Employment opportunities for central city residents in high technology industries

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Employment opportunities for central city residents in high technology industries
Moussalli, Guy A
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75 leaves : ; 28 cm


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High technology industries ( lcsh )
Job vacancies ( lcsh )
High technology industries ( fast )
Job vacancies ( fast )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references (leaves 71-75).
General Note:
Submitted in partial fulfillment of the requirements for the degree, Master of Urban and Regional Development, College of Architecture and Planning.
Statement of Responsibility:
by Guy A. Moussalli.

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University of Colorado Denver
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Auraria Library
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16734148 ( OCLC )
LD1190.A78 1987 .M687 ( lcc )

Full Text
Guy A. Moussalli
Directed by Associate Professor Thomas A. Clark
A thesis submitted to the Faculty of the Graduate Program in Urban and Regional Planning of the University of Colorado in partial fulfillment of the requirements for the degree of
Master in Planning and Community Development School of Architecture and Planning

Guy A. Moussalli
Directed by Associate Professor Thomas A. Clark
A thesis submitted to the Faculty of the Graduate Program in Urban and Regional Planning of the University of Colorado in partial fulfillment of the requirements for the degree of
Master in Planning and Community Development School of Architecture and Planning

This thesis would not be what it is without the contribution of a number of people. My deep appreciation goes to my adviser Thomas A. Clark for his diligence in directing the research and his patience and understanding when it was lagging behind. I would also like to thank LLoyd Levy for his help in the statistical portion of the research. Our informal discussions enabled me to get a better understanding of regression and correlation analysis. Many thanks also go to Elaine Taylor who was consulted at the beginning of the research regarding style and editing requirements. I am indebted to Planning Information Corporation for allowing me the use of their word processing and printing facilities. My gratitude goes to Jennifer Sebesta for her help and assistance in the printing and production of the final draft. Finally, I would like to thank my friend Ellen Mathiott who provided encouragement and hope at times when I was losing faith, and to my family without whom this endeavor would not have been possible in the first place.

Table of Contents
I. Introduction 1
II. Literature Review 5
1. High Technology as an Agent of Change 5
2. The Restructuring of Metropolitan Economies 9
3. Technology and Labor:
Upgrading or Deskilling of Tasks ? 11
III. Statement of Research 16
IV. High Technology Employment in Central Cities:
1. Cities by type 19
a. Measuring resident need 20
b. Measuring city growth or decline 22
2. High Technology: Definitions 24
a. Conceptual definition 25
b. Empirical definition and
Selection of industrial sectors for analysis 26
3. Source of data 31
4. Data handling 31
5. Data presentation 33
a. High technology employment as a share of
total employment 34
b. Profile of employment in sectors
by skill level and city type 37

c. Entry-level jobs by type of city 42
6. Conclusion 44
V. Correlation analysis 46
1. Limitations of the statistical procedure 47
2. Selection of variables and source of data 48
3. Interpretation of the correlation matrix 49
4. Testing for the significance of the correlation 53
VI. Policy options 57
VII. Conclusion 64
Appendix I 67
Appendix II 70

List of Tables
1. Large Cities by Type, 1980 23
2. Three Groups of High Technology Industries
Based on Alternative Definitions 27
3. Percentage of Workers
by Educational Level Attained, March 1982 32
4. High Tech Jobs in Two-Digit Industries As a Percentage of Total Employment
By Type of City, 1981 35
5a. Employment in Selected Two-Digit High Tech Industries
By Skill Level of Workers and Type of City, 1981 38
5b. Employment in Selected Two-Digit High Tech Industries By Skill Level of Workers and Type of City, 1981 Percent Column Totals 39
5c. High Tech and Entry-Level Jobs
By Type of City, 1981
Percent Column Totals 43
6. Correlation Matrix 50
Figure 1. Association among variables

Metropolitan economies have been impacted in recent years by changing market conditions at the international level which have forced many U.S. companies to restructure and streamline their operations in order for them to remain competitive.
The obsolescence of techniques and the lack of investment in basic infrastructure by capitalists over time precipitated the decline in such basic manufacturing industries as steel and autos. As industries declined so did the regions in which they were located, mainly the northern and eastern parts of the country.
Cities in these regions experienced a reduced tax base and an increased number of unemployed workers who, nonetheless, had a high degree of specialization in their former trade. Although these cities under stress were characterized as having a high level of resident need, they nonetheless recently underwent a specialization of their economies in advanced services, capitalizing on their former strengths as established centers.
Despite the growth in services, using the latest advances in telecommunications and information processing networks, all segments of the population have not been able to take advantage of the new economic opportunities. Inner-city residents employed

in secondary labor markets, the unemployed and underemployed, cannot match their skills to the demands of the new sectors. Former skilled blue collar workers in traditional manufacturing industries have found the transition to the new jobs difficult to make. The empirical research points out to the fact that high need cities did not have a large enough number of entry-level jobs available to their unskilled residents while, on the other hand, offering greater opportunities in professional services to skilled and educated workers.
Conditions in low need cities of the south and west are different: cities in these regions do not carry an extensive declining infrastructure and have an abundant supply of cheap labor. These factors played a role in the attraction of branch plants of high technology industries such as electronics assembly and manufacturing of high tech components such as computers.
However, analysts indicate that growth in the sunbelt is largely illusory because regional economies have come to depend on highly volatile manufacturing industries that can relocate to areas where costs are even lower, as they increase the levels of automation. Research findings suggest that, due to the standardized nature of production activities, the situation of workers in entry-level positions is insecure as they are at risk of losing their jobs whenever an industry moves out of a community. This fact is emphasized by the greater proportion of entry-level jobs in low need cities.

The development pattern in the U.S. is thus one of great regional disparities, subject to shifts in capital mobility across boundaries. In some regions, skill levels of residents do not match available jobs, while other regions have a greater share of unskilled jobs which make them more receptive to countercyclical effects when downturns in the economy occur.
Under conditions of reduced employment opportunities due to raising productivity, the issues of providing enough jobs to both skilled but dislocated workers in basic industries and unskilled workers in the secondary labor market, remain central to the development of policies aimed at correcting the dysfunctions of
the market.

Chapter I Introduction
And in a City where the only meaningful places are the ones associated with the highest functions, the space with meaning for only a few tends to be the space of exclusion for the most. Thus, between the discontinuous spatial elements of the informational City, there will remain switched-off, wireless communities, still real people in real places, yet transformed into urban shadows doomed to haunt the ultimate urban dream of the new technocracy. (Castells 1985)
Technological innovation plays a major role in the shaping of urban form. New technologies have contributed in one form or another to the expansion of cities and themselves, in turn, created a market for the application of such technologies.
At the turn of the century, for example, the electric trolley enabled the City to expand beyond the rigid geographical limits imposed by mercantile and early industrial systems of production and consumption. Since the 1930's, and more so since the 1950's, the automobile has further extended urban form (or lack thereof) into suburbia and beyond, into exurbia. In the near future, more and more salaried workers will be "telecommuting", working at home using advanced communication systems linking them to their offices (Baran 1985). This technological aspect of the "office of the future" may well exacerbate the centrifugal pressures upon the existing fabric of cities, further weakening

the notions of centrality, diversity, as well as the Jacobsonian image of tightly woven neighborhood networks.
As communication systems become increasingly perfected and commonplace, the risk develops that agglomeration economies may not play as central a role in the locational decisions of firms: the importance of factors associated with specific places may decline as a result of advancements in communication technologies. Firms are becoming "footloose" with the
homogenization of the economic attributes of "place" (Sternlieb and Hughes 1978). Castells (1985) noted : "We still have to deal with space, but we increasingly observe a space of flows substituting a space of places . . . What technology allows is
that the organization locates itself according to its strategy, and not because of its spatial constraints." Since cities have traditionally been the location where entry-level jobs could be secured by new entrants in the labor force and by rural migrants, the rationalization of the production process is likely to affect the structure of their labor markets. This thesis is an attempt to investigate the magnitude and significance of those impacts. Two types of impacts can be identified at this stage: direct impacts on entry-level workers themselves; and indirect impacts, not measured here, resulting from the creation of businesses that provide goods and services to high technology industries.
I begin the empirical research with a review of the literature in Chapter II. In the first part, linkages between technological

change and regional economic activity are explained via the product-cycle theory. Different phases of the product-cycle require different types of labor which, in turn, affect local economies. The transformation of metropolitan economies caused by a rationalization of the production process and the shift to services are presented in the second part of Chapter II. The third part focuses on the consequences of such trends on labor and the adaptation problems the workforce experiences as a result. The authors reviewed appear to be in disagreement over the impacts of technological change on labor.
The objective of the thesis is presented in Chapter III. Embedded in the objective is the two-directional approach of the research elaborated in the following two chapters.
Data on employment in high tech industries disaggregated by type of city and skill level of workers will be interpreted in Chapter IV. The methodology developed by Franklin James from UCD's Graduate School of Public Affairs to classify cities based on the criteria of their residents' economic health and the level of population change between 1970 and 1980 is first described. This typology will be useful when examining high tech industrial employment in cities characterized by different socioeconomic conditions. I will then propose a definition of high technology based on a conceptual framework developed by Edward Malecki and an empirical framework developed by the Bureau of Labor Statistics. This definition will be instrumental in the

selection of a group of industrial sectors and the collection of employment data from 1981 County Business Patterns. The third factor introduced in this analysis of high tech employment by city type is the skill level of workers. Data from the Bureau of Labor Statistics were used to breakdown total employment into three categories based on the number of school years completed.
The next step will be to study, in Chapter V, the apparent relationships among the hypothetical factors. Independent variables representing those factors will be selected from the 19 83 County and City Data Book developed by the Census Bureau. Correlation analysis will determine the degree of association between the independent variables and the dependent factor of employment. The thrust of the analysis will be performed using the product-moment correlation coefficient, known as Pearson r. The null hypothesis, that in reality r = 0, will be tested to determine whether or not two variables are associated. From the correlation matrix, a flow diagram will be constructed to represent the elements involved and their logical relation to one another.
Policy options will be explored in Chapter VI, based on the findings of the empirical research. The thesis will conclude by suggesting an alternative to current economic development strategies focused on the attraction of high technology

