Is firm training associated with a lower use of atypical contracts? Evidence from Italian firms I. Brunetti

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Il presente studio è stato realizzato da INAPP in qualità di Organismo intermedio del PON SPAO con il contributo del FSE 2014-2020, Azione 8.5.6 Attività 3

Is firm training associated with a lower use of atypical contracts?

Evidence from Italian firms

I. Brunetti

¹

, M. Raitano

²

and V. Ferri

¹

1 INAPP – Istituto Nazionale per l’Analisi delle Politiche Pubbliche

2 Dipartimento di Economia e Diritto – Università La Sapienza

12 September 2019

XXXIV AIEL – Novara

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Motivations

• The increasing role of skills updating and lifelong learning of workers to face the growing pressure induced by globalization and technological changes on the labor market (European Commission, 2007; Percival et al., 2013)

• Being able to count on a productive, trained and constantly updated workforce is a fundamental competitive asset for long-term economic growth (Esping-Andersen, 2007; Brettschneider, 2008)

• Investment in on-the-job training is a fundamental lever for fostering smart, sustainable and inclusive growth (Europe 2020)

• Training might improve workers prospects, improving their labour market outcomes (wages, employability, contractual arrangement) (see e.g., Booth, 1993; Parent, 1999).

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Two main contributions of our paper

• The innovative dataset building linking 5 different database (survey and administrative);

• The dependent variable (i.e. the share of days worked in a firm with different kind of contract) is a proxy of within-firm flexibility/segmentation (Piore, 1975; Doeringer and Piore, 1971)

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• Related literature

• The data

• The empirical strategy

• The results

• Conclusions

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Related Literature

In this paper, focusing on Italian firms, we analyze the opposite relationship:

Has on-the-job training an effect on the firm’s use of permanent contracts?

• Some studies, using data at firm level, analyse the determinants of on-the-job training, that is the relationship between workforce composition, firms characteristics, and training provided at the workplace (Booth, 1991; Frazis et al., 1995; Lynch and Black, 1998; Zwick, 2005, Messe and Rouland 2014; Isfol, Ricci, 2013; Bratti et al. 2018).

• Other studies analyse the impact of training on firm’s performance (Booth, 1993;

Parent, 1999; Dumas and Hanchane, 2010).

• Work flexibility: negative relationship between temporary work and investment in human capital. The increase in fixed-term contracts is an obstacle to investment in on- the-job training (Arulampalam et al. 2004).

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The data (1)

Original dataset built linking four administrative archives with a firm’s survey

• Comunicazioni Obbligatorie (COB): an administrative dataset provided by the Ministry of Labor and Social Policies. It tracks all events related to a job position (hiring, contractual transformation – e.g. from a fixed-term to an open-ended arrangement –, firing, dismissal);

• ASIA Archive (Firms): the Statistical Register of Active Firms provided by ISTAT. It covers the universe of Italian firms and records in detail the productive sector and the geographical location of each firm, as well as, their size and legal form;

• ASIA Archive (Employment): the Statistical Register of firm’s employment provided by ISTAT. It records information relating to the worker (e.g.

gender, age, area of birth) and the main characteristics of the employment contract.

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The data (2)

• AIDA Archive: the Italian company information and business intelligence database provided by the Bureau Van Dijk. It offers comprehensive information on the balance sheets of almost all the Italian corporations operating in the private sector, except for the agricultural and financial industries

• Rilevazione Imprese e Lavoro (RIL): a survey conducted periodically by the National Institute for Public Policies Analysis (INAPP). It records in detail numerous firms’ characteristics (e.g. the number of workers trained, costs of training, ownership structure, type of industrial relations, organization and management of personnel).

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The data (3)

• The matching key: tax codes of firms and worker s employer- employee dataset in which firm is the unit of analysis.

• We start from the RIL sample, using the 2010 and 2015 waves, enriched with additional firm’s information deriving from the ASIA archives.

