Demand for Part-time Workers in the U.S. Economy: Why is the Distribution across Industries Uneven?

SOCIAL SCIENCE RESEARCH ARTICLE NO.

27, 87–108 (1998)

S0970610

Demand for Part-time Workers in the U.S. Economy: Why is the Distribution across Industries Uneven? Melinda K. Pitts Department of Sociology, Boston University While there is substantial documentation of the growth in part-time employment, there are relatively few studies which address the uneven distribution of part-time workers across economic sectors. This paper examines the effects of relative labor costs, industry attributes and job task characteristics, and industry computer use on employment ratios of part-time to full-time workers across 155 industries. Results show that industry compensation, particularly health-care provision, has a significant effect on the distribution of part-timers across economic sectors. Conditions which increase the cost of turnover are negatively associated with the use of part-time labor; whereas, industry job attibutes which facilitate supervision are positively associated with industry use of part-time workers. Computer use does not have a uniform effect. Industry part-time employment ratios are examined with a logistic regression which takes into account simultaneity of supply and demand by estimating instruments for the endogenous variables. r 1998 Academic Press

Over the last decade there has been a growing concern about the increase of part-time employment in industrialized economies (Cuvillier, 1984; McKie, 1992; de Neubourg, 1985; Owen, 1988; Schoer, 1987). This interest in part-time employment coincides with a wider concern, and a substantial amount of literature, about rising labor market inequalities. It is well documented that part-time workers are an increasingly significant segment of the U.S. labor force. Recascino Wise (1989) estimates that during the 1980s 30% of all net employment growth in the United States was due to the growth of part-time employment. Blank (1990) calculates that from 1968 to 1987 part-time employment in the U.S. labor force climbed from 11.9 to 15.4%. By 1993, it is estimated to have reached 19% of the nonagricultural workforce (Tilly, 1996). In spite of recent interest in the actual numbers of part-time workers and jobs, less is known about the forces governing the uneven distribution of part-time labor throughout the economy. This paper will examine variation in the ratio of part-time to full-time employees across 155 industries to determine whether Address reprint requests to Melinda K. Pitts, Department of Sociology, 96-100 Cummington Street, Boston University, Boston, MA 02215. I wish to thank Charles Halaby, Robert Mare, and two anonymous reviewers for their helpful comments. 87 0049-089X/98 $25.00 Copyright r 1998 by Academic Press All rights of reproduction in any form reserved.

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FIG. 1.

Demand for Part-Time Labor

attributes differ between industries that employ large numbers of part-timers and those that employ relatively few. DEMAND FOR PART-TIME WORKERS It is the relative cost of part-timers compared to full-timers that determines whether employers will use part-time workers and to what extent (Ehrenberg, Rosenberg, and Li, 1988; Montgomery, 1988; Owen, 1979). Montgomery (1988) explains that relative labor costs consist of wages, quasi-fixed employment costs, and the productivity of labor in relation to other factors inputs (i.e., other types of labor or capital). Labor quality and turnover affect quasi-fixed employment costs and worker productivity; subsequently, they determine the relative cost of labor directly and indirectly via wages. These relationships are presented in Fig. 1. This conceptualization of the mechanisms generating employer demand for part-time labor suggests a number of empirically testable hypotheses. In particular, industry and job attributes that increase the relative cost of labor decrease the relative attractiveness of part-time compared to full-time workers and should be negatively associated with industry part-time employment ratios. Second, since industry and job properties generate relative labor costs, industry wage differentials between part-time and full-time workers should be greater where quasi-fixed employment costs are higher. The Relative Cost of Part-Time Labor Quasi-fixed employment costs are per-person expenditures which are incurred regardless of the number of hours worked (e.g., screening, supervision, training). Unless part-time workers have a comparative advantage in terms of personal abilities that reduce these costs, the relative cost of employing them will be higher than for full-timers (Owen, 1979). A number of studies have identified three very distinct demographic groups as the primary sources of part-time workers: teenag-

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ers, retirees, and married women (Blank, 1994; Kosters, 1995; Snider, 1995; Tilly, 1991). It is arguable that each one of these groups is potentially less productive than otherwise similar workers for different reasons. Furthermore, the nonmarket activities of many part-timers may increase the incidence of turnover. Worker turnover affects the relative cost of labor by increasing quasi-fixed employment costs, by raising wages when turnover costs are high, and by reducing firm productivity. Turnover, in essence, increases quasi-fixed employment costs by increasing the number of workers. Since quasi-fixed labor costs are per-person costs, when someone leaves and another worker is hired costs are incurred twice. Weiss (1990) argues that where the costs of turnover are high, employers will be hesitant to reduce wages since above average wages make job loss more costly and, subsequently, reduce worker propensity to quit. Because employers attempt to reduce turnover by raising wages, the relative labor cost for those individuals most likely to quit is higher. Many employers believe that part-timers are more likely to quit than similar full-time workers and evidence suggests they are correct. Researchers have found that part-time work tends to be a short-term alternative to some other labor market choice (Blank, 1994; Jacobs, 1993) and turnover rates are much higher for part-timers than full-timers even within the same job classification (Tilly, 1991). Turnover affects productivity if it creates disruptions in the work flow. Certain production processes are associated with greater turnover costs than others. Large-scale, capital-intensive production precludes fluctuations in the applications of machinery and the use of labor. Therefore, disruptions in the work flow result in high production costs. Studies have found that high production costs reduce the likelihood of hiring part-time workers (Montgomery, 1988; Owen, 1979). Relative Productivity of Part-Time Workers Labor quality is assumed to produce variation in individual productivity; however, conditions present at the place of employment can also affect worker productivity (Sørensen, 1994; Sattinger, 1980). The degree of complementarity between physical and human capital and the structure of job tasks determines the relative productivity of part-time compared to full-time workers. Researchers reason that there is a complementarity between physical and human capital because (1) complex, specialized equipment is associated with greater returns to on-the-job training (Idson and Feaster, 1990) and (2) the elasticity of substitution of unskilled labor for capital is higher than for skilled labor (Griliches, 1969).1 The relative productivity of part-timers compared to full-timers may be reduced in capital-intensive industries if (1) they possess less of the necessary human capital and (2) their work behavior, particularly turnover, does not enable employers to take full advantage of their potential. Using industry 1 A higher elasticity of substitution means that unskilled labor can be replaced more easily by machines than can skilled labor.

