Shifts in the boot: Understanding inequality's impact on interregional migration patterns in Italy

Di Pasquale, Giacomo; Parazzi, Elisa Article Shifts in the boot: Understanding inequality's impact on interregional migration patterns in Italy Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Di Pasquale, Giacomo; Parazzi, Elisa (2024) : Shifts in the boot: Understanding inequality's impact on interregional migration patterns in Italy, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 12, pp. 1-21, https://doi.org/10.3390/economies12120317 This Version is available at: https://hdl.handle.net/10419/329244 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Di Pasquale, Giacomo, and Elisa Parazzi. 2024. Shifts in the Boot: Understanding Inequality’s Impact on Interregional Migration Patterns in Italy. Economies 12: 317. https:// doi.org/10.3390/economies12120317 Academic Editor: Bedassa Tadesse Received: 30 May 2024 Revised: 24 July 2024 Accepted: 19 November 2024 Published: 21 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Shifts in the Boot: Understanding Inequality’s Impact on Interregional Migration Patterns in Italy Giacomo Di Pasquale * and Elisa Parazzi Nicolais School of Business, Wagner College, New York, NY 10103, USA; [email protected] *Correspondence: [email protected] Abstract: Italy has long experienced a significant developmental gap between its northern and southern regions, with the latter being less developed. The 2007–2008 financial crisis accentuated this gap, leading to distinct patterns of interregional migration. This paper aims to investigate the effects of past migration flows and income inequality on interregional mobility in Italy, using a gravity model with bidirectional dyads and three different measures of inequality as dependent variables: Gini Index, Relative Poverty, and Income Ratio. Previous research has shown that living in highly unequal regions is associated with increased mistrust and anxiety about social status, contributing to unhappiness among residents. Using bilateral gross migration flows for the period 2007–2018, the study aims to control for the potential endogeneity between interregional mobility and inequality. The results indicate a positive relationship between high levels of inequality and interregional outmobility, underscoring the need for policies aimed at reducing both horizontal and vertical inequality within and among Italian regions. Keywords: inequality; interregional mobility; Italy; north; regions; south 1. Introduction Italy has a long-standing history of regional disparities, with the southern regions being less developed than the northern ones. This gap became more accentuated in recent times during the economic crisis of 2007–2008, resulting in different patterns of interregional migration. Understanding the determinants of interregional mobility in Italy is crucial for policymakers to design effective policies that promote social cohesion and reduce regional inequalities. The economic crisis of 2007–2008 severely impacted the Italian economy, causing a sharp increase in unemployment and poverty rates, particularly in the southern regions, which were already lagging behind in terms of economic development compared to the northern regions (Vera Zamagni 1993). In fact, the southern region has historically always been poorer than the north, as it has never experienced a comparable process of economic growth; rather than an “active modernization”, the process that interested the south resembled the characteristics of a “passive modernization”, as “something extraneous to the local society” brought from outside and implemented by the extractive elite classes, without benefiting the population as a whole (Felice and Vasta 2015). Southern regions have always experienced significant out-migration, both abroad and to the northern regions, which intensified in the interwar period and peaked in the 1960s; subsequently, interregional migration declined in the 1970s but revived in the 1990s and the following decades, exacerbating existing regional disparities (Vittorio Zamagni 2008; Mussida and Parisi 2016). The demographic composition of these migrations has varied over time, and scholars have formulated various hypotheses to explain this inconsistency. Indeed, recent migration waves, mostly characterized by high-skilled individuals, generally from higher social classes, differ greatly from the mainstream low-skilled migrants of the 1960s. Despite some hypotheses suggesting demographic change or substitution over the Economies 2024,12, 317. https://doi.org/10.3390/economies12120317 https://www.mdpi.com/journal/economies Economies 2024,12, 317 2 of 21 years, Panichella argues that high-skilled migration has always existed, and has continued to persist after the 1990s. Conversely, low-skilled migration surged during the specific historical period of economic boom alongside the already present high-skilled migration flow and accounted for the increase in interregional migration in those years (Sanfilippo 2016;Panichella 2012). The 2007–2009 economic crisis exacerbated this interregional pattern of mobility from the south to the north in search of better economic opportunities and may have had different demographics compared to previous migrations (Simpson 2017) (Figure 1). Economies 2024, 12, x FOR PEER REVIEW 2 of 22 the 1960s. Despite some hypotheses suggesting demographic change or substitution over the years, Panichella argues that high-skilled migration has always existed, and has continued to persist after the 1990s. Conversely, low-skilled migration surged during the specific historical period of economic boom alongside the already present high-skilled migration flow and accounted for the increase in interregional migration in those years (Sanfilippo 2016; Panichella 2012). The 2007–2009 economic crisis exacerbated this interregional pattern of mobility from the south to the north in search of better economic opportunities and may have had different demographics compared to previous migrations (Simpson 2017) (Figure 1). Figure 1. Migration flows—Italian regions. Migration is a