Componente Socioeconómico

As noted in Chapter 4, HILDA was not primarily designed to collect data on skilling, and most of the research done so far which uses its data has focused on other aspects such as wealth and poverty, family formation, women’s career patterns and flows between employment and unemployment. However, five recent studies use the key skilling variables to examine issues of relevance to this thesis.

Carroll and Poehl (2007) apply logit modelling techniques to data from the first five waves to examine job mobility and the factors that drive it. They note that HILDA provides the first reliable large-scale data source for mapping flows within employment (as opposed to flows between employment and unemployment) in the Australian economy. Their specific interest lies in the relative importance of individual preferences or characteristics and objective considerations of job-person match in driving voluntary job change. Their findings are ultimately somewhat inconclusive, since the authors suggest that several of the observable factors that are found to reduce the odds of job separation (firm-specific human capital, tenure in present and previous jobs, marital status, union membership, public sector employment) could simply reflect unobserved heterogeneity, in that individuals with an inherent preference for stable employment may self-select into kinds of job or life situation that promise or demand greater stability. Their research is largely peripheral to the issues covered in this thesis, except in that it emphasises both the statistical importance of labour mobility as a potential source of allocative efficiency (an important element in the supply mechanism as set out in the model in Chapter 2) and the importance of job satisfaction as a determinant of mobility or worker-firm attachment.

McGuinness and Wooden (2007), in research that appears to have been conducted

specific relationships between overskilling and job mobility. Their measure of overskilling is derived from USESKILL, with those scoring the item 1-3 classified as “severely

overskilled” (15-28% of sample in each year), those scoring 4-5 as “moderately

overskilled” (25-28%) and those scoring 6-7 as “well matched”. Their variables of interest relating to job mobility are intention to leave current job, perceived probability of losing current job, and expectations of finding an equally satisfactory replacement job. These three dependent variables were regressed in a fractional logit model against the overskilling variable, a range of demographic variables, employment type and duration, and indicators of underskilling and upskilling derived from COMPLEX and NUSKILLS respectively, though the precise nature of these indicators is not described in the paper.

The results of this first analysis indicated that overskilled workers believe themselves to be more likely to quit their present job voluntarily within the next 12 months than well-

matched ones, by 10% in the case of the severely overskilled and 3% for the moderately overskilled, once other potential causal factors have been taken into account. This far the results are consistent with expectations. However, overskilled workers also proved to be more apprehensive of losing their job involuntarily, and in the case of moderately

overskilled workers, marginally less optimistic about finding a comparable replacement job. When actual job changes between waves were regressed in a random effects probit model against overskilling and the same demographic variables, overskilling proved as expected to be a significant predictor of voluntary job change (8.2% increased odds for the severely overskilled and 4.4% for the moderately overskilled), but also marginally

increased the odds of involuntary job loss.

As a final test, descriptive statistics were used to compare the USESKILL scores of

voluntary job changers between Waves 3 and 4. Only 38% of those previously classified as overskilled reported an increased score on this variable as a result of their change of job. On the basis of these analyses, the authors argue that overskilling in Australia cannot be regarded as a purely transitory or frictional phenomenon which is resolved by natural labour mobility, a conclusion which suggests that it may be symptomatic of a more durable dysfunction in the labour market.

Mavromaras, McGuinness and Fok (2007a) extend this analysis to examine the wage penalties that attach to overskilling, as evidence of lost productivity resulting from the failure to deploy workforce skills effectively. Their data are drawn from the first five waves, using a combined sample and the same definitions of overskilling as the McGuinness/Wooden paper. Inclusion of Wave 5 alters the proportions of severely

overskilled, moderately overskilled and well matched to 11%, 31% and 58% respectively of the combined sample (n = 5,843).

Their analysis uses ordinary least squares (OLS) regression to estimate the wage penalty for severe underskilling. Measured against the wages of otherwise comparable workers who are well matched, this penalty averages out at 13.3% across the sample, but is especially pronounced for graduates (23.8%). These findings proved robust when subjected to further testing based on propensity score matching (PSM) to control for the possibility of

unobserved heterogeneity (e.g. rises in participation and retention rates at the higher levels of education leading to a broader spread of ability in each qualification group). Averaging out the OLS and PSM results produced estimates of the wage penalty ranging from 20% for graduates down to 8% for employees holding vocational certificates or diplomas (a

by the OLS estimates is more modest at 4.9% and 5.1% respectively, and lost most of its significance after PSM testing.

