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Macroeconomic impact of flexicurity on the integration of people with disabilities into the labour market. A two-regime spatial autoregressive analysis

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Abstract

The aim of this paper is to examine the impact of flexicurity upon regional labour market performance in the employment of disabled people. To this purpose, we estimate a matching function by means of a two-regime spatial autoregressive model on Italian regions for the period 2006–2011. Our results highlight the importance of spatial interactions in the matching process of disabled people, which enable Northern–Central Italy regions to find a job for a number of disabled persons five times higher than the one employed by Southern Italy regions.

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Notes

  1. Among others, studies that investigate the labour market outcomes of disabled people focus on job satisfaction (Pagan and Malo 2009) and unemployment duration (Sciulli et al. 2012).

  2. Flexicurity is an integrated strategy for enhancing, at the same time, flexibility and security in the labour market. It attempts to reconcile employers’ need for a flexible workforce with workers’ need for security—confidence that they will not face long periods of unemployment. Security refers, however, to employability rather than safety from dismissal (Bekker and Wilthagen 2008; Rogowski 2008).

  3. Temporary, part-time, seasonal jobs, hourly-waged and self-employment (Gialis et al. 2014).

  4. In the case of disabled people, we cannot speak of “effective” stock of unemployed people looking for a job, because the stock of unemployed disabled people only includes people with disabilities in search of work, therefore they are all effective. Law 68/99 expressly provides that disabled people who want to work must enroll in lists that are maintained by the employment centers. Consequently, in these lists we can find only disabled persons which truly are looking for a job and not all unemployed disabled people (such as discouraged workers or other categories of unemployed who no longer are in search of a job).

  5. Lagrange multiplier (LM) tests check for a spatially lagged dependent variable and for spatial error autocorrelation; robust LM tests check for the existence of one type of spatial dependence conditional on the other. A mathematical derivation of these tests for a spatial panel data model with spatial fixed effects can be found in Debarsy and Ertur (2010). These tests are based on the residuals of the non-spatial model with spatial fixed effects and follow a χ2 distribution with one freedom degree (Elhorst 2010).

  6. “Everything is correlated, but nearer things are more correlated than things further apart” (Tobler 1970).

  7. The square distance between indicators z i  = [PopDen i , GDPpc i ] and z j  = [PopDen j , GDPpc j ] is computed as \(d_{ij}^{2} = (z_{i} - z_{j} )\varSigma^{ - 1} (z_{i} - z_{j} )^{'} ,\) where Σ is the covariance matrix computed using all the cross-section units. Also in this case we consider the initial levels (2006) of the variables over time.

  8. ISFOL does not specify whether the match variable includes also employed disabled people who are looking for a job, in addition to unemployed disabled people who find a job. Since job placement for disabled people is based on enrolment to employment centers, it is natural to think that after a job placement the disabled person is deleted from the list of unemployed people with disabilities looking for work; this suggests that a new enrolment means that the disabled person is unemployed again and therefore the match variable only includes the outflows into employment of unemployed disabled people.

  9. Through agreements, signed by interested parties (workers, employers, provincial offices for the employment of disabled workers and authorities that promote the labour integration), it is possible to define a personalized program of interventions in order to overcome barriers related to the inclusion in the workplace. The agreements represent the tool by which the legislation seeks to promote the targeted integration, through a gradual labour integration of people with disabilities, aimed at the achievement of the employment obligations.

  10. In this case, ISFOL does not make a distinction between the unemployed looking for employment and the unemployed who are not looking for a job; for this reason, the variable of unemployment will be distorted upwards.

  11. Secondo Rapporto sulla coesione sociale (http://www.istat.it/it/archivio/53075, accessed 16 November 2013).

  12. ALMPs measure can be defined as either the expenditure (as a percentage of GDP) on ALMPs or the number of participants in ALMP programmes. The empirical analysis uses the participants in programmes of active policies as a percentage of the labour force (Altavilla and Caroleo 2011). We use the number of disabled people participating in ALMP as a percentage of the labour force of people with disabilities.

  13. Law 247 of 29 December 2007 changed Article 13 of Law 68/99, providing the employer with a contribution for each disabled worker on permanent contracts by agreement in compliance with Article 11 of Law 68/99. The requirement for giving the employer a contribution for each disabled worker is that hiring has occurred under a permanent contract and that the employment relationship is still ongoing.

