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' P" 33%.; . , ‘ 1‘ . . .. - l ' ‘ ‘ r 1' m ' ~ . ‘ |' ' Mr. ‘1 ' . ‘. ‘ I? 'k I A," U 1,412"; . ’. v‘ 4 I ‘ ' NJ Iii-I7f I MICHISGAN STETA IIIIIIIII IIIlIIIIIIIIIIIIIIIIIIIIIIIIIIIIII 31293 00900 8933 IIIII This is to certify that the dissertation entitled _ _. . Adaptation to Change in Number of Industrial bstabllshmenw The Interaction Between Labor Force Movement and Environment presented by Carole Elaine Rankin has been accepted towards fulfillment of the requirements for Ph.D. Sociology degree in 1711/ [“945 46/ ml»? 6' Major professor Datefi/é' Ll; /6/76/l/ MS U i: an Affirmative Action/Equal Opportunity Institution 0-12771 LIBRARY Michigan State University I" J PLACE IN RETURN BOX to remove We checkout from your record. TO AVOID FINES return on or before due due. DATE DUE DATE DUE DATE DUE it”; I. 2 was] :FALJ ._ I 51:3?) ‘ A __J —_I MSU Is An Affirmative ActIon/Equal Opportunity Institution chG-pt QDQPTQTION TO CHANGE IN NUMBER OF INDUSTRIQL ESTRBLISHMENTS: THE INTERQCTION BETHEEN LQBOR FORCE MOVEMENT 9ND ENVIRONMENT By' Carole Elaine Rankin Q DISSERTATION Submitted to Michigan State University . in partial fullfillnent of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Sociology 1991 \ k. f \\ I‘rj w a 5\ ABSTRACT ADAPTATION TO CHANGE IN NUMBER OF INDUSTRIAL ESTABLISHMENTS: THE INTERACTION BETWEEN LABOR FORCE MOVEMENT AND ENVIRONMENT IN Carole E. Rankin Human Ecology theory and industrial establishments and interstate migration data are used to study the interaction between social structure and social behavior. Social structure is operationalized as number of industrial establishments and, implicitly, the resultant labor market. Social behaVior is operationalized as inter-state migration. This dissertation examines the relationship between out- versus-in migration and changes in the type of industry on a state-by-state basis for the entire United States for migrants age 21 to 29 and age 30 to 59.. Responsiveness to change in number of industrial establishments depends on age, occupation, and industry. Factors influencing immigration are not the mirror image of those that influence emigration. Emigration is influenced by comparison of the origin to its former condition for migrants age 30 to 59; immigrantion for age 30 to S9 is based on comparison among destinations. The behavior of migrants age 81 to 89 is complex and depends more on their occupations rather than their industries. Copyright by CAROLE ELAINE RANKIN 1991 iii Dedication This 15 dedicated to my daughter who was my reason for keeping on when I was tired. It is also dedicated to all those who made it possible and to the rest who made it necessary. Acknowledgements I want to thank the members of my committees: Pre-comprehensives: Tom Conner Stan Kaplowitz Bill Faunce Harry Schwarzweller Post-comprehensives: Craig Harris Harry Perlstadt Chris Vanderpool Jay Artis. The idea for this project was developed with the inspiration and advice of Craig Harris. His tireless listing and critical feedback were essential. But, very warm thanks and deep gratitude go to Harry Perlstadt who jumped in at the last minute and shepherded it through the tedium and drudgery of final editing and defense. TABLE OF CONTENTS List of Tables Introduction Literature Review Data and Methods Procedure and Analysis Results 70 Conclusions and Discussion References Appendix A Industry Crosstabulation Cell Values for Each State Using Migrants Age 20 to 29. Appendix 8 Industry Crosstabulation Cell Values for Each State Using Migrants Age 30 to 59. Appendix C Occupation Crosstabulation Cell Values for Each State Using Migrants 20 to 29 Years Old. Appendix D Occupation Crosstabulation Cell Values for Each State Using Migrants 30 to 59 Years Old. vi vii 14 47 65 97 107 111 122 133 144 LIST OF TABLES Table 1 Strategies Optimal for Populations With Particular Types of Occupations in Coarse Versus Fine Grained Environments Table 8 Identification of Desired Sample Table 3 Number of In- and Out-migrants per State (Ages 21 to 59) Table 4 Sex Table 5 Education, Highest Grade Completed as of 1980 Table 6 Age Table 7 Marital Status Table 8 Alabama Movers Age 81 to 29 Table 9 Chi-square Statistics for Migrant Industry by Movement Direction by State for age 21 to 29 Table 10 Results of Chi-square Analyses for Movement Direction by Migrant Industry by State for age 30 to 59 Table 11 Chi-square Statistics for Migrant Occupation by Movement Direction by State for age 21 to 29 vii 31 54 58-59 62 62 62 '53 72 76 77 78 Table 12 Chi-square Statistics for Movement Direction by Migrant Occupation by State for age 30 to 59 79 Sample Table 1 81 Sample Table 2 81 Table 13 Number of States, Average Number of Migrants, and ANOVA Results by Establishments Growth Category and Migrant Industry and Type, Age 21 to 19 84 Table 14 Number of States, Average Number of Migrants, and ANOVA Results by Establishments Growth Category and Migrant Industry and Type, for Migrants Age 30 to 59 86 Table 15 Correlations of In- and Out-migrants by Industry with Percent Change in Number of Establishments by Industry for Migrants 21 to 29 Years 91 Table 16 Correlations of Number of In- and Out-migrants by Industry with Percent Change in Number of Establishments by Industry, for Age 30 to 59 94 Table 17 Comparing Results for In- and Out-migrants. Only Statistically Significant Results are Included. 95 viii “Change is the nursery of musicke, joy, life, and eternity.” (Donne, (c) 1600). In 1960, the major employer on the British Isle of Sheppey closed down (Pahl,1984). After 1960, at least 1400 new private sector jobs were created on Sheppey. In spite of the new jobs, the unemployment rate rose to twenty percent. No increase in out-migration from the island followed this increase in unemployment, although theorists like Hawley (1950) and Greenwood (1975) would predict that out—migration would increase following such an apparent decline in employment opportunities. On the contrary, forty percent of the island’s current population moved to Sheppey after 1960. Intutitively, a location with steadily rising unemployment shouldn’t be very attractive to anyone. Why did the outsiders come? ‘Nhy didn’t the islanders leave? why did the unemployment rate rise after the new jobs came to the island? In the light of the Sheppey experience, it doesn't make sense to explain migration primarily as a response to number of jobs without considering the socioeconomic characteristics of those jobs. I propose that the crucial issue confronting a population for whom the industrial structure is changing is not the mere number of jobs gained or lost, but rather the kinds of jobs gained or lost. An influx of engineering jobs does not help a population composed primarily of manual laborers. Although this kind of change is not limited to Sheppey, it is useful and convenient to use Sheppey as an example. In his Divisions of Labour (1984), Pahl has examined in detail the nature and organization of work on this island from its early history through 1984. This includes: accounts of number and type of workplace establishments, number of employees used, rate of pay, and skill level required of employees. Sheppey has gone through major changes since 1960. These changes have resulted in a complete re-structuring of the labor market demands and industrial diversity on the island. Since Sheppey is an island and thus has the advantage of clear geographic boundaries , it can serve as a convenient example of the challenge of adapting to a changing environment. Sheppey Island In the mouth of the Thames River, just off the southeast coast of England, lies Sheppey Island. The first bridge 3 between the mainland and the island was built in 1860. The toll was a penny each way. The inhabitants rarely left the island. They worked on the island and had their own schools and social service organizations. They simply had very little reason to go anywhere else. Prior to 1960, virtually the only employer on Sheppey was the British Royal Naval Dockyard. The dockyard provided high wages, stable employment, and vocational training for the island's youth by way of apprenticeships as shipwrights. Shipwrights are carpenters who specialize in the construction and maintenance of ships. Shipwrights were treated by the dockyard as 'general constructors’ of the ships. Pahl states that this occupational classification has no parallel in private industry. Jobs tended to be handed down within families, that is, you got in at the dockyard because your father, brother, or another relative worked there. Sheppey has always had a small tourism industry. Unfortunately, the number of tourists began to decline in the late 1970’s. Tourists camped out in caravans (these seem to be some sort of mobile home) along the northwestern shore of the island. People not employed at the dockyard, particularly teenagers or school dropouts, sometimes took seasonal work selling things to tourists. Although respectable married women did not work for wages outside the 4 home, some of them did rent out rooms to tourists. No special skills were required to enter the job market. In 1960, the admiralty (Royal Naval) dockyard closed. The closing of the dockyard put 'more than 700 dockyard workers’ out of work (Pahl, 1984, pg. 169). Although the port of Sheppey is still open and used, it is no longer a major ship building and repair area. It is merely a transition point for a Japanese car importer (Toyota) or for conventional ships unloading produce for the London markets. Toyota has an auto import staging area on the island, that is, it's not a factory. They use the island more as a sort of open—air warehouse. The type of occupation employable at the dockyard changed from shipwright to stevedore. A stevedore is a person who loads and unloads goods from ships. It is an occupation which requires physical strength and few specific skills. During the late sixties and early seventies the number of dockyard workers (stevedores) increased from 360 to 380. In the 1960's, a local chapter of the stevedores union was formed. After 1960, new industries and employers did come to the Island. Between 1961 and 1975 Abbott Laboratories, a steel mill, a steel rolling mill and Toyota all brought new jobs to the island. The steel mill and Abbott Laboratories together had brought in about 1400 new jobs by 1983. The steel mill specializes in processing scrap iron into steel S rods. In spite of new establishments, the unemployment rate rose steadily to twenty percent in 1983. The island’s 1984 population was about 33,000. This means about 6,600 people were unemployed in 1983, assuming that the population level did not change significantly from 1983 to 1984. This is a very rough number because the 33,000 includes all of the population rather than just the adult population. Not all of the present inhabitants of Sheppey were born there. Two-fifths (40%) of all households on Sheppey have come to the Island since 1960. The new people came after the dockyard closed and with the new industries. The original inhabitants did not have the skills needed by the new industrial establishments. Ninety percent of the skilled jobs in the steel mill were filled by people from off the island. In fact, the steel rolling mill (a different mill from the one that makes steel rods) is owned by Italians and employs skilled Italians, not the local islanders. In the past few people commuted off the Island to work. Now, about twenty-five to thirty percent do. In the past, married women did not work outside the home. Now, women working is not considered surprising. It was not just the jobs that changed on Sheppey, but rather the nature of work itself. Previously, it had been the national goverment or private British firms that provided major employment on Sheppey. Now, the major employers are 6 multi-national firms who treat their employees very differently than had been the custom of the former British employers. The steel mill that was established in 1972 employed ’more than 800 workers’ eight years later (Pahl, 1984, p. 170). This was a Canadian based firm. In this mill, ’ninety per cent of the most skilled workers’ came from off the island (Pahl, 1984, p. 170). The new multi-nationals demanded acceptance of discipline and control of the employees by the employer. These firms laid people off at will. The old firms had adjusted hours to suit the needs of the workforce and had cutback the number of hours per employee rather than laying people off in slack periods. Previously, employers had provided job security (if not high wages) and had fostered individualistic attitudes in workers. The shipwrights could even sometimes do private jobs on the government’s time with the government’s tools and materials. They were also allowed to take scrap lumber home without charge, although they were limited at any one time to the amount they could carry untied under one arm. This is similar to the working arrangments Gouldner found in the Gypsum Plant before the management change (Gouldner, 1954). There are not many large employers on Sheppey. In 1981, of thirty-nine manufacturing enterprises only fifteen employed more than fifty workers, ten manufacturers employed between 7 twenty-one and fifty workers, and fourteen manufacturers who employed twenty or fewer workers. Including the fifteen manufacturers, the island has only 27 total employers who employ more than fifty workers. Four of the six largest companies are owned by multi-nationals. Half of the twenty- five largest firms are owned by organizations based outside the United Kingdom. The current pattern of industrial development is in striking contrast to earlier events on Sheppey. It so happens that the closing of the dockyard is not the first time that the major opportunity for employment had declined on Sheppey. As the result of expansion of the dockyard in the 1850’s associated with the Crimean War, by 1861 two-thirds of the male workforce was employed in the naval dockyard or in the military. But by 1870, there were such substantial cutbacks at the dockyard that two troopships were used to take displaced dockyard workers and their families to Canada. In light of this history, it is even more intriguing that the recent demise of the dockyard did not result in increased out-migration.-l In addition to the dockyard, development in the nineteenth century included: tourism, a steam engine factory, a glass bottle factory, and what might be termed ’independent salvaging’, (also known as smuggling). Occasionally, ships founder or are wrecked on the Channel side of the island. 8 The islanders are self-starters and willing to work late hours salvaging the cargo. (Pahl reports that this still continues. In the 1970’s, he happened to come across industrious salvagers late one evening and was soon convinced that some fieldwork opportunities are best passed by.) In the twentieth century, industrial establishments (beyond those already mentioned) include: a fertilizer factory, a glass bottle factory, pubs, knick-knack shops for tourists, and assorted shops for carpets, groceries, etcetera. However, none of these is a major employer. In addition, tourism has declined since the 1970’s. None of these establishments fostered collective organization (unions), shift work, or the hard industrial discipline demanded by most modern factories. Implications of the Sheppey Experiengg The consensus of the literature on migration (Greenwood, 1975) is that people move primarily because of economic reasons. People leave an area that is not doing well and enter an area that is doing well. In looking at what has happened to the original inhabitants of Sheppey, it seems that a different theoretical approach may be needed. An approach to migration which simply counts jobs assumes that that local people would be eager to leave and outsiders 9 would be reluctant to enter an area with a high unemployment rate. Social/Human Ecologists, such as Hawley (1950), would say that the islanders did not leave en masse after the closing of the dockyard because the arrival of new employers created new jobs for them. But the new jobs were not exclusively filled by the old inhabitants: new people moved to the island to take the new jobs. The original inhabitants stayed because there was no work for ship builders either on or off Sheppey.v The old inhabitants were largely carpenters. The new jobs required different vocational skills, or technical skills, or new work habits that were not easy for many of the islanders to adapt to. The islanders could not easily adapt to the demands of 20th century industry. The niches they had occupied had disappeared and they did not fit into the new ones. The problem faced by the islanders becomes comprehensible when it is seen as an exercise in adaptation and not merely a mysterious failure to migrate. Migration is only one possible way to solve the problem of adapting to the loss of one’s industrial niche. It is true that new niches were created on the island, but they were not compatible with the characteristics of the original population. New people, from off the island, moved 10 into the new niches. The original inhabitants could have been crowded out entirely. However, the original inhabitants developed some new niches for themselves. The women went out to work as clerks or menials: the men put themselves to work as self-employed housing rehabilitators. IThey buy houses with the income provided by other family members. ‘Then they fix-up the house and sell it to the new comers and use the profits to buy another fix-up house. The implication in Pahl (1984) is that the men only make money when they sell the house. Therefore, they are probably counted among the unemployed. The experience of Sheppey clearly illustrates that people do not have to migrate when conditions at origin become untenable. So, the question remains, under what conditions does change in the industrial structure at origin result in migration. Migration has been seen in push-pull stimulus terms (Greenwood, 1975). People move because they find conditions in another place more appealing than conditions at origin. In other words, there is the push of unfavorable conditions at origin and the pull of favorable conditions at destination. I agree that the push-pull description is accurate, but I think it is limited in the types of questions it can answer. Most importantly, push-pull tells us that conditions are unequal, but cannot tell us why conditions are appealing or not or Egg the population will react to these unequal conditions. An adaptation ll perspective explicitly asks, what are the characteristics of the population and the environment and how well do they fit together? The answer to this question leads directly to why some conditions might be more appealing than others and what the possible responses to the conditions could be. Adaptation is a powerful concept that enables us to ask much more sophisticated questions than a more simple descriptive concept like push-pull. The environment only poses the problems, it does not determine what the solution to those problems has to be. The solution to the problem is determined by the characteristic capacities of the population in conjunction with the nature of the problem that must be addressed. For example, if the population is composed primarily of shipwrights, it cannot just suddenly become a population of lab technicians or metal workers. The phenomenon of industrial change is certainly not limited to Sheppey Island. while a complete survey of industrial changes throughout the world is beyond the scope of this paper, a few examples can be given. Hass (1985) described the closing of the General Electric Metal Iron Plant in Ontario, Canada on February 28, 1982. The plant was shut down even though there was a large market for metal irons and the plant was extremely productive. The shut down occured within a year or so of General Electric specifically denying such plans to the workers and the mayor. Rothstein 12 (1986) compared the closings of steel plants in Youngstown, Ohio with plant closings in Longwy, France. "Over the years, more than one-fifth of the area’s (Youngstown’s) employment has been in primary metals” (p.116). Between 1970 and 1980 the population of Youngstown dropped from 140,090 to 115,511 (Hoffman, 1989). From 1977 to 1980 Youngstown lost over 10,000 jobs in the steel industry, or about one-third to one-fourth of local employment in that industry. This included partial or complete closing of several steel plants. The existence of industrial change is widespread and so is the need to respond effectively to it. The late seventies and early eighties was a period of significant change in the structure of the United States economy. During this period, our economy changed such that the manufacturing sector became smaller and the service sector became much larger. This contributed to relatively high unemployment rates and a sixty percent increase in the number of people working part-time for economic reasons from 1979 to 1985 (Hershey, 1986). A changing industrial structure in the United States will be used in this paper as an example of a problem the environment can pose for a population. The extent to which this problem is solved by migrating will be examined in the context of the amount of inter-state migration in selected industries. Changing numbers of industrial establishments will serve as the measure of the amount of change in 13 industrial establishments. Four industry types will be included: wholesale trade, retail trade, manufacturing, and service. The literature review will examine what is empirically known about migration and attempt to explain those empirical findings. In addition, considerable space will be spent explaining and translating Levins (1968). Levins has been chosen because he focuses on adaptation in the context of heterogeneous environments. A changing industrial structure is very likely to be heterogeneous (at least during the