Frequency vs. Size of Bank Fines in Local Credit Markets

We examine how banking supervisors affect credit at the local level by charging fines to individual banks. Using a macro approach to capture the direct effect on the fined bank and the indirect effect on the other banks operating in the local credit market, we estimate reputational, reallocation and balance sheet effects on Italian provinces over the period 2005–2016 by a fixed effects model and instrumental variables. Provincial gross bank loans expand after a fine independently of its size. The impact of fine frequency depends on the size of the provincial banking sector, but neither on bank governance/ownership nor crises. No statistically significant evidence supports reputational or balance sheet effects. Instead, our results suggest that it would behoove bank supervisors to favor frequency over size of bank fines. Bank fines seem to work more like a good housekeeping seal of approval, enhancing transparency and effective banking practices.

Introduction and Theoretical Underpinnings
This paper examines how banking supervisors influence credit at the local level-in our case the Italian geographical area defined by the province-by charging fines to banks in breach.
Enforcement actions by bank regulators-in our case the Bank of Italy (BI)occur typically in the aftermath of on-site inspections and may include pecuniary fines, prompt corrective actions, temporary cease and desist orders, and moral suasion initiatives. Most of these measures are kept strictly confidential, while a few are publicly disclosed and available to researchers. We restrict the domain of enforcement actions to published pecuniary bank fines and estimate their overall effects on Italian provinces for the period 2005-2016. In contrast to previous bank-level studies, we use aggregate data to capture both the direct impact of the sanctioning process on the fined bank and the indirect effect emanating from other banks' reactions. The latter is crucial for the analysis of the local credit market because with several banks serving the same local market, the indirect effect may dominate the direct one. 1 The macro approach, furthermore, offers two statistical advantages. First, it reduces the sample selection distortion and biased estimates resulting from severely unbalanced bank-level panel data. Second, it does not suffer from the efficiency loss due to the increase in the number of estimated variables and reduction in the number of usable observations that instead affect unbalanced bank-level panel data.
We hypothesize three transmission channels from pecuniary bank fines to provincial credit. The first is a balance sheet effect. Say an inspection reveals a breach in the classification of loans; the fine may come with a correction to shift some loans from the "good" category to the impaired loan category. The consequence of this action is an additional write-down due to the higher non-performing loans (NPL). If the bank absorbs the pecuniary fines charged to the management through higher compensation or more insurance protection, the cost of the fines affects the bank's capital ratios and income account.
If, instead, the inspection reveals a systematic management strategy of supporting friendly "zombie" firms, the fine will generate effects that go beyond an accounting adjustment: the bank in breach will be obliged to reduce its loan portfolio to lower its risk-weighted asset indicator to restore the prescribed capital and financial ratios. This second transmission mechanism of asset repricing and resizing will be larger in provinces with a concentrated banking sector, and smaller in provinces with more competitive credit markets. The indirect effects of the fine will include the reaction of rival banks with similar asset allocation of the offending bank, but yet not found in breach by the regulator; the rival banks will be induced to act preemptively by making adjustments in the classification of loans and write-downs. In time, a more transparent credit procedure would promote a more diversified allocation of resources Frequency vs. Size of Bank Fines in Local Credit Markets that, in turn, would raise bank efficiency and lead to an expansion of credit, for the same level of risk or a reduction of overall risk for the same level of credit. We call the second transmission mechanism the (provincial) reallocation effect.
The third transmission channel involves the role of borrowers. A sanctioned bank loses reputation; as a result, part of the demand for its loans is diverted elsewhere. If the market believes that the sanctioned practice is specific to the fined bank, the provincial loan supply will not change: the loss of credit suffered by the sanctioned bank will be compensated by the gain in credit supplied by other banks. But, if the market suspects that other banks may have followed the practice of the sanctioned bank, the provincial demand for credit would decline. We call this demand-side driven reaction to bank fines the reputational effect. In sum, we have three channels-balance sheet, reallocation, and reputation-that occur at different times after an inspection assessing a bank fine. With our annual data, we can disentangle the impact on provincial credit of each of the three distinct channels (through separate estimated coefficients), but not their timing sequence. In the empirical part of the paper, we will see that only the reallocation channel is statistically significant; the other two are not. Therefore, as a practical matter, the importance of the timing sequence of the three channels becomes less important.
The motivation for focusing on the Italian provincial credit market stems from the fact that the Italian economy, with its emphasis on small and medium-sized firms, is extremely dependent on bank credit: numerous small local banks and large national banks compete at the provincial level. 2 The spatial overlap of small local banks and national banks in the provincial credit markets provides a suitable environment to study the effects of bank fines and their spillovers along the lines outlined with the three transmission channels. On the other hand, bank competition in Italian provinces works against our hypothesis of the effectiveness of bank fines because credit supply adjustments tend to be smaller in competitive markets, whereas our hypothesis tests for their presence. Low pecuniary fines and high NPLs also work against our hypothesis because bank fines are spread over multiple provinces, which further reduce their impact on local credit supply, and the effects of the corrections to NPLs are harder to detect at the provincial level. Lastly, we use total loans as the dependent variable because the data on NPLs, our preferred choice, have many missing values. 3 As many accounting adjustments occur between good and impaired loans, it also works against our hypotheses. In brief, our hypothesis faces headwind forces; to the extent that Italy represents a tough case for our hypotheses, our findings are more likely to hold also in other, less competitive, environments.
The selection of the sample period was determined by data availability and institutional reforms. In particular, the starting year of 2005 coincides with a 2 The number of banks in Italy has decreased from 698 in 2008 to 497 in 2016. The sample average value of the provincial bank Herfindahl-Hirschman index (computed on the basis of provincial shares of bank branches) is 0.15 out of a maximum of 1. The low concentration of bank branches suggests a quite competitive sector. 3 It creates a further bias against finding an effect of bank fines on provincial credit supply because the bulk of balance sheet adjustments occurs in the loan category. reclassification of accounting items; the choice of the terminal date of 2016 is due to the approval of three main institutional and legal reforms that altered the governance structure of popular and cooperative banks and reorganized the whole sanctioning process; see section II below.
This paper contributes to the related literature in several respects. The first is that our empirical results at the local credit market level refute the literature on the negative effects of enforcement actions on bank performance. Our findings are close to the most recent papers, such as Leuz and Granja (2019) and Hirtle et al (2019), corroborating that bank loans expand after a bank sanction. We find that provincial credit supply is sensitive to the frequency of fines, but not to the amount of the fines. The imposition of fines, after an initial change in loan classification, can induce sanctioned banks and other banks in similar situations to revisit their lending and risk management practices, in turn leading to credit reallocation and loan expansion. The second contribution is that the paper identifies three potential effects of bank fines on local credit-balance sheet effect, the reallocation effect, and the reputational effect-and suggests a specific empirical strategy to identify them. The third contribution recognizes that the reallocation effect is the dominant one, whereas no evidence was detected for the other two effects. Finally, the impact of bank fine frequency on provincial credit is sensitive to the size of the provincial banking sector, but not to bank governance or to the crisis period of 2008-2012.
Our findings have policy implications. Supervisors may differentiate the frequency of fines among different types of banking institutions. Italian regulators have historically made a distinction between one-member-one-vote mutualistic banks and larger national joint-stock banks (Giordano 2007). Our paper finds no evidence of a similar distinction in bank governance on the effects of supervision. Instead, breaches interact negatively and significantly with bank size, the implication being that provinces with a large bank sector require a higher frequency of bank fines than provinces with smaller bank sectors to offset the drag on provincial bank credit due to larger bank size. With an average provincial bank size of €4.1 billion, 80% of Italian provinces find that the positive effect of fine frequency on provincial credit is actually more than compensated by the negative impact on credit due to provincial bank size. Our simulation shows that if fine frequency were to rise by 25%, its positive impact on credit would dominate the negative impact due to bank size in all provinces. The inference is that the fine mechanism acts as a salutary monitoring device that spurs efficiency: bank supervision acts more as a good housekeeping seal of approval than as a policeman.
The rest of the paper is organized as follows. Section 2 summarizes the literature. Section 3 describes the sanctioning process. Section 4 deals with the adopted methodological approach and hypotheses. Section 5 presents data sources and descriptive statistics. Section 6 discusses the empirical strategy and our findings. Section 7 deals with robustness checks and presents the results of a simulation exercise that evaluates the contrasting forces at work in the empirical findings. Conclusions are drawn in Sect. 8.