complex phenomenon that is influenced by a range of factors, including economic, social, cultural, and political (Harris and Todaro 1970). Previous studies have shown that economic factors, such as income inequality, are important determinants of migration (Etzo 2011; Percoco 2018). In fact, in highly unequal regions, people may feel discouraged by the lack of economic opportunities and may be more likely to migrate to other regions. Moreover, high levels of inequality can lead to increased mistrust and anxiety about social status, further motivating the desire to migrate. This study aims to investigate the effects of past migration flows and income inequality on interregional mobility in Italy. To achieve this goal, the study uses a gravity model with bidirectional dyads and three different measures of inequality, the Gini Index, Relative Poverty, and Income Ratio, for the 2007–2018 period. The use of multiple measures of inequality allows for a more nuanced understanding of the relationship between inequality and interregional mobility in Italy and provides more robustness to the model. In addition, an instrumental variables approach is adopted to control for the potential endogeneity between migration and several of the independent variables used in the model (this aspect will be discussed more in detail in the next sections of the paper). The remainder of the paper is structured as follows. The next section provides a review of the relevant literature on interregional mobility and income inequality in Italy. The Section 3 outlines the data and methodology used in the analysis, including a detailed explanation of the instrumental variables approach and the gravity model. The Section 4 presents the results of the analysis, while the Section 5 discusses policies implications. The final section concludes the paper. 2. Literature Review Italy has historically been divided between the more developed and wealthier north, and the less developed and poorer south (Musolino 2018). This divide, which lasted for most of the 20th century, was exacerbated by the 2008 economic crisis, leading to different Figure 1. Migration flows—Italian regions. Migration is a complex phenomenon that is influenced by a range of factors, including economic, social, cultural, and political (Harris and Todaro 1970). Previous studies have shown that economic factors, such as income inequality, are important determinants of migration (Etzo 2011;Percoco 2018). In fact, in highly unequal regions, people may feel discouraged by the lack of economic opportunities and may be more likely to migrate to other regions. Moreover, high levels of inequality can lead to increased mistrust and anxiety about social status, further motivating the desire to migrate. This study aims to investigate the effects of past migration flows and income inequality on interregional mobility in Italy. To achieve this goal, the study uses a gravity model with bidirectional dyads and three different measures of inequality, the Gini Index, Relative Poverty, and Income Ratio, for the 2007–2018 period. The use of multiple measures of inequality allows for a more nuanced understanding of the relationship between inequality and interregional mobility in Italy and provides more robustness to the model. In addition, an instrumental variables approach is adopted to control for the potential endogeneity between migration and several of the independent variables used in the model (this aspect will be discussed more in detail in the next sections of the paper). The remainder of the paper is structured as follows. The next section provides a review of the relevant literature on interregional mobility and income inequality in Italy. The Section 3outlines the data and methodology used in the analysis, including a detailed explanation of the instrumental variables approach and the gravity model. The Section 4 presents the results of the analysis, while the Section 5discusses policies implications. The final section concludes the paper. 2. Literature Review Italy has historically been divided between the more developed and wealthier north, and the less developed and poorer south (Musolino 2018). This divide, which lasted for most of the 20th century, was exacerbated by the 2008 economic crisis, leading to different Economies 2024,12, 317 3 of 21 patterns of interregional migration (Benassi et al. 2019). In this literature review, we explore the effects of past migration flows and income inequality on interregional mobility in Italy, focusing on the years following the 2008 crisis. We will focus on the use of the gravity model with bidirectional dyads and discuss the literature that successfully employed gravity models for regional economics. Additionally, we analyze the consequences of the economic crisis of 2008 as a trigger for interregional immigration. 2.1. Historical Context Historically, Italy has experienced a significant north–south divide in terms of economic development. The north has been characterized by high levels of industrialization and modernization, while the south has lagged behind in terms of economic growth, infrastructure, and job opportunities (Gagliardi and Percoco 2011). This divide, known as the “Questione Meridionale” [Southern Question], has been a subject of study for many years, and different theories have been advanced to explain it. Felice, Daniele, and Malanima offer contrasting positions in the ongoing debate, providing different answers to the question “Why did the south lag behind?” Felice, maintaining an “institutionalist” approach, argued that the extractive elites in the south have delayed the modernization process for private gain, a condition typical and unique to those regions. Moreover, according to him, southern regions lacked social capital, they were in an unfavorable geographic location, and they had a pronounced economic inequality, all factors that contributed to its underdevelopment. Felice (2018) discusses how one of the critical causes of this divide has been the uneven spread of industry. The industrialization of the northern regions facilitated economic growth and development, while