period of change). The migration literature is examined for suggestions about when people adapt by migrating. In particular, to what extent does the degree of congruity between population characteristics and environmental demands predict who will enter or leave a given place. The literature review will be followed by description of the databases used and definitions of the variables. There will be three main hypotheses. The hypotheses will be explicated and the method of examining them will be explained. The data will be used and the hypotheses tested in several different ways and the results given. Finally, the results will be summarized and disscussed in relation to their implications for policy. Literature review. The major traditional theories and approaches to migration in this review include: Lee (1966), Ravenstein (1889), Hawley (1950). The first two are included primarily for completeness, but my major interest is in Hawley (1950). I want to see if the ecological approach to explaining and predicting behavior can be made more precise. After Hawley (1950), I proceed to examine Levins (1968) and try to suggest how his ideas on adaptation can be used to augment and extend Hawley’s (1950) ideas about migration. The push-pull discussions of migration by Ravenstein (1889) and Lee (1966) are couched in terms of the inadequacy of locations. The primary cause of migration is some inadequacy of a location for some people and the presumed attraction of another location. This results in a push from the inadequate location and a pull toward a presumably more adequate location. In both Lee and Ravenstein, an implicit relationship exists between the needs/characteristics of people and the attributes/social-structure of a given location. This relationship is that they have to fit together. For example, if the population needs fuel to burn to heat their homes, the social structure of the location has to provide 14 15 information about and access to a fuel that can be burned such as wood or peat. If the fuel resource is eliminated, because the forest has been all chopped down or the peat has all been cut and burned or there is no alternative fuel such as coal to mine, or the people don’t know how to mine coal, then the people will have to leave this location. If the social structure of a location does not fit with the characteristic needs of the people, the people will leave the location. Because of a lack of fit, the people feel a push from the area without fuel and a pull toward a location which presumably has fuel. A recent example of the potential importance of fit is found in Howland (1988). Howland (1988) studied the effects of plant closings on worker displacement using Dun and Bradstreet data on employment and plant closures in auto manufacturing, electronic components, and the metalworking industry. This was a national study. She found that employment shifts to the south in the 1970’s were related to high rates of job creation in the Sun Belt rather than plant closures in the Frost Belt. Rates of plant closure tended to be relatively even across regions, although number of plant closures was higher in the older, industrialized states because they had more to begin with. Using the Bureau of Census’ 1984 Survey of Displaced workers, she found that a worker is as likely to be displaced in a 16 growing area as a declining one. However, displaced workers do not move easily into new occupations and industries. New, compatible jobs are frequently in the wrong region. This effect was particularly strong for older and less educated workers. Sociology: Human Ecology Hawley (1950), defines migration as non-recurrent movement from one geographic location to a different location. It ”requires readjustment of (the) population in a modified or entirely new structure of relationships" (Hawley, 1950, p 327). Non-recurrent movement is the means of change and the measurable evidence of it (Hawley, 1950). People who move to a new location and stay there are an example of change through non-recurrent movement. The fact that they are in a new location and remain there is evidence that they have made a non-recurrent change in their location. For Hawley (1950), migration depends on two things. The probability that migration will occur is related to (a) the social structure of the community of origin and (b) the ratio of population to opportunities for life at origin and destination (Hawley, 1950). Hawley (1950) describes the social structure of communities or between communities in terms of social dependence. 17 Social dependence refers to activities such as sharing information, giving emotional or psychological support, fostering a sense of identity as a member of a community, or economic or political alliances (Hawley, 1950). Social dependence can refer either to the relationship between communities or the relationship among members of a community (Hawley, 1950). Although Hawley does not extensively discuss examples of such dependence, I believe Hawley (1950) would consider reliance of one community on another community for produce or manufactured goods to be be an example of dependence between communities. He might accept an individual’s reliance on relatives in the community for defense against hostile members of the community as an example of dependence between members of the community. If the relatives lived in another town, then that would probably serve as an example of dependence of a member in one community on members of another community. Going even further out on a limb, it may be that Hawley (1950) simply uses dependence in a very general, ordinary language, way to mean some sort of regular interaction in which human beings have come to expect, or to depend on, certain behavior from other human beings. Hawley’s (1950) position on migration and dependence can be summarized as follows. There is less probability of migration from tightly knit communities in which the members are very dependent on each other, but the community itself 18 is not dependent on other communities (Hawley, 1950). An example of a tightly knit community, in which the members are dependent on each other, but the community is not dependent on other communities, might be certain religious communities such as the Amish in Pennsylvannia or the 'Hutterites in Canada. There is a greater probability of migration from communities whose members are dependent on members of other communities (Hawley, 1950). For example, during the nineteenth century, people in Europe who were economically dependent on relatives who had already emigrated to America would be more likely to migrate to America than people without such relationships. Communities in which a large proportion of inhabitants had connections to other communities would be expected to have higher out—migration than communities in which relatively fewer inhabitants had connections to other communities. Migration is more likely between communities that are dependent on each other than between communities that are independent of each other (Hawley, 1950). For example, if a group of rural towns had very little trade with each other, but each had extensive trade with the same urban center, migration between the rural areas and the urban area would be much more likely than migration between the rural areas. Dependence is used to describe how closely linked the parts 19 of the social structure are to each other. It is also used to describe the links between the parts of the structure. Hawley (1950) does not talk about the needs or characteristics of the potential migrants. Hawley (1950) is using social structure to explain behavior. There are_two major problems with depending exclusively on structure to explain behavior. First, structural explanations, (Lee, 1966: Hawley, 1950) ignore the possibility that the structure could change. The lack of capacity to address change is a serious drawback for a structural approach. Social structure changes. The conditions in a location change. Technologies, customs, mores, availabilities of resources and even climates change over time. Second, structural explanations often assume that the inhabitants of a social structure are like rodents or roaches in a skyscraper. The activities of the inhabitants are at best a nuisance and at worst a threat to the integrity of the structure. It is not recognized that structure may be a tool of the inhabitants to ensure their survival. The interest of the inhabitants in the survival of the structure in its present form may merely reflect their belief that the present structure is an essential tool in their own survival. Blau (1965) distinguished three levels of study in the study of organizations. Although I am not studying organizations 20 directly, his discussion on levels of analysis could logically apply to almost any social event, process, or entity. The first level of analysis is the individual, i.e., role analysis. The second level of analysis is the structure, i.e., structural analysis. The third level of analysis is analysis of the system of interrelated elements that characterize the organization as a whole, i.e., organizational analysis. Organizational analysis is that analysis which aims to discover the principles that govern the functioning system. In Blau’s (1965) use of the term, any study of the interaction between individuals and structure would probably count as an example of organizational analysis. Blau (1965) does not confine the term ’organization’ to formal organizations, but rather uses it to apply to any organized collectivity. Blau is making distinctions between levels so that he can discuss the interactions and relations between them. Blau is interested in the outcomes of these interactions. I am focusing on the process of the interactions themselves. The process can be described in terms of adaptation and evolution. Two mechanisms for population response (adaptation) to change in the environment (social structure is the environment) are evolution and migration. Evolution is change resulting from the steady accumulation of small 21 changes in the characteristics present in the population. For evolution to succeed, the change in the environment has to happen slowly enough so that the steady accumulation of small changes in the characteristics present in the population will be able to keep up with the changes in the environment. Evolution is a time consuming response to change. If the environment changes so quickly that there isn’t enough time for adaptation through evolution, then migration is the only strategy left. Population Ecology, as described by Aldrich (1979), is an evolutionary perspective on ’...social change which depends heavily upon the natural selection model borrowed from biological and human ecologists...’ (Aldrich, 1979, p.26). The goal of population ecology is to explain the process underlying change (Aldrich, 1979). Organizational change is explained by the nature and distribution of resources in the organizations’ environment (Aldrich, 1979). The central force in organizational activities is the competition for resources (Aldrich, 1979). Aldrich (1979) also uses the term ’niche’ which refers to a distinct combination of resources and other constraints sufficent to support an organizational form. Aldrich (1979) defines an organizational form as an organized activity system oriented toward exploiting the resources within a niche. 82 Aldrich (1979) identifies three different outcomes of the process of selection: (a) selective survival of whole organizations, (b) selective diffusion or imitation of successful innovations or partial organizations structures or activities, and (c) selective retention of successful activities resulting from variations in behavior over time. Aldrich (1979) implictly suggests that survival is a matter of finding a niche or adapting to the available niches. The concept of niche for organizational populations has also been discussed in a similar way by Hannan and Freeman (1989). The idea of niche is closely tied with the idea of adaptation (Hannan and Freeman, 1989). Niches and adaptation are more extensively discussed in Levins (1968) where he suggests that there is more than one possible way to adapt to environmental change. Levins (1968) equates adaptations with strategies. Obviously, strategy is being used in an analogical way by Levins since he applies his ideas to bacteria. The basic choice of strategy is between being a generalist or a specialist. A generalist attempts to be prepared to at least some extent for any eventuality in order to cope over a broader range of conditions. A specialist attempts to be particularly well prepared for a particular condition, but may not be able to cope at all in some other condition. A population therefore has three basic ’choices’ in the composition of its members. The choices are: (a) all 23 members are generalists, (b) all members specialize in the same thing, or (c) each member has a specialty, but more than one specialty is represented in the population. Levins (1968) wrote about adaptation when the environment changes. Although his hypotheses are specifically concerned with nonhuman (e.g., butterflies and bacteria) populations and communities, the general ideas can be applied to humans. Levins (1968) uses many terms which need to be fully explained and their re-interpretation in sociological terms requires explication. I want to use Levins because his view of adaptation is explicitly interactive. It focuses on the interaction between the characteristics of a population and an environment. Successful adaptation occurs when neither the population nor the environment imposes a set of conditions which the other cannot meet. A mechanism for responding to change is explicitly a part of his theory. Levins assumes that environments are heterogeneous. If it is heterogeneous, then it changes. Humans are faced with environments that are constantly changing in terms of what is required for survival. New technologies are discovered which change how we live and how we interact with each other, for example, the industrial revolution in the nineteenth century, the development of cars, birth control, mechanized farming, or synthetic fibers and textiles. 24 Levins calls the different combinations of change over time and space ’patterns of change’. Levins argues that environments do exhibit patterns of change and thereby influence the odds governing which responses will be successful in them. Responses to change will differ depending on the pattern of change. In Levins, the response to change (adaptation) is called a strategy. For my purposes, a strategy is a pattern or mix of occupations: it is not the occupations themselves. In the context of this paper, adaptation is the process of fitting a mix of occupations distributed in the population to a pattern of change in the distribution of industries. The result is an aspect of social structure: the labor market. Which adaptation strategy (occupational mix present in the population) a population will adopt depends on the pattern of change (in available industries) to which the population must adapt. To return to the Sheppey example, Sheppey men were able to earn a sufficient living at the dockyards to support families. Now, there has evolved a large group of men who live ’off-the-books’. The men combine odd jobs (sometimes skilled labor such as plumbing) with investing in and developing real estate. They live in one house and buy another one to fix up. When they finish fixing up the second house, they move in and sell the first house. Then 25 they start the cycle all over again. The strategy has changed from trying to pursue single occupation to combining occupations. The original occupation of shipwright required carpentry skills and the skill of directing one’s own and the work of others. Fixing houses uses carpenter and the ability to plan and direct one’s own work and work of others. These skills were originally learned shipyard and exercised in one occupation. Now, these are carpenters, housing developers, and real estate investors. Their skills are now exercised in three occupations instead of just one. Fine versus Coarse Grained Environments. work skills the in the men Levins divides patterns of change into either fine grained or coarse grained. It may help here to visualize the environment as being divided into patches. Levins assumes the environment is heterogeneous. Some conditions will be hostile and some will be beneficial. The idea that conditions may be hostile or beneficial is implicit in the claim that the environment changes and the population will only have a finite set of characteristics. Any given population of humans beings will have the skills to carry out a variety of occupations, however, no population is likely to have the skills to carry out all occupations that any human anywhere has ever practiced. (If a population 26 were so blessed, it wouldn’t be of interest here anyway. For them, adaptation would not be a challenge.) In a fine grained environment, an individual will encounter all of the conditions in the environment during its life-I span, that is, it will have to spend some time in each of the patches. In a coarse grained environment, the individual can live its entire life-span in just one of the conditions (patches) of the environment, although the population is faced with all of the conditions (patches). The number of different types of industries (niches for occupations) available on Sheppey constituted the grain of Sheppey. When the shipyard closed, the grain of Sheppey changed. It became fine. No one could ignore the closing of the shipyard. Either new niches for occupations had to be found on Sheppey, or the former shipwright had to leave Sheppey. Additive versus Multiplicative Population Characteristics. Levins categorized the characteristics of the populations as either additive or multiplicative. If the characteristics are multiplicative, then no one characteristic alone is enough to ensure survival: all characteristics are required to be present in at least some amount. If the characteristics are additive, then either a single characteristic in a very large quantity or a combination of 27 two or more characteristics in smaller quantities will enable survival. For example, suppose a population contains occupations A, B, and C. If an individual must do A+B+C to survive, then these occupations are multiplicative. If an individual can survive by doing a lot of just one of them (A or B or C) or by doing some of any two of them ( a+b or b+c or a+c), then these occupations are additive. If you have to do all of them, they are multiplicative. If you don’t have to do all of them, they are additive. Although it is convienent to speak of occupations as additive or multiplicative, what these terms really describe is how certain occupations can be successfully practiced in a certain context. The difference between ’additive’ and ’multiplicative’ is more a matter of degree than kind. It must be realized, that to some extent, all populations are required to have some sets of multiplicative characteristics. For example, no one could survive (even in an agricultural society) by literally just knowing how to pick beans. You also need to know how to get other foods, how to get shelter, how to dress yourself, and other very basic skills. The distinction between additive and multiplicative is more relevant at the level of occupations. Occupation refers to the set of activities that one usually spends most of one’s time doing and is necessary to pursue in order to survive. 28 Most adults need to dress themselves, but without an occupation to pursue for money, or raw materials for construction into garments, they won’t have anything to dress themselves in. The pattern of industries, and consequent occupational opportunities, in the evironment constitutes the grain and determines which pattern of characteristic occupations can‘ be successful. Fine grained labor markets are more likely to reward multiplicative occupations. Coarse grained labor markets may be more likely to reward additive occupations in the population. In the context of human populations, the characteristics of the population would be the occupations in which members of the population work. When the dockyards at Sheppey were open, the grain was coarse and the effect was to encourage an additive pattern of occupations: a man could just concentrate on being a shipwright. The grain was coarse because there was really just this one major industry and you could survive by just working there. When the dockyard at Sheppey closed, the grain became finer (you couldn’t work in just one industry all your life any more) and the effect was to encourage the development of multiplicative occupations such as carpenter, real estate developer, and housing rehabilitator. 