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Over-education: What influence does the workplace have?
Economics of Education Review 29 (2010) 236–245
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Over-education: What influence does the workplace have? Clive Belfield Department of Economics, Queens College, City University of New York, Flushing, NY 11367, USA
a r t i c l e
i n f o
Article history: Received 6 August 2009 Accepted 7 August 2009 Keywords: Over-education Job satisfaction
a b s t r a c t The wage and job satisfaction impacts for over-educated workers have been welldocumented; yet little attention has been paid to the consequences for firms. In this paper we examine over-education from the perspective of the workplace. Using linked employer–employee data for the United Kingdom, we derive the standard worker-level penalties on wages and job satisfaction. We then show how over-education rates across workplaces adversely influence workplace pay and workplace labor relations. For individual workers who may be at-risk of over-education, we also distinguish between workforce composition effects and workplace labor practices, such as hiring. The effect of over-education on job satisfaction is particularly strong and its effects are evident at the workplace level. Our results suggest that investigations of over-education at the level of the firm are a promising area of inquiry. © 2009 Published by Elsevier Ltd.
1. Introduction The economic literature on how over-education adversely impacts individual workers’ earnings is large. Rubb (2003a, 2003b, Table 1) summarizes 85 separate international estimates of the impact of surplus, required, and under-education on earnings: whereas each required year of schooling raises earnings by 9.6%, surplus years only raise earnings by 5.2%; and specifications with a dummy variable for over-education show a clear negative-signed coefficient. Other research has looked at differential wage impacts: by level of education (e.g. for graduates, Battu, Belfield, & Sloane, 1999; Dolton and Vignoles, 2000); by heterogeneity of skills within education levels (Brynin & Longhi, 2009; Chevalier, 2003); by race (Battu & Sloane, 2004; Lindley, 2009); over time (Bauer, 2002; Rubb, 2003a, 2003b); and within an individual firm (Groeneveld & Hartog, 2004). In addition, a strong link between overeducation and job dissatisfaction has been identified (e.g., Rumberger, 1981). Studies have also found that various definitions of over-education may be used and that alternative
E-mail address: Clive.Belfi[email protected]. 0272-7757/$ – see front matter © 2009 Published by Elsevier Ltd. doi:10.1016/j.econedurev.2009.08.001
specifications (ORU versus dummy variable models) yield consistent conclusions (Battu et al., 1999; Rubb, 2003a, 2003b). Thus, we can be confident that over-education is an important determinant of earnings and job satisfaction. However, almost all these studies have examined individual determinants of over-education; very few have examined how firm-specific characteristics influence overeducation. In their paper on over-education in this Journal in 1985, Henry Levin and Mun Tsang emphasized the management–labor relationship and how managers must use hiring, supervision, and incentive schemes to ensure that labor inputs are optimally productive. They also observed that “the welfare of workers is tied to the welfare of firms. If over-education leads to a decline in the productivity of workers, firms also suffer in their pursuit of profits” (p. 101). Profit-maximising firms might, therefore, wish to identify workforce characteristics and labor practices that either minimize the incidence of over-education and or alleviate its effects on earnings and job satisfaction. Since 1985, however, little empirical research has been performed on whether firms do this or whether it matters. This paper is one attempt to fill in this gap. We use data on samples of workers across multiple workplaces, along with workplace-specific data, from the United
C. Belfield / Economics of Education Review 29 (2010) 236–245 Table 1 Frequencies for key variables. Mean (SD) Individual variables: Skills ‘much higher’ than required Skills ‘somewhat higher’ than required Satisfied or very satisfied with work itself Content with job (all or most of time) Ln(Pay per hour) Worker-level observations
21.1% 31.9% 71.1% 36.8% 2.19 (0.56) 21,620
Workplace-level variables: Percent skills ‘much higher’ than required Management–labor harmony very good Workers share the values of organization % days lost to absenteeism during last year (BC skew) % quit in last year (BC skew) Ln(average workforce pay): worker survey Ln(average workplace pay): manager survey Workplace-level observations
21.4% 36.1% 18.1% 15.4% 10.0% 2.14 (0.38) 2.26 (0.45) 1,463
Workplace-level labor practices: Aptitude tests for hiring Competency tests for hiring New worker induction program All workers appraised Recognized union Preferential internal hiring Worker-level training Workplace-level observations
36.6% 65.3% 91.4% 46.3% 55.0% 29.2% 18.9% 1,463
Kingdom Workplace Employee Relations Survey of 2004. First, we demonstrate the consensus worker-level relationships between over-education and penalties to earnings and job satisfaction. Then, we examine whether these over-education penalties are evident at the workplacelevel; we find evidence for both penalties. Broadly, we find that workplace attributes are influential in identifying the consequences of over-education and in determining its impacts. Next, we examine the determinants of overeducation, focussing on workplace-specific attributes. 2. Theory Most research on over-education adopts the perspective of the individual worker. Given the wage penalty and the lower job satisfaction, it is plausible to regard overeducation as a sub-optimal outcome for worker. Given public subsidies for school and college, over-education is also sub-optimal for society. However, firms employ workers and so it is important to determine to what extent over-education is sub-optimal for the firm too. As pointed out by Levin and Tsang (1985), firms run the risk of lower profits as a result of poor deployment of workers. These lower profits arise primarily because of lower worker effort (higher absenteeism, etc.) induced by low job satisfaction. Certainly, for individual workers the evidence of over-education reducing job satisfaction is very strong; correspondingly, the evidence of low job satisfaction reducing worker effort is strong (see the mass of evidence in Rubb, 2003a, 2003b). But there are other reasons why over-education may impair profits. If workers’ skills do not match their jobs, they may need training which companies may have to subsidize. Also, over-educated workers may be more likely to quit: frequent hiring adds
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to firms’ personnel recruitment costs. If pay is set according to a formula, such as a worker’s credentials, then an over-educated worker may be overpaid relative to some of their job requirements. Finally, over-educated workers may impose negative externalities on co-workers, either undermining workplace morale or influencing workplace norms about effort. Potentially, over-education may be associated with lower profits because it reflects poor labor management decisions by the firm’s managers. Nevertheless, this still implies that firms would prefer lower rates of over-education. The counter-argument is that a firm could hire overeducated workers and adjust their pay to reflect any lower effort. Also, firms may have only a few workers who are over-educated and so not notice any adverse effects in terms of profit, norms of worker/workplace effort, or absenteeism. At best, then, companies might not care if their workers are over-educated. Importantly, either argument implies that firms would want to understand why workers might be over-educated. If such workers reduce profits, firms will want to avoid hiring or retaining them. If such workers do not reduce profits, firms will want to identify them so they can be paid accordingly. Yet, identifying such workers should incorporate the characteristics of the firm and not just those of the worker. In a sense, workers are only over-educated when they are given tasks at the workplace. Almost exclusively thus far, explanations of over-education have used either search theory or human capital theory from the perspective of the worker (Hartog, 2000).1 Here, we interpret these from the perspective of the firm. Under search theory, initial mismatch occurs but is temporary as the worker is redeployed or promoted to a position with appropriate skill requirements. The workerlevel test is on experience: more experienced workers should have lower rates of over-education (tenure effects are ambiguous, see Hartog, 2000). However, several workplace tests are also possible. First, over-education will be more prevalent where firms have weak hiring systems: if the firm does not properly check for worker capabilities, initial mismatches will be more prevalent. Second, overeducation will be more prevalent where the firm has rigid worker deployment over time: any initial mismatch will be perpetuated. Third, over-education may be a function of the workforce composition: if the firm requires relatively few highly skilled workers or hires mostly part-time or shift workers, few can be promoted to positions commensurate with their education. (On the importance of the internal labor market for rates of over-education, see Groeneveld & Hartog, 2004). Under human capital theory, over-educated workers accumulate skills that can then be used to switch to a higher
1 Assignment theory is a third alternative explanation for overeducation. With assignment theory, both supply and demand functions should be modelled simultaneously. Workers obtain jobs based on their rankings within the skill hierarchy relative to the available positions. If there are too many workers for a specific position, some will be overeducated and assigned jobs lower down the hierarchy. However, absent a structural model of the economy, few direct tests of this theory are possible (but see Hartog, 2000, pp. 142–144).