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dummies in an establishment level analysis, Montgomery (1988) finds that firms in characteristically capital-intensive industries are significantly less likely to use part-timers than firms in service industries. The nature of the job also shapes worker productivity. To employ part-timers, work must be structured so that part-day or part-week workers can accomplish assigned tasks without interrupting the work flow of the organization. Tilly (1996, p. 51) reports that in the insurance industry part-time jobs are described as those in which ‘‘somebody can put down their work and somebody else can pick it up’’—‘‘a routine job that we can break up.’’ When the job is boring, strenuous, or tedious, employers may realize productivity gains by reducing the length of the work shift (Owen, 1979). Quasi-Fixed Employment Costs Supervision and training costs, in particular, militate against the use of part-time labor. Both firm size and output ambiguity determine the cost of supervision. Williamson (1967) identifies a loss of control which is cumulative with size because successive hierarchical levels remove supervisors from pertinent information. To reduce supervision costs, large firms tend to invest in screening and training of employees (Barron, Black, and Loewenstein, 1989; Oi, 1983). Higher screening and training costs could explain why researchers have found larger firms tend to have relatively fewer part-timers than smaller ones (Mellow, 1982; Oi, 1991; Rebitzer and Robinson, 1991). Output ambiguity increases supervision costs because it makes assessment of worker performance difficult (Alchian and Demsetz, 1972). Moreover, output ambiguity may increase relative labor costs by raising wages. Because individual output is difficult to determine when jobs are embedded in sequential arrangements, employers may simply try to reduce poor performance by paying above average wages (Jacobs, 1994). Or employers may offer above average wages to attract a better quality of worker (Cartter, 1959) in the hope of economizing on supervision and training costs later. Stinchcombe (1990) argues that routinization of job tasks allows employers to organize a production system in which individual input and output can be controlled and evaluated. Supervision costs are reduced because discrete tasks can be monitored more easily than integrative ones. Analyzing two service industries, Tilly (1992; 1996) finds that retail firms are characterized by discrete tasks (i.e., sales) and rely heavily on part-time workers, whereas insurance firms have mainly integrative tasks (i.e., long-term maintenance of service contracts) and employ relatively few part-timers. Training costs are generally higher for part-time workers than full-time workers because the fewer the work hours, the longer it takes the individual to become proficient in the task and the longer it takes the employer to recover the investment in training (Owen, 1979). Moreover, training is a ‘‘sunk’’ cost because skill and knowledge reside in the individual. Employers, therefore, forfeit the investment upon termination of the employment relation (Becker, 1964; Parsons,

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1986). Since part-timers have higher turnover rates than full-timers, the likelihood that employers will lose their investment in training is greater for part-time workers. If training is necessary for the job, employers may simply avoid hiring part-time workers. Montgomery (1988) finds that doubling the number of perperson hours dedicated to recruiting and training a typical worker reduces the proportion of part-timers in a firm by about 1.6 percentage points. In case studies of the retail and insurance industries, Tilly (1996) finds a direct negative relation between the training time required to competently perform a task and the use of part-time workers. Finally, institutional constraints, such as unionization and fringe benefits, may also produce high quasi-fixed employment costs. Consideration of company practices and employee morale often keeps employers from differentiating between part-time and full-time workers to reduce costs (Owen, 1979). Perperson labor costs are higher in unionized industries due to administrative paperwork associated with collective bargaining agreements and the screening of potential hires since union rules regulate dismissal. In addition, unions tend to increase the relative cost of labor because they increase wages (Freeman and Medoff, 1981, 1984; Moore, Newman, and Cunningham, 1985; Perloff and Sickles, 1987). Likewise, fringe benefits that are not prorated by hours worked or by earnings are quasi-fixed labor costs (e.g., health-care premium). Examining establishment level data of the child-care industry, Montgomery and Cosgrove (1993) conclude that the fact that benefits are offered to some workers seems to make firms less likely to employ part-timers. Economic Environment and Labor Costs The weight of labor costs often depends upon the economic environment in which the employer operates. Many believe that economic factors increasingly influence employment patterns in the U.S. (Belous, 1989; Harrison and Bluestone, 1988; Katz and Murphy, 1992; Kochan, Katz, and McKersie, 1986; Murphy and Welch, 1992; Revenga, 1992; Rubin, 1995). Hamermesh (1993) contends there is a tendency to reduce employment when wages increase and for employers to shift toward relatively less expensive labor. Rubin (1995) goes further and asserts that employers are now consciously minimizing long-term employment contracts. Harrison and Bluestone (1988) argue that beginning in the late 1970s, as profits began to fall, full-time jobs increasingly were given to part-timers. Due to the industrial restructuring of the 1970s and 1980s, worker displacement made it easier for employers to do so (Rubin, 1995). Tilly (1991) finds that part-time jobs in manufacturing are basically short-term responses to economic downturns. And finally, although part-time employment steadily increased over a 20-year period, it peaked at 17% of the workforce during the 1982 recession (Blank, 1990). If macroeconomic forces are exerting more pressure, then industries experiencing

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the most unfavorable conditions should employ relatively more part-timers than otherwise similar industries experiencing less economic stress. Economic conditions will be particularly influential where the demand for labor is sensitive to changes in factor prices (e.g., wage increases, product price). The larger the elasticity of demand for the product, the larger the elasticity of demand for labor (Fleisher, Ray, and Kniesner 1987; Kaufman, 1989).2 Firms in competitive markets have horizontal product demand curves (i.e., very elastic) because consumers have numerous alternatives. Therefore, labor demand for firms in competitive markets should be very sensitive to changes in wage rates. Firms in concentrated markets, on the other hand, often deviate from costminimization/profit-maximization behavior and incur higher labor costs in order to preserve market position (DiPrete, 1990). Hage (1989) contends, however, that market power should be conceptualized in terms of level of technological advancement (R & D) and product specialization. Because customized products do not have substitutes and are generally produced by relatively few firms, labor demand curves are rather inelastic in these industries. In other words, high-tech industries generally are affected less than other firms by trends in the general economy (Hage, 1989). Since high-tech firms do not compete on labor costs and require a highly specialized labor force, it is not expected that part-time workers will be employed in large numbers in industries characterized by high levels of R & D. MODELING THE DEMAND FOR PART-TIME LABOR There have been two dominant approaches to the study of part-time employment. One has been to investigate employment ratios of part-time and full-time employees in firms and/or industry groupings (Owen, 1979; Ehrenberg et al., 1988; Montgomery, 1988; Montgomery and Cosgrove, 1993). The other has focused on the part-time/full-time wage differential itself by examining wage equations (Blank, 1990; Ermisch and Wright, 1993; Long and Jones, 1981; Kosters, 1995; Leeds, 1990; Montgomery and Cosgrove, 1995; Nakamura and Nakamura, 1983). Wage equations do not provide insight into the actual or relative number of part-time workers in particular jobs or industries. This may explain why those interested in the distribution of part-time work have investigated employment ratios. The proportion of an industry’s workforce that is employed part-time3 is assumed to be a function of average industry compensation and relative compen2 Elasticity of labor demand is defined as the percentage change in employment divided by the percentage change in the wage rate. 3 Industry employment ratios are transformed into log odds and can be written as: E(Yi) 5 ln [Pi/(1 2 Pi]: where Pi is the fraction of the work force in industry i that employed part-time. Analyses are performed in STATA and maximum-likelihood estimates are weighted by the number of workers in the industry. The weighting procedure STATA uses can be described as: F(bx) s (1 2 F(bx))) t2s, where F( ) denotes the logistic likelihood function and s is the number of successes and t is the population.