Basing their calculations on the severely underskilled only, the authors estimate the annual wage penalty at $3,979 for a vocationally qualified employee, $6,257 for one with 10 to 12 years of schooling and $13,723 for a graduate. Extrapolating from the sample to the Australian population, and assuming that the wage penalty is equivalent to the value of forgone productivity, they tentatively suggest a total annual productivity loss to the Australian economy of just under $6 billion, or around 2.6% of GDP, resulting from

inadequate deployment of the skills of the full-time employed labour force. They argue that this is likely, if anything, to be an underestimate of the true productivity cost, citing

evidence adduced in the UK by Dearden, Reed and van Reenen (2006: 414) of a “wedge” between the productivity and wage effects of training which suggests that the actual productivity differential could be as much as twice the wage differential. It should also be borne in mind that this estimate excludes productivity lost as a result of underemployment of part-time workers (i.e. persons working fewer hours than they would prefer),

underutilisation of the skills of part-time workers who are working their preferred hours, and skilled unemployment.

Mavromaras, McGuinness, O’Leary, Sloane and Fok (2007b) add an international comparative dimension to the analysis by including data from the 2004 UK Workplace Employment Relations Survey (WERS). The latter, though containing a broadly comparable question on skills-job match, differs significantly from HILDA in sampling method (random selection of employees from a cross-sectional sample of workplaces, as opposed to a longitudinal panel sample of households), the way the question is asked (“How well do the skills you personally have match the skills you need to do your present job?”) and the scaling method (5-point scale with the individual points anchored by verbal tags i.e. much higher, a bit higher, etc.). Differences in findings between the two surveys may well be due at least in part to sensitivity of the response to the last two matters in particular. In this paper the HILDA data are drawn only from the first four waves. Despite the differences, the overall incidence of moderate overskilling in both countries emerges as surprisingly similar at 33.41% for Australia and 33.36% for the UK. However, severe overskilling appears to be much more common in the UK, at 20.86% as against 14.23%, and there are marked differences between the two countries in the distribution of overskilling by educational level, occupational level and industry. Severe overskilling in the UK is more or less evenly distributed across educational categories, but in Australia its incidence declines strongly with education level, except for postgraduate qualifications. Respondents with bachelor’s degrees have the highest incidence of good matches in Australia (61.91%), but the lowest among all categories in the UK (44.43%). In both countries, the incidence of overskilling varies inversely to occupational level, but the distribution is much more even in the UK, with severe overskilling in the managerial and professional classifications running at around three times the level shown in the Australian data. The distribution of severe overskilling across industries also shows much more variation in Australia, ranging from 7% to 25% across ANZSIC major categories as against 17-27% for the comparable UK categories.

While a number of credible explanations could be put forward for these differences (e.g. higher workforce expectations, a more recent bulge in the proportion of the workforce holding degrees, or the persistence of the low-skill equilibrium in the UK), some caution is

required in interpreting the findings because it is not certain that the two scales have been appropriately matched in the comparison, or even that they measure the same construct. The verbally anchored response categories in the (Likert-type) WERS question could be seen as encouraging confidence in both the reliability and the interpretation of the response, whereas the meaning of the unlabelled individual points on the (true) Likert item in HILDA is open to a variety of interpretations besides that followed by the authors. This problem will be further discussed shortly, and is addressed in more detail in Chapter 6. Meanwhile, without supporting evidence that the difference between the findings reflects a real

difference in the working of the respective skilling systems, it remains at least open to hypothesise that the threshold of “severe” overskilling may have been set higher for the Australian than the UK data.

These doubts are somewhat offset by the results when the underskilling variables were fed into a standard wage regression for each country. The model used for the regression in this paper was different from that used in the paper previously cited, resulting in a more modest estimate of the wage penalty for severe overskilling in Australia at 8.2%. However, the corresponding penalty for the UK was almost half as high again at 12.0%. The penalties for moderate overskilling were more comparable, at 2.5% for Australia and 2.9% for the UK. If the prevalence of severe overskilling in the UK was indeed overestimated by comparison with Australia, one would expect the wage penalties for the moderate and severe categories to be closer, ceteris paribus, in the UK because the “severe” category would include many respondents whose job-person match would have qualified as them only moderately overskilled on the scale used for the Australian data. That the actual gap is so much bigger suggests, if nothing else, that cetera are not paria and there are different circumstances in the UK labour market which result in a much larger penalty for

overskilling across the full range of possible levels of mismatch, albeit still with a strong bias against the most overskilled. Such a conclusion appears to be much more robust to differing assumptions about the comparability of the two scales, and on the surface is hard to dismiss as an artefact of the method.

Disaggregating the results by educational level, the contrast between the two countries is strongest at the Year 10 level, but this time in favour of the UK, with the respective wage penalties suggesting that Australia has by far the larger problem with skills mismatch at this level. At most other levels the two countries produce broadly similar results, with holders of degrees and postgraduate qualifications experiencing the highest disadvantage

(remembering that the incidence of overskilling at these levels is much higher in the UK). In both cases the wage penalty for severe underskilling disappears at the lowest levels of education; the authors surmise that this results from the existence in both countries of minimum wage laws which limit the scope for such a penalty to be imposed. Taking all the results together, the authors calculate that 61% of the Australian labour force and 79% of the British labour force experience a wage penalty of at least 10% as the result of

underutilisation of their skills.