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level occupation or position.2 The worker-level test looks at wage growth to see if this is faster (de Oliveira et al., 2000). A workplace test would focus on the availability of training: where training options are more likely, over-educated workers can acquire firm-specific skills that complement their formal education and so progress toward higher paid positions. We test all of these arguments below using our linked employer–employee dataset for the U.K. We note here that the dataset is a cross-sectional survey. As such, only correlational relationships can be reported (as with the majority of earlier studies). However, we can control for a substantial array of worker and workplace-level characteristics. When we are examining coworker effects, it is more plausible to assume that coworkers’ behavior is independent: they are not hired jointly with their over-educated colleagues. Also, we are able to compare the relative strength of each correlation to see, for example, whether firm’s hiring practices are more influential than firm’s deployment practices. Finally, for a number of variables we have multiple measures, based on responses by managers and workers; we can therefore perform several tests for the same underlying relationship. We begin by using employee-level data to test for the influence of over-education on job satisfaction and wages.3 We denote OE, W, and JS as over-education, wages, and job satisfaction respectively; and subscript i refers to variables measured at the level of the individual worker and subscript j refers to variables aggregated to the workplace level. In accordance with the substantial evidence base, we anticipate Wi /OEi < 0 and JSi /OEi < 0. In each case we control for a full set of worker-level characteristics and a full set of workplace-level characteristics. Next we use workplace-level data to test for the influence of aggregate over-education across all workers at the firm. Specifically, we test for effects on workplace wages and satisfaction, but we also have two measures of labor effort (EF): absenteeism and the quit rate. In symmetry with the worker-level results, we anticipate: Wj /OEj < 0 and JSj /OEj < 0; we also expect EFj /JSj > 0 and EFj /OEj < 0. Here, we control for a set of workplace-level characteristics. In addition, workplacelevel data allows us to test more relationships. These tests help us to get closer to the issue of whether over-education harms firm profits. Lastly, we consider the determinants of over-education both at the worker and workplace level. We anticipate that over-education (OEi and OEj ) will be lower where firms have: more rigorous hiring practices; more flexible deploy-
2 The specific mechanism by which intra-firm opportunities may ameliorate over-education is unclear. Robst (1995) finds that the overeducated are more likely to be promoted; Rubb (2003a, 2003b) finds that three-quarters of over-educated workers will still be over-educated one year later. Our investigation is on the firm characteristics associated with more opportunities. 3 A worker-level measure of effort is not easily identifiable. We experimented with hours of work conditional on pay but the results were highly sensitive to model specification. Less satisfied workers do postsignificantly fewer hours, but the sign and size of the over-education coefficient was not robust.
ment rules; fewer part-time or shift workers; and greater availability of training programs. 3. Data and basic relationships 3.1. Workplace employee relations survey 2004 The WERS 2004 surveys are nationally representative of workplaces with more than 5 employees (for full information, see Kersley et al., 2006). A survey was administered to the manager of each workplace, with questions on workforce composition, personnel management, dispute procedures, unionization, and recruitment and training of workers. The total sample of workplaces is 2295 (a response rate of 64%). A second survey was administered to a random sample of up to 25 workers at each workplace. This survey included questions on earnings, job quality, and personal characteristics. The total sample of workers is 22,451 (a response rate of 61%). The two surveys are linked through a workplace identifier. Weights are available for each survey and applied in the regression equations. The WERS 2004 is part of a series of harmonized workplace surveys administered periodically in the United Kingdom; earlier versions have yielded a large body of evidence on the management operations of firms (Millward et al., 2000). The linked workplace and worker survey was introduced in WERS 1998, allowing for investigation of how workplace attributes influence workers and vice versa (e.g., on intraworkplace human capital externalities, see Battu et al., 2003). However, the WERS 2004 is the first to ask workers specifically about the match between their skills and their job. It is therefore the first opportunity to investigate how workplace characteristics influence over-education levels within the U.K. context. We merge the worker and workplace surveys and examine relationships at both levels of aggregation. When examining effects aggregated to the workplace we typically have two sources of information. The first is the averages of the worker responses, including worker averages for over-education, for job satisfaction, and pay. (To militate against collinearity, we exclude workplaces where fewer than five workers responded to the survey). The second source of information is the manager, who reports workplace-level pay and labor relations quality independently from the worker respondents.4 Table 1 reports data on the key dependent and independent variables at the worker and workplace level. In the worker survey of WERS 2004, workers are asked about whether their skills are higher than required for their job, with possible responses of ‘much higher’, ‘somewhat higher’ and ‘appropriate’ (very few workers respond ‘less than appropriate’). As shown in Table 1, over half of all workers reported having higher skills than their jobs required: 21% reported having much higher skills and 32% reported somewhat higher skills. We have two measures of job satisfaction: the first shows that ‘satisfaction with
4 The manager is asked to report the number of workers (aged 22 or over) in each of four pay bands. From these bands and proportions we calculate an average workplace pay.
C. Belfield / Economics of Education Review 29 (2010) 236–245
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Table 2 Influence of over-education on workers’ log hourly pay, work satisfaction and job contentment. Skills compared to requirement:
Public sector (1)
Log hourly pay Higher
Public sector (2)
−0.0261 (0.0118)** −0.0794 (0.0148)*** 0.0042 (0.0141) 0.39 7840
‘Somewhat higher’ 0.39 7840
Satisfied or very satisfied with work itself Higher
−0.0651 (0.0110)*** −0.1399 (0.0170)*** −0.0244 (0.0131)* 8170
‘Somewhat higher’ 8170
Content with job all of the time or most of the time Higher −0.0436 (0.0131)*** ‘Much higher’
0.39 13,740
−0.1513 (0.0130)*** −0.0027 (0.0111) 14,197
14,197 −0.0307 (0.0101)***
−0.0921 (0.0171)*** −0.0148 (0.0148) 8170
‘Somewhat higher’ Observations
−0.0579 (0.0122)*** −0.0218 (0.0109)* 0.39 13,740
−0.0692 (0.0216)***
‘Much higher’
Observations
Private sector (4)
−0.0363 (0.0096)***
‘Much higher’
R-Squared Observations
Private sector (3)
8170
−0.0738 (0.0126)*** −0.0017 (0.0115) 14,197
14,197
Notes: Log hourly pay estimated using OLS. Job satisfaction and job contentment estimated using weighted probit; marginal effects are reported. Robust standard errors in parentheses. Specifications control for: male; tenure; tenure squared; marital status; minority; education (5 dummy variables for highest qualification); age (6 bands); occupation (9 SOC); unionised worker; industry (3 in public sector; 9 in private sector); and constant term. *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
the work itself’ is indicated by 71% of all workers; but the second shows that ‘content with job (all or most of the time)’ is indicated by 37%. As controls, we also have data on sex, tenure, marital status, minority status, education, occupation, age, union status, and sector.
At the workplace-level, Table 1 reports the frequencies for the measures of job satisfaction, effort and wages. As reported by the manager, 36% of workplaces have ‘very good’ management–labor harmony and 18% believe ‘workers share the values of the organization’. Two measures of
Table 3 Influence of over-education on workers’ log hourly pay, work satisfaction and job contentment (controlling for firm-specific characteristics). Model specification
Effect of skills being ‘much higher’ than required on: Log hourly pay
Satisfaction with work itself
Job contentment
Public sector
Private sector
Public sector
Private sector
Public sector
Private sector
[1] Firm-specific controls for workforce composition, market structure, and organizational size
−0.0735
−0.0402
−0.1241
−0.1563
−0.0696
−0.0716
(0.0126)***
(0.0092)***
(0.0136)***
(0.0102)***
(0.0141)***
(0.0097)***
[2] Firm-specific controls as per model [1] plus controls for firm labor practices
−0.0721
−0.0402
−0.1219
−0.1530
−0.0667
−0.0692
(0.0126)***
(0.0092)***
(0.0136)***
(0.0102)***
(0.0142)***
(0.0098)***
[3] Wage equation: firm-level fixed effects; Satisfaction equations: control for pay and pay satisfaction
−0.0631
−0.0300
−0.0974
−0.1345
−0.0401
−0.0525
(0.0124)***
(0.0090)***
(0.0137)***
(0.0104)***
(0.0149)***
(0.0102)***
Firm-level observations Worker-level observations
566 7840
1,166 13,740
566 7532
1,166 13,301
566 7532
1,166 13,301
Notes: Weights applied with robust standard errors in parentheses. Log hourly pay estimated using OLS. Job satisfaction and job contentment estimated using weighted probit; marginal effects are reported. All model specifications include controls for: male; tenure; tenure squared; marital status; minority; education (5 dummy variables for highest qualification); age (6 bands); occupation (9 SOC); industry (3 in public sector; 9 in private sector). *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
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C. Belfield / Economics of Education Review 29 (2010) 236–245 Table 4 Influence of over-education on workplace average pay, relations and productivity. Effect of percent over-educated at workplace
(1) Log average workplace pay: From worker sample From managers’ workforce statement (2) Workplace harmony reported by workers: Mean of job satisfaction scores Mean of job contentment scores (3) Workplace relations reported by managers: Management–labor harmony very good Workers share the values of organization (4) Workplace effort: Percent workers absent during last year Percent quit in last year Firm-level observations
Public sector
Private sector
−0.2806 (0.1244)** −0.2019 (0.0853)**
−0.2918 (0.1392)** −0.1858 (0.0898)**
−0.2632 (0.0683)*** −0.2208 (0.0812)***
−0.2504 (0.0721)*** −0.2187 (0.0719)***
−0.2840 (0.2576) −0.0227 (0.2259)
−0.5235 (0.1983)*** −0.5280 (0.1493)***
−0.4548 (0.7651) −0.0214 (0.3436)
0.2448 (0.5199) 0.5329 (0.2190)**
515
948
Notes: Weighted estimation with robust standard errors in parentheses. Panels (1), (2), and (4) apply OLS estimation; panel (3) reports marginal effects from a probit estimation. All model specifications include controls for: age of firm; UKownership; single firm; percent female; percent part-time; percent manual; shiftwork; labor costs (3); competition (2); workplace size; and labor practices. Number of observations for absenteeism rate: 388 public, 803 private. Number of observations for quit rate: 376, 783. *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
labor effort are available: percentage of days lost to absenteeism during the previous year; and the percentage of workers who quit in the previous year. Also, two measures of workforce pay are created, based on the managers’ reports and the averages of the worker respondents. Table 1 also shows the range of labor management practices are implemented across workplaces. These are divided into practices that influence hiring and those that influence deployment of workers. Hiring practices are measured by whether the firm has hiring tests for aptitude (37%) and competence (65%) as well as induction programs (91%). Deployment practices are captured by whether the workplace performs appraisals (46%), has a recognized union (55%), and uses internal hiring to re-assign workers (29%). Finally one-in-five workplaces offer training programs. 3.2. Basic relationships First, we estimate a simple OLS worker-level wage equation which includes a dummy variable for overeducation (controlling for a vector of worker characteristics including education levels and occupation). The results are given in the top panel of Table 2. In both the public and private sectors, over-educated workers have lower earnings than appropriately educated workers (Wi /OEi < 0). However, the wage penalty is associated only with those who report having skills much higher than required (columns 2 and 4). For workers with skills ‘some-
what higher’ than required, there is no observable wage disadvantage. Next, we estimate simple probit equations for job satisfaction. These results – reported as marginal effects – are given in the bottom two panels of Table 2. Similarly, the results in Table 3 correspond with the extant evidence. Using a measure either of satisfaction with work itself or of contentment with the job, we find that the coefficient on over-education is strongly negative (JSi /OEi < 0). Again, this effect is driven almost exclusively by those workers whose skills and actual job are very badly matched. The above results are consistent with an argument derived from Chevalier (2003): genuine identification of over-education should consider both the wage penalty and the mediating links with job satisfaction. Therefore, in analysis below we focus on the 21% of workers who report having skills much higher than required and classify them as ‘over-educated’. In fact, this proportion is very close to Lindley’s (2009, Table 1) estimate of 23% and Brynin and Longhi’s (2009, Table 1) combined estimate of 17%. For these workers, the gaps are very large both for wages and job satisfaction. In the public sector the wage penalty is 7.9%; and in the private sector it is 5.8%. Similarly, in the public sector the predicted percentage of over-educated workers who are satisfied with work is 65%; for appropriately educated workers the prediction is 78%. The respective gap for the private sector is even larger, at 56% versus 73%.
