfactions, since the future welfare and security of the individual is dependent to a very large extent on the opportunities available to him in his com— munity, and whether he can, or is able to fully realize his earnings potential comensurate with those Opportuni- ties. Local economies are highly diverse in their makeup relative to each other, which generates significant dif- ferences in earnings Opportunities. It simply cannot be said that because two areas have adjusted at the same rate that adjusting forces at work in the two areas are generally of the same magnitude. Nor can it be said that, because a particular adjustment policy was successful in one area, it will also be successful in another. The two areas may in fact differ markedly with respect to resources, available technology, kinds of commodities produced, and in the case of agriculture, in applicable government programs. This indicates the probability of large varia- tions among communities in the extent of labor maladjust— ment, and once such disequilibriums are identified with respect to location and magnitude, the employment of "specific" remedial measures for each community can be made. U.) Charles E. Bishop has stated that if economists are to make the greatest contri- bution to policy formation, they must continually probe for a greater understanding of how the economy functions, why it functions as it does, and what the implications are for resource use and income distri— bution to various groups in society. This type of descriptive analysis is necessary to identify and to quantify relationships among economic phenomena. It is essential to meaningful prediction and to the establishment of norms for development. This statement is an appropriate prologue and justification for this study on returns to human resources in local communities, where much of the labor maladjustment problem stems from the effects of economic growth and development. Given the heterogeneity among small areas of the United States and the differences in impacts of public policies and programs among regions, there is a definite need for a more comprehensive understanding of the differences in these effects. Such an analysis is essential to an appraisal of alternative policies designed to promote regional growth and development. Another problem that plagues all areas, but espe- cially the local community,is that of poverty. The poverty issue, as such, is not of direct concern to this investi— gation, but it is part of the labor maladjustment story in most communities. According to Bishop, "real poverty characterizes individuals and families who own too few 1C. E. Bishop, "Purposes and Usefulness of Policy Research," Price and Income Policies, a workshop sponsored by the Agricultural Policy Institute (Raleigh: North Carolina State University, April, 1965), p. 5. assets to yield an income large enough “to sustain a minimal level of living even when their resources are optimally employed."l Problems of poverty and labor malallocation are relative, for just as individual aspirations change over time, society as a whole changes its views as to what con- stitutes adequate subsistence levels. Causes of low income are varied, and the most suitable policy approach to the problem must take into account both the nature of causal forces and empirical evidence of the extent of the problem. The real policy question surrounding this issue is what type of assistance should be offered to remedy each type of poverty and then what kinds of regulations governing this assistance should be instituted.2 Another important point emphasized by Bishop is that few resources other than personal labor are owned by persons in low—income cate— gories. Any hope of their overcoming this plight usually hinges upon increasing their returns for labor serVices. Waht are the implications of these conditions? Among other things, it suggests that although the American economy is currently enjoying by far its greatest period of prosperity in history, and is characterized by changing conditions on every hand (political, economic, technological, medical, etc.), there are still thousands of peOple scattered llbid., p. 7. 21bid., p. 8. throughout both urban and rural communities of the United States, who, for a varied number of reasons, are not receiving earnings comensurate with their potential. On the average, economic progress has improved the situation of individuals and families, but for many people conditions are no better today than they were several decades ago. They are often victims of a bad communication system, their own inabilities to interpret and pursue better Opportuni— ties, and the lack of employment opportunities in their own environments. The existence of such situations seems indicative of malallocated resources, and a potential source of economic growth. So long as it remains possible for persons to increase their real earnings in some alter- native employment, either within or outside their own community, they are not realizing their highest compensa- tion, and local and/or state and national income could be increased by their transfer to some other higher paying employment, assuming that costs involved in making a job change do not exceed the increase in earnings. But the assurance of a job for every person ready, willing, and able to work and/or provision of at least a subsistence level of wages does not necessarily rectify the problem of malallocated resources in a given area if there still eXists unutilized labor capacity, i.e., peOple who are very often regularly or part-time employed, but underemployed. It is this problem of underemployment in local labor markets--the disparity between actual and potential earnings of persons--to which this thesis proposes to address itself. The study is based on certain assumptions about individual aspirations. The most important, perhaps, is that there exists in man a basic drive to better his economic welfare, and that his values regarding work are Of significance since his livelihood depends on employment. Therefore, the search is always on for new and more rewarding job opportunities. Furthermore, individuals seem to prefer these opportunities be available in the community where they live and generally work, so they will not have to experience the frustrations so often associated with physically relocating their families. Based at least on these economic incentives, people at the community level continually search for ways in which they can restructure themselves to remain economically and socially viable. The extent to which they have been successful in these efforts largely determines the extent to which they have been able to achieve an Optimum use of their most impor- tant resource-~people. Importance of Study In short, suspicion, and some evidence, of signifi— cant inefficiencies in the allocation and use of human resources in hundreds of communities across the nation constitutes the specific nature of the problem under investigation. The importance of the study is justified on the basis of providing reliable empirical documentation on patterns of labor maladjustment among communities, and residence areas within communities, which heretofore has been unavailable in sufficient detail. But, further elaboration of circumstances underlying labor maladjust— ments will help highlight some of the problems involved and the need for exploring the selected problem area. In nearly every major region of the United States economy today there exists a multitude of develOpment projects designed to alleviate substandard levels of living and provide an employment base that will economically support the population of a given area at a level consistent with the affluence of the nation. In addition, broad development policies today are designed rather specifically to eliminate substandard levels of living through programs which make it possible to sustain increases in per capita incomes and products. In other words, major emphasis has been on the group, geographically spanning at least a multi-county area, and not the average individual in a particular community. Greater attempts need to be made, however, to develOp the "potential" of persons in various areas of the country so that problems of underutilization of human abilities and underpayment of such skills can be adjusted towards some kind of equilibrium state, in which persons more nearly realize their full earning power. It is hypothesized that an extremely large number of persons and households who are below so called poverty levels, and others on subsis- tence payments, could earn a decent standard of living if their human capabilities were fully realized. 'To design and institute policies that will allow an area to more efficiently utilize its pool of available skills requires some measure of the lag between eXpectations (potential earnings) and reality (actual earnings). Unfortunately, no such measures are available in sufficient detail at this time. Increases in aggregate income should be distributed so that all segments of the population are favored, in real terms, by the change in economic activity. According to traditional economic theory (which is discussed at more length in Chapter II), the factor adjustment process should equilibrate returns to homogeneous resources and thus eliminate disparities in per capita earnings on invested capital. Instead, unequal income distribution problems abound, in both the farm and nonfarm sectors.l A central problem, obviously, is that two very basic assumptions of the perfectly competitive enterprise system 1Daniel G. Sisler, "Regional Differences in the Impact of Urban-Industrial Development on Farm and Nonfarm Income," Journal of Farm Economics, XLI (Dec., 1959), p. 1100. are violated, that of perfect knowledge and free resource mobility. Both may generate adjustment lags that are not encompassed in traditional theory, which raises a major question: why, with our seeming awareness of the persis— tence of labor maladjustments amdincome differences, and the increasing disparity of these differences, do we not have more and better information on the problem, informa- tion that would provide the springboard for apprOpriate policy decisions on how to attack these resource allocation problems in various local areas of the United States? The answer is not readily available, largely because no such detailed analysis of earnings in local markets has been carried out for the whole country, and because little empirical documentation was heretofore possible at the local level. Only in recent years have new research techniques and data sources needed for analyzing labor resource returns in "local" labor markets and communities emerged in suffi- cient detail and accuracy. Indeed, this study must attrib- ute its conception largely to the recent availability of and relatively easy access to pertinent statistics at the local level, plus the also recent develOpment of some important methodological tools which will be reviewed in later discussions. National concern for the improvement of the quality of people and the quality of living is evidenced through 10 recent federal expenditures on massive welfare assistance type programs, by the appointment and work of the President's National Advisory Commission on Rural Poverty, through prolific writings on returns to investments in peOple, and by the priority which development economists are giving to the human factor. Better information on labor maladjustments is also needed for a detailed analysis of specific rural farm communities. Agricultural policy today is heavily con- cerned with designing programs for the agricultural sector that will allow individuals and families in this sector "parity" returns relative to their nonfarm counterparts. Empirical evidence of the extent of rural farm underemploy- ment of labor would provide norms to assist such decisions. .In recent years lack of such evidence at the local level has not only generated policies for agriculture that failed to COpe with low-income problems, but in addition has encouraged the more capital intensive commercial Operations in agriculture to reap most of the benefits of farm policy programs. In summary, then, many factors contribute to the inequitable and relatively low-earnings position of persons in local labor markets. In general, these can be categor- ized into (1) the less favorable mix of social elements affecting earning capacity in a given labor market, e.g., age, sex, education, attitudes and aspirations, capital 11 ownership, etc., and (2) relatively poor economic Oppor- tunities in local labor markets, causing participants in these markets to earn substantially less than they might reasonably eXpect to earn on the basis of their earnings capacity. It is hoped that whatever information is pro— vided in this study will afford long needed documentation for more effective human resource policy. Empirical Evidence of the Problem Some selected county data are presented in this section as an indication of a few of the statistical questions involved in this investigation. Table 1.1 shows a distribution of counties by size of per capita earnings across all areas of residence, by major regions and divisions ofthe United States. These data are provided merely to exemplify the dispersion in per capita earnings among communities within each region and division, and they suggest an important question which this study prOposes to answer. On the basis of per capita earnings potential would each of these areas exhibit the same relative distributions as they do in Table 1.1 for actual per capita earnings? Further, would computed distributions of labor maladjustments show the same relation among divisions and regions as the distri— butions of actual per capita earnings, or were the peOple in the labor force of regions and divisions with low per capita earnings more closely realizing their potential earnings in 1959 than those in other areas? l2 .mopom Honda on» CH A+3Hv mcompoa no .mpae emgmHszacs .oemH .coHpmHsdmm co oEoocH pcmEHOHQEOILHOm msHo mcmem one me3* mSmcmo .mzmcoo OQB mo smmcsm .m .D ”mopzom o e a om mm mm AH m H o H .>Ho OHLHoam m H H mH on mm mm om m o o .>Hm cHapcsoz m m HH mm mOH me mOH mm m o H conmm sympmmz o H : om mm ow HmH mmH Hm o o .>Ho .cmo .om .3 o o o H NH AH a: HmH mHH me H .>Ho .cmo .om .m H m m m mm ma HmH mmH me mm o .>Ho .Hpa .om H z m mm mm NAH mmm mo: mmm as H .mmm ccmnpsom H H o :H em mm mmm mom m: o o .>Ho .cmo .z.3 m 0 HH mm mm aeH NMH o: H o o .>Hm .cmo .z.m : H HH mm mm mmm 20m mam a: o o .wmm .cmo .02 m a HH SH mm am mm o o o o .>Hm .Hpa .eHz o H H H OH Hm Hm m o o o .>HQ .Hmcm zmz m m NH om m: we we a o o o .wmm .m .02 OH mH m: mMH me mmo new mom mo: ea H mmpmpm empHc: comm mmzm mom: man: mmmm mmzm mmmm mmzm mmmH mmzH oooH mmc< A -ooom loom: loco: noomm uooom -oomm -ooom -oomH -OOOH v .mmmH .mOpmpm OOpHCD on» mo mCOHmH>HQ new mQOHwom HOnmz pom .mmmp< mocmeHmmm HH< .Hapoe *.mwchcmm Haspoa mpHan cam an mmecsoo co QOHpanppmHouu.H.H mHmae 13 There appears to be a high correlation of county per capita earnings frequencies in Table 1.1 with low family income statistics and other poverty indexes published in other sources, i.e., the prevalence of poverty, by almost any index, generally shows the Southern region far out front. It is also evident from those data that the South contains a high percentage of the nation's total counties with low per capita earnings. In 1959, the South had 100 per cent of all counties in the $1,000 to $1,499 class, 88 per cent in the $1,500 to $1,999 class, 59 per cent in the $2,000 50 $2,499 category, and 71 per cent of all counties with less than $2,500 per capita income. This compares with 25 per cent in the over $2,500 category in the North Central States, 3 per cent in the West, and 1 per cent in the Northeast. Within regions, about 60 per cent of all Southern counties had per capita earnings less than 2,509 dollars relative to 25 per cent in the North Central states, 8 per cent in the West, and 4 per cent in the Northeast. A very disturbing consideration is that this high percentage Of counties in the South with relatively low per capita earnings may not tell the whole story of their plight. A final question must be asked. Are persons in the labor force of these counties earning near their per capita earnings potential, and if not, is the discrepancy as large or larger than that eXperienced in other major 14 regions of the country? Obviously, this is a much more encompassing index than merely looking at bare income statistics, since it is entirely possible that peOple in counties where per capita earnings are very high may be earning farther below their earnings capacity than persons in low income counties. High income counties then, may have more acute labor disequilibriums, in terms of theo— retical allocative efficiency criterion, than low income counties. This discussion only augments the nature of the problem at hand. The rest is left to subsequent analysis where labor force earnings in each major residence, for each region, division, and state of the country, will be examined in considerable detail. Objectives This is a study in the efficiency of labor resource allocation at the community (or county) level, and hence its main concern is with underpaid human resources. The overall objective of the study is to produce empirical evidence of the underutilized earning power of persons in each county of the United States, by type of residence, provide a partial analysis of possible reasons why esti— mated disparities exist between potential and actual earnings, and suggest ways and means by which given com- munities might better develOp their human resources in 15 order to reduce or remove these disparities. The thesis does not prOpose to eXpound directly on the magnitude or plight of impoverished Americans. However, it is recog- nized that no study of human resources in the local economy can remain completely devoid of at least some poverty considerations, since the very existence of poverty is partially a function of the opportunities of peOple to realize their earnings potential through local employment pursuits. Specifically, the objectives of this thesis are: (l) to define and describe theoretically problems of disequilibrium returns to labor resources among communities (counties) of the United States, (2) to identify and describe demographic and struc- tural characteristics which are closely associated with the actual and potential earnings of people, (3) to estimate the average earnings capacity of persons, county by county, by major types of residence, for the United States, for 1959, (4) to compute community labor maladjustments by contrasting per capita earnings capacity estimates with actual per capita earnings for 1959, indicating the relative magnitude and patterns of resulting earnings gaps, (5) to carefully examine labor maladjustments in the rural farm and rural nonfarm sectors of communities, 16 indicating divergences from and relationships to nonfarm earnings gaps and, (6) to describe the relative importance of major variables affecting per capita earnings potential and some adjustment and policy implications consistent with computed labor market disequilibriums. Previous Work Literally hundreds of references exist which in some form or another deal with the relative welfare position of persons and families in various areas and sectors of the United States economy. Much of this literature is con- cerned with problems of low—income (poverty) and sub- standard levels of living in the urban and rural farm sectors of the economy. We have given enormous amounts of attention to various organized efforts for combating poverty problems and general underemployment. A multitude of new federal, state, and local programs reflect this growing concern, e.g., the Economic Opportunity Act of 1964 and eXpanded efforts by the U. S. Department of Agriculture, among others. Such studies have led to the establishment of specific public assistance programs in both urban and rural areas, including several government support devices aimed at boosting farm incomes, programs which many feel have had very moderate success. 17 None of these studies, however, have dealt compre- hensively with the potential earning power of individuals in local labor markets across the country. Most have all been rather straight-forward attempts at Opportunity cost estimates utilizing national data. Research efforts focusing on local income problems have utilized actual income data for the most part to depict the relative economic condition of people at a point in time. Such research efforts do not go far enough in their eXplor- ation of the problem. Perhaps one of the main reasons for much of our ineffectiveness in raising low incomes in local economies to a level more comensurate with the national level has been the lack of detailed evidence on the factors causing variations in individual earnings. Nearly all studies that have touched on this problem have attacked it at either the national level or for three or four major regions, mostly for the South, assuming homogeneity of labor resources and comparability of other resource characteristics over broad geographical areas. Such assumptions are often unrealistic and may account for much of the unexplained variance in income variability between sectors and areas. Of the available research most relevant to the problem at hand, two recent theses in Agricultural Economics at Michigan State University appear most useful. Both have l8 expounded at length on previous research results related to income distribution problems and disequilibrium returns to the human factor. They provide major impetus to this research effort. In 1963, Bryant completed a study of the causes for income variations among agricultural communities of the United States.; In this study two measures of farm income were regressed on several eXplanatory variables in an attempt to eXplain the existence of large income varia— tions among rural communities. The methodology used for analyzing inter-community income variations for the rural farm sector is partially adaptable to a study of labor maladjustments in other sectors of the economy, an inte- gral part of the investigation undertaken in this thesis. In addition, Bryant's study provides some norms in the rural farm economy against which results obtained in this study can be checked. In a second study, Ben-David investigated farm— nonfarm income differentials at the national level for 1960.2 An entire chapter was devoted to a review of lWilfried Keith Bryant, "An Analysis of Inter- Community Income Differentials in Agriculture in the United States" (unpublished Ph.D. dissertation, Michigan State University), 1963. 2Moshe Ben-David, "Farm-Nonfarm Income Differentials, United States, 1960" (unpublished Ph.D. dissertation, Michigan State University), 1967. 19 important literature dealing with low-returns in American agriculture. Since the thesis at hand was originally conceived as a continuation of Ben-David's analysis below the national level, the reader is referred to his thesis for capsules of previous research indirectly related to this study. In his study, Ben—David placed major emphasis on the development of an earnings capacity relationship at the national level for the purpose of testing for malallocation of labor resources in United States agriculture. The estimated function contained far more independent and pre- determined variables than any previously develOped rela— tionship of the determinants of individual earnings, and explained a larger proportion of the total variance in personal earnings than previous efforts. This functional relationship will be used extensively in the present study to compute an index of labor maladjustment for each county in the United States. These estimated earnings gaps will then form the basic data for analyzing relative differences in labor force earnings potential among local communities. These two theses, especially the Ben-David study, provide an immense laboratory of literature review, data, and methodology relevant to this study. To avoid redun- dancy and conserve space, only additional literature that is directly and specifically related to this study is cited, and much of this is dispersed in appropriate places throughout the thesis. 20 In another published research undertaking, which deals with income distribution and capabilities of peOple to earn, personal income was regressed on eight major social variables (as of 1958), but only 35 per cent of the total variation in individual incomes was explained.1 At that particular time, a multiple correlation coefficient (R2) of 0.35 was considered by many to be a rather high correlation for the type of relationship being fitted, and actually only a moderate improvement in that esti- mate has been made to date. Miller correctly points out that "there is much about the determination of individual income that is not accounted for by education or by any of the other characteristics included in a census or the usual household survey." Although none of Miller's methodology is adapted for this study, he offers some important in- sights with respect to possible sources of unexplained variation nipersonal income variations. His reasoning centers primarily on the omission from measurement of several intangible type variables, e.g., ability, effort motivation, and "quality" of education. A further important contribution made to this analysis by Miller's study is his in-depth treatment of the relationship of personal income to education. Education is generally lU.S. Bureau of the Census, Income Distribution in the United States by Herman P. Miller, A 1960 Census Monograph (Washington, D.C.: U.S. Government Printing Office, 1966). 21 regarded by most researchers as the most important deter- minant of individual incomes, but since the quality of education is extremely variable across the country, use of the same basic statistic in all areas introduces a source Of error in R2 estimates. Miller offers a good treatise of the pros and cons involved in attempts to quantify educational quality. Another study, the 1967 parity report on farmers,l has also alluded to the interrelationships between earnings and several socio-economic characteristics. This report was concerned with parity returns to labor employed in agriculture, i.e., the income that a farmer could eXpect to receive in alternative employments in other sectors of the economy. Major emphasis is placed on two com— ponents, labor supplied by farm Operators and unpaid family workers, and the wage rate this labor could earn in other occupations. The idea was to produce an objec— tive measure of earnings (a wage rate) which peOple with similar attributes could command in the nonfarm economy. Results and implications of the report are important to this thesis since major attention is given to the rural farm sector of each community. In addition, problems of 1United States Department of Agriculture, Parity Returns Position of Farmers, a report to The Congress of the United States, Document No. 44, 90th Congress, 1st Session (August, 1967). 22 adjusting actual earnings in line with potential earnings is essentially a search for parity returns for labor resources, wherever they happen to be employed. It should be noted that all studies cited are dealing with labor returns at the national level, except for Bryant's, where only the local agricultural sector is analyzed. To date this writer is unaware of any compre- hensive study that utilizes county income data to compare earnings disparities in local labor markets, although a few isolated case studies of small rural communities have provided token analysis of the problem at hand. The lack of data availability for small geographic areas in a manageable form has no doubt restricted previous efforts, and still does thwart an extensive analysis. However, better cross classifications are available for the first time at the county level in the 1960 census of pOpulation, and storage on magnetic tapes made it more readily accessible for analysis. Procedure The first two chapters of this thesis are devoted to a general discussion of labor resource returns in local economies across the nation. This chapter has attempted to introduce the general nature of the problem under inves- tigation, to spell out Objectives being pursued, and very briefly review the literature most directly related to the Objectives of the study. 23 Objectives one and two are discussed at greater length in Chapter II. A review of the theory of factor. returns is Offered with particular emphasis on problems Of inequitable resource returns in local labor markets. This chapter will also discuss definitions of crucial concepts to be used throughout the remainder of this study, e.g., geographic areas analyzed, local labor mar- kets, disequilibrium returns, labor comparability, income and earnings, and others. Some structural characteristics of labor markets thought to affect the earnings potential of persons, as well as major social influences, are deliberated. The estimating procedure used to generate the average earnings capacity of individuals in each county of the United States, by major types of residence, are outlined in Chapter III. Problems surrounding the acquisition and inconsistencies of data are pointed out, and procedures for adjusting certain data are discussed. The chapter termi— nates with a discussion of those variables included in the earnings-capacity function that was used to derive esti- mated earnings potential. Empirical results obtained from application of the earnings-capacity function are presented in Chapter IV. Estimated earnings potential of individuals is compared with actual earnings for 1959, and an earnings gap com- puted for each major area of residence. Distinguishable 24 patterns of variability in actual per capita earnings, per capita earnings potential, and computed labor malad- justments are discussed at length. The relative importance of major variables affecting average earnings capacity and apparent labor maladjustments in local labor markets is discussed in Chapter V. Changes in the eXpected earnings of persons in each community are analyzed relative to national norms, i.e., national char- acteristics such as education, industry mix, age distri— bution, etc., are substituted for the same county charac- teristics to further determine the nature of a community's disequilibrium of labor resources. Questions concerning whether local labor markets are structured, or could be restructured, to more efficiently utilize available man- power are also briefly considered. The study terminates with a review of procedures and major findings and an evaluation of empirical and analytical results. This is followed by a divided appendix containing detailed tables of county data for each earnings index computed. CHAPTER II CONCEPTS AND THEORETICAL CONSIDERATIONS PERTAINING TO HUMAN RESOURCE RETURNS IN LOCAL LABOR MARKETS This chapter undertakes two important tasks. The first part is a definitional section devoted to a dis- cussion of several crucial concepts within their context of intended usage, including some of the most important Operational variables used throughout the study. The second phase of the chapter presents a theoretical dis— cussion of human resource returns. Particular emphasis is given to the relevance of theoretical considerations in studying problems of underemployment, and to problems surrounding the malallocation of human resources in and among local labor markets in various parts of the United States. In appropriate places, variations in some of the major structural characteristics of local labor markets are alluded to, as well as hypothesized effects of such differentials. Concepts and Definitions Central to the subsequent analysis of this report are the geographic units considered, i.e., the community, county, and local labor market, definitions of earnings, 25 26 earnings capacity, labor force, residence areas, census places, standard metropolitan statistical areas, and issues surrounding labor maladjustment. Local Economy Frequent reference was made in the previous chapter to sub-regional and sub—state areas of the United States economy, and heavier use of local area terminology will be used in the subsequent analysis. Therefore, it is important to define the basic geographic units at which this study is directed, and how such small areas are inter— related. Up until recently, the bulk of our economic analysis in this country dealt with resource problems at either the national scale, or for some very large geographic division of the country.. This thesis and the subsequent content of this chapter are directed at much smaller areas, communi- ties and local labor markets. The Community There are probably as many working definitions of the term community as there are studies which base their analysis on the concept. "Community" has more flexibility in terms of its application than other local units such as county, parish, section, division, etc. It implies cohe— siveness and similar aspirations among groups of people. But there are wide variations in the comprehensiveness of 27 a "community." According to the National Poverty Commission Report, a community must be defined in terms of: . an area that encompases several counties grouped about a town, city, or metropolis. The geographic size varies from one community to the next, their boundaries cutting across political jurisdictions-~city, county, or state-~and some- times overlapping. A community includes those who share common interests in the significant activities, public and private, that reach the local level. Its geographic dimensions are actually determined by the degree of economic and social integration and by the extent to which important items in the lives of the people are tied to specific locations.1 This is a modern day definition of community activity in the truest sense of the word. Not so long ago, these areas were more isolated and independent of one another. Residents of one town or county did not involve themselves, nor were they interested, in the affairs of an adjoining county or city. As a result each village or place of residence was a community in and of itself. PeOple took pride in announcing the name of some small town as their home, when asked their residence. Today the same question would likely solicit the name of a county, some geographic region of a state, or distance from some large city. Technological change and the eXpan- sion of communication media have brought peOple dispersed over wider areas into closer contact with each other, 1National Advisory Commission on Rural Poverty, The People Left Behind (Washington, D.C.: Superintendent of Documents, U.S. Government Printing Office, September, 1967), p. 121. 28 so that the community now more closely conforms to county boundaries. Another noted economist views the community as "a group of people who organize for a common purpose, and in this sense, an individual can belong to many communities."1 Hence, a community may be large or small in geographic area depending upon the community function involved. The central focus within any particular community is economic opportunity and quality of living for its inhabitants. Both of these definitions of a community are highly subjective and boundaries are very arbitrary. It would be near impossible to put together a series of data with any degree of cross-classification that would be meaningful for an area defined in this manner. Unless each community could be defined as an exact conglomerate of counties, census data could not be used for this analysis. It was decided therefore, to define "community" consistent with a "county" unit, and refer to the two areas synonymously throughout the thesis. Local Labor Markets Considerable interest is now being given to the labor force as a meaningful economic variable. Much of the 1J. Carroll Bottom, "Community Resource DevelOpment Defined," Community Resource DevelOpment, proceedings of Second National Extension WorkshOp in Community Resource DevelOpment (East Lansing, Michigan: Michigan State Uni- versity, Resource Development Department, July, 1966), p. 2. 29 decision—making involving labor utilization, from both employee and employer standpoints, is made within the con- fines Of a local labor market area. Again, one cannot avoid an injection of subjectivity when pinpointing a workable definition of the "local labor market." This study concerns itself with the earnings of people in small areas (counties to be precise), which implies the source Of their incomes to be either in their immediate locale or within some "reasonable" commuting distance. Thus, both place of employment and major depo— sition of earnings may be in the adjoining county, within the same county, or divided in some other manner. Most peOple probably spend the largest proportion of their earnings within or near places where they work. Both the availability of employment opportunities and the distri- bution of incomes are important to area growth and devel- Opment, but in choosing a local geographic unit for anal- ysis, the county affords the most statistics. Accordingly, local labor markets should be thought of as an employment medium available to labor force participants of a given county or similar area. It is a "place" where persons in a given community work and earn their livelihood, a source of varied occupational and industrial job oppor— tunities. The range of labor mobility, states Abraham L. Gitlow, sets the spatial limits or area of the labor 30 market.1 One cannot properly speak of a national labor market as such in the United States, because of the nation- wide movement of labor, i.e., because of impediments to labor mobility, there are local labor markets. Basic restrictions are probably the cost of movement and limited knowledge by workers of job opportunities outside the local labor market (home area). Gitlow proceeds to offer a more precise treatment of a local labor market as follows: Local labor markets are definable as those geo- graphic areas within which most workers seek and find their employment and between which only a minority of workers move. The physical size of these areas varies. Their boundaries are a function of transportation time and cost, as well as other impediments to mobility . . They have fringes where two or more areas may overlap. Within each area, there are a number of job markets created by differences in skill requirements and by institutional barriers to movement. Also some job markets, especially for certain skills and most professions, are much larger in spacial terms than the local areas. For the great majority of workers and employment situations, however, the local labor-market area is largely equivalent to the job market within which the process of matching jobs and workers takes place. Consistent with this definition, the Bureau of Employment Security of the U.S. Department of Labor has designated a data series for "labor-market areas," which consist of a principal city or cities and the surrounding area within some reasonable commuting distance (as based 1Abraham L. Gitlow, Labor Economics and Industrial Relations (Homewood, Illinois: Richard D. Irwin, Inc., 1957), p. 345. 2Ibid., p. 346. 31 on normal worker commuting patterns in a particular area). This series began in 1960, but no data containing information commensurate with the variables used in this study are available for these labor market areas, another reason for using the county as the basic unit of analysis. Since the focal point of the study is on average per capita earnings in each county, and since per capita earnings emanate mostly from local labor markets, there appears to be no inconsistency involved in delineating county and local labor market terminology. In some cases where a large urban center encompases an entire county, it would also be correct to talk of a "county labor market" for that county's labor force. Earnings The United States Bureau of the Census presents several indexes of welfare. Most of these are income measures, including incomes of persons, families, unrelated individuals, and earnings of persons in the labor force. Since the dominant goal of this research effort is to measure returns to human resources used in the productive process, and since the census measures total earnings in the same manner, earnings represent the standard welfare unit for this study. In addition, this study builds heavily on a previous thesis in which total earnings was the focal point of analysis.1 lBen-David, op. cit., Ch. VI. 32 Measures of underemployment and labor malallocation in local labor markets are directly dependent on estimated earnings, as reported by the United States Census of Popu— lation and Housing.1 According to the census, total income includes three major types of income: wages and salaries, self—employed income, and other income. Wage and salary income is made up Of total money earnings received for work performed as an employee, inclusive of wages, salary, commissions, tips, piece-rate payments, and cash bonuses earned. Self-employment income is defined by the census as "net money income (gross receipts minus Operating eXpenses) from a business, farm, or professional enterprise in which the person was engaged on his own account." Other income is "money income re- ceived from sources other than wages and salary and self- employment," and is composed of such sources as net rents and royalties, interest, dividends, trust funds, estates, military dependency allotments, alimony, insurance and annuity payments, and various transfer payments. Total earnings are the sum Of wage and salary plus self—employment income, and thus represents actual dollar earnings of persons from all productive pursuits. Census lU.S. Bureau of the Census, United States Census of Population: 1960, General Social and Economic Character- istics of the POpulation, 1C and 1D, U.S. Summary. 33 earnings figures represent amounts of income received before deductions for personal income taxes, Social Security, bond purchases, union dues, etc. Several problems emerge regarding the earnings data reported by the census. The major restriction is the exclusion of nonpecuniary sources of income from reported earnings. In the case of farm people, this is an important omission, since home grown food consumption is often a sig- nificant part of earnings in the farm sector. The U. S. Department of Agriculture estimates that products consumed directly on the farm in 1960 amounted to about 10.4 per cent of total net income from farming, or 7.2 per cent of total disposable personal income of the farm pOpulation from all sources.1 Other estimates show that for more than one—half Of the lower-income farms, this was about 500 to 600 dollars, an amount that would raise the per capita earnings of persons in the rural farm labor force con- siderably.2 In the subsequent analysis, the exclusion of income in kind tends to widen the gap between per capita earnings capacity and actual per capita earnings, and there- fore increase the level of labor malallocation in an area. 1U. S. Department of Agriculture, Farm Income Sit- uation, ERS—FIS-203 (Washington, D.C.: Office‘of Manage— ment Services, July, 1966), p. 49, Table 11H. 2Ben-David, op. cit., p. 159. 34 Two other important problems are associated with the census earnings figure. Included in farm returns are direct and indirect government subsidies, part of which are transfer payments that many feel should be excluded from earnings. For the most part, however, such payments are made to farmers not as a dole or direct welfare allowance, but for performing a particular action or function, such as restricting output in some way. Therefore, such transfers are actually a payment for services rendered. In addition, such payments are built into the market price mechanism and it would be near impossible to remove these returns accurately. Any error that is involved would be much smaller in making inter-community comparisons of the rural farm sector than when inter—sector comparisons were being made (urban vs. rural farm, for example). For those who advocate the removal of government payments as an unearned income, the measurement error might also be significant when contrasting labor surpluses between two rural farm sectors, one of which contained large commercial farmers as opposed to small subsistence farms in the other. The position taken in this study is that all net returns to farm labor is earned, regardless of the proportion of government support. Hence, the assumption advanced by many that the exclusion of income in kind from earnings is offset by the inclusion of government payments in the farm sector is invalid with respect to this analysis. One 35 must simply recognize that actual and potential per capita farm earnings would be somewhat higher with the inclusion of income in kind, but for this study the increase is dis- regarded, especially since relative and not absolute com- parisons are made. Another problem involves the manner in which farm earnings are reported. Wage and salary income was pre— ferred by Ben—David, but for farmers, the census reports only self-employed income. In addition, rural farm people also earn a considerable part of their income in nonfarm sectors as wages and salary. The only way, then, to report total rural farm earnings from census data was as total earnings, i.e., the sum of wage and salary plus self- employment income. Procedures for statistically analyzing this variable are discussed in the following methodological chapter. Earnings Capacity The earnings capacity of an individual is a function of the total sum of all his attributes, faculties, talents, and attitudes, plus the characteristics of his physical and social environs. The ability of a person to earn is thus dependent on his alternative opportunities, part of which are self-made and developed, and others the result Of the community and labor market structures in which he participates. 36 Thus, the potential earnings of persons are obviously the function of a conglomerate of forces, some of which can be quantified with some difficulty, such as the influence of industrial structure, age, education, and race, while others are essentially unmeasurable, for example, attitudes toward work, experience, future expectations, health, and other amenities and behaviorisms. This thesis will attempt to measure empirically the average per capita earnings capacity (or potential earnings) of people in all communities (counties) of the United States based on a national earnings capacity relation develOped by Ben—David.l The estimated earnings capacity of persons, according to Ben David, represents a measurement of the comparability of labor. By identifying and establishing the relationship between important factors affecting the earnings of people, one can estimate average per capita losses in earnings due to underemployment or malallocation of labor resources in each community. A review of relevant parts of Ben-David's methodology and statistical relation- ships adopted for this study in estimating earnings capacity are spelled out in the following chapter. Labor Force A community's pOpulation constitutes its labor poten_. tial, but generally its labor force is substantially belov; the number of people comprising the total population of tklee lBen—David, op. cit. _ 37 area. Labor force statistics are used in this study as a proxy for the universe studied by Ben-David in relating an individual's earnings capacity to explanatory variables. In contrast to the pOpulation base in the national equation, which considered all persons 14 years of age and over in households with nonzero total earnings in 1959, county data tapes from the 25 per cent sample of U. S. pOpulation presents a cross-classification only for the labor force. The total labor force is defined by the U. S. Bureau of the Census as the sum Of all employed persons 14 years of age and over plus the unemployed who are ready, willing, and able to work if given the opportunity, and members of the armed forces.1 It thus includes all persons who are at work or holding a job and those who are seeking work, while excluding people unwilling or unable to work. The "civilian" labor force excludes members of the armed forces, and where apprOpriate, is the measure used in this study, since the same exclusion was made by Ben—David. In addi- tion, to more closely approximate Ben-David's statistical universe, only employed persons were considered in certain cases, e.g., for the industry, race, and color variables. These distinctions in the use of labor force statistics for this study and specific calculating procedures are lU.S. Bureau of the Census, op. cit., pt. 10, p. XXVI. 38 detailed in Chapter III. Total persons unemployed plus members of the armed forces comprise from 5 to 10 per cent of the total labor force of most counties. Removal of earnings for members of the military service from total earnings is not attempted for those cases where only the total labor force is reported, and hence wage and salary earnings may be slightly biased upwards in a few instances. It should be emphasized that, potentially, there are other ways of measuring the labor force than just analyzing numbers. Productive potential of the labor force is reflected not only in terms of the number of persons able and willing to work, but also through output efficiency. But a measure of Uualabor force in terms of labor-hours of uniform quality, although highly desirable, is still very impracticable to make, and consequently, there is consid- erable controversy among many economists over the accuracy of labor force data in measuring employment and unemploy- ment. For example, can we argue that unemployment has increased as a result of a drop in total hours worked, when numbers Of persons employed does not decline? Can we say that underemployment has increased but not unemployment? These are problems that analysts of labor force numbers will likely have to live with for awhile, but in the mean- time they are the best data we have. One final Observation on the labor force concerns synonymous usage with the term "workers." They are not the 39 same, since the labor force involves a considerably larger number of persons than does the worker category, including in addition to workers, independent professional peOple, managerial persons, proprietors, and others. This distinc— tion is mentioned for the benefit Of those who may wish to compare data from this study with "gainful worker" statis- tics prior to 1940. Residence Areas Five types of residence are delineated in this report (referred to as sectors), and for the sake of conven- ience and clarity are defined consistent with the U. S. Census definitions.1 The census of pOpulation defines rural residents as persons living in the open country or in communities of less than 2,500 peOple. In‘census usage, small subdivi- sions outside small cities (less than 50,000) would be classed as rural, as would military installations, but farms inside an incorporated area Of 2,500 or more would be urban. Basic distinctions in the two sectors involves population density. In 1960, average density in rural areas of the United States was 15 persons per square mile, compared to 3,113 in urban areas. Seventy per cent of the American peOple are urban but they occupy less than 2 per cent of all land area.2 1U. S. Bureau of the Census, Op. cit., Pt. 1c, p. VI. 2 U. S. Department of Agriculture, Rural People in the American Economy, Agric. Econ. Report No. 1017(Washington, D.C.: U.S. Government Printing Office, Oct. 1966), p. 2. 40 Two categories of rural population are analyzed. Rural Farm areas are made up of all persons living on farms. The farm pOpulation is defined as persons living in rural territories on places of ten or more acres with sales of farm products amounting to $50 or more in 1959, or on places of less than ten acres with sales of $250 or more. Sales from farm products refers only to gross receipts from classified farm commodities produced on the place of residence. Rural Nonfarm population is made up of all rural residents who were not rural farm inhabitants, i.e., all persons who were neither living on farms or in urban places. In recent years this sector of the total pOpulace has grown tremendously with the movement of peOple to suburban housing develOpments outside of city limits, and with the migration of thousands of farm workers to nonfarm employment while maintaining their original place of residence. Urban pOpulation comprises a third important sector, and the largest one. According to the 1960 census this category is made up of . . . all persons living in (a) places of 2,500 inhabitants or more incorporated as cities, boroughs, villages, and towns (except towns in New England, New York, and Wisconsin); (b) the densely settled urban fringe, whether incorporated or unincorporated, of urbanized areas; (0) towns in New England and townships in New Jersey and Pennsylvania which contain no incorporated munic- ipalities as subdivisions and have either 25,000 inhabitants or more or a pOpulation of 2,500 to 25,000 and a density of 1,500 persons or more 41 per square mile; (d) counties in states other than New England states, New Jersey, and Pennsylvania that have no incorporated municipalities within their boundaries and have a density of 1,500 per- sons or more per square mile; and (e) unincorpor- ated places of 2,500 inhabitants or more. Thus the starting point for urban territory is at least 2,500 persons, whether incorporated or not. Disregarded as urban are thousands of people living in small towns under 2,500, but who are, following the census categor- ization, part of the rural nonfarm sector. The Total Nonfarm sector is included mainly for comparisons of rural farm data with the rest of the pOpu- lation. This residence area is the sum of the rural non- farm plus urban labor forces. The fifth sector to be analyzed was a tgtal across all residence areas, i.e., the sum of the farm and nonfarm labor forces. Type of residence is included in the overall earnings capacity functions, to be discussed fully in the next chapter, and then broken out to produce earnings capacity estimates of persons by each area of residence defined above. Census Places Magnetic census source tapes were utilized heavily for this study. The basic unit for these tapes was "places," and as defined by the census "refers to any concentration of pOpulation, regardless of the existence of legally nc‘ 42 prescribed limits, powers, or functions."1 Places listed cover incorporated cities, towns, villages, boroughs, and large unincorporated places outside urbanized areas. Thus, the total pOpulation is allocated into one type Of place or another. Standard MetrOpolitan Statistical Areas An important variable utilized in most income studies, including this one, is the SMSA. The Bureau of the Census presents a very elaborate definition of SMSA's which will not be repeated in this description.2 However, the general definition of an individual SMSA involves first, identifi- cation Of a central city or cities made up Of at least 50,000 persons, which constitutes the name of the SMSA, and a central county in which it is located. Boundaries of the SMSA follow county peripheries, Often overlapping state lines, except in the New England area where counties may be split. To be included a county must have at least 75 per cent of its labor force in the nonagricultural labor force and conform to other pOpulation density and labor force specifications. These areas are thus very important labor markets and exert far reaching influence on the economic activities of lU.S. Bureau of the Census, op. cit., pt. lc, p. VIII. 2U.S. Bureau of the Census, Op. cit., p. X. 43 included and neighboring communities, as later analysis will clearly show. Labor Maladjustment Before discussing theoretical issues surrounding the underemployment of labor in local labor markets, it seems constructive to focus directly, although briefly, on the central issue of this thesis, problems of human resource maladjustment. The concern is with economic underemploy- ment of labor relative to local alternatives. The main indicator of maladjustment in this study is the divergence of earnings of people as reflected by census data. Thus, the term labor maladjustment is technically synonymous with several other economic concepts very frequently found in welfare literature, e.g., labor—surplus, labor under— employment, labor opportunity costs, labor malallocation, and labor disequilibrium. Any reference to these terms is generally indicative of some degree of economic disequilibrium, and furthermore, implicit is the assumption that a unit of labor is fully "adjusted" at some level of economic return.1 It follows that a maladjustment of labor occurs when real returns to lLonnie Eugene Talbert, "A Study of the Extent of Labor Maladjustment and Differential Rates of Change in Labor Earnings for Specified Areas and Size and Types of Farms, 1949—1959" (Unpublished Ph.D. dissertation, Dept. of Econ., North Carolina State Univ.), 1963, p. 6. 