Temi di Discussione (Working Papers)
Legal status of immigrants and criminal behavior: evidence from a natural experiment
by Giovanni Mastrobuoni and Paolo Pinotti
813
June 2011
Number
Temi di discussione (Working papers)
Legal status of immigrants and criminal behavior: evidence from a natural experiment
by Giovanni Mastrobuoni and Paolo Pinotti
Number 813 – June 2011
The purpose of the Temi di discussione series is to promote the circulation of working papers prepared within the Bank of Italy or presented in Bank seminars by outside economists with the aim of stimulating comments and suggestions.
The views expressed in the articles are those of the authors and do not involve the responsibility of the Bank.
Editorial Board: Marcello Pericoli, Silvia Magri, Luisa Carpinelli, Emanuela Ciapanna, Daniela Marconi, Andrea Neri, Marzia Romanelli, Concetta Rondinelli, Tiziano Ropele, Andrea Silvestrini.
Editorial Assistants: Roberto Marano, Nicoletta Olivanti.
LEGAL STATUS OF IMMIGRANTS AND CRIMINAL BEHAVIOR: EVIDENCE FROM A NATURAL EXPERIMENT
by Giovanni Mastrobuoni* and Paolo Pinotti**
Abstract
We estimate the causal effect of immigrants’ legal status on criminal behavior exploiting exogenous variation in migration restrictions across nationalities driven by the last round of the European Union enlargement. Unique individual-level data on a collective clemency bill enacted in Italy five months before the enlargement allow us to compare the post-release criminal record of inmates from new EU member countries with a control group of pardoned inmates from candidate EU member countries. Difference-in-differences in the probability of re-arrest between the two groups before and after the enlargement show that obtaining legal status lowers the recidivism of economically motivated offenders, but only in areas that provide relatively better labor market opportunities to legal immigrants. We provide a search-theoretic model of criminal behavior that is consistent with these results.
JEL Classification: F22, K42, C41. Keywords: immigration, crime, legal status.
Contents
1. Introduction…………………………………………………………………………………………………………..5 2. Legal and illegal immigrants: preliminary evidence from Italy …………………………………… 7 3. Theoretical framework…………………………………………………………………………………………. 11 4. The natural experiment ………………………………………………………………………………………… 15 5. Empirical strategy……………………………………………………………………………………………….. 17 6. Results………………………………………………………………………………………………………………..23 7. Conclusions…………………………………………………………………………………………………………28 References ……………………………………………………………………………………………………………… 28 Appendix ……………………………………………………………………………………………………………….. 33 Tables and figures……………………………………………………………………………………………………. 35
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* Collegio Carlo Alberto and ** Bank of Italy, Economics, Research and International Relations.
1 Introduction1
Concerns about the effects of immigration on crime are widespread. As a matter of fact, foreigners are heavily over-represented among the prison population of all developed countries. In recent years the share of foreigners over official residents barely reached 10% in the US (and it was significantly lower in most other countries), while their incidence over incarcerated individuals was many times larger (Figure 1). Such numbers increase support for migration restrictions, which prevent part of the prospective immigrant population from (legally) residing in the host countries. At the same time, if enforcement is loose, migration restrictions might also create a pool of unauthorized immigrants that are able to (illegally) cross the frontier but, once in the host country, do not enjoy legal status and therefore cannot work in the official sector.
The implications of migration barriers for crime are then ambiguous. On the one hand, restrictive policies prevent a number of immigrants (that would be potentially at risk of committing crime) from entering the country, or expel them after entry; on the other hand, those that manage to enter anyway face worse income opportunities in official markets, which raises their propensity to engage in criminal activity. Empirically identifying the overall effect is difficult as immigrants determine, at least in part, their legal status. The decision about whether to reside legally or illegally into the destination country may in fact respond to several individual characteristics (e.g. working ability) that are also likely to enter the decision about whether to commit a crime. In addition to this problem, the size of the illegal immigrant population is usually not reported in official statistics, so their crime rate remains also unobserved.
In this paper we exploit exogenous variation in legal status, provided by the last round of the European Union (EU) enlargement, and detailed criminal records on a sample of pardoned immigrants in Italy to address these issues. After August 1, 2006, more than 9,000 foreigners were released from Italian prisons upon approval of a Collective Clemency Bill passed by the Italian Parliament; five months later, on January 1, 2007, about 800 of them acquired the right to legally stay in Italy as their origin countries, namely Romania and Bulgaria, entered the EU. We thus exploit the asymmetric effect of the EU enlargement across nationalities to estimate the effect of legal status on criminal behavior, as measured by the post-release criminal record of pardoned foreign individuals.
1Contacts: giovanni.mastrobuoni@carloalberto.org, Collegio Carlo Alberto and CeRP, Via Real Colle- gio 30, Moncalieri, Italy, and paolo.pinotti@bancaditalia.it, Bank of Italy, Via Nazionale 91, Rome, Italy. We would like to thank Federico Cingano, David Card, Raquel Fernandez, Andrea Ichino, Justin McCrary, Enrico Moretti, Alfonso Rosolia, Adriaan Soetevent, Giordano Zevi and seminar participants at the Bank of Italy, Bocconi University, Collegio Carlo Alberto, FEEM-CEPR Conference on Economics of Culture, Institutions and Crime, Center for Studies in Economics and Finance in Naples, University of Padua, University of Paris X, INSIDE Workshop in Barcelona, NBER Summer Institute 2010 (Labor Studies), Universitat van Amsterdam, Cornell University and Brown University for very useful comments. Finan- cial support from the Collegio Carlo Alberto, the W.E. Upjohn Institute for Employment Research, and Fondazione Antonveneta is gratefully acknowledged. Giovanni Mastrobuoni thanks INSIDE at Universitat Autnoma de Barcelona University for their hospitality. Giancarlo Blangiardo kindly provided the ISMU data. Any opinions expressed here are those of the authors and not those of the Collegio Carlo Alberto or of the Bank of Italy. ⃝c 2009 by Giovanni Mastrobuoni and Paolo Pinotti.
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The empirical strategy is grounded on a search-theoretic model of crime that relates legal status to the probability of committing a crime. In the tradition of economic models of crime, agents choose between legitimate and illegitimate activities by comparing the economic costs and benefits between the two. Granting access to the official sector, legal status raises the returns to legitimate activities (or the opportunity cost of illegitimate ones), which in turn lowers the probability of engaging in crime. However, there is also another effect that goes in the opposite direction. Immigrants without legal status may in fact be deported back to their own country with some positive probability, which me- chanically reduces the pool of individuals in this group that are at risk of committing an offense.
The model does also allow for self-selection into legal status. In particular, migration policy imposes some fixed cost on official entrants, so that only immigrants with higher (legitimate) income opportunities in the host country will decide to comply with it; the other ones prefer to enter unofficially and face the risk of being expelled in the future. Therefore, self-selection at the frontier implies that the distribution of individual char- acteristics potentially correlated with criminal activity differs systematically between the groups of legal and illegal immigrants; this will also be the main threat to the identification of the causal effect of legal status.
To address this issue, we focus on the difference-in-differences in the probability of (re)arrest between pardon inmates from new EU member countries and a control group of inmates from candidate member countries, before and after the EU enlargement. While there are significant differences between the average characteristics of the two groups, weighting observations by the (inverse) propensity score of belonging to each group elim- inates such differences, as well as differences in pre-enlargement outcomes. Baseline es- timates suggest that the average probability of rearrest over a six-month period declines from 5.8% to 2.3% for Romanians and Bulgarians after obtaining legal status (as a conse- quence of the EU enlargement), relative to no change in the control group. Distinguishing between different categories of potential offenders, the effect is significant only for pardoned inmates that were previously incarcerated for economically-motivated crimes and the re- duction is stronger in areas characterized by better income opportunities for legal (relative to illegal) immigrants. These results are robust to alternative estimation techniques and to several robustness checks.
We contribute to the literature on social and economic effects of immigration. Until very recently, this research area has traditionally emphasized the labor market competition between immigrants and natives (surveys include Borjas, 1994; Friedberg and Hunt, 1995; Bauer et al., 2000; Card, 2005), as well as the effects of immigration on fiscal balances (Storesletten, 2000; Lee and Miller, 2000; Chojnicki et al., 2005) and prices (Lach, 2007; Cortes, 2008). Drawing on survey evidence from 21 European countries in 2002, Card et al. (2009) show that, besides these (“economic”) issues, natives’ support for migration restrictions is shaped also (and indeed mostly) by other “compositional amenities”, among
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which crime plays a major role.2
Partly as a consequence of this increasing awareness, a few previous papers have ex-
amined the empirical relationship between immigration and crime. At the aggregate level, Butcher and Piehl (1998a), Bianchi et al. (2008) and Bell et al. (2010) document some correlation between- and within-local areas in the US, Italy and the UK, respectively, but conclude that the causal effect is not different from zero (maybe with the exception of asylum-seekers in the UK).3 At the micro level, Butcher and Piehl (1998b, 2007) use Census data to show that, keeping other individual characteristics constant, current immi- grants have lower incarceration rates than natives, while the opposite was true for former immigrants at the beginning of the XX century (Moehling and Piehl, 2007). Yet, no pre- vious study has investigated the role of legal status; this is precisely the contribution of the present paper.
We also add to a huge empirical literature on the relationship between legitimate income opportunities and criminal career. Indeed, the fact that the propensity to engage in crime should decrease with outside options in official markets is one of the key results of the economic model of crime (Becker, 1968). Over the years, several paper have examined the empirical validity of this prediction, finding in general a good deal of evidence consistent with it: a non-exhaustive list includes Witte (1980), Meyers (1983), Grogger (1998), Gould et al. (2002) and Machin and Meghir (2004). We contribute to this strand of literature by showing that, also in our sample of pardoned foreigners, access to better legitimate income opportunities (through the acquisition of legal status) lowers the individual propensity to engage in crime.
In the next section we summarize the main features of immigration in Italy, paying particular attention to the gap between legal and illegal immigrants in terms of labor market outcomes and crime. In Section 3 we provide a theoretical framework that captures these elements in a very simple way, studies the channels through which legal status may impact on crime and clarifies which are the main threats to identification. Then, Section 4 describes in detail the natural experiment while Section 5 derives the estimating equations. Finally, in Section 6 we discuss the empirical results and Section 7 concludes.
2 Legal and illegal immigrants: preliminary evidence from Italy
After centuries of massive emigration, Italy became a recipient of positive net inflows only in the late 1980s. As a consequence, the legislative framework in this respect is also very recent, the first migration law being enacted in 1990 and later amended in 1995 and 2002. Throughout these changes, Italian migration policy remained firmly grounded
2See also Bauer et al. (2000)
3Partly in contrast with these findings, Borjas et al. (2010) argue that migration has indeed an effect, but a very indirect one: by displacing black males from the labor market, immigration increases the criminal activity of this latter group.
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on the institute of the residence permit, which establishes a direct link between working condition and legal residence: the main condition for eligibility is receiving a job offer in Italy. However, the total number of residence permits issued each year is fixed on the basis of migration quotas decided by the government.
