NO. 70 GRADUATE OVER-EDUCATION AS A SHEEPSKIN EFFECT: EVIDENCE FROM NORTHERN IRELAND. SEAMUS McGUINNESS

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1 WORKING PAPER SERIES NO. 70 GRADUATE OVER-EDUCATION AS A SHEEPSKIN EFFECT: EVIDENCE FROM NORTHERN IRELAND SEAMUS McGUINNESS NORTHERN IRELAND ECONOMIC RESEARCH CENTRE

2 GRADUATE OVER-EDUCATION AS A SHEEPSKIN EFFECT: EVIDENCE FROM NORTHERN IRELAND May 2002 Seamus McGuinness Northern Ireland Economic Research Centre, University Road, Belfast, BT7 1NJ Tel: +44 (0) Fax: +44 (0) s.mcguinness@qub.ac.uk Acknowledgements I would like to thank Professor David Canning (Queen s University Belfast), Dr Stephen Roper (NIERC) and Dr Michael Anyadike-Danes (NIERC) for their useful comments.

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4 Abstract This paper examines the nature of graduate over-education amongst a group of applicants to a graduate conversion programme. It was found that whilst a substantial proportion of earnings differentials were associated with a mismatch between individual skill levels and job requirements, wage gaps were still likely to occur should such mismatches be eliminated. The evidence suggests that graduate wage levels are heavily related to sheepskin effects associated with the attainment of jobs with graduate level entry requirements. These sheepskin effects suggest that the process of job categorisation is arbitrary in nature, with stated job requirements somewhat independent of actual skill requirements. The analysis suggests that graduate over-education is better understood within the context of both skill and categorisation mismatches as opposed to skill matches alone.

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6 INTRODUCTION The standard neo-classical approach to educational participation predicts that workers will invest in an optimum amount of education and training and, on entry to the labour market, will earn a wage equating to their marginal product. However, in reality significant proportions of UK graduates fail to obtain jobs matching their education levels and are thus under-utilised within the labour market, earning wages below those of comparably educated individuals who are successful in finding graduate level employment (Dolton and Vignoles 2000, Sloane, Battu and Seaman 1999, Groot 1996). Graduate over-education represents an obvious loss to national output and welfare, yet to date, UK government policy has done little to address the problem and may actually be exacerbating it given the current expansionary nature of higher education supply. 1 Nevertheless, the existence of a raft of graduate conversion programmes 2 suggests some recognition amongst policy makers that labour market mismatches exist at graduate level, however, such programmes are generally established with the primary objective of filling skill shortages, as opposed to improving employability. This paper provides a further analysis of the determinants of over-education through the examination of data collected during the evaluation of a Northern Ireland graduate conversion programme named Premiere. The issue of over-education is usually examined within a standard wage equation framework, with the general consensus being that the phenomenon arises primarily as a result of some level of mismatch between the skill set of the worker and the skill requirements of the job. However, some gaps appear to exist within the existing literature, for instance, little effort has been made to control for the level of any worker / job mismatch, thus to a large extent, the validity of the dominant view remains untested. Secondly, there is some doubt surrounding the adequacy of the Mincerian framework for examining over-education given that the standard human capital measure applied is centred around either years of schooling or the level of credential attained. Thus little account is taken of the effect of more informal types of 1 It is the stated objective of the UK government to widen access to HE with current policy aimed at ensuring that over 50 per cent of under thirties have accessed some form of third level education. 2 For example, in Northern Ireland alone, graduate conversion programmes exist in IT, Management, Export Marketing with a further one about to be developed in electronics. 1

7 human capital investment, or allowances made, for heterogeneous skill sets implying that, at least to some level, the phenomenon of over-education may arise as a result of an omitted variables problem. This work builds on the existing literature in that (a) it includes variables designed to account for the extent of any skill mismatch in employment, and (b) the analysis allows for the existence of heterogeneous skill sets amongst individuals with similar levels education and credentials. The results of this analysis suggest that graduate wage differentials are heavily related to job credentials which are themselves somewhat independent of actual skill requirements. The work suggests that even in a labour market scenario where the skill sets of workers perfectly matched the skill requirements of their jobs, wage gaps would still occur due to disproportionate returns, or sheepskin effects 3, associated with the successful attainment of certain categories of jobs. 3 Sheepskin effects are generally defined as the difference in earnings between individuals possessing a degree (or in this case a job requiring a degree) and those who do not (see Jaeger & Page, 1996) 2

