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Mismatch Definitions

Freeman (1976) introduced the notion of education mismatch, particularly overeducation, in which education is measured objectively by comparing a worker’s level of education attainment with what is required by the worker’s job. Consequently, education mismatch arises when the educational qualifications of the workers, individually or in the aggregate, are different from the qualifications required by or specified for their jobs (Sattinger, 2012), and as such it can be an over/undereducation. Overeducation refers to the phenomenon where workers have more education than their job requires (Silles and Dolton, 2002), while undereducation occurs when workers have lower education than the requirement. In other words, a match occurs when workers have exactly the education the job requires. Here, education is defined as the highest level or years of education achieved by individuals.

Conceptually, many studies have also explored different definitions of education mismatch, such as Rumberger (1981) who defines the mismatch in three ways: firstly, as a decline in the economic position of educated individuals relative to historically higher levels; secondly, as an under-fulfilled expectation of the educated with respect to their occupational attainments; and thirdly, as the possession of greater educational skills than their jobs require. Furthermore, Oliveira et al., (2000) argue that undereducation is the outcome of a process in which market-acquired capital substitutes for insufficient school- supplied qualifications, where overeducation is associated with excess schooling but short tenure and job experience. In a similar vein, Gosling and Zhu (2010) separate overeducation definitions based on micro and macro levels. In micro level, overeducation is defined similar to Silles and Dolton’s (2002) definition. Meanwhile, in macro level, there are some characteristics of overeducation, such as a labour market which has “too many” graduates, a credentialism tendency and thus represents a disequilibrium.

As a result, a match is the most efficient condition for the economy, education mismatch, overeducation in particular, is potentially costly to the economy, the firms and the individuals. At the macroeconomic level, national welfare is potentially lower than what would be the case if the skill of all under/overeducated workers were fully utilised within the economy. It is also (probably) related to tax revenues being wasted on equipping

individuals with non-productive education. At the firm level, overeducation could be associated with lower productivity, as Tsang (1987) finds that overeducated workers have a negative effect on output. At the individual level, overeducated workers, by virtue of the fact that a proportion of their educational investment is unproductive, are likely to earn a lower return on their investment relative to similarly educated individuals whose jobs match their education (Ortiz, 2010).

According to previous empirical studies, overeducation measurement could also belong to the upper tail of the education distribution based on statistical definition. Similarly, undereducation refers to the lower tail of the distribution (Rumberger, 1981). Hartog (1997) defines overeducation as departing from more than one standard deviation from the mean, resulting in finding similar proportions of over and under educated workers - around 15 per cent of the population, if education is measured in years and the distribution of education per occupation is normally distributed. Kampelmann and Rycx (2012) find an increase of one year in the incidence of undereducation among young workers is found to decrease productivity on average by 3.5 per cent one year later.

In additions, another strand of literature on overeducation finds that there is a negative relationship between overeducation and job satisfaction (Battu et al., 1999; Chevalier, 2003). Chevalier (2000) proposes an alternative measure of mismatch based on occupation and job satisfaction, more precisely, whether the graduate is satisfied with the match between her education and her occupation. Graduates in a sub-graduate occupation who are satisfied are defined as apparently over-educated, whereas those who are dissatisfied are called genuinely over-educated. One advantage of using this definition of mismatch is that it refers to qualifications, not only education, and does not require an assessment of the educational level which requires doing the same job. Yet, it can be argued that this definition does not measure overeducation accurately, because the dissatisfaction between qualification and occupation could be due to undereducation; the dissatisfaction could reflect that despite being in a graduate job, this occupation is not related to the academic subject studied at university; and/or the job may require most of the skills that were learnt at university but also some more from a different field. To answer these criticisms, the definition of overeducation (based on job satisfaction) should be combined with the Professional Job Analysis (JA) method. Similarly, the negative correlation between overeducation and job satisfaction is also confirmed by Clark (2014).