29 Competitive Versus Complementary Population Characteristics. Levins states that characteristics of populations may be either competitive or complementary. They are competing if having one characteristic means.having less of the other. They are complementary if having one of them either has no effect or a positive effect on the existence of the other. In terms of occupations, occupations are competitive if practicing one of them diminishes one’s ability (or opportunity) to practice the other(s). Sociologically, the characteristics primarily of interest for survival are occupations. Some occupations are complementary. Tax preparer and accountant are complementary. The more you practice either one, the better you will be at the other. In fact, these occupations are so complementary they are usually combined in general practice. An occupation should not be confused with a job. If you teach part-time for two different school systems, you only have one occupation, teacher, even though you have two jobs (because you have two different employers). An example of competitive occupations would be farming and traveling salesperson. The more time you spend on farming, the less time you have to spend on traveling and selling and vice versa. One could also use the occupations of teacher and researcher as an example of competing occupations. Although 30 these occupations are combined in university faculty positions (jobs), they do tend to interfere with each other. Interaction of Population and Environggnt Characteristics. Characteristics of population and environment interact to determine the optimal strategy. A coarse grained environment in combination with competing and additive population characteristics will reward specialists. A fine grained environment in combination with complementary and multiplicative population characteristics will reward generalists. Table 1 shows the strategies most likely to be successful for the combinations of population and environment characteristics. There is one logically possible combination which is not in Table 1. That is, for populations whose occupations are multiplicative and competing. This combination would mean that more than one occupation must be exercised to survive, but practicing more of one occupation means practicing less of another occupation. Logically, two conflicting activities cannot successfully simultaneously occur. Successful adaptation is not possible if you must perform tasks that interfere with each other at the same time. The possibility of success depends on what Levins (1968) meant by ’at the same time’. I do not 31 know if ’same’ is used literally to mean simultaneous or if it is used more loosely to tasks that are in close temporal proximity but not necessarily simultaneous. Since success under conditions that demand multiplicative and competing occupations is problematic this paper will omit this condition from its scope. Table 1 shows what kind of occupations would be expected to be optimal given an environment which presents a particular kind of change. The pattern of change in an environment directly affects how the range of resources in it can be exploited. TABLE 1 Strategies Optimal for Populations With Particular Types of Occupations in Coagse Versps Fine Grained Environpents. Populations’ Occupations Environments Complementary Competing """""""""" 3;;2:23;""$335123};3""33322132' Coarse homogeneous - heterogeneous homogeneous specialists specialists specialists 722;; """"""""""""""""""""" ESSEQEZLE generalists generalists specialists 32 In Table 1, occupations are not used to mean the same thing as a job. For example, a plumber who works for Ajax Plumbing has an occupation and a job. If Ajax Plumbing goes out of business, the plumber will not have a job but he will still have his occupation, plumbing . An example of homogeneous specialists could be a population whose members all primarily practice slash and burn agriculture. In a population of heterogeneous specialists some members might primarily be farmers, some might be coop extension agents, and some might concentrate on administration of farmer ' assistance programs. In a population of generalists, each individual would have more than one occupation. For example, farmers who are also farriers, artists who are also writers, and factory workers whose factory work is unskilled but who have a home repair business they pursue part-time. Levins’ (1968) ideas can be translated into labor market terms in order to use them to describe the conditions which might facilitate or impede migration. It is possible to speak of labor markets as fine versus coarse grained. The demand for certain occupations can be described as changing quickly or slowly or over a wide area or a small area. Workers can be described as specialists or generalists. Suppose an environment changes from rewarding specialists to rewarding generalists i.e., from coarse to fine grained. The specialists can either adapt by trying to learn new 33 occupations or by out-migrating while generalists enter to replace the exiting specialists. In other words, looking at the fit between population occupations and niches for those occupations (industries), can tell us something about why a given location would or would not be attractive to a given population. Persons who can make a living in more than one way have the advantage in that they are more likely to be able to adapt to environmental change without moving. If you have more than one occupation that enables you earn a living, those occupations are potentially multiplicative. Examples of this include: summer farming and winter factory jobs, or nine months of school teaching and summer as a camp counselor. Individuals who cannot live on the income that one occupation can produce, either have to find additional things they can do, or develop a occupation that can produce an adequate income. If you have one occupation that can produce an adequate income, that occupation is potentially additive. Medicine and law are examples of occupations that are usually additive. Occupations are additive or multiplicative depending on the context in which their possessor wants to use them. Manual labor is additive if that produces an adequate income. Manual labor is multiplicative if you must combine it with vegetable farming in order to survive. If a context is such that no (or very 34 few) occupation(s) can produce an adequate income, then generalism is the most likely outcome. If it is possible to make a living from a single occupation, specialization is much more likely. Large cities have more Ispecialized stores of many kinds including food, clothing, household appliance, wine, and tobacco sellers. The presence of natural resources such lumber, minerals, or fishing opportunities may also encourage specialization. If the resources are depleted or the market for a particular specialty is becomes too small to produce an adequate income, the population which specialized in it will have to adapt to the change. The specialists will no longer be able to practice their specialty. They will have lost their niche through contraction of activities. They have to migrate or develop new niches. Sociological Literature: Organizations The organization of work is a natural place to apply Levins’ ideas. Weber (1947) exhaustively detailed the possibilities for the organization of work. He defined an occupation as specialization, specification, and combination of the functions of an individual so that it provides a reliable source of income or profit. In Levins’ terms, an occupation is the combination of skills that enable survival. Weber (1947) described three modes of occupational distribution: 35 (a) heteronomous assignment of functions, (b) specification or specialization of functions, and (c) using the services of individuals on either an autocephalous basis or a heterocephalous basis. (Heteronomous assignment of functions means that people are employed for wages or a salary.) Specification or specialization of functions implies the existence of specialists. Autocephalous means they are self-directed in their work. Heterocephalous means they are directed in their work by others. Although Weber (1947) is very informative about the organization of work, he describes it as though it were a static, given entity. A reader who is dissatisfied with the circumstances under which she works would be left with the tantalizing idea that there are alternatives, but the sad news that we have no idea how to change between alternatives. The concept of adaptation gives us a way to talk about how change might happen and what its likely consequences might be. Although my interest is in the broad process of adapting to change, I have to choose something to serve as a testing ground for the usefulness of the adaptation perspective. I have chosen migration. The literature review will allow us to see what is empirically known about migration and to examine the usefulness of the various theoretical perspectives that have been used. 36 Sociology Literature: Migration Studies of migration either ask why people migrate or what are the causes of migration. The goals include predicting when migration will happen or who will migrate so that it' can either be anticipated, prevented, or encouraged depending on the policy interests of the writer. Explanations of migration can be very roughly divided into two categories: migrant’s personal characteristics or characteristics of the environment. Under migrant personal characteristics we find discussions of migrant personality, age, sex, employment status, stage of migrant’s life, occupation, size of migrant’s household, and educational achievement. Under environmental characteristics we find demand for labor, urbanization, occupational opportunities, industrial organization, community structure, birth rates, infant mortality rates, size of population, relation of one community to another community, and per capita income. These two categories also dictate two basic ways to ask questions about migration. Questions about migration can either be stated in terms of migrants as in: "What are migrants like compared to non-migrants?": or they can be stated in terms of locations as in: "What kinds of locations have a lot of migrants?". Stating the question in terms of migrants leads to a focus on migrant characteristics and the 37 migrant as the unit of analysis. Stating the question in terms of locations leads to a focus on location or environment characteristics and the location as the unit of analysis. If one takes an adaptation approach to explaining migration, it becomes obvious that a primary focus on either migrant or environment is inadequate. It is necessary to look at the interaction between the characteristics of the migrants and the locations. Migrants act on environments, and environments influence the behavior of migrants. Two types of comparisons are commonly made to identify migrant characteristics: (a) those who did not leave the place of origin to those who left, and (b) in-migrants to original residents at destination. Migrants differ from non-migrants in several ways. They are: age (Thomas, 1938; Danzo, 1978; and Spengler and Meyers, 1977): sex, occupation, education (Thomas, 1938; Danzo, 1978; and Spengler and Meyers, 1977): and employment status (Danzo, 1978); skill, training, and enterprise (Spengler and Meyers, 1977). The typical migrant in these studies is a young, adult, educated, trained, and enterprising male who wants to pursue a highly skilled occupation. There are many aspects of locations which have a direct effect on migration. They are size, economic self- 38 sufficiency, amount of contact with other cities (Karp and Kelly, 1971), unemployment rates (Lowry, 1969), occupational opportunities (Vogelnik and Fergoli, 1978: Lowry, 1969; Spengler and Meyers, 1977), climate (Long and Hansen, 1978), degree of urbanization, household size, infant mortality, illiteracy, and percent of population engaged in agriculture (Vogelnik and Fergoli, 1978), and the relative sizes of the non-agricultural labor forces at origin and destination and industrial organization in terms of dispersal or concentration (Spengler and Meyers, 1977). Climate preferences were found by Long and Hansen (1978) to be the most frequent reason for migration after employment and desire to be near family. Studies do not usually compare the in-migrants for a particular place and time to out-migrants at that same place and time. There is an interaction between migrant and environment. The process of adaptation through migration is a process of migrants matching or fitting their characteristics to the characteristics of environments. The observable outcome of matching is a correspondence between the characteristics of in-migrants and opportunities of destination environments and'a relative lack of correspondence between out-migrants’ characteristics and origin environment opportunities. The fact that for any given place and time both in-migration and out-migration 39 occur and may occur for different reasons is obscured by the conventional use of net migration as the dependent variable. When we discuss the characteristics of a migrant which are likely to further adaptation, we usually begin by discussing what, if anything, the migrant can do for sustenance. In non-agricultural settings, this means pursuing an occupation and getting paid for doing it. Certainly, unemployment rates are often the inverse of the occupational opportunities. That is, if unemployment is high, then occupational opportunities tend to be low. However, this not always true. If the labor market were undergoing a change in the occupations it provided a niche for, and those occupations were not common in the resident population, then the unemployment rate might be high and the occupational niches abundant. When the distribution of occupational niches changes, the grain of the location changes. If there is a change in number of niches, but not a change in type of niche, the location may be becoming more coarse grained, for example, more but smaller number of farms. If there is a change in number of types of occupational niches, without an overall increase in number of occupational niches, then the location is becoming more fine grained, for example, some farmers give up farming and change to farm equipment sales or repair. 40 The importance of individuals’ characteristics and their relationship has been described by Sjaasted (1962). Sjaasted calls these characteristics ’human capital’. ’Human capital’ is the knowledge, skills, abilities, education level, experience, and training of each .individual. Sjaasted argues that migration causes a loss of human capital because the usefulness of each individual’s accumlated human capital declines from one location to another. He further states that this effect increases with age. When an occupation becomes less valued, the practitioner suffers a loss of capital. The desert nomad’s detailed knowledge of how to survive in the desert is less useful if he is suddenly transported to an urbanized area. Human capital requires time and effort to acquire. As a move is contemplated, the cost of acquiring new capital and the loss of value in current capital has to be weighed against the possible gains that may come from new capital. The idea that migration is adaptation could be expressed as the proposition that migration is an effort to preserve old capital or acquire new captial. This, however, would confuse the means with the ends. Human capital is an outcome; adaptation is a process. Lack of fit might be expressed as having accumlated inappropriate human capital, but lack of fit really encompasses more. Human capital puts an emphasis on the individual characteristics and not enough on environmental change. I would not dispute the idea that 41 human capital exists or that migration has an impact on it. I just want to focus on the process, not the product. Long and Hansen (1978) found that persons who were not college educated gave non-economic reasons for migration, such as the desire to be near family, more often than the college educated. They interpreted this to mean that these two groups actually have different reasons, in a causal sense, for migration. In contrast, economists suggest the real reasons in both cases may have been economic. Long (1978) notes that the poor moved to the south in the 1960’s and 1970’s in numbers that gave the south a net in- migration. This was a change from.the net out-migration of the 1950’s. Rees (1979) reports that manufacturing moved from the northeast to the south in the 1960’s and 1970’s and also notes that there was tremendous growth in the service sector of the economy during that time period throughout the country, including the south. This also reinforces Hawley’s (1950) point that stated motivations may have little to do with the changes preceding migration. The dual importance of social and economic factors can be seen by comparing the results of Long and Hansen (1978) with Rees (1979). The desire to be near family emphasizes that social ties to the community are important. However, the movement of both jobs and people at the same time emphasizes the crucial influence of employment. Schwarzweller’s (1971) 42 work on stem-family migration shows how the desire to be near family and be employed may be combined in practice. He found that migrants moved primarily to a location where there were relatives as well as jobs. This is quite consistent with Hawley and Thomas on the importance of Isimilarity of community structure. Certainly the presence of kin in a new community lends an aspect of similarity and dependency between origin and destination communities. If it is true that people move in response to changing occupational opportunities, then it is not surprising that Morrison (1971) finds that migration serves to adjust the labor supply, expands the range of opportunities available to the migrant, and causes urban growth. This in turn is supported by the finding that inter-regional skill distributions tend to remain constant (Horiba and Kirkpatrick, 1979). Rothberg’s (1977) conclusion that migration behavior is the outcome of the joint influence of the personal characteristics of the migrant (such as education, skills, tolerance for risk) and labor market conditions is also consistent with the idea that populations must find a way to fit with their environments and will migrate if necessary to achieve an acceptable fit. Summation of literature. The literature review has tried to make three major theoretical points. The first point is from Levins’ (1968) that adaptation is a very complex process involving an interaction between the demands of the environment and the capacities of the population members as individuals and as a population per se. Second, Hawley (1950) makes the point that migration is heavily dependent on the proportion of resources to population. Third, Hawley (1950) emphasizes community integration at origin and destination through his concept of dependence. All of these points are supported by the empirical findings, however, the findings suggest some additions to the theory. Every study reviewed emphasizes the importance of work or of factors, such as education, that influence the inhabitants’ abilities to do various kinds of work. The only exception to this is climate and that was found to be secondary to employment. The proportion of population involved in agriculture is inverse to the proportion of the population involved in other sectors of the economy. It is directly related to the relative opportunities for pursuing other industries. Urbanization influences the relative variety of industries which may be pursued (Durkheim,1933) . The larger a city, the more likely it is to have a diverse 43 44 occupational and industrial structure. Size is directly related to the tendency of organisms, organizations, and communities to differentiate. Inter-city contact would facilitate the transfer of information about job opportunities and transport of people between cities. News of low unemployment rates would certainly travel quickly and the transportation facilities would make migration much easier. The studies reviewed imply that migration would be better understood by expanding our perspective to include the interaction between migrant characteristics and location characteristics. Working from the perspective of adaptation allows explicit consideration of interaction between population behavior and environment. As previously stated, studies do not usually compare the in- migrants for a particular place and time to out-migrants at that same place and time. If the adaptation perspective is correct, the comparison of in-migration to co-occurring out-migration is essential. Adaptation would predict that in-migrants and out-migrants at a particular place and time would differ in their characteristics. For example, rural to urban migration is typically explained as result of a lack of economic opportunity in the rural location, so, one would expect out-migrants to be younger and better educated 45 persons about to start their careers and in-migrants to be retirees. Because I did not find studies of co-occuring in— and out-migration, I have to combine the results from studies comparing in-migrants to original residents and studies comparing movers to stayers to yield hypotheses about the differences between in- and out-migrants with respect to a particular location. This study is designed to address three hypotheses. First, for the majority of states, in-migrants will have different occupations or work in different industries than out-migrants. That is, whether a migrant moved into or out of a state between 1975 and 1980 is related to the industry or occupation in which he or she worked in 1980. Second, the average number of in-migrants employed in a given industry will be higher in states that are growing in that industry compared to states which are not growing in that industry. In addition, the number of out-migrants from a given state who worked in a given industry in 1980 will correlate negatively with change in number of establishments in that industry in the state of origin between 1972 and 1977. This means that number of out-migrants will decline when the number of establishments increases. Third, the relationship between changes in industrial structure and migration will vary with age. This comes from Saben (1964) who found that among migrants who moved for work related 46 reasons, the percent moving because of a transfer was much higher in 25 to 64 year old migrants. DATA and METHODS In the literature review, I explored the possibility of enhancing the human ecological perspective by expanding its implict use of adaptation to an explict use. I have suggested that the need for this is made apparent by the example of Sheppey Island where we saw the non-occurance of migration under circumstances that would intuitively suggest that migration would occur. I believe that explicit use of adaptation as a perspective can be usefully examined by looking at migration in a conventional way: by comparing a location’s out-migrants