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sation differentials between full-time and part-time workers, a vector of industry attributes and industry job characteristics, and the economic environment. The demand equation can be expressed as: Log Odds (PT) 5 f 5Cw, Cb, Dw, DB, I, J, E6

(1)

where PT 5 worker is part-time; Cw 5 average industry wage; Cb 5 percent of workforce receiving benefit coverage; Dw 5 wage differential between full- and part-time workers; Db 5 differential probabilities of benefit coverage for parttime and full-time workers; I 5 industry attributes; J 5 job task characteristics; E 5 economic environment. Wage and fringe benefit coverage differentials between part-time and full-time workers are estimated for each industry.4 The differentials are estimated as follows: ln (Wij) 5 bXij1aPij 1 eij (Bij) 5 YXij 1 dPij 1 µij

i 5 1 . . . 155

(2)

i 5 1 . . . 155

(3)

where Wij is the hourly wage5 of individual j in industry i, Bij is a dichotomous variable indicating health or pension plan coverage of individual j in industry i, Pij are dichotomous variables indicating part-time status, Xij are controls for human capital,6 region of employment, SMSA residence, a vector of job characteristics,7 eij and µij are random error terms. In equation (1), the value of a is an estimate of the difference in the wage paid to part-time workers compared to full-time workers.8 In equation (2), the value of d is an estimate of the differential in the probability that a part-time worker compared to a full-time worker is covered by these plans. The supply-side equation, the relative attractiveness of working part-time in 4 The 1987 and 1988 March CPS: Annual Demographic Files are pooled to produce a sample large enough to acquire enough part-time workers (approximately 30) to estimate differentials for industries grouped at the 3 digit Industry Census Code. Each of the samples is comprised of approximately 50,000 individuals. Eleven industries (1 in mining, 10 in manufacture) either did not have any part-time workers or too few even in the pooled sample to estimate differentials. To retain as many observations as possible, regressions for these industries were estimated from pooled samples of proximate industries. 5 Hourly wage is calculated as the annual earned income (wages) divided by the product of usual hours worked times the number of weeks worked. This procedure conforms to other studies which utilize CPS data to investigate wage determination and labor costs (Blank, 1990; Ehrenberg et al., 1988; Krueger and Summers, 1988). Following the example of others (Krueger, 1993; Krueger and Summers, 1988), individuals earning less than $1.50 or more than $250 per hour were excluded from the sample. 6 The human capital variables used for controls are years of schooling completed, proxies for labor force experience (age minus years schooling completed minus 5 and experience squared), dummies for marital status, gender, race, and veteran status. 7 Controls for job characteristics are task repetition, task precision, length of time required to master the task, and job meterability. Measures come from the file compiled by England and Kilbourne and are attached to individual observations in the March 1988 CPS. 8 Specifically, a is an estimate of the ln(Part-time/Full-time Wage) for individuals who differ only by part-time status.

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industry i, is assumed to be a function of industry compensation and the relative cost of working part-time in industry i (Ehrenberg et al., 1988). Because personal characteristics are known to affect labor market supply, vectors of individual attributes are used as controls (Blank, 1990; Hotchkiss, 1991). The supply-side equation can be written as: Lg Odds (PT) 5 f 5Cw, Cb, Dw, Db, Y6

(4)

where PT 5 worker is employed part-time; Cw 5 average industry wage; Cb 5 percent of workforce with benefit coverage; Dw 5 wage differential of part-time worker compared to full-time worker in industry i; Db 5 differential probabilities of benefit receipt of part-time workers in industry i; Y 5 vector of personal attributes for industry workforce.9 As seen from Fig. 1, compensation is endogenous to the process of demand. To correct for simultaneity of supply and demand instruments for the industry, compensation variables are estimated. The instruments are the fitted values from the regression of each of the endogenous variables on all of the exogenous variables of the demand equation, as well as variables from the supply equation (this is the reduced form equation). DATA Previous studies on part-time employment have used proxies, such as occupational distributions, to measure production technology and single variables to capture industry effects (Blank, 1990; Ehrenberg et al., 1988). Moreover, data in many of these studies is aggregated to the level of 1- or 2-digit Standard Industrial Classification (SIC) where substantial intragroup variation in industry attributes exists (Ehrenberg et al., 1988; Montgomery, 1988; Owen, 1979; Tilly, 1991). This paper will build upon previous studies by operationalizing a vector of industry attributes at the 3-digit SIC for 155 industries and job characteristics for 420 3-digit 1980 Census Occupation Codes.10 The 1987 Enterprise Statistics provide most of the industry level variables.11 The January 1991 Current Population Survey: Job Supplement File provides a measure of employer provided training and extent of industry use of computers

9 Variables are measured as averages for the industry workforce (e.g., average number of years of schooling, percent of workforce that is female). 10 Four industries, all in the mining sector, are aggregated at the 2-digit level in the CPS. The construction industry is aggregated to the 1-digit level in the CPS. Agriculture and the Private Household industries were excluded due to the informal nature of employment and a dearth of data. The financial services sector was excluded due to an absence of data at the industrial level. 11 Separate 1987 Economic Census for Manufacturing, Mining, Construction, Transportation, Wholesale, Retail, and Services provide measures of industry concentration ratios and investments in new capital assests. Industries in these Census are grouped at the 4-digit SIC code. Measures were aggregated to the 3-digit SIC code and attached to individual observations in the March 1988 CPS file by 3-digit Industry Census Codes. The 1982 Enterprise Statistics provide measures of industry sales and wage rates for 1982.

