Introduction Theoretical Model Empirical Model Conclusion
Spatial Nexus in Crime and Unemployment in Times of Crisis Povilas Lastauskas
Eirini Tatsi
CEFER Stockholm University
SOFI, 2017
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Outline
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1
Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Outline
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1
Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Motivation
“Poverty is the parent of revolution and crime” Aristotle “Everything is related to everything else, but near things are more related than distant things” Waldo Tobler
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Motivation
Space is important: unemployment and crime propagate also through space (statistical property). Lack of spatial economic theory (exception: Patacchini & Zenou, 2007). We use the 2008 global financial crisis as an experiment: The shock is exogenous (originates from the U.S. or globally). Germany is Europe’s economic powerhouse, tightly integrated into the global trade and financial network – gets “treated” by the exogenous shock. Short-lived effect – “Germany’s jobs miracle”.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Outline
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Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Contribution: Theory New economic mechanism as why income affects crime: through shocks in match productivity and adjustments in labor market tightness from both home/neighboring regions. Concept is to model reality: agents who are willing to commute to work are also willing to commit crime in regions other than their domicile. Link between criminal activity and labor markets is a shock in agents’ productivity. When a shock occurs, productivity at which firms are willing to employ an agent changes. Agents whose productivity falls below the new threshold productivity level lose their jobs. 7 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Contribution: Theory An agent acts by weighing expected gains and losses from crime or working. By allowing for in- and outmigration in job seekers we allow for changes in domestic labor market tightness as well as in reservation wages. In turn, these shape steady states of crime and unemployment: it is always the low-productive and unemployed agents who have the incentive to commit property crime. Regional crime depends on crime in neighboring regions as well as unemployment, labor market tightness, average productivity, crime wages both in home and neighboring regions and deterrence. 8 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Contribution: Empirics
Data on the 402 German regions and years 2009 − 2010. Model crime spatial multipliers (misspecification). Circumvent reverse causality by exploiting exogenous changes in unemployment due to the crisis. Model performs well for property-related crime. Unemployment rates, clearance rates and share of subsistence benefits recipients explain crime.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Outline
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Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Relevance
Local government policy: What is the most effective way (deterrence factors or labor market conditions) to combat crime? Strategic budget design by taking into account decisions of neighboring local governments: police expenditures, welfare policy.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Outline
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Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Chain of Events Crisis felt in autumn 2008, culminated in the beginning of 2009 with a steep increase of unemployment rates and stabilized thereafter (BfA, 2009). Employment: Fell in autumn 2008 and continued until spring 2009. Short-time employment in export-oriented manufacturing firms (metal, engineering, car and electrical industries). Increased in September 2009.
Unemployment: Sharp increase from January to April 2009 (a bit later) in the west and the south. Males, younger persons. Stabilizes in October 2009. 13 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
6
Unemployment Rates (%) 8 10 12 14
16
Time Evolution of Unemployment Rates
2008
2009
2010
2011
year Overall Foreign
Male Age 15−25
Female
Source: German Regional Database
Figure: Unemployment Rates, 2008-2011, Germany. 14 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Introduction Theoretical Model Empirical Model Conclusion
Time Evolution of Crime Rates
2010
2009
2010
360 340 320 300
2011
2008
2009
2010
Street Crime
−9.60% 2009
2010
−1.87% 2011
290
−1.36%
285
2.48%
−2.42%
2008
year
2009
2010 year
2011
Offenses per 100,000 Inhabitants
Drug−related Offenses
1650 1700 1750 1800 1850
Damage to Property 295
year
900 850
2008
2.41% −6.15%
year
−2.67%
2008
140
2011
−12.71%
year
Offenses per 100,000 Inhabitants
1000 950
2009
Offenses per 100,000 Inhabitants
150
−2.20%
6.48% 5.30%
130
1.09%
Theft in/ from Motor Vehicles
280
160
9.46%
Offenses per 100,000 Inhabitants
−0.72%
2008
Offenses per 100,000 Inhabitants
Theft by Burglary of a Dwelling
280
Offenses per 100,000 Inhabitants
7250 7300 7350 7400 7450
Overall Crime
2011
−3.41%
2.11%
2008
−5.70% 2010 2011
2009 year
Source: German Federal Criminal Police Office
Figure: Crime Rates and Percentage Changes, 2008-2011, Germany. 15 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Spatial Distributions Theft by Burglary of a Dwelling
Overall Unemployment Rates
Damage to Property
(1021,4129] (820,1021] (612,820] [330,612]
(282,1355] (167,282] (96,167] [29,96]
(140,488] (81,140] (38,81] [5,38]
(9.5,17.3] (6.7,9.5] (4.8,6.7] [2.0,4.8]
Theft in/ from Motor Vehicles
Street Crime
Drug−related Offenses
(337,1144] (225,337] (164,225] [59,164]
(1848,4290] (1329,1848] (829,1329] [363,829]
Figure: Unemployment Rates and Crime Rates, 2009-2010, Germany. 16 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Outline
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Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Related Research - Theory Freeman et al. (1996): spatial concentration of crime model decision between working and stealing. Number of criminals in a neighborhood increases returns to crime initially.