  14. Civilian disability pensions are not connected with national insurance contributions; they are paid to disabled people on the basis of their physical characteristics (e.g., people affected by blindness, deafness, or other types of impairments). These pensions are also paid to people with no income or insufficient income after the age of 65.

  15. http://www.istat.it/it/assistenza-e-previdenza, accessed 16 November 2013.

  16. We recall that in the construction of flexicurity indices, CDP have been considered as a negative measure and, consequently, turned into a positive measure (see Appendix).

  17. By an ‘absorbing state’, we mean that the probability of the individual subsequently exiting that state is close to zero.

  18. We use a set of instrument variable L(X, WX, W2X), that is, regressing WlnM (spatial lag of dependent variable) on L(X, WX, W2X) and where X are the regressors (except FIs at time t) with W2 we indicate the second order contiguity matrix (Anselin 1988).

  19. The interpretation is the same for other regressors.

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Authors and Affiliations

  1. Department of Economic and Legal Studies, University “Parthenope” of Naples, Naples, Italy

    Massimiliano Agovino

  2. Department of Economics, “G. D’Annunzio” University of Chieti-Pescara, Chieti, Italy

    Agnese Rapposelli

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  1. Massimiliano Agovino
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  2. Agnese Rapposelli
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Correspondence to Massimiliano Agovino.

Appendix

Appendix

1.1 Flexicurity indices: weighted arithmetic mean method

The steps in the construction of the FI are the following.

1.1.1 Normalisation

Let X = {x ij } be the matrix with n rows (geographical units) and 2 columns (indicators, e.g., ALMP, CDP). Thus, the normalized matrix Z = {z ij } is computed as follows, when the jth indicator is a good (ALMP):

$$z_{ij} = \frac{{z_{ij} - min(z_{j} )}}{{max(z_{j} ) - min(z_{j} )}}.$$
(I)

And alternatively, if the jth indicator is a bad (CDP), we have:

$$z_{ij} = \frac{{max(z_{j} ) - z_{ij} }}{{max(z_{j} ) - min(z_{j} )}}.$$
(II)

So normalized CDP variable is able to give us a measure of the reduction of dependency on board by working age people. Its increase will mean a decrease in the number of people aged 15–64 who receive CDP; it indicates, therefore, a reduction in the dependency and welfarism degree. Broadly, it can be understood as a proxy for the autonomy degree and for the participation of the disabled in both social life and labor market.

In both cases, the values of the normalized indicators vary between 0 and 1, 0 always corresponding to the worst (cross-section) performance (in terms of flexicurity) in the sample and 1 corresponding to the best performance.

1.1.2 Aggregation

The weights used for aggregation of passive and active measures are chosen arbitrarily. In particular, the FI is given by:

$$({\text{A}}) :\;\frac{{\mathbf{1}}}{{\mathbf{2}}}z_{{i{\mathbf{1}}}} + \frac{{\mathbf{1}}}{{\mathbf{2}}}z_{{i{\mathbf{2}}}} \quad \forall i = 1, \ldots ,20,$$
(III)
$$({\text{B}}) :\;\frac{{\mathbf{2}}}{{\mathbf{3}}}z_{{i{\mathbf{1}}}} + \frac{{\mathbf{1}}}{{\mathbf{3}}}z_{{i{\mathbf{2}}}} \quad \forall i = 1, \ldots ,20,$$
(IV)
$$({\text{C}}) :\;\frac{{\mathbf{1}}}{{\mathbf{3}}}z_{{i{\mathbf{1}}}} + \frac{{\mathbf{2}}}{{\mathbf{3}}}z_{{i{\mathbf{2}}}} \quad \forall i = 1, \ldots ,20,$$
(V)

where z i1 is normalized ALMP, and z i2 is normalized CDP.

The FI is computed as an arithmetic mean of the two dimension indices. This exercise allows us to check what happens to flexicurity if a government decides to assign the same weight (or different weight) to active and passive measures.

The indicators thus calculated will allow us to answer the following question: to give greater weight to a measure rather than another will promote greater the employment of disabled people?

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Agovino, M., Rapposelli, A. Macroeconomic impact of flexicurity on the integration of people with disabilities into the labour market. A two-regime spatial autoregressive analysis. Qual Quant 51, 307–334 (2017). https://doi.org/10.1007/s11135-015-0306-x

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