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Social Closure, Surnames & Crime

Paolo Buonanno1 Paolo Vanin2

1University of Bergamo

2University of Bologna

Journal of Economic Behavior & Organization, 137, 2017, 160-175

Paolo Vanin (University of Bologna) Social Closure, Surnames & Crime 2015 Presentation 1 / 33

Introduction Aim

Aim of the Paper

Aim: empirically investigate the effect of social closure on crime ratesI Social closure: tendency of groups to restrict entry to outsiders (Weber, 1978)

Theory: Social closure mayI Strengthen social control and social sanction (Coleman, 1988; Posner,

1997; Allcott et al., 2007)F Folk-theorem-like intuition: I may refrain from damaging other people if I am

likely to interact in the future with them or with people they knowI Reduce the scope of cooperation (Banfield, 1958; Tabellini, 2008)

F Better local enforcement reduces the incentive to transmit values of cooperationwith strangers (limited vs. generalized morality, Platteau, 2000)

I Foster crime through imitation of delinquent peers (Glaeser et al., 1996;Patacchini and Zenou, 2005), know-how sharing among criminals(Calvo-Armengol and Zenou, 2004) or street culture (Silverman, 2004)

Challanges:I Find a credible measure of social closureI Identify the effects of social closure on crime


Introduction Contribution

Contribution of the PaperSurnames distribution to measure social closure

I Used in Human Biology and Genetics (e.g. Cavalli-Sforza)I Innovative in EconomicsI Captures history of migration and inbreeding

AdvantagesI Relative to network-based measures (Allcott et al., 2007; Karlan et al., 2009):

available on a large scale (no need to know individual connections)I Relative to indirect measures (e.g. population share in small towns, as in

Goldin and Katz, 1999; Buonanno et al., 2012): more direct and moredisaggregated

Municipality data for Italy to analyze both crime and social closure at thehighest level of territorial disaggregation

Main result:I Lower crime rates (but higher tax evansion) in municipalities with higher

social closureI Robust to many different controls, specifications, sample splits


Social Closure & Surname Entropy Surname Distribution

Surname Distribution

Surnames began to take form during the lower Middle Age and theRenaissance period (De Felice, 1978)

In most Western Europe and in Italy they are patrilinearly transmitted:their distribution mainly reflects migration and inbreeding (apart frommutation such as misspelling or wrong translation)

Over time a community’s surname distribution essentially becomesI More diverse when men with new surnames arrive from outside to form new

householdsI Less diverse when men either leave the community or have no recognized sons

Hence, a community with a history of closure ends up with a highlyconcentrated surname distribution, whereas one with a history of opennesshas a more diverse distribution

Surname diversity to measure social openness at the municipality level


Social Closure & Surname Entropy Surnames in Genetics, Human Biology and Economics

Surnames in Genetics and Human Biology

Surname distribution is similar to that of neutral alleles of a genetransmitted by the Y-chromosome (Zei et al., 1983a,b; 1986)

Human Biology: isonomy as a proxy for inbreeding (Gottlieb, 1983; Barrai etal., 1998)

I Barrai et al. (1999): Southern Italy is on average more inbred than the North.In particular, heterogeneity is greater in the plain of the Po River, thananywhere else in Italy

Genetics: distance in surname distributions as a proxy for genetic distance(Guglielmino, Zei and Cavalli-Sforza 1991 HB)


Social Closure & Surname Entropy Surnames in Genetics, Human Biology and Economics

Surnames and Genetic Distance in Economics

Development Economics: genetic (and cultural) distance betweencommunities

I Genetic distance as proxy forF Cultural distance (Desmet et al. 2009)F Barriers to the diffusion of development (Spolaore and Wacziarg 2010 QJE,

distance from the technology frontier)F Isolation (Ashraf, Galor and Ozak 2010 JEEA, with positive effects on

development)

I Long-lasting effects of genetic distance on comparative development (Ashrafand Galor, 2009)

I Natural Selection and the Origin of Economic Growth (Galor and Moav, QJE,2002)

I Isolation and ethnolinguistic fractionalization (Michalopoulos, AER, 2011)