Burdett et al. (2003): a search equilibrium framework with interrelations among crime, unemployment and inequality. Crime generates wage dispersion and multiple equilibria. Encouragement of criminal activity if one lives in a neighborhood with high crime rates. Crime is more competitive in high crime neighborhoods.
Burdett et al. (2004): on-the-job search. 18 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Related Research - Empirics Cook & Zarkin (1985), Bushway et al. (2012): recessions and crime; link for property crime (burglary, robbery and motor vehicle theft) but none for violent (homicide). Edmark (2005): Swedish county panel (1988-1999); positive effect for burglary, car and bike theft; reverse causality of unemployment not considered. Öster & Agell (2007): Swedish municipalities panel (1990s); causal positive effect on burglary, auto theft, drug possession; robust only for burglary after controlling for neighboring unemployment. Instruments are the regional employment composition and interaction terms between share of manufacturing employment with exchange rates. 19 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Related Research - Empirics Lin (2008): U.S. panel (1974-2000); 2SLS estimates larger than respective OLS for the positive effect of unemployment; instruments are the interaction terms of changes in real exchange rate and oil prices with the percent employed and GDP in manufacturing industry; also state union membership rates. Fougère et al. (2009): French départements panel (1990-2000); causal effect of youth unemployment on burglary, theft, drug offenses; instrument is the predicted industrial structure. Hooghe et al. (2011): Belgian municipalities panel (2001-2006); significant spatial spillovers and strong impact of unemployment rates; reverse causality of unemployment not considered. 20 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
Related Research - Empirics
Deiana (2016): U.S. commuting zones (722) panel (1990 − 2007); local labor market conditions (earnings), identified by the exposure to Chinese imports on crime. Bennett & Ouazad (2016): Danish employer-employee dataset (1985 − 2000), focus on men, born 1945 to 1960, continuously in the sample; impact of job displacement on an individual’s propensity to commit crime. Dix-Carneiro et al. (2016): 1990s trade liberalization in Brazil as a shock to construct exogenous variation in local labor demand – link with crime in the medium run.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Outline
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Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
A Broad Idea Theoretical model built on Burdett et al. (2003), Boeri (2011) and Patacchini & Zenou (2007). There are labor markets, firms, productivity shocks and criminal opportunities. Introduce at least two areas to capture space: i = 1, 2 and j = 1, 2. Unemployed can look for a job in the two areas. Firms provide vacancies in each of two locations. First subscript denotes where agent is living (i) and second where an action is undertaken (j).
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Labor Market Ei + ui + ni = Eii + Eij + uii + uij + nii + nji = 1, where: Ei : number of employed workers in area i residing in i and working in i or j. ui : number of unemployed workers in area i residing in i and working in i or j. ni : number of enjailed criminals in area i residing either in i or j, but committed a crime in region i. Total labor force residing in i and working and searching in j: uij + Eij . 24 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Labor Market
Vacancy rate in j is defined as a fraction of the total mass of workers: vj / (u1j + E1j + u2j + E2j ). Job finding depends uniquely on the degree of labor market tightness, θi ≡ vi / (uii + uji ) . mi = m (vi , ui ): aggregate matching function. Unconditional probability of a vacancy to match with an unemployed worker: m(vi , ui ) ui 1 = m 1, = m 1, vi vi θi = q (θi ).