2.1 Official migration
Over the last two decades, the number of valid residence permits rose from less than 1 million at the beginning of the 1990s to more than 2 million in 2005, slightly declining thereafter; the number of foreign (official) residents increased even more steeply, from less than 600 thousands to almost 4 million (in the face of an otherwise declining population), see Figure 2. Official residents include immigrants holding a valid residence permit (and possibly their close relatives), as well as foreigners enjoying legal status in Italy for other reasons, such as being citizen of a EU member country. The divergence between the two measures (permits and residents) toward the end of the period is indeed explained by the EU enlargement, which starting in 2004 relieved an increasing number of Eastern European citizens from the need of a residence permit to legally reside in Italy.
Notwithstanding the spectacular growth of the official immigrant population, the num- ber of newly issued residence permits fell systematically short of total demand over the years, often by a large extent. For instance, 170,000 permits were issued in 2007 in front of more than 740,000 demands; the following year, the number of new residence permits even decreased to 150,000, to be primarily assigned to applications left pending the year before (thus increasing the gap between current demand and supply of permits). In addition to that, the 2002 reform of migration policy requires prospective immigrants to find a job contract before entering the country, thus hampering further the match of foreign workers with Italian employers. Stringent requirements on permit eligibility and tight rationing of migration quotas, coupled with weak border enforcement (also due to the geographic configuration and location of the Italian peninsula), resulted in an increasing number of undocumented immigrants illegally crossing the border or overstaying tourist visas.
2.2 Unofficial migration
While the very nature of unofficial migration prevents accurate estimates of its size, amnesties of formerly undocumented immigrants provide some information in this re- spect. During these episodes, immigrants illegally present in Italy can apply for a valid residence permit under very mild conditions, with clear incentives to report their illegal status.4 General amnesties have been enacted every 4-5 years since 1986, growing in size from 100 to 200-250 thousand individuals during the 1990s, and reaching a peak of 700
4Bianchi et al. (2008) and Fasani (2009) also use applications for amnesty to estimate the size of the illegal population in Italy, while several studies adopted the same methodology to count the number of undocumented immigrants in the United States after the amnesty passed with the Immigration Reform and Control Act in 1986 (see, e.g., Winegarden and Khor, 1991).
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thousand in 2002. The acceleration in official migration observed during the last few years was thus accompanied by an analogous one in unofficial inflows; see Figure 3.
The graph reports also the number of illegal immigrants expelled from the country. In Italy illegal immigrants apprehended by the police are not incarcerated. Rather, they are accompanied at the frontier and deported back to their origin country. In some cases, deportation is not enforced and the individual receives just an injunction to leave the country.5 Whilethefractionofimmigrantsthataredeportedisnotlarge,itisn’tnegligible. Looking again at the years in which there was an amnesty, the ratio of expulsions over demands for amnesty by unofficial immigrants were 17% in 1986, went up to 28% in 1998, and down again to 15% during the last amnesty program of 2002.
2.3 Criminal and labor market outcomes
Even though foreigners cannot be incarcerated for breaking migration laws, they are nev- ertheless overly represented in Italian prison population. The number of foreign inmates more than doubled since the early 1990s, from less than 10 thousand to more than 20 thousand in 2008, in the face of just a slight increase of total prison population. As a result, the share of foreigners in prison population has reached one third (Figure 4), an order of magnitude greater than the share of immigrants in the whole population. Of course, such imbalance is not necessarily driven by differences in criminal behavior, as it might also depend on statistical discrimination in law enforcement against foreigners (Becker, 1957). Yet, it is hard to believe that such a huge difference in incarceration rates between natives and foreigners is the sole product of discrimination.
However, an important distinction between official and unofficial immigrants seems to suggest that discrimination is not the whole story. While detailed statistics on convicted foreigners disaggregated by legal status are not publicly available, the Italian Ministry of Internal Affairs (2007) claims that legal immigrants represented about 6% of all individ- uals reported by the police to the judiciary authority in year 2006, which is in line with their share over total population. The disproportionate incidence of foreigners in prison population is entirely due to undocumented immigrants, who account for 70% and 80% of foreigners reported for violent and property crimes, respectively.
Then, legal status seems to have profound implications for immigrants’ criminal ca- reers. There are several reasons why this might indeed be the case. In particular, legal and illegal immigrants may face very different (legitimate) earning profiles, which in turn affect the opportunity cost of crime. Most likely, the administrative and penal sanctions (including the possibility of incarceration) faced by employers who hire undocumented immigrants, in addition to the risk of job destruction due to the expulsion of the worker, adversely impact on the demand for illegal immigrants (relative to those holding a valid
5In 2002 the last reform of migration policy (Law 189/2002) introduced the possibility of incarceration for those that did not comply with a previous injunction to leave and were later re-apprehended by the police. However, such norm was never enforced because it was deemed Anti-Constitutional by the Constitutional Court.
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residence permit). This effect could be amplified by selection into legal status. Far from being randomly distributed, legal status is strongly correlated with other individual char- acteristics, which are known to be important determinants of criminal activity.
While information in this respect is not available for a representative sample of (legal and illegal) immigrants in Italy, survey evidence from a region in the North-West is con- sistent with this conjecture. Starting in year 2001, the NGO ISMU has conducted yearly interviews on a sample of about 9,000 immigrants in the Lombardy region. The data con- tain information on labor market outcomes, along with several individual characteristics, including legal status. Sampling of illegal immigrants is attained through the “center- sampling technique” devised by the statistical team of ISMU in the early 1990s. The main idea is to exploit the social networks among the foreign population and base sampling on a number of “aggregation centers” that are attended by both legal and unauthorized immigrants (care centers, meeting points, shops, telephone centers, etc.), as opposed to administrative sources that cover only official residents; the methodology is described at length in Blangiardo et al. (2004) and Blangiardo (2008).6
Table 1 compares the characteristics of legal and illegal immigrants in the 2006 round of the survey (that is, immediately before the EU enlargement of 2007). It turns out that unauthorized immigrants are on average younger, less educated, disproportionately single males and have fewer kids. Most importantly, they tend to be employed in occupations with lower skill content and earn significantly lower wages; notice also that the wage gap is relatively more severe among the high-skilled.7
While restrictions imposed by migration laws on the employment of illegal immigrants certainly explain part of the wage gap, the striking differences in other observable charac- teristics point at the importance of selection into legal status. In particular, immigrants could voluntarily self-select when deciding whether to comply or not with migration pol- icy; for instance, individuals with better income prospects in the host country could exert greater effort in dealing with the bureaucratic difficulties imposed by (legal) entry pro- cedures. On the other hand, selection could also be involuntarily (on the part of the immigrant), whenever less skilled individuals have lower chances to receive a job offer eventually entitling them to apply for a permit. Whatever the reasons are, the differences reported in Table 1 suggest that legal status is not randomly distributed across immi- grants, which considerably hampers the empirical identification of its effect on criminal behavior.
We next present a model of crime that is consistent with the stylized facts described above and clarifies which are the main threats to identify the causal effect of legal status
6Using the ISMU data to estimate the determinants of immigrants’ earnings in Italy, Accetturo and Infante (2010) examine the reliability of the information on legal status by comparing the results of the 2002 survey with the demands for amnesty presented during the same year, concluding that the extent of under-sampling of unauthorized immigrants and/or misreporting of legal status is modest.
7Drawing on several rounds of the ISMU survey, Accetturo and Infante (2010) confirm these findings in a multivariate regression analysis. Extensive empirical evidence on the wage gap suffered by illegal immigrants is available also for the US, see for instance Bratsberg et al. (2002), Kossoudji and Cobb-Clark (2002) and Kaushal (2006).
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on crime.
3 Theoretical framework
Consider a population of infinitely lived, risk-neutral prospective immigrants. If they decide to actually move to the destination country they incur a travel cost T . In addition, official entry in the host country imposes an upfront cost B on legal immigrants (L); such cost may include, for instance, the time and money spent to deal with paperwork, acquire health certifications, pay head taxes and so on. Alternatively, immigrants may decide to cross the border illegally. In this way, illegal immigrants (I) avoid the burden imposed by migration policy, but face the risk of being apprehended and deported back to their home country at the beginning of any subsequent period.
Once in the host country, both legal and illegal immigrants may engage in crime. Criminal activities deliver an immediate payoff z, which is randomly distributed across agents in each period according to the cumulative density F (z); however, those committing a crime are arrested and sent to jail in the following period with probability π. Assuming a constant discount factor ρ < 1, the expected utility of seizing a crime opportunity z for immigrants of type k = I, L is then
Ck(z)=z+ρ[πP +(1−π)EVk] (1)
where P is utility associated with incarceration, Vk is the utility when not in prison and E denotes expectations over (future) values of z; without loss of generality, we may impose P equal to zero.
Apart from criminal proceeds, immigrants have access to labor earnings that vary with individual skills and the returns to skills for different groups of immigrants in the labor market. In particular, letting h denote the (heterogeneous) level of human capital, the wage of each immigrant is wLh if (s)he holds a residence permit and wIh otherwise, with ∆w = wL − wI ≥ 0.8 While the strict inequality would be consistent with the empirical evidence discussed in the previous section, the conservative assumption that wL is no lower than wI is sufficient for all the results that follow.
The utility of legal immigrants is
VL = max {CL(z); ρEVL} + wLh, (2)
which depends both on the decision about criminal activity (the first term on the r.h.s.) and on legitimate income (the second term). The utility of illegal immigrants is similar except for the fact that, with probability δ > 0, they are apprehended and deported back
8From now on the notation ∆ will always refer to the difference between the (potential) outcomes of an individual when legal and illegal.
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to their origin country,
VI =δVH +(1−δ)[max{CI(z);ρEVI}+wIh], (3)
where VH is utility in the home country. The latter depends, positively, on the labor market income wHh and we posit that wH < wI ≤ wL.9 From now on we assume for simplicity that those expelled from the country do not try anymore to migrate.
Individuals face three decisions: whether to migrate or not; in case they do, whether to cross the border legally or illegally; finally, once in the host country, they must choose whether to accept or reject the crime opportunities available in each period. The latter problem is at the core of the economic model of crime first proposed by Becker (1968), in which individuals choose whether to engage or not in crime depending on the rela- tive return of legitimate and illegitimate activities.10 Admittedly, our framework is very stylized in this respect, reducing such problem to a discrete choice between crime and lawfulness. In this way, we prevent continuous time-allocation choices between legitimate and illegitimate activities (Grogger, 1998), as well as ex-ante investments in human capital (Lochner, 2004). However, these simplifications are inconsequential for the empirical anal- ysis, given that our data do not contain such information. The present model captures the institutional features of Italian migration policy that are common to most other countries, namely that legal aliens face a substantial bureaucratic and economic burden upon entry and that illegal aliens may be deported back to their origin country. We next describe in greater detail the trade offs involved in each decision and solve the problem backward, starting from the choice about criminal activity.
3.1 Criminal behavior
In deciding whether to engage or not in criminal activity each individual of type k = I, L compares the expected returns from criminal activity, Ck(z), with their opportunity cost, ρEVk. Since Ck(z) depends, positively, on the value z of illicit income opportunities available in each period (while ρEVk does not) there must exist a reservation value zk∗ such that each individual of type k commits a crime if and only if z ≥ zk∗.11 Imposing the expected payoffs from crime equal to its opportunity cost for both legal and illegal immigrants, Ck(zk∗) = ρEVk (k = I,L), and substituting into (1) delivers the reservation
9Since in most cases human migration is an economic phenomenon, it seems natural to assume that wages in the destination country are higher than in the origin country. In our formulation, this occurs through higher returns to human capital, which might be at odds with standard factor proportion ex- planations of migration. On the other hand, it is consistent with more recent models stressing the effect of skill-biased technical change on labor market outcomes in destination countries, which affects in turn the relative returns of more and less educated migrants (Acemoglu, 2002, provides a survey). Also, it is consistent with extensive empirical evidence on the positive selection of immigrants; see Grogger and Hanson (2010) for a recent study.