In a similar respect, Chevalier (2000) distinguishes overeducation into two parts: apparent overeducation (i.e. overeducated only) and genuine overeducation (i.e. both overeducated and mismatched skill-wise). Moreover, a mismatch can either be vertical or horizontal or both. A vertical mismatch occurs when the level of the employee’s qualification is not the one required by the job, for instance, a graduate employee who works in a job that is typically considered a non-graduate job, in which case the graduate is over-educated. Meanwhile, a horizontal mismatch occurs when the level of the employee’s qualification is at the correct level for the job, but the type of the qualification is not, for example an individual with a degree in engineering working in a job that requires no engineering knowledge at all. A horizontal and vertical mismatch occurs when an individual may have a qualification that is both at the wrong level and of the wrong type for the job they are hired to do.

It is also worth noting that education and skill are not synonymous33_{. As Flisi et al., (2017)}

distinguish, education refers to an individual’s qualifications at a given point in time, which are bound by differences across countries and cohorts for the same level attained. By contrast, skills are acquired and lost over an individual’s entire lifespan, thereby providing a more concise and updated measure of competencies34_{. McGuinness et al.}

(2017) further that overskilled is a situation where a worker believes that they possess more skills than their current job requires, while underskilled occurs if the worker believes that their current skills do not meet the demands of the job. Mavromaras et al. (2009) argue that overskilled could be a more accurate measure of mismatch amongst existing workers than overeducation on the grounds that overeducation assumes that; (a) job entry requirements accurately reflect job skill content, and (b) a worker’s qualifications adequately reflect their total work-related human capital. Thus, the overeducation approach ignores the fact that job entry requirements may be weakly

33 _{Flisi et al. (2014) report that around 30 per cent of EU employment is overeducated (but not overskilled),} 17 per cent of them are overskilled (but not overeducated), and around 15 per cent are simultaneously mismatched; both overeducated and overskilled.

34 _{In terms of skill, World Bank (2010) has published an Indonesia skills report which reviews the main} characteristics of demand skills, documents the existence of a possible skill mismatch between employer demands and available supply and the contribution of the education and training sector to the mismatch. The analysis is based on employer and employee skill survey, involving around 473 medium and large firms and 200 employees in manufacturing and services sector in five provinces. The report highlights; an evidence of serious gap in generic and technical skills which puts young workers particularly in critical situation; at the more macro level, the employer survey suggests that skills are not yet a binding constraint to business development though the skills-for-needs issue does nevertheless appear non-trivial and, subjective assessments of difficulties of matching needs with available skills provide further evidence that skills are becoming a problematic aspect in Indonesia’s economy.

related to job content, and more reflective of qualification inflation and credentialism, while individual human capital will also consist of (non-formal and informal) skills acquired through labour market experience and training. Another reason why overskilled may be a more comprehensive measure of mismatch is that it compares all the skills and ability, irrespective of whether they are learned in the classroom or work environment, with the actual skill requirements of the worker’s current job (McGuinness et al., 2017). Although some studies find overskilled more accurate and comprehensive, there are several drawbacks of the method, such as: overskilled and underskilled are measured through separate questions, unlike education mismatch where a single question can be used to identify both over and undereducation. Also, skill mismatch is commonly measured through direct assessment by human resource specialists, and such direct measures are rarely captured in datasets. The questions adopted to investigate overskilled/underskilled vary substantially across datasets, consequently making it difficult to compare the estimates. Overskilled questions also could not allow the researcher to identify the relative importance of underused skills deriving from labour market experience, training, innate ability or formal schooling. In addition, skill mismatch measurement also could pose a bias in the estimates, for instance, the respondents’ job is related to their hobbies, as such when they formulate the response, their consideration of skills and abilities is totally unrelated to the workplace (McGuinness et al., 2017).

Mismatch Measurement

There are some basic approaches to measure a mismatch. These choices are typically restricted by data availability. Nonetheless, there is a growing literature centred on assessing the levels of consistency and potential biases associated with the various approaches. The summary of mismatch measurement is shown in Table 4.1, and the explanation follows after the table.

