Download - THE CRIME KUZNETS CURVE
SERIE DOCUMENTOS DE TRABAJO No. 155
Abril de 2014
THE CRIME KUZNETS CURVE Paolo Buonanno Leopoldo Fergusson Juan F. Vargas
The Crime Kuznets Curve
Paolo Buonanno∗ Leopoldo Fergusson† Juan F. Vargas‡
Abstract
We document the existence of a Crime Kuznets Curve in US states since the 1970s.As income levels have risen, crime has followed an inverted U-shaped pattern, firstincreasing and then dropping. The Crime Kuznets Curve is not explained by incomeinequality. In fact, we show that during the sample period inequality has risenmonotonically with income, ruling out the traditional Kuznets Curve. Our findingis robust to adding a large set of controls that are used in the literature to explainthe incidence of crime, as well as to controlling for state and year fixed effects. TheCurve is also revealed in nonparametric specifications. The Crime Kuznets Curveexists for property crime and for some categories of violent crime.
Keywords: Crime Kuznets Curve, Income inequality, Non-parametric Kernel anal-ysis.
∗Universita degli Studi di Bergamo, Department of Economics. Via dei Caniana 2 - 24127, Bergamo.E-mail: [email protected].†Universidad de los Andes, Department of Economics, Cra 1 No 18A–12, of. W812, Bogota. E-mail:
[email protected]‡Universidad del Rosario, Department of Economics. Calle 12c No. 4–69, of. 315, Bogota. E-mail:
1 Introduction
This paper documents for the first time the existence of an inverted U-shaped relationship
between crime and income within US states for the period 1970-2011. Crime increases
with per capita income until it reaches a maximum and then decreases as income keeps
rising. We provide compelling robust parametric and nonparametric support for this
“Crime Kuznets Curve” (CKC). The curve survives controlling for the standard socioe-
conomic and demographic determinants of crime, as well as including state and time
fixed effects that account respectively for potential time-invariant omitted variables and
for time shocks in crime that are common to all states (e.g. federal criminal law changes).
We rule out the obvious candidate explanation, namely that crime responds monoton-
ically to changes in income inequality and in turn inequality has an inverted U-shaped
relationship with income. This non-linearity is in fact the essence of the traditional
Kuznets Curve (KC), named after the seminal paper by Simon Kuznets (1955) who noted
a non-monotonic association between income inequality and economic growth. However,
we show that the CKC survives the inclusion of economic inequality as control. More-
over, we also establish that during our sample period inequality has been monotonically
increasing with income in most US states. Hence, the KC is not the underlying factor
explaining the CKC.
The relationship between income and crime has attracted the attention of social sci-
entists, notably economists and criminologists. The basic version of the economic model
of crime (Becker, 1968 and Ehrlich, 1973) suggests that rational agents would engage in
crime or illegal behavior as long the expected benefits offset the expected costs. Indeed,
for a given probability of apprehension and expected punishment, higher levels of income
increase the opportunity cost of crime of potential criminals, thus reducing the total time
devoted to criminal activities. However, this logic also implies that wealthier individuals
are more attractive criminal targets. This predicts a higher victimization of the rich by
the relatively poor. However, as suggested by Allen (1996) and Chiu and Madden (1998),
the rich may implement private protection strategies to tackle crime (e.g. alarm systems,
private security, etc.), thus offsetting the criminal incentives implied by the cost-benefit
calculation of the disadvantaged.
In short, the theoretical prediction regarding the relationship between income and
crime is ambiguous, and the answer is ultimately empirical. However, most theoretical
models fail to predict a non-monotonic relationship between income and crime, and thus
cannot account for the CKC. While the paper’s objective is to document the existence
and robustness of the CKC at the macro level, and not to uncover the features creating
this relationship, we discuss possible avenues for future research that could help uncover
these microeconomic underpinnings. Moreover, we show that the CKC exists for property
crime and for categories of violent crime that can be related to economic appropriation,
1
like robbery. Instead, the inverse-U pattern is less robust for violent crimes not nec-
essarily connected with economic incentives. This distinction, which is less salient in
the nonparametric analysis, may be important to help identify which causal mechanisms
underlie the CKC.
Both official country-level crime statistics and victimization surveys show that crime
rates have systematically fallen over the past decade in most of the developed world.
