Directory UMM :Data Elmu:jurnal:J-a:Journal of Economic Behavior And Organization:Vol43.Issue4.Dec2000:

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Vol. 43 (2000) 423–446

Wage equity and employee motivation in

nonprofit and for-profit organizations

Laura Leete

Public Policy Research Center, Willamette University, 900 State Street, Salem, OR 97301, USA Received 18 July 1998; received in revised form 5 July 1999; accepted 8 July 1999

Abstract

In this paper, I argue that because nonprofit organizations rely disproportionately on intrinsically motivated employees, they provide a particularly interesting context for examining the relationship between wage dispersion and employee motivation. If certain hypotheses put forth in the literature on psychology and employee motivation are correct, then wage dispersion should be less apparent in the nonprofit sector than in the for-profit sector. I examine labor market data from the 1990 US Census on nonprofit and for-profit employees and find a strong link between wage equity and sector of employment. This finding is supportive of the view that wage equity is related to worker motivation. Alternative explanations for the observed wage patterns are examined and rejected. © 2000 Elsevier Science B.V. All rights reserved.

JEL classification: J31; L31

Keywords: Wage equity; Worker motivation; Nonprofit institutions

1. Introduction

A new interest in the relationship between the economics of labor markets and the psychol-ogy of worker motivation has developed in recent years. Economists and others have been exploring previously neglected concepts, such as the relationship between organizational structure, worker motivation, envy, pay, and workplace performance (see Rabin, 1998). In this paper, I argue that because nonprofit organizations rely disproportionately on intrin-sically motivated employees, they provide a particularly interesting context for examining some of these issues. If certain hypotheses put forth in the literature on nonprofit organi-zations, psychology and employee motivation are correct, then wage dispersion should be

E-mail address: [email protected] (L. Leete).

1_{Fred H. Paulus Director for Public Policy Research and Associate Professor of Economics and Management.}

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less apparent in the nonprofit sector than in the for-profit sector. I examine labor market data from the 1990 US Census on nonprofit and for-profit employees. I find a strong link between wage equity and sector of employment. This finding is supportive of the view that wage equity is related to worker motivation. Alternative explanations for the observed wage patterns are examined and rejected.

Many authors have recognized that nonprofit organizations are organized along different motivating and operational principles than for-profit organizations. The differences between nonprofit and for-profit organizations extend, to varying degrees, to their reasons for ex-istence, organizational goals and methods, products produced, and constituencies served. I will argue here that such differences may well lead to different approaches to human resource management, in particular with regard to the distribution of wages in relation to worker motivation.

A large number of nonprofit organizations are formed for one of two reasons: to produce a certain level of quality when it is less than perfectly observed by the consumer, or to produce goods or services while abiding by certain moral, intellectual, aesthetic or religious princi-ples. Several authors have suggested that under such conditions that the organizational goals of nonprofits are often best achieved by intrinsically motivated employees and by employees who identify very closely with the goals of the organization. There are many methods of encouraging employee compliance with the goals and principles of the organization, such as incentive and monitoring schemes. I will argue below, however, that for many nonprofit organizations, the use of intrinsically motivated employees may be the most satisfactory solution. Indeed in some cases, it is the only solution; intrinsically motivated employees are sometimes inherent to the service being provided. Parishioners may object to confessing to a priest who is motivated by anything other than faith. Heart patients may be reluctant to submit to the knife of a surgeon who does not put the quality of her work above all else.

Several authors have noted the wage level consequences of hiring intrinsically motivated employees in nonprofit organizations. In particular, in his seminal work Hansmann (1980) suggests that nonprofit employers will use wages as a negative screening device by offering salaries below those in the for-profit sector. This should deter those highly motivated by monetary concerns from seeking nonprofit employment and attract those for whom love of their work dominates. This idea is formalized by Handy and Katz (1998). Leete (2001) finds evidence that is consistent with such wage setting practices in some portions of the nonprofit sector. No author to date has pursued other implications of the need for some nonprofit organizations to hire (and retain and continue to motivate) intrinsically motivated employees. Frey (1997), however, has written more generally on the relationships between intrinsic and extrinsic motivation. Others have written about the relationship between work morale and status and equity comparisons within firms of all types. In this paper, I will relate the two literature, pointing out the possible role of wage equity in maintaining intrinsic motivation and organizational identification. I will then relate this to wage setting practices in the nonprofit sector.

In Section 2 below, I provide an overview of previous literature addressing nonprofit human resource issues and the psychology of worker motivation and I relate this literature to the notion of wage equity. In Section 3, I examine empirical evidence on the distribution of wages in the nonprofit and for-profit sectors. In Section 4, I summarize the implications of the findings.

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2. Literature review

In this review, I will first summarize writing on the subject of worker motivation as it relates to perceptions of employer fairness and wage equity. I will then provide an overview of scholarship relating to worker motivation and the mission of nonprofit organizations. Finally, I will discuss the implications of these two bodies of work taken together. 2.1. The psychology of wages and worker motivation

The literature relating to worker motivation spans the disciplines of anthropology, psy-chology, sociology and economics. I will review four branches of this work: the first two of these relate worker motivation explicitly to the structure of wages, the latter two relate worker motivation to general employee perceptions of employer fairness. In the first body of work, wage equity is linked to worker morale, productivity and group cohesiveness. In the second, the literature on reciprocity and gift exchange, the perception of wage fairness is linked to worker morale. In the third group of articles, the relationship between intrinsic and extrinsic motivation is considered. Finally, the effects of organizational pride on behav-ior are discussed. In the last two cases, the relationship between wage equity and worker motivation is not made explicit, but will be drawn out here.

2.1.1. Wage equity, work morale and group cohesiveness

In the literature relating wage equity to worker motivation, the perception of wage fairness or equity is generally understood to be based on the extent to which differences in wages between an individual and the relevant reference group are considered to be justified (within the context of a culturally determined understanding). Adams (1963) writes, “Inequity exists for Person whenever his perceived job inputs and/or outcomes stand in an obverse relation to what he perceives are the inputs or outcomes of Other,” (p. 424), where ‘Person’ is an individual and ‘Other’ is a reference individual or group. The relevant reference group is generally taken to be other employees in the same occupation in the same firm, all employees in the same firm, employees in the same occupation in other firms, or other employees in other firms. Most commonly, the first two groups have been considered most relevant. As Frank (1985) discusses, local comparisons generally carry more weight than more distant ones.2

Frank and Stark (1990) have both argued that wage dispersion within a reference group diminishes work morale because it introduces relative status deprivation for those at the low end of the distribution. Clark and Oswald (1996), using survey data from a sample of 5000 British workers, find empirical evidence that relative pay does in fact affect job satisfaction and overall utility. In a closely related argument, Levine (1991) suggests that wage dispersion leads to lower group cohesiveness, which he defines as the propensity of workers to obey group norms. In turn, he argues that cohesiveness promotes productivity, 2_{Baron and Pfeffer (1994) refine this understanding by suggesting that wage inequality will generate more}

dissatisfaction the more closely people work together, the less differentiation there is among them in rank or other identifying characteristics, the more interdependent their work, the more important the social relations of work, the smaller the organization, or the less that employment is out-sourced from the organization.

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particularly when work requires cooperation between employees. He provides a variety of types of evidence to support his contentions. Lazear (1989) elaborates a similar argument. Adams (1965) suggests that workers who perceive themselves as unfairly paid will lower their work effort; the more participatory the firm and the more discretion employees have, the easier it is for employees to ‘punish’ a firm they feel is treating them unfairly.