• For all workers of RIL firms, we extract information relating to each activation/end of the contract in the period 2009-2017 recorded in the SISCO archive.

• It is possible to calculate, for each year, the actual duration, expressed in days, of each contractual relationship experienced by a worker during the year.

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The data (4)

• Only contracts with open-ended employees can last continuously between 2009 and 2017 without incurring interruptions or renewals.

• The part of the stock of employees at a firm that is never registered in the SISCO archive is identified by reconstructing for each firm, from ASIA-Employment 2015, the number of people who had a permanent contract lasting the whole of 2015 and subtracting from this stock the number of individuals who started or ended this contract from 2009 onwards.

• We focus on firms that claim to have at least 50 employees since in these firms the training activity is most significant and they are the most represented in the ASIA archive.

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The empirical strategy (1)

𝒔𝒉_𝒊𝒏𝒅𝒆𝒕_𝒊𝒕= 𝛼 + 𝛽𝑠ℎ_𝑖𝑛𝑑𝑒𝑡_𝑖𝑡−1 + γ𝑻𝒓𝒂𝒊𝒏𝒊𝒏𝒈_𝑖𝑡−1 + 𝛿₁𝑈𝑛𝑖𝑜𝑛_𝑖𝑡−1 + + 𝛿₂𝑃𝑟𝑜𝑑_{𝑖𝑛𝑛𝑖𝑡−1} + 𝛿₃𝑃𝑟𝑜𝑐_{𝑖𝑛𝑛 𝑖𝑡−1} + 𝜕𝑋_𝑖𝑡 + ϕ𝑍_𝑖𝑡−1 + 𝜀_𝑖𝑡

• 𝒔𝒉_𝒊𝒏𝒅𝒆𝒕_{𝒊𝒕−𝟏}

:

(with unbalanced panel) to take into account structural unobserved characteristics of firm (see Van Reenen, 1997; Piva and Vivarelli, 2017);

• Vector 𝑋_𝑡 includes a detailed number of checks at firm level at time t;

• Fixed effects of year, region, 2-digit sector NACE;

• Dummy variable for: firm’s size and firm’s revenue class;

• Workforce characteristics: gender, age group, citizenship;

• Vector 𝑍_𝑖𝑡−1 contains two variables controlling for firm performance: the (log) of labor productivity and the (log) of firm’s investments in physical capital.

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The empirical strategy (2)

RIF-regression (Unconditional quantile regression ) 𝑅𝐼𝐹 𝑌_𝑖; ෠𝑄_𝜏 = 𝑄_𝜏 + 𝜏 − 𝕝{𝑌 ≤ ෠𝑄_𝜏}

𝑓(𝑄መ _𝜏)

• 𝑓(𝑄መ _𝜏) is the marginal density function of the dependent variable, Y, estimated by a kernel function,

• 𝕝 𝑌 ≤ 𝑄_𝜏 is a dummy variable that specifies whether the value of the dependent variable is greater or less than the quantile 𝑄_𝜏.

• To show the results at different quantiles of the distribution of the days worked with standard contracts,

• Given the independence from the covariates, to compare among them the achieved results.

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Descriptive statistics (1)

Number of Observations The share Year

2010 2,631 33,9%

2015 5,130 66,1%

Firm size

50-249 6,057 78%

>=250 1,704 22%

Total 7,761 100%

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Descriptive statistics (2)

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Main results: OLS on all firms

Estimate of the relationship between the number of days worked with standard contracts and firm’s characteristics.

