Certification and the corporate burden: effective tax rates across three regulatory regimes in Italian small firms
Abstract
Differentiated tax regimes assume that a class of firms can be identified in advance and that membership predicts the constraint the policy is meant to relieve. This paper tests that premise on 67,899 firm-year observations of Italian certified innovative start-ups, innovative SMEs and ordinary small firms between 2014 and 2025. One effective-tax-rate equation is estimated three times: across five panel estimators, inside an unsupervised partition of the same firms, and against thirteen learning algorithms fitted to the same observations. Capitalisation and public contributions dominate, carrying seventy per cent of outof-sample predictive content, while the spread across the three registers is three points against seven for firm size alone, whose gradient is non-monotone. The partition rejects a common coefficient vector in twenty of twenty-one comparisons: the equity coefficient varies twelvefold and vanishes among grantfunded firms. Fiscal relief keyed to balance-sheet behaviour reaches this population more evenly than relief keyed to certification.
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BibTeX
@misc{leogrande2026certification,
title = {{Certification and the corporate burden: effective tax rates across three regulatory regimes in Italian small firms}},
author = {Angelo Leogrande and Mauro Di Molfetta and Valeria Notarnicola and Maria Giovanna Trotta and Antonio Volpe Plantamura},
howpublished = {HAL},
year = {2026},
month = sep,
url = {https://hal.science/hal-05734533v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Certification and the corporate burden: effective tax rates across three regulatory regimes in Italian small firms
AU - Angelo Leogrande
AU - Mauro Di Molfetta
AU - Valeria Notarnicola
AU - Maria Giovanna Trotta
AU - Antonio Volpe Plantamura
PY - 2026
DA - 2026/09/01
PB - HAL
UR - https://hal.science/hal-05734533v1
AB - Differentiated tax regimes assume that a class of firms can be identified in advance and that membership predicts the constraint the policy is meant to relieve. This paper tests that premise on 67,899 firm-year observations of Italian certified innovative start-ups, innovative SMEs and ordinary small firms between 2014 and 2025. One effective-tax-rate equation is estimated three times: across five panel estimators, inside an unsupervised partition of the same firms, and against thirteen learning algorithms fitted to the same observations. Capitalisation and public contributions dominate, carrying seventy per cent of outof-sample predictive content, while the spread across the three registers is three points against seven for firm size alone, whose gradient is non-monotone. The partition rejects a common coefficient vector in twenty of twenty-one comparisons: the equity coefficient varies twelvefold and vanishes among grantfunded firms. Fiscal relief keyed to balance-sheet behaviour reaches this population more evenly than relief keyed to certification.
ER -