Public Corruption: A Study across Regions in Italy

Germana Corrado1 Fiammetta Rossetti2

Abstract

This work explores the determinants of public corruption using a regional panel dataset on crimes perpetrated in Italy by public officials against the public administration in combination with a set of demographic and socio-economic variables. The results suggest that both the size and the composition of public spending at the local level explain corruption. We also find that regions where social capital is higher are more likely to face a lower incidence of corruption crimes. Moreover, regions which have historically placed less importance on rooting out corruption may be stuck in a vicious circle of higher levels of corruption.

JEL Classification: C2; D7; H5; R1.

Keywords: Corruption; Regions; Public Sector; Italy; Panel Data Analysis.

1 Department of Management and Law, University of Rome Tor Vergata, Via Columbia 2, 00133 Rome, Italy. Corresponding author: corrado@uniroma2.it2 JRC Institute for Prospective Technological Studies, European Commission.

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1. Introduction

A large political economy literature has highlighted that the more sizeable is the public sector the higher is the likelihood of detecting illicit activities carried out by (mis-)using public money. Corruption arises from the illicit behaviour of state-appointed bureaucrats who appropriate public funds by misleading the government about the cost and quality of public goods provision. However, from an empirical perspective the evidence of the effect of government size (i.e., the quantity and quality of public spending) on corruption is quite mixed (see, in particular, Acemoglu and Verdier, 2000; Persson and Tabellini, 2003; Mauro, 1998; Treisman, 2007; Blackburn et al., 2011).

Since Italy has a long tradition of government interventions to foster growth, to stimulate investments and to reduce unemployment, it is crucial to study the relationship between public spending and corruption. In fact, an important issue to address when dealing with grafts within public administrations relates to the relative share of public sector (with respect to the private sector) within a country. Most of all, it can be insightful to observe how public expenditures (in specific sectors) and corrupt crimes within public administration jointly evolve across geographical areas (regions) of the same country. It is indubitable that the presence of corruption hinders talented and clean activities; the best individuals - be they firms or citizens - tend to leave corrupt systems since their ethical and moral values prevent them from conforming to dishonesty, while their intellectual skills are frustrated by backward and inequitable conditions. Therefore, the threat of corruption is to create a society made of its worst individuals and entrepreneurs which is doomed to become more and more peripheral to economic development. In fact, corrupt systems tend to support public projects that are less labour intensive and more capital intensive, being the latter more prone to rent-seeking (Gupta et al., 2002). As a consequence we observe a shrinking of resources aimed to maintain social equality and inclusion (e.g., expenditure for public schools, hospitals, welfare, etc.). A big bulk of

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studies documents how criminal conduct within the public administrations reduces the wherewithal to public education system (Ades and Di Tella, 1999; Mauro, 1998) and to the healthcare system (Baicker and Staiger, 2005; Factor and Kang, 2015; Golden and Picci, 2005). Whilst Treisman (2000) focuses on the causes of corruption between-countries and finds that economic and institutional variables explain corruption less than do socio-political variables (see also Ades and Di Tella, 1996; Lambsdorff, 2005, 2006; Goel and Nelson, 2010). In a similar way we aim at detecting the main factors that help to explain the incidence of corruption crimes conducting the analysis at regional level within a country: Italy. A key feature of a country like Italy relates to the strong heterogeneity of economic traits – and of the mechanisms of social fairness connected to it - from North to South. An insightful perspective may be gained by looking at how the structure of public expenditures varies across the regions of the same country. Also, observing how regional heterogeneity of socio-economic and demographic traits relates to the incidence of illicit activities within the public administration might help for a better understanding of the geographical determinants of corruption. The key questions we want to address are:

What are the effects of the size of local public sector expenditure on the incidence of corruption across regions?

How important are geographic, cultural and historic influences in determining corruption?

We therefore believe that this analysis can contribute to the ongoing debate on regional differences brought at European level on quality of government, broadly defined, such as corruption, impartiality, and quality of public services (Charron et al., 2014; 2015). The concept of the quality of the local governance, which heavily depends on the control of corruption, is central since it is related with economic and social development, better environmental conditions, better quality of life and also more equitable society.

