Cooperative banking and local sustainable development in Italy

Cooperative banking and local sustainable development in Italy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cooperative banking and local sustainable development in Italy Giuseppe Terzo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6264324/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 paper empirically investigates the relationship between cooperative banking and local sustainable development through a panel analysis of 103 Italian provinces over the period 2005–2021. Results show that cooperative credit banks exert a positive impact on local sustainable development, although this effect manifests heterogeneously across different dimensions that characterize it. The analysis also reveals a heterogeneity in CCBs’ impact across Italian macro-regions, suggesting that the sociocultural and institutional context might play a crucial role in shaping the relationship between cooperative banking and sustainable development. These findings provide valuable insights for developing effective policy strategies for boosting local sustainability. JEL classifications: G21, Q01, R11. Cooperative banking sustainable development Italy panel data sustainable finance Figures Figure 1 Figure 2 Figure 3 1. Introduction Banking institutions play a crucial role in fostering the growth of the real economy, primarily by channelling savings into productive investment opportunities (Beck & Levine, 2004 ). In performing this intermediation function, they contribute to improving the allocative efficiency of resources, promoting economic growth, and facilitating structural transformation processes that enable regions to embark on virtuous paths of economic development (King & Levine, 1993 ; Rousseau and Wachtel, 1998 ). The banking sector is undergoing profound transformations, driven primarily by advancements in information technology, which may have diminished some of the traditional advantages associated with relationship lending — particularly characteristic of local banks. According to Wheelock and Wilson ( 2011 ), digitalization and the automation of credit evaluation processes have enhanced the ability of large banks to compete in markets traditionally dominated by local banks. Despite these changes, local banks remain key players in financing small and medium-sized enterprises (SMEs), leveraging geographical proximity and personal relationships to gather detailed information on their clients’ creditworthiness (Berger et al., 2005 ; Brevoort & Hannan, 2006 ; Ho & Ishii, 2011 ). This relational model is particularly relevant in contexts characterized by information opacity and limited access to advanced technologies, where SMEs often face challenges in accessing the services provided by larger banks (Cosci et al., 2023 ). The importance of local banks also lies in their countercyclical role, which is particularly relevant in this era of polycrisis, characterized by a succession of external shocks. Indeed, they can help stabilize credit supply during periods of economic downturns, due to their greater flexibility in client relationships and their ability to tailor loan conditions to local needs (Manitiu & Pedrini, 2017 ). However, prolonged crises may erode this adaptive capacity, limiting their effectiveness in sustaining credit supply (Migliorelli, 2018 ). In light of these premises, this paper aims to investigate the impact of cooperative credit banks (CCBs) on local sustainable development in Italy. I focus on CCBs because they represent small-sized organizations generally considered for proxying the local banking sector (Caporale et al., 2016 ). The impact of the banking sector on sustainable development is an issue of significant relevance for the empirical research. Due to their critical role as financial intermediaries, banks not only facilitate the efficient allocation of resources but might also play a pivotal role in promoting equity and sustainability (Aracil et al., 2021 ). This dual contribution positions the banking sector at the core of strategies to foster inclusive and sustainable economic development. In this context, CCBs emerge as pivotal actors since their business model incorporates the ESG (Environmental, Social, Governance) principles, translating sustainability commitments into concrete lending and investment practices at the local level. For this reason, my focus is on this specific institutional group of banks. Several theoretical and empirical studies have highlighted how cooperative banks (CBs) can promote: (a) local economic growth (e.g., Ayadi et al., 2010 ; Coccorese & Shaffer, 2021 ; Usai & Vannini, 2005 ); (b) business creation (e.g., Agostino et al., 2022 ; Errico et al., 2024 ); (c) innovation (e.g., Barra & Ruggiero, 2022 ; Cosci et al., 2023 ); (d) reduced economic and social inequalities (e.g.,; Angelini et al., 1998 ; Arestis & Phelps, 2023 ; Barra et al., 2024; Berger et al., 2008 ; D’Onofrio et al., 2019 ; Lal, 2018 ; Minetti et al., 2021 ); and (e) environmental sustainability (e.g., Bevilacqua, 2022 ; Caselli, 2022 ). The innovative contribution of this study lies in its attempt to integrate a diverse body of literature into a unified conceptual framework, which is empirically tested through an in-depth econometric analysis. Empirically examining how cooperative banking contributes to balancing efficiency, equity, and environmental sustainability is essential, as these represent the three fundamental pillars of inclusive and sustainable regional development. The empirical strategy to test the proposed conceptual framework consists of two main steps. First, I develop a composite sustainable development index that integrates three fundamental dimensions: economic dynamism, social welfare, and environmental sustainability. This multidimensional index is constructed by aggregating a set of elementary indicators representative of each dimension. Second, I perform an econometric analysis leveraging a panel dataset of 103 Italian provinces (NUTS-3), considering the time frame 2005–2021, where the dependent variable is the composite index of local sustainable development and the key explanatory variable is the territorial presence of CCBs, measured through the density of their branches at the provincial level. The Italian case presents a compelling context for investigating the relationship between cooperative banking and sustainable development. Italy has an essential cooperative tradition, which historically plays a pivotal role in supporting local economies, fostering social cohesion, and addressing inequalities. Moreover, the country’s economic structure, characterized by a significant presence of SMEs and a pronounced regional diversity, provides a fertile ground for assessing how cooperative banking practices can drive sustainable development by balancing efficiency, equity, and environmental quality. This unique combination of factors makes the Italian case an ideal setting for such an investigation. The results of the econometric analysis can be summarized as follows. Overall, CCBs positively impact local sustainable development, with results robust to several issues and scenarios. However, disaggregating the sustainable development index, it emerges that CCBs make a significant positive contribution to social welfare and environmental sustainability, while their impact on economic dynamism is not clear. Furthermore, when examining geographical heterogeneity by splitting the sample between macro-regions, the results reveal a stark contrast: CCBs positively affect sustainable development in the Centre-North, while their impact is negative in the South. These findings highlight how CCBs, due to their distinctive features, can significantly contribute to promoting sustainable development, primarily through their ability to generate social and environmental value in local communities. The negative effect observed on the economic dynamism dimension suggests the importance of a pluralistic banking system. The complementarity between different banking models emerges as a key factor: while CCBs excel in generating social and environmental value, traditional commercial banks might be more effective in sustaining economic efficiency and innovation. Hence, a balanced mix of financial institutions may be optimal for an integrated sustainable development. Finally, the heterogeneity of effects across macro-regions reveals how deep-rooted regional factors that determine the institutional quality of regions could shape the relationship between cooperative banking and sustainable development. The remainder of the paper is structured as follows. The next section presents the conceptual framework. Section 3 describes the dataset and the empirical strategy. Section 4 reports the results, while Section 5 offers some reflections on the findings. Finally, Section 6 concludes. 2. Related literature 2.1 The specificities of cooperative banking Cooperative banks (CBs), which originated in Europe in the 19th century, were specifically designed to support small businesses. Like other banks, they contribute to economic performance by transforming savings into long-term loans, assessing borrowers’ creditworthiness to mitigate default risks and finance viable projects, and fostering the efficient allocation of resources among competing uses (Coccorese & Shaffer, 2021 ). 1 The specificity of CBs lies primarily in their governance structure, which results in a banking model that is deeply rooted in local communities and responsive to their social needs. Building on the work of Minetti et al. ( 2021 ), the following characteristics of the CB model can be identified. First, ownership is non-transferable, limited to individual shares, and redeemable only at par value. In addition, the close link to local communities ensures that CB clients are also members. Second, in terms of governance, the “one member, one vote” rule characteristic of all cooperative organizations applies. Thus, voting power is not determined by the amount of capital held. Finally, as Hesse and Cihák (2007) also argue, the primary objective of CBs is to maximize social value rather than profits. Regarding the latter aspect, Christensen et al. ( 2004 ) and Ayadi and Schmidt ( 2009 ) argue that CBs can be considered institutions with a dual objective: they aim to generate profits to survive and expand, but without profit being the sole or primary ultimate goal (Becchetti et al., 2016 ). As Ayadi et al. ( 2010 ) argue, by lending locally, CBs prevent the savings of a local community from being withdrawn or transferred to other markets, thereby mitigating capital drain. This approach is closely related to relationship banking, a model in which banks focus on fostering deep, long-term relationships with their customers, reducing the risks of adverse selection and moral hazard (Aristei & Gallo, 2019 ; Berger & Udell, 2006 ; Coccorese et al., 2016 ; Ferri et al., 2019 ). These lending relationships enable banks to acquire “soft information” about customers, which can play a key role in mitigating financial exclusion. They allow the bank to gain insights into details that are not easily quantifiable, such as specific behaviours or the reputation of a customer. This information is invaluable for assessing the creditworthiness of borrowers who might otherwise be excluded from the traditional financial system, where credit decisions are primarily based on standardized quantitative data, such as credit scoring (Berger & Udell, 2006 ; Petersen & Rajan, 1994 ). Larger banks tend to have a complex and centralized organizational structure, with decisions often made at the central level. Gathering “soft information” requires direct, personal knowledge of the customer, which is more easily obtained by local banks that are physically close to borrowers and have a direct relationship with them (Agarwal & Hauswald, 2010 ). Larger banks, on the other hand, may find it difficult to gather this information due to geographical distance (Alessandrini et al., 2008). Furthermore, CBs leverage social capital and group interactions to create a system where shared responsibility and mutual oversight enhance credit quality, increase access to financing for the most vulnerable members of the community, and reduce insolvency risks (Angelini et al., 1998 ; Banerjee et al., 1994 ). Several studies (e.g., Berger & Black, 2011 ; DeYoung et al., 2011 ; Petersen & Rajan, 2002 ) argue that the advent of new information technologies has improved access to and the quality of information about borrowers, reducing the importance of soft information and geographic proximity in the credit process. In this context, new technologies would reduce the informational advantage of the relationship lending model, allowing larger banks to compete even without close local ties. However, some studies show that geographical proximity remains a key factor in the credit process, as SMEs are still predominantly financed locally (e.g., Brevoort & Hannan, 2006 ; Ho & Ishii, 2011 ; Hakenes et al. 2015 ; Hasan et al. 2017 ; Meslier et al., 2022 ; Nguyen, 2019 ; Petach et al. 2021 ). 2.2 On the concept of sustainable development Sustainable development is a key issue of international debate about human society and its future, becoming one of the most challenging concepts ever developed to ensure a dignified life in society for everyone (Alaimo & Maggino, 2020 ). Its growing importance reflects the increasing awareness that the dominant development models should be reshaped to address present and future challenges for humanity. The scientific literature encompasses a rich spectrum of conceptualizations regarding sustainable development, reflecting its theoretical complexity and multifaceted nature. This conceptual heterogeneity has fostered the emergence of diverse research streams, spanning methodological reflections and empirical applications across multiple disciplines (Wichaisri & Sopadang, 2018 ). The conceptual foundation of sustainable development is intrinsically connected to sustainability principles, defined as a system’s inherent capacity for perpetual self-maintenance (Olawumi & Chan, 2018 ). Its emergence as a central paradigm in environmental research has fundamentally transformed development policy frameworks. Since its foundational articulation in the Brundtland Report (WCED, 1987 ) 2 , and particularly following the watershed Rio de Janeiro Earth Summit, this concept has transcended theoretical boundaries to become a cornerstone of international agreements and national legislative frameworks globally (Ruggerio, 2021 ). The concept of sustainable development has evolved, reflecting its dynamic and adaptive nature (Sachs, 2015 ). While intergenerational equity — the fundamental imperative to safeguard resources for future generations — remains a cornerstone principle, contemporary interpretations have expanded significantly beyond this foundational aspect. It has gradually acquired a multidimensional nature that reflects the intricate interdependencies between economic prosperity, social progress, and environmental safeguard (D’Adamo et al., 2024). This integrated perspective represents a fundamental shift from earlier, more compartmentalized approaches, recognizing that sustainable development challenges cannot be addressed in isolation but require coordinated interventions across multiple domains of human activity (Alaimo & Maggino, 2020 ). These three pillars of sustainable development are known as “triple bottom line”, and their balance is considered essential for generating value for profits, planet, and people (Elkington, 1997 ; cited in Olawumi & Chan, 2018 ). According to the purposes of this work, I embrace this three-dimensional conceptualization of sustainable development, incorporating economic, social, and environmental aspects as equally essential and mutually reinforcing components. This holistic framework guides the research methodology, acknowledging that sustainability manifests through the synergistic interaction of these foundational pillars. 2.3 Cooperative banking and local sustainable development There is a strong tradition of empirical studies demonstrating that the banking system plays a central role in promoting macroeconomic performance (e.g., Beck & Levine, 2004 ; Cetorelli & Gambera, 2001 ; Cosci & Mattesini, 1997 ; King & Levine, 1993 ; Levine et al., 2000 ; Rousseau & Wachtel, 1998 ; Wachtel, 2001 ). Over time, a similarly well-rounded body of literature has emerged highlighting the role of local banks — which, in the Italian context, take the form of cooperative credit banks (CCBs) — in fostering the economic growth of territories. This is attributed to their unique characteristics mentioned above, which help mitigate market failures, especially those stemming from information asymmetries, ensuring a more efficient allocation of resources and preventing capital drain. Usai and Vannini ( 2005 ) demonstrate that, between 1970 and 1993, the financial sector as a whole exerted a limited influence on Italy’s economic growth. However, a disaggregated analysis reveals that CCBs and special credit institutions positively contributed to economic growth. This suggests that the overall limited impact of the financial system can be attributed to the underperformance of other institutional banking groups. Ayadi et al. ( 2010 ) highlight that CCBs fostered economic growth in countries such as Austria, Finland, Germany, and the Netherlands from 2000 to 2008. Conversely, this positive effect was not evident in other nations, including Spain, France, and Italy. Caporale et al. (2015) find that CCBs, serving for create local banking proxies, significantly enhanced local economic growth in Italy, particularly in the Northern regions. Sfar and Ouda ( 2016 ) identify a positive correlation between the development of CCBs and regional economic growth in France from 2006 to 2012. Similarly, Bernini and Brighi ( 2018 ) demonstrate that increased credit availability from CCBs in Italy translates into higher local economic growth. More recently, Coccorese and Shaffer ( 2021 ) provided empirical evidence that CCBs exerted a stronger influence on the economic growth of Italian municipalities between 2001 and 2011 compared to conventional banks. CBs can contribute to fostering more inclusive development paths in local areas. Empirical literature demonstrates their significant role in mitigating economic and social inequalities. D’Onofrio et al. ( 2019 ) show that the development of local banking helps reduce income disparities across Italian provinces — particularly through geographic mobility and urbanization—although this effect appears to be confined to the more advanced areas of the country. Similarly, Minetti et al. ( 2021 ) find that CCBs are more effective than commercial banks in mitigating income inequality in local areas, based on an empirical analysis of Italian provinces for the period 2001–2011. The critical role of CBs in addressing these disparities is largely attributable to their ability to promote financial inclusion, which is a key driver of social welfare. As highlighted by Alvarez-Gamboa et al. (2023), there is robust evidence in the empirical literature showing that financial inclusion alleviates inequality and poverty, enhances the socioeconomic conditions of the population, guaranteeing greater investments in health and education, and improves the performance of SMEs. Moreover, CBs provide valuable support to social enterprises (Zedda et al., 2021), which, as highlighted by an emerging body of literature (e.g., Terzo, 2022 ; Terzo et al., 2023), play a pivotal role in fostering the social and economic cohesion of local communities. Finance can be central to promoting sustainability (Bevilacqua, 2024). For instance, a recent study by Schoenmaker and Schramade ( 2019 ) demonstrates how it can facilitate the transition to a circular economy by directing investments toward green firms. Financial institutions, therefore, can effectively address the trade-off between sustainability objectives. However, as argued by Pisano (2012), conventional finance tends to place marginal importance on social and environmental sustainability. Various works (e.g., Avrampou et al., 2019 ; Ziolo et al.,2021) contend that a more sustainable financial model is essential for achieving the Sustainable Development Goals (SDGs), ensuring a balance between social, environmental, and economic sustainability. In the pursuit of making finance more sustainable, CBs could assume a strategic role, as their long-standing integration of ESG criteria positions them well to seize the opportunities presented by a sustainable transformation (Bevilacqua, 2024). In light of the evidence that emerged from the brief literature review, it becomes clear that CBs can serve as key players in local sustainable development processes, as they possess the necessary attributes to promote equity, efficiency, and environmental sustainability simultaneously. This is an intriguing topic that empirical literature has not yet thoroughly explored. Therefore, in the subsequent sections of this study, I aim to empirically test this conceptual framework to provide novel insights on an issue of considerable importance, particularly from a policy-making perspective. 