44 human resources are less in their present employment than they would be in other uses, or for labor of the same char- acteristics in other employment. This focuses attention on the concept of labor comparability and {gal returns. Previous efforts to define labor comparability and issues surrounding the concept were deliberated at length by Ben- David and will be discussed momentarily.l In general, though, the problem is to standardize the earnings of labor units with similar socio-economic charac- teristics. If such standardization could be accomplished, any income differences accruing to labor would be due to 3331 labor maladjustment.2 Due to difficulties involved in measuring labor comparability with respect to earnings capacity, and standardizing real returns, 3991 income gaps are unobservable. For all practicable purposes, it is impossible to determine homogeneity between any two indi- viduals or groups with respect to their income earning capabilities, in either a static or comparative framework. Because of extreme difficulties involved in attempts to quantify factors that explain the nonhomogeneity of labor and the influence of nonpecuniary elements on real returns, measures of {gal maladjustment are next to mean- ingless. In view of these problems, this study adapts the lBen-David, op. cit., Chs. III and IV. 2Talbert, Op. cit., p. 7. 45 more realistic approach of quantifying as many measurable factors as possible which affect earnings capacity, while recognizing the nonmeasurable influences of attitudinal and other nonpecuniary conditions,as well as imperfections in the capital and labor markets (e.g., imperfect knowledge and labor mobility barriers). Results are then referred to as "apparent maladjustment" rather than real maladjust- ment, as Talbert does.l Comparability of Labor Measures of the extent of underemployment or malallo- cation of labor in any sector requires a base of comparison, i.e., some meausre of the comparability of labor resources, since alternative earnings opportunities of a labor unit must be known. Many peOple talk loosely and yet theoreti— cally correct of labor comparability in their arguments, but few have attempted to develop a comprehensive measure. For example, Charles E. Bishop states: If owners are guided by real returns in allo— cating their resources, free mobility of labor will tend toward uniformity of real wages for comparable labor services in all uses. This does not imply, however, that even in a fric— tionless society money wages will be equal for comparable labor. lTalbert, Op. cit., p. 8. 2Charles E. BishOp, "Underemployment of Labor in Southeastern Agriculture," Journal of Farm Economics, XXXVI, No. 2 (1954), p. 270. 46 Another statement by Bishop is also frequently quoted:l Economic underemployment of labor exists when the real return which owners receive for the uses of labor in a particular field of resource use is less than the real return which could be obtained for comparable resource services in other uses. These statements imply that there exists a standard- ized service unit for comparison purposes, but with no suitable explanation of how such standardizing might be done. As a matter of fact, no dependable definition of labor comparability was available to the knowledge of this writer until Ben—David's development of a national earnings capacity relationship, which affords a statistical method of estimating returns to labor units having similar social and economic characteristics across diverse segments of the economy. This method too, contains several limitations which will be noted in subsequent parts of this study, but relative to other techniques it is less restrictive, and was the first to identify in such detail the important factors affecting the earnings capacity of peOple. This functional relationship was actually designed to test the hypothesis that, on the average, agriculture does not suffer from a higher degree of labor malallocation than any other sector of the American economy. Therefore, a technique was required which would estimate the earnings lBishop, Op. cit., p. 258. 47 of comparable labor in alternative uses and economic under- employment in both farm and nonfarm sectors. Because of that flexibility, the model was also adaptable to the study of malallocations in local labor markets. Previous to the development of this national equation, several relatively weak measures of labor comparability were advanced for studying chronic disequilibriums of labor resource returns in U. S. agriculture. Without the benefit of a better empirical device, it was still obvious to many noted economists that farm labor malallocations were really the root of the farm problem in modern U. S. agri- culture.1 In develOping an earnings capacity relationship for measuring labor comparability, Ben-David reviews the arguments of these researchers and summarizes our present knowledge concerning "quality" differences in labor resources, and other problems of labor comparability.2 He eXpounds at great length on the loose application of the term comparability, noting that in any study dealing specifically with opportunity costs and resource malallo- cation problems among sectors, industries, or geographic 1See for example, Dale E. Hathaway, Government and Agriculture (New Yprk: MacMillan, 1963), p. 126; Theodore W. Schultz, Agriculture in an Unstable Economy (New York: McGraw—Hill Book Company, Inc., 1945); and Heady, et al., Roots of the Farm Problem (Iowa State University Press, Ames, Iowa, 1965). 2Ben-David, op. cit., Chapters III and IV. 48 areas, careful attention needs to be given to resource homogeneity. These arguments are accepted as sufficient since issues involved are theoretically the same regardless of the size of the labor market being analyzed. Labor as a Factor In general, the economist treats labor as a produc- tive resource, encompassing all human effort. The major distinction between labor and other types of productive factors, which may be grouped together and called "capital," is that the outright purchase alternative does not exist in the case of labor. A firm can only hire labor services; it cannot purchase a laborer outright, since slavery is illegal in the U. S. But the human agent is indeed a fundamental factor of production, and in this study its returns (measured as earnings) are based on services rendered in productive pursuits. An area's productive capacity is dependent upon the quantity and quality of production factors available to it (natural resources, capital, labor, entrepreneurhip, etc.) as well as the proportions in which they occur. But with a given store of these resources, limitations on production in an area are still determined by the most limiting factor, which frequently is size of the labor force. Many communities throughout the nation are abun- dantly endowed with great quantities and varieties of natural resources, and frequently have large accumulations 49 of capital, but yet have a very restricted output because of an inadequate labor force in terms of skills, initiative, and other attributes. In other cases, numbers in the labor force have grown tremendously, but are not good measures of productive potential of the labor force because people simply are not being paid consistent with their education, skills, and other assets. In recent years the labor resource has become quite mobile, generally moving with relative ease from area to area, industry to industry, and occupation to occupation, both within and between labor markets, but the central question is whether labor has actually been mobile enough to adjust to changes in technology and demand. The fol- lowing section discusses labor mobility and labor market equilibrium. Theoretical Considerations Introduction Since this study deals with problems of underemployed, underpaid, and malallocated human resources in communities and local labor markets of the United States, it therefore appropriately involves questions surrounding the Optimum allocation of labor resources among alternative uses, and the extent to which earnings capacity is fully achieved in the labor market. 50 Presentation of all the implications of basic micro- theories of resource allocation as they relate to the subject of efficiency in human resource use Obviously cannot be attempted in one study of this nature. Scores of books and other publications have eXpounded at length on Optimum resource use and income distribution problems as they relate to human resources in the United States. It is important, though, for a study of this kind to set in proper persepctive certain norms, in theory at least, towards which an economy, be it large or small, should direct iflsself for optimum growth and output. Policies and programs which work best for a large area in achieving a more efficient allocation of its resources, as far as optimum output is concerned, do not necessarily work best for smaller areas. Of course, the Opposite is also true. Adoption of new technologies continually generate disequilibriums in resource markets by changing factor substitution ratios without changing the price ratios. When this happens, resources of comparable earning capacity are no longer receiving equal real marginal returns in all uses, current resource combinations are no longer the most efficient, and maximum economic progress is restricted. In analyzing problems of resource adjustment in any segment of the economy, such technical intersectoral relationships are constantly changing, and in reality no true equilibrium is ever achieved. But, theoretical goals can be established 51 with respect to Optimum labor allocations, some of which are discussed in this section. A brief discussion of basic theoretical premises eXplaining forces determining the employment of human resources is presented for two reasons. First, the mal- allocation of labor partially reflects dormant earnings capacity in an area, and hence fuller utilization of under- employed 1abor resources depends heavily on more efficient allocations of these resources according to the dictates of economic theory. Earnings capacity in a given community is reflected in part by the degree to which peOple are matched with employments that fully utilize their skills and pay them their best alternative wages and salaries. Secondly, no thorough discussion of relevant resource allo- cation theory relating to local labor markets was offered by Ben-David, although a broad treatment of the more impor- tant concepts was generally well covered. Labor Market Eqilibrium Theoretically, the equilibrium prices of comparable labor services in a fully competitive buying and selling market economy, like other input and productive prices, are determined by the interactions of the forces of demand and supply. In addition, equilibrium prices vary depending upon the firwn, market, or geographic area considered. In this study, attention is focused on equilibrium conditions among local labor markets. 52 There are many controversies among economists as to the determinants of both demand and supply Of labor. NO attempt is made here to summarize these arguments.1 Instead, the discussion follows the more conventional theories of labor market equilibrium under conditions of perfect compe- tition. In a purely competitive demand economy, the price of an input unit is equated with the marginal value product of that unit to the firm purchasing it, insuring the payment Of full marginal value product to each input unit. In a purely competitive supply economy, comparable labor units tend to move from lower paying to higher paying employment (assuming moving costs are accounted for), bringing real return to approximately the same level in all jobs. But for those jobs where nonpecuniary attributes differ, money rewards for like resources will not be equalized even in a fully competitive equilibrium situation. Temporarily ignoring nonpecuniary considerations, the condition for labor market equilibrium can be established. In the fol- lowing discussion "earnings" and "wage rate" are treated as the same. Under pure competition, the individual firm's demand curve for labor is determined by conditions of marginal 1See, for example, Gordon Bloom and Herbert Northrup) Economics of Labor Relations, Richard D. Irwin, Inc. Homewood, 111., 1958, Ch. 10; and Gitlow, Op. cit., p. :29j3 ff. 53 productivity theory, i.e., an employer's utilization of labor must be determined by weighing the cost of employing additional labor against the contribution which the added labor is eXpected to make to the revenue of the firm. Incremental changes in total revenue must of necessity be rough and approximate because of fluctuations in product prices, but in order to maximize profits the firm must compare the cost of a unit Of labor with anticipated changes in marginal revenue. The labor demand curve for the firm is the marginal value product obtained by the firm from employment of varying amounts of labor (referred to as an MVP curve). The "market" demand curve for labor is the sum of all MVP curves of firms in a given labor market, assuming product prices and other input prices remain constant. On the other hand, the supply curve for labor services in a given labor market is not clearly defined, since the supply of labor deals with the behavior of individuals, and it is often unclear just what they are trying to maximize. Custom, tradition, and legal and institutional restrictions largely set the number of hours of work for most individuals, and a relatively small proportion of the total labor force is able to change substantially the number of hours worked in response to a potential increase in earnings (increased wage rate). For the economy as a whole, this might indicate a completely vertical or positively sloping labor supply 54 curve, but for the individual firm in a competitive labor market, the short-run supply curve for labor is more likely completely elastic, i.e., any number of workers are avail- able at the same wage level. Because of the influence of several firms acting together and the possibility of labor mobility in and out of a local labor market, the labor supply curve for a particular market will likely be posi- tively sloping, instead of perfectly elastic or inelastic, but its position is hard to determine due to several other forces to be discussed shortly. Given the demand and supply surves for labor in a particular market, their intersection represents a compet- itive equilibrium, such that there exists no impetus to upset the price-quantity adjustment. Since the market is competitive each firm can hire as much of a "comparable" labor unit as it wants at the market price. Similarly, every qualified worker who wishes to work at the market price can obtain a job. Theoretically, then, basic market pressures would establish and maintain an earnings- employment equilibrium for comparable labor. Shifts in demand or supply would generate new pressures that would again bring the market into equilibrium. Any position away from the point of equilibrium is considered a malad- justment of labor. Geographic or regional differentials in earnings are to be eXpected because labor markets are basically local 55 in character. But over some time period, these differentials should diminish as the labor force becomes mobile and searches out the areas with maximum-pay levels, and capital searches out areas with minimum-pay levels. The following diagram illustrates the long-run movement Of labor and cap— ital needed to equalize rates of return between two labor markets. Equilibrium adjustment between occupations, indus- tries, Or regions could be shown in a similar manner. VMP of labor VMP of labor in labor in labor Mkt. Mkt. No. 2 No. l VMP Quantity of Labor per Period Figure 2.1—-Inter-Market Labor Movement. Source: Adapted from Marshall R. Colberg, Human Capital In Southern Development, 1939-1963 (Chapel Hill: University of North Carolina Press, 1965), p. 32. 56 Two different labor markets are depicted, VMP repre- 1’ senting the value marginal product curve (or demand curve for labor) for labor market one, and VMP2, labor market two's labor demand curve. Both curves assume the existing stock Of material capital fixed, but material capital per worker is assumed greater in labor market 1, accounting for its higher position in the diagram. In addition all other labor markets are assumed in equilibrium so that any move— ments of labor are between labor markets 1 and 2. Current competitive wages (or levels of earnings) are determined by the quantity of labor in each labor market, assumed to be O'X workers in labor market one, and OX in labor market two. If psychic incomes of the two markets are ignored, so that only labor market earnings are considered, the labor input per time period would need to be adjusted by MX, i.e., MX workers would have an economic incentive to leave labor market two, and migrate to labor market one. Such movement would continue until money wages in the two labor markets are equated at E. Total gain in national income would be the shaded area of Figure 2.1, EYK, a gain emanating from the better allocation of labor between the two labor markets. If labor market two were able to acquire part of the material capital stock of labor market one, and thus raise its VMP2 curve, the shaded area would diminish accordingly, i.e., a smaller out-movement of human resources from area 57 two would be needed to establish equilibrium between the two areas. Further, it should be noted that full adjust— ment of resources may not equate money wage rates, because of the presence of nonpecuniary factors in one labor market, e.g., differences in climate, congestion, recreational Opportunities, social attitudes, etc. The economic value of these are impossible to quantify accurately, but def- initely influence the mobility patterns Of individuals. The discussion up to this point is representative of the traditional view of labor allocations, i.e., through the inducement of differentials in wages (potential earn- ings in alternative jobs), and assuming that labor is moti- vated by rational economic decisions, labor mobility occurs until an "equilibrium" is established between the supply and demand of labor resources in all markets. In this View, wage rates are changing continuously and persons are moving among jobs. Thus, wage determination and labor mobility are very closely related, one conditioning the other, and an equilibrium level of earnings is achieved when there exists no inducement for any worker to change his job. At such a point, an "Optimum" distribution is presumably accomplished. A second, short-run View Of labor allocation is now rather widely recognized by unions, government adminis- trators, and many economists as being more practical.1 lGitlow, op. cit., pp. 343-345. 58 Instead of the traditional view of the labor market as a totality of jobs for which the same wage is paid, they advocate that the labor market is a mechanism which dis- tributes jobs, that is, the quantity of labor supplied becomes a function of job Opportunities instead of wage rates. Basically they argue that the level of earnings and labor mobility may be quite separate processes, and that a single wage level for comparable labor may be a sure sign of collusion. Inequalities in earnings are felt to be the result of several imperfections in the job market, such as a general lack of knowledge on job Oppor- tunities, restricted occupational evaluations of oneself, fears of uncertainties involved in movement, and inertia. Thus, workers are not viewed as very alert participants in the local labor market. These market imperfections and restrictions are thought to destroy the traditional View Of labor market equilibrium. Institutional rules relegated by unions, employers, and government policies are considered more significant sources of movements in earnings than the action of market forces (supply and demand). Hence, institutional and leadership comparisons have been substituted for labor mobility as the main basis for relating earnings, and any "equilibrium" level of earnings which exists for comparable labor units is mainly the result of policy rather than market pressures. 59 Consistent with this View, it may be that the short- run mobility of persons, especially those enticed to enter the labor force from some nonemployment status, is mostly a function of job Opportunities, independent of the earnings involved. Thus, for a particular market, short-run changes in the number of persons in the labor force become a func- tion of movements in some index of jobs available, which may or may not include the level of earnings. Both of these views of labor allocations in a given market have merit under certain conditions. The traditional marginal productivity View is mainly a full—employment argument, while in actual practice, with ups and downs in the business cycle, the latter postulate that the market distributes jobs is perhaps the more accepted. In the case of this study the second approach may also be more accep- table, because of institutional restraints, public poli- cies, and poor knowledge of job opportunities in so many local labor markets. As reported later in this study, wide disparities were found between actual and potential earnings of persons in local labor markets, while neigh- boring labor markets appeared well-adjusted, one indica- tion that peOple are not necessarily in their best alter- native employment. Finally, it is an accepted fact that market imper- fections do exist on both the demand and supply side of local markets. Differences in the level of earnings, and 60 changes in wages, evolve from all kinds of institutional forces, such as industry—wide union—employer bargaining, and current government wage and price guidelines. On the supply side, market frictions are generated by relative immobility of labor caused by imperfect knowledge, fears, distance, job availability, personal relationships, etc. It appears obvious that the earnings level is not the only instrument for reallocating labor, although it is probably the most important variable, especially during full- employment periods. During high unemployment cycles, job availability is likely more important. Thus, within a given local labor market, differentials in the level of earnings for comparable labor units results from many things, including degree of responsibilities required, regularity of employment, hazards, differences among plants in unit labor costs, product demand, and other conditions of work. Among local labor markets, differentials in the level of earnings for comparable labor services may be economically justified on the basis of differentials in the ratios of labor to capital at a point in time, differ- ent pOpulation growth rates, and different capital forma- tion capacities. Because of these differences, unemploy- ment and underemployment of labor may be experienced simul- taneously while shortages are occurring in other labor markets. 61 If all labor market areas conformed exactly with the theories of pure competition in the demand and supply of inputs, then any differences in earnings capacity per capita among labor markets would be due to differences in input quality or transfer costs and the economic and insti— tutional structure of labor markets. Thus, Optimum resource allocation does not quarantee equality of per capita earnings potential in all areas, due to wide dif- ferences in employment Opportunities and endowments. The inability of an area to attain Optimum allocation of resources does indeed reduce the earnings capacity of that area. In describing the thoery Of labor market equilibrium and eXpected labor mobility, problems of labor resource fixity have essentially been ignored, a case in which a resource in its present employment is earning less than its marginal acquisition cost (so that it would not pay an employer to acquire an additional unit), but more than its salvage value (so that it would not pay to sell units already employed). By this definition, a resource which is fixed cannot be malallocated since it is in its best alter- native employment. Hence, the transferability of labor resources for the purpose of reducing or alleviating prob- lems of malallocation considers only those labor resources for which their salvage value exceeds their earnings, i.e., those which are mobile. Problems of labor resource 62 fixity characterize many rural communities of the nation, problems that would likely disappear in an extremely long- run context. CHAPTER III METHODOLOGICAL PROCEDURES, DATA SOURCES, AND ESTIMATING TECHNIQUES Introduction A considerable part of the analysis put forth in this study revolves around estimates of the expected earnings capacity of individuals in local labor markets of the United States. As a result, substantial attention was devoted to selection of the (best alternative) method for obtaining such data, one of the several procedural problems to be discussed early in this chapter. The overall intent of the chapter is to provide a detailed resume of the procedures underlying estimation techniques utilized to generate data for various parts of the study. The study is based on an extremely large volume of empirical documentation emanating from functional relation— ships developed in a recently completed doctoral thesis.1 Because of the importance of Ben-David's empirical results to this report, his estimating techniques and their adap- tibility to this thesis are summarized at the outset. It was found necessary to redefine some of the important terms lBen-David, op. cit. 63 64 lused and offer seemingly detailed eXplanations of calcu- lating procedures in various places, mainly for the benefit of those who may wish to make modifications in my estimates, or pursue a different approach. Some important criterion and assumptions develOped by Ben-David are footnoted rather than discussed again in this chapter, but are nevertheless very important to the estimates analyzed in later chapters. Hopefully, though, the procedures outlined in the following sections zare self-contained with respect to detail. The concept of earnings capacity was defined briefly in the previous chapter and some of the reservations and limitations surrounding it were spelled out. The problem now at hand is that of designing a method for estimating the eXpected earnings capacity of persons in local areas (defined as counties) throughout the United States. One such method for estimating the potential earning power of persons or groups of persons with the same attri- butes is to compare them with persons of similar credentials in other alternative employment. The difference between what a person is earning in his present employment and what he could earn in his highest paying alternative employment constitutes earnings foregone, or his Opportunity costs of remaining where he is. His earnings capacity is said to be what he could earn in his highest paying alternative. Many studies have utilized this procedure for documenting the existence of malallocated resources in a given economy, in 65 a given industry or occupational category, or in some other sector (type of residence) of an economy. The method has also received wide application in opportunity cost studies for particular groups of people, where estimates of income losses to society are desired. For circumstances in which estimates Of the earnings capacity of an individual in a particular endeavor are desired, at a particular point in time, this process provides a feasible, quick measure. But, if the earnings potential or eXpected per capita earnings for persons in some local labor market are desired, where there is a considerable mixture of talents, skills, and aspirations, a better method is needed. Provision of such a method was the main objective undertaken in a study by Ben-David, in which human resource returns were studied in considerable detail. Earnings Capacity Estimation Process As implied above and in an earlier chapter, this study is the second of a broad project dealing with returns to human resources in all sectors of both national and local economies. This phase of the project follows an extensive analysis at the national level and is heavily dependent on some of the empirical relationships estab- lished by Ben—David. In his study, Ben—David developed several functional relations, correlating individual earnings of persons to several social and economic char- acteristics in an attempt to explain as much variation as 66 possible in the per capita earnings of individuals, while providing equations that would predict "average" per capita earnings potential. The following discussion concerns the basic procedure followed in fitting these equations. Ben-David's main objective in designing these rela- tionships was to test the so—called labor surplus hypothesis --that is, the assertion that there exists surplus and malallocated labor in the agricultural sector of the United States economy. His estimated earnings capacity functions for labor served as a means of defining and measuring "comparable labor." Population and Sample Several separate dummy variable, multiple regressions were run by Ben-David but only four were presented as acceptable. The equations were based on a cross-classification of characteristics of individuals from the one-in-one thousand sample of the 1960 population Of the United States, reported as individuals in households.1 The study sample pertains to all persons 14 years old and over with non-zero earnings in 1959 (the period in which the sample was drawn). A random sub-sample of the l/l,000 Sample Of POpulation and Housing was drawn, which finally resulted in the utilization Of 90,392 individuals that were 14 years or Older and had lU.S. Bureau of the Census, U.S. Census of Population and Housing: 1960, l/l,000 and 1/10,000, two National Samples of the POpulation of the United States: Description and Technical Documentation. 67 earnings in 1959. The reader is referred to the previous chapter for a definition of what constitutes earnings within the context of this study. All sectors of the pOpulation were sampled (i.e., both rural and non—rural places of residence) so that persons in all occupations would be represented and the functional relationship would more nearly depict a weighed average, i.e., a more accurate estimate of equilibrium levels of earnings. From the chosen sample of individuals, several characteristics were iden- tified as possible variables influencing earnings capacity. Many of these were drOpped following a test of their sig- nificance through a method described in the following section. AID Method of Variable Selectivity -There is an almost infinite number of variables which influence total earnings in some way, but unfortunately all of them could not be quantified and presented in a statistical analysis of this kind. Many influences such as attitudes, aspirations, and other behaviorisms have frustrated researchers in this area for many years, and although some crude social indexes incorporating such measures have been devised, Ben-David chose not to grapple with them, but instead, to stick with more conventional and directly quantifiable characteristics available from census data. Indeed, the census data tapes imposed a 68 limitation on the kinds of eXplanatory variables that could be used, since cross classifications of individuals were desired. Given the large number of possible variables that were available from the 1/100 census sample, the next step was to Choose those variables thought to be of greatest relative importance in explaining total variation in the earnings of persons. To accomplish this, a statistical method known as The Automatic Interaction Detector (AID) computer program was used, which maximizes one's ability to predict values of the dependent variable. This method, develOped by Sonquist and Morgan,1 is summarized in signif- icant detail in Ben-David's thesis (pp. 8-9 and pp. 93—98), and forms an integral preliminary segment of his empirical analysis. Many of the variables considered were eliminated because of their insignificance but other "indicative" influences were drOpped simply because a "causative" rela- tionship was desired for predicting earnings capacity, which contained only basic structural parameters. The Earnings Capacity Equation, Results of the Automatic Interaction Detector program were followed in deciding which explanatory variables were 1John A. Sonquist and James N. Morgan, The Detection of Interaction Effects, A report on a computer program for the selection of Optimal combinations of eXplanatory vari- ables (Ann Arbor: University of Michigan, Survey Research Center, Monograph No. 35, 1964). 69 most important for inclusion in an ordinary least squares regression. All included variables were transformed to a dummy variable format to generate better estimates of interaction effects and to avoid the assumption of linearity in the independent variables. This obviously is a very important assumption since individuals fit either one social class or another, e.g., a person is either male or female, white or nonwhite, in one age or industrial category and not another, etc. Each basic variable used (called tOpic group) was subdivided into appropriate categories (called vari- ables), and one variable was omitted from each tOpic group to permit an inverse for the regression matrix. The effect of each omitted variable is of course contained within the constant term of the estimated regression equation. The dummy variable estimating technique utilized by Ben-David makes it possible to employ one of his equations for predicting earnings capacities of persons at the local level for several areas of residence. The national equations presented by Ben-David varied from one containing 127 variables, 76 of which were inter- action variables, to one containing 37 variables and no interaction terms. Becasue of census data limitations at the local level, around which this study revolves, and the problem of modifying the constant term for any variable that would have to be omitted because of nonavailability of data, the equation without interaction variables was 70 chosen for estimating purposes in this study. Coefficients and other relevant data pertaining to this equation are presented in Table 3.3. In the discussion hereafter, this equation is often referred to as "the national estimating equation." Predictability of Equation The percentage of total variation in per capita earnings explained by the 36 variables included in the regression of Table 3.1 is shown in Table 3.2, along with other solutions to the regression matrix. At first obser- vation, the multiple correlation coefficient (0.63) and the coefficient of multiple determination (0.40) appears low, relative to R2's yielded by other types of regressions often reported in economic literature. But one should be cognizant of the nature of the relationship being fitted. Many others have attempted to run similar functional rela- tionships, and none have been successful in eXplaining this much of the total variation, except in cases where highly aggregated data were used and perhaps some isolated studies of specific areas that are unknown to this writer. Because of the importance of this statistical relationship to data generated for this study, the reliability of Ben- David's results are compared with previous measurements. Perhaps the most important recent study in which an attempt was made to estimate earnings capacity was one done 71 szmm. . 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I 2\ ://81/\F-Northeast estimated ‘0 ’ 5’ a \ 40 r : 1 \ U.S.estimated '5, 3 Northeast ac ual 2 \ 30 . . 3 30 » 2 Northeast actual ’ ' / ;><;’ U.S.actual-y’ 20 . ’ 20 > I I 10 I - / L I, a 10 > / ‘9 / “’4 ..— J a a ahb- — p- —4 { less 1000 1500 2000 2500 3000 3500 4000 4500 9000 5500 less 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 than to to to to to [C to to to or than to to to to to to to to to or $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more Dollars of Per Caplta Earnings Dollars of Per Capita Earnings farm Residence Area ’/\6—Northeast estimated 3000 3500 4000 4500 5000 5500 Percent (e) Total Non 70 f 60 b . U.S. estimated—45'; 50 p 3 I. 40 » f f): \ 30 » Northeast actual I 20 > f’ / U.S.actuagf’ 10 b / l I , _j 100a 1000 1500 2000 2500 than to to to to to to CO to to or $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more Dollara of Per Capita Earnings Figure ”.1. Distribution of Count Estimated Per Capita Earnings ies by the Magnitude of Actual and , Northeast and U. 8., 1960. 60» 50» 40' 30» 20? 80 70 60 50 40 30 20 10 «J 70» 121 Percent (a) Total, All Residence Areas Percent (b) Urban Residence Area P 80> A 70 ’ Northeast estimated-J‘ 60 > .ofU.S.Estimated I \ .. so» \ .rfi : 7‘ . .D. ’ . / . ‘0 | U.S. estimated--O,‘ " '- \4—Northeast .5 .1 Estimated 30 L Northeast actual '. \ 1/ 2° ’ I I U.S.Actual-fi/ U.S.actualdl/ I ortheast Actual 10 L / . I, I, 'o \‘ l J I / a.--a0"_/ J a 0:4‘“-—___ 1esa 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 less 1000 1500.2000 2500 3000 3500 4000 4500 5000 5500 than to to to to to to to to to or than to to to to to to to to to or $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more Dollars of Per Capita Earnings Dollars of Per Capita Earnings Percent (c) Rural Nonfarm ReSidence Area Percent (‘3) Rural Farm Regidence Area * 80 b b 70 r p A 60 ' . . \ U.S. estimated—6:, , Northeast estimatedfi I. 50 , .- .' '.\ r /\(—Northeast estimated b ."/ 1,\ ‘0 ’ '. .‘ \ U.S.estimated '3’ .' Northeast ac ual '. \ ’ - ' 3o . - Northeast act 1 ’ ”:>’\. L I 20 U.S.actuaH/ 3. r ’ . 10 I, .\ _’/A ..—!J a a I.J£L‘l---n———a , . 0'" .— less 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 less 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 than to to to to to tc to to to or than to to to to to to to to to or $1000 1499 1999 2499 2999 3499 3999 4499 4999' 5499 :rc'rc $1000 1499 1999 2499 2999 3499 3999 4499 4999 5499 more Dollars of Per Capita Earnings Dollars of Per Capita Earnings Percent (e) Total Nonfarm Residence Area 7. g 60 b U.S . estimated—H31. .:/2’/\4—Northeast estimated 40> .' SO. 30 20 10 leaa 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 than to to to to to to to to to or $1000 1499 1999 2499 2999 3499 2999 4499 4999 5499 more Dollars of Per Capita Earnings Figure “.1. Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, Northeast and U. 8., 1960. 122 Within the region, actual per capita earnings for the total labor force in the New England counties appeared to be lower in 1959 than in the counties of the Middle Atlantic division (Appendix Table B-2). Actual earnings of persons in approximately 95 per cent of the New England counties, all of which were in Maine and Vermont, fell below $2,500, compared to 17 per cent in the Middle Atlantic division. At the other extreme, actual earnings in only 9 per cent of the counties in the New England area were $9,000 or better, as opposed to 22 per cent of the Middle Atlantic counties. The heavy share of Northeastern counties in the Middle Atlantic division (74 per cent) strongly influences the regional distribution of counties among actual earnings categories. The larger proportion of counties in the Middle Atlantic division having actual earnings in the upper earnings groups reflects the prevalence of large metropolitan labor markets, such as New York, Philadelphia, Trenton, Pittsburg, and Buffalo, and their widespread influence on surrounding communities. As seen from the second distribution of Table A.2, a high proportion of the nation's counties in the largest actual earnings categories were in the Northeast in 1959. The region contained more than one—fourth of all counties in the nation with actual per capita earnings of $u,5oo or more, compared to its 7 per cent of the total 123 number of counties. All but two of these counties were located in the Middle Atlantic division near large indus— trial-urban centers. The distribution of Northeastern counties by actual per capita earnings in their urban labor force follows a dispersion similar to that for the total labor force (Figure H.1b). The modal earnings class is $3,000 to $3,499, although actual urban earnings of persons were higher than $3,500 in 52 per cent of the region's counties. In only 13 per cent of the Northeastern counties were actual earnings of urban people less than $3,000 in 1959, compared to 36 per cent for the Nation. As expected a larger proportion of counties were in the high per capita earnings categories for the urban sector than was true for the total labor force. This is due, of course, to the inclusion of earnings from all residence areas in the total labor force, and the fact that average earnings in some counties of the Northeast are heavily weighted by rural farm population, which exerts downward pressure on average earnings for the total labor force of these counties. Urban per capita earnings in over one-fourth of the New England counties were below $3,000 in 1959, compared to only 7 per cent of the Middle Atlantic counties. But the urban earnings of persons in almost three times as many Middle Atlantic as New England counties were $9,000 129 or more, resulting from the lack of any significant urban pOpulation base in most New England counties. Moving from the lowest urban earnings categories of Table 9.2 to the highest, the Northeast had an increasing proportion of U. S. counties in each category. The same trend was also true for the Middle Atlantic division, while the comparable New England distribution was very erratic. The distribution of counties according to actual per capita earnings in their rural nonfarm labor force also conforms closely to that of the total labor force (Figure 9.1c). Except for eight counties in northern Maine, actual earnings of persons in this sector averaged $2,500 or greater for every Northeastern community. Nationally, there were 1,198 counties, or 90 per cent of all U. S. counties, in which average actual earnings of persons in the rural nonfarm sector were below $2,500 in 1959. Thus, relative to the nation, actual earnings of rural nonfarm persons in most counties of the Northeast were reasonably high. Another indication of the relatively high earnings of rural nonfarm people in the Northeast is seen in the second distribution of Table “.2. Over one-third of all U.S. counties in each of the three highest rural nonfarm earnings categories were in the Northeastern region in 1959. In the lower earnings categories, the region's share of U. S. counties was very low (less than 5 per cent). 125 Again, the regional distribution for this sector closely parallels that of the Middle Atlantic, in spite of the heavy frequency of counties in the lower earnings categories in the New England division. However, 74 per cent of the Northeastern counties are in the Middle Atlantic, and over 80 per cent of these were in earnings categories of $3,000 and over. As expected, the distribution of counties by actual per capita earnings in the rural farm sector differs markedly from the three distributions discussed in the preceding paragraphs, as seen in Table 4.2 and Figure 4.ld. In only one—fourth of the Northeastern counties were the earnings of farm persons $3,000 or more. The modal class of the regional distribution was $2,500 to $2,999, but 38 per cent of all counties were in the two categories immediately below this mode. The modal earnings category in the national distri— bution of counties for the rural farm sector was even lower, however. As seen in Figure 4.1d, the U. S. distri- bution peaks betWeen $2,000 and $2,499, but in two-thirds of all counties in the country (see Table 4.3) average earnings of persons in the farm labor force were less than $2,500 in 1959. Thus, it appears from these distributions that persons in local farm labor markets of the Northeast were faring considerably better in terms of per capita _earnings than were rural farm individuals in other com- munities of the nation. 126 A much lower percentage of the nation's counties in the higher rural farm earnings categories were in the North— east than was true for the three sectors discussed in the preceding paragraphs, although the regionfsshare of counties in all earnings classes still exceeded its 7 per cent share of all counties in the country. Low farm earnings of persons in the Northeast were heavily concentrated in the upper New England counties, mostly in Maine, New Hampshire, and Vermont. Neither of these three states had a single county in which per capita earnings in their rural farm sector were as high as $3,000, and in Maine and Vermont farm earnings of persons in over 85 per cent of all counties in the state were less than $2,500. The distribution of counties was considerably better for the Middle Atlantic division, although 82 per cent of the counties in Pennsylvania, and 75 per cent in New York were in farm earnings categories less than $3,000. Ten out of thirteen Northeastern counties in which farm earnings of persons were less than $2,000 were in the northern portions of the states of Maine and Vermont, an area frequently designated as one of the chronic rural poverty "pockets" in the United States. Thus, the Northeast is not void of some of the most chronically depressed local farm labor forces in the nation, although its percentage of the total is relatively small. 127 The total nonfarm sector of each county represents the sum of persons living in the rural nonfarm and urban resi- dence areas. It is included mainly as a base against which the earnings and labor maladjustments of farm people can be compared. The distribution of counties according to actual per capita earnings in this sector closely follows, but generally lies between the urban and rural nonfarm distri— butions Just discussed. No further elaboration of this sector is offered other than the data of Tables 4.2 and 4.3, and the distributions shown in Figure 4.1e. Estimated Per Capita Earnings Estimated average earnings capacity for each residence area of each county was computed from the residence equations discussed in Chapter III. Distributions of counties by categories of average estimated (or potential) earnings of persons are summarized for the nation and the Northeast in the lower half of Tables 4.2 and 4.3, and in Figure 4.1. As noted earlier, this index provides a measure of the expected average earnings of persons based on a cross classification of individuals in the U. S. with similar social and economic characteristics. Estimated earnings capacity of persons in the total labor force of each county depicts a weighted average of individual earnings across all_residence areas. There was considerably less variation among Northeastern counties in the average estimated earnings of persons than for 128 actual earnings, regardless of the type of residence area. The regional distribution of counties by potential earnings of their total labor force is relatively narrow, with 96 per cent of all counties in the $2,500 to $3,500 range. The majority of counties (50.2 per cent) were in the same modal class as actual per capita earnings for this sector, $3,000 to $3,499 (Figure 4.1a). Another 46 per cent of the region's counties were in the $3,500 to $3,999 estimated earnings category, however, compared to only 21 per cent for the actual earnings distribution. Thus, for the total sector, variability in potential earnings of persons among counties of the Northeast was considerably less than the variability of actual earnings. As seen in Figure 4.1a, the comparable distribution of counties for the nation peaks in the same estimated earnings category as that for the Northeast. However, estimated earnings of persons in only 15 per cent of all counties in the country exceeded $3,499 in 1959, three times-fewer than the 48 per cent in the Northeast. Within divisions, particular states tended to have heavy concentrations of counties in a given earnings category, when considering the county's total labor force. In Maine, for example, estimated earnings of persons in 81 per cent of all counties were between $3,000 and $3,499, compared to 70 per cent in this class in New Hampshire, 86 per cent in Vermont, 57 per cent in New York, and 51 per 129 cent in Pennsylvania. In Massachusetts, 86 per cent of all counties were in the $3,500 to $3,999 estimated earnings category, in Rhode Island 80 per cent, Connecticut 75 per cent, and New Jersey 71 per cent. These heavy state con- centrations of counties in one of these two potential earnings groups produced an almost equal proportion of counties in the two earnings groups for the region as a whole. The Northeastern region contained a relatively large percentage of the nation's total counties having high estimated earnings in 1959. Approximately one-fourth of all counties in which average potential earnings were $3,500 or more, were in the Northeast, and more than two— thirds of these were in the Middle Atlantic division. 0f the nation's counties in which actual earnings of persons were the same amount, only 16 per cent were in the North- east. Variability of urban and rural nonfarm potential earnings among counties of the Northeast was less than that computed for the total labor force (see Figure 4.1). In 163 out of 202 (81 per.cent) Northeastern counties having an urban labor force, estimated earnings of persons were between $3,500 and $3,999. In no counties were estimated urban earnings less than $3,000 or more than $4,500. In the national distribution, potential earnings of urban persons in all but 3 per cent of all counties 130 were contained in the $3,000 to $3,999 range, but in contrast to the Northeast, only 50 per cent were in the $3,500 to $3,999 category. Fewer counties were concentrated in the modal class of potential earnings ($3,000—$3,499) for the rural nonfarm sector, but again average per capita earnings in 97 per cent of all counties were in the same modal class, $3,000 to $3,499. However, potential earnings of rural nonfarm persons in 28 per cent of the nation's counties were less than $3,000, compared to 0.5 per cent of the Northeastern counties. Variability in potential earnings of persons in the total, urban, and rural nonfarm labor forces of counties in the New England and Middle Atlantic divisions followed almost the same distributions as those shown for the Northeast region in Figure 4.1. Within divisions, however, the modal class of potential earnings varied from state to state, although the range of the distribution across classes did not change. Potential earnings of persons in the rural farm sector of most counties in this region were in either the $2,500 to $2,999 or $3,000 to $3,499 earnings categories. However, average potential earnings in almost one-half (46 per cent) of all counties were less than $3,000. Thus, the distribution of Northeastern counties for the farm sector is heavily weighted by counties in which average 131 potential earnings were low relative to the other residence areas. In comparison, the national distribution was also heavily concentrated, with potential farm earnings of persons in 58 per cent of the nation's counties contained in the $2,500 to $2,999 range, one category lower than the mode for the Northeast. The Northeast, in 1959, had a much greater percentage of the nation's counties in which potential earnings of persons in other residence areas was low. This again largely reflects the very low farm earnings of persons in the upper New England counties. The distribution of counties by average potential earnings in the total nonfarm sector lies between the urban and rural nonfarm distributions, as shown by Table 4.2. Average potential earnings for the nonfarm labor force of most Northeastern counties varied from $3,000 to $3,999, with 46 per cent of the region's counties in the lower half of this range, and 62 per cent in the upper half. The national distribution of counties for the total nonfarm labor force was almost perfectly normal, as seen from Figure 4.1e, with the mode one potential earnings category lower than that for the Northeast. In three- fourths of all counties in the U. 8. potential earnings of nonfarm persons averages less than $3,500, compared to less than one-half of all Northeastern counties. Thus, although the variation in potential earnings among counties of the 132 Northeast and the nation is about the same, the frequency distributions are quite different. In summary, the average potential earnings of persons in over 95 per cent of the Northeastern counties varied from $3,000 to $3,999 in 1960, for the county labor force of all residence areas, except the rural farm. In 95 per cent of all counties, farm persons were earning on the average, between $2,500 and $3,499. In each of the five residence sectors, the distribution of Northeastern counties favors higher earnings categories more heavily than does the national distribution of counties. Additionally, a high percentage of the counties in the U.S. in which average potential earnings of persons were above $3,000 were in the Northeast, for all residence areas, relative to the region's 7 per cent share of the actual number of U. S. counties. Apparent Labor Maladjustments The allocation of counties in the Northeastern region according to the extent of labor maladjustment is shown in Table 4.4 for the five residence sectors of each community. Labor maladjustments in each residence area are discussed separately in the following sections. Total, All Residence Areas.