Shadow Economy or Economic Driver? The Impact of Counterfeiting on Italy's Growth

Shadow Economy or Economic Driver? 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The Impact of Counterfeiting on Italy's Growth Elton Beqiraj, Silvia Fedeli, Luisa Giuriato This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5032553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the impact of economic crime, specifically counterfeiting, on economic growth in Italy after the Great Recession (2008-19), when the country experienced lower economic growth than its developed counterparts. Counterfeiting is orchestrated by mafia-like organizations that turn large areas into hubs for the production/trading of counterfeit goods, thereby damaging genuine brands. Our study, using a unique regional dataset, demonstrates that in Italy, two direct effects of counterfeiting are significant. First, it stimulates economic growth by flooding markets with counterfeit goods. Second, it undermines market modernization by violating the intellectual property rights of innovative firms in high-value sectors. Moreover, the resources absorbed by illegal activities determine an indirect negative effect: once counterfeiting is controlled, the standard drivers of economic development become more effective. This suggests that if all growth drivers were channeled into legal activities, Italy would experience a development path in line with the performance of its top competitor countries. JEL: E26; O47; C23; K14 Counterfeiting Economic growth Intellectual property rights Shadow economy Regional dynamics Italy Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction This study delves into the impact of counterfeiting on Italy's economic growth, using a unique regional dataset that comprehensively captures criminal counterfeiting activities disclosed/reported in Italy from 2008 to 2019, alongside key economic indicators. The need to study this issue in Italy arises from its inadequate/low economic development, which is always below those of its developed European and G-7 counterparts, as shown in Fig. 1 . The origins of Italy's underperformance are controversial and include historical, geographic, and institutional reasons, as well as human capital formation and cultural fractionalization. In addition, deep regional disparities persist as a contributing factor. During the period covered by this study (2008–2019), regional growth rates were higher in the Northwest, followed by the Northeast and Central regions (see Fig. 2 ). The southern regions (in particular, Calabria, Sicily and Molise) faced structural problems rooted in high unemployment rates, low productivity, competitiveness, investment attractiveness,... All these factors have contributed to the widening of the economic gap between the north and south of the country, which is a legacy of its history. This picture is combined with the significant presence of the informal economy, whose value added in 2019 was estimated at 183.9 billion euros, or 10.2 percent of GDP (Ministero dell'Economia e delle Finanze, 2022). This decades-long economic disparity in Italy raises fundamental questions about the growth and development process and its implications, also in light of recent evidence that even highly developed Italian regions have wide pockets of underdevelopment, as signaled by indicators of innovation and productivity. Traditional development theories often assume that individuals will remain compliant and law-abiding even in situations of considerable relative deprivation, such as those that have followed the series of economic crises that afflicted the Italian economic system during the period considered. This assumption may not correspond to reality. When economic crime is in the hands of criminal organizations that turn it into a lucrative option, labor-surplus economies can tolerate high doses of it, contributing to a scenario in which a country’s growth goes together with large pockets of illegality and organized crime. We investigate whether economic crimes -specifically counterfeiting run by criminal organizations- have played a significant role in the economic growth and development path of Italian regions. The choice of counterfeiting is due to the fact that the entire Italian economy heavily depends on intellectual property rights (IPR), with virtually every industry either producing or utilizing them, thus characterizing the Italian economy with an IPR intensity surpassing the EU average (OECD, 2018, p.16). Global trade in counterfeit products infringing Italian trademarks reached a value of 24.3 billion euros in 2018, equivalent to 3.6% of total Italian manufacturing sales. Italy ranks fourth among OECD countries in terms of IPR infringements by counterfeiters (OECD, 2021). In Italy, the market for counterfeit products has reached a value of 8.7 billion euros, representing 2.1% of the country's imports (OECD, 2021a). In addition, being a non-violent economic crime, counterfeiting is socially (and, hence, politically) tolerated: about one-third of Italian consumers buy at least one fake product or utilize an illegal service each year, and 73% of consumers believe that it is “normal” (Confcommercio, 2019). In principle, the expected impact of counterfeiting on Italy's economic performance is twofold. First , a negative effect results from the profitability associated with counterfeiting in high-value-added sectors. Counterfeiting infringes IPR, such as fashion, luxury goods, electronics, and automotive, undermines market confidence, reduces consumer spending on genuine products, erodes market shares for legitimate businesses, and poses barriers to international trade and investment. Second , a positive effect arises from the massive quantity of (cheap and low quality) counterfeit goods in the market, in which Italy also exploits its propensity to trade counterfeit products wherever (internally and abroad) it has a relative comparative advantage for production (OECD, 2021) and where the "Made in Italy" sounding attracts consumers. In addition , our analysis reveals a third effect that is intertwined with the previous ones. Counterfeiting is mainly managed by criminal organizations that have assumed an entrepreneurial role, using both licit and illicit capital and mixing the management of legal and illegal production activities (Camera dei Deputati, 2013; 2017). They infiltrate legitimate companies by exercising control over the entire supply chain, which determines the reallocation of inputs, the displacement of resources from legal to illegal production activities, the management of the import/export of counterfeit goods and their final subsequent sale through an extensive network of national and international distribution points. All this leads to job creation and attracts investment to the benefit of organized crime, which undermines the development path of the Italian economic system. Our analysis is based on the 20 Italian regional economies, where the presence of counterfeiting activities is not uniformly distributed, with certain regions being more affected due to higher economic activities, rich markets in metropolitan areas, and significant transportation infrastructure. By concentrating on a single country, we sidestep issues related to differences in anti-counterfeiting policies between countries, a problem that affects cross-country studies of the economic impact of crime and illegal activities (see below). There are no significant differences in anti-counterfeiting policies between Italian regions, as they are centralized in Italy. Moreover, due to the relatively short period covered by our analysis, there is no need to include variables such as population and age structure, which remain relatively stable throughout the period. Finally, Italian regions exhibit institutional homogeneity and are fully integrated economic areas, which further justifies our approach. We employ a regional dynamic panel data model with a Generalized Method of Moments (GMM) estimator. Our estimated model aligns with endogenous growth models focusing on physical capital and technology (Romer, 1987, 1990; Jones, 1995; Grossman and Helpman, 1991; Aghion and Howitt, 1992). With the introduction of counterfeiting indicators, the empirical results demonstrate the coexistence of both positive and negative effects of counterfeiting on economic growth. In addition, once counterfeiting is controlled for, the effects of wages, productivity, and R&D on economic growth are particularly amplified and aligned with those of advanced economies, suggesting the truly devastating nature of criminal activity on the Italian economy. The paper is structured as follows. Section 2 reviews the literature on economic growth and crime. Section 3 presents the data. Section 4 presents some stylized facts. Section 5 reports the model specification and empirical results. The conclusion in Section 6 encapsulates the implications of our findings and suggests policy consideration and further research. 2. Review of the literature Recent research has increasingly recognized the role of the socioeconomic environment in shaping firms' ability to absorb knowledge, improve their performance, and contribute to growth and development. In this respect, evidence from various studies indicates the relevant role of crime and highlights the many channels through which it significantly affects social, political, and economic factors that are vital determinants of growth. The presence of criminal organizations reduces capital accumulation by diverting resources from productive activities towards crime deterrence and control. It impacts private investment by increasing uncertainty and inefficiency and by reducing the attractiveness of local economic activities to national and foreign investors also due to the fears of weak law enforcement (Daniele and Marani, 2011; Forgione and Migliardo, 2023). Crime also reduces the labor supply in the legitimate business sector as criminal organizations hire workers for their own illegal activities or refrain regular workers or entrepreneurs from working in certain sectors or areas. Criminal organizations reduce the legal firms’ productivity (Centorrino and Ofria, 2008; Daniele, 2009; Albanese and Marinelli, 2013; Ganau and Rodriguez-Pose, 2019) and distort competition to their advantage by employing their larger financial resources and exerting their coercive power (Albanese and Marinelli, 2013). By imposing quasi-fixed costs on legal firms and generating credit rationing, criminal organizations can stick the economy in a low-income steady state (Balletta and Lavezzi, 2023; Fioroni et al. 2023). Mehlum et al. (2005) also show how, in the presence of criminal behaviors affecting firms’ profitability and returns from job creation, the economic dynamics are characterized by multiple regimes and can end up in a poverty trap with high crime rates, a large sector of subsistence workers and low income. Some studies have examined the impact on the economic resources available for growth-enhancing factors such as education, the quality of the workforce, and human capital accumulation (Buonanno and Leonida, 2006; Coniglio et al., 2010; Acemoglu et al., 2020). As shown in Table 1 , evidence of the impact of crime on growth has been provided in cross-country studies or in studies comparing the US states using a single country’s time series. The main problem of these studies relates to the inexact and conflicting data across countries, diverse crime definitions, underreporting, and disparate recording methodologies. The most commonly used proxy for crime is the homicide rate because alternative measures (e.g., property crimes) suffer from severe problems of underreporting that bias results. Homicides and violence create high uncertainty, create additional costs for security, lower business profitability and efficiency and, ultimately, impact on growth as discussed above. In some studies (e.g., Peri, 2004), homicides are considered the expression of organized crime with a tendency to operate in specific areas. Some cross-country studies report strong adverse effects of crime on economic growth (e.g., World Bank, 2006; Neanidis and Papadopoulou, 2013). Cárdenas (2007) finds them for a panel of 65 countries (years 1971–1999) controlling for stabilization policies, structural policies, and institutions. Burnham et al. (2004) also find a strong impact of crime on US county-level (per-capita) income growth, while Gaibulloev and Sandler (2008) show the negative impact of terrorist crimes on the growth rates in Western Europe. Using time series for a single country, Cárdenas (2007) finds a strong impact of crime on Colombia's total factor productivity and growth (1951–2005). Dettoto and Otranto (2010) apply an autoregressive model for Italian GDP growth and the monthly homicide rate (1979–2002), finding a reduction of growth due to crime and cyclical components in the growth-crime relationship, which are confirmed in Detotto and Otranto (2012). Detotto and Pulina (2013) found for Italy (1970–2004) that homicides and robberies have a crowding out effect on GDP growth (a 1 percent increase causes a 0.1 percent reduction in GDP growth per year), while all crime typologies (except robberies) cause a reduction in the employment rate. Other studies present evidence of no statistically clear effect of crime on growth (e.g., Chatterjee and Ray, 2009). Using data from 26 countries and trying to account for the different channels through which crime influences growth, Goulas and Zervoyianni (2015) provide evidence of asymmetries in the growth-crime relationship, as it is strongly negative in economic low and weakly negative or statistically insignificant in good times. Table 1 Selected empirical studies on the crime – growth nexus Authors Country, time, dependent variables, and crime measures Methodology Results Albanese and Marinelli (2013) Sample of Italian firms (2002-07) Total Factor Productivity (TFP) at firm level Mafia index OLS, FE, IV Moving from the 90th to the 10th percentile in the provincial distribution of organized crime increases firm TFP by 9–10% Results are independent of size and sector of firms Results are robust to the potential endogeneity of organized crime Cardenas (2007) Unbalanced panel of 65 countries (1971–1999) Per capita GDP growth Homicide rate OLS Homicide rate explains growth deceleration. Cardenas and Rozo (2008) Departments of Columbia (1960–2000) GDP and per capita GDP growth Violent crimes (homicide rate), coca crops Difference in differences The surge in drug trafficking significantly fueled the rise in homicides, subsequently exerting adverse impacts on overall economic growth Centorrino and Ofria (2008) Italian regions Calabria, Campania, Apulia and Sicily (1983–2005) Labor productivity Ratio between mafia homicides and population. OLS Crime negatively influences the rate of growth of labor productivity in the southern regions, especially in construction and non-tradable sectors Chatterjee and Ray (2009) 74 countries (1991–2003) GDP growth rate Rates of crimes based on crime and corruption perception by victims OLS; IV Lack of a strong association between both corruption and growth rates and between crime and growth rates. Serious crime and crime (overall) are negatively associated with growth only for countries with high crime rates. Daniele and Marani (2011) 103 Italian provinces (2002–2006) Foreign direct investment Organized crime (measured by the number of criminal offenses associated with mafia organizations) Feasible GLS estimators Negative impact of the presence of organized crime on the business climate and deterrence of foreign and domestic investment. Detotto and Otranto (2010) Italy (1979–2002) Monthly data on GDP Homicides AR; Impulse response function (IRF) A 1% increase in the crime rate results in a 0.00040% monthly reduction in real economic growth. The economic cost of crime has a significant fixed component. The negative impact of crime on the Italian economy is 5% higher during recessions than during expansions. Detotto and Otranto (2012) Italy (1991–2004) Real GDP 22 types of crimes Non-parametric dynamic factor model Seven crime types (mostly of the white-collar type) have a strong link with GDP. Detotto and Pulina (2013) Italy (1970–2004) Employment and GDP growth Homicides; robberies, extortions and kidnapping; property crimes AutoRegressive Distributed Lags; Granger causality Homicides and robberies contribute to a crowding-out effect on GDP growth. All types of crime, excluding robberies, decrease the employment rate. In the short term, all crime types contribute to fluctuations in employment. Fioroni et al. (2023) Italian regions (1996–2013) Per capita GDP Mafia crimes Semi-parametric estimation Per capita GDP dynamics exhibit various regimes determined by the initial level of organized crime. Regions with higher organized crime levels have an increased proportion of embezzled public expenditure, leading to a negative impact on per capita GDP. Forni and Paba (2000) 94 Italian provinces (1971–1991) Employment, population, real and per capita value added Murders OLS Crime has a negative impact on employment growth. The effects of crime on value-added growth are not clear Ganau and Rodriguez-Pose (2019) Italian small- and medium-sized manufacturing firms (2010–2013) Total factor productivity Mafia association, Mafia murders, extortions GMM Adverse direct impacts of organized crime on the productivity growth of businesses. Effect is more pronounced for smaller enterprises. Any potential positive influence arising from industrial clustering is significantly undermined by the robust existence of organized crime Goulas and Zervoyianni (2015) 26 countries (1995–2009) Per capita GDP growth Homicide rate GMM Strong negative relationship between per capita output growth and crime due to crime impact on levels of insecurity in the economy. Mauro and Carmeci (2007) Italian regions (1963–1995) Unemployment, output levels and growth Homicide rate Pooled Mean Group estimator Crime has a negative long-run effect on output level rather than on output growth Naddeo (2014) Italian regions (1995–2011) Per capita GDP and GDP growth Homicide rate GMM The growth rate is reduced by 0.13% for a 1% increase in the homicides rate. Peri (2004) 95 Italian provinces (1951–1991) Per capital GDP growth, employment growth rate Organised crime (measured by murder rate) OLS Annual per capita income growth is negatively affected by murder rate Pinotti (2015) Two Italian regions (Apulia and Basilicata) (1951–2007) Per capita GDP Mafia-type crimes, Homicide rate Synthetic control method Crime determines lower levels of GDP per capita (-16%) in Apulia and Basilicata, which have been exposed to a huge increase in the presence of organized crime (proxied by the homicide rate) since the mid-1970s due to a shift in tobacco smuggling routes. Part of the literature investigates growth at the local level (Forni and Paba, 2000). In