3. Empirical framework To empirically assess the conceptual framework of this study, I structure a panel dataset comprising 103 Italian provinces (NUTS-3 level) spanning the period from 2005 to 2021. The choice of this time frame is dictated by data availability constraints The dataset is structured using two primary sources: (a) Istat (the Italian National Institute of Statistics) and (b) the Bank of Italy. Concerning Istat data, multiple databases are considered, including (i) the Equitable and Sustainable Well-Being of Territories (Benessere Equo e Sostenibile, BEST), (ii) Statistics for Development Policies, (iii) the Territorial Statistical Atlas of Infrastructures; (iv) the Territorial Economic Accounts, and v) COEWEB—International trade statistics. A comprehensive overview of the variables included into the econometric model, along with the empirical strategy employed, is provided in the following subsections. 3.1 Dependent variable The dependent variable in the model is a composite index of sustainable development. The decision to construct a composite index is driven by the multidimensional nature of the concept of sustainable development, which, as I extensively argued above, encompasses three distinct dimensions: economic, social, and environmental. The following approach is adopted regarding the selection of elementary indicators. Drawing from various Istat databases (BEST, Statistics for Development Policies, Territorial Economic Accounts, the Territorial Statistical Atlas of Infrastructures, and COEWEB), all indicators that could effectively proxy the three dimensions of sustainable development are considered, excluding those for which a sufficiently long time series is unavailable. Additionally, further variables are not included to prevent information redundancy caused by high collinearity with other variables. As a result of this preliminary analysis, nine elementary indicators are selected, the details of which are presented in Table 1 . The economic dimension is characterized by indicators that make it possible to assess the economic dynamism of regions by measuring productivity, trade openness. and the ability to create new businesses. The social dimension, on the other hand, includes indicators that proxy the level of human development of regions, as they are representative of the material living conditions and health of the population, as well as the accumulation of human capital. Finally, the environmental dimension includes indicators that can serve as proxies for the pro-environmental behaviour of regions, which is essential for initiating virtuous processes of environmental sustainability. Table 1 Elementary indicators of local sustainable development Indicator Description Dimension Productivity Gross Value Added per worker Economic New firm formation Firm creation rate (total entries over working population) Trade openness Sum of imports and exports as a percentage of total value added Unemployment Unemployment rate Social Education Percentage of individuals aged 25 to 39 with a tertiary education degree Health Life expectancy at birth Separate waste collection Percentage of municipal waste subjected to separate collection relative to total municipal waste Environmental Urban waste Municipal waste produced per capita (in kg) Renewable energy consumptions Percentage of electricity consumed from renewable sources as a percentage of total electricity consumed. Regarding the choice of the aggregation method, I adopt the Jevons index, which is a geometric mean of ratios. The geometric mean represents an intermediate solution between compensatory and non-compensatory methods, offering many desirable properties from an axiomatic perspective (Diewert, 1995; OECD, 2008). The index, as outlined by Mazziotta and Pareto ( 2012 ), is calculated as follows: $$\:{LSDI}_{i}^{t}=\:\prod\:_{j=1}^{m}{\left(\frac{{x}_{ij}^{t}}{{x}_{rj}^{t}}\right)}^{\frac{1}{m}}$$ 1 Where \(\:{x}_{ij}^{t}\) is the value of the j-th indicator for province i at time t and \(\:{x}_{rj}^{t}\) is the reference value, namely the national average. The elementary indicators are thus transformed into index numbers, where values greater than 1 indicate performance above the national average, while values below 1 indicate performance below the average. The utility of this methodology in the context of sustainable development measurement lies in its ability to implicitly penalize units with imbalanced values across the various elementary indicators. This feature is crucial for measuring sustainable development, as it depends on the balance across its three dimensions — economic, social, and environmental. The weighting procedure consists of the adoption of an equal weighting scheme, attributing the same importance to all elementary indicators (Nardo et al., 2005). I consider this option since the better weighting scheme for structuring sustainable development indices is the stakeholders’ opinion (Alaimo & Maggino, 2020 ). Since this option is not available for this study, an equal weighting scheme is preferred. Figure 1 shows quantile maps of the Local Sustainable Development Index (LSDI) at the beginning and end of the period considered in the analysis, namely 2005 and 2021. The spatial distribution of this indicator is marked by strong polarization, emphasizing the traditional divide between the Centre-North and the South of the country. Notably, this distribution remains persistent, with no significant changes in the spatial patterns observed between 2005 and 2021. This stability can largely be attributed to the slow pace of structural transformation, driven by deep-seated historical, socio-cultural, and institutional factors that hinder the Southern regions’ ability to bridge the gap with the Centre-North. Specifically, disparities in infrastructure, human capital, and access to markets remain persistent, preventing meaningful improvements in sustainable development outcomes. 3.2 Independent variable The main independent variable is the number of CCB branches per 1,000 inhabitants (CCBs). Italian CCBs represent an important part of the national banking system. Founded in the second half of the 19th century, they have managed to adapt to social, economic, and regulatory changes over time. The 1993 Banking Law reduced the differences between CCBs and commercial banks while preserving the principles of mutualism, democracy, localism, and non-profit objectives. Initially focused on providing small loans to farmers and artisans in rural areas, CCBs are now able to compete with commercial banks on a broader scale, also thanks to second-level networks that increase their efficiency and competitiveness (Catturani & Stefani, 2016 ). I consider the CCBs’ branch density since, as argued by Minetti et al. ( 2021 ), the number of branches represents a key indicator for assessing the development of local banking, as it reflects the ability of CCBs to reach different local communities and provide them with accessible banking services. Hence, it can be considered a pivotal measure of financial development (Rossi & Scalise, 2022 ). Figure 2 shows the quantile maps of the branch density of CCBs in 2005 and 2021. In line with what Ferri and Messori ( 2000 ) pointed out, a higher density is mainly observed in areas with a prevalence of SMEs, especially in the North-East. Moreover, the geographical distribution of CCB branches seems to reflect that of bridging social capital, as it is one of the key factors driving the diffusion of CCBs in areas (Catturani et al., 2016 ). No significant changes in the spatial distribution can be observed over the period analyzed, as cooperative banking tends to be characterized by considerable stability, driven not only by economic factors but also by institutional and sociocultural traits that influence its presence in the territory. 3.3 Control variables For the selection of control variables, I draw on the extensive empirical literature on the determinants of regional and local growth and development. First, I include the number of branches per 1,000 inhabitants for banks other than CCBs ( Other_banks ) to account for the overall level of access to banking services across provinces. Including this variable allows for isolating the specific effect of CCBs from the broader impact of other financial institutions. Furthermore, this control captures potential structural differences between provinces regarding banking density, which may influence credit availability and local sustainable development. To control for the socio-demographic characteristics of the area, I include in the model the structural dependency ratio ( Dep_ratio ), which is the percentage of the population in the non-working age groups (0–14 and 65+) relative to the working-age population (15–64), and the percentage of the foreign population relative to the total resident population ( Foreign_pop ). Concerning the structural dependency ratio, I expect a negative sign, as a higher share of the inactive population can hurt productivity (Terzo et al., 2023, 2024 ). Furthermore, a lower share of the youth population may hinder sustainability, as suggested by Yamane and Kaneko (2021), who argue that young people are more likely to adopt a lifestyle that is consistent with the need to ensure sustainability compared to other generations. About the foreign population, I expect a positive sign because, according to a large body of literature (e.g. Ottaviano & Peri, 2006 ; Schuch & Wang, 2015 ), it represents a significant economic force that can stimulate innovation and promote the creation of new businesses. Moreover, there is evidence in the empirical literature showing that the foreign population tends to be more inclined towards pro-environmental behaviours (e.g., Argentiero et al., 2023 ; Hellwig et al., 2019 ; Raimo et al., 2025 ). For agglomeration economies, which are proxied by population density, the implications for sustainable development are uncertain. For example, as claimed by Peiró-Palomino et al. ( 2020 ), highly urbanized areas can facilitate the matching of labour supply and demand, improving the overall functioning of the labour market. However, they can also lead to an increase in energy consumption and the emission of pollutants that worsen environmental quality. Furthermore, urbanization can enhance the forms’ access to credit, hence favouring local entrepreneurship development (Carmignani et al., 2021 ). Finally, the sectoral composition of economic activity has uncertain implications for sustainable development. While the primary and tertiary sectors generally have lower employment multipliers than manufacturing (Elhorst, 2003 ), assessing the impact of each sector on environmental quality is complex. While the manufacturing sector is often associated with higher resource intensity and pollutant emissions, the primary and tertiary sectors can have different impacts on environmental sustainability depending on their specific activities and the level of technological innovation adopted (Martín-Ortega & González-Sánchez, 2023 ). 3.4 Empirical strategy According to the variables above described, I estimate the following equation: LSDI it = β 0 + β 1 CCBs it + β 2 Υ it + µ i + µ t + ε it (2) Where LSDI represents the Local Sustainable Development Index, CCBs denotes the number of CCBs branches per 1,000 inhabitants, Υ represents the set of control variables, µ i captures fixed effects and µ t encompasses a full set of time dummies. I estimate the model using a pooled OLS approach, employing clustered standard errors at the provincial level to address heteroscedasticity and autocorrelation. This estimation may suffer from omitted variable bias and reverse causality. To address these issues, I also estimate a Two-Stage Least Squares instrumental variables model (IV-2SLS). The variable CCBs is instrumented with the number of branches of rural banks (Casse rurali) per 1,000 inhabitants in 1936 ( Rural_banks ), a measure historically tied to the current distribution of CCBs. 3 This connection stems from institutional persistence: many rural banks gradually evolved into CCBs, reflecting the enduring nature of their cooperative principles and their deep integration within local communities. This historical continuity highlights how these institutions maintained their territorial presence over time, adapting to the needs of their communities while preserving their mission to support local economies. Consequently, this instrument is expected to satisfy the relevance criterion due to the strong geographic and institutional inertia of CCBs. The exogeneity criterion is also likely to be satisfied, as the spatial distribution of rural bank branches in 1936 reflects historical and political conditions of the time, such as the reorganization of the banking system imposed by the Fascist regime. These determinants are not directly linked to current levels of local sustainable development, as subsequent factors (e.g., economic, environmental, or technological changes) have significantly altered the context. Therefore, the effect of the historical distribution of rural bank branches on contemporary local development can reasonably be considered indirect, operating exclusively through the CCBs variable. To create an additional instrumental variable, I employ a shift-share instrument based on Bartik ( 1991 ). This variable is constructed by combining a time-invariant factor that varies across different regions with a time-varying factor that remains consistent for all provinces. The process involves the following steps: 1) I calculate the ratio of the average number of CCB branches in each province to the national average number of CCB branches. $$\:{s}_{i}=\:\frac{{av\_CCBs}_{i}}{{av\_CCBs}_{ita}}$$ 3 2) I determine the time-varying factor, which reflects the relative change in the number of BCC branches at the national level. $$\:{g}_{t}=\:\frac{{CCBs}_{t}-{CCBs}_{t-1}}{{CCBs}_{t-1}}\:$$ 4 3) Finally, the instrumental variable ( Bartik_CCBs ) is obtained by multiplying the share of each province by the annual changes in the number of CCB branches. Bartik_CCBs it = s i * g t (5) Since there is an instrumental variable measured in a single year — and therefore treated as fixed effects — it is not possible to include individual fixed effects in the model. This explains why I consider NUTS-1 macro-regional fixed effects to control for those unobserved factors that are time-invariant. Table 2 presents the main descriptive statistics for all the variables included in the model and Table 3 the correlation matrix of the explanatory variables, which also consists of the Variance Inflation Factors (VIFs), calculated without the inclusion of macro-regional and time fixed effects. By examining the VIFs and the correlation matrix, it is possible to rule out multicollinearity issues that could affect the estimates. Table 2 Description of variables and summary statistics Variable Description Source Mean S.D. Dependent variable LSDI Composite index of local sustainable development Multiple sources 0.912 0.237 Independent variable CCBs Number of CCB branches per 1,000 inhabitants Bank of Italy 0.082 0.086 Control variables Other_banks Number of other banks (different to CCBs) per 1,000 inhabitants Bank of Italy 0.166 0.116 Pop_dens Number of inhabitants per km 2 Istat 248.708 328.222 Dep_ratio Structural dependency ratio 55.291 4.412 Foreign_pop Foreign residents as a percentage of the total population Istat 7.509 9.692 Primary_emp Primary sector employment as a percentage of total employment Istat 5.327 4.165 Tertiary_emp Tertiary sector employment as a percentage of total employment Istat 69.259 6.737 Instrumental variables Rural_banks Number of rural bank branches per 1,000 inhabitants in 1936 Bank of Italy 0.034 0.060 Bartik_CCBs Shift-share instruments for CCB branches Own elaboration on Bank of Italy data 0.055 0.224 Table 3 Description of variables and summary statistics Variable VIF 1 2 3 4 5 6 7 1 CCBs 1.09 1.000 2 Other:banks 1.91 0.146 1.000 3 Pop_dens 1.51 -0.136 -0.018 1.000 4 Dep_ratio 1.26 0.064 0.255 -0.028 1.000 5 Foreign_pop 1.08 0.111 0.071 -0.029 0.254 1.000 6 Primary_emp 2.29 -0.112 -0.540 -0.446 -0.305 -0.092 1.000 7 Tertiary_emp 1.28 -0.159 -0.297 0.154 0.149 -0.017 -0.074 1.000 4. Estimation results 4.1 Main results Table 4 presents the estimation results using different model specifications. In Model 1, I estimate a pooled OLS regression that includes only the key variable of interest to capture its raw effect. In Model 2, I include a set of control variables to isolate the impact of the key variable of interest from potential confounding factors. In Model 3, I also include macro-regional dummies to account for time-invariant unobserved factors, and in Model 4, I include time dummies to control for cyclical fluctuations or common shocks that occur during the analysis period. This step-by-step approach makes it possible to assess the robustness of the results and evaluating how the estimated effect of the key variable of interest changes when introducing additional controls. Table 4 Estimation results I (Pooled OLS and IV-2SLS estimations) Dependent