8 THE THEORETICAL DEBATE There is no unified theory of over-education, however, there has been much debate as to how exactly the phenomenon sits within the context of existing views of the labour market. The acceptance of over-education as a non-transitory phenomenon causes considerable problems for Human Capital Theory (HCT) (Becker, 1964), under which firms are assumed to adjust their production processes in response to changes in the relative supply of labour. Also implicit within HCT are the assumptions that earnings are independent of job characteristics and that workers are paid a wage equating to their marginal product, suggesting that the returns to surplus and required education are equal. Over-education can only therefore be rationalised within the standard neoclassical framework as a short-term phenomenon, existing only for as long as it takes firms to adjust to changes in relative supply. However, HCT does not stand up well in light of evidence from studies suggesting that over-education tends to be persistent in nature (Dolton & Vignoles 2000). In defence of HCT, it can be argued that most empirical studies tend to focus on formal measures of human capital (typically years of schooling) and tend not to take account of less formal investments, such as experience and on-the-job training. Thus, the over-education phenomenon may be the result of an omitted variables problem within the standard HCT framework and there is evidence to suggest that over-educated (under-educated) workers tend to have lower (higher) amounts of informal human capital 4 (Sloane et al 1999, Sichernman 1991). However, Alba-Ramerez (1993) reports a lower return to surplus education even after controlling for on-the-job training whilst Duncan & Hoffman (1981) found evidence to suggest that general labour market experience is not treated by employers as a substitute for formal education. As an alternative to HCT, Duncan & Hoffman (1981) argue that over-education is consistent with Thurow s job competition model (1975), arising because firms labour requirements are fixed according to their production techniques and thus will not vary with relative skill supply. In support of this job competition interpretation, Rumberger (1986) reported that the extent to which individuals are free to utilise their skills and abilities varies significantly across jobs. A middle ground between the two preceding interpretations is found within the 4 Chavelier (2001) also criticises the dominant framework for assuming homogeneity across individuals with similar levels of education and demonstrates that a large amount of apparent overeducation disappears when heterogeneous skill sets are allowed for. 3

9 assignment literature (e.g. Sattinger, 1993) whereby heterogeneous workers are assigned to heterogeneous jobs. The assignment literature implies that neither human capital nor job characteristics alone can sufficiently explain away all variations in earnings as they will be determined within a hedonic price equation containing both demand and supply parameters. Those studies that have sought to determine the most appropriate specification for explaining the phenomenon of over-education have generally tended to reject the HCT and Job Competition models in favour of an assignment interpretation 5 (Dolton & Vignoles 2000, Battu et al 2000, Hartog et al 1988, Groote 1996). Finally, it should be noted that some authors have attempted to explain over-education outside the confines of any of the above models, attributing it instead to a relatively temporary phenomenon that diminishes with each successive movement along the career path (Alba-Ramerez 1993, Sicherman 1991). However, there is limited evidence to support this mobility hypothesis, and in fact Sloane et al (1999) report that an increased number of job changes does not ensure a reduction in over-education. 5 The Hartog study did report that the HCT specification was appropriate for females within their dataset. 4

10 PROGRAMME DESCRIPTION The Premiere programme was established in Northern Ireland during the late 1980s and provides training to around 250 graduates annually. The programme lasts 35 weeks during which time participants take a number of business courses interwoven with two week periods of work placement. On completion of training participants are expected to have achieved Institute of Management certificates in addition to one or more National Vocational Qualifications (NVQs) in specialist options. The objectives of the placement element are twofold, firstly, to improve the productivity of participating businesses by providing graduates capable of undertaking management tasks, and, secondly to equip participants with the skills necessary for them to obtain permanent managerial level employment. The programme was delivered by a major private sector recruitment and training specialist and supported financially by the Training and Employment Agency (T&EA). 5

11 DATA AND METHODS The data used within this study was collected during a telephone survey of 1997/98 applicants to the Premiere programme conducted during July and August Around 200 persons were contacted in total, of which 150 were admitted to the programme with the residual denied a place due to excessive demand. However, some successful applicants failed to complete their training and were subsequently excluded from the analysis. In addition a number of persons from both groups failed to provide adequate employment information and thus were also removed from the sample. Following these corrections the number of valid responses from those admitted and not admitted to the programme stood at 120 and 46 respectively. These two cohorts represent the study and control groups, however, account must be taken of potential selection biases when comparing the labour market outcomes of the two groups. If admittance to the programme contains a non-random component, for instance, if unsuccessful candidates were in some way less likely to succeed in terms of their ultimate labour market position, then the use of such a control group would introduce an upward bias into the estimated effects of assistance. The literature on such sample selection problems is well developed (Himler 2000, Puhani 2000, Heckman et al 1998) and the standard approach adopted to deal with such issues centres around the two stage approach developed by Heckman (1974). The model adopted for this analysis follows a similar framework and can be written as follows lny i = β X + ε 1i (1) e i * = δ Z + ε 2i (2) Equation (1) represents a starting wage equation with log earnings determined by a matrix of personal and /or job characteristics (X) which varies according to the specification being estimated (Mincerian / hedonic price) whilst equation 2 measures the propensity of entering a labour market programme given another vector of observed explanatory variables (Z). ε 1I and ε 2I are the mean-zero stochastic errors representing the influence of unobserved variables affecting each equation. 6