particular, some empirical studies examine the impact of crime on regional backwardness in Italy and the long-time and disrupting presence of different mafia-type organizations (Mafia in Siciliy, Camorra in Campania, ‘Ndrangheta in Calabria, Sacra Corona Unita in Apulia, along with other similar groups). Some studies conclude that organized crime hinders economic development and industrial growth in certain regions of Italy, especially in the South (Forni and Paba, 2000; Naddeo, 2014; Pinotti, 2015; Mocetti and Rizzica, 2021). Other studies show different or more nuanced conclusions. Peri (2004) reports results indicating non-linearities in the employment growth-crime (homicides) relationship, with only high crime rate provinces showing an adverse impact on growth and labor demand. Mauro and Carmeci (2007), employing pooled-mean-group estimation techniques, find that crime harms income levels of Italian regions, but it does not exert a statistically significant adverse influence on long-run growth rates. Fioroni et al. (2023) suggest that different growth patterns at the regional level depend on the initial levels of Mafia-related crimes, also due to the capture of public expenditure by criminal gangs. Our study contributes to this topic by investigating the growth impact of a specific economic crime that has been only marginally considered in the literature. Counterfeiting has particular features that are not common to other crimes: through the appropriation of IPRs and the production of fakes, it has direct effects on the economic system, while it does not impact via uncertainty and fear, like violent crimes. Counterfeiting intersects various disciplines, such as marketing, management, and behavioral economics. Macro-level studies, including EUROPOL-OHIM (2015), OECD (2008, 2009, 2016), and Frontier Economics (2016), provide insights into the quantitative dimensions of counterfeiting. At the micro level, researchers have attempted to estimate the financial losses suffered by firms due to counterfeiting, including profits, brand value, brand equity, and countermeasure expenditures (Feinberg and Rousslang, 1990). Studies have also explored strategies used by legitimate firms to differentiate themselves from counterfeiters 1 and have highlighted the heterogeneous effects of counterfeiting on legitimate sales (Qian, 2011; Yao, 2005). More recently, the impact of counterfeiting has been estimated in terms of trade exchange, labor market, intellectual property protection, and lost tax revenues for the government (Beqiraj et al., 2020; Fedeli et al., 2018; Primo Braga and Fink, 1998; Smith, 2001; OECD 2018). The impact of counterfeiting on economic growth has been almost neglected. Our study aims to fill the research gap by examining the relationship between counterfeiting and economic growth in Italy. Italy is an ideal case to study this relationship as counterfeiting is a structural feature of the Italian production system, and a deep economic divide characterizes the regions of the country. To the best of our knowledge, no empirical study has yet investigated the impact of counterfeiting on Italian economic growth, underscoring the need for comprehensive analysis in this area. 3. The data As mentioned, the impact of criminal activity on economic indicators in cross-country studies suffers from inaccurate and inconsistent data, varying definitions of crime, underreporting, and different recording procedures. As a result, the proxies for criminal activity commonly used in cross-country analyses (such as indexes of economic freedom, transparency, political instability, and violent crime indicators) do not correspond to the general notion of crime, but rather to problems related to political governance (Powell et al., 2010) or to proxies of criminal organization activities. To address this research gap, our study uses a unique and recently constructed dataset that provides comprehensive information on the criminal economic activities of counterfeiting, along with key economic indicators for the 20 Italian regions. The time span is the decade following the Great Recession (2008–2019). All variables are logarithmically transformed to facilitate the estimation of the different elasticities through a log-linear model (as explained in the following sections). For counterfeiting data, we draw from the IPERICO database, which comprehensively documents all counterfeiting-related enforcement activities since 2008. 2 We consider two indicators: the average size of all counterfeits seized in each region (CAD) and the estimated value of such seizures in euros (CAV). 3 The CAD variable serves as a reliable proxy for the presence of counterfeiting phenomena in a given region. Higher CAD values give evidence of counterfeiting production activities or the importance of the region as an exchange and distribution hub for counterfeit goods, which may also originate from abroad (IPERICO, 2012). On the other hand, CAV reflects the expected profitability associated with such illicit activities and captures the presence of counterfeiting in high-value-added sectors, thereby affecting brand credibility and trade capacity. Because CAV is less influenced by factors such as the presence of major ports or national borders, where stricter controls are typically in place, it helps mitigate the spatial bias observed in CAD, especially in regions with larger average counterfeit seizure sizes (IPERICO, 2012). The presence of these two variables also allows us to account for an expected dual impact of counterfeiting: Both contribute to income in the informal economy and disrupt markets for high-value goods. 4 It is important to recognize certain limitations in the use of these variables as proxies for criminal activity at the regional level. They primarily capture the disclosed part of the phenomena, which could lead to underestimations. However, the success of anti-crime policies in certain regions certainly signals the presence of relatively higher levels of criminal activity in those areas. Moreover, these indicators may not be suitable for long-term analysis because they depend on the intensity of law enforcement activities rather than on changes in criminal activity per se . Nevertheless, they can be effective proxies for the presence and extent of counterfeiting in Italian regions over the relatively short period considered in our study. We also use regional ISTAT data on economic indicators. Our dependent variable is economic growth. Regarding the economic explanatory variables, which serve both as regressors and as instrumental variables, great emphasis is placed on capturing regional competitiveness and the characteristics of the production system related to technology and innovation. Such regional indicators include regional labor cost; average labor productivity derived from lagged log real regional GDP minus log employment; regional investment in physical capital by firms, and research and development costs of firms; investment in human capital as proxied by education expenditure; private investment in R&D; public policies of subsidies and taxation. The regional labor cost (W) is a crucial control variable for measuring competitiveness, as it allows us to take into consideration one of the most important dimensions of regional heterogeneity, capturing the evolving economic conditions and the regional divide between the South and the Centre-North of Italy. The variable includes wages, salaries, and fringe benefits in cash, before withholding taxes and social security contributions, payable by the employer. It coincides with the taxable wages for contribution purposes, paid on a cash basis, and includes remuneration for overtime, i.e., hours worked in addition to normal working hours. Regarding the impact of average labor productivity (ALP), which complements wages in measuring competitiveness, we expect that regions with lower economic activity will experience mainly negative effects, while higher productivity will be consistent with the use of modern equipment and up-to-date skills in the more developed regions. The other variables considered to test the robustness of our estimates include "private investment in tangible goods" (INVESTMENT), which captures the dynamism of the private sector and the regional economic climate. In addition, firms' regional research and development (R&D) input costs capture competitiveness related to firms' efficiency, innovation, and the industrial attractiveness of the economic environment. The role of private firms' expenditure on research and development (R&D) is recognized as a relevant driver in the growth literature (Romer, 1987, 1990; Grossman and Helpman, 1991; Aghion and Howitt, 1992) and is important for our analysis. Firms' investment in R&D reflects their deliberate efforts to create new ideas and products (Helpman, 1992), which are subsequently protected by IPR to secure innovation and gain a competitive advantage. The OECD (2021) provides evidence that firms that own IPR generate 20% higher sales per employee and pay higher wages than their counterparts that do not have an IPR portfolio. The benefits of IPR ownership are particularly pronounced for small and medium-sized enterprises. All of this, in turn, increases productivity, promotes economic competitiveness, and should boost overall economic growth (Gould and Grube, 1996; Neves et al., 2021). For each economic regressor, if statistically significant, we expect a positive effect on regional growth. To account for the role of public intervention, we include both a tax variable, indirect taxes (INDTAX), and an expenditure variable, subsidies (SUBS). Indirect taxes also reflect the level of legal trade in the region. Subsidies express public support to productive activities: the latter has traditionally been more generous in the southern regions, in an attempt to support the economic development of backward areas. Note that in our empirical analyses, we also consider two alternative proxies for human capital. The first is an indicator that captures the regional percentage of individuals who have attained levels of education from upper secondary to Ph.D. or equivalent, as classified by the International Standard Classification of Education (ISCED11, levels 3–8). The second refers to public expenditure on education and training -which includes transfers to households and public and private institutions-, as a percentage of GDP. 5 These variables do not significantly affect our results and are found to have an insignificant impact on economic performance. Table 2 presents summary statistics for the variables considered. Table 2 Summary statistics Variable (log) Description Obs. Mean Standard Dev. Min Max Y GDP growth rate (in %) 240 0.564 0.583 -0.616 2.019 CAV* Value of the seized counterfeits 239 6.301 1.593 0.000 8.167 CAD* Average dimension of fakes’ seizures 240 5.759 1.522 0.693 8.311 W* Labor cost 240 9.892 1.118 7.364 11.985 ALP Average labor productivity 220 -2.294 0.109 -2.580 -2.063 INVESTMENT* Private investment for tangible goods 240 9.160 1.050 6.746 11.223 R&D* Research and development expenditure 240 7.554 1.105 5.211 9.631 SUBS* Subsidies to businesses 240 4.999 1.034 1.686 6.449 INDTAX* Indirect taxes 240 8.722 1.126 6.221 10.876 HC EXP Human Capital (Expenditure) 220 0.488 0.198 0.100 1.300 HC ISCED11 Human Capital (ISCED11) 220 56.300 6.265 44.007 68.458 Notes: * denotes the log values. HC EXP is expressed as a percentage of GDP. 4. Some stylized facts From 2008 to 2019, Italy experienced a period of modest economic growth as a result of unsatisfactory productivity, reduced public investment, significant deficiencies in the supply and efficiency of tangible and intangible factors of production, a quantitative and qualitative shortage of human capital, the cost and length of the civil justice process, and the quality of industrial relations (for a review, see Bugamelli et al., 2018). All this reduces support for the competitiveness of domestic firms and the attraction of foreign direct investment. Overall, the period from 2008 to 2019 witnessed a continuation of regional disparities in Italy, with the northern regions generally outperforming the southern regions in terms of economic growth and development. This picture is combined with the significant presence of the underground economy, within which counterfeiting plays a relevant and growing role, with a turnover of more than 30 billion euros and its presence in the territory is mostly determined by the strategies of criminal organizations: "Organized crime, thanks to its financial, intimidating and corrupting power, manages all stages of the counterfeit supply chain, from production to shipping, distribution and retail" (Camera dei Deputati, 2013, p. 14). In this large-scale transnational criminal enterprise, different criminal organizations employ different strategies and specialize in different sectors. Camorra is undoubtedly the most dynamic and active organization, exercising direct control over the counterfeit supply chain, mainly in Campania, Lombardy, and Lazio. In Calabria, 'Ndrangheta primarily facilitates intermediary services in production and distribution, overseeing the import and re-export of counterfeit goods to European Union member states. The Sicilian Mafia focuses on counterfeiting in the food industry. Chinese criminal organizations based in Tuscany, the northern regions, Lazio, Marche and Campania have formed alliances with Italian and African criminal groups to import and distribute Chinese counterfeit products. Finally, counterfeiting is not limited to regions with a traditional industrial presence. It can infiltrate the economy of various regions, regardless of their level of industrial activity. Counterfeiting is also a form of financing for criminal clans and a means of laundering money from other criminal activities. In addition, counterfeiting allows for the extensive control of territories (Camera dei Deputati, 2013). The range of activities involved and the network of international connections show that the artisanal and local dimension of counterfeiting has been completely surpassed, replaced by a business-oriented activity with national and transnational reach. This explains the widespread presence of counterfeiting in all regions and why counterfeiting is proliferating even in regions where the presence of organized crime is not significant (e.g. Abruzzo). The types of products -from leather goods to handbags, watches, electrical machinery, and electronics- counterfeiters produce and sell, as well as their location in Italy, influence the distribution of the phenomenon. Regions that specialize in certain types of industrial production are more prone to counterfeit infiltration. In addition, the regions with the highest seizure rates differ from those with the highest value of seized goods. To gain a first insight into the phenomenon under study, Fig. 3 shows the mean dimension of counterfeiting (measured by the average number of pieces seized, CAD) during the period from 2008 to 2019 and reveals a remarkable degree of heterogeneity across regions. Lombardy, Lazio, Campania, and Apulia emerge as the leading regions with the highest average dimension of counterfeiting, indicating a relatively higher prevalence of counterfeiting activities in these areas. Conversely, regions such as Calabria, Sardinia, Piedmont, Abruzzo, Basilicata, Umbria, Trentino-South Tyrol, and Aosta Valley have a lower mean dimension of counterfeiting, suggesting comparatively less counterfeit trade. This observed variation underscores the regional disparities in counterfeiting activity and highlights the importance of taking local context and specificities into account when addressing the issue of counterfeiting. Figure 4 , which shows the value of counterfeiting, provides further insight into the phenomenon. The graph shows a diverse landscape across regions, with significant variations in the economic scale of highly profitable counterfeiting activities. Lombardy, Sicily, Veneto, Emilia Romagna, and Liguria emerge as regions with relatively higher counterfeit values, indicating the presence of a highly lucrative market for counterfeit goods in these areas. Regions such as Sardinia, Apulia, Tuscany, Campania, Abruzzo, and Piedmont show lower but still significant values of lucrative counterfeiting activities. The differences between Figs. 2 and 3 underscore the need for tailored anti-counterfeiting strategies at the regional level, taking into account the different levels of economic impact and enforcement required in different areas. 5. The model and empirical results To assess the impact of criminal activities on Italian economic growth, we use a regional dynamic panel data model with a GMM estimator (Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). While these models may not fully capture the long-run growth dynamics, they provide valuable insights into the drivers of economic performance over relatively short to medium-term horizons. Moreover, the inclusion of the lagged dependent variable as a covariate allows the persistence of agents' behavior to be captured with a partial adjustment mechanism. The model also takes into consideration endogenous and predetermined variables across regions, thus allowing for the identification of commonalities and differences in growth dynamics that provide insights into the role of specific factors in driving short-/medium-term growth. This contributes to a better understanding of regional disparities and growth differentials, although the limited time frame does not allow for insights into regional convergence. Our benchmark equation takes the following form: \(\:{y}_{i,t}=\gamma\:\:\:\alpha\:{y}_{i,t-1}+X{{\prime\:}}_{it}\beta\:+Z{{\prime\:}}_{it}\gamma\:\:\:+{\eta\:}_{i}+{v}_{it}\) , with (1) where the error term \(\:{\epsilon\:}_{it\:}\) has two components: the fixed effects \(\:{\eta\:}_{i\:}\) and the idiosyncratic shocks \(\:{v}_{it\:}\) . Our dependent variable, \(\:{y}_{i,t\:}\) , represents economic growth in region i at time t . The matrix X’ i,t includes all the economic regressors at a regional level, whereas the matrix \(\:Z{{\prime\:}}_{it}\) includes the criminal variable tested. The correlation of the lagged dependent variable, \(\:{y}_{i,\:t-1\:}\) , with the fixed effects in the error term, causes Nickell’s (1981) dynamic panel bias, which can be overcome by taking the first differences of the original model (Eq. 2 ): $$\:\varDelta\:{y}_{it}={\lambda\:}_{1}\varDelta\:{y}_{i,t-1}+{\lambda\:}_{2}\varDelta\:X{{\prime\:}}_{it}+{\lambda\:}_{3}\varDelta\:Z{{\prime\:}}_{it}+\varDelta\:{v}_{it}$$ 2 The first difference estimator eliminates time-invariant regressors and fixed effects. To ensure robustness, we refer to both the GMM estimates in levels as our main results and the difference GMM estimation as a robustness check. 