According to The Economist (2013), in the US the fall began around 1991, in the UK
around 1995, and in France and other Western European countries crime rates have
fallen since about 2001. While this pattern holds for either property and violent crime,
the decline stage has been relatively more pronounced for property crime.1 The fall in
crime rates in developed countries has followed a relative long period of steadily increasing
rates, as documented by Buonanno et al. (2011).
A crucial concern in interpreting the evolution of crime rates from a cross-country
perspective is related with the comparability of crime data. While in other critical pol-
icy sectors data are collected systematically and according to uniform standards across
countries, the same is not true for crime rates. This has to do in part with the fact that
crime is a social phenomenon that is hidden by its illegal nature and often goes under
denounced. This complexity has been recognized by several scholars, among others Aebi
(2004), Dills et al. (2008), Goldberger and Rosenfeld (2009) and Durlauf et al. (2010).
We focus in this paper in the case of the US. This has several advantages like mitigating
some of the concerns regarding the institutional heterogeneity and data comparability
present in cross-country studies. Standards for crime measurement in the US are held at
the federal level, making the data homogenous. Besides, we believe that the US provide
a very interesting framework to study the relationship between income and crime. It is
well known that the US experienced an unexpected drop in crime rates starting in the
1990s, after a period of dramatic growth in both violent and property crimes (Levitt,
2004).
The rest of the paper is organized as follows. Section 2 describes the data. Section 3
discusses the empirical strategy both for the parametric and the nonparametric analyses.
Section 4 reports the results that lead us to claim the existence of a CKC in the US.
Finally Section 5 concludes.
1For instance, in New York City the annual number of car thefts has fallen by 93% over the last 20years. Similarly, in England and Wales reported car thefts drop from 400,000 in 1997 to just 86,000 in2012 (The Economist, 2013).
2
2 Data
We constructed a balanced panel with annual observations at the state level for 50 US
states over the period 1970-2011.2 As for the dependent variable, we consider the seven
felony offenses recorded in the FBI’s Uniform Crime Reports. In particular, we distinguish
between different forms of property crime (burglary, larceny and car theft) and violent
crime (murder, assault, robbery and rape). The distinction between property and violent
crime is motivated by the observation that property crime is more likely to depend on
economic motivations than violent crime.
Panel A of Table 1 reports the descriptive statistics of the crime outcomes, for which
rates are computed in terms of 100,000 inhabitants. Property crime rate is on average
almost 10 times as prevalent as violent crime. Within property crimes the most prevalent
is larceny and the least prevalent is car theft. In the case of violent crimes the highest
incidence corresponds to assault and the lowest to murder.
The main independent variable of interest is income. Real GDP per capita at the
state level for the entire sample period is obtained from the US Bureau of Economic
Analysis, and the base year is 2009.
We also collected a large set of standard socioeconomic and demographic variables that
are likely to be correlated with crime rates. Of these, the most important is the income
Gini coefficient.3 It is important to control for inequality for two reasons. One the one
hand inequality has been theoretically and empirically linked with criminal behavior (see
for example Becker, 1968 and Buonanno and Vargas, 2013 respectively). On the other
hand, the traditional KC features an inverted U-shaped relationship between income
and inequality and hence inequality is an obvious confounder when exploring a potential
non-linear relationship between income and crime.
We also control for population density, the state employment rate and the distribution
of the state male population across several age categories. Controlling for population
density is important as it is well documented that the incidence of crime is higher in
densely populated areas than in sparsely populated areas (Glaeser and Sacerdote, 1999).
Several reasons may explain this fact: in a dense area the pool of potential victims
is larger, criminal networks are more developed and criminal activities may experience
economies of scale due, for example, to lower search costs. Hence we expect larger
population density to be associated with higher crime rates.
In turn, controlling for the employment rate accounts for labor market opportunities
that, according to the benchmark economic model of crime (Becker, 1968 and Ehrlich,
2Consistent with previous empirical research on crime (e.g. Donohue III and Levitt, 2001; Raphaeland Winter-Ebmer, 2001 and Lin, 2008) we exclude District of Columbia from our analysis because itconstitutes an outlier.
3Inequality data are available for download at Mark W. Frank’s website(http://www.shsu.edu/∼eco mwf/inequality.html) and the data available represent an updatewith respect to the data used in Frank (2009).