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Firms and Productivity Shocks For production to occur, a worker must be matched with a job: newly-formed matches generate a periodic productivity ϕ. Match-specific productivity is subject to shocks. We can thus address the channels through which crime and the real economy are interlinked.
When a shock hits, productivity is a random draw with a fixed, known CDF F (ϕ). Shocks occur at a frequency λ and are persistent. Job destruction occurs when productivity falls below a threshold level, ϕ, ˜ endogenously determined in the model.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Labor Market Flows Probabilities to commit a crime for employed and U unemployed, respectively, φW ij (ϕ) and φij . Crime-preventing productivity is ϕC . Probability of getting caught is π and rate of release from jail into unemployment is ρ. There are three states and six possible transitions: Employed Employed Unemployed Unemployed Enjailed Enjailed 27 / 62
→ Unemployed λF (ϕ˜j ) (1 − uj − nj ) , W C → Enjailed πφ ϕj (1 − uj − nj ) , → Employed θj q (θj ) uj , → Enjailed πφU j uj , → Unemployed ρnj , → Employed 0.
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Unemployment Evolution
4uj = λF (ϕ˜j ) (1 − uj − nj ) + ρnj − θj q (θj ) + πφU uj j
(1)
Inflows: Dissolution of matches when their productivity falls below ϕ. ˜ Enjailed criminals ni released to unemployment with rate ρ. Outflows: Unemployed who meet a vacancy with probability θi q (θi ). Unemployed caught committing a crime with probability π.
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Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Steady State Unemployment
Equating (1) to zero and solving for the steady state: uj =
λF (ϕ˜j )+(ρ−λF (ϕ ˜j ))nj . λF (ϕ ˜j )+θj q(θj )+πφU j
(2)
Two key endogenous variables determining evolution of gross flows in labor market: Market tightness, θ, affecting the job creation margin. Threshold productivity level, ϕ, ˜ affecting the job destruction margin.
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Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Criminal Opportunities Immediate monetary gain from committing a crime: g. The decision space of criminal activity is partitioned for both unemployed and employed by the expected gain gj /π. Form expected payoff from crime in region j for an unemployed (employed) worker from i and Bellman equations for the unemployed, a job-worker match with current productivity ϕ, and the enjailed. Partition wage rates to determine equilibrium outcomes: employed accept any outside offer above current wage; unemployed accept any wage wij (ϕ) ≥ Rij .
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Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Several Results: Partitioning Productivity Space Lemma Workers are less likely to commit crimes when their wage incomes are higher; unemployed agents engage in criminal activities if and only if the workers employed at the reservation wage R do. Corollary If an unemployed agent in i has no incentives to commit a crime in j, then an employed agent does not have such incentives either for any given wage. Even if an unemployed agent commits a crime, it is sufficient for an employed agent to engage in criminal activities only if her wage is lower than the crime wage. Yet, if the wage she earns is larger than C , the expected losses from a crime are larger than the expected gains. 31 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Major Equations Partition the population in 4 segments: employees EjiL with a wage wij (ϕ) < Cij , employees EjiH with a wage wij (ϕ) ≥ Cij , unemployed u and enjailed criminals n. Unemployment rate (Ωi made of parameters and threshold productivities):
ui =
ρλF (ϕ˜i ) θi q (θi ) 1 − F ϕCij
+ λF (ϕ) ˜ +π
Ωi
(3)
For the sake of argument, let φU j = 1; then the crime rate over the non-imprisoned population is : ci = 32 / 62
EiiL +EjiL +ui 1−ni
=
Povilas Lastauskas and Eirini Tatsi
ρλF (ϕ˜i ) . θi q(θi )(1−F (ϕC ˜i ) i ))+λF (ϕ Spatial Nexus in Crime and Unemployment
(4)
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Major Equations
Equation (4) exemplifies the importance of shocks: set λ = 0, and we close the partitioning and economic rationale for crime. Spatial competition is embedded in the expression: Reservation/crime-preventing productivity, and home’s labor market tightness are functions of agents’ interactions at home and neighboring regions. Suppose more people are looking for jobs: the labor market becomes slacker; ceteris paribus, this makes home’s crime rate go up.