Intergenerational mobility, measured by the predictive power of individualsurnames (not distribution), Guell et al. (2015 REStud)

Difference: we look at surname diversity within a community


Data & Empirical Methodology Surname Data

Surname Data

Municipality level data

1993: entire distribution, from national telephone directory (SEAT)I 18,546,891 subscribers (1/3 of the population, virtually all households)

2004: ten most frequent surnames (below: cdf of the first five)

mean std. dev. min maxFrequency 1 5.20 4.64 .23 53.62Frequency 2 8.90 7.25 .45 66.66Frequency 3 11.88 9.11 .66 78.26Frequency 4 14.40 10.53 .84 82.60Frequency 5 16.62 11.68 1.03 87.5


Data & Empirical Methodology Surname Entropy

Surname Entropy

Entropy: our main measure of surname diversity

Entropy = −S∑

i=0

(pi logpi )

I S is the total number of surnames in a municipalityI pi is the municipality’s population share with a given surnameI Used in information theory to measure disorder within a systemI Used in ecology and biology (Shannon-Weaver diversity index) to measure α

diversity (within-habitat diversity): wehighted average of ‘rarity’, captured by−logpi (almost infinite ‘rarity’ for pi close to 0)

Alternative measures: share of the first n most frequent surnames (1 to 5)I Highly correlated with one another (above .9): use only First ShareI Correlation of First Share with Entropy around -.7I First Share captures social closure, Entropy captures social opennessI Notice: surname fractionalization has little variance


Data & Empirical Methodology Surname Entropy

Social closure of Italian municipalities

Figure: Entropy (left) and First Share (right) in 1993


Data & Empirical Methodology Crime Data

Crime data

Municipality level data over the period 2004-2010

Italy has more than 8k municipality

Crime categories: total crime, common theft, vehicle theft, burglary, robbery,injury

Average crime rates across the period (cross section)



Crime rates in Italian municipalities

Figure: Total Crime (left), Theft (center), Car Theft (right)



Crime rates in Italian municipalities

Figure: Burglary (left), Robbery (center), Injury (right)


Data & Empirical Methodology Controls

ControlsGeoraphy&Morphology:

I Share of mountainous territory, average altitude, difference in altitude,ruggeddness, distance from the sea, landlocked

Socioeconomics:I GDP and unemployment rate, at SLL levelI Human capital (share of high school and of college graduates) and social

capital (civicness, proxied by the share of television tax payers), at municipalitylevel

I New analysis: GDP at municipality level; TV tax evasion and GDP asdependent variables

Demographics:I Population and surface (to capture density)I Share of males by 5-year-age bracketsI Share of male immigrants and openness [(immigrants+emigrants)/population]

Deterrence: presence of a military police (Carabinieri) station


Data & Empirical Methodology Empirical strategy

Major worries

Entropy may capture the effect of omitted variablesI Large number of controlsI Different sets of controlsI Analysis by different subsamples

Other measures of social closure may yield different resultsI Shares of the most common surnameI Notice: surname fractionalization has too little variability

The effect of Entropy may be due to reverse causalityI Unlikely: Entropy reflects previous history, more stable than crime ratesI Surname shares in 1993: reinforce the argumentI Reverse causality would reinforce our results, if crime induces people to leave,

thus raising social closure and reducing Entropy

Observation independence is hardly granted with municipality dataI Spatial analysis


Data & Empirical Methodology Empirical strategy

Empirical strategy

Baseline: OLS estimate of β in

Crime = βEntropy + Xγ + ε

For each crime, different specifications of controls

Estimates by subsamples: north-center-south, village-town-city

Replace Entropy by First Share (share of the most common surname) in1993 and 2004

Spatial estimates: spatial lag, spatial error, both

Crime = ρWCrime + βEntropy + Xγ + λW ε

New analysis: GDP and TV Tax Evasion as dependent variables


Empirical analysis Baseline estimates

Baseline estimates

DEPENDENT Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsVARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 6.912*** 6.374*** 6.164*** 5.954*** 6.305*** 6.187*** 5.348*** 5.124*** 5.336***[0.177] [0.255] [0.283] [0.378] [0.375] [0.376] [0.400] [0.445] [0.491]