Chapter II Literature Review
The obsolescence of techniques goes hand in hand with the obsolescence of qualifications. Thus appears a growing class of "aged" workers between 40 and 45 years old, and sometimes between 30 and 35 years old only, in those occupations where new technologies evolve rapidly, and for whom the remaining half of the working life is a long decline, punctuated by sudden unemployment periods. These workers, rendered obsolete, are increasingly forming a new proletariat rejected by technological progress, exploited by it. (Touraine 1969, my translation)
1. High Technology as an Agent of Change:
Technological change occurring through the adoption of new technologies by firms and companies results in product and in process changes: product technologies generally refer to the degree of sophistication of a finished product and process technologies are technologies affecting the production process itself. While new products open up new markets, resulting in potential new employment, "new processes are often adopted because they are considered efficient, using fewer resources than older processes to yield a product of given quality. If the conserved resource is labor, a company adopting a more efficient process will need fewer employees for a fixed output level" (Office of Technology Assessment 1983).

The behavior of individual firms and the pace at which they adopt technological change can best be explained through the
product-cycle model, which has also been referred to as the profit-cycle model. I am indebted to Edward Malecki (1981) for the following presentation of the model, and its implications for regional economic development. The product-cycle model is composed of three phases.
The first phase is the innovation phase, requiring the concentration of skilled workers engaged in research and new-product development. During the second phase, technology transfers among regions take place, emphasizing the growth of production to meet market demand. Production organization and techniques developed during the innovation phase are perfected first at a small number of plant sites to lead to standardized plants that can then be located to minimize product costs. Finally, the third phase of the product-cycle model is the mature-product phase, characterized by a lack of innovation and product standardization. Firms are committed to cost reduction through mechanized production and rigid management and control. Routinized manufacturing tends to locate in developing countries and low-wage regions of developed countries.
The product-cycle model has allowed the linkage between technological change and regional economic activity to take place. Regions can be designated as innovation-phase,
growth-phase, or mature-product regions, corresponding to their

tendency toward a particular phase in the product-cycle. During the first phase, for example, the presence of new-product firms and research and development activity centers generate regional growth and spin-off industries. As they enter the third phase, however, regions are likely to decline because of their specialization in mature product manufacturing.
It is during the latter phase that process changes are likely to adversely affect employment: labor productivity (the amount of output per unit of input) is subject to rise while employment may remain the same or decline. According to Personick (1985), "while the value of output of the manufacturing sector is projected to grow by 3.0 percent annually from 1984 to 1995, employment is projected to rise only 0.6 percent annually. Manufacturing employment is projected to decline as a proportion of all jobs from 18.5 percent in 1984 to 17.2 percent in 1995." High technology is not expected to remedy the employment decline in manufacturing although "it is often touted as the source of new employment opportunities to help replace jobs lost in declining smokestack industries." She foresees that, "while faster-growing than the average for all sectors, and particularly the manufacturing sector, high tech industries are projected to account for only a small proportion of new jobs through 1995."
In a Monthly Labor Review article, Riche, Hecker, and Burgan (1983) estimated that of the 23.4 to 28.6 million new wage and salary jobs created between 1982 and 1995, between 1.0 and 4.6

million of these only will be in high technology industries. In light of this, they advise governmental and community organizations seeking to attract jobs to their regions not to limit their search to high technology industries alone.
Using the product-cycle theory, Markusen (1985) explains that "the extraordinary job growth rates of high technology industries today are in large part a function of their innovative age and the tremendous competition to perfect products like the microcomputer. Once these products become standardized, the employment growth curve will flatten out and may even decline." She warns that high technology industries may not constitute a preferred development strategy for many older regions which will be subject sooner or later to pressures resulting from the centrifugal relocation of these industries to lower-cost areas as production becomes standardized.
The issues of rising output and declining employment, due to higher productivity, form the substance of the "deindustrialization" debate, and its impacts on local communities, cities and regions. Although Kutscher and Personick (1986), for example, recognize that the loss of manufacturing employment and output is a severe problem for certain industries, they affirm that "the rise in manufacturing output seems to preclude a conclusion of deindustrialization at the macro level." However, Bluestone and Harrison (1982) regard deindustrialization the loss of the existing industrial base through production process improvements

and the consequent restructuring of industries as posing real problems for the labor force, not the least is displacement. "In essence," they say, "losing one's job as a result of deindustrialization tends to propel one downward in the industry hierarchy toward lower productivity jobs, not upward."
2. The Restructuring of Metropolitan Economies:
The restructuring of the labor process by high technology, through increased automation and rationalization, has had an impact on the urban-regional structure (Castells 1985). Old manufacturing regions and cities declined as a result of aging plants, the obsolescence of their production methods and declining shares of capital investment, as they entered the third phase of the product-cycle theory. New regions that did not have a stock of older industries emerged as manufacturing centers of standardized production because they had an abundance of low-wage labor available to perform unskilled and routine tasks. As a consequence, industries became "increasingly freed from reliance upon the pools of labor skills typically concentrated in and accessible only through the metropolitan labor market" (Braverman 1974 ) .
At the same time, technological changes at the level of production have resulted on one hand in an increasing demand for managers, professionals, technicians, and clerical employees and, on the other, in the centralization of administrative and planning functions (Stanback and Noyelle 1982). A new economic

base, premised in part on technology appears to have emerged in some of the largest metropolitan areas. It is founded on the development of high tech information processing activities as well as related functions such as product engineering and marketing, research and development, and corporate strategy and planning (Clark et al. 1984; Stanback and Noyelle 1983).
The shift, from an employment base founded on the production of goods and their distribution to an employment base founded on the performance of services, has impacted the structure of city economies. Changes at the local level reflect changes taking place in the national economy, where "employment in goods-producing industries (construction and manufacturing) increased by just 6 percent, in distribution industries (transportation, communication and public utilities, wholesale and retail trade) by 36 percent, and in service-performing industries by 72 percent between 1970 and 1981" (Clark et al. 1984). With the relocation of standardized manufacturing to other locales and overseas, large cities (which tend to be in skill-rich regions) are becoming more and more specialized in the production and export of high level services rather than goods, and in the generation of innovation streams (Stanback and Noyelle 1983; Malecki 1980).
This transformation, coupled with the increased automation of both service and nonservice production activities, has had major implications for metropolitan labor markets. In the following

section, I review the consequences of such trends on the labor force as presented in the literature on this topic.
3. Technology and Labor: Upgrading or Deskilling of Tasks?
Authors and researchers appear to disagree over the impacts of technology on labor. Some argue that "a process of secular deskilling" has taken place, "in which, as capital is substituted for labor, so is less skilled labor substituted for more skilled labor" (Scott 1982). For others, the embodiment of technological advances in industry opened up new opportunities for skilled workers through an upgrading of the work process. Baran (1985) who has studied office automation in the insurance industry says that, "because computers are best able to perform highly structured functions, narrow routine clerical jobs are
disappearing the most rapidly; skill levels should rise as routine clerical work is eliminated and professional work becomes more complex." As skills levels generally rise, so do educational requirements. The effects are obvious on
educationally disadvantaged residents. Auletta, in his study of the underclass (1982), explains that, "by valuing education and training, the post industrial economy can offer little but permanent joblessness or only intermittent employment to large numbers of the unskilled, thereby dooming them to underclass status."
The movement from an industrial to a service economy resulted in a "white-collarization of the labor force," with "a greater

differentiation between upper-level and lower-level jobs" (Stanback and Noyelle 1983). It is assumed that there will be fewer jobs in the long run and that "employment should fall, at least in relation to output and probably in absolute terms" (Baran 1985). Furthermore, "the jobs in the new high tech industries do not come close to making up for the jobs lost through deindustrialization" (Bluestone and Harrison 1982).
As workers are replaced by automated processes or by cheaper labor, both at home and overseas, dislocation and displacement occur. Flaim and Sehgal (1985) define dislocated workers as "persons who have lost jobs in which they had a considerable investment in terms of tenure and skill development and for whom the prospects of reemployment in similar jobs are rather dim."
The hardest hit workers are those employed in the old-line, heavy manufacturing industries which have traditionally offered a range of skilled and semiskilled blue collar jobs at high wage levels: "Improvements in industrial technology invariably involve both the disintegration of specific skills originally residing in the hands and brains of workers and the reembodiment of those skills in machinery and equipment" (Scott 1982).
The decline of the mill-based industries through changes in the production process meant the loss of jobs situated in the middle of the skill and wage spectrum, "jobs that have provided the backbone of the U.S. economy in the postwar period," (Wiewel et al. 1984), and reduced workers' chances for upward mobility,