to a location’s in-migrants during the same period of time. To test the usefulness of the adaptation perspective, I will compare a state’s out- migrants to that state’s in-migrants during a particular period of time during which the number of the state’s industrial establishments may change. There are three major hypotheses: (I) whether migrants exit or enter a state is related to the industries and occupations in which they work, (II) the number of migrants who enter or exit a state depends on the existence of change in number of industrial establishments, (III) the relationship between migration and change in number of industrial establishments will vary with migrant age. 47 48 The ideal circumstance to study adaptation to change is one where the environment is changing in a measureable way without the consent of the pre-change population or without the pre-change population trying to make the change happen. In addition, we would know how that population had previously lived, how they lived during the period of change, and how (or where) they lived after the period of change. This would allow clear comparison of pre-change behavior and post-change behavior. It would be even better if the environment were similar to other environments so that we could generalize with confidence to the other populations and environments. The beauty of the Sheppey Island example was that it met these requirements. The U.S.A. has experienced great changes historically in its industrial structure. The labor force of the U.S. has had to adjust from an economy that was primarily agricultural at its founding to an industrial one after WW II, and currently seems to be changing to a service economy. The locations of concentrations of industry have also shifted over time (Rees, 1979). The populace of the U.S. has had to adjust both its occupations and its locations to adapt to these changes. Fortunately, the U.S. Census of Population and Housing, which is conducted every ten years, asks for current 49 occupation and industry and where the respondant lived five years earlier. This makes it possible to examine a location’s in-migrants and out-migrants with respect to their occupations and industries. The U.S. Census of Business and Industry provides state level information every five years on the number of business and industry establishments for each state. This makes it possible to determine what changes have occurred in a particular state‘ during a particular period of time. Armed with these two sources on information one can compare the changes in industrial establishments to the occupations (and industries) of people who came to the state or left the state during the period of interest. If the adaptation perspective is correct, then a state which is declining in certain industries ought not to be attractive to people who will be working in those industries. Therefore, as hypothesized earlier, there ought to be a relationship between a migrant’s occupation and the industries available in a given enivronment. OPERATIONALIZATION OF CONCEPTS AND VARIABLES: Agaptation was operationaliaap as interstate migration that occured after the beginning of change in number of industrial establishments. It is for this reason that the period of labor market change was chosen to be as close as possible to the time in which migration was observed, and 50 still have the labor market change start before the migration could start. Because the Census of Business and Industry is only conducted in years ending in ’7’ or ’2’ and the Census of Population and Housing is only conducted in years that end with ’0’ ( it asks about residency five years earlier), it was impossible to choose a period of industrial change that would not overlap the migration period to some extent. I chose a period of industrial change (1972 to 1977) that allows three full years of change to occur before begining to observe migration (1975 to 1980). This should be sufficent lead time to allow me to argue that the migration is more likely to have followed the industrial change rather than to have simply co-occured with or stimulated it. Change in the labor market was operationalized as a change in the number of establishments. Most of this study was limited to four sectors of the economy: manufacturing, wholesale trade, retail trade, and service. This was done to maximize the generalizability of results and yet keep the domain investigated within a reasonable size. The period of change measured was between 1972 and 1977. Number of establishments included those with and without payroll. Establishments without payroll were included to include persons who were self-employed but had no other employees. Migration is operationalized as interstate migration between 1975 and 1980 within the U.S.A.. Any person whose reported 51 residence in 1980 was different from the reported residence in 1975 was considered to be an interstate migrant. If the state in 1980 was identified, but the state in 1975 was not, then that person was assumed to be an interstate migrant. In order to test the hypotheses, it was necessary to maximize the likelihood that migration was due to changing jobs. Migration by persons under the age of 21 in 1980 may have been most influenced by the need to obtain training or education for employment or to remain with a family that the migrant was too young to move away from independently in 1975. Persons over 59 may be thinking about retirement and migration may be undertaken with that in mind. Persons not in the labor force in 1980 are assumed not to have moved directly in response to fluctuations in the demand for labor. In addition, people who were continuously in either school or the military are unlikely to have moved primarily because of changes in civilian occupational demand during the period they were in those institutions. These characteristics are summarized below. This study is restricted to migrants who: a) did not live in the same state in both 1975 and 1980, b) were in the labor force in 1980, c) were not in college in both 1975 and 1980, d) were not in the armed forces in both 1975 and 1980, and e) were aged 21 to 59 inclusive in 1980. 52 UNIT OF ANALYSIS: The state is the major unit of analysis: the migrant is not the major unit of analysis. The independent variable is the change in number of industrial establishments in a state between 1972 and 1977. The dependent variables are the numbers of people who entered and exited the state between 1975 and 1980. The dependent variable is gap the rate of entrances or the rate of exits nor the net gain or loss. The relationship between the independent and dependent variables will be tested with Chi-square, correlations, and ANOVAs as detailed in the discussion of the analyses for each hypothesis. Information on establishments was collected at the state level from tables published in the U.S. Census of Business and Industry for the years 1972 and 1977. Changes in number of industrial establishments are the basis for categorizing the states as growing or not growing. The specific cut-off points for growing versus not growing were set separately for each of the four industrial sectors. These will be detailed in the procedure and analysis section. For states, I collected the number of establishments with or without payroll in 1972 and 1977. Number of establishments was only collected for the wholesale, retail, manufacturing, and service industries. 53 Aggregate level data about interstate migrants for the variables of interest was not so easily available. For this reason, it was necessary to obtain a file of data on migrants and group them into categories according to lcoation, occupation, industry,'and age. For migrants, I collected: (a) state in 1975, (b) state in 1980, (d) industry in 1980, (e) occupation in 1980, (f) age in 1980, (9) sex, and (f) labor force status in 1980. A description of this data file follows the description of how the migrant data was aggregated. Before aggregating the migrant data, it was necessary to identify the cases which were suitable for inclusion in the sample. Table 2 shows the stages of narrowing the sample and the resulting number of cases for aggregation. Originally 846,543 cases were available in the data file. After eliminating those who were too young, too old, continuously in the military or in school, persons not in the labor force, and non-interstate migrants, the resulting sample size is 290,237. 54 Table 2 Identification of Desired Sample Stage of Sample Qpnstruction No. ofggases Original Data Available on Tape 846,534 Soldiers, Students and Non-interstate Migrants 122,433 Cases Remaining 724,101 Non-labor Force Migrants 381,657 Cases Remaining 342,444 Under 21yrs or over 59yrs (in 1980) 52,207 Final Population Remaining 290,237 Procedure for creating aggregate migrant data. A person who lived in a particular state in 1975 was grouped as an out-migrant for that state. He or she was grouped as an in-migrant for the state reported as the 1980 residence. The number of migrants in each industry and in each occupation was obtained by selecting all the in—migrants for that state and all the out-migrants for that state and then cross-tabulating the direction of migration (in- versus out) by the migrant’s industry or occupation in 1980. Separate crosstabulations were done for occupation and industry. A dummy crosstabulation is shown below: 55 Dummy Table State "Florida" Migrants’ Industries in 1980 Service Manufacturing Retail Wholesale In-Migrants (In Florida in 1980) Out-Migrants (In Florida in 1975) The aggregate migrant data and the business and industry data were then put in a combined file which had one record for each state describing the changes in number of establishment for each of the four industries and the number of in and out-migrants in each of those industries for that state. Number of migrants in each occupation was not included in the aggregate data file because I did not have information about employment in each occupation by state. As a result of creating aggregate migrant data, fifty groups of in-migrants and fifty groups of out-migrants were created. Table 3 is provided to show the size of each of the aggregated groups. That is, Table 3 shows the number of in- and out-migrants for each state. For example, there were 3,874 people who ostensibly moved into Alabama for job purposes between 1975 and 1980. There were 3,081 people who moved out of Alabama during the same time period for similar reasons. 56 For each group of in-migrants and each group of out- migrants, I determined the number in each migrant group who had worked in each industrial sector and in each occupational sector in 1980. Tables for number of each migrant group in each industrial and occupational subgroup fare not provided here to avoid overwhelming the reader. These tables are presented in the Appendices. In-migration location data was available for all subjects. Out-migration location data was missing for 63,432 (22%) of the subjects. By "missing", I mean that I do not have information about where they lived in 1975. They are subjects who did not respond to the census question asking them to identify the state they lived in in 1975. Migrants were identified by requiring that "state-in-1980 not equal state-in-1975". Because state in 1980 was not missing for any subject, subjects who were missing state-in-l975 became included in the data file (because missing was not equal to anything for in-migrants). However, they are not counted in the analysis of out-migrants. The analysis of out-migrants required that the state-in-7S be identified. Census data does have certain limitations. First, the poor are underrepresented. The Census does miss some people and the poor are more likely to be missed than the middle class. However, the Census does try to count the poor by visiting places where the poor or homeless are known to congregate 57 such as inner—city church missions, truck stops, all-night diners, other locations which are not necessarily in-doors, but are known to the police (Bureau of Census, 1987). The second limitation is in the definition of migrant. People who moved from a place after 1975 and returned to it before 1979 will not be considered migrants. Number of In- 58 Table 3 and Out-migrants per State (Ages 21 to 59) Out-migrants State In-miggants Alabama 3874 Alaska 1464 Arizona 6771 Arkansas 2749 California 36461 Colorado 7805 Connecticut 4203 Delaware 940 D. of C. 1862 Florida 19720 Georgia 7772 Hawaii 2461 Idaho 2016 Illinois 10836 Indiana 5029 Iowa 2852 Kansas 3537 Kentucky 3263 Louisiana 4275 Maine 1416 Maryland 6342 Massachusetts 5949 Michigan 6492 Minnesota 4067 Mississippi 2436 3081 1267 3861 2223 19507 4943 3796 935 2242 10684 5046 1851 1402 11680 5108 3282 3368 3149 3179 1154 5368 6559 7386 3636 2370 59 Table 3 (continued) Number of In— and Out-migrants per State (Ages 21 to 59) ,gtate In-migrants :Qut-migrants Missouri 5365 5297 Montana 1369 1192 Nebraska 1906 2144 Nevada 3326 1267 New Hampshire 2123 1300 New Jersey 9159 7892 New Mexico 2522 1951 New York 14546 18698 N. Carolina 6710 5438 N. Dakota 988 1031 Ohio 7760 10105 Oklahoma 4672 3021 Oregon 5404 2746 Pennsylvania 7921 9642 Rhode Island 1076 1096 S. Carolina 4102 2968 S. Dakota 847 1011 Tennessee 5301 4135 Texas 21791 9371 Utah 2479 1487 Vermont 858 769 Virginia 9714 7049 Washington 8536 3768 W. Virginia 1700 1577 Wisconsin 3823 3955 Wyoming 1647 818 Missing_f 0 63432 Totals 290237 290237 * Subjects who did not respond to the census question asking them to identify the state they lived in in 1975. Migrants were identified by requiring that "state-in-1980 not equal state-in-1975". Because state in 1980 was not missing for any subject, subjects who were missing state-in-1975 became included in the data file (because missing was not equal to anything for in-migrants). However, they are not counted in the analysis of out-migrants. The analysis of out-migrants required that the state-in-75 be identified. 60 SOURCE OF MIGRANT DATA: Using information obtained from the 1980 U.S. Census of Population and Housing, the U.S. Census Bureau creates the Public Use Microdata Sample A (PUMSA) . PUMSA includes the one fourth of the households that received the long form of the census questionnaires. It covers 11 million persons and over four million households. The PUMSA is a five percent sample of the national population. On a national scale, the migration data is only available for half of the five pencent sample. Thus, the PUMSA migration data is a 2.5 percent national sample. The out-migrant data file supplied by the Applied Population Laboratory (APL) is not a 2.5 percent national sample. The Applied Population Laboratory uses the Public Use Microdata Sample A (PUMSA) as a source of information about individuals who lived in a given state in 1975 and lived elsewhere in the U.S. in 1980. The Applied Population Laboratory at the University of Wisconsin-Madison constructs samples of migrant data for each state. The migrant data for this study was obtained from a data set constructed from the Public Use Microdata Sample A (PUMSA) by the APL. For the purpose of this study, the outmigrant files for all the states were provided in one large file. The file I received contained 1,163,180 records. In census data, 61 records do not equal individuals. The census data consist of two kinds of records: household and person records. For each household surveyed, there is one household record describing features that would apply to each member of the household (such as where they are currently living) and a set of one or more person records which describe features thathould or could be unique to each individual (such as age or occupation). Thus, for each person there are at least two records in the file. There is a person record unique to the person and a household record which may or may not be shared with other individuals depending on the number of individuals in that household. The result of this file structure is that it will contain more records than individuals. The file provided by the Applied Population Laboratory had 846,534 individuals. Not all of the individuals in the file were migrants. All the individuals in a household were included in the out-migrant file even if only one member of the household migrated. General Demographic Characteristics of Migrants. I present this information to demonstate that the migrants in this study are similar to migrants in other studies and therefore the results obtained from studying them are likely to be applicable to other instances of migration. Tables 4, 5, and 6 show that as in other studies of migration, migrants were mostly male, young and educated (Ravenstein, 62 1889: Thomas, 1938: Danzo, 1978: and Spengler and Meyers, 1977). Table 7 shows that, unlike other studies of migration, most of these migrants were married (Ravenstein, 1889). Table 4 Sex Sex Number Percent Males 174,137 60.0 Females 116,100 40.0 Total 290,237 100.0 Table 5 Education, Highest Grade Completed as of 1980 Education Level Number Percent eighth grade or less 17,102 5.9 ninth to eleventh 22,797 7.9 twelfth 86,424 29.8 Some College 54,071 18.6 Four yr college or more 109,843 37.9 Totals 290,237 100.0 Table 6 Age Age Group Number Percent 21 thru 29 131,527 45.3 30 thru 39 90,061 31.0 40 thru 49 42,325 14.6 50 thru 59 26,324 9.1 Total 290,237 100.0 63 Table 7 Marital Status Marital status Number Percent married 183,392 _ 63.2 widowed 3,120 1.1 divorced 27,295 9.4 separated 9,199 3.2 single 67,231 23.2 Total 290,237 100.0 It should be noted that the data file treats married couples as.two separate people so there is no risk of double counting or omitting spouses. A high proportion of this sample is college educated and it may be that such people are more likely to be married than the less educated. One implication of a high number of married couples is that it may dilute the apparent impact of occupation. Because the analysis treats the couple as two separate moving units rather than as one moving unit. In a married couple, it could be that only one person’s labor is un-marketable. Treating them as independent has the effect of diluting the proportion of the same which may be suspect to the effects of a changing occupational or industrial structure. In this study, the movement of a married couple counts as two movers. Each person’s movement and occupation or industry is treated separately, whereas in reality, the move may have been due to only been one person’s opportunity to 64 move while the spouse came along in order to remain with the mover. Traditionally, it has been the husband’s work which would have dictated the couple’s behavior. It seems to me that many women have recently begun to consider their own career development more seriously now than in the past. If this is true, then some of the movers in this study may have been men who were following their wives. In future studies, it would be desirable to examine the behavior of the sexes separately by marital status, but to do so here would open up a whole new area of inquiry much beyond the scope and intent of this paper. It has to be remembered that this is not a case study. I don’t have the opportunity to quiz these movers in depth about their attitudes or values. This is a secondary analysis of data that was collected by other people for their own purposes. The strength of this study is the breadth of the population that is covered and the increased confidence which that permits one to have in the results. The drawback is that the depth one can get from the knowledge of specific details about individuals is lost. PROCEDURE AND ANALYSIS The analysis is designed to address three hypotheses. Hypothesis One: Whether a migrant moved into or out of a state between 1975 and 1980 is related to the industry and occupation in which he or she worked. The existence of this relationship was tested with chi- square. The chi-square was calculated as a by-product of the migrant aggregation crosstabulation step. Hypothesis Two: Number of migrants will change in response to change in number of establishments. Number of migrants, instead of rate of migration, is used because I am assessing the net result of change in number of establishments, not the rate of change of migrations. Further, because I only have one observation of migration per migrant, I do not have the necessary information to calculate rates of migration. 65 66 Change in number of establishments for a given state can be measured in two ways. (A) The state is growing in comparision to other states. For example, Texas may be considered growing in eggplant proccessing if it has gained more eggplant processing plants than some other state. (8) The state is growing in comparison to its own earlier number of establishments. For example, Texas could be considered to be growing in eggplant processing if it now has more processing plants than it did five years ago, regardless of whether it now has more or fewer eggplant processing plants than some other state. Therefore, Hypothesis two is stated and tested in two ways to correspond to these measurement possibilities. II (A) The average number of in~migrants employed in a given industry will be higher in states that are growing in that industry compared to other states which are not growing in that industry. Hypothesis IIA was tested using Analysis of Variance of the effect of change in number of establishments in a given industry on the number of in- or out-migrants who worked in the given industry in 1980. For the ANOVA, the industrial sectors were categorized with a simple division of the states into two parts. The 67 dividing line put the fifty percent with the most growth into the growth category and the rest into the non-growing category. This resulted in two groups: one group with 26 states and the other with 24 states. The division is not exactly 25/25 because the dividing line was drawn at the point where a frequency distribution of change-in-number-of— establishments met or exceeded the fifty percent mark. Specific Growth Cut-off Points: Because 26 states had an increase in number of wholesale establishments greater than or equal to 124, the change in number of wholesale establishments in a state had to be greater than or equal to 124 for that state to be categorized as growing in wholesale. For retail establishments, the change in number of establishments had to be greater than or equal to 818. For manufacturing establishments, the change in number of establishments had to be greater than or equal to 397. For service establishments, the change in number of establishments had to be greater than or equal to 2,796. Thus, the definitions of growth used are specific to each kind of industry and have an implicit comparison of growth in one kind of industry in one state to growth in that same industry in another state. 