Theft 4.797*** 3.505*** 3.365*** 3.173*** 3.247*** 3.202*** 3.138*** 2.830*** 2.848***[0.098] [0.138] [0.157] [0.206] [0.208] [0.212] [0.218] [0.247] [0.285]

Car theft 0.518*** 0.427*** 0.401*** 0.363*** 0.371*** 0.371*** 0.373*** 0.398*** 0.360***[0.012] [0.017] [0.019] [0.043] [0.047] [0.049] [0.051] [0.058] [0.066]

Burglary 0.600*** 0.199*** 0.234*** 0.219*** 0.202*** 0.198*** 0.258*** 0.223*** 0.213***[0.019] [0.023] [0.026] [0.047] [0.057] [0.052] [0.054] [0.059] [0.069]

Robbery 0.109*** 0.091*** 0.083*** 0.077*** 0.078*** 0.079*** 0.082*** 0.107*** 0.073***[0.004] [0.005] [0.007] [0.012] [0.013] [0.014] [0.015] [0.023] [0.014]

Injury 0.198*** 0.212*** 0.211*** 0.212*** 0.209*** 0.203*** 0.175*** 0.187*** 0.202***[0.005] [0.008] [0.008] [0.010] [0.010] [0.009] [0.010] [0.011] [0.012]

CONTROLSGeography No Yes Yes Yes Yes Yes Yes Yes YesPop&Surface No No Yes Yes Yes Yes Yes Yes YesGDP&Unemployment No No No Yes Yes Yes Yes Yes NoMale Age Shares No No No No Yes Yes Yes Yes YesMigration No No No No No Yes Yes Yes YesPolice No No No No No No Yes Yes YesEducation&Civicness No No No No No No No Yes YesProvince FE No Yes Yes Yes Yes Yes Yes Yes NoSLL FE No No No No No No No No Yes

Observations 8,074 8,067 8,067 8,065 8,065 8,065 8,065 8,065 8,065N clust . . . 686 686 686 686 686 686



Baseline estimates

Column (1): Entropy ’s correlation with crime rates (no controls)I Positive and significant for all crimesI High explanatory power: R2 between 7% (robbery) and 23% (theft)I Relevant in magnitude: a sd increase in Entropy associated to rises

F Between 1/4 sd (robbery) and 1/2 sd (theft)F Detail: .4 sd (total crime, car theft and injury), .33 sd (burglary)

Columns (2)-(9): progressive inclusion of controls (and FE)I Entropy ’s sign and significance confirmed for all crimes and specificationsI Even magnitude little affected by different specifications

Implication: the effect does not seem to be driven by omitted variables



(1) (2) (3) (4) (5) (6)VARIABLES Total Crime Theft Car Theft Burglary Robbery Injury

Entropy 5.336*** 2.848*** 0.360*** 0.213*** 0.073*** 0.202***[0.491] [0.285] [0.066] [0.069] [0.014] [0.012]

Ruggedness -0.002 -0.003 -0.000 -0.002*** -0.000* -0.000[0.004] [0.003] [0.000] [0.000] [0.000] [0.000]

Mountain Share -0.031*** -0.023*** -0.002*** -0.003*** -0.000*** -0.000*[0.009] [0.005] [0.001] [0.001] [0.000] [0.000]

Altitude Difference -4.506*** -1.480 -0.089 0.640*** -0.024 -0.048[1.708] [0.970] [0.070] [0.172] [0.022] [0.051]

Altitude 6.854** 1.605 0.054 -0.766** 0.003 0.100[2.789] [1.428] [0.107] [0.322] [0.036] [0.089]

Sea Distance -19.219 -12.897 -5.256 -3.607 -0.166 0.503[33.312] [20.246] [3.447] [3.876] [0.608] [0.872]

Landlocked -9.930*** -6.398*** -0.224 -0.786*** 0.012 -0.259***[1.366] [0.865] [0.152] [0.159] [0.067] [0.036]

Population 22.432* 16.433*** 3.104*** -2.122*** 0.990** -0.155[11.853] [6.316] [0.860] [0.671] [0.405] [0.206]