This third paper briefly examines the impact on wages of job discretion, an analysis only foreshadowed in the earlier paper. Inexplicably, the authors do not merely overlook the task discretion variables in HILDA, but actually assert that no such variables exist (2007b: 24). Hence, the analysis is carried out only for the UK. Using the same regression model as the other analyses in the same paper, they find a strong positive association between wages and job discretion, most marked in the case of the variable “involvement in decision- making” (directly corresponding to HAVESAY). They find that the wage effects of

overskilling are offset, albeit modestly, when workers have reasonable control over their work, and suggest that this results from the workforce having more discretion to determine its own level of productivity.

The work of McGuinness, Mavromaras and their colleagues, presumably still in progress, has considerable relevance to this thesis and has laid invaluable groundwork for the analyses to be carried out in subsequent chapters. In many respects, given their far greater command of advanced inferential methods, they set an example which the present author cannot hope to equal in the timeframe of this thesis. They also make a contribution that will not even be attempted here by setting a dollar value (however tentative) on the

productivity that is lost across the economy as a result of underutilisation of the skills of the workforce. This said, two potentially important differences exist between their

methodological paradigm and focus and those to be followed in this thesis:

• The work of these authors incorporates many assumptions of the traditional labour economics and human capital paradigms, albeit with an important distinction. They see their findings as supporting the class of theories on the operation of the labour market known collectively as assignment models (Mavromaras et al, 2007a: 24; Sattinger, 1993). These models replace the more traditional assumption among quantitative economists that productivity is directly determined by the individual characteristics of the worker with the premise that the main problem for the labour market is to assign the available workers among the available jobs in a way that produces the best match – a function parallel to that of allocation in the model set out in Chapter 3 above1. Assignment models acknowledge the importance of exogenous factors, e.g. the availability of complementary assets, in shaping the range of jobs that is actually on offer at any given time, and thus provide an account of how it is possible for some workers to be deployed, and/or remunerated, at less than their optimal productivity even in the absence of market imperfections. They thus represent a kind of middle path between conventional human capital theory and system approaches that takes account of many of the confounding factors listed in 3.1 above. Nevertheless, they still ultimately assume the theoretical possibility of equilibrium, and hence depart from the assumption of constitutive dynamic imbalance which underlies the system approach.

• The main focus of such models is different from that of the analysis undertaken in this thesis. The ultimate aim of such econometric approaches is to develop a model that can predict efficiency wages in a range of circumstances, or at the least, can account for observed levels and patterns of wage inequality. Skills (generally treated as fixed attributes of the individual worker) feature in such models as an important independent variable, but are not the true object of interest. By contrast, the analysis of skilling systems outlined in this paper treats skill, as exercised at the point of production, as its key output variable. Wages may enter the equation as an independent variable, an indicator or possibly even a proxy, but the model does not purport to explain or predict wage levels in any comprehensive fashion.

1

Strictly speaking, assignment in these models is treated as an aspect of efficiency that is reflected in outcomes. Allocation, in the model developed in this thesis, is an observable process or mechanism that requires study in its own right, with considerations of its efficiency determined in the context of the overall skilling system or its subsystems. Although subtle, the distinction seems sufficiently substantive to justify using different terms for the two constructs.

While both these differences are fundamental at the level of underlying concept and mechanism, they do not necessarily rule out individual analyses or findings which are common to both kinds of model. At the level of analysis currently under discussion, the practical differences between the two approaches are unlikely to be significant, but the gap can be expected to widen as the analyses become more ambitious. One issue on which the distinction is already apparent is that this present research will not attempt its own

calculation of the productivity costs of skill underutilisation because a systems model, in which all the elements are in constant, asynchronous adjustment to one another, does not allow for such straightforward and unequivocal counterfactuals. This said, the work done by Mavromaras et al provides a very useful order-of-magnitude indication of the degree of flexibility that exists within the Australian NSS as currently configured.

A more strictly technical point of methodology also needs to be signalled. The present research follows a more conservative approach to the interpretation of scores on each question than that taken by McGuinness et al. On the face of it, it is difficult to see why a score of 5 on the HILDA question (i.e. marginal agreement with the proposition “I use many of my skills and abilities”) should be included in the range of responses classified as overskilling. When it comes to comparing the Australian and UK findings, it seems at least as problematic in principle that the range of responses classified as overskilling should cover four points on a seven-point scale in Australia, but only two points on a five-point scale in the UK. The difficulty is heightened by the way the questions are put in the different instruments: in WERS the category “moderately overskilled” is explicitly anchored on the response scale, whereas the wording of the HILDA question introduces a second ambiguity (How many of one’s skills and abilities counts as “many”?) that further complicates interpretation of the response.

A pragmatic response would be to exclude the “moderate overskilling” category from the analysis, given its relatively low significance in the wage equations for both countries, and concentrate on those responses which clearly and unequivocally indicate overskilling. If