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while the October 1989 Current Population Survey: School Enrollment12 provides measures of qualitative uses of computers. Measures of job characteristics come from the Occupational Measures from the Dictionary of Occupational Titles for 1980 Census Detailed Occupations, a file compiled by Paula England and Barbara Kilbourne. Occupational and industry level data are attached to individual observations in the March 1988 Current Population Survey: Annual Demographic File and then aggregated to produce industry level measures in the form of averages. Because part-time status, occupation, and industry of employment in the CPS refer to the longest job held last year, the effective year of coverage is 1987, the same year for the industry data. See Table 1 for definitions of the variables as well as the sources and the unit of analysis of the data. SAMPLE DESCRIPTIVE STATISTICS As seen from Table 2, there is considerable variation in the use of part-time labor across broad economic sectors. While roughly 17% of the sample works part-time, in the service sector part-time workers account for as much as 21 to 41%. This is in marked contrast to manufacturing, where part-time workers account for no more than 7% of the workforce. While wholesale and construction have more part-time workers than manufacturing, mining industries have fewer. Results of regressions in which industry sectors are effect coded13 are presented in Tables 3 & 4. Effect coding produces regression coefficients that reveal how far above or below the grand mean a sector is in terms of the dependent variable. Thus, examination of the table shows how selected attributes vary across industries. Regression coefficients and standard errors of the coefficients for each sector are presented in the rows. At the bottom of each column is the constant, or sample average of the dependent variable listed at the top of the column, and the F-statistic for the regression equation. Reading across the rows provides information of particular industrial sectors. However, because the units of the dependent variables vary, it is not appropriate to compare coefficients across rows. All comparisons should be made down the columns. Estimates of compensation differentials between part-time and full-time workers across broad sectors of the economy are presented in columns 1–3 of Table 3. Because they are differentials, negative values indicate higher differentials between full and part-time workers (i.e., part-timers receiving less) and values approaching zero indicate lower differentials (i.e., more equality). Concretely, on average, part-timers in the manufacturing sector earn less than full-time workers in similar jobs and they receive fewer benefits. Part-timers in the service sector, on 12 The CPS is a national probability sample. The two outgoing rotations comprise the sample for the computer items. 13 Effect coding simply involves coding the sector ‘binaries’ so that the regression coefficients sum to zero. This coding schema produces an intercept term which is the sample average of the dependent variable and slope coefficients which measure the deviations of sectors from the average over all the sectors (Johnson, Johnson, and Buse, 1987, p. 189).

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TABLE 1 Definition and Descriptive Statistics of Variables Aggregated to 3 Digit SIC: (Data Source and Unit of Analysis of Original Data) Source/variable

Label

Mean

S.E.

n

(.017)

155

Industrial census

Unit of analysis Industry (3 digit SIC)

% sales generated by top 8 firms

CNSAL8

.30

% employed by top 8 firms

CNEMP8

.19

(.012)

154

ln (employees/# firms)

LGFIRM

2.99

(.142)

155

ln (employees/$ sales)

LGSALEP

11.51

(.059)

155

ln ($ new capital/# firms)

LGNWCAPF

10.75

(.158)

155

% of workforce covered by union a

UNCOV

.15

(.013)

155

High tech industry b

HTECH

.15

(.028)

155

1987 $ Sales/1982 $ sales

GRWSAL8287

1.55

(.071)

155

1987 avg. wage/1982 avg. wage

WGCHG8287

1.23

(.006)

155

Current population surveys

Individual

% Trained after hire by firm

INDTRAIN

.18

(.011)

149

% Using computers every day

INDCOMP

.28

(.012)

149

Avg. # info. processing uses

CINFO

.46

(.035)

148

Avg. # tracking trans. uses

CTRACK

.36

(.018)

148

Avg. # administrative uses

CADMIN

.20

(.015)

148

% Workforce female

%FEMALE

.40

(.017)

155

% Workforce white

%WHITE

.90

(.004)

155

% Workforce Hispanic ethnicity

%HISPANIC

.10

(.004)

155

% Workforce veteran

%VETERAN

.16

(.007)

155

% Workforce married

%MARRIED

.61

(.008)

155

% Workforce with kids ,6

KIDS6

.26

(.007)

155

Avg. number of children per worker

NUMFAM

% Workforce located in MSA

%MSA

% Workforce 55 and over

%55OVER

Average # of years of worker education

EDUC

% Workforce Part-time

%PART-TIME

Estimated wage differential Estimated pension probability differential

3.09

(.016)

155

.76

(.000)

155 155

.12

(.004)

12.53

(.079)

155

.17

(.011)

155

WAGEDIF

2.06

(.026)

153

PENDIF

2.22

(.016)

153

Estimated health care probability differential

HLTHDIF

2.34

(.022)

153

Avg. industry hourly wage c

AVGWAGE

9.87

(.268)

155

% Workers with employer pension plan

%Pension

.35

(.016)

155

% Workers with employer health care

%Health

.56

(.016)

155

21.96

(.760)

155

DOT

Occupation (3 digit census code)

Avg. job training time (months)

JBTRAIN

% Jobs with set tolerances or standards

STS

.47

(.013)

155

% Jobs characterized by repetitive work

REPCON

.27

(.011)

155

% Jobs with measurable verifiable criteria

MVC

.34

(.012)

155

a

This comes from Curme, Hirsch, and McPherson (1990). Figures for 1987/1988 were averaged.

b

Hadlock, Hecker, and Gannon’s (1991) typology is employed to indicate high-tech industries at the 3-digit SIC. They define

high-tech industries as those whose proportion of employment in R & D is at least equal to the average proportion for all industries. Note: Qualitative Uses of computers are industry averages of a count of different types of uses grouped into three qualitatively distinct uses: CINFO: Computer Assisted Design, Programming, Analysis, Databases, Spreadsheets, Communications, and Graphics; CTRACK: Inventory Control, Sales, Bookkeeping and Invoicing; CADMIN: Word Processing, Desktop Publishing, and Calendar or Scheduling.

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DEMAND FOR PART-TIME WORKERS TABLE 2 Distribution of Part-time Workers across Industrial Sectors: March 1988 CPS

Sector

Part-time workers % total (s.e.)

Mining Construction Manufacturing–Nondurable Manufacturing–Durable Transportation Wholesale–Nondurable Wholesale–Durable Retail Business Services Personal Service Entertainment Professional Service Total F-Statistic (d.f.)

.037 (.000) .100 (.000) .073 (.012) .046 (.004) .163 (.029) .118 (.013) .105 (.008) .341 (.027) .213 (.034) .288 (.054) .413 (.025) .278 (.026) .168 (.011) 26.006* (11)

N CPS

Number industries

531 4,328 5,151 7,556 1,430 1,394 1,406 13,219 3,146 1,961 1,029 8,590 49,741

4 1 25 38 4 9 10 28 9 7 3 17 155

Note. Part-time is defined as someone who usually works 35 hours or less a week. * p-value , .000.

the other hand, tend to earn hourly wages that are closer to full-time wages in these industries. In addition, the disparity in benefit receipt for part-time and full-time workers is less in the service sector. Industry compensation figures by sector are shown in columns 4–6 of Table 3. Industries with higher average wages and a substantial number of workers receiving benefits are the ones with greater, negative compensation differentials between part-time and full-time workers. In other words, where compensation is higher, part-time workers receive less relative to full-time workers. Theory suggests that industry attributes which (1) make supervision difficult, (2) make turnover costly, and (3) produce institutional constraints in terms of labor deployment, should increase labor costs, particularly wages. Regression coefficients of industry attributes for effect coded sectors are shown in Table 4. With the notable exception of construction, sectors characterized by above average wages tend to have above average levels of attributes associated with higher quasi-fixed labor costs—large firms, unions, substantial investments in new capital equipment and a larger fraction of the work force receiving employer provided training (coefficients are positive and significant). These same sectors (mining and manufacture) employ very few part-time workers. Whether relative labor cost differentials between full- and part-time workers play a prominent role in shaping industry demand for part labor once industry attributes, economic variables, and job characteristics are controlled for will be examined with a logistic regression.

