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Local employment concentration and hourly earnings.

BACKGROUND AND SIGNIFICANCE

The impact of the labor market structure (types and distribution of employment) on individual earnings is well documented. For example, individuals employed in peripheral sector industries have lower earnings than those in the core sector (Beck et al., 1978, p. 710). Stanback et al. (1981, p. 78) find shifts in the U.S. economy from manufacturing to service employment have created a polarization of earnings. Kasarda (1985, p. 43) and Wilson (1987, p. 39) find the geographic relocation of manufacturing firms has created a mismatch between worker skills and job requirements in urban areas. As a result, unemployment and poverty among inner city minorities has grown. Lorence (1991, p. 772) finds employment shares in social and distributive services are negatively related to median Metropolitan Statistical Area earnings.

But, beyond determining what jobs are available to individuals, the local labor market may affect individuals' earnings in another way. Thompson (1965, pp. 71-3) argues the relatively high wages of the export sector 'rollout' or spread to other industries within an area. That industries have the capability of influencing the wages paid to one another's workers is suggested by the results of Colclough and Tolbert (1990, p. 15). In markets dominated by high tech industries, high tech workers have higher mean earnings than other workers; in markets dominated by agricultural employment, however, the mean earnings of high tech workers are the lowest. Also, South and Xu (1990, p. 596) find the earnings impact of local industrial dominance (share of local employment found in a worker's industry) varies across industries. Manufacturing workers benefit from large shares of employment in their industry; construction workers, on the other hand, receive higher earnings when their industry's share of local employment is low.

The implication of the local labor market having this second additional effect on hourly earnings is that some individuals are twice blessed and some twice cursed in terms of rewards for employment. Some individuals have good paying jobs and are employed in a labor market whose employment structure is favorable to earnings. Other individuals have poor paying jobs and are employed in a labor market whose employment structure depresses earnings. Finally, individuals may have a good paying job in an unfavorable market or a poor paying job in a favorable market.

In his discussion of the intra-area rollout effect, Thompson does not specify what processes are involved in creating this rollout effect. In this article, I propose an explanation of how the rollout effect is created and operates. Although I can not directly test the proposed relations in the model, I do examine the hourly earnings impact of local labor market structure through the concept of local employment concentration. Local employment concentration is the share of local employment found in different industries. Local employment concentration in high paying industries is expected to positively influence individual hourly earnings, whereas local employment concentration in low paying industries is expected to have a negative earnings impact.

INTRA-AREA ROLLOUT EFFECT

Figure 1 shows the intra-area rollout effect consists of two processes through which local labor market structure is thought to influence individual earnings. One process involves the interaction of labor demand and supply; the other process involves the reinvestment of funds into the local economy.

First, local labor market structure influences local labor demand. Labor demand varies across industries depending upon the distribution of tasks performed within each industry and the skills required to perform these tasks. For example, a higher proportion of producer service employees have a college education (27 percent) than do personal service employees (7.1 percent) (U.S. Government, 1993, p. 409).

Next, local labor demand interacts with local labor supply to influence worker competition. Markets characterized by high paying, high skill employment tend to have an inadequate labor supply. Often, training and educational programs are provided to increase the pool of qualified labor available (Thompson, 1965, p. 72; Cobb, 1986, p. 21). Worker competition is low in these markets. On the other hand, local labor supply tends to exceed demand in markets characterized by low paying, low skill employment. Labor supply exceeds demand because: (1) almost anyone is capable of performing jobs that require few skills and (2) opportunities for employment in good paying jobs are scarce. Therefore, worker competition is high in these markets.

Last, worker competition negatively affects individual earnings. When worker competition is keen, employers can be selective of whom they hire. They can offer lower pay to workers because there is a large labor reserve army waiting for employment and the alternatives available to dissatisfied workers are another relatively poor paying job or unemployment. However, when worker competition is low, workers can be selective of employers and demand higher pay. If the employers are not willing to pay decent wages, then the employers have positions which remain unfilled, must recruit workers from outside the market, or have their other employees work longer hours.

In the second process of the intra-area rollout effect, local labor market structure influences consumer expenditures. Aggregate disposable income is higher in markets with large shares of employment in high paying industries than in other markets. Disposable income is positively associated with consumer expenditures (Wonnacott & Wonnacott, 1982, p. 146).

Next, consumer expenditures positively affect employer profits. Businesses are not only selling more products or services, but can also charge more for their goods or services. In some cases, increased production may actually lower the production cost per unit that businesses incur. Hence, income received from production or providing services will exceed costs of production or for providing services.