--This section is concerned with apparent labor maladjustments (earnings gaps) in the total labor force of counties in the Northeast. In addition TABLE 4.4.-—Distribution of Counties by Magnitude of Labor 133 Maladjustment, for Each of Five Residence Areas, 1959. NORTHEAST REGION County Area $100 or $101— $401- $701 Totall of Residence less $400 $700 or more Number Total 99 65 35 18 2172 Urban 104 49 31 18 2022 Rural Nonfarm 105 58 30 15 2082 Rural Farm 55 54 56 41 206 Total Nonfarm 103 60 36 18 217 Percentage Distribution within Region Total 45.6 30.0 16.1 8.3 100.0 Urban 51.5 24.3 15.3 8.9 100.0 Rural Nonfarm 50.5 27.9 14.4 7.2 100.0 Rural Farm 26.7 26.2 27.2 19.9 100.0 Total Nonfarm 47.4 27.6 16.6 8.3 100.0 Per Cent of U.S. Total3 Total 12.0 10.3 4.8 1.9 6.9 Urban 12.8 9.3 7.0 5.0 9.4 Rural Nonfarm 13.8 9.7 4.3 1.5 6.8 Rural Farm 9.9 13.6 8.8 2.8 6.7 Total Nonfarm 12.3 9.1 4.8 2.0 6.9 1 This column represents the number of counties for the whole region. 2Some counties had no population in this residence area in 1960. 3 Each percentage in this distribution represents the proportion of U.S. counties, with labor maladjustments of a given size, that were in the Northeast, for a given area of residence in the county. Thus, rows and columns do not sum to any particular total. 134 to summary Table 4.4, a shaded map is provided in Figure 4.2 showing the extent of labor maladjustment (average dollar amount per person in the total labor force) for each county in the United States. As seen in Table 4.4, almost 46 per cent of all counties in the Northeast were relatively free ($100 or less) of labor maladjustment problems in their total labor force in 1959. On the other hand, 18 counties, or slightly over 8 per cent of the region's total counties, were in the "severely" maladjusted category (greater than $700). In the next largest maladjustment class, $401 to $700, were another 16 per cent of all counties in the region. Thus, in one-fourth of the Northeastern counties labor maladjustments were greater than $400, a proportion that appears relatively small compared to the nation's 54 per cent in this same maladjustment group (Table 4.5). Nationally, only 26 per cent of all counties had a well- adjusted total labor force, a little more than half the comparable proportion in the Northeast. This distribution of counties for the Northeast actually peaks in the lowest labor maladjustment category, with a rapidly diminishing proportion of counties in each successively larger category. But for the country as a whole, the distribution was relatively flat across the four levels of labor malad- justment shown in Table 4.5. 135 TABLE 4.5.-—Distribution of Counties by Magnitude of Labor Maladjustments, for Each of Five Residence Areas, 1959. UNITED STATES County Area of $100 or $101- $401— $701 or T t 11 Residence less $400 $700 more 0 a Number Total 822 634 733 942 3,131 Urban 814 528 443 357 2,1422 Rural Nonfarm 762 601 700 1,015 3,0782 Rural Farm 553 396 640 1,475 3,0642 Total Nonfarm 840 656 745 889 3,1302 Percentage Distribution Within the Country Total 26.3 20.2 23.4 30.1 100.0 Urban 38.0 24.6 20.7 16.7 100.0 Rural Nonfarm 24.8 19.5 22.7 33.0 100.0 Rural Farm 18.0 12.9 20.9 48.1 100.0 Total Nonfarm 26.8 21.0 23.8 28.4 100.0 1This column represents the number of counties in the nation having a labor force in the indicated area of residence. 2Some counties had no population in this residence in 1960. Of all counties in the nation with a severe maladjust- ment of labor (Table 4.5), less than 2 per cent were in the Northeast, and one-half of these were in the northern-most counties of Maine, as shown by the heaviest shaded counties of Figure 4.2. The remaining counties were widely scattered throughout the region. The characteristics of the labor force in these counties were such that either low or negative coefficients were aggregated in the earnings capacity 136 Figure 4.2--Apparent Labor Maladjustments by Counties, Total Labor Force, 1959. K u. 5'" I; "“4 CI! LAM/ff" . , w Lulu .' "d!" mum A . - PM: an m. 1 .2.» m... Michmnn m. _, XVI-m Cunmz AkmnI-m, nun ”u numb-u angfiown m...— 7 u... M . l. ,1": M Cfl’m’" m...“ ‘1 Minn... SCAL 2 50 ‘00 150 00 Total Labor Force a. M1259 350 400 460 m m Figure 4.2——Apparent Labor Maladjustments By Counties, Atlnnlic Octan 21 138 equation for several of the dummy variables. For example, in the northern counties of Maine, a relatively high proportion of the total labor force in each county is either {rural nonfarm or rural farm, in either the youngest or oldest age group of the labor force, has relatively low jlevels of educational attainment, and is salaried. In axidition, a relatively large share of peOple are in indus- ‘tries with coefficients negatively related to earnings, such as agriculture and retail trade. Generally speaking, these characteristics prevailed in all Northeastern counties in which severe labor maladjustments were com— puted. Thus, in spite of the lower average earnings poten— tial of persons in the total labor force of these counties, resulting from an undesirable mixture of social and economic characteristics, labor maladjustments were still high because actual per capita earnings in these counties were relatively low. Nearly all counties in the Northeast in which there were no computed labor maladjustments (solid white in Figure 4.2) were located in or next to Standard Metropolitan Statistical Areas (SMSA's). The effect of different size SMSA variables on potential earnings of individuals was included in the estimating equation, and the relationship between the magnitude of county labor maladjustments and near‘ness to a large industrial-urban center is very clear from Figure 4.2. All counties in the industrial-urban 139 strip from Boston to Philadelphia were in the two lowest labor maladjustment classes, and most had earnings gaps in their total labor force under $100. In reality then, only in a few counties of central Pennsylvania, the Adirondack Mountains of New York, and the states of Maine and Vermont were there any serious labor maladjustments in the total labor force of North- eastern counties in 1959. Severe labor disequilibriums in the total labor force of these counties appeared to result mainly from relatively low actual earnings of persons rather than from high potential earnings. The earnings gap might have been even greater in these counties, except for their heavy endowment of persons with low educational attainment, and their high proportion of employment in agricultural, retail, and professional industries, all variables in the estimating equation with significant negative coefficients. Urban Areas of Residence.-—The aid of a map depicting labor maladjustments in the urban sector of each county, such as that of Figure 4.2, would be highly beneficial, but because of time and cost involved, only one map was pro- vided. However, as seen in Table 4.4, the distribution of counties in the Northeast according to the magnitude of estimated labor maladjustments in their urban labor force is roughly the same as that for the total labor force. Thus, the map is useful for limited reference in this section. 140 Apparent labor maladjustments in 76 per cent of all counties in both total and urban distributions were $400 or less, although there was a slightly larger percentage of "well-adjusted" counties ($100 or less) when only the urban labor force of each county was considered. The proportion of counties in the severe maladjust- ment category was between 8 and 9 per cent for both the urban and total labor force distributions. Since most counties in which labor maladjustments in the urban labor force were large (greater than $400) were also counties in which labor maladjustments for the total labor force were large (this relation will be shown statistically in Chapter VI), they can easily be located by the two darkest shades among the Northeastern counties in Figure 4.2. This parallel was due to the absence of any urban centers in these counties that were large enough to offer an indus- trial structure with higher paying job opportunies. As a matter of fact, several counties in which labor maladjust— ments were greater than $700 in the total residence sector, had no urban population in 1960 according to the census definition. As previously noted, such counties tended to have a heavy rural farm population, with very small towns and cities serving the consumption needs of the farm popu— lace. With little industry in the small urban areas of these basically rural counties, actual per capita earnings in the urban sector were relatively low. 141 Nationally, there were only 2,142 counties with an urban labor force, and in 357 of these (17 per cent), esti- mated labor maladjustments were greater than $700. Only 2 per cent of these were in the Northeast, however. In another 21 per cent of the nation's counties with an urban population base, the earnings gap varied from $401 to $700, compared to 15 per cent in the Northeast. Thus, there were 54 per cent more counties in the nation with urban labor maladjustments in the two largest categories than in the Northeast. Within the region, apparent urban labor maladjust— ments in less than 6 per cent of the Middle Atlantic counties were greater than $700 in 1959, compared to almost 17 per cent in the New England counties. In 59 per cent of the Middle Atlantic counties, these computa- tions show no apparent labor maladjustment problems, which was true of only 34 per cent of the New England counties. The regional distribution is obviously strongly influenced by variations in urban labor maladjustments in counties of the Middle Atlantic division. As seen from the last distribution of Table 4.4, the Northeast had a much larger share of all U. S. counties with low labor maladjustments than of those counties with high urban labor maladjustments. 142 Rural Nonfarm Areas of Residence.—-Variability in rural nonfarm labor maladjustments among communities of the North- east closely conformed to the patterns established for the total and urban sectors. In 15 Northeastern counties, or just over 7 per cent of the total, apparent labor maladjust- ments exceeded $700, and importantly, 13 of these counties also had severe earnings gaps in their total and urban labor forces. But the population base of nearly all these counties were heavily rural in 1960, and therefore endowed with social and economic characteristics known to be asso— ciated with low individual earnings. Again, most of the Northeastern counties in which the largest rural nonfarm labor maladjustments occurred were in the upper New England area (12 of 15). In 18 per cent of the counties in this division rural nonfarm labor maladjust— ments were severe, compared to 2 per cent in the Middle Atlantic division. Among states, the extreme case of labor maladjustment was in Maine, where the rural nonfarm earnings gap in 63 per cent of all counties exceeded $700, and in 94 per cent, the gap was greater than $400. In over half the Northeastern counties, labor malad- justments in the rural nonfarm sector were $100 or less, but within the region, there was maximum variation among states. For example, Maine and Vermont had no counties with a fully adjusted labor force in this sector, while 100 per cent of Connecticut's counties were in the lowest category. 143 In more than 80 per cent of the counties in the region's two largest states, New York and Pennsylvania (which together comprise 59 per cent of all Northeastern counties), esti- mated earnings gaps were $400 or less, and the majority of these were in the lowest labor maladjustment category. Considering the weight which these two states carry in the computations for both the Middle Atlantic and Northeast region, it is not surprising that extreme distributions of counties in smaller states have relatively little influence on the regional distribution. Compared with the nation, the Northeastern distribu— tion showed a much larger percentage of counties in the two lowest labor maladjustment classes, and the proportion of counties with severe rural nonfarm labor maladjustments was only one—fifth that in the national distribution (7 per cent vs. 33 per cent). Thus, the nation's communities are much more evenly distributed across the four rural non— farm labor maladjustment categories than are communities of the Northeast. In summary, there appeared to be little serious labor maladjustment in the rural nonfarm sector of most Northeastern counties in 1959, with the exception of several counties in Maine, New Hampshire, and Vermont. Throughout the region, only 22 per cent of all counties incurred labor maladjustments exceeding $400, with two- thirds of these between $401 and $700. A majority of the 144 people living in rural nonfarm residence areas of North- eastern communities held jobs in the several industrial- urban centers of the region, which resulted in actual per capita earnings at a level comensurate with potential rural nonfarm earnings. Only those counties relatively isolated from sizable industrial centers experienced large labor maladjustments in their rural nonfarm labor force. Rural Farm Areas of Residence.-—Labor maladjustments in the rural farm labor force of communities in the North— east were much more serious than for the other major resi- dence sectors. Table 4.4 shows the counties to be more evenly distributed across the four indicated earnings gaps, with the largest proportion (27 per cent) in the $401 to $700 category. In approximately one-fifth of all counties, rural farm labor maladjustments were over $700, more than twice the proportion in this category for any other resi- dence area. This still leaves a majority of the region's counties (53 per cent) in the two lowest farm labor malad— justment classes, but only half of these were in the well— adjusted category of $100 or less. This means there were considerably more Northeastern communities with large labor maladjustments in their farm sector than in their other major residence areas. The distribution still compares favorably with the national distribution, however. Nationally, 48 per cent of all rural farm communities experienced severe labor 145 maladjustments in 1959, 45 per cent more than the North- eastern share. In addition, of all counties in the nation having a farm labor force, only 31 per cent had rural farm labor maladjustments of $400 or less, compared to the 53 per cent in the Northeast. Less than 3 per cent of all counties in the nation with farm maladjustments of $700 or greater were in the Northeast, far below the region's 6.7 per cent of all rural farm communities shown in the last column of Table 4.4. On the other hand, the region had more than its rightful share of farm communities with relatively little labor maladjustment, 9.9 per cent in the lowest category and 13.6 per cent in the second lowest. In which Northeastern counties were the worst rural farm labor maladjustment problems? These are shown in more detail in Appendix Tables D—lO through 12. Of the 206 rural farm communities in the Northeast, 41 encountered earnings gaps of over $700, and two-thirds of these were in the upper New England states. Maine alone had 15 of these 41 counties, while New Hampshire and Vermont had 5 each. Of the remaining one—third, which were located in the Middle Atlantic division, almost two-thirds of these were in the Appalachian and Allegheny Mountain areas of Northeastern and Southwestern Pennsylvania. In both divisions, these counties were characterized by low actual and potential earnings of persons. Furthermore, in nearly every one of these counties, at least one—third of the 146 farm labor force had no more than 8 years of schooling, and over one-half had less than a high school education. Employment was predominately in agriculture, forestry and fisheries, and a relatively large proportion of the labor force was over 65 years of age. All these variables are known to have a depressing effect on personal earnings. In general, labor maladjustments in the farm labor force of those counties located closest to large urban areas tended to be the smallest. This is consistent with the results of a study by Keith Bryant,l in which it was found that the single most important variable determining variations in rural farm family incomes among Northeastern communities was industrial—urban concentrations. The relationship was highly significant and negative, emanating, no doubt, from increased off—farm job opportunities, higher wage rates, lower transportation costs, and higher land values, the closer to the city the farm community is located. Total Nonfarm Areas of Residence.--The distribution of Northeastern counties by the degree of computed labor maladjustment in their total nonfarm areas of residence is almost identical to the total labor force distribution discussed in the first part of this section on Northeastern labor maladjustments, as shown in Table 4.4. Since the lBryant, op. cit., pp. 103—105. 147 two distributions are the same, and the nonfarm labor force is a composite of the urban and rural nonfarm labor forces, it can be assumed that the rural farm labor force did not have a very significant influence on the variation in total labor maladjustments among Northeastern counties. Within the region, the number and per cent of counties in each labor maladjustment category for both the New England and Middle Atlantic divisions are approximately the same for both the total and nonfarm residence areas. In addition, state distributions are also about the same for both sectors. Hence, given these similarities, no further dis- cussion of labor maladjustments in the nonfarm sector or Northeastern counties is offered. North Central Region The North Central region is composed of two census divisions, the East North Central, which includes the states of Ohio, Indiana, Illinois, Michigan, and Wisconsin; and the West North Central, which contains the states of Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, and Kansas. Summary results pertaining to each individual state and division are found in the tables of Appendices B, C, and D, while summary tables and charts for the North Central region are presented in the following text. 148 Actual Per Capita Earnings Total variability among the North Central counties in actual per capita earnings of persons in the total labor force is shown in Table 4.6 and Figure 4.3a. The distri- bution is fairly normal, with 35 per cent of all counties in the modal earnings class ($2,500 to $2,999). The next lowest category had 23 per cent, and the next highest 22 per cent of all counties. Thus, in slightly over 80 per cent of the North Central communities, per capita earnings of the total labor force varied in the range from $2,000 to $3,499 in 1960. In only 4 per cent of the counties were the actual earnings of persons less than $2,000, compared to 16 per cent with earnings equal to or greater than $3,500. Overall, average per capita earnings varied from a low of $1,637 in Jerauld County, South Dakota to a high of $6,213 in Johnson County, Kansas. As evident from Table 4.3 and Figure 4.3a, the North Central counties were distributed in actual earnings categories very similar to the national distribution, the major difference being the much larger percentage of counties in the under $2,000 category of the national distribution (16 vs. 4 per cent). In addition, the modal classixu*the nation, although the same, had 29 per cent fewer counties. Of all the counties in the nation in which actual earnings of persons in the total labor force were in the .00:300 020 CH mocothop .conmL Hmppcoo nope: 0:0 CH coma 50H£3 .mmeo mmchgmo co>Hm m CH mmchgmo ..m.: 0:» CH moHpcsoo HHm do :oHoLoqogo 62p mucommgomg :oHpsoHLomHo cho :H .Hmoop pmHonpgmq 0:0 Op Eon coHpangomHo mHzo CH mzop Lo mcESHoo LocpHoz oo mogm co>Hn 0 p00 wonnmo Loo gown ammo cmogqo £000 1149 H 0.0 0.0 0.0 0.00 0.0. 0.00 0.0 0.0 0 0 0.0 0.0 5600co: Haoos 9.9 0.0 0.0 0.:H :.mm m.av m.mm 0.0H o o 0.0 0.0 Epmm ngsx 0.0 0.; H.0 0.0 0.00 0.0: H.0 0.0 0 0 0.0 0.0 anmacoz Hmtzm 0.0 2.0 0.0 0.00 0.0: 0.0H 0.0 0.0 0 0 0.0 0.0 0000: 0.0 0.0 0.0 0.00 0.00 0.00 0.0H 0.0 0 0 0.0 0.0 Hmooe H.0.0 0o 0:00 0:0 0.0:H 0.0 1.0 0.H 0.0 H.00 0.01 0.H 0.0 0.0 0.0 ,.0 Eemacoz Hmooe R H0H 0.0 0.0 0.0 H.0 0.H 0.00 0.00 0.0 0 0 0.0 0.0 5000 H0000 R .0H 0.0 0.0 0.H 0.0 0.0H 0.00 0.0 0.0 0 0 0.0 0.0 560006: H0600 0. 00H 0.0 0.0 0.0 0.H 0.00 0.00 0.0 0.0 0 0 0.0 0.0 00660 .00H 0.0 0.0 0.0 0.0 H.0H 0.00 0.0 H.0 0 0 0.0 0.0 H0060 conom eHon: mtchgdw Uupmlwpmm no mmflucso:o ho coHusoHpumHQ pcou L6; 0.0: 0.0 0.00 0.00 0.00 0.0: 0.H0 0.00 0 0 0.0 0.0 Eamocoz Hmooe 0.0 0.0H 0.0H 0.0 0.0H 0.Hm 0.00 0.0: 0 H 0.0 0.0 5000 H0600 0.00 H.00 0.00 H.0: 0.00 0.00 H.0: 0.00 0 0 0.0 0.0 Enwucoz H0000 0.00 0.0H 0.00 0.00 0.00 H.00 0.00 H.0 0 0 0.0 0.0 00060 0.00 0.0 0.00 0.00 H.Hm 0.00 0.0: 0.00 0 0 0.0 0.0 Hence H 0.0 0o 0060 600 0.00H 0.0 H.0 0.H 0.0 0.0H 0.00 0.00 0.0H 0 H 0.0 0.0 5000coz Hmooe 0.00H H.0 0.0 0.0 0.0 0.0 0.0 0.00 :.H0 0 H 0.H H.0 E000 H0600 0.00H 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0 0 0.0 0.0 Eamacoz H0600 0.00H 0.0 0.0 H.0 0.0H 0.00 0.00 0.00 0.0 0 0 0.0 0.0 cane: 0. 00H 0.0 H.0 0.H 0.0 0.0 0.00 0.00 0.00 0 a 0.0 0.0 Hmooe :onom eHnon mwcHCme H05p0< an mmecsoo mo COHoonppmHQ ucoo pom otoe 00000 00000 00000 00000 00000 00000 00:00 000H0 000H0 000H0 00::o0 :H Hmpoe to 0» Op 0» 0» 0p 0» on o» o» cocoonmm . 00000 00000 00000 00000 00000 00000 00000 00000 000H0 000H0 v 0o mot< onomm HHm MO comm .Hom .mwchamm mpHomo Log owmpo>< ooumEHpmm ocm Hmzuo< an moHuczoo mo coHuonHpmHollm. : m4m m > mfi IO ’ 30> 1 1.81000150020002500110035004000450050005500 lean 1000150020002500360035009020150050005500 0m to to to to to to or than to to to to to to cr 31000 1499 1999 2999 2999 3499 3999 4499 4999 5499 lnre $1000 1999 1999 21:99 2999 3:199 3999 “99 9999 52.99 m mum of Her mpm Eamim Dollars of For capita Eamiro Pei-cent. (c) Total Nonfarm Residence Area P ‘IoL 60" 20F less 1000 1500 2000 2500 3000 3500 4000 4500 5000 5,500 than to to to to to to to to to to $1000 140‘) 1999 2409 299‘) 3499 309‘? 4409 4999 5499 m Tbllam of Per Papita l-Lamings Figure 4. 3. Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, Northcentral Region and U.S., 1959. 151 modal class of the county distribution, i.e., between $2,500 and $2,999, 45 per cent were in the North Central region (Table 4.6). In no other class of actual earnings was the region's share greater than 40 per cent, although it exceeded 30 per cent in a total of six classes. This compares with the region's 34 per cent share of all counties in the nation. Within the region, the largest proportion of counties with high per capita earnings appeared to be in the East North Central division, where actual earnings of persons in 60 per cent of all counties were $3,000 or greater, compared to only 23 per cent of the West North Central counties. With the heavy frequency of counties in the West North Central division in earnings categories below $3,000, and the division's 59 per cent share of all counties in the North Central region, it is understandable why the regional distribution conforms to that of the Western division. Except for Ohio and Missouri, actual per capita earnings in approximately 90 per cent of the counties of all North Central states varied between $2,000 and $4,000. In Ohio, the actual earnings of persons in 21 per cent of the state's counties were above this range, while in Missouri, per capita earnings averaged less than $2,000 in 18 per cent of the state's 115 counties. Some of the largest metropolitan areas in the nation are located in the North Central region, and the fact that 152 they are mainly concentrated in the East North Central states partially explains why actual earnings are higher in that division. These labor markets provide stable employ— ment for several million persons scattered in hundreds of surrounding communities, and as a result contribute sub- stantially to relatively high per capita earnings in the total labor force of those communities. In addition to the urban influence on actual earnings of persons in this division, the area has a commercialized agricultural base which affords relatively high earnings to many persons in the rural farm sector. Variations in urban_per capita earnings levels among communities of the North Central region were not as great as for the total labor force. Actual per capita earnings in less than 3 per cent of all counties were under $2,500 in 1960, compared to 11 per cent in the national distri— bution for this sector (Table 4.3). However, the per- centage of counties with high urban per capita earnings (greater than $4,000) was the same in this region as for the nation. As shown in Figure 4.3b, both distributions peaked at the same level of urban earnings, but the North Central region had a greater frequency of counties with average urban earnings between $3,000 and $4,499. The median earnings level in the urban community distribution is between $3,000 and $3,499, but because of the heavy concentrations of persons in the urban labor 153 force of many counties in this region, average urban per capita earnings for the whole region is probably higher than the median class. This computation was not made from the magnetic source tapes, and is not available in the published census data for 1959. As-anticipated, the distribution of North Central counties by urban per capita earnings is shifted more towards higher earnings levels than any other residence distribution in this region. This results from the fact that in most counties throughout this region, average earnings of persons in the urban labor force are higher than in the labor force of other residence areas due to such factors as better job Opportunities and higher wages for urban people, their higher levels of educational attainment, more regular employment, the presence of unions, etc. Within the region, three—fourths of the counties in the western division had average urban earnings less than $3,500, compared with 40 per cent of the East North Central counties. But urban earnings in 40 per cent of the Western division counties were concentrated between $3,000 to $3,499. In the eastern division heaviest con- centration occurred in the $3,500 to $3,999 category. As examples of extreme variations among states, urban earnings of persons in over one—fourth of the counties in Michigan and Ohio averaged better than $4,000, while at the other 154 extreme, urban earnings in almost 13 per cent of the counties in South Dakota and in 9 per cent of those in Missouri, were under $2,500 in 1959, a result of the relative absence of urban centers in these states. It should be noted that there were 337 North Central counties (32 per cent less than the total number of North Central counties) which did not have an urban labor force in 1960, and 80 per cent of this number were in the West North Central division. This is partially indicative of the higher relative degree of rurality in the West North Central division. Forty per cent or more of the nation's counties in 4 out of the 6 highest urban earnings categories were in the North Central region in 1959, and over two-thirds of these were in the East North Central states. The region had no counties in which urban per capita earnings were less than $2,000, and only 8 per cent (17 of 209) of the nation's counties with urban earnings between $2,000 and $2,500 were in the North Central region. Variability of per capita earnings in the rural nonfarm labor force among North Central communities conforms closely to variations in actual county earnings for the total labor force. As seen in Figure 4.3(c) the distribu- tion of counties for this residence area approaches nor-' mality and lies to the right of the national distribution. Although the maximum concentration class of the two 155 distributions is the same ($2,500-$2,999), over a third of the North Central counties fell into this category, compared to slightly less than one—fourth of the nation's counties. In one-fourth of all North Central counties, the average earnings of rural nonfarm people were below the mode of the distribution, i.e., less than $2,500. This compares with almost 38 per cent of the region's counties, contrasted with 31 per cent nationally. Thus, although the dispersion in average rural nonfarm earnings of persons among commun- ities of this region was very wide, ranging from $1,359 in Washabaugh County, South Dakota, to $7,219 in Cuyahoga County (Cleveland), Ohio, rural nonfarm earnings in most counties (over 80 per cent) averaged between $2,000 and $3,500 (Table 4.6). The median of the distribution was somewhere in the $2,500 to $2,999 category. Appendix Table B-8 shows the dispersion of rural nonfarm earnings among communities of the East North Central and West North Central divisions. Again, as for the two residence areas previously discussed, a greater percentage of East North Central counties had high rural nonfarm per capita earnings than did the West North Central counties. In almost two-thirds of the East North Central counties actual earnings of persons in this sector were $3,000 or greater, compared to one-fifth of the counties in the Western division. The large proportion (80 per cent) of West North Central counties with rural nonfarm earnings of 156 persons less than $3,000 was heavily weighted by the county distributions of Missouri, North Dakota, and Nebraska. Over 90 per cent of the counties in these states were in categories under $3,000, reflecting, among other charac- teristics, the nonavailability of higher paying industrial job opportunities. The rural nonfarm labor force in most of these counties was composed of persons living and working in small towns under 2,500, which are not classified as urban population by the census. In the three lowest rural nonfarm earnings cate- gories, the North Central region contained a very small proportion of the national total, but at all earnings levels above $2,000, the region's share was consistent with its 34 per cent proportion of all counties in the nation. Among rural farm communities of the North Central states, actual per capita earnings ranged from an average low of $340 in Keweenaw County in the extreme upper penin- sula of Michigan, to $5,872 in Grant County, Kansas. Two- thirds of the region's counties in which there was a rural farm labor force in 1960 had per capita farm earnings between $2,000 and $2,999. The mode of this distribution, which was relatively low ($2,000 to $2,499), included 41 per cent of all rural farm communities in the North Central region. Almost one—fourth of the region's counties had rural farm earnings less than $2,000, compared to 10 per cent over $3,000. 157 Figure 4.3d offers a comparison of the dispersion of communities by per capita farm earnings in the nation and the North Central region. The range of the two distribu- tions is the same, but more North Central counties than national had farm earnings of persons exceeding $2,000, 78 vs. 62 per cent, respectively. Thus, in 38 per cent of the rural farm communities in the nation, average farm earnings of persons were less than $2,000, as opposed to 22 per cent in the North central region. Of the nation's counties in the two most heavily concentrated rural farm earnings categories ($1,500—$2,499), almost half were in the North Central states, a far greater regional share than for any of the other categories of farm earnings. As was partially evident in the other residence distributions, the degree of rurality appears much greater among the West North Central counties. In 30 per cent of the farm communities of this division, per capita earnings averaged less than $2,000, compared to 14 per cent in the East North Central Division. Among states, more than 50 per cent of the counties in Minnesota and Missouri exper- ienced average farm earnings of persons below $2,000. In Minnesota, these were mostly the isolated northern tier of counties, and in Missouri, the Ozark counties. In addition to these counties, the states of North and South Dakota also contain heavy concentrations (about 30 per cent) of counties with actual per capita farm earnings averaging less than $2,000. 158 Among the East North Central states, almost one- third of the counties in Michigan and Wisconsin had average per capita farm earnings below $2,000, nearly all of which were located in the upper Great Lakes area. These results are consistent with other recent studies which have defined heavy concentrations of counties in the upper Great Plains, the Ozarks, and upper Great Lakes as rural poverty areas.1 'There appeared to be little difference in the vari- ability of the total and total nonfarm distributions, as shown in Table 4.6 and Figure 4.3. In their combined urban and rural nonfarm labor forces, one-third of the North Central counties had per capita nonfarm earnings between $2,500 and $2,999. In 17 per cent of the counties, non- farm earnings of persons averaged less than $2,500, leaving approximately one-half above $3,000. Because of the lower average earnings in the farm sector, the distribution for the total labor force had more counties in the lower earnings categories than the nonfarm distribution, and fewer in the higher categories. As shown in Figure 4.3(e), the distribution of North Central counties by per capita earnings in their nonfarm labor force is shifted more towards the higher earnings levels than the comparable distribution for the 1National Advisory Commission on Rural Poverty, op, cit., pp. 3-4. 159 nation. In addition, only one-half the nation's counties were concentrated in the two middle earnings categories ($2,500-$3,499), compared to 62 per cent of the North Central counties. Summarizing, actual average per capita earnings in most North Central counties appeared higher in their urban labor force than in either of the other four residence areas analyzed, and lowest in the rural farm sector. In all residence areas examined, except the rural farm, actual earnings of persons averaged $2,500 or more in approxi- mately three-fourths of the region's counties. County distributions for all residence areas compared favorably with the comparable national distributions, with slightly heavier concentrations at the higher earnings levels. Estimated Per Capita Earnings Frequency distributions of North Central counties by categories of estimated per capita earnings are summarized in the last part of Table 4.6, and in Figure 4.3. There was considerably less variation among North Central counties in average potential earnings of persons, for all residence areas considered, than was true for the distributions of counties by actual earnings. Minimum variations occurred in the urban sector and maximum vari- ation in the farm sector. 160 Considering only the total labor force of each county, potential earnings in three—fourths of all North Central counties averaged between $3,000 and $3,499. Fourteen per cent were in the next highest category, and 12 per cent in the next lowest. Only 4 counties, 2 in each division, had potential earnings which averaged $4,000 or more. This was a relatively high concentration compared to the national county distribution of estimated earnings, as shown in Figure 4.3. The range of the two distributions was approximately the same, however. In the modal cate- gory of the distribution ($3,000-$3,499), 43 per cent of the nation's counties were in the North Central region, and 34 per cent of all U. S. counties were in the next highest category, which is approximately equal to the region's share of all counties. Fewer East North Central counties than West North Central had potential earnings which averaged below $3,000, 2 per cent vs. 20 per cent (Appendix Table C-2). Con— versely, potential earnings in 25 per cent of the East North Central counties averaged $3,500 or more, compared to 7 per cent of the West North Central counties. Frequen- cies in the lower tail of the regional distribution are due to relatively heavy concentrations of counties in potential earnings categories less than $2,500 in the states of Minnesota, North and South Dakota, and Nebraska. 161 The range in average urban potential earnings of persons was essentially confined to only two categories. In three-fourths of all North Central counties, estimated urban earnings averaged $3,500 to $3,999, and in almost another one-fourth, $3,000 to $3,499. Only one per cent of the region's counties lay outside this range. The relation of the North Central distribution to that for the nation is shown in Figure 4.3b. The North Central distribution is much more peaked and has a 50 per cent larger proportion of counties with urban potential earnings $3,500 or greater. Half of the nation's counties in the $3,500 to $3,999 modal class were in the North Central region in 1959, a share that was heavily weighted by counties in the East North Central division. Of all counties in the country with an urban labor force averaging between $3,000 and $3,499 per person, 18 per cent were in this region. Thus, only in one category was the regional share greater than what might have been expected, relative to the region's 34 per cent of all U. S. counties. Concentration was even greater when counties were distributed according to their average level of rural nonfarm earnings potential, as evident from Figure 4.3c- and Table 4.6, but the mode of the distribution was one earnings division (of Table 4.6) lower than that for the urban sector. In 85 per cent of the North Central 162 munities, average rural nonfarm potential earnings were estinuated to be $3,000 to $3,499, as opposed to 63 per cent in tlris category for the U. S. distribution. In all but 3 pEI‘ cent of the remaining North Central counties, poten- tiaJ. rural nonfarm earnings were $3,500 or greater. Natitnmally, 27 per cent of all counties had potential earnirgfi in their rural nonfarm labor force less than $3,00CL Therefore, the North Central estimated earnings distxribution for this sector lies further to the right (Figurme4.3c), and has a smaller range than the U. S. estimated distribution. There were 2,223 counties in the U. S. in 1959 which had rulral nonfarm potential earnings in the range between $3,0CH3 and $3,999, and slightly over 40 per cent of these were ix1the North Central region. Potential rural nonfarm earnings did not exceed $4,000 for any county in the region, however. Within the region, potential earnings in the rural nonfarm sector of most counties in both the East and West North Central divisions (80 and 88 per cent, respectively) were concentrated between $3,000 and $3,499. Another 20 per Cent of the East North Central counties had estimated per capita earnings for this sector varying from $3,500 to $3,999, compared to 7 per cent in the West North Central states. 163 Among the rural farm communities of the North Central stattas there was considerably less concentration at any one level.cd‘estimated per capita earnings. However, potential flmnn earnings of persons in 96 per cent of all North (knnxral counties having a rural farm labor force averaged fron1 $2,500 to $3,499, with about 3 per cent of the counties hmmediately below this range and one per cent just above. Therwefore, considering combined categories, potential farm earnirgfi were still as heavily concentrated as the distri— buticnqs for the other residence areas. But no other resi- dence: distribution showed potential earnings below $3,000 hirmore than 13 per cent of all counties in the region, cxmnxared to 59 per cent below $3,000 in the farm distribu- tion. As observed from Table 4.6 and Figure 4.3d, the regicndal and national distributions almost coincide, the major difference being the larger percentage of farm communities in the North Central states in which potential earnings of persons averaged $3,000 and over (41 vs. 32 per cent). Unlike the previous distribution discussed, a relatively larger proportion of the nation's counties having low potential farm earnings were estimated to be in the North Central region in 1959, and a relatively smaller prOportion of the counties with high potential farm earnings were in this region (last percentage distribution of Table 14.6). 164 Potential farm earnings in the majority of the East North Central counties (60 per cent) averaged $3,000 or more, while in the West North Central division 72 per cent averaged less than $3,000 (Appendix Table C-ll). Greatest concentration in any one state occurred in Wisconsin, where per capita potential farm earnings in 88 per cent of all farm communities in the state averaged between $2,500 and $2,999. However, this concentration was followed closely by 87 per cent in Minnesota, 81 per cent in North Dakota, 82 per cent in South Dakota, and 75 per cent in Nebraska. Potential earnings of persons in the nonfarm labor force of most North Central counties (68 per cent) varied between $3,000 and $3,499, and all but 2 per cent were contained in the $3,000 to $3,999 range. Nationally, about 80 per cent of all counties had an average nonfarm earnings potential within the same range. Similarities of the regional and U. S. distributions for this sector are shown in Figure 4.3e. As expected, the distribution of counties for the nonfarm sector lies between the urban and rural nonfarm distributions, but coincides more closely with the rural nonfarm distribution, mainly because there were 48 per cent more Northeastern counties with a rural nonfarm labor force than of those with an urban labor force. In summary, for each of the five residence areas considered, average per capita earnings potential among 165 the North Central counties was heavily concentrated between $3,000 and $3,999, except for the rural farm sector. Average potential farm earnings of nearly all farm commun- ities were concentrated in the $2,500 to $3,499 range. Further, as shown by Figure 4.3, the distribution of counties according to the level of estimated per capita earnings in each residence area lies much further to the right than the actual distributions, and does not contain the extreme high and low average earnings that are included in the actual distributions. The following section provides an index of the divergence of actual and potential earnings for each residence area. Apparent Labor Maladjustments This part is concerned with the differential between actual per capita earnings and estimated (or potential) per capita earnings in each of the county residence areas discussed in the two previous sections. Summary Table 4.7 shows the distribution of counties in the North Central region by four different levels of labor maladjustment, while the Tables of Appendix D present a much more detailed set of distributions. Total, All Residence Areas.--Only labor maladjust— ments in the total labor force of the North Central counties are considered in this section. In addition to Table 4.7, reference is again made to Figure 4.2 for the subsequent discussion. 166 TABLE 4.7.--Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five County Residence Areas, 1959. NORTH CENTRAL REGION County Area $100 or $101— $401- $701 Totall of Residence less $400 $700 or more Number Total 253 248 323 232 1,056 Urban 301 195 144 79 7192 Rural Nonfarm 258 204 277 313 1,0522 Rural Farm 124 165 333 431 1,0532 Total Nonfarm 279 239 282 255 1,0552 Percentage Distribution within Region Total 24.0 23.5 30.6 22.0 100.0 Urban 41.9 27.1 20.0 11.0 100.0 Rural Nonfarm 24.5 19.4 26.3 29.8 100.0 Rural Farm 11.8 15.7 31.6 40.9 100.0 Total Nonfarm 26.4 22.7 26.7 24.2 100.0 Per Cent of U.S. Total3 Total 30.8 39.1 44.1 24.6 33.7 Urban 37.0 36.9 32.5 22.1 33.6 Rural Nonfarm 33.9 33.9 39.6 30.8 34.2 Rural Farm 22.4 41.7 52.0 29.2 34.4 Total Nonfarm 33.2 36.4 37.9 28.7 33.7 1 proportion of U.S. This column represents the number and distribution of counties for the whole region. 2Some counties had no population in this residence area in 1960. 3Each percentage in this distribution represents the counties, with labor maladjustments of a given size, that were in the North Central region, for a given area of residence in the county. Thus, rows and columns do not sum to any particular total. 167 As seen in Table 4.7, only one-fourth of the North Central counties were considered free (<$100) of labor maladjustment problems in their total labor force in 1959. In contrast, 22 per cent, or almost another one—fourth of the counties in the region, experienced severe labor maladjustments (>$700). In the two broad middle categories, apparent labor maladjustments varied from $101 to $400 in 24 per cent of the counties, and from $401 to $700 in 31 per cent. Thus, the county distribution for this region according to these four levels of labor maladjustments is almost flat, with about the same proportion of counties in each category. The national distribution of counties, as previously presented in Table 4.5, shows almost the same pattern of county dispersion. Labor maladjustments exceeded $700 in a smaller proportion of North Central counties than national counties (22 vs. 30 per cent), but exceeded the national share in the $401—$700 category by about the same amount. Hence, in both distributions, about half the counties were in the two largest earnings gap categories, and half in the two lowest. The proportion of North Central counties in which the total labor force was well- adjusted was slightly lower than that for the nation, however. The North Central region contained one-fourth of all counties in the nation in which there were severe labor 168 maladjustments in 1959. Although this appeared to be a relatively high proportion, it was almost one-fourth lower than the region's 34 per cent share of all counties in the nation. But of the nation's 733 counties with labor malad- justments between $301 and $700, 44 per cent were in the North Central states. The region contained 31 per cent of the 822 well-adjusted counties in the U. S., and 39 per cent of the 634 counties in the $101 to $400 group. The regional location of counties in each of the four labor maladjustment divisions can easily be observed from Figure 4.2. As eXpected, the greatest concentration of counties in which computed labor maladjustments were less than $100, or between $100 and $400, occurred in the East North Central states of Illinois, Indiana, Ohio, and in southern Wisconsin and Michigan, all areas with very heavily populated urban centers. Two-thirds of the 437 counties in this division had average labor maladjustments less than $400 and 58 per cent of these were in the lowest maladjust— ment class. This dispersion was strongly influenced by Ohio and Illinois. In 84 per cent of the counties in Ohio, earnings gaps were $400 or less, and in Illinois 73 per cent were below this level. There were two noticeable concentrations of West North Central counties in which labor maladjustments in the total labor force were estimated to be below $400. One area includes most of Iowa and southern Minnesota, while 169 a second encompasses a strip of western counties in the great plains states of North and South Dakota, Nebraska, and Kansas. All of these are relatively high farm income areas. But in only 35 per cent of all counties throughout the whole division were labor maladjustments $400 or less. No state in the division had as many as one half of its counties in the two lowest labor maladjustment classes, while in the East North Central division, approximately 50 per cent or more of the counties in all states had an earnings gap of $400 or less. The influence of large metropolitan areas in the North Central region is obvious from Figure 4.2. Sur- rounding each Standard Metropolitan Statistical Area is a contingent of counties (white shade) in which labor malad— justments were $100 or less (a balanced total labor force), and as the distance of the county from the SMSA increases, the shading progresses from white to dark, i.e., the computed level of labor maladjustment in the county's total labor force increases. This pattern is the result of the levels of both actual and potential per capita earnings in these counties, as discussed earlier. The closer the county is located to industrial—urban activity in the region, the higher are actual per capita earnings in the total labor force of the county, relative to poten- tial per capita earnings. Average potential earnings of persons did not increase as rapidly as actual earnings in 170 moving towards an SMSA, due to the nature of the estimating equation for potential earnings. Coefficients in the equation were based on a cross-classification of individual and area characteristics in all parts of the country in 1959, and not just the North Central region. Therefore, actual earnings are being compared to potential earnings based on the interaction of a set of national variables which are moderated by conditions in other regions of the country. From Figure 4.2, it appears that those SMSA's exerting the greatest downward pressure on the level of labor maladjustments in the North Central counties were Cleveland, Columbus, Cincinnati, Indianapolis, Detroit, Chicago, Milwaukee, St. Louis, Kansas City, and Minneapolis- St. Paul. These are not necessarily listed in their order of economic importance. There were also very noticeable geographic concen- trations of North Central counties which had either severe or heavy labor maladjustments. Most important were those counties-of the Missouri Ozarks, the eastern and upper Great Plains, and the upper Great Lakes. These were mostly located in the West North Central division, where it was estimated that labor maladjustments in 65 per cent of the counties in the division were greater than $400. States having an equal or larger percentage than the division included Missouri, which contained almost one-fifth of the division's counties (85 per cent), North Dakota (68 per 171 cent), and Nebraska (65 per cent). In Missouri, labor maladjustments exceeded $700 in 67 per cent of the state's counties, but in no other North Central state did this share exceed 30 per cent, and it reached a low of 3.4 per cent in Ohio. Labor maladjustments in a majority (over 50 per cent) of the counties of all West North Central states exceeded $400, however, and were generally concen- trated in each state, as shown in Figure 4.2. In the East North Central division, there was only one distinct pattern of counties with heavy (>$400) labor maladjustments, the upper great lakes counties in Wisconsin and Michigan. In 17 per cent of the counties in Michigan, labor maladjustments were severe, and in 48 per cent, they were greater than $400. These shares compare with 16 and 52 per cent, respectively, in Wisconsin. For the division as a whole, severe labor maladjustments were present in only 9 per cent of all counties, and exceeded $400 in 34 per cent. As seen in Figure 4.2, those North Central counties for which severe labor maladjustments were computed are located in relatively rural areas at extreme distances from SMSA's or urban areas of any size. Actual per capita earnings, as pointed out in the previous discussion, were low in these counties compared to other North Central com- munities located closer ‘mb urban influences, mostly because of the lack of nonagricultural job opportunities. 172 In comparison, potential earnings of persons in these counties were still relatively high since, compared to a sample of individuals across the nation, their attributes were statistically associated with higher earnings. In summary, because of the facility of the map in Figure 4.2, greater attention was given to a state0by—state analysis of labor maladjustments in the total labor force of the North Central counties. It is concluded that there are concentrated areas in which county labor maladjustments in this region were severe (>$700) in 1959, mainly in the West North Central division where the proportion of counties in this category approaches one-third. Except for a shelf of upper Great Lakes counties, and a few communities in southern Illinois, Indiana, and Ohio, the East North Central division had no severe labor disequil— .ibrium problems within the total labor force. Urban Areas of Residence.