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TABLE 3 Regression Coefficients for Effect Coding by Industry Sector: Industry Compensation and Estimated Compensation Differentials Dependent Variable b (s.e.) Estimated differentials part-time/full-time Sector

Wagedif

Pendif

Hlthdif

Industry compensation AvgWage

% Pension

% Health

2.088 (.094)

2.303 (.059)

2.175 (.079)

21.391 (1.115)

.266 (.059)

.229 (.060)

.010 (.183)

.056 (.115)

.079 (.154)

.761 (1.894)

2.043 (.115)

2.050 (.117)

Manufacturing–Nondurable

2.051 (.045)

2.032 (.028)

2.047 (.038)

.552 (.461)

.176 (.028)

.141 (.028)

Manufacturing–Durable

2.069 (.039)

2.076 (.025)

2.031 (.033)

1.528 (.406)

.223 (.025)

.218 (.025)

Mining Construction

.091 (.094)

.009 (.059)

.004 (.079)

.188 (.975)

2.007 (.059)

.012 (.060)

Wholesale–Nondurable

2.071 (.066)

2.061 (.041)

2.009 (.055)

2.198 (.680)

.034 (.041)

.109 (.042)

Wholesale–Durable

Transportation

2.123 (.063)

2.045 (.039)

2.182 (.053)

.563 (.651)

.033 (.039)

.096 (.040)

Retail

.023 (.043)

.110 (.027)

.068 (.036)

22.667 (.445)

2.155 (.027)

2.175 (.027)

Business services

.038 (.066)

.094 (.041)

.034 (.055)

2.383 (.680)

2.127 (.041)

2.087 (.042)

Personal services

.110 (.078)

.166 (.049)

.126 (.066)

22.515 (.758)

2.223 (.046)

2.195 (.047)

Entertainment

.834 (.108)

.075 (.067)

.103 (.091)

21.391 (1.115)

2.192 (.068)

2.254 (.069)

Professional services

.047 (.051)

.007 (.032)

.030 (.043)

.213 (.528)

.015 (.032)

2.045 (.032)

2.061 (.026)

2.222 (.016)

2.339 (.022)

9.875 (.268)

.350 (.016)

Constant a F-Statistic

1.389

N

153 a

7.464* 153

2.700* 153

8.544* 155

22.91*

.556 (.016) 22.020*

155

155

This is the grand mean for the sample population.

* Significant at .00.

RESULTS Results from a logistic regression are shown in Table 5. The issue of simultaneity of supply and demand is taken into account in columns 1 and 2, where instrumental variables are employed for the compensation variables. Comparing the IV estimates to the results presented in columns 3 and 4, it is apparent that not treating compensation as endogenous alters the substantive interpretation of the results. In particular, the signs of the coefficients change for two of the computer variables (INDCOMP, CINFO) and the measure of industry training (INDTRAIN). The compensation variables themselves are, for the most part, unchanged by instrumentation. The pronounced negative effect of the pension variables (PENDIF, %PENSION), however, is reduced once instruments are estimated. By examining column 1 of Table 5, patterns in interindustry variation in part-time employment emerge. It is apparent that compensation is important. Part-time workers are a larger fraction of the workforce in industries which reward them less relative to full-time workers. Specifically, lower hourly wages and a lower probability of health care coverage for part-timers (WAGEDIF, HLTHDIF) compared to full-timers increases the likelihood of industry use of part-time workers. Although the coefficients for the fringe benefit variables are all negative, the magnitude of the health care variables (HLTHDIF, %HEALTH) is striking. Montgomery and Cosgrove (1993) also find that health care provision

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DEMAND FOR PART-TIME WORKERS TABLE 4 Regression Coefficients for Effect Coding by Industry Sector: Selected Variables Dependent Variable b (s.e.) Sector

LGFIRM

UNCOV

LGNWCAP

INDTRAIN

JBTRAIN

Mining .415 (.517) .171 (.047) 2.161 (.574) .073 (.040) .397 (2.761) Construction 2.344 (1.004) .082 (.091) 2.561 (1.115) 2.062 (.077) 6.040 (5.362) Manufacture–Nondurable 1.869 (.244) .095 (.022) 2.182 (.271) .045 (.019) 2.249 (1.305) Manufacture–Durable 1.587 (.215) .118 (.019) 1.831 (.239) .047 (.017) 2.310 (1.148) Transportation 2.230 (.517) .056 (.047) 21.372 (.574) .002 (.040) 24.608 (2.761) Wholesale–Nondurable 2.263 (.360) 2.054 (.032) 2.046 (.400) 2.004 (.028) 24.653 (1.926) Wholesale–Durable 2.458 (.345) 2.082 (.031) 2.290 (.383) 2.028 (.026) 21.322 (1.842) Retail 2.355 (.236) 2.101 (.021) 2.355 (.262) 2.029 (.018) 26.632 (1.258) Business services 2.402 (.360) 2.084 (.032) 2.581 (.400) 2.005 (.028) 2.283 (1.926) Personal services 2.976 (.402) 2.108 (.036) 2.801 (.446) 2.065 (.033) 23.604 (2.145) Entertainment 2.369 (.591) 2.029 (.053) 2.979 (.656) 2.022 (.045) .841 (3.157) Professional services 2.471 (.280) 2.064 (.025) 21.189 (.311) .048 (.021) 9.197 (1.495) 2.986 (.142) .151 (.013) 10.753 (.158) .166 (.011) 21.961 (.760) Constant a F-Statistic 13.047* 13.918* 16.861* 2.843* 8.998* n 155 155 155 155 155 a

This is the grand mean of the sample population. * Significant at .00.

has a much stronger negative effect on the use of part-time labor than does pension coverage. They argue this is due to the fact that, unlike pensions, health care premiums cannot be prorated and, therefore, are quasi-fixed labor costs, which increase the unit labor costs of part-timers. Although costs appear to be important, part-time employment is used more extensively in those industries experiencing sales growth rates (GRWSAL8287) and not those suffering from decline over the previous five years. And the coefficient for industry wage increases over the past five years (WGCH8287) is not statistically significant, albeit positive. Training, as expected, negatively affects industry use of part-time labor. Industries that train a larger fraction of their workforce (INDTRAIN) hire relatively fewer part-time workers, other things equal. Although the coefficient for industry averages of training time required to master the job (JBTRAIN) is negative, it is statistically insignificant. It appears to be the cost associated with training and not the difficulty of tasks which militates against the use of part-time workers. While concentration of resources and market specialization (CNEMP8, CNREC8, HTECH) are negatively associated with industry use of part-time labor, as predicted, several of the industry attributes known to increase quasi-fixed labor costs are not. In particular, unionization, large firm size, and large acquisitions of new capital (UNCOV, LGFIRM, LGNWCAP) all have statistically significant, positive effects. These attributes may impose costs which induce employers to