10See also Ehrlich (1973), Grogger (1998) and Machin and Meghir (2004) for later developments; Burdett et al. (2003), Lochner (2004) and Lee and McCrary (2009) provide extensions in dynamic settings that are most similar to ours.
11Notice the analogy with the notion of “reservation wage” commonly adopted in equilibrium search models of labor (see Rogerson et al., 2005, for a survey)
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values
zk∗ = ρπEVk. (4)
Conditional on legal status, the reservation value completely characterizes criminal behavior. In particular, the probability of committing a crime for legal immigrants simply equals the probability of receiving a crime opportunity worth more than zL∗ ,
cL =1−F(zL∗); (5) for illegal immigrants, we must first condition the probability of committing a crime on
the risk of deportation,
cI =(1−δ)[1−F(zI∗)]. (6) The log-probability of committing a crime for each individual, conditional on legal
status, may be written compactly as
ln c(h) = ln cI (h) + β(h)L,
where L = 1 if the immigrant is legal and L = 0 otherwise, and β(h) ≡ ∆lnc(h) is the causal effect of legal status conditional on h; using equations (5) and (6),
β(h) ≈ δ − [F (zL∗ ) − F (zI∗)] . (7)
The sign of (7) depends on two effects. On the one hand, holding constant the propen- sity to engage in criminal activity, deportation of illegal aliens (at rate δ) lowers the number of crimes they commit relative to legal immigrants. This is the incapacitation effect of migration restrictions, which is apparent from the first term on the right hand side, mov- ing the crime rate upward after the removal of migration restrictions. On the other hand, the propensity to criminal behavior does also change with legal status, because different labor market opportunities for legal and illegal immigrants entail also a difference in terms of opportunity cost of crime. This second effect, which is apparent from the last term of the equation (namely the change in the probability of accepting a crime opportunity for formerly unofficial immigrants) increases with the wage premium to legal status, ∆w ≥ 0. However, its sign and strength, as well as the direction of the overall effect in (7), depend crucially on the equilibrium distribution of h across legal and illegal immigrants, which we examine next.
3.2 Equilibrium
In the remainder of this section we provide a graphical representation of the equilibrium and the intuition behind all results; formal proofs are presented in the Appendix. First notice that wH < wI implies that there exists a threshold for h above which individuals decide to leave the origin country. In particular, Figure 5a shows that all individuals characterized by h ≥ hI prefer to unofficially cross the border rather than staying home.
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What about the option of entering the destination country by complying with migra- tion policy? Notice that, once in the host country, all immigrants prefer to be legal rather than illegal, i.e. E∆V = E(VL − VI ) > 0. By a simple revealed-preference argument, in fact, all those willing to (illegally) migrate prefer to live in the destination than in the origin country; therefore, these same individuals are better off avoiding the risk of being deported back. Moreover, the differential E∆V increases with h; intuitively, better la- bor market opportunities in the destination country mean a greater utility loss in case of expulsion.12
After arrival, legal immigrants are thus better off holding a valid residence permit; however, upon arrival, they must bear the entry cost B, so they apply for legal status if and only if E∆V ≥ B. Since E∆V (h) is upward sloped, the latter condition must hold beyond some threshold hL; see Figure 5b. The diagram clearly illustrates the self selection of immigrants at the frontier: individuals in the upper tail of the distribution of skills comply with migration policy, while those in the intermediate range [hI,hL] enter unofficially in the country.
Therefore, labor skills sort out immigrants from non-immigrants and, among the for- mer, legal from illegal entrants. Figure 5c shows then how the equilibrium distribution of h determines within- and between-group differences in criminal behavior. The probabil- ity of committing a crime for legal immigrants, cL, is inversely related to the reservation value zL∗ , which in turn is proportional to expected utility EVL; the same is true for illegal immigrants, conditional on not being deported. Therefore, EVL(h) > EVI(h) implies that, conditional on h, unauthorized immigrants that are not deported commit more crimes than legal immigrants: c ̃I = 1 − F (zI∗) ≥ 1 − F (zL∗ ) = cL. However, the unofficial population includes also those that are deported at the beginning of each period (before committing a crime), so deportation shifts the crime rate for this group down from c ̃I to cI.
The causal effect of legal status on crime is then the average (log) difference between the curves cL and cI over the interval [hI , hL ]
β≡E[β(h)|hI ≤h≤hL]=E[lncL(h)|hI ≤h≤hL]−E[lncI(h)|hI ≤h≤hL]. (8)
The sign of (8) is a priori ambiguous, depending upon whether the average (relative) reduction in criminal activity by formerly unofficial immigrants after the concession of legal status, E[(F(zL∗)−F(zI∗))|hI ≤h≤hL], is strong enough to counterbalance the increase in crime brought by the end of deportations. Therefore, determining the effect of legal status on criminal activity is ultimately an empirical issue.
3.3 Identification
Assuming to have data on criminal activity for a sample of legal and illegal immigrants, the main threat to empirically identify β is that each individual is commonly observed only in one state (either with or without legal status), so the first term on the right hand side of
12The Appendix presents the formal proof that E∆V ≥ 0 and ∂E∆V ≥ 0 for h ≥ hI . ∂h
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equation (8), namely the (counterfactual) log probability of committing a crime for illegal immigrants conditional on obtaining legal status, is not observed (in the terminology of Rubin, 1974, it is the “potential outcome”). Missing this element, one could alternatively conduct a naive comparison between legal and illegal immigrants,
β ̃=E[lncL(h)|hL ≤h]−E[lncI(h)|hI ≤h≤hL];
however, E [ln cL(h)|hL ≤ h] ≤ E [ln cL(h)|hI ≤ h ≤ hL] (because crime decreases with h),
so β ̃ would provide a downward biased estimate of β, β ̃=β+E[lncL(h)|hL ≤h]−E[lncL(h)|hI ≤h≤hL]<β,
SELECTION BIAS
see the last diagram in Figure 5.
The last round of the EU enlargement provides an exogenous source of variation in
legal status that allows us to remove the selection bias. The next section describes in detail this quasi-experimental setting.
4 The natural experiment 4.1 The EU enlargement
With the fall of the Eastern Bloc and the EU enlargement toward the east, immigrants from central and eastern Europe became a large and growing share of total inflows in Italy (see Figure 2). A first round of the enlargement took place in 2004 with the admission of Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia and Slovakia. Then, on January 1st, 2007, Bulgaria and Romania also joined the EU; and the process of enlargement is far from over, as several countries are “candidate members” of the EU. In particular, Croatia, Turkey and the Former Yugoslavian Republic of Macedonia are already negotiating admission conditions, while such negotiations should start soon for Albania, Bosnia and Herzegovina, Kosovo, Montenegro and Serbia. New member and candidate member countries are shown in Figure 6.
Article 39 of the European Commission Treaty would in principle allow citizens of new member countries to i) look for a job in any other country within the EU, ii) work there without needing any permit, iii) live there for that purpose, iv) stay until the end of the employment relationship, v) enjoy equal treatment with natives in access to employment, working conditions and all other social and tax advantages that may help to integrate in the host country. In practice, however, several countries in Europe maintained significant restrictions to the free movement of immigrants from new member countries.
The application of the EU directives was at the center of a heated public debate also in Italy until the very last weeks before the enlargement, mostly because of the alleged impacts on crime. However, in the end (on December 28, 2006) the center-left government
15
led by Romano Prodi guaranteed free movement to all new EU citizens and completely liberalized access to the labor market in the following sectors: agriculture, hotel and tourism, managerial and highly skilled work, domestic work, care services, construction, engineering and seasonal work. These sectors account for the bulk of foreign employment, both before and after the enlargement. And in the rest of the official economy (basically the manufacturing sector) migration quotas were also eased in order to accommodate the larger number of workers from Romania and Bulgaria.
The removal of migration restrictions led to sharp changes in the composition of for- eign population in Italy. The left graph of Figure 7 compares the number of (official) residents coming from new member and candidate member countries before and after the EU enlargement. Until 2006, the combined size of Romanian and Bulgarian communities was about half of the other group, the difference between the two remaining constant over the period. Then, in the wake of admission to the EU, the number of Romanians and Bulgarians officially residing in Italy nearly doubled, while the size of the other group continued to grow at approximately the same rate as in the previous years.
A similar pattern arises among the individuals arrested by the Italian police between 2006 and 2007. However, the (differential) increase was much less pronounced in this case, so the ratio of arrested over total official residents actually declined for Romanians and Bulgarians, while no significant change is observed for the control group; see the right graph in Figure 7. At a first sight, one might be tempted to conclude that the removal of migration restrictions favored a decline in criminal activity. Yet, the increase in Romanians and Bulgarians between 2006 and 2007 includes both inflows from abroad and acquisitions of legal status by (formerly unofficial) immigrants already in Italy, the two components being undistinguishable from each other. Figure 7 would be indeed consistent with a decline in the crime rate if the sharp increase in the left graph was driven largely by inflows of new immigrants after the EU enlargement. On the other hand, if it was caused by changes in legal status of foreigners already residing in Italy before 2007, the decline in the incidence of arrests would be due to the fact that formerly unofficial immigrants impact, positively, on total official population only after 2007, but affect the number of crimes both before and after that period.
One way to circumvent this problem is to focus on a sample of immigrants that were already present in Italy before the enlargement.
4.2 The July 2006 Collective Pardon
Italian collective pardons eliminate part of the sentence, typically 2 or 3 years, to all prison inmates; then, all those whose residual sentence is below such length are immediately released. In this way, pardons generate sudden releases of large numbers of inmates. The only ones excluded are Mafia members, terrorists, kidnappers, and sexual offenders, but even violent criminals like murderers and robbers can be pardoned. Whenever a pardoned prisoner recommits a crime within five years, the commuted prison term gets added to the
16
new term.
Collective clemency bills are deeply rooted in Italian history; over the last 40 years
there has been on average a pardon every 5 years (Barbarino and Mastrobuoni, 2010). The last one was voted by the Italian Parliament in July 2006 and enacted shortly after (on August 1). About 22,000 individuals, corresponding to more than one third of total prison population, were freed within a few days. More than 8,000 of them were foreigners, reaching the figure of 9,642 by the end of 2006.
