Abstract
We investigate whether the geographic concentration of non-EU immigrants in the various Italian provinces affects labour tax avoidance (LTAV) practices adopted by firms located in the same provinces, as well as in the neighbouring provinces, and operating in construction and agriculture industries that mostly employ immigrants in Italy. For this purpose, we develop a LTAV proxy based on the financial accounting information of a sample of 993,606 firm-years, disseminated throughout the 108 Italian provinces, over the period 2008–2016. Our results, based on a Spatial Durbin Model panel regression, reveal a statistically significant positive association between the concentration of non-EU immigrants and LTAV at province level, as well as the presence of spillover effects among neighbouring provinces. Our findings are robust to several additional analyses, including instrumental variable estimations. Our study provides empirical support to previous structuralist or marginalization theories holding that socioeconomically marginalized groups, such as non-EU immigrants, are more likely to be involved in labour exploitation practices, which could underlie our LTAV outcomes. Furthermore, it supports the need for tax authorities to strengthen labour inspections, coordinated at national level, especially in those contexts where non-EU immigrants are mostly employed. On the other hand, a greater social integration, assistance, and recognition of rights of immigrants may help to alleviate their situation of weakness that makes them more vulnerable to LTAV practices. Finally, tackling LTAV, associated with the underemployment of immigrants, may prevent its negative effects for society arising from the reduction of public resources to sustain the social welfare and finance public goods and services.
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Notes
In our study, according to the official statistics, we consider an immigrant any resident with non-EU nationality, namely citizens of countries that do not belong either to the EU or the European economic area.
The most recent labour market reform in Italy, the so-called Jobs Act, was enacted by the Renzi government in 2014.
AIDA is a database managed by Italian Bureau Van Dijk, which includes financial statements and other relevant details of 1 million companies in Italy, with up to 10 years of history.
We extracted the accounting data from AIDA over the first 5 months of 2018, when accounting data for fiscal year 2017 were not available yet.
Data on immigration in Italy are provided by the Italian Institute of Statistics (ISTAT) and publicly available on: http://stra-dati.istat.it/.
NACE (for the French term: nomenclature statistique des activités économiques dans la Communauté européenne) is the industry standard classification system used in the European Union. The current version is revision 2 and was established by Regulation (EC) No 1893/2006.
The regions of Italy are the first-level administrative divisions of Italy, constituting its second NUTS (Nomenclature of Territorial Units for Statistics) administrative level. Each of the 20 regions is divided into provinces.
NUTS (Nomenclature of Territorial Units for Statistics) is a geocode standard, developed by the European Union, for referencing the administrative divisions of EU countries for statistical purposes.
Mezzogiorno or Meridione d'Italia is an economic macro-region traditionally comprising the territories of the former Kingdom of the two Sicilies (all the southern section of the Italian Peninsula and Sicily) as well as the island of Sardinia.
The reference legislation on social security contributions, including their computation rules and settlement, includes law no 335 of August 8th, 1995 and other following circulars of INPS (the national social security institute).
The social security tax base is defined by the Legislative Decree n. 314 of 1997.
Italian accounting regulation for private companies is based on the Italian Civil Code (articles from 2423 to 2429), compliant with 2013/34/UE Directive, and accounting standards issued by Organismo Italiano di Contabilità (Italian Accounting Standard Setter).
Most of SSCs reported as expenses in the income statement are likely to be fully paid given that Italian social security regulation obliges the employer to pay them within the 16th day of the month following the last salary payment period.
We repeat our estimations using three-digit NACE rather than two-digit NACE and the results obtained are qualitatively analogous to those presented.
We deflate all variables by natural logarithm of lagged total assets to address the nonlinearity of the model. An untabulated analysis of residuals shows that this expedient significantly improves the explanatory power of the model.
We include this variable to exclude inventory adjustments from the possible determinants of the regression residuals ultimately affecting our LTAV measure.
We specifically apply the Skillings-Mack (SM) test (Skillings and Mack 1981), which is a generalization of the Friedman test in the presence of missing data. This test may be suitable for our analysis given that several firms do not appear in the observations of all years of the period 2008–2016.
Untabulated ttests show that variable AbSSCs is not significantly different from 0 in any year of the period 2008–2016.
In 2008, Italian GDP dropped by 1.05% (The World Bank 2018).
We elaborate the ratio based on data provided by ISTAT.
We repeat the analysis by omitting Milan or Florence and the results obtained are qualitatively analogous to those presented.