12 Although the term e * * I is unobserved we can define a dummy variable e i =1 if e i >=0 and e i =0 otherwise. Selection bias becomes an issue in the event of the unobserved error terms ε 1I and ε 2I from both equations being correlated i.e. individuals with higher expected earnings are most likely to be selected for training. Assuming that both error terms are drawn from a bivariate normal distribution, the following regression equation can be derived to include a selection control term, which generates unbiased estimates of the latent participation dummy within the wage equation: E(Y i \ e i ) = β X + ρσ 1 λ i (3) Where ρ is the correlation coefficient between ε 1 and ε 2, σ 1 is the standard deviation of ε 1 in equation 1 whilst λ i the Inverse Mills Ratio (IMR) is given by λ = φ (δ Z ) / Φ (δ Z ) for e i = 1 (4) λ = -φ (δ Z )/ [1- Φ (δ Z) ] for e i = 0 (5) Where φ and Φ are density and distribution functions of the standard normal distribution. Over-education is measured subjectively within the dataset based on the question What was the minimum level of education required for this job? respondents were then given a range of standard educational aggregates from which to choose from. The merit of various subjective and objective measures has been debated extensively within the literature (Hartog 2000, Chavelier 2000, Cohn & Kahn 1995), however, Groot & van den Brink (2000) demonstrate that the definition adopted within this analysis has little or no impact when estimating either the incidence of, or returns to, over-education. Given that our sample was relatively restricted from the outset and had the potential to be reduced further through the reluctance of some respondents to provide sensitive information relating to their annual earnings, it was decided to adopt a banding approach when collecting annual wage data. Consequently, the dependent 7

13 variable was calculated as the log of the wage band mean. 6 The use of log wages as the second stage dependant variable allowed direct comparison with the standard Mincer type human capital regressions typically adopted within the existing literature. Finally, whilst information was collected on the entry requirements of both first and current employment the wage data relates to first employment only. The questionnaire used in the survey was quite extensive and, in addition to standard human capital, job and environmental controls, the dataset contained a number of variables developed specifically in an attempt to fill perceived gaps within the existing literature. Firstly, respondents were asked to subjectively assess their skill levels in 16 key areas relating to their employability and, accepting that there may be some subjective measurement error, an explicit control was introduced for any heterogeneity in the skill levels of respondents (see Chavelier, 2000). The data also controls for the level of mismatch between the skill requirements of the firm and the skill attributes of the respondent, thus enabling an explicit examination of the relationship between worker utilisation and wage rates. 6 The following five bands were used: < 10,000, 10,000-12,000, 12,000-15,000, 15, , 20,000-25,000, > 25,000. As the two bands at extremes of the distribution were open-ended, they were assigned a mean value equal to the cut off points ± 1000 ( 9000 & 26000). 8

14 SAMPLE CHARACTERISTICS Premiere is extremely popular, attracting around 2000 applications annually, which equates to around 25 per cent of the yearly full-time output of the region s universities. The popularity of Premiere suggests that a significant proportion of NI graduates perceive the local labour market to be particularly depressed and / or believe that they are relatively ill equipped to compete in it. When we compare the distribution of Premiere applicants in 1997/98 with that of all qualifiers from NI universities in the same year, it becomes obvious that the Premiere cohort represents a distinct sub-grouping of the new graduate population. Figure 1 plots the academic distribution of 1997/98 university leavers against the proportion of labour market entrants from these faculties employed within what are generally assumed to be the non professional SOC groups 4-9 six months following graduation. Assuming that a location in a non-professional occupation roughly proxies an inability to find graduate level employment, Figure 1 demonstrates that well over a third of entrants from Arts, Science, Social Science and Business backgrounds are likely to be in non-graduate jobs six months following graduation, conversely students from more vocationally orientated backgrounds tend to have relatively higher rates of labour market success. If we then examine the academic distribution of our cohort of applicants to the Premiere programme, we find that they are disproportionately drawn from backgrounds associated with lower rates of graduate employment. Thus the Premiere cohort is not representative of the overall graduate population, it instead characterises Figure1: Subject Distribution of FT university leavers 1997/98, Proportion of graduates entering SOC Groups /98, Subject Distribution of Premiere applicants 1997/ % NI Dist soc 4-9 Prem Arts Science Maths, Comp, Eng Agri, Tour, Law, Other Social Sc Business subject 9