variable: LSDI Mod.1 OLS Mod.2 OLS Mod.3 OLS Mod.4 OLS Mod.5 IV-2SLS Mod.6 IV-2SLS Mod.7 IV-2SLS Mod.8 IV-2SLS CCBs 1.141*** 0.812*** 0.512*** 0.541*** 0.632** 0.779*** 0.647** 0.546*** (0.217) (0.199) (0.163) (0.174) (0.304) (0.288) (0.289) (0.164) Other_banks 0.143 -0.045 0.380** 0.397** 0.426** 0.401** 0.375** (0.117) (0.109) (0.172) (0.167) (0.168) (0.166) (0.182) (ln) Pop_dens -0.029 -0.055** -0.041* -0.039 -0.034 -0.038 -0.027 (0.028) (0.025) (0.024) (0.024) (0.023) (0.023) (0.022) Dep_ratio -0.000 -0.007** -0.018*** -0.017*** -0.017*** -0.017*** -0.017*** (0.002) (0.003) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign_pop 0.001* 0.001* -0.000 -0.001 -0.001 -0.001 -0.000 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Primary_emp -0.029*** -0.024*** -0.019*** -0.019*** -0.019*** -0.019*** -0.018*** (0.005) (0.005) (0.004) (0.005) (0.005) (0.005) (0.004) Tertiary_emp -0.006** -0.003 -0.002 -0.002 -0.002 -0.002 -0.004* (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.002) Macro-regional FE No No Yes Yes Yes Yes Yes Yes Time FE No No No Yes Yes Yes Yes Yes Summary results for first-stage regressions Instrumental variables Rural_banks Bartik_CCBs Rural_banks Bartik_CCBs Bartik_CCBs CCBs_lag Weak id. test 42.586 126.906 90.904 779.290 Underid. test (p-value) 0.017 0.010 0.034 0.000 Overid. test (p-value) 0.624 0.692 Number of provinces 103 103 103 103 103 103 103 103 Number of observations 1751 1751 1751 1751 1751 1751 1751 1236 Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request). In all the estimated models, the independent variable of interest consistently exhibits a positive and statistically significant coefficient (p < 1%). These findings provide strong preliminary evidence supporting the hypothesis that CCBs play a pivotal role in fostering local sustainable development. In Model 5, the variable Other_bank also demonstrates a positive and statistically significant coefficient; however, its marginal effect on the local sustainable development index is notably smaller than that associated with the CCBs variable. The results regarding the control variables are generally consistent with expectations; however, they should be interpreted with caution, as they represent simple correlations rather than causal relationships. Of particular interest are the negative coefficients linked to population density — which is also found, for example, in the work of Muringani (2022) — and the variables that account for the sectoral composition of economic activity. As previously discussed, the relationship between cooperative banking and local sustainable development can be characterized by reverse causality or simultaneity. Additionally, the pooled OLS estimates may be influenced by omitted variable bias. To address the issue of endogeneity, I estimate IV-2SLS models. In models 5 and 6, I perform IV-2SLS estimation by including each of the two instrumental variables individually to test their significance. In Model 7, both instrumental variables are considered together. Finally, in Model 8, I replace the Rural_banks variable with an internal instrumental variable, which is represented by the 5-year lag of the endogenous variable. This deep lag is commonly used in empirical literature when appropriate exogenous instrumental variables are not available (Aresu et al., 2023 ). This approach allows us to assess the robustness of our results against different hypotheses regarding the endogeneity mechanism, providing a stronger empirical foundation for the conclusions. In all models, the key variable of interest maintains a coefficient that is both positive and statistically significant. The instrumental variables are significant, as evidenced by a high F-statistic from the weak identification test in all models. Furthermore, models 8 and 9 show that Hansen’s test confirms the exogeneity condition is satisfied. 4.1 Panel estimations using fixed and random effects models After estimating the Pooled OLS and IV-2SLS models, it is important to assess the robustness of the results by estimating panel models with fixed effects (within-estimator). This approach allows for additional control over unobserved heterogeneity at the provincial level, capturing specific time-invariant factors that may be correlated with the error term, thereby mitigating potential estimation bias. However, it should be noted that the fixed-effects panel estimation may not be ideal for the characteristics of the dataset, as the dependent variable, along with many explanatory variables, exhibits low within-variance. This limitation reduces the informational content available for coefficient estimation, potentially leading to identification issues and imprecise or insignificant estimates. Despite these challenges, this approach serves as an additional check to ensure that omitted variable bias is not driving the results, providing further validation of the robustness of the findings from other model specifications to this stricter control for heterogeneity. For robustness purposes, I also estimate a random effects (RE) model, which, unlike the fixed effects (FE) model, treats region-specific effects as random and, therefore, uncorrelated with the error term. In addition to unobserved heterogeneity, another potential issue could be spatial dependence. By violating the assumption of independence of observations, spatial dependence may lead to biased and inefficient estimates (Anselin, 2022 ; LeSage, 2015 ; LeSage & Pace, 2009 ). For this reason, in addition to the traditional FE and RE estimations, I propose estimating spatial econometric models. First, I estimate two traditional models: the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). The SAR model incorporates the spatial lag of the dependent variable as an additional regressor. The equation can thus be formalized as follows: LSDI it = β 0 + ρ \(\:\sum\:_{j=1}^{n}{w}_{ij\:}{LSDI}_{jt}\) + β 1 CCBs it + β 2 Υ it + µ i + µ t + ε it (6) Compared to Eq. (2), ρ represents the spatial dependence parameter of the dependent variable, w ij denotes the elements of the spatial weight matrix, and µ i captures the provincial fixed effects. The SEM, on the other hand, focuses on analyzing spatial dependence within the error term and can be formalized with the following equation: LSDI it = β 0 + β 1 CCBs it + β 2 Υ it + µ i + µ t + u it u it = λ \(\:\sum\:_{j=1}^{n}{w}_{ij\:}{u}_{jt}\) + ε it (7) Where λ is the spatial dependence parameter of the errors. Finally, I also estimate a Spatial Durbin Model (SDM), which accounts for spatial dependence in both the dependent variable and the explanatory variables: LSDI it = β 0 + ρ \(\:\sum\:_{j=1}^{n}{w}_{ij\:}{LSDI}_{jt}\) + β 1 CCBs it + β 2 Υ it + θ 1 \(\:\sum\:_{j=1}^{n}{w}_{ij\:}{CCBs}_{jt}\) + + θ 2 \(\:\sum\:_{j=1}^{n}{w}_{ij\:}{\gamma\:}_{jt}\) + µ i + µ t + ε it (8) Where θ indicates the spatial spillover effects of explanatory variables. These spatial models are estimated using RE, as in the case of a panel with a relatively short time dimension (t = 17) and strong persistence of key variables, fixed effects would not be estimated correctly or consistently (Basile & Mínguez, 2018 ). Thus, in equations (6), (7), and (8), the notation µ i represents macro-regional fixed effects. Given the irregular geographical configuration of Italian provinces, the k-nearest neighbours (k = 5) contiguity matrix is considered. This approach assigns the same number of neighbours to each province, regardless of the shape or size of the territorial unit. The choice of k = 5 is not arbitrary but empirically determined through a model selection process, where this parameter provided the best fit in terms of information criteria and predictive performance compared to other specifications with different values of k. This specification is preferable to a traditional contiguity matrix, which could produce unbalanced spatial connections due to the irregular territorial configuration of Italy. Table 5 presents the estimation results. Model 9 corresponds to the traditional FE estimation and Model 10 the RE estimation, while the subsequent models report the spatial estimations obtained using a maximum likelihood estimator. The log-likelihood function includes the Jacobian determinant ln |In – ρw| for the SAR and SDM models, where In is the identity matrix of dimension n. This term is necessary to account for the simultaneity introduced by spatial dependence. In the case of the SEM model, the Jacobian term becomes ln |In − λw| (Elhorst, 2014 ; Lee & Yu, 2010 ). In all the estimated models, the key variable of interest exhibits positive and statistically significant coefficients. Notably, in all cases, these coefficients are higher than those observed for the variable Other_banks , which are not always statistically significant. Regarding the control variables, no substantial changes are observed. Both ρ and λ exhibit positive and statistically significant coefficients in the estimated models, indicating the presence of spatial dependence in both the dependent variable and the error term. 4 The SDM proves particularly valuable as it incorporates spatial lags of explanatory variables, thereby indirectly capturing the effect of unobserved variables that follow similar spatial patterns. Additionally, through its spatial structure, the model helps mitigate potential risks of simultaneity (Anselin et al., 2008 ; Le Gallo & Ndiaye, 2021 ). In this latter model, it can be observed that the spatial lag of CCBs is positive and statistically significant, indicating the presence of positive spatial spillovers. Due to the presence of spatial lags in both the dependent and explanatory variables, the interpretation of coefficients is not straightforward. From the reduced form of the SDM, one can derive direct effects (the impact of a change in variable X in one unit on its own Y) and indirect effects (the impact on other units, spatial spillovers, non-diagonal elements) (Kelejian & Piras, 2020 ; LeSage & Pace, 2009 ). For the independent variable of interest, both direct effects (coeff. = 0.556, p-value = 0.000) and indirect effects (coeff. = 1.239, p-value = 0.000) are positive and statistically significant. Therefore, it appears that CCBs generate spatial spillover effects, which are larger than the direct effects. Table 5 Estimation results II (FE and RE estimations) Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Dependent variable: LSDI Mod.9 FE Mod.10 RE Mod.11 SAR Mod.12 SEM Mod.13 SDM CCBs 0.676* 0.588** 0.493** 0.506** 0.507*** (0.350) (0.253) (0.219) (0.240) (0.189) Other_banks 0.220** 0.243** 0.179** 0.186* 0.107 (0.103) (0.101) (0.088) (0.106) (0.095) (ln) Pop_dens 0.006 -0.004 -0.009 -0.011 -0.020 (0.033) (0.021) (0.018) (0.020) (0.018) Dep_ratio -0.014*** -0.015*** -0.009** -0.007 0.002 (0.004) (0.004) (0.004) (0.002) (0.005) Foreign_pop 0.000 0.000 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) Primary_emp 0.008 -0.001 -0.002 -0.005 -0.006 (0.007) (0.006) (0.006) (0.006) (0.006) Tertiary_emp 0.004 0.000 -0.001 -0.003 -0.003 (0.005) (0.003) (0.003) (0.003) (0.003) ρ 0.352*** 0.249*** (0.061) (0.067) λ 0.370*** (0.075) θ CCBs 0.805** (0.370) Within R 2 0.195 0.190 0.211 0.173 0.271 Between R 2 0.007 0.624 0.654 0.628 0.648 Overall R 2 0.018 0.577 0.605 0.578 0.606 Provincial FE Yes No No No No Macro-regional FE No Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes Number of provinces 103 103 103 103 103 Number of observations 1751 1751 1751 1751 1751 [Table 5 about here ] 4.2 Sample split Given Italy’s well-documented regional dualism, it is crucial to conduct a sample split between Centre-North and South to analyze the impact of CBs on regional sustainable development. This division is warranted by the profound socio-cultural and institutional differences between these macro-areas, which could significantly influence the relationship under investigation. The historical divergence in social capital, institutional quality, and economic development patterns between these regions suggests that CBs might operate and impact local sustainable development through different channels and with varying effectiveness across these two distinct institutional and socio-cultural environments. Table 6 reports the estimation results from OLS and IV-2SLS specifications across Centre-North and South subsamples. As the IV-2SLS methodology addresses the aforementioned endogeneity concerns through appropriate identification strategies, it represents the preferred specification. Therefore, all subsequent empirical analyses are conducted employing the IV-2SLS estimation framework to ensure the consistency and efficiency of our estimates. Table 6 Estimation results III (IV-2SLS estimations) Dependent variable: LSDI Centre-north South Mod.14 OLS Mod.15 IV-2SLS Mod.16 OLS Mod.17 IV-2SLS CCBs 0.546*** 0.632** -0.526 -1.602* (0.184) (0.264) (0.667) (0.845) Other_banks 0.255 0.275 0.552*** 0.466*** (0.243) (0.234) (0.162) (0.176) (ln) Pop_dens -0.059* -0.057* -0.011 -0.025 (0.035) (0.034) (0.021) (0.024) Dep_ratio -0.019*** -0.018*** -0.014*** -0.011* (0.005) (0.005) (0.005) (0.006) Foreign_pop 0.000 0.000 -0.001* -0.001** (0.000) (0.000) (0.000) (0.000) Primary_emp -0.025** -0.026** -0.023*** -0.026*** (0.011) (0.011) (0.005) (0.005) Tertiary_emp -0.001 -0.001 -0.015*** -0.017*** (0.003) (0.007) (0.004) (0.004) Macro-regional FE Yes Yes Yes Yes Time FE Yes Yes Yes Yes Summary results for first-stage regressions Instrumental variables Rural_banks Rural_banks Bartik_CCBs Bartik_CCBs Weak id. test 97.269 20.103 Underid. test (p-value) 0.048 0.012 Overid. test (p-value) 0.964 0.231 Number of provinces 67 67 36 36 Number of observations 1139 1139 612 612 Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request). The results reveal a striking geographical heterogeneity in the relationship between cooperative banking and sustainable development. While the Centre-North exhibits a positive and statistically significant relationship between CCBs and sustainable development, the Southern subsample shows a negative and significant coefficient. This divergent pattern suggests that the effectiveness of CBs in promoting sustainable development is heavily contingent on the institutional and socio-economic context in which they operate. The positive impact in the Centre-North might reflect the region’s stronger social capital, more developed institutional framework, and deeper historical roots of cooperative banking, which enable these institutions to effectively channel resources toward sustainable development initiatives. Conversely, the negative coefficient in the South could indicate that in areas characterized by weaker institutional quality and lower social capital endowment, CCBs might face greater challenges in promoting sustainable development, possibly due to less efficient resource allocation or different operational constraints. 4.3 Analysis across sustainability dimensions To provide a more granular understanding of how cooperative banking influences sustainable development, we decompose our analysis across the three fundamental dimensions of sustainability. By examining the economic, social, and environmental components separately, it is possible to identify whether CCBs exhibit heterogeneous effects across different sustainability domains. Hence, this dimensional analysis is useful to pinpoint the specific channels through which cooperative banking potentially influences local sustainable development. Table 7 shows the results of the estimates, which are summarized, for the variable CCBs , in Fig. 3 . Decomposing the analysis across sustainability dimensions unveils nuanced patterns in the relationship between cooperative banking and different aspects of sustainable development. Indeed, the estimation results show that while the CCBs variable displays a negative but not statistically significant coefficient for the economic dimension, it exhibits positive and statistically significant effects on social and environmental domains. This finding suggests that CCBs’ impact on sustainable development is not uniform across dimensions, with their strongest contributions manifesting in social and environmental domains. The lack of statistical significance in the economic dimension, coupled with positive significant effects in social and environmental spheres, indicates that CCBs might be particularly effective at promoting sustainability objectives that extend beyond traditional economic metrics, possibly reflecting their distinctive operational model and institutional mission. Table 7 Estimation results IV (IV-2SLS estimations) Economic Social Environmental Mod.18 OLS Mod.19 IV-2SLS Mod.20 OLS Mod.21 IV-2SLS Mod.22 OLS Mod.23 IV-2SLS CCBs -0.056 -0.036 0.408*** 0.531*** 1.306*** 1.842*** (0.187) (0.250) (0.106) (0.168) (0.458) (0.679) Other_banks 0.480** 0.483** 0.383*** 0.407*** 0.167 0.270 (0.217) (0.222) (0.119) (0.120) (0.387) (0.394) (ln) Pop_dens 0.076*** 0.077*** -0.000 0.004 -0.199*** -0.182*** (0.028) (0.027) (0.017) (0.018) (0.058) (0.057) Dep_ratio -0.007 -0.007 0.002 0.003 -0.044*** -0.043*** (0.008) (0.007) (0.007) (0.003) (0.010) (0.010) Foreign_pop -0.001 -0.001 -0.000 -0.000 -0.001 -0.001 (0.001) (0.001) (0.000) (0.000) (0.001) (0.001) Primary_emp -0.024*** -0.024*** -0.010*** -0.010*** -0.019* -0.019* (0.006) (0.006) (0.003) (0.003) (0.011) (0.011) Tertiary_emp -0.010*** -0.010*** -0.000 -0.000 0.005 0.005 (0.003) (0.003) (0.002) (0.002) (0.005) (0.005) Macro-regional FE Yes Yes Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes Yes Summary results for first-stage regressions Instrumental variables Rural_1936 Rural_1936 Rural_1936 Bartik_CCBs Bartik_CCBs Bartik_CCBs Weak id. test 90.904 90.904 90.904 Underid. test (p-value) 0.034 0.034 0.034 Overid. test (p-value) 0.558 0.322 0.314 Number of provinces 103 103 103 103 103 103 Number of observations 1751 1751 1751 1751 1751 1751 Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request). 4.4 Alternative IV-2SLS estimations To further validate the above findings and ensure more robustness to endogeneity concerns, I implement the heteroscedasticity-based instrumental variable approach developed by Lewbel ( 2012 ). This methodology provides an alternative identification strategy by constructing internal instruments from the model’s exogenous regressors, exploiting second-moment restrictions and heteroscedasticity in the first-stage regression. The Lewbel’s approach generates instruments by exploiting heteroscedasticity in the first-stage regression through: S = ( \(\:Z-\:\stackrel{-}{Z}\) )ν (9) Where \(\:\stackrel{-}{Z}\) represents the mean of Z and ν are the first-stage residuals. The validity of this approach relies on the presence of heteroscedasticity in the first-stage regression and two key assumptions: E(Zϵ) (10) E(Zνϵ) (11) Cov (Z, ν 2 ) ≠ 0 (12) This complementary identification strategy serves as a crucial robustness check, allowing us to assess whether our main findings persist under different identification assumptions. Table 8 presents estimation results based on Lewbel’s heteroskedasticity-based identification strategy. Model 24 employs only heteroskedasticity-based instruments, while Model 25 combines heteroskedasticity-based and standard instruments. The coefficient of the key independent variable remains positive and statistically significant across both specifications, further supporting the robustness of the findings concerning endogeneity issues. Table 8 Estimation results V (IV-2SLS estimation) Mod.24 IV-2SLS Mod.25 IV-2SLS CCBs 0.685*** 0.730*** (0.204) (0.264) Other_banks 0.408** 0.417** (0.163) (0.166) (ln) Pop_dens -0.037 -0.036 (0.023) (0.023) Dep_ratio -0.017*** -0.017*** (0.004) (0.004) Foreign_pop -0.019*** -0.019*** (0.005) (0.005) Primary_emp -0.002 -0.002 (0.003) (0.003) Tertiary_emp -0.001 -0.001 (0.001) (0.001) Macro-regional FE Yes Yes Time FE Yes Yes Summary results for first-stage regressions Instrumental variables Heteroscedasticity-based instruments Heteroscedasticity-based instruments + standard instruments Weak id. test 30.059 44.536 Underid. test (p-value) 0.016 0.000 Overid. test (p-value) 0.313 0.362 Number of provinces 103 103 Number of observations 1751 1751 Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request). 