resulting in an increased bifurcation of the labor force: "With a missing middle in the economy," displaced workers "had to either make the leap up to the higher-skill jobs in the top end of the labor market or settle for work that is lower-skill, lower-wage, and more unstable" (Bluestone and Harrison 1982).
High technology industry generally demands such a bifurcated labor force: computer programming, systems installation, technical consulting, engineering, research and development on one hand; routine, standardized manufacturing of computer hardware and semiconductors on the other. Assembly jobs in high technology industries are not necessarily more secure than the older mill-based production jobs, quite the contrary. Alic and Harris (1986) report that "semiskilled and unskilled workers in semiconductors, computer manufacturing, and consumer electronics industries are more likely than other workers to lose jobs because of technology, imports, and offshore production. Continuing advances in both products and processes leave relatively fewer openings for unskilled and semiskilled workers." Assembly workers have been declining in the computer manufacturing industry because of "a growing emphasis on research and development activities, continuing automation of the production line, and the movement offshore of production facilities" (Einstein and Franklin 1986). To them, the high priority of research and development explains the concentrations of engineers, technicians and computer analysts in the computer manufacturing industry. This trend is expected to continue into

the future while assembly-related occupations will be characterized by the lowest ratios of production workers to total employment for any manufacturing industry. The outlook for high technology industrial employment is one of increasing separation between two strata of workers with restricted mobility between them, and different occupational structures.
Under conditions of stagnating, if not declining, employment in traditional goods production and high tech standardized manufacturing, "services have been hard pressed to supply the kinds and numbers of jobs demanded by a rapidly expanding labor force, particularly in those places where production employment has declined dramatically" (Stanback and Noyelle 1983). For blue collar workers formerly employed in traditional manufacturing industries, the transition to the new jobs is a difficult, if not impossible, task. They lack the technical skills to be absorbed into the highly qualified white collar technological jobs while, on the other end, they risk to be trapped into jobs in retail trade and consumer services that are unrewarding and do not provide the financial security of their former mill-based jobs. Furthermore, the income and occupational mobility channels characteristic of large manufacturing enterprises may not have their counterpart in new service enterprises. As a result, employees (especially women and minorities) are likely to remain permanently trapped in the poorest paid jobs characterized by low-pay, low-skill, and lack of opportunities for advancement (Stanback and Noyelle 1982).

Employment in nonmanufacturing sectors of the economy is likely to slow as well because of the spread of automation technologies and increased productivity, further compounding the problem of providing jobs for dislocated workers as well as new labor force entrants who would previously have found skilled and semiskilled manufacturing jobs (Office of Technology Assessment 1983). According to Kasarda (1983), unemployment is also likely to increase for lesser skilled job seekers (the reserve army of the unemployed), as entry-level job deficits characterize the employment base of the new urban centers of information processing. The gap between skill requirements for urban employment and skills possessed by disadvantaged residents worsens under conditions of overall central city employment gains in financial and administrative services.

Chapter III Statement of Research
The main objective of the research is to investigate whether or not high technology industries offer employment opportunities for unskilled central city residents through the creation of entry-level jobs. This objective suggests two directions of research:
The first direction can be characterized as descriptive in nature: by interpreting data on employment in high tech industries categorized by city type and skill level of workers, it will be possible to identify where does high tech industrial development constitute a significant proportion of local employment, as well as the share of entry-level jobs in the total picture. Conditions in declining cities may differ significantly from conditions in stable or growing cities, as an example. The cross-sectional analysis of the data takes place in Chapter IV.
The second direction of research is an attempt, in Chapter V, to explain what factors promote high tech development and, therefore, determine the urban context in which high tech enterprises are likely to emerge. By looking at simple correlations among variables, I offer a modest contribution toward the construction of a predictive equation by examining those variables that have a high degree of association with the dependent variable and testing for the significance of this association.

Relationships among factors addressing the research objective yield the following hypotheses, elements of which will be verified for their degree of association:
- Automation generally eliminates the most unskilled tasks, especially in manufacturing;
- Persons with a high level of educational attainment adapt more easily to changes resulting from technological advances. They are more flexible than persons with a minimum level of education;
- Jobs available in central cities are of the white-collar type. Few opportunities exist for unskilled persons to fill those jobs except in unrewarding service occupations (e.g., janitorial work, deliveries, cleaning services, etc...);
- Despite this, high technology firms can indirectly generate ancillary business activities which may offer employment opportunities to central city residents. It is assumed that the larger the size of the local population, the greater the rate of indirect job generation;
- Finally, the more a given firm invests in technology, such as robots or other automated processes, thereby increasing the value added of its manufacture, the greater the impact on employment since low level jobs may be eliminated as a result. At the same time, demand for more sophisticated production equipment can create entry-level jobs in standardized production activities and assembly lines, although these may be located outside central cities.

Empirical findings will help determine if high technology-enterprises can be justified as producing entry-level jobs in those areas that need them the most. If there are no substantial direct benefits to low skill workers, then should communities continue to engage in "chipchasing" and pursue a high tech development strategy, or rather reorient their efforts toward "low tech" local industries in an effort to create entry-level jobs?

Chapter IV
High Technology Employment in Central Cities:
A 1981 Profile
A profile of employment in high technology industries for selected central cities of SMSA's is developed in this chapter, broken down by skill level of workers. The chapter consists of five sections: in the first section, I describe the two dimensions of resident need and city growth or decline that have been used by Franklin James to develop the typology of cities incorporated in the research. Breaking down the cities into
types based on these two dimensions will help determine whether high technology sectors are equally able to generate entry-level jobs across all city classes.
A definition of high technology is proposed in the second section and has been used to select a group of industries for which employment data have been collected. The source of data is then presented. Data handling procedures are discussed in the fourth section. A geographic profile of high tech employment is developed in the last section, with an emphasis on entry-level jobs.
1. Cities by Type:
Using an empirical indicator of city distress, Dr. Franklin James from the Graduate School of Public Affairs, University of Colorado at Denver, developed a typology of cities based on

hardship and need. The thesis applies this typology to the research effort outlined in the preceding chapter. Two factors define the community typology:
a. Resident need: a function of poverty rates, unemployment, and per capita income growth providing a measure of economic hardship among city residents; and
b. City growth and decline: a function of the pace at which population change takes place. Growth or decline shapes fiscal and economic resources available to municipalities to provide services and address problems. Only large cities are considered, with a 1980 population in excess of 250,000.
James based his decision to develop a two-dimensional community typology with independent measures of the economic problems of people as well as those of places on the need to reflect "a partial and preliminary diagnosis that rapid deterioration during the seventies in the economies of a number of cities was in large part responsible for mounting urban distress" (James 1985). This deterioration was in part caused by shifts of capital and industrial restructuring and in part by changing federal priorities.
a. Measuring Resident Need:
Resident need is a composite index of four specific economic indicators measuring overall economic opportunities for urban dwellers. The indicators are:
Percent of city population with incomes below the poverty level in 1979, providing a measure of the relative level of

population in poverty and indicating the population in need of assistance. A weight of 40 percent in calculating resident need was assigned by James, considering that poverty rates are among the most widely accepted indicators of economic deprivation.
The remaining three indicators have been assigned equal weights of 20 percent each. They are:
- Net change in real per capita income between 1969 and 1979 (in 1969 dollars): records absolute dollar growth or decline in the average income level in the city;
- Percentage growth in real per capita income between 1969 and 1979: indicates relative income growth;
- Unemployment rate in 1980: reflects job availability and provides an indicator of general economic health.
In sum, these indicators measure the economic well-being of persons in cities such as: their access to jobs, the adequacy of their incomes to support above poverty standards of living, and the pace of improvement in their average standard of living.
To provide a context within which to interpret the findings, the index was normalized so that a value of 100 indicates a national average in terms of resident need. Cities whose index of resident need scored above the national average were considered to have "high need"; cities whose need index fell below 60 percent of the national average were termed "low need"; and cities with in-between scores were characterized as "moderate
need" cities.

b. Measuring City Growth or Decline:
Population change is highly correlated with changes in job opportunities within cities and their regions. Cities were ranked according to population growth or decline. Three general groups were defined by James:
- Declining population: Cities that lost 10 percent or more of their population between 1970 and 1980;
- Stable population: Cities whose population gain or loss between 1970 and 1980 was less than 10 percent; and
- Growing population: Cities that gained population by 10 percent or more between 1970 and 1980.
The results of applying the two-dimensional community typology based on city hardship appear in Table 1 where the 56 largest American cities are listed by their levels of resident need in 1980 and the pace of their population growth or decline between 1970 and 1980. It is clear, from looking at the table, that a strong relationship exists between population change and resident need: 70 percent of cities undergoing population decline showed high levels of resident need in 1980 while, at the other end, 67 percent of growing cities showed low levels of resident need. There are exceptions: for example, two cities with declining population levels Minneapolis and St. Paul exhibited low resident need in 1980 which suggests that they may be adapting somewhat successfully to change, possibly through a
diversification of their economies. At the other end, El Paso

Table 1
Large Cities by Type, 1980
Population Change: 1970 - 1980
Resident Decline Growth
Need -10Z +10Z
1980 or more Stable or more
High Atlanta Birmingham El Paso
Baltimore Miami
Boston New Orleans
Buffalo Chicago Cincinnati Cleveland Detroit Louisville New York Newark Norfolk Philadelphia St. Louis Oakland
Moderate Kansas City Columbus San Antonio
Milwaukee Indianapolis San Diego
Pittsburgh Jacksonville Tucson
Washington Long Beach
(D.C.) Los Angeles Memphis Sacramento San Francisco Tampa Toledo
Low Minneapolis Dallas Albuquerque
St. Paul Denver Austin
Fort Worth Charlotte
Nashville Honolulu
Oklahoma City Houston
Omaha Phoenix
Portland San Jose
Seattle Tulsa Wichita Virginia Beach
Source: City Need and Distress in the United States: 1970 1980, By Franklin J.James, July 1985.

showed high levels of resident need in 1980 despite the fact that its population expanded at a rate of 10 percent or more between 1970 and 1980. El Paso's growth has been caused by an influx of unskilled migrant workers with low earning power therefore placing a burden on the ability of the city to manage growth and provide services. However, these exceptions do not appear to deny the validity of the general observation that needy cities and population loss go together.
In sum, the typology suggests that, during the period from 1970 to 1980, northern cities suffered disproportionate rates of population decline and reflected higher rankings of resident need. While southern and western cities fared better, even there some central cities reflected slower rates of growth and/or stable population levels. These trends were emphasized in the eighties as the energy boom subsided, affecting their regional economies.
2. High Technology: Definitions
Since a large number of industries use one form or another of technological knowledge in their production process, it was necessary to narrow them down for the purpose of empirical research by developing a definition that would take into account some unique characteristics of high technology.