Lazear (1991) suggests, however, that the effect of wage inequity need not necessarily imply a net loss of motivation or productivity. The loss of status to those at the bottom of the wage distribution is (by definition) equal to the gain to those at the top. While there may be an efficiency gain from harmony in the work place, there can also be efficiency gains spawned by competition between workers to win tournaments (stressed by Lazear and Rosen, 1981). However, as Rabin notes, the human dislike of loss (relative to reference points or to the status quo) is greater than the love of gains. This casts doubt then on whether the benefits of status gains to winners, and their productivity implications, will outweigh the costs to losers.3

2.1.2. Reciprocity and gift exchange

Closely related to the wage equity literature is the literature based on reciprocity and gift exchange. In this view, derived from anthropology and emphasized by Akerlof (1982), employees ‘give’ to the employer in proportion to what they perceive they have received. Along these same lines, Rabin notes the reciprocal nature of preferences: people will be altruistic if they perceive others being altruistic. Rabin cites evidence of a strong positive correlation between subjects’ contributions to a public good and their beliefs about how much others are contributing. Furthermore, Rabin argues that individuals determine their disposition toward others according to the motives they attribute to them, not solely ac-cording to their actions. Thus, employees will put forth more effort when they feel they are receiving a fair wage.

2.1.3. Intrinsic and extrinsic motivation

Frey (1993a,b, 1997) writes extensively about intrinsic motivation and the factors that contribute to or detract from it. He writes: “Persons are intrinsically motivated if work is performed for work’s sake. Many different conceptualizations of intrinsic preferences exist (see Deci and Ryan, 1985). . . For our purpose intrinsic work motivation is identified with work morale or work ethic” (Frey, 1997, p. 429). Frey argues that intrinsic motivation is both costly and fragile. It is (psychically) costly to muster and to maintain the inner forces required to support intrinsic motivation. Furthermore, it can be either crowded-in or crowded-out by elements and conditions that he identifies, particularly by externally imposed commands, rewards or sanctions related to the performance of the job (extrinsic motivation). His argument rests on the premise that individuals are motivated (in part) by the desire for “feelings of competence and self-determination” (Deci, 1975). These feelings can be diminished when extrinsic motivation (either positive or negative) is introduced. In addition, Frey argues that individuals operate under what amounts to a principle of conservation of motivation. They muster only the intrinsic motivation needed to accomplish the task, given extrinsic conditions. Following Deci and Ryan (1980), Frey identifies a

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number of conditions under which intrinsic motivation is more likely to be crowded out by external factors. These include, among other things, when the employee perceives unfairness in their relationship with the employer.

While Frey does not explicitly discuss the relationship between wage equity and intrinsic motivation, several possible connections are clear. First, perceptions of workplace unfairness are, according to Frey, likely to promote the crowding out of intrinsic motivation. Second, if Frey is correct, then productivity gains from status differentiation, such as those discussed by Lazear and Rosen may not be forthcoming when productivity depends in an important way on intrinsic motivation. Because status differentiation amounts to a heightening of extrinsic motivation, under certain conditions (discussed by Frey), this may serve only to crowd out intrinsic motivation among both winners and the losers. Thus, all else equal, one might expect firms that rely more heavily on intrinsic motivation to rely less on status differentiation as a motivating mechanism.

2.1.4. Organizational pride

In a related literature, Smith and Tyler (1997) also identify fairness as an important principle affecting the attitude and behavior of individuals in organizations. They define pride as the status of the group one belongs to, and respect as one’s position within the group. They write that “Feelings of pride are linked to judgements that group authorities are trustworthy, neutral and respectful. . . These results support the argument that people care about fair treatment by authorities because the fairness of those procedures indicates to those involved that they are respected members within their groups and that their groups are positive and valuable” (p. 147). They go on to note that “people who feel proud or well respected will be more likely to endorse or engage in conforming group behaviors” (p. 151). They provide experimental and survey evidence that this is the case. Thus, feelings of pride and respect can encourage conforming behavior, but depend at least in part on the perception of fairness in the workplace. The wage structure could well be central to such an assessment, and thus, again would be linked to employee motivation.

2.2. Employment relations in the nonprofit sector

While the literature discussed above relates to worker motivation more generally, non-profit scholars have frequently noted requirements for employee motivation that are par-ticular to nonprofit settings. These needs are generally identified as being rooted in the reasons for the formation of nonprofit organizations. The economic theory of nonprofit or-ganizations and the resulting typology of nonprofit oror-ganizations are well summarized in a number of places, in particular in Hansmann and Rose-Ackerman (1996), and thus, will not be reproduced in full here. Instead, two basic types of nonprofits will be discussed explicitly here: those formed as a result of information asymmetries, and those formed to uphold the ideological principles of the stakeholders.4 Examples of nonprofits that are formed due to information asymmetries include those that provide education, medical, nursing home and childcare services. In these and other cases, it is often difficult for the purchaser of the 4_{Other types of nonprofits that are not as relevant to the discussion here include clubs (those formed to serve the}

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services to monitor and/or evaluate the quantity and/or the quality of the good or service purchased. If quantity or quality is costly to produce, for-profit organizations pose a risk of moral hazard to the consumer. Managers motivated by monetary incentives may skimp on either quality or quantity while skimming profits. In some cases, this may be at the expense of those who are least able to express their needs or to care for themselves. Other nonprofits exist explicitly to conduct activities in accordance with the religious, aesthetic, moral, ethical and/or intellectual values of the organizations’ stakeholders. Examples in-clude religious organizations, civil rights and civil liberties watchdog groups, museums, symphonies, arts preservation or arts appreciation groups. In these cases, the very existence of the organization is predicated on the assumption that it will uphold the values of the stakeholders.

Both Hansmann and Rose-Ackerman discuss the participation of intrinsically motivated managers in the nonprofit sector in the context of these types of organizations. Hansmann highlights the importance to nonprofits of managers who are motivated more by the desire to produce a quality product than by monetary rewards. According to Hansmann, the in-ability of nonprofits to distribute residual earnings (the nondistribution constraint) leads to an efficient match between nonprofit organizations and intrinsically motivated managers. His discussion does not extend beyond this observation. Rose-Ackerman, however, goes further. She suggests that ideological customers may prefer patronizing organizations run by like-minded individuals. Ideological founders are those motivated by ideas rather than profit — those ‘with strong beliefs about the proper way to provide a particular service’ (p. 719). They will then seek to hire managers and employees who share their vision and, thus, need little monitoring to ensure that they work towards the same goals as the organi-zation founder.

There is some evidence to support the conjectures of Hansmann and Rose-Ackerman. Weisbrod (1988, p. 32) discusses evidence of the sorting of nonprofit and for-profit managers by goals and personality type. Mirvis and Hackett (1983) analyze Quality of Employment Survey data for 1977 by sector of employment and find that nonprofit employees are more likely (than government or for-profit sector employees) to report that “their work is more important to them than the money they earn” (p. 7). In a comparison of matched samples, they also find that nonprofit employees reported the most variety and challenge in their jobs, the most autonomy (defined as freedom and responsibility to decide what to do and when), and the least extent of ‘overeducation’. Furthermore, nonprofit workers were also less likely to report “that their jobs sometimes go against their conscience” (p. 9), and reported higher levels of intrinsic rewards from the job, such as feelings of accomplishment and self-respect when they do their jobs well. Newman and Wallender (1978) report that nonprofit workers develop a ‘mystique’ about their organization.