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Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Testable Implications Proposition Unemployment and crime depend on average productivity in both regions, labor market tightness, crime wage and exogenous variables (probability of catching a criminal, rate of release into unemployment, and a match-specific shock). 1. An increase in the frequency of match-specific shocks tends to increase the crime rate; 2. An increase in the exogenous variables that drive cutoff productivity ϕ ˜i (e.g. subsistence income) increases the crime rate. 3. An exogenous shock to labor market tightness changes the crime rate at the home region. Hence, a slacker labor market (e.g., due to an increase in job-seekers from other regions) is associated with a higher crime rate. 4. The crime rate increases if the productivity of matches of the unemployed agents from i in region j increases. Hence, an influx of more productive employees from i to j who raise the productivity of a match in j leads to an increase in crime in j. 34 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Testable Implications Considering crime rates in a neighborhood yields
ln ci = ln cj + ln ui − ln uj + ln
− ln
θi q(θi )+λF (ϕ˜i )+π (θi q(θi )(1−F (ϕCi ))+λF (ϕ˜i )+π)
θj q(θj )+λF (ϕ ˜j )+π (θj q(θj )(1−F (ϕCj ))+λF (ϕ˜j )+π)
+ ln(1 − nj ) − ln(1 − ni ). (5)
Crime rate in region i depends on: 1 2 3 4
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Crime rate in neighboring region j. Unemployment rate in regions i and j. Non-imprisoned population in regions i and j. A function of the probability of filling a vacancy (slacker labor market) in regions i and j, the average productivity in regions i and j, crime wages in regions i and j as well as the probability of being caught committing a crime and sent to jail. Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Sketch of a Model
Testable Implications In reality, home region i usually has more than one neighbor; say, j = 1, 2, ..., k neighboring regions. Therefore, we introduce weights wj for each neighbor j 6= i P such that normalization j wj = 1 applies. Taking the weighted sum of equation (5) yields P
ln ci = + ln
wj ln cj + ln ui −
−
P
j wj ln
+
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P
j
P
j wj ln uj θi q(θi )+λF (ϕ˜i )+π (θi q(θi )(1−F (ϕCi ))+λF (ϕ˜i )+π) j
θj q(θj )+λF (ϕ ˜j )+π (θj q(θj )(1−F (ϕCj ))+λF (ϕ˜i )+π)
(6)
wj ln (1 − nj ) − ln (1 − ni ) .
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Outline
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1
Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Econometric Model Ynt = λW n Ynt + Znt δ1 + W n Znt δ2 + X nt β1 + W n X nt β2 + αn + θt ιn + εnt (7)
Ynt : n × 1 vector of observations on crime rates. W n : nonstochastic and constant-over-time n × n spatial weights matrix. W n Ynt : n × 1 vector of crime’s spatial effect. Znt , W n Znt : n × 1 vectors of unemployment and spatial effects. X nt , W n X nt : n × k matrix of observations on time-varying controls and spatial effects. n × 1 vector αn denotes district fixed effects and θt time fixed effects (ιn : n × 1 vector of ones). 38 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Econometric Model: Estimation
W n Ynt , is endogenous by construction of the model: 0 E (W n Ynt εnt ) = σε2 W n (In − λW n )−1 6= 0. True even if W n exogenous. Quasi Maximum Likelihood (QML) on the reduced form (Lee & Yu, 2010). To comply with theory all variables are in natural logarithms.
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Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Econometric Model: Interpretation
Parameter λ always captures spatial dependence in crime λ > 0: similarity. λ < 0: dissimilarity. λ = 0: no spatial dependence.