3
1973), reduce the amount of time devoted to criminal enterprises. This occurs for two
reasons. First, the expected returns from legal activity increase if the probability of
being employed is higher. Second, given a downward sloping labor demand curve, more
employment is associated with a higher wage rate. In both cases the opportunity cost of
crime increases, thus discouraging this activity. We then expect higher employment rates
to be associated with lower crime rates. This prediction has been confirmed by recent
empirical scholarship that employs panel data techniques at the state or regional level
(e.g. Raphael and Winter-Ebmer, 2001; Gould, Weinberg and Mustard, 2002; Lin, 2008;
and Fougere, Kramarz and Pouget 2009, Oster and Agell, 2007).
Finally, controlling for the state-level share of male population across different age
brackets serves the double purpose of accounting for the well-documented age-crime pro-
file as well as the male crime bias. These controls ensure that estimates are not contam-
inated by age and gender differences across states and overtime.
Panel B of Table 1 reports the descriptive statistics of the independent variables. The
real income per capita, measured in thousands of dollars of 2009, is on average $28,553.
The average value of the Gini coefficient of income inequality is 0.54. On average the
employment rate over the state population is 55% and there are about 169 individuals
per squared mile. The fraction of males is decreasing with age.
Since it is impossible to control for the myriad of factors that may potentially affect
crime our list of controls is likely to be incomplete. For this reason, in all regressions
we include both state and year fixed effects. The former account for any remaining
heterogeneity across states as long as it is time invariant. The latter absorbs any shock
that may affect all states simultaneously at any moment of time. The empirical approach
is explained in more detail in the next section.
3 Empirical strategy
Kuznets (1955) based his inverted U hypothesis on the observed time series evolution of
three developed countries (US, UK and Germany), as well as on cross sectional obser-
vations of three developing nations (India, Ceylon and Puerto Rico). Since the work of
Ahluwalia (1976a and 1976b) the empirical relevance of the KC has been investigated in
a more systematic way by several scholars, leading however to little consensus. The exis-
tence or not of a KC depends on the nature of the empirical investigation, whether based
on cross-sectional country-level variation or based on the time series evolution within
individual countries (see Gallup, 2012 and Kanbur, 2012 for recent reviews of the KC
literature).
To estimate whether there is an inverted U-shaped relationship between income and
the incidence of crime we run the following benchmark specification in which we include
4
a quadratic polynomial of income:
Crimeit = αi + γt + β1Incomeit + β2Income2it + δXit + εit, (1)
where Crimeit is either of the measures of the crime described in section 2 in state i at
time t; Incomeit it real GDP per capita in state i at time t; Xit is a vector containing the
state-specific time-varying controls discussed in section 2 and αi and γt are respectively
state and time fixed effects. Finally εit is the error term.
A more recent empirical debate, surrounding the existence or not of an Environmen-
tal Kuznets Curve (EKC) has taken place among scholars from across a wide range of
disciplines.4 Several papers contributing to the EKC debate use however a higher order
polynomial of income to parametrically estimate its effect on the concentration of pollu-
tants. For robustness we follow this approach in investigating whether there is a CKC
in the US. Thus we include an additional cubic term of income. The idea is to allow
the data to reveal in a flexible way potential non-linearities. In particular, a third-degree
polynomial has the advantage over the quadratic specification that it is not symmetric,
hence the cubic function may rise faster than it declines or vice-versa. This approach
was first used to investigate the EKC by Grossman and Krueger (1995), Holtsz-Eakin
and Selden (1995) and Cole et al. (1997) among others. Our preferred specification thus
estimates:
Crimeit = αi + γt + ρ1Incomeit + ρ2Income2it + ρ3Income
3it + θXit + ηit (2)
Even in the case of the flexible third degree polynomial of income the parametric
approach may be arbitrarily restrictive. Thus, if the parametric assumptions are wrong
the results may be compromised. Thus, as a further robustness exercise we use nonpara-
metric methods to estimate the relationship between income and crime. By doing so we
relax any parametric assumption imposed on the data generating process and let the data
determine the appropriate model. We estimate a generic nonparametric equation of the
form:
Crimeit = f(Incomeit) + µit (3)
where f(Incomeit) is an unknown function. To estimate this equation we use Kernel-
weighted local polynomial smoothing. We do so for each state separately. As in the case
4Following the seminal contribution of Grossman and Krueger (1993) several studies have foundthat some pollutants follow an inverse U-shaped pattern relative to countries’ incomes (see for exampleGaleotti et al., 2006 and 2009; and Andreoni and Levinson, 2001). However, others have rejected theexistence of an EKC (Stern, 2004 and Poudel et a. , 2009). Dasgupta et al. (2002) and Dinda (2004)provided comprehensive reviews. The existence or not of an EKC ultimately depends, according to Lieb(2002) on the pollutant analyzed, the sample of countries studied and the empirical method used (eithercross-section or time series as in the case of the KC).