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4. Over-education and the workplace
4.2. The impacts of over-education across workplaces
4.1. Penalties for over-education across workers
Now we examine whether the consequences of overeducation – both in terms of pay and harmony – are evident at the workplace-level. Based on the worker responses, we calculate the ‘percent over-educated’ as the average probability of being over-educated within the workplace. This variable represents the proportion of workers at each workplace who are over-educated. The correlation between this workplace proportion and workplace average pay and workplace harmony are given in Table 4 (see the table notes for controls). The proportion of over-educated workers has a strongly negative impact on average earnings across a workplace, both in the public and private sectors (Panel 1). The result is evident using two measures of average workplace earnings. The first measure is the average pay of all the workers who responded to the survey; this is a weak test because we already know that those individuals who report being over-educated have lower pay (Table 2). The second measure is the average pay at the workplace as reported by the manager; this is a much stricter test as it is based on a report for all the workers at the firm, regardless of whether they were surveyed or not. The effect is substantive: going from a firm with no over-educated workers to one where all workers are over-educated would reduce everyone’s pay by between 13% and 26%. This result is strongly suggestive of negative externalities from having over-educated co-workers: all workers have lower pay; also, the wage penalty is larger than the effect per individual worker.5 It is also weakens the likelihood that lower worker productivity arising from over-education is fully reflected in the individual worker’s wages. Similarly, the effects of over-education on job satisfaction are identifiable at the workplace (Panel 2). Based on the mean satisfaction with work and job contentment scores of the worker samples, higher proportions of over-educated workers mean lower workplace morale. Again, these are weaker tests because they are aggregations of worker-level specifications. More compellingly, managers themselves are able to identify adverse workplace consequences in terms of morale from over-education (Panel 3). Based on two questions to the managers, there is a strong negative correlation between percent over-educated and management–labor relations and whether managers believe workers share the values of the organization. The effect is strong in both sectors, although it is only statistically significant in the private sector. Finally, we employ two additional tests to see whether over-education has deleterious consequences in terms of worker effort for workplaces (Panel 4). Our measures of effort are workplace absenteeism rates and the quit rates (as reported by the manager).6 The results are statistically significant only for the private sector quit rate, where
Here we identify if workplace characteristics and labor practices affect the wage and job satisfaction penalties for over-education across workers. For each penalty, we estimate two common models: [1] includes controls for workplace characteristics; and [2] includes controls for worker characteristics and labor practices. In addition, we include an extra model [3] with specifications that vary across the wage and job satisfaction equations. The first column of Table 3 reports the workers’ wage penalty. Model [1] includes workplace characteristics and model [2] adds workplace labor practices as well. The wage penalty falls to 4–7%, compared to 6–8% without these controls. Next we estimate a simple workplace fixed effects specification; given in column 3, this reduces the wage penalty to 3–6%. Thus, firm attributes are driving one-quarter to one-half of the over-education wage penalty. Parallel estimations of models [1] and [2] are reported for satisfaction with work and job contentment. Being over-educated is associated with a 12-point or 15-point reduction in satisfaction with work; this effect is almost unchanged when we introduce controls for workplace attributes. Being over-educated is associated with a 9point reduction in job contentment in the public sector (Table 2 above); controlling for workplace characteristics and practices, this figure falls to 7. In the private sector, the role of the workplace is more muted: job contentment is unchanged after we control for workplace characteristics. For job satisfaction, we estimate a model [3] where we explicitly control both for wages and satisfaction with wages. Over-educated workers report lower pay, so we might expect them to report lower job satisfaction. However, when we control for own pay, over-education still has a negative effect on job satisfaction. Yet, this does not fully address the issue of whether the worker is paid less than their marginal product. As noted above, we cannot measure labor productivity independently and so cannot tell whether these workers are ‘underpaid’. Our partial solution is to control for whether a worker feels underpaid; this feeling might be interpreted as a divergence between marginal product and pay. Model [3] reports the impact of over-education on job satisfaction, controlling for actual pay and satisfaction with pay (as well as variables from Model [2]). The coefficient for over-education is still statistically significant and strongly negative: even controlling for own pay and for satisfaction with pay, over-education exerts a strongly negative effect on both satisfaction with the work and job contentment. Overall, workplace differences do appear to offset some of the labor market consequences of over-education. Nevertheless, as found by Groeneveld and Hartog (2004) we reject the argument that over-education penalties are exclusively due to firm fixed effects. In particular, workplace morale is adversely affected by overeducation, even after adjustment for the lower wage payments.
5 To repeat, we cannot determine whether in fact poor managerial quality is responsible for both the higher rates of over-education and the lower wages. 6 The absenteeism rate is measured as the percentage of work days lost in the last 12 months. The quit rate is the proportion of workers who
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Table 5 Determinants of over-education across workers. Public sector workers
Worker characteristics: Male Minority Graduate degree
A-levels O-levels CSE/GCSE (high) CSE/GCSE (low) Unionised worker Disability Workplace characteristics: Shiftworkers % workers part-time % workers manual Age of firm UK-owned Single workplace % workers female Labor costs <25% Labor costs 25–50% Labor costs 50–75%
(2) Model (1) and workplace attributes
(3) Model (2) and labor practices
(1) Worker
(2) Model (1) and workplace attributes
(3) Model (2) and labor practices
0.0817 (0.0143)*** 0.0866 (0.0243)*** 0.0594 (0.0245)** 0.0710 (0.0165)*** 0.0248 (0.0182) 0.0175 (0.0180) −0.0150 (0.0127) −0.0084 (0.0164) −0.0005 (0.0111) 0.0528 (0.0175)***
0.0819 (0.0158)*** 0.0761 (0.0248)*** 0.0460 (0.0250)* 0.0769 (0.0172)*** 0.0261 (0.0188) 0.0229 (0.0186) −0.0149 (0.0130) −0.0109 (0.0166) 0.0028 (0.0118) 0.0558 (0.0178)***
0.0816 (0.0158)*** 0.0764 (0.0250)*** 0.0478 (0.0252)* 0.0782 (0.0173)*** 0.0264 (0.0188) 0.0250 (0.0187) −0.0135 (0.0130) −0.0128 (0.0165) 0.0023 (0.0119) 0.0544 (0.0177)***
0.0565 (0.0098)*** 0.0353 (0.0190)* 0.0869 (0.0257)*** 0.0654 (0.0139)*** 0.0395 (0.0159)** 0.0319 (0.0175)* 0.0223 (0.0107)** −0.0215 (0.0133) 0.0325 (0.0108)*** 0.0592 (0.0144)***
0.0644 (0.0107)*** 0.0274 (0.0191) 0.0968 (0.0263)*** 0.0724 (0.0144)*** 0.0358 (0.0161)** 0.0361 (0.0181)** 0.0244 (0.0109)** −0.0179 (0.0136) 0.0211 (0.0112)* 0.0586 (0.0145)***
0.0669 (0.0108)*** 0.0278 (0.0190) 0.0971 (0.0264)*** 0.0735 (0.0144)*** 0.0369 (0.0162)** 0.0360 (0.0180)** 0.0237 (0.0109)** −0.0176 (0.0136) 0.0161 (0.0121) 0.0588 (0.0145)***
0.0397 (0.0126)*** 0.0293 (0.0295) 0.0424 (0.0526) −0.0000 (0.0001) −0.0006 (0.0241) −0.0030 (0.0172) −0.0156 (0.0388) −0.0490 (0.0250)* −0.0054 (0.0193) −0.0064 (0.0123)
0.0434 (0.0139)*** 0.0219 (0.0296) 0.0449 (0.0516) −0.0001 (0.0001) 0.0041 (0.0250) 0.0014 (0.0177) −0.0126 (0.0391) −0.0414 (0.0263) 0.0047 (0.0202) −0.0051 (0.0120)
0.0320 (0.0100)*** 0.1145 (0.0270)*** 0.0749 (0.0177)*** −0.0001 (0.0001) 0.0035 (0.0099) −0.0207 (0.0126)* −0.0426 (0.0280) 0.0251 (0.0135)* 0.0103 (0.0125) 0.0094 (0.0142)
0.0305 (0.0106)*** 0.1045 (0.0272)*** 0.0715 (0.0178)*** −0.0001 (0.0001) 0.0011 (0.0100) −0.0235 (0.0126)* −0.0314 (0.0283) 0.0259 (0.0136)* 0.0107 (0.0125) 0.0112 (0.0142)
C. Belfield / Economics of Education Review 29 (2010) 236–245
College degree
Private sector workers
(1) Worker
Few competitors Many competitors
0.0334 (0.0294) 0.0322 (0.0265)
7847
−0.0295 (0.0124)** 0.0358 (0.0142)** 0.0085 (0.0216) −0.0184 (0.0110)* −0.0156 (0.0128) 0.0039 (0.0175) 7847
Labor practices: Worker-specific training Recognized union Induction program All workers appraised Hiring tests Prefer internal hiring Observations
8170
14,197
−0.0245 (0.0135)* −0.0143 (0.0135)
−0.0212 (0.0135) −0.0104 (0.0136)
13,744
−0.0484 (0.0115)*** 0.0151 (0.0119) −0.0296 (0.0170)* −0.0009 (0.0092) −0.0208 (0.0106)* −0.0008 (0.0092) 13,744
Notes: Probit estimation with robust standard errors in parentheses. Marginal effects reported. All specifications also include: industry (3 in public sector; 9 in private sector); age (6 bands); marital status; children; worker tenure; occupation (9 SOC); organizational size; number of employees; and constant term. *** Significant at p < 0.01. ** Significant at p < 0.05. * Significant at p < 0.1.