5. Comments The estimation results show that CCBs can play a primary role in promoting the transition towards a more sustainable economy overall. Indeed, in all estimated baseline models, the coefficient of the independent variable of interest is positive, providing statistically significant evidence of the positive link between cooperative banking and local sustainable development. There is also fairly robust evidence that other institutional groups of banks (in particular, mutual banks and commercial banks) can benefit local sustainable development. However, the marginal effects of the variable representing the other institutional bank groups are significantly lower than those observed for cooperative banking group. This highlights the prominent role that these banks can play at the local level in promoting sustainability. Interesting evidence emerges from the spatial analysis, which reveals that CCBs could generate positive spatial spillover effects on sustainable development, confirming how they can significantly influence credit dynamics (Basile et al., 2024 ). These spillover effects likely operate through two main channels: first, through inter-territorial lending networks that extend beyond administrative boundaries, and second, through the diffusion of sustainable business practices across contiguous areas. From a methodological standpoint, the primary concern of endogeneity has been effectively addressed through the proposed mix of estimation methods. Notably, rather than simultaneity or reverse causality, the main threat to identification stemmed from omitted variable bias. This assessment is supported by the strong temporal persistence in CCB density (as evidenced by its high correlation with rural bank branch density in 1936). Given our relatively contained time horizon, it is unlikely that sustainable development could have exerted significant effects on cooperative banking patterns, thus mitigating reverse causality concerns. The results obtained disaggregating the LSDI in its different dimensions has shown interesting evidence that underscore the critical importance of maintaining a pluralistic banking system. The multidimensional nature of sustainability challenges demands diverse institutional responses: CBs excel in promoting social welfare and environmental sustainability, while commercial banks typically drive economic efficiency. This complementarity becomes particularly relevant in light of current debates surrounding banking sector consolidation in Italy. The trend toward reduced institutional diversity through mergers and acquisitions may compromise the banking system’s capacity to effectively address the complex, multifaceted challenges of sustainable development. Of particular significance are the varying performances of CCBs observed across the Centre-North and South macro-regions. These differentials highlight the importance of institutional context in shaping the relationship between CBs and sustainable development. This pattern is not surprising, considering how local specificities exert profound influence on CCB operations (Agostino et al., 2023 ). For instance, Catturani et al. ( 2016 ) demonstrate that CCBs require adequate social capital to operate effectively, as without a robust network of trust relationships, a banking model based on relationship lending may not fully achieve its potential effectiveness. This, in my view, represents a plausible interpretative framework, as the distinction between Centre-North and South, despite its internal heterogeneity, effectively reflects the territorial divide in bridging social capital endowment — that form of social capital from which generalized trust originates (De Blasio & Nuzzo, 2010 ). Regarding control variables, the structural dependency index yields results consistent with our expectations. Results for other control variables are either ambiguous or lack statistical significance to draw definitive conclusions. However, given their role as control variables, we must exercise caution in interpretation, limiting our analysis to the observation of correlative patterns rather than causal linkages. 6. Concluding remarks This study has examined the impact of CCBs’ territorial presence on local sustainable development in Italy. The findings demonstrate that CCBs can effectively support sustainable development, although their effects exhibit significant heterogeneity across macro-regions and through the various constituent dimensions of local sustainable development. Specifically, the analysis reveals that CCBs’ contribution to sustainable development is primarily concerned with promoting social welfare and environmental sustainability, while no evidence emerges supporting their positive impact on economic dynamism. Furthermore, the analysis reveals differential effects between the Centre-North and South macro-regions, suggesting that the institutional context may shape the relationship between cooperative banking and sustainable development. These findings yield two significant policy implications. First, they underscore the critical importance of preserving banking system pluralism. The differential impact of CCBs across sustainability dimensions suggests that a diversified banking system, where distinct institutional types complement each other, is essential for addressing the multifaceted challenges of sustainable development. In this context, the current trend toward banking sector consolidation in Italy raises concerns about the system’s capacity to effectively support all dimensions of local sustainable development. Furthermore, the territorial heterogeneity in CCBs’ impact on sustainable development indicates how stronger social cohesion, more developed support networks, and more effective local governance in Central-Northern regions can amplify CCBs’ positive impact. Conversely, in Southern regions, where we can observe a generally lower institutional quality, the context may constrain the effectiveness of CCBs. This heterogeneity emphasizes the need for policies that account for local specificities, focusing on strengthening the social and institutional fabric in areas where cooperative banking’s effectiveness appears limited, rather than adopting uniform national approaches. This study exhibits several limitations. The primary constraint pertains to the temporal scope of the analysis which, given the high stability and persistence of key variables, has permitted only a static rather than dynamic analytical approach. Investigating the dynamic relationship between cooperative banking and sustainable development would require data spanning a significantly longer period to capture structural changes that occur gradually. Indeed, a dynamic analysis would yield additional and more robust evidence concerning the potential causal relationship between cooperative banking and sustainable development. Although this represents a crucial objective for future research in this field, collecting regional data over extended periods could be problematic due to limited availability. While cross-sectional analysis presents an alternative methodological approach, it demonstrates less effectiveness in addressing endogeneity concerns. A second limitation addresses territorial heterogeneity. 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J money credit Bank, 657–678 Sachs JD (2015) The age of sustainable development. Columbia University Schoenmaker D, Schramade W (2019), October Financing environmental and energy transitions for regions and cities: creating local solutions for global challenges. In Paper for an OECD/EC Workshop on financing environmental and energy transitions Schuch JC, Wang Q (2015) Immigrant businesses, place-making, and community development: a case from an emerging immigrant gateway. J Cult Geogr 32(2):214–241 Sfar FEH, Ouda OB (2016) Contribution of cooperative banks to the regional economic growth: Empirical evidence from France. Int J Econ Financial Issues 6(2):508–514 Terzo G (2022) Investigating the link between social cooperation sector and economic well-being of Italian provinces through the lens of social capital. 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Res Int Bus Finance 68:102186 Ziolo M, Bak I, Cheba K (2021) The role of sustainable finance in achieving sustainable development goals: Does it work? Technological Economic Dev Econ 27(1):45–70 Footnotes According to the International Cooperative Alliance (ICA), a CB is “an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly-owned and democratically-controlled enterprise. Cooperatives are based on the values of self-help, self-responsibility, democracy, equality, equity and solidarity. In the tradition of their founders, co-operative members believe in the ethical values of honesty, openness, social responsibility and caring for others” (ICA,1995). The Brundtland Report provided what has become the most influential definition of sustainable development, characterizing it as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987 , p. 43). The rural banks were credit cooperatives established to provide financial support to farmers and artisans, tailored to the needs of local and family economies. Originating in Prussia in 1849 under F.W. Raiffeisen, they were introduced in Italy in 1883 by L. Wollenborg, who founded the first rural banks in Loreggia (Padua). Supported by the clergy and concentrated particularly in Trentino-Alto Adige, they peaked in 1922 with over 3,500 institutions. In 1937, their activities expanded to include artisans, leading to a name change. Following the 1993 banking reform, they became CCBs , retaining their mutualistic mission to foster economic and social development. Considering the possibility that the estimation results of the spatial econometric models might be influenced by the choice of the spatial-weighting matrix, I also estimate the models using alternative matrices. The results of these additional estimations, which are not significantly different from those illustrated here, are available upon request. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6264324","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431737524,"identity":"6deac601-7d45-482a-9d87-7923668076ae","order_by":0,"name":"Giuseppe Terzo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZhiDvQFIGFgQqeUAiMEDIg0kiLQJrEUiAUwSVi3vzmPA/KHinj2/5POrG34USDDwt3cn4NVieJjHgOHAmWJmydk5ZTd7gA6TOHN2A34tzUAtB9sS2Axu56Td4AFqMZDIJUbLvwQeg5tn0m7+IUaLPDNIS0OChMEN9mO3ibLFgJmt4MCZYwkGkj05bLdlDCR4CPpFvv/wxgcVNQn2/OzHn91888dGjr+9l4AtB6DRAoxLAzCJVznYlgY4k/0BQdWjYBSMglEwMgEALVZDJJyuiggAAAAASUVORK5CYII=","orcid":"","institution":"LUISS University, Department of Business and Management","correspondingAuthor":true,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Terzo","suffix":""}],"badges":[],"createdAt":"2025-03-19 20:20:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6264324/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6264324/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79067131,"identity":"388d258b-4b4e-46fb-ac7c-be8e66fa63fd","added_by":"auto","created_at":"2025-03-24 04:43:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":774292,"visible":true,"origin":"","legend":"\u003cp\u003eLocal sustainable development index (Source: own elaboration on multiple sources)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6264324/v1/a7b1f5aad8b834ef48a44b7b.jpeg"},{"id":79066840,"identity":"01de09a8-8036-4b12-a199-013a298ae81e","added_by":"auto","created_at":"2025-03-24 04:35:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":822151,"visible":true,"origin":"","legend":"\u003cp\u003eCCBs branch density (Source: own elaboration on Bank of Italy data)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6264324/v1/48a1ffcc71d5f14122cb36a1.jpeg"},{"id":79066845,"identity":"98024767-73bb-4469-a7d9-52a3c754c831","added_by":"auto","created_at":"2025-03-24 04:35:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158626,"visible":true,"origin":"","legend":"\u003cp\u003eIV-2SLS estimation results for the variable CCBs (Coefficient plot)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6264324/v1/77a52e5acb2dc5b580bac7bd.jpeg"},{"id":79067255,"identity":"f6a97d81-7465-4305-8a61-98629df64bbf","added_by":"auto","created_at":"2025-03-24 04:51:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3349496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6264324/v1/ca46ef04-a6f5-43bf-a12c-5e79247bf04b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCooperative banking and local sustainable development in Italy\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBanking institutions play a crucial role in fostering the growth of the real economy, primarily by channelling savings into productive investment opportunities (Beck \u0026amp; Levine, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In performing this intermediation function, they contribute to improving the allocative efficiency of resources, promoting economic growth, and facilitating structural transformation processes that enable regions to embark on virtuous paths of economic development (King \u0026amp; Levine, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Rousseau and Wachtel, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe banking sector is undergoing profound transformations, driven primarily by advancements in information technology, which may have diminished some of the traditional advantages associated with relationship lending \u0026mdash; particularly characteristic of local banks. According to Wheelock and Wilson (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), digitalization and the automation of credit evaluation processes have enhanced the ability of large banks to compete in markets traditionally dominated by local banks. Despite these changes, local banks remain key players in financing small and medium-sized enterprises (SMEs), leveraging geographical proximity and personal relationships to gather detailed information on their clients\u0026rsquo; creditworthiness (Berger et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Brevoort \u0026amp; Hannan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ho \u0026amp; Ishii, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This relational model is particularly relevant in contexts characterized by information opacity and limited access to advanced technologies, where SMEs often face challenges in accessing the services provided by larger banks (Cosci et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe importance of local banks also lies in their countercyclical role, which is particularly relevant in this era of polycrisis, characterized by a succession of external shocks. Indeed, they can help stabilize credit supply during periods of economic downturns, due to their greater flexibility in client relationships and their ability to tailor loan conditions to local needs (Manitiu \u0026amp; Pedrini, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, prolonged crises may erode this adaptive capacity, limiting their effectiveness in sustaining credit supply (Migliorelli, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn light of these premises, this paper aims to investigate the impact of cooperative credit banks (CCBs) on local sustainable development in Italy. I focus on CCBs because they represent small-sized organizations generally considered for proxying the local banking sector (Caporale et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The impact of the banking sector on sustainable development is an issue of significant relevance for the empirical research. Due to their critical role as financial intermediaries, banks not only facilitate the efficient allocation of resources but might also play a pivotal role in promoting equity and sustainability (Aracil et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This dual contribution positions the banking sector at the core of strategies to foster inclusive and sustainable economic development. In this context, CCBs emerge as pivotal actors since their business model incorporates the ESG (Environmental, Social, Governance) principles, translating sustainability commitments into concrete lending and investment practices at the local level. For this reason, my focus is on this specific institutional group of banks.\u003c/p\u003e \u003cp\u003eSeveral theoretical and empirical studies have highlighted how cooperative banks (CBs) can promote: (a) local economic growth (e.g., Ayadi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Coccorese \u0026amp; Shaffer, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Usai \u0026amp; Vannini, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); (b) business creation (e.g., Agostino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Errico et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); (c) innovation (e.g., Barra \u0026amp; Ruggiero, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cosci et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); (d) reduced economic and social inequalities (e.g.,; Angelini et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Arestis \u0026amp; Phelps, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barra et al., 2024; Berger et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; D\u0026rsquo;Onofrio et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lal, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Minetti et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and (e) environmental sustainability (e.g., Bevilacqua, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Caselli, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The innovative contribution of this study lies in its attempt to integrate a diverse body of literature into a unified conceptual framework, which is empirically tested through an in-depth econometric analysis. Empirically examining how cooperative banking contributes to balancing efficiency, equity, and environmental sustainability is essential, as these represent the three fundamental pillars of inclusive and sustainable regional development.