OLS (1)

OLS (2)

M1 M2 M1 M2

Share of trained employees 0.023***

(0.0005)

0.023***

(0.0007)

0.0046**

(0.002)

0.0042**

(0.002)

Share of days with PC in t-1 0.904***

(0.0126)

0.905***

(0.0127)

Unions 0.0103*

(0.0057)

0.0108*

(0.0057)

-0.0013 (0.0016)

-0.001 (0.0016)

Product Innov. 0.0047

(0.0057)

0.0013 (0.0018)

Process Innov. -0.0089

(0.0058)

0.0037 (0.0019)

Labor prod. (log) 0.010 (0.0072)

0.0037 (0.0026)

Other controls Yes Yes Yes Yes

R2 0.52 0.53 0.93 0.97

Num. Observations 6956 6956 6955 6955

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Main results: OLS by firm’s size not controlling for the lagged dependent

M1 M1 M2 M2

50-249 >=250 50-249 >=250

Share of trained employees

0.023***

(0.007)

0.028***

(0.009)

0.023***

(0.007)

0.027***

(0.009)

Unions 0.014**

(0.007)

-0.023**

(0.009)

0.013**

(0.006)

-0.022**

(0.01)

(0.006)

-0.004 (0.008)

Process Innov. -0.009

(0.006)

-0.002 (0.008)

Labor prod. (log) 0.011

(0.0083)

0.0105 (0.0106) Other controls Yes Yes Yes Yes

R2 0.52 0.68 0.51 0.69

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Main results:

OLS by firm’s size controlling for the laggged dependent

M1 M1 M2 M2

50-249 >=250 50-249 >=250

Share of trained employees 0.004**

(0.0022)

0.0052*

(0.0027)

0.0037*

(0.002)

0.0051*

(0.002) Share of days with PC in t-1 0.905***

(0.0134)

0.888***

(0.235)

0.904***

(0.0134)

0.887***

(0.0232)

Unions -0.0009

(0.0018)

-0.0042 (0.003)

-0.001 (0.0018)

-0.0041 (0.003)

(0.002)

0.001 (0.002)

Process Innov. 0.0033

(0.002)

-0.0012 (0.002)

Labor prod. (log) 0.0054*

(0.0031)

0.0011 (0.0025) Other controls Yes Yes Yes Yes

R2 0.93 0.96 0.93 0.96

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Main results: RIF regression

P10 P25 P50 P75

0.0673***

(0.0241)

0.0246***

(0.0095)

0.0069 (0.0063)

0.0007 (0.0042)

Unions 0.0120

(0.021)

0.0076 (0.008)

0.0070 (0.005)

0.0108**

(0.0034)

Other Controls Yes Yes Yes Yes

R2 0.30 0.35 0.25 0.26

Numb. Observations 7013 7013 7013 7013

P10 P25 P50 P75

0.0640***

(0.0225)

0.0246**

(0.0097)

0.0083 (0.0060)

0.0018 (0.0040)

Unions 0.0134

(0.0213)

0.0087 (0.0086)

0.0076 (0.0054)

0.0108***

(0.0033)

Product Innov. 0.0368*

(0.022)

0.0051 (0.0086)

-0.0034

(0.0056)

0.0030 (0.0040)

Process Innov. -0.0202 (0.0194)

-0.0049 (.0082)

-0.0069 (0.0055)

-0.0064*

(0.0038)

Labor prod. (log) 0.0236 (0.024)

0.0078 (0.007)

-0.338 (0.0042)

-0.0105***

(0.0028)

Other controls Yes Yes Yes Yes

R2 0.29 0.28 0.30 0.32

Numb. Observations 6997 6997 6997 6997

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Conclusions

• The application of simple linear regression models and the use of an innovative dataset constructed by linking five different databases, RIL, COB, ASIA Firms, ASIA Employment and AIDA, allow us to demonstrate that firm provided training is positively associated with a lower use of contractual flexibility within the, even when we take into account a wide range of control variables both for employment and firm characteristics

• This positive correlation is higher in very large firms.

• The estimates along the distribution highlight that the positive relationship between permanent contracts and on-the-job training characterizes firms that position themselves up to the median distribution of number of days worked with permanent contracts, where room for increasing the share of workers with standard arrangements emerge.

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THANK YOU!

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Irene Brunetti – i.brunetti@inapp.org

Is firm training associated with a lower use of atypical contracts? Evidence from Italian firms I. Brunetti