Table 4.1: Mismatch Measurement

Type of

mismatch Measurements Approach Method Education

Mismatch Objective Measures Normative/Professional Job Analysis (JA) Using information provided by professional job analysts: comparing job titles with actual education attainments

Statistical/Realised

Match (RM) Comparing education attainments with the mean of education level (± standard deviation) Subjective Measures Self- declared/ Self- reported/ Self- assessment Direct measures (DSA)

Asking respondents directly whether they are over/undereducated/matched

Indirect measures (ISA)

Asking respondents to give information on minimum job

requirements and individual's acquired education

Mixed/ Alternative methods (EMX)

Mixed between those methods

Adapted from: Filsi et al., 2014.

(1) Objective measures

Overeducation can be assessed objectively: (1) the Job Analysis (JA) method; by using the information provided by professional job analysts/JA (such as in the Standard Occupational Classification System in the UK or the Dictionary of Occupational Titles in the US) to determine an individual’s required education on the basis of their job title and again comparing this with their actual level of education (Rumberger, 1987); (2) Statistical/Realised Match (RM);by calculating the mean education level for a range of occupations with an individual defined as being overeducated if their qualifications are more than one standard deviation above their occupation’s mean education level (Sicherman, 1991); while undereducation occurs if the actual education attainment is lower than the mean level of education within their occupation (known as Realised Matches/RM). Apart from the mean, median or mode of educational level in an occupation can be used as well (Kiker et al., 1997).

These measures are also open to criticisms such as: (1) some occupations may contain a number of skill levels, so that in fact people with the same job titles may be doing very different jobs, for instance, the tasks undertaken by managers are likely to vary widely; and (2) rising education levels in the economy imply that employers will allocate workers

differently. For example, Mason (1996) reports that managers are now employing university graduates in mid-clerical positions, the posts traditionally held for persons educated to O and A levels standard (predominantly high school graduates). Thus, the educational requirements of various occupations will evolve with changes in relative supply; a factor that is not always readily incorporated into occupational classification systems that tend to be relatively static in nature. Hartog (2000) adds that a carefully conducted job analysis method should not lead to any systematic bias. Yet, this requires a regular update of the classification scheme. Otherwise, a general upgrade of skill requirements due to skill-biased technological change might lead to overestimation of the incidence of overeducation.

In terms of empirical evidence, Kiker et al. (1997) study the determinants of overeducation and undereducation in Portugal using Personnel Records (Quadros de Pessoal) dataset in 1991, collected by the Portuguese Ministry of Labour. Years of schooling variable is used to calculate the mismatch. The study employs three alternative methods to measure overeducation and undereducation: (1) the Verdugo and Verdugo (1989) or VV model, where job requirement is defined as actual occupation attainments of workers within occupations disaggregated at a 3-digit level. Workers whose education attainments fall within plus or minus one standard deviation of the mean value within the occupation are considered to be adequately educated; (2) the Mode model, where educational attainments equal the modal (mode) value within each occupation, overeducation or undereducation equals to modal education level plus or minus the standard deviation; and (3) the measurement based on job analysts’ opinion. The study finds that overeducation and undereducation exist in the Portuguese labour market. Comparing these methods, over and undereducation are highest under the third method, around 33.1 per cent and 37.5 per cent of the sample, respectively. Meanwhile, the second method (mode) results are only 25.5 per cent for overeducation and 17 per cent for undereducation. The lowest estimation is reported by VV method, only 9.4 per cent for overeducation and 5 per cent for undereducation. The study also reports another period (1985) of the same data and by using the same method finds that there is an increase of overeducation from around 18 – 26 per cent in 1985 to 25 – 33 per cent in 1991 and a similar increase for undereducation as well. Furthermore, overeducated workers are more likely to be the young members of the employed labour force, while undereducated workers are more likely to be the older members. The finding from Kiker et al. (1997)

seems to be more supportive of the role of technological change rather than of human capital in explaining overeducation/undereducation for the Portuguese economy; characterised by intensive efforts to promote economic growth, modernization of the industrial structure, and the upgrade of educational qualifications.