Although there is no widely accepted definition of what high technology industries are, a method that attempts to define them by focusing on the nature of the product alone and its degree of technical sophistication has many limitations and is highly subjective. Rather, two definitions are proposed in this section: A conceptual one and an empirical one. The selection of a high tech study group is based on linkages between the two.
a. Conceptual Definition;
In conceptual terms, Malecki (1984) defines high technology as "nonroutine economic activities directed toward developing new processes and toward small volume production of innovative products and services." He therefore distinguishes between industrial sectors that have routine, standardized production activities, and those that have nonroutine, nonstandardized production activities.
The focus in routine manufacturing, whether of textile products or of high tech goods such as microelectronic chips and personal computers, is on standardized production activity which is highly mobile: Capital in this case is attracted to low-wage labor areas either in some parts of the U.S., such as the Sunbelt, or abroad because there is no reliance in the production process on the technical or scientific skills of workers. In contrast, "emerging technologies and unstandardized activities that require skill-intensive labor should be considered the only real high tech industries High tech activities are specialized and

cannot be manipulated easily over the short term" (Malecki 1984). Among innovative and nonroutine production activities he includes: Research and development, experimental and prototype manufacturing, and small volume production of new and changing products such as medical diagnostic scanners and cruise missiles. Capital linked to nonroutine production activity is extremely stable in terms of locational requirements because linked to the availability of skilled and trained workers. Malecki points out that technical and professional workers needed in the nonroutine activities are the greatest single location factor for new products and high technology. This explains the degree of research and development concentration in such areas as California's Silicon Valley, along Route 128 near Boston, Route 1 near Princeton in central New Jersey, and the Research Triangle in North Carolina, to name a few of the better-known centers. Leading universities, in particular, provide the breeding ground for new industries and confer a competitive advantage on the regions in which they are located.
b. Empirical Definition and Selection of Industrial Sectors:
In empirical terms, the Bureau of Labor Statistics (1983) developed three definitions of high technology industries, ranging from very broad to very narrow, using the criteria of research and development expenditures, the ratio of scientific and technical personnel to total employment, and product sophistication. Table 2 presents the industrial sectors of the U.S. economy broken down into three groups, according to the

Table 2
Three Groups of High Technology Industries Based on Alternative Definitions
High tech group
SIC Industry I
131 Crude Petroleum and Natural Gas X
162 Heavy construction X
281 Industrial inorganic chemicals X
282 Plastic materials and synthetics X
283 Drugs X
284 Soaps and cleaners X
285 Paints and allied products X
286 Industrial organic chemicals X
287 Agricultural chemicals X
289 Miscellaneous chemical products X
291 Petroleum refining X
301 Tires and inner tubes X
324 Cement, hydraulic X
351 Engines and turbines X
352 Farm and garden machinery X
353 Construction and mining machinery X
354 Metalworking machinery X
355 Special industry machinery X
356 General industrial machinery X
357 Office,computing,accounting mchns X
358 Refrigeration & srvc ind machinery X
361 Electric transmission equipment X
362 Electrical industrial apparatus X
363 Household appliances X
364 Electric lighting & wiring equipmt X
365 Radio & TV receiving equipment X
366 Communication equipment X
367 Electronic components & accessories X
369 Miscellaneous electrical machinery X
371 Motor vehicles and equipment X
372 Aircraft and parts X
376 Guided missiles and space vehicles X
381 Engnrg, lab, scntfc, & rsrch instrm X
382 Measuring & controlling instruments X
383 Optical instruments and lenses X
384 Surgical, medical, & dental instrm X
386 Photographic eqpmnt & supplies X
483 Radio and TV broadcasting X
489 Communication services, n.e.c. X
491 Electric services X
493 Electric, gas and other utilities X
506 Wholesale trade, electrical goods X
508 Wholesale trade, machinery, eqpmnt X
737 Computer & data processing services X
7391 Research & development laboratories X
891 Engineering & architectural service X
892 Educ, scientific, & rsrch orgnztn X
Source: Bureau of Labor Statistics, November 1983.

different definitions, and classified at the three-digit Standard Industrial Classification (SIC) code level. Classification of an industrial establishment is based on its primary activity, determined by its principal product or group of products produced or distributed, or by services rendered. Using Malecki's concepts, I will identify one among the three groups of industries for the purpose of empirical study.
* Group I; The criterion for inclusion into this group is solely the utilization of technical personnel. This group comprises industries with a proportion of technology-oriented workers (engineers, life and physical scientists, mathematical specialists, engineering and science technicians, and computer specialists) at least 1.5 times the average for all industries. A total of 48 industries are included in this group, making it the broadest of the three. Based on Malecki's stricter conceptual definition, industries in this group cannot be readily considered high tech due to the fact that, "when above average sectors are considered high technology, a number of rather routine, standardized, and large-volume industries are included that would not be considered emerging or knowledge-based These industries conduct little research and development but have relatively high percentages of technical employees" (Malecki 1984). Among them are: Crude petroleum and natural gas, construction and mining machinery, household appliances, electric lighting and wiring equipment, and automobile manufacturing.

* Group II: Industries in this group have a ratio of research and development expenditures to net sales at least twice the average for all manufacturing industries. This group, with only six industries, is the narrowest of the three. The Bureau of Labor Statistics limited its selection to manufacturing industries heavily involved in research and development, such as drugs and communication equipment, excluding other industries. This group also does not include some of the advanced business services that have been playing an increasingly important role in the shaping of metropolitan economies and the restructuring of their labor markets, and that Malecki considers high tech "based on their occupational structure, new product orientation, and linkage with innovative manufacturing." Advanced services meeting these characteristics are computer programming, data processing services, and research, development, and testing laboratories.
* Group III: Industries in this group have a proportion of technology-oriented workers equal to or greater than the average for all manufacturing industries, and a ratio of research and development expenditures to sales close to or above the average for all industries. This is the Bureau of Labor Statistics intermediate definition: it includes all of the industries in Group II and some of the industries in Group I, distinguishing "among the activities related to the various product lines of individual firms" (Malecki 1984). This definition excludes those industries that exclusively have a high proportion of technical workers such as crude petroleum and natural gas, or

motor vehicles and equipment, because they do not focus on
science-based, emerging products and processes and they do not operate based on state-of-the-art knowledge. As Malecki indicates, this definition includes "the research and development activity of a firm like Procter and Gamble, but not its
manufacture of various soaps and cleaners," which explains the inclusion in this definition of the "soaps, cleaners and toilet preparations" industrial sector. "Similarly," he says, "a firm like General Electric not only produces light bulbs, which are not high tech, but also jet engines and electronic defense
systems, which are." This definition also includes two
nonmanufacturing industries which provide technical support to high tech manufacturing industries: computer and data processing services and research and development laboratories.
The intermediate definition (Group III) has been retained for the purposes of data gathering and interpretation. On the one hand,
it provides the benefit of including all of the Group II
industries but without being as restrictive. On the other, it
selects from the broader definition industries that have
production activities emphasizing the output of new products and the employment of skilled workers while excluding those that are cyclically sensitive to downturns in the economy, such as automobile manufacturing.
A summary description of Group III industries classified by major group at the two-digit SIC level is presented in Appendix I.

3. Source of data;
Data on total employment in selected high tech industries were collected from the 1981 edition of the County Business Patterns. The U.S. Bureau of the Census tabulated data by detailed kinds of business based on the 1972 edition of the Standard Industrial Classification manual. The data cover most of the economic divisions of the economy: agricultural services, mining,
construction, manufacturing, transportation, public utilities, wholesale trade, retail trade, finance, insurance, real estate, and services.
Because County Business Patterns statistics provide information on establishments, payroll, and employment by industry classification and county location, they are useful for analyzing the industrial structure of regions. Although the focus of the research is on cities, data were collected for the county in which the central city of an SMSA is located, assuming a one-to-one relationship between central city and central county of the SMSA.
4. Data Handling:
The raw employment data were assembled to present a profile of 1981 high technology employment conditions in central cities of SMSA's classified by need, and were further disaggregated based on the skill level of workers.

Three types of skill categories were identified, based on education levels: jobs employing people having fewer than 12 years of education were defined as entry-level jobs (4 years of high school or less, translating into a low education level); those employing people with 12 to 15 years of education were defined as moderate-level jobs (1 to 3 years college, translating into a moderate education level); finally, jobs employing people with 16 or more years of education were defined as high-level jobs (4 or more years of college, translating into a high education level).
Table 3 below presents the percentage of workers in each skill
category, relative to total employment in each major industrial
group (two-digit SIC classification). The percentages were
derived from Bureau of Labor Statistics data on educational
attainment of U.S. workers by industry for 1982. Educational
attainment was identified as years of school completed.
Table 3. Percentage of Workers by Educational Level Attained, March 1982:
Educational Level
Industry Low Moderate High
Chemicals & Allied Products 56.5 18.5 25.0
Petroleum Refining 47.9 10.2 18.9
Machinery, except Electrical 67.1 18.5 14.4
Electronic Equipment 64.3 18.7 16.9
Transportation Equipment 64.6 18.0 17.4
Instruments & Related Prdcts 59.1 20.2 20.8
Professional Services 28.4 19.9 51.7
Source: Educational Attainment of Workers, March 1982-83,
U.S. Department of Labor, Bureau of Labor Statistics, April 1984, Bulletin 2191.