All of this suggests that intrinsic motivation and identification with organizational goals play a more important role in the nonprofit sector than elsewhere. However, as noted by Frey (1993a), intrinsic motivation is both costly and fragile. Thus, nonprofit firms must not only find appropriately motivated employees, but must also direct and support their motivation. To this end, one might expect nonprofit organizations to rely more heavily (than for-profit organizations) on practices that strengthen intrinsic motivation, improve adherence to group norms, and organizational pride. In order to accomplish this, following the discussion above, they may rely more heavily on wage equity. As Frank highlights,

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localized wage comparisons take on a particular importance. However, what is ‘local’ will be defined by the scope of the particular labor market. Thus, one might expect wage equity to occur within or across all occupations within an organization, or within an occupation but across organizations in a particular industry. Any of these comparisons may apply only to higher level employees — managerial and professional employees, and technical and administrative support staff — those who have the most information about relative wages and whose workplace conduct is most dependent on self-determination. Furthermore, to the extent that wage inequity in the US has race- and gender-related components, one might expect these to be diminished in nonprofit organizations as compared with for-profit organizations.

This emphasis on wage equity in the nonprofit sector is not meant to downplay the possible importance of such factors in for-profit settings, but rather to highlight the nonprofit sector as a context in which these factors may be relatively more concentrated as compared with the economy at large. While motivated employees or organizational pride might be productivity or sales enhancing in the for-profit sector, they may be the sine quibus non of the nonprofit sector.5 Furthermore, the implication that wage equity is important to maintaining worker motivation can only be taken to suggest that wage equity may be a necessary precondition, not that it is a sufficient one. For example, Freeman and Medoff (1984) note that while wage equity is greater among union than among non-union employees, stated job satisfaction is not.

3. Evidence of wage equity in the nonprofit sector

If nonprofit organizations require more intrinsically motivated and organizationally ori-ented employees, and if wage equity is central to maintaining intrinsic motivation, group cohesiveness and organizational pride, then one would expect the wages of nonprofit em-ployees to be less dispersed either within or between organizations than those of for-profit organizations. Previous researchers have collected some evidence that is consistent with this hypothesis. Mirvis and Hackett and Mirvis (1992) examine survey data for 1977 and 1990, respectively, and find some suggestions that this is the case. In both years, they find that nonprofit employees are more likely than for-profit employees to report that they are paid fairly “as compared to what other people doing my kind of work are paid.” Mirvis notes further that there is “usually less disparity in wages and working conditions from top to bottom in nonprofits” (p. 26).

In this Section, I use 1990 US Census data to examine whether these perceptions of greater wage equity in the nonprofit sector are supported by evidence on differences in nonprofit and for-profit sector wage structures. Of course, if wage equity within or between organizations is to gain expression in the wage distribution of an entire sector this requires either that there be no countervailing factors, or that those factors be properly controlled for. For example, wages could be less dispersed within nonprofit organizations than within for-profit organizations, but nonprofit organizations themselves could be more dispersed 5_{Of course, to the extent that either ideology or quality is costly, ideological employees could be a liability to a}

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across the wage distribution than for-profit ones. Therefore, I control for as many underlying determinants of the wage distribution as possible.

The basic measure examined here is the difference in the variance of wages between the two sectors. Raw wages are decomposed into predicted and residual components using OLS regression. The variance differential for predicted wages is further decomposed into the portions attributable to the differences in returns and differences in characteristics. The variance differentials are examined for all workers, and within both broad and detailed oc-cupations and industries. I also examine differences in race and gender wage discrimination between the two sectors.

3.1. Data

The dataset used here is the 5-percent Public Use Microdata Sample (PUMS) of the 1990 Census. This includes a sample of 4.1 million individuals employed in the private sector, of whom 8.5 percent work in the nonprofit sector. These data were self-reported by individuals receiving the ‘long form’ survey of the census. Sector of employment was identified by asking individuals if they were an employee of ‘a private for-profit company

. . . a private not-for-profit tax-exempt, or charitable organization, local government, state government, etc.’6 In addition to type of employment, the PUMS reports individuals’ wage and salary income for 1989, occupation and industry of employment, weekly hours of work, number of weeks worked, and individual characteristics, such as age, education (including type of degree held), gender, race, area of residence, language fluency, and disability status. Hourly wages are imputed as annual wage and salary income divided by total hours worked in 1989. Government workers are eliminated from the sample so that the comparisons made are strictly between for-profit and nonprofit workers. The sample was also limited to those who were not currently enrolled in school and who did not report having a disability that limited their ability to work.7 The sample was not restricted in any other way.8 Sample means of the variables used are reported in Table 1 by organizational status. Nonprofit workers average higher hourly wages, are more likely to be part-time, female, and fluent in English, and they have on average more years of potential labor market experience (defined as age minus years of education minus six). The racial mix in the nonprofit sector is slightly more white, and educational levels are higher. All of these characteristics are as expected given the preponderance of white-collar, service sector occupations in the nonprofit sector. 6_{Responses were checked for consistency with answers to questions on employer name, location, industry and}

occupation. As part of the consistency check, data processors could use a directory of company names to identify the correct industry code and legal form of an organization. They could then recode the answer to the ‘class of worker’ question accordingly. For a detailed discussion of the implications of misreporting of nonprofit status, see Leete (2001).

7_{Disabled workers are eliminated because degree of disability is not sufficiently identified and severely disabled}

clients of ‘sheltered workshops’ in the nonprofit sector whom earn some pay will alter the distribution of nonprofit wages.

8_{The discussion in this paper relates primarily to the major classes of nonprofit organizations (formed as a result of}

information asymmetries or on ideological grounds). These nonprofits are predominately found in the professional services sector of the economy, while other types of nonprofits are found elsewhere. However, because this sector accounts for 82 percent of nonprofit employment, limiting the analysis here to this sector of the economy has little effect on the results.

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Table 1

Means of dependent and independent variables by sectora

Variable For-profit Nonprofit

Means

Ln(hourly wage 1989) 2.23 2.27

Years of potential experience (age-Ed-6) 19.9 21.8

Percent

Female 43.9 66.6

Not fluent in English 3.1 1.3

Average hours worked per week, 1989 40.6 37.9

Working<10 h per week, 1989 1.8 3.8

Working part-time (<25 h per week), 1989 9.8 16.5

Average weeks worked, 1989 45.1 45.1

Working<13 weeks, 1989 5.7 5.1

Race

White 81.5 83.8

African–American 9.4 9.5

Hispanic 3.4 2.5

Asian 2.6 2.4

Other race 3.2 1.9

Educational attainment

No school 0.8 0.4

Nursery school 0.0 0.0

Kindergarten 0.0 0.0

1st–4th grade 0.8 0.3

5–8th grade 4.0 2.0

9th grade 2.5 1.1

10th grade 3.7 1.7

11th grade 3.6 1.7

12th grade, no diploma 3.8 2.0

High school graduate or GED 35.2 21.7

Some college, no degree 20.5 17.6

Associate degree, occupational program 3.8 5.2

Associate degree, academic program 2.9 4.2

Bachelors degree 13.5 22.6

Masters degree 3.0 12.6

Professional degree 1.5 3.7

Doctorate degree 0.4 3.1

N 3822413 323548

a_{Private sectors workers, not disabled or enrolled in school.}

3.2. The decomposition of variance differentials

Using a human capital earnings function, actual wages can be decomposed into the por-tions attributable to the presence of certain characteristics, the returns to those characteris-tics, and parts unexplainable. Differences in the wage distribution between two sectors can be attributed to differences in the distribution of any of these components. Thus, I use OLS

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regression to estimate a standard human capital earnings function separately for workers in the for-profit and nonprofit sectors. I then examine the distribution of actual, predicted and residual wages for the two sectors. The estimated equation is

Wk=ak+bkZk+εk. (1)

where W represents the natural log of the hourly wage,εthe error term of the equation. The superscript k is an indicator for either the nonprofit or for-profit status. Control variables summarized in Z are: dummy variables for six 1-digit occupations, 11 1-digit industries, location in an urban area, lack of fluency in speaking English, 10 race-gender categories, and part-time work (<25 hours per week). In addition, continuous variables for years of education, potential experience and potential experience-squared are included.9This equa-tion is estimated on samples of 323,521 nonprofit workers and 3,822,020 for-profit workers for whom no data were missing for any of the included variables. A full set of estimated coefficients for each sector are shown in Appendix A.