C. Belfield / Economics of Education Review 29 (2010) 236–245
0.0296 (0.0286) 0.0289 (0.0258)
243
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C. Belfield / Economics of Education Review 29 (2010) 236–245
over-education is associated with a higher quit rate.7 These results affirm our presumption that over-education will likely impair firm profitability by raising personnel costs.
Table 6 Determinants of percent over-educated across workplaces.
Shiftworkers
4.3. Determinants of over-education across workers % workers part-time
Given these effects, it is important to identify firmspecific influences on over-education. We begin with individual worker-level analyses. Table 5 reports estimation of the determinants of over-education, splitting the sample of workers into public and private sectors. Model (1) follows the literature and specifies over-education purely as a function of worker characteristics. Notably, over-education rates are consistently higher among male workers and workers with disabilities. (Although not reported in Table 5, but included in each specification, occupations are highly statistically significant). In the public sector, minority workers also report more over-education, but this is not the case in the private sector. Although the incidence of over-education rises with education levels, this gradient is particularly steep in the private sector. Also, model (1) indicates that unionized workers in the private sector are more likely to report being over-educated; the insignificant effect in the public sector may occur because of the very high rates of unionization. Finally, tenure at the workplace is not statistically significant (not reported). This suggests that workers do not become more appropriately matched over time, lending support to theories of overeducation through hiring rather than labor deployment. Model (2) supplements the worker model with workplace characteristics which capture the composition of the workforce. Across both the public and private sector, workplaces which rely heavily on shiftworkers have higher levels of over-education. In the private sector, workplaces with more part-time workers and more manual workers also report more over-education. However, other variables are not significant: these include the competitiveness of the market, the capital–labor ratio, and the size of the enterprise (not reported). Model (3) introduces labor practices which might be anticipated to ameliorate over-education. There is modest evidence for each practice. Hiring tests and induction programs are associated with lower over-education (most clearly in the private sector). Appraisal programs play some role in the public sector, but the effect is only statistically significant at the 10% level. More clearly, workers who receive more training report lower levels of over-education. However, preferences for internal ‘hiring’, which is best interpreted as promotion or redeployment, is not statistically significant. Notably, a workplace with a recognized union strongly raises over-education in the public sector, even conditional on the workers’ own union status. This suggests that union skill demarcation rules may impair firms’ ability to reduce over-education levels over time.
stopped working at the firm over the last year. Because the rates are truncated at zero, we apply Box–Cox transformation so that the skewness is set at zero. 7 However, in sensitivity testing, the absenteeism rate in the private sector was also found to be higher with more over-educated workers. Details available from the author.
% workers manual Age of firm UK-owned Single workplace % workers female Labor costs <25% Labor costs 25–50% Labor costs 50–75% Few competitors Many competitors Labor practices: Worker-specific training Recognized union Induction program All workers appraised Hiring tests Prefer internal hiring R-Squared Observations
Public sector
Private sector
0.0091 (0.0271) −0.0544 (0.0540) 0.1378 (0.0867) −0.0001 (0.0001) 0.0112 (0.0379) −0.0131 (0.0329) −0.0439 (0.0581) 0.0765 (0.0884) −0.0404 (0.0323) −0.0288 (0.0240) 0.0253 (0.0437) 0.0718 (0.0353)**
0.0251 (0.0244) 0.1465 (0.0423)*** 0.0718 (0.0329)** 0.0000 (0.0002) −0.0057 (0.0276) −0.0795 (0.0211)*** −0.1562 (0.0498)*** 0.0260 (0.0254) 0.0057 (0.0281) 0.0045 (0.0265) 0.0043 (0.0261) 0.0244 (0.0271)
−0.0578 (0.0817) 0.0515 (0.0268)* 0.0473 (0.0323) −0.0651 (0.0229)** −0.0146 (0.0252) −0.0638 (0.0375)*
−0.0624 (0.0617) 0.0368 (0.0230) −0.0320 (0.0242) 0.0142 (0.0199) −0.0275 (0.0174) −0.0040 (0.0176)
0.20 515
0.21 948
Notes: Probit estimation with robust standard errors in parentheses. All specifications also include: industry (3 in public sector; 9 in private sector); organizational size (2); workplace size; and constant term. * Significant at p < 0.1. ** Significant at p < 0.05. *** Significant at p < 0.01.
In summary, there is some evidence that firm-specific labor practices do influence the level of over-education across workers controlling for workers’ own characteristics. These practices tend to be associated with initial hiring and deployment decisions, although on-the-job training and appraisal programs also have some modest influence. 4.4. The determinants of over-education across workplaces Finally, we turn to identification of over-education rates across workplaces. Theory suggests that over-education will be affected by the usage of specific labor market practices and by workforce composition, but not by general features of the product market unrelated to labor markets. These relationships are tested by regressing the percent over-educated at the workplace against workplace char-
C. Belfield / Economics of Education Review 29 (2010) 236–245
acteristics (taken from the managerial survey). Results are reported in Table 6. For private sector enterprises there is some support for workforce composition influences: rates of over-education are higher where workplaces have more part-time workers, more manual workers, and more male workers. In contrast, structural and market characteristics are not important: the age/size of the workplace, the market competitiveness, and the capital–labor ratio are all insignificant predictors. However, labor market practices do not appear to influence over-education within private sector workplaces. For public sector workplaces, there are very few predictors of over-education: labor force compositional effects are weak, as are structural and market characteristics. However, there is some evidence of the importance of labor practices: unionization rates increase over-education, whereas worker appraisal programs and internal hiring programs reduce it. 5. Conclusions In their analysis of over-education, Levin and Tsang (1985) observed that, with high levels of over-education, firms suffer as well as the workers themselves. Since then, little attention has been paid to whether that observation has merit. In our investigation using the WERS 2004 from the United Kingdom, we can affirm that it does. As the rate of over-education rises, firms do make lower wage payments but, even controlling for wages and expectations of wages, they still report significantly worse workplace harmony. Moreover, workplace characteristics – both workforce composition and labor practices – do influence over-education prevalence, although the relationship is much more easily discernible at the level of the individual worker. Given the overwhelming evidence on individual earnings and job satisfaction impacts, it may
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be appropriate to shift attention to the role of the workplace either in terms of mitigating the consequences of over-education or reducing its prevalence. References Battu, H., & Sloane, P. J. (2004). Over-education and ethnic minorities in Britain. Manchester School, 72, 535–559. Battu, H., Belfield, C. R., & Sloane, P. J. (1999). Overeducation among graduates: A cohort view. Education Economics, 7, 21–38. Battu, H., Belfield, C. R., & Sloane, P. J. (2003). Human capital spillovers within the workplace: Evidence for Great Britain. Oxford Bulletin Of Economics And Statistics, 65, 575–594. Bauer, T. K. (2002). Educational mismatch and wages: a panel analysis. Economics of Education Review, 21, 221–229. Brynin, M., & Longhi, S. (2009). Overqualification: Major or minor mismatch? Economics of Education Review, 28, 114–121. Chevalier, A. (2003). Measuring over-education. Economica, 70, 509–531. de Oliveira, M. M., Santos, M. C., & Kiker, B. F. (2000). The role of human capital and technological change in overeducation. Economics of Education Review, 19, 199–206. Dolton, P., & Vignoles, A. (2000). The incidence and effects of overeducation in the UK graduate labour market. Economics of Education Review, 19, 179–198. Groeneveld, S., & Hartog, J. (2004). Overeducation, wages and promotions within the firm. Labour Economics, 11, 701–714. Hartog, J. (2000). Over-education and earnings: Where are we, where should we go? Economics of Education Review, 19, 131–147. Kersley, B., Alpin, C., Forth, J., Bryson, A., Bewley, H., Dix, G., & Oxenbridge, S. (2006). Inside the Workplace: Findings from the 2004 Workplace Employment Relations Survey. London: Routledge. Levin, H. M., & Tsang, M. (1985). The economics of overeducation. Economics of Education Review, 4, 93–104. Lindley, J. (2009). The over-education of UK immigrants and minority ethnic groups: Evidence from the Labour Force Survey. Economics of Education Review, 28, 80–89. Millward, N., Bryson, A., & Forth, J. (2000). All Change at Work? British Employment Relations 1980–98. London: Routledge. Robst, J. (1995). Career mobility, job match, and overeducation. Eastern Economic Journal, 21, 539–550. Rubb, S. (2003a). Overeducation in the labor market: A comment and re-analysis of a meta-analysis. Economics of Education Review, 22, 621–629. Rubb, S. (2003b). Overeducation: A short or long run phenomenon for individuals? Economics of Education Review, 22, 389–394. Rumberger, R. W. (1981). The rising incidence of overeducation in the US labor market. Economics of Education Review, 7, 185–194.