We employ a panel dataset for Italian regions on crimes perpetrated by public officials against the public administration for more than a decade (from 2000 to 2011) in combination with a set of economic and

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socio-demographic variables identified by the prevailing literature to be causes and effects of corrupt systems. We therefore use a measure of corruption that has the advantage to be directly observable and therefore is ‘objective’ although it suffers of several significant well-known problems. For example, while data are reported annually by the official statistics, there is an unknown, and most likely variable, time lag between crimes and convictions; in addition, data give little to no indication as to the seriousness of the incidence of corruption, but they just reveal the tip of an iceberg of a vast word of corruption and other illicit practices. In fact, the data cover only those officials who are caught and, of course, convicted. In fact, our data refer to illicit activities that involve public officials and persons in charge of public offices who have been prosecuted and convicted for such crimes. The database employed in the present analysis offers a variety of indicators at the regional level that are likely to reveal the latent existence of corruption, and mirror the effects of the statistics on crimes. Regional characteristics included in the empirical analysis are the extent of public sector, complemented by the breakdown of government spending in: general public services, social welfare, public safety, education and the healthcare system. Finally, the analysis encompasses the degree of economic development reflected by regional GDP per capita, the level of education and youth unemployment3 conditions of local communities, and the attitude towards volunteering.4

The main findings of our work confirm that the dimension of the Italian public sector matters in explaining the incidence of corruption crimes along with the socio-economic and “environmental” (cultural) conditions that may greatly vary across Italian regions. In particular, the results suggest that individuals who reside in regions where corruption is higher and persistent are less likely to be satisfied with public services and to benefit from higher levels of social capital.

The paper proceeds as follows. Section 2 gives a picture of the socio-economic and historical conditions that lie behind the different levels of corruption across Italian regions. Section 3 describes the data employed.

3 See Mocan (2008)4 See Del Monte and Papagni (2007)

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Sections 4 and 5 deepen the empirical methodology and discuss results. Section 6 concludes.

2. Mapping Corruption across Italian Regions

The battle against corruption is now explicitly declared in the policy agenda of many national and international institutions. The Italian case is definitely a blatant example of how corruption frustrates hopes for a balanced growth and an equitable society. Countries like Italy are particularly under scrutiny since they belong to the group of developed countries, therefore they should have been responsible of an accountable management of their internal public resources. Recent literature has pointed out that in Italy one of the main factor that explains regional path dependencies is the presence of consolidated “clientelistic networks” existing in regions with historically unconstrained rulers (Charron and Lapuente, 2013). As highlighted by Del Monte e Papagni (2007) “...For politicians the probability of being elected was much more linked to the number of favori (favours) that they could offer their “clients” than to the efficiency of public expenditure and the probability of being apprehended” (p. 387). This preferential treatment enjoyed by individuals and groups of citizens keen to show compliance with the dominant political classes was initially fed by the possibilities offered by periods of economic boost, but then ended with pauperising the economic resources - and hindering growth - until present days. Favourable economic conditions and available resources could have been used to create more equitable growth paths, instead corrupt practices along with economic crises depleted resources. The very heterogeneous histories in the North and South of Italy generated diverse socio-economic paths, and the cultural background was always considered the heart of clear-cut economic and political divergences between the two macro areas of the country. Italy, as a unified republic, enjoyed an economic boom after the Second World War, along with an increasing decentralization of its public system since 1948. It is within this peculiar portrait that the political and economic dynamics

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of Italy take place, and in particular the floods of corruption-related crimes between the 70s and the 90s (La Porta et al., 1999). Figure 1 maps the regional incidence of crimes5 against the public administration committed by public officials who have been prosecuted and convicted by the Italian judiciary system. These data have been released from the Italian National Institute of Statistics (ISTAT) for the period 2000-2011. The episodes of misconduct are classified by differentiating between the broader class of unspecified misbehaviour and illegal activities committed by public officials who abused of their discretionary power. We should recall that statistics about unlawful behaviours may underestimate the true phenomenon, since they refer only to crimes reported to the police and prosecuted (and convicted) by the Italian judiciary system. In Figure 1, a darker shade denotes regions with a higher average incidence of recorded crimes against the public administration during the period under consideration: Italian Central and Southern regions along with the two northern regions of Lombardia and Piemonte report on average a higher number of crimes. An increase in the number of recorded crimes may not necessarily be due to an increase in the number of actual crimes; rather it might be caused by an improvement in factors such as the willingness of individuals to report crimes, the adequacy of police to detect illicit activities, or the efficiency of judicial institutions to prosecute crimes.In the following section we conduct a panel data analysis across Italy’s regions. Regional data allow to better identify how structural changes in institutions, social norms and population demographic characteristics over time may impact on corruption within the public sector; the only drawback relates to the fact that regional data do not allow the use of variables that are the same for all entities (here regions) such as the legal system or the administrative rules and law.