the southern regions remained predominantly agricultural. This industrial concentration in the north led to a more robust economic infrastructure, higher productivity, and better employment opportunities, further widening the economic gap. Transportation costs have also played a significant role in perpetuating the north– south divide. The northern regions benefit from better transportation infrastructure, which reduces costs and facilitates trade and mobility. In contrast, the southern regions face higher transportation costs due to less developed infrastructure, hindering economic activities and increasing the cost of doing business. Market integration has been another crucial factor. The northern regions are more integrated into European markets, benefiting from greater access to consumers and suppliers. This market integration has allowed for quicker economic growth and recovery from economic downturns. On the other hand, the southern regions, with lower levels of market integration, struggle to attract investments and expand economic activities. Conversely, Daniele and Malanima’s main argument rests primarily on geographical factors. Indeed, in post-unitary Italy, the north benefited from more investment due to its proximity to the other European industrial hubs, which attracted more investors, consolidating an “industrial triangle” ideally connecting Genoa, Turin, and Milan. They argue that economic forces, instead of spreading industrialization across the Italian peninsula, created a thriving economy in the north to the detriment of the south (Ciccarelli et al. 2021). The geographical proximity of the northern regions to major European markets provides a competitive advantage in terms of export opportunities and foreign investments. This proximity has made it easier for northern regions to establish and maintain economic ties with European partners, contributing to their economic success. The southern regions, being farther from these markets, face disadvantages in attracting investments and expanding economic activities. The north’s advantageous location, with better access to European markets, has historically facilitated trade and economic integration, contributing to its economic prosperity. In contrast, the south’s geographical challenges, including rugged terrain and less accessible locations, have impeded similar levels of economic integration and development. The recent literature also stresses the importance of innovation in promoting regional economic development (Di Quirico 2010). Manioudis and Angelakis (2023) emphasize that the creative economy is tightly associated with sustainable development and Sustainable Economies 2024,12, 317 4 of 21 Economic Goals (SDGs). Their study focuses on the region of Attica, illustrating how smart specialization strategies and the Entrepreneurial Discovery Process (EDP) methodology can foster sustainable regional growth. The deployment of a robust innovation ecosystem requires engaging and mobilizing regional actors, identifying their needs and priorities, and fostering long-term institutional learning and policy co-design. These findings underscore the critical role of both education and innovation in regional development and the need for policies that support creative and knowledge-based industries to drive economic growth and sustainability (Manioudis and Angelakis 2023). The existing literature reveals that education, innovation, and physical geography are fundamental in understanding and addressing regional economic disparities. Policies aimed at enhancing educational opportunities, fostering innovation, and addressing geographical challenges are essential for promoting balanced regional development and transitioning to sustainable economic growth. The integration of these elements can help create a more cohesive economic landscape across regions, reducing disparities and promoting overall national prosperity. However, it is also important to mention the impact of exogenous events on long-term disparities. In fact, more recently, the 2007–2009 economic crisis had a profound impact on Italy, exacerbating regional disparities and influencing interregional mobility patterns. The crisis led to a sharp contraction in economic activity, with significant declines in industrial production, exports, and employment. The northern regions, being more industrialized and integrated into the European market, experienced severe but relatively shorter economic contractions. In contrast, the southern regions faced prolonged economic difficulties due to their reliance on less dynamic sectors, such as agriculture and public sector employment. The effects of this regional economic disparity have been significant and long-lasting. For instance, poverty rates in the south have consistently been higher than in the north, and the region has experienced higher levels of emigration as a result (Boschini et al. 2007). Additionally, the south has suffered from high levels of unemployment, particularly among young people, and a lack of investment in key sectors such as education and infrastructure (Lisciandra et al. 2022). Figures 2–4illustrate levels of inequality, measured by the Gini Index, Relative Poverty, and Income Ratio, across 20 Italian regions during the observed years. Economies 2024, 12, x FOR PEER REVIEW 4 of 22 that the creative economy is tightly associated with sustainable development and Sustainable Economic Goals (SDGs). Their study focuses on the region of Attica, illustrating how smart specialization strategies and the Entrepreneurial Discovery Process (EDP) methodology can foster sustainable regional growth. The deployment of a robust innovation ecosystem requires engaging and mobilizing regional actors, identifying their needs and priorities, and fostering long-term institutional learning and policy co-design. These findings underscore the critical role of both education and innovation in regional development and the need for policies that support creative and knowledge-based industries to drive economic growth and sustainability (Manioudis and Angelakis 2023). The existing literature reveals