68 II (B) The number of out-migrants from a given state who worked in a given industry in 1980 will correlate negatively with percent change in number of establishments in that industry in the state of origin between 1972 and 1977. That is, out-migration will decrease as the percent change in Inumber of establishment increases. Hypothesis 118 was tested using correlation of number and type of migrants in each industry with percent change in number of establishments in each industry. This consisted of Pearson product moment correlations of the number of in- and out-migrants in a particular type of industry with change in number of establishments for that industry type (and, incidentally, for the other three industry types as well). Essential Differences Between HIIA and HIIB, and HI: The analyses for Hypotheses IIA and 118 have one very important diffference from the analysis for Hypothesis I. The Hypothesis I analysis uses all of the eight major categories of industry used by the Census Bureau or all of the six major categories of occupation as appropriate. The analyses for Hypotheses IIA and 118 uses only four industry types: wholesale, retail, manufacturing, and service. 69 It will be possible to surmise the most likely causal direction of the effect of change in number of establishments because the change in establishments is measured between 1972 and 1977 while the migration had to have occured between 1975 and 1980. Since the change in number of establishments starts before the migration, the most likely direction of causality is that change in number of establishments has an impact on migration. Hypothesis Three: The relationship between changes in number of industrial establishments and migration will vary with migrant age. This comes from Saben (1964) who found that among migrants who moved for work related reasons, the percent moving because of a transfer was much higher in migrants aged 25 to 64 years old. This hypothesis was tested by doing the analyses for Hypotheses I, IIA, and 118 separately for migrants age 21 to 29 and age 30 to 59 and then comparing the results for each age group to see whether or not they were different from each other. RESULTS SECTION The results section will primarily address one hypothesis at a time. The exception to this will have to be hypothesis III. Hypothesis III is concerned with age differences. Because the analyses for hypotheses I and II include separate results for each age group, there will necessarily be some mention of age differences in the results described for hypotheses I and II. At the end of the results section, the differences between age groups will be summarized so that hypothesis III can be considered and discussed in its own right. Hypothesis I: For the majority of states, in-migrants will work in different industries or have different occupations than out-migrants. These analyses will be done separately to control for age and to allow comparison between the age groups. The crosstabulation for industry by in— versus out-migrant using migrants 21-29 years old for Alabama is given as an example in Table 8 (for all crosstabulations’ cell values, see the Appendices.) The industry categories in which migrants worked in 1980 are the columns of the table. For the rows, ’in’ means people who moved into Alabama between 1975 and 1980: ’out’ means people who moved out of Alabama between 1975 and 1980. For example, there were 150 people who were working in ’afm’ (agriculture, fisheries, and minerals) in 1980 and had moved to Alabama between 1975 and '70 71 1980. There were 123 people who were working in 'afm’ in 1980 and moved out of Alabama between 1975 and 1980. The point of this crosstabulation is ”Q: to compare number of people who moved in to people who moved out in a particular industry. The point is to determine whether a relationship exists between direction of migrants' movements and the migrants’ industries (or occupations) in 1980. The Chi—square test of independence proceeds in the following manner. Based on the row and column totals and the total number of observations possible for the table, an 'expected value' is calculated for each cell. The expected value is equal to the number of observations that would be expected to be in each cell if the two variables of interest were independent of each other. Chi-square then compares the expected value for each cell to the actually observed value for each cell. If the gattern of differences between expected and observed values is statistically unlikely, then the null hypothesis of independence is rejected. In this Chi-square calculation, a statistically significant result simply means that the two variables in the table have some relationship to each other. In this case, statistical significance does not imply anything at all about causality or the direction or the strength or the form of the relationship between the two variables. It simply means that some kind of relationship exists. 72 Table 8 Alabama Movers Age 21 to 29 Movement Directions Number of movers in each industry in 19 0 4gfm mfg tgc whl ret fin ser puba totals In 150 357 166 71 .242 75 401 91 1493 Out 123 273 B3 59 190 111 443 66 134B Totals 273 630 189 13m 432 186 844 157 2841 The Chi-square for this tabulation equals 29.75. This crosstabulation (see Table 8) was repeated for each of the fifty states and each of the two age groups for industries and occupations. Chi-square statistics were calculated for each of the resulting 200 crosstabulations. Migrant occupation and industry were categorized using the major divisions used by the census of population. The Census Bureau uses eight major divisions of industry. The Census Bureau uses six major divisions of occupation. The numbers in Tables 9, 10, 11, and 12 are the values of the Chi—square statistics for each of the separate cross tabulations of mover direction by mover occupation and industry for each mover age group and each state. Altogether, Tables 9 through 12 represent 20% separate crosstabulations and calculations of the Chi-square statistic. 73 Tables 9 and 10 show the Chi-square test results by state from the crosstabulation of mover direction by mover industry. The industrial sectors used in Tables 9 and 10 include: 1. Agriculture, Forestry, and Fisheries 2. Manufacturing 3. Transportation, Communication, and Public Utilities 4. Wholesale Trade 5. Retail Trade 6. Finance, Insurance, and Real Estate 7. Services, All Kinds a. Business and Repair Services b. Personal Services c. Entertainment and Recreation Services d. Professional and Related Services 8. Public Administration Table 9 shows the results for migrants 21 to 29 years old. Table 10 shows the results for migrants aged 30 to 59. Thirty-eight (38) states show a statistically significant relationship between mover industry and mover direction for movers aged 21 to 29. Forty-one (41) states showed a statistically significant relationship between mover industry and mover direction for movers aged 30 to 59. Thus, for both age groups, mover direction is related to industry. Industry may be more important for the older migrants than for the younger group based on the observation 74 that there are a larger number of statistically significant relationships for industry for the older group. Tables 11 and 12 show the Chi-square test results from the crosstabulation of movement direction by mover occupation by state. The occupation categories for Tables 11 and 12 include: 1. Managerial and Professional 2. Technical Sales and Administrative Support 3. Service 4..Farming, Forestry, and Fishing 5. Precision Production, Craft, and Repair 6. Operators, Fabricators, and Laborers Table 11 is for movers 21 to 29 years old. Table 12 is for movers 30 to 59 years old. Forty (40) states show a statistically significant relationship between movement direction and mover occupation for age 21 to 29. Thirty- seven (37) states showed a statistically significant relationship between movement direction and mover occupation for age 3% to 59. For both age groups there is a relationship between movement direction and mover occupation. Occupation may be more important for younger migrants than for older migrants based on the observation that there were a larger number of significant relationships for the younger age group. 75 It is apparent that direction of movement (entering or leaving a state) between 1975 and 1980 is not independent of occupation or industry in 1980. Tables 9 through 12 show that this dependency relationship exists in the vast majority of states. The first hypothesis is supported. It is supported for both age groups although the younger migrants may be more affected by occupational considerations and the older migrants may be more affected by industrial considerations based on the differences in their patterns of statistically significant results. The younger migrants had a larger number of significant relationships for occupational sectors than the older migrants. The older migrants had a larger number of significant relationships for industrial sectors than did younger migrants. This would be expected from Sarben's (1964) finding that intra- company transfers were more common in migrants ages 25 to 64 years old. It may also be that the younger migrants, with less time to acquire work experience, may be choosing work primarily on the basis of their training or education which may have been more directed toward an occupation than toward an industry. Table 9 Chi-square Statistics for Migrant Industry by Movement Direction by State for age 21 to 29. State Chi-square ;:§tate Ath-sguare Alabama 29.75*** Montana 8.50 Alaska 77.27*** Nebraska 8.28 Arizona 18.90** Nevada 86.19*** Arkansas 8.86 New Hampshire 20.79%! California 49945.00*** New Jersey 94.49*** Colorado 19.69** New Mexico 57.94*** Connecticut 14.74% New York 363.50fiii Delaware 7.49 N. Carolina 29.91*** Florida 96.44*** N. Dakota 15.89% Georgia 23.16** Ohio 50.45*** Hawaii 89.68*** Oklahoma 11.78 Idaho 17.76* Oregon 20.33%! Illinois 123.07*** Pennsylvannia 39.48*** Indiana 20.67%! Rhode Island 29.97*** Iowa 20.70** S. Carolina 12.67 Kansas 22.37** S. Dakota 4.35 Kentucky 34.63mee Tennessee 5.96 Louisiana 56.72%em Texas 52.32*** Maine 4.45 Utah 12.51 Maryland 109.00** vermont 11.51 Massachusetts 83.53%ee Virginia 126.50*** Michigan 78.50eme washington 21.96** Minnesota 24.86*** N. virginia 30.39*** Mississippi 9.69 wisconsin 38.98*** Missouri 9.82 Wyoming 79.73*** * significant at the :05 level ** significant at the .01 level *** significant at the .001 level 77 Table 10 Results of Chi-square Analyses for Movement Direction by Migrant Industry by State for age 30 to 59. State Chi-square State Chi-sguare Alabama 8.43 Montana 15.71 * Alaska 60.70 Nebraska 18.22 * Arizona 7.72 Nevada 156.70 *** Arkansas 21.87 * New Hampshire 16.18 ** California 157.20 *** New Jersey 74.36 *** Colorado 20.80 * New Mexico 22.44 ** Connecticut 71.41 *** New York 114.40 *** Delaware 5.14 N. Carolina 38.55 *** Florida 129.80 *** N. Dakota 29.07 *** Georgia 8.67 Ohio 49.38 *** Hawaii 37.26 *** Oklahoma 14.57 * Idaho 18.80 ** Oregon 43.70 *** Illinois 89.59 *** Pennsylvannia 48.28 *** Indiana 48.65 *** Rhode Island 22.30 ee Iowa 9.79 S. Carolina 16.69 9 Kansas 18.65 ** S. Dakota 7.16 Kentucky 24.82 *** Tennessee 8.45 Louisiana 16.44 * Texas 52.45 *** Maine 19.82 ** Utah 9.17 Maryland 108.50 *** Vermont 9.32 Massachusetts 115.80 *** Virginia 132.80 *** Michigan 90.06 *** Nashington 52.16 *** Minnesota 27.79 *** w. Virginia 24.72 *i* Mississippi 7.90 Nisconsin 33.50 *** Missouri 22.86 *i Hyoming 31.48 *** * significant ** significant ems significant at the .05 levél .01 level .001 level at the at the 78 Table 11 Chi- square Statistics for Migrant Occupation by Movement Direction by State for age 21 to 29. gtate Chi-square State Chi-sguare Alabama 31.52!!! Montana 3.44 Alaska 47.00!!! Nebraska 24.75!!! Arizona 6.55 Nevada 46.90!!! Arkansas 37.57!!! New Hampshire 22.56!!! California 18616.00!!! New Jersey 77.56!!! Colorado 21.97!!! New Mexico 7.23 Connecticut 36.30!!! New York 68.78!!! Delaware 11.96! N. Carolina 12.05! Florida 37.97!!! N. Dakota 10.69 Georgia 6.45 Ohio 85.23!!! Hawaii 39.97!!! Oklahoma 24.92!!! Idaho 14.97! Oregon 18.88!! Illinois 135.30!!! Pennsylvannia 137.30!!! Indiana 73.32!!! Rhode Island 81.15!!! Iowa 14.53! S. Carolina 5.66 Kansas 17.45!! S. Dakota 16.25!! Kentucky 9.04 Tennessee 13.86! Louisiana 10.97 Texas 35.60!!! Maine 19.23!! Utah 40.37!!! Maryland 21.44!!! Vermont 10.21 Massachusetts 36.74!!! Virginia 50.70!!! Michigan 51.40!!! Washington 14.44! Minnesota 8.56 W. Virginia 19.50!! Mississippi 11.03 Wisconsin 109.00!!! Missouri 19.54!! Wyoming 16.43!! ! significant at’fhe .05 level !! significant at the .01 level !!! significant at the .001 level Table 12 Chi-square Statistics for Movement Direction by Migrant Occupation by State for age 30 to 59. State Chi-square State Chi-square Alabama 10.27 Montana 23.53 !!! Alaska 25.64 !!! Nebraska 15.57 !! Arizona 8.82 Nevada 40.31 !!! Arkansas 9.57 New Hampshire 4.93 California 63.56 !!! New Jersey 86.93 !!! Colorado 8.75 New Mexico 7.12 Connecticut 22.80 !!! New York 204.70 !!! Delaware 7.99 N. Carolina 34.75 !!! Florida 49.24 !!! N. Dakota 23.18 !!! Georgia 9.94 Ohio 40.27 !!! Hawaii 29.06 !!! Oklahoma 20.92 !!! Idaho 6.27 Oregon 12.85 ! Illinois 95.33 !!! Pennsylvannia 46.61 Indiana 33.59 !!! Rhode Island 22.07 !!! Iowa 4.03 S. Carolina 19.87 !! Kansas 8.06 S. Dakota 17.70 !! Kentucky 19.28 !!! Tennessee 34.31 !!! Louisiana 21.97 !!! Texas 35.34 !!! Maine 15.29 !!! Utah 14.56 ! Maryland 19.70 !! Vermont 5.30 Massachusetts 67.30 !!! Virginia 16.73 !!! Michigan 27.49 !!! Washington 19.44 !! Minnesota 10.70 W. Virginia 14.47 !! Mississippi 30.05 !!! Wisconsin 50.68 !!! Missouri 60.11 !!! Wyoming 15.42 !! ! significant at the {05 leveTT !! significant at the .01 level !!! significant at the .001 level 80 RESULTS FOR HYPOTHESES II(A) AND 11(8): The analyses for Hypothesis I included eight categories of industry. The analyses for Hypotheses II(A) and II(B) did NOT include as many of the industrial sectors as did the analysis of Hypothesis I. Hypotheses II(A) and II(B) use only four sectors: wholesale, retail, service, and manufacturing. Hypotheses II(A) and II(B) are a direct assessment of the effect of growth in number of four types of industrial establishments on movement direction of people employed in those four types of industrial establishments. In this paper, GROWTH NEVER REFERS TO MIGRATION; GROWTH ONLY REFERS TO NUMBER OF ESTRBLISHMENTS. The categorization of states into growing versus non—growing categories will follow the 26/24 split described in the Data and Methods section. The twenty-six (26) states with the greatest gains in number of industrial establishments will be categorized as growing. The identification of the states with the largest increases was determined separately for each of four industrial sectors: manufacturing, wholesale, retail and service. 81 Hypothesis II(A): The average number of in-migrants employed in a given industry in 1980 will be higher in states that are increasing in number of establishments of that industry. This analysis will be done separately to control for age and to allow comparison of results between the age groups. Younger Migrants Table 13 presents the results for eight separate one-way ANOVAs. There is one ANOVA for each industrial sector and each direction of movement of people employed in that industrial sector. Examples: Sample Table 1 States Growing States Not Growing in Manufacturing in Manufacturing Establishments Establishments In-movers in manufacturing sector Sample Table 2 States Growing States Not Growing in Manufacturing in Manufacturing Establishgents Establishment; Out-movers in manufacturing sector For migrants 21 to 29 years old, growth in number of establishments had a significant effect on both in- and out—migration in the manufacturing and service sectors (see Table 13). For the 26 states that were growing in manufacturing compared to other states, the average number 82 of in-migrants who worked in manufacturing was 545.23. For the 24 states that were not growing in manufacturing compared to other states the average number of in-migrants was 310.75. Clearly, the average number of in-migrants who worked in manufacturing was higher in states that were growing in manufacturing compared to other states. The F statistic for this comparison is 5.001 and is sigificant at the 0.030 level. For states that were growing in manufacturing, the average number of out-migrants was 451.23. For states that were not growing in manufacturing, the average number of out-migrants was 279.04. The F statistic for this comparison is 4.035. It is significant at the 0.050 level. This result was not expected. I did not expect average gggrmigration to be higher in states that are growing in industrial establishments than in states that are not growing in industrial establishments. It is particularly suprising in light of the fact that the establishments and the migrants are assumed to be in the same industry. Migrant industry was observed in 1980. Establishment industry was observed from 1972 to 1977. This finding is anomalous if we assume that migrant industry was the same in 1975 as it was in 1980. If migrant worked in manufacturing in 1975 and the state was growing in number of manufacturing establishments between 1972 and 1977, why did the migrant migrate? This will be explored more after presentation of the service 83 sector results because the service sector results show the same pattern. The same pattern of results was observed for the service sector. The average number of in-migrants agg_the average number of out-migrants who worked in service in 1980 was higher in states that were growing in number of service establishments compared to other states between 1972 and 1977 (see Table 13). There were no statistically significant results in the 21 to 29 age group in the wholesale or retail trade sectors. This means that, if a state was growing in number of manufacturing (or serVice) establishments between 1972 and 1977, it was more likely than non-growing states to have more manufacturing ( or service) workers both enter and leave. I expect people who work in an industry to enter a state that is growing in that industry, but why would people who work in an industry leave a state that is growing in that industry9 There are two possible explanations. The first is that migrant industry may have changed between 1975 and 1980. If some people did not work in manufacturing or service in 1975, then change-in number of manufacturing or service establishments between 1972 and 1977 would not have influenced their behavior. The second possibility takes advantage of the two year overlap between period of establishment change and migration period. Change in 84 establishments is observed from 1972 to 1977. Inter-state migration is observed from 1975 to 1980. Migration and change in establishments overlap from 1975 to 1977. The potential migrant may have worked briefly in the growing industry before leaving in order to make himself more (employable at another location which was desired for some unknown reason. Table 13 Number of States, Average Number of Migrants, and ANOVA Results by Establishments Growth Category and Migrant Industry and Type, Age 21 to 29. Non-Growth Growth ANOVA Average (N) Average (N) F (p) Migrant Industry and Type: Manufacturing In-migrants 310.75(24) 545.23(26) 5.001(.030)! Out-migrants Wholesale In-migrants Out-migrants Retail In-migrants Out-migrants Service 279.04(24) 67.67(24) 75.13(24) 265.96(24) 304.54(24) 451.23(26) 107.62(26) 83.42(26) 434.54(26) 330.08(26) 4.035(.050)! 3.069(.086) 0.182(.671) 3.872(.055) 0.099(.754) 48.593(.000)! 24. 317 (. 000) ! 1042.38(26) 1028.54(26) 278.33(24) 247.54(24) In-migrants Out-migrants Results for Older Migrants Table 14 presents the same analysis as Table 13, but for the 30 to 59 year old movers. The average number of in-migrants who worked in manufacturing for states that were growing in number of manufacturing establishments was 891.31. The 85 average number of manufacturing in-migrants for states that were not growing in number of manufacturing establishments was 432.54. The F statistic for this comparison was 4.838. It was significant at the 0.033 level. The same pattern of results was found for states growing in number of wholesale and retail trade establishments. However, in the service sector we find the same pattern of results that we found in the service sector for the 21 to 29 year old age group. The average number of service in-migrants and the average number of service out-migrants are higher in states that are growing in number of service establishments (see Table 14). The possible reasons for this are the same as those offered for the 21 to 29 age group. Hypothesis II(A) is supported, but the total picture is mixed. Younger migrants show significant effects in the manufacturing and service industries. The older migrants show significant effects for all four industries. In the results for hypothesis I, it was observed that there were more significant results in the analysis of industry for the older migrants. That observation is certainly consistent with the results of hypothesis II(A). There are more statistically significant results for the effect of industry growth for older migrants than for younger migrants. Further, the pattern of significant results is more interpretable for the older migrants, with the exception of the service sector. It was noted in the literature review 86 that in the 1970’s the service sector of the economy grew enormously in all areas of the country. This may explain why growth in number of service establishments does not have a consistent effect. Table 14 Number of States, Average Number of Migrants, and ANOVA Results by Establishments Growth Category and Migrant Industry and Type, for Migrants Age 30 to 59 Non-Growth Growth ANOVA Average (N) Average (N) F (p) Migrant Industry and Type: Manufacturing In-migrants Out-migrants Wholesale In-migrants Out-migrants Retail In-migrants Out-migrants Service In-migrants Out-migrants 432.54(24) 383.33(24) 95.04(24) 106.96(24) 262.2l(24) 279.25(24) 393.00(24) 294.21(25) 891.31(26) 539.35(26) 196.08(26) 125.31(26) 537.23(26) 324.58(26) 1548.42(26) 1185.69(26) 4.838(.033)! 