15 a distinctly more homogeneous subset of graduates drawn together on the basis of relatively low expectations of labour market success. Table 1 reports the incidence of over-education within both the total sample and its individual components for both first 7 and current employment. The rate of underemployment within the total cohort was estimated at 29 per cent for first employment and 24 per cent for current employment. These rates appear quite high, nevertheless, they are somewhat below what we might expect given that the cohort is more heavily drawn from subject groupings that are associated with underemployment rates (based on the SOC measure) of between 37 and 46 per cent. Whilst some reduction in over-education rates was apparent over the years in which the cohort was active in the labour market, the evidence seems to confirm the observation that over-education is persistent in nature and cannot therefore be attributed to temporary disequilibriums in either firm or worker behaviour. Separating out the analysis for the individual sample components, we see that the rate of underemployment, particularly for current job, is substantially higher within the control group, however, given the potential problems associated with selection bias it would be naive to draw any significant conclusions from this observation. The levels of over-education found within our sample are somewhat lower than the 38% and 30% reported by Dolton & Vignoles (D&V) who applied the same measurement technique in their 2000 UK study. The relatively better performance of the Premiere sample runs somewhat against a priori reasoning given the D&V dataset was representative of the aggregate graduate population, however, as D&V point out, their results may be somewhat atypical given that their cohort entered the labour market during particularly poor economic conditions in the early 1980s. Table 1: Incidence of Over-education in First and Current Employment (%) First Job Current Job Total Sample Premiere Sample Control Group N First employment refers to a post Premiere scenario in the case of successful applicants and a post university scenario in the case of unsuccessful applicants. 10

16 The distribution of over-education by entry level requirement is reported for the total sample within Table 2. Almost half of all individuals reporting over-education in their first jobs believed that the position could have been secured with either A-levels or a vocational equivalent. GCSE represented the next significant entry requirement aggregate, with just under a third of those over-educated in their first employment falling into this category, whilst the remaining aggregates account for just over 10 per cent of the cohort. The distributions for both first and current employment are very similar. Table 2: Distribution of over-educated workers in aggregate sample First Job Current Job No qualifications GCSE A-levels / vocational Other sub-degree Total Whilst some reduction in the incidence of over-education was observed within the cohort between first and current employment, almost a quarter of the cohort remained in non-graduate employment despite over two years of labour market experience. This apparent inertia is somewhat surprising given that a quarter of the cohort changed jobs over the period, thus considering the prior evidence of higher rates of occupational mobility amongst the over-educated we might have expected to see a more dramatic reduction in the rate of under-employment. However, a closer examination of the data revealed that most of the mobility tended to be concentrated within over-educated categories as opposed to between them. This is illustrated by the transition matrix of Figure 2, which demonstrates that whilst the over-educated tend to be mobile (with 40% changing jobs compared to 17% of the suitably educated cohort), a substantial amount of moves (41%) resulted in workers changing from one state of over-educated employment to another. Thus whilst the analysis confirms that over-educated workers tend to be more mobile (Alba-Ramerez 1993, Sicherman 1991) it also supports the contention that increased job mobility does not necessarily guarantee an exit from an over-educated state (Sloane 1999). 11

17 Figure 2 First Job overeducated First Job not over-educated Second Job over-educated Second Job not over-educated 12

18 EMPIRICAL ANALYSIS The empirical analysis is centred around three sample selection wage equations. Equation one measures the impact of human capital variables on wages whilst equation two constitutes a more inclusive model containing human capital, job characteristic and environmental controls. Finally, equation three assesses the effect of introducing the job utilisation variables into the inclusive regression. The first step of the empirical analysis involved the estimation of a probit model describing entry to the Premiere programme. The model s explanatory variables included controls for gender, faculty, religion, degree classification, post graduate study, self assessed skill levels, social exclusion and experience (see data appendix for details). The results of the programme entry model are reported in Table 3, the equation is quite poorly specified with only the self-assessed skill variable proving significant, suggesting that more capable graduates were more likely to be admitted onto the programme. Table 3: Selection equation for programme admittance Dependant: Programme Participation (binary) Variable Test Statistic Constant [0.723] Arts Degree (base case Agr, Tour, Law & Other) [0.433] Social Science Degree [0.477] Science Degree [0.504] Business Studies Degree [0.474] Maths, Engineering or Computing Degree [0.729] Post Graduate in [0.531] Experienced [0.077] TSN [0.322] Female [0.268] Urban [0.378] Degree classification [0.178] Catholic [0.285] Self-assessed skill-level [0.068]*** Log likelihood function Significance level

19 Despite the relatively uninformative nature of the regression, the model specification was significant and consequently the IMR was retained as a selection control to be used in subsequent estimations. In order to ensure that the sample selection models were properly specified, care was taken to ensure that the first specification included at least one variable that was absent in the starting wage equation 8. As a very basic test of HCT, the starting wage equation was initially estimated in a reduced form (RF) to include only human capital components and, despite the postgraduate variable being weakly significant, it was not possible to reject the hypothesis that the coefficients were jointly zero (Table 4). Dependant: Log wages Table 4: Reduced form wage equation (OLS) Variable Test Statistic Constant [0.205]*** Art Degree [0.0990] Social Degree [0.106] Science Degree [0.132] Business Studies Degree [0.101] Maths, Engineering or Computing Degree [0.157] Post Graduate [0.064]* Self-assessed skill-level [0.022] Female [0.057] Unemployment > 6 months since qual [0.105] Experience [0.014] Degree classification [0.039] Premiere Programme [0.322] λ [0.193] R F-test 0.49 Nevertheless, the failure to find positive significant slopes is not sufficient for us to reject HCT, in fact, such a result is not really surprising given the homogenous nature of the cohort being examined. HCT essentially predicts a positive relationship between years of schooling and earnings, with the model generally performing well 8 Such exclusions should have an economic rationale, for instance the second stage models excludes the TSN variable on the basis that whilst coming from a TSN area might impact a person s chances of gaining access to a government programme it should have little impact on earnings. 14