6 We are aware of the fact that the correlation between the differenced lagged dependent variable (and other endogenous variables) and the disturbance process may still remain. For this reason, along with the internal instruments employed in the present empirical setup, we also instrument the variables of the GMM with external instruments, given that the usage of internal instruments may not be sufficient to remove the endogenous components in the data. 7 To assess this issue, we rely on the Arellano-Bond test, on the over-identifying restrictions of Sargan/Hansen tests, and on the instrument-diagnostic suggested by Bazzi and Clemens (2013). Furthermore, we present estimates obtained with OLS and fixed effects (FE) regressions as a robustness check, focusing specifically on the lagged dependent variable. The OLS regression accounts for the positive correlation between the lagged dependent variable and the error term due to fixed effects, leading to an upward-biased coefficient. Conversely, the FE estimator yields a downward biased coefficient. The robust GMM estimates of the lagged dependent variable are expected to lie between these bounds. Referring to the above framework, our empirical strategy consists of estimating, first , the model related to the economic growth of the Italian economy in the absence of counterfeiting. Table 3 provides the output of the two-step GMM regression of regional economic growth in the absence of counterfeiting (column A), where we select among the variables considered in the literature by adopting a general-to-specific methodology (Hendry, 1993; and Hoover and Perez, 1999). As a robustness check, columns B and C present the OLS and FE regressions, respectively. Second , Table 4 provides the results of the two-step GMM regression of regional economic growth, taking into consideration the effect of counterfeiting (column A), with columns B and C presenting the OLS and FE regressions, respectively. In either table, the standard errors are adjusted for the correction of Windmeijer (2005), and the inclusion of the lagged dependent variable accounts for the persistence of the time series (Watt and Janssen 2003). In both cases, we use instruments to mitigate potential endogeneity bias in some regressors in both the “GMM-style” and “IV-Style” matrices. These include proxies for regional competitiveness (lagged), such as labor productivity ( ALP ), private investments for tangible goods, and R&D , but also economic policy variables like social benefits and indirect taxation. We include other lagged regressors in Table 4 , such as CAD and CAV , as internal instruments based on endogeneity tests. The validity of the instruments is assessed using the Sargan test of over-identifying restrictions and the Hansen test, both confirming the appropriateness of our instruments. We also conduct the instrument diagnostics suggested by Bazzi and Clemens (2013) to address potential identification problems. The under-identification test based on the Kleibergen-Paap LM statistic and the overidentification test based on the Hansen J statistic from an extended instrumental variables estimation further support the validity of the instruments. Additionally, we employ the Arellano-Bond test to detect autocorrelation in the idiosyncratic disturbance term, which can render some lags invalid as instruments even after controlling for fixed effects. The Arellano-Bond test confirms the suitability of our instruments. We also conduct a test of the endogeneity of the regressors, specifically related to counterfeiting activities ( CAD and CAV ), following Baum et al. (2007). The test indicates that these potentially endogenous variables can be considered exogenous in the empirical investigation. However, we acknowledge that this test does not fully address all issues related to endogeneity, which we mitigate by employing the GMM estimator. Finally, we include the Hansen test, excluding group statistics related to the exclusion restriction. Nevertheless, we recognize that the test for the exogeneity of instrument subsets may be weakened due to the limited degree of overidentification (Roodman, 2009b). Table 3 reports the GDP growth estimates, revealing significant regional economic inertia through the significant influence of lagged GDP growth on current economic performance, thereby highlighting the path-dependent nature of regional economic development. This inertia reflects the embeddedness of past economic activities and outcomes in shaping future growth trajectories, underscoring the persistent effect of historical growth rates on current and future economic dynamics. The underlying mechanisms of this inertia include the slow adjustment of markets to shifts in investment levels, consumer spending patterns, and regulatory changes, which collectively influence the economic fabric of regions over time. The analysis reveals a robust positive relationship between labor costs, W, our measure of competitiveness, and economic growth. This association implies that regions with higher labor costs, possibly due to the presence of specialized labor, contribute more significantly to GDP growth, indicating the benefits of investing in human capital and specialized skills. Similarly, labor productivity, ALP, another measure of competitiveness, has a positive and statistically significant impact on growth. The higher elasticity of labor costs compared to productivity calls back the productivity gap between the north-central and the southern regions of Italy, which drives development disparities and economic divisions. The gap discrepancy has been explained by the prevalence of skill-intensive production processes only in some parts of the country. Daniele (2021) underlines that firms in Southern regions pay lower average wages than in the Center-North and employ workers with lower qualifications and productivity. This difference depends on the characteristics of the regional production structure, namely the firm size (see Berlingeri et al., 2018), the sectoral composition in which they operate (e.g., manufacturing is more present in the Center-North) and product diversity. The uneven distribution of high-skill industries suggests that regions that have succeeded in developing or attracting such sectors have moved ahead, exacerbating the productivity gap. Southern regions, facing a shortage of skill-intensive sectors, are also experiencing a "brain drain" as skilled workers migrate to more prosperous areas in search of better opportunities and wages. The findings emphasize the intertwined nature of wages and productivity. High wages can both reflect and foster high productivity, creating a virtuous circle of growth in regions where both are present. This interplay is crucial for policymakers to understand, as it highlights the need to create conditions conducive to both skilled labor and productivity growth in order to reduce regional economic disparities. Such conditions can include investments in education and training, incentives for industries to locate in lagging regions, and infrastructure development that facilitates the mobility of skilled workers and the distribution of goods and services from these new productivity centers. Table 3 GDP growth and the main determinants in the absence of shadow economy Variables A GMM B OLS C FE Wald chi2(13) = 7.09e + 08 Prob > chi2 = 0.000 F(13, 166) = 92428.30 Prob > F = 0.0000 R-squared = 0.9999 Adj R-squared = 0.9999 Root MSE = .01342 R-squared: Within = 0.9317 Between = 0.9995 Overall = 0.9994 F(13,147) = 154.15 Prob > F = 0.0000 Y t−1 0.809*** (0.069) 0.828 (0.029) 0.323 (0.050) W 0.131*** (0.0566) 0.114 (0.026) 0.403 (0.059) ALP 0.035*** (0.015) 0.042 (0.013) 0.239 (0.039) INVESTMENT 0.039*** (0.017) 0.037 (0.010) 0.0459 (0.015) R&D 0.024*** (0.005) 0.024 (0.005) 0.048 (0.029) TIME DUMMIES YES YES YES Constant 0.346*** (0.110) 0.320 (0.060) 3.105 (0.556) Sargan test: χ 2 = = 39.25 Prob > χ 2 = 0.026 Hansen test χ 2 = 8.51 P > χ 2 = 0.998 Arellano-Bond test for AR(1): z = -2.39 Pr > z = 0.017 Arellano-Bond test for AR(2): z = 1.06 Pr > z = 0.290 Difference-in-Hansen tests of exogeneity of instrument subsets GMM instruments for levels Hansen test excluding group: χ 2 = 8.51 Prob > chi2 = 0.970 Difference (null H = exogenous): χ 2 = 0 Prob > chi2 = 1.000 IV L.logsubs L.logindtax L2.logALP L2.loginvestment L.logR&D time) Hansen test excluding group: χ 2 = 3.63 Prob > chi2 = 1.000 Difference (null H = exogenous): χ 2 = 4.88 Prob > chi2 = 0.560 Notes : Number of observations = 180, number of groups = 20. The empirical investigation is performed under small sample adjustments. Standard errors are in parenthesis. *** , ** , and * correspond to the 1%, 5% and 10% level of significance, respectively. Consistent with previous research (Romer, 1987, 1990; Jones, 1995; Grossman and Helpman, 1991; Aghion and Howitt, 1992), private investment in physical goods and R&D emerge as important determinants of growth. These investments, which are crucial for technological progress and innovation, have implications for a region's growth trajectory and long-term economic development. In this regard, IPR protection plays a critical role in securing the returns on these investments and fostering an environment conducive to innovation, economic expansion, and industrial development. Effective IPR enforcement can help address historical disparities in regional economic development and ensure that all regions benefit from private investment and innovation-driven growth. Thus, the interplay between private investment, R&D activities, and IPR protection is fundamental to achieving balanced and sustainable regional economic growth. In Table 4 , the main indicators of criminal activity directly related to counterfeiting, the value of the seized counterfeits and the average dimension of fakes’ seizures, respectively, CAD and CAV , are added to the model. The results for both coefficients are highly significant and statistically different from each other, with a positive impact of the dimension of counterfeit ( CAD ) and a negative impact of the value of counterfeit ( CAV ), signaling omitted variable misspecification in the apparently robust estimation results in Table 3 . In particular, the variable representing the presence of counterfeiting activities, namely CAD , shows a positive and statistically significant correlation with the level of economic growth. This finding mainly captures the role of counterfeiting in certain Italian regions, where a massive amount (mostly of low-value or low-quality) of counterfeit goods are either imported or produced locally, falsely labeled, and then distributed abroad or to other regions. However, this is only one facet of the phenomenon. More importantly, our estimates reveal a negative and statistically significant relationship between the estimated value of counterfeits ( CAV ) and economic growth. The estimated negative coefficient implies that high-value and lucrative counterfeiting activities negatively impact economic growth, thereby affecting regional economic activity by infringing IPR and suppressing legal production and legitimate exchange. This finding underscores the vulnerability of prestigious Italian brands to counterfeiting practices. Moreover, the results highlight the dualistic relationship between different dimensions of counterfeiting activity and economic growth. While the positive coefficient of CAD suggests that the overall presence of counterfeiting activity stimulates economic growth, the negative coefficient of CAV highlights the detrimental impact of highly profitable counterfeiting ventures on economic activity at the regional level. The contrasting effects of the size and value of counterfeits that produce a positive net economic impact of counterfeiting on growth go beyond the mere volume and value of illicit production and exchange. Interestingly and unexpectedly, when controlling for the presence of counterfeits, there is a significant increase in the estimated elasticities of most of the conventional drivers of economic growth (the impact of gross wages, productivity, and R&D on economic growth becomes more pronounced than in Table 3 , where we do not control for counterfeiting) and a strong reduction the coefficient of the lagged GDP growth rate that indicates that past economic performance reduces its influence on current growth when isolating counterfeit. Dwelling into the implications of these results that disclose the effect of each variable on the legal market once illegal activities have been controlled for, the increased estimated elasticity of gross wages on growth suggests the presence of the real negative effect of counterfeit on the development path. Counterfeiting absorbs low-paid jobs in the illegal manufacturing sectors. The higher impact of wages on growth could be, therefore, also indicative of innovative industries that, in the legal market, move towards more sophisticated, high-quality goods and services, which require more skilled labor, which, in turn, further enhances productivity and, thus, economic growth. In addition, since workers in the legal sectors are likely to earn higher wages, they could further stimulate consumption and economic activity. Regarding productivity, the increased estimated elasticity of ALP, when controlling for counterfeiting activities, shows that the real productivity gains of legal activities become more important for economic growth than what emerges when discarding the activities of criminal organizations. In other words, counterfeiting erodes the competitive advantage of genuine producers via reduced returns on investment in innovation and low-quality improvements. In the absence of illicit activities, productivity gains in genuine sectors would have a larger impact, close to that of advanced countries, as they help to maintain competitiveness, promote innovation, and support economic growth. This interpretation underscores the importance of efficiency and innovation in economies faced with illicit economic activity. Table 4 GDP, counterfeiting and the conventional determinants Variables A GMM B OLS C FE Wald chi2(15) = 2.68e + 08 Prob > chi2 = 0.000 F(15, 164) = 82102.07 P > F = 0.0000 R-squared = 0.999 Adj R-squared = 0.9999 Root MSE = .01325 R-squared: Within = 0.9320 Between = 0.9995 Overall = 0.9994 F(15,145) = 132.59 Prob > F = 0.0000 Y t−1 0.7094 *** 0.0578 0.7956 0.0350 0.3228 0.0502 CA V -0.0084439 *** 0.0028656 -0.0024 0.0010 -0.0001 0.0009 CA D 0.0137 *** 0.0042 0.0040 0.0020 -0.0023 0.0030 W 0.1824 *** 0.0354 0.1318 0.0269 0.4073 0.0593 ALP 0.0486 *** 0.0183 0.0471 0.0155502 0.2365 0.0393 INVESTMENT 0.065*** 0.0278 0.0445 0.0118 0.0438 0.0154 R&D 0.0382*** 0.0074 0.0287 0.0068 0.0440 0.02998 TIME DUMMIES YES YES YES Constant 0.5652133 *** 0.1111545 0.3891 0.0820 3.1249 0.5588 Sargan test: χ 2 = = = 27.45 Prob > chi2 = 0.386 Hansen test χ 2 = = 4.03 Prob > chi2 = 1.000 Arellano-Bond test for AR(1): z = -2.51 Pr > z = 0.012 Arellano-Bond test for AR(2): z = 0.96 Pr > z = 0.339 Difference-in-Hansen tests of exogeneity of instrument subsets GMM instruments for levels Hansen test excluding group: χ 2 = 3.28 Prob > χ 2 = 1.000 Difference (null H = exogenous): χ 2 = 0.75 Prob > χ 2 = 1.000 iv(L.logsubs L.logindtax L.ogCAD L2.logCAV L2.logALP L2.loginvestment L.logrd time) Hansen test excluding group: χ 2 = 2.10 Prob > χ 2 = 1.000 Difference (null H = exogenous): χ 2 = 1.93 Prob > χ 2 = 0.983 Notes : Number of observations = 180, number of groups = 20. The empirical investigation is performed under small sample adjustments. Standard errors are in parenthesis. *** , ** , and * correspond to the 1%, 5% and 10% level of significance, respectively. The observation that the elasticity of investment also increases after controlling for counterfeiting may reflect the nuanced role of investment that could be channeled either into legal sectors to combat the negative effects of counterfeiting or into sectors that have robust protection against counterfeiting. Such investments could include advanced manufacturing technologies, intellectual property protection measures, or sectors with high barriers to entry. This strategic reallocation of investment yields higher legal returns regarding economic growth, as it not only combats the direct effects of counterfeiting but also fosters innovation and productivity in less affected sectors. The increased elasticity of R&D underscores innovation's critical role in driving legal market economic growth. In contexts where counterfeiting is a significant concern, the low incentives for R&D can dilute the average legal returns on investment in new technologies and products. However, our findings suggest that when counterfeiting is explicitly controlled, R&D contribution to economic growth becomes more relevant. R&D activities can help legal firms and economies maintain a competitive edge, ensuring that they stay ahead of counterfeit competitors through continuous innovation and improvement. Finally, our analysis reveals a significant refinement in the understanding of regional economic growth determinants. Notably, we observe a marked reduction—approximately 10%—in the coefficient of lagged GDP growth. This reduction signifies that, when accounting for the presence of counterfeit activities, past economic performance exerts a diminished influence on current growth rates. Counterfeiting, by introducing fake products into the market, undermines the profits of legitimate businesses, thereby dampening their ability to invest in innovation and development. This, in turn, stymies the economic dynamism necessary for overcoming inertia and fostering sustainable growth. Moreover, the widespread presence of counterfeit goods can tarnish regions' reputation as hubs of legitimate production, adversely affecting their attractiveness to external investments and complicating the market signals necessary for identifying genuine economic opportunities. The implications of these findings are profound, suggesting that effective policies against counterfeiting are not merely a tool for protecting intellectual property but also a critical lever for mitigating regional economic inertia. By bolstering the enforcement of intellectual property rights, enhancing international cooperation against the flow of counterfeit goods, and raising awareness about the detrimental impacts of counterfeiting, policymakers can stimulate a more dynamic and sustainable regional economic growth pathway. This insight urges policymakers and scholars alike to reconsider the mechanisms of regional growth, advocating for a clean economic space where genuine innovation and productivity can flourish unimpeded by the shadow of counterfeiting. In summary, our main findings are as follows: 1- A dual effect of counterfeiting, with an ultimately net positive impact on growth. 