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Economics of Education Review journal homepage: www.elsevier.com/locate/econedurev
Over-education: What influence does the workplace have? Clive Belfield Department of Economics, Queens College, City University of New York, Flushing, NY 11367, USA
a r t i c l e
i n f o
Article history: Received 6 August 2009 Accepted 7 August 2009 Keywords: Over-education Job satisfaction
a b s t r a c t The wage and job satisfaction impacts for over-educated workers have been welldocumented; yet little attention has been paid to the consequences for firms. In this paper we examine over-education from the perspective of the workplace. Using linked employer–employee data for the United Kingdom, we derive the standard worker-level penalties on wages and job satisfaction. We then show how over-education rates across workplaces adversely influence workplace pay and workplace labor relations. For individual workers who may be at-risk of over-education, we also distinguish between workforce composition effects and workplace labor practices, such as hiring. The effect of over-education on job satisfaction is particularly strong and its effects are evident at the workplace level. Our results suggest that investigations of over-education at the level of the firm are a promising area of inquiry. © 2009 Published by Elsevier Ltd.
1. Introduction The economic literature on how over-education adversely impacts individual workers’ earnings is large. Rubb (2003a, 2003b, Table 1) summarizes 85 separate international estimates of the impact of surplus, required, and under-education on earnings: whereas each required year of schooling raises earnings by 9.6%, surplus years only raise earnings by 5.2%; and specifications with a dummy variable for over-education show a clear negative-signed coefficient. Other research has looked at differential wage impacts: by level of education (e.g. for graduates, Battu, Belfield, & Sloane, 1999; Dolton and Vignoles, 2000); by heterogeneity of skills within education levels (Brynin & Longhi, 2009; Chevalier, 2003); by race (Battu & Sloane, 2004; Lindley, 2009); over time (Bauer, 2002; Rubb, 2003a, 2003b); and within an individual firm (Groeneveld & Hartog, 2004). In addition, a strong link between overeducation and job dissatisfaction has been identified (e.g., Rumberger, 1981). Studies have also found that various definitions of over-education may be used and that alternative
E-mail address: Clive.Belfi[email protected]. 0272-7757/$ – see front matter © 2009 Published by Elsevier Ltd. doi:10.1016/j.econedurev.2009.08.001
specifications (ORU versus dummy variable models) yield consistent conclusions (Battu et al., 1999; Rubb, 2003a, 2003b). Thus, we can be confident that over-education is an important determinant of earnings and job satisfaction. However, almost all these studies have examined individual determinants of over-education; very few have examined how firm-specific characteristics influence overeducation. In their paper on over-education in this Journal in 1985, Henry Levin and Mun Tsang emphasized the management–labor relationship and how managers must use hiring, supervision, and incentive schemes to ensure that labor inputs are optimally productive. They also observed that “the welfare of workers is tied to the welfare of firms. If over-education leads to a decline in the productivity of workers, firms also suffer in their pursuit of profits” (p. 101). Profit-maximising firms might, therefore, wish to identify workforce characteristics and labor practices that either minimize the incidence of over-education and or alleviate its effects on earnings and job satisfaction. Since 1985, however, little empirical research has been performed on whether firms do this or whether it matters. This paper is one attempt to fill in this gap. We use data on samples of workers across multiple workplaces, along with workplace-specific data, from the United
C. Belfield / Economics of Education Review 29 (2010) 236–245 Table 1 Frequencies for key variables. Mean (SD) Individual variables: Skills ‘much higher’ than required Skills ‘somewhat higher’ than required Satisfied or very satisfied with work itself Content with job (all or most of time) Ln(Pay per hour) Worker-level observations
21.1% 31.9% 71.1% 36.8% 2.19 (0.56) 21,620
Workplace-level variables: Percent skills ‘much higher’ than required Management–labor harmony very good Workers share the values of organization % days lost to absenteeism during last year (BC skew) % quit in last year (BC skew) Ln(average workforce pay): worker survey Ln(average workplace pay): manager survey Workplace-level observations
21.4% 36.1% 18.1% 15.4% 10.0% 2.14 (0.38) 2.26 (0.45) 1,463
Workplace-level labor practices: Aptitude tests for hiring Competency tests for hiring New worker induction program All workers appraised Recognized union Preferential internal hiring Worker-level training Workplace-level observations
36.6% 65.3% 91.4% 46.3% 55.0% 29.2% 18.9% 1,463
Kingdom Workplace Employee Relations Survey of 2004. First, we demonstrate the consensus worker-level relationships between over-education and penalties to earnings and job satisfaction. Then, we examine whether these over-education penalties are evident at the workplacelevel; we find evidence for both penalties. Broadly, we find that workplace attributes are influential in identifying the consequences of over-education and in determining its impacts. Next, we examine the determinants of overeducation, focussing on workplace-specific attributes. 2. Theory Most research on over-education adopts the perspective of the individual worker. Given the wage penalty and the lower job satisfaction, it is plausible to regard overeducation as a sub-optimal outcome for worker. Given public subsidies for school and college, over-education is also sub-optimal for society. However, firms employ workers and so it is important to determine to what extent over-education is sub-optimal for the firm too. As pointed out by Levin and Tsang (1985), firms run the risk of lower profits as a result of poor deployment of workers. These lower profits arise primarily because of lower worker effort (higher absenteeism, etc.) induced by low job satisfaction. Certainly, for individual workers the evidence of over-education reducing job satisfaction is very strong; correspondingly, the evidence of low job satisfaction reducing worker effort is strong (see the mass of evidence in Rubb, 2003a, 2003b). But there are other reasons why over-education may impair profits. If workers’ skills do not match their jobs, they may need training which companies may have to subsidize. Also, over-educated workers may be more likely to quit: frequent hiring adds
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to firms’ personnel recruitment costs. If pay is set according to a formula, such as a worker’s credentials, then an over-educated worker may be overpaid relative to some of their job requirements. Finally, over-educated workers may impose negative externalities on co-workers, either undermining workplace morale or influencing workplace norms about effort. Potentially, over-education may be associated with lower profits because it reflects poor labor management decisions by the firm’s managers. Nevertheless, this still implies that firms would prefer lower rates of over-education. The counter-argument is that a firm could hire overeducated workers and adjust their pay to reflect any lower effort. Also, firms may have only a few workers who are over-educated and so not notice any adverse effects in terms of profit, norms of worker/workplace effort, or absenteeism. At best, then, companies might not care if their workers are over-educated. Importantly, either argument implies that firms would want to understand why workers might be over-educated. If such workers reduce profits, firms will want to avoid hiring or retaining them. If such workers do not reduce profits, firms will want to identify them so they can be paid accordingly. Yet, identifying such workers should incorporate the characteristics of the firm and not just those of the worker. In a sense, workers are only over-educated when they are given tasks at the workplace. Almost exclusively thus far, explanations of over-education have used either search theory or human capital theory from the perspective of the worker (Hartog, 2000).1 Here, we interpret these from the perspective of the firm. Under search theory, initial mismatch occurs but is temporary as the worker is redeployed or promoted to a position with appropriate skill requirements. The workerlevel test is on experience: more experienced workers should have lower rates of over-education (tenure effects are ambiguous, see Hartog, 2000). However, several workplace tests are also possible. First, over-education will be more prevalent where firms have weak hiring systems: if the firm does not properly check for worker capabilities, initial mismatches will be more prevalent. Second, overeducation will be more prevalent where the firm has rigid worker deployment over time: any initial mismatch will be perpetuated. Third, over-education may be a function of the workforce composition: if the firm requires relatively few highly skilled workers or hires mostly part-time or shift workers, few can be promoted to positions commensurate with their education. (On the importance of the internal labor market for rates of over-education, see Groeneveld & Hartog, 2004). Under human capital theory, over-educated workers accumulate skills that can then be used to switch to a higher
1 Assignment theory is a third alternative explanation for overeducation. With assignment theory, both supply and demand functions should be modelled simultaneously. Workers obtain jobs based on their rankings within the skill hierarchy relative to the available positions. If there are too many workers for a specific position, some will be overeducated and assigned jobs lower down the hierarchy. However, absent a structural model of the economy, few direct tests of this theory are possible (but see Hartog, 2000, pp. 142–144).