We were granted access to the criminal records, from August 2006 through December 2007, of all prison inmates released with the Collective Clemency Bill.13 The most im- portant information for our purposes is the nationality and the date of rearrest (if any). Figure 8 shows that, among foreigners, a large number of pardoned inmates were rear- rested over the following year and a half. In particular, 795 individuals were back to jail by the end of 2006, before the EU enlargement, growing to 1654 one year later, after the EU enlargement. The main idea behind our empirical strategy will be then to exploit differ- ences in the probability of rearrest, before and after the EU enlargement, across different nationalities in our sample.14
5 Empirical strategy
In this section we devise a difference-in-differences estimator that exploits the unique characteristics of our sample to estimate the effect of legal status on the probability of committing a crime. If legal status does indeed affect the criminal behavior of immigrants, we should observe a change in such probability for Romanians and Bulgarians after the EU enlargement. In terms of our theoretical model, the average log probability for this group in year 2006 (before the policy change) is
E[lnc(h)]=E[lncI(h)|hI ≤h
17
Subtracting (9) from (10) delivers the change after the extension of legal status to all (formerly illegal) immigrants from new EU member countries,
E ln c′(h) − ln c(h) = βG(hL) + Ψ, (11) where Ψ is the (unobserved) counterfactual change between the two periods absent the
policy shock,
Ψ= E[lnc′I(h)−lncI(h)|hI ≤h
H0 : d∗it = new EUi + post∗t (17) H1 :d∗it = new EUi +post∗t +βnew EUi ×post∗t,
where post∗t = 1 after t∗ and post∗t = 0 otherwise, and d∗it = 1 if individual i was re-arrested in period t and d∗it = 0 otherwise; notice that estimating β by OLS is an alternative way of calculating the difference-in-differences reported in Table 3. Then, for any possible break point t∗ we compute the R-squared ratio between models H1 and H0, as a measure of the importance (in terms of explanatory power) of any differential change in criminal behavior between the two groups at date t∗.
The placebo estimated coefficients and R-squared ratio for Italy, as well as for the North and Center-South areas, are presented in the the left plots in Figure 12. The most likely break point for Italy as a whole is December 12, which is indeed quite close to the date of the enlargement and consistent with immigrants rationally anticipating the policy change and modifying their behavior as uncertainty about the policy change gradually unravels
25
(see Section 4.1).26 When the same test is run separately for North and Center-South, the estimated break-points are very similar (December 1 and December 7), but while for the North the additional explanatory power is considerable, for the Center-South the R- squared increases by just a little; moreover, in the latter case the difference-in-differences effect is positive. This might be explained by the absence of significant improvements of the income opportunities of formerly unofficial immigrants after obtaining legal status, so that in Center-Southern regions the incapacitation effect prevails also for economically- motivated offenders. Another thing to notice is that the magnitude of the effect (gray line) is always higher as we let the data “choose” the break point, rather than fixing it on January 1, 2007.
The right plots in the figure provide additional visual evidence in this respect, showing the discontinuity in criminal behavior at the estimated break point. The plots are predicted (daily) hazard rates of rearrest as a function of a third order polynomial in time for Romanians and Bulgarians (solid line) and for control group (dashed line), before and after the break point. In line with the results in Tables 7 and 8, the discontinuity for the first group is particularly relevant in Northern regions, reaching 8%, as opposed to no discontinuity at all for the control group.
One interpretation of the general increase in the magnitudes is that estimating the break point addresses the measurement error induced by the fact that individuals will not in general stick to the official date of the policy change when adjusting their behavior. On the other hand, when we choose the break point by maximizing the explanatory power of the difference-in-differences, specification search bias implies that the coefficient estimated using the same data will have a nonstandard distribution (Leamer, 1978); in particular, conventional test statistics reject too often the null hypothesis that the coefficient is equal to zero. For this reason, we stick to the (more conservative) estimates reported in the regression tables.27
6.4 Threats to identification
In the final part of this section we discuss some additional identification issues. One first concern is that legal status affects the probability of being arrested and/or incarcerated conditional on having committed an offense. For instance, immigrants found without documents may be carefully scrutinized and additional evidence may become available at closer inspection. Also, conditional on the severity of charges, official immigrants could have easier access to sanctions alternative to institutionalization (like for instance home
26A likelihood ratio test statistic based on Cox’s proportional hazard model gives very similar results. See Ichino and Riphahn (2005) for a similar exercise.
27In a study on the dynamics of segregation, Card et al. (2008) address this source of bias by randomly splitting the sample and using different subsamples to estimate the break point and the size of the change. However, their data set includes about 40,000 census tract observations from 114 Metropolitan Areas, so even after splitting the sample into 2/3 and 1/3 subsamples for estimating the break point and the coefficient, respectively, they end up with a reasonably large number of observations in both steps. On the other hand, with just over 2000 individuals, we run into serious troubles in terms of statistical power.
26
detention). If this is the case, the reduction in incarceration that we observe could be ex- plained by changes in the probability of ending up in jail conditional on criminal behavior, as opposed to changes in criminal behavior itself.
While we cannot directly address this issue (because the conditional probability of incarceration remains unobserved), there are several reasons why we believe that it is possible to exclude these alternative stories on the basis of the available empirical evidence. First of all, changes in the probability of arrest and incarceration should matter after the EU enlargement, while all the tests identify the break point before that date, which is more consistent with expectation-induced changes in (criminal) behavior. In addition, there is little or no reason for these alternative explanations to impact differentially in Northern and Center-Southern regions, or among economic and violent offenders. Finally, as to the possibility of sanctions alternative to incarceration, the pardon status precludes all individuals in our sample from accessing this opportunity.
Another issue is that legal and illegal immigrants may be characterized by a different willingness to travel back to their origin country, as they retain the right to return to Italy at any subsequent moment. While we consider at risk all individuals released with the pardon, spending less time in Italy would decrease the probability of committing a crime there for Romanians and Bulgarian after the acquisition of legal status. While this would also imply a reduction in the crime rate of this group, the mechanism would be totally different from the one proposed in this paper. However, also this alternative channel (like the ones discussed before) should work mostly after the policy change and, again, it should impact similarly on all (regardless of the type of crime previously committed and the region of residence in Italy).
A related concern is that, after obtaining the right to free movement, immigrants might consider moving to other European countries that offer relatively better labor market opportunities to legal immigrants. In addition to the usual counter-arguments in terms of break point and heterogeneity of effects (which hold true also in this case) we should notice in addition that migration to other EU states would not occur instantaneously; indeed, if this was really driving the change in re-incarceration, the effect should be increasing over time, as more and more Romanians and Bulgarians exit from the pool at risk. Therefore, we can investigate the empirical relevance of this alternative explanation by looking at the evolution of the differential effect over time. For this reason, we re-estimate the model (15) truncating the longitudinal dimension at each week after the EU enlargement. The results, shown in Figure 13, are remarkable stable over time, suggesting if anything that most of the action takes place in the first weeks after the enlargement, when migration to other countries plays probably little or no role.
One final issue concerns the possibility of interactions in crime between different com- munities of immigrants. In particular, the change in the criminal behavior of Romanians and Bulgarians after the EU enlargement could have affected the activities of the other individuals in our sample, thus “contaminating” the control group. In particular, substi- tution between the criminal activity of the two groups would bias our estimates upward,
27
in that other immigrants would increase their criminal activity in response to the decrease by Romanians and Bulgarians; the opposite is true in the case of complementarity.
While interactions in crime raise formidable estimating issues (which we do not address in this paper), descriptive evidence seems supportive of the complementarity hypothesis. Figure 14 plots the change (between 2006 and 2007) in the number of crimes committed by all Romanians in Italy against the same changes for (some of) the nationalities included in the control group, for different types of crime.28 It emerges clearly a positive correlation between the two. While we can hardly attach any causal interpretation to this finding, the latter seems more consistent with the existence of complementarity in the criminal activities of the two groups (as opposed to substitution), in which case our estimates would be biased downward.
7 Conclusions
We use a natural experiment, namely the last round of the EU enlargement, to identify the effect of legal status on immigrants’ crime. We provide a theoretical framework that illustrates the two main effects of legal status: on the one hand, it increases crime by precluding deportation of potential foreign criminals; on the other hand, it lowers the propensity to engage in crime by providing immigrants with better income opportunities. Evidence from a sample of former prison inmates released in Italy a few months before the enlargement suggests that the second effect prevails. In particular, the probability of rearrest decreases by more than half after obtaining legal status as a consequence of the EU enlargement.
Besides the effect on the pool of undocumented immigrants already in the host country, the concession of legal status (either through subsequent rounds of the EU enlargement or though amnesties) is also likely to attract new immigrants from abroad: indeed, thousands of Romanians and Bulgarians were standing along the European borders on New Year’s Eve of 2007. It is then likely that changes in migration policy affect also the quantitative dimension of incoming flows, as well as their composition. Policy makers would also need an estimate of the cost and benefits of these additional consequences, but this goes beyond the scope of this paper.
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32
Appendix
In this Appendix we characterize the expected utility functions EVL and EVI defined in section 3, as well as the difference between the two, E∆V = E (VL − VI ).
Starting with EVL, we may subtract ρEVL from both sides of equation 2 to obtain VL − ρEVL = max {CL(z) − ρEVL; 0} + wLh.
Taking expectations with respect to z and recalling that zk∗ = ρπEVk (from equation 4)
delivers
EVL (1 − ρ) = after integrating by parts,
[1 − F (z)] dz + wLh; (18) In a similar way, one obtains that the expected utility of illegal immigrants may be written
as
zL∗
EVI (1−ρ)=δ[VH −ρEVI]+(1−δ)
+∞
[1−F(z)]dz+wIh . (19)
EVL (1 − ρ) =
+∞ ∗
(z − zL) dF (z) + wLh;
zL∗
+∞
zI∗
Proof that E∆V (h) ≥ 0 ∀h ≥ hI . By contradiction: assume that ∃h′ ≥ hI such that
E∆V (h′) < 0, which implies (after combining 18 and 19)
+∞ zL∗
[1−F(z)]dz+wLh<δ[VH −ρEVI]+(1−δ)
+∞
[1−F(z)]dz+wIh (20)
Since [VH − ρEVI] < 0 over h ≥ hI and ∆w ≥ 0, a necessary condition for (20) to hold is that zL∗ − zI∗ = ∆z∗ > 0 > E∆V ; but ∆z∗ and E∆V having a different sign contradicts condition (4).
∂ E ∆ V ∂ E ∆ V ∂ z L∗ ∂ z I∗
Proof that ∂h ≥ 0. Using again equation (4), ∂h ≥ 0 ⇔ ∂h ≥ ∂h ; also, equations
(18) and (19) become, respectively,
+∞
= [1 − F (z)] dz + wLh
zI∗
∗ 1−ρ(1−δ)
zL
∗1−ρ
πρ zI
πρ
zL∗
= δVH +(1−δ)
+∞
[1−F(z)]dz+wIh .
33
zI∗
Applying the implicit function theorem and the Leibniz rule we obtain
∂ zL∗ π ρwL
∂h = 1−β+2πρ1−FzL∗
∂zI∗ (1−δ)πρwI
∂h = 1−β(1−δ)+2πρ(1−δ)1−F zI∗.
Then, a sufficient condition for having ∂E∆V = 1 ∂∆z∗ ≥ 0 is that ∆z∗ = πρE∆V ≥ 0, ∂h πρ∂h
which is always true over the interval h ≥ hI .
34
60
50
40
30
20
10
0
Figure 1: share of foreigners over total and prison population
NLD GRC BEL AUT
USA (fed.)
ITA DEU ESP PRT FIN
% foreigners over prison population % foreigners over total (official) population
Note: The figure shows the incidence of foreigners over prison and total population, respectively, in some OECD countries around year 2000. The source is the OECD for all countries other than the US. The data for the US are taken from Stana (2005) and refer exclusively to federal prisons; representative data on state and local jails are not publicly available.
35
Figure 2: legal immigrants
12.0 3600
10.0 3000
8.0 2400
6.0 1800
4.0 1200
2.0 600
0.0
0
-2.0 -600
Note: The figure shows net migration inflows in Italy, as well as foreign official residents and valid residence permits during the period 1971-2007. Source: ISTAT and Ministry of Interiors.