-—There were 337 fewer North Central counties with an urban labor force than with a total labor force in 1960, and 271 of these were in the West North Central division (Table 4.7). As anticipated, the distribution of counties by the level of urban labor maladjustment showed a much heavier concentration in the two lowest categories than was true when only the total labor force was considered. In 496 counties, or 69 per cent of the total, urban labor maladjustments were esti- mated to be $400 or less, and $100 or less in 42 per cent. 173 In only 11 per cent of the region's urban communities were the earnings gaps greater than $700, one-half the share in the previous distribution, and in 31 per cent, labor malad— justment estimates exceeded $400. For the U. S., labor maladjustments were $400 or less in 63 per cent of all counties, and greater than $400 in 27 per cent. Based on this division, the two distributions were almost identical. In the nation, however, there was a greater percentage cfi'counties with. severe urban labor maladjustments than in the North Central region (17 vs. 11 per cent), and a smaller proportion with a balanced urban labor force (38 vs. 42). As shown in the last distribution of Table 4.7, in each urban labor maladjustment category, except the highest, approximately one-third of all counties in the nation were in the North Central states, but the region contained only 22 per cent of the nation's counties in which urban labor maladjustments exceeded $700. Basic computations for each county, which are not published with this report, show that those 79 North Central counties in which severe urban labor maladjustments occurred were generally counties which also had severe earnings gaps in their total labor force. All but 16 of these were in the West North Central division, and 29 per cent were in the state of Missouri alone (Appendix Table D—4). In the West North Central division, apparent urban labor maladjustments in 46 per cent of the counties 174 exceeded $400, compared to 17 per cent in the East North Central division, and were greater than $700 in 18 per cent, as Opposed to 4 per Cent in the East North Central states. In only a fourth of the communities in the Western division were earnings gaps in the urban sector less than $100, while in the Eastern division, almost 60 per cent of all counties were in this category. Hence, as in the previous case of the total labor force, the North Central region appeared to be relatively free of severe labor maladjustments in the urban labor force of most counties, but the existence of such dispar- ities were greater in the Western division of the region than the Eastern division. Heaviest concentrations of counties in categories over $700 occurred in the states of Missouri (33 per cent) and South Dakota (38 per cent). Nearly all counties in the region falling into the highest categories of labor maladjustments contained no towns or cities large enough to support an industrial base that would afford good employment opportunities. The urban labor force in these counties is small in number, agri- culturally oriented, and contains a relatively large proportion of self-employed. Thus the urban sector in these communities is heavily geared to an agricultural populace. 175 Rural Nonfarm Areas of Residence.——Variations among counties in rural nonfarm labor maladjustments are shown in Table 4.7. This distribution differs very little from the total labor force distribution discussed at the outset of this section. Differentials between actual and poten- tial rural nonfarm earnings exceeded $400 in 56 per cent of the North Central counties, compared to 53 per cent in the.total labor force distribution. In a fourth of the counties in both distributions, labor maladjustments were $100 or less. In 30 per cent of the North Central counties, rural nonfarm labor maladjustments were greater than $700, com- pared to one-third of the counties in the nation. Fifty- six per cent of all counties in the U. S. were in the two largest rural nonfarm labor maladjustment divisions, compared to the same percentage in the North Central states. Hence, the total dispersion of counties was about the same in both the national and regional distributions. In addition, the North Central region contributed about one-third of the total counties in each of the four rural nonfarm labor maladjustment categories, as shown in the last distribution of Table 4.7. Again, most counties in which severe labor malad- justments occurred for this sector were located in the West North Central division, which had 264 of the 313 counties in this category. The largest proportion of these were in 176 Missouri and Nebraska, which together had 45 per cent of the division total. In the West North Central division as a whole, 43 per cent of all counties had an average earnings gap exceeding $700, and in three-fourths of the counties the gap was greater than $400. In only 11 per cent of the counties in this division was the rural nonfarm labor force relatively well—balanced. Almost the exact opposite pattern prevailed in the East North Central division, where 43 per cent of the counties were in the $100 or less category and 11 per cent in the over $700 category. The proportion of counties with severe rural nonfarm labor maladjustments was greatest in Wisconsin (26 per cent) and least in Indiana (2 per cent). On the other hand, Ohio had the largest proportion of well— adjusted counties (60 per cent), and Wisconsin the least (31 per cent). In summary, severe labor maladjustments were more prevalent in the rural nonfarm labor force of North Central counties in 1959 than in the total or urban labor force. The earnings gap exceeded $700 in 30 per cent of the region's counties, but the problem was much more acute in the West North Central division, which, because of its larger proportion of counties and lack of an industrial- urban base, appeared to strongly influence the regional distribution. 177 Rural Farm Areas of Residence.--As expected, the per capita earnings potential of farm people in most communities of the North Central states was much greater than their actual per capita earnings in 1959. This differential exceeded $400 in almost three-fourths (72 per cent) of all counties in the region, and was greater than $700 in 41 per cent. In only 12 per cent of the counties in the region were persons in the farm labor force realizing their estimated average earnings potential. The dispersion of rural farm communities by degree of labor maladjustment was approximately the same in the West and East North Central divisions, the only residence area for which this was true. As shown in Appendix Table D—ll, in just over 70 per cent of the counties in both divisions, farm labor maladjustments varied upward from $400. A slightly smaller proportion of East North Central counties had earnings gaps of more than $700, however (37 vs. 44 per cent). The proportion of counties with a well- balanced rural farm labor force was relatively small in both divisions, 10 per cent in the eastern division com- pared to 13 per cent in the western division. Among states farm labor maladjustments varied from $1,936 in Cook County, Minnesota to -$2,907 in Grant County, Kansas. Heaviest county concentrations of severe farm labor maladjustments occurred in Missouri (76 per cent), Michigan (57 per cent), and Minnesota (54 per cent), 178 and most of these counties were in designated rural poverty areas, i.e., the Ozarks, upper Great Plains, and upper Great Lakes. The regional distribution does not compare as favorably with the national county dispersion as it did for the three previous residence areas. As seen from Tables 4.5 and 4.7, the farm earnings gap was greater than $700 in 15 per cent fewer North Central counties than U. S. counties, but it was also less than $100 in 34 per cent fewer counties. Thus, a smaller proportion of farm communities in the North Central region than in the nation were realizing their average farm earnings potential. In spite of the prevalence of severe rural farm labor maladjustments in the North Central states, the region still had only 29 per cent of the nation's counties in this category. However, the region contained over half the counties in the country in which the farm earnings gap was $401 to $700, and 36 per cent of the counties in the two largest categories combined ($400 and above). Its share of well-adjusted counties was only 22 per cent, relative to 34 per cent of all counties in the U. S. Summarizing, high labor maladjustments in the North Central region were much more prevalent in the local faaa labor force than for any other residence area. In almost three-fourths of the counties in the region, and in each division, the farm earnings gap exceeded $400. Heaviest 179 concentrations of these counties were in the Ozarks, the eastern and northern Great Plains, and the upper Great Lakes counties. These are all areas in which actual per capita earnings in the farm sector are very low relative to actual earnings in other areas of residence, and compared to what people with similar characteristics throughout the nation are earning on average. As previously indicated by the shadings in Figure 4.2, these counties are relatively isolated from the influence of industrial urbanization, and the social characteristics possessed by the local farm labor force are those known to be associated with low earnings, e.g., more than one-half the farm labor force in most North Central counties where labor maladjustments weregreater than $400 had less than a high school educa- tion, and a relatively high percentage were in older age groups. Perhaps most important, however, is the lack of off-farm employment opportunities within a reasonable distance of these communities. Thus, even though rural farm people in these communities possess characteristics which, for the nation as a whole, are associated with much higher average earnings (i.e., their potential per capita earnings), they do not have the opportunities locally to exploit these potentials, and therefore large earnings gaps are generated as just described. 180 Total Nonfarm Areas of Residence.--About one—fourth of the North Central counties were distributed in each of the four nonfarm labor maladjustment categories of Table 4.7, a distribution almost identical to that for the nation, and very similar to the total and rural nonfarm distribu- tions previously described. Since these three distributions show approximately the same allocations, it would appear that the urban labor force is contributing very little influence to either the nonfarm or total distributions. However, it should be noted that there wereEX)per cent more counties in the region with a rural nonfarm labor force than of those with an urban labor force, a difference which gives the rural nonfarm sector considerably more weight. There were large differences among divisions (as indicated by Appendix Table D-l4). In the more heavily populated, highly industrialized eastern division, the nonfarm labor force in 46 per cent of the counties appeared to be well-adjusted, and nonfarm labor maladjustments were $400 or less in almost three-fourths of all counties. In only 8 per cent of the counties were potential earnings as much as $700 greater than actual earnings. In contrast, nonfarm labor maladjustments were severe in 36 per cent of the counties in the West North Central division, and were greater than $400 in over two-thirds of the counties. Only 13 per cent of the communities in the western division had a well-adjusted nonfarm labor force. 181 The prevalence of heavy nonfarm labor maladjustments in this region was serious, but slightly less so than that at the national level. Severe labor maladjustments in this sector were highly concentrated in the West North Central counties (222 out of 255 counties), especially in the states of Missouri, Nebraska and South Dakota, which contained 60 per cent of the 255 counties in this cate- gory. Nearly all these counties were relatively isolated from any industrial centers. Southern Region The Southern states were allocated into three major divisions by the U. S. census. The South Atlantic includes the states of Delaware, Maryland, Virginia, West Virginia, North Carolina, South Carolina, Georgia, and Florida, plus Washington, D. C., while the states of Kentucky, Tennessee, Alabama, and Mississippi make up the East South Central division. In the third division, the West South Central, are the states of Arkansas, Louisiana, Oklahoma, and Texas. Combined, these states constitute the second largest region in the nation, in terms of land area. Actual Per Capita Earnings Actual earnings for the Southern counties are sum- marized in the first half of Table 4.8 and in Figure 4.4. Average per capita earnings of the total labor force in the majority of the Southern counties is known to be low, both 182 in absolute and relative terms, and the data in Table 4.8 offer further documentation of the extent of low earnings among these counties. In over 60 per cent of the Southern counties, actual earnings in the total labor force averaged less than $2,500, and in 31 per cent of all counties, per capita earnings were under $2,000. In another 20 per cent, the average ranged from $2,500 to $2,999, which means that actual per capita earnings in 80 per cent of all counties in the South averaged less than $3,000 in 1959. Direct poverty implications are not intended here, since such measures are usually based on "family" total incomes below $3,000, and these data are average individual earnings for persons in the county labor force. It is obvious, however, that these earnings are low relative to the nation. Figure 4.4 and Table 4.3, show the contrast in the National and Southern distributions. As seen in Figure 4.4a, the distribution of Southern counties by average per capita earnings of their total labor force lies much further to the left, with much heavier frequencies in the lower per capita earnings categories. Nationally, actual earnings in 1,983 counties, or 64 per cent of all counties were less than $3,000, compared to the 80 per cent in the South. Of this total, 1,133, or 57 per cent, were Southern counties. It follows then, that a large proportion of U. 8. counties allocated to those earnings categories below :183 .0pczoo map CH mocoonmn no 0000 co>0w a Low .coHavL :Lozpsom 0:0 :0 0L0: 300:3 .mmmHo macflcgmo ce>00 0 :0 00:0:000 000000 Lea £003 ..m.: 0:» CH mowpcsoo Ham Ho coHunonoga 0:0 mucomognop coflpsnflg . 0000 0H00 :H mHMuzmogmo Loam .Hmpou gmasowpgmn 0C0 00 Son coflpsoflgumflo mng :0 mzop Lo mcESHOO LozgflmzH 0.0 0.0 0.0 H.0 0.HH 0.00 0.00 0.H0 0.00H 0.0 0.0 eamacoz H0060 0.0 0.0 0.0 0.00 0.00 0.00 ..00 0.00 0.00 0.00H 0.0 5000 H0020 0.0 0., 0.0 0.0 0.0 3.00 h.H: 0.00 0.00H 0.0 0.0 Eamoco: H0000 0., 0.0 0.0 0.0H 0.0H 0.00 0.0 H 0.0 0.0 0.0 0.0 0000: 0.H 0.; 0.0 0.0H 0.0H 0.00 o.H0 0.00 0.00H 0.0 0.0 H0000 H.0.0 0o 0:;0 noa 0.00H 0.0 lfila 0.0 H.0 lim0 0.0: 1100 0.H H.0 0:0 0.0 etmacoz H0090 0.00H 0.0 0.0 0.0 H.0 H.H 0.0H 0.00 0.0H 0.H H.0 0.0 5000 Hna:0 0.0:H 0.0 0.0 0.0 0.0 0.0 0.00 0.00 H.c 0.0 0.0 0.0 snaaco: H0000 0.00H 0.0 0.0 ..0 0.0 0.;H 0.00 e.0 0.0 0.0 0.0 0.0 00000 c.00H 0.0 0.0 0.0 H.0 «.0 0.00 0.00 0.0 0.0 0.0 0.0 H0060 COHU$J C00.“ .0 ME. mmcflcpmm oopmeflpmm an moHOC3oo go coflpanmewz 0:00 000 0.0H 0.00 0.0H 0.0H 0.0H 0.00 0.00 H.00 0.00 0.00 0.00 Enmacoz Hmooe 0.00 0.00 0.00 0.00 0.00 0.00 0.H0 0.H0 0.00 0.00 0.00 5000 H0000 0.0 H.00 0.0H 0.0H 0.0H 0.00 0.00 0.00 0.00 H.00 0.00 somacoz H0000 0.00 0.0H 0._0 0.00 H.H0 0.00 0.00 0.00 0.00 0.00H 0.0 00000 0.0H 0.00 0.00 0.00 0.H0 0.00 0.00 0.00 0.00 0.00H 0.00 Hmooe H.0.: 06 0000 000 0.00H H.0 0.0 0.0 0.0 0.0 H.0H 0.00 0.H0 0.0H 0.0 H.0 5000coz Hmpoa 0.00H 0.0 0.0 0.0 0.H 0.0 0.0 0.0 0.00 0.00 H.00 0.0 5000 H0000 0.00H 0.0 0.0 0.0 0.H 0.0 0.0 0.H0 0.00 0.00 0.0 0.0 Ena0coz H0000 0.00H 0.0 0.0 0.H 0.0 0.0H 0.00 0.00 0.0H 0.0 0.0 0.0 00000 0.00H H.0 0.0 0.0 0.0 0.0 H.0H 0.00 0.00 0.00 0.0 H.0 Hmooe :o00om eanHz wuchpmm Hmzuo< an moflpcsoo do cofipsoHLumHo 0Com 00m 0.85 00000 00000 00000 00000 00000 00000 00000 000H0 000H0 000H0 0ocooo cH H0000 no on 00 o» Op 00 Op 00 Op 00 mocmoflmom . 00000 00000 00000 00000 00000 00000 00000 00000 000H0 000H0 v 00 000< onomm zmmrfiaom .mmmH .mmon< cocooamom m>Hm ho comm 00m .mwcac00m «pHQmo pom owmpo>< oopmsfipmm ocm H0300< an moHpczoo 00 c00psoflnpm0allm.0 mqm$700) were in the South, compared to the South's 52 per cent share of all counties in the $401-$700 category. Among divisions, the South Atlantic contained the smallest proportion of counties (45 per cent) with urban earnings gaps greater than $400, followed by the West South Central with 51 per cent, and the East South Central with 56 per cent (Appendix Table D—5). In two states, Mississippi and Arkansas, three— fourths of the counties had an urban earnings gap in excess of $400 per capita, while at the other extreme, persons in the urban labor force of at least one—half the counties of Delaware, Maryland, Virginia, and West Virginia were realizing their average earnings potential, and in 4 other Southern states the earnings gap was $400 or less in 204 more than 50 per cent of the counties in each state. Overall in 8 of the 16 Southern states, however, estimated urban labor maladjustments exceeded $400 in a majority (>50 per cent) of the counties. These states represent exactly half of all counties in the South with an urban labor force. Inclusion of a map showing labor maladjust— ments in the urban sector of each county would have been difficult since a third of the Southern counties had no urban population, but such a map would show a much smaller percentage of dark shaded counties, as evident from Table 4.9. Those 249 Southern counties in which urban labor maladjustments were less than $100 were mostly located near SMSA's and/or contained cities large enough to pro— vide a higher paying industrial base. Thus, actual urban earnings were higher, on average, in these counties, but because of the inferior industrial mix and social char- acteristics prevailing in these communities relative to the nation as a whole, potential earnings were about in line with actual earnings. Rural Nonfarm Areas of Residence.—-In 1960, there were 1,381 Southern counties with a rural nonfarm labor force, and the proportion of counties in each labor malad— justment category of Table 4.9 was estimated to be about the same as for the total labor force. In almost one- half (46 per cent) the counties potential rural nonfarm 205 earnings of persons averaged at least $700 more than actual rural nonfarm earnings, and in 69 per cent of all counties, labor maladjustments were $400 or more. The rural nonfarm labor force was well-adjusted in only 15 per cent of the 1,381 counties. Nationally, slightly over half of the 3,078 counties with a rural nonfarm population had labor maladjustments greater than $400, and 56 per cent of these were in the South. The South, however, contained only 45 per cent of all counties with a rural nonfarm labor force. One—fourth of the 3,078 rural nonfarm communities in the U. S. had well-adjusted labor forces, but only 26 per cent of these were in the South. Of all counties in the $101 to $400 category, 38 per cent were in the South. Division and state distributions for the rural non- farm sector are shown in Appendix Table D-8. In the South Atlantic and West South Central divisions, labor malad- justments in a smaller proportion of counties exceeded $400 than was true for the whole region (64 vs. 69 per cent), but in the East South Central division, over 80 per cent of all counties were in the two largest categories. Among states, there was almost extreme variation in the extent of labor maladjustment in the rural nonfarm sector. Labor maladjustments in all three counties in Delaware were less than $100, while in 95 per cent of the counties in Arkansas, the per capita earnings gap exceeded 206 $400. In 85 per cent of the counties in Arkansas, and in more than 50 per cent of the counties in Mississippi, Tennessee, Alabama, Georgia, and North Carolina, the gap exceeded $700. Summarizing, estimated rural nonfarm labor malad- justments were severe in each of the three divisions of the South, but especially so among counties of the East South Central states, plus North Carolina, South Carolina, Georgia, Arkansas, and Oklahoma. The South claimed well over half of all counties in the nation in which labor maladjustments in this sector were greater than $400. Rural Farm Areas of Residence.--Previous comparisons of the variability in actual and potential farm earnings among Southern counties strongly indicated the existence of large labor maladjustments in this sector, and this is evidenced by the data in Table 4.9. For the Southern region as a whole, the farm earnings gap was greater than $400 in four—fifths of all counties, and greater than $700 in two—thirds of the counties. Potential farm earnings were realized in only 13 per cent of the Southern farm communities, and in only a fifth of the counties were labor maladjustments less than $400 in 1959. In the U. S. distribution, just over two-thirds of all counties (2,115) had farm labor maladjustments in excess of $400, and 51 per cent of these were Southern counties. But of the 1,475 counties in the severe labor 207 maladjustment (>$700) category, 62 per cent were in the South. Surprisingly, the South contained almost a third of all farm communities in the well-adjusted category, and most of these (68 per cent) were located in western Texas and down the Florida mainland. Farm labor maladjustments were again most severe in the East South Central division, where the earnings gap exceeded $700 in 83 per cent of the counties (Appendix Table D—ll), and was greater than $400 in 93 per cent. In comparison, farm labor maladjustments were greater than $700 in half the West South Central counties, and in two- thirds the South Atlantic counties. But the farm earnings gap exceeded $400 in a much larger prOportion of the counties of these two divisions, 64 per cent in the West South Central division, and 82 per cent in the South Atlantic. There appeared to be a definite relationship between low farm labor maladjustments and the location of counties. From basic calculations it was found that two patterns existed. First, estimated farm labor maladjustments were found to be very low in the commercial farm belts of western Texas and Oklahoma, and in Florida. Most counties in these areas, which can be distinguished as mostly the white shaded counties of Texas, Oklahoma, and Florida in Figure 4.2, had relatively high actual per capita farm earnings in 1959. Farm earnings in many of these counties are also 208 boosted by oil sales off individual farms, but off-farm work in these counties is relatively low. Secondly, nearly all the 129 counties of the other Southern states in which farm labor maladjustments were under $400, were located "reasonably" close to an industrial- urban center. This allowed farmers in these counties to take off—farm employment during certain parts of the year. In many cases, and to a larger extent than in other parts of the country, Southern farmers worked off-farm year around, and only farmed on the side while maintaining their farm residence. Southern farm operations are more conducive to this type of arrangement since average farm size is relatively low and specialization in cash crops, such as cotton, tobacco, soybeans, or peanuts, predominates. These results are consistent with those obtained in a 1963 study,1 where it was found that the single most important determinant of variations in income levels of both white-and nonwhite rural farm families in the South was nearness to industrial-urban concentrations. In those Southern counties where agriculture alone provides the main source of income, the average level of earnings is generally much lower. Since it was not possible to separate rural farm earnings in each county into that part derived from off-farm lKeith Bryant, op. cit., pp. 140-143. 209 pursuits, and that part directly from farming, the true relationship between size of labor maladjustments in the farm sector and proximity to industrial—urban centers cannot be analyzed. However, the data utilized in this study more accurately reflect the total earnings position of farmers and to that extent, provide a better index of comparison. In summary, rural farm labor maladjustments in every division of the South were severe, but less so in the West South Central states. Farm labor maladjustments exceeded $700 in over 90 per cent of the counties of every East South Central state,where most farms were small in size and relatively isolated from the influence of urban centers. The same pattern was found in all South Atlantic states, with the exception of the highly urbanized states of Delaware and Maryland, and in Florida. Total Nonfarm Areas of Residence.--Every Southern county had a nonfarm labor force in 1960, i.e., no county in the South was 100 per cent rural farm population. Labor maladjustments in this sector were allocated in about the same pattern as for the total labor force, as_shown in Table 4.9. In both cases, labor maladjustments in about two-thirds of the Southern counties exceeded $400, and in most instances these were the same counties. This means that the seriousness of labor maladjustments in the total labor force of most Southern counties was heavily conditioned 210 by the characteristics and earnings of persons in the urban and rural nonfarm labor forces, but when the farm labor force alone was considered, labor maladjustments became larger, in a greater proportion of Southern counties. Statistical correlations between labor maladjustments in various sectors will be presented in greater detail in Chapter VI. Estimates of nonfarm labor maladjustments in about half the counties in the nation (1,634) exceeded $400, and 57 per cent of these were widely dispersed in the Southern states. Figure 4.2 accurately portrays this distribution, since labor maladjustments in the total and total nonfarm labor forces of Southern counties were closely related. In general, the same discussion as that presented for the total labor force applies to this sector. As seen from the map, severe nonfarm labor maladjustments were most heavily con- centrated among the counties of Mississippi and Arkansas and least concentrated in the upper South Atlantic division. The paucity of urban areas in the South Eastern states (there were only 12 SMSA's) are reflected in the larger proportion of counties in this division in which nonfarm labor maladjustments exceeded $400 (76 per cent). With this noticeable absence of job opportunities in the division, actual earnings were relatively low in the nonfarm sector compared to the earnings of persons with similar characteristics in other areas of the country. In the more 211 urbanized South Atlantic and West South Central states, nonfarm labor maladjustments still exceeded $400 in just over 60 per cent of the counties. Western Region The Western states comprise 50 per cent of the total land area in the United States, but only 14 per cent of the counties. Following the census classification, the West was subdivided into the Mountain division, which includes Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, and Nevada, and the Pacific division, which encom- passes Washington, Oregon, and California, plus the out— lying states of Alaska and Hawaii. U. S. data presented in this report include Alaska and Hawaii unless otherwise denoted. Actual Per Capita Earnings Variability in actual earnings among Western counties, for each residence area, is shown in Table 4.10 and Figure 4.5. Actual earnings of persons in the West are known to be high relative to per capita earnings in other parts of the United States, and this is borne out by the county data of Tables 4.3 and 4.10. In the total labor force of just over two-thirds of the Western counties, actual earnings averaged $3,000 or more. Almost a third of the counties were concentrated in the $3,000 to $3,499 category, and on each side of this category were concentrated another 24 per 2212 .mocsoo who CH oosooflmmp mo mopm co>flw m pom .COHmoL compmo3 who Ca ohms zoflzz .mmmao mwcflcsmm co>flm m CH mwcflcpmo mpfiowo Lou cows ..m.0 mg» cfi moflpcsoo Ham mo coHQLOQOLQ on» mocomohaoo cofipzpfitumwo mflno CH oamocmotod sown flmDOu LmHSoHuLmd mom Op Ezm coflpsofizomfio mficp CH msoh no mcssaoo LocpHoZH 0.0 0.0 0.0 m.o: 0.mm m.mH 0.H 0.0 0.0 0.0 0.0 Esmwcoz Hmpoe 0.00H 0.0 0.00H m.m: s.mm m.mfi m.HH H.m H.0 0.0 0.0 Ecmm Hmczm 0.0 0.00H 0.0 0.0m m.mm m.wH m.: H.H 0.0 0.0 0.0 Epmpcoz Hmzsm 0.0 0.0 0. 0.5m :.0m 0.: 0.0 0.0 0.0 0.0 0.0 cash: 0.0 0.0 0.0 0.m: m.mm :.ma H.m 0.0 0.0 0.0 0.0 fiance H.m.0 mo once com 0.00H 0.0 0.0 0.0 m.m m.H: m.qm 0.m 0.0 0.0 0.0 0.0 Etmmco: Hmuoa 0.00H m.0 0.0 m.0 m.0 s.m :.m: :.s: m.H m.0 0.0 0.0 Ecmm Hmczm 0.00H 0.0 m.0 0.0 m.0 m.ma 0.Hs m.» m.0 0.0 0.0 0.0 Epmoco: Ampsm 0.00H 0.0 0.0 0.0 H.m m.0w m.:H 0.0 0.0 0.0 0.0 0.0 smog: .0.00H 0.0 0.0 0.0 0.m m.mm m.m0 s.m 0.0 0.0 0.0 0.0 - Hmpoa cancmm Canoaa mmcficpmm poowEHpmm mo mmfluczou mo cofipzofltpmfio pcoo pom 0.0m m.mm m.mm m.mm m.Hm m.mH m.0H s.z H.H 0.0 0.0m Eptmcoz HmuOB 0.0: w.m: 0.mm m.0: m.mm 2.2m m.0m 0.0H m.m H.H 0.0H Ecmm Hmosm m.mm :.mfl H.0H 0.mm m.0m 0.:m 0.:H m.0 m.a 0.0 m.:H Epmmcoz amusm 0.0 m.mm m.mm m.:m 0.Hm m.HH H.m :.H 0.0 0.0 0.0 cant: 0.0m m.mm 0.mm m.mm 0.mm m.mm H.MH 0.: m.0 0.0 0.0m Hmooe H.m.0 mo pcoo pom 0.00H m.0 H.H m.m m.» m.sm 0.0m m.Hm 0.m 5.0 0.0 m.0 Epmmcoz Hmpoa 0.00H w.m m.H :.m m.z H.HH m.mH m.mm :.mm m.m m.0 m.0 Epmm Hmpzm 0.00H m.0 m.0 H.H m.m :.ma s.mm 0.0m m.HH H.H 0.0 m.0 Epmmcoz Hmosm 0.00H 0.0 m.m m.m 0.0H :.mm H.sm m.m H.H 0.0 0.0 0.0 owns: 0.00H m.0 H.H m.m 0.0 m.mm m.Hm m.mm m.m m.o 0.0 m.0 Hmpoe coawmm eacpaa mwcHCme Hodpo< an mmfipczou no coapzofipumam pcoo pom mace mmqmm mmmzw mmzzw mmmmw mmamm mmmme mmqm» mmmfiw mmzfim ooofim. mpcsoo 2H Hmpoe Lo ow on on on 0p 0» Op 09 on v mocmcfimom comma 000mm oomzw 0002» 00mm» 000mm 00mm» ooomw coma» oooaw mo mmp< onomm 2mm8mm3 .mmma .mmop< secondmmm o>Hm mo comm mom .mwcfiCme mpaqmo pom mwmpo>¢ ooumEapmm 0cm Hmzpo< an moapcsoo mo :ofipsnfippmfiolloa.: mqm less 1000 1500 20m 2500 3000 3500 1:000 1:500 5000 5500 than to to to to to to or 81000 11:99 1999 2499 2999 3499 3999 4499 1‘999 5499 more Dollars of Her Capita Earnim Figure 4. 5. Distribution of Counties by the Magnitude of Actual and Estimated Per Capita Earnings, West and U.S., 1959. 214 cent of the counties. Thus, per capita earnings ranged from $2,500 to $3,999 in 79 per cent of the counties in the West. In 13 per cent of the counties, actual earnings averaged more than $4,000, compared to 8 per cent in cate- gories below $2,500. As shown in Figure 4.5a, the distri- bution of Western counties by the level of actual per capita earnings in their total labor force follows a normal distribution across the 11 earnings categories indicated. The Western distribution of counties is much more heavily concentrated in the higher actual earnings cate- gories than the national distribution, as seen in Figure 4.5a, although average actual earnings in some counties of both distributions were extremely low and, in others, extremely high. The proportion of U. S. counties in those actual earnings categories above $3,000 that were in the West was more than double the region's 14 per cent share of all counties in the country. On the other hand, only 7 per cent of all counties in the nation with per capita earnings under $3,000 were in the Western states, and most (62 per cent) of these were in Montana, Idaho, and Colorado. Variability among counties in the two Western divisions was about the same as for the region, with 80 per cent of the counties concentrated in the $2,500-$3,999 range. In the Pacific division, however, about one-fifth of the counties had actual earnings greater than $4,000, compared to 9 per cent in the Mountain division. From the 215 state distributions shown in Appendix Table B-3, it appears that actual earnings were highest in California, averaging $3,500 or more in over two—thirds of all counties, and lowest in Idaho, where per capita earnings in 64 per cent of all counties were less than $3,000. On average, nrban per capita earnings in the Western counties were higher than actual earnings for the total labor force, as indicated by the data of Table 4.10. In 1959, urban earnings in 62 per cent of the Western counties exceeded $3,500, and was greater than $3,000 in 89 per cent of the counties. The modal category ($3,500-$3,999) contained 37 per cent of the counties in the region in which there was an urban labor force. The position of the entire urban distribution and its relation to the national urban distribution is shown in Figure 4.5b. Again the western distribution overlaps and lies to the right of the national distribution and is more peaked. The West accounts for about one—fourth the total number of counties in the nation which had urban earnings of $3,500 or above, but contained only 10 per cent of those counties in categories under $3,500. Urban distributions for the Mountain and Pacific divisions show little deviation from the regional distri- bution (Appendix Table B-5). A slightly larger percentage of Mountain counties than Pacific counties had average actual earnings below $3,000. For example, in only 30 216 counties in the entire Western division did actual urban earnings average under $3,000, and 24 of these were in the Mountain division states. But urban earnings in a much larger percentage of the Pacific counties (37 per cent) averaged over $4,000, largely because of the influence of California. In California, 51 of the state's 59 counties contained an urban labor force in 1960, and in 45 of these, per capita earnings were at least $4,000. Thus, both the Pacific and regional distributions are influenced by high urban earnings in this state. Rural nonfarm earnings among Western counties were heavily concentrated in the $2,500-$3,999 range. Only 20 per cent of the 437 Western counties with a rural nonfarm labor force had per capita earnings outside this range, with 12 per cent below the range and 8 per cent above. The mode of the distribution, as seen in Figure 4.5c, was $3,000-$3,499, which contained one-third of the 437 counties. Thus, average earnings in this sector for most counties, although slightly lower than earnings in the urban labor force, were still relatively high compared to the nation. Of the 999 counties in the nation in which rural nonfarm earnings were greater than $3,000, one—fourth were in the West, and of those in the West, 50 per cent were in the three conterminous states of the Pacific division, California, Oregon, and Washington. Closer examination of the rural nonfarm population of these 217 states revealed a heavy concentration around large metro— politan complexes where average wages tend to be much higher than the national average. In 1960, there were 424 Western counties with a rural farm labor force, 273 in the Mountain division and 151 in the Pacific. Except for the Pacific counties, however, the farm labor force in most Western communities was very small and relatively isolated from the urban populace geographically. Farm earnings were lower on average than the earnings in other sectors, as shown in Table 4.10. In 58 per cent of all the Western counties, farm earnings averaged less than $3,000, compared to a previous low of 42 per cent in the rural nonfarm sector. In the national distribution, however, per capita farm earnings in 85 per cent of all counties were below $3,000, due almost totally to low farm earnings among the Southern counties. Of the 2,604 counties in the nation with a rural farm labor force, only 252 (10 per cent) were in the West. These were well dispersed throughout the region. In 41 per cent of the Western farm communities, per capita earnings were greater than $3,000, compared to only 15 per cent nationally. The West contributed well over one-third of the nation's farm communities with average earnings above $3,000, and these were about equally divided among the two divisions. In the Pacific division, farm 218 earnings averaged over $3,000 per person in 57 per cent of the counties, and in California 75 per cent of the counties were in this category. In the Mountain division, about one— third of the counties had average farm earnings above $3,000, with no one state predominating. Variability in total nonfarm earnings among the Western counties closely followed the total distribution discussed previously. This results from the relatively small prOportion (4 per cent) of rural farm peOple in the total labor force of the West in 1960. Among Western counties, the total labor force was composed of at least 75 per cent nonfarm residents in nearly every community. The relationship of the Western nonfarm earnings distribution to the national distribution is shown in Figure 4.5e. The Western distribution favors the upper earnings categories more heavily, and in 1960, the West contained over a fourth of all counties in the nation with average nonfarm earnings over $3,000, and these were evenly distributed among the two divisions of the West (Appendix Table B-15). In summary, per capita actual earnings in most Western counties were relatively high in 1959, for each residence area. In each of the five distributions shown in Figure 4.5, a much larger percentage of Western counties than national had actual earnings which averaged over $3,000, and the Western distribution showed a greater concentration 219 of counties in the modal earnings categories. Even in the West, however, per capita earnings in a few counties were extremely low, except in the urban labor force. Estimated Per Capita Earnings Reference is again made to Tables 4.10 and 4.3, and to Figure 4.5 for the purpose of comparing variability among counties in the average earnings capacity of persons in different residence areas. As seen in the lower half of Table 4.10, potential earnings in a very high propor— tion (over 90 per cent) of the Western counties were con- centrated between $3,000 and $3,999 for every residence area except the rural farm. In the rural farm sector, the degree of concentration was just as great, but the range was $2,500 to $3,499. In the bbbal labor force of 63 per cent of the Western counties, potential earnings averaged between $3,000 and $3,499. The average varied from $3,500 to $3,999 in 30 per cent of the counties, and was estimated to exceed $3,500 in 32 per cent. Nationally, only 15 per cent of all counties had total potential earnings in excess of $3,500, and 30 per cent of these were in the West. The Western total labor force distribution and its relation to the comparable U. S. distribution is shown in Figure 4.5a. A comparison of the actual and potential distributions from Figure 4.5a also indicates that actual earnings in the 220 total labor force of a majority of the Western counties were commensurate with earnings potential in these counties. The mode of both distributions was the same, but the potential distribution contained twice the proportion of counties in the modal earnings category. This relation is fully analyzed in the following section. Concentration of counties in the two Western divisions was approximately the same as for the region, with esti— mated earnings potential of the total labor force in well over 90 per cent of all counties fluctuating between $3,000 and $3,999 (Appendix Table C-2). In the Pacific division, however, potential earnings averaged over $3,500 in 46 per cent of the counties, compared to 23 per cent in the Mountain division, and 32 per cent for the whole region. Variations in nrban earnings potential among Western counties was extremely low, as indicated by the distribution of Figure 4.5b. In 80 per cent of the 273 counties in the West with an urban labor force in 1960, potential earnings averaged from $3,500 to $3,999. In the category just below this mode ($3,000-$3,499) were 15 per cent, and in the class just above ($4,000-$4,499) were 5 per cent. There was more variability in the national distribu- tion, which lay slightly to the left of the potential dis- tribution for the West. The West contributed more than one- third of all counties in the nation with urban potential earnings over $3,500, and of these, over two-thirds were from the states of Washington, Oregon, and California. 221 Potential urban earnings in the Mountain and Pacific counties conformed closely to the regional distribution of Figure 4.5b. In the majority of these counties, persons in the urban labor force were, on average, earning at least their estimated earnings capacity. The average earnings capacity of persons in the £2221 nonfarm sector of almost three-fourths the Western counties was between $3,000 and $3,499, one earnings category lower than the mode of the urban distribution. Potential rural nonfarm earnings in 92 per cent of the Western counties was greater than $3,000, however, which compares with 73 per cent nationally. The overall relation between the national and Western distributions is shown in Figure 4.5c. The potential rural nonfarm earnings distribution for the West lay further to the right of the actual rural nonfarm earnings distribution than was true in the total or urban residence areas. This implies that varying levels of labor maladjustments may have been more prevalent in the rural nonfarm sector of many Western counties than in the total or urban sectors. Further discussion of this postu- late will follow shortly. There was little variation in potential rural nonfarm earnings among counties in the Mountain division, with 80 per cent of the counties averaging between $3,000 and $3,499 (Appendix Table C-8). By comparison, 57 per cent of the Pacific counties were in this category, but another 50 per cent fell in the $3,500-$3,999 range. 222 Average earnings potential of persons in all but 9 per cent of the Earn communities in the West was contained in the $2,500 to $3,499 interval. This compares with 89 per cent in the same range for the country as a whole. However, in over half the Western farm communities, poten- tial earnings exceeded $3,000, as opposed to only one-third of all farm communities in the U. S. Nationally, there were 998 farm communities with an average earnings capacity of $3,000 or more, and 21 per cent of these were in the West, one and half times the region's share of total counties in the U. S. As shown in Figure 4.5d, the rural farm estimated distributions are flatter and contain more variation in potential earnings among counties than the estimated dis- tributions in other residence areas. In addition, judging from the graphic relation of the actual and estimated dis- tributions, rural farm labor maladjustments do not appear to be as severe as those for the nation as a whole, or as those found in other regions of the country. Variation in total nonfarm potential earnings among counties does not follow either of the other four residence area distributions. Concentration occurred in two earnings categories. In 54 per cent of the 441 Western counties with a nonfarm labor force, potential earnings averaged between $3,000 and $3,499, and in another 42 per cent, estimated earnings potential ranged from $3,500 to $3,999. 223 Thus, 96 per cent of all counties were contained in a $1,000 range. As shown in the last distribution of Table 4.10, the Western share of all counties in the nation increases with each successively higher potential nonfarm earnings cate- gory, up to 46 per cent of the nation's counties in which nonfarm earnings potential averaged $4,000 to $4,499. The total relationship between the regional and national dis- persion of counties by estimated earnings is shown in Figure 4.5e. To recapitulate, the average earnings capacity of’ persons in the labor force of five different residence areas in each Western county was computed, based on the estimating equations discussed in Chapter III. It was found that average potential earnings in over 90 per cent of the Western counties were heavily concentrated between $3,000 and $3,999, for each residence area except the rural farm sector. Potential farm earnings showed the same degree of concentration in the $2,500-$3,499 range. Thus, the variability among counties in average potential earnings was far less than variations in average actual earnings, as shown in the distributions of Figure 4.5. In the following section, an examination of differences in actual and poten- tial earnings among Western counties is made, a differen- tial which is not evident from information presented in the last two sections. 224 Apparent Labor Maladjustments Based on the earnings of a thorough cross-classification of individuals in every social and economic strata of the United States, what were the eXpected earnings of people in local labor markets of the West as of 1959, and how do these potential earnings compare with earnings actually received? The first part of this question was discussed in the previous section; an answer to the second part is the task of the following discussion. Total, All Areas of Residence.——Reference is made both to Table 4.11 and Figure 4.2 in explaining differences in total labor maladjustments among Western counties. Out of 441 Western counties in 1960, it was estimated that persons in the total labor force of 224 (51 per cent) counties were- averaging within at least $100 of their potential earnings and in another 107 counties (24 per cent), within at least $400 of their earnings potential. Hence, in only one-fourth of the Western counties was the differential between actual and potential per capita earnings greater than $400. Total labor maladjustments exceeded $700 in only 10 per cent of the counties, and these were concentrated in two separate areas of the Mountain division, which are discussed shortly. Compared with the national distribution of labor maladjustments reported in Table 4.5, where the earnings gaps in 47 per cent of all counties in the country were less than $400, Western counties had relatively well-adjusted 225 TABLE 4.ll.--Distribution of Counties by Magnitude of Labor Maladjustment, for Each of Five Residence Areas, 1959. WESTERN REGION County Area $100 or $101- $401- $701 Totall of Residence less $400 $700 or more Number Total 224 107 65 us 441 Urban 160 56 38 19 2732 Rural Nonfarm 198 113 72 SA 4372 Rural Farm 197 61 76 90 4242 Total Nonfarm 213 109 71 48 441 Percentage Distribution within Region Total 50.8 24.3 14.7 10.2 100.0 Urban 58.6 20.5 13.9 7.0 100.0 Rural Nonfarm 45.3 25.9 16.5 12.4 100.0 Rural Farm 46.5 14.4 17.9 21.2 100.0 Total Nonfarm 48.3 24.7 16.1 10.9 100.0 Percent of U.S. Total3 Total 27.3 16.9 8.9 4.8 14.1 Urban 19.7 10.6 8.6 5.3 12.7 Rural Nonfarm 26.0 18.8 10.3 5.3 14.2 Rural Farm 35.6 15.4 11.9 6.1 13.8 Total Nonfarm 25.4 16.6 9.5 5.4 14.1 1This column represents the number and distribution of counties for the whole region. 2Some counties had no population in this residence area in 1960. 3Each percentage in this distribution represents the proportion of U.S. counties, with labor maladjustments of a given size, that were in the West, for a given area of resi— dence in the county. Thus, rows and columns do not sum to any particular total. 226 labor forces. Of the 822 counties in the U. S. in which labor maladjustments were $100 or less, 27 per cent were in the West. By comparison, the West contributed 17 per cent of all counties in the $101-$400 labor maladjustment category, 9 per cent of those in the $401—$700 category, and only 5 per cent of those with total labor maladjust— ments over $700. Apparent labor maladjustment data pertaining to Western divisions and states are presented in Appendix D. From estimates shown in Appendix Table D—2, it is clear that most labor maladjustment problems were in the counties of the Mountain division. Of the 278 counties in this division, total labor maladjustments were less than $100 in 40 per cent, and were $400 or lower in 67 per cent. Thus, total labor maladjustments exceeded $400 in one-third of the Mountain counties, and were considered severe (over $700) in 15 per cent. By comparison, the total labor force in 112 (69 per cent) Pacific counties, including Alaska and Hawaii, was estimated to be rela- tively free of labor maladjustments, and in another 20 per cent of the counties the earnings gap per capita was $400 or less. In only four counties of this division was the earnings gap over $700, and three of these were in Alaska. In only 18 counties, or a total of 11 per cent, did labor maladjustments in the total labor force exceed $400. Hence, in terms of county units, the Mountain 227 division had twice the proportion of communities in which labor disequilibriums were serious. The level of apparent labor maladjustment in the total labor force of each Western county is shown in Figure 4.2. One is first awed by the dearth of Western counties, relative to other regions of the country, that are dark shaded, and secondly by the degree of concentration of these few counties. One such area encompassed the counties of south central Colorado and north central New Mexico. In Colorado the total labor force in 12 of 63 counties (19 per cent) failed to average within $700 of their estimated earnings capacity, and the earnings gap in 43 per cent was over $400. The problem was less serious in New Mexico, where labor maladjustments exceeded $700 in 13 per cent of the state's 31 counties, and was over $400 in 29 per cent. The industrial distribution in most of these counties showed that the labor force was heavily employed in mining and construction occupations, and/or in retailing and service jobs. Although employment in these industries are known to be weakly or negatively associated with earnings, potential earnings still averaged over $3,000 in nearly every county in the area, mainly because of relatively high education levels and the large propor- tion of people in age categories between 25 and 45. How- ever, actual earnings were relatively low, below $2,500 in most counties, and therefore labor maladjustments were large. 228 A second concentration of counties with severe labor maladjustments occurred in southwestern Utah. In this state the estimated earnings gap exceeded $400 in 52 per cent of the 29 counties, but were severe in 38 per cent. These counties are characterized by very dry salt flats and other desert land, and the population is extremely scarce. Those who reside in these counties are employed mostly in agri- culture, mining, service, and professional industries. One other state had scatterings of severe labor maladjustments. The earnings gap in 7 of 44 counties (16 per cent) in Idaho exceeded $700, and in one—half the counties the gap was greater than $400. But as shown in Figure 4.2, these counties were dispersed throughout the state, and were characterized by high employment rates in agriculture and retail trades, relatively slow-growth industries. Again, the close relationship between low labor maladjustments and nearness to industrial—urban development is evident, but this relation among the Western counties is less clear from mere observation of Figure 4.2 because of the large number of Western counties with low labor malad- justments that are relatively isolated geographically from urban areas. Only one of the severe labor maladjustment concen- trations in the West, Colorado and New Mexico, corresponds to a designated poverty area in the U. S. In this area 229 there is a large Mexican—American population, and the average earnings of these people are known to be very low, in many counties less than $2,000. Summarizing, the total labor force in most Western counties was found to be relatively well—adjusted in 1959, with average actual earnings in over half the counties within at least $100 of potential earnings, and within $400 or less of potential earnings in 75 per cent of the counties. Severe labor maladjustments were prevalent only in the states of New Mexico, Colorado, Utah, and Idaho, but approached the national average only in the state of Utah (see Appendix Table D-2). Urban Areas of Residence.——There were 273 Western counties with an urban labor force in 1960, 168 fewer than the total number of counties. Of this number estimated urban labor maladjustments in 59 per cent were $100 or under. In another 21 per cent urban residents were averaging within $101 to $400 of their average potential earnings, which means that urban labor maladjustments were moderate to absent in four-fifths of all Western counties. Thus, in only one—fifth of the counties did the estimated earnings gap exceed $400, and in only 7 per cent (19 counties) was the gap over $700. This compares with 37 andIL7per cent, respectively, in the national distribu- tion shown in Table 4.5. The proportion of the nation's counties that were in the West in each of the three highest 230 labor maladjustment categories of Table 4.11 was less than 11 per cent, but 20 per cent of all counties in the well- adjusted category were contributed by the Western states. Division and state allocations are shown in Appendix Tables D-4, 5 and 6 for the urban sector. In the Mountain division urban labor maladjustments in 11 per cent of the 152 counties exceeded $700, compared to only 2 per cent of the 121 Pacific counties with an urban sector. Almost half of those in the Mountain division were in Colorado, and in the Pacific division, Washington and Oregon each contained one county in the severe maladjustment cate- gory. However, there appeared to be no strong correlation between the occurrence of large total and urban labor maladjustments in the same county. In about one-third of the Mountain counties, urban labor maladjustments were greater than $400, compared to 7 per cent in the Pacific, and 20 per cent for the region. The urban labor force appeared well-adjusted in 46 per cent of the Mountain counties, and in 74 per cent of the Pacific counties. Among all contiguous states in the West, the largest proportion of counties with urban labor maladjustments of $100 and under occurred in California (92 per cent), and the smallest proportion in Idaho (24 per cent), followed closely by Colorado (31 per cent). This was the expected pattern, however, given the extremely large urban base of California (86 per cent of the total 231 population was urban in 1960), and the prevalence of large industrial-urban complexes. In Idaho and Colorado, and most other Mountain states, the urban pOpulation of most counties is associated with very small towns and cities which do not afford employment Opportunities comparable to those on the West Coast. Rural Nonfarm Areas of Residence.