Predicted log wages in a given sector are calculated as ˆ

Wk= ˆak+ ˆbkZˆk (2)

whereaˆk andbˆkare estimated coefficients for that sector. Residual log wages are then calculated as

Wk=Wk− ˆWk (3)

As a basic measure of differences in wage equity between the two sectors, we examine the variance of each of these wage measures (actual, predicted and residual) and compare them across sectors. Following Freeman (1980), the difference in the variance of predicted wages can also be decomposed into the portions attributable to differences in characteristics and differences in returns to those characteristics between sectors. This decomposition presents the classic index number problem of any such decomposition (e.g. Oaxaca, 1973). The differences attributable to different characteristics can be weighted by the returns of either the nonprofit or for-profit sector, and vice versa. The variance differential for predicted wages is

Var(Wˆfp)−Var(Wˆnp)=Var(aˆfp+ ˆbfpZfp)−Var(aˆnp+ ˆbnpZnp)

i,j

bfp_i bˆfp_jcov(Zfp_i Z_jfp)−X

i,j

b_inpbˆnp_j cov(Z_inp, Znp_j ) (4)

which in turn can be expressed either as

i,j

[bˆ_ifpbˆ_jfp− ˆbnp_i bˆnp_j ]cov(Z_ifp, Z_jfp)+X

i,j

b_inpbˆnp_j [cov(Z_ifp, Zfp_j)−cov(Znp_i , Z_jnp)] (5a)

9_{Indicators for firm size and union status are two variables that would ideally be included here but are not}

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Or as

i,j

[bˆ_ifpbˆ_jfp− ˆbnp_i bˆnp_j ]cov(Z_inp, Z_jnp)+X

i,j

bfp_i bˆfp_j [cov(Z_ifp, Zfp_j)−cov(Znp_i , Z_jnp)] (5b) where i, j=1. . . n, and n is the number of independent variables in Z. In both Eqs. (5a) and (5b), the first term represents the variance differential attributable to different returns to characteristics in the nonprofit and for-profit sectors. In (5a) these differences are weighted by for-profit characteristics; in (5b) they are weighted by nonprofit characteristics. Similarly, the second term of each equation is the difference attributable to differences in worker characteristics between the sectors, weighted by the returns of either sector.

3.3. Differences in wage variances between two sectors

The variance of actual wages is 0.587 across the entire for-profit sector, and 0.494 across the nonprofit sector. Of these amounts, 0.165 and 0.097, respectively, are attributable to the predicted portion of the wage. The remaining variance in each sector is a function of residual wages.10 The for-profit/nonprofit variance differentials and their decomposition are shown in Table 2, for all workers and by broad occupation and industry categories. All differentials shown represent the variance in the for-profit sector minus the variance in the nonprofit sector; a positive number indicates that variance in the nonprofit sector is lower and that wages in the nonprofit sector are less dispersed than in the for-profit sector. Results are shown for all workers, for white collar and blue collar workers separately, for executive and non-executive white collar workers, and for workers in finance, insurance and real estate, entertainment and recreation services, and professional services.11 All differences are statistically significant at the 0.005 level or higher (F-test). As shown in line 1, actual hourly wages are more tightly clustered in the for-profit sector than in the nonprofit sector across all groups. The size of the differences is also meaningful, with variances in the for-profit sector typically 10–20 percent higher than in the nonprofit sector.

Of course, the distributions of a multitude of characteristics underlie the distribution of actual wages. The variance differential of predicted wages is shown in line 2 of Table 2. Again, the differences are all positive and significant. This explained portion can be decom-posed into its components. Regardless of the set of weights chosen, the largest share of the difference is attributable to differences in returns to characteristics (lines 3a and 3b) rather than to differences in the characteristics themselves (4a and 4b). Finally, in line 5, I show the difference in the variance of residual wages. If the theory discussed above implies behav-ioral differences in wage setting across nonprofit and for-profit institutions, then we would expect this to manifest itself either in the differences in returns to particular characteristics 10_{While not shown here, the magnitude of each type of variance in each sector is comparable across the different}

occupation and industry sub-groups analyzed.

11_{White collar workers are defined as census occupational categories: ‘managerial and professional workers’}

and ‘technical, sales and administrative workers’. Executives are identified as census occupation codes 0–22. The 1-digit industries shown are those in which there is the most significant level of nonprofit/for-profit competition. See Table 3 for nonprofit/for-profit percentage compositions of these industries.

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or in differences in unexplained residual wages. These elements are captured in lines 3a, 3b and 5. Together they comprise most of the observed differences in wage distribution between the sectors.

The pattern across occupations evident in Table 2 is quite striking. The differences are consistently largest among white collar workers, especially among white collar workers classified as executives. For blue collar workers, the differences in actual and predicted wages are relatively small but still positive, but the differences in residual wages become negative — i.e. for blue collar workers the residual wage is more dispersed among nonprofit workers than among for-profit workers. Interestingly, the pattern among white collar workers is strongest among executives. These findings are consistent with the perception that there are significant differences in the way that nonprofit and for-profit executives are compensated. However, this is not the sole explanation for the differences between the sectors: differences still persist down through the ranks of white collar workers.

The lower wage dispersion in the nonprofit sector apparent for white collar workers also holds within industry categories in which there is significant representation in both the nonprofit and for-profit sector. In all three industries shown, actual, predicted and resid-ual wages are less dispersed in the nonprofit sector than in the for-profit sector. Only in finance, insurance and real estate is the variance attributable to the distribution of worker characteristics higher in the nonprofit sector than the for-profit sector.

Table 2 only captures the broadest measures of differences in wage distributions across sectors, however. In order to confirm these findings at a more detailed level, I also make similar calculations within each 3-digit occupation and within important 3-digit industries as well. Here I estimate Eq. (1) within each detailed industry and occupation (once across both nonprofit and for-profit workers) and compare the variance of the residual wages for nonprofit and for-profit workers.12 _{The results are summarized in Table 3 for those} occupa-tions and industries in which there is significant nonprofit and for-profit representation.13 In 48.5 percent of 262 occupations, Var(W¯np)was significantly lower than Var(W¯fp) (follow-ing an F-test with P≤0.05), while it was significantly higher in only 26.3 percent. Similarly, nonprofit wages are less dispersed in 25 out of 31 detailed industries, especially in those industries classified as professional services (where nonprofits are most dominant). The pat-tern varies, however, by broad occupational classification. Nonprofit wages are distinctly more equitable than for-profit wages in managerial and professional occupations and in service occupations, where Var(W¯np)is lower in nearly 70 percent of included occupa-tions. Var(W¯np)is also more likely to be lower among nonprofit workers in technical, sales and administrative occupations. In contrast, there is little difference between the sectors when comparing precision, craft and repair occupations and among operators, fabricators and laborer occupations, Var(W¯np)is higher in over 60 percent of the occupations. These patterns are consistent with the previous findings and with the suggestion that if equity is used to support intrinsic motivation and organizational identification in nonprofit organiza-tions, one would most expect to see it manifested in the wages of white collar employees:

12_{While not separately decomposing the differences due to returns and characteristics, this comparison of residual}

wages will capture the differences in returns and unexplained differences of interest here.