20 when applied to heterogeneous panels of data (See Harmon & Walker 2000). However, datasets such as this (and that of D&V 2000) are concentrated around a very narrow segment of the distribution with little variation in the human capital component, thus the finding that HCT perform poorly in explaining wage variations when applied to such cohorts is largely unsurprising. The model was re-estimated in a more expansive form to include information on job characteristics, however, before this could be done cognisance had to be taken of a second potential source of selection bias. Some programme participants entered the labour market either directly as a result of their work placements or through the programme deliverer s recruitment agency on completion of training. Thus, it is important to guard against the possibility that better candidates were being put forward for placements with a high likelihood of permanent employment or sent to the deliverer s corporate clients at the end of the training period. Consequently a probit model for labour market entry (through the programme) was estimated and a second IMR extracted for inclusion in the second stage models 9. The results of the labour market entry regression indicate that females and persons who had experienced a spell of long-term unemployment were less likely to be placed in permanent employment through the programme (Table 5). 9 Note that due to the fact that those persons failing to complete the course were removed from the sample, in essence, we are controlling for bias in both programme selection and completion. 15

21 Table 5: Selection probit for labour market entry through programme Dependant: Labour market entry through premiere (binary) Variable Test Statistic Constant [0.704] Arts degree [0.445] Social Science degree [0.439] Science degree [0.568] Business Studies degree [0.452] Maths, computing or engineering degree [0.653] Post Graduate [0.475] Experienced [0.070] Female [0.264]** NI Universities [0.312] Urban [0.374] Degree classification [0.190] Catholic [0.262] LT Unemployed [0.425]*** Self-assessed skill-level [0.063] Local unemployment rate [0.004] Log likelihood function Significance level When estimating the inclusive model a parsimonious approach was adopted on the basis that the combination of a relatively small sample size and a large number of variables might serve to obscure important relationships. Consequently, the variable with the lowest t statistic was repeatedly removed until only variables with a t statistic greater than or approaching 1 remained, however, the selection terms were only removed if both they and the controlled variable proved insignificant. The results of the inclusive starting wage models in both parsed and unparsed form are reported in Table 6 and it is immediately obvious that the model is much better specified relative to the RF model with both human capital and job characteristic variables proving significant. 16

22 Dependant: Log Wages Table 6: Inclusive wage equation (OLS) Variable Unparsed Parsed Constant [0.225]*** [0.134]*** Human Capital Variables Arts degree [0.098] Social Science degree [0.119] Science degree [0.143] [0.086] Business Studies degree [0.097] [0.062]** Maths, Engineering & Comp [0.153] Post Graduate [0.065]* [0.059]** Self-assessed skill-level [0.030] [0.018]** Female [0.064] Premiere [0.461] [0.228]** LT Unemployed [0.113] Degree Classification [0.038]* [0.034]* Experience [0.014] Job Related Variables Small firm [0.055]*** [0.050]*** PREMJOB [0.222] [0.14] Manufacturing [0.067] SUB [0.140] No qualifications [0.169] ALEVVOC [0.080]* [0.071] GCSE [0.091]*** [0.839]*** Environmental / Other Urban [0.084] Catholic [0.063]* [0.054]** Local unemployment rate [0.000]* [0.000]** TSN [0.079] λ [0.272] [0.135]** λ [0.134] [0.091] R F-test 1.87** 3.34*** Dealing firstly with the human capital control variables, possession of a postgraduate qualification earned a 13 per cent starting wage premium whilst business studies graduates tended to enjoy a similar advantage relative to the base case. Graduates 17

23 who reported higher skill levels and / or had gained a higher degree class earned starting salaries that were significantly below those of their less able / less qualified counterparts. Such a finding runs contrary to both intuition and the D&V (2000) analysis, which reported a positive and weakly significant relationship between starting earnings and degree class. However, this apparent contradiction can most readily be explained through an increased availability of professional level opportunities for graduates relative to the comparatively depressed period of the late 80 s and an absence of controls in the model for professional / firm specific training. The basic argument is that better quality students are more likely to gain access to professional occupations that require firms to invest in further training which is usually provided at a cost to the individual of deferred higher earnings implying lower starting salaries 10. This is confirmed by an earlier analysis of the data which revealed a significant inverse relationship between skill levels and the probability of being over-educated, implying that better quality students tended to get better quality jobs 11 (McGuinness 2002). Turning to the job characteristic controls, whilst it was found that workers employed within small firms (<=50) earned less than their counterparts in larger firms, the most significant results related to the over-education variables. Whilst over-educated workers generally earned less than their counterparts, the results were significant only for workers employed in jobs requiring GCSEs and A level / vocational qualifications (the A level / vocational variable was not significant within the parsed model) with these employment states associated with pay penalties of 44 and 10 per cent respectively. The result confirms the observation that over-educated persons earn less than persons with similar levels of education who are fully utilised in their jobs, thus casting further doubt on the usefulness of HCT as a predictor of wage rates within such cohorts. However, although the result implies that over-educated workers are at a relative disadvantage compared to their adequately educated counterparts, it in no way implies a negative return to years of surplus educational investment i.e. overeducated workers may still have gained something for each year of excess education (see Cohn & Kahn 1995). 10 We might expect to observe a positive relationship between degree classification / ability and current earnings, however D&V (2000) report the relationship to be positive but non-significant and cite this as evidence of HCT failure. 18