that education, innovation, and physical geography are fundamental in understanding and addressing regional economic disparities. Policies aimed at enhancing educational opportunities, fostering innovation, and addressing geographical challenges are essential for promoting balanced regional development and transitioning to sustainable economic growth. The integration of these elements can help create a more cohesive economic landscape across regions, reducing disparities and promoting overall national prosperity. However, it is also important to mention the impact of exogenous events on long-term disparities. In fact, more recently, the 2007–2009 economic crisis had a profound impact on Italy, exacerbating regional disparities and influencing interregional mobility patterns. The crisis led to a sharp contraction in economic activity, with significant declines in industrial production, exports, and employment. The northern regions, being more industrialized and integrated into the European market, experienced severe but relatively shorter economic contractions. In contrast, the southern regions faced prolonged economic difficulties due to their reliance on less dynamic sectors, such as agriculture and public sector employment. The effects of this regional economic disparity have been significant and long-lasting. For instance, poverty rates in the south have consistently been higher than in the north, and the region has experienced higher levels of emigration as a result (Boschini et al. 2007). Additionally, the south has suffered from high levels of unemployment, particularly among young people, and a lack of investment in key sectors such as education and infrastructure (Lisciandra et al. 2022). Figures 2–4 illustrate levels of inequality, measured by the Gini Index, Relative Poverty, and Income Ratio, across 20 Italian regions during the observed years. Figure 2. Inequality (Gini Index)—Italian regions. Figure 2. Inequality (Gini Index)—Italian regions. Economies 2024,12, 317 5 of 21 Economies 2024, 12, x FOR PEER REVIEW 5 of 22 Figure 3. Inequality (Relative Poverty)—Italian regions. Figure 4. Inequality (Income Ratio)—Italian regions. The economic crisis of 2007–2008 exacerbated these regional disparities, particularly in terms of employment and income. During this period, the north fared better than the south in terms of job creation and economic growth, which led to a significant increase in interregional migration from the south to the north (Odoardi and Muratore 2019). This crisis highlighted the urgency of addressing the underlying economic disparities between the two regions, as the consequences of this inequality continue to impact the country’s economic and social development. 2.2. Gravity Models in Regional Economics As mentioned earlier in the paper, this study is conducted at the regional level. Gravity models have been widely used in regional economics to explain patterns of migration and trade flows among regions. In the context of migration, gravity models have been used to understand the determinants of interregional migration flows. The basic intuition behind gravity models is that the magnitude of flows between two regions is proportional to the size of their respective populations and inversely proportional to the distance between them. This is similar to the law of gravitation, which posits that the force between two objects is proportional to their masses and inversely proportional to the distance between them (Ashby 2007). Figure 3. Inequality (Relative Poverty)—Italian regions. Economies 2024, 12, x FOR PEER REVIEW 5 of 22 Figure 3. Inequality (Relative Poverty)—Italian regions. Figure 4. Inequality (Income Ratio)—Italian regions. The economic crisis of 2007–2008 exacerbated these regional disparities, particularly in terms of employment and income. During this period, the north fared better than the south in terms of job creation and economic growth, which led to a significant increase in interregional migration from the south to the north (Odoardi and Muratore 2019). This crisis highlighted the urgency of addressing the underlying economic disparities between the two regions, as the consequences of this inequality continue to impact the country’s economic and social development. 2.2. Gravity Models in Regional Economics As mentioned earlier in the paper, this study is conducted at the regional level. Gravity models have been widely used in regional economics to explain patterns of migration and trade flows among regions. In the context of migration, gravity models have been used to understand the determinants of interregional migration flows. The basic intuition behind gravity models is that the magnitude of flows between two regions is proportional to the size of their respective populations and inversely proportional to the distance between them. This is similar to the law of gravitation, which posits that the force between two objects is proportional to their masses and inversely proportional to the distance between them (Ashby 2007). Figure 4. Inequality (Income Ratio)—Italian regions. The economic crisis of 2007–2008 exacerbated these regional disparities, particularly in terms of employment and income. During this period, the north fared better than the south in terms of job creation and economic growth, which led to a significant increase in interregional migration from the south to the north (Odoardi and Muratore 2019). This crisis highlighted the urgency of addressing the underlying economic disparities between the two regions, as the consequences of this inequality continue to impact the country’s economic and social development. 