Surface 0.005 0.002 -0.000 0.001** -0.000 0.000[0.006] [0.004] [0.001] [0.001] [0.000] [0.000]

Immigrant Male Share 0.031 -0.056 0.000 -0.045* 0.002 0.021***[0.147] [0.082] [0.009] [0.024] [0.004] [0.005]

Openness 1.125*** 0.498*** 0.026** 0.146*** 0.007 0.008*[0.183] [0.107] [0.010] [0.024] [0.007] [0.005]

Police 3.391*** 0.309 -0.039* -0.211*** -0.026*** 0.107***[0.431] [0.208] [0.021] [0.043] [0.006] [0.014]

High School 0.262*** 0.217*** 0.005 0.027** -0.003 -0.003[0.095] [0.052] [0.004] [0.012] [0.002] [0.002]

Graduate 0.154 0.119 0.006 0.013 0.007** -0.002[0.152] [0.086] [0.011] [0.018] [0.003] [0.004]

Civicness -0.256*** -0.122*** -0.006** -0.017*** -0.001 -0.003***[0.034] [0.020] [0.003] [0.004] [0.002] [0.001]

Male Age Shares Yes Yes Yes Yes Yes YesSLL FE Yes Yes Yes Yes Yes YesObservations 8,065 8,065 8,065 8,065 8,065 8,065R-squared 0.347 0.447 0.681 0.544 0.688 0.378N clust 686 686 686 686 686 686


Empirical analysis Robustness: north-center-south

Subsamples: northNORTH Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 6.833*** 6.581*** 5.968*** 5.694*** 6.151*** 5.844*** 4.769*** 4.299*** 4.746***[0.275] [0.337] [0.358] [0.523] [0.512] [0.510] [0.529] [0.592] [0.627]

Theft 4.563*** 3.423*** 3.118*** 2.926*** 3.078*** 2.928*** 2.775*** 2.280*** 2.442***[0.150] [0.188] [0.207] [0.283] [0.285] [0.288] [0.290] [0.343] [0.373]

Car Theft 0.427*** 0.318*** 0.283*** 0.258*** 0.274*** 0.268*** 0.248*** 0.263*** 0.278***[0.012] [0.017] [0.017] [0.052] [0.058] [0.060] [0.058] [0.070] [0.085]

Burglary 0.507*** 0.162*** 0.216*** 0.210*** 0.218*** 0.193*** 0.282*** 0.204** 0.200**[0.027] [0.030] [0.033] [0.068] [0.080] [0.072] [0.076] [0.082] [0.093]

Robbery 0.085*** 0.063*** 0.046*** 0.043*** 0.045*** 0.044*** 0.045*** 0.045*** 0.050***[0.003] [0.005] [0.004] [0.007] [0.008] [0.008] [0.008] [0.010] [0.011]

Injury 0.190*** 0.204*** 0.194*** 0.195*** 0.202*** 0.194*** 0.165*** 0.176*** 0.194***[0.006] [0.010] [0.011] [0.013] [0.013] [0.012] [0.014] [0.014] [0.016]





Subsamples: centerCENTER Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 7.619*** 6.145*** 6.054*** 6.068*** 6.554*** 6.811*** 6.561*** 6.105*** 5.906***[0.336] [0.549] [0.612] [0.738] [0.795] [0.804] [0.876] [0.994] [1.129]

Theft 5.271*** 3.707*** 3.661*** 3.528*** 3.717*** 3.837*** 4.045*** 3.607*** 3.540***[0.195] [0.305] [0.330] [0.357] [0.406] [0.418] [0.457] [0.486] [0.578]

Car Theft 0.419*** 0.465*** 0.466*** 0.437*** 0.459*** 0.466*** 0.495*** 0.506*** 0.508***[0.020] [0.036] [0.038] [0.108] [0.118] [0.117] [0.121] [0.136] [0.151]

Burglary 0.651*** 0.264*** 0.312*** 0.289*** 0.303*** 0.323*** 0.368*** 0.398*** 0.321***[0.042] [0.053] [0.057] [0.058] [0.070] [0.062] [0.065] [0.096] [0.112]