These percentages were then used as multipliers to breakdown employment data into skill level categories described earlier. It was assumed, in doing so, that the job distribution at the local level reflected conditions existing in the nation as a whole. This distribution is presented in Tables 5a and 5b.
5. Data Presentation:
Table 4 presents 1981 high technology employment as a share of total employment in the economy in cities classified by resident need. The data in this table outline the relative importance of high technology industrial sectors in local economies and the differences that exist among cities.
Tables 5a and 5b present a profile of high tech employment by industry, by skill level of workers, and type of city. While Table 5a presents the raw data, Table 5b highlights the distribution of employment as a share of the total in each category, by city type.
Table 5c presents a breakdown of entry-level jobs by type of city as a percentage of total entry-level employment in high tech industries. Thus, a geography of jobs across cities emerges, pointing out those that have a larger concentration of low skilled jobs. High tech employment in cities as a share of the

total is also presented, providing a point of reference in comparing the two.
a. High Technology Employment as a Share of Total Employment:
As indicated in Table 4, 1981 high tech employment constituted a larger share of total employment in relatively low need cities than in other cities: 10.8 percent of total jobs in these cities were in high tech industries, compared to 7.8 percent in moderate need cities and 4.2 percent in high need cities. Thus, cities that were financially better off in 1980 seemed to capture a larger share of jobs in high tech industries than those that were beset by social and economic problems. This observation is emphasized by the fact that the total job base in high need cities was almost twice as large as in low need cities, thereby lessening even more the impact, in absolute terms, of high technology employment in those high need cities.
Industrial sectors that had the greatest share of total employment in low need cities in 1981 were: electronic machinery and equipment (3.4 percent of total jobs), transportation equipment (2.8 percent of the total), and general machinery (1.8 percent of the total). Electronic machinery and equipment also had a larger share of total employment in high need cities, relative to other industries (1.3 percent of the total). However, this percentage was well below the level for low need

s in Two-Digit Industries ;e of Total Employment
t, 1981 City Tot Emp (000s) Percent Of Total Mchnry Electro Transpt Instr . Chem Petrol
ly High Need 11,717 4.2 0.5 1.3 0.3 0.5 1.0 0.0
Need 7,135 7.8 0.7 2.2 2.4 0.6 0.9 0.1
ly Low Need 6,421 10.8 1.8 3.4 2.8 0.8 0.8 0.2
25,273 6.9 0.9 2.1 1.5 0.6 0.9 0.1
County Business Patterns, U.S. Bureau of the Census, 1981.
The Decline in Entry Level Jobs in Denver and Other Large Cities, PCD Studio Paper, June 1985.
Authors calculations derived from the above, February 1987.

High need cities had a relatively larger share of total employment in chemicals and allied products compared to other sectors and to other classes of cities as well. The relative importance of chemical related employment in high need cities is explained by the fact that most of the nation's chemical plants are generally located in the north and northeast.
The locational geography of jobs also explains the relative importance of the electronics and transportation equipment sectors in the economies of low need cities: a large portion of the electronics research, development, and assembly operations, and the aeronautical industry are in California, Texas, and Arizona, which is where a number of moderate and low need cities are located.
Employment in professional services, comprising computer and data processing services and research and development laboratories, formed a larger share of total employment in low and moderate need cities than in high need cities. Advanced business services came to play an increasingly important role in the economies of a number of central cities, as indicated in Chapter II. Further research is needed at this point to document whether or not high need cities, many of them large centers of information processing and diffusion, have experienced increased employment in advanced services, relative to other sectors and other regions as well, since these data were collected.

b. Profile of Employment in Sectors by Skill Level by City Type: The purpose of the analysis is to distinguish among sectors offering a larger proportion of entry-level jobs relative to the total, and assess if all types of cities bear an equal share of entry-level jobs. The interpretation is based on percentages in Table 5b.
The raw data in Table 5a yields some preliminary observations: in absolute terms, total high tech employment in 1981 was most abundant in low need cities where the concentration of entry-level jobs was also greater than in moderate or high need cities. In all cities, jobs appeared to be concentrated at either end of the skill structure, with low skill jobs at one end and high skill jobs at the other end, illustrating the fact that high technology generates a dichotomized labor structure.
* High need cities:
The largest share of total high tech employment in 1981 was in electronics (31.3 percent of the total) and chemicals (24.1 percent). The share of professional services was 12 percent. Entry-level jobs (4 years of high school or less) were generally concentrated in electronics (34.9 percent of the total), chemicals (23.6 percent), and machinery (14.3 percent ). In contrast, 6 percent of entry-level jobs were in the category of professional services.

Table 5a
Employment in Selected Two-Digit High-Tech Industries By Skill Level of Workers and Type of City, 1981
Entry-Lev Non Entry Lev
Tot Emp Low Ed Mod Ed High Ed
High Need Cities
Chemicals & Allied Prdcts 117,368 66,340 21,729 29,299
Petroleum Refining 4,314 2,065 442 813
Machinery, excpt Electric 60,025 40,247 11,127 8,651
Electronic Mach & Equip 152,533 98,138 28,556 25,805
Transportation Equipment 40,652 26,275 7,324 7,080
Instruments & Related Prd 53,411 31,541 10,769 11,100
Professional Services 58,974 16,760 11,739 30,501
Total 487,277 281,367 91,687 113,249
Moderate Need Cities
Chemicals & Allied Prdcts 66,724 37,714 12,353 16,657
Petroleum Refining 10,222 4,893 1,046 1,927
Machinery, excpt Electric 50,922 34,144 9,440 7,339
Electronic Mach & Equip 158,515 101,987 29,676 26,817
Transportation Equipment 168,049 108,619 30,276 29,268
Instruments & Related Prd 44,795 26,453 9,032 9,310
Professional Services 54,606 15,519 10,870 28,242
Total 553,833 329,329 102,694 119,559
Low Need Cities
Chemicals & Allied Prdcts 52,578 29,719 9,734 13,125
Petroleum Refining 12,743 6,100 1,304 2,403
Machinery, excpt Electric 114,587 76,831 21,242 16,514
Electronic Mach & Equip 218,371 140,498 40,882 36,943
Transportation Equipment 182,609 118,030 32,899 31,804
Instruments & Related Prd 51,274 30,279 10,339 10,656
Professional Services 59,461 16,899 11,836 30,753
Total 691,623 418,355 128,236 142,198
Source: County Business Patterns, U.S. Bureau of the Census, 1981 Educational Attainment of Workers, March 1982-83,
U.S. Dept, of Labor, Bureau of Labor Statistics,
April 1984, Bulletin 2191.

Table 5b
Employment in Selected Two-Digit High-Tech Industries By Skill Level of Workers and Type of City, 1981 Percent Column Totals
Entry-Lev Non Entry Lev
Tot Emp Low Ed Mod Ed High Ed
High Need Cities
Chemicals & Allied Prdcts 24.1 23.6 23.7 25.9
Petroleum Refining 0.9 0.7 0.5 0.7
Machinery, excpt Electric 12.3 14.3 12.1 7.6
Electronic Mach & Equip 31.3 34.9 31.1 22.8
Transportation Equipment 8.3 9.3 8.0 6.3
Instruments & Related Prd 11.0 11.2 11.7 9.8
Professional Services 12.1 6.0 12.8 26.9
Total 100.0 100.0 100.0 100.0
Moderate Need Cities
Chemicals & Allied Prdcts 12.0 11.5 12.0 13.9
Petroleum Refining 1.8 1.5 1.0 1.6
Machinery, excpt Electric 9.2 10.4 9.2 6.1
Electronic Mach & Equip 28.6 31.0 28.9 22.4
Transportation Equipment 30.3 33.0 29.5 24.5
Instruments & Related Prd 8.1 8.0 8.8 7.8
Professional Services 9.9 4.7 10.6 23.6
Total 100.0 100.0 100.0 100.0
Low Need Cities
Chemicals & Allied Prdcts 7.6 7.1 7.6 9.2
Petroleum Refining 1.8 1.5 1.0 1.7
Machinery, excpt Electric 16.6 18.4 16.6 11.6
Electronic Mach & Equip 31.6 33.6 31.9 26.0
Transportation Equipment 26.4 28.2 25.7 22.4
Instruments & Related Prd 7.4 7.2 8.1 7.5
Professional Services 8.6 4.0 9.2 21.6
Total 100.0 100.0 100.0 100.0
Source: County Business Patterns, U.S. Bureau of the Census, 1981 Educational Attainment of Workers, March 1982-83,
U.S. Dept, of Labor, Bureau of Labor Statistics,
April 1984, Bulletin 2191.
Authors calculations, March 1987.

Non entry-level jobs requiring a high level of education (4 or more years of college) were generally found in services: 26.9 percent of all high skill jobs were in this category in 1981, followed by chemicals (25.9 percent) and electronics (22.8 percent). Chemicals and electronics offered a greater share of total non entry-level jobs requiring moderate education (1 to 3 years college), relative to other sectors. The share of jobs in professional services was also relatively important (12.8 percent of the total).
The data suggest that, while jobs in professional services were concentrated in the high skill range of the labor market, chemicals and electronics in high need cities offered a more or less equal range of employment opportunities across all skill categories.
* Moderate need cities:
The greatest share of 19 81 total high tech employment in these cities was in transportation equipment (aircraft and parts, and missiles and space vehicles) with a share of 30.3 percent of the total, electronics (28.6 percent), and chemicals (12.0 percent). The share of professional services was 9.9 percent of total high tech employment.
Entry-level jobs were generally in the transportation sector (33 percent of all entry-level jobs), electronics (31 percent), and chemicals (11.5 percent).