2500
2000
1500
1000
500
0
net inflows (x 1000 inhab.) residence permits permits, central and eastern europe official residents
Figure 3: illegal immigrants
amnesties, applications for regularization
residence permits
deportations
Note: The figure shows the number of valid residence permits (since 1971), applications for regular- ization of formerly unofficial immigrants during the amnesty programs (1986, 1990, 1995, 1998, 2002) and the number of deportations of undocumented immigrants over the period 1984-2006. Source: Ministry of interior.
36
1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
resident permits and official residents, thousands
thousands
1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
net inflows, x 1000 inhabitants
Figure 4: prison inmates
70,000 40
60,000
50,000
40,000
30,000
20,000
10,000
35 30 25 20 15 10 5
00 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
% foreign over total prison population (right axis)
foreign inmates
total prison population
Note: The figure plots the number of native and foreign prison inmates in Italy during the period 1992-2008, as well as the incidence of foreigners over total prison population. Source: Ministry of Justice.
37
foreign inmates
incidence over prison population
ρEVI(h)
VH(h)
ρEVI(h)-VH(h)
hI illegals h Figure 5a: illegal immigrants
B
Figure 5: theoretical model
ρEVL(h)
ρEVI(h)
ρEΔV(h)
T
zI*/π=ρEVI(h)
illegals, δ=0 (no enforcement)
lncI = ln(1-δ)[1-F(zI*)] lncL = ln[1 – F(zI*)]
hL legals h Figure 5c: criminal behavior
hI illegals
Figure 5b: self selection into legal status
hL legals h
zL*/π=ρEVL(h)
̃
lnc = ln[1 – F(z *)] II
E(lncI|I) E(lncL|I)
E(lncL|L)
β
β hL legals h
BIAS
hI
illegals
hI
illegals
38
Figure 5d: selection bias
Figure 6: new EU member and candidate member countries
Note: The map shows the countries admitted to the EU during the last round of the enlargement (in black), as well as the group of candidate member countries (in gray). Source: European Commission.
39
Figure 7: immigrants in Italy, new EU member and candidate member countries
official residents in Italy (thousands)
2002 2003 2004 2005 2006 2007 2008
Romanians & Bulgarians EU candidate countries
people reported by the police, x 100k residents
2006q1 2006q2 2006q3 2006q4 2007q1 2007q2 2007q3 2007q4
Romanians & Bulgarians EU candidates (Albania & Serbia-Mont)
Note: The left graph plots the number of citizens of new EU member and candidate member countries officially residing in Italy during the period 2002-2008. The right graph shows instead the ratio of arrested by the police over official residents in each quarter during the period 2006-2007. In both graphs the vertical line refers to the date of the last EU enlargement. Source: ISTAT and Ministry of Interior.
Figure 8: the Collective Clemency Bill
9000 200
8000 7000 6000 5000 4000 3000 2000 1000
180 160 140 120 100 80 60 40 20
00
aug jan may sep 2006 2007 2007 2007
released
rearrested
Note: The figure plots the number of foreign prison inmates released after the Collective Clemency Bill in August 2006 (on the left axis), as well as those rearrested until December 2007 (right axis). The vertical line refers to the moment of the EU enlargement. Source: Ministry of Justice.
40
released
rearrested
0 200
400
600 800
0
1000
2000
3000
no weighting
Pardon (08/2006) EU enlargement (01/2007)
Romanians and Bulgarians
Pardon (08/2006)
PS weighting
EU enlargement (01/2007)
Romanians and Bulgarians control
Figure 9: propensity score weighting
0 .2 .4 .6 .8 1 propensity score
new EU members control group
Note: The figure shows the kernel density of the estimated propensity score across groups. The propensity score is the probability of belonging to the groups of citizens of new EU member countries, conditional on observable characteristics. The estimate is based on a logit regression of a dummy for being Romanian and Bulgarian on a flexible specification of the individual information included in our sample.
Figure 10: hazard rates of rearrest
Hazard rate of rearrest (log scale) .01% 0.02% 0.04%
Hazard rate of rearrest (log scale) .01% 0.02% 0.04%
0123
control
Note: The figure plots the non-parametric (Nelson-Aalen) estimates of daily log hazard rates of rearrest between between August 2006 and May 2007 for Romanians and Bulgarians (solid line) and for the control group (dashed line). The scale on the vertical axis reports the (estimated) hazard rate of rearrest in each day. In the right graph observations are weighted by the (estimated) propensity score according to formula (14).
41
Figure 11: differences between north and south
Enforcement of migration restrictions (incapacitation effect)
Unofficial economy (opportunity cost effect)
.87
.48
.31
.073
Note: The left map shows the ratio of legal over total (legal and illegal) immigrants across Italian regions; the right map shows instead the relative size of the unofficial economy. In both cases darker colors refer to higher values. Source: ISTAT and Ministry of Interior.
42
Figure 12: structural break test
ITALY – structural break test
12dec2006
R2 ratio
NORTH – structural break test
01dec2006
R2 ratio DID CENTER-SOUTH – structural break test
07dec2006
R2 ratio DID
ITALY – discontinuity
12dec2006
New EU
NORTH – discontinuity
01dec2006
DID
control
R2 ratio R2 ratio R2 ratio 024 024 024
-6% -3% 0 3% DID
-6% -3% 0 3% DID
-6% -3% 0 3% DID
hazard rate
0 0.04% 0.08%
hazard rate
0 0.04% 0.08%
hazard rate 0 0.04%
0.08%
Note: The left graphs plot (black line and left axis) the ratio of the R2 of the difference-in-differences model H1 in (17), estimated at each possible date in the sample period, over the R2 of the restricted model H0. The estimated coefficient of the interaction term in H1 is also shown (gray line and right axis). The vertical solid line corresponds to the day that maximized the R2-ratio (i.e. the most likely break point), while the vertical dashed line is the official date of the EU enlargement. The right graphs plot instead the predicted (daily) hazard rates of rearrest as a function of a third order polynomial in time for Romanians and Bulgarians (solid line) and for the control group (dashed line), before and after the break point.
43
New EU
CENTER-SOUTH – discontinuity
07dec2006
New EU control
control
Figure 13: effect over time
0 10 20 30 40 50 truncation of the sample (n-th week in 2007)
point estimate confidence interval
Note: The graph plots the estimated β in model (15), namely the difference-in-differences between the log-odds of rearrest for Romanians and Bulgarians relative to the control group, before and after the EU enlargement, when the longitudinal dimension of the sample is truncated at each week during year 2007.
Figure 14: substitution of criminal activity
copyright
murder3 smuggling
murder2
forgery cyber2
pedophilia
terror prostitution
org_crime att_murder
extor
money_lau theft mischiefthreat
nkaidrncoatpicpsing stolen_goordosbbery arson2
abuse sexual
battery assault
arson
murder1
-1 -.5 0 .5 1 1.5 Percentage change in crimes 2006-2007, Romanians
Note: The figure plots the (percentage) change between 2006 and 2007 in the number of Romanians arrested in Italy for different types of crime against the same change for citizens of candidate member countries. The area of markers is proportional to the total number of offenses committed in each category. Source: Ministry of Interior.
44
Percentage change in crimes 2006-2007, EU candidates -1 -.5 0 .5 1
-3 -2 -1 0 1
Table 1: legal and illegal immigrants, individual characteristics and labor market outcomes
variable
age
female
married
number of kids
college
low skilled
income (euros per month) college premium
illegals legals
obs mean obs mean
diff.
1280 31.29 7343
(8.94)
1281 0.39 7353
(0.49)
1281 0.34 7353
(0.47)
1279 0.76 7339
(1.19)
1281 0.14 7353
(0.34)
1281 0.12 7353
(0.33)
34.63 -3.34∗∗∗ (9.36) (0.28)
0.44 -0.05∗∗∗ (0.50) (0.01)
0.59 -0.26∗∗∗ (0.49) (0.01)
1.18 -0.41∗∗∗ (1.28) (0.04)
0.16 -0.02∗∗∗ (0.37) (0.01)
0.09 0.04∗∗ (0.28) (0.01)
1130 -306∗∗∗ (371) (652) (22)
949 824 5339
949 9 5339 112
(35)
-103∗ (25) (62)
Note: This table reports the average characteristics of legal and illegal immigrants, as well as the between-group difference in each variable. The source is the 2006 round of the ISMU survey, the sample is representative of the entire immigrant population of the Italian region of Lombardy. Robust standard errors are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote between- group differences that are statistically significant at the 90% confidence, 95% confidence and 99% confidence, respectively.
45
Table 2: sample statistics by group
NON-WEIGHTED SAMPLE
new EU control diff
PROPENSITY SCORE WEIGHTING
age
low education
no education education missing married
economic crimes violent crimes sentence (months) residual sentence
obs mean obs
725 31.083 1622
(7.597)
725 0.339 1622
(0.474)
725 0.017 1622
(0.128)
725 0.539 1622
(0.499)
725 0.257 1622
(0.437)
725 0.84 1622
(0.367)
725 0.295 1622
(0.456)
725 20.31 1622
(20.706)
725 9.305 1622
(10.615)
mean mean
33.269 -2.187∗∗∗
obs
700
700
700
700
700
700
700
700
700
mean obs
33.335 1493
(8.528)
0.437 1495
(0.496)
0.015 1493
(0.122)
0.437 1493
(0.496)
0.266 1493
(0.442)
0.857 1493
(0.35)
0.284 1493
(0.451)
32.115 1493
(30.63)
13.349 1493
(12.917)
mean
32.716
(7.914)
0.422
(0.494)
0.018
(0.133)
0.450
(0.498)
0.277
(0.448)
0.877
(0.328)
0.262
(0.44)
33.269
(30.593)
13.83
(14.13)
(8.088)
0.461
(0.499)
0.017
(0.128)
0.404
(0.491)
0.288
(0.453)
0.894
(0.308)
0.242
(0.428)
(0.355)
-0.122∗∗∗ (0.022)
-0.0001
(0.006)
0.135∗∗∗ (0.022)
-0.031
(0.02)
-0.054∗∗∗ (0.015)
0.053∗∗∗ (0.02)
39.183 -18.873∗∗∗ (32.33) (1.306)
15.727 -6.423∗∗∗ (14.784) (0.609)
new EU control
diff
Mean
0.619
(0.38)
0.015
(0.023)
-0.003
(0.006)
-0.013
(0.024)
-0.011
(0.021)
-0.02
(0.016)
0.022
(0.021)
-1.154
(1.435)
-0.481
(0.646)
Note: This table compares the characteristics of Romanians and Bulgarians in our sample with the group of citizens from candidate EU member countries. The first tree columns report non-weighted averages for each group, as well as the between-group difference for each variable. In the last three columns observations are weighted by the inverse propensity score, according to (14). Robust standard errors are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote between-group differences that are statistically significant at the 90% confidence, 95% confidence and 99% confidence, respectively.
Table 3: difference in difference
economic crimes
non-economic crimes
post
pre
diff.
new EU
0.023
(0.005) [0.006]
0.058
(0.013) [0.014]
-0.035∗∗ (0.014) [0.014]
control
0.054
(0.008) [0.008]
0.057
(0.007) [0.008]
-0.003
(0.011) [0.011]
diff.
-0.031∗∗ (0.010) [0.010]
0.001
(0.015) [0.015]
-0.032∗ (0.017) [0.018]
new EU
0.047
(0.020) [0.021]
0.033
(0.028) [0.019]
0.014
(0.034) [0.028]
control
0.034
(0.014) [0.014]
0.043
(0.021) [0.022]
-0.009
(0.025) [0.027]
diff.