--There were 437 counties in the West in 1960 in which there was a rural nonfarm labor force, only 4 less than the total number of counties (Table 4.11). Of this number, 45 per cent were in the lowest labor maladjustment category, 26 per cent in the $101—$400 category, 17 per cent in the $401-$700 cate- gory, and in 12 per cent of the counties, rural nonfarm labor maladjustments exceeded $700. In total, 29 per cent of all Western counties had rural nonfarm labor maladjust- ments greater than $400, and 71 per cent $400 or less. Nationally, the rural nonfarm earnings gap exceeded $400 in 1,717 counties (56 per cent), but only 126 (7 per cent) of these were in the West. But of the 1,363 counties in the country in which rural nonfarm earnings were less than $400, 311 (23 per cent) were in the West. The Mountain and Pacific distributions for the rural nonfarm sector (Appendix Table D—8) barely resemble one another. In the Mountain division, labor maladjustment 232 estimates exceeded $700 in 18 per cent of the 277 counties in which rural nonfarm residents resided, compared to only 3 per cent of the 160 Pacific counties. Only 34 per cent of the Mountain counties had a well-adjusted rural nonfarm labor force, as opposed to 64 per cent of the Pacific counties. The labor force of this sector in 82 per cent of the counties in California was well—adjusted, compared to lows of 21 per cent in Idaho and 23 per cent in New Mexico. In four conterminous Western states, Oregon, California, Wyoming, and Arizona, there were no counties with severe rural nonfarm labor maladjustments. To capsule, the prevalence of severe labor malad- justments (>$700) in the rural nonfarm sector of Western counties, although disturbing, is certainly not a cause of major concern since the proportion is very small rela— tive to other areas of the country. The problem appears more serious in the Mountain division, than the Pacific. Among states, estimates show that Colorado had the largest proportion of counties With severe labor maladjustments in this sector, while in California, labor disequilibriums were negligible. This region contributes heavily to the nation's well-adjusted counties, but only a token per— centage of those counties in which rural nonfarm earnings gaps were over $400. Rural Farm Areas of Resdience.--In 1960 only 4 per cent of the total labor force in the West lived on farms, 233 compared with 8 per cent in the Mountain division, 3 per cent in the Pacific division, and 7 per cent nationwide. The proportion varied by individual states, but in all but 3 of the Western states the rural farm component was less than 10 per cent of the total The rural farm population of the West was dispersed across 424 counties, as seen from Table 4.11, and as sus- pected, heavy labor maladjustments in the farm sector were more prevalent than for the other residence areas. The estimated farm earnings gap was at least $700 in just over one—fifth the 424 Western counties, and exceeded $400 in 39 per cent of these counties. Nevertheless, Western farmers in almost half the counties were averaging within $100 or less of their estimated earnings capacity in 1959, and within $400 or less of their earnings potential in 70 per cent of the counties. As seen in the last distribution of Table 4.11, only a small percentage of the farm communities in the nation with earnings gaps averaging over $400 were in the West, while the region contributed over a third of the nation's counties in which the farm labor force was well-adjusted. How were these farm communities allocated within the region, and in what part of the West were the heaviest farm labor maladjustment problems located? This information is contained in Appendix Tables D-10 and 11. In 1959, the degree of farm labor maladjustment among the Mountain 234 division counties was almsot indentical to that for the whole Western region. One-fourth of the Mountain counties had severe farm labor maladjustments, and 68 per cent of these were in Idaho, Colorado, and Utah. In the Pacific division, farm labor maladjustments exceeded $700 in only 22 of the 151 (15 per cent) farm communities, 8 of which were in Alaska. Removal of Alaska reduces the share to 10 per cent. Nine of the 22 counties were in the state of Washington. The farm labor force was estimated to be well adjusted in 43 per cent of the Mountain counties and in 53 per cent of those in the Pacific division. This share varied widely among states, from highs of 87 per cent in Nevada and 77 per cent in California, to lows of 10 per cent in Utah, and 16 per cent in Idaho. As was true for other regions of the country, low farm labor maladjustments and nearness of off-farm employ— ment opportunities appear to be correlated among Western communities. As previously indicated the percentage of counties with heavy to severe farm labor maladjustments was greater in the Mountain than Pacific division, but in the Mountain states there were very few cities that were large enough to provide off—farm employment to farmers, and the few that could only serve a small number of farm communities located close by. On the other hand, large industrial-urban centers abound in the three 235 contiguous states of the Pacific division, and except for several Western counties in Washington, most farm commun- ities are favorably located with respect to off-farm employment and farm product markets. Therefore, actual total farm earnings, as previously discussed, tend to be higher among the Pacific counties, and the farm earnings gap lower. In addition, it is a well-documented fact that commercialized farming operations, in which capital investments and capital/labor input ratios are high, produce much higher average returns to agricultural labor. A very high percentage of such farm communities in the West are located in the three Pacific Coast states, where in many cases actual farm earnings are on par with or higher than average earnings in the nonfarm economy. Again, these results are consistent with Bryant'sl findings, where he concluded that the major determinant of inter-community diferentials in income levels of white rural farm families in the West was proximity to industrial- urban concentrations, followed by average value of land and buildings per farm. In summary, farm labor maladjustments in excess of $400 were relatively more prevalent in the farm sector of Western counties than either of the other residence areas. In one-fifth of the Western farm communities, farm labor lKeith Bryant, op. cit., pp. 148-149. 236 maladjustments were estimated to be severe, but over three— fourths of these were in the Mountain division where agri— cultural returns per farm were much lower than in the Pacific division. The West contributed far more than its 14 per cent share of total counties to the nation's well- adjusted farm communities (36 per cent), and only a token percentage of all communities with severe farm labor maladjustments. The lack of an industrial-urban base in the Mountain division produces a depressing effect on the average earnings of farmers in local communities, since there are few Opportunities for off-farm employment rela- tive to the Pacific division. Nonfarm Areas of Residence.——All 441 Western counties contained a nonfarm labor force in 1960, which means no county labor force contained only rural farm peOple. The distribution of these counties by level of nonfarm labor maladjustment is shown in Table 4.11, and since the distri— bution almost exactly parallels that for the total labor force, this sector is given only brief treatment. The urban component of the nonfarm labor force was obviously small and did not carry very much weight in the labor maladjustment computations for this sector. This follows from the close parallel of the rural nonfarm and total nonfarm distributions with the total distribution shown in Table 4.11. In addition, there were only two-thirds as many counties in the West with an urban labor force. 237 It also follows from this parallel that the rural farm labor force did not weigh heavily in the total dis- tribution. In the total labor force of most counties, average earnings of the rural farm segment were far out- weighed by average earnings of the rural nonfarm and urban labor force. Hence, the total, rural nonfarm, and total nonfarm distributions were very similar, while the distributions for the rural farm and urban sectors were unique. Within the Mountain counties, the rural nonfarm component of the total nonfarm labor force was much more important than for the region as a whole. As a result, the proportion of counties with heavy to severe labor maladjustments was greater than for the region (37 vs. 27 per cent), and the proportion of counties with a well- adjusted nonfarm labor force was much smaller (36 vs. 48 per cent). In the Pacific division, only 10 per cent of the counties contained nonfarm labor maladjustments over $400, and in 70 per cent, there were no nonfarm earnings gaps over $100. In both divisions, these proportions parallel those found in the total labor force, and in most Western counties, maladjustment levels in the total and total nonfarm labor forces varied together. As a result, the patterns established in Figure 4.2 for the total labor force also hold for the nonfarm labor force. 238 Labor Maladjustments Among Regions The discussion up to this point has focused on labor maladjustments within the four major census regions of the U. S., with little attention to inter-regional comparisons. Within a particular residential area, important differences in labor maladjustment patterns do exist, however. In summarizing labor maladjustments among regions, reference is made almost solely to Figure 4.6, but the basic data underlying this pictogram are found in various tables of Appendix D. Overall, communities in the West and Northeast appeared to have the fewest labor maladjustment problems, as shown in Figure 4.6a. Among regions the largest prOportion of counties with a well-adjusted total labor force was in the West, followed very closely by the Northeast. In the North Central and Southern regions, the proportion of well-adjusted counties was slightly less than the national share. Severe labor maladjustments (>$700) occurred in a very small percentage of Northeastern and Western counties, relative to the U. S. and South. The prOportion of counties in this category for the South was much larger than the national average, and far above every other region. The rural farm component of the total labor force was relatively large in almost all counties of the South, and in a majority of the North Central counties. less -.ts¢¢§ E Esml 9 to :5 $400 8 (a) Tbtal S 3‘ .... _ g ,;30 mm EEEEEEE §T=73337F""‘ 8m1!II .3; J 15 257 30 40 50 0 10 Parcent 0 10 20 30 40 59 60 419_ $100 or less T-TT'TT.. -Vvavv‘vv‘vv fifiéfi$flkfléfixfi_ (d) Rural Farm Size of Labor Mhladjustmant 0 5 O ""%ww 0.03.9...A.A.‘ Fflfifid Figure 4.6. 239 Percent vvvvvvv‘ fhfihfiaifiifihfihfih (b) Urban 20 30 4O 50 6O 70 O 10 2O Parcent (e) Total Nonfarm 10 20 30 40 50 Percent Percentage Distribution of Counties by Magnitude of Labor Maladjustment in Five Residence Areas, U.S. and Major Regions, 1959. 240 The lower average earnings of the farm labor force reduce actual earnings for the total labor force and thus widens the earnings gap between actual and potential earnings. Consequently, in those regions where the farm labor force constitutes a high proportion of the county total labor force, i.e., the South and North Central states, the prevalence of counties with severe labor maladjustments was found to be greater, as noted in Figure 4.6a. In every region, the prevalence of high nrban_labor maladjustments was less than for the total labor force, as evidenced by Figures 4.6a and 4.6b, but the ordering of regions was the same. Over half the counties in the West and Northeast were well-adjusted with respect to their urban labor force, and in the North Central states the proportion was greater than for the nation. Only the South had a lower share of well-adjusted counties than the nation as a whole. At the other extreme, severe urban labor maladjust— ments existed in less than 11 per cent of the counties of all regions but the South, where the earnings gap exceeded $700 in a fourth of the urban communities. At this level of disparity the South was the only region to exceed the national.proportion. There was little difference among regions in the proportion of counties with urban labor maladjustments between $101 and $400, while in the next highest category, 241 $401-$700, the South and North Central regions, respec- tively, again had the largest proportions. The paucity of urban centers in the West North Central states and the relatively low industrial wage scale in the Southern cities appear largely responsible for the higher prOportion of counties with urban earnings gaps above $400 in these two regions. The fastest growing segment of the population in most communities across the country is the rural nonfarm sector, which includes anyone not living inside a census urban place or on a farm. Rural nonfarm earnings are relatively low in many communities of the South and North Central states because of the type of jobs available in local labor markets. In the Northeast and West, a large proportion of the rural nonfarm labor force lives near and works in large metropolitan areas where average earnings tend to be higher. Thus, as shown in Figure 4.6c, the rural nonfarm labor force in a much higher prOportion of counties in these two regions were realizing their earnings potential than in the South or North Central regions, both of which were below the national level. The proportion of counties in the two middle rural nonfarm labor maladjustment categories did not vary among regions by more than 11 percentage points, but severe rural nonfarm labor maladjustments were much more prevalent in the South and North Central states. The proportion was relatively low in the Northeast and West. The U. S. 242 percentage of counties in this category was heavily weighted by the large number of Southern and North Central communities in which the estimated rural nonfarm earnings gap was greater than $700. It is a well-documented fact that total rarn earnings in every region of the country average less per capita than average earnings in the nonfarm economy, and that those persons in agriculture are less endowed with charac- teristics needed to command higher earnings. It was also found in this study that large farm labor maladjustments result mainly from extremely low actual earnings, and not from low potential earnings, although potential earnings do tend to be lower in the farm sector than the other residence areas. It follows then that those regions with high densities of farm population should also have the highest frequency of communities with severe farm labor maladjustments and the lowest prOportion of counties with a well-adjusted farm labor force. An analysis of Figure 4.6d confirms this hypothesis. Among regions, rurality was greatest in the South and North Central regions, both of which had relatively low proportions of well-adjusted farm communities and relatively high proportions of severely maladjusted rural farm communities, especially the South. Additionally, because 80 per cent of the farm communities in the U. S. 243 are in these two regions, the frequency of severe farm labor maladjustments among all counties in the nation was also high. The influence of the South and North Central distributions on the national distribution for each farm labor maladjustment category is shown in Figure 4.6d. Apparent labor maladjustments in the total nonfarm labor force were eXpected to conform closely to those in the total labor force, since the nonfarm labor force con- stitutes a very high proportion (80 to 90 per cent) of most communities in the United States. A comparison of Figures 4.6a and 4.6e bears this out. Since the pattern is the same as that for the total labor force, the same discus- sion applies. It should be noted, however, that the relative influence of the farm sector in the communities of each region is also reflected in the nonfarm distributions. The proportions of counties in each labor maladjustment category for the Northeast and West are almost identical in the total and nonfarm distributions of Figures 4.6a and 4.6e, while in the South and North Central regions the higher degree of rurality generates a larger proportion of counties with heavy labor maladjustments in the total labor force, and a lower proportion of well-adjusted counties. Recapitulation The main purpose of this chapter was to study the incidence of apparent labor maladjustments among the communities in each of the major regions of the United 244 States. Since the labor maladjustment index is defined in this study as a measure of the disparity between average actual earnings per capita and average earnings capacity per capita, three computational steps were required. In the first, actual earnings were computed directly from U. S. census data for each county. Secondly, earnings capacity, or potential earnings, was estimated from a national equation which relates individual earnings to social and economic variables thought to be most impor- tant in explaining earnings variability among persons in the labor force. Thus, the equation generates an estimate of the eXpected average earnings of the labor force in a county based on what persons throughout the national labor force were earning in 1959 with the same social and economic characteristics as those prevailing in the county. In the third computational step, actual earnings were subtracted from potential earnings, and counties in each state, division, and region were allocated by the magnitude of the resulting differential. Within each geographic area studied, 1abor_malad- justments were computed for the labor force in each of five residence areas, the total, urban, rural nonfarm, rural farm, and total nonfarm sectors. For each of the four major regions, the Northeast, North Central, South, and West, patterns of variability among counties in actual and potential earnings, and in labor maladjustments within each residence area were separately described. 245 Within each residence area, both actual and potential earnings averaged less in the South than for either of the other three regions. In addition, the South had a much larger proportion of counties in which actual and poten- tial per capita earnings were below $3,000 than the other regions, for all five residence areas. In each region and residence area, there was considerably less variability in potential earnings among counties than actual earnings. Potential earnings were highly concentrated in a $1,000 range, with the modal earnings category generally highest for the urban sector and lowest for the rural farm sector. Potential earnings distributions departed most from actual earnings distributions in the South, a partial indication that the frequency of heavy labor maladjustments would be greater in that region. Apparent labor maladjustments in excess of $700 were found in every region and for each residence area, but the proportion of counties in this severe maladjustment cate— gory was highest in the South, for all five residence areas, followed by the North Central region. In each case the Southern proportion was significantly greater than the comparable share for the nation, and the North Central share slightly less than the national share. Except for the rural farm residence area, labor maladjust- ments-exceeded $700 in about 10 per cent or less of the counties in both the West and Northeast. Farm labor 246 maladjustments were $700 or greater in 20 per cent of the Western and Northeastern counties. More important perhaps than severe maladjustment problems was the incidence of well—adjusted counties, i.e., labor maladjustments of $100 or less. Proportions of well-adjusted counties were highest in the Northeast and West and generally lowest in the South. In every sector except the urban, both the South and North Central shares of well—adjusted counties were less than the national share, while in the Northeast and West, this share was considerably larger than for the nation. Differences among regions in the proportions of counties in the two middle categories of labor maladjust- ments were not as great, and varied from one residence area to another. Finally, two very important patterns of labor malad- justment were distinguished within each region. There appeared to be a close positive correlation between size of estimated labor maladjustment in each residence area of a county and nearness of that county to an SMSA, i.e., as,distance of a county from an.SMSA decreases, the com- puted labor maladjustment decreases. Also, the larger the SMSA, the more extended was the influence of the SMSA into surrounding counties. Secondly, there seemed to be a high positive corre- lation between the designated poverty areas of the U. S. 247 and the concentration of counties with the highest levels of labor maladjustment, for each residence area. Thus, one possible conclusion is that the existence of poverty in these areas emanates, partially at least, from the great disparity between actual and potential earnings of individuals. This gap is doubtless the result of many things, but the major element is often the dearth of job Opportunities in local labor markets and the lack Of education to fill jobs that do exist. CHAPTER V RELATIVE IMPORTANCE OF VARIABLES AFFECTING LABOR MALADJUSTMENTS Introduction The resource adjustment process in and between local labor markets across the nation is, according to static economic theory, supposed to reduce or minimize the differ- entials in earnings between various factors, individuals, communities, and major areas. But the results of this study indicate that returns to the human factor (measured in terms of earnings) within and among hundreds of commun- ities in the U. S. in 1959 appeared far from a state of equilibrium. This conclusion was based on an index Of apparent labor maladjustments (or local labor market disequilibriums) as of 1959. The index compared the average per capita earnings of persons in particular residence areas of each county in the U. S. with the average earnings capacity per person in the same residence area and county, as based on the earnings of a cross section of individuals throughout the country with the same social and economic attributes as the average of those in the county. Thus, the index measures the 1959 earnings gap between the current actual 248 249 earnings and potential earnings of an "average" individual in a particular county. For this average individual, living in a particular residence area and working within a given local labor market structure,the labor maladjustment index represents the average increase or decrease in earnings that he could expect for his services, relative to the national earnings of persons with his socioeconomic characteristics. Obviously, potential earnings for a particular individual in a given local labor market might depart sub- stantially from average potential earnings for the whole community, but based on the per capita average, severe labor maladjustments were frequently found in the labor force of each residence area. In some communities, states, and divisions of the country, the presence of extremely large and acute earnings disparities was much more preva- lent. Details Of these patterns were discussed at length in Chapter IV. It would be impossible in this study, or perhaps any one study, to consider all the implications of these estimates for each community and to evaluate poten- tial adjustment possibilities for each case. In reality, the labor force of every county has a different mixture of social and economic characteristics which influence the average level of earnings. Thus, specific adjustment policies and programs vary from one community to another. I250 But, some variables, or labor force characteristics, were found to be of particular importance in determining the average earnings potential of the labor force in a‘ particular county residence area. The purpose of this chapter is to explore broadly some Of the possibilities for adjustment by examing the structure of the community labor force in various areas and the effect on potential earnings exerted by some of the more important variables in the earnings capacity function. The level of potential earnings and the extent of labor maladjustment, or underemployment of the human factor in local labor markets, has meaning in this study, or perhaps any study, only in the sense of relativity. To present absolute values without some suitable norm of comparison is pointless. Herman P. Miller indicates that "inadequate incomes can never be eliminated in any final sense because we as human beings always tend to judge incomes below the average as inadequate. If this conclu- sion is assumed correct, low incomes become a matter not of the size of the income but of the prevailing attitude toward the distribution."1 Thus, in the same context, labor returns of the community labor fOrce must be compared with some common base, and the usual base of comparison is a broad geographic area such as a region or the nation. As implied throughout, the U. S. is the base in this study. lHerman P. Miller, Op. cit., p. 31. 251 Examination of community labor force social and economic characteristics indicates substantial deviations from average national attributes. Although potential earnings in the labor force of some communities were con- siderably in excess of actual earnings, the overall char- acteristics of the labor force in many such communities were still found to be inferior to those for the nation as a whole. The large labor maladjustment simply resulted from very low actual earnings. The labor force of other communities, although endowed with attributes superior to the national average was also found to contain large labor maladjustments because of low actual earnings. Thus, in order to examine relative differences in average potential earnings of individuals, and the relative effect of particular variables in the estimating equation, average characteristics prevailing in the national labor force of each residence area in 1959 were substituted, one by one, for the average characteristics Of the county labor force. Adjusted earnings capacity estimates per capita were then computed for each county residence area, and com- pared with the original estimates Of potential earnings. Since actual earnings in the local labor force might have been lower or higher under a different set of social and economic characteristics, a revised estimate of the labor maladjustment index based on constant actual earnings- would have had little meaning. Under such a constant 252 earnings condition, the level of labor maladjustment would automatically increase or decrease with increases or decreases in potential earnings. Hence, adjustment consid- erations in the following analysis are based on changes in "potential" earnings in local labor markets. Space does not permit an in-depth discussion of the results for each individual community, or even by states. Thus, a general discussion of the results is presented by the six variables (see Table 5.1) for which substitutions were made. These variables were: the national labor force age distribution, percentage of females and nonwhites in the labor force, the distributions of educational attainment and industrial employment, and per cent of salaried persons in the labor force. Since the direction of change in potential earnings was the same in almost all communities for the total, total nonfarm, rural nonfarm, and urban labor force, and the correlations between labor maladjustments in these areas were found to be relatively high, only the total and rural farm sectors are considered in the following discussion. Age The entire national age distribution shown in Table 5.1 was substituted for the county age distribution in the estimating equation in comparing the local and national effects of age structure on potential earnings. Of the 253 TABLE 5.1.--Per Cent Distribution of Persons in the U. S. Labor Force, 1960, by Area of Residence and Demographic Variables Rural Rural Total Variable Total Urban Nonfarm Farm Nonfarm AGE1 "18:24 .1390 .1366 .1144 .1186 .1405 25-34 .2107 .2133 .2225 .1485 .2160 35-44 .2360 .2385 .2355 .2107 .2379 45-54 .2055 .2057 .2671 .2302 .2191 55-64 .1309 .1322 .1158 .1608 .1287 65+ .0451 .0435 .0392 .0790 .0426 FEMALE .3207 .3431 .2767 .2136 .3286 NONWHITE .1059 .1134 .0832 .0928 .1013 EDUCATION1 Elem., 5-8 .2968 .2704 .3432 .4108 .2876 H.S., 1-3 .2338 .2360 .2330 .2155 .2353 H.S., 4 .2452 .2564 .2245 .1995 .2489 Col., 1-3 .0886 .0998 .0643 .0536 .0914 Col., 4+ .0652 .0761 .0443 .0229 .0686 SALARIED .7452 .7721 .7274 .5042 .7620 INDUSTRYl “REFIET: For., Fish. .0673 .0111 .0795 .6038 .0255 Mining and Construction .0692 .0614 .1076 .0442 .0711 Manufacturing, Dur. .1521 .1591 .1587 .0625 .1590 Manufacturing, Non.-Dur. .1140 .1225 .1278 .0577 .1236 Trans.,Comm., Public Ut. .0690 .0748 .0634 .0245 .0724 Wholesale .0342 .0387 .0259 .0117 .0360 Retail .1482 .1573 .1478 .0570 .1553 Finance, Ins., Real E. .0417 .0495 .0242 .0098 .0442 Business and Repairs .0249 .0272 .0227 .0072 .0263 Private Household Ser. .0297 .0296 .0333 . .0203 .0304 Personal Services .0300 .0336 .0249 .0079 .0318 Entertainment .0078 .0089 .0058 .0017 .0083 Professional Ser. .1172 .1266 .1063 .0516 .1223 .0496 .0546 .0417 .0191 .0519 Public Adm. lPercentages do not sum to totals subclass in the dummy variable analysis. Source: POpulation: Economic Characteristics, pt. U.S. Bureau of the Census, U.S. Census of 1960, General Social and because of one omitted l, U.S. Summary. 254 six age variables in the equation, the lowest (18-24) and highest (65+) had relatively large negative regression coefficients, compared to relatively large positive coefficients for the four in—between categories. The earned income of persons between 18 and 24 tends to be relatively low for several reasons, but mainly because most young people Of this age are just entering the labor force. In addition, most students working part—time are in this category, and in counties which contain a sizeable college student population, the proportion of persons in the 18—24 age category was found to be several percentage points higher than the same share in other counties. Partial retirees, widowers, and many other part-time workers are included in the 65 and over category. In many communi- ties, this age category is also heavily populated by farmers. Thus, substituting the age distribution of the national labor force for the county labor force age distri— bution, it was expected that the labor force in those counties having a large percentage of persons in the labor force between 18 and 24 and/or 65 and over, relative to the nation, would experience an increase in potential earnings since less of the negative regression coefficient would enter the potential earnings estimate. A heavy concentra— tion of persons between 35 and 54 years of age might Offset such an increase, however. 255 In the total labor force of most Northeastern commun- ities, substitution of the entire national age distribution was found to have very little effect on average potential earnings. In a majority of the counties, average poten- tial earnings showed a decrease Of less than one per cent. Thus, in general the prevailing age distribution in the total labor force of the New England counties in 1960 was slightly better than the national distribution in terms of higher earnings expectations. An exception to this pattern occurred in some of the upper New England counties, where very large earnings gaps were detected (see Figure 4.2). These very rural communities were found to have an Older labor force than the nation, especially in the 65 and over category, and hence, substitution of the national age dis- tribution.slightly increased potential earnings, although less than $75 per person in most cases. Average potential earnings in the farm labor force of most Northeastern communities increased slightly after substitution of the national farm age structure, i.e., relative to the nation, the age structure Of the farm labor force of most Northeastern communities was slightly infer- ior to that at the national level, in terms of the level of earnings associated with the six age variables. Except for only a few Northeastern communities, the increase in potential farm earnings varied between $25 and $100, or from less than 1 per cent to about 3 per cent. 256 The exceptions were generally counties located within or near SMSA's. In the upper New England farm communities, where a much larger percentage Of the labor force was above 55 years of age than nationally, potential farm earnings per capita also increased with the substitution. In the North Central and Southern regions, patterns of change in potential earnings of the total and farm labor forces were less (filear among communities. However, it is clear that substitution of the national age distri- tNJtion did not produce the expected effect, although the Ctirection of change was as anticipated. It was expected tllat potential earnings in most communities of these two Imagions would increase substantially because of the higher Euge structure in agriculture, an industry which is very important to both areas. Except for a very few counties, changes in potential earnings in the total labor force of communities in these two regions varied from about —2 per cent to 5 per cent, and in a majority of counties there was a slight increase under the national age distribution. A very high per- centage of the counties in the West North Central, East South Central, and South Atlantic divisions, experienced increased potential earnings, but only rarely did the gain exceed $100 per person, or about 3 per cent. In the total labor force of the West North Central communities, there was a relatively high percentage of persons in the 65 and 257 over category, while the proportion of young persons (18—24) in the labor force of communities in this division was about the same or slightly less than the nation's 14 per cent (Table 5.1). This is consistent with the out—migration trend of the young populace from the bascially rural Mid- western communities. In most communities of the Southeastern and South Atlantic divisions, plus Texas in the Southwest division, the increase in potential earnings was the result of a inelatively high proportion of young people in the total liabor force. This largely reflects the relatively high Chrop-Out rate from high school and college of Southern ycnang people, who then enter the labor force, plus the Snualler proportion of youth who go on to college. These ycnxng people often continue to live at home and work in local urban areas. The more urbanized communities of these two regions tended to have a larger proportion Of young people in the total labor force than the national distribution, and slightly smaller proportion of persons in the two Older age categories. Thus, substitutions of the national age distribution increased potential earnings in the majority of these counties by l to 2 per cent. These counties are located throughout the East North Central division and near SMSA's in other parts of the Southern and North Central regions. 258 Throughout both the South and North Central states changes in potential earnings in the farm residence area did not increase as sharply as expected, nor did the pattern depart very much from that of the total labor force. Most Southern and North Central farm communities were found to contain a larger proportion of persons in the 65 and over category than the national share (8 per cent), and a smaller proportion of 18-24 year Olds than the nation (12 per cent). Thus, potential farm earnings increased by 1 to 3 per cent 111 the majority of the farm communities, although many eaxperienced a decrease, depending on whether the national Stuastitution affected the lowest or highest age category more. There also resulted no clear pattern of change among true Western communities, except in the three Pacific states. In Washington, Oregon, and California, average potential earnings per individual in the total labor force decreased from 1 to 3 per cent in most counties. This was because the nation as a whole had a larger labor force participation rate from 18 to 24 year Olds than communities in these three states. Similar patterns were also found in the farm com- munities of the far West. To summarize, substitution of the national age struc- ture for the community age distribution did not produce any dramatic and distinct patterns of change in potential earnings. It does appear clear, however, that whether 259 potential earnings in a community increased or decreased under the national age distribution mostly depended on whether the proportion of persons in the two extreme age categories were lower or higher than these same proportions in the national distribution. If the county proportion was lower, then a larger share of the negative regression coefficient was aggregated,and potential earnings generally increased, while the opposite was true for a larger county gproportion in these two age categories. Only rarely did tame change exceed plus or minus 3 per cent in either the tcital or farm labor force. In general, the age distribution in communities of tlie NOrtheast and West appeared to be associated with legher per capita earnings potential than in communities (bf the North Central and Southern regions. In 1959, com— munities of the South appeared to have an over-abundance of young people in the total labor force, as opposed to a large proportion of persons 65 and over in counties Of the North Central region. In both the South and North Central regions, there was a relatively large proportion of persons 65 and over in the farm labor force. Overall, alterations in the age distributions of the total and farm labor force in most communities does not appear to offer significant adjustment potential in terms of raising per capita earnings, except perhaps in the South. If the implication is correct that the higher 260 prOportion of young participants in the total labor force of Southern communities are college and high school dropouts, then programs designed to keep these people in school might be a solution, but this would be more consistent with some kind of an education policy, to be discussed momentarily. bar Average wages paid to female participants in the labor force are known to be lower than the average male wage, even for equivalent responsibilities. In addition, specialty ;jObs usually associated largely with women, such as secre— 1:ardal positions, nursing, public school teachers, clerical Mnark, etc., generally carry relatively low stipends, Thus, ttle female variable in the earnings capacity equation was ruegatively associated (8 = $700) with average earnings Of individuals in the labor force. As shown in Table 5.1, 32 per cent of the nation's labor force were women in 1959, compared with only 21 per cent of the national farm labor force. In communities where the proportion of females in the labor force exceeded the national share, potential earnings would increase with the substitution, and vice versa. It was expected that potential earnings would increase in those communities in which there was a large urban base and decrease in the more rural areas. This expected pattern would result from the higher female participation rate in the urban labor force. 261 The effect of this substitution on the total and farm labor forces of most communities throughout the country was meager, generally varying between ~$50 and $50, or from about -1.5 to 1.5 per cent. Two patterns were detected. A majority of the counties in every region experienced a decrease in per capita earnings potential in the total labor force when the nation's 32 per cent share of females was substituted for the county's proportion. This general decrease in every region was possible because of the :increase which occurred in those communities with a very liarge urban labor force. This pattern was less distinct irl the Northeast and South, however, where basic calcula- txions indicate that the female participation rate in many (nommunities far exceeds the national rate. Substitution industrial job opportunities in the nonfarm economy kuad higher female participation rates than the nation, and tdierefore mild increases in potential earnings. The participation rate of women in the nonfarm labor force of most communities has been gradually increasing over time, but unfortunately their wage rates do not appear to be rising commensurate with their increased training and responsibilities. Thus, as more and more females enter the labor force, it appears that more local effort should and must be directed at ways and means of eliminating wage discrimination between men and women, in addition to policies aimed at raising women's wages to a level con- sistent with their marginal value products in local labor markets. 264 sage. The general welfare state of the nonwhite populace in the U. S. has received an overwhelming amount of atten- tion in recent years. The positive association of non- whites with low wages is well—documented in the literature. In the earnings capacity function used in this study, the nonwhite variable took on a negative regression coefficient of -$339, less than half the magnitude of the female coefficient discussed above. Nationally, just under 11 per cent of the total labor fkarce was composed of nonwhites in 1959, compared to about $9 per cent of the farm labor force (Table 5.1). But the ruonwhite population was not distributed among communities irl the same manner as the white population. Two types of ruonwhite concentrations were evident. Perhaps most impor- tant is the heavy density of negroes in every area of resi- dence of most Southern communities, although out-migration from many of these areas has been very high in recent years. The second type of concentration is in the areas to which most Southern Negroes are migrating, the large northern cities. The out—migration of nonwhites from local labor markets of the South has most Often been for economic gains, and hence to minimize moving eXpenses, most of these migrants appear to prefer the nearest large metropolitan area. Thus large cities of the nonsouth located closest to the Southern region have built up very large concentrations 265 of nonwhites in recent years. As many as could find jobs have entered the urban labor force of these cities, but because of their generally inferior skills and know-how, they have received relatively low wages, which has had the effect of restraining average per capita earnings, both actual and potential, in many metropolitan areas of the North. Thus, it was eXpected that substitution of the national proportion of nonwhites in the total and farm labor forces for the nonwhite ratio in the total and farm labor forces of most Southern communities would result in a rather sizeable increase in potential earnings, and likewise for counties in which there was a large urban base. Analysis Of the statistical results indicate this to be the case. The substitution analysis indicates there were fewer than 11 per cent nonwhites in the total labor force, and fewer than 9 per cent in the farm labor force of almost every community in the Northeast, North Central, and Western regions. Per capita earnings potential decreased by $25 to $50 in most cases, or about 1 per cent, which Ineans that most communities in these states had at least. a token share of nonwhites in the labor force. The only Iioticeable exceptions to this pattern in these three regions (Occurred in counties which were a part of a large SMSA, and tshese were all in the Northeast and North Central regions. 266 On the other hand, some very significant increases in potential earnings occurred in many Southern communities when given the national percentage of nonwhites. Nearly every Southern community incurred an increase in average per capita earnings potential, ranging from $25 to $250. In many communities where per capita earnings potential was relatively low, this often represented an increase Of up to 10 per cent. In cases where these very large increases were detected, nonwhites often made up 40 to 50 per cent of the total labor force and frequently more of the farm labor force, depending on the labor intensity of farm crops produced in the county and the importance of share-cropping and Negro tenancy. Exceptions to this pattern among Southern communities were found in the fringe areas of the region, e.g., Delaware, Maryland, northern Virginia, West Virginia, the Appalachian counties of southeastern Kentucky, western North Carolina, and eastern Tennessee, and commercial farming areas of Oklahoma and Texas. Changes in potential farm earnings were almost identical to changes in total earnings potential in communities of the Northeast, North Central, and Western divisions, after substituting the nation's 9 per cent share of nonwhites in the farm labor force. But, in most of the Southern communities, especially those in which a large increase in total earnings potential occurred, the 267 increase in farm earnings potential slightly exceeded the total, which indicates that earnings in the farm labor force were relatively more restricted than earnings in the total labor force as a result Of the substitution of national percentages of nonwhites in these two sectors. In summary, it is quite obvious that the prevalence of nonwhites in the farm and nonfarm labor forces poses a real adjustment problem to most Southern communities, and to many large cities. The problem is actually two-fold in that the nonwhite prOportion of the labor force is not only large in the South, but these people are generally the very lowest wage earners, partly because Of outright wage discrimination, but perhaps more so because of the lack of salable skills. In Chapter IV, the evidence presented showed average per capita earnings in 1959 to be extremely low in a large prOportion of Southern counties, regardless of residence area. The extremely low earnings of nonwhites and their importance in the labor force appear to be very large contributors to these low averages. Policies are now being rapidly instituted to dis— courage racial wage discrimination in these areas, but local (zommunity effort must be expanded to attract employment Opportunities and raise wage rates for nonwhites. The :result will obviously be an increased per capita earnings Ibotential in these counties, but because actual earnings will Ibrobably increase more, the problem of severe labor malad- giustments in these areas should diminish substantially. 268 Education The relationship between educational attainment and income has probably been researched as much or more than any other area of human resource develOpment. Increases in monetary rewards with higher levels of educational attainment needs no further documentation for purposes of this study. In the multiple regression earnings capacity equation employed in this study, the effect of six different levels of educational attainment on individual earnings was tested, the regression coefficients becoming progressively larger with increased amounts of schooling. Levels of education considered and beta coefficients asso- ciated with each were presented in Table 3.1. The effect Of the first category (0—4 years of elementary school) on the individual earnings was included in the constant term. The next two education variables, percentage of the labor force with 5 to 8 years of elementary schooling, and per- centage with l to 3 years of high school, contain large, significant regression coefficients, —$975 and -$489, respectively. These compare with slope coefficients of $58 for 4 years of high school, $462 for l to 3 years of college, and $2,258 for14years or more of college. Besides the fact that increased schooling generally leads to better paying jobs in most communities of the ‘U. 8., it was hoped that inclusion of this variable in the estimating equation would also reflect changes in the 269 quality of the labor force among communities and relative to the nation. However, all the benefits to increased education are not strictly material; cultural and social values are also enhanced, as well as the advantage of man having more awareness of the environment around him and better rapport with his fellow-man.l But these gains cannot be measured in monetrary terms, and must be implied. In addition, measuring labor force quality strictly in terms of years of school completed ignores differences in quality of the same year of schooling in various areas, or differences in quality between a year of schooling in a white vs. a Negro school, or differences in the quality of teachers and administration officials among communities. Often, these differences play a major role in determination of monetary rewards for given levels of education, espe— cially at the college level where some employers are known to prefer employees from one school over those from another. The earnings capacity function utilized in this study does not make allowances for these deviations, and in most cases little allowance can be made without the inclusion of several interaction variables. But, because of county data limitations no interactions were used for estimating county earnings potential. One simply must ¥ lHerman P. Miller, op. cit., p. 123 ff. 270 recognize and accept some of the restrictions imposed in using years of school completed as a measure of the effect of education on earnings. Inferior distributions of educational attainment relative to the nation were expected to show up strongest in Southern communities, and in those counties where very large labor maladjustments in the total labor force were found (see Figure 4.2). Substitution of the nation's distribution of educational attainment for each county's distribution produced the following results in the total and rural farm sectors. In the Northeast, per capita earnings potential of the total labor force in almost every community decreased slightly under the national distribution of educational attainment, with the exception of three specific areas. In a few of the counties of the upper New England division, the Adirondack mountains of New York, and the Appalachian mountains of Pennsylvania, potential earnings increased by as much as 5 per cent, but in most cases the gain was much smaller. The general level of education in these counties was found to be substantially inferior to the nation (the national distribution of educational attainment is shown in Table 5.1), with 10 to 20 per cent more persons in the 5 to 8 years of schooling category, and about one-half the national average with 4 years of college education. The lack of college trained people in the total labor force 271 of these communities appeared to be the principal reason for the relatively low earnings potential. Educational attainment of farm people in the North- east was found to be considerably higher than the national level of farm education in 1959. With the exception of only four or five Northeastern farm communities, potential farm earnings decreased significantly (from 5 to 20 per cent in most cases) in every county with the substitution of the nation's educational distribution, the smallest decreases occurring in some of the counties Of upper New England and central Pennsylvania. Most farm communities in the region had 15 to 25 per cent fewer people than the nation with less than a high school education, and a much larger proportion of farm people with a college education than the nation. Surprisingly, the total labor force of most North Central communities was found to be less educated than the nation's total labor force, although this pattern was less distinct in the East North Central division than the West North Central. Substitution of the national distribution of educational attainment led to an increase in per capita earnings potential that varied from about $50 to $200, or from 1 to 6 per cent. Largest increases were noted in those communities with low actual earnings and high labor maladjustments, i.e., the Ozarks, and the upper Great Plains and Great Lakes counties. 