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level occupation or position.2 The worker-level test looks at wage growth to see if this is faster (de Oliveira et al., 2000). A workplace test would focus on the availability of training: where training options are more likely, over-educated workers can acquire firm-specific skills that complement their formal education and so progress toward higher paid positions. We test all of these arguments below using our linked employer–employee dataset for the U.K. We note here that the dataset is a cross-sectional survey. As such, only correlational relationships can be reported (as with the majority of earlier studies). However, we can control for a substantial array of worker and workplace-level characteristics. When we are examining coworker effects, it is more plausible to assume that coworkers’ behavior is independent: they are not hired jointly with their over-educated colleagues. Also, we are able to compare the relative strength of each correlation to see, for example, whether firm’s hiring practices are more influential than firm’s deployment practices. Finally, for a number of variables we have multiple measures, based on responses by managers and workers; we can therefore perform several tests for the same underlying relationship. We begin by using employee-level data to test for the influence of over-education on job satisfaction and wages.3 We denote OE, W, and JS as over-education, wages, and job satisfaction respectively; and subscript i refers to variables measured at the level of the individual worker and subscript j refers to variables aggregated to the workplace level. In accordance with the substantial evidence base, we anticipate Wi /OEi < 0 and JSi /OEi < 0. In each case we control for a full set of worker-level characteristics and a full set of workplace-level characteristics. Next we use workplace-level data to test for the influence of aggregate over-education across all workers at the firm. Specifically, we test for effects on workplace wages and satisfaction, but we also have two measures of labor effort (EF): absenteeism and the quit rate. In symmetry with the worker-level results, we anticipate: Wj /OEj < 0 and JSj /OEj < 0; we also expect EFj /JSj > 0 and EFj /OEj < 0. Here, we control for a set of workplace-level characteristics. In addition, workplacelevel data allows us to test more relationships. These tests help us to get closer to the issue of whether over-education harms firm profits. Lastly, we consider the determinants of over-education both at the worker and workplace level. We anticipate that over-education (OEi and OEj ) will be lower where firms have: more rigorous hiring practices; more flexible deploy-
2 The specific mechanism by which intra-firm opportunities may ameliorate over-education is unclear. Robst (1995) finds that the overeducated are more likely to be promoted; Rubb (2003a, 2003b) finds that three-quarters of over-educated workers will still be over-educated one year later. Our investigation is on the firm characteristics associated with more opportunities. 3 A worker-level measure of effort is not easily identifiable. We experimented with hours of work conditional on pay but the results were highly sensitive to model specification. Less satisfied workers do postsignificantly fewer hours, but the sign and size of the over-education coefficient was not robust.
ment rules; fewer part-time or shift workers; and greater availability of training programs. 3. Data and basic relationships 3.1. Workplace employee relations survey 2004 The WERS 2004 surveys are nationally representative of workplaces with more than 5 employees (for full information, see Kersley et al., 2006). A survey was administered to the manager of each workplace, with questions on workforce composition, personnel management, dispute procedures, unionization, and recruitment and training of workers. The total sample of workplaces is 2295 (a response rate of 64%). A second survey was administered to a random sample of up to 25 workers at each workplace. This survey included questions on earnings, job quality, and personal characteristics. The total sample of workers is 22,451 (a response rate of 61%). The two surveys are linked through a workplace identifier. Weights are available for each survey and applied in the regression equations. The WERS 2004 is part of a series of harmonized workplace surveys administered periodically in the United Kingdom; earlier versions have yielded a large body of evidence on the management operations of firms (Millward et al., 2000). The linked workplace and worker survey was introduced in WERS 1998, allowing for investigation of how workplace attributes influence workers and vice versa (e.g., on intraworkplace human capital externalities, see Battu et al., 2003). However, the WERS 2004 is the first to ask workers specifically about the match between their skills and their job. It is therefore the first opportunity to investigate how workplace characteristics influence over-education levels within the U.K. context. We merge the worker and workplace surveys and examine relationships at both levels of aggregation. When examining effects aggregated to the workplace we typically have two sources of information. The first is the averages of the worker responses, including worker averages for over-education, for job satisfaction, and pay. (To militate against collinearity, we exclude workplaces where fewer than five workers responded to the survey). The second source of information is the manager, who reports workplace-level pay and labor relations quality independently from the worker respondents.4 Table 1 reports data on the key dependent and independent variables at the worker and workplace level. In the worker survey of WERS 2004, workers are asked about whether their skills are higher than required for their job, with possible responses of ‘much higher’, ‘somewhat higher’ and ‘appropriate’ (very few workers respond ‘less than appropriate’). As shown in Table 1, over half of all workers reported having higher skills than their jobs required: 21% reported having much higher skills and 32% reported somewhat higher skills. We have two measures of job satisfaction: the first shows that ‘satisfaction with
4 The manager is asked to report the number of workers (aged 22 or over) in each of four pay bands. From these bands and proportions we calculate an average workplace pay.
C. Belfield / Economics of Education Review 29 (2010) 236–245
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Table 2 Influence of over-education on workers’ log hourly pay, work satisfaction and job contentment. Skills compared to requirement:
Public sector (1)
Log hourly pay Higher
Public sector (2)
−0.0261 (0.0118)** −0.0794 (0.0148)*** 0.0042 (0.0141) 0.39 7840
‘Somewhat higher’ 0.39 7840
Satisfied or very satisfied with work itself Higher
−0.0651 (0.0110)*** −0.1399 (0.0170)*** −0.0244 (0.0131)* 8170
‘Somewhat higher’ 8170
Content with job all of the time or most of the time Higher −0.0436 (0.0131)*** ‘Much higher’
0.39 13,740
−0.1513 (0.0130)*** −0.0027 (0.0111) 14,197
14,197 −0.0307 (0.0101)***
−0.0921 (0.0171)*** −0.0148 (0.0148) 8170
‘Somewhat higher’ Observations
−0.0579 (0.0122)*** −0.0218 (0.0109)* 0.39 13,740
−0.0692 (0.0216)***
‘Much higher’
Observations
Private sector (4)
−0.0363 (0.0096)***
‘Much higher’
R-Squared Observations
Private sector (3)
8170
−0.0738 (0.0126)*** −0.0017 (0.0115) 14,197
14,197
Notes: Log hourly pay estimated using OLS. Job satisfaction and job contentment estimated using weighted probit; marginal effects are reported. Robust standard errors in parentheses. Specifications control for: male; tenure; tenure squared; marital status; minority; education (5 dummy variables for highest qualification); age (6 bands); occupation (9 SOC); unionised worker; industry (3 in public sector; 9 in private sector); and constant term. *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
the work itself’ is indicated by 71% of all workers; but the second shows that ‘content with job (all or most of the time)’ is indicated by 37%. As controls, we also have data on sex, tenure, marital status, minority status, education, occupation, age, union status, and sector.
At the workplace-level, Table 1 reports the frequencies for the measures of job satisfaction, effort and wages. As reported by the manager, 36% of workplaces have ‘very good’ management–labor harmony and 18% believe ‘workers share the values of the organization’. Two measures of
Table 3 Influence of over-education on workers’ log hourly pay, work satisfaction and job contentment (controlling for firm-specific characteristics). Model specification
Effect of skills being ‘much higher’ than required on: Log hourly pay
Satisfaction with work itself
Job contentment
Public sector
Private sector
Public sector
Private sector
Public sector
Private sector
[1] Firm-specific controls for workforce composition, market structure, and organizational size
−0.0735
−0.0402
−0.1241
−0.1563
−0.0696
−0.0716
(0.0126)***
(0.0092)***
(0.0136)***
(0.0102)***
(0.0141)***
(0.0097)***
[2] Firm-specific controls as per model [1] plus controls for firm labor practices
−0.0721
−0.0402
−0.1219
−0.1530
−0.0667
−0.0692
(0.0126)***
(0.0092)***
(0.0136)***
(0.0102)***
(0.0142)***
(0.0098)***
[3] Wage equation: firm-level fixed effects; Satisfaction equations: control for pay and pay satisfaction
−0.0631
−0.0300
−0.0974
−0.1345
−0.0401
−0.0525
(0.0124)***
(0.0090)***
(0.0137)***
(0.0104)***
(0.0149)***
(0.0102)***
Firm-level observations Worker-level observations
566 7840
1,166 13,740
566 7532
1,166 13,301
566 7532
1,166 13,301
Notes: Weights applied with robust standard errors in parentheses. Log hourly pay estimated using OLS. Job satisfaction and job contentment estimated using weighted probit; marginal effects are reported. All model specifications include controls for: male; tenure; tenure squared; marital status; minority; education (5 dummy variables for highest qualification); age (6 bands); occupation (9 SOC); industry (3 in public sector; 9 in private sector). *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
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C. Belfield / Economics of Education Review 29 (2010) 236–245 Table 4 Influence of over-education on workplace average pay, relations and productivity. Effect of percent over-educated at workplace
(1) Log average workplace pay: From worker sample From managers’ workforce statement (2) Workplace harmony reported by workers: Mean of job satisfaction scores Mean of job contentment scores (3) Workplace relations reported by managers: Management–labor harmony very good Workers share the values of organization (4) Workplace effort: Percent workers absent during last year Percent quit in last year Firm-level observations
Public sector
Private sector
−0.2806 (0.1244)** −0.2019 (0.0853)**
−0.2918 (0.1392)** −0.1858 (0.0898)**
−0.2632 (0.0683)*** −0.2208 (0.0812)***
−0.2504 (0.0721)*** −0.2187 (0.0719)***
−0.2840 (0.2576) −0.0227 (0.2259)
−0.5235 (0.1983)*** −0.5280 (0.1493)***
−0.4548 (0.7651) −0.0214 (0.3436)
0.2448 (0.5199) 0.5329 (0.2190)**
515
948
Notes: Weighted estimation with robust standard errors in parentheses. Panels (1), (2), and (4) apply OLS estimation; panel (3) reports marginal effects from a probit estimation. All model specifications include controls for: age of firm; UKownership; single firm; percent female; percent part-time; percent manual; shiftwork; labor costs (3); competition (2); workplace size; and labor practices. Number of observations for absenteeism rate: 388 public, 803 private. Number of observations for quit rate: 376, 783. *** Significant at p < 0.01, ** Significant at p < 0.05, * Significant at p < 0.1.