3. Data and Statistics

5 These crimes embrace to a broader group of illicit activities that involve public officials and persons in charge of public offices who have been prosecuted and convicted for such crimes.

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This study employs a dataset which provides information for as many as twenty Italian regions over a span of eleven years 2000-2011. Data come predominantly from the Italian National Institute of Statistics (ISTAT) and Eurostat. We use the information gathered across Italian regions on the number of crimes perpetrated by public officials against public administration relative to the population which have been reported, prosecuted and convicted with an irreversible provision of sentence by the judicial authority and this constitutes our measure of corruption. These data6 refer to a broader group of illicit activities that ISTAT classifies as crimes against public administration and thus constitute a satisfactory proxy for the diffusion of corruption within the Public Administration; in the following we refer to this variable as “Crimes against the PA”. We assume the existence of a significant correlation between recorded crimes and the actual misbehaviour within public offices, even if a high number of denounces can reflect both a large number of corrupt cases and a high propensity to report, detect, and prosecute them. Also we know that this measure of corruption certainly represents a lower bound as the level of actual corrupt practices may be higher than the measured one but at least we can rely on an ‘objective’ measure of corruption not biased by subjective perceptions.

The majority of variables are rescaled by the regional population, and turned in logarithmic terms for ease of interpretation. Table 1 reports a description of the variables included in this study along with summary statistics and data sources. We aim at detecting whether Italian regions witness the association between illicit practices within the public administration and different types of government expenditure. In particular, the way public spending is framed gives indication on whether more public resources are allocated to labour-intensive or capital-intensive public goods. In fact, strong differences in terms of rent-seeking activities are associated to investment in public infrastructures rather than in health and education (Rose-Ackerman, 1997). Traditional 6 The following specifications of crimes committed by public officials (i.e. individuals in charge of public offices) have been considered in our analysis: 1. corruption; 2. embezzlement; 3. extortion; 4. bribery; 5. abuse, misuse or breach of public office duties. Data source: data are gathered from the Italian Judicial Register (Casellario Giudiziario) and reported in the crime statistics by from Italian National Institute of Statistics (ISTAT).

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information such as GDP per capita is also included, more precisely the GDP per capita is included with a lag of one year to deal with potential endogeneity. Whilst among socio-demographic and cultural characteristics, we consider the fraction of people engaged in tertiary education, the percentage of young people who are neither employed nor in education or training (the variable NEETs), and the intensity of volunteering. We finally include data on the quality of roads as declared by respondents and reported in the statistics on households’ living conditions released by ISTAT. We expect a positive correlation between the level of corruption within the public administration and the deterioration of some infrastructures such as roads.

4. Estimation strategy and empirical evidence

We sketch the relationship of crimes against the public administration committed by public officials with the size of the public sector and the type of government expenditure by means of pooled OLS regressions. Crimes against PA i,t = α +β j x i ,t + ui,t

(1)

where the variable “Crimes against PA” denotes crimes against the public administration (PA) committed by public officials, xit is the vector of covariates we control for, and ui,t are respectively the region specific error and the idiosyncratic error. In the variable “Crimes against PA” the following specifications of crimes committed by public officials are comprised: corruption, embezzlement, extortion, bribery, and abuse, misuse or breach of public office duties. The first column of Table 2 shows a baseline regression performed on a minimum number of socio-economic and cultural characteristics of the region (model (a)). Other regional controls are added in the second regression reported in the second column of Table 2 (model (b)) where we consider distinct items of public expenditure (social security and welfare, public safety, education, general public services, healthcare

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system) plus other socio-economic controls. In particular, we add two controls (i.e., the volunteering and the quality of the roads) in order to account both for the level of local social capital and the quality of the public service offered in the area of residence.Overall, evidence at this stage confirms the strong effect exerted by size of public sector as a background for illicit activities (Acemoglu and Verdier, 2000). In fact, higher levels of public spending definitely create a background of opportunities to raise irregular profits from businesses with the public administration thus causing corruption (see column one of Table 2). Whilst the negative relationship with public spending in education and with individuals’ education attainment supports the argument that higher public investment in the formation of human capital could lead to less crime (Ben-Porath, 1967; Eicher et al., 2009). This finding is also in line with main literature7 originated by Lipset (1960) according to which education represents a way to keep citizens politically involved, and civic engagement is a means to monitor them more closely (Putnam et al., 1994; Glaeser et al., 2004). Therefore, under the so-called Lipset’s hypothesis voters with higher education levels (and income) are expected to be more willing and also more capable to monitor public employees and to take action (punish) when the latter violate the law (Fiorino and Galli, 2010). The negative relationship between income per capita (GDP) and corruption across Italian regions seems to confirm again the Lipset’s hypothesis: poor regions experience more corruption than rich ones.As shown in Table 2 (column two), the augmented model displays a positive association between illicit practices within public administration and the extent of unemployment of untrained young people (the NEETs). When work opportunities are scarcer more people might engage in informal or even illegal businesses, this may be especially the case for untrained individuals with lower educational attainments. Therefore individuals with a precarious income and a poorer cultural level may more easily decide to engage in illicit activities with the public sector in order to carry their needs out. This might lead eventually to a vicious self-