3
Frequency vs. Size of Bank Fines in Local Credit Markets

Related Literature
The literature on the effects of banking supervision on local credit markets is of relatively recent vintage and is not very extensive. We group studies according to our three transmission channels: the balance sheet effect, the reallocation effect, and the reputational effect.
A first strand of the literature takes a microeconomic perspective in analyzing the effects of bank supervision on bank performance. The empirical works typically focuses on supervisory output data, such as on-site inspections, and resulting formal enforcement actions, which estimate the effectiveness of bank supervision more precisely than earlier research based on supervisory input indicators. Using US data, Peek and Rosengren (1995) investigate the direct link between supervisory actions carrying civil penalties and the dynamics of loans in bank dependent sectors: banks subject to formal enforcement actions shrink credit activity at a faster rate than those with no enforcement actions. This is consistent with our balance sheet effect. Delis and Staikouras (2011) examine whether supervisors inspecting banks more regularly and adopting stricter enforcement are better positioned to restrain banks' risk-taking appetite, an issue we take up in our empirical work. The authors, in line with our balance sheet channel, find that the impact of on-site examinations on their measures of bank risk is non-linear with a relatively high threshold level of audits beyond which their relationship with risk becomes negative. Agarwal et al. (2014) note that stricter supervision in the US, due to the mandatory rotation between state and federal regulators, leads banks to deleverage and achieve higher capital ratios. Similarly, but for euro area banks, Eber and Minoiu (2016) study the effects of stricter supervision scrutiny on bank balance sheets and find that European banks adjust to stricter supervision by shrinking assets-especially securities-rather than raising capital. Based on a US sample, Delis et al. (2017) assess the effect of formal enforcement actions directly in relation to core financial safety and soundness. Their findings suggest that enforcement actions reduce risk-weighted assets, principally through less lending and NPLs.
A second strand of the literature follows an aggregated approach in analyzing the impact of supervisory actions on macroeconomic variables. Bassett et al. (2015) show that elevated supervisory stringency has a noticeable dampening effect on future lending activity. The authors estimate the relationship between changes in CAMELS ratings and changes in small businesses lending and find that small banks with rating downgrades during 2007-2010 exhibit lower lending growth to small businesses compared with banks with unchanged ratings. For Germany, Berger et al. (2016) show how supervisory actions disrupt liquidity creation. Danisewicz et al. (2017) use a macro approach to examine at the level of US counties how enforcement actions on banks and liquidity creation exert negative effects on per-capita income growth, the number of establishments and unemployment with the identified macro-financial linkages operating through reductions in bank lending. However, these adverse effects on the real economy seem to be temporary and typically last 1 year. Finally, analyzing the Italian case, Caiazza et al. (2018) focus on spillovers of enforcement actions on non-sanctioned banks under the assumption that sanctions serve the twin objectives of penalizing the offending banks and warning other banks. The authors find that credit risk-related fines assessed on Italian banks reallocate lending in a way that non-sanctioned banks tend to behave more similarly to sanctioned bank, evidence bearing on our reallocation effect channel.
Apart from the last paper, all cited studies tend to associate closer supervision with bank deleveraging and adopting tighter criteria in making new loans, a finding that tends to support the balance sheet effect, operating through significant asset resizing. Moreover, stricter supervision seems to have a disciplinary impact upon sanctioned banks through more effective market discipline, which in turn could trigger the reputational effect resulting from public disclosure of audits' findings and more transparent financial reporting.
The above negative relationship between enforcement actions and lending activity growth raises a welfare concern about the effectiveness of banking supervision: does stricter supervision have a negative effect on credit growth and economic activity? On this question, the literature is far from being conclusive. A few recent papers corroborate the prevalence of the reallocation effect over the balance sheet and reputational effect, suggesting that stricter supervision can boost credit growth. For example, Leuz and Granja (2019) show for the US that the transition from a more to a less tolerant supervision raises the amount of loans disbursed to small business, suggesting a positive value added by more demanding supervisors. Their findings also suggest that stricter supervision can overcome frictions in bank management and raise the amount of loans, indirect evidence in favor of the dominance of the reallocation effect. Echoing Leuz and Granja, Hirtle et al. (2019) underscore the positive contribution of supervision in mitigating banking sector risk. Exploiting the structure of the US supervisory system, the paper finds that Federal Reserve examiners spend proportionally more time at the largest banks in their district, and those banks hold less risky loan portfolios and are less sensitive to industry downturns, without experiencing adverse consequences on their growth or profitability. While the authors do not discuss the specific channel which may account for their findings, they posit that more attentive supervision may assuage principal-agent problems and hence promote a more careful risk-taking assessment. For example, a recent contribution by Bonfim et al. (2020) finds that on-site inspections of bank credit portfolios by Portuguese bank supervisors reduce future lending to "zombie" firms. This positive outcome, furthermore, lasts when inspectors are long gone. The implication is that either sanctioned banks learn from the supervisors or they take into account the threat and the costs of future inspections. Both explanations are consistent with the idea that on-site supervision can affect future bank lending decisions providing additional evidence of the role played by the reallocation effect.
Another successful impact from enforcement actions is provided by the analysis of the syndicated loans sector by Deli et al. (2019), where a causal effect emerges of financial penalties on the cost of borrowing: the cost of loans originated after a bank receives a fine is significantly lower compared to loans originated before the fine. This finding can be attributed to competitive and reputational effects: a fine makes the sanctioned bank less reputable and consequently may induce management to apply better lending terms to retain customers. According to the authors, 1 3 Frequency vs. Size of Bank Fines in Local Credit Markets this effect persists for about four years after the sanction has been levied and constitutes a strong welfare case for enforcement actions.
Our brief review of the literature highlights a variety of outcomes on the impact of enforcement actions on bank credit. We have seen that sanctions affect not only the behavior of the offending banks, but also that of the non-sanctioned banks. In our paper, the effects on these two groups of banks emerge indirectly because our key variable is provincial credit, where both sanctioned and non-sanctioned banks operate. In sum, whether less lenient supervision reduces or boosts lending and the economic activity remains an empirical issue. We note that, apart from Danisewicz et al. (2017), published papers have not analyzed the local aggregate impact of bank fines. Passalacqua et al. (2020) apply a diff-in-diff estimation to a proprietary (Bank of Italy) dataset on a subset of Italian banks, banche di credito cooperativo (BCC). Their working paper finds that bank inspections exert a negative, temporary, balance sheet effect on inspected banks (by forcing write-offs) and a positive reallocation spillover for healthy firms (by squeezing zombie lending). These results suffer, however, from identification and endogeneity problems that the authors mitigate by using bank subsamples, thus reducing the general validity of their findings. 4 Yet, the authors use the results on BCCs to provide a more general interpretation of their findings.
Our paper applies data from the Italian banking universe to a growth model of credit, and it estimates the overall impact of bank supervision on the real economy at the provincial level. By design, this approach does not suffer from selection bias because we consider all the banks. We address endogeneity issues with instrumental variables. Our work, in sum, intends to fill the gap in the literature on the connection between bank supervision and local (provincial) credit.