Robbery 0.083*** 0.078*** 0.077*** 0.072*** 0.078*** 0.080*** 0.092*** 0.090*** 0.083***[0.004] [0.008] [0.007] [0.008] [0.009] [0.009] [0.010] [0.011] [0.012]

Injury 0.231*** 0.232*** 0.242*** 0.248*** 0.219*** 0.225*** 0.223*** 0.246*** 0.238***[0.013] [0.021] [0.022] [0.034] [0.033] [0.031] [0.033] [0.041] [0.051]

Observations 1,000 999 999 999 999 999 999 999 999N clust . . . 133 133 133 133 133 133




Subsamples: southSOUTH Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 6.931*** 5.377*** 5.361*** 5.126*** 5.077*** 4.809*** 4.062*** 4.458*** 4.494***[0.228] [0.348] [0.403] [0.456] [0.481] [0.456] [0.500] [0.516] [0.557]

Theft 4.704*** 3.262*** 3.178*** 2.948*** 2.774*** 2.644*** 2.614*** 2.777*** 2.657***[0.119] [0.182] [0.215] [0.245] [0.265] [0.272] [0.293] [0.303] [0.322]

Car Theft 1.049*** 0.750*** 0.691*** 0.628*** 0.613*** 0.602*** 0.642*** 0.658*** 0.563***[0.031] [0.050] [0.057] [0.102] [0.118] [0.119] [0.131] [0.135] [0.114]

Burglary 0.545*** 0.257*** 0.331*** 0.296*** 0.247*** 0.223*** 0.231*** 0.291*** 0.283***[0.027] [0.047] [0.057] [0.060] [0.067] [0.064] [0.071] [0.087] [0.077]

Robbery 0.252*** 0.156*** 0.132*** 0.124*** 0.130** 0.125** 0.133** 0.175** 0.085**[0.015] [0.019] [0.021] [0.044] [0.057] [0.058] [0.064] [0.074] [0.036]

Injury 0.248*** 0.221*** 0.231*** 0.233*** 0.199*** 0.195*** 0.155*** 0.170*** 0.186***[0.010] [0.015] [0.017] [0.022] [0.024] [0.023] [0.024] [0.026] [0.026]




Empirical analysis Robustness: village-town-city

Subsamples: villages (less than 2K inhabitants)VILLAGES Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 6.468*** 6.062*** 8.358*** 8.272*** 8.243*** 6.947*** 6.732*** 6.234*** 6.558***[0.482] [0.758] [0.892] [1.123] [1.097] [1.084] [1.097] [1.038] [1.076]

Theft 4.264*** 3.279*** 3.979*** 3.944*** 3.883*** 3.305*** 3.330*** 2.868*** 2.954***[0.253] [0.381] [0.455] [0.568] [0.555] [0.550] [0.549] [0.546] [0.571]

Car Theft 0.107*** 0.072*** 0.043*** 0.043*** 0.038** 0.013 0.018 0.008 0.027[0.014] [0.016] [0.014] [0.015] [0.015] [0.014] [0.015] [0.013] [0.019]

Burglary 1.037*** 0.548*** 0.657*** 0.654*** 0.660*** 0.531*** 0.561*** 0.504*** 0.569***[0.047] [0.056] [0.073] [0.085] [0.083] [0.082] [0.084] [0.079] [0.089]

Robbery 0.018*** 0.008* 0.012** 0.011** 0.009* 0.005 0.007 0.005 0.006[0.005] [0.005] [0.005] [0.005] [0.005] [0.006] [0.006] [0.006] [0.006]

Injury 0.123*** 0.143*** 0.141*** 0.138*** 0.140*** 0.117*** 0.113*** 0.121*** 0.129***[0.012] [0.021] [0.026] [0.031] [0.030] [0.031] [0.031] [0.031] [0.035]





Subsamples: towns (between 2K and 10K inhabitants)TOWNS Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 8.843*** 9.494*** 11.831*** 11.688*** 11.358*** 9.515*** 8.631*** 8.491*** 8.272***[0.356] [0.691] [0.913] [1.007] [1.021] [1.036] [0.978] [0.989] [1.257]