2.2. Gravity Models in Regional Economics As mentioned earlier in the paper, this study is conducted at the regional level. Gravity models have been widely used in regional economics to explain patterns of migration and trade flows among regions. In the context of migration, gravity models have been used to understand the determinants of interregional migration flows. The basic intuition behind gravity models is that the magnitude of flows between two regions is proportional to the size of their respective populations and inversely proportional to the distance between them. This is similar to the law of gravitation, which posits that the force between two objects is proportional to their masses and inversely proportional to the distance between them (Ashby 2007). Economies 2024,12, 317 6 of 21 Gravity models have been applied in a wide range of contexts, from international migration to interregional migration within countries. In the context of Italy, gravity models have been used to study interregional migration flows. For example, Bonifazi et al. (2017) used a gravity model to analyze interregional migration flows in Italy during the period of 2000–2005. They found that economic factors such as per capita GDP and unemployment rates, as well as demographic factors such as population size and the age structure, were significant determinants of interregional migration flows. In recent years, researchers have also used gravity models to study the impact of inequality on migration flows. In the context of Italy, for instance, Piras (2020) used a gravity model to study the impact of income inequality on interregional migration flows. He found that higher levels of income inequality correlated with higher levels of out-migration from the more unequal regions to the more equal ones. The choice of variables and the dyadic model used in this study is informed by the previous literature on interregional migration flows in Italy. The use of bilateral gross migration flows as the dependent variable captures the interdependence between regions in terms of migration flows. Moreover, the use of three different measures of inequality, namely the Gini Index, Relative Poverty, and Income Ratio, allows for a more comprehensive understanding of the impact of inequality on migration flows. The specific measure of income inequality, “Relative Poverty”, deserves particular attention to explain what it describes and why it is relevant in our paper. In our study, Relative Poverty is defined as the condition in which individuals or groups within a society experience a standard of living that is significantly lower than the average or median standard of living in that society. This measure captures the extent to which individuals are deprived of the resources and opportunities that are available to the majority of the population. Relative Poverty is typically assessed by comparing household incomes to a specific threshold, usually set at a certain percentage of the median income. For instance, a common threshold is 50% or 60% of the median household income. Households with incomes below this threshold are considered to be in Relative Poverty, as they have significantly fewer financial resources compared to the average household. This measure is crucial in understanding economic inequality and social exclusion, as it highlights the disparities in living standards and the extent to which certain populations are marginalized within a society. Unlike absolute poverty, which is concerned with the minimum level of resources necessary for physical survival, Relative Poverty emphasizes social participation and the ability to maintain a decent standard of living relative to the broader community. By incorporating the measure of Relative Poverty, our study aims to provide a nuanced understanding of economic disparities and their impact on interregional mobility in Italy. As mentioned above, this measure complements the Gini Index and Income Ratio, offering a comprehensive view of how income inequality influences migration patterns and regional development (Table 1). Table 1. List of inequality measures. Variable Description Gini Index Statistical measure of income distribution. The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality. Relative Poverty Index Statistical measure describing economic struggle to use goods and services in specific geographic areas, in relation to the average economic level of the same geographic areas. Top 20% Income/Bottom 20% Income Index describing the amount of people in the top 20% of the income level scale in a specific geographic area, compared to the amount of people in the bottom 20% of the income level scale, in the same geographic area. Economies 2024,12, 317 7 of 21 2.3. Effects of Income Inequality on Interregional Mobility The relationship between income inequality and interregional mobility has been a topic of interest for many scholars in recent years. A number of studies have found that high levels of income inequality can lead to lower levels of interregional mobility, as individuals may be more hesitant to leave their current region if they feel they have fewer opportunities to succeed in a new location (Jargowsky 2015;Bailey et al. 2017). In addition, regions with high levels of inequality may also experience higher levels of social tension, complicating individuals’ adjustment to a new environment (Glaeser et al. 2009). On the other hand, some research suggests that high levels of inequality may actually increase interregional mobility, as individuals may be more motivated to leave their current region in search of better opportunities elsewhere (Borjas 1995). Monras (2018) investigates how economic shocks, such as changes in local labor market conditions, influence internal migration within the United States. The paper finds that regions experiencing negative economic shocks see out-migration, while regions with positive economic shocks attract migrants. According to the author, this migration helps to equilibrate regional economic disparities. Similarly, Molloy et al. (2017), examine the decline in long-distance migration in the U.S. and its relationship to job changes. While the focus is on the overall decline, the authors also discuss how regional economic differences continue to drive migration. They argue that economic inequality across regions remains a significant factor in individuals’ decisions to relocate for better job opportunities. With a focus on Europe, Coulter and Scott (2015) analyze self-reported reasons for residential mobility in the UK, finding that economic reasons, such as seeking