24 In relation to the public policy variable, participants on the Premiere programme earned substantially more relative to members of the control group, however, there was some selectivity in the sample with Premiere type persons generally expected ex ante to earn 30 per cent less than randomly distributed individuals. This would seem to suggest that assistance was effective in that it was targeted towards individuals who would generally be expected to be in receipt of lower earnings and had the effect of raising the starting wage rates of such individuals. However, exactly why Premiere participants might be expected to earn less ex ante is unclear at this point, consequently the issue shall be re-addressed at a later stage of the analysis. A disturbing element of the inclusive regression is the persistence of the Catholic variable in both parsed and unparsed models, which, given the large range of controls in the model, suggests that their may exist some wage discrimination 12. Finally, in relation to the environmental controls, an inverse relationship was reported between local unemployment rates and starting salaries, suggesting that in small regions such as NI where every location is accessible to the main urban conurbation s, firms in relatively depressed areas offer a slight premium in order to attract and retain educated labour. As previously mentioned the dataset contains a variable, which proxied the imbalance between a person s skill attributes and their utilisation within organisations. Implicit within this variable is recognition of the heterogeneity of both individual skill levels and firm requirements. Thus, if over-education arises because of an unwillingness and / or an inability on the behalf of firms to adjust their production processes to changes in the relative supply of skilled labour, then we might expect most, if not all, of the earnings disadvantage to disappear when controls for the inertia of firms are included within the model. As a further control for job under-utilisation, a responsibility index incorporating the extent to which an individual had authority over factors such as staffing, policy, budget and innovation was included in the model. The results of the re-estimated inclusive regression are reported in Table 7, the influence of both controls (but most particularly for the utilisation variable) was positive and significant within the parsed regression, indicating that starting salaries 11 Degree classification was not significant within the incidence models 12 There is a significant literature on this issue, see Borooah (1999), Gudgin & Breen (1996). 19

25 were higher within organisations where individuals had greater freedom to express their talents. Table 7: Inclusive wage equation with utilisation controls (OLS) Dependant: Log Wages Variable Unparsed Parsed Constant [0.278]*** [0.168]*** Human Capital Variables Arts degree [0.097] Social Science degree [0.114] Science degree [0.141] [0.083] Business Studies degree [0.093] [0.060]* Maths, Engineering or Comp [0.147] Post Graduate [0.629]** [0.558]** Self-assessed skill-level [0.031]** [0.018]*** LT Unemployed [0.109] Experience [0.014] Degree classification [0.037]* [0.033]* Premiere [0.477]** [0.230]*** Female [0.063] Job related variables Small firm [0.053]*** [0.049]*** Manufacturing [0.064] Responsibility [0.017] [0.016]* Utilisation [0.054]*** [0.046]*** PREMJOB [0.216] [0.141] SUB [0.136] No qualifications [0.165] ALEVVOC [0.080] GCSE [0.095]*** [0.083]*** Environmental / Other Urban [0.084] Local unemployment rate [0.000] [0.000]* TSN [0.076] Catholic [0.061]* [0.052]** λ [0.277]* [0.134]** λ [0.129] 0.231[0.088] R F-test 2.34*** 4.17*** 20