7 See also Ades and Di Tella, (1999), Golden and Picci, (2005) and Mauro (1998).9

sustained circle of corruption with youth unemployment and inactivity (NEETs). “These young people, excluded from the labor market for years, are referred to as “Generation Jobless”…Youth have little faith in their skills or qualifications as they attempt to navigate a system they perceive to be rife with corruption” (World Bank, 2014, pag.26).

These results appear to be in line with the relevant literature (Schneider and Enste, 2000), and are further followed up by exploiting the potential of the panel database. Longitudinal data have the advantage to follow the dynamic of the same cross sectional units (i.e. Italian regions in our case), and to enable the inclusion of individual heterogeneity. In this study the relevant heterogeneity relates to unobservable socio-economic and cultural factors that change over time but not across entities. The panel data model we estimate can be specified as follows:

Crimes against PAi,t = α +β j x i ,t+γt +ε i + ui,t (2)

where again the variable “Crimes against PA” denotes crimes against the public administration (PA) committed by public officials, xit is the vector of covariates we control for, γt is a set of year dummies, and ε i and ui,t are respectively the region specific and the idiosyncratic error terms. The term ε i represents regional traits (i.e. socio-economic traditions and cultural values) assumed to be time-invariant (i.e. fixed effects) but heterogeneously distributed within the country. The two models identified with pooled OLS are now tested under random effects and fixed effects hypothesis. The former assumes that the variation across regions is random and uncorrelated with the independent variables – in other words ε i is orthogonal to xit. The latter relaxes the strong orthogonality assumption by time-demeaning the data in order to purge observations from their time invariant components which the model does not control for, but that may intertwine with relevant - however difficult to frame - regional aspects.

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Table 2 (columns 3 and 4) presents the results of the baseline model where corruption is regressed against a set of socio-economic variables using both panel random (RE) and fixed (FE) effects (see columns three and four). For choosing the best fitting model we performed the Hausman test: the random effects estimation is preferred due to higher efficiency, in fact the null hypothesis8 of those two estimators being different is not rejected. Consequently, we are going to analyse the RE model in Table 2 (column 4). Results are overall similar with respect to pooled OLS; in particular, we can note that the coefficient of (lagged) GDP per capita remains negative and highly significant.Finally, we consider the augmented the RE model in column six of Table 2. Results highlight that the poor quality of local infrastructure might positively impact on the incidence of corruption crimes. For example, ordinary maintenance and operations of physical infrastructures, such as roads, might be intentionally neglected so that they have to be rebuilt. This might create opportunities for (corrupt) officials to extract bribes and kickbacks from new public investment in large infrastructure projects. As highlighted by Tanzi and Davoodi (1998) in some phases of a large civil engineering project contract, such as for road infrastructures, “a strategically placed high-level official can manipulate the process to select a particular project. He can also tailor the specifications of the design to favour a given enterprise.” (p.4, Tanzi and Davoodi, 1998).A negative relationship between misbehaviours or misconduct within public administration and people’s participation in voluntary organizations comes to be highlighted. Average participation in volunteering can be used as a measure of local social capital which captures the existence at regional level of differences in people’s general attitude towards corruption. In mainstream literature social capital is thought to measure the ability to cooperate and the level of trust and honesty in society (Putnam et al., 1994); it is then intuitively obvious that regions in which people appear to be more honest and trustworthy are more likely to experience less corruption (Del Monte and Papagni, 2007).

8 The RE model is preferred under the null hypothesis due to higher efficiency; while under the alternative FE model is at least consistent and thus preferred.