\u003c/p\u003e \u003cp\u003eThe empirical strategy to test the proposed conceptual framework consists of two main steps. First, I develop a composite sustainable development index that integrates three fundamental dimensions: economic dynamism, social welfare, and environmental sustainability. This multidimensional index is constructed by aggregating a set of elementary indicators representative of each dimension. Second, I perform an econometric analysis leveraging a panel dataset of 103 Italian provinces (NUTS-3), considering the time frame 2005\u0026ndash;2021, where the dependent variable is the composite index of local sustainable development and the key explanatory variable is the territorial presence of CCBs, measured through the density of their branches at the provincial level.\u003c/p\u003e \u003cp\u003eThe Italian case presents a compelling context for investigating the relationship between cooperative banking and sustainable development. Italy has an essential cooperative tradition, which historically plays a pivotal role in supporting local economies, fostering social cohesion, and addressing inequalities. Moreover, the country\u0026rsquo;s economic structure, characterized by a significant presence of SMEs and a pronounced regional diversity, provides a fertile ground for assessing how cooperative banking practices can drive sustainable development by balancing efficiency, equity, and environmental quality. This unique combination of factors makes the Italian case an ideal setting for such an investigation.\u003c/p\u003e \u003cp\u003eThe results of the econometric analysis can be summarized as follows. Overall, CCBs positively impact local sustainable development, with results robust to several issues and scenarios. However, disaggregating the sustainable development index, it emerges that CCBs make a significant positive contribution to social welfare and environmental sustainability, while their impact on economic dynamism is not clear. Furthermore, when examining geographical heterogeneity by splitting the sample between macro-regions, the results reveal a stark contrast: CCBs positively affect sustainable development in the Centre-North, while their impact is negative in the South.\u003c/p\u003e \u003cp\u003eThese findings highlight how CCBs, due to their distinctive features, can significantly contribute to promoting sustainable development, primarily through their ability to generate social and environmental value in local communities. The negative effect observed on the economic dynamism dimension suggests the importance of a pluralistic banking system. The complementarity between different banking models emerges as a key factor: while CCBs excel in generating social and environmental value, traditional commercial banks might be more effective in sustaining economic efficiency and innovation. Hence, a balanced mix of financial institutions may be optimal for an integrated sustainable development. Finally, the heterogeneity of effects across macro-regions reveals how deep-rooted regional factors that determine the institutional quality of regions could shape the relationship between cooperative banking and sustainable development.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows. The next section presents the conceptual framework. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the dataset and the empirical strategy. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the results, while Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e offers some reflections on the findings. Finally, Section 6 concludes.\u003c/p\u003e"},{"header":"2. Related literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eThe specificities of cooperative banking\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eCooperative banks (CBs), which originated in Europe in the 19th century, were specifically designed to support small businesses. Like other banks, they contribute to economic performance by transforming savings into long-term loans, assessing borrowers\u0026rsquo; creditworthiness to mitigate default risks and finance viable projects, and fostering the efficient allocation of resources among competing uses (Coccorese \u0026amp; Shaffer, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003csup\u003e1\u003c/sup\u003e The specificity of CBs lies primarily in their governance structure, which results in a banking model that is deeply rooted in local communities and responsive to their social needs. Building on the work of Minetti et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the following characteristics of the CB model can be identified. First, ownership is non-transferable, limited to individual shares, and redeemable only at par value. In addition, the close link to local communities ensures that CB clients are also members. Second, in terms of governance, the \u0026ldquo;one member, one vote\u0026rdquo; rule characteristic of all cooperative organizations applies. Thus, voting power is not determined by the amount of capital held. Finally, as Hesse and Cih\u0026aacute;k (2007) also argue, the primary objective of CBs is to maximize social value rather than profits. Regarding the latter aspect, Christensen et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and Ayadi and Schmidt (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) argue that CBs can be considered institutions with a dual objective: they aim to generate profits to survive and expand, but without profit being the sole or primary ultimate goal (Becchetti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs Ayadi et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) argue, by lending locally, CBs prevent the savings of a local community from being withdrawn or transferred to other markets, thereby mitigating capital drain. This approach is closely related to relationship banking, a model in which banks focus on fostering deep, long-term relationships with their customers, reducing the risks of adverse selection and moral hazard (Aristei \u0026amp; Gallo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Berger \u0026amp; Udell, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Coccorese et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ferri et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These lending relationships enable banks to acquire \u0026ldquo;soft information\u0026rdquo; about customers, which can play a key role in mitigating financial exclusion. They allow the bank to gain insights into details that are not easily quantifiable, such as specific behaviours or the reputation of a customer. This information is invaluable for assessing the creditworthiness of borrowers who might otherwise be excluded from the traditional financial system, where credit decisions are primarily based on standardized quantitative data, such as credit scoring (Berger \u0026amp; Udell, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Petersen \u0026amp; Rajan, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Larger banks tend to have a complex and centralized organizational structure, with decisions often made at the central level. Gathering \u0026ldquo;soft information\u0026rdquo; requires direct, personal knowledge of the customer, which is more easily obtained by local banks that are physically close to borrowers and have a direct relationship with them (Agarwal \u0026amp; Hauswald, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Larger banks, on the other hand, may find it difficult to gather this information due to geographical distance (Alessandrini et al., 2008). Furthermore, CBs leverage social capital and group interactions to create a system where shared responsibility and mutual oversight enhance credit quality, increase access to financing for the most vulnerable members of the community, and reduce insolvency risks (Angelini et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Banerjee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies (e.g., Berger \u0026amp; Black, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; DeYoung et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Petersen \u0026amp; Rajan, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) argue that the advent of new information technologies has improved access to and the quality of information about borrowers, reducing the importance of soft information and geographic proximity in the credit process. In this context, new technologies would reduce the informational advantage of the relationship lending model, allowing larger banks to compete even without close local ties. However, some studies show that geographical proximity remains a key factor in the credit process, as SMEs are still predominantly financed locally (e.g., Brevoort \u0026amp; Hannan, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ho \u0026amp; Ishii, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hakenes et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hasan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Meslier et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Petach et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eOn the concept of sustainable development\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSustainable development is a key issue of international debate about human society and its future, becoming one of the most challenging concepts ever developed to ensure a dignified life in society for everyone (Alaimo \u0026amp; Maggino, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its growing importance reflects the increasing awareness that the dominant development models should be reshaped to address present and future challenges for humanity. The scientific literature encompasses a rich spectrum of conceptualizations regarding sustainable development, reflecting its theoretical complexity and multifaceted nature. This conceptual heterogeneity has fostered the emergence of diverse research streams, spanning methodological reflections and empirical applications across multiple disciplines (Wichaisri \u0026amp; Sopadang, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conceptual foundation of sustainable development is intrinsically connected to sustainability principles, defined as a system\u0026rsquo;s inherent capacity for perpetual self-maintenance (Olawumi \u0026amp; Chan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Its emergence as a central paradigm in environmental research has fundamentally transformed development policy frameworks. Since its foundational articulation in the Brundtland Report (WCED, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1987\u003c/span\u003e)\u003csup\u003e2\u003c/sup\u003e, and particularly following the watershed Rio de Janeiro Earth Summit, this concept has transcended theoretical boundaries to become a cornerstone of international agreements and national legislative frameworks globally (Ruggerio, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of sustainable development has evolved, reflecting its dynamic and adaptive nature (Sachs, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While intergenerational equity \u0026mdash; the fundamental imperative to safeguard resources for future generations \u0026mdash; remains a cornerstone principle, contemporary interpretations have expanded significantly beyond this foundational aspect. It has gradually acquired a multidimensional nature that reflects the intricate interdependencies between economic prosperity, social progress, and environmental safeguard (D\u0026rsquo;Adamo et al., 2024). This integrated perspective represents a fundamental shift from earlier, more compartmentalized approaches, recognizing that sustainable development challenges cannot be addressed in isolation but require coordinated interventions across multiple domains of human activity (Alaimo \u0026amp; Maggino, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These three pillars of sustainable development are known as \u0026ldquo;triple bottom line\u0026rdquo;, and their balance is considered essential for generating value for profits, planet, and people (Elkington, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; cited in Olawumi \u0026amp; Chan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the purposes of this work, I embrace this three-dimensional conceptualization of sustainable development, incorporating economic, social, and environmental aspects as equally essential and mutually reinforcing components. This holistic framework guides the research methodology, acknowledging that sustainability manifests through the synergistic interaction of these foundational pillars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cem\u003eCooperative banking and local sustainable development\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThere is a strong tradition of empirical studies demonstrating that the banking system plays a central role in promoting macroeconomic performance (e.g., Beck \u0026amp; Levine, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cetorelli \u0026amp; Gambera, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cosci \u0026amp; Mattesini, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; King \u0026amp; Levine, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Levine et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rousseau \u0026amp; Wachtel, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wachtel, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Over time, a similarly well-rounded body of literature has emerged highlighting the role of local banks \u0026mdash; which, in the Italian context, take the form of cooperative credit banks (CCBs) \u0026mdash; in fostering the economic growth of territories. This is attributed to their unique characteristics mentioned above, which help mitigate market failures, especially those stemming from information asymmetries, ensuring a more efficient allocation of resources and preventing capital drain.\u003c/p\u003e \u003cp\u003eUsai and Vannini (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) demonstrate that, between 1970 and 1993, the financial sector as a whole exerted a limited influence on Italy\u0026rsquo;s economic growth. However, a disaggregated analysis reveals that CCBs and special credit institutions positively contributed to economic growth. This suggests that the overall limited impact of the financial system can be attributed to the underperformance of other institutional banking groups. Ayadi et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) highlight that CCBs fostered economic growth in countries such as Austria, Finland, Germany, and the Netherlands from 2000 to 2008. Conversely, this positive effect was not evident in other nations, including Spain, France, and Italy. Caporale et al. (2015) find that CCBs, serving for create local banking proxies, significantly enhanced local economic growth in Italy, particularly in the Northern regions. Sfar and Ouda (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) identify a positive correlation between the development of CCBs and regional economic growth in France from 2006 to 2012. Similarly, Bernini and Brighi (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrate that increased credit availability from CCBs in Italy translates into higher local economic growth. More recently, Coccorese and Shaffer (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) provided empirical evidence that CCBs exerted a stronger influence on the economic growth of Italian municipalities between 2001 and 2011 compared to conventional banks.\u003c/p\u003e \u003cp\u003eCBs can contribute to fostering more inclusive development paths in local areas. Empirical literature demonstrates their significant role in mitigating economic and social inequalities. D\u0026rsquo;Onofrio et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) show that the development of local banking helps reduce income disparities across Italian provinces \u0026mdash; particularly through geographic mobility and urbanization\u0026mdash;although this effect appears to be confined to the more advanced areas of the country. Similarly, Minetti et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that CCBs are more effective than commercial banks in mitigating income inequality in local areas, based on an empirical analysis of Italian provinces for the period 2001\u0026ndash;2011.\u003c/p\u003e \u003cp\u003eThe critical role of CBs in addressing these disparities is largely attributable to their ability to promote financial inclusion, which is a key driver of social welfare. As highlighted by Alvarez-Gamboa et al. (2023), there is robust evidence in the empirical literature showing that financial inclusion alleviates inequality and poverty, enhances the socioeconomic conditions of the population, guaranteeing greater investments in health and education, and improves the performance of SMEs. Moreover, CBs provide valuable support to social enterprises (Zedda et al., 2021), which, as highlighted by an emerging body of literature (e.g., Terzo, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Terzo et al., 2023), play a pivotal role in fostering the social and economic cohesion of local communities.\u003c/p\u003e \u003cp\u003eFinance can be central to promoting sustainability (Bevilacqua, 2024). For instance, a recent study by Schoenmaker and Schramade (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrates how it can facilitate the transition to a circular economy by directing investments toward green firms. Financial institutions, therefore, can effectively address the trade-off between sustainability objectives. However, as argued by Pisano (2012), conventional finance tends to place marginal importance on social and environmental sustainability. Various works (e.g., Avrampou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ziolo et al.,2021) contend that a more sustainable financial model is essential for achieving the Sustainable Development Goals (SDGs), ensuring a balance between social, environmental, and economic sustainability. In the pursuit of making finance more sustainable, CBs could assume a strategic role, as their long-standing integration of ESG criteria positions them well to seize the opportunities presented by a sustainable transformation (Bevilacqua, 2024).\u003c/p\u003e \u003cp\u003eIn light of the evidence that emerged from the brief literature review, it becomes clear that CBs can serve as key players in local sustainable development processes, as they possess the necessary attributes to promote equity, efficiency, and environmental sustainability simultaneously. This is an intriguing topic that empirical literature has not yet thoroughly explored. Therefore, in the subsequent sections of this study, I aim to empirically test this conceptual framework to provide novel insights on an issue of considerable importance, particularly from a policy-making perspective.