Theft 6.250*** 5.128*** 6.490*** 6.352*** 6.004*** 4.881*** 4.669*** 4.379*** 4.287***[0.209] [0.448] [0.584] [0.666] [0.671] [0.684] [0.650] [0.655] [0.823]

Car Theft 0.297*** 0.491*** 0.414*** 0.394*** 0.422*** 0.287*** 0.286*** 0.267*** 0.300**[0.026] [0.046] [0.055] [0.077] [0.084] [0.091] [0.087] [0.088] [0.117]

Burglary 1.212*** 0.473*** 0.833*** 0.822*** 0.762*** 0.503*** 0.514*** 0.474*** 0.337**[0.044] [0.079] [0.118] [0.143] [0.134] [0.128] [0.128] [0.133] [0.137]

Robbery 0.049*** 0.090*** 0.065*** 0.064*** 0.070*** 0.026 0.028 0.033 0.058**[0.009] [0.016] [0.018] [0.024] [0.023] [0.029] [0.029] [0.025] [0.028]

Injury 0.162*** 0.271*** 0.233*** 0.236*** 0.244*** 0.211*** 0.191*** 0.222*** 0.203***[0.012] [0.018] [0.024] [0.025] [0.027] [0.027] [0.028] [0.029] [0.035]





Subsamples: cities (more than 10K inhabitants)CITIES Coefficient of Entropy in a regression of each dependent variable on Entropy and ControlsDEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime 12.603*** 12.140*** 11.648*** 11.500*** 10.241*** 9.277*** 9.181*** 8.100*** 8.653***[0.456] [0.700] [0.740] [0.781] [0.770] [0.783] [0.781] [0.848] [1.257]

Theft 7.837*** 7.168*** 6.720*** 6.454*** 5.534*** 5.046*** 5.021*** 4.387*** 4.869***[0.308] [0.456] [0.456] [0.467] [0.493] [0.510] [0.512] [0.557] [0.792]

Car Theft 0.418*** 1.148*** 1.119*** 0.994*** 1.030*** 1.001*** 1.000*** 0.969*** 1.131***[0.079] [0.084] [0.088] [0.135] [0.156] [0.155] [0.157] [0.172] [0.221]

Burglary 0.501*** -0.009 0.027 0.036 -0.090 -0.145 -0.148 -0.190 -0.203[0.048] [0.054] [0.060] [0.100] [0.094] [0.091] [0.090] [0.120] [0.165]

Robbery 0.036 0.314*** 0.289*** 0.277*** 0.322*** 0.319*** 0.321*** 0.283*** 0.304***[0.033] [0.034] [0.039] [0.036] [0.057] [0.062] [0.064] [0.054] [0.066]

Injury 0.149*** 0.175*** 0.179*** 0.199*** 0.207*** 0.177*** 0.175*** 0.170*** 0.162***[0.015] [0.020] [0.023] [0.029] [0.029] [0.028] [0.028] [0.030] [0.041]




Empirical analysis Robustness: first surname share

Share of the most common surname, 2004Coefficient of First Share 04 in a regression of each dependent variable on First Share 04 and Controls

DEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime -1.130*** -0.830*** -0.776*** -0.730*** -0.689*** -0.598*** -0.387*** -0.312*** -0.361***[0.056] [0.102] [0.101] [0.116] [0.109] [0.105] [0.092] [0.088] [0.093]

Theft -0.846*** -0.456*** -0.428*** -0.393*** -0.353*** -0.310*** -0.241*** -0.182*** -0.184***[0.032] [0.051] [0.050] [0.059] [0.056] [0.054] [0.049] [0.047] [0.047]

Car Theft -0.085*** -0.042*** -0.039*** -0.033*** -0.025*** -0.023*** -0.015*** -0.013*** -0.011**[0.004] [0.004] [0.004] [0.006] [0.006] [0.006] [0.005] [0.005] [0.005]

Burglary -0.132*** -0.035*** -0.036*** -0.033*** -0.026*** -0.018*** -0.021*** -0.016** -0.022***[0.006] [0.006] [0.006] [0.007] [0.007] [0.006] [0.007] [0.006] [0.007]