5
of the fixed effects parametric panel model, this accounts for the large heterogeneity that
exists across states, for instance in terms of per capita income which constitutes the main
variable in our analysis.
4 Results
4.1 Parametric results
Table 2 reports the results from estimating equation 2 (odd columns) and equation 3 (even
columns) on three different aggregations of crime. Columns 1 and 2 look at the effect
of income on the total crime rate, that aggregates the entire set of property and violent
crimes as reported in Panel A of Table 1. Columns 3 and 4 focus on the aggregation of
the property crimes only (burglary, larceny and car theft), and columns 5 and 6 on the
aggregate violent crime rate (composed by murder, assault, rape and robbery). Individual
crime categories are analyzed in Table 4.
All columns include state and year fixed effects. In Panel A, we add no further
controls, whereas Panel B includes as well the entire set of state-level time-varying controls
described in Panel B of Table 1. In Panel A, in most cases the coefficient of income is
positive and significant and that of income squared is negative and significative. This
combination is consistent with an inverted U-shaped relationship between income and
crime. The only exception is in the last column, that looks at the effect of income on the
rate of violent crime using a cubic polynomial of income. In that case, none of the terms
in the polynomial is statistically significant. Results for Panel B are very similar (both in
terms of the sign and size of coefficients for the polynomial terms). The only difference
worth highlighting is in the last column, where no term was significant without controls
yet with controls there is a positive linear term (significant at the 90% confidence level).
The main messages from this table can therefore be summarized as follows. First, there
is a robust CKC for total crime and for property crime. Second, since the results are very
similar with and without the set of controls often highlighted in theories of crime, the CKC
is not be driven by a correlation between income and such characteristics. Notably, since
inequality is included in the set of controls, the comparison between Panel A and Panel
B implies that the CKC is not a mere reflection of the traditional KC where income and
inequality have an inverse-U relationship. And finally, while the CKC is also apparent in
some specifications for violent crime, this relationship is less robust. In particular, in the
most convincing cubic specification allowing for a more flexible non-symmetric U-shape
we find that there is no CKC for violent crimes (regardless of whether state controls are
include or not).
Table 2 also reports in the last line the implied income for the crime turning point
in each of our specifications. Since results with and without controls are similar, we
6
report these only for the more demanding specifications with state controls. Imposing a
quadratic polynomial affects the estimated turning point. While in quadratic polynomials
the income threshold at which crime starts to fall is around 35 to 37 thousand dollars (of
2009) per capita, once a cubic term is included this falls to 20 to 24 thousand dollars per
capita. Hence, imposing symmetry does seem to be restrictive.
Table 3 looks more closely at the possibility that the traditional KC is driving our
results. While this is unlikely given our comparison of Panel A and B in Table 2, we
can also run our basic specifications, equations 2 and 3, with income inequality as the
dependent variable. We run this both with state controls (columns 1 and 2) and without
the controls (columns 3 and 4). Clearly, regardless of whether the quadratic or cubic
specification is used (in the odd and even columns respectively), there is simply no tradi-
tional KC in our sample period. Hence, we confirm that the CKC in our US sample is a
phenomenon that extends beyond Simon Kusnetz’ famous income-inequality relationship.
Table 4 looks at the individual components of property crime (columns 1 to 6) and of
violent crime (columns 7 to 14). As in Table 2, the odd columns estimate the quadratic
income regression (equation 2) and the even columns the cubic one (equation 3). Each
one of the three crime categories that compose the property crime rate features the
inverted-U relationship with income. The only exception is the rate of car theft in the
cubic polynomial specification. As for the categories comprising the violent crime rate,
the only one for which there seems to be a CKC is robbery. This lies in line with the
observation from Table 2 that the CKC is less robust for violent crime. The weaker
CKC for violent crime in Table 2 is driven solely by the behavior of robbery, which in
turn is closely related with property appropriation albeit its violent nature. In short, the
parametric results provide robust evidence of the existence of a CKC for property-related
crimes.