272 In most North Central communities containing a large urban base, the total labor force was found to be slightly better eudcated than the national total labor force, and hence potential earnings were decreased with the substitu- tion. However, in the extremely large SMSA's, such as Chicago, Detroit, and Cleveland, potential earnings remained about the same with the substitution. This was due to the large build—up of relatively low educated in- migrants to these cities, especially the southern Negro. This group tended to raise significantly the proportion of people in the two lowest categories of educational attainment, and lower the percentage of the labor force with a college education. The 1959 farm labor force of most North Central communities was found to be better educated than the nation's farm labor force. Potential farm earnings decreased sub— stantially (5 to 15 per cent) in the commercial farm areas of the West North Central division, and to a slightly lesser extent in the East North Central states. There were some exceptions, however. In the farm communities of southern Illinois, the upper Great Lakes, and the Missouri Ozarks, farm earnings potential increased with the substi- tution of the national educational distribution for farm people. In these communities, it was Often found that one—half or more of the farm labor force had less than an elementary education, compared to 41 per cent nationally 273 (Table 5.1), which accounts for the increased potential farm earnings in these counties with the national sub- stitution. In 1960, the median level of education in the South was a full year less than the nation for persons 25 and over (10.6 vs. 9.6), and almost half the Southern total labor force had a maximum of an eighth grade education. Only Delaware and Florida exceeded the national median, while Maryland, Oklahoma, and Texas had about the same median. Thus, substitution of the national distribution of educational attainment had a very significant effect on per capita earnings potential in most Southern commun- ities. The increase was most apparent in counties of the East South Central division, where, except for a few largely urban communities, potential earnings increased by 5 to 15 per cent as a result Of each county having the nation's educational distribution. Increases in earnings potential were least in the West South Central states, especially Oklahoma and Texas. In all the Western counties Of this division potential earnings decreased by l to 3 per cent with the substitution, but increased slightly in the eastern counties of the division. Increases in potential earnings in the South Atlantic states varied from very mild to unchanged:h1Maryland, Dela- ware, northern Virginia, and Florida, to as high as 15 per 274 cent in many communities of North Carolina, South Carolina, and Georgia. But overall, potential earnings in the total labor force of counties in this division were not enhanced as much with the substitution as counties in the South- eastern states. The educational attainment of the farm labor force in most Southern communities was found to deviate less from the level of schooling of the national farm labor force than the community total labor force from that of the national total labor force. In most South Atlantic and Southwestern communities potential farm earnings decreased by l to 5 per cent with the national distribution of schooling shown in Table 5.1. In many farm communities of Texas, however, the decrease often exceeded 15 per cent. The big difference was in the percentage of the farm labor force in the 5 to 8 years of schooling category, which was much smaller for these counties than for the nation. In general, these are the people who have left the farm sector of these communities and taken employment elsewhere, thus boosting farm earnings potential. An exception to this pattern was found in the South— eastern states, where potential farm earnings increased slightly in a majority of the counties. Relative to the national farm labor force, there appeared to be about one- half to three-fourths as many people in the farm labor force of these communities with better than a high school educa- tion. 275 In both the total and rural farm sectors of nearly every Western community, per capita earnings potential decreased substantially when given the national distribu- tion of educational attainment. Also, in nearly every community decreases in the farm labor force were much greater than in the total labor force, which indicates that relative to the nation, local labor markets in the West had a more superior farm labor force than their total labor force in terms of educational attainment. Decreases in potential earnings of the total labor force in Western counties ranged from about 2 to 12 per cent, while farm earnings potential decreased by 5 to 15 per cent. In the farm sector, largest decreases were found in the commercial farming communities of Washington, Oregon, and California. The smallest decreases in the total labor force were found in counties of the Mountain states, eSpecially Colorado, Montana, and Idaho, areas for which some very large labor maladjustments were estimated. To summarize, potential earnings appear to be more sensitive throughout all geographic regions to levels of educational attainment than any other variable discussed so far. Overall, communities of the Northeast and West exper- ienced decreases in per capita earnings potential in both the total and farm labor forces when, instead of their own distribution of educational attainment, they were given the national distributions shown in the first and fourth 276 columns of Table 5.1. The decreases were much larger in communities of the West than those of the Northeast. Very significant increases in potential earnings were evidenced in counties of the North Central and Southern states, especially the latter, after substitution of the national distribution of educational attainment, Increases were much greater in Southern communities than Western communities, however. In most communities of all four regions the farm labor force appeared better educated than the total labor force, relative to the national farm labor force and total labor force, respectively. In all but the North Central region, changes in potential farm earnings were in the same direc- tion as those in the total labor force. At least one very important pattern was established in this analysis. In nearly all communities in the U. S. in which apparent labor maladjustments in excess of $700 were detected (see Figure 4.2), substitution of the nation's distribution of educational attainment led to an increase in per capita earnings potential, and in most cases, the increase was substantial. In nearly all these communities, a relatively large proportion of the total labor force had only an elementary education or less, and the proportion of college graduates in the total labor force was far below the national average. These results strongly imply that human labor maladjustment problems could be improved 277 immensely through community education programs designed to reduce school drop-outs, encourage college attendance, and reduce the out-movement of the educated through better job opportunities. Type of Earnings As noted earlier in this study, a very high per— centage (generally three-fourths or more) of total income consists of wage and salary earnings, in nearly all areas of the country. Exceptions to this generalization were found in those communities where agriculture predom- inates. Because farmers are categorized as self-employed, the proportion of salaried persons in the total labor force of these counties was much lower than in less rural communities. In the farm labor force, the percentage of self-employed is obviously mich higher. In the estimating equation, the effect of the source of income on variations in individual earnings was measured by two variables, percentage of the labor force that was salaried and percentage that was self-employed. The regression coefficient for the salaried variable was —$447, compared to $815 for the self—employed variable. It was expected that substitution of the percentage Of salaried persons in the nation's total and farm labor force (Table 5.1) for the comparable share in each county would produce mild effects in most communities, with the greatest change 278 in potential earnings occurring in those communities where income accrues mostly from self-employment. Nearly every Northeastern community was found to have a slightly higher proportion of salaried persons in both the total and farm labor forces than the nation as a whole. Thus, per capita earnings potential increased by only 1 per cent or less in most local labor markets Of this region with the national substitution. This same trend was found to hold in most communities of the East North Central, South Atlantic, East South Central, and Pacific states. The only major exceptions within these divisions were the states of Kentucky and Tennessee, where most counties had a smaller proportion of salaried persons in the labor force of both the farm and total sectors than the nation. In the relatively rural communities of the West North Central, West South Central, and Mountain divisions, per capita earnings potential decreased slightly, due to the high proportion of self-employed persons in agriculture. The effect of this variable on potential earnings appears relatively insignificant in most counties of the U. S. Its effect appears greatest in the commercial farming areas of the country where the proportion of salaried persons in the labor force is low relative to the nation, but even in these areas the change was very small. 279 Industry of Employment Most recent literature dealing with low-income prob- lems and alternatives for adjustment of malallocated labor resources have focused heavily on occupational and indus- trial structures. Without doubt, inadequate job opportun- ities in local labor markets are the greatest nemesis faced by communities where such problems abound. Indus- trialization per se is not a remedy for every community. The industry must be matched with the needs of each indi- vidual area, since the impact of industrialization varies tremendously. Obviously, the industrial needs Of each individual county cannot be analyzed in this study. But by substi- tuting the nation's industrial mix in 1960 for each county's industrial distribution, some generalizations can be made with respect to changes in per capita earnings potential among local labor markets. Fourteen separate industry variables were included in the earnings capacity equation presented in Chapter III. The regression coefficients were negative and highly sig- nificant for seven of these, the largest (-$1308) asso- ciated with agriculture, forestries, and fisheries, and the next largest (—$627) with personal services. Indi- vidual earnings in these industries are known to be rela- tively low. In communities where total and farm employment in these and other low-paying industries was heavy relative 280 to the nation, per capita earnings potential was expected to increase markedly, unless Offset by a very heavy con— centration of the labor force in an industry where the regression coefficient was large and positive, such as Finance, Insurance, and Real Estate, or Manufacturing. The following discussion indicates regional results, after substitution Of the entire national industrial distribution. Throughout the country per capita earnings potential was found to be extremely sensitive to the industrial dis- tribution in each community. In most counties differences between the national and county industrial structure were greater in the total sector than the farm sector, but in both the farm and total labor forces, the industry variable definitely exerted the greatest influence on potential earnings of the six variables analyzed in this chapter. Both total and farm earnings potential decreased sub- stantially in well over three-fourths of the Northeastern coun- ties, which means that in terms of income producing potential, the industrial employment structure throughout most of the Northeast was superior to the national distribution. Decreases for the total labor force varied from 3 to 10 per cent in most communities, but in several counties, especially those lying near or contained within large metrOpolitan areas, the decrease exceeded 20 per cent. Only in a few counties of upper New England, northern New York, and central Pennsylvania did potential earnings increase with the nation's industrial mix. 281 In almost every farm community of this region both absolute and percentage decreases in farm earnings potential, based on the industrial distribution of the nation's farm labor force (Table 5.1), were much larger than in the total sector, generally ranging from 5 to 20 per cent. But, had potential earnings been as high in the farm sector as the total sector, the percentage decrease in the farm sector would have been even larger. Opposite trends were found in the total labor force of the other three regions. A very high percentage of the communities in the North Central, Southern, and Western states experienced large to extremely large increases in per capita earnings potential following the national sub— stitution. In the East North Central and South Atlantic states, the gain in potential earnings varied from less than 1 per cent up to about 10 per cent. Exceptions were the states Of South Carolina and Georgia, where the increases were in general much larger. Largest increases were found in communities of the West North Central region, where potential earnings Often increased by at least $800 per capita, or well over 30 per cent. Gains were almost as large in counties of the Southeast, Southwest, and Mountain states, where potential earnings Often climbed by 15 to 20 per cent, while in most of the Pacific counties, the increase generally ranged from 2 to 12 per cent. 282 Substitution of the nation's industrial distribution of the farm labor force for the same distribution at the county level produced very large increases in potential farm earnings in practically all communities of the West North Central, Southwest, and Western states. Increases were largest in the Great Plains and Mountain states, varying between 10 and 20 per cent in most cases. Poten— tial farm earnings decreased, however, in_most counties of the East North Central, South Atlantic, and Southeastern states, due to the high frequency of off-farm employment among persons in the farm labor force. Decreases were largest in the two Southern divisions, ranging downward by 5 to 15 per cent in most instances. To summarize, several definite patterns were estab- lished for both the total and farm sectors. First, a check of the industrial mix in those counties where increases occurred indicates that the size of the increase in per capita earnings potential is directly and positively related to the proportion of the total labor force employed in agriculture, forestries, and fisheries, relative to the national proportion. Secondly, it follows that the largest gains were experienced in the commercialized farming com- munities of the West North Central states, plus Texas, Oklahoma, and California, all areas where agricultural employment comprises a very large share of the total labor force employment (from 30 to 50 per cent in most cases, compared to only 7 per cent nationally). 283 In almost all communities located within or near an SMSA, both total and farm earnings potential remained stable or decreased with the national substitution. This was due to the very small proportion Of people employed in agri- culture in these counties and the relatively large share of persons in manufacturing and trade industries. Because of the concentration of the national labor force in SMSA's and other large cities (70 per cent of the total labor force was urban in 1960), most communities had a larger percentage of persons in agriculture than the nation as a whole, and thus the major reason for increases in potential earnings in the majority of the nation's counties. Finally, in those counties of the nation designated as acute poverty areas, and for which very large labor maladjustments were computed, substantial increases in earnings potential resulted from substitution of the national industrial mix. Since these counties contained a relatively large proportion of persons in agriculture, there is a direct implication that poverty and acute labor maladjustments are at least partially associated with the degree of rurality in an area. This analysis clearly indicates the tremendous variability among communities in industrial structure, and the relative importance of this variable in determining per capita earnings potential. Opportunities for adjustment of earnings disparities in many local labor markets may 284 well depend on the extent to which communities are able to align their industrial structure with that at the national level. Summary The last chapter provided overwhelming evidence of serious labor maladjustments in hundreds of local labor markets throughout the United States. This chapter has focused on the mixture of elements in these communities that affect earnings capacity, and therefore labor malad- justments, e.g., age, sex, color, education, type of earnings, and industry of employment, by measuring the loss or gain in per capita earnings potential resulting from inequalities in the economic and social structure among communities. Using national characteristics as a norm, it is clear that the two most important variables affecting the level of earnings in both the total and farm sectors of most communities were industrial mix and education, in that order. Tremendous variations from the national industrial mix were found, generally resulting in very large gains in potential earnings. The nation's indus- trial distribution for the total labor force is largely determined by concentrated employment in industrial-urban centers, while the industrial mix for the national farm labor force is heavily influenced by the commercial farm areas of the nation. 285 This analysis strongly indicates that Opportunities for improvement of the level of per capita earnings are poorest in relatively rural communities, where a large segment of the total labor force has less than a high school education, is relatively young or old, and has a high percentage of females, nonwhites, and salaried people in the labor force. Thus, to institute an effective human resource development program that will raise per-capita earnings and reduce labor maladjustments appears highly consistent with successful anti-poverty goals in most communities, since the causal forces are similar. In addition to an inferior industrial structure and a general lack of emphasis on educational achievement in many counties, there is also an enormous underutilization of the present productive capacity of people, especially nonwhites and females. But, on the basis of the relative importance of variables examined in this study, it appears that major attention should be directed at educational and industrial adjustments. Unfortunately, exploration of Specific labor adjustment programs for different commun- ities and residence areas cannot be attempted in this study, and must be left for a separate undertaking. CHAPTER VI SUMMARY AND CONCLUSIONS General Summary The high level of sustained growth and performance of the American economy over the past few decades must be viewed as an outstanding achievement. But despite pros- perity and widespread benefits, economic develOpment is not a smooth, evenly distributed process. There are both adjusting and maladjusting forces continuously functioning throughout all sectors of the local and national economies. Thus, even in the U.S. economy, thousands of persons have experienced economic losses in the midst of prosperity. This study has attempted to produce evidence not only of where the incidence of economic losses among local commun- ities throughout the nation has been greatest, but suggest reasons why average labor force earnings in many communities have fallen far behind average per capita earnings of the national labor force. The study has dealt mostly with underemployed and underpaid human resources at the community (or county) level. The primary purpose of the study was to develop an empirical index for measuring and examining the seriousness of apparent labor maladjustments in local labor markets 286 287 throughout the United States in 1959, by type of residence area, with respect to (1) magnitude of the earnings gap between potential and actual earnings in each county, (2) variation in labor maladjustments among residence areas within a community, (3) variation in labor maladjustments among counties, (4) differences in the earnings gap among major geographic regions, and (5) the relative importance of variables influencing per capita earnings potential, and hence labor maladjustments. ‘ The first two chapters were devoted to a general discussion of the importance of the human resource in the process of community growth and develOpment, as well as reasons for undertaking this study, develOpment of important concepts frequently used in the analysis, and some under— lying theory of labor allocation in local labor markets. Detailed procedures for measuring apparent labor maladjust— ments were presented in Chapter III. Empirical results of the study are based on a national multiple regression equation which statistically relates individual earnings to several socio—economic variables. This estimating function, termed an earnings capacity equation, was fitted by zero—one dummy variable techniques using a national subsample of census data on individua's By avoiding averages in the process of fitting the equation more of the total variation of the residuals was accounted for than previous attempts to explain variability of 288 earnings among persons. In addition, inclusion of the data in the form of dummy variable subclasses permitted any of the eXplanatory variables to be treated as a dependent variable. Application of this statistical relation to local labor market estimates of earnings capac1ty required some basic assumptions regarding county data. Most important was the assumption that persons in the labor force of each county were uniformly distributed across all major variables in the estimating equation, e.g., across all age groups, educational attainment classes, industrial categories, etc. Secondly, it must be recognized that county earnings esti- mates derived from the earnings capacity equation depict an "average individual" in the county, and that computed labor maladjustments are also per capita values in 1959 current dollars. "Labor maladjustment" as used in this study reflects apparent maladjustment rather than real maladjustment, since certain factors influencing differentials in earnings among individuals could not be quantified, such as nonpecuniary variables and imperfect knowledge about employment Oppor— tunities, all of which affect income earning capacities. The existence of maladjusted labor resources assumes that, at some alternative earnings level, comparable units of labor would be in their best alternative employment. Thus, maladjustment Of labor in a community refers only to actual 289 labor earnings which fall below some standard. The standard in the case of this study was potential earnings derived from the national earnings capacity equation. "Earnings" as used in this study is the sum of wage and salary plus self-employment income, i.e., income paid to labor as a factor in the production process. Within this framework, an index of apparent labor maladjustment was computed for five residence areas in each county as an indicator of the extent to which labor resources were malallocated in local labor markets of the U.S. in 1959. The index (referred to as an earnings gap) measures the disparity between average per capita "actual" earnings in a county and average per capita earnings "potential" in that county. It is believed that this index affords con— siderably more information about labor maladjustment in an area with respect to location and magnitude, both absolute and relative, than measures based on average returns to labor, or other previous studies involving comparable detail. In each residence area major attention was focused on labor earnings which fell below potential earnings, i.e., on positive measures of apparent labor maladjustment. How- ever, the statistical distributions presented in Chapter IV, and in the Appendix, do include negative computations (counties in which actual exceeded potential earnings), although the labor force was considered well-adjusted in such counties. 290 An enormous volume of statistics were computed for this study, which presented some organization problems. It was found highly impractical to include all computations for each county and hence, the results were summarized by allocating each county into size categories of actual earnings, potential earnings, and apparent labor maladjust— ments, for each of the five residence areas considered (total, urban, rural nonfarm, rural farm, and nonfarm). The text discussion was devoted mostly to regional and national labor maladjustment patterns, but empirical documentation is also provided for divisions and states in the Appendix. Counties in which apparent labor maladjustments were estimated to be $100 or less were considered to have a well— adjusted labor force, while an earnings gap in excess of $700 was termed severe. Two additional categorizations were made; moderate labor maladjustments ($lOl-$UOO), and heavy labor maladjustments ($UOl-$700). Although summaries of procedures and results are provided throughout the text, a brief recapitulation of major findings is justified at this point. Based on the empirical evidence of this study several conclusions were reached regarding patterns of per capita earnings and labor maladjustment variability. First, variability of actual per capita earnings among counties of each major region and county residence area were found to be extremely large, while potential earnings tended to 291 be concentrated within a relatively narrow range of $1,000 per capita. The modal earnings category for both the actual and potential earnings distributions varied from one resi- dence area to another, but in every region the mode of the rural farm distribution favored lower per capita earnings categories than distributions in the other four sectors. In addition, the potential earnings distribution of counties either coincided or lay to the right of the actual earnings distribution. These relations are graphically summarized in Chapter IV. Average per capita earnings (both actual and potential) of the labor force in each residence area were lowest among Southern communities, but actual earnings much more so than potential earnings. The South, with 45 per cent of all counties in the U.S., had an extremely depressing effect on the national distributions, especially the nation's dis- tribution of counties by actual earnings. Variations in actual and potential earnings among North Central counties were found to closely parallel national variations relative to the other regions, while the Northeastern and Western county distributions lay significantly to the right of the national distributions. In every region both actual and potential per capita earnings were highest in the urban sector and lowest in the rural farm sector. Only in the most agriculturally oriented communities of the Midwest and South did the farm labor force exert a significant 292 depressing influence on per capita earnings of the total labor force. The larger the urban sector in a community, the greater was its influence on per capita earnings of the total labor force in the community. Severe underemployment of labor was indicated in every major region and division of the United States, but in varying degrees. For each residence area, the South contained a much higher prOportion of counties in which apparent labor maladjustments exceeded $700 than any other region, and generally the smallest proportion of well— adjusted counties. In every region the incidence of serious labor maladjustments was greatest in the farm sector and least in the urban sector. For any one region, the greatest frequency of severe labor maladjustments was found in the rural South, where the farm earnings gap exceeded $700 in two—thirds of all communities. Outside the South, the farm earnings gap was severe in only 20 per cent of the Northeastern and Western communities, and in #1 per cent of the North Central farm communities. In contrast, the largest prOportion of communities with a well-adjusted labor force occurred in the West, where urban labor maladjustments were $100 or less in almost 60 per cent of all Western counties. By comparison, urban labor maladjustments were estimated to be under $100 in only 26 per cent of the Southern counties, H2 per cent of the North Central counties, and almost 52 per cent of the Northeastern 293 communities. These, and other regional relationships, were previously summarized in Figure “.6. For each of the five areas of residence examined, there was a definite pattern of geographic concentration of communities for which severe labor maladjsutments were computed, although the pattern was less distinct in the South where large earnings gaps (over $400) were more the rule than the exception. Demarcation of these areas is clear from Figure “.2. They include most communities of upper rural New England, the Adirondacks and Appalachian Mountains of New York and Pennsylvania, respectively, the upper Great Plains and Great Lakes counties, the Ozarks of Missouri, Arkansas, and Oklahoma, the Mexican-American con— centration of the Southwest, often referred to as the "four- corners" area (of Colorado, Utah, Arizona, and New Mexico), and most of the South, especially Appalachia, the Black Belt, and the Mississippi Delta counties. Farm labor maladjustments in these communities were especially acute. A very important correlation was established with respect to these areas of severe labor maladjustments. Many recent studies have designated these same areas as the most chronic poverty areas of America. Thus, there appears to be a striking parallel between the incidence of poverty and economic underemployment of labor among many communities of the United States. A comparison of the characteristics of poverty areas with the attributes of persons in the farm 29“ and nonfarm labor forces of communities with severe labor maladjustments indicates reasons for the parallel drawn. In both cases there was a relatively large prOportion of persons in agriculture and other slow-growth industries, a very low level of formal educational attainment, relatively high proportions of young and old peOple in the labor force, as well as unemployed, a very small industrial sector which affords very poor off-farm job Opportunities, as well as inferior jobs to those in the urban labor force, and in most cases institutions and other public facilities that do exist are of poor quality. These characteristics are self-perpetuating in most of these communities because of the eXperiences and biases of the older generation in the labor force and their influence on the youth of the community. At least one additional striking parallel was drawn from this study. Size of apparent labor maladjustment in each area of residence analyzed was positively related to distance of a county from an industrial—urban center, up to some maximum range, and generally the larger the industrial complex, the further out was its effect extended. This relation is easily seen for the total labor force in Figure “.2. Farm labor maladjustments were especially affected by nearness of a community to urban areas, since off-farm employment was an important source of farm earnings in 1959 to those communities which happened to be located 295 near nonfarm employment opportunities, particularly in the South. The labor force in the farm and nonfarm sectors of some communities appeared well-adjusted in 1959, however, in spite of relative isolation from an industrialized sector. Most such communities were characterized by a capital intensive, commercialized agriculture, from which gross returns were high enough to produce parity earnings to farm labor. These communities were mostly located in the Midwest and western Great Plains, western Oklahoma and Texas, California, and some sections of Florida. This analysis indicates that overall, the West and Northeast were relatively free of any serious labor malad- justment problems, with mild concentrations in the most rural communities of the two regions. But these two regions combined contain only one-fifth of all counties in the nation, and the prevalence of severe labor maladjustments in the North Central and Southern regions makes the problem of utmost importance to the nation, since the persistence of such maladjustments is a drag on the national economy. Summary of Statistical Correlations A major phase of this study involved the computation and comparison of apparent labor maladjustments among county residence areas. Throughout the study, and in the preceding summary remarks, several implications were made regarding the relationship between the earnings gap of various areas 296 of residence. Because of the large number of observations involved (approximately one per county), several scatter— grams were plotted on the Calcomp mechanical plotter in the Michigan State University Computer Center, and a supporting simple linear regression analysis run, in order to statistically analyze the relationship of labor dis- equilibriums among selected residence areas. Some of the more important relationships are summarized in the following discussion. The earnings gap in each area of residence was regressed on the earnings gap in each of the other resi- dence areas, this set of regressions including the combined effect of all regions. A second set of regressions was run for each of the four regions, in which the relation between labor maladjustments in the rural and nonrural sectors was analyzed. All regression results are presented in Appendix Tables E.l and E.2. Nationally, there was definitely a positive relation- ship between tgtal and rural farm labor maladjustments, as seen from Figure 6.1, although very large variations around the estimated line of regression, especially in the negative range of the plot, are evident. This is substantiated by a relatively large standard error of estimate ($“39) and a relatively low simple correlation coefficient (0.5772). Residuals for the extreme outlying observations were very large and therefore exert downward pressure on the slope A AA A A A AA A.” . . A .._s.¢n .. .AA AA A A A A; AA AA A A A . A A A“AAAA.A A“. _- f A AA A ‘ .I A r& nA‘ A A 9A....- A AAA O AA AA 6 .‘ flA§AAA9rr¢ A. k... «A 5. Q . in nu .. A hat-‘1 A. {APAAH AAA ”HAHN... A .A AA... MA“. turn... . a.» A}. Aguiks. 1 ACWA‘: A 5‘. A. gunk“ ‘éfiw AA 5“ ”MAIN .AAH 5 ‘ ‘ AA A A N . . .5... A manna“ the AAA A A AN‘AAM ‘\A A . A0. 3. .s"?\ s . .. . .Q .A .O" A 297 A. . A A A flA' AA AA’ AAAAMA A A A AA A ‘ k L! A A A AAA. AAA A AAAAA ‘A AA A A “A A AA. A A A ‘ A AAA A AA A f A A A A AA A AA A A A ‘ A AAI..|A.II{.11.|M A AMAA AA ‘ A A A A A A A AA l;;( Cval. arent Labor Maladjust- P 10'. LL. q.”— H Mr ' ABC - 11C ments in the Labor Force of the Rural Farm and Total Residence Areas of each County in the United States, 1959. Figure 6.l--Relationship Between Ap 298 coefficient (0.36). Thus, the relationship is far less than a l to 1 ratio as one might first surmise from the cluster- ing in the scattergram. Rural farm labor maladjustments, on the other hand, were found to vary almost directly with the earnings gap in the total labor force. With the farm earnings gap as the dependent variable, the regression coefficient was 0.92, ‘but the standard error of estimate a very large $700. The scattergram corresponding to this regression may be seen 'by rotating Figure 6.1 180° and viewing it from the back side of the page. Extreme outlying observations seen in Figure 6.1, i.e., counties in which both the total and rural farm labor forces were well-adjusted, were characterized by a commer- cialized agriculture, and a relatively low prOportion of young persons, nonwhites, and females in the labor force. Hence, actual per capita earnings in most of these counties equaled or slightly exceeded potential earnings. Within regions, the total vs. rural farm labor malad- justment relation was strongest in the North Central and Southern regions, where agricultural employment is rela- tively more important to the region's total economy, and relatively weak in the Northeast and West. However, the size of the farm earnings gap was found to be much more sensitive to the total earnings gap than the reverse relation, as seen by the regression coefficients presented in Appendix Table E.2. 299 The overall relationship between the earnings gap in the farm and nonfarm labor forces in 1959 is shown in Figure 6.2. As expected this correlation was found to be very similar to that of Figure 6.1, with a slightly smaller slope coefficient (0.26) and about the Same standard error of estimate. Variations in the farm earnings gap were much more responsive to variability of nonfarm labor malad- justments, however (B = 0.72). The lower s10pe coeffi- cient obtained for the farm vs. nonfarm correlation (rela— tive to the farm vs. total correlation) is due to the fact that total nonfarm labor maladjustments are not weighed by labor maladjustments in the rural farm sector. This corre- lation was not run within regions because of the extremely high correlation between the total and nonfarm earnings gaps at the national level (Appendix Table E.l). Figure 6.3 depicts a nationwide comparison of county labor maladjustments in the urban_and rural farm sectors. Urban labor maladjustments were expected to be rather insensitive to size of the rural farm earnings gap, due to the independence of the urban sector with respect to employment Opportunities. This suspicion is confirmed by both the scattergram and the regression parameters. The slope coefficient was only 0.16, the correlation coeffi- cient 0.29, and the standard error of estimate $““5. Size of farm labor maladjustments were found to be considerably more sensitive to magnitude of the urban earnings gap, but 300 NONFARM ‘ .. . x A: , L. . 7 .. ... L .. A. uu _ Arr. .... w ...W .. _ 0" hi . ._ .I , ... 5 M)...‘ a. . g _ .1 5A 4:. a. . I I “‘ ..upc. 5.”. ”a... One” , .réfi.¢%f. . av“ L... ...-”H . 11 ... .... .... $32.. .. a... ..n Joanna .. . .\ . «A . ... A . A .muw .. UM. UM. ‘r . c1v¥ ._ . .... . r .. . u." 5 ..hr v.73.“ u” or. ..... . 3 . ....» .. ......fi 3 a. . .... ._ ._ i . . ”A. ..A x .1 1 it .52.... . u .._.. ....» ..u “rash... 1...!“ .k ...m A ._ 1 . .. . L . . . .. ... 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A ‘e A u A .. .. .. .. 5 .1 A A .u ... . A . .o A. a. A . A A A n .. . - . ._ i. . .. L m A A AA A A AA A A AA A . o. .. A A} 3 .. a A 1. A A A A A. a A p A .— A AA A n A 5 co .A A A L u— AN“ . u a. A4 01) 0.3 A A A A. A A .. 1 .u . ... : .. i A A A A A A 5 ‘ A 1 b A A a .. A A A a A . _ A . A H ._ X. .o A. A A A A .1 .. A .. A A .n u. \ L A A . A; . \ 1 A . A . A A a A In 3 a. A u A— l 1 AA- A . \n. A A A ... A 4 a 4 a .. c as. ‘u A A A A A s L L ‘ a nu. I A Jan 5 A A A g. A A .6 A A A .— A n .— A A A L A . A A . A u A A s . . w A o. A 1 m A. o a A .. A a L: M. A A A A A A 1 A . A A A A .. A A A A . A A A m A if L *.-..r.,..b tthOIIuI-xl # F u L L L b - bl A p P p > L . [4‘ "CS CNN. c‘d $1. ,9? 8a (43. 311. C2. - 0.54 - o. a: - Figure 6.2--Relationship Between Apparent Labor Maladjust- ments in the Labor Force of the Rural Farm and Total Non- farm Residence Areas of each County in the United States, 1959. 301 the lepe coefficient was still relatively low (0.53). From these results it is apparent that one can say very little about eXpected labor maladjustment in either the farm or urban labor force of a county by knowing the earnings gap in the other. Within regions, the regression of urban on rural farm labor maladjustments, as well as the reverse case, was much stronger than for the nation, but the regression coefficients were below 0.60 in every case. Response of the farm earnings gap to the urban earnings gap was about the same in all four major regions, varying from 0.U3 in the North Central region to 0.50 in the South. SlOpe coefficients for the reverse relation were much lower. These results partially substantiate previous findings, where it was concluded that size of labor maladjustments in the non-urban sectors appeared positively related to distance of a community from industrial-urban activity. The rural nonfarm labor force of most counties encompasses many of the same characteristics as those found in the farm labor force. Many persons in the rural nonfarm sector were once rural farm residents, and a large percentage have shifted Jobs without physically relocating. 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O 9 9+9 9 A 9 9 9 9 9 99 n O O 9 9 99. 9. 9 Q Q 9 9 9 99 9 9 9 9 9 9’ o. 9* O 9 9 9 9 9 9 9 99 9 9 9 9 9 .. 9 9 9 9 9 9 9 9 9 9 9 9 o 99 9 9 9 9. 9 A 9 9 9 9 9 ¢ 9 9 99 9 9 09 O 9 O 9 9 9 O9 9 9 O 9 9 9 9 9 L 9 9 9 9 9 9 9 w 99 9 99 9 9 9 99 9 9 9 9 9 9 9 9 O L 99 9 9 9 9 9 9 9 9 9 9 9 9 A 9 9 9 9 9 9 9 9 .. 9 99 u . . . 9 9 9 9 9 9 99 9 L 9 9 9 9 9 9 9 9 9 9 0 T x4P F b P P F L h b b F >1 \P b > P b b + 54 Cga ,3 Q3 g .84 84- DJ]. 015- 084. Cain- t...“ 1m '18) -‘A.O - 1020 Figure 6.3--Relationship Between Apparent Labor Maladjust- ments in the Labor Force of the Rural Farm and Urban Residence Areas of each County in the United States, 1959. 303 and nonfarm labor forces, as did the rural nonfarm earnings gap. The relation of the total and rural nonfarm earnings gap among communities is shown in Figure 6.“. From the scattergram this is obviously a closer relation than the three preceding relations. The estimated regression coef- ficient was 0.56, the standard error of estimate $392, and the simple correlation coefficient 0.68. Very large residual terms reduced the lepe coefficient substantially, generating a regression line much flatter than the positive, 1 to 1 ratio which is visually depicted in the plot. Size of the rural nonfarm earnings gap was more sensi- tive to the total earnings gap (8 = 0.83) than the reverse case Just noted, although the standard error of estimate was larger ($u76). This correlation was stronger in every region than at the national level, especially those regions where a large proportion of the total labor force in local labor markets was composed of persons from the rural nonfarm sector, such as the South, West, and Northeast. But even in the relatively rural North Central region, the slope coeffi- cient was 0.78. The simple correlation coefficient was 0.88 or above in every region. The reverse relation was even stronger, with the regression coefficients varying from 0.86 in the South to 1.06 in the Northeast. 304 The relation between labor maladjustments in the urban and rural nonfarm sectors was not eXpected to be as strong as that between the total and rural nonfarm sectors, as seen by comparing Figures 6.“ and 6.5. Size of urban labor maladjustments were relatively insensitive to rural nonfarm labor maladjustments (8 = 0.33), but variation in the rural nonfarm earnings gap was more closely associated with variations in the urban earnings gap (8 = 0.69). In both cases the standard error of estimate exceeded $400. Within regions the slope coefficients for the urban vs. rural nonfarm correlation were considerably higher than for the nation. Apparently, combining observations from each region introduced a much larger source of variability and increased the standard error of estimate. Statistical results of all possible correlations between apparent labor maladjustments in the total, urban, and nonfarm sectors are presented in Appendix Table E.l. As anticipated, these relations are represented by high regression coefficients (0.90 or above in each case) and relatively low standard deviations. Thus, it was felt that scattergrams for these correlations would contribute little to the analysis. To briefly capsule these statistical results, it appears that nationally, positive correlation of labor maladjustments was greatest in those residence areas where the social and economic characteristics of peOple were '13) L220 7 1CD. I '1090 Y 305 TOTAL J l A A A 9qu AL m " Figure 6.4--Relationship Between Apparent Labor Maladjust- ments in the Labor Force of the Rural Nonfarm and Total Residence Areas of each County in the United States, 1959. ii - 1m .1020 -'Aa0 “450 '180 lm. am. am “JD. 1230 11“? 306 URBAN 3+ 4 A A A A ‘ A A . 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' 4f 1 J L 1 I 1 A A 1 A 4% A A 1 A J LEO 1020 - - fl W0 w m 1m 3m H.0 ‘40 1220 l'I‘L‘ RLJRAL 7‘} Figure 6.5--Relationship Between Apparent Labor Maladjust- ments in the Labor Force of the Rural Nonfarm and Urban Residence Areas of each County in the United States, 1959. 307 most homogeneous, i.e., in the total, total nonfarm, and urban areas of residence. The most striking contrasts in such characteristics were detected in the rural farm and urban labor forces of most communities, and hence, there was a relatively weak association of labor maladjustments in these two sectors, both at the national and regional level. The rural farm earnings gap was found to be very sensitive to variations in labor maladjustments in the total labor force (8 = 0.92), moderately sensitive to non- farm labor maladjustments (fi = 0.72), and only slightly sensitive to urban labor maladjustments (8 = 0.53). The influence of the urban sector on farm labor maladjust- ments is low mainly because of the large prOportion of farm communities in the U.S. which were relatively isolated from industrial-urban activity. Rural nonfarm labor maladjust- ments were found to be moderately sensitive to urban, non- farm, and total labor maladjustments. Within regions, the slope and correlation coeffi— cients for each relation analyzed were greater than the national coefficients, except when the rural variable was regressed on the total or urban variable. 0f the regional relations tested, strongest associations were found between labor maladjustments in the rural nonfarm and total labor forces of local labor markets, and the weakest correlations involved the urban and rural farm 308 earnings gap. The influence of the agricultural sector on correlations involving the farm and nonfarm variables appeared to be strongest in the North Central region and weakest in the West. Some Implications An examination of the relative importance of major variables affecting the per capita earnings potential in each county was made by contrasting the estimated earnings capacity of a community's labor force with potential earnings in that same labor force after substitution of national characteristics. The analysis indicated that industrial mix and educational attainment were by far the most important of six major variables examined, particu- larly among Southern and North Central communities. Because of the relatively high proportion of agricultural employment in most counties of these two regions, substi- tution of the nation's industrial mix boosted earnings potential substantially, often as much as “0 per cent in some of the rural Midwestern communities. Substitution of the national distribution of educational attainment also produced very significant increases in the potential earnings of communities in these two regions, but in most counties of the West and Northeast, the labor force in all five residence areas was better educated than the corres- ponding national labor force, and hence potential earnings decreased with substitution. 309 With the national industrial mix and distribution of schooling, per capita earnings were most improved in those communities of the nation designated as acute poverty areas, and for which very large labor maladjustments were computed. Examination of the employment and social structure of these counties indicates large proportions of people in agricul- ture, an industry that carries a very high negative regres- sion coefficient in the estimating equation. Thus, the implication is that the incidence of acute labor maladjust- ments and poverty are not only highly complementary, but are generally associated with the degree of rurality in an area. Further, it appears that policies dealing with poverty problems at the community level, especially those in the farm sector, would be highly consistent with policies designed to alleviate severe labor maladjustment problems. Thus, poverty programs that have already been instituted should, if effective, also help eliminate serious malallo- cations of labor in some communities. A thorough examination of other alternative programs aimed at community problems of economic underemployment, utilizing the evidence generated by this study, would con- stitute a separate major undertaking in itself. But on the basis of the national substitutions analyzed in Chapter V, it appears evident that priorities in most communities must be given to the development of a better industrial mix and improvement of education facilities. With only a 310 subsistence agriculture and a few other low-paying retail and service-type jobs, the employment structure character- izing hundreds of communities in 1959, it appears highly unlikely that the earnings potential of the labor force can ever by fully realized within the community in the absence of social and economic structural changes. Theoretically, it would appear that mobility of people from communities of severe labor maladjustment might help solve problems of underemployment, but many studies. have shown that the populace of these areas would prefer to stay if adequate jobs and public facilities were available. If these amenities are strong enough to prevent adequate mobility, severe maladjustments of human resources may persist for some time, especially in the rural areas of residence. It seems somewhat paradoxical that Opportuni- ties for improvement of the level of per capita earnings are poorest in those very communities where the most severe labor maladjustments exist, but such was obviously the case in 1959. In a recent study, however, Talbertl found that labor returns over the l9u9 to 1959 period increased most rapidly in those areas where out-migration from agriculture was greatest, but net out-migration of labor from agriculture was heaviest in communities where net farm income was lowest and labor maladjustments were lLonnie E. Talbert, op, cit., p. 113. 3ll greatest. It must be recognized, though, that these were percentage increases calculated from a very low 1949 base, and that absolute increases in labor returns have not been very large in these areas. This study helps substantiate the position of those who argue for specific development and resource adjustment programs at the local level. Poverty and suppressed earnings of individuals in local labor markets have evolved as central issues in the American economy, and it appears that public officials must now recognize the interrelation- ships of all sectors of the.economy, and direct public action toward specific community needs accordingly. One individual economy cannot be manipulated successfully apart from society around it. It was found that in most communities the estimated labor maladjustment, regardless of magnitude, was more the result of low actual earnings than low potential earnings. In the South, for example, potential earnings were on average somewhat lower than at the national level or in any other region, but actual earnings in the South were substan- tially lower. Thus, the incidence of heavy labor malad- justments was much more prevalent in the South. In commun- ity after community this is indicative of unused labor productivity. It would seem that national and local priorities should be directed towards ways of utilizing the potential labor productivity that exists in many of these 312 areas before instituting additional programs to raise the productivity of individuals in preparation for jobs that are not available, which is the case in a large prOportion of relatively isolated communities with severe labor maladjust- ments. More realistically, however, it may be that modera- tion of underemployment problems can be accomplished only in conjunction with increased job Opportunities by elim- inating discriminatory hiring practices, and by educational and vocational training of low-productivity workers to enable them to enhance their earnings potential. For the large number of communities across the country in which "farm" labor maladjustments were severe, possibil- ities for adjustment of the farm earnings gap are very limited. Farmers in these communities do not have adequate land or capital to expand into commercialized agricultural production and hence must depend on part-time, off-farm employment to augment their earnings. But in most such communities, the industrial base is very small and possi- bilities for part-time employment in the nonfarm sector are at best very limited. If such opportunities are not available, then migration may be the only solution, unless industry can be attracted into these rural, small-town settings. At any event, it must be realized that adjust- ment, especially agricultural adjustment, is not a once-and- for-all process-~it is a process of adaption to continuous change. 313 In contrast, the labor force in many farm communities appeared well-adjusted in 1959, for at least two reasons. First, such situations generally reflect the location of the more capital intensive, commercialized farming areas of the nation. But, in many other instances farm communities with a relatively poor agricultural base (small, low produc- tivity farms, high tenancy, eth had relatively mild labor maladjustment problems due to the high proportion of supple- mental earnings from non-farm employment. Such communities were often geographically located in or near Standard Metropolitan Statistical areas, where the larger industrial base frequently affords seasonal off-farm employment Oppor- tunities. The importance of off-farm earnings to total farm income for the nation as a whole is reflected in the fact that in 1967, for the first time, income earned by farmers from off-farm sources was approximately equal to net income from farming. From this discussion one may be left with the impres— sion that all implied remedies to problems of labor malad- justment advocate public action strictly at the community leVel, and that relative immobility of peOple from areas of severe labor maladjustment is assumed. 