Last, employer profits are positively related to individual earnings. Businesses invest in capital or labor if income generated from expansion exceeds costs created by expansion (Wonnacott & Wonnacott, 1982, p. 673). Capital intensity is found to be positively related to earnings (Farkas, England, & Barton, 1988, p. 281). High profits also give employers flexibility in wage concessions and creates an expectation of high wages among workers (Hodson, 1986, p. 281).

Thus, high levels of local employment in high paying industries are expected to positively influence earnings because low worker competition and the re-investment of relatively large sums of money into the local economy. High levels of local employment in low paying industries are expected to negatively influence earnings because of intense worker competition and the re-investment of relatively small sums of money into the local economy.

LOCAL EMPLOYMENT CONCENTRATION

The conceptualization of labor market structure varies across studies. Hodson (1978, p. 449) views the market as consisting of 3 industrial sectors (monopoly, competitive, and state sectors). Lorence (1991, p. 769) conceptualizes the labor market as consisting of 4 service sectors and a non-service sector. South and Xu (1990, p. 593) view the labor market as consisting of a manufacturing sector and a non-manufacturing sector. Colclough and Tolbert (1990, p. 11) classify markets as service, high tech, agricultural, or manufacturing dominated.

In this article, labor market structure is conceptualized as local employment concentration. Local employment concentration is defined as the share of local employment found in different industries. These industries consist of four manufacturing, seven service, construction, and extractive industries.

One benefit of measuring a local labor market's structure this way is the earnings impact of each and every industry in the market is taken into account. Second, one can compare local labor markets in terms of their employment structures and determine which market, on average, can be expected pay an individual higher hourly earnings. These objectives can not be accomplished using other conceptualizations.

DATA AND METHODS

The data come from the 1980 PUMS D files and publications of the U.S. Census Bureau. The PUMS D files are used to obtain information on individuals; the publications provide county level information, which are transformed into Labor Market Area (LMA) data. LMAs are used to designate local labor markets because: (1) an individual's place of work and place of residence are in the same area; (2) workers who live in areas surrounding cities but work inside the city are included; and (3) rural labor markets are not excluded (Killian & Tolbert, 1993, pp. 71-72).

The sample consists of individuals who were: (1) age sixteen or older; (2) employed in 1979 and 1980; (3) wage/salary employees or self-employed; and (4) received positive earnings income in 1979. The dependent variable is logged individual hourly earnings. Hourly earnings are operationalized as annual earnings income (sum of wage/salary, farm, and self-employment incomes) divided by annual hours worked (product of usual hours worked per week and weeks worked in 1979). Hourly earnings are logged to reduce the effects of extremely high earners (outliers) on model estimation results (Xu, 1990, p. 61).

The main independent variable used in the analysis is LMA employment concentration. LMA employment concentration is operationalized as the percent of LMA employment found in different industries and treated as a series of dummy-like variables in the analysis. The percent LMA employment for each industry is entered into the models as a separate variable in the analyses, with one industry used as a reference category. The industry used as a reference category - producer services - is the industry whose median hourly earnings rate of $5.58 is closest to the median hourly earnings rate for all workers ($5.52). Those industries whose median hourly earnings are higher than that of producer services are considered to be high paying industries; industries whose median hourly earnings are lower than producer services are considered to be low paying industries. LMA employment concentration in high paying industries is expected to positively, while employment concentration in low paying industries is expected to negatively, impact individual hourly earnings.

Furthermore, the magnitude of the earnings impact of concentration is expected to vary among industries. The magnitude of the earnings impact depends upon the size of the difference between an industry's median wage rate and the median wage rate of the reference industry. Where the difference in the median wage rate of an industry and the reference is largest, that industry is expected to have a greater impact on individual earnings than the other industries. Likewise, if the difference between an industry's median wage rate and the reference's wage rate is small, that industry is expected to have a small impact on individual earnings.

Table 1 displays the expected direction and relative magnitude of the employment concentration earnings effects for each industry. Relative to concentration in producer services, concentration in transportation services, chemical manufacturing, machine manufacturing, metal manufacturing, construction, education services/public administration, and miscellaneous manufacturing industries, are expected to positively impact individual hourly earnings. Employment concentrations in health services, extractive, food/textile manufacturing, social services, trade services, and personal service industries are expected to negatively impact individual hourly earnings.

