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A New Multidimensional Approach to Measuring Precarious Employment

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Abstract

This article proposes a new methodology to measure precarious employment with a multidimensional approach. The adjusted multidimensional precariousness rate employed to measure job precariousness is calculated on a counting approach and exhibit several advantages, including its decomposability according to the relative contribution to total precariousness of different dimensions and sub-populations. For illustrative purposes, the methodology is applied to the Spanish case using microdata from the Encuesta de Estructura Salarial (Wage Structure Survey) and considering three precariousness dimensions of jobs (low wages, fixed-term contracts and part-time work). The evidence obtained shows that at the beginning of the economic crisis there was an increase in the incidence and intensity of precariousness for new jobs created in the Spanish economy. Moreover, obtained evidence shows that the incidence of precarious employment is particularly high in certain economic sectors and for females.

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Notes

  1. See, for example, European Commission (2001), Leschke et al. (2008), Leschke et al. (2012), ILO (2004) and UNECE (2014), and, for the Spanish case, Arranz et al. (2016), Iglesias et al. (2011) and Royuela et al. (2008, 2009), among others.

  2. It must be noted that a psychometric approach to measure precariousness has been developed in parallel with the economic approach considered in this work (Vives et al. 2015).

  3. Leschke and Keune (2008) make the concept of precariousness operational by considering as precarious those jobs which are precarious in two of these three dimensions.

  4. Although other dimensions of precariousness related to undesirable working conditions could be considered, such as shift work, night work or working under dangerous conditions, which have been considered in previous studies (Rodgers 1989; Barbier 2004), or self-employment, which is usually connected to the partial access to or absence of social benefits such as unemployment benefit (Fudge et al. 2002; OECD 2015b), the limitations of the data source used in our research prevent us from doing so.

  5. According to Eurostat data, in 2014 approximately 64 % of all Spanish workers (70 % for men and 62 % for women) employed in part-time jobs had such type of jobs involuntarily.

  6. The measures proposed by Alkire and Foster, M1 (intensity) and M2 (inequality) can not be used in this context because they require cardinal data.

  7. The concept of the intensity of precariousness used in this article refers to the number of precariousness dimensions in precarious jobs (Alkire and Foster 2011). In this respect, M 0 overcomes one of the shortcomings of the multidimensional precariousness rate H, namely that this measure does not increase when the number of precariousness dimensions of the jobs increases.

  8. The wave of 2010 also covers the branch of activity corresponding to Public Administration and defence and obligatory Social Security.

  9. The hourly wage has been calculated using the wage corresponding to a representative month of reference of the year in which the survey was carried out (October), divided by the number of hours worked in this month. It is expressed in gross terms and its calculation includes any type of payment made by the companies, including commissions, bonuses for night work and weekends and overtime pay.

  10. Although the ESS questionnaire distinguishes several types of contracts according to the key contract as for the Spanish Social Security, EES microdata distinguish just between fixed-term and permanent contracts according to its duration.

  11. Part time work carried out by the workers is indicated in the EES for each company, as in the case of each worker either part-time work or full-time work was chosen as a response to the following question: “4.1 Type of working day. Part-time work is considered to be shorter than a normal working day of the company or in the case where this does not exist, shorter than the legal maximum hours established (it should be stipulated in the contract)”.

  12. Given the variability of the results at different thresholds to determine low wages, we have also obtained the values of the precariousness indicators using different thresholds (40 and 80 % of the median wage). The results show some sensitivity in the levels of H and Mo to changes in wage thresholds, but not in the variations over time of both indicators. These results are included in an annex that will be sent on request of the reader.

  13. Note that in terms of evolution over time the contribution of the sectors to precarious employment tends to adapt in general to the restructuring of the population weights in the jobs during the period of economic crisis considered.

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Acknowledgments

This study has received funding from Spain’s Ministry of Economy and Competitiveness (Project CSO2014-55780-C3-2-P) and from Autonomous Community of Madrid and European Commission (Project S2015/HUM-3416-DEPOPOR-CM).

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Authors and Affiliations

  1. Faculty of Economics and Management Sciences, University of Alcalá, Plaza de la Victoria, 2, 28802, Alcalá de Henares, Spain

    Carmelo García-Pérez

  2. University of Valladolid, Valladolid, Spain

    Mercedes Prieto-Alaiz

  3. University of Alicante-IEI-IEB, Alicante, Spain

    Hipólito Simón

Authors
  1. Carmelo García-Pérez
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  2. Mercedes Prieto-Alaiz
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  3. Hipólito Simón
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Correspondence to Carmelo García-Pérez.

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Appendix

Appendix

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Table 4 Descriptive statistics
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García-Pérez, C., Prieto-Alaiz, M. & Simón, H. A New Multidimensional Approach to Measuring Precarious Employment. Soc Indic Res 134, 437–454 (2017). https://doi.org/10.1007/s11205-016-1447-6

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