Wage differentials among Italian graduates.

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Wage differentials among Italian graduates.

Temporary versus permanent contracts

Irene Brunetti, Valeria Cirillo and Valentina Ferri

INAPP - National Institute for Public Policy Analysis

Il presente contributo è stato realizzato da INAPP in qualità di Organismo intermedio del PON SPAO con il contributo del FSE 2014-2020, Azione: 8.5.6

lOth International Academic Conference –

Global and Contemporary Trends in Social Science

6 November, 2018 - Barcellona

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• European countries witnessed a significant expansion of temporary employment.

• In 2018, in EU, temporary contracts are 16% of total contracts; in Italy, 15,5%.

An increase of 400.000 temporary contracts with respect of 2017.

Motivations

4 6 8 10 12 14 16 18

% of Temporary Employment 2000-2017

(wage and salary workers)

ITA EU28

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• Different working conditions for workers with the same level of competence should result in a wage premium for temporary workers to off set the disadvantages.

• The empirical evidence shows a wage penalty for temporary jobs.

• We investigate the temporary-permanent wage gap on graduates workers in the Italian labour market.

• We decompose the wage differential along the entire wage distribution.

Motivations

Wage differentials among Italian graduates. Temporary versus permanent contracts

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Overview

• Related literature

• The metodology

• The empirical analysis

• Conclusions and future research.

THEORETICAL LITERATURE

• Temporary contracts can be stipulated to maximize workers' on the job effort.

Fixed-term contracts can have a positive effect on effort if workers perceive that the rehiring probability depends on past performance (Dolado, García- Serrano and Jimeno, 2002).

• Temporary employment is used by firms as a flexible mechanism to adjust employment to fluctuations in the business cycle (Blanchard and Landier, 2002).

• Temporary jobs can be used as a screening device. To hire workers with temporary contracts in order to screen them, and permanently retain the ones who proved to be more productive (OFlaherty and Siow, 1995; Portugal and Varejão, 2010; Faccini, 2014).

Related Literature

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EMPIRICAL LITERATURE

Theory of compensating wage differentials: a competitive labor market should reward any “adverse conditions” the workers face workers with the same level of competence should receive different wages if their working conditions are different: A positive wage differential for temporary workers (Rosen, 1974; Smith, 1979 for a review).

BUT

• A positive wage differential in favor of permanent workers (Jimeno and Toharia, 1993; Bentolila and Dolado, 1994; Booth and Francesconi, 2002;

Blanchard and Landier, 2002; Picchio,2008; Bosio, 2014; Dias da Silva and Turrini, 2015).

• The contract discrimination is higher at the bottom of the wage distribution and tend to decrease as considering higher quantiles (Mertens e McGinnity, 2005; Barbieri e Cutuli, 2009; Bosio, 2009; Comi and Grasseni, 2012).

Related Literature

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THE ECONOMETRIC MODEL Mincer equation:

Three methods:

• One wage OLS equation including the type of contract as a dummy variable in the equation.

• Two wage OLS equations, one for temporary and another for permanent employees.

• First a probit selection equation, second a linear regression including the derived correcting factor, or the Heckman procedure at two stages (Heckman, 1979; Davia and Hernanz, 2004).

The methodology

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OAXACA-BLINDER Decomposition

• To disentangle the endowments and coefficients effects in the explanation of wage differentials and to evaluate the presence of discrimination in the rate of return for temporary contracts (Oaxaca, 1973; Blinder, 1973).

∗ ∗ ∗ )}

where the first term on the right-hand side of equation is the "explained component”, the second term is the "unexplained component“ (i.e. the wage discrimination).

• This approach assumes linearity, can only be applied to the mean of distribution, and it is sensitive to the choice of the base group.

• To overcome the first and second limits Alternative approach: RIF decomposition (Firpo et al., 2007).

The methodology

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Recentered Influence Function (RIF) Decomposition Two steps:

• The estimation of a RIF regression model of quantiles, where the RIF of a general distribution statistics v(f(x)) is given by the sum of the statistics itself and its Influence Function (IF).