1.315(.257) 4.515(.039)! 0.337(.565) 4.512(.039)! 0.272(.604) 22. 050 (. 000) ! 26.995(.000)! II(B) The number of out-migrants from a given state who worked in a given industry in 1980 will correlate negatively with percent change in number of establishments in that industry in state of origin between 1972 and 1977. This analysis will be done separately for each age group to control for age and to permit comparison between the age groups. 87 Results for Younger Migrants: Table 15 presents the results for movers age 21 to 29 years old. It is implicit in calculation of percent change in establishments for each state that we are comparing the present status of each state to its past status. This is a different conceptualization of growth than used in hypothesis II(A) and must be kept in mind when interpreting the results. Reading down the first column of Table 15, the correlation between percent change in number of service establishments and number of service sector in-migrants is -0.1399. This is not statistically significant. However, the correlation between percent change in number of service establishments and number of service sector out-migrants is -0.2869 and this is statistically significant at the 0.022 level. This means that the states with the largest increase in number of service establishments (compared to the past number) had the smallest number of out-movers who worked in the service sector. Moving through the rest of Table 15, percent change in number of service establishments was negatively correlated with out-migration in manufacturing and wholesale and with in—migration in manufacturing and wholesale. It is the significant negative correlations with number of in-migrants 88 that are counter-intuitive here. A negative correlation between percent change in number of service establishments and number of in-migrants means that the larger the states’ percent change in number of establishments, the smaller number of in—migrants. This result was found even when the industries of the establishments and the industries of the migrants are the same. I want to finish describing Table 15 before I try to tackle this. Continuing with Table 15 column two, percent change in number of manufacturing establishments was negatively correlated with out-migration in all industries. In column three, percent change in number of retail establishments was negatively correlated with out-migration in the service industry and in-migration in the maufacturing industry. In column 4, percent change in number of wholesale establishments was not correlated with in- or out-migration in any industry. Negative correlations between out-migration and percent change in number of establishments means that number of out- migrants for a state decreased with an increase in percent change in number of establishments. This is what I expected. I did not expect a negative correlation between number of in-migrants and percent change in number of establishments. A negative correlation between in-migration and percent change in number of establishments means that as 89 percent change in number of establishments gets larger, the number of in-migrants gets smaller. This happened and was statistically significant for four out of twelve statistically significant correlations in Table 15. Although unexpected, it is consistent with the finding for hypothesis IIA that out-migration can be higher in states that have increased in number of establishments in comparison to other states. In both cases, these counter- intuitive findings are found in the 21 to 29 age group. If we assume that people 21 to 29 years old are just entering the labor market, it may be that an increase in demand for labor due to an increase in number of establishments is largely met by the 21 to 29 year olds newly entering the labor market at origin. If this is the case, there would be relatively little incentive for 21 to 29 year olds from other states to move in. Other factors to consider interpreting these results is that persons may work at different jobs for different reasons at different times in life. One may take whatever one can get to earn money while concentrating on gaining training or education for some other occupation. The degree of experience one has in a particular industry may not matter much for entry level jobs, but may be essential for higher level jobs. Location and job may weigh differently for young single people compared middle—age married people who are in mid-career. 90 Not all increases in number of establishments will lead to a proportionate increase in number of jobs. A large increase in number of small establishments may not increase the number of jobs available as much as an increase in the number of large industrial establishments. Some jobs such as secretary or bookeeper can be practiced in a range of industries. Such occupations may not be as responsive to industrial establishment change as occupations which can only be practiced in a particular industry, e.g.,' shipwrights. Occupations which can only be practiced in particular industry require adaptation to their decline by migration or a change of occupation. If a person cannot practice his or her original occupation at origin or choose a new occupation at origin, then that person will have to migrate. 91 Table 15 Correlations of In- and Out-migration by Industry with Percent Change in Number of Establishments by Industry for Migrants 21 to 29 years (n=50) r,(p)! Establishment Industry Sectors: Service Manufacturing Retail Wholesale Migrant Industry Sectors: Service In -.1399 -.2048 -.0931 .0383 (.166) (.077) (.260) (.396) Out -.2869 -.4332 -.3021 -.1374 (.022)! (.001)! (.017)! (.171) Manufacturing In -.2927 -.3376 -.2580 -.0819 (.020)! (.008)! (.035)! (.286) Out -.2381 -.3649 -.2173 -.0714 (.048)! (.005)! (.065) (.311) Retail In -.1327 -.0769 .0162 .0954 (.179) (.298) (.456) (.255) Out -.2083 -.3294 -.1784 -.0481 (.073) (.010)! (.108) (.370) Wholesale In -.2381 -.1883 -.1474 .0376 (.048)! (.095) (.154) (.398) Out —.2754 —.3611 -.2200 -.0624 (.026)! (.005)! (.062) (.333) Results for Older Migrgnts: Table 16 presents the same analysis as Table 15, but is for movers 30 to 59 years old. Reading down the first column of Table 16, for migrants age 30 to 59, the correlation between percent change in number of service establishments and number of service sector out-migrants is -0.0183. This correlation is not statistically significant. The only statistically significant correlation in the first column of 92 results is the correlation between percent change in number of service establishments and number of wholesale sector out-migrants which is -0.2241 and significant at the 0.044 level. This means that the larger a state’s percent change in number of service establishments, the smaller the number (of out-migrants in the wholesale sector. Moving through the rest of Table 16, in column two, percent change in number of manufacturing establishments is significantly negatively correlated with out-migration in all four industrial sectors. This means that the larger the state’s percent change in number of manufacturing establishments, the smaller was the number of out-migrants in all four industrial sectors. In column three, change in number of retail establishments is not significantly correlated with number of out-migrants in any industrial sector, although it is close for the manufacturing and wholesale sectors. In column four, change in number of wholesale establishments is not significantly correlated with either in or out-migration in any industry. Unlike the younger migrants, this group does not show any statistically significant correlations with an unexpected sign. However, there are only five statistically significant correlations while the younger migrants had twelve. 93 It is quite interesting that some of the significant correlations are between percent change in number of establishments in a certain industry and migrants in another industrial sector. This could point to a 'domino effect' rippling through a location. It is also interesting that in this older group, percent change in number of establishments is only related to number of people who leave a state and has no relationship with how many people enter a state. It seems that the decision to migrate may be separate from the choice of destination. In the younger migrants (Table 15) the percent change in number of establishments had a relationship with number of in-migrants in four instances. 94 Table 16 Correlations of Number of In- and Out-migrants by Industry with Percent Change in Number of Establishments by Industry, for Age 30 to 59 (n=50), r (p)! Establishments Industry Sectors: Service Manufacturing Retail Wholesale Migrants Industry Sectors: Service In -.0183 -.0637 .0489 .1358 (.450) (.330) (.368) (.173) Out -.1434 -.2686 -.1487 .0093 (.160) (.030)! (.151) (.474) Manufacturing In -.1141 -.1903 -.0793 .0465 (.215) (.093) (.292) (.374) Out -.2339 A -.3810 -.2260 f.0592 (.051) (.003)! (.057) (.342) Retail In ‘ -.0215 .0235 .1149 .1593 (.441) (.436) (.213) (.135) Out —.1643 -.2698 -.1258 -.0007 (.127) (.029)! (.192) (.498) Wholesale In -.0843 -.0606 -.0156 .1372 (.280) (.338) (.457) (.171) Out -.2441 -.3706 -.2310 -.0367 (.044)! (.004)! (.053) (.400) Hypothesis III: The relationship between changes in industrial structure, as measured by change in number of establishments will vary with age. Tatmle 17 presents a brief comparison of the number and Pat terns of statistically significant results between the AVC'Llnger migrants and the older migrants from Tables 9 95 through 16. It appears that hypothesis III is supported. The migrants do show different results by age group. Table°17 Comparing Results for In- and Out-migrants. Only Statistically Significant Results are Included. Age Group Test Tables 21 to 29 30 to 59 X2 Occupation ( 9 & 10) 41 significant 37 significant x2 Industry (11 & 12) 38 significant 41 significant ANOVA (13 8 14) in— for service in- all industries - in- for manufacturing out- service out- for service out- for manufacturing Correlation 8 signficant for out 5 significant (15 & 16) 4 significant for in for out- None for in— What they do for a liVing is about equally important for both age groups for influencing where they are going to go. Remember that the ANOVA uses a measure of growth that compares each state to all the other states while the correlation uses a measure of growth that compares each state to its previous condition. As Table 17 (reading down) Shows, the older migrants are more consistently likely to 19%ave a state that is doing worse than it used to and to “Cive to a state that is doing better than other states than a?“ e younger migrants. 96 For the younger migrants, the pattern of results is less clear. States in which the number of establishments is changing appear to gain or lose 21 to 29 year olds regardless of the direction of the change. This could be due to older migrants being unwilling or unable to change the industry in which they work, or to older migrants possibly being more sophisticated in terms of seeking information about other places. An additional possiblity, is that young migrants, who may have less work experience, have difficulty finding that first job and have to go where ever they can get one. Young migrants are moving to their first jobs. Older migrants are moving to continue their line of work. If one conceptualizes migration as niche seeking behavior and a line of work as a niche, it would appear that the type of niche which one seeks may grow less flexible over the course of the life span. are £011 943? SN: low ...u exf'o' Fig] CONCLUSIONS AND DISCUSSION I began this study by asking why the Sheppey Islanders didn’t respond to increasing unemployment by migrating. I suggested that the questions usually asked about migration are not well formed questions and suggested an alternate point of view, adaptation. Push-pull studies (Greenwood, 1975; Lee, 1966) typically study movement between an origin and a destination. The movement between origin and destination is then referred to as the migration stream. One or more migration streams may be examined, but each stream is considered separately from the others. To examine the adequacy of push-pull theory by first selecting an instance of migration and then asking what was bad about origin and good about the destination assumes that push-pull is a true and adequate explanation of migration behavior and would only accidentally let one discover whether it really was a true and adequate explanation of migration or not. A more adequate explanation of migration would lead to predictions about when it would not happen as well as when it would happen. The push-pull explanation doesn’t predict when conditions might be bad in one place and better in another place, but migration doesn't happen or allow for the possibility that migrants may come from many places to a single destination 97 98 or come from a single origin to a variety of places. That the latter possibility is overlooked is hard to explain to residents of the U.S. since most of their ancestors came from several different places to the U.S.. The push-pull explanation is revealed as inadequate by the existence of a counter example such as Sheppey Island. If a lack of jobs causes people to move, then whenever there are not enough jobs people should move. If a situation is found where there are not enough jobs and people don’t move, then there must be more to explaining migration. Hawley (1950), Karp and Kelly (1971), and Howland (1988) argue that migration is a response to environmental change and a result of insufficient opportunities at origin. Karp and Kelly (1971) assert that migration is related to opportunities for functional expansion of activities. Clearly, decline in number of establishments represents a decline in the potential for functional expansion of activities. If there are fewer establishments, then there are fewer opportunities to earn a living and a person might have to move. The results of this study support all of them. A decline in number of establishments represents a change in the environment which directly lowers and eventually results in in-sufficent opportunities at origin. When the number of establishments decline, the older workers 99 move out. When the older workers move, they choose places that are increasing in number of establishments compared to other states. Lowry (1969) asserts that migration depends more on conditions at destination than at origin. I do not agree with that. Significant relationships were found for both out-migration and in-migration. The older migrants left places that were declining and went to places that were growing. I would argue that conditions at destination are very important, but not necessarily more important than conditions at origin. The results of this study would seem to imply that age, occupation, and industry of the potential migrants are all important factors. The different patterns of results for in- versus out-movement suggest to me that the decision to emigrate and the decision about specifically where to go are governed by separate considerations. The desire to emigrate for older workers may be chiefly governed by conditions at origin (hypothesis IIB), but it seems that the decision about where to go is based on comparison amoung alternate locations (hypothesis IIA). At the very least, I have demonstrated that the factors that influence in-migration are not merely the mirror image of the factors that influence out-migration. While migrant 100 industry and/or occupation certainly do have a relationship with entering or leaving a particular place, the relationship is not nearly as simple as a push-pull perspective might suggest, especially for the younger migrants. I suggest that age of migrant, probably because of its relationship to opportunity for experience, can make a difference in how relatively important conditions at origin or destination are. In hypothesis 1, the younger migrants had more statistically significant relationships for the relationship between their occupations and the direction of their movement with respect to any particular state. In contrast, the older migrants had more statisically significant relationships between their industrial sectors and the direction of their movement with respect to a particular state. This may suggest that occupation is the most salient consideration for younger migrants, while industry is the most salient consideration for older migrants. If this difference does exist, it would imply that younger migrants ought to show fewer significant relationships between their movements and changes in number of industrial establishments, which is what I found. In the analyses of hypotheses IIA and 118, there were additional differences between young migrants and old migrants. Hypothesis IIA measured growth by comparing each n) 101 state to all other states. The younger migrants showed statistically significant relationships here, but did not seem to distinquish between moving in or moving out of a state that was undergoing change with respect to other states. In contrast, the older migrants had most of their significant relationships for in-migration. It seems very likely to me that the older migrants were choosing a destination based on how attractive one state was compared to another state, whereas it does not seem as if the younger migrants were making such a distinction. This again would be.explained if younger migrants are looking for their first jobs and choosing more on the basis of their occupational education or training than on their industrial experience. The analysis of hypothesis IIB used a definition of growth that compared each state to its own previous condition without regard for the condition of any other state. Again, age differences occured. The majority of significant correlations for young migrants are for out-migration. The majority of significant results for older migrants are for out-migration. For both groups, the correlations are negative. It just doesn't seem as if the younger migrants are responding to changes in number of establishments to the extent that older migrants do. The combined results of hypotheses IIA and IIB suggest to me that older migrants leave states that are not doing as well as they used to and enter states that are doing better than other states. This 102 behavior seems eminently rational. However, the most logical explanation for the younger migrant’s behavior may be that changes in number of industrial establishments just don’t determine their behavior as much as something else does. The results of hypothesis I suggest that the something else could be occupation. The direction of movement for 21 to 29 year olds is definitely linked to migrants’occupations more frequently than to migrants’ industries. Intuitively, it makes sense that 21 to 29 year olds might behave differently than 30 to 59 year olds if one considers the possibility that 21 to 29 year olds have just finished their training and/or their education and may not have much work experience related to the occupation they want to pursue or the industry in which they want to work. Additionally, it may be relatively easy to get an entry level job in an industry without specific work experience in that industry, but that higher level jobs require industry specific experience. This would have the consequence that industry experience or conditions are relatively unimportant early in one’s work life, but become more critical over time. This would agree with and help explain Howland’s (1988) finding that older displaced workers have a harder time adjusting to industrial change. ‘Yl 103 The different results for young migrants and migrants over 30, combined with Howland’s finding that older migrants are less likely to be able to find new jobs after displacement suggests that niche seeking may indeed be a factor in- migration. Howland’s findings imply that there is a difference in ease of finding a niche at different ages. In my study, it appears that older migrants were more likely to leave in response to change in number of establishments. This would certainly be consistent with a relative lack of ability to find or keep a suitable new niche at origin. I would agree that there is a difference in ease of niche finding at different ages. The most intriguing finding is that out-migration of people 21 to 29 years who work in a given industry can be higher for states that are growing in that industry than in states that are not growing in that industry. If this result is confirmed by further research, it would certainly suggest that merely creating additional jobs in a state is not a panacea for slowing out-migration, especially of the younger migrants. The jobs must be the kinds of jobs that the pre- change population can be or is trained to do. If industry is confirmed by other research to be as critical for workers 30 to 59 as it is in my study, then it seems that the new jobs need to be in similar or compatible industries to the original industries. In the case of Sheppey Island, the pre-change population was composed primarily of carpenters. 