Institutional Background
BI is the supervisory authority of the Italian banking sector, jointly with the European Central Bank (ECB) since 2012. The sanctioning process of BI is regulated, at the primary level, by law and, at the secondary level, by various norms issued by BI. 5 Within this legal framework, BI has been empowered to levy administrative fines to banks in breach of regulations designed to protect the sound and prudent management of banking activity, the correctness and transparency of conduct, and the prevention of money laundering. The main entities subject to the sanctioning procedure are people in bank administration, management, and control, and auditing entities. 6 Recently, a major revision of the sanctioning process was undertaken at the European level. It started in July 2011 with the Capital Requirements Regulation (CRR) and the Capital Requirements Directive (CRD IV) and it was aimed at defining in more detail, among other things, the sanctioning process for banks. The maximum fines were set, in principle, at a dissuasive level, that is to nullify the incentive perpetrators may derive from a breach. The same objective was set for other measures, such as public reprimands, orders to cease, and temporary suspensions of functions. In May 2016, the Italian regulatory framework was made compatible with the European one, a change that has no direct bearing on our research given that 2016 is the end of our sample period. 7 We recall that Italy has a dual banking system, where a significant number of mutual banks coexist alongside few national banks. In addition to serving local markets, mutual banks differ from joint-stock banks in that their depositor-members can cast only one vote irrespective of the amount of their capital shares; so memberbased governance is also another characteristics of mutual banks.
The two main groups of local banks are banche popolari (BP) and banche di credito cooperativo (BCC). A 2015 law restructured BPs to alter the traditional governance principle (Visco 2018). According to this law, BPs with total assets in excess of €8 billion had either to convert into a joint stock legal structure or reduce their size below €8 billion. A 2016 law restructured BCCs with the objective of increasing economies of scale and scope and improving the quality of management. Each BCC had the option of either becoming a joint-stock company (and thus give up the one-member-one vote governance) or joining a group of BCCs anchored to a joint-stock parent bank. The intent of the law was to engineer consolidation, risk diversification, and reduce opaque or friendly lending (Coccorese and Shaffer 2018). Both laws, whose draft proposals were debated for years, accelerated the process of bank consolidation through the formation of holding group structures. 8 The ensuing consolidation process was largely anticipated by banks and could have reduced differences in governance way before 2016. 8 The empirical evidence about the role of small local banks versus large commercial bank on economic growth is vast but inconclusive suggesting that there is no optimal bank size. Many papers find a positive impact of small local banks on regional growth, corroborating the presence of comparative advantages of small institutions in acquiring and using soft information through personal and repeated interactions with local customers. For instance, in the case of Italy, Lucchetti et al. (2001), Usai and Vannini (2005), Coccorese and Shaffer (2018) find positive effects of the presence of small cooperative banks on local economic growth. In contrast and more recently, Stefani et al. (2016) show that during the great financial crisis local banks' lending policies were riskier, produced more NPLs, eventually outweighing the benefits of customer proximity and personal relationships. 6 BI ascertains violations, conducts preliminary investigations, and imposes pecuniary fines. The sanctioning procedure is divided into the following phases: assessment of violations; dispute of violations; presentation of rebuttals and possible personal hearings; evaluation of all relevant instructive elements; proposal to BI Directorate for the imposition of fines or dismissal of the proceeding; adoption of the sanctioning provision or filing of the proceeding by the Directorate; notification and publication of the provision containing the details of the levied fine. 7 An indirect effect could have occurred with the banks anticipating the new regulatory framework.

Frequency vs. Size of Bank Fines in Local Credit Markets
Our paper focuses on the outcome of the supervisory process leading to fines levied by BI, the only information in the public domain. We collected data on pecuniary fines over the period 1998-2014 from the BI Monthly Supervision Bulletin and for 2015-2016 from BI's website. For each fine we also collected the monetary amount disaggregated by the area of infringement, and for sanctioned people. Breaches related to credit risk disclosure and management, internal controls, conflict of interest and governance were aggregated under the rubric of credit and credit-related violations (BC), whereas breaches related to money laundering, salary and compensation, transparency, and controls exercised by the Supervisory Board were grouped under the rubric of non-credit violations (BO). Our classification is somewhat similar to that by Caiazza et al (2018) but differs in that we have a more expansive list of breaches. 9 Furthermore, in our empirical work we treat fines both as dummy variables and pecuniary values, whereas Caiazza et al. (2018) use only dummy variables.
Our annual data do not allow to estimate the timing of the effects of our three transmission channels. However, the overall duration of the sanctioning process is crucial to determine the lag structure of the empirical model. Three factors are at play: the duration of the inspections culminating in a fine, the impact of the fine on the bank in breach, and the spillovers from the fined bank to other banks operating in a province. The duration of an inspection depends on various factors such as the type of inspection, its results, and the size of the bank. According to Passalacqua et al. (2020, p 14), on-site inspections last, on average, 66 days, but there is a great spread around this average. Within 90 days from the end of the inspection, inspectors submit their findings and observations to the management of the inspected bank. The recipients have up to 30 days to respond (BI 1999, Titolo VI, Capitolo 4, Sezione II). If the inspectors uncover a sanctionable infraction, BI has 90 days to notify the sanction to the bank and relevant parties; these are given 30 days to respond. The final decision on the sanction, taken by BI, can take up to 240 days (BI 1999, Titolo VII, Capitolo 1, Sezione II; BA 2012, Sezione II). The two procedures, inspection and sanctioning, can well stretch beyond a year. To that one must add the time related to the impact of the fine on the activities of the bank in breach and the time required for the spillovers to unfold from the bank in breach to other banks operating in a given province. 10 In sum, the timing of the effects of the three transmission channels across the inspection process, the bank in breach, and spillovers to other banks is such to justify a dynamic specification of our empirical model.

Methodology and Hypotheses
For our empirical investigation we use a credit model at the provincial level built around a standard economic growth model (Barro 1991;Mankiw et al 1992;King and Levine 1993;Levine et al 2000;Law and Singh 2014). The growth of gross provincial bank credit (ΔlnCR j,t ) depends on the log of its lagged level (lnCR j.t-1 ), a demand factor given by the current growth of per-capita GDP (ΔlnYpc j,t ) and a supply factor given by a set of lagged provincial control variables (CTRL j,t-1 ). These include a measure of bank liquidity captured by the ratio of bank liquidity to total bank assets (LR j,t-1 ), a measure of bank leverage captured by the ratio of total bank assets to bank equity (LEV j,t-1 ), a measure of bank concentration captured by the Herfindahl Index of bank branches in the province (HI j,t-1 ), and a measure of bank profitability given by the ratio of bank profits to bank total assets (ROA j,t-1 ).
According to different specification of the model, we augment our model to include a measure of bank fines frequency, BCn, and a measure of bank fines amount, BCa. The full model and the relative testable implications are summarized in the following equation: BCn works through a credit reallocation process: the bank is sanctioned because of risky and non-transparent practices skewed towards higher-risk loans. The discovery of the breach, followed by a fine, lowers the bank manager's risk-taking preference and raises his or her incentive to achieve a more diversified, efficient, and transparent loan portfolio, i.e. with fewer loans to zombie firms. Other banks, in a similar situation, could preemptively adjust their loan portfolio to dodge a fine. The aggregate impact is affected by the distribution and exposure of banks operating in the provinces. The testable implication is that receiving fines has a positive impact on bank credit due to the reallocation effect; this is HYP 1: 1 > 0 . Its alternative is that supervision is driven by political goals and aims at making zombie firms survive.
The second fine variable, BCa, acts as a pecuniary cost borne by the sanctioned bank. Even if the provincial impact is proportional to the exposure of the fined bank in a province, competing banks, in a similar situation, could also expect a fine and decide to raise pre-emptively reserves. The testable implication is that this cost has a negative impact on the bank's balance sheet and hence on bank credit; this is HYP 2: 2 < 0 . The alternative is that a fined bank takes more risk because it relies on an implicit safety net.
The third effect arises with the public losing trust in banking when an individual bank is fined. The stigma can either be limited to the sanctioned bank or generalized to other banks at the provincial level. In such a case, the banking system as a whole (1a) loses credibility and borrowers switch to other sources of financing. The testable implication is that bank credit will be less sensitive to a demand variable like Ypc and that the interaction between Ypc and bank fines will affect bank credit negatively; this is HYP 3: 3 < 0 . The alternative is that the breach raises confidence and trust in the banking system because the "bad" bank was identified and punished. The three hypotheses appear in the second line of Eq. (1). Finally, we test for possible asymmetric effects due to the dominance of different types of bank governance or bank size in the province. Differences between banks reflect the specific nature of mutual or community banks and large joint-stock banks. These two types of banks may differ in terms of governance: ownership staff management and board of directors are always locally based in the case of mutual banks (Fonteyne 2007;Kim and McKillop 2019) and size affects the capacity to obtain and process hard and soft information (Alessandrini et al 2010). Banks interaction at the provincial level could show aggregate effects beyond the bank level. HYP 4 tests whether the provincial impact of fines may be sensitive to bank size, measured as the logarithm of total credit, rather than to bank governance (see the third line of Eq. 1). In particular, our hypothesis is that fines to large banks are geographically less specific than fines to small banks; hence they send a weaker signal and have a smaller impact to the local credit market; this is HYP 4: 1 < 0 . Assuming that banks do not anticipate the reforms, HYP 5 proposes an alternative to HYP 4, namely that the provincial impact of fines discriminates between mutual banks (noSPA) and joint-stock banks (SPA). In this case, in sympathy with the extended literature on positive effects of mutual and community banks on local credit, our hypothesis is that bank fines to large banks have smaller negative effects on local credit; this is HYP 5: 2 < 0.
In a nutshell, our null hypotheses can be summarized as follows: Null Hypothesis Description