0.013
(0.025) [0.025]
-0.009
(0.035) [0.029]
0.023
([0.043]) [0.039]
Note: This table reports the fraction of individuals in our sample that are re-incarcerated, distinguishing between citizens of new EU member countries and candidate member coun- tries, as well as between the period before (“pre”) and after (“post”) the EU enlargement; the difference-in-differences is reported in the South-East corner of the table. The left and right panel show the cross tabulation for the subsamples of former inmates that were previously incarcerated (before the pardon) for economic and violent crimes, respectively. Observations are weighted by the inverse propensity score according to (14). Robust standard errors are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote between-group differences that are statistically significant at the 90% confidence, 95% confidence and 99% confidence, respectively. Boot- strapped standard errors, based on 400 replications, are also reported in square brackets.
46
Table 4: economic crimes, parametric and semiparametric estimates
(1) 0.091
(0.219)
-0.404∗∗ (0.167)
-0.744∗∗ (0.289)
(2) (3) (4)
Logistic regression
(5) (6)
Cox model
new EU
post
new EU × post time
time2
age
age2
married
residual sentence
Observations Subjects
Week dummies pseudo R2
Log Likelihood
0.091
(0.220)
-0.162
(0.354)
-0.740∗∗ (0.288)
-0.004
(0.011)
-0.000
(0.000)
0.071 0.073
(0.222) (0.222)
-0.162
(0.354)
-0.733∗∗ -0.736∗∗ (0.289) (0.289)
-0.004
(0.011)
-0.000
(0.000)
0.090 0.090
(0.058) (0.058)
-0.001 -0.001
(0.001) (0.001)
-0.381∗∗ -0.380∗∗
(0.007) (0.007)
124019 115428 1918 1918 NO YES 0.016 0.042 -1691 -1631
0.022
(0.218)
-0.249
(0.363)
-0.679∗∗ (0.308)
0.002
(0.219)
-0.277
(0.364)
-0.668∗∗ (0.308)
0.088
(0.059)
-0.001
(0.001)
-0.283∗
(0.158) -0.021∗∗∗
(0.006)
3547 1918 . 0.009 -1786
(0.176)
(0.176) -0.020∗∗∗ -0.020∗∗∗
124019 1918 NO 0.009 -1704
124019 1918 NO 0.010 -1702
3547 1918 . 0.003 -1797
Note: The table shows the results of parametric and semiparametric estimates of the probability of incarceration for immigrants from new EU member and candidate member countries before and after the EU enlargement. The parametric model in columns (1)-(4) is a logit equation for the log-odds or incarceration estimated on a panel of individual-weeks observations; the panel is unbalanced because we include only the individuals that are at risk of rearrest in any given week. The semiparametric model in columns (5)-(6) is a proportional hazard Cox model for the log hazard rate of incarceration. The sample includes all inmates from new EU member and candidate member countries released after the July 2006 collective pardon that were previously incarcerated for having committed an economically-motivated offense. The dummy postt is equal to 1 in the weeks after the EU enlargement (January 1, 2007), while time and time2 are linear and quadratic trends in time, respectively. Regressions are weighted by the inverse propensity score according to (14). Robust standard errors clustered by Italian region and country of origin are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote coefficients significantly different from zero at the 90% confidence, 95% confidence and 99% confidence, respectively.
47
Table 5: non-economic crimes, parametric and semiparametric estimates
(1) (2) (3)
Logistic regression
(4) (5)
Cox model
-0.276 -0.215
new EU
post
new EU × post time
time2
age
age2
married
residual sentence
Observations Subjects pseudo R2
Log Likelihood
-0.281 -0.283
(1.030) (1.030)
-0.546 0.044
(0.585) (0.999)
0.251 0.251
(1.144) (1.143)
-0.222
(0.998)
0.051
(1.005)
0.245
(1.138)
-0.015
(0.027)
-0.000
(0.001)
0.223
(0.193)
-0.003
(0.003)
0.111
(0.588)
-0.008
(0.023)
18105 272 0.015 -200.6
(0.691)
0.490
(0.838)
0.256
(0.864)
(0.691)
0.493
(0.845)
0.243
(0.865)
0.229
(0.212)
-0.003
(0.003)
0.108
(0.420)
-0.007
(0.020)
531
-0.015
(0.027)
-0.000
(0.001)
18105 272 0.004 -202.9
18105 272 0.008 -202.1
531
272 0.012 -150.2 -148.6
272 0.002
Note: The table shows the results of parametric and semiparametric estimates of the probability of incarceration for immigrants from new EU member and candidate member countries before and after the EU enlargement. The parametric model in columns (1)-(3) is a logit equation for the log-odds or incarceration estimated on a panel of individual-weeks observations; the panel is unbalanced because we include only the individuals that are at risk of rearrest in any given week. The semiparametric model in columns (4)-(5) is a proportional hazard Cox model for the log hazard rate of incarceration. The sample includes all inmates from new EU member and candidate member countries released after the July 2006 collective pardon that were previously incarcerated for having committed (only) violent offenses. The dummy postt is equal to 1 in the weeks after the EU enlargement (January 1, 2007), while time and time2 are linear and quadratic trends in time, respectively. Regressions are weighted by the inverse propensity score according to (14). Robust standard errors clustered by Italian region and country of origin are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote coefficients significantly different from zero at the 90% confidence, 95% confidence and 99% confidence, respectively.
48
Table 6: differences between northern and southern Italy
Total sample
New EU
Candidate countries
North Center-South North/CSouth
1244 1103 1.1 348 377 0.9 896 726 1.2
economic structure (labor mkt opportunities)
GDP per capita
shadow economy (%GDP) employment rate
30066 20947 1.4 9% 18% 0.5 48% 37% 1.3
illegal condition in 2002 (incapacitation)
residence permits, ths. 832 616 1.4 illegals (applications for amnesty), ths. 366 336 1.1 illegals/permits 31% 35% 0.9
Note: The table displays the average characteristics of Northern and Center-Southern regions, as well as the ratio between the two (in the third column). Source: ISTAT and Ministry of Interior.
Table 7: North vs. South, difference-in-differences northern regions southern regions
post pre diff.
new EU
0.014
(0.006)
0.066
(0.020)
-0.052∗∗ (0.021)
control
0.061
(0.010)
0.053
(0.009)
0.007
(0.014)
diff. new EU
-0.046∗∗ 0.034 (0.012) (0.009)
0.013 0.049
(0.022) (0.017)
-0.059∗∗ -0.015 (0.025) (0.020)
control diff.
0.046 -0.013
(0.013) (0.016)
0.063 -0.014
(0.012) (0.021)
-0.017 0.001
(0.017) (0.026)
Note: This table reports the fraction of individuals in our sample that are re-incarcerated, distinguishing between citizens of new EU member countries and candidate member coun- tries, as well as between the period before (“pre”) and after (“post”) the EU enlargement; the difference-in-differences is reported in the South-East corner of the table. The left and right panel show the cross tabulation for the subsamples of former inmates that were released from a prison in the North and Center-South of Italy, respectively. Observations are weighted by the inverse propensity score according to (14). Robust standard errors are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote between-group differences that are statistically significant at the 90% confidence, 95% confidence and 99% confidence, respectively.
49
Table 8: North vs. South, parametric and semiparametric estimates
(1) (2) (3) (4)
Northern regions
(5) (6) (7) (8)
Center-southern regions
Logistic
0.226 0.246
(0.254) (0.246)
Cox model
0.214 0.234
(0.287) (0.287)
Logistic
Cox model
-0.224 -0.256
(0.339) (0.340)
-0.142 -0.612
(0.560) (0.524)
-0.323 -0.331
new EU
post
new EU × post age
age2
married
residual sentence
-0.085
(0.355)
-0.123
(0.351)
-0.612
(0.524)
-0.465
(0.347)
0.028
(0.086)
-0.000
(0.001)
0.168 0.170 -0.277 -0.343 -0.612
(0.432)
-0.948∗∗ (0.413)
(0.432)
-0.933∗∗ (0.422)
0.151∗ (0.083)
-0.002∗ (0.001)
(0.477)
-0.940∗∗ (0.407)
(0.478)
-0.923∗∗ (0.408)
0.151∗ (0.085)
-0.002
(0.001)
(0.524)
-0.459
(0.348)
(0.472)
1566 flex. 0.003
(0.472)
0.022
(0.081)
-0.000
(0.001)
0.117
(0.231)
-0.022∗∗ (0.009)
1566 flex. 0.009
-0.599∗∗∗
-0.022∗∗ -0.022∗∗∗ -0.019∗
(0.009) (0.007) (0.010)
-0.605∗∗∗
(0.195) (0.217) (0.315)
Observations 68151 68151 1982 1982 55868 55868 Timetrend quad. quad. flex. flex. quad. quad. pseudo R2 0.011 0.021 0.005 0.015 0.011 0.015 LogLikelihood -989.9 -979.9 -950.9 -941.0 -710.4 -706.9
-679.6 -675.7
-0.087
Note: The table shows the results of parametric and semiparametric estimates of the probability of incarceration for immigrants from new EU member and candidate member countries before and after the EU enlargement, distinguishing between Northern and Center-Southern regions. The parametric model (columns 1-2 and 5-6) is a logit equation for the log-odds or incarceration estimated on a panel of individual-weeks observations; the panel is unbalanced because we include only the individuals that are at risk of rearrest in any given week. The semiparametric model (columns 3-4 and 7-8) is a proportional hazard Cox model for the log hazard rate of incarceration. The sample includes all inmates from new EU member and candidate member countries released after the July 2006 collective pardon from a prison in the North (columns 1-4) and in the Center-South (columns 5-8). The dummy postt is equal to 1 in the weeks after the EU enlargement (January 1, 2007), while time and time2 are linear and quadratic trends in time, respectively. Regressions are weighted by the inverse propensity score according to (14). Robust standard errors clustered by Italian region and country of origin are reported in parenthesis. ∗, ∗∗ and ∗∗∗ denote coefficients significantly different from zero at the 90% confidence, 95% confidence and 99% confidence, respectively.
50
N. 788 – N. 789 – N. 790 – N. 791 – N. 792 –
N. 793 – N. 794 – N. 795 – N. 796 – N. 797 – N. 798 – N. 799 – N. 800 – N. 801 – N. 802 – N. 803 – N. 804 – N. 805 – N. 806 – N. 807 – N. 808 –
FaMIDAS: a mixed frequency factor model with MIDAS structure, by Cecilia Frale and Libero Monteforte (January 2011).
Policies for local development: an evaluation of Italy’s “Patti Territoriali”, by Antonio Accetturo and Guido de Blasio (January 2011).
Testing for east-west contagion in the European banking sector during the financial crisis, by Emidio Cocozza and Paolo Piselli (February 2011).
Are incentives for R&D effective? Evidence from a regression discontinuity approach, by Raffaello Bronzini and Eleonora Iachini (February 2011).
Evaluating the impact of innovation incentives: evidence from an unexpected shortage of funds, by Guido de Blasio, Davide Fantino and Guido Pellegrini (February 2011).
Credit availability and investment in Italy: lessons from the “Great Recession”, by Eugenio Gaiotti (February 2011).
Bridging the gap between migrants and the banking system, by Giorgio Albareto and Paolo Emilio Mistrulli (February 2011).