Robbery -0.017*** -0.007*** -0.006*** -0.005*** -0.003* -0.002 -0.001 -0.003 -0.001[0.001] [0.001] [0.001] [0.001] [0.002] [0.002] [0.002] [0.002] [0.001]

Injury -0.036*** -0.029*** -0.027*** -0.026*** -0.021*** -0.019*** -0.012*** -0.011*** -0.012***[0.002] [0.003] [0.003] [0.003] [0.003] [0.003] [0.003] [0.002] [0.003]




Empirical analysis Robustness: first surname share

Share of the most common surname, 1993Coefficient of First Share 93 in a regression of each dependent variable on First Share 93 and Controls

DEP. VAR. (1) (2) (3) (4) (5) (6) (7) (8) (9)

Total Crime -1.059*** -0.788*** -0.744*** -0.711*** -0.678*** -0.605*** -0.420*** -0.363*** -0.398***[0.045] [0.060] [0.061] [0.074] [0.074] [0.073] [0.069] [0.066] [0.071]

Theft -0.745*** -0.416*** -0.393*** -0.369*** -0.338*** -0.301*** -0.243*** -0.193*** -0.195***[0.026] [0.031] [0.032] [0.040] [0.041] [0.041] [0.039] [0.038] [0.040]

Car Theft -0.070*** -0.037*** -0.034*** -0.029*** -0.024*** -0.022*** -0.015*** -0.014*** -0.011***[0.003] [0.003] [0.003] [0.005] [0.005] [0.005] [0.004] [0.004] [0.004]

Burglary -0.115*** -0.038*** -0.039*** -0.037*** -0.033*** -0.026*** -0.029*** -0.025*** -0.030***[0.005] [0.004] [0.004] [0.005] [0.006] [0.005] [0.005] [0.005] [0.006]

Robbery -0.013*** -0.006*** -0.006*** -0.005*** -0.003** -0.003* -0.001 -0.003* -0.002[0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.002] [0.002] [0.001]

Injury -0.031*** -0.024*** -0.023*** -0.022*** -0.018*** -0.017*** -0.011*** -0.010*** -0.011***[0.001] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.003]




Empirical analysis Robustness: spatial analysis

Spatial estimates (non-standardized contiguity matrix)(1) (2) (3) (4) (5) (6)

Total crime Theft Car theft Burglary Robbery Injury

Panel A: Spatial Error ModelEntropy 4.603*** 2.234*** 0.267*** 0.156*** 0.052*** 0.184***

[0.341] [0.181] [0.015] [0.029] [0.005] [0.009]

λ 0.823*** 1.253*** 2.256*** 1.029*** 1.817*** 0.987***[0.099] [0.097] [0.068] [0.084] [0.064] [0.111]

Panel B: Spatial Lag ModelEntropy 5.258*** 2.744*** 0.330*** 0.229*** 0.062*** 0.202***

[0.335] [0.179] [0.016] [0.029] [0.005] [0.009]

ρ 0.620*** 0.978*** 1.789*** 1.846*** 2.369*** 0.284***[0.103] [0.096] [0.074] [0.083] [0.071] [0.104]

Panel C: Spatial Error and Lag ModelEntropy 4.574*** 2.206*** 0.259*** 0.172*** 0.052*** 0.178***

[0.338] [0.178] [0.014] [0.029] [0.005] [0.009]

λ 0.882*** 1.367*** 2.469*** 0.837*** 1.796*** 1.221***[0.098] [0.096] [0.063] [0.085] [0.065] [0.107]

ρ -.381*** -.618*** -1.482*** 0.942*** 0.120 -1.085***[0.147] [0.151] [0.127] [0.121] [0.126] [0.158]

Observations 8,065 8,065 8,065 8,065 8,065 8,065SLL FE Yes Yes Yes Yes Yes Yes


Empirical analysis Implications

Implications

The association of social openness, as captured by surname entropy, to crimerates is positive, significant and quantitatively relevant

It does not seem to be driven by omitted variables, by particular crimes orsubsamples, by entropy vs. other statistics of the surname distribution, byreverse causality or by spatial effects

Should we conclude that social closure is desirable?