Among the high paying industries, the earnings impact of employment concentration in transportation services is expected to be the largest; the earnings impact of employment concentration in miscellaneous manufacturing is expected to be the smallest. Among the low paying industries, the earnings impact of employment concentration in personal services is expected to be the largest, while the earnings impact of employment concentration in health services is expected to be the smallest.

Other independent variables are included in the analysis as controls. Individual level variables used as controls are individuals' human capital (education, work experience, and work experience squared), demographic (sex, race/ethnicity), and work characteristics (industry, occupation, and sector of employment). LMA level variables used as controls are LMA demographic (age distribution and percent female in the labor force, population size, racial/ethnic distribution of the population, and geographic region) and economic characteristics (mean firm size, labor force growth, Right to Work State location, and percent employment in occupations). Ordinary Least Squares regression is used to estimate the average impact that LMA employment concentration has on an individual's net hourly earnings.
Table 1. Expected Impact (Direction and Magnitude) of Local
Employment Concentration on Individual Hourly Earnings by Industry

Industry                                    Direction    Magnitude

Transportation Services                      Positive     Largest
Chemical Manufacturing
Metal Manufacturing
Machine Manufacturing
Construction
Education/Public Administration
Miscellaneous Manufacturing                  Positive     Smallest
Producer Services                            Reference        -
Health Services                              Negative     Smallest
Extractive
Food/Textile Manufacturing
Social Services
Trade Services
Personal Services                            Negative     Largest




RESULTS

Table 2 displays the partial results of the regression analysis. Approximately one half of the coefficients are positive, one half negative. Local employment concentration in transportation services, chemical manufacturing, machine manufacturing, metal manufacturing, miscellaneous manufacturing, extractive, and food/textile manufacturing industries positively affects individual net hourly earnings. Local employment concentration in construction, education service/public administration, health service, social service, trade service, and personal service industries negatively impacts individual net hourly earnings. The direction of some of these effects differ from those expected in Table 1. Local employment concentration in construction and education service/public administration industries is expected to positively influence earnings; instead, they have a negative impact. Local employment concentration in extractive and food/textile manufacturing industries is expected to negatively impact earnings; instead, concentration in those industries has a positive effect.

The size of the earnings effects for local employment concentration in industries does vary across industries as expected. For example, relative to each percentage point of employment in producer services, each percentage point of employment in social services lowers individual hourly earnings by an average of 2.5 percent. On the other hand, a percentage point of employment in trades services, relative to a percentage point of employment in producer services, lowers individual hourly earnings by an average of .5 percent.
Table 2. Partial OLS Regression Results: Impact of Local Employment
Concentration on Individual Net Hourly Earnings

Industry                                             B         S.E

Transportation Services                            .010(**)   .003
Chemical Manufacturing                             .003       .002
Metal Manufacturing                                .006(**)   .002
Machine Manufacturing                              .012(**)   .006
Construction                                      -.001       .003
Education/Public Administration                   -.006(**)   .002
Miscellaneous Manufacturing                        .001       .002
Producer Services                                 reference      -
Miscellaneous Manufacturing                        .001       .002
Health Services                                   -.008(**)   .003
Extractive                                         .001       .002
Food/Textile Manufacturing                         .005(*)    .002
Social Services                                   -.025(**)   .006
Trade Services                                    -.005       .003
Personal Services                                 -.011(**)   .003
[R.sup.2]                                          .256
Number of Cases                               397154

** p [less than] .01 (2 tailed test)
** p [less than] .05 (2 tailed test)




The pattern of the magnitude of these effects, however, is not the same as expected in Table 1. For example, among high paying industries, the size of the earnings effect of concentration in chemical manufacturing is expected to be larger than that of metal manufacturing. Instead, the impact of chemical manufacturing is found to be smaller than that of metal manufacturing. Likewise, the negative earnings impact of concentration in personal services is expected to be larger than the negative earnings impact of concentration in any of the other low paying industries. Yet, in Table 2, concentration in social services has the largest negative impact among low paying industries.

There are a few reasons why the results shown in Table 2 are not the same as the expectations in Table 1. First, the expected earnings impact of local employment concentration is discussed in terms of whether an industry is high paying or low paying. There are other factors which may influence an industry's earnings impact. For example, large shares of employment in a low paying industry may reduce unemployment in an area, benefitting all individuals in an area. Second, the explanation does not take into account the possibility of industrial complements. That is, concentration in one industry tends to be positively associated with the level of concentration in another industry. Hence, markets with high concentrations of employment in a high paying industry may also have high concentrations of employment in a low paying industry.