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical framework","content":"\u003cp\u003eTo empirically assess the conceptual framework of this study, I structure a panel dataset comprising 103 Italian provinces (NUTS-3 level) spanning the period from 2005 to 2021. The choice of this time frame is dictated by data availability constraints\u003c/p\u003e\n\u003cp\u003eThe dataset is structured using two primary sources: (a) Istat (the Italian National Institute of Statistics) and (b) the Bank of Italy. Concerning Istat data, multiple databases are considered, including (i) the Equitable and Sustainable Well-Being of Territories (Benessere Equo e Sostenibile, BEST), (ii) Statistics for Development Policies, (iii) the Territorial Statistical Atlas of Infrastructures; (iv) the Territorial Economic Accounts, and v) COEWEB\u0026mdash;International trade statistics.\u003c/p\u003e\n\u003cp\u003eA comprehensive overview of the variables included into the econometric model, along with the empirical strategy employed, is provided in the following subsections.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 \u003cem\u003eDependent variable\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe dependent variable in the model is a composite index of sustainable development. The decision to construct a composite index is driven by the multidimensional nature of the concept of sustainable development, which, as I extensively argued above, encompasses three distinct dimensions: economic, social, and environmental.\u003c/p\u003e\n \u003cp\u003eThe following approach is adopted regarding the selection of elementary indicators. Drawing from various Istat databases (BEST, Statistics for Development Policies, Territorial Economic Accounts, the Territorial Statistical Atlas of Infrastructures, and COEWEB), all indicators that could effectively proxy the three dimensions of sustainable development are considered, excluding those for which a sufficiently long time series is unavailable. Additionally, further variables are not included to prevent information redundancy caused by high collinearity with other variables.\u003c/p\u003e\n \u003cp\u003eAs a result of this preliminary analysis, nine elementary indicators are selected, the details of which are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The economic dimension is characterized by indicators that make it possible to assess the economic dynamism of regions by measuring productivity, trade openness. and the ability to create new businesses. The social dimension, on the other hand, includes indicators that proxy the level of human development of regions, as they are representative of the material living conditions and health of the population, as well as the accumulation of human capital. Finally, the environmental dimension includes indicators that can serve as proxies for the pro-environmental behaviour of regions, which is essential for initiating virtuous processes of environmental sustainability.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eElementary indicators of local sustainable development\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross Value Added per worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEconomic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew firm formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirm creation rate (total entries over working population)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum of imports and exports as a percentage of total value added\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of individuals aged 25 to 39 with a tertiary education degree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife expectancy at birth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeparate waste collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of municipal waste subjected to separate collection relative to total municipal waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMunicipal waste produced per capita (in kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenewable energy consumptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of electricity consumed from renewable sources as a percentage of total electricity consumed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRegarding the choice of the aggregation method, I adopt the Jevons index, which is a geometric mean of ratios. The geometric mean represents an intermediate solution between compensatory and non-compensatory methods, offering many desirable properties from an axiomatic perspective (Diewert, 1995; OECD, 2008). The index, as outlined by Mazziotta and Pareto (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), is calculated as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:{LSDI}_{i}^{t}=\\:\\prod\\:_{j=1}^{m}{\\left(\\frac{{x}_{ij}^{t}}{{x}_{rj}^{t}}\\right)}^{\\frac{1}{m}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{ij}^{t}\\)\u003c/span\u003e\u003c/span\u003e is the value of the j-th indicator for province i at time t and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{rj}^{t}\\)\u003c/span\u003e\u003c/span\u003e is the reference value, namely the national average. The elementary indicators are thus transformed into index numbers, where values greater than 1 indicate performance above the national average, while values below 1 indicate performance below the average. The utility of this methodology in the context of sustainable development measurement lies in its ability to implicitly penalize units with imbalanced values across the various elementary indicators. This feature is crucial for measuring sustainable development, as it depends on the balance across its three dimensions \u0026mdash; economic, social, and environmental.\u003c/p\u003e\n \u003cp\u003eThe weighting procedure consists of the adoption of an equal weighting scheme, attributing the same importance to all elementary indicators (Nardo et al., 2005). I consider this option since the better weighting scheme for structuring sustainable development indices is the stakeholders\u0026rsquo; opinion (Alaimo \u0026amp; Maggino, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since this option is not available for this study, an equal weighting scheme is preferred.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows quantile maps of the Local Sustainable Development Index (LSDI) at the beginning and end of the period considered in the analysis, namely 2005 and 2021. The spatial distribution of this indicator is marked by strong polarization, emphasizing the traditional divide between the Centre-North and the South of the country. Notably, this distribution remains persistent, with no significant changes in the spatial patterns observed between 2005 and 2021. This stability can largely be attributed to the slow pace of structural transformation, driven by deep-seated historical, socio-cultural, and institutional factors that hinder the Southern regions\u0026rsquo; ability to bridge the gap with the Centre-North. Specifically, disparities in infrastructure, human capital, and access to markets remain persistent, preventing meaningful improvements in sustainable development outcomes.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 \u003cem\u003eIndependent variable\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe main independent variable is the number of CCB branches per 1,000 inhabitants (CCBs). Italian CCBs represent an important part of the national banking system. Founded in the second half of the 19th century, they have managed to adapt to social, economic, and regulatory changes over time. The 1993 Banking Law reduced the differences between CCBs and commercial banks while preserving the principles of mutualism, democracy, localism, and non-profit objectives. Initially focused on providing small loans to farmers and artisans in rural areas, CCBs are now able to compete with commercial banks on a broader scale, also thanks to second-level networks that increase their efficiency and competitiveness (Catturani \u0026amp; Stefani, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eI consider the CCBs\u0026rsquo; branch density since, as argued by Minetti et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the number of branches represents a key indicator for assessing the development of local banking, as it reflects the ability of CCBs to reach different local communities and provide them with accessible banking services. Hence, it can be considered a pivotal measure of financial development (Rossi \u0026amp; Scalise, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the quantile maps of the branch density of CCBs in 2005 and 2021. In line with what Ferri and Messori (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) pointed out, a higher density is mainly observed in areas with a prevalence of SMEs, especially in the North-East. Moreover, the geographical distribution of CCB branches seems to reflect that of bridging social capital, as it is one of the key factors driving the diffusion of CCBs in areas (Catturani et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). No significant changes in the spatial distribution can be observed over the period analyzed, as cooperative banking tends to be characterized by considerable stability, driven not only by economic factors but also by institutional and sociocultural traits that influence its presence in the territory.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 \u003cem\u003eControl variables\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eFor the selection of control variables, I draw on the extensive empirical literature on the determinants of regional and local growth and development. First, I include the number of branches per 1,000 inhabitants for banks other than CCBs (\u003cem\u003eOther_banks\u003c/em\u003e) to account for the overall level of access to banking services across provinces. Including this variable allows for isolating the specific effect of CCBs from the broader impact of other financial institutions. Furthermore, this control captures potential structural differences between provinces regarding banking density, which may influence credit availability and local sustainable development. To control for the socio-demographic characteristics of the area, I include in the model the structural dependency ratio (\u003cem\u003eDep_ratio\u003c/em\u003e), which is the percentage of the population in the non-working age groups (0\u0026ndash;14 and 65+) relative to the working-age population (15\u0026ndash;64), and the percentage of the foreign population relative to the total resident population (\u003cem\u003eForeign_pop\u003c/em\u003e). Concerning the structural dependency ratio, I expect a negative sign, as a higher share of the inactive population can hurt productivity (Terzo et al., 2023, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, a lower share of the youth population may hinder sustainability, as suggested by Yamane and Kaneko (2021), who argue that young people are more likely to adopt a lifestyle that is consistent with the need to ensure sustainability compared to other generations. About the foreign population, I expect a positive sign because, according to a large body of literature (e.g. Ottaviano \u0026amp; Peri, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schuch \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), it represents a significant economic force that can stimulate innovation and promote the creation of new businesses. Moreover, there is evidence in the empirical literature showing that the foreign population tends to be more inclined towards pro-environmental behaviours (e.g., Argentiero et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hellwig et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raimo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor agglomeration economies, which are proxied by population density, the implications for sustainable development are uncertain. For example, as claimed by Peir\u0026oacute;-Palomino et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), highly urbanized areas can facilitate the matching of labour supply and demand, improving the overall functioning of the labour market. However, they can also lead to an increase in energy consumption and the emission of pollutants that worsen environmental quality. Furthermore, urbanization can enhance the forms\u0026rsquo; access to credit, hence favouring local entrepreneurship development (Carmignani et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, the sectoral composition of economic activity has uncertain implications for sustainable development. While the primary and tertiary sectors generally have lower employment multipliers than manufacturing (Elhorst, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e), assessing the impact of each sector on environmental quality is complex. While the manufacturing sector is often associated with higher resource intensity and pollutant emissions, the primary and tertiary sectors can have different impacts on environmental sustainability depending on their specific activities and the level of technological innovation adopted (Mart\u0026iacute;n-Ortega \u0026amp; Gonz\u0026aacute;lez-S\u0026aacute;nchez, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 \u003cem\u003eEmpirical strategy\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eAccording to the variables above described, I estimate the following equation:\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSDI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCCBs\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026Upsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cem\u003eLSDI\u003c/em\u003e represents the Local Sustainable Development Index, \u003cem\u003eCCBs\u003c/em\u003e denotes the number of CCBs branches per 1,000 inhabitants, \u0026Upsilon; represents the set of control variables, \u0026micro;\u003csub\u003ei\u003c/sub\u003e captures fixed effects and \u0026micro;\u003csub\u003et\u003c/sub\u003e encompasses a full set of time dummies.\u003c/p\u003e\n \u003cp\u003eI estimate the model using a pooled OLS approach, employing clustered standard errors at the provincial level to address heteroscedasticity and autocorrelation. This estimation may suffer from omitted variable bias and reverse causality. To address these issues, I also estimate a Two-Stage Least Squares instrumental variables model (IV-2SLS). The variable \u003cem\u003eCCBs\u003c/em\u003e is instrumented with the number of branches of rural banks (Casse rurali) per 1,000 inhabitants in 1936 (\u003cem\u003eRural_banks\u003c/em\u003e), a measure historically tied to the current distribution of CCBs.\u003csup\u003e3\u003c/sup\u003e This connection stems from institutional persistence: many rural banks gradually evolved into CCBs, reflecting the enduring nature of their cooperative principles and their deep integration within local communities. This historical continuity highlights how these institutions maintained their territorial presence over time, adapting to the needs of their communities while preserving their mission to support local economies. Consequently, this instrument is expected to satisfy the relevance criterion due to the strong geographic and institutional inertia of CCBs. The exogeneity criterion is also likely to be satisfied, as the spatial distribution of rural bank branches in 1936 reflects historical and political conditions of the time, such as the reorganization of the banking system imposed by the Fascist regime. These determinants are not directly linked to current levels of local sustainable development, as subsequent factors (e.g., economic, environmental, or technological changes) have significantly altered the context. Therefore, the effect of the historical distribution of rural bank branches on contemporary local development can reasonably be considered indirect, operating exclusively through the \u003cem\u003eCCBs\u003c/em\u003e variable.\u003c/p\u003e\n \u003cp\u003eTo create an additional instrumental variable, I employ a shift-share instrument based on Bartik (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). This variable is constructed by combining a time-invariant factor that varies across different regions with a time-varying factor that remains consistent for all provinces. The process involves the following steps:\u003c/p\u003e\n \u003cp\u003e1) I calculate the ratio of the average number of CCB branches in each province to the national average number of CCB branches.\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:{s}_{i}=\\:\\frac{{av\\_CCBs}_{i}}{{av\\_CCBs}_{ita}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e2) I determine the time-varying factor, which reflects the relative change in the number of BCC branches at the national level.\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:{g}_{t}=\\:\\frac{{CCBs}_{t}-{CCBs}_{t-1}}{{CCBs}_{t-1}}\\:$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e3) Finally, the instrumental variable (\u003cem\u003eBartik_CCBs\u003c/em\u003e) is obtained by multiplying the share of each province by the annual changes in the number of CCB branches.\u003c/p\u003e\n \u003cp\u003eBartik_CCBs\u003csub\u003eit\u003c/sub\u003e = s\u003csub\u003ei\u003c/sub\u003e * g\u003csub\u003et\u003c/sub\u003e (5)\u003c/p\u003e\n \u003cp\u003eSince there is an instrumental variable measured in a single year \u0026mdash; and therefore treated as fixed effects \u0026mdash; it is not possible to include individual fixed effects in the model. This explains why I consider NUTS-1 macro-regional fixed effects to control for those unobserved factors that are time-invariant.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the main descriptive statistics for all the variables included in the model and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e the correlation matrix of the explanatory variables, which also consists of the Variance Inflation Factors (VIFs), calculated without the inclusion of macro-regional and time fixed effects. By examining the VIFs and the correlation matrix, it is possible to rule out multicollinearity issues that could affect the estimates.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of variables and summary statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.D.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComposite index of local sustainable development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of CCB branches per 1,000 inhabitants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank of Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of other banks (different to CCBs) per 1,000 inhabitants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank of Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of inhabitants per km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIstat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e328.