The effect of local taxes on firm performance
Evidence from geo‐referenced data
Bibliographic Data
| ID | 21352772 |
|---|---|
| Authors | Federico Belotti (0000-0003-3169-0641, Department of Economics and Finance University of Rome Tor Vergata Rome Italy, corresponding author), Edoardo Di Porto (0000-0003-3598-8744, DISES‐CSEF, University of Naples Federico II Naples Italy), Gianluca Santoni (0000-0002-8853-0051, CEPII Paris France) |
| Year | 2021 |
| Volume | 61 |
| Issue | 2 |
| Pages | 492-510 |
| Publication date | 2021-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Regional Science (JOURNAL) |
| Journal identifiers | ISSN: 0022-4146 • E-ISSN: 1467-9787 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/jors.12518 |
| OpenAlex | W2256167351 |
| Language | EN |
| Citations received | 2 |
| References cited | 19 |
This paper investigates the effect of local property tax for businesses on firm performance by using a panel of Italian manufacturing firms. To identify this effect, we implement a pairwise spatial‐differenced estimator and exploit the exogenous variation in local property tax rates caused by the political alignment of local and central governments. We find that business property taxation has a sizeable negative impact on equipment, the more volatile part of tangible assets, employment, and value‐added. We interpret these results as evidence of a distortionary mechanism. When heavy equipment are included in the business property tax base, as was the case in Italy during our estimation period, business property taxation depresses investment and induces firms to also reduce output and downsize
Capital (architecture) · Econometrics · Economics · Endogeneity · Estimator · Exploit · Local government · Macroeconomics · Microeconomics · Pairwise comparison · Panel data · Productivity · Spatial econometrics · Total factor productivity · Unobservable · Work (physics) · Computer Science · Economic Policies and Impacts · Fiscal Policy and Economic Growth · Housing Market and Economics
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2023 (2) |
| Highly cited | No |