better employment opportunities, are among the primary drivers. This supports the idea that economic disparities between regions motivate people to move. However, we would like to point out that these findings are not consistent across all studies and may depend on the specific context and measures used to capture inequality (Bailey et al. 2017). One way in which income inequality can affect interregional mobility is through its impact on educational opportunities. For example, regions with high levels of inequality may have fewer resources available for public education, leading to lower educational attainment among the population (Reardon and Bischoff 2011). This, in turn, can limit the job prospects available to individuals in that region, making it more difficult for them to move to a new area with better economic opportunities (Bailey et al. 2017). On the other hand, limited educational opportunities at origin can stimulate out-migration of the lower-skilled segments in the society, where migration to countries or regions with higher uneducated wages is often considered as a substitute for education (Azarnert 2012). Furthermore, high levels of inequality may also reduce social capital, hindering an individual’s ability to make social connections and find job opportunities in new regions (Putnam 2000). This lack of social capital can be particularly problematic for individuals who are already facing social and economic barriers, such as those living in poverty or belonging to marginalized groups (Bailey et al. 2017). In summary, the relationship between income inequality and interregional mobility is complex and multifaceted, and is influenced by a variety of economic, social, and cultural factors. While some studies have found a negative relationship between the two variables, others suggest that the relationship may be more nuanced and context-dependent. Regardless, it is clear that addressing issues of income inequality is crucial for promoting greater interregional mobility and reducing economic disparities across regions. 2.4. The Economic Crisis of 2008 as a Trigger for Interregional Immigration The economic crisis of 2008 had a profound impact on the Italian economy and society, significantly influencing interregional migration patterns. The crisis triggered a sharp increase in unemployment rates and a decrease in GDP, which in turn led to a reduction in the standard of living for many households. As a result, the crisis had a profound effect on the migration behavior of individuals and families across Italy. Economies 2024,12, 317 8 of 21 Research shows that the economic crisis had disparate impacts on different regions of Italy, with the south of the country suffering more severely than the north. For example, a study by Accetturo et al. (2014) found that the crisis led to a decline in employment rates in the south that was twice as severe as that experienced in the north. This disparity in economic outcomes between regions may have contributed to the patterns of interregional migration that emerged in the aftermath of the crisis. In particular, research suggests that the crisis led to an increase in migration from the south to the north of Italy. For example, Gagliardi and Percoco (2011) found that the Great Recession led to a significant increase in migration flows from southern regions to the northern regions of the country. Similarly, a study by Bonifazi et al. (2017) found that the crisis led to an increase in migration from the south to the north, as individuals sought to find work in regions with better employment prospects. The economic crisis may have also led to changes in the characteristics of migrants, with a greater proportion of highly skilled individuals migrating in search of better job opportunities (Cannari et al. 2000). A study by Ceriani and Verme (2012) found that during the crisis period, the probability of highly educated individuals migrating from the south to the north of Italy increased significantly. Overall, the 2008 economic crisis significantly influenced patterns of interregional migration in Italy, with a marked increase in migration from the south to the north of the country. These changes in migration behavior may have been influenced by the differential impact of the crisis on different regions and the changing demographic of migrants. 3. Methodology To investigate the effects of past migration flows and income inequality on interregional mobility in Italy, we applied a gravity model with bidirectional dyads and three different measures of inequality: Gini Index, Relative Poverty, and Income Ratio. The gravity model is a widely used method for studying bilateral migration flows between regions or countries. It is based on the analogy of gravitational attraction between two objects, with the attractiveness of one region for migrants being proportional to its size and inversely proportional to the distance from the origin (Greenwood 1975;Borjas 1989;Ashby 2007). The basic equation of the gravity model is Mij =GPβ1 iPβ2 j Dα ij (1) In the equation, M ij describes gross migration from State ito State j. G is a constant, while P i and P j represent populations in State iand State j. D ij represents the distance between two states. β1,β2, and αare corresponding coefficients. By taking logs on both sides of the equation, a reduced-form model is created to analyze migratory behavior empirically: mij =α0+α1pi+α2pj+α3yi+α4yj+α5dij +z(·)+εij (2) The lower-case variables in Equation (2) represent the log form of the coinciding upper-case variables in Equation (1); y i and y j represent income for both states; and d ij denotes the travel costs between states. Since this is difficult to measure, we use the distance between regions’ capitals as a proxy variable (Borjas 1987). The variable z( · ) includes the attributes of the origin and destination states, and e ij serves as the conventional error term. Some of these attributes may include economic conditions (Borjas 1989) or measurements of political and/or civil freedoms (Gastil 1990). To satisfy the purpose of the study, the gravity model used in this paper is an