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the employees who have may have the most information about relative wages and whose conduct is most likely to affect organizational outcomes.

3.4. Race and gender wage differences

Wage inequity in the US economy has often taken the specific form of wage differences along race and gender lines (see, for example, Blau and Beller, 1992; Bound and Freeman, 1992). These differences could reflect, among other things, preference based discrimination (i.e. Becker, 1957), statistical discrimination (Aigner and Cain, 1977), or occupational crowding (Bergmann, 1974). The discussion above suggests that the greater apparent need for nonprofit organizations to achieve fairness and neutrality should imply lower levels of race and gender wage discrimination in the wage structure there. I will use the 1990 US PUMS data to investigate this aspect of wage equity in nonprofit and for-profit employment. I estimate an equation similar to Eq. (1) above:

lnW =a+bX+cForprof×Race×Gender+dNonprof×Race×Gender+ε

(6) However, Eq. (6) has been augmented in a number of ways. First, race and gender wage ef-fects are estimated separately in both nonprofit and for-profit organizations. Race×Gender and Nonprof×Race×Gender together represent a series of 19 dummy variables repre-senting all race/gender/organization groups except the comparison group, white males in for-profit organizations. In addition, control variables summarized in X are in some cases more detailed than those included in Z in Eq. (1):14 X includes dummy variables for 46,933 detailed occupation/industry cells,15 367 urban and rural areas,16 lack of fluency in speaking English, part-time work (<25 hours per week), 17 categories of educational at-tainment and type of degree earned, as well as potential experience and potential experience-squared.17

Coefficients in the vector c (c1–c9) represent average wage differences in fprofit or-ganizations between white males and other demographic groups. Coefficients in vector d (d0–d9) can be normalized (as d1−d0, d2−d0, and so on) to similarly represent average wage differences in nonprofit organizations between white males and others. The coeffi-cients c1–c9and the normalized coefficients d1−d0through d9−d0are presented in Table 4. The results are quite striking. In every case, race and gender wage differences are dimin-ished in the nonprofit sector as compared with the for-profit sector. The nonprofit effects are between 22 and 45 percent lower than the for-profit effects. All differences between

14_{The specification in Eq. (1) was computationally limited by the need to calculate a decomposition of predicted}

values and residuals from that specification.

15_{These 46,933 industry/occupation cells constitute the non-empty intersection of 490 occupation and 234 industry}

identifiers.

16_{Dummy variables represent each separate Census MSA/PMSA area as well as the non-MSA/PMSA areas of}

each state.

17_{For an analysis of how detailed occupation/industry cell controls affects the estimates of race and gender wage}

(16)

Table 4

Estimates of race and gender wage differences within sector of employmenta

Wage differences relative to white males own sector For-profit Nonprofit

White men 0 0

Black men −0.155 −0.095b

Hispanic men −0.138 −0.078b

Asian men −0.143 −0.111b

Other men −0.113 −0.062b

White women −0.277 −0.198b

Black women −0.288 −0.186b

Hispanic women −0.310 −0.217b

Asian women −0.303 −0.195b

Other women −0.289 −0.210b

Controls included in specification

Education Yes (17)

Potential experience Yes

Potential experience2 _Yes

MSA/PMSA Yes (367)

Not fluent in English Yes Part-time work status Yes Occupation/industry cells Yes (46933)

N 4145608

R2 _0.358

a_{Private sectors workers in the 1990 PUMS, not disabled or enrolled in school.} b_{For-profit/nonprofit difference significant at 0.05 level or higher (F-test).}

the sectors are statistically significant. These results are supported by similar findings by Preston (1990) and Shackett and Trapani (1987).

As was suggested above, wage equity generated by the motivational needs of the organi-zation might be expected to gain greater expression in the wages of white-collar workers. The analysis presented in Table 4 is repeated by broad occupational classification to fur-ther investigate this contention. The results are displayed in Table 5. The relative race and gender wage equity in nonprofits so apparent in Table 4, is in fact limited to white collar occupations.

3.5. Alternate explanations

While the greater wage equity and the diminished race and gender differences apparent in nonprofit wages are supportive of the view that nonprofits use wage equity to provide appropriate motivational conditions, there are several classes of alternate explanations that must be considered as well.

First, the greater reliance of nonprofit organizations on government funding may increase the requirement that they pursue affirmative action in hiring and promotion. This in turn could account for the diminished race and gender wage differences within nonprofits. A

(17)

Table 5

Estimates of race and gender wage differences within sector of employment by occupation groupa

Wage differences relative to white males own sector Managerial and professional

Technical sales and administrative

Service

For-profit Nonprofit For-profit Nonprofit For-profit Nonprofit

White men 0 0 0 0 0 0

Black men −0.201 −0.047b ₋_0.202 ₋_0.121 ₋_0.110 ₋_0.126

Hispanic men −0.155 −0.093b ₋_0.180 ₋_0.147 ₋_0.061 ₋_0.057

Asian men −0.121 −0.095 −0.207 −0.119b ₋_0.127 ₋_0.165

Other men −0.177 −0.017b ₋_0.154 ₋_0.082b ₋_0.065 ₋_0.052

White women −0.296 −0.193b −0.273 −0.180 −0.207 −0.188b Black women −0.336 −0.187b ₋_0.284 ₋_0.175b ₋_0.225 ₋_0.195b

Hispanic women −0.330 −0.217b ₋_0.309 ₋_0.188b ₋_0.227 ₋_0.261

Asian women −0.319 −0.182b ₋_0.319 ₋_0.203b ₋_0.180 ₋_0.182

Other women −0.358 −0.191b ₋_0.293 ₋_0.191b ₋_0.217 ₋_0.235

Controls included in specification

Education Yes Yes Yes

Potential experience Yes Yes Yes

Potential experience2 Yes Yes Yes

MSA/PMSA Yes Yes Yes

Not fluent in English Yes Yes Yes

Part-time work status Yes Yes Yes

Occupation/industry cells Yes (9859) Yes (12541) Yes (3347)

N 923128 1314486 473564

R2 _0.321 _0.305 _0.184

Farm, forestry and fishing Precision, craft and repair Operators, fabricators and laborers

For-profit Nonprofit For-profit Nonprofit For-profit Nonprofit

White Men 0 0 0 0 0 0

Black men −0.188 −0.252 −0.151 −0.165 −0.107 −0.063b Hispanic men −0.062 0.108 −0.117 −0.088 −0.105 −0.028b Asian men −0.042 −0.202 −0.102 −0.071 −0.125 −0.115 Other men −0.046 −0.089 −0.091 −0.091 −0.082 −0.041 White women −0.244 −0.205 −0.278 −0.255 −0.269 −0.209b

Black women −0.311 −0.313 −0.294 −0.378 −0.279 −0.203b

Hispanic women −0.183 −0.211 −0.385 −0.306 −0.333 −0.265b

Asian women −0.216 −0.305 −0.331 −0.219 −0.325 −0.307 Other women −0.194 −0.292 −0.323 −0.138b ₋_0.280 ₋_0.236