We adopt a threshold distance of 57.14 km, beyond which the elements of W are set to 0. This is the best threshold distance based on the results of a Lagrange multiplier test. W is spectrally normalized so that its largest eigenvalue is 1. The choice of the spatial weight matrix is justified based on a theoretical argument on the mobility pattern and possibilities of immigrants.
This age restriction is also motivated by the related data availability from the Italian Institute of Statistics (ISTAT).
Using Milan or Florence as a reference province, rather than Rome, leads to qualitatively similar results to those presented in this study.
The Italian Tax Code (Presidential decree 22, December 1986) sets the derivation principle in the Article 83, stating that taxable income is computed based on the accounting income that should only be adjusted, when accounting standards differ from tax rules.
Regional-level variables are not spatially differentiated like Eq. (4) SDM regression.
The Hausman test [χ2(12) = 109.65; p < 0.01] suggests that the fixed-effect specification is more adequate than the random effect.
Abbreviations
- AbSSCs :
-
Abnormal social security contributions
- ISTAT:
-
Italian Institute of Statistics
- LTAV:
-
Labour tax avoidance
- NSSCs:
-
Normal social security contributions
- SAR:
-
Spatial autoregressive model
- SDM:
-
Spatial durbin model
- SEM:
-
Spatial error model
- SSCs:
-
Social security contributions
- UDW:
-
Undeclared work
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Appendix
Appendix
Definition of Variables
Variable definition of Eq. (3)
AbSSCs abnormal SSCs equal to residuals from Eq. (2) simultaneously estimated with Eq. (1), PROVINCE dummy variable for each of 107 Italian provinces, CONTROLS firm-level control variables of Eq. (3) regression model: SIZE natural logarithm of total assets in thousands of euros, AGE age of the firm in years, LEVER total debt divided by total assets, CAPINT net fixed assets and net intangible assets divided by total assets, ROA net income divided by total assets, LOSS dummy variable that takes a value of 1 if the firm had two or more consecutive years of negative income including the current and 0 otherwise, GROW percentage change in net sales relative to previous year, DAC discretionary accruals estimated based on the performance-adjusted modified Jones model (Ravenda et al. 2018), AbMATL abnormal material costs equal to residuals from the following Eq. (5) with material costs (MAT), including both raw materials and merchandise, as dependent variable, estimated cross-sectionally for each two-digit NACE industry-year
AbSERV abnormal service costs equal to residuals from Eq. (5) with service costs (SERV) as dependent variable, estimated cross-sectionally for each two-digit NACE industry-year, CASHTA cash and cash equivalents divided by total assets, ETR abnormal effective tax rate equals to industry- and size-matched GAAP ETR minus firm’s GAAP ETR, where GAAP ETR is the total tax expense divided by pre-tax income. Industry- and size-matched GAAP ETR is the average GAAP ETR for the portfolio of firms in the same quintile of total assets and the same two-digit NACE industry-year, SD_ROA standard deviation of ROA over the past four years, INVENTA inventory divided by total assets, INDUSTRY dummy variable for each three-digit NACE industry.
Variable definition of Eq. (4)
LTAV_PROV LTAV measure at province level equal to the estimated coefficients on PROVINCE in Eq. (3), W inverse distance spatial weight matrix with a threshold distance of 57.14 km, spectrally normalized, IMMIGR non-EU immigrant concentration, computed as the fraction of non-EU residents per 1000 residents in each province and year, restricted to the population between 18 and 59 years of age, and spatially differentiated from the province of Rome (source: ISTAT), CONTROLS province-level control variables of Eq. (4) regression model: DENSITY province population per km2, spatially differentiated from the province of Rome (source: ISTAT), CRIME natural logarithm of crimes reported by police forces to judicial authorities per 1000 residents, spatially differentiated from the province of Rome (source: ISTAT), UNEMPL annual unemployment rate, spatially differentiated from the province of Rome (source: ISTAT), HGRSAL employee hourly gross salary (CPI deflated, 2016 equivalents), spatially differentiated from the province of Rome (source: ISTAT), ∆GDP gross domestic product growth rate, spatially differentiated from the province of Rome (source: ISTAT), u province fixed-effect (PROVINCE FE).
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Ravenda, D., Valencia-Silva, M.M., Argiles-Bosch, J.M. et al. The Effects of Immigration on Labour Tax Avoidance: An Empirical Spatial Analysis. J Bus Ethics 170, 471–496 (2021). https://doi.org/10.1007/s10551-019-04393-4
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DOI: https://doi.org/10.1007/s10551-019-04393-4