Descriptive Statistics
Our dataset includes 989 supervisory interventions leading to 1930 pecuniary fines. Figure 1 shows the evolution of BC and BO expressed in euro amounts from 1998 to 2016. The early years, 1998-2004, display a relatively minor sanctioning activity; we omitted them from the sample. To arrive at a homogeneous sample, we chose 2005 as the starting year of our analysis because in that year a new accounting classification was introduced. In the same vein, we selected 2016 as the terminal year because relevant changes occurred in the legal banking framework (and institutional change in mutual banks' governance and a new sanctioning process). As to the size of bank fines, Fig. 1 underscores that BC fines are higher than BO fines. The two series, on the other hand, are positively correlated.
In the empirical work, we use BC as a determinant of bank credit, whereas BO is employed as an instrument to handle potential endogeneity between bank credit and BC. The intuition is that the absence of non-credit fines signals to the regulator a general disposition of legality in the banks operating in the province, whereas the absence of credit-related fines is a manifestation of legality in a specific sector.
Concerning the remaining data, we drew accounting data of Italian banks from Associazione bancaria italiana (ABI). All data-accounting and fine data-have an annual frequency and were transformed from individual-bank data to provinciallevel data. This transformation was done by multiplying balance sheet data for each bank by the ratio of its provincial branches to its total branches at the national level. The final dataset is obtained by summing each variable of interest at the provincial level; see Appendix for further details.
Tables 1 and 2 presents descriptive statistics both at the bank level and at the provincial level respectively. In Table 1 (bank level), the first five variables -credit, liquidity, total assets, equity and profits-are in thousands of euros whereas the next three variables are ratios; LR is liquidity as a ratio of total assets, LEV is leverage measured as total assets divided by equity; ROA is return on assets. The next group of variables-SPA, noSPA, BCC, BP and CRISIS-are dummy variables. In particular, CRISIS is a dummy equal to 1 from 2008 to 2012 capturing both the great Some aspects of the statistics are worth mentioning. ROA is extremely low with a mean value of 0.22%. Its distribution is extremely wide, ranging from a minimum of -66.9% to a maximum of 19.3%; there is a high number of outliers and the frequency distribution has a left tail. Regarding the corporate structure, 70% of Italian banks are either credit cooperative banks or popular banks; the remainder 30% are joint-stock banks. Bank fines average €7032 for credit-related breaches and €1700 for non-credit related breaches. The highest bank fine in the sample was €7,154,200. These amounts appear, at first sight, to be too small to expect a significant material impact on bank behavior. Our fine variable has a high number of outliers and its frequency distribution is leptokurtic. Table 2 presents descriptive statistics at the provincial level. The number of observations in most cases is 1293. The first five variables of the table are provincial data collected by ISTAT: per-capita GDP (Ypc); the net number of firms in the Chamber of Commerce registry (Firm_ENR); the labor participation rate (Partrate); the share of cooperative firms as a proxy of social capital (Cooprate); the number of thefts (Robrate). All other variables are obtained by the noted transformation from bank-level data to provincial data using our weighting scheme based on shares of banks' provincial branches. In particular, BCa is the amount of the provincial euro Variables: LIQ liquidity (in thousands of euros), TA total assets (in thousands of euros), EQ capital (in thousands of euros), PRO profits (in thousands of euros), LR liquidity/TA ratio, LEV leverage as total asset to equity, ROA return on assets, SPA dummy equal to 1 for joint stock banks, noSPA dummy equal to 1 for non-joint-stock banks, BCC dummy equal to 1 for credit cooperative banks, BP dummy equal to 1 for popular banks, CRISIS dummy equal to 1 for the period 2008-2012, BCa euro amount of creditrelated bank breaches, BOa euro amount of credit-unrelated bank breaches