If λ 6= 0 and because |λ| < 1 E (Ynt ) = (I n − λW n ) −1 (Znt δ1 + W n Znt δ2 + X nt β1 + W n X nt β2 )
(8)
Therefore, the partial derivative with respect to an explanatory variable is a non-linear function of parameter λ.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Econometric Model: Interpretation Rewrite expected value of reduced form as
E (Ynt ) = I n + λW n + λ2 W 2n + λ3 W 3n + · · · (Znt δ1 + W n Znt δ2 + X nt β1 + W n X nt β2 )
W 2n represents neighbors of region i’s neighbors; W 3n neighbors of neighbors of region i’s neighbors etc. Marginal effects: Change from region i (average direct impact – ADI). Change from from region i’s neighbors (average indirect impact – AII).
If λ = 0, report estimated coefficients (comparable to a model estimated with OLS).
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Data 2009 − 2010 : global financial crisis hits the German economy (exogenous shock in unemployment). Offenses rate: number of offenses reported to the police per 100,000 inhabitants. Unemployment rates and youth unemployment share. Market tightness: number of vacancies (job creation) over number of unemployed. Probability of catching a criminal (π): clearance rates. Average productivity: labor productivity (product produced by one hour of labor), proportion of graduates without secondary education qualification and with general higher education qualification. 42 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Data
Rate of release in unemployment: number of employees in firms in insolvency procedures. Crime wage or expected gains from committing a crime and wages: disposable income of private households. Share receiving reservation wage: percent of subsistence benefits recipients. Influx of migrants and changes in the non-imprisoned population: interregional migration.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Data Wn , parametrizes spatial dependencies: 402 regions. Neighbors: districts that share immediate geographical proximity - suffices to capture plausible commuting times in Germany. Elements wii on the main diagonal take value zero, as no district is a neighbor to itself. Element wij takes value one if districts i and j share common borders and zero otherwise. Row-normalize (a spatial effect can be interpreted as the average effect of the neighbors).
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Data
Use lags to avoid reverse causality of control variables (market tightness, clearance rate, disposable income, percent of subsistence benefits recipients, interregional migration). Sources: German Federal Criminal Police Office (crime and clearance rates), German Federal Agency for Employment (unemployment, vacancies, employment), German Federal Office of Cartography and Geodesy (weights matrix), German Regional Database (rest).
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Summary Statistics: Crime & Clearance Rates Variable Overall Crime Rate Clearance Rate (lag) Theft By Burglary of a Dwelling Rate Clearance Rate (lag) Theft in/ from Motor Vehicles Rate Clearance Rate (lag) Street Crime Rate Clearance Rate (lag)
Mean
Std.Dev.
6,528
2,805
60.093
6.715
106
94
23.659
13.439
221
190
16.967
11.375
1,452
775
22.127
5.736
Damage to Property Rate
854
358
Clearance Rate (lag)
26.919
6.618
Drug-related Offenses Rate Clearance Rate (lag)
278
173
95.727
4.003
N=402, T=2009, 2010 46 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Summary Statistics: Unemployment Rates
Variable
Mean
Std.Dev.
Unemployment Rate
7.521
3.354
Unemployment Rate Age 15-25
7.078
3.233
Unemployment Share Age 15-25
3.824
1.886
Male Unemployment Rate
7.648
3.478
Female Unemployment Rate
7.391
3.272
Foreign Unemployment Rate
16.361
6.247
N=402, T=2009, 2010
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Summary Statistics: Controls Variable
Mean
Std.Dev.