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5. Controlling for endogeneity: A GMM approach

By tracking the same cross-sectional units over time panel data offer the opportunity to exploit the temporal dimension in order to gain insights about possible causality issues among variables. With reference to corruption and other types of illicit conduct within public offices a reasonable question is whether it is more the composition of government expenditure that causes the misbehaviour by creating the opportunities for it, or it is rather the unlawfulness which alters the composition of government expenditure by creating inefficiencies within the economic system. To answer this question, we should account for the potential endogeneity bias (reverse causality) between corruption and public spending (and possibly other covariates). In fact, government expenditures are liable of endogeneity issues since they can be influenced by (corrupt) public officials who are interested in the creation of opportunities for bribery thus affecting government spending (as shown by Mauro, 1998). In addition we include the lagged value of the variable “Crimes against PA” as a predictor. We employ the system generalized method-of-moments (system GMM)9 particularly suitable for panel analysis (Arellano and Bond, 1991). This estimation method can account for (i) the fact that the process (i.e. illicitness within public sector) may be dynamic, with current realizations of the dependent variable influenced by past; and (ii) that some controls may be not be exogenous. In the estimated model lags of the dependent variable are supposed to alleviate the misspecification caused by the possible omission of relevant variables. These dynamic panel data models and their estimation overcome the severe bias caused by adding a lagged dependent variable to a panel data model and then estimating it via the within regression or the GLS estimator.

9 In the Arellano-Bond estimator (i.e. command xtabond2 of the software Stata 13) we estimate two equations (one differenced and one in levels) which are instrumented by first-differences only (system GMM).

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Therefore equation (2) is complemented with the inclusion of the lagged values10 of Crimes within PAi,t-1 and estimated by the system GMM11:

Crimes against PAi,t = δ1Crimes against PAi,t-1 +β j xi ,t+ γt + ε i + ui,t (3)

The time-invariant region characteristics (fixed effects) are contained in the error term of equation (3). It accounts for unobserved region-specific effects and for observation-specific errors. The system GMM estimates a system of two equations: one differenced and one in levels. By adding the second equation additional instruments can be obtained. Variables in levels from the second equation are instrumented with their own first-differenced values in order to increase efficiency.Table 3 shows that today’s level of misbehaviour within the public sector is significantly determined by its previous realizations. The persistency across time of illicit activities within Italian regions might imply the existence of a “culture” of corruption that is not easy to eradicate. As a consequence some regions are more likely “to be stuck into a vicious circle” characterized by pervasive corruption. We then can argue that those regions that have historically placed less importance in combating corruption, and thus have weak anti-corruption social and cultural norms (more tolerance for corruption) might experience higher and stronger levels of unlawfulness as time elapses. Therefore, as stressed by Aidt (2003), the political and economic institutions, and possibly history, might be among the main determinants of local corruption. In other words, a historical inertia of institutions that induce corruption might be persistent (Goel and Nelson, 2010).The positive relationship between youth (and untrained) unemployment and corruption, NEETs, is still confirmed (as shown in column one of

10 In the Arellano-Bond system GMM estimator the first-differenced lagged dependent variable is also instrumented with its past levels to address for the autocorrelation problem.11 The Hansen test of over-identifying restrictions is reported in Table 3. We also perform the Arellano-Bond test for autocorrelation that presents no evidence of model misspecification: a significant AR(1) serial correlation and lack of significant AR(2) serial correlation.

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Table 3). The positive coefficient of the variable NEETs highlights that existing inequalities in employment and education conditions of the young adults (age class 15-24) are likely to strengthen local unfairness and corrupt practices across regions.The augmented model (in column two of Table 3) shows that there is evidence that corruption is negatively (but weakly) associated with higher (per-capita) levels of expenditure on general public services that represents a very large share of total public spending (as they also include interest payments on debt). Again a negative relationship between misbehaviours or misconduct within public administration and people’s participation in voluntary organizations is found. The dynamic panel confirms the existence of a strong causal effect from (high) social capital to (less) corruption. In fact, social capital might be thought to measure the level of trust and honesty in society (Uslaner, 2001), therefore a negative correlation between corruption crimes and local social capital is expected to be found: regions in which people appear to be more honest and trustworthy ought to experience less levels of corruption.12 Finally the estimates confirm the presence of a positive correlation between the quality of public infrastructures, Roads, and the incidence of corruption crimes (see column two of Table 3) thus suggesting that a high percentage of paved roads in poor condition may be the result as well as a cause of corrupt practices: the deterioration of roads caused by insufficient maintenance is likely to increase the quota of public funds spent on investments for new infrastructure projects (repairing damaged roads or building new ones) which create a fertile ground for illicit and corrupt practices to thrive.