Empirical Strategy and Results
Our empirical strategy goes as follow. We estimate Eq.
(1) first with fixed time and provincial effects (FE) and then with fixed effects and instrumental variables (FEIV) applying two-stage least squares (2SLS) so as to address endogeneity problems. 11 Three right-hand side variables of Eq. (1) potentially suffer from endogeneity: percapita income, Ypc, and the two measures of fines, BCn and BCa. Income and credit are positively related. When income starts growing credit tends to move in the same direction, but it is also the case that when credit grows income follows. Hence, we cannot exclude bidirectional causality. A similar argument applies to the relationship between credit and credit-related fines. While the paper starts with the premise that BC has an impact on CR, one cannot exclude that, as business conditions and credit deteriorate, supervisors increase their activity and discover more breaches. After all, deficiencies and malfeasance tend to emerge especially in times of distress. We treat the endogeneity issue by utilizing four sets of instruments in the first stage. As in Danisewicz et al. (2017), we include as instrument the non-credit related fines. The reason, as mentioned previously, is that, in addition to following the literature, BO signals the banking legal provincial disposition. We also add indicators of business activity, indicators of social capital, and indicators of criminal activity, all at the provincial level. These variables capture the ease with which one can start and maintain a business in a specific province, a factor that influences the local demand for credit. Apart for the non-credit related fines, the choice for the other instruments was dictated by data availability. Potentially better instruments lacked sufficient coverage or were unavailable at the provincial level. Furthermore, other instruments with a sufficient data coverage turned out to be statistically insignificant and failed to pass basic IV tests; or, if they marginally passed them, the estimated coefficients were not different from those obtained under fixed effects. Our instruments pass endogeneity test (Wooldridge 1995), reject overidentification test (Sargan 1958), and show statistically significant estimated coefficients. For brevity, we report only FEIV estimates using instruments that: (i) have a sufficient data coverage in term of years and provinces (i.e. data availability); (ii) pass basic IV tests suggesting that FEIV estimates are better than straight FE estimates; and (iii) generate statistically significant coefficients that are different from those estimated by FE estimates. In sum, FEIV estimates are more efficient and consistent than FE estimates, but we report both for comparison purposes. We follow the standard procedure to use the residuals of the first stage as regressor in the second stage equation. Table 3 shows the estimate of Eq. (1) using bank size as the heterogeneity discriminant (i.e., HYP 1-4). Estimates are run under the assumption that banks anticipate the reforms on mutual banks and adjust pre-emptively. The first five columns of the table refer to FEs estimations and the second three refer to FEIV estimations. Column 1 displays the regression of the standard growth model: lnCR t -1 is negative and highly significant; the impact of demand, ΔlnYpc, is positive with marginal statistical significance; liquidity (LR t -1 ), concentration (HI t -1 ), and ownership (SPA) are three among bank supply factors that are statistically significant. Column 2 shows that the provincial fine frequency, BCn t -1 , has the expected positive impact on the growth of provincial credit (HYP1), while the provincial pecuniary fine, BCa t -1 , exerts the opposite effect on the growth of provincial credit (HYP2). On the other hand, ΔlnYpc vanishes in statistical significance. Column 3 shows that there are no reputational effects (HYP3). Column 4 tests for asymmetries due to differences in bank size (HYP4). These asymmetric effects cannot be rejected when bank size (lnCR t -1 ) interacts with the frequency of bank fines (BCn t -1 ) but are rejected Table 3 Impact of bank fines on local credit; asymmetries due to bank size   when bank size interacts with the pecuniary fine. These findings are consistent with the proposition that the frequency of breaches is doing the bulk of the work, by triggering a strong "positive" credit adjustment mitigated by a minor "negative" credit adjustment due to the interaction with the bank size; on the other hand, the puny size of the fine does not affect bank behaviour. Column 5 adds reputational terms to the specification of column 4. These terms are not statistically significant; in contrast, the significance of the bank-size asymmetries is confirmed. Columns 6 through 8 display the corresponding FEIV regressions of the FE regressions of columns 3 through 5. The number of endogenous variables ranges from five of column 6 to seven of column 8; the number of exogenous variables ranges is 15. While there is a clear case of over-identification, the Sargan's test shows that the additional instruments are valid: the endogeneity test passes the hurdle. The key outcome is that the FEIV estimation corroborates the FE estimation. The asymmetric effects due to bank size are restricted to the frequency of breaches; the pecuniary fine appears to be irrelevant: all the BCa coefficients are statistically insignificant (see column 7). When fine variables are combined with reputational terms, the latter vanish, and the former remain significant at the 5% level (see column 8). Table 3 can be summarized as follows: fine frequency triggers credit reallocation that are sensitive to bank size; pecuniary fines are irrelevant, for which the best explanation is that fines are too small to do the job they are intended to do. Table 4 considers asymmetries due to bank ownership (HYP5). It implicitly assumes that banks are unable to correctly predict the consequences of reforms on mutual banks or that these reforms are not credible. The structure of the table is similar to Table 3, except that the first two specifications are omitted for brevity. Bank ownership is identified by the two dummy-based variables, SPA and noSPA that interact with BCn j,t-1 and BCa j,t-1 . As SPA and noSPA are complementary by design, of the triplet BCn j,t-1 , BCn j,t-1* SPA, and BCn j,t-1* noSPA, and the corresponding triplet involving BCa j,t-1 , only two are independent. In Table 4, we have dropped the fine variable from the triplet. Therefore, the interaction terms capture the combined direct effect of the fine variable and the indirect effect through the interaction with SPA or noSPA. In column 1, with FE estimation, BCn j,t-1* SPA exerts a positive impact on provincial credit growth, whereas BCa j,t-1* noSPA pulls in the opposite direction. The interaction terms have high statistical significance. On the other hand, the corresponding interaction terms with noSPA are not statistically significant at the 10% confidence level. Column 2 adds the reputational term, which is statistically irrelevant. The story changes sharply under FEIV estimations in Columns 3 and 4, in the sense that none of the four interaction terms are statistically significant. 12 The economic and statistical significance of bank fines disappears when banks are distinguished according to type of ownership and governance. The popular notion that breaches and fines penalize unduly community banks is not confirmed with this dataset.

3
Frequency vs. Size of Bank Fines in Local Credit Markets Table 4 Impact of bank fines on local credit; asymmetries due bank ownership FE fixed effects, FEIV fixed effects instrumental variables. Dependent variable: credit growth (ΔlnCR). Independent variables: lnCR = logarithmic of bank total loans, ΔlnYpc logarithmic of GDP per capita, LR = liquidity ratio, LEV leverage, HI Herfindal Index of provincial bank branches, SPA joint-stock bank index, noSPA mutual and community bank index, BCn provincial frequency of credit-related breaches, BCa provincial amount of bank breaches to total assets Variables of interest in bold ***p < 0.01, **p < 0.05, *p < 0.10 a See Hausman ( In sum, breaches exert an impact on credit reallocation through the signal emanating from its frequency and working through the size of the bank.

Robustness Checks and Counterfactual Exercises
In this section, we present some robustness checks and two counterfactual exercises that simulate the effects of changes in the frequency of fines and in the size of the provincial banking sector. On robustness of our findings, we perform two econometric exercises. The first consists of adding to specification (1) an additional bank supply variable, the return on assets (ROA). In the second, we add the crisis dummy variable CRISIS, which is equal to one for the years 2008 through 2012. We recall from our discussion of descriptive statistics that a key characteristic of Italian banks was a very low level of efficiency (profitability). ROA has a sample average of 0.22% and a frequency distribution spanning from a minimum of -67.9% to a maximum of 19.8%. As to CRISIS, Italy was particularly penalized by the Great Recession of 2008-2009 and the subsequent sovereign debt turmoil (Bofondi et al 2018). The CRISIS dummy replaces time dummies. In sum, both ROA and CRISIS are good candidates for a robustness exercise.
In Table 5, the 1-year lag of ROA is added in Model 3 and Model 4 of Eq.
(1). Model 3 includes the core specification, the two fine variables, and the asymmetries due either to bank ownership or bank size; model 4 adds the reputational term to model 3. The models are estimated, as in the previous two tables, first with FE and then with FEIV. The key result of the table is that ROA does not alter the findings of Tables 3 and 4. ROA then is replaced with CRISIS in Table 6. The difference between the two tables is that CRISIS is very significant, whereas ROA is not. For the rest, the two tables corroborate the findings of Tables 3 and 4. For the counterfactual scenario, we use the estimate of column 7 in Table 3 to simulate the impact of the statistically significant fine variables on provincial bank credit. We prefer column 7-8 because its specification is more parsimonious. For the simulation, we change the fine frequency, BCn, by ± 25% and the provincial banking sector size, CR j,t-1 , by ± 50%. We then calculate the average marginal effects under each scenario. The purpose of the exercise is to compare the two opposing forces acting on provincial bank credit, the push of fine frequency and the pull of bank size. Figure 2 displays nine maps of Italian provinces; the maps code changes in provincial credit according to three colours and their intensity: green for a positive change, white for no change, and red for a negative change. For each row, as we move from west to east provincial bank size changes (-50%, 0, + 50%), for a given provincial fine frequency. For each column, as we move from north to south, provincial fine frequency changes (-25%, 0, + 25%), for a given provincial bank size.
The key result is that as we move from the center map (row 2, column 2) to the north eastern map, the color changes from white to predominantly red, and as we move from the center map to the south western map, the color changes to predominantly green. A change in the number of fines exerts an amplification effect on provincial lending activity. This result is in line with Rezende and Wu (2014) who find that, given the minimum frequency of examinations required by US regulation, those 1 3 banks that are exempted from examination show lower profitability resulting from increasing loan losses and delinquencies. In essence, an increase in provincial bank credit can be obtained by raising the frequency of bank fines; it supports the notion that fines act more like a good housekeeping seal of approval than a penalty on the banking sector. Figure 3 shows the provincial distribution of the impact of bank fines. The left panel of the figure, based on the estimate of column 7 in Table 3, shows that given the existing local banking sector size, the impact of fine frequency on provincial credit is negative in about 83% of Italian provinces. Everything else equal (in particular bank size), a 25% increase in BCn would shift the distribution sufficiently to the right by making a positive increase in provincial bank credit in all provinces. In sum, fine frequency has a sizeable economic impact on provincial credit.