labor effort are available: percentage of days lost to absenteeism during the previous year; and the percentage of workers who quit in the previous year. Also, two measures of workforce pay are created, based on the managers’ reports and the averages of the worker respondents. Table 1 also shows the range of labor management practices are implemented across workplaces. These are divided into practices that influence hiring and those that influence deployment of workers. Hiring practices are measured by whether the firm has hiring tests for aptitude (37%) and competence (65%) as well as induction programs (91%). Deployment practices are captured by whether the workplace performs appraisals (46%), has a recognized union (55%), and uses internal hiring to re-assign workers (29%). Finally one-in-five workplaces offer training programs. 3.2. Basic relationships First, we estimate a simple OLS worker-level wage equation which includes a dummy variable for overeducation (controlling for a vector of worker characteristics including education levels and occupation). The results are given in the top panel of Table 2. In both the public and private sectors, over-educated workers have lower earnings than appropriately educated workers (Wi /OEi < 0). However, the wage penalty is associated only with those who report having skills much higher than required (columns 2 and 4). For workers with skills ‘some-
what higher’ than required, there is no observable wage disadvantage. Next, we estimate simple probit equations for job satisfaction. These results – reported as marginal effects – are given in the bottom two panels of Table 2. Similarly, the results in Table 3 correspond with the extant evidence. Using a measure either of satisfaction with work itself or of contentment with the job, we find that the coefficient on over-education is strongly negative (JSi /OEi < 0). Again, this effect is driven almost exclusively by those workers whose skills and actual job are very badly matched. The above results are consistent with an argument derived from Chevalier (2003): genuine identification of over-education should consider both the wage penalty and the mediating links with job satisfaction. Therefore, in analysis below we focus on the 21% of workers who report having skills much higher than required and classify them as ‘over-educated’. In fact, this proportion is very close to Lindley’s (2009, Table 1) estimate of 23% and Brynin and Longhi’s (2009, Table 1) combined estimate of 17%. For these workers, the gaps are very large both for wages and job satisfaction. In the public sector the wage penalty is 7.9%; and in the private sector it is 5.8%. Similarly, in the public sector the predicted percentage of over-educated workers who are satisfied with work is 65%; for appropriately educated workers the prediction is 78%. The respective gap for the private sector is even larger, at 56% versus 73%.
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4. Over-education and the workplace
4.2. The impacts of over-education across workplaces
4.1. Penalties for over-education across workers
Now we examine whether the consequences of overeducation – both in terms of pay and harmony – are evident at the workplace-level. Based on the worker responses, we calculate the ‘percent over-educated’ as the average probability of being over-educated within the workplace. This variable represents the proportion of workers at each workplace who are over-educated. The correlation between this workplace proportion and workplace average pay and workplace harmony are given in Table 4 (see the table notes for controls). The proportion of over-educated workers has a strongly negative impact on average earnings across a workplace, both in the public and private sectors (Panel 1). The result is evident using two measures of average workplace earnings. The first measure is the average pay of all the workers who responded to the survey; this is a weak test because we already know that those individuals who report being over-educated have lower pay (Table 2). The second measure is the average pay at the workplace as reported by the manager; this is a much stricter test as it is based on a report for all the workers at the firm, regardless of whether they were surveyed or not. The effect is substantive: going from a firm with no over-educated workers to one where all workers are over-educated would reduce everyone’s pay by between 13% and 26%. This result is strongly suggestive of negative externalities from having over-educated co-workers: all workers have lower pay; also, the wage penalty is larger than the effect per individual worker.5 It is also weakens the likelihood that lower worker productivity arising from over-education is fully reflected in the individual worker’s wages. Similarly, the effects of over-education on job satisfaction are identifiable at the workplace (Panel 2). Based on the mean satisfaction with work and job contentment scores of the worker samples, higher proportions of over-educated workers mean lower workplace morale. Again, these are weaker tests because they are aggregations of worker-level specifications. More compellingly, managers themselves are able to identify adverse workplace consequences in terms of morale from over-education (Panel 3). Based on two questions to the managers, there is a strong negative correlation between percent over-educated and management–labor relations and whether managers believe workers share the values of the organization. The effect is strong in both sectors, although it is only statistically significant in the private sector. Finally, we employ two additional tests to see whether over-education has deleterious consequences in terms of worker effort for workplaces (Panel 4). Our measures of effort are workplace absenteeism rates and the quit rates (as reported by the manager).6 The results are statistically significant only for the private sector quit rate, where
Here we identify if workplace characteristics and labor practices affect the wage and job satisfaction penalties for over-education across workers. For each penalty, we estimate two common models: [1] includes controls for workplace characteristics; and [2] includes controls for worker characteristics and labor practices. In addition, we include an extra model [3] with specifications that vary across the wage and job satisfaction equations. The first column of Table 3 reports the workers’ wage penalty. Model [1] includes workplace characteristics and model [2] adds workplace labor practices as well. The wage penalty falls to 4–7%, compared to 6–8% without these controls. Next we estimate a simple workplace fixed effects specification; given in column 3, this reduces the wage penalty to 3–6%. Thus, firm attributes are driving one-quarter to one-half of the over-education wage penalty. Parallel estimations of models [1] and [2] are reported for satisfaction with work and job contentment. Being over-educated is associated with a 12-point or 15-point reduction in satisfaction with work; this effect is almost unchanged when we introduce controls for workplace attributes. Being over-educated is associated with a 9point reduction in job contentment in the public sector (Table 2 above); controlling for workplace characteristics and practices, this figure falls to 7. In the private sector, the role of the workplace is more muted: job contentment is unchanged after we control for workplace characteristics. For job satisfaction, we estimate a model [3] where we explicitly control both for wages and satisfaction with wages. Over-educated workers report lower pay, so we might expect them to report lower job satisfaction. However, when we control for own pay, over-education still has a negative effect on job satisfaction. Yet, this does not fully address the issue of whether the worker is paid less than their marginal product. As noted above, we cannot measure labor productivity independently and so cannot tell whether these workers are ‘underpaid’. Our partial solution is to control for whether a worker feels underpaid; this feeling might be interpreted as a divergence between marginal product and pay. Model [3] reports the impact of over-education on job satisfaction, controlling for actual pay and satisfaction with pay (as well as variables from Model [2]). The coefficient for over-education is still statistically significant and strongly negative: even controlling for own pay and for satisfaction with pay, over-education exerts a strongly negative effect on both satisfaction with the work and job contentment. Overall, workplace differences do appear to offset some of the labor market consequences of over-education. Nevertheless, as found by Groeneveld and Hartog (2004) we reject the argument that over-education penalties are exclusively due to firm fixed effects. In particular, workplace morale is adversely affected by overeducation, even after adjustment for the lower wage payments.