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural dependency ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForeign residents as a percentage of the total population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIstat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary sector employment as a percentage of total employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIstat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary sector employment as a percentage of total employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIstat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of rural bank branches per 1,000 inhabitants in 1936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank of Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShift-share instruments for CCB branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwn elaboration on Bank of Italy data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of variables and summary statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther:banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Estimation results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 \u003cem\u003eMain results\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the estimation results using different model specifications. In Model 1, I estimate a pooled OLS regression that includes only the key variable of interest to capture its raw effect. In Model 2, I include a set of control variables to isolate the impact of the key variable of interest from potential confounding factors. In Model 3, I also include macro-regional dummies to account for time-invariant unobserved factors, and in Model 4, I include time dummies to control for cyclical fluctuations or common shocks that occur during the analysis period. This step-by-step approach makes it possible to assess the robustness of the results and evaluating how the estimated effect of the key variable of interest changes when introducing additional controls.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results I (Pooled OLS and IV-2SLS estimations)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eDependent variable: LSDI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.7\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.141***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.512***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.541***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.647**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.546***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.401**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e(ln) Pop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.055**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro-regional FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary results for first-stage regressions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs_lag\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak id. test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn all the estimated models, the independent variable of interest consistently exhibits a positive and statistically significant coefficient (p\u0026thinsp;\u0026lt;\u0026thinsp;1%). These findings provide strong preliminary evidence supporting the hypothesis that CCBs play a pivotal role in fostering local sustainable development. In Model 5, the variable \u003cem\u003eOther_bank\u003c/em\u003e also demonstrates a positive and statistically significant coefficient; however, its marginal effect on the local sustainable development index is notably smaller than that associated with the \u003cem\u003eCCBs\u003c/em\u003e variable. The results regarding the control variables are generally consistent with expectations; however, they should be interpreted with caution, as they represent simple correlations rather than causal relationships. Of particular interest are the negative coefficients linked to population density \u0026mdash; which is also found, for example, in the work of Muringani (2022) \u0026mdash; and the variables that account for the sectoral composition of economic activity.\u003c/p\u003e\n \u003cp\u003eAs previously discussed, the relationship between cooperative banking and local sustainable development can be characterized by reverse causality or simultaneity. Additionally, the pooled OLS estimates may be influenced by omitted variable bias. To address the issue of endogeneity, I estimate IV-2SLS models. In models 5 and 6, I perform IV-2SLS estimation by including each of the two instrumental variables individually to test their significance. In Model 7, both instrumental variables are considered together. Finally, in Model 8, I replace the \u003cem\u003eRural_banks\u003c/em\u003e variable with an internal instrumental variable, which is represented by the 5-year lag of the endogenous variable. This deep lag is commonly used in empirical literature when appropriate exogenous instrumental variables are not available (Aresu et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This approach allows us to assess the robustness of our results against different hypotheses regarding the endogeneity mechanism, providing a stronger empirical foundation for the conclusions. In all models, the key variable of interest maintains a coefficient that is both positive and statistically significant. The instrumental variables are significant, as evidenced by a high F-statistic from the weak identification test in all models. Furthermore, models 8 and 9 show that Hansen\u0026rsquo;s test confirms the exogeneity condition is satisfied.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 \u003cem\u003ePanel estimations using fixed and random effects models\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eAfter estimating the Pooled OLS and IV-2SLS models, it is important to assess the robustness of the results by estimating panel models with fixed effects (within-estimator). This approach allows for additional control over unobserved heterogeneity at the provincial level, capturing specific time-invariant factors that may be correlated with the error term, thereby mitigating potential estimation bias. However, it should be noted that the fixed-effects panel estimation may not be ideal for the characteristics of the dataset, as the dependent variable, along with many explanatory variables, exhibits low within-variance. This limitation reduces the informational content available for coefficient estimation, potentially leading to identification issues and imprecise or insignificant estimates. Despite these challenges, this approach serves as an additional check to ensure that omitted variable bias is not driving the results, providing further validation of the robustness of the findings from other model specifications to this stricter control for heterogeneity. For robustness purposes, I also estimate a random effects (RE) model, which, unlike the fixed effects (FE) model, treats region-specific effects as random and, therefore, uncorrelated with the error term.\u003c/p\u003e\n \u003cp\u003eIn addition to unobserved heterogeneity, another potential issue could be spatial dependence. By violating the assumption of independence of observations, spatial dependence may lead to biased and inefficient estimates (Anselin, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; LeSage, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; LeSage \u0026amp; Pace, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). For this reason, in addition to the traditional FE and RE estimations, I propose estimating spatial econometric models. First, I estimate two traditional models: the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). The SAR model incorporates the spatial lag of the dependent variable as an additional regressor. The equation can thus be formalized as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSDI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026rho;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}{w}_{ij\\:}{LSDI}_{jt}\\)\u003c/span\u003e\u003c/span\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCCBs\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026Upsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eCompared to Eq. (2), \u0026rho; represents the spatial dependence parameter of the dependent variable, w\u003csub\u003eij\u003c/sub\u003e denotes the elements of the spatial weight matrix, and \u0026micro;\u003csub\u003ei\u003c/sub\u003e captures the provincial fixed effects. The SEM, on the other hand, focuses on analyzing spatial dependence within the error term and can be formalized with the following equation:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSDI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCCBs\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026Upsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;u\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026lambda;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}{w}_{ij\\:}{u}_{jt}\\)\u003c/span\u003e\u003c/span\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eWhere \u0026lambda; is the spatial dependence parameter of the errors. Finally, I also estimate a Spatial Durbin Model (SDM), which accounts for spatial dependence in both the dependent variable and the explanatory variables:\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSDI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026rho;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}{w}_{ij\\:}{LSDI}_{jt}\\)\u003c/span\u003e\u003c/span\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCCBs\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026Upsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}{w}_{ij\\:}{CCBs}_{jt}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e+ \u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{n}{w}_{ij\\:}{\\gamma\\:}_{jt}\\)\u003c/span\u003e\u003c/span\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eWhere \u0026theta;\u003c/em\u003e indicates the spatial spillover effects of explanatory variables. These spatial models are estimated using RE, as in the case of a panel with a relatively short time dimension (t\u0026thinsp;=\u0026thinsp;17) and strong persistence of key variables, fixed effects would not be estimated correctly or consistently (Basile \u0026amp; M\u0026iacute;nguez, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, in equations (6), (7), and (8), the notation \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents macro-regional fixed effects.\u003c/p\u003e\n \u003cp\u003eGiven the irregular geographical configuration of Italian provinces, the k-nearest neighbours (k\u0026thinsp;=\u0026thinsp;5) contiguity matrix is considered. This approach assigns the same number of neighbours to each province, regardless of the shape or size of the territorial unit. The choice of k\u0026thinsp;=\u0026thinsp;5 is not arbitrary but empirically determined through a model selection process, where this parameter provided the best fit in terms of information criteria and predictive performance compared to other specifications with different values of k. This specification is preferable to a traditional contiguity matrix, which could produce unbalanced spatial connections due to the irregular territorial configuration of Italy.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the estimation results. Model 9 corresponds to the traditional FE estimation and Model 10 the RE estimation, while the subsequent models report the spatial estimations obtained using a maximum likelihood estimator. The log-likelihood function includes the Jacobian determinant ln |In \u0026ndash; \u0026rho;w| for the SAR and SDM models, where In is the identity matrix of dimension n. This term is necessary to account for the simultaneity introduced by spatial dependence. In the case of the SEM model, the Jacobian term becomes ln |In\u0026thinsp;\u0026minus;\u0026thinsp;\u0026lambda;w| (Elhorst, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee \u0026amp; Yu, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). In all the estimated models, the key variable of interest exhibits positive and statistically significant coefficients. Notably, in all cases, these coefficients are higher than those observed for the variable \u003cem\u003eOther_banks\u003c/em\u003e, which are not always statistically significant. Regarding the control variables, no substantial changes are observed. Both \u0026rho; and \u0026lambda; exhibit positive and statistically significant coefficients in the estimated models, indicating the presence of spatial dependence in both the dependent variable and the error term.\u003csup\u003e4\u003c/sup\u003e The SDM proves particularly valuable as it incorporates spatial lags of explanatory variables, thereby indirectly capturing the effect of unobserved variables that follow similar spatial patterns. Additionally, through its spatial structure, the model helps mitigate potential risks of simultaneity (Anselin et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Le Gallo \u0026amp; Ndiaye, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this latter model, it can be observed that the spatial lag of CCBs is positive and statistically significant, indicating the presence of positive spatial spillovers. Due to the presence of spatial lags in both the dependent and explanatory variables, the interpretation of coefficients is not straightforward. From the reduced form of the SDM, one can derive direct effects (the impact of a change in variable X in one unit on its own Y) and indirect effects (the impact on other units, spatial spillovers, non-diagonal elements) (Kelejian \u0026amp; Piras, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; LeSage \u0026amp; Pace, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). For the independent variable of interest, both direct effects (coeff. = 0.556, p-value\u0026thinsp;=\u0026thinsp;0.000) and indirect effects (coeff. = 1.239, p-value\u0026thinsp;=\u0026thinsp;0.000) are positive and statistically significant. Therefore, it appears that CCBs generate spatial spillover effects, which are larger than the direct effects.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results II (FE and RE estimations) Note: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eDependent variable: LSDI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.9\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.11\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.12\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.13\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSDM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.588**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.493**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.506**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e(ln) Pop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lambda;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026theta; CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBetween R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvincial FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro-regional FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003eabout here\u003c/em\u003e]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 \u003cem\u003eSample split\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eGiven Italy\u0026rsquo;s well-documented regional dualism, it is crucial to conduct a sample split between Centre-North and South to analyze the impact of CBs on regional sustainable development. This division is warranted by the profound socio-cultural and institutional differences between these macro-areas, which could significantly influence the relationship under investigation. The historical divergence in social capital, institutional quality, and economic development patterns between these regions suggests that CBs might operate and impact local sustainable development through different channels and with varying effectiveness across these two distinct institutional and socio-cultural environments.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e reports the estimation results from OLS and IV-2SLS specifications across Centre-North and South subsamples. As the IV-2SLS methodology addresses the aforementioned endogeneity concerns through appropriate identification strategies, it represents the preferred specification. Therefore, all subsequent empirical analyses are conducted employing the IV-2SLS estimation framework to ensure the consistency and efficiency of our estimates.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results III (IV-2SLS estimations)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eDependent variable: LSDI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentre-north\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.14\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.15\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.16\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMod.17\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.546***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-1.602*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.466***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e(ln) Pop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.059*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.025**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.023***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.026***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro-regional FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary results for first-stage regressions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak id. test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe results reveal a striking geographical heterogeneity in the relationship between cooperative banking and sustainable development. While the Centre-North exhibits a positive and statistically significant relationship between CCBs and sustainable development, the Southern subsample shows a negative and significant coefficient. This divergent pattern suggests that the effectiveness of CBs in promoting sustainable development is heavily contingent on the institutional and socio-economic context in which they operate. The positive impact in the Centre-North might reflect the region\u0026rsquo;s stronger social capital, more developed institutional framework, and deeper historical roots of cooperative banking, which enable these institutions to effectively channel resources toward sustainable development initiatives. Conversely, the negative coefficient in the South could indicate that in areas characterized by weaker institutional quality and lower social capital endowment, CCBs might face greater challenges in promoting sustainable development, possibly due to less efficient resource allocation or different operational constraints.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 \u003cem\u003eAnalysis across sustainability dimensions\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eTo provide a more granular understanding of how cooperative banking influences sustainable development, we decompose our analysis across the three fundamental dimensions of sustainability. By examining the economic, social, and environmental components separately, it is possible to identify whether CCBs exhibit heterogeneous effects across different sustainability domains. Hence, this dimensional analysis is useful to pinpoint the specific channels through which cooperative banking potentially influences local sustainable development.