As in high need cities, the share of entry-level employment in professional services was small (4.7 percent), relative to other sectors. This sector generally demands a skilled labor force, as the multipliers in Table 3 indicate, and 23.6 percent of total high skill jobs in moderate need cities were in professional services.
The data suggest that, as is the case with electronics and chemicals, the transportation sector offered a range of employment opportunities spread across skill levels, with an emphasis on unskilled jobs, possibly in assembly tasks. Chemicals, however, did not play as important a role in the moderate need cities as in high need cities. Employment in computer and data processing services and research and development laboratories was also smaller than in high need cities.
* Low need cities:
The largest share of total high tech employment in 1981 in the low need cities was in electronics (31.6 percent), transportation equipment (26.4 percent), and machinery (16.6 percent). All three sectors offered greater employment opportunities in entry-level jobs. The share of unskilled electronics employment was 33.6 percent of the total, while the share of these types of jobs in the transportation sector was 28.2 percent.

The data indicate that employment in electronics was greater in low need cities than in both high and moderate need cities. In contrast, the amount of employment in chemicals was much smaller. Employment in professional services followed the same pattern as in high and moderate need cities, with a low percentage of entry-level jobs (4 percent) compared to other skill levels. The share of total employment in professional services was however smaller in low need cities than in other cities.
Similarly, total employment in the sector of instruments and related products was lower in low need cities than in moderate and high need cities. Across cities, however, this sector consistently offered greater employment opportunities for unskilled workers than the advanced services sector, but less than the "big four": chemicals, electronics, machinery, and transportation.
c. Entry-Level Jobs by Type of City:
Table 5c presents a distribution profile of entry-level jobs across cities, relative to the total. Whereas Table 5b presented the distribution of jobs by industrial sector and across skill levels, this table focuses on the distribution of entry-level jobs by type of city.
A key characteristic that almost overshadows all others is the fact that low need cities held a disproportionately larger share of unskilled jobs than either moderate need or high need cities:

Table 5c
High-Tech and Entry-Level Jobs ByT^pe of City, 1981 Percent Column Totals
% Column Total
High Tech Entry-Lev High Tech Entry-Lev
Type of City Tot Emp Jobs Tot Emp Jobs
Relatively High Need 487,277 281,367 28.1 27.3
Declining Population 438,760 253,927 25.3 to
Stable Population 48,517 27,440 2.8 2.7
Moderate Need 553,833 329,329 32.0 32.0
Declining Population 86,875 51,422 5.0 5.0
Stable Population 466,958 277,907 26.9 27.0
Relatively Low Need 691,623 418,355 39.9 40.7
Stable Population 326,369 197,597 18.8 19.2
Growing Population 365,254 220,758 21.1 21.5
Total 1,732,733 1,029,051 100.0 100.0
Source: Authors calculations, February 1987.

40.7 percent of all high tech jobs filled by people with 4 years of high school or less were in low need cities, compared to 32 percent in moderate need cities and 27.3 percent in high need cities. Of the 40.7 percent, 21.5 percent of the jobs were in cities that had experienced a population increase between 1970 and 1980. The proportion of unskilled jobs in low need cities was also higher than the proportion of high tech employment in those cities.
The proportion of entry-level jobs in high need cities (27.3 percent) was lower than in the moderate need cities (32 percent). Of the 27.3 percent, 24.7 percent of the jobs were in cities with declining populations between 1970 and 1980.
In a way, these data augment the observations made while interpreting Table 5b: the high number of unskilled workers in low need cities of the south and west were in assembly-type jobs such as electronics, machinery equipment and, to a lesser extent, transportation equipment. The relatively greater importance of advanved services in the economy of high need cities in 1981 may have been a factor in their relatively lower levels of unskilled workers.
6. Conclusion:
The cross-sectional analysis suggests that a number of industries defined as high tech employed a larger proportion of workers in 1981 with 4 years of high school or less rather than with a

college or post-college education. What explains this is the fact that these workers were engaged in assembly-type operations which are generally standardized and routine in nature, thus accounting for their higher numbers in occupations defined as entry-level. Certainly this underscores the bifurcated nature of high technology employment with two pools of workers at each end of the job structure, one employed in production and the other in design, research, and development. Based on the analysis, however, it is possible to advance that high technology industries do hold some promise for disadvantaged central city residents provided all other factors remain constant, such as the location of those jobs and whether or not central city residents compete against suburban workers for them.

Chapter V
Correlation Analysis
A series of subhypotheses were presented in Chapter III, assuming probable relationships among factors addressing the research objective. In this chapter, I propose to look at the apparent relationships among variables representing the factors.
The relations of independent variables (the hypothetical factors) both to each other and to the dependent variable (representing employment in high technology industries in 1981) can be performed through correlation analysis. Some variables which were originally thought to be of importance in explaining the hypothesis may prove unrelated, while other variables may appear that show important relations although they were previously thought of little significance.
I first discuss the limitations inherent to correlation analysis, then present the variables selected and the source of data. Simple correlations between the independent variables and the dependent variable are examined and a flow diagram developed to represent the elements involved and their relation to one another. Finally, an attempt has been made to determine whether or not two variables were associated through the testing of the null hypothesis, that is the hypothesis of no association.

This chapter constitutes a partial attempt, rather than a complex statistical procedure, at examining interactions among elements of the hypothesis to assess in a rather incomplete fashion the validity of the assumptions that drove this research.
1. Limitations of the Statistical Procedure;
The assumption that the relation between two variables is a linear one e.g., as one increases, so does the other-underlies the Pearson product-moment correlation coefficient, or Pearson r. The correlation coefficient tells how closely are two variables associated to each other: it measures the strength of the relationship that exists among them. What it does not tell is how one variable is influenced by the other, that is how the Y variable changes with changes in the X variable.
Although correlation is a necessary feature of a causal relation, it is not sufficient to prove that a causal relation exists. Correlation is not causation: two variables correlated to each other, for example, may be caused by a third unknown variable (Phillips 1982). Ezechiel and Fox (1959) underline the fact that, "correlation analysis itself can never provide the interpretation of cause and effect, it can only establish the facts of the relations and nothing else." Real and significant correlation between two variables reflects relation to a common factor or factors, yet gives no inference as to direct causal connections. Thus, a correlation between two variables may be

due to both being influenced by common causes although neither may in any conceivable way influence the other.
With these limitations in mind, I now turn to the presentation and interpretation of the correlation coefficients.
2. Selection of Variables and Source of Data:
Independent variables representing the hypothetical factors have been selected from the 1983 County and City Data Book for the purpose of correlation analysis.
The County and City Data Book is developed by the U.S. Bureau of the Census and presents summary socioeconomic and housing statistics for states, counties, cities of 25,000 or more, and places of 2,500 or more. For the 1983 edition, sample data from the 1980 Census of Population and Housing on such subjects as education, labor force, and income, have been emphasized. Other topics covered are: population, government finance and
employment, households, housing, manufactures, poverty, retail and wholesale trade, school enrollment, and vital statistics. The data were collected from Table C (for cities) which covers a total of 170 items.
The independent variables are:
- Total population of central city in 1980, (Totpop);
- Percent population change between 1970 and 1980, (Pctchge);
- % of persons with 16 years + of school completed, (Yrscomp);

- Percent unemployed, (Unemploy);
- Percent employed in manufacturing, (Manu);
- Percent employed in professional services, (Services);
- Percent employed in finance, insur., and real estate, (Fire);
- Median household income, (Hhinc);
- Percent of total families below poverty, (Fampov);
- Percent of government expenditures for education, (Expeduc);
- Value added by manufacture, (Valuadd).
Abbreviations in parentheses refer to variable labels used in SPSSx for the statistical job run.
3. Interpretation of the Correlation Matrix:
Table 6 presents the correlation coefficients measuring associations among variables.
With a correlation coefficient of .63, there is a strong positive relationship between value added and employment in high tech industries (the dependent variable, EmployB). Value added by manufacture is derived by subtracting the cost of materials, supplies, containers, fuel, purchased electricity, and contract work from the value of shipments (products manufactured plus receipts for services rendered). As high tech employment increases, it appears that value added also increases.
An even higher relationship exists between value added and unemployment. A coefficient of .837 suggests that the higher the value added in an industry, the greater the unemployment, which

1.000 0.490 0.118 -0.196 0.405 0.582 -0.339 -0.244 0.003 0.217 -0.195 0.630
0.490 1.000 -0.264 -0.278 0.800 0.160 -0.064 0.418 -0.366 0.616 0.377 0.980
0.118 -0.264 1.000 -0.309 -0.571 0.210 -0.682 -0.404 0.612 -0.636 -0.564 -0.241
-0.196 -0.278 -0.309 1.000 -0.311 -0.742 0.291 0.619 -0.182 -0.012 -0.114 -0.343
0.405 0.800 -0.571 -0.311 1.000 0.423 -0.035 0.205 -0.193 0.476 0.252 0.837
0.582 0.160 0.210 -0.742 0.423 1.000 -0.510 -0.746 0.440 -0.193 -0.326 0.326
-0.339 -0.064 -0.682 0.291 -0.035 -0.510 1.000 0.220 -0.877 0.720 0.839 -0.128
-0.244 0.418 -0.404 0.619 0.205 -0.746 0.220 1.000 -0.366 0.324 0.253 0.270
0.003 -0.366 0.612 -0.182 -0.193 0.440 -0.877 -0.366 1.000 -0.944 -0.921 -0.302
0.217 0.616 -0.636 -0.012 0.476 -0.193 0.720 0.324 -0.944 1.000 0.890 0.582
-0.195 0.377 -0.564 -0.114 0.252 -0.326 0.839 0.253 -0.921 0.890 1.000 0.296
0.630 0.980 -0.241 -0.343 0.837 0.326 -0.128 0.270 -0.302 0.582 0.296 1.000
stical Package for the Social Sciences (SPSSx), March 1987 .
and County Data Book, U.S. Bureau of the Census, 1983.