2- Upon controlling for the influence of counterfeiting, the rise in the elasticities of traditional drivers of economic growth mirrors patterns observed in developed nations, whereas the significant reduction in the lagged GDP growth coefficient shows that illegal activities are a critical lever for exacerbating regional economic inertia. All this indicates the primary adverse effects of criminal activities on the economic framework. Eliminating counterfeiting infiltration from the Italian production system and employing all input in the legal sectors would be associated with increased economic growth. In addition, these results shed light on the complex interplay between counterfeiting and economic dynamics. It suggests that once counterfeiting is isolated, we find enhanced contributions from wages, productivity, investment, and R&D to sustain and drive economic growth while mitigating the economic inertia. This result confirms the importance of considering criminal organizations’ counterfeiting activities for the variables that most impact Italian economic growth and underscores the necessity of purging economic analyses of such distortive influences to attain a clearer, more accurate depiction of economic health and growth potential. Businesses’ adaptation to counterfeiting may also involve strategic shifts towards sectors and activities less vulnerable to counterfeiting, heightened emphasis on innovation, and the development of products and services that offer genuine value beyond what counterfeit goods can provide. These findings underscore the need to consider the presence of counterfeiting as a critical factor when evaluating and formulating policies to promote economic growth. Policymakers can use these findings to develop targeted strategies prioritizing initiatives that strengthen legitimate growth drivers. 6. Conclusions The phenomenon of counterfeiting in Italy represents a multifaceted challenge with far-reaching economic and social implications. We have shown that variables traditionally associated with economic growth, such as gross wages, productivity, R&D, and investment, all positively impact Italian GDP growth rates. In addition, the estimates show a quite strong inertia of the development path. When we include counterfeiting measures in our analysis, we discover a dualistic relationship between this illicit activity and economic growth. While the value of counterfeiting is found to have a negative impact on GDP growth, potentially crowding out legitimate economic activity, the average size of counterfeiting is positively correlated with growth rates. The positive net effect may reflect the impact of the broader economic activity that accompanies increased levels of counterfeiting, despite the potentially harmful effects of this illicit trade. Including counterfeiting variables also results in a significant change in the estimated effects of the conventional drivers of economic growth. Notably, once controlling for counterfeit, the estimated elasticities of all these variables -wages, productivity, investment, R&D - increase significantly, showing a furtherdetrimental impact on economic growth. In addition, the lagged GDP growth rate is strongly reduced. The interaction of these economic effects shapes the characteristics of Italy's recent development path, where modernization generated by innovative high-growth businesses coexists with large areas of underdevelopment and illegality. Therefore, the overall impact of counterfeiting poses a challenge in combating this crime. There is an urgent need to rethink policy approaches and attitudes towards these forms of economic crime, which are often considered "soft" -i.e., less dangerous and disruptive than violent crime- and are therefore tolerated. Indeed, low growth rates in Italy support the argument that significant portions of the economy may be susceptible to falling into a stagnation trap induced by crime. In other words, the links between economic crime and growth create a vicious circle, resulting in persistently low growth rates and potentially high or rising levels of economic crime, as illustrated in models where the economic dynamics in the presence of crime are characterized by multiple regimes and can end up in a poverty trap with high crime rates and low incomes (Mehlum et al., 2005; Fioroni et al., 2023). This vicious circle underscores the need to combat economic crime in order to embark on a sustainable path of social and economic development, in which high growth rates contribute to lower crime rates, thereby fostering additional economic growth and development. Strengthening law enforcement, coupled with incentives to leave the illegal economy and attend educational programs, could strike a balance between curbing an illegal sector and drawing its workers into the legal economy, fostering positive employment and development effects. 8 Declarations Author Contribution All authors prepared the manuscript No funding was received for conducting this study. The authors have no competing interests to declare that are relevant to the content of this article. References Acemoglu, D., De Feo, G., and De Luca G. (2020). Weak states: Causes and consequences of the Sicilian mafia. Review of Economic Studies , 87 (2): 537–581, https://doi.org/10.1093/restud/rdz009 Aghion, P. and Howitt, P. 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Rome MISE-CENSIS (2018). Il valore economico e l’impatto fiscale della contraffazione . Rome. Mocetti., S. and Rizzica, L. (2021). La criminalità organizzata in Italia: un’analisi economica . Quaderni di Economia e Finanza, n. 661 Naddeo, A. (2014). How crime affects the economy: evidence from Italy . MPRA Paper No. 65419 Neanidis, K. C. and Papadopoulou, V. (2013). Crime, fertility, and economic growth: Theory and evidence. Journal of Economic Behavior & Organization, 91, 101– 121 Neves, P.C., Afonso, O. Silva, D. and Sochirca, E. (2021). The link between intellectual property rights, innovation, and growth: A meta-analysis. Economic Modelling, 97, 196-209 Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica , 49(6), 1417-1426. OECD (2008). The Economic Impact of Counterfeiting and Piracy. OECD Publishing, Paris. OECD (2009). Magnitude of Counterfeiting and Piracy of Tangible Products: An Update. OECD Publishing, Paris. OECD (2016). Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact, OECD Publishing, Paris. OECD (2018), Trade in Counterfeit Goods and the Italian Economy: Protecting Italy's intellectual property . OECD Publishing, Paris. OECD (2019). Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact . OECD Publishing, Paris. OECD (2021). Global Trade in Fakes: A Worrying Threat, Illicit Trade . OECD Publishing, Paris, https://doi.org/10.1787/74c81154-en. Peri, G. (2004). Socio-cultural variables and economic success: evidence from Italian provinces 1951-1991. Topics in Macroeconomics, 4(1), 1-36. Pinotti, P. (2015). The economic costs of organised crime: Evidence from Southern Italy. The Economic Journal , 125(586), F154-F178. Powell, B., Manish, G.P. and M. Nair (2010). Corruption, Crime, and Economic Growth. In B. Benson and P. Zimmerman (eds), Handbook on the Economics of Crim e. Cheltenham, UK: Edward Elgar Publishing, pp. 328-341 Primo Braga, C. A., and Fink, C. (1998). The relationship between intellectual property rights and foreign direct investment. Duke Journal of Comparative & International Law , 9(1), 163-188. Qian, Y. (2008). Impacts of Entry by Counterfeiters. The Quarterly Journal of Economics , 123(4) 1577-1609. Qian, Y. (2011). Counterfeiters: Foes or Friends? How Do Counterfeits Affect Different Product Quality Tiers? NBER Working Paper No. 16785. Qian, Y. (2014). Brand management and strategies against counterfeits. Journal of Economics & Management Strategy , 23(2), 317-343. Romer, P. M. (1987). Growth Based on Increasing Returns Due to Specialization. American Economic Review , 77(2), 56–62. Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy , 98(5), S71–S102 Roodman, D. (2009a). How to do xtabond2: an introduction to difference and system GMM in Stata. STATA Journal , 9 (1), 86–136. Roodman, D. (2009b). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71 (1), 135–158. Smith, P. J. (2001). How do foreign patent rights affect U.S. exports, affiliate sales and licenses? Journal of International Economics , 55(2), 411-439. Soares, R. (2009). Welfare costs of crime and common violence: A critical review. In Skaperdas S. et al. (eds), The Costs of Violence . Washington, DC: The World Bank, pp. 27-47. Staake, T., Thiesse, F. and Fleisch, E. (2009). The emergence of counterfeit trade: a literature review. European Journal of Marketing , 43(3/4), 320-349. Watt, A. and Janssen, R. (2003). Unemployment and Labour Market Institutions: Why Reforms Pay Off. In IMF World Economic Outlook , Chapter IV. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of econometrics , 126(1), 25-51. World Bank (2006). Brazil. Crime, Violence and Economic Development in Brazil. Elements for Effective Public Policy. World Bank. Report No. 36525-BR Yao, J. T. (2005). How a luxury monopolist might benefit from a stringent counterfeit monitoring regime. International Journal of Business and Economics , 4(3),177-92 Footnotes See, for example, Banerjee, 2006; Grossman and Shapiro, 1988; Qian, 2008 and 2014; Chaudhry and Zimmerman, 2009. The dataset collects all the anti-counterfeiting activities carried out in Italy during the period 2008–2019, distinguishing them on the basis of 11 goods-related categories: clothing; accessories; shoes; watches; jewellery; electrical appliances; electronic and computer equipment; toys and games; CDs and DVDs; sunglasses; make-up cosmetics; perfumes and fragrances. It should be noted that the categories of goods in the database do not include those goods that are often the subject of counterfeiting, such as medicines or food, which fall under the jurisdiction of the Italian Ministry of Health. It is important to note that the data on the number of seizures represent the universe of observations, while the value of seized goods is estimated according to a methodology developed by the General Directorate of the Ministry of Economic Development (IPERICO, 2012). These estimates are based on the value assigned to the seized goods by the customs agency, according to both the quality of the goods and the differences between the expected selling price of the counterfeit and the market price of the original good. In a similar way, but using a theoretical neo-Kaleckian model, Capuano and Purificato (2012) show the dual impact of criminal organisations on the legal sector, as they both drain resources from the legal sector and allocate criminal revenues to purchase legal goods and services. For regional comparisons, one could focus only on public expenditure on final consumption, which still represents more than 80% of total expenditure, thus ensuring its relevance as a robust proxy for human capital. The latter is not reported, but available from authors upon request. The easiest way to instrument our not strictly exogenous variables is the 2SLS estimator, within the Anderson-Hsiao (1982) difference and levels estimator (see Roodman, 2009a). According to a report by MISE-CENSIS (2018), if the production of counterfeit products were to take place within the legal economy, it is estimated that it would require the participation of approximately 104,000 additional workers. These job opportunities could be filled primarily by workers currently employed in the counterfeiting sectors. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5032553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363193876,"identity":"938edaf3-66b6-4686-beb0-c1e7b0883a1a","order_by":0,"name":"Elton Beqiraj","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Elton","middleName":"","lastName":"Beqiraj","suffix":""},{"id":363193877,"identity":"0d07c285-06f1-419e-8016-88d4b5986ef9","order_by":1,"name":"Silvia Fedeli","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Fedeli","suffix":""},{"id":363193878,"identity":"88084c0a-9d3e-41b4-b4c0-325230672fe3","order_by":2,"name":"Luisa Giuriato","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACPgbGBhDNwwBh2DAwMIMlJBjYcGhhQ9FygCENpsWCgQ2HHlThAwyHYcwKdDmEFonk5g8/GGplDG4fbvz8oeJ84nZ23oePbtRIMPDJN+DQktgm2cNwnMfgXGKzxIEztxN3NrMbG+cck8DtMKAWoEeO8RicYWyQONh2O3HDYTY26Rw2vFqaP/6BaGn+cbDtHFTLP7xaGqR5GGpAWtqAthyAaMltw6OF52GbtIzBAR5JoBaLM2eSjYFamI1z+yR42NgSsGrhZ09//PFNRZ093xn2xzcqKuxkN5w/xvg451udnHzzAezWgIHBYUwxHjzqQaCOgPwoGAWjYBSMaAAAnT9UwXMpMFEAAAAASUVORK5CYII=","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":true,"prefix":"","firstName":"Luisa","middleName":"","lastName":"Giuriato","suffix":""}],"badges":[],"createdAt":"2024-09-04 14:54:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5032553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5032553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66157941,"identity":"c659f1b6-0ffd-473a-97f6-86ef77aaee36","added_by":"auto","created_at":"2024-10-08 08:53:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGDP growth rates (in percent) in Italy as compared to the G7 and the euro area countries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: own elaboration on OECD data\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5032553/v1/09c48e5eb2cd580f454cbf41.png"},{"id":66157122,"identity":"0e09758a-4562-4db1-be19-0f02d809af48","added_by":"auto","created_at":"2024-10-08 08:45:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEconomic growth in Italian regions (average over 2008-2019)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5032553/v1/324dcb60a2939b06166fc974.jpeg"},{"id":66157124,"identity":"57ad2d7f-d983-4fcc-b98c-ff42146497f4","added_by":"auto","created_at":"2024-10-08 08:45:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":266082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean dimension of counterfeiting \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e(CAD)\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the Italian regions (average 2008-2019; number of seized pieces)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5032553/v1/6bd378b0e3ef51b6aa313df4.jpeg"},{"id":66157121,"identity":"d0be26fa-5483-4b61-b674-adad5cd421ba","added_by":"auto","created_at":"2024-10-08 08:45:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":262317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValue of counterfeiting (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCAV)\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the Italian regions (average 2009-2018; thousands of euros)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5032553/v1/f5f6319d83512de66c0ee933.jpeg"},{"id":69033216,"identity":"0965f171-319e-442f-9238-37bebbd92e9f","added_by":"auto","created_at":"2024-11-14 20:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1724844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5032553/v1/b8640519-8b21-4188-b4e2-0533c54620f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shadow Economy or Economic Driver? The Impact of Counterfeiting on Italy's Growth","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThis study delves into the impact of counterfeiting on Italy's economic growth, using a unique regional dataset that comprehensively captures criminal counterfeiting activities disclosed/reported in Italy from 2008 to 2019, alongside key economic indicators. The need to study this issue in Italy arises from its inadequate/low economic development, which is always below those of its developed European and G-7 counterparts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe origins of Italy's underperformance are controversial and include historical, geographic, and institutional reasons, as well as human capital formation and cultural fractionalization. In addition, deep regional disparities persist as a contributing factor. During the period covered by this study (2008\u0026ndash;2019), regional growth rates were higher in the Northwest, followed by the Northeast and Central regions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The southern regions (in particular, Calabria, Sicily and Molise) faced structural problems rooted in high unemployment rates, low productivity, competitiveness, investment attractiveness,... All these factors have contributed to the widening of the economic gap between the north and south of the country, which is a legacy of its history. This picture is combined with the significant presence of the informal economy, whose value added in 2019 was estimated at 183.9\u0026nbsp;billion euros, or 10.2 percent of GDP (Ministero dell'Economia e delle Finanze, 2022).\u003c/p\u003e \u003cp\u003eThis decades-long economic disparity in Italy raises fundamental questions about the growth and development process and its implications, also in light of recent evidence that even highly developed Italian regions have wide pockets of underdevelopment, as signaled by indicators of innovation and productivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTraditional development theories often assume that individuals will remain compliant and law-abiding even in situations of considerable relative deprivation, such as those that have followed the series of economic crises that afflicted the Italian economic system during the period considered. This assumption may not correspond to reality. When economic crime is in the hands of criminal organizations that turn it into a lucrative option, labor-surplus economies can tolerate high doses of it, contributing to a scenario in which a country\u0026rsquo;s growth goes together with large pockets of illegality and organized crime.\u003c/p\u003e \u003cp\u003eWe investigate whether economic crimes -specifically counterfeiting run by criminal organizations- have played a significant role in the economic growth and development path of Italian regions.\u003c/p\u003e \u003cp\u003eThe choice of counterfeiting is due to the fact that the entire Italian economy heavily depends on intellectual property rights (IPR), with virtually every industry either producing or utilizing them, thus characterizing the Italian economy with an IPR intensity surpassing the EU average (OECD, 2018, p.16). Global trade in counterfeit products infringing Italian trademarks reached a value of 24.3\u0026nbsp;billion euros in 2018, equivalent to 3.6% of total Italian manufacturing sales. Italy ranks fourth among OECD countries in terms of IPR infringements by counterfeiters (OECD, 2021). In Italy, the market for counterfeit products has reached a value of 8.7\u0026nbsp;billion euros, representing 2.1% of the country's imports (OECD, 2021a). In addition, being a non-violent economic crime, counterfeiting is socially (and, hence, politically) tolerated: about one-third of Italian consumers buy at least one fake product or utilize an illegal service each year, and 73% of consumers believe that it is \u0026ldquo;normal\u0026rdquo; (Confcommercio, 2019).\u003c/p\u003e \u003cp\u003eIn principle, the expected impact of counterfeiting on Italy's economic performance is twofold. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFirst\u003c/span\u003e, a negative effect results from the profitability associated with counterfeiting in high-value-added sectors. Counterfeiting infringes IPR, such as fashion, luxury goods, electronics, and automotive, undermines market confidence, reduces consumer spending on genuine products, erodes market shares for legitimate businesses, and poses barriers to international trade and investment. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSecond\u003c/span\u003e, a positive effect arises from the massive quantity of (cheap and low quality) counterfeit goods in the market, in which Italy also exploits its propensity to trade counterfeit products wherever (internally and abroad) it has a relative comparative advantage for production (OECD, 2021) and where the \"Made in Italy\" sounding attracts consumers. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn addition\u003c/span\u003e, our analysis reveals a third effect that is intertwined with the previous ones. Counterfeiting is mainly managed by criminal organizations that have assumed an entrepreneurial role, using both licit and illicit capital and mixing the management of legal and illegal production activities (Camera dei Deputati, 2013; 2017). They infiltrate legitimate companies by exercising control over the entire supply chain, which determines the reallocation of inputs, the displacement of resources from legal to illegal production activities, the management of the import/export of counterfeit goods and their final subsequent sale through an extensive network of national and international distribution points. All this leads to job creation and attracts investment to the benefit of organized crime, which undermines the development path of the Italian economic system.\u003c/p\u003e \u003cp\u003eOur analysis is based on the 20 Italian regional economies, where the presence of counterfeiting activities is not uniformly distributed, with certain regions being more affected due to higher economic activities, rich markets in metropolitan areas, and significant transportation infrastructure. By concentrating on a single country, we sidestep issues related to differences in anti-counterfeiting policies between countries, a problem that affects cross-country studies of the economic impact of crime and illegal activities (see below). There are no significant differences in anti-counterfeiting policies between Italian regions, as they are centralized in Italy. Moreover, due to the relatively short period covered by our analysis, there is no need to include variables such as population and age structure, which remain relatively stable throughout the period. Finally, Italian regions exhibit institutional homogeneity and are fully integrated economic areas, which further justifies our approach.\u003c/p\u003e \u003cp\u003eWe employ a regional dynamic panel data model with a Generalized Method of Moments (GMM) estimator. Our estimated model aligns with endogenous growth models focusing on physical capital and technology (Romer, 1987, 1990; Jones, 1995; Grossman and Helpman, 1991; Aghion and Howitt, 1992). With the introduction of counterfeiting indicators, the empirical results demonstrate the coexistence of both positive and negative effects of counterfeiting on economic growth. In addition, once counterfeiting is controlled for, the effects of wages, productivity, and R\u0026amp;D on economic growth are particularly amplified and aligned with those of advanced economies, suggesting the truly devastating nature of criminal activity on the Italian economy.\u003c/p\u003e \u003cp\u003eThe paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the literature on economic growth and crime. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the data. Section 4 presents some stylized facts. Section 5 reports the model specification and empirical results. The conclusion in Section 6 encapsulates the implications of our findings and suggests policy consideration and further research.\u003c/p\u003e"},{"header":"2. Review of the literature","content":"\u003cp\u003eRecent research has increasingly recognized the role of the socioeconomic environment in shaping firms' ability to absorb knowledge, improve their performance, and contribute to growth and development. In this respect, evidence from various studies indicates the relevant role of crime and highlights the many channels through which it significantly affects social, political, and economic factors that are vital determinants of growth. The presence of criminal organizations reduces capital accumulation by diverting resources from productive activities towards crime deterrence and control. It impacts private investment by increasing uncertainty and inefficiency and by reducing the attractiveness of local economic activities to national and foreign investors also due to the fears of weak law enforcement (Daniele and Marani, 2011; Forgione and Migliardo, 2023). Crime also reduces the labor supply in the legitimate business sector as criminal organizations hire workers for their own illegal activities or refrain regular workers or entrepreneurs from working in certain sectors or areas. Criminal organizations reduce the legal firms\u0026rsquo; productivity (Centorrino and Ofria, 2008; Daniele, 2009; Albanese and Marinelli, 2013; Ganau and Rodriguez-Pose, 2019) and distort competition to their advantage by employing their larger financial resources and exerting their coercive power (Albanese and Marinelli, 2013). By imposing quasi-fixed costs on legal firms and generating credit rationing, criminal organizations can stick the economy in a low-income steady state (Balletta and Lavezzi, 2023; Fioroni et al. 2023). Mehlum et al. (2005) also show how, in the presence of criminal behaviors affecting firms\u0026rsquo; profitability and returns from job creation, the economic dynamics are characterized by multiple regimes and can end up in a poverty trap with high crime rates, a large sector of subsistence workers and low income. Some studies have examined the impact on the economic resources available for growth-enhancing factors such as education, the quality of the workforce, and human capital accumulation (Buonanno and Leonida, 2006; Coniglio et al., 2010; Acemoglu et al., 2020).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, evidence of the impact of crime on growth has been provided in cross-country studies or in studies comparing the US states using a single country\u0026rsquo;s time series. The main problem of these studies relates to the inexact and conflicting data across countries, diverse crime definitions, underreporting, and disparate recording methodologies. The most commonly used proxy for crime is the homicide rate because alternative measures (e.g., property crimes) suffer from severe problems of underreporting that bias results. Homicides and violence create high uncertainty, create additional costs for security, lower business profitability and efficiency and, ultimately, impact on growth as discussed above. In some studies (e.g., Peri, 2004), homicides are considered the expression of organized crime with a tendency to operate in specific areas.\u003c/p\u003e \u003cp\u003eSome cross-country studies report strong adverse effects of crime on economic growth (e.g., World Bank, 2006; Neanidis and Papadopoulou, 2013). C\u0026aacute;rdenas (2007) finds them for a panel of 65 countries (years 1971\u0026ndash;1999) controlling for stabilization policies, structural policies, and institutions. Burnham et al. (2004) also find a strong impact of crime on US county-level (per-capita) income growth, while Gaibulloev and Sandler (2008) show the negative impact of terrorist crimes on the growth rates in Western Europe. Using time series for a single country, C\u0026aacute;rdenas (2007) finds a strong impact of crime on Colombia's total factor productivity and growth (1951\u0026ndash;2005). Dettoto and Otranto (2010) apply an autoregressive model for Italian GDP growth and the monthly homicide rate (1979\u0026ndash;2002), finding a reduction of growth due to crime and cyclical components in the growth-crime relationship, which are confirmed in Detotto and Otranto (2012). Detotto and Pulina (2013) found for Italy (1970\u0026ndash;2004) that homicides and robberies have a crowding out effect on GDP growth (a 1 percent increase causes a 0.1 percent reduction in GDP growth per year), while all crime typologies (except robberies) cause a reduction in the employment rate.\u003c/p\u003e \u003cp\u003eOther studies present evidence of no statistically clear effect of crime on growth (e.g., Chatterjee and Ray, 2009). Using data from 26 countries and trying to account for the different channels through which crime influences growth, Goulas and Zervoyianni (2015) provide evidence of asymmetries in the growth-crime relationship, as it is strongly negative in economic low and weakly negative or statistically insignificant in good times.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected empirical studies on the crime \u0026ndash; growth nexus\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry, time, dependent variables, and crime measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbanese and Marinelli (2013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample of Italian firms (2002-07)\u003c/p\u003e \u003cp\u003eTotal Factor Productivity (TFP) at firm level\u003c/p\u003e \u003cp\u003eMafia index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS, FE, IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoving from the 90th to the 10th percentile in the provincial distribution of organized crime increases firm TFP by 9\u0026ndash;10%\u003c/p\u003e \u003cp\u003eResults are independent of size and sector of firms\u003c/p\u003e \u003cp\u003eResults are robust to the potential endogeneity of organized crime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardenas (2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnbalanced panel of 65 countries (1971\u0026ndash;1999)\u003c/p\u003e \u003cp\u003ePer capita GDP growth\u003c/p\u003e \u003cp\u003eHomicide rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomicide rate explains growth deceleration.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardenas and\u003c/p\u003e \u003cp\u003eRozo (2008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartments of Columbia (1960\u0026ndash;2000)\u003c/p\u003e \u003cp\u003eGDP and per capita GDP growth\u003c/p\u003e \u003cp\u003eViolent crimes (homicide rate), coca crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifference in differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe surge in drug trafficking significantly fueled the rise in homicides, subsequently exerting adverse impacts on overall economic growth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentorrino and Ofria (2008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItalian regions Calabria, Campania, Apulia and Sicily (1983\u0026ndash;2005)\u003c/p\u003e \u003cp\u003eLabor productivity\u003c/p\u003e \u003cp\u003eRatio between mafia homicides and population.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrime negatively influences the rate of growth of labor productivity in the southern regions, especially in construction and non-tradable sectors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatterjee and Ray (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 countries (1991\u0026ndash;2003)\u003c/p\u003e \u003cp\u003eGDP growth rate\u003c/p\u003e \u003cp\u003eRates of crimes based on crime and corruption perception by victims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS; IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLack of a strong association between both corruption and growth rates and between crime and growth rates.\u003c/p\u003e \u003cp\u003eSerious crime and crime (overall) are negatively associated with growth only for countries with high crime rates.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaniele and Marani (2011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 Italian provinces (2002\u0026ndash;2006)\u003c/p\u003e \u003cp\u003eForeign direct investment\u003c/p\u003e \u003cp\u003eOrganized crime (measured by the number of criminal offenses associated with mafia organizations)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeasible GLS estimators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative impact of the presence of organized crime on the business climate and deterrence of foreign and domestic investment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetotto and Otranto (2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly (1979\u0026ndash;2002)\u003c/p\u003e \u003cp\u003eMonthly data on GDP\u003c/p\u003e \u003cp\u003eHomicides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR; Impulse response function (IRF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA 1% increase in the crime rate results in a 0.00040% monthly reduction in real economic growth. The economic cost of crime has a significant fixed component.\u003c/p\u003e \u003cp\u003eThe negative impact of crime on the Italian economy is 5% higher during recessions than during expansions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetotto and Otranto (2012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly (1991\u0026ndash;2004)\u003c/p\u003e \u003cp\u003eReal GDP\u003c/p\u003e \u003cp\u003e22 types of crimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-parametric dynamic factor model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeven crime types (mostly of the white-collar type) have a strong link with GDP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetotto and Pulina (2013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly (1970\u0026ndash;2004)\u003c/p\u003e \u003cp\u003eEmployment and GDP growth\u003c/p\u003e \u003cp\u003eHomicides; robberies, extortions and kidnapping; property crimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutoRegressive Distributed Lags; Granger causality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomicides and robberies contribute to a crowding-out effect on GDP growth.\u003c/p\u003e \u003cp\u003eAll types of crime, excluding robberies, decrease the employment rate. In the short term, all crime types contribute to fluctuations in employment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFioroni et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItalian regions (1996\u0026ndash;2013)\u003c/p\u003e \u003cp\u003ePer capita GDP\u003c/p\u003e \u003cp\u003eMafia crimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-parametric estimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePer capita GDP dynamics exhibit various regimes determined by the initial level of organized crime. Regions with higher organized crime levels have an increased proportion of embezzled public expenditure, leading to a negative impact on per capita GDP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForni and Paba (2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 Italian provinces (1971\u0026ndash;1991)\u003c/p\u003e \u003cp\u003eEmployment, population, real and per capita value added\u003c/p\u003e \u003cp\u003eMurders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrime has a negative impact on employment growth.\u003c/p\u003e \u003cp\u003eThe effects of crime on value-added growth are not clear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGanau and Rodriguez-Pose (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItalian small- and medium-sized manufacturing firms (2010\u0026ndash;2013)\u003c/p\u003e \u003cp\u003eTotal factor productivity\u003c/p\u003e \u003cp\u003eMafia association, Mafia murders, extortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdverse direct impacts of organized crime on the productivity growth of businesses.\u003c/p\u003e \u003cp\u003eEffect is more pronounced for smaller enterprises.\u003c/p\u003e \u003cp\u003eAny potential positive influence arising from industrial clustering is significantly undermined by the robust existence of organized crime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoulas and Zervoyianni (2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 countries (1995\u0026ndash;2009)\u003c/p\u003e \u003cp\u003ePer capita GDP growth\u003c/p\u003e \u003cp\u003eHomicide rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong negative relationship between per capita output growth and crime due to crime impact on levels of insecurity in the economy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMauro and Carmeci (2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItalian regions (1963\u0026ndash;1995)\u003c/p\u003e \u003cp\u003eUnemployment, output levels and growth\u003c/p\u003e \u003cp\u003eHomicide rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled Mean Group estimator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrime has a negative long-run effect on output level rather than on output growth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaddeo (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItalian regions (1995\u0026ndash;2011)\u003c/p\u003e \u003cp\u003ePer capita GDP and GDP growth\u003c/p\u003e \u003cp\u003eHomicide rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe growth rate is reduced by 0.13% for a 1% increase in the homicides rate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri (2004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 Italian provinces (1951\u0026ndash;1991)\u003c/p\u003e \u003cp\u003ePer capital GDP growth, employment growth rate\u003c/p\u003e \u003cp\u003eOrganised crime (measured by murder rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual per capita income growth is negatively affected by murder rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinotti (2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo Italian regions (Apulia and Basilicata) (1951\u0026ndash;2007)\u003c/p\u003e \u003cp\u003ePer capita GDP\u003c/p\u003e \u003cp\u003eMafia-type crimes, Homicide rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSynthetic control method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrime determines lower levels of GDP per capita (-16%) in Apulia and Basilicata, which have been exposed to a huge increase in the presence of organized crime (proxied by the homicide rate) since the mid-1970s due to a shift in tobacco smuggling routes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePart of the literature investigates growth at the local level (Forni and Paba, 2000). In particular, some empirical studies examine the impact of crime on regional backwardness in Italy and the long-time and disrupting presence of different mafia-type organizations (Mafia in Siciliy, Camorra in Campania, \u0026lsquo;Ndrangheta in Calabria, Sacra Corona Unita in Apulia, along with other similar groups). Some studies conclude that organized crime hinders economic development and industrial growth in certain regions of Italy, especially in the South (Forni and Paba, 2000; Naddeo, 2014; Pinotti, 2015; Mocetti and Rizzica, 2021). Other studies show different or more nuanced conclusions. Peri (2004) reports results indicating non-linearities in the employment growth-crime (homicides) relationship, with only high crime rate provinces showing an adverse impact on growth and labor demand. Mauro and Carmeci (2007), employing pooled-mean-group estimation techniques, find that crime harms income levels of Italian regions, but it does not exert a statistically significant adverse influence on long-run growth rates. Fioroni et al. (2023) suggest that different growth patterns at the regional level depend on the initial levels of Mafia-related crimes, also due to the capture of public expenditure by criminal gangs.