These results are consistent with the idea that property crimes are more likely to
depend on economic motivations than violent crimes. This is indeed the essence of the
benchmark economic model of crime of Becker (1968) and Ehrlich (1973).
However, our conclusion that there is a CKC in the US may depend on parametric
assumptions regarding the functional form of the true relationship between income and
crime. To deal with this concern, several scholars have adopted semi-parametric and
nonparametric techniques, which do not specify a functional form a priori (e.g. Bertinelli
and Stobl, 2005; Frazer, 2006 and Galeotti et al., 2006). The advantages of nonparametric
empirical analysis are summarized by DiNardo and Tobias (2001). We now explore the
robustness of our conclusions regarding the existence of CKC in the last four decades in
the US using a more flexible and unconstrained nonparametric specification.
7
4.2 Nonparametric results
We now show graphically the nonparametric Kernel estimates for each state over the
support of the state-specific real per capita income during the sample period. This allows
us to account for the large heterogeneity in states’ income. Overall, we observe a robust
inverted U-shaped relationship between income and total crime (Figure 1), property crime
(Figure 2) and also violent crime (Figure 3), with the exception of very few states.5 There,
crime rates either fail to revert to lower level as income grows and remain fluctuating near
the maximum level, or keep rising monotonically with income.6
Just as we ruled out in Table 3 the existence of a classical KC using our parametric
approach, we can also examine this with our nonparametric approach. Figure 4 shows
that during our sample period there is no evidence for a state level KC. The relationship
between inequality (measured in the vertical axis using the income Gini coefficient) and
income (measured in the horizontal axis) is monotonically increasing. This is true for all
states. Thus, again we are confident in claiming that the CKC in the US is a phenomenon
that is not explained by the behavior of income inequality.
As in the parametric specifications, the existence of a CKC is more evident in the
case of property crime than for violent crime. However, the contrast is not as sharp as in
the parametric results: Violent crimes also follow an inverted U-shaped relationship with
income in several states (Figure 3). This is confirmed by the equivalent nonparametric
estimates of the seven disaggregated outcomes, reported in Figures 5 to 11. Outcomes
related to property appropriation (including robbery, as discussed in the previous sub-
section) feature and inverted U-shaped relationship with income in most states, which is
consistent with the CKC stylized fact put forward by this paper. In addition, outcomes
related with violent crime (beyond robbery) also display such pattern in various states.
This is confirmed by figure 12, which shows the nonparametric estimates for the aggre-
gate violent crimes excluding robbery. Even in such case that focuses on the essentially
violent (an non appropriative) crimes, the inverted U relationship survives for at least
half of the states.
While the nonparametric results attenuate the differences across crime types identified
in the parametric analysis, the fact that the results are still much stronger and preva-
lent across states for property crime is likely to be related to the different motivation
in committing a crime. Indeed, property crime is more likely to depend on economic
motivations and rational opportunity cost calculations than violent crime. Thus, when
considering the relationship between crimes and income one should distinguish between
different types of crime.
5Each state sub-figure includes the 95 percent confidence interval (grey-shaded area).6Examples of this are Arkansas and West Virginia in the case of total crime in Figure 1 (top left
sub-figure and right-most sub-figure of the sixth line, respectively), and Alaska, Arkansas, Delaware,Hawaii, Kansas and others in the case of violent crime in Figure 3.
8
5 Conclusions
This paper documents for the first time the existence of a Crime Kuznets Curve at the
state level in the US since the 1970s. As real income per capita grows crime rates first
grow and then fall, effectively describing an inverted U-shaped relationship of the type
suggested by Simon Kuznets in 1955 for the relationship between income and inequality.
We however rule out the possibility that the CKC is explained by the evolution of
income inequality. Moreover our results are robust to controlling for a set of determinants
of crime identified by the previous literature, as well as for state-level and year fixed
effects. We document the existence of a CKC using both a flexible parametric specification
that includes a third-degree polynomial of income in the set of explanatory variables and
a nonparametric approach.