26 However, whilst the pay penalty associated with A-level / vocational employment became non-significant, the penalty associated with GCSE level employment remained both substantial and significant despite falling by almost 30 per cent. This result demonstrates that even if the over-educated were fully utilised within their current jobs, they would still incur a wage penalty i.e. they would not, as HCT predicts, earn the equivalent of their marginal product. The results suggest that a large proportion of the wage penalty associated with being over-educated is independent of the level of skill utilisation within firms. One possible explanation for this is that graduate wage levels are heavily related to sheepskin effects, however, the disproportionately large return is gained on the attainment of a graduate level job as opposed to the attainment of a degree per se. These sheepskin effects imply that the process of classifying graduate jobs is rather arbitrary in nature and by no means wholly related to the actual skill requirements of the job. Thus, the evidence suggests that a significant proportion of what is observed as graduate over-education may, in fact, be due to a classification as opposed to a skill mismatch. Finally, in an attempt to assess the most adequate theoretical framework to describe the wage determination process of this particular cohort, the hypotheses that the coefficients on the human capital and job description variables within the final inclusive models were jointly zero were tested. This analysis generated test statistics of F 7, and F 6, respectively, demonstrating that both human capital and job characteristics are important factors in determining wage rates within this cohort, thus lending support to the assignment interpretation of the labour market and rejecting both the HCT and Job Competition models as appropriate specifications. Returning to the impact of public policy, the results suggest that Premiere leavers are more likely to earn significantly less than a student randomly selected from a distribution of new labour market entrants with relatively low labour market probabilities. Such a finding might lead us to believe that Premiere entrants are in some way less competent relative to unsuccessful candidates implying that the assistance agency actually pursued a policy of picking losers, however, the selection equation for programme entry would suggest that the opposite is in fact true. Therefore, Premiere leavers must have a greater tendency to enter jobs with characteristics that are associated with lower earnings, for example, SMEs or 21

27 companies with relatively low capabilities of utilising the talents of graduate level employees. To cast further light on the matter, sample selection models were estimated for both small and under-utilising firms, whilst the SME regression provided little insights, the under-utilisation model was more useful and the results are presented in Table 8. Dependant: Utils Table 8: Job quality equation (OLS) Variable Test Statistic Constant [0.365]*** Arts degree [0.168]** Social Science degree [0.178] Science degree [0.228] Business Studies degree [0.170] Maths, Engineering or Computing degree [0.264] Post Graduate [0.110] Female [0.112]** Self-assessed skill-level [0.037]*** Premiere [0.535]*** LT Unemployed [0.188] Experience [0.025] Degree classification [0.0648] PREMJOB [0.366]** λ [0.322]** λ [0.222] R F-test 3.22*** Higher skilled individuals were more likely to enter highly utilised employment, as were those entering the labour market through the programme deliverer s placement / recruitment efforts, these variables are also associated with lower starting wages which tends to support the job quality / training / wage relationship outlined earlier. 22

28 The Premiere selection variable indicates that entrants to the programme are significantly more likely, ex ante, to enter highly utilised employment relative to a randomly distributed individual cohort member. However, the variable proxying participation indicates that Premiere leavers are, for some inexplicable reason, more likely to enter under-utilised employment. Thus qualifiers from the programme, not entering the labour market through components of the programme, are more likely to enter lower quality employment, however, once in these jobs they tend to do rather well, enjoying higher starting salaries. Given the low quality nature of their employment, it is unlikely that the future earnings growth of Premiere qualifiers (not entering the labour market through the programme) will keep pace with the their counterparts who are in more highly utilised employment. Thus, the results of the policy intervention are generally less than convincing, however, given that a high proportion of business graduates appear to be having difficulty in gaining graduate level employment, it is perhaps not surprising that significant benefits did not arise as a result of converting graduates from other backgrounds into business. 23

29 SUMMARY AND CONCLUSIONS It is clear that a significant proportion of UK graduates are being under-utilised within the economy 13 and that the phenomenon represents a substantial loss to both national output and welfare. This paper examined the nature of over-education and the effects of a public policy on a cohort of graduates drawn towards a graduate conversion programme on the basis of a relatively homogenising set of labour market expectations. Dealing firstly with the issue of over-education, the empirical analysis suggests that, relative to HCT and job competition models, the wage determination process of this relatively homogenous cohort is best described through assignment theory. However, it is not sufficient to attribute the existence of over-education wholly to the failure of firms to adequately adjust their production processes to the skill attributes of workers as lower returns to surplus education are still likely to be evident in the wake of such adjustments. The persistence of lower wages amongst over-educated workers despite the inclusion of skill mismatch controls, suggests that graduate wage levels are, at least to some extent, determined by other external factors. Sheepskin effects associated with the attainment of jobs where the possession of a degree is an explicit requirement is the most obvious explanation for such persistent differentials, implying that, within the graduate labour market, the job categorisation process is somewhat arbitrary and independent of the actual skill requirements.. The impact of public policy in this case was less than convincing, whilst there was a wage premium arising from programme participation, it existed only in the relative context of programme participants having a higher tendency to enter low pay / utility employment and doing better in these low quality jobs. However, the relative ineffectiveness of the programme is not particularly surprising given that the subject area into which students were being converted was already relatively over-supplied. The analysis highlights the importance of selecting an appropriate conversion platform within the context of any policy initiative designed to tackle the issue of graduate over-education. 13 Existing studies estimate the level of graduate over-education to lie between 11% and 40% (Dolton and Vignoles 2000, Battu, Belfield and Sloane 2000, Sloane, Battu and Seaman 1999, Groot, 1996) 24