6. Conclusions

This study investigates misbehaviours within public administration in Italy throughout a panel analysis on regional statistics of crimes recorded

12 Our results confirm the findings of Paldam and Svendsen (2002) and Bjørnskov and Paldam (2004) who report that higher level of social capital is a significant cause of less corruption, although the direction of causality between social capital and corruption is not clear-cut.

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by the judiciary authority. Episodes of crimes are examined in connection with the dimension of the public sector, government expenditures by type, economic and social characteristics of regions. We observed that crimes within the public administration associate with the size of the public sector, poorer quality of local infrastructure (quality of roads) and inequitable conditions (high rates of young people not in employment, education or training and lower levels of social capital). The estimated model also suggests that the persistence of unlawfulness for those regions that delay their firm intervention against the misbehaviours of public officials increases the risk to remain stuck in a harmful “vicious circle” which may lead to tolerate public crimes instead of counteracting it. Our work contributes to the ongoing policy debate which is being developed at the European level with the objective to disentangle the determinants of local corruption. This analysis highlights the need to gather more accurate and complete information in order to enhance the understanding of such a subtle phenomenon. This might help to better understand the deeper and more hidden mechanisms that may start local and that – if not unveiled – may foster illegal practices within the national public sector.

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References

Acemoglu, D., & Verdier, T. (2000). The choice between market failures and corruption. American Economic Review, 90(1), 194-211.

Ades, A., & Di Tella, R. (1999). Rents, competition, and corruption. American Economic Review, 89(4), 982-993.

Ades, A. & R. Di Tella (1996). The Causes and Consequences of Corruption: A Review of

Recent Empirical Contributions, Liberalization and the New Corruption, ed. by B. Harris–White and G. White, (Brighton: Institute of Development Studies Bulletin, 27, pp. 6-12.

Aidt, T. (2003), Economic analysis of corruption: A survey, The Economic Journal, 113, F633-F652.

Arellano, M., & S. Bond (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies, 58, 277-297.

Baicker, K., & Staiger, D. (2005). Fiscal shenanigans, targeted federal health care funds, and patient mortality, Quarterly Journal of Economics, 120 (1), 345-386

Ben-Porath, Y. (1967). The production of human capital and the life-cycle of earnings, Journal of Political Economy, 75, 352-365.

Bjørnskov, C. & Paldam, M. (2004). Corruption trends, in J. Graf Lambsdorff, M. Schramm and M. Taube (eds), The new institutional economics of corruption: Norms, trust and reciprocity, 59-75. London: Routledge.

Blackburn, K., Bose, N. & Haque, M.E. (2011). Expenditures, Bureaucratic Corruption and Economic Development, The Manchester School, Vol. 79(3), 405-428.

Charron, N., Dijkstra, L., & Lapuente, V. (2015). Mapping the regional divide in Europe: A measure for assessing quality of government in 206 European regions, Social Indicators Research, 122(2), 315-346.

Charron, N., Dijkstra, L. & Lapuente, V. (2014). Regional governance matters: Quality of government within European Union member states, Regional Studies, 48(1), 68-90.

Charron, N., & Lapuente, V. (2013). Why do some regions in Europe have a higher quality of government? The Journal of Politics, 75(03), 567-582.

16

Del Monte, A., & Papagni, E. (2007). The determinants of corruption in Italy: Regional panel data analysis. European Journal of Political Economy, 23(2), 379-396.

Eicher, T., García-Peñalosa, C., & van Ypersele, T. (2009). Education, corruption, and the distribution of income. Journal of Economic Growth, 14(3), 205-231.

Factor, R., & Kang, M. (2015). Corruption and population health outcomes: an analysis of data from 133 countries using structural equation modeling. International Journal of Public Health, 60 (6), 633-641.

Fiorino, N., & Galli, E. (2010). An analysis of the determinants of corruption: Evidence from the Italian regions. POLIS Working Papers No. 171, Department of Public Policy and Public Choice, Università Piemonte Orientale.

Glaeser, E.L., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2004). Do institutions cause growth? Journal of Economic Growth, 9(3), 271-303.

Goel, R.K. & Nelson, M.A. (2010). Causes of corruption: History, geography and government. Journal of Policy Modeling, 32(4), 433-447.

Golden, M.A., & Picci, L. (2005). Proposal for a new measure of corruption, illustrated with Italian data. Economics and Politics, 17(1), 37-75.