5‘s 5}- \Eah 104 The new industries did not use carpenters. It might have been more useful to the islanders if the new industries had been wooden furniture makers, wooden toy makers, pre—fab home builders, or makers of small wood products such as toothpicks, popsicle sticks, or‘wooden pallets used for stacking and shipping merchandise. At present, education seems to tend toward development of specific technical skills. This may not be the best strategy in a rapidly changing world. Specific technical skills can become outdated. General skills such as organization, logical reasoning, and the ability to think and reason with numbers may be much less likely to become outdated and can be applied in a wide range of industries. I would also suggest that we should provide opportunities for re-training of older workers. Learning new skills should be a life long activity. The idea that you can learn all you will ever need to know in the first part of your life and then just coast on that knowledge is antiquated. People should be encouraged to develop a wide range of skills and to accquire new ones throughout their lives. Learning is a skill. The more things that you practice learning, the better you get at learning. The habit of life-long learning gives you the advantage of having a wider range of skills to fall back on if the environment changes 105 and the advantage of being better able to learn and use new information that comes along. Future research in this area should further explore the relationship between age, industry of employment, changes in industrial structure, and migration. If it were possible to collect the data, it might be interesting to look at the kind of education and training the migrants had experienced and to do more detailed examination of the specific skills involved in the occupations and industries in which they worked. It would be useful to repeat this study with the 1990 census data to confirm these findings, but it would be even more useful if the migrant occupational data could in some way be linked to data on changes in the opportunities to practice those occupations in the states entered and exited. The age analysis might to be extended by breaking down the 30 to 59 year old group into smaller age groups, perhaps a group for each decade. If the frequency and significance of industry in comparison to occupation continued or even increased, this would lend additional strength to the idea that individuals tend to get sort of ’frozen’ into particular industries and find it increasingly difficult to change to new industries as they become older. 106 I have demonstrated that destination, origin, and migrant characteristics are all important to understanding migration. I have shown that an adaptation perspective is useful for understanding migration and provides more detailed and more focused understanding of migration than the push-pull approach. The success of the adaptation perspective rests in its consideration of the population and environment and the interaction between them. I have produced a counter-example, Sheppey Island, to the predictions of the push-pull hypothesis. I have shown the greater explanatory, and thus predictive, power of the adaptation perspective. I have shown that the decision to migrate and the ch0ice of destination are separate decisions. I have shown that the probabiltiy of movement and the factors which predict movement are related to age. I have shown that migration is a rational response to environmental change. LIST OF REFERENCES 107 Bureau of the Census, Census Bureau Presentation. 1987. Presented March 4, 1987 in Lansing, MI. Danzo, J. 1987. Does unemployment affect migration-evidence from microdata. 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Geographic Mobility and Employment Status, March 1962 and March 1963. Monthly Labor Review. 1964, 87(8), pp 873- 881. Schwarzweller, Harry K., Brown, James S., and Mangalam, J. J. 1971. Mountain Fagilies in Transition. University Park: The Pennsylvannia University Press. Ravenstein, E.S. 1889. The Laws of Migration, Journal of the Royal Statistical Society Vol. 52 (June 1889) pp. 241- 301. 110 Thomas, Dorothy S. 1938 Research Memorandum on Migration Differentials. Social Science Research Council, N.Y.:N.Y. Vogelnik, D. and Ferligoj, A. 1978 Ecological determinants of change. In The Social Ecology of Chang . Zdravko Mlinar and Henry Teune (Ed.s), Sage Studies in International Sociology, California. APPEND 1 CES APPEND 1 X A 111 APPENDIX A Industry Crosstabulation Cell Values for Each State. Using Migrants Age 20 to 29. Number of migrants who moved In or Out of each state 1975 to 1980 by Industries in which -those migrants worked in 1980. Industry Abbreviations: afm agriculture, forestry, fisheries, and mining mfg manufacturing t&c transportation, communication, and other public utilities whl wholesale ret retail fin finance, insurance, and real estate ser service puba public adiministration Alabama state indus 1 afm mfg t&c whl ret fin ser puba totals in 150 357 106 71 242 75 401 91 1493 out 123 273 83 59 190 111 443 66 1348 totals 273 630 189 130 432 186 844 157 2841 expected values for each of the cells above 143.5 331.1 99.32 68.32 227.00 97.75 443. 129.5 298.9 89.68 61.68 205.00 88.25 400. Alaska state indus 2 afm mfg t&c whl ret fin ser in 70 45 54 12 91 31 184 out 38 121 35 17 72 24 117 total 108 166 89 29 163 55 301 5 82.51 5 74.49 Chisq 29.75 puba totals 83 570 33 457 116 1027 chisq 77.27 I: II t t Arizona state 4 afm in 364 out 215 total 579 Arkansas state afm mfg in 140 out 135 total 275 California state 6 afm in 1542 937 total 2479 out Colorado state 8 afm in 544 out 333 total 877 Connecticut state 9 afm in 83 out 112 total 195 indus mfg t&c 412 136 322 107 734 243 indus t&c whl 249 63 209 78 458 141 indus mfg t&c 3913 811 1487 553 5400 1364 indus mfg t&c 526 257 368 155 .894 412 indus mfg tac 490 98 489 92 979 190 whl ret 100 525 56 276 156 801 ret fin 40 148 35 170 75 318 whl ret 617 2632 302 1364 919 3996 whl ret 139 651 96 371 235 1022 whl ret 60 215 73 280 495 133 112 fin 175 103 278 ser 48 62 110 fin 1049 408 1457 fin 294 126 420 fin 146 142 288 SEY‘ 774 434 1258 puba 265 261 526 591” 4411 2139 6550 591‘ 1144 626 1770 ser 519 634 1153 puba totals 107 2593 72 1635 179 4228 chisq 18.9 totals 35 988 36 986 71 1974 chisq 8.86 puba totals 518 15493 376 7566 894 23059 chisq 49945 puba totals 165 3720 100 2175 265 5895 chisq 19.69 puba totals 51 1662 74 1896 125 3558 chisq 14.74 P J9 Delaware state indus 10 afm mfg in 43 89 out 32 82 total 75 171 t&c 27 28 55 District of Columbia state indus 11 afm mfg in 30 39 out 37 75 total 67 114 Florida state indus 12 afm mfg in 872 914 out 484 810 total 1356 1724 Georgia state indus l3 afm mfg in 209 531 out 178 414 total 387 945 Hawaii state indus 15 afm mfg in 77 54 out 93 171 total 170 225 t&c 39 69 108 t&c 486 280 766 tac 258 129 387 t&c 48 75 123 whl 13 10 23 whl 12 20 32 whl 274 209 483 whl 159 95 254 whl 17 46 63 ret 69 68 137 ret 85 97 182 ret 1520 782 2302 ret 540 321 861 ret 196 162 358 fin 19 19 38 fin 53 67 120 fin 515 294 809 fin 200 153 353 fin 60 42 102 ser 116 154 270 SEY’ 405 362 767 SE!“ 2129 1270 3399 ser 979 655 1634 ser 270 217 487 puba totals 20 396 24 417 44 813 chisq 7.491 puba totals 215 878 167 894 382 1772' chisq 33.16 .puba totals 261 6971 175 4304 436 11275 chisq 86.44 puba totals 157 3033 113 2058 270 5091 chisq 23.61 puba totals 56 778 68 874 124 1652 chisq 89.68 Ka- SI 1r. Ou tc Idaho state 16 afm in 140 out 85 total 225 Illinois state 7 afm in 264 out 392 total 656 Indiana state 18 afm in 162 out 182 total 344 Iowa state 19 afm in 125 141 total 266 out Kansas state 20 afm in 155 183 total 338 out indus mfg 116 116 232 indus mfg 1312 858 2170 indus mfg 599 492 1091 indus mfg 297 286 583 indus mfg 349 273 622 t&c 59 47 106 t&c 333 307 640 t&c 142 130 272 t&c 69 102 171 t&c 108 112 220 whl 30 27 57 whl 251 191 442 whl 83 98 181 whl 64 65 129 whl 64 82 146 114 ret 132 127 259 ret 704 747 1451 ret 349 390 739 ret 205 256 461 ret 239 224 463 fin 40 30 70 fin 325 331 656 fin 115 144 259 fin 68 110 178 fin 73 97 170 SE)" 226 181 407 ser 1541 1619 3160 SE)“ 706 716 1422 SE!“ 470 594 1064 591“ 411 481 892 puba totals 56 799 22 635 78 1434 chisq 17.76 totals 4912 4621 9533 puba 182 176 358 chisq 123.7 puba totals 91 2247 74 2226 165 4473 chisq 20.67 puba totals 59 1357 43 1597 102 2954 chisq_ 20.7 puba totals 65 1464 64 1516 129 2980 chisq 22.37 115 Kentucky state indus 21 afm mfg t&c whl ret fin ser puba totals in 186 207 82 64 229 57 387 63 1275 out 126 295 96 72 245 100 415 72 1421 total 312 502 178 136 474 157 802 135 2696 chisq 34.63 Louisana state indus 22 afm mfg t&c whl ret fin ser puba totals in 372 267 130 81 317 85 486 71 1809 out 185 276 114 46 203 97 438 62 1421 total 557 543 244 127 520 182 924 133 3230 chisq 56.72 Maine state indus 23 afm mfg t&c whl ret fin ser puba totals in 56 131 18 21 96 27 207 24 580 out 50 132 24 11 86 27 197 25 552 total 106 263 42 32 182 54 404 49 1132 chisq 4.453 Maryland state indus 24 afm mfg t&c whl ret fin ser puba totals in 166 255 148 67 342 143 962 410 2493 out 164 331 128 63 321 137 713 161 2018 total 330 586 276 130 663 280 1675 571 4511 chisq 109 Massachusetts state indus 25 afm mfg t&c whl ret fin ser puba totals in 105 708 124 90 370 197 1196 126 2916 out 236 557 170 109 435 212 1142 130 2991 total 341 1265 294 199 805 409 2338 256 5907 chisq 83.53 Michigan state indus 26 afm mfg in 190 824 out 319 630 total 509 1454 Minnesota state indus 27 afm mfg in 160 423 out 163 261 total 323 684 Mississippi state indus 28 afm mfg in 111 206 out 136 233 total 247 439 Missouri state indus 29 afm mfg in 204 441 out 245 '433 total 449 874 Montana state indus 30 afm mfg in 102 52 out 104 76 total 206 128 t&c 153 179 332 tac 140 110 250 t&c 55 88 143 t&c 150 159 309 t&c 39 30 69 whl 110 124 234 whl 100 69 169 whl 37 56 93 whl 100 114 214 whl 18 21 39 116 ret 477 544 1021 ret 290 281 571 ret 136 163 299 ret 328 357 685 ret 109 95 204 fin 139 203 342 fin 136 113 249 fin 47 74 121 fin 125 167 292 fin 26 22 48 ser 873 1019 1892 SE?“ 689 618 1307 SE!” 211 329 540 52?” 734 732 1466 ser 171 182 353 puba totals 119 2885 138 3156 257 6041 chisq 78.5 puba totals 68 2006 52 1667 120 3673 chisq 24.86 puba totals 44 847 61 1140 105 1987 chisq 9.694 puba totals 105 2187 95 2302 200 4489 chisq 9.827 puba totals 37 554 29 559 66 1113 chisq 8.508 Nebraska state indus 31 afm mfg in 97 133 out 105 143 total 202 276 Nevada state indus 32 afm mfg in 147 91 out 54 86 total 201 177 New Hampshire state indus 33 afm mfg in . 60 278 out 40 127 total 100 405 New Jersey state indus 34 afm mfg in 150 815 out 194 560 total 344 1375 New Mexico state indus 35 afm mfg in 191 66 out 127 149 total 318 215 t&c 71 65 136 t&c 69 39 108 tac 52 31 83 t&c 199 218 417 t&c 62 54 116 whl 49 39 88 whl 31 16 47 whl 33 20 53 whl 160 135 295 whl 31 33 64 117 ret 121 152 273 ret 204 117 321 ret 105 94 199 ret 438 576 1014 ret 184 144 328 fin 56 68 124 fin 66 37 103 fin 64 40 104 fin 280 217 497 fin 45 57 102 ser 283 330 613 591” 594 140 734 ser 273 202 475 ser 1031 1183 2214 ser 327 247 574 puba totals 44 854 33 935 77 1789 chisq 8.286 puba totals 59 1261 15 504 74 1765 chisq 86.19 puba totals 21 886 22 576 43 1462 chisq 20.79 puba totals 153 3226 133 3216 286 6442 chisq 94.49 puba totals 62 968 58 869 120 1837 chisq 57.94 118 New York state indus 36 afm mfg t&c whl ret fin ser puba totals in 270 1408 317 279 963 487 2208 191 6123 out 555 1296 440 298 1297 1045 4136 505 9572 total 825 2704 757 577 2260 1532 6344 696 15695 chisq 363.5 North Carolina state indus 37 afm mfg t&c whl ret fin ser puba totals in _ 197 583 134 96 408 128 731 100 2377 out 243 541 171 106 355 172 809 151 2548 total 440 1124 305 202 763 300 1540 251 4925 chisq 29.91 North Dakota state indus 38 afm mfg t&c whl ret fin ser puba totals in 72 36 34 21 85 23 125 25 421 out 64 73 41 22 102 45 198 30 575 total 136 109 75 43 187 68 323 55 996 chisq 15.89 Ohio state indus 39 afm mfg t&c whl ret fin ser puba totals in 225 891 202 157 551 204 1017 145 3392 out 387 883 231 210 751 278 1405 156 4301 total 612 1774 433 367 1302 482 2422 301 7693 chisq 50.45 Oklahoma state indus 40 afm mfg t&c whl ret fin ser puba totals in 330 347 132 81 295 98 474 64 1821 out 197 260 98 57 192 74 398 55 1331 total 527 607 230 138 487 172 872 119 3152 chisq 11.78 119 Oregon state indus 41 afm mfg t&c whl ret fin ser puba totals in 314 523 127 90 433 112 696 96 2391 out 143 193 80 56 216 66 366 54 1174 total 457 716 207 146 649 178 1062 150 3565 chisq 20.33 Pennsylvannia state indus 42 afm mfg t&c whl ret fin ser puba totals in 235 802 204 113 495 195 1170 130 3344 out 336 841 259 175 647 287 1636 246 4427 total 571 1643 463 288 1142 482 2806 376 7771 chisq 39.48 Rhode Island state indus 44 afm mfg t&c whl ret fin ser puba totals in 19 157 15 20 54 18 139 18 440 out 36 102 17 22 77 35 183 21 493 total 55 259 32 42 131 53 322 39 933 chisq 29.97 South Carolina state indus 45 afm mfg t&c whl ret fin ser puba totals in 124 337 85 52 252 89 424 64 1427 out 162 290 89 62 232 92 384 75 1386 total 286 627 174 114 484 181 808 139 2813 chisq 12.67 South Dakota state indus 46 afm mfg t&c whl ret fin ser puba totals in 55 43 29 18 77 17 135 21 395 out 68 73 38 33 89 29 191 30 551 total 123 116 67 51 166 46 326 51 946 chisq 4.352 U6?” Ch I IKU Art 1.4 Tennessee state 47 afm in 183 out 161 total 344 Texas state 48 afm in 1511 570 total 2081 out Utah state 49 afm in 158 out 96 total 254 Vermont state 50 afm in 41 out 33 total 74 Virginia state 51 afm in 293 out 285 total 578 126.5 indus mfg 456 410 866 indus mfg 1853 767 2620 indus mfg 181 92 273 indus mfg 98 63 161 indus mfg 425 565 990 t&c 162 113 275 tac 654 270 924 t&c 67 49 116 t&c 15 24 t&c 206 210 416 whl 92 78 170 whl 438 143 581 whl 48 33 81 whl 13 15 28 whl 93 103 - 196 120 ret 348 297 645 ret 1443 602 2045 ret 187 103 290 ret 75 66 141 ret 553 484 1037 fin 123 102 225 fin 585 215 800 fin 72 44 116 fin 19 31 50 fin 225 188 413 ser 607 571 1178 SE!" 2330 1140 3470 ser 433 196 629 SE!“ 138 140 278 ser 1341 947 2288 puba totals 83 2054 82 1814 165 3868 chisq 5.965 puba totals 317 9131 201 3908 518 13039 chisq 52.32 puba totals 65 1211 49 662 114 1873 chisq 12.51 puba totals 13 412 14 371 27 783 chisq 11.51 puba totals 463 3599 225 3007 688 6606 chisq 2: ’- an st in out tct v.5 Washington state indus 53 afm mfg in 435 672 out 188 324 total 623 996 West Virginia state indus 54 afm mfg in 120 113 out 80 172 total 200 285 Wisconsin state indus 55 afm mfg in 128 493 out 129 380 total 257 873 Wyoming state indus 56 afm mfg in 308 y 37 out 65 44 total 373 81 t&c 231 122 353 t&c 55 46 101 t&c 99 107 206 t&c 72 36 108 121 whl ret 155 565 63 298 218 863 whl ret 24 122 19 125 43 247 whl ret 66 242 67 299 133 541 whl ret 22 141 21 46 43 187 fin 185 94 279 fin 24 51 75 fin 89 135 224 fin 25 25 50 ser 963 485 1448 SE)“ 219 236 455 SE)" 574 688 1262 SE?“ 168 96 264 puba 139 115 254 chisq puba 35 33 68 totals 3345 1689 5034 21.96 totals 712 762 1474 chisq 30.39 puba totals 67 1758 90 1895 157 3653 chisq 38.98 puba totals 27 800 22 355 49 1155 chisq 79.73 APPENDIX B 122 APPENDIX 8 Industry Crosstabulation Cell Values for Each State. Using Migrants 30 to 59 years old. Number of migrants who moved in or out of each state 1975 to 1980 by industries in which those migrants worked in 1980. Industry Abbreviations: afm agriculture, forestry, fisheries,and farming mfg manufacturing t&c transportation, communication, and other public utilities whl wholesale ret retail fin finance, insurance, and real estate ser service puba public administration Alabama state indus 1 afm mfg t&c whl ret fin ser puba totals in 208 487 157 100 312 96 608 175 2143 out 157 294 99 71 183 74 471 113 1462 total 365 781 256 171 495 170 1079 288 3605 Chisq 8.438 Alaska state indus 2 afm mfg t&c whl ret fin ser puba totals in 99 39 58 ‘ 13 7s 38 220 111 654 out 130 103 61 33 96 34 193 58 708 total 229 142 119 46 172 72 413 169 1362 chisq 60.7 Arizona state indus 4 afm mfg t&c whl ret fin ser puba totals in 469 524 292 ’183 579 235 1217 243 3897 out 265 364 157 85 304 123 634 127 2059 total 734 988 449 268 883 408 1851 375 5956 chisq 7.715 123 Kansas state indus 5 afm mfg t&c whl ret fin ser puba totals in 223 377 93 74 256 76 483 81 1663 out 157 254 76 68 154 73 251 53 1086 total 380 631 169 142 410 149 734 134 2749 chisq 21.87 California state indus 6 afm mfg t&c whl ret fin ser puba totals in 1688 4480 1146 861 2456 1357 5919 813 18720 out 1104 2115 816 513 1595 728 3548 686 11105 total 2792 6595 1962 1374 4051 2085 9467 1499 29825 chisq 157.2 Colorado state indus , 8 afm mfg tac whl ret fin ser puba totals in 468 575 319 192 477 286 1142 266 3725 out 327 413 157 136 348 172 825 146 2524 total 795 988 476 328 825 458 1967 412 6249 chisq 20.8 Connecticut state indus 9 afm mfg tac whl ret fin ser puba totals in 116 817 124 121 200 221 718 73 2390 out 127 443 123 86 201 183 650 89 1902 total 243 1260 247 207 401 404 1368 162 4292 chisq 71.41 Delware state indus 10 afm mfg t&c whl ret fin ser puba totals in 35 154 30 33 62 21 155 14 504 out 43 129 33 27 55 22 142 21 472 total 78 283 63 60 117 43 297 35 976 chisq 5.139 124 District of Columbia state indus 11 afm mfg t&c whl ret fin ser puba totals in 25 51 58 14 74 54 418 250 944 out 74 119 91 21 126 76 501 281 1289 total 99 170 149 35 200 130 919 531 2233 chisq 34.22 Florida state indus 12 afm mfg t&c whl ret fin ser puba totals in 1321 1746 917 618 2180 1033 3607 554 11976 out 647 1124 434 255 795 387 1724 309 5675 total 1968 2870 1351 873 2975 1420 5331 863 17651 chisq 129.8 Georgia state indus 13 afm mfg t&c whl ret fin ser puba totals in 322 850 342 249 551 306 1211 254 4085 out 238 500 193 147 372 202 785 170 2607 total 560 1350 535 396 923 508 1996 424 6692 chisq 8.668 Hawaii state indus 15 afm mfg t&c whl ret fin ser puba totals in 102 76 89 46 175 74 405 111 1078 out 60 128 63 26 131 53 301 71 833 total 162 204 152 . 72 306 127 706 182 1911 chisq 37.26 Idaho state indus 16 afm mfg t&c whl ret fin ser puba totals in 184 151 94 36 186 65 367 76 1159 out 126 123 54 27 91 54 189 35 699 total 310 274 148 63 277 119 556 111 1858 chisq 18.8 125 Illinios state indus 17 afm mfg t&c whl ret fin ser puba totals in 321 1587 367 340 616 352 1811 255 5649 out 555 1484 430 337 843 488 2097 275 6509 total 876 3071 797 677 1459 840 3908 530 12158 chisq 89.59 Indiana state indus 18 afm mfg t&c whl ret fin ser puba totals in 181 811 190 128 368 137 796 80 2691 out 260 641 144 120 369 182 759 104 2579 total 441 1452 334 248 737 319 1555 184 5270 chisq 48.65 Iowa state indus 19 afm mfg tac whl ret fin ser puba totals in 116 313 100 88 198 90 495 85 1485 out 150 332 120 78 175 101 505 72 1533 total 266 645 220 166 373 191 1000 157 3018 Chisq 9.793 Kansas state indus 20 afm mfg t&c whl ret fin ser puba totals IA 132 418 112 95 243 124 589 7a 1841 out 189 324 140 . 102 251 118 515 93 1732 total 371 742 252 197 494 242 1104 171 3573 chisq 18.65 Kenucky state indus 21 afm mfg t&c whl ret fin ser puba totals in 220 352 113 67 215 76 540 90 1673 out 139 335 119 ’ 90 208 96 467 90 1544 total 359 687 232 157 423 172 1007 180 3217 chisq 24.82 Lo st! Ou‘ to? Na sta 1r. Oul tc‘. 126 Louisana state indus 22 afm mfg t&c whl ret fin ser puba totals in 379 362 182 109 324 121 630 107 2214 out 218 259 134 82 184 96 485 92 1550 total 597 621 316 191 508 217 1115 199 3764 chisq 16.44 Maine state indus 23 afm mfg t&c whl ret fin ser puba totals in 59 180 32 15 91 37 271 58 743 out 39 116 31 26 63 39 177 23 514 total 98 296 63 41 154 76 448 81 1257 chisq 19.82 Maryland state indus 24 afm mfg t&c whl ret fin ser puba totals in 222 351 216 100 378 248 1281 675 3471 out 209 463 209 140 7383 203 1053 371 3031 total 431 814 425 240 761 451 2334 1046 6502 chisq 108.5 Massachusetts state indus 25 afm mfg t&c whl ret fin ser puba totals in 137 827 134 89 291 156 1127 133 2894 out 217 686 200 243 371 231 1242 169 3359 total 354 1513 334 . 