\u003c/p\u003e \u003cp\u003eOur study contributes to this topic by investigating the growth impact of a specific economic crime that has been only marginally considered in the literature. Counterfeiting has particular features that are not common to other crimes: through the appropriation of IPRs and the production of fakes, it has direct effects on the economic system, while it does not impact via uncertainty and fear, like violent crimes.\u003c/p\u003e \u003cp\u003eCounterfeiting intersects various disciplines, such as marketing, management, and behavioral economics. Macro-level studies, including EUROPOL-OHIM (2015), OECD (2008, 2009, 2016), and Frontier Economics (2016), provide insights into the quantitative dimensions of counterfeiting. At the micro level, researchers have attempted to estimate the financial losses suffered by firms due to counterfeiting, including profits, brand value, brand equity, and countermeasure expenditures (Feinberg and Rousslang, 1990). Studies have also explored strategies used by legitimate firms to differentiate themselves from counterfeiters\u003csup\u003e1\u003c/sup\u003e and have highlighted the heterogeneous effects of counterfeiting on legitimate sales (Qian, 2011; Yao, 2005). More recently, the impact of counterfeiting has been estimated in terms of trade exchange, labor market, intellectual property protection, and lost tax revenues for the government (Beqiraj et al., 2020; Fedeli et al., 2018; Primo Braga and Fink, 1998; Smith, 2001; OECD 2018). The impact of counterfeiting on economic growth has been almost neglected. Our study aims to fill the research gap by examining the relationship between counterfeiting and economic growth in Italy. Italy is an ideal case to study this relationship as counterfeiting is a structural feature of the Italian production system, and a deep economic divide characterizes the regions of the country. To the best of our knowledge, no empirical study has yet investigated the impact of counterfeiting on Italian economic growth, underscoring the need for comprehensive analysis in this area.\u003c/p\u003e"},{"header":"3. The data","content":"\u003cp\u003eAs mentioned, the impact of criminal activity on economic indicators in cross-country studies suffers from inaccurate and inconsistent data, varying definitions of crime, underreporting, and different recording procedures. As a result, the proxies for criminal activity commonly used in cross-country analyses (such as indexes of economic freedom, transparency, political instability, and violent crime indicators) do not correspond to the general notion of crime, but rather to problems related to political governance (Powell et al., 2010) or to proxies of criminal organization activities.\u003c/p\u003e \u003cp\u003eTo address this research gap, our study uses a unique and recently constructed dataset that provides comprehensive information on the criminal economic activities of counterfeiting, along with key economic indicators for the 20 Italian regions. The time span is the decade following the Great Recession (2008\u0026ndash;2019). All variables are logarithmically transformed to facilitate the estimation of the different elasticities through a log-linear model (as explained in the following sections).\u003c/p\u003e \u003cp\u003eFor counterfeiting data, we draw from the IPERICO database, which comprehensively documents all counterfeiting-related enforcement activities since 2008.\u003csup\u003e2\u003c/sup\u003e We consider two indicators: the average size of all counterfeits seized in each region (CAD) and the estimated value of such seizures in euros (CAV).\u003csup\u003e3\u003c/sup\u003e The CAD variable serves as a reliable proxy for the presence of counterfeiting phenomena in a given region. Higher CAD values give evidence of counterfeiting production activities or the importance of the region as an exchange and distribution hub for counterfeit goods, which may also originate from abroad (IPERICO, 2012). On the other hand, CAV reflects the expected profitability associated with such illicit activities and captures the presence of counterfeiting in high-value-added sectors, thereby affecting brand credibility and trade capacity. Because CAV is less influenced by factors such as the presence of major ports or national borders, where stricter controls are typically in place, it helps mitigate the spatial bias observed in CAD, especially in regions with larger average counterfeit seizure sizes (IPERICO, 2012). The presence of these two variables also allows us to account for an expected dual impact of counterfeiting: Both contribute to income in the informal economy and disrupt markets for high-value goods.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIt is important to recognize certain limitations in the use of these variables as proxies for criminal activity at the regional level. They primarily capture the disclosed part of the phenomena, which could lead to underestimations. However, the success of anti-crime policies in certain regions certainly signals the presence of relatively higher levels of criminal activity in those areas. Moreover, these indicators may not be suitable for long-term analysis because they depend on the intensity of law enforcement activities rather than on changes in criminal activity \u003cem\u003eper se\u003c/em\u003e. Nevertheless, they can be effective proxies for the presence and extent of counterfeiting in Italian regions over the relatively short period considered in our study.\u003c/p\u003e \u003cp\u003eWe also use regional ISTAT data on economic indicators. Our dependent variable is economic growth. Regarding the economic explanatory variables, which serve both as regressors and as instrumental variables, great emphasis is placed on capturing regional competitiveness and the characteristics of the production system related to technology and innovation. Such regional indicators include regional labor cost; average labor productivity derived from lagged log real regional GDP minus log employment; regional investment in physical capital by firms, and research and development costs of firms; investment in human capital as proxied by education expenditure; private investment in R\u0026amp;D; public policies of subsidies and taxation.\u003c/p\u003e \u003cp\u003eThe regional labor cost (W) is a crucial control variable for measuring competitiveness, as it allows us to take into consideration one of the most important dimensions of regional heterogeneity, capturing the evolving economic conditions and the regional divide between the South and the Centre-North of Italy. The variable includes wages, salaries, and fringe benefits in cash, before withholding taxes and social security contributions, payable by the employer. It coincides with the taxable wages for contribution purposes, paid on a cash basis, and includes remuneration for overtime, i.e., hours worked in addition to normal working hours. Regarding the impact of average labor productivity (ALP), which complements wages in measuring competitiveness, we expect that regions with lower economic activity will experience mainly negative effects, while higher productivity will be consistent with the use of modern equipment and up-to-date skills in the more developed regions.\u003c/p\u003e \u003cp\u003eThe other variables considered to test the robustness of our estimates include \"private investment in tangible goods\" (INVESTMENT), which captures the dynamism of the private sector and the regional economic climate. In addition, firms' regional research and development (R\u0026amp;D) input costs capture competitiveness related to firms' efficiency, innovation, and the industrial attractiveness of the economic environment. The role of private firms' expenditure on research and development (R\u0026amp;D) is recognized as a relevant driver in the growth literature (Romer, 1987, 1990; Grossman and Helpman, 1991; Aghion and Howitt, 1992) and is important for our analysis. Firms' investment in R\u0026amp;D reflects their deliberate efforts to create new ideas and products (Helpman, 1992), which are subsequently protected by IPR to secure innovation and gain a competitive advantage. The OECD (2021) provides evidence that firms that own IPR generate 20% higher sales per employee and pay higher wages than their counterparts that do not have an IPR portfolio. The benefits of IPR ownership are particularly pronounced for small and medium-sized enterprises. All of this, in turn, increases productivity, promotes economic competitiveness, and should boost overall economic growth (Gould and Grube, 1996; Neves et al., 2021). For each economic regressor, if statistically significant, we expect a positive effect on regional growth.\u003c/p\u003e \u003cp\u003eTo account for the role of public intervention, we include both a tax variable, indirect taxes (INDTAX), and an expenditure variable, subsidies (SUBS). Indirect taxes also reflect the level of legal trade in the region. Subsidies express public support to productive activities: the latter has traditionally been more generous in the southern regions, in an attempt to support the economic development of backward areas.\u003c/p\u003e \u003cp\u003eNote that in our empirical analyses, we also consider two alternative proxies for human capital. The first is an indicator that captures the regional percentage of individuals who have attained levels of education from upper secondary to Ph.D. or equivalent, as classified by the International Standard Classification of Education (ISCED11, levels 3\u0026ndash;8). The second refers to public expenditure on education and training -which includes transfers to households and public and private institutions-, as a percentage of GDP.\u003csup\u003e5\u003c/sup\u003e These variables do not significantly affect our results and are found to have an insignificant impact on economic performance.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents summary statistics for the variables considered.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable (log)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGDP growth rate (in %)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCAV*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eValue of the seized counterfeits\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCAD*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage dimension of fakes\u0026rsquo; seizures\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eW*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLabor cost\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage labor productivity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINVESTMENT*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrivate investment for tangible goods\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eResearch and development expenditure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSUBS*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSubsidies to businesses\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINDTAX*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndirect taxes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHC EXP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman Capital (Expenditure)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHC ISCED11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman Capital (ISCED11)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: * denotes the log values. \u003cem\u003eHC EXP\u003c/em\u003e is expressed as a percentage of GDP.\u003c/p\u003e"},{"header":"4. Some stylized facts","content":"\u003cp\u003eFrom 2008 to 2019, Italy experienced a period of modest economic growth as a result of unsatisfactory productivity, reduced public investment, significant deficiencies in the supply and efficiency of tangible and intangible factors of production, a quantitative and qualitative shortage of human capital, the cost and length of the civil justice process, and the quality of industrial relations (for a review, see Bugamelli et al., 2018). All this reduces support for the competitiveness of domestic firms and the attraction of foreign direct investment.\u003c/p\u003e \u003cp\u003eOverall, the period from 2008 to 2019 witnessed a continuation of regional disparities in Italy, with the northern regions generally outperforming the southern regions in terms of economic growth and development.\u003c/p\u003e \u003cp\u003eThis picture is combined with the significant presence of the underground economy, within which counterfeiting plays a relevant and growing role, with a turnover of more than 30\u0026nbsp;billion euros and its presence in the territory is mostly determined by the strategies of criminal organizations: \"Organized crime, thanks to its financial, intimidating and corrupting power, manages all stages of the counterfeit supply chain, from production to shipping, distribution and retail\" (Camera dei Deputati, 2013, p. 14). In this large-scale transnational criminal enterprise, different criminal organizations employ different strategies and specialize in different sectors. Camorra is undoubtedly the most dynamic and active organization, exercising direct control over the counterfeit supply chain, mainly in Campania, Lombardy, and Lazio. In Calabria, 'Ndrangheta primarily facilitates intermediary services in production and distribution, overseeing the import and re-export of counterfeit goods to European Union member states. The Sicilian Mafia focuses on counterfeiting in the food industry. Chinese criminal organizations based in Tuscany, the northern regions, Lazio, Marche and Campania have formed alliances with Italian and African criminal groups to import and distribute Chinese counterfeit products. Finally, counterfeiting is not limited to regions with a traditional industrial presence. It can infiltrate the economy of various regions, regardless of their level of industrial activity. Counterfeiting is also a form of financing for criminal clans and a means of laundering money from other criminal activities. In addition, counterfeiting allows for the extensive control of territories (Camera dei Deputati, 2013). The range of activities involved and the network of international connections show that the artisanal and local dimension of counterfeiting has been completely surpassed, replaced by a business-oriented activity with national and transnational reach. This explains the widespread presence of counterfeiting in all regions and why counterfeiting is proliferating even in regions where the presence of organized crime is not significant (e.g. Abruzzo).\u003c/p\u003e \u003cp\u003eThe types of products -from leather goods to handbags, watches, electrical machinery, and electronics- counterfeiters produce and sell, as well as their location in Italy, influence the distribution of the phenomenon. Regions that specialize in certain types of industrial production are more prone to counterfeit infiltration. In addition, the regions with the highest seizure rates differ from those with the highest value of seized goods.\u003c/p\u003e \u003cp\u003eTo gain a first insight into the phenomenon under study, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean dimension of counterfeiting (measured by the average number of pieces seized, CAD) during the period from 2008 to 2019 and reveals a remarkable degree of heterogeneity across regions. Lombardy, Lazio, Campania, and Apulia emerge as the leading regions with the highest average dimension of counterfeiting, indicating a relatively higher prevalence of counterfeiting activities in these areas. Conversely, regions such as Calabria, Sardinia, Piedmont, Abruzzo, Basilicata, Umbria, Trentino-South Tyrol, and Aosta Valley have a lower mean dimension of counterfeiting, suggesting comparatively less counterfeit trade. This observed variation underscores the regional disparities in counterfeiting activity and highlights the importance of taking local context and specificities into account when addressing the issue of counterfeiting.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which shows the value of counterfeiting, provides further insight into the phenomenon. The graph shows a diverse landscape across regions, with significant variations in the economic scale of highly profitable counterfeiting activities. Lombardy, Sicily, Veneto, Emilia Romagna, and Liguria emerge as regions with relatively higher counterfeit values, indicating the presence of a highly lucrative market for counterfeit goods in these areas. Regions such as Sardinia, Apulia, Tuscany, Campania, Abruzzo, and Piedmont show lower but still significant values of lucrative counterfeiting activities. The differences between Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e underscore the need for tailored anti-counterfeiting strategies at the regional level, taking into account the different levels of economic impact and enforcement required in different areas.\u003c/p\u003e "},{"header":"5. The model and empirical results","content":"\u003cp\u003eTo assess the impact of criminal activities on Italian economic growth, we use a regional dynamic panel data model with a GMM estimator (Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). While these models may not fully capture the long-run growth dynamics, they provide valuable insights into the drivers of economic performance over relatively short to medium-term horizons. Moreover, the inclusion of the lagged dependent variable as a covariate allows the persistence of agents' behavior to be captured with a partial adjustment mechanism. The model also takes into consideration endogenous and predetermined variables across regions, thus allowing for the identification of commonalities and differences in growth dynamics that provide insights into the role of specific factors in driving short-/medium-term growth. This contributes to a better understanding of regional disparities and growth differentials, although the limited time frame does not allow for insights into regional convergence.