Gupta, S., Davoodi, H.R.,& Alonso-Terme, R. (2002). Does corruption affect income inequality and poverty? Economics of Governance, 3(1), 23-45.

Gupta, S., Davoodi, H.R., & Tiongson, E. (2000). Corruption and the provision of health care and education services, in Jain A.K. (eds.), The Political Economy of Corruption, 111-141. London: Routledge.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. (1999). The quality of government. Journal of Law, Economics, and organization, 15(1), 222-279.

Lambsdorff, J. Graf (2005). Between two evils-investors prefer grand corruption!. Univ. Passau, Wirtschaftswiss. Fak., Discussion Paper V-31-05.

Lambsdorff, J. Graf (2006). Causes and consequences of corruption: what do we know from a cross-section of countries. In S. Rose-Ackerman (Ed.), International handbook on the economics of corruption, pp. 3–51. Cheltenham: Edward Elgar.

Lipset, S.M. (1960). Political man: The social bases of politics. Garden City, NY: Doubleday.

17

Mauro, P. (1998). Corruption and the composition of government expenditure. Journal of Public Economics, 69(2), 263-279.

Mocan, N.H (2008). What determines corruption? International evidence from microdata. Economic Inquiry, 46(49), 493-510.

Mungiu-Pippidi, A. (2015). Corruption: Good governance powers innovation. Nature, 518, 295-297.

Paldam, M., & Svendsen, G.T. (2002). Missing social capital and the transition in Eastern Europe. Journal for Institutional Innovation, Development and Transition, 5, 21-34.

Persson, T. & Tabellini, G. (2003). The economic effects of constitution, Cambridge, MA: MIT Press.

Putnam, R.D., Leonardi, R., & Nanetti, R.Y. (1994). Making democracy work: Civic traditions in modern Italy. Princeton University Press.

Rose-Ackerman, S. (1997). The political economy of corruption, in K. A. Elliott (eds.), Corruption and the global economy, 31-60. Institute for International Economics, Washington DC.

Schneider, F. & Enste, D.H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 38, 77-114.

Tanzi, V., & Davoodi, H. (1998). Corruption, public investment, and growth. Tokyo, Japan: Springer.

Treisman, D. (2000), The causes of corruption: A cross-national study, Journal of Public Economics, 76, 399-457.

Treisman, D. (2007). What have we learned about the causes of corruption from ten years of cross-national empirical research? Annual Review of Political Science, 10 (1), 211-244.

Uslaner, E.M. (2001). Volunteering and social capital: how trust and religion shape civic participation in the United States, in P. Dekker and E.M. Uslaner (eds.), Social capital and participation in everyday life, 104-117. London: Routledge.

World Bank (2014). Youth inactivity and unemployment, in Breaking the Barriers to Youth Inclusion, Chap.3, 23-35. The World Bank Group, Washington D.C.

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Table 1. Summary statistics and description of the variables

Variable Description Obs Mean Std. Dev. Min Max Sourc

e Unit

Crimes against the PA (crimes committed by public officials againstthe public administration and prosecuted).

264 -10.5 0.6 -12.4 -8.6 ISTAT per capita; logarithmic

Share of public sector (government expenditure per capita). 240 9.1 0.2 8.6 9.8 ISTAT per capita; logarithmic

General public services 264 6.6 0.3 6.0 7.7 ISTAT per capita; logarithmic

Education expenditure 264 7.0 0.2 6.5 7.5 ISTAT per capita; logarithmic

Public healthcare care system expenditure 264 7.4 0.2 7.0 7.8 ISTAT per capita; logarithmic

Social security and welfare 264 5.5 0.5 4.5 6.8 ISTAT per capita; logarithmic

Public safety expenditure 264 6.2 0.1 6.0 6.7 ISTAT per capita; logarithmic

Volunteering (number volunteers during the last 12 months). 242 -2.3 0.4 -3.3 -1.4 ISTAT per capita; logarithmic

GDP per capita 264 10.1 0.3 9.5 10.5 ISTAT per capita; logarithmic

Gini Index 198 -1.3 0.1 -1.5 -1.1 ISTAT logarithmic

Tertiary Education (number of students enrolled in the first stage of tertiary education). 245 -3.6 0.6 -7.1 -2.9 EUROSTA

Tper capita; logarithmic

NEETs (young people aged 15-24 neither in employment nor in education and training). 241 2.6 0.5 1.1 3.5 EUROSTA