Conclusions
Fines are an important enforcement mechanism of bank supervision, and naturally one would expect that they would exert an impact on the credit market. Yet, these actions have received scarce attention in the literature. This paper has examined the nexus between bank fines and provincial bank credit. Theoretically, fines trigger a reputational effect on the demand for credit, and credit reallocation and balance sheet effects on the supply side. Using a dataset of Italian banks over the period 2005-2016, we estimate these effects using a fixed effect model and instrumental variables to remove potential endogeneity problems. The three  Table 3 testable implications are: a negative effect on provincial bank credit due to pecuniary fine reducing banks' net income; a positive effect on provincial bank credit due to fines triggering a more efficient and transparent resource allocation; and a reduction of provincial bank credit due to a stigma effect associated with a bank fine. Our key findings are that provincial total bank loans expand following the decision of a fine but are insensitive to the size of the fines.
Compared with the findings in the literature, the novel result of our paper is that bank fines can induce changes in bank management that streamline and increase lending activity at the provincial level. Furthermore, the impact of bank fine frequency on local credit is sensitive to the size of the provincial banking sector but not either to differences in types of bank governance/ownership or to the crisis period of 2008-2012. One explanation for the insensitivity to bank governance/ownership is that banks may have anticipated the policy reforms of 2016. Finally, we find no evidence of either reputational or other balance sheet effects. Our policy recommendation is that it would behoove bank supervisors to raise the frequency over the size of bank fines. The latter seem to work more like a good housekeeping seal of approval, enhancing transparency and effective banking practices, and less like a penalty.

Appendix: Construction of Provincial Breach Variables
In the following table we illustrate the transformation of bank-level breach variables in provincial breach variables: the notation is: x = breach amount, d = breach dummy, y = total assets and z = breach amount / total assets. Observed data are shown in bold. As we do not know the provincial allocation of an individual bank's breach, we assume that it is proportional to the relative incidence of branches. This allows to construct a continuous variable from a dummy = 1 when there is a breach and zero otherwise. In the dataset, this variable is denoted by BCn, i.e. the (relative) number of credit-related breaches. For pecuniary sanctions, we construct a weighted provincial breach and weighted provincial bank assets, each of which aggregated as shown above, and then calculate the ratio. The two weighted sums reflect the relative incidence of banks in the province (numerator) and the relative bank size in the province (denominator). In the dataset, this variable is shown as BCa, i.e. the amount of the sanction (See Tables 7, 8 and 9).

Table 7
Description of the variables used in the paper (3) (4) (8)  Tables 3 and Table 4 respectively

Table 9
Impact of the size of bank fines on local credit; asymmetries due to bank size and ownership


s2fieldsofstudy: Economics Business

text:
THE ROLE OF DIGITAL MARKETING AND BUSINESS CAPITAL IN INCREASING INCOME OF MSMES IN DENPASAR CITY IN THE CONDITIONS OF COVID-19

This study aims to determine the role of digital marketing and business capital in increasing Denpasar city income amid Covid-19. This study used 100 respondents who were selected using random sampling spread in Denpasar City, then the data were analyzed using multiple linear regression. The results of the simultaneous analysis show that the variables of digital marketing and business capital have a significant effect on increasing the income of MSMEs in Denpasar City. Meanwhile, the results of the partial analysis of digital marketing and business capital variables have a significant effect on increasing MSME income in Denpasar City. Digital Marketing is the dominant variable that affects the increase in income, so people will find it easier to get information about the products being sold so that the buying and selling transaction process increases. Purchases will increase and income will increase. Once with business capital, the higher the capital will increase the amount of additional production, thereby increasing income.

In 2020, the whole world was hit by the corona virus (Covid-19) which made the whole world affected by the virus. Many countries have locked down, but there are still some countries that are reluctant to do lockdown in order to avoid a recession in a country.
In Indonesia The government itself does not do a lockdown but provides policies to regions to limit activities or activities outside the home that are considered unnecessary. However, the reality is that many from various sectors are experiencing the impact of , and what is most felt by the community is the economic sector. The impact on the economic sector is also felt by people who experience layoffs (PHK) and also the decline in income from Micro, Small and Medium Enterprises (MSMEs).
The micro, small and medium enterprises (MSME) sector also has a contribution in terms of increasing the amount of gross domestic product. Until now, MSMEs are considered as an effective way to raise the level of the economy. The role of MSMEs since the 1998 monetary crisis can be said to be a saviour in the process of recovering the national economy, both in terms of encouraging the rate of economic growth and also in terms of employment (Ningsih, 2018). MSMEs are a source of livelihood for many people and are able to provide jobs for those who are educated and low-skilled and able to reduce poverty (Idawati and Pratama, 2020).
In the city of Denpasar, there were recorded that there were 32,026 MSMEs assisted by the Denpasar City Cooperatives Service in 2020, from various sectors. The large number of Micro, Small and Medium Enterprises (MSMEs) in Denpasar City is a separate asset that will strengthen the foundation of the regional economy, in essence it can be a source of income for local governments, besides that the participation of MSMEs can realize economic growth, equity, and increase people's income. job creation, and poverty alleviation Maintaining stable income from MSMEs requires business capital in the form of financing funds with small fees / interest and also the need for Digital marketing so that these businesses can reach buyers or consumers who cannot directly shop offline to these sellers during this Covid-19 period. Business capital is a factor that has a fairly important role in the production process, because business capital is needed when entrepreneurs want to establish or run a new business or to expand an existing business. obtained (Utari and Dewi, 2014). Digital marketing is also very much needed to increase sales and also increase revenue. Digital marketing at this time of Covid-19 really helps MSMEs and the community to make it easier and faster to carry out buying and selling transactions without the need to come to the store. Based on the phenomena written on the background above, the research questions are as follows: is there any influence of Digital Marketing on increasing MSME income in Denpasar City; is there an effect of Business Capital on increasing MSME income in Denpasar City. Gitman et al. (2015) explains that working capital/business capital is the amount of current assets that are part of the investment that circulates from one form to another in a business activity. Abbass (2018) said that working capital/business capital is an investment in short-term assets or investment in current assets. Working capital can be categorized into two, namely gross working capital and net working capital. Gross working capital is total current assets, and net working capital is total current assets minus current liabilities.

LITERATURE REVIEW
Digital marketing is also defined as marketing activities that use internet-based media (Wardhana, 2015). Marketing activities cannot be separated from the influence of digital technology. The term digital-based marketing (digital marketing) has evolved from the initial activities of marketing goods and services using digital channels to a broader understanding, namely the process of acquiring consumers, building consumer preferences, promoting brands, nurturing consumers, and increasing sales (Sulaksono and Zakaria, 2020).
Income is a very important element in a business, because in doing a business, of course you want to know the value or amount of income earned during doing the business (Artini, 2019). If income increases then it illustrates that there is a development in a business.

METHODS OF RESEARCH
This research was conducted in Denpasar City, the main reason for choosing this location is the number of MSMEs in Denpasar City is the largest of several districts in Bali. The object of this research is the development of the influence of digital marketing, the business capital used on the income of MSMEs in the Denpasar City area. Data were collected by distributing questionnaires and supported by the results of interviews with respondents. The population in this study were all MSMEs in Denpasar City. The number of population taken in this study was 32,026 MSMEs and the number of samples was 100 MSMEs with a sampling technique using Stratified Random Sampling. Multiple linear regression data analysis techniques, The hypothesis in this study was tested using the simultaneous test (F-test) and partial test (t-test) to determine the effect between variables. The classical assumption tests used in this research are normality test, heteroscedasticity test, and multicollinearity test.

