Fiscal policy, public investment and structural change: a P-SVAR analysis on Italian regions

ABSTRACT

This study analyses the regional impact of public expenditures focusing on three domains central to the Italian National Recovery and Resilience Plan (NRRP): green, digital and knowledge. Relying on a regional public expenditures sectoral dataset for the period 2000–19, we perform a panel structural vector autoregressive (P-SVAR) model showing that fiscal policy has positive and long-lasting effects on gross domestic product (GDP) and private investments. A relevant heterogeneity is detected, relative to: (1) the effects of sectoral spending in crowding-in investment; (2) the impact on regions’ ‘structural upgrading’; and (3) a discrepancy in fiscal multipliers across macro-areas. Nevertheless, the results suggest that the NRRP may help in reducing the Italian divide.

KEYWORDS:

• fiscal multipliers
• panel structural vector autoregressive (P-SVAR)
• Italian regions
• North–South divide

JEL:

• C33, E62, H70, R58

ACKNOWLEDGEMENTS

We thank the Associate Editor and two anonymous referees for their helpful comments and suggestions. We are grateful to Giuseppe Celi, Paolo D’Imperio, Matteo Deleidi, Francesco Simone Lucidi, Mario Pianta, Massimiliano Tancioni, Annamaria Simonazzi, Valeria Patella, Gennaro Zezza and the participants at the 63^ma Riunione Scientifica Annuale (RSA) della SIE – Società Italiana di Economia, for their comments on previous versions of this work. All the usual disclaimers apply.

DISCLOSURE STATEMENT

This study is part of the activities funded by the Dezernat Foundation in the context of the European Macro Policy Network project. All the usual disclaimers apply.

Notes

1. See Ministry of Economy and Finance (https://www.mef.gov.it/en/focus/The-Recovery-and-Resilience-Plan-Next-Generation-Italia/).

2. See Presidenza del Consiglio dei Ministri (https://italiadomani.gov.it/it/home.html).

3. For a comprehensive review of fiscal multipliers and their use for policy analysis, see Ramey (2011, 2019), Batini et al. (2014) and Castelnuovo and Lim (2019).

4. Other purely empirical methods include the local projections approach pioneered by Jordà (2005) and later integrated in SVAR models (Auerbach & Gorodnichenko, 2017). Recent studies show that SVAR and LP models produce the same IRF, and are equally robust to non-linearities (Plagborg-Møller & Wolf, 2021).

5. See Caldara and Kamps (2017) for a discussion of identification schemes in SVARs.

6. For model-based estimates see, among others, Piacentini et al. (2016) and Canelli et al. (2022).

7. As in Marrocu and Paci (2010) and Piacentini et al. (2016).

8. In the Centre–North, the investment multiplier at impact ranges between 7.8 for Trentino and 1.3 for Tuscany.

9. In contrast to models following the real business cycle and NK tradition, the SMM extends the ‘Keynesian hypothesis’ to the long run (Garegnani, 1992). Output growth is driven by the growth rate of the autonomous non-capacity-creating components of aggregate demand (such as public expenditures, export or credit-financed consumption), while the Keynesian multiplier effect is combined with an investment function grounded on the flexible accelerator principle (e.g., Girardi & Pariboni, 2019).

10. In ISTAT REA, public consumption follows the COFOG definition. However, it is not possible to distinguish between categories of expenditures. Investment spending is instead broken down in three sectors only (education, healthcare, other), so the matching between public consumption and investment for each economic sector is not possible.

11. See https://www.contipubbliciterritoriali.it/CPTDE/catalogo/CPTDE_CatalogoCPT.html.

12. Figures A1–A5 in Appendix A in the supplemental data online show the individual cross section of government expenditures ($g_i^j$), private investment ($i_i$) and GDP ($\mathrm{gd}{p}_i$), and the degree of export ($\mathrm{spe}{c}_i^{\mathrm{XD}}$) and high-tech ($\mathrm{spe}{c}_i^{\mathrm{HT}}$) specialisation.

13. For a detailed analysis, see Celi et al. (2018).

14. From 2009, with the adoption of the new ATECO 2007 classification, sectors with dynamic world demand are CE, CF, CI, CJ, CL, M, R and S.

15. See Canova and Ciccarelli (2013) and Pedroni (2013) for a review of SVARs in a panel setting.

16. Table A3 in Appendix A in the supplemental data online shows the sample average for (A) economic dependency – defined as the ratio of net imports to GDP – and (B) the specialisation in manufacturing – defined as the share of manufacturing VA in total VA. Looking at the distribution of regions across groups (North/South, exporter/importer and manufacturer/traditional), a strong overlap is detected, with Northern regions being either a net exporter or running a balanced CAB, and Mezzogiorno regions mostly specialised in traditional sectors.

17. Lucidi (2022) shows how computing multipliers with the standard methodology would bias upward the results. We performed the same exercise, reaching similar conclusions. The results are available from the authors upon request.

18. Notice that: j ε {total public expenditure in green, digital, and knowledge-related sectors, excluding current and capital transfers}. This means that we estimate a separate model for each public expenditure aggregate.

19. It is well known that altering the ordering of the variables in the VAR can lead to dramatic changes in the results. However, our results are robust to different variable orderings.

20. While our results are on the upper bound in terms of magnitude, macro-areas GDP multipliers display similar dynamics to other studies in the literature on Italian regions. In Lucidi (2022), the government consumption multiplier at impact is equal to 1.7 in the North and 1.3 in the South, but converges to 1.2 and 1.1, respectively, at the eighth horizon. Similarly, the government investment multiplier is equal to 2.5 in the North and 1.5 in the South, but increases over time, even though a regional gap persists.

21. The evidence in Destefanis et al. (2022), which is the only other study in a regional setting that includes private investment, points to rather different results. Crowding-out effects on private investment due to shocks to government investment are reported for all but five regions, while government consumption shocks crowds-in private investment in all but seven regions, mostly located in the North.

Additional information

Funding

This study is part of the activities funded by the Dezernat Foundation in the context of the European Macro Policy Network project.