30 References Alba-Rammírez, A. (1993). Mismatch in the Spanish labor market. Journal of Human Resources, 28 (2) Barooah, V. K. (1999). Is there a penalty to being a catholic in Northern Ireland: An econometric analysis of the relationship between religious belief and occupational success. European Journal of Political Economy, 15 (2) Battu, H. Belfield, C.R. & Sloane, P.J. (2000). How well can we measure graduate over-education and its effects? National Institute Economic Review, 171, Becker, G. (1964). Human capital: A theoretical and empirical analysis with special reference to education, Columbia University Press, New York. Chevalier, A. (2000). Graduate over-education in the UK. Report published by the Centre for the Economics of Education, London School of Economics. Cohn, E. & Khan, P. (1995). The wage effects of overschooling revisited. Labour Economics, 2, Dolton, P. & Vignoles, A. (2000). The Incidence and effects of over-education in the U.K. graduate labour market. Economics of Education Review, 19, Duncan, J. D. & Hoffman, S. D. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1 (1), Groot, W. (1996). The incidence of, and returns to over-education in the UK. Applied Economics, 28, Harmon, C. & Walker, I. (2001). The Returns to Education: A review of the evidence, issues and deficiencies in the literature. Department for Education and Employment, Research Report RR254. Groot, W. & van den Brink, H. (2000). Over-education in the labor market: a metaanalysis. Economics of Education Review, 19, Gudgin, G. & Breen, R. (1996). Evaluation of the ratios of unemployment rates as an indicator of fair employment. Central Community Relations Unit Report. Jaeger, D. & Page, M. (1996). Degrees Matter: New evidence on sheepskin effects in the returns to education. Review of Economics and Statistics, 78 (4), Hartog, J. (2000). Over-education and earnings: Where are we, where should we go? Economics of Education Review, 19, Hartog, J. & Oosterbeek, H. (1988). Education, allocation and earnings in the Netherlands: Overschooling? Economics of Education review, 7 (2),

31 Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47 (1), Heckman, J., Ichimura, H., Smyth, J. & Todd, P. (1998). Characterizing selection bias using experimental data. Econometrica, 66 (5), Himler, M. J. (2001). A comparison of alternative specifications of the college attendance equation with an extension to two-stage selectivity-correction models. Economics of Education Review, 20, McGuinness, S. (2002). Private sector postgraduate training and graduate overeducation: Evidence from Northern Ireland. International Journal of Manpower (Forthcoming). Puhani, P. A. (2000). The Heckman correction for sample selection and its critique. Journal of Economic surveys, 14 (1), Rumberger, R. W. (1997). The impact of surplus schooling on earnings and productivity. The Journal of Human Resources, 22 (1), Sattinger, M. (1993). Assignment models of the distribution of earnings. Journal of Economic Literature, 31, Sicherman, N. (1991). Over-education in the labour market. Journal of Labor Economics, 9 (2), Sloane, P.J., Battu, H. & Seaman, P.T. (1999). Over-education, undereducation and the British labour market. Applied Economics, 31, Thurow, L. C. (1975). Generating Inequality. Basic Books, New York. 26

32 Data Appendix Art Degree: Dummy indicating that individual held an Arts degree Social Science Degree: Dummy indicating that individual held a Social Science degree Science Degree: Dummy indicating that individual held a Science degree Business: Studies Degree: Dummy indicating that individual held a Business Studies degree Maths Engineering or Computing Degree: Dummy indicating that individual held a degree in Maths, Engineering or Computing a. Small Firm: Dummy indicating if individual was employed in a firm of less than 50 persons Post Graduate: Dummy for possession of a postgraduate degree B. Female: Gender Dummy Self Assessed Skill Level: Respondents were asked, on a scale of 1 to 10, to subjectively assess their skill levels immediately following university in 16 areas relating to their potential employability. The skill areas assessed were word-processing, spreadsheets, data management, knowledge of IT packages, Internet use, corporate finance, product / process management, quality assurance, customer awareness, HRM, corporate statutory requirements, interpersonal skills, leadership skills, organisational skills, team building. An unweighted mean measure of skill / employability was derived with the variable taking a value within the 1 to 10 range with 1 denoting the lowest mean skill level and 10 the highest. Catholic: Religion Dummy Urban: Dummy denoting residence in one of the main urban conurbations of Belfast or Derry. Premiere: Dummy denoting programme participation. LT Unemployed: Dummy indicating that an individual had been unemployed for 6 months or more since leaving Premiere / university. Manufacturing: Sector Dummy (base case service sector). Experience: It was assumed that most candidates spent 22 years out of the labour market (pre-school period + education duration), thus potential labour market experience is proxied by age at the time of survey less 22. Degree Classification: Took the value 1 if candidate achieved pass level (or HND), 2 if achieved 2:2, 3 if achieved 2:1 and 4 if candidate achieved first class honours. A Degrees were allocated to subject areas using the Higher Education Statistics Agency (HESA) classification system, the faculty base case was Other (comprised of Agriculture, Law & Other). B In the programme selection equation checks were made to ensure that the postgraduate dummy was restricted to those cases were the qualification was obtained prior to applying for the programme. 27