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ABSTRACT
This paper provides firm-level evidence on how subsidies to buyers of advanced digital production technologies work in sustaining private investments in innovation, and how such investments correlate with firms’ demand for labor. By exploiting the introduction in 2017 of a fiscal incentive granted to all Italian companies purchasing tangible goods instrumental to their digital transformation, we are able to quantify the volume of the subsidized investments within the national economy, to correlate the decision to invest with ex-ante structural and strategic characteristics of the beneficiary firms, and to evaluate the labor market effects (hirings and separations) of these investments at the firm level, for different classes of workers and firms. Overall, the analysis suggests that the policy has been so far an effective means to support the advanced digital technology transformation of the Italian production system and that such transformation has been positively correlated with employment growth at investing firms.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Empirical evidence based on previous recessions is mixed on whether technology adoption occurs faster in the aftermath of economic downturns (Hershbein and Kahn Citation2018; Graetz and Michaels Citation2017).
2 As regards the predicted labor market effects arising from the spread of information technology, see Autor, Levy, and Murnane (Citation2003) and Goos and Manning (Citation2007) for early theory and evidence of the so-called ‘routinization’ hypothesis.
3 For evidence suggesting this trend, see, for instance, the most recent World Economic Forum’s Future of Jobs Survey (Citation2020), in which, among the business leaders surveyed, over 80% report that they are not only expanding their use of remote work, but also accelerating the digitalization of their work processes. A significant 50% also indicate that they are set to accelerate the automation of jobs in their companies.
4 A 40% increase in tax allowance was also admitted for some types of disembodied digital technologies (such as cloud computing and other data management software), conditioning on firms being also granted the 150% increase in tax allowance for ‘smart’ capital goods.
5 This figure is estimated applying the corporate tax rate, which is equal to 24%, to the amount of the extra depreciation deduction (150 thousand euros in this example), not considering the discount of the multi-annual cash flow.
6 Moreover, as of 2016, small and medium enterprises could take advantage of payable interest relief on debt incurred for investments in tangible and intangible capital goods, by making a request to the Ministry of Economic Development, subject to availability of the funds.
7 According to the Italian Law, the tax depreciation rate on ICT appliances is, indeed, constant across industries and equal to 20%; instead, the tax depreciation rate on other machinery and equipment varies according to the specific nature of the capital good and by industry. For instance, within the food, beverage, and tobacco industry it ranges from 7.5% to 15.0%, while in pharma industry from 10.0% to 17.5%. Prudentially, we take the median of the tax depreciation rates relevant within each 2-digit NACE sector.
8 We also could not directly estimate the value of investments for 519 companies for which information on their sector of activity was unavailable.
9 Results not reported but available upon request.
10 By controlling for the use of R&D credit and super-depreciation as of 2016 we aim for our estimated parameters to capture the effect of additional investments, made possible through the hyper-depreciation in 2017, regardless of whether firms also benefitted from other supply-side incentives in place. For studies devoting specific attention to the impact of the so-called ‘hidden effect’, which is likely to occur when the outcome of a given policy is influenced by the presence of other policies, see Guerzoni and Raiteri (Citation2015) and Caravella and Crespi (Citation2021).
11 See later, footnote 17, for details on the occupational classification used.
12 The same is true also for the different subgroups of the Treated populations separately analyzed in the rest of the paper. Results of the corresponding balancing tests are available upon request.
13 The existence of a positive relationship between firm size and return from IT investments has been explicitly verified by Dhyne et al. (Citation2018).
14 The‘Credito di imposta Mezzogiorno’, as regards limited liability firms, was used by 11.900 companies in 2018 and by 17.400 companies in 2019, for a tax credit amount of 0.9 and 1.3 billion euros respectively.
15 Through the so-called ‘Bonus assunzioni Sud’ program, hiring incentives, in the form of exemptions from social security contributions, were exclusively in place for firms located in the South of Italy in 2017, and more generous than elsewhere in subsequent years.
16 We use the occupational classification CP2011, adopted by ISTAT in line with the International Standard Classification of Occupations ISCO-08, and define the following 5 classes: High skilled science-based occupations, like science, engineering and ICT professionals (codes 2.1, 2.2, 2.3, 3.1, 3.2); High skilled not science-based occupations, like managers, business and legal professionals (codes 1, 2.4, 2.5, 2.6, 3.3, 3.4); Middle skilled white collar occupations, including clerical support, services and sales workers (codes 4, 5); Middle skilled blue collar occupations, including craft and related trades workers, as well as plant and machine operators and assemblers (codes 6 e 7); Low-skilled occupations, including a variety of elementary occupations (code 8).
17 Firm-level information on the stock of employed workers in 2015 was retrieved by merging data from ASIA, the firm register maintained by ISTAT, while employment levels for 2016 onward were calculated as: for t = 2016, 2017, 2018, Q12019.
We could not replicate the analysis on sub-populations of workers, since only firm-level data on the total number of employees in 2015 were available.
18 Almost 27 billion euro has been allocated specifically to the digitization, innovation and competitiveness of the productive system. The main intervention is the so-called Transition 4.0 program (in the form of multi-year tax credits promoting investments in ADP technologies embedded in capital goods, research development and innovation projects related to digitalization, as well as training activities for digitization), which constitutes an evolution of the Industry 4.0 program, introduced in 2017, that is the focus of the present study. The plan provides also for the institution of a scientific committee with the mandate of an ex-post economic effects evaluation of the Transition 4.0 program. Other interventions include the completion of the broadband project, the construction of ultra-fast fiber optic networks, 5G and investments in satellite monitoring.