RESULTS AND DISCUSSION
There are 46 male respondents or 46%, far less than 55 female respondents or 54%. Judging from age, respondents aged 21 to 30 years were 17 people or 17%, aged between 31 years to 40 years were 31 people or 31%, aged between 41 to 50 years were 26 people or 26%, and aged over 51 years as many as 26 people or 26%. Judging from the length of business, respondents who have been in business for less than 1 year are 22 people or 22%, respondents who have been in business for 1 year to 5 years are 38 people or 38% and respondents who have been in business for more than 5 years are 40 people or 40%.
The results of the validity and reliability test showed that the questionnaire instrument proposed in this study through various questionnaire items was valid and reliable. Testing the relationship in the classical assumption test, namely: data normality test, multicollinearity, heteroscedasticity, and autocorrelation showed that the entire questionnaire instrument had met the requirements for hypothesis testing through regression.     The results of the regression calculation can be seen that the adjusted R square value is 0.884. This means that 88.4% percent of the variation in the Income Increase variable (Y) can be explained by Digital Marketing (X1) and Business Capital (X2) variables while the remaining 11.6% percent is explained by other variables not included in this research model.
From the data above, it can be seen that the significance value is 0.000 <0.05, so it can be concluded that simultaneously, the Digital Marketing (X1) and Business Capital (X2) variables have an effect on the Income Increase variable.
The test results show the tcount X1 (16,225) with a significant value of 0.000 < 0.05, then H0 is rejected and H1 is accepted, so it can be concluded that there is a positive and partially significant effect between Digital Marketing variables on Income Increase.
The test results show the value of tcount X2 (6,498) with a significant value of 0.000 < 0.05, then H0 is rejected and H1 is accepted, it can be concluded that there is a partially significant effect between the variables of Business Capital on Increasing Income. Based on the results of the analysis, it is concluded that the Digital Marketing variable has a positive and partially significant effect on increasing MSME income. Where the indicators used are ease of use, convenience of use, providing complete information, use of Facebook social media, use of Instagram social media, use of e-commerce. These results indicate that Digital Marketing is one of the factors that play an important role in determining the level of income that will be obtained by a business. In the midst of Covid-19, people are reluctant to shop out so that the use of digital marketing is very helpful in marketing products from MSMEs. If we do digital marketing, people will find it easier to get information about the products being sold so that the buying and selling transaction process increases. Purchases will increase and income will also increase.
Likewise, Business Capital has a positive and significant partial effect on increasing MSME income. Where this business capital consists of several indicators, namely obtaining capital on credit, the amount of capital, obstacles to capital sources, and sources of own capital and sources of capital from outside. These results indicate that business capital is one of the factors that play an important role in determining the level of income that will be obtained by a business. The higher the business capital, the more product variants that are sold so that more consumers come to shop, this will encourage an increase in MSME income.


ASA 2021 Statistics and Information Systems for Policy Evaluation : BOOK OF SHORT PAPERS of the on-site conference

This book includes 40 peer-reviewed short papers submitted to the Scientific Conference titled Statistics and Information Systems for Policy Evaluation, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Dept. of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring.

SESSION DECISION MAKING
Measuring the effectiveness of COVID-19 containment policies in Italian regions: are we doing enough? 75

Demetrio Panarello, Giorgio Tassinari
Motivation of basketball players: a random-effects logit model for the probability of winning 81 Silvia Bacci, Tijan Juraj Cvetković Reducing inconsistency in AHP by combining Delphi and Nudge theory and network analysis of the judgements: an application to future scenarios 87

Simone Di Zio
Mapping and factoring the 2007 ATECO categories in regard to specialised human capital 93

Luigi Fabbris, Paolo Feltrin
Modelling the spatio-temporal dynamic of traffic flows with gravity models and mobile phone data 99

Giorgio Tassinari, Demetrio Panarello
The role of the extra-man play actions in elite water polo matches: which elements lead to a good shot? 111

Alessandro Lubisco
Big data analysis and labour market: an analysis of Italian online job vacancies data 117

Introduction
This study is part of a project aimed at supporting the weaknesses of young people who are experiencing, or are at risk of reaching, NEET condition (Not in Education, Employment or Training; aged between 15 and 29 or 34, according to different definitions).
At present the entire project consists of three phases. The first, concluded in 2020 with the volume "From Neet to Need" (see also Bollani, Rota, 2018), presents the European statistical situation of the Neet phenomenon and its sociological implications; it includes an empirical study, based on thorough interviews with people in Neet condition intended to collect their life stories (Merril, West, 2012); the purpose is to classify them as to their internal and external needs, in order to suggest adequate support for intervention. The second phase, to be completed in 2021 with a second book, is the object of this study: it searches for harbingers of Neet status in the difficulties encountered during high school (which in Italy is divided into an initial two-year set and a subsequent three-year set); in fact, in general it is more simple and effective to tackle minor problems by a preventive approach, starting from school age. Of course students as such are not Neets and the anonymity required for a survey dealing with very personal issues does not allow a longitudinal study to be carried out on the same individuals. However, the construction of a comparison base referring to a generic population, relative to the incidence of certain states of weakness (or some of their combinations), will allow for a subsequent comparison with the incidence of the same difficulties in school age for those who find themselves in a Neet situation. The third phase, started in 2021, is about identifying good practices in order to grow Neets into working adults; the people selected to accompany the Neets in this process are now fully trained, while the first groups of Neets will be involved by the end of the year. The research group was identified among members of the InCreaSe Association (Innovation Creativity Settings; www.increasegroup.org), which boasts transversal research skills. Numerous local authorities are involved in the project, which is supported by Compagnia di San Paolo Foundation.
As to the specific object of this paper, the first signs of the extreme discomfort caused by Neet condition were sought by investigating signs of weakness in high school students in the Piedmont, Valle d'Aosta and Liguria regions. A survey questionnaire was administered to students, an operator being present in the classrooms, shortly before the start of the pandemic. Collected results will therefore be more directly linked to school activities "in presence", even if discontinuities in teaching methods and characteristics caused by the pandemic might have brought in alterations that will need to be monitored over time.

Themes of investigation and subjects involved
School transmits knowledge accumulated and elaborated by society over the centuries, allowing younger generations to experience social, cultural and territorial belonging and to be citizens of the society with its rights and duties.
The proposed survey is embedded in the context of training institutions challenged to cope with rapid change and face uncertainty about the impact of offered training courses on personal Personal weaknesses recognized by high school students in the North-West of Italy growth and access to work in the current context. The intention of the study is to highlight positive states of well-being, certainly present in a school context, but at the same time identify situations of discomfort among students which, if not adequately prevented, can develop in a harmful way. It was decided to address the issue from within the school itself, administrating questionnaires to students engaged in secondary education. The topics of the survey, included in the questionnaire within specific sections, were: • You and the school (role attributed to the school) • The relationship with your school • The relationship with your teachers • The relationship with your schoolmates • You and your future (for the three-year period only) • The relationship with your family • Personal and family data Special attention was devoted to the field organization, with an operator present in each class during the survey in order to ensure correct presentation of the questionnaire and homogeneous assistance as well as sense of anonymity towards teachers during its compilation.
It should be mentioned that the Italian secondary education system is divided into an initial compulsory two-year period ("biennio" below) and a subsequent voluntary three-year period ("triennio"); it is also divided into a more academic (classical or scientific) school called "liceo", a technical school ("tecnico") and a vocational school ("professionale").
Students were selected following a multi-stage process, considering schools and classes as first and second level units and students as third level units. The first stage units, i.e. the schools, reflect different territorial situations, with several focuses in the three regions. In particular, for Piedmont distinctions were made according to size and location of the centres and among Turin city areas with different socio-economic patterns. The choice of second stage units, i.e. classes, allowed to reach, to a certain extent, an overall balance among different educational paths; finally, for third stage units, i.e. students, a complete survey of the students present in classrooms at the time of the survey was opted for. In this way, 14 schools (some classes for each) were surveyed, in line with the three Italian regions of interest. In Piedmont 10 schools are considered: three are in Turin city (see Torino1, 2 and 3 in table 1), two in the city belt (in the municipalities of Nichelino and Settimo Torinese), five in other areas of Piedmont (one in the municipality of Pinerolo, close to Turin, and four in different provinces). In Valle d'Aosta and Liguria, two schools were surveyed (see Aosta1 and 2 for Valle d'Aosta and two schools in the municipalities of Genova and Savona for Liguria). Globally, 931 students were surveyed, keeping a sufficient number for each type of educational path, as shown in table 1.