\u003c/p\u003e \u003cp\u003eOur benchmark equation takes the following form:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,t}=\\gamma\\:\\:\\:\\alpha\\:{y}_{i,t-1}+X{{\\prime\\:}}_{it}\\beta\\:+Z{{\\prime\\:}}_{it}\\gamma\\:\\:\\:+{\\eta\\:}_{i}+{v}_{it}\\)\u003c/span\u003e \u003c/span\u003e, with \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003ewhere the error term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e has two components: the fixed effects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{i\\:}\\)\u003c/span\u003e\u003c/span\u003e and the idiosyncratic shocks \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e. Our dependent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,t\\:}\\)\u003c/span\u003e\u003c/span\u003e, represents economic growth in region \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e. The matrix \u003cem\u003eX\u0026rsquo;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e includes all the economic regressors at a regional level, whereas the matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Z{{\\prime\\:}}_{it}\\)\u003c/span\u003e\u003c/span\u003e includes the criminal variable tested. The correlation of the lagged dependent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,\\:t-1\\:}\\)\u003c/span\u003e\u003c/span\u003e, with the fixed effects in the error term, causes Nickell\u0026rsquo;s (1981) dynamic panel bias, which can be overcome by taking the first differences of the original model (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{y}_{it}={\\lambda\\:}_{1}\\varDelta\\:{y}_{i,t-1}+{\\lambda\\:}_{2}\\varDelta\\:X{{\\prime\\:}}_{it}+{\\lambda\\:}_{3}\\varDelta\\:Z{{\\prime\\:}}_{it}+\\varDelta\\:{v}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe first difference estimator eliminates time-invariant regressors and fixed effects. To ensure robustness, we refer to both the GMM estimates in levels as our main results and the difference GMM estimation as a robustness check.\u003csup\u003e6\u003c/sup\u003e We are aware of the fact that the correlation between the differenced lagged dependent variable (and other endogenous variables) and the disturbance process may still remain. For this reason, along with the internal instruments employed in the present empirical setup, we also instrument the variables of the GMM with external instruments, given that the usage of internal instruments may not be sufficient to remove the endogenous components in the data.\u003csup\u003e7\u003c/sup\u003e To assess this issue, we rely on the Arellano-Bond test, on the over-identifying restrictions of Sargan/Hansen tests, and on the instrument-diagnostic suggested by Bazzi and Clemens (2013). Furthermore, we present estimates obtained with OLS and fixed effects (FE) regressions as a robustness check, focusing specifically on the lagged dependent variable. The OLS regression accounts for the positive correlation between the lagged dependent variable and the error term due to fixed effects, leading to an upward-biased coefficient. Conversely, the FE estimator yields a downward biased coefficient. The robust GMM estimates of the lagged dependent variable are expected to lie between these bounds.\u003c/p\u003e \u003cp\u003eReferring to the above framework, our empirical strategy consists of estimating, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003efirst\u003c/span\u003e, the model related to the economic growth of the Italian economy in the absence of counterfeiting. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides the output of the two-step GMM regression of regional economic growth in the absence of counterfeiting (column A), where we select among the variables considered in the literature by adopting a general-to-specific methodology (Hendry, 1993; and Hoover and Perez, 1999). As a robustness check, columns B and C present the OLS and FE regressions, respectively. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSecond\u003c/span\u003e, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the results of the two-step GMM regression of regional economic growth, taking into consideration the effect of counterfeiting (column A), with columns B and C presenting the OLS and FE regressions, respectively. In either table, the standard errors are adjusted for the correction of Windmeijer (2005), and the inclusion of the lagged dependent variable accounts for the persistence of the time series (Watt and Janssen 2003).\u003c/p\u003e \u003cp\u003eIn both cases, we use instruments to mitigate potential endogeneity bias in some regressors in both the \u0026ldquo;GMM-style\u0026rdquo; and \u0026ldquo;IV-Style\u0026rdquo; matrices. These include proxies for regional competitiveness (lagged), such as labor productivity (\u003cem\u003eALP\u003c/em\u003e), private investments for tangible goods, and \u003cem\u003eR\u0026amp;D\u003c/em\u003e, but also economic policy variables like social benefits and indirect taxation. We include other lagged regressors in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, such as \u003cem\u003eCAD\u003c/em\u003e and \u003cem\u003eCAV\u003c/em\u003e, as internal instruments based on endogeneity tests.\u003c/p\u003e \u003cp\u003eThe validity of the instruments is assessed using the Sargan test of over-identifying restrictions and the Hansen test, both confirming the appropriateness of our instruments. We also conduct the instrument diagnostics suggested by Bazzi and Clemens (2013) to address potential identification problems. The under-identification test based on the Kleibergen-Paap LM statistic and the overidentification test based on the Hansen J statistic from an extended instrumental variables estimation further support the validity of the instruments. Additionally, we employ the Arellano-Bond test to detect autocorrelation in the idiosyncratic disturbance term, which can render some lags invalid as instruments even after controlling for fixed effects. The Arellano-Bond test confirms the suitability of our instruments. We also conduct a test of the endogeneity of the regressors, specifically related to counterfeiting activities (\u003cem\u003eCAD\u003c/em\u003e and \u003cem\u003eCAV\u003c/em\u003e), following Baum et al. (2007). The test indicates that these potentially endogenous variables can be considered exogenous in the empirical investigation. However, we acknowledge that this test does not fully address all issues related to endogeneity, which we mitigate by employing the GMM estimator. Finally, we include the Hansen test, excluding group statistics related to the exclusion restriction. Nevertheless, we recognize that the test for the exogeneity of instrument subsets may be weakened due to the limited degree of overidentification (Roodman, 2009b).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the GDP growth estimates, revealing significant regional economic inertia through the significant influence of lagged GDP growth on current economic performance, thereby highlighting the path-dependent nature of regional economic development. This inertia reflects the embeddedness of past economic activities and outcomes in shaping future growth trajectories, underscoring the persistent effect of historical growth rates on current and future economic dynamics. The underlying mechanisms of this inertia include the slow adjustment of markets to shifts in investment levels, consumer spending patterns, and regulatory changes, which collectively influence the economic fabric of regions over time.\u003c/p\u003e \u003cp\u003eThe analysis reveals a robust positive relationship between labor costs, W, our measure of competitiveness, and economic growth. This association implies that regions with higher labor costs, possibly due to the presence of specialized labor, contribute more significantly to GDP growth, indicating the benefits of investing in human capital and specialized skills.\u003c/p\u003e \u003cp\u003eSimilarly, labor productivity, ALP, another measure of competitiveness, has a positive and statistically significant impact on growth. The higher elasticity of labor costs compared to productivity calls back the productivity gap between the north-central and the southern regions of Italy, which drives development disparities and economic divisions. The gap discrepancy has been explained by the prevalence of skill-intensive production processes only in some parts of the country. Daniele (2021) underlines that firms in Southern regions pay lower average wages than in the Center-North and employ workers with lower qualifications and productivity. This difference depends on the characteristics of the regional production structure, namely the firm size (see Berlingeri et al., 2018), the sectoral composition in which they operate (e.g., manufacturing is more present in the Center-North) and product diversity. The uneven distribution of high-skill industries suggests that regions that have succeeded in developing or attracting such sectors have moved ahead, exacerbating the productivity gap. Southern regions, facing a shortage of skill-intensive sectors, are also experiencing a \"brain drain\" as skilled workers migrate to more prosperous areas in search of better opportunities and wages.\u003c/p\u003e \u003cp\u003eThe findings emphasize the intertwined nature of wages and productivity. High wages can both reflect and foster high productivity, creating a virtuous circle of growth in regions where both are present. This interplay is crucial for policymakers to understand, as it highlights the need to create conditions conducive to both skilled labor and productivity growth in order to reduce regional economic disparities. Such conditions can include investments in education and training, incentives for industries to locate in lagging regions, and infrastructure development that facilitates the mobility of skilled workers and the distribution of goods and services from these new productivity centers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGDP growth and the main determinants in the absence of shadow economy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003cp\u003eFE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWald chi2(13)\u0026thinsp;=\u0026thinsp;7.09e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF(13, 166) = 92428.30\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F = 0.0000\u003c/p\u003e \u003cp\u003eR-squared = 0.9999\u003c/p\u003e \u003cp\u003eAdj R-squared\u0026thinsp;=\u0026thinsp;0.9999\u003c/p\u003e \u003cp\u003eRoot MSE = .01342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR-squared:\u003c/p\u003e \u003cp\u003eWithin = 0.9317\u003c/p\u003e \u003cp\u003eBetween =\u0026thinsp;0.9995\u003c/p\u003e \u003cp\u003eOverall\u0026thinsp;=\u0026thinsp;0.9994\u003c/p\u003e \u003cp\u003eF(13,147) = 154.15\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809***\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003cp\u003e(0.050)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131***\u003c/p\u003e \u003cp\u003e(0.0566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINVESTMENT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0459\u003c/p\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTIME DUMMIES\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.346***\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003cp\u003e(0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.105\u003c/p\u003e \u003cp\u003e(0.556)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargan test: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e= = 39.25 Prob\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.026\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHansen test χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;8.51 P\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.998\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArellano-Bond test for AR(1): z = -2.39 Pr\u0026thinsp;\u0026gt;\u0026thinsp;z = 0.017\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArellano-Bond test for AR(2): z = 1.06 Pr\u0026thinsp;\u0026gt;\u0026thinsp;z = 0.290\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDifference-in-Hansen tests of exogeneity of instrument subsets\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eGMM instruments for levels\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHansen test excluding group: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;8.51 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.970\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDifference (null H\u0026thinsp;=\u0026thinsp;exogenous): χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 1.000\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIV L.logsubs L.logindtax L2.logALP L2.loginvestment L.logR\u0026amp;D time)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHansen test excluding group: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.63 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 1.000\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDifference (null H\u0026thinsp;=\u0026thinsp;exogenous): χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;4.88 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.560\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Number of observations\u0026thinsp;=\u0026thinsp;180, number of groups\u0026thinsp;=\u0026thinsp;20. The empirical investigation is performed under small sample adjustments. Standard errors are in parenthesis. \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e, and \u003csup\u003e*\u003c/sup\u003e correspond to the 1%, 5% and 10% level of significance, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsistent with previous research (Romer, 1987, 1990; Jones, 1995; Grossman and Helpman, 1991; Aghion and Howitt, 1992), private investment in physical goods and R\u0026amp;D emerge as important determinants of growth. These investments, which are crucial for technological progress and innovation, have implications for a region's growth trajectory and long-term economic development. In this regard, IPR protection plays a critical role in securing the returns on these investments and fostering an environment conducive to innovation, economic expansion, and industrial development. Effective IPR enforcement can help address historical disparities in regional economic development and ensure that all regions benefit from private investment and innovation-driven growth. Thus, the interplay between private investment, R\u0026amp;D activities, and IPR protection is fundamental to achieving balanced and sustainable regional economic growth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the main indicators of criminal activity directly related to counterfeiting, the value of the seized counterfeits and the average dimension of fakes\u0026rsquo; seizures, respectively, \u003cem\u003eCAD\u003c/em\u003e and \u003cem\u003eCAV\u003c/em\u003e, are added to the model. The results for both coefficients are highly significant and statistically different from each other, with a positive impact of the dimension of counterfeit (\u003cem\u003eCAD\u003c/em\u003e) and a negative impact of the value of counterfeit (\u003cem\u003eCAV\u003c/em\u003e), signaling omitted variable misspecification in the apparently robust estimation results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In particular, the variable representing the presence of counterfeiting activities, namely \u003cem\u003eCAD\u003c/em\u003e, shows a positive and statistically significant correlation with the level of economic growth. This finding mainly captures the role of counterfeiting in certain Italian regions, where a massive amount (mostly of low-value or low-quality) of counterfeit goods are either imported or produced locally, falsely labeled, and then distributed abroad or to other regions. However, this is only one facet of the phenomenon. More importantly, our estimates reveal a negative and statistically significant relationship between the estimated value of counterfeits (\u003cem\u003eCAV\u003c/em\u003e) and economic growth. The estimated negative coefficient implies that high-value and lucrative counterfeiting activities negatively impact economic growth, thereby affecting regional economic activity by infringing IPR and suppressing legal production and legitimate exchange. This finding underscores the vulnerability of prestigious Italian brands to counterfeiting practices. Moreover, the results highlight the dualistic relationship between different dimensions of counterfeiting activity and economic growth. While the positive coefficient of CAD suggests that the overall presence of counterfeiting activity stimulates economic growth, the negative coefficient of CAV highlights the detrimental impact of highly profitable counterfeiting ventures on economic activity at the regional level. The contrasting effects of the size and value of counterfeits that produce a positive net economic impact of counterfeiting on growth go beyond the mere volume and value of illicit production and exchange.\u003c/p\u003e \u003cp\u003eInterestingly and unexpectedly, when controlling for the presence of counterfeits, there is a significant increase in the estimated elasticities of most of the conventional drivers of economic growth (the impact of gross wages, productivity, and R\u0026amp;D on economic growth becomes more pronounced than in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where we do not control for counterfeiting) and a strong reduction the coefficient of the lagged GDP growth rate that indicates that past economic performance reduces its influence on current growth when isolating counterfeit.\u003c/p\u003e \u003cp\u003eDwelling into the implications of these results that disclose the effect of each variable on the legal market once illegal activities have been controlled for, the increased estimated elasticity of gross wages on growth suggests the presence of the real negative effect of counterfeit on the development path. Counterfeiting absorbs low-paid jobs in the illegal manufacturing sectors. The higher impact of wages on growth could be, therefore, also indicative of innovative industries that, in the legal market, move towards more sophisticated, high-quality goods and services, which require more skilled labor, which, in turn, further enhances productivity and, thus, economic growth. In addition, since workers in the legal sectors are likely to earn higher wages, they could further stimulate consumption and economic activity.