0n the contrary, farm and nonfarm residents in hundreds of communities may never realize their full earnings potential except through migration. The physical, social, and economic attributes of such areas are simply not attractive to most types of 314 industry seeking eXpansion or relocation. The industrial base that currently exists is structured mainly around small subsistence farming which is rapidly declining, due to insuf- ficient capital and other inefficiencies of production. Thus, it may be that underemployment problems in many commun- ities can be alleviated only through the shifting of labor resources to the rural nonfarm or urban labor force, either within or outside the community. This is not to suggest, however, that the total burden of adjustment for communities with high earnings gaps in one or more sectors should fall heavily or completely on the nonfarm economy. Even if human resources must be shifted out of the county in order to raise per capita earnings, the community can and shoudl assume much of the responsibility for public education and where possible, develOpment of vocational and technical programs. In cases where the capital base simply is not available for adequate education facilities, perhaps the proper course of action would be stepped-up state and/or federal assistance at the community level. With the mounting pOpulation pressures now being experienced by many urban centers of the U.S., a strong case can be made for maintaining a decentralized system of public education. Public education must be provided where the population resides, while college and technical training can be taken outside the area. 315 Evaluations A brief evaluation of procedures used in this study are in order. Several restrictions were imposed by the source of county data utilized in the earnings capacity equation. The PC reports of the U.S. Census do not report individual characteristics at the county level, nor is the cross-classification of social and economic characteristics in adequate detail. Thus, the earnings capacity equation used in this analysis does not include certain interaction variables which are known to be important in explaining variations in individuals earnings, e.g., age and education, race and age, and sex and education, among others. In addition, differences in individual preference functions, quality of schooling among regions and residence areas, and other nonpecuniary effects were unaccounted for, but the predictive capacity of the equation (R2 = .UO) was still considered relatively high compared with other attempts to explain variation in individual earnings, and the nature of the relation fitted. Inclusion of a variable to measure differences in the quality of schooling among regions, for example, probably would have widened the gap in potential per capita earnings between the South and Nonsouth. Earnings capacity estimates based on the socio-economic characteristics of each major region, instead of national characteristics, undoubtedly would have produced lower estimates of potential earnings for the South, perhaps 316 higher earnings for the Northeast and West, and little change for the North Central region. However, this omission does not appear critical to this study, since relative comparisons of the index of labor maladjustment are not directly dependent on the absolute level of either potential or actual earnings, but on the difference in the two. In addition, the use of a common base (the'nation) for all areas renders relative comparisons more meaningful. It is important to note that the equation used predicts "average" per capita earnings capacity for a given county at one point in time, which covers up extreme fluctuations of potential earnings for particular individuals in a given county. Neither the actual or potential earnings figures of this study account for income in kind in the rural farm sector, since it is not reported in the census statistics. However, given this omission from both estimates, size of the estimated farm earnings gap should also remain unaffec- ted, i.e., both actual and per capita earnings should increase in the same proportion, changing the earnings gap very little. But, due to the weighting process in the earnings capacity equation, potential earnings would likely increase slightly less than actual earnings, and hence the earnings gap would have been somewhat smaller by acccounting for the value of farm products consumed. In a 1962 study, 317 LeRoy and Reederl estimate that the value of products consumed in farm households amounted to more than $500 on about 65 per cent of the farms in a low-income area. Other estimates show that farm families need about 80 to 85 per cent as much money income as nonfarm families to maintain comparable levels of consumption. Ben-David argues that for the nation as a whole these estimates reduce the differential between actual and potential farm earnings substantially, and except for the lowest age group (20) in the labor force, it means that farm labor was reasonably well adjusted in 1959, since all his other estimates showed the earnings gap to be about $500 or less. His position is that a substantial part of the farm labor force would not have benefitted from employment elsewhere, on the basis of their estimated earnings potential, and are therefore relatively fixed in farm employment.2 This argument does not seem to hold for the local farm labor force on the basis of evidence in this study, even if one assumes that potential earnings would have remained unchanged with the inclusion of income in kind. The farm earnings gap in over one-half of all counties in the nation exceeded $600 in 1959, was greater than $800 in #0 per cent lNelson L. LeRoy and William w. Reader, Ex-Farm Operators in a Low Income Area (Ithaca, New York: Cornell Ugivgrsity, Agric. Expt. Sta. Bul. 67-2, Nov. 1965), pp. 3 -3 . 2Ben—David, 93. cit., pp. 156-159. 318 of all farm communities, and in excess of $1,000 in 26 per cent of all counties. As previously noted, these commun- ities tended to be heavily concentrated in the South where income in kind is perhaps more important than in other areas of the country, but increases in earnings in these areas by some reasonable per capita value of income in kind would not have prevented a high frequency of heavy labor maladjustments throughout the South, and in at least a fourth of the counties in the nation. It may be that actual earnings realized by the large, capital intensive farmers in the Midwest and other commercial farm areas offset the low earnings of those farm communities just described, greatly diminishing the problem of farm labor malallocation for the nation as a whole. In addition, it also appears that Ben-David may have misinterpreted some of his results, since he apparently does not consider the fact that poten- tial earnings of farm people would also increase if an allowance were made for income in kind when fitting the national earnings capacity equation. The extremely large array of data presented with this study are most important in terms of their "relative" value. The data accurately protray relative differences in labor maladjustments among residence areas, communities, and regions, but should be used cautiously in an absolute sense due to the weighting of so many interaction effects .in the estimating equation which could not be quantified, and the fact that they are the result of a predictive model. 319 A different categorization of the variables used in the estimating equation might have produced entirely different estimates of per capita earnings capacity, since there are large differentials in per capita earnings within each of the labor force age categories, educational attain- ment classes, and major industries, differences which are averaged out in the estimating equation. For example, a division of age into five year intervals, instead of ten years, undoubtedly would produce more accurate regression coefficients for the age variable. The same reasoning holds for a finer breakdown of the education and industry variables. In particular, the industry variable is subdivided into categories which cover up extreme earnings differentials among individuals within a given industry. Because of county data limitations and the major revisions that would have been required for the national estimating equation, further subclassification of the eXplanatory variables was not attempted for this study. Further Study The results of this study leave many unanswered ques- tions pertaining to maladjustments in local labor markets, and suggest several additional areas of investigation. First, all evidence indicates a very strong correlation between size of labor maladjustment and distance of a county from major industrial activity. In addition, the 320 earnings gap of the labor force in communities surrounding urban centers appears to be positively related to size of the urban area. A more rigorous statistical test of these relations is needed. This study has been largely devoted to an examination of the seriousness of labor maladjustments among communi- ties and residence areas with only descriptive eXplanations of variability in the earnings gap. A more comprehensive analysis of factors influencing variations in labor malad- justments among both communities and residence areas would provide greater leverage to policy-makers in dealing with labor malallocation problems. The prOper approach might be to treat the earnings gap as the dependent variable and include as many eXplanatory variables as possible. At least one independent variable should be some index of urban-industrialization. The dummy-variable regression used in this study permits one to examine the seriousness of labor malalloca- tion at the county level by specific age categories, educational attainment levels, race, sex, and industry of employment. A separate treatment of labor maladjustments in local labor markets for each of these variables would provide much more detailed information on the location and magnitude of human resource adjustment problems. The very fact that the age, education, and industrial structure varies so widely among communities indicates that such a study is warranted. 321 Considerably more attention needs to be given to the implications for labor mobility from various county residence areas on the basis of evidence in this study and further analysis of the data. The effect of conditions in local labor markets and surrounding cities, if any, on adjustment poten- tials in the farm and nonfarm labor forces of various commun- ities needs to be studied in greater detail. Our knowledge concerning factor market phenomena, particularly at the local 7 level, is weak. We know far too little about how economic. growth affects the demand for agricultural resources below the national level, particularly human resources. This study has examined labor earnings disparities for all counties (3,105) in the conterminous United States. But, it was impossible to analyze the computed earnings gap in any great detail for any one county. The data contain con- siderably more information about the socio-economic structure of communities than that reported herein, which suggests the possibility of a more rigorous state or sub—regional study which might also give more attention to Specific labor adjustment policies. Assuming that 1970 census data are made available for county units on magnetic source tapes, a comparative analysis of apparent labor maladjustments for 1959 and 1969 should be extremely revealing. Of particular importance would be the shifts in local labor market structure over the decade, as well as changes in the patterns of geographic concentration 322 of community underemployment. However, such an analysis would probably necessitate the derivation of an up-to-date earnings capacity function to be consistent with the 1970 social and economic conditions in the national labor force. Assuming adequate availability of county data, consideration should be given to a more detailed breakdown of the dummy variables, and to the inclusion of other eXplanatory var- iables known to affect the earnings capacity of individuals. In addition, more predictive accuracy might be gained if the dimensions (units of measurement) of some variables were changed. BIBLIOGRAPHY 323 BIBLIOGRAPHY Allen, Clark L., Buchanan, James M., and Colberg, Marshall R. Prices, Income, and Public Policy, The ABC's of Economics. New York: McGraw-Hill Book Company, Inc., 195“. Becker, Gary. "Investment in Human Capital: A Theoret- ical Analysis," Journal of Political Economy, LXX (Supp., Oct. 1962), pp. 9:39. Ben-David, Moshe. "Farm-Nonfarm Income Differentials, U.S., 1960." Unpublished Ph.D. dissertation, Depart— ment of Agricultural Economics, Michigan State University, 1967. Bishop, Charles E. ”Purposes and Usefulness of Policy Research," Price and Income Policies, A Workshop Sponsored by the Agricultural Policy Institute. Raleigh, North Carolina: North Carolina State Uni- versity, Department of Economics, API Report No. 17, April 1965. Bishop, Charles E. "Underemployment of Labor in South- eastern Agriculture," Journal of Farm Economics, XXXVI (May 195u), pp. 258-272. Bloom, Gordon, and Northrup, Herbert. Economics of Labor Relations, Homewood, Illinois: Richard D. Irwin, Inc., 1958. Bottom, Carrol J. "Community Resource Development Defined," Community Resource Development, Proceedings of Second National Extension Workshop in Community Resource Development. 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"Urban—Industrial DevelOpment and Income Differentials Between Occupations," Journal of Farm Economics, XLVI (Feb. 196“), pp. 56—66. Johnson, D. Gale. "Comparability of Labor Capacities of A Farm and Nonfarm Labor," American Economic Review, XLIII (June 1953), pp. 296—313. Johnson, D. Gale. "Labor Mobility and Agricultural Adjust— ment," Agricultural Adjustments in a Growing Economy. Ames: Iowa State College Press, 1958. Johnston, J. Econometric Methods. New York: McGraw- Hill Book Company, Inc., 1963. Knight, Frank. Risk, Uncertainty, and Profit. Boston: Houghton Mifflin Company, 1921. LeRoy, Nelson L. and Reeder, William W. Ex-Farm Operators in a Low Income Area. Ithaca, New York: Cornell University, Agr. Expt. Sta. Bul. 67-2 (Nov. 1965). Maddox, James G., gt a1. ThegAdvancing South: Manpower Prospects and Problems. New York: Twentieth Century Fund, 1967. Marshall, Alfred. Principles of Economics. 8th edition; London: Macmillan, 1961. Michigan State University Computer Laboratory. Temporary Description of U.S. Census of POpulation and Housing; 1960, Educational, Employment, and Occupational Data, 25 Percent Sample. East Lansing, Michigan: Michigan State University Computer Center. (Mimeographed). Morgan, J. N., David, M. H. Cohen, W. J., 33 a1. Income and Welfare in the United States. New York: McGraw-Hill Book Company, Inc., 1962. 326 National Advisory Commission on Rural Poverty. Rural Poverty in the United States. Washington: U.S. Government Printing Office, May 1968. National Advisory Commission on Rural Poverty. The People Left Behind. Washington: U.S. Government Printing Office, Sept. 1967. Pfannestial, Maurice. "Adjustment of the Size of the Labor Force: An Analysis of Selcted Labor Market Areas in the United States," American Economic Review, LVII (May 1968), pp. 212-226. Schultz, T. W. "Education and Economic Growth," Social Forces Influencing American Education. Chicago: University of Chicago Press, 1961. Schultz, T. W. "Investment in Human Capital," American Economic Review, LV (March 1961), pp. 1-17. Jill..-‘ " Schultz, T. W. "Underinvestment in the Quality of Schooling: The Rural Farm Areas," Increasing Under- standing of Public Programs and Policies: 1963. Chicago: Farm Foundation, 1963, pp. 12-3u. Sisler, Daniel G. "Regional Differences in the Impact of Urban-Industrial DevelOpment on Farm and Nonfarm Income," Journal of Farm Economics, XLI (Dec. 1959), pp. 1100-1112. Sonquist, John A. and Morgan, James N. The Detection of Interaction Effects, A Report of a Computer Program for the Selection of Optimal Combination of Explan- atory Variables. Ann Arbor: University of Michigan, Survey Research Center, Monograph No. 35, 196A. Stam, Jerome. "An Application of Macroeconomic Analysis to the Demand for Human Skills and Knowledge." Unpublished Ph.D. Dissertation, Department of Agri- cultural Economics, Michigan State University, 1966. Talbert, Lonnie Eugene. "A Study of the Extent of Labor Maladjustment and Differential Rates of Change in Labor Earnings for Specified Areas and Size and Types of Farms, 19M9-1959." Unpublished Ph.D. dissertation, Department of Economics, North Carolina State Uni- versity, 1963. 327 Bureau of the Census. Income Distribution in the United States by Herman P. Miller (A 1960 Census Monograph). Washington, D.C.: U.S. Government Printing Office, 1966. Bureau of the Census. County and City Data Book: 1962. Washington, D.C.: U.S. Government Printing Office, 1962. Bureau of the Census. U.S. Census of POpulation and Housing: 1960, 1/1,000 and 1/10,000 (Two National Samples of the Population of the United States: Description and Technical Documentation). Bureau of the Census. United States Census of Population: 1960, General Social and Economic Characteristics of the POpulatoin, IC and ID, U.S. Summary. Department of Agriculture. Farm Income Situation, ERS, FIS-203. 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APPENDICES 328 APPENDIX A ALLOCATION OF COUNTIES BY STANDARD METROPOLITAN STATISTICAL AREAS 329 APPENDIX TABLE A.l DEFINITIONS OF STANDARD METROPOLITAN STATISTICAL AREAS This is the official listing of standard metropolitan statistical areas together with their constituent counties and independent cities, or, in the case of New England, towns and cities. See Geographic Concepts and Codes, p. XI. ABILENE, TEXAS Jones County Taylor County AKRON, OHIO Summit County ALBANY, GA. Doughcrty County AIBANY-SCHI-NECTADY-TROY, N.Y. Albany County Rennselaer County Saratoga County achnmotmty County ALBUQUERQUE, N. MEL Bernalillo Counw W—BE’IW—EASTON, PA.-N.J. Lehigh County, Pa. Northampton County, Pa. Warren County, NJ. ALIOONA, PA. Blair County AMARIILO, TEXAS Potter County Randall County ANN ARBOR, MICH. Vashtenav County ASHEVIILE, N.C. Buncombe Counw A'I'IANTA, GA. Clayton County Cobb County De Kalb County Fulton County Gvinnett Coun‘w ATLANTIC CITY, N.J. Atlantic County AUGUSTA, GA.-5.C. Richmond Counw, Ga. Aiken County, 8.0. wens, TEXAS Travis County mm, CALIF. Kern County ' BALTIMORE, MD. Baltimore city Anne Arundel County Baltimore County Carroll County Howard Coun‘w BATON ROUGE, LA. East Baton Rouge Parish BAY CITY, MICK. Bay County BFJWDNT-PORT ARTHUR, TEXAS Jefferson County Orange Counw BILLINCS, MONT. Yellowstone County BINGHAHI‘ON, N.Y. Broome County BIRMINGHAM, ALA. Jefferson County MN, MASS. Essex County (part) Beverly city Iynn city Peabody city Salem city Danvers town Hamilton town Lynnfield town Manchester town Marblehead tam Middleton town BOSTON, MASS.-—Con Essex County (part)-—Con. Nahant town Saugus town Svampscott town Topsfield town Venham town Middlesex County (part) Cambridge city Everett city Holden city Madford OSW Melrose city Nevton city Somerville city Waltham city Voburn city Arlington tom Ashland town Redford town Belmont town Burlington town Concord town Framingham tom Lexington town Lincoln town Natick town North Reading town Reading town Stoneham town Sudbury town Nakefield town Vatertovn town Vsyland town Veston town Wilmington town Winchester town Norfolk County (part) Quincy city Braintree town Brookline town Canton town Cohssset town Dedham tom Dover town Holbrook town Medfield town Milton town Needham town Norfolk town Nor-wood town Randolph town Sharon town Valpole town Vellesley town Vestvood town Veymouth town Plymouth County (part) Duxbury town Hanover town Hingham town Hull town Marshfield town Norwell town Pembroke town Rockland town Scituate town Suffolk County Boston city Chelsea ciw Revers city Vintln'op town BRIDGEPORT, CONN. Fairfield County (part) Bridgeport city Shelton city Fairfield town Monroe town Stratford town Trumbull town New Haven County (part) Milford town BROCK'ICN MASS Bristol Counw (part) Easton town Norfolk County (part) Avon town Stoughton town Plymouth County (part) Brockton city Abington town Bridgevater town East Bridgewater town Hanson town Vest Bridgevater town Whitman town BROWSWHE-WINGEN-SAN BENITO, TEXAS Cameron County BUFFALO, N.Y. Erie County Niagara County CANTON, OHIO Stark County CEDAR RAPIDS, IOJA Linn County CHAMPAIGN-URBANA, ILL. Champaisn Comm! CHARLESTON, S. C. Charleston County CHARIHS'I‘ON, V. VA. Kanavha County CHARLOTTE, N. C. Mecklenbm‘g County CHA'I'I‘ANOOGA, TENN.-GA. Hamilton County. Tenn. Walker County, Ga. CHICAGO, ILL. Cook County Du Page County Kane County Lake County McHenry County Will County CINCINNATI, OHIO-KY. Hamilton County, Ohio Campbell County, Ky. Kenton County, Ky. CLEVPZIAND, OHIO Cuyahoga County Lake County COLORADO SPRINCS, COLD. El. Paso County COLIMBIA, 8.6. Lexington County Richland County oostus GIL-ALA. Chattahooche County, Ca. Muscogee County, Ca. Russell County, Ala. COLIMBUS, OHIO Franklin CORPUS CHRISTI, TEXAS Nucces County DALLAS, TEXAS Collin County Dallas County Denton County Ellis County DAVENmRT-ROCK ISLAND-HOLINE, I ' )‘w'A- I LII. SCUM. County, IOVI Rock Island County, Ill. DAYTON, OHIO Greene County Miami Counw Montgomery County DECATUR, ILL. Macon County DENVER, COLD. Adams County Arapahoe County Boulder County Denver County Jefferson County DES MOINES, 1041A Polk County DETROIT, MICH. Macomb County Oakland County Hayne County DUBUQUE, IONA Dubuque County DULUTH-SUPERIOR. MAN-WIS. St. Louis County, Minn. Douglas County, Via. DURHAM, N.C. Durham County EL PASO, TEXAS El Paso County nus, PA. Erie County EUGENE, cam. Lane County EVANSVIIIJI, IND. -KY. Vanderburgh County, Ind. Henderson County, IQ. FALL RIVER, MASS.-R.I. Bristol County, Mass. (part) Fall River city Somerset town M11888 town Vestport town Newport County, R.I. (Pm) Tiverton town FAROO-MOORHEAD, N.DAK.~MINN. Cass Comty, N.Dak. Clay County, Minn. FmIIBURG—WINSTER, MASS. Middlesex County (part) Shirley town Worcester Counw (part) Fitchburg city Iecminster city Lunenburg taun FLINT, MICH. Genesee County PORT LAUDKRDAIx-mYVOOD, FLA. Bmvard County II APPENDIX TABLE A . l . MITH, ARK. stian Counw ‘AYNE, IND. n County ORTH, TEXAS son Coun‘w 'ant Counw I, CALIF. .no County 24, ALA. rah Counw WON-TEXAS CITY, TEXAS 'eston County WMOND—EAST CHICAGO, IND. :County or County RAPIDS, MICE. ; County FALLS, PDNT. :ade Counw BAY, WIS. an County SBORO—HIGH POINT, N.C. .ford Counw TILLE, S. C. :nville County PON-MIDDISTOdN, OHIO Ler County SBURG, PA. aerland County shin County 3RD, CONN. tford County (part) artford city ast Windsor tom afield town annington town lastonbury town anchester town evington town Ody Hill town insbury town outh Windsor toun uffield town est Hartford town ethersfield town indsor town incisor locks town dlesex County (part) mu town land County (pert) ernm town ”LU: HAVAII Olulu County OH, mus 1'16 County NGmN-ASHIAND, V. VA. -KI. -OHIO ell County, W. Va. ne County, W. Va. d County, NAPOLIS, IND. ion County ON: MICK, hm Conny 0" HIss. ‘8 County CONTINUED JACKSONVIILE, FLA. Duval County JERSEY CITY, N.J. Hudson County JOHNSTOVN, PA. Cambria County Sanerset County XALAMAZOO, MICH. Kalamazoo County KANSAS CITY, M0.-KANS. Clay County, Mo. Jackson County, Mo. Johnson County, Kane. Wyandotte County, Kane. KENOSHA, WIS. Kenosha Counw KNOXVILLE, TENN. Anderson County Blount County Knox County LAKE CHARLES, LA. Calcasieu Parish LANCASTER, PA. Lancaster County LANSING, MICH. Clinton County Eaton County Inng County LAREDO, TEXAS Webb County LAS VEIIAS, NEV. Clark County IAWRENCE—HAVERHIIL, MASS.-N.H. Essex County, Mass. (part) Lawrence city Haverhill city Andover town Groveland town Methuen town North Andover town Rockingham County, ma. (part) Pleistow town Salem town LANTON, OKLA. Camnche County LMSTON-AUBURN, MAINE ' Androscoggin Conniw (part) Auburn city Lewiston city Lisbon town LEXINGTON, KY. Fayette County LIMA, OHIO Allen County LINCOLN, NEBR. Lancaster County LITTLE ROCK-NORTH LITTLE ROCK, ARK. Pulaski Counw LORAIN-ELYRIA, OHIO Lorain County LOS ANGELm-LONG BEACH, CALIF. L08 Angeles County Orange County LOUISVILLE, KY.-IND. Jefferson County, 1w. Clark County, Ind. Floyd County, Ind. LWEIL, MASS. Middlesex County (part) Lowell city Billerica town Chelmsford town Dracut town Tewksbury town lyngsborough town LUBBOCK, mas Lubbock County LYNCHBURC, VA. Iynchburg city Amherst County Campbell County MACON, GA. Bibb County Houston County MADISON, VIS. Dane County ' MANCHESTER, N.“ . Hillsborough County (part) Manchester city Goffstown town was, mm. Shelby County MERIDEN, CONN. New Haven Counw (part) Meriden city MIAMI, FLA. Dade County MIDLAND, mas Midland County MILVAUKEE, WIS. Milwaukee County Vaukesha County MINNEAPOLIS-ST. PAUL, MINN. Anoka County Dakota County Hennepin County Ramsey County Washington County MOBILE, ALA. Mobile County mNROE, LA. Ouachita Parish MONTGCMEIU, ALA. Montganery County MUNCIE, IND. Delaware County MIBKECON-MUSICEXEON HEIGHTS, MICK. Muskegon County NASHVILLE, TENN. Davidson County NEW BEDFORD, MASS. Bristol County (part) New Bedford city Acushnet town Dartmouth town Fairhaven town Plymouth County (part) Marion town Mattapoisett town NEW BRITAIN, CONN. Hartford County (part) New Britain city Berlin town Pla‘inville town Southington town NEW HAVEN, CONN. New Haven County (part) New Haven city Branford town East Haven town Guilford town Hamden town North Haven town Orange town West Haven town Woodbridge town NEW IONDON-GROTON-NORWICH CONN. New Iondon County (parts New London city ' Norwich city East Iyme town Groton town Lechrard town ‘NEW LONDON-OmN-NORWICH, CONN.—Con. New London County (part)-Con. Montville town Preston town Stonington town Haterford town NEW ORLEANS, LA. Jefferson Parish Orleans Parish St. Bernard Parish NEW YORK, N.Y. New York City Bronx County Kings County New York County Queens County Richmond County Nassau County Rockland Comty Suffolk County Westchester County NEWARK, N.J. Essex County Morris County Union County NEWPORT NEWS-HAMPTON, VA. Newport News ciw Hampton city York County NORFOLK—PORTSAOUI'H, VA. Norfolk city South Norfolk city Portsmouth city Virginia Beach city Norfolk County Princess Anne Comfy NORWALK, CONN. Fairfield County (part) ' Norwalk city Westport town Wilton town coma, mus Ector County OGDEN, UTAH ‘ Weber County OKLAIW CITY, OKLA. Canadian County Cleveland County Oklahoma County (NARA, NEBR.-IOWA Douglas County, Nebr. Sarpy County, Nebr. Pottawattamie County, Iowa ORLANDO, FLA. Orange County Seminole County PAMSON-CLIFTON-PASSAIC, N.J. Bergen County Passaic Coun‘w PENSACOLA, FLA. Escambia County Santa Rosa County PEORIA, ILL. Peoria County Tazewell County PHILADELPHIA, PA.-N.J. Bucks County, Pa. Chester County, Pa. Delaware County, Pa. Montgomery County, Pa. Philadelphia Counw, Pa. Burlington County, N. J. Camden County,N . J. Gloucester County, N. J. PHOENIX, ARIZ. Maricopa County PITTSBURGH, PA. Alleghemr County Beaver County Washington County Westmoreland County APPENDIX TABLE A.l. PITISFIEID, MASS. Berkshire County (part) Pittsfield city Dalton town Lee town Lenox town PORTLAND, MAINE Cumberland County (part) Portland city South Portland city Westbrook city Cape Elizabeth town Falmouth town PORTLAND, OREG.-WASH. Clackamas County, Oreg. Multnomah County, Oreg. Washington County, Greg. Clark County, Wash. PROVIDENCE-PAWUCKET, R. I. -MA.SS. Bristol County, R.I. Barrington town Bristol town Warren town Kent County, R.I. (part) Warwick city Coventry town East Greenwich town West Warwick town Newport County, NJ. (Wt) Jamestown town Providence County, NJ. (P879) Central Falls city Cranston city East Providence ciw Pawtucket city Providence city Woonsocket city Burrillville town Cumberland town Johnston town Lincoln town North Providence town North Snithfield town Snithfield town Washington County, R.I. (part) Narragansett town North Kingstown town Bristol County, Mass. (part) Attleboro city North Attleboro town Seekonk town Norfolk County, Mass. (part) Bellingham town Franklin town Plainville town Wrentham town Worcester County, Mass. (part) Blackstone toun Millville tom PROVO-ORm, UTAH Utah County PUEBLO, COLO. Pueblo County RACINE, WIS. Racine County RALEIGH, N.C. Wake County READI NC, PA. Berks County RENO, NEV. Washoe County RICHAOND, VA. Richnond city Chesterfield Couwty Henrico County CONTINUED ROANOKE, VA. Roanoke city Roanoke County ROCHESTER, N.Y. Monroe County ROCKFORD, ILL. Winnebago County SACRAMENTO, CALIF. Sacramento County SAGINAW, MICH. Saginaw County ST. JOSEPH, MO. Buchanan County 81‘. IDUIS, I'D.-ILL. St. Louis city, Mo. Jefferson County, Mo. St. Charles County, Mo. St. Louis County, Mo. Madison County, Ill. St. Clair County, Ill. SALT LAKE CITY, UTAH Salt Lake County SAN ANGELO, TEXAS Tom Green County SAN ANTONIO, TEXAS Bexar County SAN BERNARDINO—RIVERSIDE- ONTARIO, CALIF. Riverside County San Bernardino County SAN DIEGO, CALIF. San Diego County SAN FRANCISCO-OAKLAND, CALIF. Alameda County Contra Costa County Marin County San Francisco County San Mateo County Solano County SAN JOSE, CALIF. Santa Clara County SANTA BARBARA, CALIF. Santa Barbara County SAVANNAH, CA. Chatham County SCRANTON, PA. Lackawanna Counw SEATTLE, WASH. King County Snohmish County SHREVEPORT, LA. Bossier Parish Caddo Parish SIOID( CITY, IOWA Woodbury County SIOUX FALIS, S. DAK. Minnehaba County SOUTH BEND, IND. St. Joseph County SPOKANE, WASH. Spokane County SPRINGFIELD, ILL. Sangamon County SPRING‘IELD, m. Greene Cmmty SPRINGFIELD, OHIO Clark County SPRINCFIEID-CHICOPEE-HOLYOKE, MASS. Hampden County (part) Chicopee city Holyoke city Springfield city Westfield city Agawam town East Longmeadow town Lonnmeadow town Ludlow town Monson town Palmer town West Springfield town Wilbraham town Hampshire County (part) Northampton city Easthampton town Hadley town South Hadley town Worcester County (part) Warren town STANFORD, CONN. Fairfield County (part) Stamford city Darien town Greenwich town New Canaan town STEUBENVILLE-WEIRTON, OHIO-W. VA. Jefferson County, Ohio Brooke County, W. Va. Hancock County, W. Va. STOCKTON, CALIF. San Joaquin County SYRACUSE, N.Y. Madison County Onondaga County Oswego County TACOVIA, WASH. Pierce County TAMPA-ST. PEPERSBURO, FLA. Hillsborough County Pinellas County TERRE HAUTE, IND. Vigo County TEXARKANA, TEXAS-ARK. Bowie County, Texas Miller Counw, Ark. TOLEDO , OHIO Lucas County TOPEKA, KANS. Shawnee County TRENTON, N. J. Mercer County TUCSON, ARIZ. Pima County TUISA, OKLA. Creek County Osage County Tulsa County TUSCALOOSA, ALA. Tuscaloosa County TYLER, TEXAS Snith County UTICA-RGE, N.Y. Herkimer County Guide County WACO, TEXAS MC Lennon County WASH I NC'mN, D. C. -MD. -VA. District of Columbia Montgomery County, Md. Prime Georges County, Md. Alexandria city, Va. Falls Church city, Va. Arlington County, Va. Fairfax County, Va. WATERBURY, CONN. Litchfield County (part) Thomaston town Wetertown town New Haven County (part) Waterbury city Naugatuck borough Beacon Falls town Cheshire town Middlebury toun Prospect town Wolcott town WATERLOO, ICNA Black Hawk County WEST PAIM BEACH, FLA. Palm Beach County WHEELIm, W. VA.-0HIO Marshall County, W. Va. Ohio County, W. Va. Belmont County, Ohio WICHITA, HANS. Sedgwick County WICHITA FALIS, TEXAS Archer County Wichita County WILKES-BARRE-HAZIEION, PA. Luzerne County WIIMINCTON, DEL-N.J. New Castle County, Del. Salem County, N.J. WINSTON-Sum, N. C. Forsyth County WORCI-STER, MASS. Worcester County (part) Worcester city Auburn town Berlin town Boylston town Brookfield town East Brookfield town Grafton town Holden town Leicester town Millbury town Northborough town Northbridge town North Brookfield town Oxford town Shrewsbury town Spencer town Sutton town Upton town Westborough town West Boylston town max, PA. York County YOUNGSTOWN-WARREN, OHIO Mahming County Tnnbull Counw APPENDIX TABLE A-2 Distribution of Counties in Standard Metropolitan Statistical Areas Into Three Size Categories, l96O 50,000 to 100,000 Etowah, Ala. Miller, Ark. Sebastian, Ark. Dougherty, Ga. Dubuque. Iowa Androscoggin, Maine Berkshire, Mass. Washtenaw, Mich. Buchanan, Mo. Cascade, Mont. Yellowstone, Mont. Washoe, Nev. Hillsborough, N.H. Comanche, Okla. Minnehaha, S. Oak. Bowe, Texas Ector, Texas Midland, Texas Smith, Texas Tom Green, Texas Webb, Texas lO0,000-l million Jefferson, Ala. Madison, Ala. Mobile, Ala. Montgomery, Ala. Russel, Ala. Tuscaloosa, Ala. Maricopa, Ariz. Pima, Ariz. Pulaski, Ark. Fresno, Calif. Kern, Calif. Riverside, Calif. Sacramento, Calif. San Bernardino, Calif. San Joaquin, Calif. Santa Barbara, Calif. Santa Clara, Calif. Adams, Col. Arephoe, Col. Boulder, Col. Denver, Col. El Paso, Col. Jefferson, Col. Pueblo, Col. Fairfield, Conn. Hartford,_Conn. lOO,OOOJl mil. continued Litchfield, Conn. Middlesex, Conn. New Haven, Conn. New London, Conn. Tolland, Conn. New Castle, Del. Broward, Fla. Dade, Fla. Duval, Fla. Escambia, Fla. Hillsborough, Fla. Orange, Fla. Palm Beach, Fla. Pinellos, Fla. Santa Rosa, Fla. Seminole, Fla. Bibb, Ga. Chatham, Ga. Chattahoochee, Ga. Houston, Ga. Muscogee, Ga. Richmond, Ga. Walker, Ga. Honolulu, Hawaii Champaign, Ill. Macon, Ill. Peori, Ill. Rock Island, Ill. Sangamon, Ill. Tazewell, Ill. Winnebago, Ill. Allen, Ind. Clark, Ind. Delaware, Ind. Floyd, Ind. Lake, Ind. Marion, Ind. Porter, Ind. St. Joseph, Ind. Vanderburg, Ind. Vigo, Ind. Black Hawk, Iowa Linn , Iowa Polk, Iowa ‘ Pottawattamie, Iowa Scott, Iowa Woodbury, Iowa Sedgwick, Kansas Shawnee, Kansas Boyd, Ky. 'lO0,000-l mil} fgygjlnmxi Fayette, Ky. Henderson, Ky. Jefferson, (y. Bossier, La. Caddo. La. Calcasieu, La. East Baton Rouge, La. Jefferson, La. Orleans, La. Ouachita, La. St. Bernard, La. Cumberland, Maine Bristol, Mass. Worcester, Mass. Bay, Mich. Clinton, Mich. Eaton, Mich. Genesee, Mich. Ingham, Mich. Jackson, Mich. Kalamazoo, Mich. Kent, Mich. Muskegon, Mich. Saginaw, Mich. Clay, Minn. St.¥Louis, Minn. Hinds, Miss. Greene, Mo. Douglas, Neb. Lancaster, Neb. Sarpy, Neb. Clark, Nev. Rockingham, N.H. Atlantic, N.J. Hudson, N.J. Mercer, N.J. Salem, N.J. Warren, N.J. Bernalillo, N. Mex. Albany, N.Y. Broome, N.Y. Herkimer, N.Y. Madison, N.Y. Monroe, N.Y. Oneida, N.Y. Onondaga, N.Y. Oswego, N.Y. Rensselaer, N.Y. Saratoga, N.Y. Schenectady, N.Y. APPENDIX TABLE A-2 continued lOO ,OOOTI milTi on continued lO0,000él million continued continued Buncombe, N.C. Durham, N.C. Forsyth, N.C. Guilford, N.C. Mecklenburg, N.C. Wake, N.C. Cass, N. Oak. Allen, Ohio Belmont, Ohio Butler, Ohio Clark, Ohio Franklin, Ohio Greene, Ohio Jefferson, Ohio Lorain, Ohio Lucas, Ohio Mahoning, Ohio Miami, Ohio Montgomery, Ohio Stark, Ohio Summit, Ohio Trumbull, Ohio Canadian, Okla. Cleveland, Okla. Creek, Okla. Oklahoma, Okla. Osage, Okla. Tulsa, Okla. Clackamos, Okla. Lane, Ore. Multnomah, Ore. Washington, Ore. Berks, Pa. Blair, Pa. Cambria, Pa. Cumberland, Pa. Erie, Pa. Lackawanna, Pa. Lancaster, Pa. Lehigh, Pa. Luzerne, Pa. Northhampton, Pa. Somerset, Pa. York, Pa. Bristol, R.I. Kent, R.I. Newport, R.I. Providence, R.I. Washington, R.I. Aiken, S.C. Charleston, S.C. Greenville, S.C. Lexington, S.C. Richland, S.C. Anderson, Tenn. Blount, Tenn. Davidson, Tenn. Hamilton, Tenn. Knox, Tenn. Shelby, Tenn. Archer, Texas Bexar, Texas Cameron, Texas El Paso, Texas Galveston, Texas Jefferson, Texas Johnson, Texas Jones, Texas Lubbock, Texas McLennan, Texas Nueces, Texas Orange, Texas Potter, Texas Randall, Texas Tarrant, Texas Taylor, Texas Travis, Texas Wichita, Texas Salt Lake, Utah Utah, Utah Weber, Utah Amherst, Va.. Campbell, Va. Chesterfield, Va. Henrico, Va. Norfolk, Va. Princess Anne, Va. Roanoke, Va. York, Va. Clark, Wash. Pierce, Wash. - Spokane, Wash. Brooke, West Va. Cubell, West Va. Hancock, West Va. Kanawha, West Va. Marshall, West Va. Ohio, West Va. Wayne, West Va. Brown, Wis. Dane, Wis. Douglas, Wis. Kenosha, Wis. Racine, Wis. iflijlion Alameda, Calif. Contra Costa, Calif. Los Angeles, Calif. Marin, Calif. Orange, Calif. San Diego, Calif. San Francisco, Calif. San Mateo, Calif. Solano, Calif. District of Columbia Clayton, Ga. Cobb, Ga. DeKalb, Ga. Fulton, Ga. Gwinnett, Ga. Cook, Ill. DuPage, Ill. Kane, Ill. Lake, Ill. McHenry, Ill. Madison, Ill. St; Clair, Ill. Will, Ill. Johnson, Kan. Wyondotte, Kan. Campbell, Ky. Kenton, Ky. Anne Arundel, Md. Baltimore, Md. Baltimore City, Md. Carroll, Md. Howard,Md. Montgomery, Md. Prince Georges, Md. Essex, Mass. Hampden, Mass. Hampshire, Mass. Middlesex, Mass. Norfolk, Mass. Plymouth, Mass. Suffolk, Mass. Macomb, Mich. Oakland,_Mich. i‘h‘nTo’o‘oT- ‘mi‘lTi'o 5"" APPENDIX TABLE A-2 continued >l million continued Wayne, Mich. Anoka, Minn. Dakota, Minn. Hennepin, Minn. Ramsey, Minn. Washington, Minn. Clay, Mo. Jackson, Mo. Jefferson, Mo. St. Charles, Mo. St. Louis, Mo. St. Louis City, Mo. Atlantic, N.J. Bergen, N.J. Camden, N.J. Essex, N.J. Gloucester, N.J. Morris, N.J. Passaic, N.J. Union, N.J. Erie, N.Y. Nassau, N.Y. New York, N.Y. Niagra, N.Y. Rockland, N.Y. Suffolk, N.Y. STlfillion continued Westchester, N.Y. Cuyahoga, Ohio Hamilton, Ohio Lake, Ohio Lawrence, Ohio Allegheny, Pa. Beaver, Pa. Bucks, Pa. Chester, Pa. Delaware, Pa. Montgomery, Pa. Philadelphia, Pa. Washington, Pa. Westmoreland, Pa. Collin, Texas Dallas, Texas Denton, Texas Ellis, Texas Harris, Texas Arlington, Va. Fairfax, Va. King, Wash. Snohomish, Wash. Milwaukee, Wis. Waukesha, Wis. Source: U. S. Bureau of the Census, County and City_Data Book: l962 (Washington, D. C.: U. S. Government Printing Office, 1962 APPENDIX B DISTRIBUTION OF COUNTIES BY MAGNITUDE OF "ACTUAL" PER CAPITA EARNINGS, FOR THE U.S., REGIONS, DIVISIONS, AND STATES, BY AREAS OF RESIDENCE, 1959 336 l. l. l l l l l l o n N m A“ am an .m» mm A c «_2_em_> P. ........ c: - o ........... o .......... ,H-l-lllbslllfo-l- o o c cl--- ----l...u.....c-.zen-2_x-m.<.xl----llé .« a a u v o n c a a o c244>m~c u~czmmxwa zmz llw:ll-l-cl- m-ll-llm-lnlll~alllllx~ m- a a o- p- .. ::l. --llvmo>:2mz,: . 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IHN97 HNHN - H99H N.» .9.9 H.” l-9U9Hll9.nn-:nva 9.9 9.9 9.9 9.9 9.9 zohbma-uwmbzmo-mpuoz N99H n.Hl-l9.N 9:9 9.NH .13. mun-clo-H-NH 9.9 9n.9_l-.-.9.-9 9.9 . 233969.22 - l... x99H 9.9 m. -l .-l-sN 9Hllmwnm-l9.99 N.9 9 9 9.9 9.9 9H. >mmxmnleuz H99H . 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N NH 9N 9 99 NH 9.99 9.9 r9-n9 9.9 - ll-9me9H u -::. 9 ll- - 9 so»; l-ll- -llu99H 9.Hlll99H ”.ml 9 .N 9 .9H 9. .9N 9. 9m 9 .9H 9. 9 9.9 9.9 N99H «TH «9.9-ll . n9 9mlwlmlml99Nllblmv 9H9. 9...-9 9.9 9.9 03:: L999“ 9. . 9. H n. 99 m .9." 999» 9. H» 99 9H -. A llll 54:29: ...-ll . .ll-ll - Olfizd Mow 0.9m 9 9 9 N. c O O c O >~ 7 2. Gt fioofl 69o v90 9 9 lllmWanlanMllmlMN 095 h9olllb9bl- N96 2 udh D Noe." 99° 990 N9." 990 099 nwaw c90N v9om N90.“ -o9-o lc-..cl----l-l znuoum-hmwKJl Road-ll.--owd-llo-Mclnqu-lnma c w oulowlw-H-w-n n99Nl-H90 999 9.9 mcxmb 9 . . 9 9 H 9 9.9 H.9H 9.9N 9.H9 H H .- -1-999999Y9..1- -:-- .llu 9Hl l9-pl-l9-ol. 9.9. . .. . H «H 9 H 9 9 979 9 9 nocd O96 N9c ll09°.llo9olllbdblllnwm- one D9 9mm 0.9le 5.90l C90 m<~zpsz .NOlOHll 90° Dub 0. no? 090 Oofifl B mN Q .NN No NH n 9." C-oOll-l-ll‘w-Hl-Dl-o P w < ”4 . -- ..- 999H 9.9 .. -.le-9.9 9 9 N.H 9. 9 .l9,.9H H.99l 9.. 9Nl N.H .NH 19999 999: L92 9.9 u.“ “.9 9.9 m... 9.9 9. 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E .1 .. 1 ooom 1 00m: 00:: , comm 1ooom ..oomm 1ooom 100m: 1ooo: mom: 00:0: . @520: .02 0. 113. . . . .11:11Qqq111111!.u3nqroaoz_zw¢3111 0.: 0.: 0.: 0 0 0.0 0.: 0H: 0.“ 0.” m.“ 0.: 0204.202 1 . . n.~ v.: 0.: o.: m 0 o v . -1. - O Ofi O O a 0 Duo Occ c O wC(3(JmQ . 0.: :.m 0.0 0.: 0.: 0 0 0 2 . 0 m c o g o o N vow? Noon o o >~o U—hZQn—hd IF 0 11 . . :.:: 0.0 :.0 0 :: : 0: : 0 . o 1 :10: 0 0d a ON a 0 0m 0 o a am 2 uwmm1 h m Goad BuON n.0fi 0.0H mHOfi "HON ”a“? Ho”0 “.00 We” o.o w:o 07mg 10am: :mm: 0.0: 0.0 0.: :.:: :.:: 0.0: : :0 0 . 0. 2:020:m_2 00° 900 Oak flow '9” NON Non OHC Ono 0.6 a.” 7~O fiH7wU IhmOZ Pmdm :.:n 0.0 «.00 m.:n m.m~ n.0m m.:¢ o.om :.e 0.0 0.0 zo:omm :0ayzmu thczl1 OoOfl 00° he? 5 N V V V o u o c 0.0 >wm¢ww IWZ . . .m 0.: v.0 o o o o o o o o 1 00° 5 ON 0 B he 0 O 0.0 Ooq Ode 00° ado vac, Imz o.om o.o o.:: «.0 o.» 5.» ~.m o.o o.o o.o o.o >~o 0:074:04 m4oo_z 0.0» :.:u 0.00 0 :: : 0: w 0 . . . . :.: boo—Humzzoo . .0 :.w 0.0 :.o o o o o 0 o o 0 00° N 0 O O O 0 H00 00° 00° GOO GOO 02(Jmu ”10105171..“ 1 0.: 0.: 0.: 0 0 m 0 0 0 .. . .0 0.0 0.: 0::mwmmu0mw22 . . 0.0 0.: 0.: 0.0 m 2 0 0 0 O O O O o o o 9.0 N00 0.0 696 0.0 szmmw> 11 o.o o.o M.“ m m m.“ M.” «.0 0.: o.o o.o o.o mamrmozax 2&2 00° 60° 0 o o o o o a 09° m2~m m Hz o 11 . 0 2 2:3 . 1111/11/111111111/11/11/111/11 MHW~QIIADVQJrI|IE:!II;IE THAN 5500 WERE ’l 5M99 5000 "999 ”500- “000- hh99 3999 3500- 3000- 3399 2500- 2999 4~r~wv~ - 2000- 2h99 1500- 1999 1000- 1h99 LESS THAN 1000 APPENDIXENfiHEZB-l5. > .— H ¢t< D O 422 ual—0b! D d 24.1 .. .... H00 7 0.7 O 7 (3.110: w W (LL11 ¢ D 0 (Iraq ufl-m ~0m7¢ > nu. y. ‘nfluu44 xm¢m «(t J< ax< >0 2) O‘DIDMZIDICDHO O2 2(“32 (~22 ~ Ixouhqm¢mfizw1q (a «so ammo L'JD-t-P-QOIDD-ZEIDD‘H44 Zt-IXO N14 10 .mmmzoqczzdmoxaqu~2< 43—<>umw HLL‘OOUJJIDLDLUJ—UJCEOXIDO‘OG>OLU¢IPUJH22mcm xp > .— O-i << D O <22 it." C d 7.1.1 I- ~0- uoo 7 a7 a 7 4 0mm m w mm 4 u o n «vac ufim ~0m7¢ > an F 7 ;~~oo¢< xmu m 12> ”DIUMZWImfiO a2124w2 <~zzoumw4<: ~moom4mww4~wmoxwoowahwn3zmcm Xb‘: (JOFHHI—302432k30042 .— h- (:7 "" m m w: o ( LU (IO ( LU 3 1* a g. m w 3 I: .1 I: .7 I: I_—‘ APPENDIX C DISTRIBUTION OF COUNTIES BY MAGNITUDE OF "ESTIMATED" PER CAPITA EARNINGS, FOR THE U.S., REGIONS, DIVISIONS, AND STATES, BY AREAS OF RESIDENCE, 1959 352 1110 -- P - 0 - 0 - 9. .0. , 0 -- 1.0.1.;. 0- 1 0 0 .u.0.2020z:zm02 0 0 0 0 N 0 m: 0 0 0 0 0224222: 11101 1-. 0 . 0 -0_ -1 «.1 ~ - 0 11.0 : 1.0 0 0 m22220m0 . 0 0 0 0 0~ 0am :0» 0m 0 0 0 2:0 0:220:22 2.200 11011. -0-- -0 :-m . -1-00--- 000 1 mmo --apm. 1 zn.---- 0. 0 20:002 22000 0 0 0 N 0 00 c 0 0 0 0 0002.2 1101.11--0- -1.01-1- 0--111-0::.-1100. 1 -001111 0;: 1--0.- 1 0 . 0 . . 020(00w2 0 0 0 0 0 0» an N 0 0 0 «20200 1:00m 1101 - 0 0 --.0 -0 - am am - 0 -.0.- 0 - 0 020220 22202 0 0 0 0 o 00: n 0 0 0 0 :mnomm:z 11011111101 -1.0:1111-0-1- 1101.- 1100 ,11:m11111-0-11 11011. -10. 0 220: 0 0 0 . 0 0 on 00 0 0 0 0 «20mm72:z 110 110 --.110-;1111~.-11110c..-1-~m¢ 11.-H::1-n-1111.911- 110; .. 0 2:0 .2200 1:007 20m: 0 0 0 0 N: mm 0 0 0 0 0 2:mzoom:2 .1101111:.0 .10 -1 --0 .11-m~ .1100 .. 0. -Fr-111101. .1 0, .. 0 2<0:20:: 0 0 0 N 00 mo 0 0 0 0 0 0:02:00: 1011-. 01.11.0111 11-0.11-1110011-11-00.11 01-- -- -.011 1111-01.11-10.-- - -- -0- - «2:02: 0 0 0 0 00 mm 0 0 0 0 0 0:20 110 -0.. 0 0.111110001111oan-11;m-..-1 @111-11p111 1.0;---- 0 - 2:0 .22m0 2:002 202m 0 0 0 v 0.0 0:: 200 0 0 0 0 20:0mm 402:2m0 22002 Iib1111.10- .1.0 :1 1:0 -.1.~n..- .00 ..1 011111,0.- 11.01. 0 . 0 .0:2<2:2m?zm¢ 0 0 0 a a: n 0 0 0 0 0 2mwmw5 zmz 1.01-11.01111011101110011-10.01.11...01110110 1-.....0; 0- : . 2202 32 0 0 0 n . a: 02 u 0 0 0 0 2:0 0:22<:22 m400:z .1101111110:-1 1101.1 1101;111.m-111--01--11-01 ....... 1119111111011--..01 0: 220:20m2200 0 0 0 a v 0 0 0 0 0 0 0204u: moorm 101-3- 0, -..-01 . .0 .. 2,- .- ~ - ._ 0. - -..0: :;_0 _ - 0 0 02002020022 0 0 0 0 0 «0 N 0 0 0 0 hzoymm2 .11b1111--c-- - 0 10:. 1 n- . .2 0 - -02.---10-. --0 0. ma:zmnz<: zmz 0 0 0 0 n n: 2 0 0 0 0 m7:«: 1101---. 0 0 .~ mm m». N 1-011..1-0 0. 0 2:0 02<402m 202 0 0 0 m 00 00: c 0 0 0 0 70:0mm 2mm mu:023cu no 20:»2m0mhm:a .dAvmaaarxfiamEm< -0 - . 0 .0 0~ v.2 - 0010- ,000 .00 0 0. 0 0200: m0 0 0 0 0 0 N o 0 0 0 0 ::020: 1.9.. . 0 0 .0 --0 .1-~0- ..c 1 0 - 0 - 0. -- 0 0xm<0< 0 0: 0 v 00 0w 0 0 0 0 0 0:2mcu:000 110 :1 -0. 0 .0 .100 on .1 0 0 .0 - 0. 0 200000 0 0 0 0 0m 00 0 0 0 0 0 20:02:1002 119111 ; 0; .0 - 0.- :--001111-00-1-.;0- 1.1 0 - 0 - 0 0- w , 2:0 0:1:0<¢ 0 0 0 0 n n: 0 0 0 0 0 - 00.2w: 11011111 0; 1 0.-- : 0:. 1100111:10011111011111-0-- - 0-.- --0-- 11-011, 12:: 0 0 0 0 c 00 0 0 0 0 0 «200:00 0 - 0 - 0 . 0 _- 0 A_.m~ . :.0 0 0 0 0 000202 202 HH01HWw- 0 -H_0... ~ . ..100 wywnc11m.-~ . 0. n.0,---,.01 m.9.1-1 1,00000000 0 0 0 0 0 00 0 0 0 0 0 02:2022 110111.:10 . -‘101 ..-0-.;;112--1:11001111-0 - 1-0- ..0 - 1.0- -1 0.-- . 0:00: 0 0 0 0 0. an 00 0 0 0 0 (20220: 110111-..0-1 - 0 1 0 1-11100.111auo: 1100-1- 0 ..0-. 0.- ; 0- - , 2:0 2:002:02 0 0 0 0 000 22w 0 0 0 0 0 20:0mm 2mm: 11p . 0 - 0 0 ..o: . .000 00 0 0, 0 0 m.xm: . 0 0 0 0 0 no m 0 0 0 0 .2010JYQ 1101111:.0, -..0 - - 0 .111011111:on.;-110~ . 0 , .0 ..1 0 - 0 <2«:m:000 0 0 0 0 0 00 ow 0 0 0 0 00020202 1101111110- -11-p1.; -=0 -1111~011111000-11-000, .-0-2 . 0.1.11 0 - ..... 0- - - 2:0 .02mu 20000 0002 . 0 0 0 0 0 0 mm 00 m 0 0 :aa:mm:mm:z 1.9-1..- 1 0 0 0 - -.." .-..1....11.0~ 1... on . .. n. 0 - 0 . 0 .. 4.240404 0 0 0 0 n on um N 0 0 0 mmmmw2zm: 11011111 0 : 1110-- . 0 -.:11~--111100.11-1m0,1,1f0 -1 3.011--1 0. -..:0-- -.1.; -1 2:000zm2 0 0 0 0 o 20: 00~ 0w 0 0 0 2:0 .hzwn 1:000 000m 11p11111.01;:-1101- 1 0 ..:::2;z.-1-nn 11.1mm -1; n -.0 . 0 -0 00:00:: 0 0 0 0 0 mm a: v." 0 0 0 1000mm 119-1 . 0 1 ..0 0 .0 ..~0 0m . 0 0 0 0 02:00200 10200 0 0 0 0 0 an 0m 0 0 0 0 02:00200 Irmnz 1101111.:0 1.11.0.- - 0- - -0.-.:1-0n -.1-w0 ‘ 0 - 0 -0: - 0 «:z:00:2 0mm; 0 . 0 0 0 0 cm 00‘ 0 0 0 0 . .:2:02:2 00mm coca 25:9 000m 000:: 000.: 000m 000m mmmm 00:00 0000 0000 23:0 was: ..000m ..00m0 10000 100mm .1000m 100mm 1000~ 100m0 10000 0mm< mmmfl QHEfiHZQu.dAumHBEVNdHfiEm< [‘1' I!I.<|‘il§! IIIII'I‘J. 0000 0.0 0.0 0 0 0.0 0.0 0.000 0.0 0.0 0.0 0.0 0.0 .0.0.7000201003 0000 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 q.0 0.0 0zwuwn0x 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 00020000 0000 0.0 0.0 0.0 0.0 00% 0.00 0.00 .0.0, 0.0 000 0.0 >00 000200rw.x0000 0000 0.0 0.0 .0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 200000 10000 0000 0.0 0.0 No.0 0.0 0.0 0.00 0.0 0.0 0.0 0.0 0.0 04M700 0000 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 00000002 0000 0.0 0.0 0.0 0.0 0.0 0.00. 0.00 0.0 0.0 0.0 0.0 000000 10000 0000 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 000000 10002 III0000 000 0.0 0.0 0.0 0.0 0.00 000 0.0 .0.0 bub 0.0 0000000: 0000 0.0 0.0 0.0 0.0 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0000 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.0 0.0 0.0 0.0 00000220: 0000. 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0. >00 .0200 10002 0003 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0. 0.0 0.0 0.0 0.0 200200003 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0000:0_x 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 00020000 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 020—020 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 ii 0010 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 .0700 00002 0000 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.00 L0.0 0.0 0.0 0.0 200umm 0000200 10002 0000 0.0 0.0 0.0, 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 000000002200 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 000001 302 :1. . 0000 0.0 0.0 0.0 «.0 0.0m m.cm 0.0 0.0 0.0 0.0 0.0 «mo» :02 Mmcd coc o.o o.o now most noov mod o.o o.o coa o0c >~Q D~h7 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 000xmuz01 0m: 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0200: 0000 0.0 0.0 0.0 0.0 0.00 ~.mm1,0.0 0.0 0.0 0.0 0.0 .000,0z<0000:002 0000 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 0.0 0.0 0.0 70.000 0000:0002 - 0000 , , 0000 -- —0ama_lvovm 0000 0000— 0000 —oovn_ ooomb 000~_ 0000—00«0_.z<10 00000— 0002—--qu.uqum-qu0v_-0000 fi0000_-000~ +0000 T00m0fi0000 —0004: 0mm0 - - .4000 - .e- 100 >0 0zmuama 0003008 00.002.00.00 0000... :00. 0001037 «23.0-0.0..100030 0.00000 ..muu.m0mfinfieamm<{ APHQEHXUMEU3C#2.CDNEDNED WERE TEEN 5503 3500- uooo- usoo- 5000- 3999 ”“99 “999 5&99 3000 — 3N99 2999 2500- 1500- 1999 1000 1&99 IESS THEN 1000 100‘ 100; 100i 106? 100* 100: 100: 100! lam. 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.6 10.6 1.7 1.7 6108 3700 17.6 5000 3202 3902 909 6692 73.0 34.8 5908 6709 2604 59.0 A580? 2501 1 2000- 2499 0.0_59o2 4.5 8.8 5. 0.0 3.0 0.0 15.2 0.6 0.0 I 0.0 0.0 0.0 01v va iRDLIM sOuTH C‘ROLINA GEORGIA W‘- wFsT VIQGINIA NORTH C VIRGINI‘ FLORIDA EAST SOUTH CEVT. KENTUCKY TENNESSEE ALABAMA MISSISSIPPI NEST SOUTH CENT. _— 100! CO or) .00 cm hm . C 0" '0") F00 I40 CO CO CO CO CC CC 100! 100! 100$ 100! 100! 100! 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.9 25.0 25.6 37.9 14.0 8103 64.9 77.3 75.0 71.4 58-6 4 0.0 3. 0.0 0.0 0.0 ‘ m7 ‘4 (nu-u 2U) 4-4 X: 0:0 (.1 #— ARIZONA OKLAHOMA TEXAS MOUNIAJN DIV MONTANA IDAHO HYDMJEE COLORADO NEW MEXICO UTAH NEV‘DA PACIFIC DIV NEST REGION 00 CO CO GO GO 51.3 44.4 48.7 52.3 OD GO 0. GO CO CO WASHINGToN OREGON 100! 100! GO 100! 100‘ at: 0-0 GO cc: CC CALIFORNIA HAWAII ALASKA US ToTAL woF ESTIMATED EARNINGS (unumslfiwm__ TOTAL PERCtNT U.S. pxsTptauTiow 0F COUNTIES a? MAGNITUDE 3.- AHHEEEXIUHEEIC- 1 TOTAL _-M0RE TH‘N 55Q1_ 00 00 CV mm 4500- 4999 CO 00 0V V? 3500’ 3999 3000- 3499 mzsnu- 2909 2000- 2499 DO (:0 n) tit-O 1000f 1499 Less_ THAN 1on0 AREA l NoaTHEAST REGIov h :3 Cl C O D \ N. O C O O D AND DIV ...,“ k NEH ENG OD CDC; COCO . .I. .‘ coinln I i NO CD DD :30 DC DC C‘C‘OC .04 CC CC D.C" C'C‘ OOOODO DDDDDD coco .1 DO COCO 060060 000900 :..-r h... o a.» MA§SAQ§JSETTS RHOIE ISLANO CONVECTICUT .. MIDDLE ATLAVTIC DIV —- ~¢~—..-.¢.-.—pq._~ ‘oo ‘oobo I I HOBVBfldeOMO ...-.1 .04. ‘ ov-Ajocroooo N O O O O D O O ‘OO OOO H O O O O O O 0 OD NEd JERSEY PEVVSYLVANIA NEH YOR4 NORTH GO GO D” 00 CO '00 NTRAL REGION CE gOO‘OO 00° 0° DIV SENT. EAST ypaTH CO CO 0° 0° 0° l flm’ -.-.-q-..’ v..;uu~asro~abro , 'v.a OD CC) 0° 00 OD CO (3° 0° INDIANA _-_————.._.._-— OHIO °°~ D OD 00‘ DO ODIOO cot-cluoouQC CO v-l N HO OD CC O‘O CO DD 00 OD 00 DO ILLINOIS MICHIGAN °r= F: mnmmo I O O D D O C O HISCONSIN NEST vow“ 0331:: can ...; 6°C -9 I '00 NO 0 C Is 8‘ GOO COO 00°C 00°C 0°C CO C C 900° O‘D D DIV T. I" E” MINNESOTA IONA a ‘1- 0.. MISSOURI ON 3 1'4 V '0 0 O D O .. SOUTH.DAKOTA NORTH DAKOTA NEBRAS O O Q n . O D O o O O O . 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I xfimc-I«3:340:01:osnx’a'cx'xmon~vvwo~n~ow~ao .........C.........-.....C....-. mHOCOOOOVHOHnoflONHmn¢fllnNnnNOQOHO a m i N I ‘ - m;fi . . . . I - I . I I 1 I . I . I ' . I I 2 2 ' . I I I I I I I I ID¢OCVDQCVCOVOCOOCVRNOVNU‘KVONOOVOI I... .0... ...-.0 ..ICCCCOOI...-....~ -GCI‘OCIIOC"IOGODIOC°GCIOO ’ H I O- OOC‘CNCIv-IMRO’CIDIC‘C’IOC- I r! I ' com - I I I . I ' I = I I I I I I I I I I . OODODODOOOQQOOOOOOODOOOOOOOOVOOO . .- . C - C . .' . -' . - - - . . - O O - . .I - . - - I I I I l I _H...H_.-_ H -41... _fi—. H .-..--- QQSDDCDSDDDDCD'DQ'ODDD DDIDDDDDDODDD ' : 3 : f : ' I ' I I 0"" I ' ! I I I I I I ' 5 I I I I I I 7 I ; I ' . I I . I I I I I . . i I COD:0c0609060000000OOC‘DOOOOOOOOO .... ... ....I. ... .. ...-............ COCOOOOOCOQOQCC‘C‘ODOOOOOOOC‘GDOOOO I I , (:7 : I i I I I f I I I HI I I I I I I I I I - I I GOODOOOOOOOOOOOOODOOOODOOOOOOOOO o...cocoon-o.oooooiocotoooooooooo cocoODOOOOOOOCDODOO°°°°°°°.°°°°°° I. -..-4.. ‘ .vuv. - ...-.....m H. , > > > I t—o h-o I o 0—0 ' C C_ > U u: {'2 Q ‘ ‘ -- 0' t: D—C I <2 o 4 «Id: C, L >_ '-‘ PZPU . ”,O’.‘ I 5’ I ...." I I I . H. 1' mqno— |>2-¢Z' j I 2 . 00 i .u I 2; o m mJUI— UI|ll() m7-«ub Io-tddd' ’b-m‘Ob- OD. xv—I—cr-fi(04b <‘ZHOD-LU 012.1244 :HII‘HKHJ~ u!_37 3mc7< '7dn'o-uc—Ztuo'7dtrb—Hrrm7-r-<>I: xa-«ztrmoz grzer—oaqomozzzntxacnza<_1:rm 7dlllllldTOUIIHUIUII—777..JF-‘H7O-00HCOUI'du-o llldd~ t—uJJZZI>ZID_IZZCLZ O~~23 ZHZZU’Z‘X'JJIQZI U) _. D I UJF- . I— . I I LUI— <'t ' I?) ‘ U'JJ- (I) - '13 mu. .4 l < LL! 0 I2 3: ' ILL! “It IV) .— r— 0— rr (Y I ; '3 C O I : C IIDRE THAN 35oo- uooo-— usoo- 5000—- 3999 "“99 “999 5&99 5500 3000- 3&99 2000- 2500 ‘- 1500- 1999 2N99 2999 1000 - 1&99 THAN 1000 APPENDIX TABLE C-12 . CONTINUED ' I I I I , I I I I I I E I ‘ooIooIoo'ooIooLoLo'colaoi'poIcooIoo‘oorooLpoI 0.. .I..'. o .-I. ..- -I. .1. c .- -I -O .t.. .- -.-- OOIOOIOOOOIOOOODODOOODOIODDODOOOOOOI I ' -‘ I I I I , I I O I I ' I DI c i I I ; I '4 ; I . , I “I H. . I I I I I I ‘ I I I -. I I I ‘ , 1 . OCOOOOOOOOOCDOOOOOOO OODOOOOGOCC'I o o o o o o o o o o o o o o o o o o o o o o o o o o c o o o . OOODOOOOOOOODOOOOODG OOOQOOGOOCDI I I. I I I I ' I I I I I ' 1 ' I I I I , I I ' I ' I I I I OOOOOOOOOOOODOOOOOOO’OOOOOOOOOOOI I ... ..... ..‘C.... ...-- ..-.. .. -... ooocooooooooocoooooo’oooooooooooI . I c I I . c c: I v-O ! I . H '4 | I I I I OOOOOODOOODODOOOO‘CDO COODOOODOI‘O‘OI 00..-.-.000000-000000.0.0.0.... DOOOOOOOODOQDDDOQIOOO ODDOQONDO¢01 N NV I V HN . I : ‘ I ' ' I I ‘ I I I I I omoorxo'lxtxoobmcmowmvsm oNNItNV’NVNONI | I omooHOddooooomomONHv-Il omdnmnundomI . .1-0 ION I -v-I' I 1 . ' I I I I I ‘ j I I Q I ' I I I GOOv-ONQNFOHCDOOU‘U‘VINNQOQ.mOFOIDNNNQBNVI ou¢oocuo‘o-oooooao-Ou ...-o.o..o. fifiHONHVNfiODDfid’JVO‘Ou-ifl ONHDNDONNDOI I ' I fl HH ' I ‘ . I I I I I I . I . I I I I I I . I I ‘I . I I’ . I ' I I I | , ' I I l 1 I : ' . N314? .‘OD'NN'1\0¢1\K\DNV:ONNJ\;D > I 3 H u I <14 C. CL (22 ”DINO-'- 0 I Z..J.J '— ~0— ----00 4‘: {1.2 . I , CI 2 «I LDCZCZ LU LIJ (LLU‘ <1. ‘ UI . O '— dfluq “qu HUI/It'd § fo—I: I; r- ? I HHUU<< xw0 0: _ Z) HHAUWifilUAHCa Q2 Z‘tLL'IZ ‘duzz'04' H IILDHPDUJ<~PZUJIUI «(O‘mmz‘o DDHOuXI Lfihr—hauZJhémm’.~;—Q'<. Z’E—I 7C 5'14 7t'I-~mI IIWCIDOOOZZDCDILIJ< u—IIICOHI-IU)IIIUI_l—mflnffl1104no >OlllfYF-lllu—4IY<_I- >SZCDCDLL xi—dz (JDPHh-Z-O ZDZflDZuXOD‘tZ ,I- I v— . £92 I I—v. :1) ‘ U) L113 30 '4 LL; EDI < In 2 2. CL . *- I l I U) 7 ° ' “I I .‘I! I ll .1“‘ Itfi‘ o o o a m ma n a o c o oz<4>xqz o o o D H m o c o o o mm~c uupzqqp< :knom o o o m Km mos mam mm a o o rouumx Ihaom o o o m N» on a o a c o w_c .hzwu 1»aoy 5mm: 9 o o o ow me o c o o o 2*mzoummz o o o a on Amv o o o o o p_c .hzmu zhwoy pnqm a a o m «on “mu om m o o o :ohomm 44xhzwu rhaoz a a o a on an o o o o o <~¢424>m72m1 o o o “ kg m a a o o o >mum.n zmz o o o m mm mm a a o g o vac» 1m: 9 c o n am no a o o o o >_a o~bl¢gh¢ m4mo_z o, o o fl 0 a a o o a o pnuwpgm72nu o o o a ¢ 9 o o o o o azgqug mOOTx o o o 9 ma a a o o o o mppmmuzu o o o o n h o o o o o ma~xmnz~x zmz o o o o m «a a o o o o m;.4z o a o m mm mm a a o o o >»o o‘4457u 1&2 o c o m mad no a o o o o yo~oux bmquhaoz oomm . — ooofi zm mu 2300 no zo_h3m::w~a .MHIQ 8mg”. gnu—”Eng. . o c 0 mm 050 mm»« ocm cm a o a J_o o~m~umz o c o a ma ow H c a o o 14»: o c o o. v .o« o c o c o <20-w< a o o a ad oa o c o a o oouxmz zmz o o o m mm on a o o o o anamoJOQ o o o o a“ «a a a o a o czuyo>z No a a a ma an o o o o o ow~o .pzmu zpnom mex a o o o o a“ cm w o o o _aaamummm_z - o o o o a an wm h o o o xgopzmx o a o o u no“ um“ o a o o >~c .ppwu Lprom qum o‘ o o o 5 mm om m o o o «cazog* o o o o a mm nag Am a a o «_waamu o o o o c we om m o c o «2.40mqo xpanm a o o o n nv am a o o o «2H42aco thrnz c o o c 5 av n o o c o <~z_on~> 5mm; a o o o o“ 00 mm m o a o «~“Hum_> comm coca zm~c .p7wu zpacz pmmz .Iiubbmiiltbumll:pho o.o o.o m.cv w.om o.o o.o onmélsbhbls:euciiz- I 2Hmzoum_fl.:-;a nocd o.o o.o o.o N.“ o.av c.5m o.o o.o o.o :.: c.c 7~a .k7mu Ipacz hm4>m22ma :l!fibbfllil.o.o cuo can w.c o.nm nnma o.o Pho .ch c.o anew::;: ;:i:_mex a :uz‘- need o.c o.o o.o N.» m.m¢ o.wm w.“ o.o o.o a.o :.: - . vac» 3&2 - :Ilmmma o.c o.o DUO o.w n.mm onw«:!~.o o.o o.o m.p::-c.c : >~c.o_»7<4h< w499_x mead o.o o.o o.o m.~a c.m~ m.nfl o.o °.a o.c o‘d .d;c.,:: :-:. pnu.»owzzcu . necw ©uP-:nP»o o.o o.o“ o.om nub 1-0.9 o.o o.c o.c c.o . oz<4m_.waozm mood o.o o.° o.o o.o o.mo ".N o.o o.° o.o ada:z c.mu .ambhwm5104mqu_ upmwttazbuc mac o.o o.o a.h o.mo o.o :.: o.o =.mzalc4m:-:::;;aé: :-kzozaw>5 noca o.o c.o o.o o.o o.o» o.@~ o.o o.o o.c.. c.a ¢.c um~zmnz41 :mz :aummbMII--o.c--1oqo:-imuo:1amnmtzimdams-mwao o.o °.o excai:cnm:- can , wz_4z mood o.c o.o o.o o.m «.Nv n.ov o.o o.o o.c c.o c.c >_a o2<4w2m 3m? :3 be-.- oaoisfb n.~ camifmf magismumib..c c5 5.. 720% $3532 odmm cocr :ili:a£- ,238 oovm oooc oovv Goon omen noom mavm: oaoa gave 24:» 4m pzmuawm l‘nl.‘ Ii.'lll.-1Il’. . ifilil I'll . \I.. -III. .I utlil - taming 3.2-33 -.cm.».99599uwummi1:iz nuimb9ml!uc9uma:zbu9 9.9 9.9 9.99 :.:: 9.9 9.9 9.9 9.9 9.9 99<>wz 999: 9.9 9.9 9.9 9.9 :.:: m.vn 9.9 9.9 9.: 9.9 9.9 1:-1111amcp::sxa::!z .Ilmm9mnyxz9»: mm: 9.9 9.9 :.:m «.99 9.9 9.9 9.9 9.9 9.: «29~_:< :99: 9.: 9.9 9.9 m.» m.mn 9.99 9.9 9.9 9.: 9.9 9.9:s::!:ilsizduwxmxzzwzlli::::- gxlmppw. pm: 9.9 9.9 9.. :.:: :.:: o.“ 9.9 9.9 9.9 9.9 99999999 :99: 9.: 9.9 949 9.9 N.vm :.m9 9.9 9.9 9.9 9.9 9.9. =-:::,:z9zo»3:zsgqi1;z. INS.“ 9.9 :..: 9.9 9.9 :.:: mi: 9.4: 9.9 9.9 9.9 9.9 919: .kywn zpnom pmwx lllmppa P»: 9.9 9.9 9.9 9.9 mum~ rho: 9.: 9a: 9umtu|:.cii:;i11i-:Wfim~mm_wm_mai. -.-: - :99: 9.9 9.9 9.9 9.9 m.: 9.ov :.:: :.: 9.: 9.9 9.9 -hm:z. - LEM - 9.9 9.9-19.9 9.9 uh»---w.mm- 9.99im.m:lo.9 9.9. 9.9 1255) 99mm 999: 24:: man: 9:99 999: mmmm mmzm mmmm mmzm mam: :99: 24:: AdEDB mac: -ooom noom: nooo: loomm nooom loomm ‘ooom nooma :oooa mmmu «mm< u: ‘ QEBEEZQU.aalumZMerHBfiEm< WISTRI3UTION 0F COUNTItS RY MAI .‘ -—-... ..--_- s I NHENDDKTWEIICQEi NITUDE 0F ESTIMATED EARNINGS (DMIARS)M-9” TOTAL PEHC:NT U.S NONfiARMA TOTAL I Mose THAN 5500 5000- 5499 4500- 4999 4000- 4499 3500- 3999 3000- 3499 2999 2500' 2000- 2499 1900- 1999 1000- 1499 I LESS THAN 130U Ic’ I I i II II *c13cac<3:flcc:b¢:;>ot:cu=cfic1=c [OOOOOOOOO I O O O O O I C O O . coat: I I I I I I I O, O .' C I I l I 0 ....-fi-.., . ’ l I I cu=c>0&3cromaowacaclacnocacnac> Iowac>of3cfiow3c>cqscacacuac:cacc: I . I , I I I I I . 