Growth of weakness as an accumulation process
A first research objective concerns how weakness is formed and how it can grow. One possibility that also seems to emerge from life stories is that there is an accumulation phenomenon. A perhaps tolerable weakness emerging for a particular aspect of life in school age may be aggravated by a second circumstance of discomfort, and so on, with the consequence of reaching a very intense state of discomfort due to a sum of causes, each of which is not particularly relevant.
In this perspective, the approach used for this paper consists in considering the key questions of each section of the questionnaire, which, each in view of a specific aspect, detected a condition of well-being or weakness of the answering student. The number of difficult situations encountered for each student was then added; this sum enabled to consider further degrees of individual difficulty.
In particular, ten questions were examined, two for each section of the questionnaire; they are presented below; labels used in figures and the percentage of students in a state of weakness (see ending "_yes") for each of them appear in brackets.
As regards the first part of the questionnaire, related to the role students attribute to the school and the relationship they have with it (first two sections), the selected questions were on: -the importance of school in one's life (Life_yes: 4.40%) -the school's ability to foster personal growth (Growth_yes: 13.43%) -I thought about changing school (Change_yes: 47.91%) -prolonged absences (Absences_yes: 30.08%) An aspect fundamental in determining the categories of students most in difficulty certainly is the one concerning the relationship with classmates, which if problematic can lead to important episodes of exclusion and marginalization; thus the chosen questions were whether they: -were teased or isolated by school friends (Isolated_yes: 24.92%) -go out with classmates outside school hours (Outside_yes: 33.30%) Other elements taken into consideration are the average grade and the relationship with their teachers; the selected questions concerned: -whether they ask teachers questions when they do not understand (Questions_yes: 39.74%) -which was their average grade in the last term (Grades_yes: 48.44%) Finally, two questions were chosen from the section about the relationship with one's family, i.e. whether: -students feel supported by their family (Support_yes: 16.33%) -they have confidence with any adults (Confide_yes: 21.27%) The answers to each of these ten questions were coded in terms of presence or absence of weakness; the resulting synthetic variable indicates the amount of weakness situations, which can vary from zero (absence of weakness) to ten (maximum weakness intensity) for each answering student. The frequency distributions of this variable, called "intensity of weakness", for all respondents and for those living in different territorial areas of interest for the survey, are presented in figure 1.
The shapes of the frequency distributions shown in the figure are very similar, although Turin belt and Liguria display the highest average levels of weakness intensity (3.0 and 3.1) and city of Turin and belt of Turin show the highest variability (standard deviation 2.0 and 1.9).
In all cases, at higher degrees of weakness frequencies progressively decrease. Referring to a more stable condition, intensity of weakness is also measured more synthetically as follows, assuming to set only three qualitative degrees of weakness: students with intensity of weakness 0 and 1 are categorized as students "without weakness" (or nearly so), those with scores 2 and 3 are considered to have "lower weakness", while students with a score higher than 3 fall into the "greater weakness" category. The subdivision into bands respects the subdivision into tertiles, considering the variable starting from the most disadvantaged in the general total column 47 (obtaining following frequencies: greater weakness 33.94%; lower weakness 39.53% and without weakness 26.53%).

Figure 1 -Frequency distributions of the intensity of weakness (0-10)
In order to check the usefulness of the considered variable empirically, as to the degree of weakness, a multiple correspondence analysis (MCA) was performed on the binary codified ten variables used for its construction; the intensity of weakness, as both ten level and three synthetic level variables, are maintained as supplementary. The MCA map is shown in Figure 2; software R and FactoMineR package were used for the analysis (Escofier, Pagès, 2008). Note: the MCA, given its coding scheme, underestimates the percentage of inertia explained by the first factorial dimensions (Abdi, Valentin, 2007). In literature, some methods of reevaluation of variances explained by first factorial dimensions are indicated to overcome this drawback (Benzécri, 1973;Greenacre, 2017). Using Greenacre's method (more parsimonious), we obtain a variance percentage of 74.65% for the first dimension and of 3.97% for the second (overall, therefore, the first factorial plan would explain the variance of 78.62%, which seems satisfactory; R software and in particular the ca package was used for this re-evaluation, as in Nenadic, Greenacre, 2007).
It can be observed that the intensity of weakness may be used as a good synthesis of the ten variables, as it displays quite homogeneous levels, growing from left to right of the graph and substantially following the first axis of the map. This is congruent with the presence of almost all "_no" (absence of weakness) statements for almost all variables on the left of the map, while the "_yes" (presence of weakness) statements are on the right.
It should also be noted that negative responses to the fact that school is useful for one's life and for personal growth, corresponding to a state of weakness visible in the map at the top right in an eccentric position (Life_yes and Growth_yes), are generally less recurrent than other types of weakness (4.40% for Life_yes and 13.43% for Growth_yes).

Living conditions that can facilitate weakness
Considering Figure 2 again, the most important reading direction is -as already shown -from the far left (absence of weakness) to the far right (greater weakness). However, a second useful piece of information can be found using the bottom-up dimension, especially in the quadrants to the right of the graph where the points are vertically more dispersed and can provide information on the type of discomfort experienced. In fact, staying on the right, towards the top there are the already known negative answers on the importance of school for one's life and growth and also situations of prolonged absence (Absences_yes): this could suggest a difficulty deriving from lack of motivation and escape. On the downside, however, there is a lack of support and attention from adults (Confide_yes; Support_yes), as well as isolation at school and outside peer groups (Isolated_yes, Outside_yes).  On the left side of the map, where difficulty is generally low, we note the simultaneous greater 49 presence of both parents in the family unit of cohabitation (par. home_2), a high parental qualification (at least for one of the two parents; par. degr. high), "liceo" enrolment; for students of the second three-year period (who were asked more questions) also the conviction that school prepares for the future and they will to continue studying. Compared to "liceo" students, those from other paths ("professionale", "tecnico") generally show more weakness. Females appear to be in a better position than males. Piedmontese schools seem to be in a better position than those of Valle d'Aosta or Liguria.
Looking at the top right of the map, students of the second three-year period, who do not think school prepares for the future (Prepare_no) and do not want to continue studying (Continue_no) are in the same position as those already discussed with lack of motivation and orientation to escape in Figure 2.
On the bottom right, which is the already discussed area with more students unsupported by adults and isolated by peers, Figure 3 adds the information concerning greater presence in this area of parents with low qualification (par. degr. low) and difficulty of presence of both parents in the family unit of cohabitation (par. home_0 or par. home_1).

Conclusions
In this study, which considers secondary education institutions from the inside by a survey conducted on students, a transversal look at the various sections of the survey questionnaire was maintained to highlight one of the key aspects of the research, aimed at observing situations of well-being, but also of progressive weakness of the students themselves.
The focus is on observing in the sense of describing and therefore favouring a reflection on causes determining weakness: a method to place surveyed students in a scale polarized between well-being on the one hand and extreme discomfort on the other was proposed; subsequently it was shown how some characteristics (to be explored as possible motivations), derived above all from the school and parenting context, can accompany the different situations of intensity of individual weakness.
Furthermore, in the last three-year school period, attention was devoted to how one's "feeling" within the school context can influence the desire for a future perspective for oneself and how it can influence choices.