Regional multipliers across the Italian regions
Dados Bibliográficos
| ID | 21353040 |
|---|---|
| Autores | Sergio Destefani (0000-0003-3568-3414, Department of Economics and Statistics Università degli Studi di Salerno Fisciano Italy, autor correspondente), Mario Di Serio (0000-0001-7486-2947, Department of Economics and Statistics Università degli Studi di Salerno Fisciano Italy), Matteo Fragetta (0000-0001-7764-0813, Department of Economics and Statistics Università degli Studi di Salerno Fisciano Italy) |
| Ano | 2022 |
| Volume | 62 |
| Fascículo | 4 |
| Páginas | 1179-1205 |
| Data de publicação | 2022-09-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Journal of Regional Science (JOURNAL) |
| Identificadores do periódico | ISSN: 0022-4146 • E-ISSN: 1467-9787 |
| Editora | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/jors.12592 |
| OpenAlex | W3034647182 |
| Idioma | EN |
| Citações recebidas | 8 |
| Referências citadas | 39 |
This paper estimates the multipliers of different types of government spending in the 20 Italian administrative regions throughout 1994–2016. We derive region‐specific multipliers through a Bayesian random effect panel vector autoregressive model. We find that the EU structural funds, compared to the other types of government spending, provide the largest and most pervasively significant GDP multipliers, whereas the effectiveness of nationally funded government investment and, especially, government consumption shocks is more limited. An exploratory analysis of the regional multipliers suggests that they are positively associated with the amount of unused resources and the size of the regional economy
Autoregressive model · Bayesian probability · Consumption (sociology) · Econometrics · Economics · Government (linguistics) · Government spending · Investment (military) · Macroeconomics · Political science · Statistics · Welfare · Fiscal Policy and Economic Growth · Mathematics · Monetary Policy and Economic Impact · Regional Development and Policy
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| Obras citantes distintas | 8 |
|---|---|
| Citações por ano | 2 |
| Intervalo de citações | 2022 - 2025 (4) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 8 |