\u003c/p\u003e \u003cp\u003eRegarding productivity, the increased estimated elasticity of ALP, when controlling for counterfeiting activities, shows that the real productivity gains of legal activities become more important for economic growth than what emerges when discarding the activities of criminal organizations. In other words, counterfeiting erodes the competitive advantage of genuine producers via reduced returns on investment in innovation and low-quality improvements. In the absence of illicit activities, productivity gains in genuine sectors would have a larger impact, close to that of advanced countries, as they help to maintain competitiveness, promote innovation, and support economic growth. This interpretation underscores the importance of efficiency and innovation in economies faced with illicit economic activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGDP, counterfeiting and the conventional determinants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003cp\u003eFE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWald chi2(15) = 2.68e\u0026thinsp;+\u0026thinsp;08 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF(15, 164) = 82102.07\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;F\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e \u003cp\u003eR-squared\u0026thinsp;=\u0026thinsp;0.999\u003c/p\u003e \u003cp\u003eAdj R-squared = 0.9999\u003c/p\u003e \u003cp\u003eRoot MSE = .01325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR-squared:\u003c/p\u003e \u003cp\u003eWithin = 0.9320\u003c/p\u003e \u003cp\u003eBetween =\u0026thinsp;0.9995\u003c/p\u003e \u003cp\u003eOverall\u0026thinsp;=\u0026thinsp;0.9994\u003c/p\u003e \u003cp\u003eF(15,145) = 132.59\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7094 ***\u003c/p\u003e \u003cp\u003e0.0578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7956\u003c/p\u003e \u003cp\u003e0.0350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3228\u003c/p\u003e \u003cp\u003e0.0502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCA\u003c/em\u003e\u003csub\u003e\u003cem\u003eV\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0084439 ***\u003c/p\u003e \u003cp\u003e0.0028656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0024\u003c/p\u003e \u003cp\u003e0.0010\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCA\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0137 ***\u003c/p\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0023\u003c/p\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1824 ***\u003c/p\u003e \u003cp\u003e0.0354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1318\u003c/p\u003e \u003cp\u003e0.0269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4073\u003c/p\u003e \u003cp\u003e0.0593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0486 ***\u003c/p\u003e \u003cp\u003e0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0471\u003c/p\u003e \u003cp\u003e0.0155502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2365\u003c/p\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINVESTMENT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065***\u003c/p\u003e \u003cp\u003e0.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0438\u003c/p\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0382***\u003c/p\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0440\u003c/p\u003e \u003cp\u003e0.02998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTIME DUMMIES\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5652133 ***\u003c/p\u003e \u003cp\u003e0.1111545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3891\u003c/p\u003e \u003cp\u003e0.0820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1249\u003c/p\u003e \u003cp\u003e0.5588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargan test: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e= = = 27.45 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.386\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHansen test χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e= = 4.03 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 1.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArellano-Bond test for AR(1): z = -2.51 Pr\u0026thinsp;\u0026gt;\u0026thinsp;z = 0.012\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArellano-Bond test for AR(2): z = 0.96 Pr\u0026thinsp;\u0026gt;\u0026thinsp;z = 0.339\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDifference-in-Hansen tests of exogeneity of instrument subsets\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eGMM instruments for levels\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHansen test excluding group: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.28 Prob\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1.000\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDifference (null H\u0026thinsp;=\u0026thinsp;exogenous): χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.75 Prob\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1.000\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eiv(L.logsubs L.logindtax L.ogCAD L2.logCAV L2.logALP L2.loginvestment L.logrd time)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHansen test excluding group: χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;2.10 Prob\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1.000\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDifference (null H\u0026thinsp;=\u0026thinsp;exogenous): χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1.93 Prob\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.983\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Number of observations\u0026thinsp;=\u0026thinsp;180, number of groups\u0026thinsp;=\u0026thinsp;20. The empirical investigation is performed under small sample adjustments. Standard errors are in parenthesis. \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e, and \u003csup\u003e*\u003c/sup\u003e correspond to the 1%, 5% and 10% level of significance, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe observation that the elasticity of investment also increases after controlling for counterfeiting may reflect the nuanced role of investment that could be channeled either into legal sectors to combat the negative effects of counterfeiting or into sectors that have robust protection against counterfeiting. Such investments could include advanced manufacturing technologies, intellectual property protection measures, or sectors with high barriers to entry. This strategic reallocation of investment yields higher legal returns regarding economic growth, as it not only combats the direct effects of counterfeiting but also fosters innovation and productivity in less affected sectors.\u003c/p\u003e \u003cp\u003eThe increased elasticity of R\u0026amp;D underscores innovation's critical role in driving legal market economic growth. In contexts where counterfeiting is a significant concern, the low incentives for R\u0026amp;D can dilute the average legal returns on investment in new technologies and products. However, our findings suggest that when counterfeiting is explicitly controlled, R\u0026amp;D contribution to economic growth becomes more relevant. R\u0026amp;D activities can help legal firms and economies maintain a competitive edge, ensuring that they stay ahead of counterfeit competitors through continuous innovation and improvement.\u003c/p\u003e \u003cp\u003eFinally, our analysis reveals a significant refinement in the understanding of regional economic growth determinants. Notably, we observe a marked reduction\u0026mdash;approximately 10%\u0026mdash;in the coefficient of lagged GDP growth. This reduction signifies that, when accounting for the presence of counterfeit activities, past economic performance exerts a diminished influence on current growth rates. Counterfeiting, by introducing fake products into the market, undermines the profits of legitimate businesses, thereby dampening their ability to invest in innovation and development. This, in turn, stymies the economic dynamism necessary for overcoming inertia and fostering sustainable growth. Moreover, the widespread presence of counterfeit goods can tarnish regions' reputation as hubs of legitimate production, adversely affecting their attractiveness to external investments and complicating the market signals necessary for identifying genuine economic opportunities. The implications of these findings are profound, suggesting that effective policies against counterfeiting are not merely a tool for protecting intellectual property but also a critical lever for mitigating regional economic inertia. By bolstering the enforcement of intellectual property rights, enhancing international cooperation against the flow of counterfeit goods, and raising awareness about the detrimental impacts of counterfeiting, policymakers can stimulate a more dynamic and sustainable regional economic growth pathway. This insight urges policymakers and scholars alike to reconsider the mechanisms of regional growth, advocating for a clean economic space where genuine innovation and productivity can flourish unimpeded by the shadow of counterfeiting.\u003c/p\u003e \u003cp\u003eIn summary, our main findings are as follows:\u003c/p\u003e\n\u003cp\u003e1- A dual effect of counterfeiting, with an ultimately net positive impact on growth.\u003c/p\u003e\n\u003cp\u003e2- Upon controlling for the influence of counterfeiting, the rise in the elasticities of traditional drivers of economic growth mirrors patterns observed in developed nations, whereas the significant reduction in the lagged GDP growth coefficient shows that illegal activities are a critical lever for exacerbating regional economic inertia.\u003c/p\u003e \u003cp\u003eAll this indicates the primary adverse effects of criminal activities on the economic framework. Eliminating counterfeiting infiltration from the Italian production system and employing all input in the legal sectors would be associated with increased economic growth. In addition, these results shed light on the complex interplay between counterfeiting and economic dynamics. It suggests that once counterfeiting is isolated, we find enhanced contributions from wages, productivity, investment, and R\u0026amp;D to sustain and drive economic growth while mitigating the economic inertia. This result confirms the importance of considering criminal organizations\u0026rsquo; counterfeiting activities for the variables that most impact Italian economic growth and underscores the necessity of purging economic analyses of such distortive influences to attain a clearer, more accurate depiction of economic health and growth potential.\u003c/p\u003e \u003cp\u003eBusinesses\u0026rsquo; adaptation to counterfeiting may also involve strategic shifts towards sectors and activities less vulnerable to counterfeiting, heightened emphasis on innovation, and the development of products and services that offer genuine value beyond what counterfeit goods can provide. These findings underscore the need to consider the presence of counterfeiting as a critical factor when evaluating and formulating policies to promote economic growth. Policymakers can use these findings to develop targeted strategies prioritizing initiatives that strengthen legitimate growth drivers.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe phenomenon of counterfeiting in Italy represents a multifaceted challenge with far-reaching economic and social implications. We have shown that variables traditionally associated with economic growth, such as gross wages, productivity, R\u0026amp;D, and investment, all positively impact Italian GDP growth rates. In addition, the estimates show a quite strong inertia of the development path. When we include counterfeiting measures in our analysis, we discover a dualistic relationship between this illicit activity and economic growth. While the value of counterfeiting is found to have a negative impact on GDP growth, potentially crowding out legitimate economic activity, the average size of counterfeiting is positively correlated with growth rates. The positive net effect may reflect the impact of the broader economic activity that accompanies increased levels of counterfeiting, despite the potentially harmful effects of this illicit trade. Including counterfeiting variables also results in a significant change in the estimated effects of the conventional drivers of economic growth. Notably, once controlling for counterfeit, the estimated elasticities of all these variables -wages, productivity, investment, R\u0026amp;D - increase significantly, showing a furtherdetrimental impact on economic growth. In addition, the lagged GDP growth rate is strongly reduced.\u003c/p\u003e \u003cp\u003eThe interaction of these economic effects shapes the characteristics of Italy's recent development path, where modernization generated by innovative high-growth businesses coexists with large areas of underdevelopment and illegality. Therefore, the overall impact of counterfeiting poses a challenge in combating this crime. There is an urgent need to rethink policy approaches and attitudes towards these forms of economic crime, which are often considered \"soft\" -i.e., less dangerous and disruptive than violent crime- and are therefore tolerated. Indeed, low growth rates in Italy support the argument that significant portions of the economy may be susceptible to falling into a stagnation trap induced by crime. In other words, the links between economic crime and growth create a vicious circle, resulting in persistently low growth rates and potentially high or rising levels of economic crime, as illustrated in models where the economic dynamics in the presence of crime are characterized by multiple regimes and can end up in a poverty trap with high crime rates and low incomes (Mehlum et al., 2005; Fioroni et al., 2023). This vicious circle underscores the need to combat economic crime in order to embark on a sustainable path of social and economic development, in which high growth rates contribute to lower crime rates, thereby fostering additional economic growth and development. Strengthening law enforcement, coupled with incentives to leave the illegal economy and attend educational programs, could strike a balance between curbing an illegal sector and drawing its workers into the legal economy, fostering positive employment and development effects. \u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors prepared the manuscript\u003c/p\u003e\u003cp\u003eNo funding was received for conducting this study. 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How a luxury monopolist might benefit from a stringent counterfeit monitoring regime. \u003cem\u003eInternational Journal of Business and Economics\u003c/em\u003e, 4(3),177-92\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e See, for example, Banerjee, 2006; Grossman and Shapiro, 1988; Qian, 2008 and 2014; Chaudhry and Zimmerman, 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The dataset collects all the anti-counterfeiting activities carried out in Italy during the period 2008\u0026ndash;2019, distinguishing them on the basis of 11 goods-related categories: clothing; accessories; shoes; watches; jewellery; electrical appliances; electronic and computer equipment; toys and games; CDs and DVDs; sunglasses; make-up cosmetics; perfumes and fragrances. It should be noted that the categories of goods in the database do not include those goods that are often the subject of counterfeiting, such as medicines or food, which fall under the jurisdiction of the Italian Ministry of Health.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e It is important to note that the data on the number of seizures represent the universe of observations, while the value of seized goods is estimated according to a methodology developed by the General Directorate of the Ministry of Economic Development (IPERICO, 2012). These estimates are based on the value assigned to the seized goods by the customs agency, according to both the quality of the goods and the differences between the expected selling price of the counterfeit and the market price of the original good.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In a similar way, but using a theoretical neo-Kaleckian model, Capuano and Purificato (2012) show the dual impact of criminal organisations on the legal sector, as they both drain resources from the legal sector and allocate criminal revenues to purchase legal goods and services.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For regional comparisons, one could focus only on public expenditure on final consumption, which still represents more than 80% of total expenditure, thus ensuring its relevance as a robust proxy for human capital.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The latter is not reported, but available from authors upon request.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The easiest way to instrument our not strictly exogenous variables is the 2SLS estimator, within the Anderson-Hsiao (1982) difference and levels estimator (see Roodman, 2009a).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e According to a report by MISE-CENSIS (2018), if the production of counterfeit products were to take place within the legal economy, it is estimated that it would require the participation of approximately 104,000 additional workers. These job opportunities could be filled primarily by workers currently employed in the counterfeiting sectors.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Counterfeiting, Economic growth, Intellectual property rights, Shadow economy, Regional dynamics, Italy","lastPublishedDoi":"10.21203/rs.3.rs-5032553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5032553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of economic crime, specifically counterfeiting, on economic growth in Italy after the Great Recession (2008-19), when the country experienced lower economic growth than its developed counterparts. Counterfeiting is orchestrated by mafia-like organizations that turn large areas into hubs for the production/trading of counterfeit goods, thereby damaging genuine brands.\u003c/p\u003e\n\u003cp\u003eOur study, using a unique regional dataset, demonstrates that in Italy, two direct effects of counterfeiting are significant. First, it stimulates economic growth by flooding markets with counterfeit goods. Second, it undermines market modernization by violating the intellectual property rights of innovative firms in high-value sectors. Moreover, the resources absorbed by illegal activities determine an indirect negative effect: once counterfeiting is controlled, the standard drivers of economic development become more effective. This suggests that if all growth drivers were channeled into legal activities, Italy would experience a development path in line with the performance of its top competitor countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL:\u003c/strong\u003e E26; O47; C23; K14\u003c/p\u003e","manuscriptTitle":"Shadow Economy or Economic Driver? The Impact of Counterfeiting on Italy's Growth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 08:45:51","doi":"10.21203/rs.3.rs-5032553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04c91c3b-7666-4192-995b-53ed75d500a9","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-14T20:09:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 08:45:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5032553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5032553","identity":"rs-5032553","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}