Controls included in specification

Education Yes Yes Yes

Potential experience Yes Yes Yes

Potential experience2 _Yes _Yes _Yes

MSA/PMSA Yes Yes Yes

Not fluent in English Yes Yes Yes

Part-time work status Yes Yes Yes

Occupation/industry cells Yes (617) Yes (9392) Yes (11177)

N 99180 549643 785607

R2 _0.131 _0.280 _0.256

a_{Private sectors workers in the 1990 PUMS, not disabled or enrolled in school.} b_{For-profit/nonprofit difference significant at 0.05 level or higher (F-test).}

(18)

related explanation derives from the fact that nonprofit organizations often rely for revenue on long-term relationships with outside funders. Their continued viability depends in part on their public reputation, which may include their reputation as a fair employer. Thus, reputational considerations could lead nonprofit organizations to pursue a more equitable wage structure, both generally and along the lines of race and gender. However, one would expect either of these explanations to manifest themselves across the occupational structure. This is inconsistent with the limitation of more equitable nonprofit wages to white-collar employees demonstrated above.

A second possible set of explanations for the findings here with regard to male/female wage differences could stem from the occupational mix of the sectors and the effect of occupational segregation on wages. Men in the nonprofit sector are relatively more con-centrated in ‘traditionally female’ occupations than are men in the for-profit sector. The average male nonprofit employee works in an occupation/industry cell that is 79 percent female, while his for-profit counterpart works in an occupation/industry cell that is only 41 percent female.18 As is well known, wages fall as the percent female in an occupation rises (see Leete, 1998b, for a summary of this literature). This effect could cause the wages of men in nonprofits to be relatively lower, and gender wage differences in the nonprofit sector to be diminished. I investigate this contention with three alternate specifications of Eq. (6):

lnW =a+bY+cForprof×Race×Gender+dNonprof×Race×Gender+ε

(6a)

lnW=a+bY+cForprof×Race×Gender

+dNonprof×Race×Gender+ePct Fem+ε (6b)

lnW=a+bY+cForprof×Race×Gender+dNonprof

×Race×Gender+ePct Fem+fPct Fem×Female+ε (6c) In Eq. (6a), the variable vector Y is substituted for the vector X in Eq. (6). Y includes controls for 3-digit occupation and industry categories only. This equation provides a basis for comparison for the next one. In Eq. (6b), the variable Pct Fem is added to the specification to measure the percent of employment in each occupation/industry cell that is female.19 If the results here are in fact due to occupational mix, Eq. (6b) should not generate the gender wage differences shown in Tables 4 and 5. Finally, while nonprofit men are disproportionately employed in ‘traditionally female’ occupations, it is also possible that men earn less in those occupations than comparable women do. This could result from either worse performance by men or discrimination against men in these occupations. To account for this possibility, in Eq. (6c) an additional interaction term, Pct Fem×Female, is added. The results of these three estimations are shown in column (1) through (6) in Table 6. In each specification, the

18_{Author’s calculations from the 1990 PUMS.}

19_{Leete (1998b) discusses how this kind of ‘percent female’ specification differs from specifications that are more}

(19)

Table 6

Estimates of race and gender wage differences within sector of employment controlling for percent female in occupation/industry cella

Wage differences relative to white males own sector

(1) (2) (3) (4) (5) (6)

For-profit Nonprofit For-profit Nonprofit For-profit Nonprofit

White men 0 0 0 0 0 0

Black men −0.160 −0.095b −0.160 0.096b −0.159 −0.095b

Hispanic men 0.144 0.082 0.144 0.083b −0.143 −0.084b

Asian men 0.151 −0.130 0.149 −0.128 −0.148 0.128

Other men −0.118 −0.064b −0.118 −0.066b −0.118 −0.067b White women −0.286 −0.203b ₋_0.276 ₋_0.197b ₋_0.301 ₋_0.227

Black women −0.301 −0.193b ₋_0.291 _0.187b ₋_0.316 ₋_0.218b

Hispanic women −0.322 −0.226b ₋_0.312 ₋_0.220 ₋_0.338 ₋_0.251b

Asian women 0.316 −0.201b ₋_0.307 ₋_0.194b ₋_0.332 ₋_0.225b

Other women −0.301 −0.213b ₋_0.290 ₋_0.207b ₋_0.315 ₋_0.237

Controls included in specification

Education Yes (17) Yes (17) Yes (17)

Potential experience Yes Yes Yes

Potential experience2 _Yes _Yes _Yes

MSA/PMSA Yes (367) Yes (367) Yes (367)

Not fluent in English Yes Yes Yes

Part-time work status Yes Yes Yes

Occupation/industry cells

Occupation Yes (490) Yes (490) Yes (490)

Industry Yes (234) Yes (234) Yes (234)

Percent female in occupation/ industry cell

Yes Yes

Female×percent female in occupation/industry cell

Yes

N 4145608 4145608 4145608

R2 _0.343 _0.343 _0.343

a_{Private sectors workers in the 1990 PUMS, not disabled or enrolled in school.} b_{For-profit/nonprofit difference significant at 0.05 level or higher (F-test).}

relative race and gender wage effects are virtually unchanged from the original findings. Thus, none of these possible explanations account for the existing pattern of race and gender wage differences in nonprofit organizations.

A third group of possible explanations hinges on the crudeness of the measures of human capital used here. Only the most basic measures are included in Census data: e.g. age, education, and language fluency. Of course, if different demographic groups exhibit different levels of unobservable (to the researcher) but productivity relevant characteristics, measures of race and gender wage differences can mistakenly reflect these differences. Here, a relevant possibility is that women and non-whites in the nonprofit sector have higher levels of unmeasured human capital relative to white males, than do women and non-whites in the for-profit sector. If this were the case, it could account for the observed pattern of

(20)

wage differences. This might result if nonprofit firms had more family friendly or less discriminatory policies, allowing women and racial minorities there to accumulate more actual experience than comparable women in the for-profit sector. To empirically test this possibility, I estimate a final set of variants of Eq. (6) in which the returns to potential experience, education and language fluency are allowed to vary first by gender and nonprofit status, and then by race and nonprofit status. The resulting patterns of wage differentials are unchanged from those presented in Table 4.20

Finally, it is possible that the observed wage patterns reflect differences between the nonprofit and for-profit sector that are not measured here. Some authors have suggested that, as a result of the non-distribution constraint, nonprofit managers operate with greater managerial discretion than for-profit managers (e.g. Feldstein, 1971). As discussed above, many suspect that attitudes and preferences differ systematically between the sectors. If this difference in preferences included a greater value being placed on wage equity in the non-profit sector, then the increase in managerial discretion there might be used to accomplish this. It is unclear, however, why a greater desire for equity would gain expression only in white-collar occupations and not throughout the occupational structure. Similarly, differ-ences in the distribution of wages could result from other systematic differdiffer-ences between the sectors. If nonprofit and for-profit organizations adopt and use technology differently, this could have implications for their wage structures, even after controlling for detailed occupations and industries.