I fondi comuni aperti in Italia: performance delle società di gestione del risparmio, by Michele Leonardo Bianchi and Maria Grazia Miele (February 2011).
Securitization is not that evil after all, by Ugo Albertazzi, Ginette Eramo, Leonardo Gambacorta and Carmelo Salleo (February 2011).
Reserve management and sovereign debt cost in a world with liquidity crises, by Flavia Corneli and Emanuele Tarantino (March 2011).
Managerial incentives, financial constraints and ownership concentration, by Marco Protopapa (March 2011).
Bootstrap LR tests of stationarity, common trends and cointegration, by Fabio Busetti and Silvestro di Sanzo (March 2011).
Performance pay and shifts in macroeconomic correlations, by Francesco Nucci and Marianna Riggi (March 2011).
Monetary and macroprudential policies, by Paolo Angelini, Stefano Neri and Fabio Panetta (March 2011).
Imperfect information, real-time data and monetary policy in the euro area, by Stefano Neri and Tiziano Ropele (March 2011).
Financial subsidies and bank lending: substitutes or complements? Micro level evidence from Italy, by Amanda Carmignani and Alessio D’Ignazio (April 2011).
Il miglioramento qualitativo delle produzioni italiane: evidenze da prezzi e strategie delle imprese, by Valter di Giacinto and Giacinto Micucci (April 2011).
What determines annuity demand at retirement?, by Giuseppe Cappelletti, Giovanni Guazzarotti and Pietro Tommasino (April 2011).
Heterogeneity and learning with complete markets, by Sergio Santoro (April 2011).
Housing, consumption and monetary policy: how different are the U.S. and the euro area?, by Alberto Musso, Stefano Neri and Livio Stracca (April 2011).
The monetary transmission mechanism in the euro area: has it changed and why?, by Martina Cecioni and Stefano Neri (April 2011).
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Economia, v. 67, 1, pp. 1-20, TD No. 451 (October 2002).
P. ANGELINI, P. DEL GIOVANE, S. SIVIERO and D. TERLIZZESE, Monetary policy in a monetary union: What role for regional information?, International Journal of Central Banking, v. 4, 3, pp. 1-28, TD No. 457 (December 2002).
F. SCHIVARDI and R. TORRINI, Identifying the effects of firing restrictions through size-contingent Differences in regulation, Labour Economics, v. 15, 3, pp. 482-511, TD No. 504 (June 2004).
L. GUISO and M. PAIELLA,, Risk aversion, wealth and background risk, Journal of the European Economic Association, v. 6, 6, pp. 1109-1150, TD No. 483 (September 2003).
C. BIANCOTTI, G. D’ALESSIO and A. NERI, Measurement errors in the Bank of Italy’s survey of household income and wealth, Review of Income and Wealth, v. 54, 3, pp. 466-493, TD No. 520 (October 2004).
S. MOMIGLIANO, J. HENRY and P. HERNÁNDEZ DE COS, The impact of government budget on prices: Evidence from macroeconometric models, Journal of Policy Modelling, v. 30, 1, pp. 123-143 TD No. 523 (October 2004).
L. GAMBACORTA, How do banks set interest rates?, European Economic Review, v. 52, 5, pp. 792-819, TD No. 542 (February 2005).
P. ANGELINI and A. GENERALE, On the evolution of firm size distributions, American Economic Review, v. 98, 1, pp. 426-438, TD No. 549 (June 2005).
R. FELICI and M. PAGNINI, Distance, bank heterogeneity and entry in local banking markets, The Journal of Industrial Economics, v. 56, 3, pp. 500-534, No. 557 (June 2005).
S. DI ADDARIO and E. PATACCHINI, Wages and the city. Evidence from Italy, Labour Economics, v.15, 5, pp. 1040-1061, TD No. 570 (January 2006).
S. SCALIA, Is foreign exchange intervention effective?, Journal of International Money and Finance, v. 27, 4, pp. 529-546, TD No. 579 (February 2006).
M. PERICOLI and M. TABOGA, Canonical term-structure models with observable factors and the dynamics of bond risk premia, Journal of Money, Credit and Banking, v. 40, 7, pp. 1471-88, TD No. 580 (February 2006).
E. VIVIANO, Entry regulations and labour market outcomes. Evidence from the Italian retail trade sector, Labour Economics, v. 15, 6, pp. 1200-1222, TD No. 594 (May 2006).
S. FEDERICO and G. A. MINERVA, Outward FDI and local employment growth in Italy, Review of World Economics, Journal of Money, Credit and Banking, v. 144, 2, pp. 295-324, TD No. 613 (February 2007).
F. BUSETTI and A. HARVEY, Testing for trend, Econometric Theory, v. 24, 1, pp. 72-87, TD No. 614 (February 2007).
V. CESTARI, P. DEL GIOVANE and C. ROSSI-ARNAUD, Memory for prices and the Euro cash changeover: an analysis for cinema prices in Italy, In P. Del Giovane e R. Sabbatini (eds.), The Euro Inflation and Consumers’ Perceptions. Lessons from Italy, Berlin-Heidelberg, Springer, TD No. 619 (February 2007).
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J. SOUSA and A. ZAGHINI, Monetary policy shocks in the Euro Area and global liquidity spillovers, International Journal of Finance and Economics, v.13, 3, pp. 205-218, TD No. 629 (June 2007).
M. DEL GATTO, GIANMARCO I. P. OTTAVIANO and M. PAGNINI, Openness to trade and industry cost dispersion: Evidence from a panel of Italian firms, Journal of Regional Science, v. 48, 1, pp. 97- 129, TD No. 635 (June 2007).
P. DEL GIOVANE, S. FABIANI and R. SABBATINI, What’s behind “inflation perceptions”? A survey-based analysis of Italian consumers, in P. Del Giovane e R. Sabbatini (eds.), The Euro Inflation and Consumers’ Perceptions. Lessons from Italy, Berlin-Heidelberg, Springer, TD No. 655 (January 2008).
R. BRONZINI, G. DE BLASIO, G. PELLEGRINI and A. SCOGNAMIGLIO, La valutazione del credito d’imposta per gli investimenti, Rivista di politica economica, v. 98, 4, pp. 79-112, TD No. 661 (April 2008).
B. BORTOLOTTI, and P. PINOTTI, Delayed privatization, Public Choice, v. 136, 3-4, pp. 331-351, TD No. 663 (April 2008).
R. BONCI and F. COLUMBA, Monetary policy effects: New evidence from the Italian flow of funds, Applied Economics , v. 40, 21, pp. 2803-2818, TD No. 678 (June 2008).
M. CUCCULELLI, and G. MICUCCI, Family Succession and firm performance: evidence from Italian family firms, Journal of Corporate Finance, v. 14, 1, pp. 17-31, TD No. 680 (June 2008).
A. SILVESTRINI and D. VEREDAS, Temporal aggregation of univariate and multivariate time series models: a survey, Journal of Economic Surveys, v. 22, 3, pp. 458-497, TD No. 685 (August 2008).
2009
F. PANETTA, F. SCHIVARDI and M. SHUM, Do mergers improve information? Evidence from the loan market,
Journal of Money, Credit, and Banking, v. 41, 4, pp. 673-709, TD No. 521 (October 2004).
M. BUGAMELLI and F. PATERNÒ, Do workers’ remittances reduce the probability of current account
reversals?, World Development, v. 37, 12, pp. 1821-1838, TD No. 573 (January 2006).
P. PAGANO and M. PISANI, Risk-adjusted forecasts of oil prices, The B.E. Journal of Macroeconomics, v.
9, 1, Article 24, TD No. 585 (March 2006).
M. PERICOLI and M. SBRACIA, The CAPM and the risk appetite index: theoretical differences, empirical similarities, and implementation problems, International Finance, v. 12, 2, pp. 123-150, TD No. 586 (March 2006).
U. ALBERTAZZI and L. GAMBACORTA, Bank profitability and the business cycle, Journal of Financial Stability, v. 5, 4, pp. 393-409, TD No. 601 (September 2006).
S. MAGRI, The financing of small innovative firms: the Italian case, Economics of Innovation and New Technology, v. 18, 2, pp. 181-204, TD No. 640 (September 2007).
V. DI GIACINTO and G. MICUCCI, The producer service sector in Italy: long-term growth and its local determinants, Spatial Economic Analysis, Vol. 4, No. 4, pp. 391-425, TD No. 643 (September 2007).
F. LORENZO, L. MONTEFORTE and L. SESSA, The general equilibrium effects of fiscal policy: estimates for the euro area, Journal of Public Economics, v. 93, 3-4, pp. 559-585, TD No. 652 (November 2007).
R. GOLINELLI and S. MOMIGLIANO, The Cyclical Reaction of Fiscal Policies in the Euro Area. A Critical Survey of Empirical Research, Fiscal Studies, v. 30, 1, pp. 39-72, TD No. 654 (January 2008).
P. DEL GIOVANE, S. FABIANI and R. SABBATINI, What’s behind “Inflation Perceptions”? A survey-based analysis of Italian consumers, Giornale degli Economisti e Annali di Economia, v. 68, 1, pp. 25- 52, TD No. 655 (January 2008).
F. MACCHERONI, M. MARINACCI, A. RUSTICHINI and M. TABOGA, Portfolio selection with monotone mean- variance preferences, Mathematical Finance, v. 19, 3, pp. 487-521, TD No. 664 (April 2008).
M. AFFINITO and M. PIAZZA, What are borders made of? An analysis of barriers to European banking integration, in P. Alessandrini, M. Fratianni and A. Zazzaro (eds.): The Changing Geography of Banking and Finance, Dordrecht Heidelberg London New York, Springer, TD No. 666 (April 2008).
A. BRANDOLINI, On applying synthetic indices of multidimensional well-being: health and income inequalities in France, Germany, Italy, and the United Kingdom, in R. Gotoh and P. Dumouchel (eds.), Against Injustice. The New Economics of Amartya Sen, Cambridge, Cambridge University Press, TD No. 668 (April 2008).
G. FERRERO and A. NOBILI, Futures contract rates as monetary policy forecasts, International Journal of Central Banking, v. 5, 2, pp. 109-145, TD No. 681 (June 2008).
P. CASADIO, M. LO CONTE and A. NERI, Balancing work and family in Italy: the new mothers’ employment decisions around childbearing, in T. Addabbo and G. Solinas (eds.), Non-Standard Employment and Qualità of Work, Physica-Verlag. A Sprinter Company, TD No. 684 (August 2008).
L. ARCIERO, C. BIANCOTTI, L. D’AURIZIO and C. IMPENNA, Exploring agent-based methods for the analysis of payment systems: A crisis model for StarLogo TNG, Journal of Artificial Societies and Social Simulation, v. 12, 1, TD No. 686 (August 2008).
A. CALZA and A. ZAGHINI, Nonlinearities in the dynamics of the euro area demand for M1, Macroeconomic Dynamics, v. 13, 1, pp. 1-19, TD No. 690 (September 2008).
L. FRANCESCO and A. SECCHI, Technological change and the households’ demand for currency, Journal of Monetary Economics, v. 56, 2, pp. 222-230, TD No. 697 (December 2008).
G. ASCARI and T. ROPELE, Trend inflation, taylor principle, and indeterminacy, Journal of Money, Credit and Banking, v. 41, 8, pp. 1557-1584, TD No. 708 (May 2007).