332 662 387 2369 302 6253 chisq 115.8 Michigan state indus 26 afm mfg t&c whl ret fin ser puba totals in 193 1078 198 133 398 169 1193 146 3508 out 309 870 275 179 496 256 1200 147 3732 total 502 1948 473 ’312 894 425 2393 293 7240 chisq 90.06 127 Minnesota state indus 27 afm mfg t&c whl ret fin ser puba totals in 142 485 114 106 250 159 682 97 2035 out 169 363 113 131 228 99 609 83 1795 total 311 848 227 237 478 258 1291 180 3830 chisq 27.79 Mississip state indus 28 afm mfg t&c whl ret fin ser puba totals in 168 321 111 56 186 68 414 85 1409 out 114 202 91 53 140 54 339 63 1056 total 282 523 202 109 326 122 753 148 2465 chisq 7.929 Missouri state indus 29 afm mfg t&c whl ret fin ser puba totals in 290 670 246 152 406 152 928 149 2993 out 206 533 217 150 359 185 907 137 2694 total 496 1203 463 302 765 337 1835 286 5687 chisq 22.86 Montana state indus 30 afm mfg t&c whl ret fin ser puba totals in 133 63 67 30 101 53 269 42 758 out 93 65 45 31 67 33 173 53 560 total 226 128 112 _ 61 168 86 442 95 1318 chisq 15.71 Nebraska state indus 31 afm mfg t&c whl ret fin ser puba totals in 88 152 114 44 116 58 349 57 978 out 126 157 89 63 164 77 354 66 1096 total 214 309 203 ’107 280 135 703 123 2074 chisq 18.22 128 Nevada state indus 32 afm mfg tac whl ret fin ser puba totals in 208 134 149 50 302 126 893 118 1980 out 93 112 53 23 113 146 950 129 1619 total 301 246 202 73 415 272 1843 247 3599 chisq 156.7 New Hampshire state indus 33 afm mfg t&c whl ret fin ser puba totals in 87 366 62 40 131 75 386 52 1199 out 59 149 28 31 76 43 226 37 649 total 146 515 90 71 207 118 612 89 1848 chisq 16.18 New Jersey state indus - 34 afm mfg t&c whl ret fin ser puba totals in _ 255 1599 495 335 578 434 1720 254 5670 out 307 1043 302 225 561 341 1361 196 4336 total 562 2642 797 560 1139 775 3081 450 10006 chisq 74.36 New Mexico state indus 35 afm mfg tac whl ret fin ser puba totals in 219 115 107 57 211 76 515 107 1407 out 145 130 88 45 151 49 302 73 983 total 364 245 195 102 362 125 817 180 2390 chisq 22.44 New York state indus 36 afm mfg t&c whl ret fin ser puba totals in 304 1991 548 395 945 622 3007 317 8129 out 653 2105 802 485 1264 860 3571 464 10204 total 957 4096 1350 880 2209 1482 6578 781 18333 chisq 114.4 129 North Carolina state indus 37 afm mfg t&c whl ret fin ser puba totals in 260 957 213 146 383 180 1040 156 3335 out 232 559 162 129 336 174 778 129 2499 total 492 1516 375 275 719 354 1818 285 5834 chisq 38.55 North Dakota state indus 38 afm mfg tac whl ret fin ser puba totals in 68 32 46 26 55 18 174 40 459 out 47 72 40 23 45 26 136 23 412 total 115 104 86 49 100 44 310 63 871 chisq 29.07 Ohio state indus 39 afm mfg t&c whl ret fin ser puba totals in 243 1262 277 205 513 226 1365 164 4255 out 421 1284 330 264 674 328 1636 238 5175 total 664 2546 607 469 1187 554 3001 402 9430 chisq 49.38 Oklahoma state indus 40 afm mfg t&c whl ret fin ser puba totals in 412 466 213 126 342 143 738 154 2594 out 222 265 111 102 213 112 430 80 1535 total 634 731 324 228 555 255 1168 234 4129 chisq 14.57 Oregon state indus 41 afm mfg t&c whl ret fin ser puba totals in 317 560 226 119 478 158 971 145 2974 out 180 68 123 83 186 91 441 78 1250 total 497 628 349 202 664 249 1412 223 4224 chisq 146.3 Pennsylvannia state indus 42 afm mfg t&c in 285 1236 out 378 1025 total 663 2261 Rhode Island state indus 44 afm mfg t&c in 31 199 out 33 121 total 64 320 South Carolina state indus 45 afm mfg t&c in 215 607 out 124 327 total 339 934 South Dakota state indus 46 afm mfg t&c in 52 43 out 65 57 total 117 100 Tennessee state indus 47 afm mfg t&c in 242 710 out 188 455 total 430 1165 130 whl ret fin ser puba totals 272 206 510 227 1515 241 4492 286 258 574 282 1514 236 4553 558 464 1084 509 3029 477 9045 chisq 48.28 whl ret fin ser puba totals 24 17 55 35 184 22 567 27 20 72 36 203 26 538 51 37 127 71 387 48 1105 chisq 22.3 whl ret fin ser puba totals 123 71 266 115 654 101 2152 105 58 155 89 391 71 1320 228 129 421 204 1045 172 3472 chisq 16.69 whl ret fin ser puba totals 33 19 67 22 128 25 389 27 12 63 32 132 31 419 60 31 130 54 260 56 808 chisq 7.16 whl ret fin ser puba totals 269 174 393 152 959 140 3039 170 123 256 129 625 105 2051 439 297 649 281 1584 245 5090 chisq 8.451 Texas state indus 48 afm mfg t&c whl in 1665 2263 778 out 653 917 369 total 2318 3180 1147 Utah state indus 49 afm mfg t&c whl in 150 207 104 out 95 128 64 total 245 335 168 Vermont state indus 50 afm mfg t&c whl in 33 111 24 out 39 78 24 total 72 189 48 Virginia state indus 51 afm mfg t&c whl in 356 631 296 out 269 653 250 total 625 1284 546 Washington state indus 53 afm mfg t&c whl in 541 996 338 out 209 296 179 total 750 1292 517 131 ret fin ser puba totals 705 1582 738 3193 484 11408 224 575 303 1404 287 4732 929 2157 1041 4597 771 16140 chisq 52.45 ret fin ser puba totals 62 177 53 349 102 1204 30 95 51 243 69 775 92 272 104 592 171 1979 chisq 9.172 ret fin ser puba totals 11 51 23 178 14 445 13 43 14 119 17 347 24 94 37 297 31 792 chisq 9.326 ret fin ser puba totals 148 520 334 1734 871 4890 142 433 226 1174 373 3520 290 953 560 2908 1244 8410 chisq 132.8 ret fin ser puba totals 240 549 321 1373 239 4597 94 253 97 624 128 1880 334 802 418 1997 367 6477 chisq 52.16 132 West Virginia state indus 54 afm mfg tac whl ret fin ser puba totals in 196 178 77 29 120 37 269 66 972 out 94 143 58 36 83 44 225 40 723 total 290 321 135 65 203 81 494 106 1695 chisq 24.72 Wisconsin state indus 55_afm mfg t&c whl ret fin ser puba totals in 188 506 101 95 249 97 715 96 2047 out 128 370 111 85 233 142 695 86 1850 total 316 876 212 180 482 239 1410 182 3897 chisq 33.5 Wyoming state indus 56 afm mfg t&c whl ret fin ser puba totals in 276 40 76 25 93 38 204 54 806 out 100 46 52 21 . 61 23 114 22 439 total 376 86 128 46 154 61 318 76 1245 chisq 31.48 APPENDIX C 133 APPENDIX C Occupation Crosstabulation Cell Values for Each State. Using Migrants 20 to 29 years old. Number of migrants who moved in or out of each state 1975 to 1980 by Occupation in which they worked in 1980. Occupation Abbreviations: man t&s ser a&f p.pro o&f Alabama , state in out totals Alaska state in out totals Arizona state in out totals operators, managerial and professional specialty technical, service farming, sales, and administrative support fishing, and forestry precision production, craft, and repair fabricators, and laborers occup 1 man tas ser aaf p.pro.o&f totals 311 462 167 14 218 321 1493 383 415 155 15 142 238 1348 694 877 322 29 360 559 2841 occup 2 man' t&s ser aaf p.pro.o&f totals 153 181 96 16 60 64 570 72 158 75 23 92 100 520 225 339 171 39 152 164 1090 chisq 47.00 occup 4 man t&s ser aaf p.pro.o&f totals 589 855 360 69 354 366 2593 385 532 200 31 246 241 1635 974 1387 560 100 600 607 4228 chisq 6.55 r) 134 Arkansas state occup 5 man t&s ser a&f p.pro.o&f totals in 183 246 121 44 148 246 988 out 217 331 102 18 134 184 986 totals 400 577 223 62 282 430 1974 chisq 37.57 California state occup 6 man tas ser a&f p.pro.o&f totals in 3173 4893 2168 572 1826 2861 15493 out 1567 2284 1083 191 1112 1329 7566 totals 4740 7177 3251 763 2938 4190 23059 chisq 18616 Colorado state - 8 man t&s ser a&f p.pro.o&f totals in 890 1229 511 68 527 495 3720 out 561 640 258 55 311 350 2175 totals 1451 1869 769 123 838 845 5895 chisq 21.97 Connecticut state 9 man tas ser a&f p.pro.o&f totals in 571 500 146 20 167 258 1662 out 606 557 206 25 158 162 1714 totals 1177 1057 352 45 325 420 3376 chisq 36.3 Delaware . state ocCup 10 man t&s ser a&f p.pro.o&f totals in 125 104 52 14 38 63 396 out 114 143 46 5 48 61 417 totals 239 247 98 19 86 124 813 chisq 11.96 135 District of Columbia state occup 11 man t&s ser in 375 341 92 out 301 372 100 totals 676 713 192 Florida state occup 12 man tas ser in 1562 2280 1049 out 962 1429 541 totals 2524 3709 1590 Georgia state occup 13 man tas ser in 868 1055 336 'out 555 see 243 totals 1423 1737 579 Hawaii state occup 15 man t&s ser in 160 271 168 out 159 301 132 totals 319 572 300 Idaho state occup 16 man t&s ser in 186 212 105 out 138 217 71 totals 324 429 176 a&f p.pro.o&f 3 29 38 3 44 74 6 73 112 chisq a&f p.pro.o&f 200 933 947 83 573 716 283 1506 1663 chisq a&f p.pro.o&f 38 298 438 34 223 321 72 521 759 chisq a&f p.pro.o&f 24 72 83 9 121 152 33 193 235 chisq aaf p.pro.o&f 57 111 128 25 89 95 82 200 223 chisq totals 878 894 1772 24.29 totals 6971 4304 11275 37.97 totals 3033 2058 5091 6.446 totals 778 874 1652 39.97 totals 799 635 1434 14.97 136 Illinios state occup 17 man t&s ser a&f p.pro.o&f totals in 1343 1482 586 50 440 1011 4912 out 1413 1514 572 73 496 553 4621 totals 2756 2996 1158 123 936 1564 9533 chisq 135.3 Indiana state occup 18 man t&s ser a&f p.pro.o&f totals in 529 646 286 30 266 490 2247 out 683 734 241 33 226 309 2226 totals 1212 1380 527 63 492 799 4473 chisq 73.32 Iowa state occup 19 man t&s ser a&f p.pro.o&f totals in 398 385 161 43 153 217 1357 out 524 499 171 45 164 194 1597 totals 922 884 332 88 317 411 2954 chisq 14.53 Kansas state occup 20 man t&s ser a&f p.pro.o&f totals in 344 456 165 34 200 265 1464 out 444 479 147 25 191 230 1516 total 788 935 312 59 391 495 2980 chisq 17.45 Kentucky state occup 21 man t&s ser a&f p.pro.o&f totals in 306 373 149 36 184 227 1275 out 350 447 164 27 162 271 1421 totals 656 820 313 63 346 498 2696 chisq 9.04 Louisana state 22 in out totals Maine state 23 in out totals maryland state 24 in out totals Massachusetts state 25 in out totals Michigan state 26 in out totals occup man 424 388 812 occup man 159 139 298 occup man 815 570 1385 occup man 1037 1126 2163 occup man 740 965 1705 137 t&s ser 602 186 446 155 1048 341 t&s ser 142 78 183 76 325 154 t&s ser 947 285 747 241 1694 526 t&s ser 930 350 897 326 1827 676 t&s ser 842 343 948 372 1790 715 a&f 22 8 30 a&f 26 33 a&f 36 35 71 a&f 17 46 63 a&f 49 59 108 p.pro.o&f 278 297 196 228 474 525 chisq p.pro.o&f 74 101 62 85 136 186 chisq p.pro.o&f 166 244 180 245 346 489 chisq p.pro.o&f 210 372 290 306 500 678 chisq p.pro.o&f 310 601 363 451 673 1052 chisq totals 1809 1421 3230 10.97 totals 580 552 1132 19.23 totals 2493 2018 4511 21.44 totals 2916 2991 5907 36.74 totals 2885 3158 6043 51.4 NED. 138 Minnesota state occup 27 man t&s ser a&f p.pro.o&f totals in 586 680 243 32 187 278 2006 out 487 573 207 42 167 191 1667 totals 1073 1253 450 74 354 469 3673 chisq 8.556 Mississippi ' state occup 28 man t&s ser a&f p.pro.o&f totals in 194 240 88 16 127 182 847 out 261 384 126 12 142 215 1140 totals 455 624 214 28 269 397 1987 chisq 11.03 Missouri state occup 29 man t&s ser a&f p.pro.o&f totals in 616 649 280 52 232 358 2187 out 680 773 233 34 239 343 2302 totals 1296 1422 513 86 471 701 4489 chisq 19.54 Montana state occup 30 man t&s ser a&f p.pro.o&f totals in 126 158 84 19 86 81 554 out 138 159 71 16 79 96 559 totals 264 317 155 35 165 177 1113 chisq 3.442 Nebraska state occup 31 man tas ser aaf p.pro.o&f totals in 224 219 110 42 101 158 854 out 273 304 91 26 114 127 935 totals 497 523 201 68 215 285 1789 chisq 24.75 Nevada New New New New state in out totals Hampshire state in out totals Jersey state in out totals Mexico state in out totals York state in out totals occup man 260 78 338 occup 33 man 206 181 387 occup man 993 1017 2010 occup man 237 178 415 occup 36 man 1981 2461 4442 t&s 316 172 488 t&s ,305 139 494 t&s 1050 1136 2186 t&s 285 295 580 t&s 1888 2502 4390 139 ser 371 87 458 ser 88 68 156 SE!“ 320 420 740 SE?“ 123 119 242 521" 796 934 1730 a&f 19 13 32 aaf ~JD\D a&f 25 35 60 a&f 25 23 48 aaf 62 85 147 p.pro.o&f 151 144 71 83 222 227 chisq p.pro.o&f 114 164 64 66 178 230 chisq p.pro.o&f 277 561 276 332 553 893 chisq p.pro.o&f 160 138 130 124 290 262 chisq p.pro.o&f 471 925 702 808 1173 1733 chisq totals 1261 504 1765 46.9 totals 886 576 1462 22.56 totals 3226 3216 6442 77.56 totals 968 869 1837 7.247 totals 6123 7492 13615 68.78 140 North Carolina state occup 37 man t&s ser a&f p.pro.o&f totals in 618 699 273 27 275 485 2377 out 608 789 311 37 344 459 2548 totals 1226 1488 584 64 619 944 4925 chisq 12.05 North Dakota state occup 38 man t&s ser a&f p.pro.o&f totals in 101 127 54 21 58 60 421 out 151 194 77 10 71 72 575 totals 252 321 131 31 129 132 996 chisq 10.69 Ohio state occup 39 man t&s ser aaf p.pro.o&f totals in 998 927 407 49 333 678 3392 out 1324 1426 482 73 453 553 4311 totals 2322 2353 889 122 786 1231 7703 chisq 85.23 Oklahoma state occup 40 man t&s ser a&f p.pro.o&f totals in 389 563 205 32 259 373 1821 out 371 413 133 28 176 210 1331 totals 760 976 338 60 435 583 3152 chisq 24.92 Oregon state occup 41 man t&s ser a&f p.pro.o&f totals in 504 698 329 127 293 440 2391 out 306 360 150 42 133 183 1174 totals 810 1058 479 169 426 623 3565 chisq 18.88 141 Pennsylvannia state occup 42 man t&s ser a&f p.pro.o&f totals in 961 977 416 46 357 587 3344 out 1645 1454 408 52 388 480 4427 totals 2606 2431 824 98 745 1067 7771 chisq 137.3 Rhode Island state occup 44 man t&s ser asf p.pro.o&f totals in 119 116 34 4 51 116 440 out 176 150 70 4 42 51 493 totals 295 266 104 8 93 167 933 chisq 51.15 South Carolina state occup 45 man t&s ser a&f p.pro.o&f totals in 352 452 167 24 156 276 1427 out 317 467 146 19 179 258 1386 totals 669 919 313 43 335 534 2813 chisq 5.656 South Dakota state occup 46 man t&s ser a&f p.pro.o&f totals in 100 101 64 20 54 56 395 out 156 188 55 29 59 64 551 totals 256 289 119 49 113 120 946 chisq 16.25 Tennessee state occup 47 man t&s ser a&f p.pro.o&f totals in 513 652 213 29 230 417 2054 out 505 572 204 13 212 308 1814 totals 1018 1224 417 42 442 725 3868 chisq 13.86 142 Texas state occup 48 man t&s ser a&f p.pro.o&f totals in 2139 2854 972 127 1386 1653 9131 out 878 1205 460 105 530 730 3908 totals 3017 4059 1432 232 1916 2383 13039 chisq 35.6 Utah state occup 49 man t&s ser a&f p.pro.o&f totals in 271 369 172 21 189 189 1211 out 198 225 69 24 90 56 662 totals 469 594 241 45 279 245 1873 chisq 40.37 Vermont state occup 50 man t&s ser a&f p.pro.o&f totals in 128 98 69 18 42 57 412 out 130 102 40 9 45 45 371 totals 258 200 109 27 87 102 783 chisq 10.21 Virginia state occup 51 man t&s ser a&f p.pro.o&f totals in 1185 1251 420 50 321 372 3599 out 819 1040 329 41 351 427 3007 totals 2004 2291 749 91 672 799 6606 chisq 5&7 Washington state occup 53 man t&s ser a&f p.pro.o&f totals in 720 1013 465 131 508 508 3345 out 385 544 229 48 205 278 1689 totals 1105 1557 694 179 713 786 5034 chisq 14.44 West Virginia state occup 54 man in 177 out 212 totals 389 Wisconsin state occup 55 man in 465 out 623 totals 1088 Wyoming state occup 56 man in 175 out 66 totals 241 143 t&s ser 211 79 227 83 438 162 t&s ser 510 187 676 217 1186 404 t&s ser 188 83 114 40 302 123 a&f a&f a&f 13 9 22 46 42 88 22 17 39 p.pro.o&f totals 110 122 712 67 164 762 177 286 1474 chisq 19.5 p.pro.o&f totals 195 355 1758 164 173 1895 359 528 3653 chisq 109 p.pro.o&f totals 170 162 800 55 63 355 225 225 1155 chisq 16.43 APPENDIX D 144 APPENDIX 0 Occupation Crosstabulation Cell Values for Each State. Using Migrants 30 to 59 years old. Number of Migrants who moved into and out of each state 1975 to 1980 by occupation in which they worked in 1980. Occupational Abreviations: man managerial, and professional specialty t&s technical, sales, and administrative support ser service a&f farming, fishing, and forestry . p.pro precision production, craft, and repair o&f operators, fabricators, and laborers Alabama state Occup 1 Man T&S Ser A&F P.Pro 08F totals in 724 578 171 40 282 348 2143 out 521 427 125 24 176 189 1462 totals 1245 1005 296 64 458 537 3605 Chisq 10.27 Alaska state Occup 2 Man T&S Ser A&F P.Pro O&F totals in 243 184 77 14 78 58 654 out 189 203 79 27 117 93 708 totals 432 387 156 41 195 151 1362 chisq 25.64 Arizona , state Occup 4 Man T&S Ser A&F P.Pro 08F totals in 1218 1204 409 67 564 435 3897 out 657 606 182 38 310 266 2059 totals 1875 1810 591 105 874 701 5956 chisq 8.822 145 Arkansas state Occup 5 Man T&S Ser A&F P.Pro O&F totals in 431 418 175 83 265 291 1663 out 309 302 89 26 165 195 1095 totals 740 720 264 109 430 486 2758 chisq 9.565 California state Occup 6 Man T&S Ser A&F P.Pro 08F totals in 6125 5271 2096 595 2162 2462 18711 out 3645 3243 1241 211 1436 1329 11105 totals 9770 8514 3337 806 3598 3791 29816 chisq 63.56 Colorado state Occup ' 8 Man T&S Ser A&F P.Pro 08F totals in 1339 1196 338 53 461 338 3725 out 944 748 215 50 313 254 2524 totals 2283 1944 553 103 774 592 6249 chisq 8.751 Connecticut state Occup 9 Man T&S Ser A&F P.Pro O&F totals in 1144 614 155 13 193 271 2390 tot 870 545 138 22 173 154 1902 totals 2014 1159 293 35 366 425 4292 chisq 22.8 Delaware state Occup 10 Man T&S Ser AaF P.Pro O&F totals in 184 148 34 7 66 65 504 out 208 109 30 9 62 54 472 totals 392 257 64 16 128 119 976 chisq 7.989 146 District of Columbia state Occup 11 Man in 524 out 531 totals 1055 Florida state Occup 12 Man in 3387 out 1729 totals 5116 Georgia state Occup 13 Man in 1481 out 1016 totals 2497 Hawaii state Occup 15 Man in 346 out 271 totals 617 Idaho state Occup 16 Man in 358 out 239 totals 597 T&S Ser 248 85 382 145 630 230 T&S Ser 3951 1451 1713 575 5664 2026 T&S Ser 1268 342 793 224 2061 566 T&S Ser 307 182 288 99 595 281 T&S Ser 309 117 190 57 499 174 AGF othe- A&F 259 94 353 A&F 51 35 86 AGF 39 46 A&F 60 37 97 P.Pro 37 92 129 P.Pro 1557 812 2369 P.Pro 441 274 715 P.Pro 119 97 216 P.Pro 157 75 232 O&F 49 131 180 chisq 08F 1371 752 2123 chisq O&F 502 265 767 chisq O&F 85 71 156 chisq O&F 158 101 259 chisq totals 944 1289 2233 58.54 totals 11976 5675 17651 49.24 totals 4085 2607 6692 9.935 totals 1078 833 1911 29.06 totals 1159 699 1858 6.268 Illinois state 17 in out totals Indiana state 18 in out totals Iowa state 19 in out totals Kansas state 20 in out total Kentucky state 21 in out totals Occup Man 2152 2548 4700 Occup Man 892 968 1860 Occup Man 580 592 1172 Occup Man 666 667 1333 Occup Man 547 555 1102 147 T&S Ser 1451 534 1899 563 3350 1097 T53 Ser 714 246 707 190 1421 436 T&S Ser 399 134 449 121 848 255 T&S Ser 520 173 502 151 1022 324 T88 Ser 425 172 445 139 870 311 A&F 1 1 A&F naF AGF A&F 41 07 48 ea 44 7e 35 41 76 45 29 74 36 16 52 P.Pro 567 678 1245 P.Pro 342 334 676 P.Pro 150 156 306 P.Pro 223 174 397 P.Pro 222 184 406 O&F 904 714 1618 chisq O&F 469 336 805 chisq O&F 187 174 361 chisq O&F 214 209 423 chisq 08F 271 205 476 chisq totals 5649 6509 12158 95.33 totals 2691 2579 5270 33.59 totals 1485 1533 3018 4.031 totals 1841 1732 3573 8.06 totals 1673 1544 3217 19.28 Louisana state Occup 22 Man in 759 out 566 totals 1325 Maine state Occup . 23 Man in 1476 out 616 totals 2092 Maryland state Occup 24 Man in 1476 out 1290 totals 2766 Massachusetts state Occup 25 Man in 1260 out 1461 totals 2721 Michigan state Occup 26 Man in 1325 out 1444 totals 2769 148 T&S Ser 580 212 473 143 1053 355 T&S Ser 1063 368 451 109 1514 477 T&S Ser 1063 368 909 256 1972 624 T88 Ser 716 244 906 273 1622 517 T&S Ser 885 333 1040 348 1925 681 AlF A&F A&F AGF A&F 37 22 59 30 16 46 30 41 71 25 32 57 34 45 79 P.Pro 351 179 530 P.Pro 255 124 379 P.Pro 255 284 539 P.Pro 244 334 578 P.Pro 363 402 765 08F 275 167 442 chisq O&F 279 138 417 chisq O&F 279 251 530 chisq O&F 405 253 658 chisq 08F 568 453 1021 chisq totals 2214 1550 3764 21.97 totals 3471 1454 4925 15.29 totals 3471 3031 6502 19.7 totals 2894 3259 6153 67.3 totals 3508 3732 7240 27. 49 149 Minnesota state Occup 27 Man T88 Ser A8F P.Pro 08F totals in 853 572 174 44 179 213 2035 out 698 567 164 39 173 154 1795 totals 1551 1139 338 83 352 367 3830 chisq 10.7 (Mississippi state Occup 28 Man T88 Ser A8F P.Pro 08F totals in 395 369 153 41 185 266 1409 out 343 319 101 11 141 140 1055 totals 738 688 254 52 326 406 2464 chisq 30.05 Missouri state Occup 29 Man T88 Ser A8F P.Pro 08F totals in 1043 830 282 101 328 409 2993 out 1085 817 228 35 261 268 2694 totals 2128 1647 510 136 589 677 5687 chisq 60.11 Montana state Occup 30 Man T88 Ser A8F P.Pro O8F totals in 276 194 81 37 91 79 758 out 186 147 50 20 84 111 598 totals 462 341 131 57 175 190 1356 chisq 23.53 Nebraska state Occup 31 Man T88 Ser A8F P.Pro 08F totals in 338 278 105 34 106 117 978 out 423 367 87 27 119 103 1126 totals 761 645 192 61 225 220 2104 chisq 15.57 New New New New New Hampshire state Occup 32 Man in 472 out 190 totals 662 Hampshire state Occup 33 Man in 479 out 286 totals 765 Jersey state Occup 34 Man in 2283 'out 1730 totals 4013 Mexico state Occup 35 Man in 486 out 327 totals 813 York state Occup 36 Man in 3036 out 4105 totals 7141 T88 567 227 794 T88 334 181 515 T88 1522 1315 2837 T88 410 281 691 T88 2074 3032 5106 1 50 Ser A8F P.Pro 490 26 240 101 11 106 591 37 346 Ser A8F P.Pro 79 13 157 43 7 71 122 20 228 Ser A8F P.Pro 419 27 524 322 35 497 741 62 1021 Ser A8F P.Pro 116 47 200 99 19 149 215 66 349 Ser A8F P.Pro 1056 73 649 945 83 996 2001 156 1645 08F 185 94 279 chisq 08F 137 61 198 chisq 08F 895 437 1332 chisq O8F 148 108 256 chisq 08F 1241 1043 2284 chisq totals 1980 729 2709 40.31 totals 1199 649 1848 4.925 totals 5670 4336 10006 86.93 totals 1407 983 2390 7.108 totals 8129 10204 18333 204.7 151 North Carolina state Occup 37 Man T88 Ser A8F P.Pro 08F totals in 1160 902 299 62 347 557 3327 out 916 776 206 29 268 304 2499 totals 2076 1678 505 91 615 861 5826 chisq 34.75 North Dakota state Occup 38 Man T88 8er A8F P.Pro 08F totals in 148 132 63 21 51 44 459 out 152 127 30 16 46 80 451 totals 300 259 93 37 97 124 910 chisq 23.18 Ohio state Occup 39 Man T8S Ser A8F P.Pro O8F totals in 1760 1050 360 23 447 615 4255 out 2087 1485 384 46 584 589 5175 totals 3847 2535 744 ' 69 1031 1204 9430 chisq 40.27 Oklahoma state Occup 40 Man T88 Ser A8F P.Pro 08F totals in 782 703 274 45 401 389 2594 out 533 439 131 37 203 192 1535 totals 1315 1142 405 82 604 581 4129 chisq 20.92 Oregon state Occup 41 Man T88 Ser A8F P.Pro 08F totals in 913 835 330 99 388 409 2974 out 515 392 131 51 178 183 1450 totals 1428 1227 461 150 566 592 4424 chisq 12.85 Pennsylvannia state Occup 42 Man in 1761 out 1923 totals 3684 Rhode Island state Occup 44 Man in 207 out 210 totals 417 South Carolina state Occup 45 Man in 665 out 490 totals 1155 South Dakota state Occup 46 Man in 122 out 152 totals 274 Tennessee state Occup 47 Man in 1000 out 794 totals 1794 152 T88 Ser 1139 392 1305 372 2444 764 T88 Ser 127 41 149 50 276 91 T88 Ser 615 201 358 96 973 297 T88 Ser 102 39 107 45 209 84 T88 Ser 817 290 546 150 1363 440 A8F A8F A8F A8F 51 48 99 40 18 58 18 15 33 33 31 64 P.Pro 514 461 975 P.Pro 62 58 120 P.Pro 270 174 444 P.Pro 62 63 125 P.Pro 364 255 619 08F 608 444 1052 chisq 08F 125 64 189 chisq 08F 361 184 545 chisq 08F 46 108 154 chisq 08F 535 275 810 chisq totals 4465 4553 9018 46.61 totals 567 538 1105 22.07 totals 2152 1320 3472 19.87 totals 389 490 879 17.7 totals 3039 2051 5090 34.31 Texas Utah Verm state in out totals state in out totals ont state in out totals Virginia Washington state in out totals state in out totals Occup Man 3725 1713 5438 Occup Man 413 308 721 Occup Man 186 125 311 Occup Man 2158 1485 3643 Occup Man 1504 667 2171 153 T88 Ser 3423 1012 1314 384 4737 1396 T88 Ser 326 117 212 80 538 197 T88 Ser 100 40 93 27 193 67 T88 Ser 1467 428 1037 286 2504 714 T88 Ser 1332 416 548 172 1880 588 A8F 180 112 292 A8F 16 14 30 A8F 66 45 111 A8F 146 47 193 P.Pro 1617 615 2232 P.Pro 158 85 243 P.Pro 52 50 102 P.Pro 409 346 755 P.Pro 671 203 874 08F 1451 594 2045 chisq 08F 174 76 250 chisq 08F 59 43 102 chisq 08F 371 334 705 chisq 08F 578 243 821 chisq totals 11408 4732 16140 35.34 totals 1204 775 1979 14.56 totals 445 347 792 5.304 totals 4899 3533 8432 16.73 totals 4647 1880 6527 19.44 154 West Virginia state Occup 54 Man T88 8er A8F P.Pro 08F totals in 286 215 99 18 183 171 972 out 230 203 66 7 112 105 723 totals 516 418 165 25 295 276 1695 chisq 14.47 Wisconsin state Occup 55 Man T88 8er A8F P.Pro O8F totals in 781 515 235 73 217 226 2047 out 759 542 154 20 218 157 1850 totals 1540 1057 389 93 435 383 3897 chisq 50.68 Wyoming state Occup . 56 Man T88 8er A8F P.Pro 08F totals in 213 194 85 22 168 124 806 'out 143 116 37 21 73 49 439 totals 356 310 122 43 241 173 1245 chisq 15.42