4. Summary and implications

A growing literature suggests that wage equity and employee perceptions of employer fairness may be important to developing and maintaining employee motivation. Further-more, theory suggests that because of their unique organizational needs, nonprofit employers will be more likely than for-profit employers to rely on intrinsically motivated employees. If both of these propositions hold, then one would expect nonprofit organizations to ex-hibit more wage equity than for-profit organizations as part of an organizational strategy that must both seek out and maintain employee motivation to a greater degree. The find-ings here are consistent with these ideas: wage equity is more apparent across the earnfind-ings of employees of nonprofit organizations than of for-profit organizations. This is true both across the board, within detailed occupations and industries, and along the lines of race and gender wage differences. These findings are supported by survey data regarding employee perceptions of pay equity in the nonprofit sector, and by limited previous scholarly findings with regard to race and gender wage differentials in the nonprofit sector. The differences in wage structure found here are largest among managerial and professional employees, particularly executives, and diminish as one moves down the occupational structure from white to blue collar occupations. This is consistent with the theoretical expectation that the motivational requirements of nonprofit organizations are most differentiated from those of the for-profit organization at the managerial and white-collar level.

(21)

While a number of other possible explanations for these findings are explored here, they are mainly inconsistent with the results of further tests. Of course, all possible alternate explanations can not be entirely eliminated. For instance, this paper addresses only one aspect of total compensation. Non-wage compensation (benefits) has been virtually unstud-ied in this context. It is quite possible that differences in benefits paid (and differences in their distribution across occupational groups) in the nonprofit and for-profit sectors could compensate for the differences in the wage structure found here.

The nonprofit sector is highlighted here as an important context in which to study the issue of wage structure and worker motivation. However, as the economy at large increasingly comes to resemble the nonprofit sector in some respects, the significance of these issues may spread. Social, educational, health and human services have always dominated the nonprofit sector. Employment in such services is now becoming more prevalent in the for-profit sector of the economy as well. To the extent that the motivational issues highlighted here are in part related to the services produced (and not just to organizational form), their importance will grow. Furthermore, newly emerging industries may face similar motivational issues. In the burgeoning computer programming industry, for instance, work quality can be difficult to monitor and the intrinsic motivation of workers to produce a quality product can be cru-cial to a firm’s success.21 The same may be true in other industries to the extent to which they involve service provision or production technologies which are inherently difficult to monitor and in which product quality depends in large part on employee performance. The shortcoming of this work is the indirect nature of evidence that is based on individ-ual wage records drawn from a national sample. It points to the need for future research conducted at the organization level on the relationship between wage equity and worker motivation.

Acknowledgements

The author gratefully acknowledges financial support from the Aspen Institute Nonprofit Sector Research Fund for research related to this project. This work greatly benefited from comments and suggestions by two anonymous referees, Neil Bania, Bruce Kingma, Rich Parkin, Rich Steinberg, Dennis Young, participants at the annual meetings of ARNOVA, in seminars in the Department of Economics and the Mandel Center at Case Western Reserve University, and members of WINE’99.

Appendix A

A full set of estimated coefficients for each sector is given in Table 7.

21_{It is difficult for an employer to observe a priori that a programmers work is thoroughly bug- and crash-free.}

Witness for example, the hidden inclusion of a full-scale version of Microsoft’s Flight Simulator program as an ‘easter-egg’ in theirexcel_{97 program.}

(22)

Table 7

Estimated coefficients from Eq. (1)a

Variable Nonprofit workers For-profit workers

Coefficient S.E. Coefficient S.E.

Intercept 1.346 0.009 1.410 0.003

Years of education 0.050 0.000 0.057 0.000

Potential experience 0.021 0.000 0.030 0.000

Potential experience2 _0.000 _0.000 _0.000 _0.000

Not fluent in English −0.036 0.010 −0.044 0.002

part-time work −0.027 0.003 0.049 0.001

Urban location 0.202 0.003 0.188 0.001

Race/gender

Black male −0.080 0.007 −0.174 0.002

Hispanic male 0.022 0.011 −0.090 0.002

Asian male 0.035 0.012 −0.065 0.003

Other male −0.041 0.013 −0.075 0.002

White female −0.165 0.003 −0.367 0.001

Black female −0.150 0.005 −0.379 0.002

Hispanic female −0.135 0.010 −0.330 0.003

Asian female 0.010 0.010 −0.288 0.003

Other female −0.213 0.011 −0.341 0.003

1-Digit industry

Agriculture, forestry and fishing −0.044 0.017 −0.175 0.004

Mining 0.245 0.048 0.188 0.004

Construction 0.023 0.012 0.021 0.002

Manufacturing 0.038 0.008 0.096 0.001

Transportation, communication, utilities 0.171 0.007 0.146 0.002

Wholesale trade 0.034 0.015 0.036 0.002

Retail trade −0.169 0.007 −0.190 0.001

Finance, insurance, real estate 0.174 0.005 0.073 0.002

Business and repair services −0.027 0.009 −0.083 0.002

Personal services −0.158 0.014 −0.182 0.002

Entertainment and recreation services −0.087 0.008 −0.151 0.003 1-Digit occupation

Technical, sales and administrative −0.178 0.003 −0.232 0.001

Service occupations −0.367 0.004 −0.453 0.001

Farm, forestry and fishing −0.351 0.015 −0.501 0.004

Precision, craft and repair −0.022 0.007 −0.247 0.001

Operators, fabricators and laborers −0.272 0.007 −0.396 0.001

R2 _0.191 _0.281

N 323521 3822020

a_{Dependent variable: ln hourly wage 1989 (private sectors workers in the 1990 PUMS, not disabled or enrolled}

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Table 6

Estimates of race and gender wage differences within sector of employment controlling for percent female in occupation/industry cella

Wage differences relative to white males own sector

(1) (2) (3) (4) (5) (6)

For-profit Nonprofit For-profit Nonprofit For-profit Nonprofit

White men 0 0 0 0 0 0

Black men −0.160 −0.095b −0.160 0.096b −0.159 −0.095b

Hispanic men 0.144 0.082 0.144 0.083b −0.143 −0.084b

Asian men 0.151 −0.130 0.149 −0.128 −0.148 0.128

Other men −0.118 −0.064b −0.118 −0.066b −0.118 −0.067b White women −0.286 −0.203b ₋_0.276 ₋_0.197b ₋_0.301 ₋_0.227

Black women −0.301 −0.193b ₋_0.291 _0.187b ₋_0.316 ₋_0.218b

Hispanic women −0.322 −0.226b ₋_0.312 ₋_0.220 ₋_0.338 ₋_0.251b

Asian women 0.316 −0.201b ₋_0.307 ₋_0.194b ₋_0.332 ₋_0.225b

Other women −0.301 −0.213b ₋_0.290 ₋_0.207b ₋_0.315 ₋_0.237

Controls included in specification

Education Yes (17) Yes (17) Yes (17)

Potential experience Yes Yes Yes

Potential experience2 _Yes _Yes _Yes

MSA/PMSA Yes (367) Yes (367) Yes (367)

Not fluent in English Yes Yes Yes

Part-time work status Yes Yes Yes

Occupation/industry cells

Occupation Yes (490) Yes (490) Yes (490)

Industry Yes (234) Yes (234) Yes (234)

Percent female in occupation/ industry cell

Yes Yes

Female×percent female in occupation/industry cell

Yes

N 4145608 4145608 4145608

R2 _0.343 _0.343 _0.343

a_{Private sectors workers in the 1990 PUMS, not disabled or enrolled in school.} b_{For-profit/nonprofit difference significant at 0.05 level or higher (F-test).}

relative race and gender wage effects are virtually unchanged from the original findings.

Thus, none of these possible explanations account for the existing pattern of race and gender

wage differences in nonprofit organizations.