Market Tightness (lag)
12.301
8.497
Gross Domestic Product per Working Hour
40.599
7.097
Disposable Income per Capita (lag)
18,653
2,274
Share Subsistence Benefits Recipients (lag)
0.358
0.177
Share Employees in Insolvent Firms
0.613
1.260
Share Graduates w/o SEQ
6.861
2.125
Share Graduates GHEQ
28.670
10.356
Share of Arrivals (lag)
5.333
1.298
Share of Departures (lag)
5.445
1.096
5
-
Weights Matrix N=402, T=2009, 2010
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Outline
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1
Introduction Motivation Contribution Relevance The Global Financial Crisis in Germany Related Research
2
Theoretical Model Sketch of a Model
3
Empirical Model Econometric Model and Data Estimation Results
4
Conclusion Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
No Space
Unemployment for
UR
UR15-25
US15-25
URMale
URFemale
URForeign
Overall Crime
0.373*
0.157
0.037
0.340*
0.337
0.156
(0.204)
(0.106)
(0.040)
(0.198)
(0.205)
(0.126)
Theft by Burglary of a Dwelling
0.506*
0.240
0.073
0.415
0.486
0.030
(0.302)
(0.180)
(0.147)
(0.311)
(0.297)
(0.268)
Theft of/from Motor Vehicles
0.419
0.258
0.089
0.399
0.407
0.098
(0.375)
(0.193)
(0.150)
(0.353)
(0.370)
(0.251)
Note: 804 observations (402 regions for years 2009, 2010).Estimation with OLS including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
No Space
Unemployment for
UR
UR15-25
US15-25
URMale
URFemale
URForeign
Street Crime
0.386*
0.209*
0.068
0.347
0.369
0.182
(0.234)
(0.116)
(0.056)
(0.225)
(0.231)
(0.149)
Damage to Property
0.357*
0.118
-0.024
0.308
0.340*
0.200
(0.209)
(0.123)
(0.088)
(0.215)
(0.203)
(0.124)
-0.205
-0.185*
-0.176
-0.088
-0.328
-0.058
(0.183)
(0.108)
(0.115)
(0.149)
(0.210)
(0.123)
Drug-related Crime
Note: 804 observations (402 regions for years 2009, 2010). Estimation with OLS including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Overall Crime
UR
bλ Unemployment
WUnemployment
UR15-25
US15-25
URMale
URFemale
URForeign
0.063
0.026
0.023
0.065
0.047
0.038
(0.057)
(0.058)
(0.058)
(0.057)
(0.058)
(0.058)
0.413***
0.164***
0.003
0.394***
0.365***
0.164***
(0.054)
(0.036)
(0.040)
(0.053)
(0.053)
(0.037)
-0.076
-0.174
-0.169
-0.060
-0.123
-0.063
(0.162)
(0.166)
(0.170)
(0.162)
(0.162)
(0.161)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Theft by Burglary of a Dwelling
bλ Unemployment – ADI
WUnemployment – AII
UR
UR15-25
US15-25
URMale
URFemale
URForeign
0.227***
0.232***
0.230***
0.232***
0.224***
0.229***
(0.051)
(0.051)
(0.051)
(0.051)
(0.051)
(0.051)
0.472***
0.291***
0.121
0.425***
0.400**
0.007
(0.168)
(0.110)
(0.121)
(0.163)
(0.166)
(0.115)
-0.309
-0.518**
-0.519**
-0.493
0.160
-0.457*
(0.370)
(0.234)
(0.238)
(0.352)
(0.355)
(0.240)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Theft of/from Motor Vehicles
bλ Unemployment – ADI
WUnemployment – AII
UR
UR15-25
US15-25
URMale
URFemale
URForeign
0.167***
0.149***
0.142***
0.164***
0.160***
0.140***
(0.053)
(0.053)
(0.053)
(0.053)
(0.053)
(0.054)
0.654***
0.330***
0.093
0.687***
0.571***
0.190*
(0.157)
(0.105)
(0.116)
(0.152)
(0.155)
(0.106)
-1.191***
-0.124
0.098
-1.285***
-0.776**
-1.151***
(0.328)
(0.204)
(0.207)
(0.310)
(0.314)
(0.211)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Street Crime
UR
bλ Unemployment – ADI
WUnemployment – AII
UR15-25
US15-25
URMale
URFemale
URForeign
0.134***
0.095*
0.090*
0.126**
0.126**
0.106**
(0.050)
(0.051)
(0.052)
(0.051)
(0.051)
(0.051)
0.456***
0.206***
0.008
0.423***
0.426***
0.196***
(0.061)
(0.041)
(0.047)
(0.060)
(0.060)
(0.042)
-0.497***
-0.075
0.056
-0.478***
-0.361***
-0.345***
(0.125)
(0.078)
(0.081)
(0.119)
(0.120)
(0.079)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Damage to Property
UR
bλ Unemployment
WUnemployment
UR15-25
US15-25
URMale
URFemale
URForeign
0.068
0.060
0.048
0.069
0.061
0.053
(0.054)
(0.054)
(0.055)
(0.054)
(0.054)
(0.054)
0.431***
0.184***
0.010
0.404***