(2) Subjective measures

Overeducation can also be measured subjectively: (1) the Indirect Self-Assessment (ISA); by asking the respondents to give information on the minimum requirements of their jobs and then comparing this with the individual’s acquired education - some studies employed this method such as Duncan and Hoffman (1981) and Hartog and Oosterbeek (1988); or (2) the Direct Self-Assessment (DSA); by simply asking the respondents whether or not they are overeducated - used by Halaby (1994). And the last method is mixed or alternative method (EMX); by combining two or more methods above (Chevalier and Lindley, 2009).

There are some criticisms against these measures: (1) overeducated workers may be less likely to respond to questionnaires due to higher levels of job apathy which may lead to underestimation of the incidence of overeducation; (2) workers in smaller and/or less structured organisations may lack sufficient benchmarks against which they can assess their job requirements, a factor which will again lead to measurement error; and (3) even where benchmarks are available, respondents may be applying differing criteria when assessing their job requirements, i.e. the actual level of education required to do specific tasks or the formal educational requirements necessary to get the job.

Hartog (2000) highlights the obvious bias in self-assessment for the measure that is based on the assessment of the required level to get the job. These measures provide an indication of the credentials gap (Livingstone, 1998); nevertheless, the concept of credentials and overeducation is not exactly the same. For instance, employers might increase hiring standards in response to cyclical or structural oversupply of educated workers, possibly causing the level required to get the job to deviate from the actual level required to do the job. Conversely, if the self-assessment measure is based on this last level, the bias is likely to be less severe. Nonetheless, there might still be a problem if individuals tended to inflate the status of their own position or if they adapted their answers to their personal ambitions and expectations. These biases might also be a

problem for measures that are based on DSA. Moreover, these indicators might measure skill mismatches instead of education mismatches, particularly if they were based on questions regarding skill utilisation.

In addition, Groot and Brink (2000) study education mismatch in the US and EU, using meta-analysis. By analysing 25 studies, they obtain 50 estimates on the incidence of overeducation and 36 estimates on the incidence of undereducation. They find that the unweighted average of the overeducation incidence is 23.3 per cent (with a standard deviation of 9.9 percentage points), while the unweighted average incidence of undereducation is 14.4 per cent (with a standard deviation of 8.2 percentage points). The unweighted averages of the rates of return to the different educational components are: 5.6 per cent for years of education attained, 7.8 per cent for years of education required for the job, 3.0 per cent for years of overeducation and 21.5 per cent for years of undereducation. They also find that the different definitions lead to large differences in the incidence of overeducation. Overeducation rate in the EU also appears lower than the US; the average value of overeducation among studies for the United States is 26.3 per cent, compared to 21.5 per cent among the European studies. In terms of aggregate trend, they also point out that the trend of overeducation and undereducation declines between 1970s and 1990s. With the decline in the incidence of overeducation over time, the average rate of return to years of overeducation has declined as well. With a similar trend for undereducation, subsequently, the joint decline in overeducation and undereducation suggests that skill mismatches in the labour market have decreased since the 1970s. McGuinness (2006) also documents many studies on mismatch for various education levels, particularly overeducation in the US, Canada, Hong Kong, the UK and seven European countries35_{, using RM and years of schooling as education attainment variable.}

The study concludes that objective measures with RM (mean) is found to generate lower estimate; around 22 per cent, some 7 per cent points below the comparable subjective figure. For cross-country comparisons The Netherlands yields the lowest incidence level under both subjective and objective measures (Hartog and Oosterbeek, 1988; Groot and Brink, 2000), whilst studies of the US labour market generate the highest incidence level irrespective of the measurement approach adopted (Tsang et al., 1991; McGoldrick and Robst, 1996).

In a similar vein, Chua and Chun (2016) study the mismatch in Asian countries, using the World Bank survey data for Armenia, the People’s Republic of China (PRC, Yunnan), Georgia, the Lao People’s Democratic Republic (Lao PDR), Sri Lanka, and Viet Nam between 2012 and 2013. The sample per country comprise around 3,000 adults aged 15 to 64 who were located in urban areas. The method used to estimate the mismatch is ISA.