can presumably be attributed to more efficient processes of production where labor costs have been curtailed.
A moderate relationship exists between value added and manufacturing employment (.326) and a strong one between high tech employment and manufacturing employment (.582), which suggests that employment gains in manufacturing occur in those industries that are high tech.
On the other hand, there is a negative relationship between high tech employment and employment in professional services. The
negative coefficient of .339 indicates that as high tech jobs in central cities go up, employment in such industries as computer and data processing services, and research and development laboratories goes down. This trend may be caused by increased productivity in office occupations, thus reducing the necessary amount of input per unit of output.
Total population is positively associated to both unemployment and high tech employment. As the size of a city increases, so do unemployment levels (r = .800). Also characteristic of urban
areas is the number of jobs in high tech industries. The
correlation coefficient is .490 when total population and high tech employment are associated.
In summary, high tech development in urban areas may result in an increase in the value added of manufactures. Unemployment may

Figure 1
Association among Variables

Source: Table 6, Correlation Matrix.

develop as a corollary of the growth of high tech and there may actually be a decrease in professional services jobs. These findings corroborate what has been presented in the literature review section of the research: the growth in high tech manufacturing employment has mainly been in assembly and standardized production. This is where the growth has mostly been visible. These jobs have generally been located in suburban locations and rural areas. As the product-cycle model indicates, once production reaches a standardized stage where innovation is not likely to occur, it becomes highly volatile in its locational requirements. This may explain the association between central city population and total employment, since most of the new jobs are not within immmediate reach of central city residents who then compete with suburban residents to gain access to them. Studies have also indicated that suburban residents hold a majority of jobs in central cities, generally in advanced services occupations. However, care should be exercised in drawing inferences of causality among variables due to the limitations of correlation analysis, as indicated earlier. Figure 1 is a pictorial representation of the association among some of the variables.
4. Testing for the Significance of r:
The null hypothesis that r = 0 may be tested directly by using the table in Appendix II. To use the table, the degrees of freedom associated with r are first calculated, using the following formula:

df = N 2,
where N is the number of observations. The number of cities for which data were collected was 50. Therefore, df = 48.
If the observed value of r equals or exceeds the table value, then the null hypothesis, that is the hypothesis of no association between variables, is rejected. If the observed value is less than the table value, we fail to reject the null hypothesis. To test the hypothesis of no association (r = 0) between the dependent variable and each of the independent variables in Table 6, the steps are as follows:
a. Null hypothesis: There is no correlation between any of the independent variables and employment in high technology in the fifty cities. Or, the correlation between the two is zero.
b. Decision rules: Let the significance level be .01, with a one-tail test, and df = 48.
c. The null hypothesis is rejected if r is greater than or equal to .322, and it cannot be rejected if r is less than .322.
As Table 6 indicates, the observed value of r is larger than the critical value of .322 found in the table in Appendix II for a number of independent variables correlated with the dependent variable of employment: total population (r = .490), unemployment (r = .405), manufacturing employment (r = .582), and value added of manufactures (r = .630). Therefore, we can reject the
hypothesis of no association between high tech employment and the variables listed above. A reasonable inference is that the size

of a city, unemployment levels, manufacturing employment, and value added are associated to employment in high tech industries. Together, these variables determine the urban context in which high tech enterprises are likely to emerge. If the share of high tech jobs in high need cities, and as a corollary the proportion of entry-level jobs, is not as large as in low need cities, then it may well be that one or more of the independent variables shown to be associated to the employment variable do not play as important a role in the economy of high need cities as they do in low need cities.
The majority of high need cities, for example, lost population at a rate greater than 10 percent between 1970 and 1980, while a majority of low need cities had stable population levels or grew by 10 percent or more during the same time interval. High technology labor intensive assembly plants offering entry-level employment opportunities have been shown to be generally in the south and west. Thus, the loss of population in high need cities may account for lower levels of entry-level jobs in these types of industries.
Another factor accounting for the regional disparities may be explained by the fact that manufacturing industries in high need cities have shrunk considerably during the recessions of the seventies and early eighties, and have never regained prerecessionary levels of employment. As previously indicated, manufacturing employment and high technology employment are

positively correlated, and the absence of a large manufacturing base in high need cities may account for overall low levels of employment in high technology industries. As the manufacturing base shrinks, so does value added by manufacture since the value of shipments cannot offset the cost of materials and other necessary inputs to the production process.
Due to regional disparities and the preeminence of some factors over others, high need cities seeking to increase entry-level employment opportunities should reorient their efforts toward other strategies. Indeed, the analysis suggests that high tech industries cannot be justified as producing enough entry-level jobs to benefit the unskilled portion of the workforce residing in high need cities. For these communities, high technology as an economic development strategy may not be a viable solution to resolving the problems of unemployment, underemployment, and job insecurity for central city residents.

'Chapter VI Policy Options
Nevertheless, the United States is alone among advanced industrial nations in allowing capitalist accumulation to proceed largely outside the confines of national governmental planning and welfare measures. The explanation for American divergence lies in the uniqueness of the political system. There is no political force to demand a coherent national urban and regional policy, even one which would only require the shortterm discipline of business interests to assure their long-run aims The future then is one of continued social inequality and uneven development, devoid of the comfort that we are still the richest country on earth. (Sternlieb and Hughes 1978)
Two implicit policy options stem from the empirical research. The first one is aimed at the retention of entry-level jobs in low need cities: however abundant these may be, they are not stable blue-collar production jobs (Markusen 1984). The second one targets the creation of entry-level jobs in high need cities where "the urban residential composition-job opportunity mismatch, and corresponding minority unemployment rates, can worsen even under conditions of overall central city employment gains" (Kasarda 1983).
Policies to mitigate the negative effects of worker displacement and dislocation when plant closings occur can be one way to save jobs and protect communities against the impacts of industrial disinvestment. Efforts of this sort have materialized in this

country, generally involving large unionized industries such as steel and automobile manufacturing. Collective bargaining
agreements between labor and management have increasingly emphasized job security and, in the event of a plant shutdown, management assistance to labor in finding employment, the provision of retraining, and the maintenance of benefits such as health insurance and pension benefits. One such example is the approach taken by Ford Motor Company when it announced the impending shutdown of its San Jose assembly plant in the fall of
1982. The joint United Auto Workers Union-Ford management initiative provided assistance to dislocated workers in the form of orientation sessions, assessment and testing, basic education, vocational exploration courses, in-plant seminars, targeted vocational retraining, prepaid tuition assistance, on-the-job training, job search training and placement, and preferential placement (Hansen 1985).
Although this type of palliatives does not address the long term issue of job retention advocated by this author, it does however provide some protection against the unchecked and unfettered mobility of capital that often leaves entire communities in shambles. The retraining of workers displaced by economic changes is a central theme of industrial policy. However, it "implies that someone had a previous job; it does nothing for the long-term unemployed" (McGahey and Jeffries 1985).
In order for job retention to be realized, it would be necessary

to restrict the mobility of capital and provide those most directly affected (workers and communities) with the right to make decisions about capital transfer and investment (New Initiatives for Full Employment Committee 1987). Bluestone and Harrison (1982) report that many European countries, such as Sweden, Germany, and France, have such protective mechanisms of control to restrict outright disinvestment and that this has not affected the performance of the firms involved, although some could question this in light of the sluggish performance of European economies at the present time.
Stanback and Noyelle (1982) maintain that "mobility of the large corporation constitutes a serious threat to the long-term viability of the jobs held by many workers in the lower occupational strata Capital mobility is not simply restricted to the older places but occurs in fast growing places as well." Job retention could also be accomplished by alternative forms of ownership and worker buyouts of closing plants, although financial and political considerations can stand in the way of implementation (Bluestone and Harrison 1982; Mayor's Task Force on Steel and Southeast Chicago 1985). The difficulty is heightened by the fact that most high tech enterprises in low need cities are often branch plants of large corporations with minimal linkages to the community in which they are located.
The situation is somewhat different in high need cities. After a

period of decline and disinvestment caused by the dismantling of basic industries, some cities have generally been able to exploit their emerging service sector. More often than not, this has left unskilled inner-city residents out of the new economic renaissance. A good case in point is provided by New York City. This city, "capitalizing on its strength as an international financial and administrative center, experienced a net increase of 167,000 jobs between 1977 and 1981. Yet, while the city's total employment base was expanding, its overall minority unemployment rates continued to climb. This is because virtually all of New York's employment expansion was concentrated in white-collar service industries, whereas manufacturing employment dropped by 55,000 jobs and wholesale and retail trade employment declined by an additional 9,000 jobs during the four-year period" (Kasarda 1983) .
Other high need cities are capitalizing on their strengths as production centers and are engaging in an effort to revive their basic manufacturing industries. Chicago, for example, is launching a reconstruction effort to save its steel industries: "The City, State, and other government bodies in the region are urged to build on not to neglect the existing industrial base . . . Although southeast Chicago where three major steel
producers and a great number of steel-related enterprises are located has suffered severe economic hardship, it retains many comparative advantages. Among these are access to
transportation, availability of large tracts of industrial land

at reasonable prices, and an experienced workforce" (Mayor's Task Force on Steel and Southeast Chicago 1985). Among other
industry-specific measures, the task force has advocated that "the city devise more efficient strategies to facilitate adaptation of unemployed workers to the changing job market through retraining, educational, and job placement programs."
Industrial policy advocates who emphasize retraining of
dislocated workers, so that they can upgrade their skills to remain in the same industry or acquire new ones and move to another sector, seem to overlook the fact that high technology industries are highly capital intensive, therefore reducing the overall number of workers needed. As discussed in Chapter II, high technology enterprises will eliminate more jobs than they will create: innovations raise the productivity level, but they also result in the elimination of a number of production jobs. "Unless the economy can boom in some unforeseen way," say McGahey and Jeffries (1985), "it is hard to see how enough labor demand will be generated to reemploy displaced industrial workers, much less hire those who are chronically unemployed." This point is emphasized by the fact that, as the Chicago experience shows, "the employment transition for the worker displaced from a southeast Chicago factory has been slow and painful. Many workers have joined the pool of the long-term unemployed; when they have found new work, it is frequently for much less pay and with limited future opportunity" (Mayor's Task Force on Steel and Southeast Chicago 1985).