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results of the estimates, which are summarized, for the variable \u003cem\u003eCCBs\u003c/em\u003e, in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Decomposing the analysis across sustainability dimensions unveils nuanced patterns in the relationship between cooperative banking and different aspects of sustainable development. Indeed, the estimation results show that while the \u003cem\u003eCCBs\u003c/em\u003e variable displays a negative but not statistically significant coefficient for the economic dimension, it exhibits positive and statistically significant effects on social and environmental domains. This finding suggests that CCBs\u0026rsquo; impact on sustainable development is not uniform across dimensions, with their strongest contributions manifesting in social and environmental domains. The lack of statistical significance in the economic dimension, coupled with positive significant effects in social and environmental spheres, indicates that CCBs might be particularly effective at promoting sustainability objectives that extend beyond traditional economic metrics, possibly reflecting their distinctive operational model and institutional mission.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results IV (IV-2SLS estimations)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEconomic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.18\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.19\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.20\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.21\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.22\u003c/p\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.23\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.306***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.842***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.480**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.383***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.407***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e(ln) Pop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.199***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.182***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro-regional FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary results for first-stage regressions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_1936\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_1936\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural_1936\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBartik_CCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak id. test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 \u003cem\u003eAlternative IV-2SLS estimations\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eTo further validate the above findings and ensure more robustness to endogeneity concerns, I implement the heteroscedasticity-based instrumental variable approach developed by Lewbel (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This methodology provides an alternative identification strategy by constructing internal instruments from the model\u0026rsquo;s exogenous regressors, exploiting second-moment restrictions and heteroscedasticity in the first-stage regression.\u003c/p\u003e\n \u003cp\u003eThe Lewbel\u0026rsquo;s approach generates instruments by exploiting heteroscedasticity in the first-stage regression through:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Z-\\:\\stackrel{-}{Z}\\)\u003c/span\u003e\u003c/span\u003e)\u0026nu;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{Z}\\)\u003c/span\u003e\u003c/span\u003e represents the mean of Z and \u0026nu; are the first-stage residuals. The validity of this approach relies on the presence of heteroscedasticity in the first-stage regression and two key assumptions:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE(Zϵ)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(10)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE(Z\u0026nu;ϵ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCov (Z, \u0026nu;\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026ne;\u0026thinsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis complementary identification strategy serves as a crucial robustness check, allowing us to assess whether our main findings persist under different identification assumptions.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents estimation results based on Lewbel\u0026rsquo;s heteroskedasticity-based identification strategy. Model 24 employs only heteroskedasticity-based instruments, while Model 25 combines heteroskedasticity-based and standard instruments. The coefficient of the key independent variable remains positive and statistically significant across both specifications, further supporting the robustness of the findings concerning endogeneity issues.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results V (IV-2SLS estimation)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMod.24\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMod.25\u003c/p\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCCBs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.685***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOther_banks\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.408**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e(ln) Pop_dens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDep_ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eForeign_pop\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePrimary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTertiary_emp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMacro-regional FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTime FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary results for first-stage regressions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeteroscedasticity-based instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHeteroscedasticity-based instruments\u0026thinsp;+\u0026thinsp;standard instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWeak id. test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e44.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUnderid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOverid. test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNumber of provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: Standard errors clustered by province in parentheses. Level of significance: 10% (*), 5% (**), and 1% (***). Weak identification test: Kleibergen-Paap rk Wald F statistic. Overidentification test: Hansen J statistic. Underidentification test: Kleibergen-Paap rk LM statistic. First-stage estimates are not reported (available upon request).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Comments","content":"\u003cp\u003eThe estimation results show that CCBs can play a primary role in promoting the transition towards a more sustainable economy overall. Indeed, in all estimated baseline models, the coefficient of the independent variable of interest is positive, providing statistically significant evidence of the positive link between cooperative banking and local sustainable development. There is also fairly robust evidence that other institutional groups of banks (in particular, mutual banks and commercial banks) can benefit local sustainable development. However, the marginal effects of the variable representing the other institutional bank groups are significantly lower than those observed for cooperative banking group. This highlights the prominent role that these banks can play at the local level in promoting sustainability. Interesting evidence emerges from the spatial analysis, which reveals that CCBs could generate positive spatial spillover effects on sustainable development, confirming how they can significantly influence credit dynamics (Basile et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These spillover effects likely operate through two main channels: first, through inter-territorial lending networks that extend beyond administrative boundaries, and second, through the diffusion of sustainable business practices across contiguous areas.\u003c/p\u003e \u003cp\u003eFrom a methodological standpoint, the primary concern of endogeneity has been effectively addressed through the proposed mix of estimation methods. Notably, rather than simultaneity or reverse causality, the main threat to identification stemmed from omitted variable bias. This assessment is supported by the strong temporal persistence in CCB density (as evidenced by its high correlation with rural bank branch density in 1936). Given our relatively contained time horizon, it is unlikely that sustainable development could have exerted significant effects on cooperative banking patterns, thus mitigating reverse causality concerns.\u003c/p\u003e \u003cp\u003eThe results obtained disaggregating the LSDI in its different dimensions has shown interesting evidence that underscore the critical importance of maintaining a pluralistic banking system. The multidimensional nature of sustainability challenges demands diverse institutional responses: CBs excel in promoting social welfare and environmental sustainability, while commercial banks typically drive economic efficiency. This complementarity becomes particularly relevant in light of current debates surrounding banking sector consolidation in Italy. The trend toward reduced institutional diversity through mergers and acquisitions may compromise the banking system\u0026rsquo;s capacity to effectively address the complex, multifaceted challenges of sustainable development.\u003c/p\u003e \u003cp\u003eOf particular significance are the varying performances of CCBs observed across the Centre-North and South macro-regions. These differentials highlight the importance of institutional context in shaping the relationship between CBs and sustainable development. This pattern is not surprising, considering how local specificities exert profound influence on CCB operations (Agostino et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, Catturani et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrate that CCBs require adequate social capital to operate effectively, as without a robust network of trust relationships, a banking model based on relationship lending may not fully achieve its potential effectiveness. This, in my view, represents a plausible interpretative framework, as the distinction between Centre-North and South, despite its internal heterogeneity, effectively reflects the territorial divide in bridging social capital endowment \u0026mdash; that form of social capital from which generalized trust originates (De Blasio \u0026amp; Nuzzo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding control variables, the structural dependency index yields results consistent with our expectations. Results for other control variables are either ambiguous or lack statistical significance to draw definitive conclusions. However, given their role as control variables, we must exercise caution in interpretation, limiting our analysis to the observation of correlative patterns rather than causal linkages.\u003c/p\u003e"},{"header":"6. Concluding remarks","content":"\u003cp\u003eThis study has examined the impact of CCBs\u0026rsquo; territorial presence on local sustainable development in Italy. The findings demonstrate that CCBs can effectively support sustainable development, although their effects exhibit significant heterogeneity across macro-regions and through the various constituent dimensions of local sustainable development. Specifically, the analysis reveals that CCBs\u0026rsquo; contribution to sustainable development is primarily concerned with promoting social welfare and environmental sustainability, while no evidence emerges supporting their positive impact on economic dynamism. Furthermore, the analysis reveals differential effects between the Centre-North and South macro-regions, suggesting that the institutional context may shape the relationship between cooperative banking and sustainable development.\u003c/p\u003e \u003cp\u003eThese findings yield two significant policy implications. First, they underscore the critical importance of preserving banking system pluralism. The differential impact of CCBs across sustainability dimensions suggests that a diversified banking system, where distinct institutional types complement each other, is essential for addressing the multifaceted challenges of sustainable development. In this context, the current trend toward banking sector consolidation in Italy raises concerns about the system\u0026rsquo;s capacity to effectively support all dimensions of local sustainable development. Furthermore, the territorial heterogeneity in CCBs\u0026rsquo; impact on sustainable development indicates how stronger social cohesion, more developed support networks, and more effective local governance in Central-Northern regions can amplify CCBs\u0026rsquo; positive impact. Conversely, in Southern regions, where we can observe a generally lower institutional quality, the context may constrain the effectiveness of CCBs. This heterogeneity emphasizes the need for policies that account for local specificities, focusing on strengthening the social and institutional fabric in areas where cooperative banking\u0026rsquo;s effectiveness appears limited, rather than adopting uniform national approaches.\u003c/p\u003e \u003cp\u003eThis study exhibits several limitations. The primary constraint pertains to the temporal scope of the analysis which, given the high stability and persistence of key variables, has permitted only a static rather than dynamic analytical approach. Investigating the dynamic relationship between cooperative banking and sustainable development would require data spanning a significantly longer period to capture structural changes that occur gradually. Indeed, a dynamic analysis would yield additional and more robust evidence concerning the potential causal relationship between cooperative banking and sustainable development. Although this represents a crucial objective for future research in this field, collecting regional data over extended periods could be problematic due to limited availability. While cross-sectional analysis presents an alternative methodological approach, it demonstrates less effectiveness in addressing endogeneity concerns.\u003c/p\u003e \u003cp\u003eA second limitation addresses territorial heterogeneity. Although the analysis employs provincial-level data (NUTS-3) to ensure a comprehensive dataset, this geographic aggregation may mask significant intra-provincial heterogeneity that could be better captured through municipality-level analysis. Using more granular spatial data would enable a more detailed examination of local variations in the relationship between cooperative banking and sustainable development.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwal S, Hauswald R (2010) Distance and private information in lending. Rev Financial Stud 23(7):2757\u0026ndash;2788\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostino M, Errico L, Rondinella S, Trivieri F (2022) Do cooperative banks matter for new business creation? Evidence on Italian manufacturing industry. 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Technological Economic Dev Econ 27(1):45\u0026ndash;70\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e According to the International Cooperative Alliance (ICA), a CB is \u0026ldquo;an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly-owned and democratically-controlled enterprise. Cooperatives are based on the values of self-help, self-responsibility, democracy, equality, equity and solidarity. In the tradition of their founders, co-operative members believe in the ethical values of honesty, openness, social responsibility and caring for others\u0026rdquo; (ICA,1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The Brundtland Report provided what has become the most influential definition of sustainable development, characterizing it as \u0026ldquo;development that meets the needs of the present without compromising the ability of future generations to meet their own needs\u0026rdquo; (WCED, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, p. 43).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The rural banks were credit cooperatives established to provide financial support to farmers and artisans, tailored to the needs of local and family economies. Originating in Prussia in 1849 under F.W. Raiffeisen, they were introduced in Italy in 1883 by L. Wollenborg, who founded the first rural banks in Loreggia (Padua). Supported by the clergy and concentrated particularly in Trentino-Alto Adige, they peaked in 1922 with over 3,500 institutions. In 1937, their activities expanded to include artisans, leading to a name change. Following the 1993 banking reform, they became \u003cem\u003eCCBs\u003c/em\u003e, retaining their mutualistic mission to foster economic and social development.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Considering the possibility that the estimation results of the spatial econometric models might be influenced by the choice of the spatial-weighting matrix, I also estimate the models using alternative matrices. The results of these additional estimations, which are not significantly different from those illustrated here, are available upon request.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Guido Carli Free International University for Social Studies","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":"Cooperative banking, sustainable development, Italy, panel data, sustainable finance","lastPublishedDoi":"10.21203/rs.3.rs-6264324/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6264324/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper empirically investigates the relationship between cooperative banking and local sustainable development through a panel analysis of 103 Italian provinces over the period 2005–2021. Results show that cooperative credit banks exert a positive impact on local sustainable development, although this effect manifests heterogeneously across different dimensions that characterize it. The analysis also reveals a heterogeneity in CCBs’ impact across Italian macro-regions, suggesting that the sociocultural and institutional context might play a crucial role in shaping the relationship between cooperative banking and sustainable development. These findings provide valuable insights for developing effective policy strategies for boosting local sustainability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classifications: \u003c/strong\u003eG21, Q01, R11.\u003c/p\u003e","manuscriptTitle":"Cooperative banking and local sustainable development in Italy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 04:35:05","doi":"10.21203/rs.3.rs-6264324/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":"72995703-7873-482a-9ef0-4a7003cb7639","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T04:35:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 04:35:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6264324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6264324","identity":"rs-6264324","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}