Another item worth noting about the size of the earnings effects is how small they are. Most of the earnings effects of concentration are less than one percent. An issue related to the small earnings effects of concentration is the amount of explained variation in earnings explained by the model. When comparing the amount of earnings variation explained by this model (.25611) with a model sans the concentration variable (.25579), the gain in R Square is .03 percent. The small earnings effects and gain in R Square suggest that individuals' earnings returns to employment are determined primarily through the jobs they receive and their individual characteristics.

However, these results should not be considered as an indication that the local labor market's structure does not influence individual rewards and opportunities. Depending on the local labor market's structure, the job opportunities that are available varies. Within a local labor market, individuals are assigned to jobs based on their educational attainment, gender, race/ethnicity, work experience, and so forth. The assignment of individuals with a particular set of characteristics varies across markets, according to the types of jobs present and the relative availability of these jobs. This is one issue future research should address.

CONCLUSION

This study proposes and indirectly examines the influence of local labor market structure on individual hourly earnings through the intra-area rollout effect. The results from the analysis suggest that the intra-area rollout effect on earnings is weak. The inclusion of local employment concentration as a factor impacting individual net hourly earnings is, at best, a small refinement of the earnings determination model.

Hence, future research should continue to examine other possible ways that the local labor market influences individual opportunities and rewards. Some questions worth addressing are: (1) how individuals are channeled into different jobs; (2) the consequences this channeling has on individual opportunities; and (3) how the types of jobs individuals receive varies across local labor market structures. When considering these opportunities and outcomes, research should also consider - in addition to hourly earnings - how the organization of the local labor market conditions (1) the opportunity to be employed; (2) number of hours per week and weeks of employment; and (3) nonmonetary rewards associated with employment (health, vacation, and retirement benefits, and job satisfaction).

Acknowledgment: The author wishes to thank Shelley A. Smith, Jimy M. Sanders, John Skvoretz, and Patrick D. Nolan for their comments on earlier drafts of this article.

REFERENCES

Beck, E. M., P. M. Horan, and C. M. Tolbert II. (1978.) Stratification in a Dual Economy: A Sectoral Model of Earnings Determination. American Sociological Review, 43(5):704-720.

Cobb, J. C. 1986. Y'All Come on Down: The Southern States Pursuit of Industry. Southern Exposure, 14(5-6): 18-23.

Colclough, G. and C. M. Tolbert II. (1990). High Technology, Work, and Inequality in Southern Labor Markets. Work and Occupations, 17(1):3-29.

Farkas, G., P. England, and M. Barton. (1988). Structural Effects on Wages: Sociological and Economic Views. In G. Farkas and P. England. (Eds.), Industries, Firms, and Jobs. New York: Plenum.

Hodson, R. (1986). Industrial Structure as a Worker Resource. The Social Science Journal, 23 (3):277-292.

Kasarda, J. D. (1985). Urban Change and Minority Opportunities. In P. E. Petersen. (Ed.), The New Urban Reality. Washington: Brookings Institution.

Lorence, J. (1991). Growth in Service Sector Employment and MSA Gender Earnings Inequality: 1970-1980. Social Forces 69(3): 763-783.

South, S. J. and W. Xu. (1990). Local Industrial Dominance and Earnings Attainment. American Sociological Review, 55(4): 591-599.

Stanback, T. M., P. J. Bearse, T. J. Noyelle, and R. A. Karasek. (1981). Services: The New Economy. Tottowa: Allanheld Osmun.

Thompson, W. R. (1965). A Preface To Urban Economics. Baltimore: Johns Hopkins University.

U.S. Bureau Of Census. (1993). Statistical Abstract Of The United States. Washington: U.S. Government.

Wonnacott, P. and R. Wannacott. (1982). Economics, 2nd Edition. New York: McGraw-Hill.

Wilson, W. J. (1987). The Truly Disadvantaged. Chicago: University of Chicago.

Xu, W. (1990). The Multidimensional Labor Market: The Effects of Industry, Occupation, and Place on Earnings. Ph.D. dissertation, State University of New York-Albany.

Lisa A. Eargle teaches at Southern Illinois University, Carbondale. Her research interests include local employment and hourly earnings.
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Title Annotation:Focus on American Indian Studies
Author:Eargle, Lisa A.
Publication:The Social Science Journal
Date:Oct 1, 1997
Words:3271
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