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Technology-related investments and employment composition


Academic year: 2021

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Technology-related investments and employment composition

Evidence from the Italian business sector, 2011-2015

S. De Santis, G. Perani, F. Rapiti


The socio-economic impact of technological change

INAPP - Roma, 29-30 November 2018


o Exploring the level of capital-skill complementarity as a preliminary stage of an ISTAT monitoring project on the impact of technology on jobs

o Key research questions:

Has the skill structure of Italian limited companies changed as a result of the post-crisis recovery effort (2011 to 2015)?

Has the investment dynamics of Italian limited companies affected their skill structure in the 2011-2015 period?

Capital-skill complementarity



Empirical evidence

o ISTAT Register of employees in enterprises. Years 2011-2016.

It includes information on all the persons employed by Italian businesses on a yearly basis. Data come from administrative sources.

o Competitiveness Research Network (CompNet) Database (ECB).

Years 2011-2015.

Systematic collection of balance sheets’ data for analytical purposes as well as for those of the National Accounts.

o Digitalisation taxonomy based on the ICT survey 2016-17 dataset.


o Investments. Calculated as the difference between the capital stock (both tangible and intangible) reported by a limited companies in a fiscal year compared to the previous one.

o Enterprise. A statistical unit matching a legal entity which is an economic actor required by law to disclose its accounting data.

o Employee. A person who has a contract to carry out work for an eenterprise and receives a compensation for it.

o Capital. Tangible and intangible (non financial) assets.

o Skills. Calculated, at employee level, as a combination of

occupational status, level of formal education attainment and tenure (period in the enterprise, years).

Definitions and assumptions



The skills’ grid

Blue collars White collars Managers

Lower secondary Higher secondary Tertiary

0 to 1 1 to 3 More than 3

Occupational status

Level of education

Tenure (years)


Skill groups: level of education


9.4% 9.1% 6.2%


5.1% 4.6%


3.9% 3.2% 2.6%


Lower Sec.

Higher Sec.




Skill groups: tenure


9.4% 9.1% 6.2%


5.1% 4.6%


3.9% 3.2% 2.6%


<1 year 1 to 3 years

> 3 years


Skill groups: occupational status


9.4% 9.1% 6.2%


5.1% 4.6%


3.9% 3.2% 2.6%


Blue collars White collars




Skill structure: 2011, 2015

0 1 2 3 4 5 6 7 8

YEAR 2011 YEAR 2015

More than 3_LowSec_Manag Less than 1_LowSec_Manag Less than 1_Tert_Manag Less than 1_HighSec_Manag More than 3_HighSec_Manag Years 1-3_LowSec_Manag Less than 1_Tert_Blue Years 1-3_Tert_Manag More than 3_Tert_Manag Less than 1_LowSec_White Years 1-3_Tert_Blue Years 1-3_LowSec_White Years 1-3_HighSec_Manag Less than 1_Tert_White More than 3_Tert_Blue Less than 1_HighSec_White Years 1-3_Tert_White Less than 1_HighSec_Blue Years 1-3_HighSec_White Years 1-3_HighSec_Blue More than 3_Tert_White More than 3_LowSec_White Less than 1_LowSec_Blue Years 1-3_LowSec_Blue More than 3_HighSec_Blue More than 3_HighSec_White More than 3_LowSec_Blue

24% 22%

16% 15%

9% 9%

9% 9%


o First step. Matching between the Register of employees (ASIA-Occ, more than 16 M individual rekords) and the CompNet Database (more than 400K enterprise, around 11 M employees).

o Second step. Descriptive statistics. Variables are highly correlated.

High heterogeneity found across size classes and economic activities.

o Third step. Exploratory analysis. Weak evidence of a mutual

moderating effect between indicators on investments (per capita net capital, composed rate of investment) and the skill dynamics (both considering the single components and their combination).

o Fourth step. Calculation of indicators according to the Taxonomy by digital intensity.

Data integration



ISTAT Digital intensity taxonomy (2016-2017 data)


Digital intensity, capital and skills, 2011-2015

1. Key indicators



Digital intensity, capital and skills, 2011-2015

o Firms are recovering after the crisis but at different speed.

o Further increase of employment is reported until 2017.

o Productivity is still decreasing along the observed period (at the best is stagnant).


Digital intensity, capital and skills, 2011-2015

2. Capital indicators


o Capital stock increases but is unable to keep pace with the employment.

o Arguably, automation is not yet the dominant paradigm.

o High differences in average investment ratios across the business sector.


Digital intensity, capital and skills, 2011-2015


Digital intensity, capital and skills, 2011-2015

3. Human capital indicators



Digital intensity, capital and skills, 2011-2015

o On average, formal education levels are on the increase (also because of a similar trend in the whole population).

o Technology seems associated to a demand for better educated employees.

o Average work tenure is increasing but is also largely affected by the legislation on retirement.


Preliminary evidences and open issues

o A huge inertia affects the digital transformation of Italian enterprises. A large percentage of SMEs (as well as micro-

enterprises, not considered here) with low productivity makes size and features of technological phenomena less visible at macro level.

o As a strategy to recover from the crisis, firms invest more on labour than on capital. To be understood whether because of the specific national conditions (incentives, labour market) or as a strategic positioning.

o Skill measurement is still an issue. More information at individual and firm level are being collected by ISTAT (incl. ISCO codes, work contracts, etc.) to shed light on this key topic.


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