Measuring matching

In this section, I propose a new measure of matching that reflects the link between the type of education that has been undertaken by the worker and the tasks she will be performing in her work.

There are three approaches to measure mismatch (Desjardins and Rubenson, 2011;

Groot and Brink, 2000; Nordin et al., 2010). Mismatching may be determined through (1) self-assessment, when workers are asked whether they consider to have the right qualifications for their position; (2)job analysis, where statistical institutes or job analysts provide normative nomenclatures that link majors to occupations; or (3)realised matches, that look at the actual distribution of workers within occupations to find a corresponding job requirement for each occupation. So far, the last method has been only applied

to compute a required educational attainment. For instance, R. R. Verdugo and N. T.

Verdugo (1989) compute the average year of schooling in each occupation and define workers as overeducated when their education is greater than one standard deviation above this mean. Kiker et al. (1997) uses the modal value: workers are considered as overeducated (undereducated) when their educational attainment is above (below) this value.

I adopt this third approach and apply it to degree fields (horizontal mismatching) in-stead of educational attainment (vertical mismatching). It circumvents several limitations of the self-reporting or job analysis approaches. The main drawback of the self-reporting approach is measurement errors that might be correlated with a worker’s education and the fact that it may reflect job dissatisfaction rather than skill mismatch. The second approach in turns allows for arbitrary assignments of degrees to occupations. The main limitation of the third approach is the subjectivity in the choice of the threshold (the modal value or the one standard deviation in previous examples) and the fact that it pro-vides categorical indicators as workers are either matched or mismatched. My measure will address these two limitations.

I rely on an additional dataset provided by the French statistical institute (INSEE).

The French Labor Force Survey (“Enquˆete Emploi”) describes the labor activity of French residents in 2007. This dataset being representative of the entire French active population, I use it as a benchmark to measure the quality of match between each field of study and each occupation⁴. My measure is based on realized job matches and reflects the outcome of supply and demand in the French labor market. The intuition is as follows: the fact that a field of study is rarely or not observed across workers in a given occupation suggests that this educational background may not provide the right training for this occupation. At the national level, firms seem to be reluctant to hire people with this major for this specific occupation. By contrast, the fact that a degree field is very common across workers in a given occupation implies that this degree is appropriate for this type of work. Employers are likely to hire workers with this educational background for this position.

Based on this intuition, I compute a matching coefficient for each pair of degree field edu and occupation occ. This measure is defined as:

M atchingedu,occ = Share_edu,occ

Share^max_occ ∈[0; 1] with Shareedu,occ = N_edu,occ N_occ and Share^max_occ ≡max

edu {Share_edu,occ} whereN_edu,occ represents the number of workers with the majoreduworking in occupation occ. N_occ is the total number of workers in occupation occ. Therefore, Share_edu,occ is the share of workers in occwho have a degree in the fieldedu. I rescale the measure and take into account heterogeneity across occupations by dividing this share by the maximum value that can be observed over all education types within this occupation⁵, denoted

4289’425 individuals are surveyed among which 147’542 are active. Using the same data between 1996 and 2002, Chardon (2005) shows that the relationship between majors and occupations is very stable over time. Degree fields and occupations are respectively disaggregated into 83 and 486 different categories.

The occupational nomenclature corresponds to the ISCO-3 digit classification.

5Rescaling the measure is important. For instance if an occupation is not specialized, workers with all types of majors may be occupied in this activity. If the number of workers with each major is equal, the computed shares (Shareedu,occ) would be very low. Workers would then be considered as mismatched.

Dividing by the maximum share, which is low in this case, yields a relative measure of matching. Workers are then considered as matched as their different degrees are equally appropriate for this job.

Share^max_occ .

This computation provides us with coefficients that range between 0 and 1. A coef-ficient equal (or close) to zero implies that the pair edu−occ is never (rarely) observed at the national level. This is interpreted as evidence that the educational background edu does not really correspond to the job requirement of occupation occ. One example of such a coefficient in the data is the pair IT research and development engineers and managers andgeography, or the pairfood sellers andeconomics: workers with a degree in economics are apparently not qualified to work as food sellers. By contrast, a coefficient of one implies that Share_edu,occ = Share^max_occ . The frequency of the degree field edu is very strong within occupationocc. An example of such a good match is given by the pair lawyer and law. This means that most lawyers have a degree in law. An illustration and additional comments on the measure are provided in the appendix.

The advantages of this measure are many. First, it is based on statistical information and realized matches rather than subjective or arbitrary criteria. Similarly, this measure does not imply that there is only one best degree for each occupation. Multiple good matches may be observed in any given occupation. Finally, this indicator is continuous.

We can thus observe a full range of coefficients varying with the relatedness of education and works. This contrasts with most papers on skill mismatch. For instance, Abel and Deitz (2012) alternatively use indicators from self-assessments and an occupational crosswalk provided by the U.S. Department of Education’s National Center for Education Statistics that links college majors to occupations. In both cases, the matching variable is dichotomous. Robst (2007) or Andini et al. (2013) use subjective indicators based on questionnaires. In Robst (2007), workers are then categorized as matched, partially mismatched or completely mismatched. In Andini et al. (2013), workers either have appropriate work experience or qualifications for their job or not.

Two shortcomings might remain. First, the matching index may be partially en-dogenous as I implicitly assume that the actual distribution of skills within occupations reflects the requirements for the job. I make progress on this issue. First, I rely on a comprehensive and exhaustive labor force survey for evaluating mismatch. As long as the entire French active population is not completely mismatched, the way I evaluate matching is unlikely to provide endogenous or biased estimates. In addition, Verhaest and Omey (2006) or Hartog (2000) show that the realized matches approach provides us with the lowest average level of mismatching. Other approaches tend to overestimate the importance of mismatching. Second, I use two different datasets for the matching computation and the estimations. The first includes a large number of employed workers while the second focuses on school leavers. Therefore, the latter are not necessarily in-cluded in the labor force survey or only represent a very small fraction of observations.

The second potential limitation is that this computation gives high weights to big cities as they represent a high number of observations in the sample. As larger areas contribute more to the computation of the matching index, we may suspect the relationship between employment density and matching to be systematic. To make the index more exogenous, I perform a series of robustness checks in Section 2.5 by excluding the largest cities or regions from the two samples, alternatively. I then show that matching indexes are not sensitive to the exclusion of dense cities and do not result from an over-representation of Paris or other large areas.

I assign these matching indexes to my sample of school leavers based on their last de-gree field and their first occupation. Table 2.1 shows that new entrants’ average matching index is about 0.46 with a median value at 0.3. Among the sample, 26% of workers have

a degree corresponding to the best match and 34.6% have a matching index above 0.8.

These numbers are in line with existing measures of skill match.