• Apply a decomposition analogous to the Oaxaca – Blinder approach.

; ;

where is the total wage effect. Adding and subtracting the term _; in the last line of the previous equation, we get the following decomposition:

; ; ; .

• We are interested in the second term: a positive value indicates that the returns to temporary characteristics are lower than those of permanent and this obviously points out at “discrimination”. A negative value implies the reverse.

The methodology

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THE DATA

• Source: “ Inserimento Professionale dei Laureati “ (Istat).

• Year: 2015.

• Sample: 28000 graduates employees (17296 with permanent contract and 11048 with a temporary contract).

• Variable of interest: monthly net wage.

• Exclusion variable : final grade at college.

• Control variables (individual characteristcs and occupation charcteristics): gender, type of degree, fields of degree, part time/full time, sector of activity, occupation (ISCO08).

The empirical anlysis

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DESCRIPTIVE ANALYSIS

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DESCRIPTIVE ANALYSIS

Perm-Temp q10

Perm-Temp q50

Perm-Temp q90

Ln monthly net wage (full-time equivalent) 0.288 0.145 0.107

Ln monthly net wage (only full-time) 0.251 0.145 0.165

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Full time eq. Only full time workers

Contract Dummy (=1 if temp) ^-0.136***

(0.001)

-0.153***

(0.00)

Part time ^0.069***

(0.002)

-0.510***

(0.00)

Grade of University degree ^0.0013***

(0.00)

0.001***

(0.00)

Duration of studies ^0.002***

(0.001)

0.002***

(0.004)

Master University degree ^.054***

(0.002)

0.060***

(0.00)

Female ^-0.063***

(0.00)

-0.064***

(0.00)

Work in the south ^-0.085***

(0.00)

-0.104***

(0.00)

Selection bias (Inverse Mill’s Ratio) ^-0.008*

(0.089)

-1.201***

(0.00)

Sectors Dummy YES YES

Occuparion Dummy YES YES

Fields of study Dummy YES YES

Intercept ^1.038***

(0.001)

1.446***

(0.00)

N 26032 26032

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OAXACA – BLINDER DECOMPOSITION

OB_FulltimeEquiv

Permanent 7.386***

(0.003)

Temporary 7.197***

(0.006)

Difference 0.188***

(0.007)

Explained 0.051***

(0.004)

Unexplained 0.137***

(0.006)

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RIF DECOMPOSITION

Note: Bbootstrapped standard errors; in parenthesis. ***, ** and * denote statistical significance at the .01, .05 and .10 levels, respectively

10° percentile 50° percentile 90° percentile

Permanent 7.108*** 7.424*** 7.759***

(0.003) (0.002) (0.004)

Temporary 6.847*** 7.274*** 7.664***

(0.009) (0.003) (0.006)

Difference 0.261*** 0.150*** 0.095***

(0.010) (0.004) (0.008)

Explained 0.047*** 0.028*** 0.017***

(0.004) (0.002) (0.004)

Unexplained 0.214*** 0.122*** 0.078***

(0.009) (0.004) (0.007)

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Conclusions

• Four years after graduation, to find a work means to find a temporary work and the final grade helps to find this job.

• Aftr controlling for possible selection bias, among graduates workers there is evidence of a penalty in terms of wage for temporary workers.

• The Oaxaca-Blinder decomposition shows that about 72% of the difference between temporary and permanent workers is due to the discrimination effect.

• The wage gap is higher at the bottom of the wage distribution.

Future Research

• To estimate two separeted two stage OLS wage equation : one for permanent and one for temporary workers.

Conclusions

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THANK YOU FOR YOUR ATTENTION

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Irene Brunetti –i.brunetti@inapp.org ; Valeria Cirillo –v.cirillo@inapp.org; Valentina Ferri– v.ferri@inapp.org

Wage differentials among Italian graduates.