Appendix: Matching Index – illustration and com- com-ments

Dans le document Essays on location choice: agglomeration, amenities and housing (Page 94-100)

Table 2.A.1: IT research and development engineers and managers

Rank Degree field Obs. Shareedu,occ Matchingedu,occ

1 Informatics and Information Sciences 565 42% 1.00

2 Electrical and Electronic 129 10% 0.23

engineering-related technologies

3 Robotics, and Automation 127 10% 0.22

4 Pluriscientific specialties 82 6% 0.15

5 Industrial technologies 51 4% 0.09


50 Literature and Arts 1 0.1% 0.002


82 Geography 0 0% 0

83 Health 0 0% 0

This table provides an illustration of the computation of matching indexes for the occupation IT research and development engineers and managers (“IT engineers” here-after). Workers are ranked according to their degree field. The table shows that among the sample, 565 IT engineers have a degree in Informatics and Information Sciences, 126 in Electrical and Electronic engineering while only one IT engineer in the dataset has a degree in Literature and Arts.

I first compute the proportion of IT engineers holding each degree. I find for instance that 10% of workers have a degree in Electrical and Electronic engineering. In order to rescale the measure to the [0-1] range, these proportions are divided by Sharemaxocc , the maximum value of Shareedu,occ that is observed for this occupation. This computation provides us with a matching index for each degree field. This index equals one for the educational fieldInformatics and Information Sciences, meaning that it is the most preva-lent degree field among IT engineers. If many employers hire IT engineers with a degree in informatics and information sciences, it suggests that this major is pretty well suited for this occupation. The lowest matching index is about 0.002 (0.1%42%) for Literature and Arts, meaning that this educational background is not the most appropriate major for this occupation. Employers seem to be reluctant to hire workers with a degree in Literature and Arts for this occupation. A coefficient of zero is then manually assigned to degrees for which we have no observation in the dataset for this occupation. Examples in the data includeGeographyorHealth. The matching index between these fields and the occupation IT engineers is zero as any IT engineers hold a degree in Geography or in Health. They are then considered as mismatched.

Comment on the occupational perspective This index reflects the distribution of degrees within occupations. There are several motives for this choice. First, the aim of this measure is to identify the most appropriate degrees to work in each occupation. It does not seek to determine a worker’s preferred occupation depending on her educational background. Besides, the number of observations is higher within degrees than within jobs. The occupational classification is therefore a too fine grid for precisely estimating the quality of a match. Matching indexes associated with many occupations would indeed be very low if they were computed at the level of degree fields rather than occupations. I have computed a symmetric index from the education perspective. The average matching index in the sample is eventually lower when using this symmetric measure but the two are significantly correlated. As expected, coefficients using the symmetric index are less precisely estimated. This justifies the fact that matching indexes are computed on the basis of occupations rather than degrees.


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