T logarithmic

Roads (number of families declaring that streets in their area of residence are in very bad conditions, per 100 families with the same characteristics). 242 2.8 0.4 1.5 3.6 ISTAT logarithmic

Trade (used as instrumental variable) 240 -17.1 1.1 -19.5 -14.4 ISTATper capita; logarithmic

Polluted Air (used as instrumental variable) 242 2.3 0.4 1.1 3.2 ISTAT logarithmic

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Table 2. Crimes against the PA. Pooled OLS and Panel estimates

Pooled OLS Panel

CONTROLS Model (a)

Model (b)

Model (a) Model (b)

(1) (2)FE

(3) RE (4)

FE (5)

RE (6)

GDP per capita (-1) -0.648** -0.669 2.221 -1.025** 0.729 -0.578

(0.307) (0.505) (2.158) (0.466) (2.344) (0.568)Gini Index 0.893 0.578 -0.256 0.146 -1.036 -0.607

(0.607) (0.654) (0.646) (0.597) (0.668) (0.620)Tertiary Education -0.118 0.0089 -

0.951** -0.211 -0.287 -0.149 (0.111) (0.114) (0.460) (0.205) (0.422) (0.169)

NEETs 0.368 0.419* 0.060 0.153 -0.059 0.108 (0.233) (0.240) (0.267) (0.240) (0.254) (0.225)

Share of Public Sector 0.703** __ 0.657 0.612 __ __

(0.270) (0.768) (0.453)General Public Services 1.355*** 0.974

1.474*** (0.388) (1.071) (0.529)

Social Security/Welfare

0.147 -0.003 0.0388 (0.244) (0.309) (0.263)

Public Safety Expenditure

0.0176 -6.095 -1.473 (1.372) (3.921) (1.501)

Education Expenditure -0.769** -0.163 -0.369

(0.368) (0.769) (0.512)Public Health Care System -1.602** -1.234 -1.167

(0.714) (0.912) (0.757)Volunteering -0.268 -0.411 -0.486*

(0.255) (0.287) (0.250)Roads -0.0279

0.525** 0.438**

(0.175) (0.212) (0.188)

Time Dummies √ √ √ √ √ √

Observations 163 161 163 163 161 161

R-squared within 0.449 0.428 0.474 0.458R-squared betweenR-squared overall 0.515 0.506

0.0660.034

0.5350.492

0.0660.037

0.5260.494

Hausman Test (Prob>χ2)

6.26 (0.9362)

25.15 (0.1208)

Notes: Standard errors in parentheses; significance level *** p<0.01, ** p<0.05, * p<0.1; FE stands for fixed effects; RE stands for random effects; Constants are

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Pooled OLS Panel

CONTROLS Model (a)

Model (b)

Model (a) Model (b)

(1) (2)FE

(3) RE (4)

FE (5)

RE (6)

included.

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Table 3. Crimes by public officials against PA. GMM estimatesModel (a) Model (b)

CONTROLS (1) (2)

Crimes against PA (-1) 0.595*** 0.577*** (0.116) (0.114)

GDP per capita (-1) 0.028 0.191 (0.299) (0.786)

Gini Index -0.443 -0.726 (0.886) (0.929)

NEETs 0.485* 0.515 (0.193) (0.397)

Tertiary Education 0.030 -0.171 (0.086) (0.134)

Share of Public Sector -0.238 __ (0.330)

Roads 0.424* (0.231)

General Public Services 1.196* (0.617)

Social Security/Welfare -0.298 (0.305)

Public Safety Expenditure

0.885

(1.497)Education Expenditure -1.379

(1.016)Public Health Care System

-1.565

(0.964)Volunteering -0.478**

(0.216)

Time Dummies √ √

Observations 145 145

Arellano-Bond test for AR(1)

-3.33 (0.001)

-2.83 (0.005)

Arellano-Bond test for AR(2)

1.27 (0.206)

1.31 (0.191)

Sargan test of overid. restrictions

24.35 (0.711)

42.83 (0.170)

Hansen of overid. restrictions

3.32 (1.000)

0.00 (1.000)

Notes: Standard errors in parentheses; significance level at *** p<0.01, ** p<0.05, * p<0.1. Instruments: exogenous variables (Trade, Polluted Air) and Tertiary Education, Volunteering, GDPt-1, Year dummies.

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23

Figure 1. Crimes against the Public Administration. Years 2000-2011 (averages)

Source: Crime Statistics released by the Italian National Institute of Statistics (ISTAT).

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