5 To repeat, we cannot determine whether in fact poor managerial quality is responsible for both the higher rates of over-education and the lower wages. 6 The absenteeism rate is measured as the percentage of work days lost in the last 12 months. The quit rate is the proportion of workers who
242
Table 5 Determinants of over-education across workers. Public sector workers
Worker characteristics: Male Minority Graduate degree
A-levels O-levels CSE/GCSE (high) CSE/GCSE (low) Unionised worker Disability Workplace characteristics: Shiftworkers % workers part-time % workers manual Age of firm UK-owned Single workplace % workers female Labor costs <25% Labor costs 25–50% Labor costs 50–75%
(2) Model (1) and workplace attributes
(3) Model (2) and labor practices
(1) Worker
(2) Model (1) and workplace attributes
(3) Model (2) and labor practices
0.0817 (0.0143)*** 0.0866 (0.0243)*** 0.0594 (0.0245)** 0.0710 (0.0165)*** 0.0248 (0.0182) 0.0175 (0.0180) −0.0150 (0.0127) −0.0084 (0.0164) −0.0005 (0.0111) 0.0528 (0.0175)***
0.0819 (0.0158)*** 0.0761 (0.0248)*** 0.0460 (0.0250)* 0.0769 (0.0172)*** 0.0261 (0.0188) 0.0229 (0.0186) −0.0149 (0.0130) −0.0109 (0.0166) 0.0028 (0.0118) 0.0558 (0.0178)***
0.0816 (0.0158)*** 0.0764 (0.0250)*** 0.0478 (0.0252)* 0.0782 (0.0173)*** 0.0264 (0.0188) 0.0250 (0.0187) −0.0135 (0.0130) −0.0128 (0.0165) 0.0023 (0.0119) 0.0544 (0.0177)***
0.0565 (0.0098)*** 0.0353 (0.0190)* 0.0869 (0.0257)*** 0.0654 (0.0139)*** 0.0395 (0.0159)** 0.0319 (0.0175)* 0.0223 (0.0107)** −0.0215 (0.0133) 0.0325 (0.0108)*** 0.0592 (0.0144)***
0.0644 (0.0107)*** 0.0274 (0.0191) 0.0968 (0.0263)*** 0.0724 (0.0144)*** 0.0358 (0.0161)** 0.0361 (0.0181)** 0.0244 (0.0109)** −0.0179 (0.0136) 0.0211 (0.0112)* 0.0586 (0.0145)***
0.0669 (0.0108)*** 0.0278 (0.0190) 0.0971 (0.0264)*** 0.0735 (0.0144)*** 0.0369 (0.0162)** 0.0360 (0.0180)** 0.0237 (0.0109)** −0.0176 (0.0136) 0.0161 (0.0121) 0.0588 (0.0145)***
0.0397 (0.0126)*** 0.0293 (0.0295) 0.0424 (0.0526) −0.0000 (0.0001) −0.0006 (0.0241) −0.0030 (0.0172) −0.0156 (0.0388) −0.0490 (0.0250)* −0.0054 (0.0193) −0.0064 (0.0123)
0.0434 (0.0139)*** 0.0219 (0.0296) 0.0449 (0.0516) −0.0001 (0.0001) 0.0041 (0.0250) 0.0014 (0.0177) −0.0126 (0.0391) −0.0414 (0.0263) 0.0047 (0.0202) −0.0051 (0.0120)
0.0320 (0.0100)*** 0.1145 (0.0270)*** 0.0749 (0.0177)*** −0.0001 (0.0001) 0.0035 (0.0099) −0.0207 (0.0126)* −0.0426 (0.0280) 0.0251 (0.0135)* 0.0103 (0.0125) 0.0094 (0.0142)
0.0305 (0.0106)*** 0.1045 (0.0272)*** 0.0715 (0.0178)*** −0.0001 (0.0001) 0.0011 (0.0100) −0.0235 (0.0126)* −0.0314 (0.0283) 0.0259 (0.0136)* 0.0107 (0.0125) 0.0112 (0.0142)
C. Belfield / Economics of Education Review 29 (2010) 236–245
College degree
Private sector workers
(1) Worker
Few competitors Many competitors
0.0334 (0.0294) 0.0322 (0.0265)
7847
−0.0295 (0.0124)** 0.0358 (0.0142)** 0.0085 (0.0216) −0.0184 (0.0110)* −0.0156 (0.0128) 0.0039 (0.0175) 7847
Labor practices: Worker-specific training Recognized union Induction program All workers appraised Hiring tests Prefer internal hiring Observations
8170
14,197
−0.0245 (0.0135)* −0.0143 (0.0135)
−0.0212 (0.0135) −0.0104 (0.0136)
13,744
−0.0484 (0.0115)*** 0.0151 (0.0119) −0.0296 (0.0170)* −0.0009 (0.0092) −0.0208 (0.0106)* −0.0008 (0.0092) 13,744
Notes: Probit estimation with robust standard errors in parentheses. Marginal effects reported. All specifications also include: industry (3 in public sector; 9 in private sector); age (6 bands); marital status; children; worker tenure; occupation (9 SOC); organizational size; number of employees; and constant term. *** Significant at p < 0.01. ** Significant at p < 0.05. * Significant at p < 0.1.
C. Belfield / Economics of Education Review 29 (2010) 236–245
0.0296 (0.0286) 0.0289 (0.0258)
243
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C. Belfield / Economics of Education Review 29 (2010) 236–245
over-education is associated with a higher quit rate.7 These results affirm our presumption that over-education will likely impair firm profitability by raising personnel costs.
Table 6 Determinants of percent over-educated across workplaces.
Shiftworkers
4.3. Determinants of over-education across workers % workers part-time
Given these effects, it is important to identify firmspecific influences on over-education. We begin with individual worker-level analyses. Table 5 reports estimation of the determinants of over-education, splitting the sample of workers into public and private sectors. Model (1) follows the literature and specifies over-education purely as a function of worker characteristics. Notably, over-education rates are consistently higher among male workers and workers with disabilities. (Although not reported in Table 5, but included in each specification, occupations are highly statistically significant). In the public sector, minority workers also report more over-education, but this is not the case in the private sector. Although the incidence of over-education rises with education levels, this gradient is particularly steep in the private sector. Also, model (1) indicates that unionized workers in the private sector are more likely to report being over-educated; the insignificant effect in the public sector may occur because of the very high rates of unionization. Finally, tenure at the workplace is not statistically significant (not reported). This suggests that workers do not become more appropriately matched over time, lending support to theories of overeducation through hiring rather than labor deployment. Model (2) supplements the worker model with workplace characteristics which capture the composition of the workforce. Across both the public and private sector, workplaces which rely heavily on shiftworkers have higher levels of over-education. In the private sector, workplaces with more part-time workers and more manual workers also report more over-education. However, other variables are not significant: these include the competitiveness of the market, the capital–labor ratio, and the size of the enterprise (not reported). Model (3) introduces labor practices which might be anticipated to ameliorate over-education. There is modest evidence for each practice. Hiring tests and induction programs are associated with lower over-education (most clearly in the private sector). Appraisal programs play some role in the public sector, but the effect is only statistically significant at the 10% level. More clearly, workers who receive more training report lower levels of over-education. However, preferences for internal ‘hiring’, which is best interpreted as promotion or redeployment, is not statistically significant. Notably, a workplace with a recognized union strongly raises over-education in the public sector, even conditional on the workers’ own union status. This suggests that union skill demarcation rules may impair firms’ ability to reduce over-education levels over time.
stopped working at the firm over the last year. Because the rates are truncated at zero, we apply Box–Cox transformation so that the skewness is set at zero. 7 However, in sensitivity testing, the absenteeism rate in the private sector was also found to be higher with more over-educated workers. Details available from the author.
% workers manual Age of firm UK-owned Single workplace % workers female Labor costs <25% Labor costs 25–50% Labor costs 50–75% Few competitors Many competitors Labor practices: Worker-specific training Recognized union Induction program All workers appraised Hiring tests Prefer internal hiring R-Squared Observations
Public sector
Private sector
0.0091 (0.0271) −0.0544 (0.0540) 0.1378 (0.0867) −0.0001 (0.0001) 0.0112 (0.0379) −0.0131 (0.0329) −0.0439 (0.0581) 0.0765 (0.0884) −0.0404 (0.0323) −0.0288 (0.0240) 0.0253 (0.0437) 0.0718 (0.0353)**
0.0251 (0.0244) 0.1465 (0.0423)*** 0.0718 (0.0329)** 0.0000 (0.0002) −0.0057 (0.0276) −0.0795 (0.0211)*** −0.1562 (0.0498)*** 0.0260 (0.0254) 0.0057 (0.0281) 0.0045 (0.0265) 0.0043 (0.0261) 0.0244 (0.0271)
−0.0578 (0.0817) 0.0515 (0.0268)* 0.0473 (0.0323) −0.0651 (0.0229)** −0.0146 (0.0252) −0.0638 (0.0375)*
−0.0624 (0.0617) 0.0368 (0.0230) −0.0320 (0.0242) 0.0142 (0.0199) −0.0275 (0.0174) −0.0040 (0.0176)
0.20 515
0.21 948
Notes: Probit estimation with robust standard errors in parentheses. All specifications also include: industry (3 in public sector; 9 in private sector); organizational size (2); workplace size; and constant term. * Significant at p < 0.1. ** Significant at p < 0.05. *** Significant at p < 0.01.
In summary, there is some evidence that firm-specific labor practices do influence the level of over-education across workers controlling for workers’ own characteristics. These practices tend to be associated with initial hiring and deployment decisions, although on-the-job training and appraisal programs also have some modest influence. 4.4. The determinants of over-education across workplaces Finally, we turn to identification of over-education rates across workplaces. Theory suggests that over-education will be affected by the usage of specific labor market practices and by workforce composition, but not by general features of the product market unrelated to labor markets. These relationships are tested by regressing the percent over-educated at the workplace against workplace char-
C. Belfield / Economics of Education Review 29 (2010) 236–245
acteristics (taken from the managerial survey). Results are reported in Table 6. For private sector enterprises there is some support for workforce composition influences: rates of over-education are higher where workplaces have more part-time workers, more manual workers, and more male workers. In contrast, structural and market characteristics are not important: the age/size of the workplace, the market competitiveness, and the capital–labor ratio are all insignificant predictors. However, labor market practices do not appear to influence over-education within private sector workplaces. For public sector workplaces, there are very few predictors of over-education: labor force compositional effects are weak, as are structural and market characteristics. However, there is some evidence of the importance of labor practices: unionization rates increase over-education, whereas worker appraisal programs and internal hiring programs reduce it. 5. Conclusions In their analysis of over-education, Levin and Tsang (1985) observed that, with high levels of over-education, firms suffer as well as the workers themselves. Since then, little attention has been paid to whether that observation has merit. In our investigation using the WERS 2004 from the United Kingdom, we can affirm that it does. As the rate of over-education rises, firms do make lower wage payments but, even controlling for wages and expectations of wages, they still report significantly worse workplace harmony. Moreover, workplace characteristics – both workforce composition and labor practices – do influence over-education prevalence, although the relationship is much more easily discernible at the level of the individual worker. Given the overwhelming evidence on individual earnings and job satisfaction impacts, it may
245
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