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TABLE 5 Logistic Regressions of Industry, Job and Environmental Effects on the Log Odds of Being a Part-Time Worker: Maximum Likelihood Estimation IV Variables Compensation wagedif pendif hlthdif avgwage %pension %health Industry Characteristics uncover cnemp8 cnsal8 htech lgfirm Production Technology lgnwcap lgsalep indcomp cinfo ctrack cadmin Training indtrain jbtrain Economic environment wgch8287 grwsal8287 Job characteristics repcon sts mvc Human capital %female %white %Hispanic %veteran %married %children ,6 avg. # children avg. # yrs. educ. avg. # yrs. experience experience squared % 55 or older % in MSA Constant LgLikelihood # of industries

(1)

(2)

(3)

(4)

2.684 (1.5) 2.178 (0.3) 2.614 (1.0) .030 (0.5) 2.223 (0.2) 24.679 (5.2)

2.364 (1.4) 2.568 (5.4) 2.829 (2.6) 2.099 (3.6) 1.221 (2.4) 22.460 (4.0)

2.396 (2.0) 2.641 (1.9) .054 (0.2) 2.002 (0.1) 21.416 (3.2) 23.159 (8.6)

.408 (2.7) 2.495 (1.9) .002 (0.0) 2.166 (9.1) 1.589 (5.6) 23.213 (11.8)

1.078 (2.0) 2.186 (0.5) 2.116 (0.5) 21.184 (7.1) .407 (1.6)

1.394 (4.0) .078 (0.2) 2.117 (0.5) 21.025 (7.3) .020 (1.0)

.061 (2.2) .017 (0.3) 2.036 (0.1) .583 (2.2) 2.287 (1.4) 21.517 (2.9)

.051 (2.1) .076 (1.6) .680 (1.5) 2.009 (0.0) 2.464 (2.5) 2.432 (0.9)

21.304 (1.8) 2.000 (0.0)

1.112 (2.3) 2.002 (0.2)

.258 (0.7) .220 (3.3)

2.105 (0.3) .131 (2.6)

.501 (1.3) .271 (0.9) 1.399 (2.9)

1.199 (3.3) 2.072 (0.3) 1.063 (2.6)

22.866 (2.8) 220082.91 130

.305 (1.0) 1.378 (2.7) 25.367 (9.1) 22.337 (3.2) 21.526 (2.1) 1.647 (3.0) .376 (2.4) .164 (2.6) 2.269 (3.4) .007 (3.7) .575 (0.3) 1.270 (4.9) 22.376 (2.0) 220161.09 153

23.122 (4.5) 219996.05 130

.042 (0.2) .407 (1.0) 22.755 (5.9) 21.599 (2.5) 2.231 (0.5) .727 (1.7) 2.275 (1.9) .243 (6.2) 2.163 (2.7) .004 (2.4) .663 (0.5) .864 (3.8) 2.739 (0.8) 222837.80 153

Note. Absolute value of z-score in parentheses. Six industry occupational percentage variables are entered. 100

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DEMAND FOR PART-TIME WORKERS

restructure their workforce and hire part-timers in spite of higher administrative, supervision, or production costs. Researchers have found that, faced with institutional rigidities, firms are increasingly employing an array of ‘‘flexible’’ workers, including part-timers (Davis-Blake and Uzzi, 1993; Pfeffer and Baron, 1988), as a buffer against permanent workers. Flexible workers provide a buffer because their use enables firms to respond to market fluctuations or minimize costs without jeopardizing the job security of permanent employees. Doeringer, Christensen, Flynn, Hall, Katz, Keefe, Ruhm, Sum, and Useem (1991) contend that many firms are creating a two-tier workforce of core employees with job security and benefits and a second tier which is typically hired for finite periods with no benefits. They find that among 521 of the largest U.S. corporations most part-time workers are placed in this second tier (ibid., p. 143). Separating computer technology into distinct applications reveals that computers do not have a uniform effect on industry use of part-time workers. While industry use of computers to perform standard administrative tasks and track transactions have negative effects on the likelihood of working part-time, using computers to analyze or process information has a positive effect. This could be due to the structure of job tasks in industries and the diffusion of computer technology. Tracking of transactions and administrative tasks may be interdependent or organizationally specific work tasks. For example, what a secretary types may end up on the table of an executive meeting. In fact, many administrative tasks may entail follow-up or interaction with other workers which demands consistency of input and knowledge of particulars. Industries which rely on computers to process and analyze information, on the other hand, may be able to break some tasks into discrete units. The diffusion of computer software has no doubt reduced the specificity of many applications. Scharpf (1990) argues that advancements in storage mediums for information technology (e.g., diskettes, CDs) decoupled production from consumption (or conception of a ‘‘mental service’’ from its execution) and enabled employers to ‘‘rationalize’’ the production of services. Baran (1987) describes the rationalization of job tasks in the insurance industry as computers began to perform the task of risk assessment with decision rules built into the machine (ibid., p. 33). The outcome of this process was the restructuring of the job task of underwriting into two distinct tasks performed by different workers— routine data-entry for standard policies and skilled underwriters for nonroutine applications. The high concentration of part-time workers in the professional service industries may be due, in part, to the diffusion of computer software and the extensive use of computer technology. More than any other group of industries in this sample, professional service industries have a higher percentage of workers who directly use computers at work every day (Pitts, 1997). Job characteristics believed to reduce supervision costs are positively associated with industry use of part-time workers. Industries characterized by repetitive

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tasks (REPCON) and jobs with measurable and verifiable criteria for execution (MVC) tend to hire relatively more part-timers than otherwise similar industries. Likewise, industries characterized by jobs demanding precision tend to hire more part-time workers (STS). Part-time workers may have a comparative advantage over full-time employees in jobs requiring precision due to the physical or mental demands placed upon them (Owen, 1979). Finally, x2 tests of significance are presented in Table 6 for groups of explanatory variables taken as a whole. The null hypothesis of no significance can be rejected at the 5 or 10% level for all sets except the two training variables. Controlling for a number of industry and job attributes does not reduce the significance of industry occupational distributions, however. Whether task characteristics or institutional conditions associated with occupations, not measured here, could explain the association between part-time employment and industry occupational distributions is uncertain but worth investigating. Testing the wage and fringe benefits variables separately reveals that it is the coefficients for the health care measures which are significant. It is worth restating: industries that provide health care benefits to a larger fraction of their workforce tend to hire fewer part-timers and those industries that employ more part-timers tend to differentiate in terms of health care benefits between part-time and full-time workers. Results from the supply equation are presented in columns 2 and 4 of Table 5. Most of the compensation variables do not behave as expected. Being female, having children under the age of 6, an increase in the number of children per worker, all increase the chances that employees will want to work part-time. Being married and having more work experience decrease the likelihood that individuals will desire part-time work14 (%MARRIED, EXPER). DISCUSSION Structural features of industries determine employment relations (Storper and Walker, 1989). Industry specific conditions shape employment patterns because labor is a derived demand. Whether jobs differ depends upon the set of activities and constraints that firms face in their competition to deliver goods and services. The general concern regarding the increase in part-time employment is that these jobs have unfavorable attributes which place part-timers at a disadvantage in the labor market. It is clear that certain industry configurations are conducive to the use of part-time labor. However, this analysis is unable to determine whether certain industry properties necessarily imply disadvantages for part-time workers in relation to full-time workers, particularly in those industries with high concentra14 Research has shown that the effect of marital status on part-time employment varies by gender (Blank, 1990; Pitts, 1997). While married men, generally, choose not to work part-time the opposite is true for married women. Differential gender effects of marital status are lost when estimated as an industry average.