A third group of possible explanations hinges on the crudeness of the measures of human

capital used here. Only the most basic measures are included in Census data: e.g. age,

education, and language fluency. Of course, if different demographic groups exhibit different

levels of unobservable (to the researcher) but productivity relevant characteristics, measures

of race and gender wage differences can mistakenly reflect these differences. Here, a relevant

possibility is that women and non-whites in the nonprofit sector have higher levels of

unmeasured human capital relative to white males, than do women and non-whites in

the for-profit sector. If this were the case, it could account for the observed pattern of

(2)

wage differences. This might result if nonprofit firms had more family friendly or less

discriminatory policies, allowing women and racial minorities there to accumulate more

actual experience than comparable women in the for-profit sector. To empirically test this

possibility, I estimate a final set of variants of Eq. (6) in which the returns to potential

experience, education and language fluency are allowed to vary first by gender and nonprofit

status, and then by race and nonprofit status. The resulting patterns of wage differentials

are unchanged from those presented in Table 4.

Finally, it is possible that the observed wage patterns reflect differences between the

nonprofit and for-profit sector that are not measured here. Some authors have suggested

that, as a result of the non-distribution constraint, nonprofit managers operate with greater

managerial discretion than for-profit managers (e.g. Feldstein, 1971). As discussed above,

many suspect that attitudes and preferences differ systematically between the sectors. If this

difference in preferences included a greater value being placed on wage equity in the

non-profit sector, then the increase in managerial discretion there might be used to accomplish

this. It is unclear, however, why a greater desire for equity would gain expression only in

white-collar occupations and not throughout the occupational structure. Similarly,

differ-ences in the distribution of wages could result from other systematic differdiffer-ences between

the sectors. If nonprofit and for-profit organizations adopt and use technology differently,

this could have implications for their wage structures, even after controlling for detailed

occupations and industries.

4. Summary and implications

A growing literature suggests that wage equity and employee perceptions of employer

fairness may be important to developing and maintaining employee motivation.

Further-more, theory suggests that because of their unique organizational needs, nonprofit employers

will be more likely than for-profit employers to rely on intrinsically motivated employees.

If both of these propositions hold, then one would expect nonprofit organizations to

ex-hibit more wage equity than for-profit organizations as part of an organizational strategy

that must both seek out and maintain employee motivation to a greater degree. The

find-ings here are consistent with these ideas: wage equity is more apparent across the earnfind-ings

of employees of nonprofit organizations than of for-profit organizations. This is true both

across the board, within detailed occupations and industries, and along the lines of race and

gender wage differences. These findings are supported by survey data regarding employee

perceptions of pay equity in the nonprofit sector, and by limited previous scholarly findings

with regard to race and gender wage differentials in the nonprofit sector. The differences

in wage structure found here are largest among managerial and professional employees,

particularly executives, and diminish as one moves down the occupational structure from

white to blue collar occupations. This is consistent with the theoretical expectation that the

motivational requirements of nonprofit organizations are most differentiated from those of

the for-profit organization at the managerial and white-collar level.

(3)

While a number of other possible explanations for these findings are explored here, they

are mainly inconsistent with the results of further tests. Of course, all possible alternate

explanations can not be entirely eliminated. For instance, this paper addresses only one

aspect of total compensation. Non-wage compensation (benefits) has been virtually

unstud-ied in this context. It is quite possible that differences in benefits paid (and differences in

their distribution across occupational groups) in the nonprofit and for-profit sectors could

compensate for the differences in the wage structure found here.

The nonprofit sector is highlighted here as an important context in which to study the issue

of wage structure and worker motivation. However, as the economy at large increasingly

comes to resemble the nonprofit sector in some respects, the significance of these issues may

spread. Social, educational, health and human services have always dominated the nonprofit

sector. Employment in such services is now becoming more prevalent in the for-profit sector

of the economy as well. To the extent that the motivational issues highlighted here are in

part related to the services produced (and not just to organizational form), their importance

will grow. Furthermore, newly emerging industries may face similar motivational issues. In

the burgeoning computer programming industry, for instance, work quality can be difficult

to monitor and the intrinsic motivation of workers to produce a quality product can be

cru-cial to a firm’s success.

The same may be true in other industries to the extent to which

they involve service provision or production technologies which are inherently difficult

to monitor and in which product quality depends in large part on employee performance.

The shortcoming of this work is the indirect nature of evidence that is based on

individ-ual wage records drawn from a national sample. It points to the need for future research

conducted at the organization level on the relationship between wage equity and worker

motivation.

Acknowledgements

The author gratefully acknowledges financial support from the Aspen Institute Nonprofit

Sector Research Fund for research related to this project. This work greatly benefited from

comments and suggestions by two anonymous referees, Neil Bania, Bruce Kingma, Rich

Parkin, Rich Steinberg, Dennis Young, participants at the annual meetings of ARNOVA, in

seminars in the Department of Economics and the Mandel Center at Case Western Reserve

University, and members of WINE’99.

Appendix A

A full set of estimated coefficients for each sector is given in Table 7.

21_{It is difficult for an employer to observe a priori that a programmers work is thoroughly bug- and crash-free.}

Witness for example, the hidden inclusion of a full-scale version of Microsoft’s Flight Simulator program as an ‘easter-egg’ in theirexcel_{97 program.}

(4)

Table 7

Estimated coefficients from Eq. (1)a

Variable Nonprofit workers For-profit workers

Coefficient S.E. Coefficient S.E.

Intercept 1.346 0.009 1.410 0.003

Years of education 0.050 0.000 0.057 0.000

Potential experience 0.021 0.000 0.030 0.000

Potential experience2 _0.000 _0.000 _0.000 _0.000

Not fluent in English −0.036 0.010 −0.044 0.002

part-time work −0.027 0.003 0.049 0.001

Urban location 0.202 0.003 0.188 0.001

Race/gender

Black male −0.080 0.007 −0.174 0.002

Hispanic male 0.022 0.011 −0.090 0.002

Asian male 0.035 0.012 −0.065 0.003

Other male −0.041 0.013 −0.075 0.002

White female −0.165 0.003 −0.367 0.001

Black female −0.150 0.005 −0.379 0.002

Hispanic female −0.135 0.010 −0.330 0.003

Asian female 0.010 0.010 −0.288 0.003

Other female −0.213 0.011 −0.341 0.003

1-Digit industry

Agriculture, forestry and fishing −0.044 0.017 −0.175 0.004

Mining 0.245 0.048 0.188 0.004

Construction 0.023 0.012 0.021 0.002

Manufacturing 0.038 0.008 0.096 0.001

Transportation, communication, utilities 0.171 0.007 0.146 0.002

Wholesale trade 0.034 0.015 0.036 0.002

Retail trade −0.169 0.007 −0.190 0.001

Finance, insurance, real estate 0.174 0.005 0.073 0.002

Business and repair services −0.027 0.009 −0.083 0.002

Personal services −0.158 0.014 −0.182 0.002

Entertainment and recreation services −0.087 0.008 −0.151 0.003 1-Digit occupation

Technical, sales and administrative −0.178 0.003 −0.232 0.001

Service occupations −0.367 0.004 −0.453 0.001

Farm, forestry and fishing −0.351 0.015 −0.501 0.004

Precision, craft and repair −0.022 0.007 −0.247 0.001

Operators, fabricators and laborers −0.272 0.007 −0.396 0.001

R2 _0.191 _0.281

N 323521 3822020

a_{Dependent variable: ln hourly wage 1989 (private sectors workers in the 1990 PUMS, not disabled or enrolled}

in school).

References