Consistent with the idea that income increases the opportunity cost of engaging in
appropriative illegal behavior, the CKC is present for crimes related to property but is
less robust for violent crimes. The only exception is robbery that in spite of its violent
nature is often motivated by the appropriation of someone else’s valuables. However,
a simple version of the “opportunity-cost theory” of crime is unable to account for the
patterns presented in this paper. Indeed, crime should fall monotonically with income if
the opportunity cost of crime is its main driver.
It is therefore necessary to develop and test new theories that can account for the
documented relationship. We conclude by suggesting some possible avenues for future
research, though of course many other interesting possibilities are open.
One hypothesis is that the provision of certain public goods with the potential to
reduce crime only increases significantly after communities have attained a sufficiently
high level of average income. These could be public goods affecting crime directly, like
police expenditures or investments in judicial efficiency, or indirectly, like schooling and
certain types of public infrastructure and amenities. A demand-side mechanism could
create this relationship if households exhibit the type of non-homothetic preferences often
used in problems involving subsistence levels of consumption and models of structural
change (e.g. Stone-Geary preferences). If public goods that are key drivers of crime
are outside the bundle of “subsistence” goods, then we would expect a strong demand
for them only after some minimal income has been attained. Conceivably, supply-side
mechanisms with the same spirit could also explain these patterns, if communities are
only able to provide certain public goods once a certain level of development has been
achieved.
Other hypotheses could emphasize the opposite direction of causality. For instance,
suppose that in early stages of development the types of activities that can enrich societies
are not too threatened by environments with relatively high incidence of crime. Perhaps
activities like resource extraction or industrial development intensive in physical capital
9
could survive or even thrive in spite of high rates of property and violent crime. However,
as the most productive activities in the technological frontier start demanding high levels
of human capital, it may be especially important to have an environment that attracts
highly-qualified individuals willing to live in these communities. In this hypothesis, only
when communities are able to diminish crime rates they may increase their income beyond
a certain threshold. The upward portion of the CKC could then be driven by some of
the theoretical mechanisms currently emphasized in the literature (like the fact that
wealthier communities are more attractive criminal targets) yet in the downward portion
the decrease in crime rates causes the rise in income.
As noted in the introduction, an interesting aspect of our results that may help guide
the search for the causes of the CKC is the apparent difference between property and
violent crimes. For instance, the first hypothesis would be consistent with our results
only to the extent that the public goods that are provided after certain level of income
have a stronger impact on property than on violent crimes. The second hypothesis seems,
prima facie, harder to reconcile with the presence of a CKC in property and not so much
in violent crimes. Indeed, highly-qualified individuals are likely to be just as unwilling to
live in places with high property crime rates as in places with high violent crime rates.
However, it is also clear that violent crime rates are much smaller, and therefore that
large decreases in crime rates are mainly associated with falls in property crimes. In any
case, these are all key questions that must be tackled to uncover the reasons behind the
CKC established in these paper.7
Our findings are also relevant for policy as they suggest that violent conflict cannot
be tackled solely by the trickle-down forces of economic growth and both more effective
policing as well as preventive strategies are needed. However, the scope of this paper
is limited in that it only describes a robust empirical pattern. As noted, this pattern
deserves more attention from both theorists and applied social scientists. Future efforts
in these two fronts are likely to converge in a better characterization of the micro drivers
of the macro patterns presented in this paper. Moreover, similar analyses testing the
existence of a CKC need to be done for other developed and developing countries. This
will examine the external validity of the stylized facts that we describe for the US.
We predict that the coming agenda of studying the CKC both theoretically and em-
pirically will be as active and vibrant as is the current debate on similarly policy relevant
issues like the EKC.
7We also emphasize that in some specifications the CKC is also apparent for violent crimes (namely,in our quadratic parametric exercises and in our nonparametric exercises for a number of states). Hence,while we are quite confident about the existence of a CKC, the idea that it is present mostly for thosetypes of crime more close related with economic appropriation is highly suggestive but not entirelyconclusive.
10
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Gould, E., Weinberg, B., & Mustard, D. (2002). “Crime and Local Labor market
Opportunities in the United States: 1979-1997”. The Review of Economics and
Statistics , 87(3), 411- 422.
Grossman, G., & Krueger, A. (1995). “Economic growth and the Environment”. The
Quarterly Journal of Economics , 110 (2), 353-377.