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TABLE 6 x2 Tests of Significance of IV Estimates: Demand Equation Variables

x2

Significance

Compensation Differentials Wagedif Pendif Hlthdif Industry Averages Avgwage %Pension %Health Wage Wagedif Avgwage Pension Pendif %Pension Health Care Hlthdif %Health Industry structure Uncov Lgfirm Cnemp8 Cnsal8 Htech Production technology Lgnwcap Lgsalep Indcomp Cinfo Ctrack Cadmin Training Indtrain Jbtrain Economic environment Wgch8287 Grwsal8287 Job characteristics REPCON STS MVC Occupational distribution Professional Administrative Sales Service Blue-Collar Skilled Agricultural All variables

153.12 12.61

.000 .006

112.09

.000

2.61

.270

0.07

.964

29.72

.000

100.10

.0000

20.08

.000

3.58

.167

20.66

.000

14.51

.002

188.50

.000

5562.76

.000

Note. Omitted category for occupational distribution is unskilled Blue-collar workers. 103

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tions of part-timers. Associations between industry part-time employment ratios and particular industry attributes, not actual job conditions of part-time workers, were examined. For example, while it is clear that industry applications of computer technology do affect the distribution of part-time employment, it is unclear whether part-timers actually use computers at work. The above discussion presumes a uniformity in the characteristics of part-time workers and part-time jobs. However, studies have shown that part-time workers and jobs vary widely in their attributes and rewards (Blank, 1990; Nardone, 1995; Tilly, 1992). Contrary to popular belief, some part-time workers are very skilled and have job rewards, particularly wages, comparable to those of full-time workers (e.g., professional part-timers—see Blank, 1990; Pitts, 1997). While the ‘‘good’’ part-time jobs do not pose many concerns in terms of public policy, the same cannot be said of the so-called ‘‘bad’’ part-time jobs (Tilly, 1996). The apparent negative association between part-time employment and the provision of health care is an area of potential concern. Recent research indicates that employers increasingly reward training and competency with newer technologies, and push for higher levels of individual productivity at work (Harrison and Bluestone, 1988; Katz and Murphy, 1992; Krueger, 1993; Lillard and Tan, 1992; Murphy and Welch, 1992; Noyelle, 1987). Part-time workers, for example, in industries and jobs with little employerprovided training, most likely will be disadvantaged regardless of the amount of labor force experience gained in part-time employment. Jacobs (1993) finds that most part-time workers do not remain in part-time jobs. Subsequent employment attributes, however, may vary widely for part-time workers. As firms in the U.S. face heightened competition, extract concessions from unions, increasingly rely on training received outside the firm, and reduce their commitment to long-term employment contracts (Appelbaum, 1987; Harrison and Bluestone, 1988; Noyelle, 1987; Rubin, 1995), many believe part-time employment will continue to grow. While most of the growth is due to the expansion of retail and service industries, Tilly (1996) reports an increased rate of part-time employment within most major industries. Research in the area suggests that certain types of jobs are prone to the use of part-time labor. Expansion of part-time employment across economic sectors may indicate a long-term trend in the organization of work and employment relations, with consequences for full-time workers as well. REFERENCES Alchian, A. A., and Demsetz, H. (1972). ‘‘Production, information costs, and economic organization,’’ American Economic Review 62:5, 777–795. Appelbaum, E. (1987). ‘‘Restructuring work: Temporary, part-time, and at-home employment,’’ in Computer Chips and Paper Clips: Technology and Women’s Employment, Vol. 2 (Heidi I. Hartmann, Robert Kraut, and Louise Tilly, Eds.), pp. 269–310, National Research Council, National Academy Press, Washington, DC. Baran, B. (1987). ‘‘The technological transformation of white-collar work: A case study of the insurance industry,’’ in Computer Chips and Paper Clips: Technology and Women’s Work, Vol. 2,