S. COLAROSSI and A. ZAGHINI, Gradualism, transparency and the improved operational framework: a look at overnight volatility transmission, International Finance, v. 12, 2, pp. 151-170, TD No. 710 (May 2009).
M. BUGAMELLI, F. SCHIVARDI and R. ZIZZA, The euro and firm restructuring, in A. Alesina e F. Giavazzi (eds): Europe and the Euro, Chicago, University of Chicago Press, TD No. 716 (June 2009).
B. HALL, F. LOTTI and J. MAIRESSE, Innovation and productivity in SMEs: empirical evidence for Italy, Small Business Economics, v. 33, 1, pp. 13-33, TD No. 718 (June 2009).
2010
A. PRATI and M. SBRACIA, Uncertainty and currency crises: evidence from survey data, Journal of
Monetary Economics, v, 57, 6, pp. 668-681, TD No. 446 (July 2002).
L. MONTEFORTE and S. SIVIERO, The Economic Consequences of Euro Area Modelling Shortcuts, Applied
Economics, v. 42, 19-21, pp. 2399-2415, TD No. 458 (December 2002).
S. MAGRI, Debt maturity choice of nonpublic Italian firms , Journal of Money, Credit, and Banking, v.42,
2-3, pp. 443-463, TD No. 574 (January 2006).
R. BRONZINI and P. PISELLI, Determinants of long-run regional productivity with geographical spillovers: the role of R&D, human capital and public infrastructure, Regional Science and Urban Economics, v. 39, 2, pp.187-199, TD No. 597 (September 2006).
E. IOSSA and G. PALUMBO, Over-optimism and lender liability in the consumer credit market, Oxford Economic Papers, v. 62, 2, pp. 374-394, TD No. 598 (September 2006).
S. NERI and A. NOBILI, The transmission of US monetary policy to the euro area, International Finance, v. 13, 1, pp. 55-78, TD No. 606 (December 2006).
F. ALTISSIMO, R. CRISTADORO, M. FORNI, M. LIPPI and G. VERONESE, New Eurocoin: Tracking Economic Growth in Real Time, Review of Economics and Statistics, v. 92, 4, pp. 1024-1034, TD No. 631 (June 2007).
A. CIARLONE, P. PISELLI and G. TREBESCHI, Emerging Markets’ Spreads and Global Financial Conditions, Journal of International Financial Markets, Institutions & Money, v. 19, 2, pp. 222-239, TD No. 637 (June 2007).
U. ALBERTAZZI and L. GAMBACORTA, Bank profitability and taxation, Journal of Banking and Finance, v. 34, 11, pp. 2801-2810, TD No. 649 (November 2007).
M. IACOVIELLO and S. NERI, Housing market spillovers: evidence from an estimated DSGE model, American Economic Journal: Macroeconomics, v. 2, 2, pp. 125-164, TD No. 659 (January 2008).
F. BALASSONE, F. MAURA and S. ZOTTERI, Cyclical asymmetry in fiscal variables in the EU, Empirica, TD No. 671, v. 37, 4, pp. 381-402 (June 2008).
F. D’AMURI, O. GIANMARCO I.P. and P. GIOVANNI, The labor market impact of immigration on the western german labor market in the 1990s, European Economic Review, v. 54, 4, pp. 550-570, TD No. 687 (August 2008).
A. ACCETTURO, Agglomeration and growth: the effects of commuting costs, Papers in Regional Science, v. 89, 1, pp. 173-190, TD No. 688 (September 2008).
S. NOBILI and G. PALAZZO, Explaining and forecasting bond risk premiums, Financial Analysts Journal, v. 66, 4, pp. 67-82, TD No. 689 (September 2008).
A. B. ATKINSON and A. BRANDOLINI, On analysing the world distribution of income, World Bank Economic Review , v. 24, 1 , pp. 1-37, TD No. 701 (January 2009).
R. CAPPARIELLO and R. ZIZZA, Dropping the Books and Working Off the Books, Labour, v. 24, 2, pp. 139- 162 ,TD No. 702 (January 2009).
C. NICOLETTI and C. RONDINELLI, The (mis)specification of discrete duration models with unobserved heterogeneity: a Monte Carlo study, Journal of Econometrics, v. 159, 1, pp. 1-13, TD No. 705 (March 2009).
L. FORNI, A. GERALI and M. PISANI, Macroeconomic effects of greater competition in the service sector: the case of Italy, Macroeconomic Dynamics, v. 14, 5, pp. 677-708, TD No. 706 (March 2009).
V. DI GIACINTO, G. MICUCCI and P. MONTANARO, Dynamic macroeconomic effects of public capital: evidence from regional Italian data, Giornale degli economisti e annali di economia, v. 69, 1, pp. 29- 66, TD No. 733 (November 2009).
F. COLUMBA, L. GAMBACORTA and P. E. MISTRULLI, Mutual Guarantee institutions and small business finance, Journal of Financial Stability, v. 6, 1, pp. 45-54, TD No. 735 (November 2009).
A. GERALI, S. NERI, L. SESSA and F. M. SIGNORETTI, Credit and banking in a DSGE model of the Euro Area, Journal of Money, Credit and Banking, v. 42, 6, pp. 107-141, TD No. 740 (January 2010).
M. AFFINITO and E. TAGLIAFERRI, Why do (or did?) banks securitize their loans? Evidence from Italy, Journal of Financial Stability, v. 6, 4, pp. 189-202, TD No. 741 (January 2010).
S. FEDERICO, Outsourcing versus integration at home or abroad and firm heterogeneity, Empirica, v. 37, 1, pp. 47-63, TD No. 742 (February 2010).
V. DI GIACINTO, On vector autoregressive modeling in space and time, Journal of Geographical Systems, v. 12, 2, pp. 125-154, TD No. 746 (February 2010).
S. MOCETTI and C. PORELLO, How does immigration affect native internal mobility? new evidence from Italy, Regional Science and Urban Economics, v. 40, 6, pp. 427-439, TD No. 748 (March 2010).
A. DI CESARE and G. GUAZZAROTTI, An analysis of the determinants of credit default swap spread changes before and during the subprime financial turmoil, Journal of Current Issues in Finance, Business and Economics, v. 3, 4, pp., TD No. 749 (March 2010).
A. BRANDOLINI, S. MAGRI and T. M SMEEDING, Asset-based measurement of poverty, Journal of Policy Analysis and Management, v. 29, 2 , pp. 267-284, TD No. 755 (March 2010).
G. CAPPELLETTI, A Note on rationalizability and restrictions on beliefs, The B.E. Journal of Theoretical Economics, v. 10, 1, pp. 1-11,TD No. 757 (April 2010).
S. DI ADDARIO and D. VURI, Entrepreneurship and market size. the case of young college graduates in Italy, Labour Economics, v. 17, 5, pp. 848-858, TD No. 775 (September 2010).
A. CALZA and A. ZAGHINI, Sectoral money demand and the great disinflation in the US, Journal of Money, Credit, and Banking, v. 42, 8, pp. 1663-1678, TD No. 785 (January 2011).
2011
S. DI ADDARIO, Job search in thick markets, Journal of Urban Economics, v. 69, 3, pp. 303-318, TD No.
605 (December 2006).
E. CIAPANNA, Directed matching with endogenous markov probability: clients or competitors?, The RAND Journal of Economics, v. 42, 1, pp. 92-120, TD No. 665 (April 2008).
FORTHCOMING
M. BUGAMELLI and A. ROSOLIA, Produttività e concorrenza estera, Rivista di politica economica, TD No.
578 (February 2006).
G. DE BLASIO and G. NUZZO, Historical traditions of civicness and local economic development, Journal of Regional Science, TD No. 591 (May 2006).
F. SCHIVARDI and E. VIVIANO, Entry barriers in retail trade, Economic Journal, TD No. 616 (February 2007). G. FERRERO, A. NOBILI and P. PASSIGLIA, Assessing excess liquidity in the Euro Area: the role of sectoral
distribution of money, Applied Economics, TD No. 627 (April 2007).
P. E. MISTRULLI, Assessing financial contagion in the interbank market: maximun entropy versus observed
interbank lending patterns, Journal of Banking & Finance, TD No. 641 (September 2007).
Y. ALTUNBAS, L. GAMBACORTA and D. MARQUÉS, Securitisation and the bank lending channel, European
Economic Review, TD No. 653 (November 2007).
M. BUGAMELLI and F. PATERNÒ, Output growth volatility and remittances, Economica, TD No. 673 (June
2008).
V. DI GIACINTO e M. PAGNINI, Local and global agglomeration patterns: two econometrics-based indicators, Regional Science and Urban Economics, TD No. 674 (June 2008).
G. BARONE and F. CINGANO, Service regulation and growth: evidence from OECD countries, Economic Journal, TD No. 675 (June 2008).
S. MOCETTI, Educational choices and the selection process before and after compulsory school, Education Economics, TD No. 691 (September 2008).
P. SESTITO and E. VIVIANO, Reservation wages: explaining some puzzling regional patterns, Labour, TD No. 696 (December 2008).
P. PINOTTI, M. BIANCHI and P. BUONANNO, Do immigrants cause crime?, Journal of the European Economic Association, TD No. 698 (December 2008).
R. GIORDANO and P. TOMMASINO, What determines debt intolerance? The role of political and monetary institutions, European Journal of Political Economy, TD No. 700 (January 2009).
F. LIPPI and A. NOBILI, Oil and the macroeconomy: a quantitative structural analysis, Journal of European Economic Association, TD No. 704 (March 2009).
Y. ALTUNBAS, L. GAMBACORTA, and D. MARQUÉS-IBÁÑEZ, Bank risk and monetary policy, Journal of Financial Stability, TD No. 712 (May 2009).
P. ANGELINI, A. NOBILI e C. PICILLO, The interbank market after August 2007: What has changed, and why?, Journal of Money, Credit and Banking, TD No. 731 (October 2009).
G. BARONE and S. MOCETTI, Tax morale and public spending inefficiency, International Tax and Public Finance, TD No. 732 (November 2009).
L. FORNI, A. GERALI and M. PISANI, The macroeconomics of fiscal consolidations in euro area countries, Journal of Economic Dynamics and Control, TD No. 747 (March 2010).
A. DI CESARE and G. GUAZZAROTTI, An analysis of the determinants of credit default swap spread changes before and during the subprime financial turmoil, in C. V. Karsone (eds.), Finance and Banking Developments, Nova Publishers, New York., TD No. 749 (March 2010).
G. BARONE, R. FELICI and M. PAGNINI, Switching costs in local credit markets, International Journal of Industrial Organization, TD No. 760 (June 2010).
G. GRANDE and I. VISCO, A public guarantee of a minimum return to defined contribution pension scheme members, Journal of Risk, TD No. 762 (June 2010).
P. DEL GIOVANE, G. ERAMO and A. NOBILI, Disentangling demand and supply in credit developments: a survey-based analysis for Italy, Journal of Banking and Finance, TD No. 764 (June 2010).
G. BARONE and S. MOCETTI, With a little help from abroad: the effect of low-skilled immigration on the female labour supply, Labour Economics, TD No. 766 (July 2010).
S. MAGRI and R. PICO, The rise of risk-based pricing of mortgage interest rates in Italy, Journal of Banking and Finance, TD No. 778 (October 2010).
A. ACCETTURO and G. DE BLASIO, Policies for local development: an evaluation of Italy’s “Patti Territoriali”, Regional Science and Urban Economics, TD No. 789 (January 2006).