extended form of the one reported above. Specifically, to incorporate measures of inequality into the gravity model, we estimate three different regression models, one for each measure of Economies 2024,12, 317 15 of 21 Table 5. Effect of inequality on interregional mobility (2SLS model). Independent Variables Migration Flows Migration Flows Migration Flows Lagged Migration Flows 0.967 *** (254.69) 0.967 *** (257.09) 0.968 *** (257.80) Lagged Gini Origin 10,805.9 *** (2.94) Lagged Gini Destination 166.5 (0.17) Lagged Relative Poverty Origin 19.93 *** (3.48) Lagged Relative Poverty Destination −5.501 (−1.45) Lagged Income Ratio Origin −190.9 *** (−4.50) Lagged Income Ratio Destination 107.6 *** (4.08) Distance −1.995 *** −2.099 *** −2.132 *** (−12.41) (−13.60) (−13.59) Distance20.001 *** 0.001 *** 0.001 *** (12.29) (13.74) (13.77) Lagged Unemployed Origin −8.538 0.105 −12.76 (−0.74) (0.01) (−1.68) Lagged Unemployed Destination −26.98 *** −21.46 *** −8.056 (−5.15) (−3.52) (−1.33) Lagged Population Origin 0.000 *** 0.000 *** 0.000 *** (15.08) (29.76) (29.08) Lagged Population Destination 0.000 *** 0.000 *** 0.000 *** (34.31) (35.40) (33.53) Lagged Education Level Origin 21,016.1 28,922.1 ** −1478.939 (1.84) (2.48) (−0.14) Lagged Education Level Destination 61,253.3 *** 56,741.5 *** 70,417.2 *** (5.44) (5.05) (6.29) Lagged Crime Origin 14.47 *** 19.92 *** 20.30 *** (4.25) (5.99) (6.48) Lagged Crime Destination −4.000 −5.005 −5.656 * (−1.24) (−1.62) (−1.82) Constant −3181.9 *** −571.0 *** −277.4 * (−3.73) (−5.13) (−1.92) Region Fixed Effects Yes Yes Yes Period Fixed Effects Yes Yes Yes R-squared 0.709 0.726 0.722 Observations 3038 3038 3038 Underidentification Test 140.258 1284.983 1097.769 KP-F Statistics 18.500 17.353 14.228 Weak Identification Test 72.850 1108.580 808.171 Sargan Statistics 0.517 0.817 0.162 Chi-sq(1) p-value 0.419 0.366 0.687 Number of Regions 20 20 20 tstatistics in parentheses; * p< 0.1, ** p< 0.05, *** p< 0.01. 5. Conclusions This study analyzes the effect of regional inequality, together with economic, social, and demographic factors, on mobility at the regional level in Italy. The primary hypothesis is that high levels of inequality increase bilateral migration flows within the country. This analysis constitutes a first and explorative attempt to consider inequality as a significant driver of interregional mobility in Italy. Below, we focus on discussing in depth theoretical and policy implications. Economies 2024,12, 317 16 of 21 5.1. Theoretical Implications Our findings reveal that income inequality at the origin significantly influences interregional mobility. The positive effect of income inequality suggests that the perception of the existing gap between the rich and poor drives mobility at the regional level. This indicates that wealth redistribution policies can shape the social and economic landscape of different regions, potentially exacerbating or reducing existing levels of inequality (Zhang et al. 2019). Future research could benefit from analyzing inequality from a different perspective, such as examining the income levels of the first and ninth deciles of the population to understand their impact on regional migration patterns. Unemployment levels also provide valuable insights into migration trends. High unemployment at the origin correlates with reduced migration, aligning with the literature that emphasizes the role of conditional cash transfers (Angelucci 2012), social networks (Epstein and Gang 2006), and remittances (García 2018) in mobility decisions. Additionally, distance plays a crucial role in migration decisions, with individuals preferring to migrate to nearby regions, influenced by personal, climatic, and environmental factors (Etzo 2011; Biagi et al. 2011). The presence of personal networks is another significant factor influencing migration, as they assist migrants in adjusting to new environments and provide essential information on jobs and housing (Piras 2020). This paper focuses on the period following the 2007– 2009 financial crisis, a unique context that may have influenced our results. To isolate the crisis’s effects, we included time and region-to-region fixed effects in our model. We have used standard errors clustered at the regional level to account for potential intraregional correlation and to provide robust inference. Future research could compare data from before and after the crisis to better understand its impact on interregional migration trends. 5.2. Policy Implications The study highlights important social and political implications of migration from poorer to wealthier regions. The results suggest the need for significant investments in regions with high inequality and out-migration to enhance wealth redistribution, reduce the gap between the rich and the poor, and improve living conditions in areas still affected by the Great Recession. Reducing vertical inequality within regions could indirectly reduce horizontal inequality among regions by fostering brain retention, thus promoting more uniform development across the country. Based on our findings, we recommend the following policy measures: 1. Economic Development Programs Implement economic development initiatives focused on southern regions to create job opportunities and stimulate local economies. This includes supporting local businesses, promoting entrepreneurship, and attracting investment to underdeveloped areas. 2. Investment in Education and Infrastructure Increase investment in education and infrastructure in less developed regions to improve human capital and connectivity. Enhancing educational facilities and access, particularly in the south, can reduce regional disparities and foster inclusive growth. Improved infrastructure can facilitate better integration with national and European markets. 3. Social Cohesion Programs Develop programs that foster social cohesion and reduce mistrust and anxiety caused by high inequality. Social cohesion initiatives can include community-building activities, support for local cultural projects, and policies aimed at reducing social exclusion. 