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I II— t— (Y (1' C.‘ C) 7 7 DIV ::VT. In" EAST VJQTH u I ..O‘. ..- -‘. I I I I J I 0413 I - . DDODO . O 1 IVJIANA capcacaocacna I Grandacaocac .. GCC‘DODGIC.‘ 00000000 3 I I . COD 0.. 00000000 ILUINOIS MI “IGAV NISCONSII 4E$T VJQTH DDODDDDD I caocataowacao o.’ OOOCOOOD 31v VT, .I- c 0’. OI. . c of. ... ocag>ocaowaow3c3omabwa IIIII clacabcpcackacaowpc>o o'oooc'oo'oo‘ouoo ciacaowaiacgacacw=c>o I I I I I I I I I I acacaowacaoqacaowacwo .I-ooooooo;o-oo DOODOOOCODOOO I I I I I I III I I #690006 I I I I . I ray-«mama .I. 0.001. CIOCOOICO Ofacac4ataoc>a~¢¢3vro I I I I I I l : 1 I I I * I I -'------‘------- QNVSODNQHV'OOO F. f ' ‘H' I I I I I I ' I I I I I . cvm~0ca¢¢:h.m1=urue~vo -'...'--.‘....7.. mnn-nmnwnvovaooo ‘ I I I ; 1 . I I I I ND=D$03NnNDmO - o .0... o .- ..- ”OODCHDIO¢NDHO I . , om I I I I : I QOGNNCBNC‘C‘C cool-cocoon. COCVVCfiVC'OC' OH.“ ”O me ---‘O-----.- GODDDDDDDDD ; go I ’ I H. I I ' I DO -I. D: OCDQDCDD o.o-o.o. OOOODCCO eta: .. . GOO CO CO I I § ‘ I I . . DCDC’DOOD OOC‘ODODO ODD .0. COO CDC, .0. p c - NTIC gIV'“ DA > H H <14 C‘ C) 4"? rub—co— O ' '7.J_1 y— o—olI— . 1 , ~00 ? a7 . O . z 4: 'firY'Y m m nm 4 . L) O -- 11an (3).!“ ”(31/1‘14 > C‘-— r- I Hr—Du-td yaw-1m <43: ---4 00x4 ‘ >0 C: /> HCAI UV: MILD—O C42. 4:41;.LI4 <~4¢Ld~ H TI'.9HI-'3lUdu-ot-.Z'.DIIU) <0 ~rrxo DQ—nOLLx-w (”kb-v-na"b>n1UIT<—da 2F‘I Sc: NJQ (Cir—(IQ Trh’YfifiDfiZ’d'n'fiijx7—024 041-04)!)«731144‘! NHIOOUI J") Hill! Io—omn’nvmndno >nmnrv—mu-an 4 _Id >T7IDT’JLI. (~45: «(Job—chi“ ‘xuzqnzmxau- ’— wz‘ --. ...l '1’ ‘n IU‘DI L)‘ <1. <1 LU (10' «q' t- 11] 't 5: .0, O F- ‘. i— (D I I I LU ' (r. ‘{ '3 APPENDIX D DISTRIBUTION OF COUNTIES BY MAGNITUDE OF "APPARENT LABOR MALADJUSTMENT," FOR THE U.S., REGIONS, DIVISIONS, AND STATES, BY AREAS OF RESIDENCE, 1959 368 .‘ll. o o o o o o o o o o o a .u.c.zo»oy~zm«x o a a o a o n o m m « ¢a o244>m~o ufipz‘4h4 xhnom mow sud nmm mud on“ no oca ooa mm mm we no“ 23.0mm xhaom o n 0 o« as ea Ha o v s m ow m~o .pzwu zhmoz pmwz o v m m o« m w s m w A ca z_myoum~z o n u c ad 0 n m m m 0 mm 7~o .hzmu Thaoz pmqm «a «v mo as no“ mag cc“ me“ do me mm mod ,oaomm 44>mzzwa a a o o o o a a a o a ma >w¢mma xmz o o a a a a m m m o a an yxo» zmz o o a n n n ea nu ma ma N“ on >~a o~b7 a a a o a o a m o x H a mn.xmaz~a Q744uzm 1m? a w m o m ma ma am ad om ow no youuwx hmcurhyoy a. oQH . coma coma coca can ask coo ocm oov com com ocH zHo uHN_umz H H m v N H H n H a n N I3 o H H m n n N m v n n N oxHo quNyaor o N 0H 0H mH nN NN mm mm on He ncH 20.0mm Nmmz n vH Nn oH oN NH nN oH o nH HH oo m~o .szu xNaom wa1 HH nN 0N N v v v H N N o H HaaHmmHmNHX v mH 0H m o v v n o n H m <7<m<4< NN oH oH o m c N c N H n n mmmmmzsz oH nH NH MH 0 a HH oH n n n NH >zuakzmz on oN . mN Hm HN ON oN oH N o N HN >Ho .szn INaom Nm<m o v v n c n N 9H n v 0 9H «QHNaHN oH oN on NH nH NH 0 nH N n H N «Homage o v ¢H v n m m o H H o o HzchmHo INacm v HN HN o HH N NH 0 v N N H «2.40:40 xNzoz o H o m N H N c n v H oH «HzHo«H> Nmuz v o nN N HH 0 m NH N N N mm «H7H3IH> 00H oomH OONH OOOH com OON com com 00: com com 00H z¢mH mm>o IHOOH IHow iaow IHow IHom 3H0: cHom IHON IHOH .nooo mmmq <mm< nMSZHEfib tfiAHMdEEVXdEfiEm< EARNINGS GAP (DOLLARS) PERCENT 3v Row TOTAL DISTRIBUTION or COUNTItS av MAGNITUDE 0F APHQEHXINEEEI}2. owaaltmnfiL 1200 1290 l l l 1001- 801— 1000 701- 800 601- 700 501- 600 401- 500 301- 400 201- 300 101- 200 l 000— 100 LESS THAN ;00 AREA Q(DCDOHDCDOMDCDOMDCanithCDOWOvi?(DOHDH\GH3U\OJDCWD H‘OU‘OOOOOOOOOVNHHDMU‘OV)”OOVNNNO‘OOO N o. o c: .- or o a» a c» .»I. o. .»‘O c» a» c’ c» o .t a o o - NONODNOOOHOOQVOQmQNNF‘mNn N“ N v4.4 dd H H H o . Wink f. H 18. BU‘U‘OMOODOOOMNNnv-COQQOOHWMOOMONOOD ”NNOVOOONOOVNMNHVVNONOHHF‘DO‘NNOOD Viv-0H ‘ H VON YOGOOOHOODDOU‘NPOPO'O (Nfl)oc>oh~owanupcatcahcuv ... v-i dfi InOQOQODOMfiOOOO‘ONNOHH‘OONU‘NmmONOOO V) 0 PIC-i N r0 a 0 v4 '4 N 0 CH '4“ #ooonOc-qonn H H V4 V4 '4 I 74 Ht-IH 'vhfivmfi HHH Hr! '4 the?”OONNOO‘U‘OVHOVMNQHNF‘OQU‘NOOMO VNQV‘OFOVJU‘U‘O‘OONNONO II'I N¢n0¢ooohflmVONQHNOOOONWNOOQNCOCO O‘DVOOVOOOOQQVOOHVQ‘O‘OOOOOVMOQNNOOOO c-l NNN ...; r-CV-IHC-i H H OOWOMHODOHOVNVHOOOQmOHONMNQNOOnO 0! Cl. C ' .Q . «.0-col. mooovhooowvmuoom v-I OOOOOVOMOmcvonQHN'Q v-l H r! NflOOOHONchntOVQ or Q.” .V A {PIFIV‘ 3'! 9r MONO H N HH Q? .. NV") V 0,. o o O 0‘ .1 CI .2 .0 o NHNIDNYOVJPOVOQO NOONNO‘ON’O “\OCDOVOIOOCDO cwscaocacwacaowhman\Nc3MDoaoocrvwocaownOJOQrQBOGMD 00.00000!“ nv¢caoc304:wuxc:hmo . O‘DOONONOOK‘H (13va” (O F) NORTHEAST HEGIOV v-I New ENGLAND DIv MAINE 4 NEN HAMoswqu VERMOVT MASSACHJSFTTS RHODE ISLAND TICUT MIDDLE ATLAVTIC DIV CONNE NEW YORK 18.5 DIV 28.3 T. I I RSEY EAST NURT4 CEm PENNSYLVANIA NORTH CENTRAL REGION NEN J OHIO 45.5 21.7 INDIAVA ILLINOIS MICHIGAN 27.2 26.5 man u o o o (“v-CON NHHC'O > n C] g. 2 ZUJ< o—DI— U) C) 77m 09-11.] DCIZ< (1)32“! ~2~O 3 '20-. p. (D LL) 2 0! 0! OI 0| 0| 0| 0| 0. 0. OI 0' 0| 0| MISSOJRI ...... mot-OCOVV v-Ov-Iv-cv-Ov-i > i-O «a O I-—|- CC 0 xx N «<34 5- DQK 7. mm a 11:44 _I v—r—crmzo— 1311204 OOLde—o ZCDZIOI un— ND 0 It!) .— D C) U) p - _ DELAHAR MARYLAND quOIOIOU" APPENDIX TABLE D-2. CONTINUED 201- 301- A01- 300 400 500 I OVER 1200 ‘l 1001 1200 701- 801- 800 1000 601- 700 501- 600 101- 200 'IUI‘AL 100$I LESS 000' wars 100 100:- 100x 100! 100$ 100$ 100$ 100$ 100% 100% 100: 100* 100$ 100x 100x 100$ 100$ 100$ 100$ 100$ 1001‘- 100$ 100$ 100$ 100x 100$ 100x 100$ 100$ 100x 100: 100$ 100: I I i flOOOMOOONOONNOQNV¢OODNNOVON‘OOONOFI ”OVO‘OO‘OIBW‘O‘OOOONHHHOOO”nowodNOOVOV Viv-IN r! N OQONOHMQDV¢VBOV|DONOFODQNOVOOOOONOU‘ o.o-coocoucoobocooooococoons-...- oddQNOOoonoooomv-ONONOvnonooooo¢0h N '4 fifiNNNde-Ofi hmov’mv-IBNOVNIAOHVO‘OOVIAMOONONMOOOONON -..----.-.O-----.---- ----------.- NVHOVOOVODVVOVOVPODVONONnONOOOOOVOc-O Hv-O‘NMN NdNNNv-CNH v4 '0’ H VHDthmmanNkommomnVONNOQnODDDOOQ .O-.-----------------------COCOCD mO‘O‘QDV'QNOVQNOO‘ONMU‘mv-CO”WDWOOOOOOOO 1". V" V'. '4 f" mNOmNflQOMOovnQQOVOMQWOODOOOOQOOOU‘ ----.-uooooooou-.-ooc-.-.-.----.- wNflomommmochmfihknmn‘omNOD‘OOOONOOON HH onovmannmooonnaumno¢mo~ho¢onr~moooo ......OOCOOOOOOOOOOOO .....C...... {vHhthmOVOVBdOVomONs-OVNOOWONNNOOON I .4 rd r! H r \ NOOONONN¢OOOHNVOflfimMOOOMOVOlflnHONOr-I ..ooooo‘ooooccoooooooo 0.00-0...... OWNOU‘OBONOVNMVOooommcboanmOV-IOVOQ '4'4 ‘74 'C'IV'I flnOONNOMNMNVNQNU‘ONOQ‘DU‘HOMOOONNOO‘O .-.-o.ooooooooo00.00....000...... H H H H HH H V" VMON¢LDOInHOLnOOhHmmOOv-QYOQNVOVMMMQ?POOO~ o. o o- .0 o! o~ at oi .0 a! Cl 00 0' 0 OI 0' 00 Cl Cl 0? o 0| o. 9. at o- o; a. on o' .0 c} . LnanNVVv-INNONVOQ'OMNQVOQVO¢MOMONMQOM v-I H r! VMONOflmU‘Hmman-IOdQMQQU‘MMMOOQO‘OVNON mNNNv-QONNngN¢NVOVJMQQMONOOVOOONMFOVOU‘ (\ Iv- Iw .“ If.“ vmooooommmcommonnonaommmvnmoorxdomom I“dud-lOld-lOlufltt-ld-ld-I-ldd- Q-l-Ioluonotqqdq. mHNoon«undovanmvoomomooaomonfiavom “Is-41H v-Ors n: mOOVNQONva-HOOQNU‘MMONOHOv-OCDOOONKOO ......0..........CCOOCOOOOCCOOOOO anHovmmonhHOHHhrxv-cdcmvrxmovcom«nuanced I") H H '4 N anmamwnmwomvnnccw > > H ... «q C- C: 477. HUI-l“ O O 2.1.! .— HD- o—C‘JC‘. 2‘ :12 O 2 <1 ocrcz Lu LU CLLU < L) O t-0 ("<14 ()>UJ ~13m7< > or. $- 2 ~~UD<< ace/34m (<3: ~< GDX< >0 fr 7) natumrmrm—‘o :37 74L“? (H7704.— v-o IIO~h3uJ<~l—ZU)IU) (ca-«:20 can—coux—o (Dr—hh-IIOIDt-anmD4H44 ZD—IZO NI< Io—m< ILDIDDOOZZ<JDOKDJXZ~Z<OJZ~<>DMJJJ<2 ~wcow4mww4~mmoxwo<oc3>owmhw~<cz<4< >32<DCSLN act-<2 (_lOt-Hv-Z~ZUZ<DZLNIOU<I .— I- 07 - _J (n (D m: U 4 <1 UJ (:0 < l- Lu T t CL C) v— Ib— U) LL! U) "I ‘3 m1?“ : EARNINGS GAP (mm) TOTAL DISTRIBUTION OF COUNTItS 9v MAGNITUDE 0F PERCtNT u.s. ARHQHHIWDMEIZDbB. TOTAL (NEH; 1001- 1200 501- 601- 701- 801— 700 800 600 401— 500 301- 400 000- 101- 201- 200 300 LESS THAN 1200 ; 1000 100 100 COOOOOOOOOOONOOOOOONOOOOQDOVOOOO ......O...................--.... c>au=c=c>cm=c»au:c:auo¢:c>cu=c:=n0c:c:cu:cacuaoqvwo¢3c> ‘w rt a _uaa nnnooooococooovvonnunnmononnucoo 00.0.0.0coco-00000000000000.0000 HHHOOOOOoooconOOOHfiOHHOOHOHmVCOO N H [\N VdmnDnOOHDOOv-OOOOOOVNN'HONMOc-OHO'OO fiHOOOOOOOOOOU‘VDOfiHHONflNflNnv-OOOOOO N on (D DODmOVOOVQOOU‘VOO‘NNov-CQQQNQNOOO V0 ------u-ucoooocnouuoouuu9-0000---- NO “3 OOOOOfiOOfllfiOODNHOO‘LfiNONnMVNv-‘OOO l’) N mm Hoovovoonoonmhovxonnmdooomow")?ova NOOOOOOOHOOHOVOOHNVVNU‘VDI‘ONWOQU‘HOOO V04 VN ovocuc:o¢=c:ocaéwo¢>o1H¢>muohvnaovnnranzahunlaacac>o VNHDv-OOOONOOHOBNVFDMVOONOMV)NmomhGOO VT. N 70" rawHDVrnhmcac>OHncavdv'v1uvdolwwficahud‘rriv1>N30anauhc NMOOHOOOYOOOP’JOM‘HU‘FON'OVOVU‘NMNMVNQOHO '00-! V04 . V’O'NflOQOQOVOONQOv-CO‘OCDNOO‘ONOOU‘QOOO ...-.00.-..-OOOOCOOOOCCOQOQQOOOO ”H N VN NNmOv—IMOOdhmmmonmva-zMOOOONONmmOv-OO 0- 0o 0. 0 0| 0 00 0‘ 0‘ C 0 0 II CD 0| 0' 03 0. 0 0- 0 00 0t 0. 0 0 0‘ 0| 0 0| 0 ONOOHOOOQNOVVJNVOBNJdmIflHHNdNQVOq-Oo H VN N NH “\MONONOOHOOOOOVIOOFOHONNVONNMOv-lCDOv-OO VVONOdOOOmOU‘VNVJBMHflOHNOv-ONVMONOHO :v- -v- '00-! -v- (\w COIDOOOO‘DOVO‘OQOK‘DU‘OU‘VOOQMOOHQNVU‘OOD | 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| ONOOOOv-OONVOHU‘VVNQmVHHNOOnv-OHOMOOO MA! 0004 OOOOOOOflOhkN‘fifiHONVJVIflVLQflNMHNOONMdN NNOOOOOHovmnoorgmvrfi;NOflHdOv-0HNO:OONO n H NV! '4 P074 > > > b. 0-0 0 H D O > U I.“ U) a 0—0 0 (7 O-CA C, 0 O “ C! C > M O—Zt-U HOD- ’- I-O- . o— I LL<D~ >2"; 2 CO L) 2 Q U) (DJUO- “(OLD ZLUC XX 0-0 C) 7 O TU1~7Ym>uI¢J UB7HL)’- H440! r-wcvr- OD th-I—OO-<CZCZ.JO: (~40! O 132! 7.12:!) 0-07 (7!) n-10ua>— T 7003770) '1 mm ((47 '34JJIO(.ULUD->-§U)_JO— (ZO-OOr-LU CII<< .13.!” LU_JZ XWOZ< ZdCIOHHIUCZZ<¢DPhIIMZI~<>I I’D—03131032 21213~340mozzmmanzs<.110) ZdLLLLI<IOLLLUUJLhF-ZIZJ—HZNC—Cowdw LL<<1 FLUZZ>ZCZUJZZQZ ONO-OZ? ZHZZWZKCDIQZZ m C.) LUF- .- u].— (‘t D DU) U) Q'D LL'LU H <1 LU 0 1’7 2' IL“ I It!) .— O— ’— rr or D O O O ‘1 APPENDIXTUEflEID-B.(IIHINUED l 801- 1001- OVER 1000 1200 1200 701- 800 601— 700 501- 600 a 401- 500 301— 1 201- 300‘ 101- 200 000‘ 100 LESS THAN 100 [ AREA NJNVfiQfiVNNv-CVONNFONOOBNHNO oooflocoooooo vi '4 ~ “OfiOQOOBHHHmmONnNHOOOOQOQOOQOOQOO 00.00.00-00000000000000000000...- 'flfi flNd C H O'ONONQMfi'momOVOOOO'O”VO'OVOOOVOO 000.000.0000....000000.00-0.0.00- DOQHHHomQ‘OOODnnonNOOOHDDOOOOOOOOO 7' N N C I" ”NNQNHU‘ONNm‘OflMOHVflmnOVMOVnnOOOnOO ------nuuasooo-------0-00--..--.- ONmnOHOmemQV’Nv-OOVVOOO04000100960000 C'C N d H C3 H Mvmoovorcaovnovnvommvvoounoounooooooo - - - - - - - C - -- - - - - - - - - - . C"- - - - - - - O - - - C) H INONMONOVOVGOBOBv-IOMVOOPOO HOOOO'OVOOOO ..----.-------------.----C----.-. wnvdmdommmfimdmmooooaoNooaoooooooo H a '4 00000000000000-000000.00-0.000000 NONncnwnflfiHnOfiV‘OONHNONHODOHflOOOOD '1 O H flQBOmQNanO‘OOva-OVOQ-INOO OQOVOIDOOOVOD C . ... ... ... . .......'.. ............ MOVNMNOVNHHVHHNOONV'NONOOOOMHfiOOOO H IIOmNNQNNBMHVONOVJu-IOOOO‘O NOOflOOVNVOOO -.....ooooocaooooouuo-..-00.00.... OdNCJVMOFOHv-OOFJOHVONVOOFOHONdOHOnONOODD fl “'4 '0 I". SONIDQ'OQ‘OHOv-ONOOQONOMNFO OOHMIOQNLHV-lfibo 0 0 0 000 000 0Q0|0|0I0‘0 0'0t0:090:0‘0-0i0 .‘“.‘.‘.'.‘.'.‘.',. 0 nHNoanHHOHOOv-OVOVNNVNH dfiflOOVNOv-idoo v-i H04 . O '4 O‘NH‘ONNONONVONv—iflhnmmchh NHHOOOOQflOOO MNdOflNU‘HOfiHdfifldNHva-ld NflHOODnNflOOO (v- C\.v-1 ‘C v4 MONOOU‘WQQOONOFON'NNPOQQQNNQQNOQU‘WO‘OO conoun-lo!ol-Iduct-nonolanal-Iclot-concu-I-I-n-IolououoI-tq- VOF‘DOU‘VHHDOHOWT‘OU‘U‘F‘V‘F.”Hf‘HHO‘HNVOOO '4 (\‘v-J ('5 r'l QONOHMNQMQNNNfiomQNva-OO OU‘flHNOVv-lv-OVIDO C C O C C mNODHHMHOOOMONOONFJNv-‘N NHHHHVNNNNOO H C) 1 1 2 1 H > > H H d( O D {22 0-0—0- 0 0 2.1.... F- HI— ----(_JC; 4: 0.2 O 2 '4 00:: u: L“ EL“ 4 U O H «04¢! u>u' HLJm7‘ > 00" F 7 «H0044 (WC!!! (<1: 0“! (DDX( >0 I 7) "0?!)MTMTMb-on D7 741“? ¢H7704~ ‘- IIZD-dF-DJJCHF-ZMIU) (CD—01:0 QQHOux—O L'Jt—t—P-CIZLIDk-ZIDCDZI‘dfi-fidd ZF-IZO NI< IUF‘MQ IJJIDODOZZ<WOKDJXZ~Z<DJZH<>UU3LLIJ<Z HUJCOLUJWLLLUJHUJQOXWDQCD>CLLCIPLU~<K<J< >IZUJCJLL 1P4: (JOP—~PZHXUZ<DZLLZOU<I .- b- (92 ”'0 _l U) (D LU: U 4 d LU (to 4 I— UJ 1 3? Q 0 >- l- (D LU U) I 3 "' 1.. A.” o o o a o a o o o o a a .o.o.zo»o7~xm<z « o o o N N o o o a H n“ oz.4>m(z o a o a o a a o a o o n mm«x<4mo a o« oN oN .NN cm on nv 0N «N mu om >~o u~»244»< zhaom Nu «m «Na No mm on do“ cad mu mm on ooa zouuma zpaom a o v m o m m m m o m o m<mz<x o o n v N o o n 0 v a v czm<mmmz « a o n o a N n v a N n .pcx4o :»30m 9 o o a c a a a a a n N .pox<s Ipxoz N n o o o n o o v m N o ”muomm_z o o v N m o ow m ca 0 0 ma «Ian 9 a a N v m n a“ o o o o“ <»ommzz.2 n m mN on N» am mm 0» on v» NN do >~o .pzwu xbmoz bum: o a H n a a m o m o 0 ad z~myoum~z o a a o a, o m N m n v N» 7<m.:o_z a o a n N n m m o o “0 vv . m.nz~44_ a o N a N c o N ca Na 0 mN <7<~gz~ o o N o a a a v o m n“ Nm oqzo o N N N 0 ma Nu ¢N mm N» nv an“ >_a .pzmo rbmoz ~m<m n N N» N» on o« um om «N on me . omN 7o_omm 4<mpzwu thoz N a o n N a n m n m 0 am «~2<>4>mzzwm a o o a o o N o N o a ma >mmzmw xmz a o a a a N n N o o m cm ran» 3mz N a a 0 m 9H N a ma «a Na Nu >~o o~»7<4h< mgoouz o o a o o o o o o o a o . knunhumzzou o a a a o a o o o a N o oz«4m. moOzm a o a a a a N a a N o o mbhwmzxu<mm<x « a o o o a a N n N o o h7ozmm> o o o a o o H N a o m o m«_1maz<x zmz « a N a N a N n a a o o mz.«z N a m N N n o N m o N Na >~a 27<4o2m 3wz v N o o n na na ea Na N“ am cm youcwz pmquhmoz mmzu.aooa -aoo -695 [name -Hom ‘-Hov lac» uHoN, -flOH{ .050 A mmNJ «gm< z Ammqagxc 1<o moz_zm¢m no monk—zo‘ - , N on” Axma cam“ good can ecu coo 9cm _ o°¢— can ooN ac” quk «m z m: E mmtzaou .3 22.52539 5.9 WEE. finzmmfi mN OO omH NHH nOH HmH OOH OOH OOH OmH NOH Nee 4¢NON O: O O O O O O O ‘:O H H O N _H«;Hx O O O O O O O O O O O o «xm444 O O O O O O O H H N m Ne . HHymnuHHHO O O H O N O n O n v N vH yoouao O H O O O H O v H m m OH zONuyHIOHx O H H O N H n m o NH NH ON )HO OHNHOHN O O O O O O O O O O H o ¢O<>wz O N H O H N O O O H H N :4»: O O O O O O H O O n n N «7ONHm4 O O O N H o n H N O H O OOmez zwz O H N c O a N N H n H O OOHOOHOO O O O H H O O N H N O O OszO>2 H O N O O H o v O O H m O1<OH O O H O N H n e H H N O <7<Nzoz H n o N m NH mH NH OH OH OH OO >HO 2H<N7002 H v N N N OH OH OH oH NN NN OOH onoma waz o . mN OH HH NH oH HH OH NH mH He quwh O v HH N N O o N o n m N Hznzqqxo O N v n v n O O N m n . OH <7<HmHOOH H O OH O n o c m m N H N m<mz<za< O OH mm NN ON NN ON H» vN NN «N No >HO .szu TNOOm wax H O o O O O o m N H H N HaaHmOHOOHz N H OH H v O OH O N H H N Hy<O<H< O o m v N n OH N O n m m mwmmmzsz H O . n H N O OH O o N v OH >zu3hzwx v OH NN OH NH nH on ON ON N HH mm >HO .szu :Nnom Nm<m O H N N N n O c o n O O «OHOOHN H NH OH OH N OH OH N m m H OH «HOOONO N c m N m c v o v N H n «ZHHOOHO INacm n H m o v N 4O mH m m c O HzHHOaHO INxoz O O H O O O H m H O O OH «HzHOaHO Nmmz H H - N , O H o v o N m , o NN ‘ «HyHOrH> ‘ ... OOH OONH OONH OOOH OOO OON com com OO: OOm OON OOH z<ma mm>o -HOOH IHOO uHON uHOO [Hon uHO: uHOm :HON IHOH ..OOO mmmu <mm< QagfiHZQO.qAHmQ@§pxfiafiEm< RCENT BY ROH APPENDIX mans; n.5, DISTRIBUT10N 0F COUNgéES BY MAGNITUDE 0F EARNINGS GAP (DOLLARS) l I unagu 501- 601- 701- 801— movovm mm. 600 700 800 1000 1200 1200 ' 401- 500 301- 400 000- 101- 201- 100 200 300 100 LESS THAN \ l l | l NMNOOOOOHOOWOOOOOOOOOONO'DOHNOU‘O v-l ONHODDOONDOOOLDOOOOQ'OOnONOOV000° fiHNOOOOOOOOHHOOODHHHv-IOVOVDOU‘U‘OOO OMDOONOMNOOOmOVMHVOQNOFIOOKNNNNOOO coucocccoocuuoonuouuoccon.-----. nwV°°N°N°H°°¢HNNHHHNHmnOONOHNOOO 0" N." '4 '4 v" O¢HHOOOODOOOflOONVOnONOdomDnmnOOO nnNHOODDNflOVU‘HOr-InommnwnONOQOMOOO r0 '6 04d «\vr0c>Owac>Ow4(“Orann0cnnrachOHONOOHOONHro«Jocawwo Nnvooooomaonmfiommdfioooncomnmmacro v-l flN 0-0 H «rraocaonNc>oc=M>OHH1>01~o~Nuo:>O¢>.40.Hnun-oc»oc:mso ......O...-.....-O............-. omOOONOONnOnovvaONmNovNccmoOOOO v4 N v"! f! N v" VNnHOVOOOO‘QOOONVNNQflQNOVCMVMNFOOOO 00000.00.000000000000000o000000. ODVHO OOVV¢¢NmfiB'mQQOVNflNQmDOOOOO fiflo" fi-V“ ‘v'O 2.4.4 -"' -""'§ 000.00..............,.-........- HNNN A C“. H H H" O‘lfirfiOOhOOflfilflOOVv-tnmfionhfiwHINVOVOO‘V000 ' 0. 0- .1000:0- 0 0| 0! 0 0; 010 0» 01 0' 0| 0. 010 0| 0 0| 0. 0i 0 0! 010! 0‘ 0: C QQNOONOOOMO¢OOBN~OMQONNIDNOOQNBOOQ P) 04 r! H H0404 H04 NHOO¢OOBDONN~OOQOOMQMHNv-QNHOO'OOOO V ...-......O..................... (D OKOOMOONO‘OQO‘QOV‘OVOO‘QONNVOMK‘OOOO v- ‘C\3FC\ t0- ‘v-I :v-I ' {w'v- I . O~OOOODomVOOQOOU‘Hmomnmv-ia‘#N'OVOFOOOPOO I-I OI 0| 0| -I 0| 0| 0! 00 0| 0| 0| O! 0| 0| 0| 0| 0| 0| 00 Cl OI 0| 0| 0| M It 0| CI 00 0| 0| GnomOOONGDVO¢OHID04N~OOOO~MNHVNQOOOMO 04 ll‘ 904 0-0 v-iviv-dv-O '4 (\t OMOOONDOMNNQQNOOOMMMV000mmOO”0’6 ......OOOOOOOOOOOOOOO0....0....O HoooocomommNNf‘HVoNnkmNMVNOMO30°00 VN V hmmwnnvcmmmmamwd‘wwuwnmgcz > > > o— 0-0 0 -- C: C.) > L) L” U) o 0—0 0 IV PC! (7 0 0 (d O D > —- F-ZF—U -—cn— k F-h w 0-0 I UJQDH >2»; 2‘ DO U 2 D (I! €0.10!— LU<<DLU ZLU< X! 0-0 O 7 O Dm~7x¢n>mo m7~()l- ~44< v—tuou— OD Zh-I-roi-dtrmJCr (“(0) O moor 20:20 O-o7 <7!) ()_JOLU>— T 70(57TU) '5 mm («(7 O<mIO<LULUl—>-')U)_J!- (ZHOl-UJ OII<< _JIJ" LU_IZ ZUJCJZ< Z4EOH~IUOEZ<¢DFPCIUJZF<>I IOfiI‘ZiflOZ‘ Izzmo~aqomozzmrramzo<qazw 2«mum:lomwmwh2124—~z—o~oow<— LL14“ F-L|.IIIZ>}'.'KC.J_IZ20.2 o—o—zz ZHZZWZXOIQZI U) 0 th— )- wt- 4‘! D DU) (n (2:) LL'UJ - 41 LU 0 T7 17 TL“ ‘.t It!) 0— t— t- rr C! D O O O 7. 7 U) . ‘1. APPENDIX TABLE D—S. CONTINUED TUTfiL 701— 801- 1001- OVER 800 1000 1200 1200 301- u01— 501— 601- uoo 500 600 700 201- 300 101- 200 000‘ 100 LESS THAN 100 E l l 1 . H . 100! 100x 100: R O O 0'. 100$ 100$ 100£ 100: 100x 100$ 100x 100x 100: 100% 100: 100$ 100$ 100$' 100: 190: 100x; 100%- 100:; 100x 100x 100% 1ooxj .100! 100$] 100: 100x 100; 0" “\OMQHOOOOOOVOOON'NOOD OOOOOOOOOOON fiOVVHOfiHOnNHNOONOOOVO OOODOOOOOOOH mommOOv-doomtommmmmaooovocal-0000100000 000000000000000000000000000000000 HOHONNBOOv—HDVv-IVOONHNOOOnoonoonoooon 04 0'0 '4 '0 0'! OOOONOOOMONOVNONOONOD OOONOOOVOOOVO ”NNFO¢VONVQFONMONQMNMVQO OOOOOOOVOOOOB 040-0 04 Ndv-ON 040-! OOF‘QU‘ON‘ONQVQOQVV‘OOOO” CHOOOODOOOON 0.0-00000000000000.000-000000-0000- OOOV‘OMOdOv-Owkmmnomvooonoooooooooom 0" H 0'. 0'0 lflOdOVOMVOP’JMNQONVO‘OVOMOM OmONOKOOOOOQ 00000-00-0-000-0000-000000-00-000 HOOHNnmnnNommNn‘ONF’QOOVOVOOOflDOOOOV F. HDOflhIDKO'OnflflmfiNOfiONDD NODWDONODODO 000000000000000000000 000000000000 ~05600605‘0‘OQI.‘\VO'\0'-Cl.f\°‘°¢'\¢vO"\~)°",°°")°°°°N ‘ H '4 H H “'4 V" fao~rau\h.«acav-h.nannU\¢rcannh.<>o~u~c:c:<><>rac3causcar>c>c:&>¢: ‘0000000 0 00 00000-00 000000000 000 owmonN‘O‘O‘OQNNNOOO‘OONVOonNDONOOOOOD fi‘f‘lfififl C" -"'.N '4 -" flnNnQQOQNHNOQVOO‘OONOmOmOOOflQOOOOO 000000-00000000000-000000-0000000 OVNVNNHVdOOOOlfiHOOQOON O¢°°°¢NON°°Q “'N“. H.404 V" '01" dd“ '4 DooanNmMNflOOQNHOONO’O VHOOOONVOOOOV 0‘ 0. 0.‘ 0t 0, 0o 03 0' O! 0 0O 0! 0 0o 08 00 0! 0' 0x 0! 0O 0 0. 0 0! 02 0' 0! 0| 0: 03 0| 0 Omnomaoonmxrhmnovsmovoonoooomnomomm 0-4 0404 040-0 04 N 01 N OVOQFOOFIPODQOVOVOVOOHONOU‘ MOVKOOfiQOOOQ NHKVU‘U‘MMU‘HNVOVOO‘U‘OQOVON OOflOOO‘OMNDOU‘K -(\o .0- 10- 30- (\ friv- HVHVHBO‘OWQOOOQU‘NVCOMOO V'J‘VNMOWUOQOOQ clOIOIOIOOOIOUfl-I-IOICI-l0|“OIOIDIGIOIOlddddOIOl-tIIOIIIOII OHONHmQ'Omv-ONKNU‘QCDQOQVOMVHOVOOOOOOK 0* 0- N 04 0" onmfiMOBflnhv-ONO‘OO‘VU‘U‘K‘ON OOOBBIAN'OCOON ......O.....-........ ......C..... OV¢NOBVHONVOMVHNOO~NOONOOOBVK‘QNOOO vm “0401M 0s 0-1 mammmmmmwvmvccvvmomn - 0-0 > > an 00 (4 C3 C3 427. 0—0~0—o 0 ' 2.1.! D— ~0- -—00 2 a2. 0 2 d (30:0: m L“ CLUJ 4 O O --0 ("'44 U)!“ ”(3107‘ > O” F- 7 ~~UQ<< soc/3(a) (4! 0-‘< ODX( >0 0:, 7) HDTL)MTMTMHO D7 74“]? Cu770‘0-0 --0 IIUHPDLU<~PZCDIUD (CO-.120 DD~OLN¥~0 OhhhmmthmMD<~<< Zt-IZO NI< Ina—m4 YWOC‘DDOOZZdLDOZDsz—‘Z4O.J‘C-O<>DU)'U.J<3 ~UJOOLU_JUJLULU_J~(D(IOXUJO<OO>OLDCZPLU~<OL<J< >ZZUJCDLL XI—(Z (_IOO-Hl—ZHIUZ<DZLLZOU<I 0n- 0— 02 0-0 .J (D (D L113 U ( <t LIJ mo <1 ’— LD 2 1' O. C) b- N- (I) m U) 3 D EARNINGS. GAP (mm) TOTAL URBAN DISTRIBUTION OF coulees BY MAGNITUDE 0F PERCtNT U.S. AHWNDD(TMEEI}6. (NEH 000- 101- 201— 301- 401- 501- 601- 701- 801— 1001- 200 300 500 600 700 800 1200 100 LESS AREA laxr 1000 400 THAN 100 OOOOOOODOOOOOOOOOOOOOOOOOOOOOOOO O O C O O C. O -- O C O O. O C C O. O C O. C. O O- O. O OQQOVOOOOOOQNOOOOOONOOGOVOOON(DVD 0' ore fi‘O‘OOOOOO‘OOOOOHOOO‘OOQOOKOOOONBCOO 00000000000000000000000000000000 ”HQ-0°COOOHOOflOMOODHflNHOVOflOOOOGOO ’4 RN (DN'OODOOOOOOOLBU‘MMOOOOOOQOOOON‘OGOO ”'00-'00OOOOOOOOVF‘HOODOONU‘ONHNHQGOO N (‘01 700000OOOOOOKOHOONONQQWOONOK‘VOODD 230-0000OOOWOONWOOONONOflowONV‘DVU‘hODD N ma 00000OOOOOOOOQOOOOOFIOOKOOOQU‘VODD CO----------‘------.------O.--.. #040400OOONdOfiOU‘OHHflHflVJVQMOHKHOOHO n LhN 0000000000000000000000000000-000 QNOOOoocOfiova-OONMNNOMMNOOU‘P)ONOHO n". N mm ONHU‘U‘dcafiomON‘fimNfiOfim‘efliNMHNOVoDOG VOWHOOdOONHOfiOJJJN'NNJJLgVOHP’JNHOCOO ID“ VN‘OHHOOONHO‘OU‘OVOKOHNOQONIDOO'OVO‘OOOO ..... .-.-..............-..-..-.. NMflHv-CDOOMHONONNnNHWNmNnOflfiNNNCOO PO04 H OQ‘OONOOONV‘HNVQ’M‘ODVQOVONONNQN‘OODD 2 0! 0! 0| 0‘ 0) 04 0t 0‘ 0- 0 0.3 09 0. 0| 0! 09 0d 0» 0- 0! 03 0| 0: 0!. 0t 0 0« 0. 0a 0] 0r 0 ONOOHOOONVfidOOVJmDVNO¢mNONNNHmooo 0-0 7004 N V04 OQOOMMOOOQONQFONOQOQmQflNOOU‘BMNOOO ..... ...............-..-..... .-. 6106000dochnondonhnfindnmnoommnmcoo 0- V“‘ (\ 0’)". OQOOOONONQOVONQQOVONOVNQOOOOOOOO 0‘00.OIOI'IOIOIIIOIOI0'0.0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0U0|01 m~tonoonondommmmmommmmwdaconmmcoo 0* mm . Mrs cakewac:OCDOwdnHDancaomo¢H>Ov¢U\m1rMHn1>v(MOHACHN ”H000OOOSMNMONQVOVNONNHOOOHOMONO 0-4 MN N04 > > > 0—0 0-0 0 H D D > U LU U) D 0-0 0 (7 PC) ‘7 0 0 “ C) O > H D-ZF-U HO!- 0- 0-0— 0 —' I “.3423" >ZHZ 2 CC 0 2 Q (I) (1)-l00- LU<ULU ZLU( XX H O 7 fl Dm-H7Ym>u'u "57”le 0~<d¢ FLUCO— QC) tF-I—‘F—(CZCZJO: ("(0) O :1on ZCEZCS III-o7 d7!) ClJOUI) '1’ 70017.10) 7" mm (447 O<LUIO<LULUH>7WJI~ (z—‘OO—UJ 01144 JFK-3'“ 8.1.3.32 IMDZ< 2<mo~~lomzamhr~mmzh<>z era—ozrr'noz I!?IO~QJUU’OZZM-IDZDZO<JICD 24mm<10wwwwr—2124~~z~o—oom<— LU<< hmzz>zcru_lzzmz 0—0—023 z—OZZUJZXUIQZI (I) D LUV- .- LUI- (‘I D DU) (D (1') LULIJ H d LU 0 I7 3.- 1!.“ I TU) *- F— .- Q’ n: D O O O 7 7 (D OVER [1200 1001- 1200 801- 1000 l 701- 800 601- 700 501- 600 l “01- 500 000* 101- 201- 301- 100 200 300 “00 100 LESS THAN l APPENDIX TABLE EF6. CONTINUED c:c:c:cac»c:c>c:c:c:c:c»c:c:c>c:c:c>c>c:cac:c:c:c>c>=»cacacacacpc> .. ... .... ...-...--.O. ... ......... VON”?OOVOQ'O'OO‘OV'O'OOOOOOOOOOOOO H H N H o . H coonmoOov0HoannnnooooOOHoooooooo .. C .. ......OOCCC CC... ... ......... HOHooHmoovaonooovoooHoonoHHooooo H N HN o ‘ , H ")0 OMMONMQDOOVUOMQQMO”00060000000 NN -----.o-cuuccccucopog-.--0------- flonnofiNHnQWmON"Q'nOflO‘HOOOOOOOOOOOO '4 '0 1" O f. 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(D ‘K '.J [rt-I W" Hal-1 t r .n\ n o P“ 11 HEP. o o o o o a o o o o o o .u.o.zo»u7_xm<z o o H o H o n H H N o eH ez.4>x«z o o o o o o o o o o H N wm<z<4mo vH um no ow mv mm nv «v Hm vm oH mu >Ho uH»244»< xhaom ooH omH mcm HHH VoH HHH ocH no Ha Km «v omH onomm zhaom w m HH nH mH HH «H a m n n mH m<mz<x n HH oH mH 0 OH oH o n H m o H¢m<mmmz n «H 0H 0 9H m o n m m H v Hhqua zkncm o m oH HH a o n a n H H o HHOXHQ tzoz NH nH on o o s o m n n H m Hanowm_z o H o o vH HH mH HH nH v a a «go— H u oH mH nH v o s o v m HH «HommZZH: om cm cHH ex on hm om ow mm mH om av >~o .kzmu xbmoz Hmmz o H cH » OH H c m H n m «H szyoum_z a m m oH m m c o v n m cm 7<u_zo_z H o u H v mH m HH a HH OH H» mHOzHJHH o o H H m c HH ~H HH HH o on <7<HazH o o H N H N o m 5H m o mv c.1o H n mu RH mm cm on we em mm He mVH >Ha .szu eroz Hm<m nu ma onH Ho oOH Ho co em on Hm Ho HoH :oHuwm 44mpzmu zhmoz o o H H m m u o o H v mm HHz<>J>mzzmm o a o a a o o H H o a HH >mm1mw zuz o a H o a o H n v m oH on van» zmz o o m H m m m vH vH mH vH on >~a onhyaJH< wJQQHr o o o o o o o o o o o a Haquumzzoo o o o o o o a H m o o m oz<4mH maozm o o a o H o H o H c m w mphumsru<mw<z o o a H s H H N m o o a H7ozxw> p o o H o H H v m o H o w«_1maz<x zwz o v m H H m m o H o o o mZHHz o v m n o v m H m o n mH >Ho Q7<ngm zmz o v u v HH 0 nH Hm mm mH NH mm yoHomm Hm<mzpaoz 8mH ooNH 20H 2; 2: 23 com S: com com S: 2:: mmzu.HooH IHom nHom uHoo uHom anv uHon anm THOH taco mum; <ma< GEBV a3 moizfm zm<uzoz 4<xnm no wonwnzo<z >m munkznoo no 20.»:mHmhmHQ .fiAHmaBBvxfiflfiEm< wooaov'v-cOO-comdooucm OH nmw OOv OHO NON Ovm ONO mcmi OOH HOH OOH com HOHOH OO O O O O O O O ..OOO. O H O OHOOH: H H O O O H O m H H OH me<O< O O O ,O O O n H O O OO «HymouHJHu O O O H m n n m o O OH OOOOOO O O H n H O n v n O OH 2OH37HIO<3 H H H v n o O NH OH OH «O >OO OOOHOHO O H O H O O m H H O OH HOHOOz N O n m m m H O O O. O :4»: O O O H O H H H O n m <70~qu v H O O H m w v O O H OOOxmz zwz O O O H n O O O O O OH OOOOOJOO O O O O H O O H O O O O7OOO>O H v v H O O m O. O O c OIOOH O n v m n O O H v m OH <7<Hzor HH Hm HH nH Om nu On OH ow Hm Oc OHO z_<H2:cr OH Om OH HH om OO On O» OO cc OOH zoOOOO Hmwx OH Om HO OH HH HO mm OH HH OH NH Ov OHXOH OH O H O OH OH H O u v N O HzOIHon H m vH n v O m n v v m HH <7<HOHOOJ ON HO OH O O O O HO O H H O OHOZHOO< Ov cm OH OO O» HO HO ON ON ON OO no OHO,.H2OO :HOOO Hmwz O mm OH O O m n O O H H m HOOHOOHOOOI 0 HH OH H O O O O H n H m <y<O<H< nH Ow OH O m H O O H O n n mmmmmzzwh OH OH OH OH OH HH H O H m H OH >OOOszx On On OH O» ON ON ON mm O O O HO >OO .Hzmu OHOOO Hm<m H O O n m H O O c O O OH «OHOOHO O On On HH OH NH O OH OH O H OH «HOOOOO O v OH O o c m H n H O O HzHHcmqo :Haom O OH OO O O OH H o H O O n HzOOOOHO :Hrcz O H m v O O m H N v n Om (HzchHO Hmmz H O OH OH O ‘ O OH \wO: OH x O O OH , HHyOOIO> .\ oQH OOmH OONH OOOH OOO OOH com com OO: com com OOH 24mg mm>o IHOOH IHOO IHOO uHom uHom 1H0: «Hem uHom IHOH .uooo mmmH «mm4 QBOGHZCU.gflnuwmwarxbefimmd EARMNGS GAP (DOLLARS) PERCENT BY ROH DISTRIBUTfON or COUNTIES BY HAGNITUDE 0F APHDHHXTUELEILB. l RURAL NONFARM OWEfl'KflmL' 1200 1001- 1200 801— 1000 701- 800 601- 700 l 501- 600 JJ' 1; 401- 500 ono— 101— 201— 301— 1no 200 300 400 100 a ,. LESS THAN AREA l l | ‘ILI 1nd; 100! 100‘< 100x. 100: 10oS' 100$ 100$ 100: 100* 100‘. 1oox‘ 100$ 100$ 100* 100$, 100$ 100$ 100$ 100$ 100$! 100$ 1oox‘ ,100$: ‘100£ n O O f. .100; 100x; 100x 100x 100x. 190* OOOOOOOOOOOOONOOOOONV‘OOOU‘NONOOOO OODOOOOOOOOONOOOVOOOVFOOVOVnflNNOOO '0 OflODOOOOOODO'BGOD'VonOQOOQQO'OOO HOMOOOOOOODOK‘OOOONflQNI-Om0Nv-‘VFOOOOO N .474 VOMOOOOO'QOIBV-OVVOHOOOQ V ”N m - -- .- ”NHOOOOOHflOflnOv-OfloOONOO‘OO'OO‘ONOOVD- V) rt Ndv-C Nv-ONv-Ov-Odd '0 Hr! WOMOONOOVOOOlnv-IflmoOM‘OOv-COMOOI’JU‘NOPJO mnOOONODflOOnOmdNnOVNVVBKV v-l Ln v-‘v-Ov-OH v-OH coco-.-.....-.-o.oouccoocoooouoo N‘ONONDDOflOOnQNNQ'NOOOVH‘ONNODQO°06 “H '1 V" "I V‘ '4“ IOVOU‘OflNOOO‘DOfiNfimaOONfiONOmOQVONOOOO .- - ... C. O. I O C Q. . .-.. O. C C O .‘C O . ONNONNOOU‘“00(050NVVU‘O'OU‘NU‘OOMBBOMO '45“ “Iv-'4 1'4 {'4 '4" 1'4 deconoooomnHOHODGQHmDv-Cvomln‘Ov-OOOVOO .-....ccoo....-oocooouoouoooooou °°°°¢°°°owmmo°mnachooflVOVOBBQOVO I41”! VH N '4' f. 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O 7 a T)U:~?¥m>w() ID7O-UO- ”4‘4 PLUG.- OQ Zh—IH‘F-(CZIJCZ (”(U) C) EDD! 20:20 0-07 <7!) l)_JOlll>- I 70071”) 3 mm (447 rs<mxo<mmv—>~‘1m4u— (Z—OOI—LU OII<( .13.)" uJJZ {(0924 ZdaoHHIUQZCMthmZP<>I Iiflm'ZfZ'JIOZ RIZIO~QJUWOZI?DOZD’DZO<JIU) ZdLUUJ<IOUJLULULhD~ZIZJfi—Z~OHOCUJ<H H.344 D—LUZZ>ZKD.JZZCLZ o—‘O-‘II ZWIZWZXOIQZI (I) C) UJF- F- UJF- . 4'! C1 DUI U) «3 LUUJ - 41 w 0 I7 1' It.“ 3 III) I'- . I— F- E (r I) O O O 7. Z (I) Wag APHQKHXTNEHEIDJL.CONEEUED TUNE: 1001- OVER 1200 1200 IEBS vaq a 100 1057' 100$ 100) .100) 100‘. 100‘ _1001 100$ 100! 100‘ 100$ 100‘ 100‘ 1001 100! 100$ 100$ 100$ 100’ 100$ 100% 1:) 1:) 1:) 1:) 1‘1 11‘ 11‘ 11‘ f‘- 1:) 1)‘ 1‘1 1‘- ‘1) 1:) 11‘ 11) 1‘1 1:) 1:) 1:) v-1 1:) 1‘) 1:) urs u-O 1:) 1‘1 1') 1)‘ ‘1? 1:) 1:) 1-1 1') 11‘ ‘I'. 1‘) 1:) 1:) 1:) 0') 1‘1 ‘l"1)‘ 11‘ 1:) 1)‘ ‘1’ 1)‘ F‘- 1:) 1:) 1‘) 1:) 1K) 1" ‘1) 1:) 'I'. 11) f‘- 1:) 1‘s 1‘] ‘l’ 1:) 1‘1 1:) 1‘1 1r. 1') Ir1 1‘1 1:)*!') 11) 11‘ l1) 1‘- 1:) 1:) 1-1 1:) ‘1) 11‘ ‘1’ ‘1) ‘1) 1‘1 ‘1' f‘- 1)‘ 1:) 1-1 1!! 1:) 1:) 1‘1 ‘1) 1:) 11) 1‘) 11‘ 1)‘ 1:) 1‘1 1') 1:) f‘~ 1)‘ 1‘» 11) 1') 1:) 1") 1)‘ 1)‘ 1') 1!) l‘~ 1!) 1:) 11‘ ‘I’ 1)‘ 1‘» 1‘1 ‘1) l1) ‘1) 1') ‘3’ 11) 1‘1 1:) v-I c-i 1‘1 21‘ 1:) !‘-»:1‘ ‘1)> 1‘» 1‘1 ‘1? 21‘ 1‘1 1)‘ 1‘» P1) 1:) 11) 1)‘-11) 11) 11‘ 1‘1 11) ‘I’ 1') 1!) f‘- 1:) 1‘- 1)‘ f‘~ 1‘s ‘1) 1‘~ 1‘1 ‘1) 1;) 11) 11‘ 11) 11) 1:) ‘1’ 1‘! 1') ‘1) 1:) 1)‘ 1:) 1rd 11‘ 11) 1') 1:) 1‘: 1): 1:) 1)‘ 1F. l‘~ ‘1) 1’) 11) 11) 11‘ 1:) 1') l‘~ 1:) 11‘ 1‘1 11‘ 11‘ ‘1) ‘1) F1) 1‘: <1” I‘~ 1)‘ 11) ‘1) 1!) 11) ‘1) 1‘] F1) 1!) 1:) 1‘1 11) ‘1) 1)‘ 1‘- ‘1'11‘ 1)‘ 11‘ 1:) 11) ‘1’ 1)‘ 1)‘ 1K) 11) ‘l’ 11) 1P1 ‘1) 11‘ 11) 1') ‘1)"1) 1!) f‘- ‘1' 11‘ 1:) ‘l’ 1:) 11‘ (I) 1:) 11‘ ‘v-1 1!) 1') ‘1) 1:) 11‘ 1') 1rd 11‘ 11) v-1 “1‘ 1:) 1') 1‘~ 1') ‘1) 1‘~ 1)‘ 11‘ 1') 11‘ 1‘) 10,01 01.. 01.1 cl 0. 010'. .1 .10. . (:3 1') f‘- ‘1) ‘1) ‘1) 1‘1 11‘ «PI v-0 1:» 11‘ 1‘1 ‘1) 1‘! ‘1) ‘1) ‘1) (‘1 ‘Ir ‘1' v-I 1-1 1') 1:) 1‘1 1)‘ v-O 11‘ 1‘] 1:) 11‘ 1‘1 1!! f1) 1') 1‘1 1)‘ ‘-1 11' 1:) ‘1) 1') 11' 1‘~ 1‘1 1‘1 1r1 1‘1 1‘1 ‘1' 1:) ‘1' 1r. 11‘ 1F! ‘1) 11‘ 11‘ ‘1:) 1)‘ 1‘- F1) 11) .1 '1 C1 01 0| 3| 0| ‘1 11‘» 11’ 1:) 1:) r1) 1‘] 11) 1‘- 1‘1 1:) 11‘ ‘11’ 1:) 11‘ 1‘1 1): 1!) 11‘ 11‘ ‘rfl 11‘ ‘1) ‘1) P1) 1:)>‘1) 11‘ 11‘ ‘r: 1') P1) 1‘] 1') 1:) f‘~ 11‘ 1!) ‘l’ 1" 1‘: 1)‘ f‘~ DIV TENNESSEE ALABAWA IDAHO WYOMIVG NORTH CAROLIVA TEXAS SOUTH CAROLIVA GEORGIA FLORIDA wEST VIQGINIA 5151 SOUTH CENT. VIRGTNIA KENTUCKY MISSISSIPPI NEST SOUTH CENT. BIV1 ARKANSAS LOUISIANA OKLAHOMA MONTANA MOUNTAIN DIV NEST REGION I 1091‘ 100! 100x 100; 100* 100‘ 100* 100‘ 100* II. 1:) 1:) 1'1 100$ 100$ 11‘ 1‘1 1:) ‘l’ 1:) P1) ‘1) 1:) 1:) 1‘1 1:) 1" ‘1) f1) 1:) F1) 1:) UFO 1‘1 1:) 1:) ‘1'1:) 11‘ 11‘ 1)‘ 1:) 1)‘ (:3.“) 1:) 1:) 1:) 1‘] 1:) 1‘1 ...-00...... ‘1) 1‘1 1:) ‘1) 1:) 1:) 1:) 1:) 1:) ‘3’ 1:) CI) IF1 11‘ 1‘1 1:) l1) 1') ‘1) 1:) 1:) 1:) 1‘1 1:) 1') ‘1’ F1) 1:) 1:) ‘1) 1:) 1:) 1:) 1:) ‘11’1:) 1‘) ,.; 1F. ..c 1:) 1:) 1:) f1) 1:) ‘1) ‘1) 1:) 1:) 1:) 1:) 1F. ------u---u-' OOODOONOOOO" v4 ‘1) 11‘ <v-I 1)‘ I1) 11‘ 1)‘ 11) 1:) 1:) 1:) 11‘ 1!! ‘1) l‘~ ‘1) ‘1) 1‘! f‘- 1‘! 1:) 1:) 1:)'f‘~ 1F. 1‘) 1:) 1)‘ 1:) 1" ‘1)*‘1) 1:) 1:) 1:) 1:) 11) I1) 1:) ‘1) 1:) 1-1 1‘! 11‘ 1:) 1:) 1:) 1‘- 11‘ v-1 '-1 1)‘ 1:) 11) I1) 1') 1:) 1‘1 1:) ‘1’ O O O. ... ‘1) ‘1) 1‘: ‘1) 1:) 1') 11‘ 1!) 1:) ‘11’ 1:) 1‘» tIFi omvaoonnOOd 1‘1 ‘1) 1“ 1') 1‘) 11‘ f‘~ 1!) 11‘ 1:) 1:) E‘- 1?! hr! ‘ 1‘1 1)‘ 1-1 1:) 1') 11‘ 11‘ 1)‘ 11) l') 1:) 1‘1 ' .01... o.olo 01.10401. 1') 1‘1 f‘~ 1:) ‘1) 1‘s 1:) 1') 1r1 11) 1:) ‘1) s—1 qr! ‘-1 1)‘ 1') 1)‘ 1') l1) 1)‘ l‘~.!1‘ 1‘1 1:) ‘1' an c; I! a: up up an an an up or «- 1:) 1‘) ‘l’ ‘1) ‘1) 1F1 1“ ‘1) 1:) ‘11’1:) 11‘ 2v-v'v-c 1' 1"?“ .V (‘1 11‘ 1:) 1:) ‘1) 1rd l‘~ 11) 1‘1 11‘ (‘1 1’) l1) 1)‘ ‘1) 1‘~ 11‘ f‘~ (I) ‘l’ 1:) up! 1:) ‘1? 1') 1:) C)‘ 11) 1') 1:) 1‘1 1:) ‘1' c|o|o|030|¢|o|“0.40.0101... 111‘ 11‘ 1‘) 1:) 1:) ‘1) 1rd 1:) 1‘) 1!! 1-1 ‘1’ 1-1 f‘u 1‘! 11' 1:) 1)‘ 01) 1P1 11‘ 1!) 1:) 1r! 1:) 1:) 1-1 11‘ 11) ‘1’ ‘l’ 1:) 11‘ __————‘ T 1"?“ v4.1 " N 1“ ‘1) 1‘» ‘1) 11‘ 11‘ 11) 1F. ‘1’ 1‘- 1:) ‘1’ ...-......- ‘1' 1‘1 11‘ 1‘» 1‘1 1‘1 ‘1) ‘1) 1!) ‘1) 1:) 1)‘ 1‘J 1‘. 11) 1‘: ‘13 11‘ 1'3 1': ‘1) ‘1) ‘1' 1r1 o 2 ‘ U D "‘ O” 1'— 7 1:) )1I‘<K :i’Il) 13E _ <1 111 27' <I 1'! 37’137 1:)‘11 Ill 1!: IE: 1:) 1:) 1:) ""1:) 145. )1: U-‘ 1:) 1‘1 :1: <1 :1: 1:) "0 1’) <1 ..1 3K '-0 <1: :’*1J) 1’) .11 ..1 <1? 3! 1:) LL) 1!: IF- 11: "1 <1 1!: <1 ..l'<l 1.) :2: '<1 2:) 43!: 11.. :3: 1:) 1.) ‘<1. :1: 0.3 (J) “I 11. US TOTAL EARNINGS GAP (DOLLARS) TOTAL PERCtNT U.S. DISTRTEUTYON 0F COUNTTtS BY MAGNITUDE 0F AFBQEEXTHEEEI>£L RURAL NONFARM AREA OOOOOOOOOOOOONOOBDonBOOONNU‘H'OOO o.ogoocoooooooooo....ooouocooc-o OOOOOOODOOOOOOOOOOOOOONONNHVOOOO N ’1 '1 B“ 0000001300000“NOODQVnQ'dONnOv-CU‘OOO FOHC-OOOODOODOONHOODOOC-IOONNVVNHNOOO N N “N NNNOOOOOII‘NONNQNNNNVOONnVOVNNNONO ....-.....-............o-....... '0 N “‘1‘! QVU‘M‘U‘OOOM‘OOU‘KQOU‘U‘O O‘U‘QNOI-OO‘OOU‘OOO ' acoupoocnucuo--------OQ-OUOOO--- V V) 101‘! INC"OOVOOOOOO‘VUU‘VONNMOO°ooanwflOVO vnoonoocoooonoooamurnmonnvnovcoooo v.4 ......OOOOOOOCOOC......OOOOOOOOO Ndooocoooooorxvofimnmndvmnmvvwdocc var: VN “NO#VQOOU‘VOHNN‘ZQNOQOOOU‘VJOVfiOOOnD ooooouco-ooco coco-000.00.00.00 ”H (V Vf" NOONQOVOOHVnNNOOmNflMOMflONmnMVOVO .---....cocoon-ooaoooooooooooooo QNOVCOOOOU‘NOPOVQNU‘V”NgNVNOflNnOOOOO 1')" V“ U‘Nfl‘oomoonflU‘NO‘NNNNfiU‘NfiOOOOO‘OOMOU‘O o .1 0’ o 0. 0) c: 01.3.. .1 01 o. .1 o o o a on .1 .1 a 0* o o o co 0| 0', .0 o o HVOHHOHONNOVK‘NU‘U‘VNDQMOHHfiv-ONBOOOO H IOU-4 [OH OOOOOOOOOQDNmmOOOQQQ'VQON‘OQv-O'ONO ..... ... ..-.. .. .. ............... O‘OOOOOOOOVovoonOOflflONNflOflOHVVOC-CO V21!- “ 103'- NQO‘OONOOQOOVNNVVOOQOONOOONQM‘O‘OOO 101010IOI"0101010101OIo.o.doldoldo|u|o.o|o'Ildddoflo|dolol OHOOOHOOGOONOVU‘U‘OMVNMVOOOv-OHU‘OOOO a N 1'" CocoonnnkkONfiQ€DNNMNQMQONOInKVOMIOO VNOOOV‘IOHHVNVM;P\.11‘11‘VN®HfiOOOv-1NONOND 1H v-I MN NH > > > 'H H . ~-- 0 D > L) 1.11 m D -- o 1" ho <7 0 o (d O .D > 0-0 F—ZD—U Ho.- 1- 1—1— . ... I LL<DH >2—Z 2 CO 1.) 2 13 m (DJUP- 11141311.! ZLU< xx N O 7 a :‘hmo—7vm>wu m7~oh and“ #11105- 03 zv-I—oo—qmrnchr <~<m C) (29:)! 7.12:3 —7 d7!) 1)-lnlll>- “I 7019771]! “:1 mm (<47 0<JJIO<LULu1->-ficn_lt- (Zach—Lu OII<< .JZJ-o 1.1.1.)? 210024 z<mo~~locrz<mhhcrm2h<>1 .zr'a—oz’rmoz ther—oQJUJIOZImfrDDZDGIJI'J) 2<ww<lowwwmhzx24n~2~0-oom<~ 11.144 hmzz>zmogzzmz (DH—oz? 2—022102201023 U" 0 ml— b— 1.1!!- 4‘: D om U) 033 1.1.1111 .— <1 11.! 0 I7 t 1111 2 Im Ir- r- r- 0' rr 3 O O O 7 2 (n APHQHHXTNEEEIDéh CUMUEMED l ‘ fl 1 (NEE I1200 1001 1200 801- 1000 701— 800 601— 700 501- 600 U01- 500 301- U00 201— 300 101- 200 l 000- 100 100 IESS THU! l l BONOhNfln-GO'OOOK OONOOOQ HOVMMNO N '4 YOU-C vvmooonOe-noonnomohnovo NOIflHHOhVO‘VOflQNMNVVOOD N N H ONHNQNI’OO V - V0 VOflO‘VP‘OVv-OKOO Naonhfiov¢vvwnnaommoac '4 H DDd¢QV~OONNflQQVDWMOQQO OHVflNflmVMFOVNu-UfiNNMIDQ-Odo . H H ”NOOONU‘ONYOVNOKDM v-‘IN'ONU‘NNU‘Nv-OVOVOHVN H H "JVOVNDONODHHVDNND nnmdmmnvmmmmoavm a a #0 ammmmaomnmnmao vonmnnmnmdaov-Imno fi-vwnc»¢:huoronhfir~h4:oaMUN f6cacununr0caranuvr+ou3v4r30 ‘ ‘fl NOOONHBOU‘MOOOHDO oi oi. WWMHI0v4fl\nr¢!0c3c3cHOv4GlPHD r0 VVNVOQQVOODOVOVVO NNdOHVIflVOOv—OOVONNO of. .A .‘ v4 0: 0].! Ir- “0‘ ”ONVO htflNOD flth-OQ ONMNO H '1 OVOOM .‘ OI maniac-co r1 MOQON omwnfi (\Wr‘ ONOKOMNOOKOO 00.0.0000... ”OOOOflOOOOOO C H OOOQO'OOOVOO ...-.00....- “HOOOOOOOOOD O F. NNONNNOOONOO .-.--.a'QQQQQ NODOOOOOOOOO 0 Fl cacao------- OQOHODDDODOO D H VOQOVKVOVOOOO --.---oooooo occocflfloaooo O H HVDQOOVQODDD ......OOOOOO Guac:cuac4c>ou:c>cm: o '4 ONVOOOOVOOC'OO 0.00-.00.... dNOOONOv-Ooooo ‘C r'l nchONNNNooo cocooooooooo ”OOOOnf‘v-CHDOO o H onOmndomocc not... oval-VODo.o,oluo HNOOOVONNOv-IOO O v-O O‘NNOQQOOONO .. C O. . O . .... ”NdflOOv-IFOFOOv-IO 1'- 3: v4 DONOOV‘OOQ‘O‘ODODNNNMOO‘O OODDOQOOD‘OOO OIOIOIOIOIQOIOId-lflflfl ")HHOONMOHOONOM HR '4 . NVU‘OBNM N VIRGIVII p) NEST VI?GINIA IO . Ol'lcifld CIGIOI‘ Olold-ld-ldddd- Benton MOHflOan-OVDOO‘ (\0 v4 MHMOOQBHU‘VKNU‘ (nearerHOCMCDCHDIDCDrOcunlnvdr+ChH NORTH CAROLINA SOUTH CAROLINA GEORGIA FLORIDA EAST SOUTH CENT, 01v KENTUCKY TENNESSEE ALABAMA MISSISSIPPI v-O > DI. D O p. 2 LU ( {30374 ((1: I‘D—O I—ZU‘DI D<H< O‘CDJ waox (_ID ... U) LU I TEXAS NEST REGION N '4 MOUNTAIN DIV MONTANA IDAHO WYOMIVG C) H H mNQnNHnNMNnO 0.0.0.0....- NV‘IOHHVNNONOO 9" 0 'fi EXICO ARIZOVA HASHIVGTON OREGOV CALIFORNIA ALASKA COLORADO UTAH NEN M NEVADA HAWAII PACIFIC DIV US TOTAL o o o o o a o o a o o o .u.c.2o»u7~1m<z a o o o m ~ u a m n o «a o244>x<z o o o a o a a o o a a « mm«z<4wo om .ana ca” ov um cm ca nw ma mu m on >~o unpz‘4»< Ihaom Nan gum onm no mo us an ac an an an ¢ma zo.mmu :haom « o “a «a ca «a s m m . m m a“ m<mz<x n m o“ ca o a” n o n m n Na .zm<mmw2 « o A o v m m n m a N am .pox<a Ipaow o n o u v o v o v o N 0 2.3.3 1582 ow an ad ad «a N n m a m o m ”maomm_z v n n“ n ma mm m“ «a m m m o «:0. n m" a“ o ca 5 o m n m n m «hommzzuz wv as no on no mu v. ac cm «a «H on >~o .bzmu thoz bmwx n n o v m m ca m o n m o zumyocmuz «a «a ow o u s o o c a o v 2<w~xu_z « v n« ca 5 «a 0 ca .n« v o «A w~oz~44~ « a ea c a ma ma o N u m n ¢7<~az~ o ow ca «a ma mu 5 v m o a v onzo oa an oo mv «c an ac an ow ma v“ on >~o .pzwo thoz hmqm on ooa me” No” mo“ «na no on mm mm ow no yahomm 4<myzwu I»moz a a n m m n v“ o v o N ow «~2<>4>mzzma « a a o a a a o a v o ca >mmmmw 2wz a o o n n o m o o m m n“ van» zmz m a n o o ca a“ A“ n“ ma 5 mm >~o u_h7<J»« m4oo~x o o o o a o a o o a o m Fan—bumzzou o o o o o a o o o o o v oz<4m~ media a o a a m H a N a o a v mphmmnxu<wqu a a n a m a a m m o a o pyozzw> « o a n o o n a o a o a manzmnzcx zwz v m v m o a o o o o o o gz_<z m o o u m c m m m m m ma >.n 37<Jazm zwz s n «a ma cu ma cm mm ma «a 0 cc yonomx pmamzhmor . . oqn .ocNH coma good com com coo com oov con com as“ 241» AmEfiv.éood uaom nucm uaoo uacm uacv nae» nfiow -flofl -oco mmmg «mm< zm«u 4<max Aggy a; moz~zm<w ...o mcak~204z >m mnnkznou no 20w»:m~mhm~a .OHIQ EH. XHszmmd. Nov NNN va NNN NNN NmN NNH NNH NNH NNH NN Nov HHNNN m: N o N ‘N N N o N H H o N HH<z<I o o N o N N o o N o o N <2m<4< H 4o H H H o n N n H m on HHymoNHJHN N N H H N N N n N n N NH youuao H H m N H v m n n o N NH zNNN7~zm43 N H o v NH N NH N o m N NN >HN NHNHNHN H H a o N o N o o o o NH ¢N«>mz v H c N v n n H H H N N 1<NN N o H o o N o H o H o a <7NN_m< o v N H H N N v n H H NH ou_xmz 2N2 n N N v N v o N H n m NN na‘moJou o H o H H H H H H N v HH N7~zo>x H n NH N H N n a o H n o N:«N~ H N v H N o N v H v m vN <7<Nzoz NH vH on NH HH NN NH NH NH NH NH No >HN zHHNzacr NN NH on NH HN on ma HN NN NH mN NNH zcnowm wa1 NH NN Nn HN NH NH 0 NH N N N No NHxNN N OH NH 0 N N N N N n N N .NNIHHxN NH HH NH v N N v H H m H N ¢7<HNHNNH NN NH NH N H N N N N N H H m<m2<x¢¢ HN No NN N» NN «N NH NH NH NH NH NNH >HN..N2mu TNNNN wax Hv NH HH H N N H N N N N N _amummem_z n» NH N N N N H H H N o H <y<m<4< my oN HH N n H H o N N H o mwmmmzZNN on NH NN N N NH N v n H H N >zu3szx NNH oN om NH NH NH o N N H N o >~N .Nzwo INNNN Nw<m m c o m H N m N o m N NH «NHNNHN Nn Nv on NH N N N o N N N N «HNNNNN N NN NH N N N N N N N N N <2_Hcmau INNNN NN Nn NN o v n H H N N N N <z_HNmaN xNzNz HH NH N o v n N m H a o H «_zHNa~> NNN: NH - NH , NH NH m NH - m ;m c N , N o .HyHNI_> s NNH NNNH NNNH NNNH NNN NNN NNN Nom NN: NNm NNN NNH ZNNB mmSo -HNNH ..HNN ANN ..HNN -Hom AN: uHNm ANN -HNH .08 NE: a QEgfiHZRV.odAHmflBBVXEHfiEm< EARNINGS GAP (DOLLARS) PERCENT BY Row DISTRIBUTION OF COUNTIES BY MAGNITUDE 0F APflamflXTUElElldl. RURAL FARM Al I l l \ 801— 1001-m' MAL 1000 1200 1200 701- SM 601' NO 501' 6% 401’ SM 301- 4% 201- M0 000' 101- 200 1M LESS THAN 1% AREA I l 100x 100: 100‘ 100; 100: 100; 100$ 100x 100: 100‘ 100x 100: 100x 100‘ 100% 100$ 100x 100$ 100‘ 100; 100: 100x 100x 100; 100: 100‘ 100: 1001 100$ 100$ 100! 100‘ o.o.o-00.....-.....ooooooooooooo ”NMOOOOOHfimOmnOHfl'OVOQVNOHan‘OOOO Nd NV! nNmoHnoonnoouuonmmmnoHNNvmmovoco on...-----o-n-u-c.----pooococcouuoo0v NONONQOOMU‘ONOomQONU‘O‘ONQOWDO‘OQOOO Hf!” H flHflH whoonhomvnoonvaOVmefiOMONNKOONO .-----OOOOOOOOOC--I--.’--.-.----. NNoovo NommNNovooNNoHnoNoomvvoNN HH H H H v-lv-Av-l- H NNNNHnooamo-oNNNNchNNNHnmNONNNNN .....O... ...-..O.-.........:‘.. NNNNNNNNNNNNNNMNNNHHNNNHNHMNNNNN N '4 Hv-iv-AHNv-I fir! N w! vie-i NNooHoommNoNNnonmonHNHomNmNNNNNo ......IOCCOCOO......COOCOO .... HNNNNNNNNNNHNHNoNovNonNNNNNNNNNN H n HH n.H-H . NNOOMNOOdOOOmewmwv-AONHmflmmmnNOnO o.o-o.ooooncooo.oooooooooonoopoo OhoovOOONVOMNQVOOONOMNdmvovnfiovo d 7".“ Hr. H H H 1'" IOHOOWOODNOOdOO§NNOONVHOU‘U‘W‘OBNONO ‘.‘OQOIOACUOOOH. CIOIOVO‘O o‘moao o'ooo-~ooooovo¢oo:o‘ol0 unoov'oooozmovommwoovnmorsrxth-cmmooo '4 '0fiOOOOOIAOQNflnHfiNONnan-GOOU‘VOVVMOO .......-....-. .....-‘..........- ”MOOODONDQNOMVONnflQNNMHOflMflNNnno iv- fr'v'! (x V)!"- VflOOv-CMOOOOOOBJVHVOOOV)?COCONONU‘MOO IOIOI“OOOAOAOAOAOICid-l0!Ol‘lOlCODA-lIlddflfild‘ldil‘lfld’l VMOONQOOU‘CDOMNVOHU‘U‘ONNMNOMFOPOv-CFIHVOOD m noooonomv”ONflfimanVNnomnonv-CNOFOQO 0.0.0.000... 00...... 0.. a... NOOOOMONnNmmggvnOVHONOQ-zfiomgfl‘onl‘o (\‘CV MCCQNU‘" v4 '1“ HNHV‘AV‘I "JV > > > r-o u—o o 5" C3 C.) > D A.” m G o—o . IV Po ‘7 o o “ D C > H F‘ZFU ”OF- 5- P.- . D U) (".10.- ILI‘CLU ZLUC K! W O. 7 O 3m~7¥UI>UJU M7HU.' ”‘4‘ FHIOI- 33 tD-I'?F-(mm.1(r (~40) o ([30! 2121.5 "‘7 d7!) L)’_IOUI> T 70(577111 .3 mm ‘4‘? UCNIO<UJLUH>§§DJF (ZHOF-LU OII<< .JIJH Lb-AZ {“3024 2dmoflfiIUQZ‘wFmeZF-(>I 2<IUJLU<1CJLULULLJUJPZIZJnflzfiouoow<~ UJ<4 FLUZZ>XOCUJZZQZ 0".sz ZNZZWZXUIDZI m Q “J‘- .- LU.— (3 D um m (:3 LL'UJ u « LU ~C I? 1.. IL“ 2 I‘D .— .I- .- CY O.’ D O O O 2 2 m APHQEHXTHEEEI}JJ.(XWTDWED l l 1001- OVER TOTAL 1200 1200 801- 1000 701- 800 ' 101- 201- 301- "01- 501- 601- 200 300 400 500 600 700 000 100 1 I£3S THUG 100 VIRGIVIA NEST VIQGINIA NV) mnvomHvonnNVHnomm¢nN¢ DMOU‘v-INNOU‘OFONu-IHODVOVON at. CHOOHOHNNHOMOfiONva-Q'OV 14 fl Caesar-0mm N VdDDDD’WflDNHNN'Nv-OVKON .. ... .- . -O . momNmnanNnmmannmNm‘r H H Hr! NOODOOU‘OHMNOOMDU‘OMOQN .. MOfiOfiBNU‘Hflv-GNOOO'OU‘U‘M’OV HHNNwoonNmmeromoNoc-ONN ...-..O. -.‘O... MOHOWCflDOflNnOc-(OMMVNOV v4 deOnHBmHMOVOOVQNQwON o' .0 a! .0 .9 a! on o. o! 00 o? 0' of a, o! 0; 0| 0| . NORTH CAROLINA SOUYH CAROLlNA GEORGIA FLORIDA EAST SOUTd CENT. 0| 00 0. 0| on on on 0| 00 NDOOfiV’OOHOONHHMWmOO‘OO mnooovmowoom D HNNNnonoNNNHhoomNov-non Noooanooooovmrxnnvvbwm 100$ 100$ 100$ 100$ 100$ 100x 100$ 100$ 100$ 100$ 100$ 100$ OOMONMOOOOOH o.o..o-oooco 'OVnOU‘NOHoon -7.“ V '9 ”MDVNNOOOOOH MMOWOONOOOO") v1 v4 Inhv-OQOOQOQMOID .----.QOCQOQ ”OBMOONNHVJO¢ OHOODOfiQQOOV OWOOONU‘NV‘IODN ”MOOOOONQOOQ -----.-..¢--. ”MOMOONNHOOO v-O N ODDNDOOQDODN coco-coon... OOONDVOQOOOQ H H moonoomonooo OOOOOONU‘U‘OOU‘ vi '4 nnflVonNPOIDOO'U‘ MMNFOOMINQPOOOU‘ v-l OOOVOONOV3¢DOH 9 o- o. o .0 o- 0‘ 0| .0 up .2 on o HOOVOOONU‘IAOU‘V’ H N OWHVOMOMQOOV Q’P’JBYOOVOOQ‘QOU‘I‘) '(\ HOOOWmOQdOOQMOO‘v-IO'OOQN NMOOOOFOOQODID 0'060|0‘0!"OIOIOOOOOIOIGMOIOIfl-ldflOIIIOIO H mmoonmmoommmnddmonov-co ......O. ...... moHNNHHOOOONom N fl nvnv V > H D 0 HF— (Ll UJ mm 4 >u1 ~0m7< > ¥m<w ((2 N4 (.5 umrmfrmu-on D7 7 Dm<~hzmrm (OM fi—ZCDU)D<--<< ZD—IZ 22<TDOKDJXZHZ<O mung—mmoxwo<oo> Kh<t (_JOD—HO—I—‘X .— (92 U) m: LL) are 2 I ... m w “I HMWMNVOOV‘IVNOVJ ......OOCOOC OWV’OVOQO‘OOOOU‘ ”VOfiCVMMOVU‘v-l O 2 d O O *0 O” I'- 7 QXC >15 0: (III? (”77.04" (EEO GOO-00k!” 0 N14 IO~W< J3~<>UCDLUJ<T OLUthJH<CI<J< UZ<DZLLIOU<I 0‘ U ( CL US TOTAL EARNINGS GAP (DOLLARS) DISTRIBUTVON 0F COUNTItS BY MAGNITUDE 0'" APPENDIX TABLE D-12 . TOTAL PERCtNT U.S. RURAL FARM 501- 601- 701- 801- 1001-OVER. 600 700 300 1000 1200 1200‘ l [ I!) 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QOO'Q'MU‘U‘QU‘OBNOVODOND POv-OII‘MQHHVNHNU‘NNNKQOONO v! d Q'-°°°Q°¢¢°°° -.---------- AOOOOv-OOODDDD c H .flNNv-CmNLnfOOOOONv-ONONU‘OHO fl omDOOQmOmOOO ----0..----- HOOfiovonoooo D d QoovomNCVDOfimocndnomm ------ooouccucuuuoooo NHdHDOBMHHHHOHMOOmv-Ooo H 1". ODDOOQO'NDOOD ..-.-.ooaoo- doomomddoooo O H ncac>omuniecav>awu¢\naonanc>caéunv- ......C.............. vmmonomvooaooonvaomwo v4 FOOMDF~Owo¢DrHNCD¢m3 oo...o‘.o.o,oo Foowavicnncurhacabm: ‘C‘ r. N'OOOQQQNOOO 00.0.0-0...- ANOOOVHdv-Cooo c 0'. GDcHOcahun¢3Hr0~o<>nuocucac30JOv4hno twaaauaviunnraauacahwpoacunroahflsdcr ' a COOOONNVOONOOOOOOQVOO finocn¢¢NODfiOOOnONNNOO d (nircuncarOVHo'tcaéu: |-;¢mo.ovolo!ovotolpo ONODONNHNOOO O NQOOOOQVOOONOGIMOIDDOQQ o abouolc|fluuoaolotoedoQfloiolc.o|.to MOOOflVVNfiOONOOOMNOOVO H Hr! H Omacnoc30Ha¢Scacuac> ............ nunvrficavwocvvmovdo :c rc (Ic>cu=o~Ouacaauac3v1>o~0d\u\owhcwo ..... ......... ....... dOOOflVddOOOOdVNOBNMdv-O 11H .rlr VOOOV'U‘VNNOOMNNU‘VVNOMNONOOONV'OGOOO oyo|o|o|o|d0|o|0|“find-l“0|dolo'dolfiflo.ol0|o|o|o|o|0140 NOOONMNHdOOU‘v-OHVOOOHU‘H?Inv-OOODQNOIBOOO '4 ' “ON C H swoomooommVO ......O ..... #NfiONLnNNCDv-loo H a H NQNHOV can... OWOanCDN V4MIV NOOV‘OO‘MONV Ninth w) ”OOOOndI-‘OOONOHH O DIV WEST VIQGINIA NORTH CAROLINA SOUTH CAROLINA :Kv GEORGIA FLORIDA EAST SOUTH CENT. MISSISSIPpl dEST SOUTH CENT. DIV NEW MEXICO ARIZOVA UTAH HASHIVGTON VIKGIVIA TENNESSEE ALABANA ARKANSAS LOUISIANA OKLAHOMA YEXAs MONTANA IDAHO NYOMlVG COLORADO NEVADA OREGOV CALIFORNIA ALASKA HANAI! KENfU PACIFIC Dlv NOUVTAIN DIV AEST REGION US TOTAL a o a a a o H . o o H a o o o o «H av 90 ov me no mnH new nNH BHH a w o o 0H m o mH 0H s c OH 0H m h o m o o 0 HH mH AN 0H m o a m 5 HH H o o HH 5 oH ac cm as no a H o n o a m m n n o o m n n a o H o N a o H o N o n Hm o om oH. no HoH um mm a o o n n a o o o o o o H o o a o H n n a o o a o a o o H a o o H o H o o H H N a o a H H o v n m o o v m n v o v o m s a a o o o o v to H H a a o a o Hm mm o« o. ”N nHH HNH mOH on Am HH HH 0 HH m NH o A .A m c N m n H o o H H v H OH H v c HH AH NH a HH 9 «H H a HH no oo «v «H n» OH H o H c H m o o H OH 0 m HH HH 0 0 OH oH mH m n . HH A an on n» Av ac no no HA on nu c o m o o o o H H H H N v o a a HH 9H 0H mH o o o o H o H a o o o N o H n m w m H H o H N o v m n H H a v o m n o 0 am 0H 0H ”N mm H K‘Hfl o~vc:reauawaownaaowshuncaomu~3¢<>«uncanwnnnnaoo.4rua fl 1w '5 H .u.o.zoposzm<z m.oomH oomH 2:: com S: H can cam co on» com ocfi loco mH c2¢4>m<z N mm¢z<4mo co >Hc uHAzHJH< xHaom smH onomm Ipaom «H m<mz<x o «zm<anmz m .pcxHa zpacm o .poxHo zhzoz m HanommHz nH «on NH <Hommzsz He >Ha .pzwu xHaaz Hmwz mH szyoume mm 7<meon on manHJHH um ¢7<HazH me ono HcH >Ha .Hzmu yhmoz pmHm mum 7oHomm 4<mhzwu thoz Hm HH2<>J>mzzm¢ mH >mmmmu zmz «m ran» zwz as >_o OHH7<4b< mJQoHr u Ase—Huwzzoo o cz<JmH moozm o m»»mmnrn<mm<z o byozxm> o mnHzmnzax zmz o mzHHz nH >Ha 37<Hazm rmz cm yoHomr quwrhmoz QOH 241» mme «ua< “5&6 .33 How , -HS , .30 $54.88 28 moizmqm uaom uacv saon uaom naoa zm<nzoz Jqu» no 23:23: E 3:238 3 20232529 .25 as figfi Lllllifi -- :-.H Oo noH an NNN NNN nvN NNN mmN vNN NoH ocH HNo JHNON m3 O O . O O O - O O O O H N N . HH<341 H H H O O O H O N H H oH «xm<4< O O O O O O O H O v N Nv «HszLHJHO O O O O H N N v n m m 0H yooumo H O O H H H n N m m m mH ZONO7HIw<z N H H H N a o N OH OH OH co. >HO O_LHOHN O O H O H O O H N O O HH Ho<>mz H N v n N N H O H N N O x<Na O O O O O O H O O v N N «yoNHm4 H H H N N H v v N n O OH oqumz 2N2 n m o H N o o o c n N «H OOHNOJOO O O O O N O N H N N N NH O7on>z O N O v N m o N n o N N oz<o~ o o n n m o v o o o N HH <7<Nzoz m OH mH nH oH Om vN NN oN NN NH NN >Ho ZHHNznoz N HH oH vH NH nN On mm on vv mm oNH onumm Nmmz 0 NH ow oN ON oN HN mH oH NH vH ov w<me o o v N N N nH o N N n o HtNIHJxo O n . nH n m v o v v n n oH <7<Hmuacq mH mH NN c N H o H O H N H m<m24z1< NN on oN cm on N» ow oN On vN NN NN >~O,.Nzwo xNaom wa1 N NH oN NH N n n m N N O H HamOmmHmmHz n N NH OH H o n N O N O N <y<O<H< OH nH NN o o n o o n N v n mmmmmzzmh HH O vH NH NH HH O OH HH v c «H >xOOH2wz 0N Nv oN nv an oN nN On oH OH o mN >Oo .szu TNnom Nm<m O N m n m N HH O N v HH O «OHOOHL N On on 0H NH nH o o VH v H N «Hoaomo O N mH N v n o N N N O O HZOJONHU INaow . O .N NH O NH. O HH N O N O HzHHOmHO Ihxcz O O. m N n NH H N N o N ON (_ZOONH> Hmmz H 0 HH HH - N N ”H \ NH NH o OH NO .O7HO1_> oofl coma OONH OOOH cow CON com com 00: com com 00H z¢me muse IHOOH sHow aHON uHom cHom -Hoz uHom uHom naoa ..ooo mmmH <mm< QagnHZHv.M¢AHMHEEONw§mEm< EARNINGS GAP (DOILARS) PERCENT BY now APPENDIX TABLE D-lll. DISTRIBUTiON or COUNTItS BY MAGNITUDE OF TOTAL NONFAPH lmonu.. [ (“EB 1200 l‘ 1001' 1200 801- 1000 l 701- 800 501- 601- 600 700 -.I" ' 201- 301- 401- 300 400 500 101- 200 000' 100 LESS THAN 100 - \ AREA uuuuuuuuunuunuuuuuuuuuuuuuuuuuuu OOOOOOOOOOODODDOOOOODOOOOOOOOODO COCOOOOOOOOOOOOOOOOOOOOOOOOOOOOO flflflflflflfififlflflHfiHfififlHHHflHHHHfififlHv-lflfl OOOOOODODOOONOOOOOOOHONDONOOF‘OOO OOOOOOOOOOOOV‘IODOOOONHOQDonovmaoc GOODOOOOOOOOHNOOO'VU‘OOOOOOOMVONO HomeOOOOOOOOVOOOONV‘I‘OOOMOVOHOQO¢O N '4 r! h4hfl\cndc:c>ow:caouncurcomaovo(“Ire ------u----ooo-.---oo ”NNONOOONOOVQNOONFOVNN fiH N H". 7.1 2 N) .-.-o.ouooooooooo-oo Vomcvooondooommoowvo V" H v4?! 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CDO‘OOONONNv-llnHHOVOU‘DIDOPOMNON'OOVOIDVONO POH V QVMQHNMU‘NMMN rid “flu-000° H > > > an '- o o— D C) > U m m G 0-0 0 0' Po ‘7 I o C‘ O C: > t-0 fi—Zb-U HOO- |-- l—O— - - 51 quDF" >ZH2 2 00 U ' 2 C3 (0 (0.10!- LU<OLU ZUJ( X! N O 7 a TIUP~7Y¢D>|UD ((37-00- O-dl‘l‘ Fulfil}.- OD throat-((20:41 (H40) 0 EDD! 2320 on? 47!.) ()JOLU>- 3’ 700,710) '.‘1 (DU) ((47 0(LflIO4LULUO->-')U).Jfi- (ZHOO-LU 0:144 _JIJF" LUJZ 20,924 ZdCZONMIULIZ<UDPO~CIUJZIP<>I ‘ZL'J—t'lmeZ 232x0~240m921mx3m20<41rm 2<LULU<IOLUUJUJthZIZJth—O—‘chfl—t LU<¢ FLUZZ>ZCZUJZZQ2 0H.-022 ZHZZU’ZXQIDZI U) D 1110- ‘- wu— (3 D DUI (I) ' DVD was ~ <1 UJ O 1'37 1’ ILL! I It!) .— I— l— (r (I :3 O C) O APPENDIX TABIE D—lll. CON'I‘DIUED OVER TOTfiL 1200 “01— 500 I 1001- 1200 801- 1000 701- 800 601— 700 501- 600 301- MOO 201- 300 101- 200 000' 100 100 THAN LESS WOONNHnnvvmmnnoooHoom neondmoomoomnhaomnndo 0MBVQMVOMONOOOONOBOOQ QNOVONOOONOO vnonaawoocom OOOOVONNU‘U‘U‘NOOQVOQDOO ..-ocooooooocoocuooco OOVOVOBOOVNU‘OONNHV'OOO '4 N ONOOOOOOONON NnOOOOOOOVOO DOOMO‘OO‘IABO‘DVOORNNM‘OOIDO VTOQVQnNNnHFOQOVv-GVNFOOVO '0 V". fldN N H mfiO‘OO‘OONNOflml‘onNNOVnO OOmNDQnOOOONOO. o-.----.---o---o----o------.--.0- QOVNQNOfinDNOOOU‘an‘mO conOMOOOOOVov-C NM“ NHNNndNN '0 fl 7. ~OMOMO~OODOOOV F'ODDOONOOOON vi DWNVOVv-IOOVVNIDVO‘N'OVIDOO H Hr! 70H NMOODNOQOOOH MOOOOv-ONNOOON VU‘OBBOflDMOO‘nnmv-OOHOQIBM K‘MCDCDONOOOOOQOBONVMQVQ H .4 “NOODQOODDOID OMOOOv-ONU‘OOOB VQDbVONNNVNDDOfiONNflVD mflnOOOggnanHOONU‘hOv-OO NH fl fir. COCONNMNmmNQOVfiMQOOOM MOdVO§N~OONOB a... no. O .0. ...... o... .0...- OHQOIDVOOOOV.MOOJO.QOQBDQONhnérghmoVow '1 v" .U" fl '4 mooonnddhoom O‘NOOOVU‘HV‘OON '4 H )Mv-OU‘U‘NCQOOOlnv-IONU‘NOU‘MV rid H r! 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(:0 4 0- Lu ‘1 1’ CL 0 t— 9— m w (I) ‘3 3 EARNlNGS GAP (IDLLARS) TOTAL DISTRIBUTION OF COUNTIES RY MAGNITUDE 0F PERCtNT U.S. APH3HHXTHEEEI}45. TOTAL NONFARM l OWE! 1203 000' 101' 201- 301- 4UI- 501- 601- 701- 801- 1001- 200 300 400 500 600 700 800 1000 1200 100 LESS THAN 100 AREA i OOOOOOOOOOOOOOOOOOOOHOflDVF’ONnOOO o...-.....0000...coco-cocoooccoo N N '4 NH f.flflOOOODDODQnQOOOOMKOOQONV‘OOVOMO . C. . .... .. .... C. .. ... C. . ..-...... NNNOODOODOOONflOOOHOOOONNU‘VHOU‘OOO N ON 0 onoonnoonnoooonnnnto'onmonovmdooo r.v-OOOOODOOOOOQU‘OOHfiNZNv-CNFIVVNU‘VOOO VON VNOVVDVOnOOMmOOOn”WONONVVOODODOO ------o--.----.----------------- ”NOD:OOOHOOfiNnOOflHannanomnoooo '0 "O “‘1'. HQOVOVOONJOOIOVOOOMv-OOVOflOU‘Ov-‘IV'IOU‘QOVO YOHOOOOOOHDOHBOOOv-OVONQFOVnVnnNHOOOD thud sonooooooccoonnvuouusnohoononooo O0.0.0..........OOOOOOCOOOOOOOOO ndooooooNOOdovoNvNvmnvmndvvonooo Vrl VN nnthhVOONOMMOflNNQOMVNNNBOv-QVHOBO O O O O O O O. C O .- 0 O. C I O O O C O O O C ... 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VOmOOHQmMVmVHdnmodeOOOOHODOOOOOO d a o u ‘ H 88 «nmommmnoomwumaooomooQuoovovvoooo ‘0“ o-oocooccucouoo'ooocoo0-9-0-0---.- munthvdenhnNnmthoooooooooocooo d a o a SonNflOOONN‘NHVOON'NVIDOV'laooNtloaooo cocoa-o.ocououooooooc-.....ooooo- NVthNOVfinfinOv-INQOQNNONooccdoooooo V". H O ‘ f. mVonnOVonHflQNNOBOOmNBNDVVONHBOVOO ......O....‘... ....-C'..-....... VONnNVONndHONNVNJOv-CNONdOOONv-COOOOD n ’ v4 HOBOQVonVOddVNOVOOOOVOBDOVOOKVOOD mocnnnNtnnNHOdNovHMMaNdaoonOdoooo d 301- “01" 501- “00 500 600 ‘ «a o ' _ H HO vowonowonoo¢oofimvonnomoo¢omwnoooo 89‘ O! In CI .0 a! 0! 0| 0| 0. o o. .9 q o. 0. cl .0 00 .5 0| 0! I 0| at M of on o .1 00 u o . MOMOOONVflOOnOflMQMQNflOdOOOOVNHOOOO . fi 88 OOOOOOdcooonmOandocmmOOOdmmcmmc HN ‘ O C O C O O . O O O O O O O O O O O O O - O C . C C C C O C C . nnOdNNmNflddNOfidONVVHdflflNdOONNNOOO m AM m H ONNOOMBVVOODNOOMNdNNNHONNONOQHONO “OlddfiidOId‘ldqOD‘VOICIOIO.d‘IOIO|‘-|OCOO0|40ICO“Old. Mafiooovmmconddfioooafidvoaaoon«VOdo 7. NH ‘v‘ C) 1'4 om- 1m HOVOONBdVOflBflVODmNOOOHWOMOMNVOVNO mnDOdflnNOfiOOONDBONfiv-OH NHHF‘IV‘VNNNNOO r! m$ mm 1m N" 00 d‘ H > > O-i H <14 0 Q <22 Fit-0." O O 244 h wk ~00 2 a2 0 z < cam m m mm 4 o o — «a<« u>m ~0m7¢ > o— h 7 ~~ou<< zm<m «(z ~¢ max< >9 m 7> uozumrmIm—o or 7cm? ¢~7zo<~ H 113~»3m<—hzm1m <o~mzo nououx— whbhmmnhzmm34~<< thzo N14 zo~m< rm1300022<m0134xz~z<o43~<>omm4<z ~woow4www4~wmoxmo<on>owmhwu<m<4< >zzmou ¥h<t <40»—»z~302<:zmzoo<x b h oz n J m m m: u 4 c m mo 4 b m z t a o p h m w m 2 3 APPENDIX E RESULTS OF SIMPLE CORRELATION ANALYSIS 3814 \PPENDIX TABLE E.1 RESULTS OF SIMPLE LEAST SQUARES CORRELATION ANALYSIS )F COUNTY LABOR MALADJUSTMENTS, BY TYPE OF RESIDENCE CORRELATED, U.S.1960. INDEPENDENT VARIABLES , DEPENDENT VARIABLES Rura1 Rura1 Trban —T(Tta1 Tota1 Farm Nonfarm ' Nonfarm 'SijJe COPVGIEIIORMFQSIIjEISPIé Rura1 Farm 1.0000 0.63087 70.2910 0.4339 0.5772 Rura1 Nonfarm 0.6308 1.0000 0.4806 0.7115 0.6849 Urban 0.2910 0. 4806 1.0000 0.9287 0. 8876 11Ita1 Nonfarm 0.4339 0.7115 0.9287 1.0000 0.9487 Tota1 0. 5772 0.6849 0. 8876 0. 9487 1.0000 Simp1e L.S. S1ope (B) Coefficientg thra1 Farm 1.0000 0.8280 0.5310 0.7157 0.9195 Rura1 Nonfarm 0.4805 1.0000 0.6927 0.8941 0.8311 Urban 0.1595 0.3335 1.0000 0.9335 0.8688 'Tota] Nonfarm 0.2630 0.5662 0.9239 1.0000 0.9162 'TotaJ 0.3623 0.5644 0.9068 0.9823 1.0000 Std. Error of Sim 'e L.S. (0) Rura1 Farm 0.0000 0.0183 0.0379 70.0266 0.0233 Rura1 Nonfarm 0.0106 0.0000 0.0275 0.0158 0.0159 Urban 0.0114 0.0132 0.0000 0.0081 0.0098 Tota1 Nonfarm 0.0098 0.0100 0.0080 0.0000 0.0055 Tota1 0.0092 0.0108 0.0102 0.0059 0.0000 Simp1e L.S. StdA.__Error of Esti.m_a_t.e_ Rura1 Farm 0.00 665.04 812.30 106:17' 699.86 Rura1 Nonfarm 506.63 0.00 588.13 458.76 475.73 Urban ~ 445.20 408.07 0.00 172.57 214.37 Tota1 Nonfarm 468.12 365.08 171.68 0.00 164.31 Tota1 439.34 392.02 219.06 170.15 0.00 F Va1ue fg£_Shmplg_L:S. (B) Rura1 Farm Large‘ 2056.27 95.99 TERIWRT 1554.85 Rura1 Nonfarm 2056.27 Large* 636.27 3191.12 2749.33 Urban 195.99 636.27 Large*‘ 13282.19 7862.30 Tota1 Nonfarm 721.70 3191.12 13282.19 Large‘ 28003.26 Tota1 1554.85 2749.33 7862.30 28003.26 Large“‘ ‘ Significance of F Va1ue§ Rura1 Farm " <0.0005 <0.0005 <0.0005 <0.0005 <0.0005' Rura1 Nonfarm <0.0005 <0.0005 30.0005 <0.0005 <0.0005 Urban <0 .0005 <0.0005 < . 0005 <0.0005 <0.0005 Tota1 Nonfarm <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 Tota1 <0.0005 <0.0005 <0.0005 <0.0005 <0 . 0005 *Extremer 1arge va1ues were obtained when a variab1e was corre1ated with itse1f. and hence, did not appear on the computer printout. \PPENDIX TABLE E.2 RESULTS OF SIMPLE LEAST SQUARES CORRELATION ANALYSIS )F LABOR MALADJUSTMENTS, BY TYPE OF RESIDENCE AREA CORRELATED, FOR MAJOR REGIONS, 1960. *Variab1es STAIISTIC COMPUTED Corre1ated and "Y" STope Simfi1e F’ 0.F. Sig. of Region Intercept Coef. (B) Corre1. Stat. F 1) Tota1 vs. Rura1 Farm Northeast - 41.11 0.5383 0.6293 125.26 191 <0.0005 Northcentra1 - 46.59 0.7351 0.7182 530.53 498 <0.0005 South 123.39 0.6123 0.7827 665.59 421 <0.0005 cht 89.92 0.4331 0.6379 252.52 ' 368 <0.0005 2) Rura1 Farm vs. Tota1 Northeast 240.13 0.7357 0.6293 125.26 191 <0.0005 Northcentra1 329.21 0.7017 0.7182 530.53 498 <0.0005 South 169.73 1.0004 0.7827 665.59 421 <0.0005 Nest 7.12 0.9397 0.6379 '252.52 368 <0.0005 3) Urban vs. Rura1 Farm Northeast 12.00 0.4101 0.4447 43.40 176 <0.0005 Northcentra1 -172.00 0.5941 0.5049 120.11 351 <0.0005 South 119.97 0.3612 0.4262 62.81 283 <0.0005 west 53.32 0.2197 0.3201 27.18 238 <0.0005 4) Rura1 Farm vs. Urban ‘“_Northeast 249.75 0.4823 0.4447 43.40 176 ,<0.0005 Northcentra1 506.51 0.4292 0.5049 120.11 351 <0.0005 South 541.53 0.5028 0.4262 62.81 283 <0.0005 West 47.88 0.4665 0.3201 27.18 238 <0.0005 :5) Tota1 vs. Rura1 Nonfarm Northeast 89.84 0.8154 0.9301 1250.04 195 <0.0005 Northcentra1 61.12 0.7782 0,8806 1719.90 498 <0.0005 South 25.95 0.9396 0.8997 1903.83 448 <0.0005 Nest -37.64 0.8684 0.8766 1324.18 399 <0.0005 I6) Rura1 Nonfarm vs. Tota1 ' "Northeast -85.12. 1.0610 0.9301 1250.04 195 <0.0005 Northcentra1 32.43 0.9964 0.8806 1719.90 498 <0.0005 South 95.94 0.8615 0.8997 1903.83 448 <0.0005 West 74.02 0.8849 0.8766 1324.18 399 <0.0005 (7) Urban vs. Rura1 Nonfarm Northeast 126.45 0.6955 0.7249 199.29 180 <0.0005 Northcentra1 12.83 0.6459 0.7367 417.70 352 <0.0005 South 3.01 0.6970 0.7561 393.81 295 <0.0005 west -18.84 0.6142 0.6166 155.80 254 <0.0005 (8) Rura1 Nonfarm vs. Urban IINortheast -81.41 0.7555 0.7249 199.29 180 <0.0005 Northcentra1 128.80 0.8401 0.7367 417.70 352 <0.0005 South 213.64 0.8203 0.7561 393.81 295 <0.0005 West 87.76 0.6190 0.6166 155.80 254 <0.0005 M11111311111@11111111111111111111ES