Retraining of dislocated workers is also likely not to affect a large number of unskilled inner city residents due to the fact that many of them are not employed in large unionized manufacturing industries. They are more likely to be employed in the secondary labor market, which involves "low wages, poor working conditions, considerable variability in employment, harsh and arbitrary discipline, and little opportunity to advance" (Piore 1977). Piore distinguished between a secondary labor market and a primary one, which involves "high wages, good working conditions, employment stability and job security, equity and due process in the administration of work rules, and chances for advancement."
It is in the primary firms that the consequences of raising productivity and technological advancements can be, to a certain extent, alleviated for workers through retraining and the maintenance of benefits. McGahey and Jeffries (1985) stressed the fact that the small, secondary firms that provide a good deal of inner-city employment do not attach great importance to training and skills, as these are often labor-intensive service or manufacturing firms with few skilled jobs who depend on the reserve army of the unemployed to fill their needs. When displacement does occur, these firms are not likely to provide assistance to their dislocated workers in the form of training programs or help in transferring to other jobs.
While skilled workers can count on retraining and education to

upgrade their skills and ease the transition keeping in mind, however, raising productivity levels and a shrinking manufacturing base workers at the lower ends of the socioeconomic ladder, and for this purpose the underemployed and unemployed as well, cannot count on the same provisions. When they are employed in high technology industries such as electronics assembly, they are subject to job instability since standardized plants are likely to be mobile, in search of reduced labor costs. In high need cities that have operated a conversion to advanced services, they are likely to be confined to the secondary labor market and its pitfalls.

Chapter VII Conclusion
Despite the fact that welfare programs have been criticized for having the effect of fostering "cultural dependency" and perpetuating "subsidized anchoring" of minorities in areas of greatest need (Kasarda 1983), it is the opinion of this author that they are necessary to maintain the social safety net, as living conditions have deteriorated in a number of cities over the past few years. They should, however, be tied to local community economic development objectives to break away from the pattern of assistance and dependency.
A local approach is therefore advocated in light of the finding that high technology industries did not produce enough entry-level jobs in those areas that need them the most and in the sectors that could maximize employment opportunities for central city residents. Local community economic development would also overcome the dichotomized job structure of high technology enterprises, characterized by restricted mobility among skill levels. This would have the potential of generating a more meaningful work environment by establishing linkages between work and community, therefore reducing the alienation of entry-level workers and providing the elements for self-identification.
Where applicable, affected communities may have to rethink their

economic development strategies and follow a "low tech" approach, oriented toward the promotion and expansion of local firms, rather than trying to attract high technology industries from the outside, or "chipchasing". The Rocky Mountain Institute (1984) estimated that "each year only a few hundred companies relocate a major facility, yet some 25,000 community development groups are seeking to entice a new company to come to town. The competition is intense, and the vast majority of these traditional economic development efforts are fruitless, wasting development resources which could, for example, have been used to strengthen existing enterprises." In the end, "the community could well find the real net result of a successful chase is imported workers and exported profits." Such an end result suggests that benefits accrued to the local labor force will be, in the long range, minimal.
Perhaps the most-widely celebrated example of community renewal and development, and one that has been widely publicized, is the small business incubator. Briefly defined, "an incubator is either an organization or a network of organizations providing individuals with skills and knowledge about business operations and opportunities, and motivating individuals to start a business" (Allen 1985). When promoted with the goal of improving the economic conditions of low and moderate income persons, incubators can help stabilize neighborhoods or meet the needs of targeted populations by creating self-sustaining small businesses for community residents. By subsidizing early

business operations in declining areas, jobs and ownership opportunities can be created for inner-city residents who would have otherwise been confined into entry-level jobs in high technology industries with few prospects for advancement, when such jobs are available.
Ultimately, this "bottom-up" strategy can work far better than a "top-down" approach to secure employment opportunities for central city residents, because of the greater emphasis placed on improving conditions for segments of the population usually not targeted in traditional economic development efforts.

Appendix I
Description of Industries by Major Group
* Group 28; Chemicals and allied products
This major group includes establishments producing basic chemicals and establishments manufacturing products by predominantly chemical processes. Establishments in this major group manufacture three general classes of products:
- Basic chemicals such as organic and inorganic chemicals, acids, alkalies, salts;
- Chemical products to be used in further manufacture such as synthetic fibers, plastic materials, dry colors and pigments;
- Finished chemical products to be used for ultimate consumption such as drugs, cosmetics, and soaps, or to be used as materials or supplies in other industries such as paints, fertilizers, and explosives.
* Subgroup 291: Petroleum refining
Establishments primarily engaged in producing gasoline, kerosene, distillate fuel oils, residual fuel oils, lubricants, and other products from crude petroleum.
* Group 35: Machinery, except electrical
This major group includes establishments engaged in manufacturing machinery and equipment, other than electrical and transportation equipment. Machines powered by built-in or detachable motors ordinarily are included in this major group, with the exception

of electrical household appliances. This group includes
portable tools, both electrically and pneumatically powered.
* Group 36: Electrical and electronic machinery, equipment, and supplies
This major group includes establishments engaged in manufacturing machinery, apparatus, and supplies for the generation, storage, transmission, transformation, and utilization of electrical energy. The manufacture of household appliances is included in this group although not considered high technology. Establishments primarily engaged in manufacturing instruments for indicating, measuring, and recording electrical quantities are included in major group 38.
* Group 37: Transportation equipment
This major group includes establishments engaged in manufacturing equipment for transportation of passengers and cargo by land, air, and water. Establishments primarily engaged in manufacturing or assembling complete aircraft as well as guided missiles and space vehicles are the only ones considered high tech in the context of the research. These industries also include related establishments engaged in research and development.
* Group 38: Instruments and related products
This major group includes establishments engaged in manufacturing instruments (including professional and scientific) for

measuring, testing, analyzing, and controlling, and their associated sensors and accessories; optical instruments and lenses; surveying and drafting instruments; surgical, medical, and dental instruments, equipment, and supplies; and photographic equipment and supplies. Establishments engaged in the production of ophtalmic goods, watches and clocks, while included in this major group, were not considered high technology industries.
* Group 73: Business services
This major group includes establishments primarily engaged in rendering services to business establishments on a fee or contract basis. Computer and data processing services fall in this category as well as research and development laboratories. Research and development laboratories of companies which manufacture the products as a result of their research activities are classified as auxiliary to the manufacturing establishments

Appendix II
table I significant values of r for testing H0.
' ' Two-Tailed Test ' One-Tailed Test
df o = .05 '. ft II o ft II o t-n a = .01
1 .997 .9999 .988 .9995
2 .950 .990 .900 .980
3 .878. . .959 .805 .934
4 .811 .917. .729 .882
5 ' .754 .874 .669 .833'
6 . .707 .834 .622 .789
7 . .666 .798 .582. ..750
8 .632 .765 .549 ' .716
. 9 . .602 .735 .521 . .685
TO .576 . -.708 .497 .658
11 .553 - .684 .476 .634
12 .532 - .661 .458 .612
13 -514 - .641 ' .441 .592
; 14 .497 .623 .426 .574
15 .482 .606 .412 .558
.1.6 .468 .590 .400 .542
17 .456 .575 .389 .528
18 .444 .561 .378 -.516
19 .433 .549 .369 .503
20 .423 .537 .360 .492
21 .413 .'526 ; .352 .482
22 .404 .515 .344 .472
23 .396 .505 .337 .462
24 .388 .496 .330 .453
25 .381 .487 .323 .445
26- . .374 .479 .317 ' .437
27 .367 .471 .311 .430
28 .361- .463 .306 .423
29 .355 .456 ' .301 .416
30 .349 ' .449 . .296 .409
35 .325. .418 ' .275" ' .381
40 ..304 .393 .257 .358
45 .288 .372 .243 .338
50 .273 .354 '.231'; .322
60 . .250 .325 .211 -.295
70 .232 .303 ' .195 .274
80 . .217 .283 .183 .256
90 .205 .267 .173 .242
100 .195 .254 .164 .230
R. A. Fisher and F. Yates, Statistical Tables'for Biological, Agricultural aud Medical Research, 6th edition. London: Longman Group Ltd., 1974, Table VII, p. 61. (Previously published by Oliver &. Boyd,
Edinburgh.) Reprinted by permission of the authors and publishers!
Source; Essential Statistics for
" Social Research by Michael Malec

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