4. Incentives for High-Skilled Workers Provide incentives for high-skilled workers to remain in or move to southern regions, thereby reducing the skill drain to the north. This can be achieved through tax incentives, grants, and support for innovation and research activities in the south. Economies 2024,12, 317 17 of 21 5. Combating Organized Crime Strengthen legal and institutional frameworks to combat organized crime in the south. A stable and secure environment is essential for attracting investment and fostering economic development. 6. Supporting Innovation and Creative Economies Encourage innovation and support creative industries to drive sustainable regional development. Implementing smart specialization strategies and the Entrepreneurial Discovery Process can help regions identify and leverage their unique strengths, promoting long-term growth and resilience (Manioudis and Angelakis 2023). 7. Universal Basic Income (UBI) Introduce a Universal Basic Income (UBI) to provide financial stability to all citizens, particularly benefiting the medium and lower classes given their higher proportional propensity to consume. This policy would help retain talent in disadvantaged regions by ensuring a basic level of economic security, reducing both vertical and horizontal inequalities. UBI can stimulate local economies through increased consumer spending and reduce the economic disparities that drive migration. Evidence suggests that UBI can improve social cohesion, reduce poverty, and foster a more inclusive economy (Widerquist 2018;Standing 2017). By addressing these areas, policymakers can mitigate the disparities that drive interregional migration and promote balanced economic development across Italy. To our knowledge, this paper represents a pioneering effort to analyze the impact of inequality on mobility at the subnational level in Italy, using both gravity and instrumental variable approaches. Author Contributions: Conceptualization, G.D.P.; methodology, G.D.P.; software, G.D.P.; validation, G.D.P. and E.P.; formal analysis, G.D.P.; investigation, G.D.P.; resources, E.P.; data curation, E.P.; writing—original draft preparation, G.D.P.; writing—review and editing, G.D.P. and E.P.; visualization, G.D.P.; supervision, G.D.P.; project administration, G.D.P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Effect of Inequality on Internal Mobility (2SLS Model, with two time-lags). Independent Variables Migration Flows Migration Flows Migration Flows Two Lags Migration Flows 0.967 *** (254.7) 0.967 *** (257.1) 0.968 *** (257.8) Two Lags Gini Origin 6108.8 ** (2.16) Two Lags Gini Destination 819.1 (0.86) Two Lags Relative Poverty Origin 16.29 *** (3.12) Two Lags Relative Poverty Destination −8.123 ** (−2.18) Economies 2024,12, 317 18 of 21 Table A1. Cont. Independent Variables Migration Flows Migration Flows Migration Flows Two Lags Income Ratio Origin −160.2 *** (−4.09) Two Lags Income Ratio Destination 116.0 *** (4.54) Distance −2.048 *** −2.112 *** −2.114 *** (−13.16) (−13.73) (−13.53) Distance20.001 *** 0.001 *** 0.001 *** (12.97) (13.73) (13.63) Two Lags Unemployed Origin 7.941 2.809 −8.021 (0.87) (0.37) (−0.99) Two Lags Unemployed Destination −31.28 *** −18.63 *** −6.100 (−6.01) (−3.01) (−0.97) Two Lags Population Origin 0.000 *** 0.000 *** 0.000 *** (19.85) (28.71) (28.30) Two Lags Population Destination 0.000 *** 0.000 *** 0.000 *** (35.26) (35.15) (33.81) Two Lags Education Level Origin 12,982.7 19,825.5 * −121.9 (1.24) (2.48) (−0.02) Two Lags Education Level Destination 52,869.4 *** 46,846.7 *** 62,232.9 *** (5.05) (4.43) (5.86) Two Lags Crime Origin 16.71 *** 19.41 *** 19.71 *** (5.05) (6.40) (6.46) Two Lags Crime Destination −5.948 * −6.703 ** −6.952 ** (−1.87) (−1.62) (−1.82) Constant −2137.4 *** −471.0 *** −308.7 ** (−3.26) (−4.43) (−2.14) Region Fixed Effects Yes Yes Yes Period Fixed Effects Yes Yes Yes R-squared 0.721 0.727 0.724 Observations 3038 3038 3038 Underidentification test 230.552 1450.932 1201.297 Chi-sq(2) p-value 0.000 0.000 0.000 Weak identification test 124.183 1385.212 990.623 Sargan statistics 0.014 0.821 0.063 Chi-sq(1) p-value 0.907 0.365 0.805 Number of regions 20 20 20 tstatistics in parentheses; * p< 0.1, ** p< 0.05, *** p< 0.01. Table A2. Effect of Inequality on Internal Mobility (2SLS Model, with five time-lags). Independent Variables Migration Flows Migration Flows Migration Flows Five Lags Migration Flows 0.967 *** (254.7) 0.967 *** (257.1) 0.968 *** (257.8) Five Lags Gini Origin 20,469.2 ** (2.13) Five Lags Gini Destination 2103.4 (1.37) Five Lags Relative Poverty Origin 18.85 ** (2.30) Five Lags Relative Poverty Destination −1.850 (−0.39) Five Lags Income Ratio Origin −234.2 *** (−2.64) Five Lags Income Ratio Destination 87.80 ** (2.56) Economies 2024,12, 317 19 of 21 Table A2. Cont. Independent Variables Migration Flows Migration Flows Migration Flows Distance −1.872 *** −2.134 *** −2.168 *** (−7.72) (−10.73) (−10.76) Distance20.001 *** 0.001 *** 0.001 *** (7.71) (10.81) (10.87) Five Lags Unemployed Origin −61.35 −6.199 −38.01 (−1.52) (−0.44) (−1.61) Five Lags Unemployed Destination −52.57 *** −39.13 *** −17.71 * (−6.08) (−4.16) (−1.66) Five Lags Population Origin 0.000 *** 0.000 *** 0.000 *** (7.99) (19.33) (18.97) Five Lags Population Destination 0.000 *** 0.000 *** 0.000 *** (25.12) (26.03) (26.31) Five Lags Education Level Origin 18,517.8 24,914.3 * −3868.6 (1.39) (1.92) (−0.25) Five Lags Education Level Destination 47,312.3 *** 44,995.7 *** 56,598.1 *** (3.57) (3.56) (4.36) Five Lags Crime Origin 16.13 *** 22.38 *** 26.77 *** (2.83) (5.69) (6.76) Five Lags Crime Destination 1.437 1.336 −0.471 (0.34) (0.34) (−0.12) Constant 5790.1 ** −481.7 *** 65.72 (−2.43) (−3.71) (0.19) Region Fixed Effects Yes Yes Yes Period Fixed Effects Yes Yes Yes R-squared 0.692 0.729 0.723 Observations 3038 3038 3038 Underidentification test 35.332 649.978 292.874 Chi-sq(2) p-value 0.000 0.000 0.000 Weak identification test 17.868 491.133 171.958 Sargan statistics 0.001 0.450 0.383 Chi-sq(1) p-value 0.976 0.502 0.536 Number of regions 20 20 20 tstatistics in parentheses; * p< 0.1, ** p< 0.05, *** p< 0.01. 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