0.397***
0.228***
(0.074)
(0.049)
(0.055)
(0.073)
(0.073)
(0.050)
-0.297**
-0.216**
-0.098
-0.345**
-0.222
-0.104
(0.145)
(0.093)
(0.097)
(0.141)
(0.139)
(0.093)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Drug-Related Crime
UR
bλ Unemployment
WUnemployment
UR15-25
US15-25
URMale
URFemale
URForeign
0.039
0.050
0.052
0.045
0.032
0.052
(0.053)
(0.053)
(0.053)
(0.053)
(0.053)
(0.053)
-0.075
-0.169**
-0.174*
0.071
-0.206
-0.028
(0.130)
(0.085)
(0.094)
(0.126)
(0.127)
(0.088)
-0.762***
-0.047
0.015
-0.792***
-0.708***
-0.331**
(0.250)
(0.158)
(0.163)
(0.240)
(0.239)
(0.160)
Note: 804 observations (402 regions for years 2009, 2010). Estimation with QML including regional and time fixed effects. All variables are in logarithms. *, **, *** denote significance at 10%, 5% and 1% respectively. W denotes neighboring effects.
57 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Significant Controls
Clearance rates Overall crime own effect: around −0.45. Pure property crime and street crime own effect: around −0.08.
Share of subsistence benefits recipients Theft by burglary of dwelling own effect: around 0.10. Theft of/from motor vehicles neighbors’ effect: around 0.16. Street crime own and neighbors’ effect: around 0.04 and 0.14. Damage to property own and neighbors’ effect: around 0.05 and 0.07.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Policy Implications
The 402 German “Kreise” are administrative units between Federal States and municipalities. The “Kreis” council is elected every 5 years (6 in Bavaria) and is – among other things – responsible for Social, old-age and youth welfare. Implementation of labor market policies (Hartz policies). Maintenance of hospitals and state schools. Provision of savings banks. Public transport and natural parks. Accommodation and integration of foreign refugees.
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Policy Implications For pure property crime, street crime, and damage to property, reduction of domestic unemployment is the most effective way to combat crime. For overall, pure property, street and damage to property crime, lower crime rates by out-migration of unemployed: crime rates decrease directly from a decrease in home unemployment and indirectly from an increase in neighboring unemployment. In principle, spatial competition in unemployment benefits: offer 1 euro less than neighbors; infeasible as Hartz benefits are uniform for whole Germany. Improve efficiency of jobcenters or increase their number. 60 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Econometric Model and Data Estimation Results
Policy Implications Local governments should aim more at labor market conditions in times of crisis than deterrence factors in order to combat crime (magnitude). Reduce crime rates through a decrease in the share of subsistence benefits recipients either directly through the home region or indirectly through neighbors. The home region can “free-ride” on poverty-reduction policies in the neighborhood. Also, for pure property crime and street crime, the crime b > 0 propagates any decreasing spatial multiplier due to λ effect so that a poverty reduction policy is doomed to be more efficient than initially intended. 61 / 62
Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
Introduction Theoretical Model Empirical Model Conclusion
Conclusion We develop and test spatial economic theory to elucidate the nexus between unemployment and crime. New economic mechanism: shocks in match productivities. Model works in times of crisis and for property-related crime as intended. Use model and identification strategy to link labor markets and crime rates in Europe after the war in Syria, (influx of refugees). And...
Space is important!
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Povilas Lastauskas and Eirini Tatsi
Spatial Nexus in Crime and Unemployment