Its correlation with and TV Tax Evasion and GDP suggests some caveats


Empirical analysis TV Tax Evasion and GDP

TV Tax Evasion

Dependend variable: TV Tax Evasion(1) (2) (3) (4) (5) (6) (7) (8) (9)

Entropy -2.921*** -2.010*** -1.957*** -1.973*** -1.774*** -1.650*** -1.548*** -1.386*** -1.803***[0.104] [0.293] [0.317] [0.352] [0.330] [0.327] [0.347] [0.392] [0.226]

Observations 8,074 8,067 8,067 8,064 8,064 8,064 8,064 8,064 8,064R-squared 0.089 0.477 0.479 0.479 0.508 0.584 0.584 0.591 0.723N clust . 103 103 103 103 103 103 103 686

CONTROLSGeography No Yes Yes Yes Yes Yes Yes Yes YesPop&Surface No No Yes Yes Yes Yes Yes Yes YesIncome No No No Yes Yes Yes Yes Yes YesMale Age Shares No No No No Yes Yes Yes Yes YesMigration No No No No No Yes Yes Yes YesPolice No No No No No No Yes Yes YesEducation No No No No No No No Yes YesProvince FE No Yes Yes Yes Yes Yes Yes Yes NoSLL FE No No No No No No No No Yes



GDP

Dependend variable: Income(1) (2) (3) (4) (5) (6) (7) (8) (9)

Entropy 1.357*** 1.227*** 1.169*** 1.175*** 1.045*** 1.069*** 1.105*** 0.394*** 0.325***[0.026] [0.053] [0.067] [0.072] [0.061] [0.056] [0.061] [0.058] [0.070]

Observations 8,071 8,071 8,064 8,064 8,064 8,064 8,064 8,064 8,064R-squared 0.252 0.567 0.574 0.577 0.607 0.626 0.626 0.760 0.810N clust . 103 103 103 103 103 103 103 686

CONTROLSGeography No No Yes Yes Yes Yes Yes Yes YesPop&Surface No No No Yes Yes Yes Yes Yes YesMale Age Shares No No No No Yes Yes Yes Yes YesMigration No No No No No Yes Yes Yes YesPolice No No No No No No Yes Yes YesEducation No No No No No No No Yes YesProvince FE No Yes Yes Yes Yes Yes Yes Yes NoSLL FE No No No No No No No No Yes



TV Tax Evasion and GDP

Column (1): Entropy ’s correlation (no controls)I Negative and significant with TV Tax EvasionI Positive and significant with GDPI High explanatory power:

F R2 = 9% for TV Tax EvasionF R2 = 25% for GDP

I Relevant in magnitude: a sd increase in Entropy associated toF Reduction in TV Tax Evasion by 1/3 sdF Increase in GDP by 1/2 sd

Columns (2)-(9): progressive inclusion of controls (and FE)I Entropy ’s sign and significance always confirmedI Magnitude reduced in the full specification

F by 1/3 for TV Tax EvasionF by 3/4 for GDP



TV Tax Evasion and GDP

Observations on TV Tax EvasionI The effect of Entropy on TV Tax Evasion is unlikely to be due to endogeneityI TV Tax Evasion is virtually free of social sanctions

Observations on GDPI Entropy can be endogenous to GDP: hard to make causal claimsI Income reporting

F Entropy ’s coefficient on GDP would be upward biased if social closure wereassociated to higher tax evasion in general (lower reported income)

F Yet, its sign and significance are robust to the inclusion of TV Tax Evasionamong the controls

Possible interpretationI Social closure poses a trade-off

F It strengthens social sanctions (and thus improves local enforcement of law)F But it weakens generalized morality (and reduces the radius of cooperation)

I Coherent with Tabellini (2008)


Conclusions

Conclusions

To measure the effect of social closure on crime, we looked for data as closeto the phenomenon of interest as possible

We find that social closure is negatively related to crime rates and positivelyrelated to TV tax evation

Such correlations are strong in magnitude and explanatory power, statisticallysignificant, robust and unlikely to be driven by endogeneity

They are coherent with insights from sociology and economics on thetrade-off between local enforcement and generalized morality

Ultimately, social closure is also correlated to lower income, but hereendogeneity is harder to rule out


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