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(Heidi I. Hartmann, Robert Kraut, and Louise Tilly, Eds.), pp. 25–62, National Research Council, National Academy Press, Washington, DC. Barron, J. M., Black, D. A., and Loewenstein, M. A. (1989). ‘‘Job matching and on-the-job training,’’ Journal of Labor Economics 7:1, 1–19. Becker, S. (1964). Human Capital, National Bureau of Economic Research, Columbia Univ. Press, New York. Belous, R. (1989). The Contingent Economy: The Growth of the Temporary, Part-Time and Subcontracted Workforce, National Planning Association, Washington, DC. Blank, R. (1994). The Dynamics of Part-Time Work, National Bureau of Economic Research Working Paper Series No. 4911, National Bureau of Research, Inc., Cambridge, MA. Blank, R. (1990). ‘‘Are part-time jobs bad jobs?,’’ in A Future of Lousy Jobs?: The Changing Structure of U.S. Wages (Gary Burtless, Ed.), pp. 123–155, The Brookings Institution, Washington, DC. Cartter, A. M. (1959). Theory of Wages and Employment, Richard D. Irwin Inc., Homewood, IL. Curme, M., Hirsch, B., and MacPherson, D. (1990). ‘‘Union membership and contact coverage in the U.S., 1983–1988,’’ Industrial and Labor Relations Review 44:1, 5–33. Cuvillier, R. (1984). The Reduction of Working Time: Scope and Implications in Industrialized Market Economies, International Labour Office, International Labour Organization, Geneva, Switzerland. Davis-Blake, A., and Uzzi, B. (1993). ‘‘Determinants of employment externalization: A study of temporary workers and independent contractors,’’ Administrative Science Quarterly 38:2, 195– 223. de Neubourg, C. (1985). ‘‘Part-time work: An international quantitative comparison,’’ International Labour Review 124:5, 559–576. DiPrete, T. A. (1990). ‘‘Is there a nonspurious link between the market power and the wage structure of firms?: Some plausible mechanisms,’’ Research in Social Stratification and Mobility 9, 283–306. JAI Press Inc., Greenwich, CT. Doeringer, P. B., Christensen, K., Flynn, P. M., Hall, D. T., Katz, H. C., Keefe, J. H., Ruhm, C. J., Sum, A. M., and Useem, M. (1991). ‘‘The two-tiered workforce in U.S. corporations,’’ in Turbulence in the American Workplace, pp. 140–155, Oxford Univ. Press, New York. Ehrenberg, R. G., Rosenberg, P., and Li, J. (1988). ‘‘Part-time employment in the United States,.’’ in Employment, Unemployment and Labor Utilization (Robert Hart, Ed.), pp. 256–281, Unwin Hyman, Inc., Boston, MA. England, P., and Kilbourne, B. (1988). Occupational Measures from the Dictionary of Occupational Titles for 1980 Census Detailed Occupations. Computer file distributed by Inter-University Consortium for Political and Social Research, Ann Arbor, MI. Ermisch, J. F., and Wright, R. E. (1993). ‘‘Wage offers and full-time and part-time employment by British women,’’ The Journal of Human Resources 28:1, 111–133. Fleisher, B. M., Ray, E. J., and Kniesner, T. J. (1987). Principles of Economics, Wm. C. Brown Publishers, Dubuque, IA. Freeman, R. B., and Medoff, J. L. (1984). What Do Unions Do? Basic Books Inc., New York, NY. Freeman, R. B., and Medoff, J. L. (1981). ‘‘The impact of the percentage organized on union and nonunion wages,’’ Review of Economics and Statistics 63:4, 561–572. Griliches, Z. (1969). ‘‘Capital-skill complementarity,’’ Review of Economics and Statistics, 51:4, 465–468. Hage, J. (1989). ‘‘The sociology of traditional economic problems: Product markets and labor markets,’’ Work and Occupations, 16:4, 416–445. Hamermesh, D. S. (1993). Labor Demand, Princeton Univ. Press, Princeton, NJ. Harrison, B., and Bluestone, B. (1988). The Great U-Turn: Corporate Restructuring and the Polarizing of America, Basic Books, New York, NY. Hotchkiss, J. L. (1991). ‘‘The definition of part-time employment: A switching regression model with unknown sample selection,’’ International Economic Review 32:4, 899–917. Idson, T. L., and Feaster, D. J. (1990). ‘‘A selectivity model of employer-size wage differentials,’’ Journal of Labor Economics 8:1, 99–122.

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Jacobs, D. (1994). ‘‘Organizational theory and dualism: Some sociological determinants of spot and internal labor markets,’’ Research in Social Stratification and Mobility 13, 203–235, JAI Press Inc., Greenwich, CT. Jacobs, J. A. (1993). Trends in Wages, Underemployment, and Mobility Among Part-Time Workers, Institute for Research on Poverty Discussion Papers #1021-93, Institute for Research on Poverty, University of Wisconsin–Madison. Johnson, A. C., Jr., Johnson, M. B., and Buse, R. C. (1987). Econometrics: Basic and Applied, Macmillan Publishing Co., New York. Katz, L. F., and Murphy, K. M. (1992). ‘‘Changes in relative wages, 1963–1987: Supply and demand factors,’’ The Quarterly Journal of Economics 107:1, 35–78. Kaufman, B. E. (1989). The Economics of Labor Markets and Labor Relations, Dryden Press, Chicago, IL. Kochan, T., Katz, H. C., and McKersie, R. B. (1986). ‘‘A strategic choice perspective on industrial relations’’ in The Transformation of American Industrial Relations, pp. 3–20, Basic Books, New York. Kosters, M. H. (1995). ‘‘Part-time pay,’’ Journal of Labor Research 16:3, 263–274. Krueger, A. B. (1993). ‘‘How computers have changed the wage structure: Evidence from microdata, 1984–1989,’’ The Quarterly Journal of Economics 108:1, 33–60. Krueger, A. B., and Summers, L. H. (1988). ‘‘Efficiency wages and the inter-industry wage structure,’’ Econometrica 56:2, 259–293. Leeds, M. A. (1990). ‘‘Part-time status and hourly earnings of Black and White men,’’ Economic Inquiry 28:3, 544–554. Lillard, L. A., and Tan, H. W. (1992). ‘‘Private sector training: Who gets it and what are its effects?’’ in Research in Labor Economics 13, pp. 1–62, JAI Press Inc., Greenwich, CT. Long, J. E., and Jones, E. B. (1981). ‘‘Married women in part-time employment,’’ Industrial and Labor Relations Review 34:3, 413–425. McKie, C. (1992). ‘‘Part-time work in the North Atlantic Triangle: The United States, the United Kingdom, and Canada’’ in Working Part-Time: Risks and Opportunities (Barbara D. Warme, Katherina L. P. Lundy, and Larry A. Lundy, Eds.), pp. 21–41, Praeger Publishers, New York. Mellow, W. (1982). ‘‘Employer size and wages,’’ Review of Economics and Statistics 64:3, 253–282. Montgomery, M. (1988). ‘‘On the determinants of employer demand for part-time workers.’’ Review of Economics and Statistics 70:1, 112–117. Montgomery, M., and Cosgrove, J. (1995). ‘‘Are part-time women paid less? A model with firm-specific effects,’’ Economic Inquiry 33:1, 119–133. Montgomery, M., and Cosgrove, J. (1993). ‘‘The effect of employee benefits on the demand for part-time workers,’’ Industrial and Labor Relations Review 47:1, 87–98. Moore, W. J., Newman, R. J., and Cunningham, J. (1985). ‘‘The effect of the extent of unionism on union and nonunion wages,’’ Journal of Labor Research 6:1, 21–44. Murphy, K. M., and Welch, F. (1992). ‘‘The structure of wages,’’ The Quarterly Journal of Economics 107:1, 285–326. Nakamura, A., and Nakamura, M. (1983). ‘‘Part-time and full-time work behaviour of married women: A model with doubly truncated dependent variable,’’ Canadian Journal of Economics 16:2, 229–256. Nardone, T. (1995). ‘‘Part-time employment: Reasons, demographics, and trends,’’ Journal of Labor Research 26:3, 275–292. Noyelle, T. J. (1987). Beyond Industrial Dualism: Market and Job Segmentation in the New Economy, Westview Press, Boulder, CO. Oi, W. Y. (1991). ‘‘Low wages and small firms,’’ Research in Labor Economics 12, 1–39, JAI Press Inc., Greenwich, CT. Oi, W. Y. (1983). ‘‘The fixed employment costs of specialized labor’’ in The Measurement of Labor Cost (Jack E. Triplett, Ed.), National Bureau of Economic Research: Studies in Income and Wealth, Vol. 48, pp. 63–116, Univ. of Chicago Press, Chicago, IL.

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