Holtz-Eakin, D., & Selden, T. (1995). “Stoking the fires? CO2 emissions and economic
growth”. Journal of Public Economics , 57 (1), 85 - 101.
Kanbur, R. (2012). “Does Kuznets Still matter?” (Working Papers No. 128794). Retrieved
from http://EconPapers.repec.org/RePEc:ags:cudawp:128794
Kuznet, S. (1955). “Economic growth and Income Inequality”. The American Economic
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Levitt, S. (2004, Winter). “Understanding Why Crime Fell in the 1990s: Four Factors
that Explain the Decline and Six that Do Not”. Journal of Economic Perspectives ,
18 (1), 163-190.
Lieb, C. (2003). The environmental kuznets curve : A survey of the empirical evidence
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Lin, M.-J. (2008). “Does Unemployment Increase Crime?: Evidence from U.S. Data
19742000”. Journal of Human Resources , 43 (2), 413-436.
Oster, A., & Agell, J. (2007). “Crime and Unemployment in Turbulent Times”. Journal
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Poudel, B., Paudel, K., & Bhattarai, K. (2009). “Searching for an Environmental Kuznets
Curve in Carbon Dioxide Pollutant in Latin American Countries”. Journal of
Agricultural and Applied Economics , 41 (01), 13-27.
Raphael, S., & Winter-Ember, R. (2001, April). “Identifying the Effect of Unemployment
on Crime”. Journal of Law and Economics , 44 (1), 259-83.
Stern, D. (2004). “The Rise and Fall of the Environmental Kuznets Curve ”. World
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12
briefing/21582041-rich-world-seeing-less-and-less-crime-even-face
-high-unemployment-and-economic
13
Table 1: Descriptive statistics
Mean Std. Dev. Min. Max.
Panel A: Dependent variablesa
Property crime rate 4,029.474 1,207.784 1,269.464 7,996.011
Violent crime rate 430.349 223.443 38.08 1,244.326
Burglary rate 1,005.14 425.61 1.087 2,906.741
Larceny rate 2,645.335 757.274 243.673 5,106.13
Car theft rate 376.229 206.021 70.138 1,571.088
Murder rate 6.32 3.685 0.157 20.349
Assault rate 262.617 143.039 25.12 785.723
Rape rate 32.357 13.72 4.16 102.184
Robbery rate 129.005 95.417 6.396 684.006
Panel B: Independent variables
Real income per capitab 28.553 8.231 12.123 58.097
Gini coefficient 0.542 0.056 0.41 0.709
Employment ratec 54.762 6.668 36.72 77.055
Population densityd 169.082 235.975 0.547 1,189.316
Share males 0-15 years old 11.529 1.51 8.403 17.236
Share males 15-19 years old 4.128 0.629 2.912 5.777
Share males 20-24 years old 4.032 0.658 2.748 7.703
Share males 25-29 years old 3.795 0.616 2.513 6.476
Share males 30-34 years old 3.672 0.573 2.387 6.515
Share males 35-39 years old 3.486 0.596 2.268 5.719
Share males 40-44 years old 3.293 0.606 2.154 5.173
Share males 45-49 years old 3.03 0.588 1.872 4.575
Share males 50-54 years old 2.75 0.536 1.701 4.293
Share males 55-59 years old 2.414 0.432 1.548 3.896
Share males 60-64 years old 2.077 0.317 0.998 3.519
Notes: The number of observations in all cases is 2,100 (50 states × 42 years). a Crime
rates computed in terms of 100,000 inhabitants. b Real income per capita measures in
thousands of dollars using 2009 as the base year. c The employment rate is computed
using the employment figures of the US Bureau of Labor Statistics as the percentage
of employed individuals over the state population. d Population density is computed as
number of inhabitants per squared mile.
14
Table 2: Aggregate crime categories
Total crime Property crime Violent crime
(1) (2) (3) (4) (5) (6)
Panel A: Without state controls
Income 385.9*** 1,028*** 351.7*** 987.5*** 33.98** 40.42
(80.45) (148.0) (74.76) (140.1) (12.83) (26.11)
Income squared -5.377*** -25.97*** -4.929*** -25.32*** -0.445*** -0.651
(1.059) (3.926) (0.978) (3.814) (0.154) (0.697)
Income cubed 0.209*** 0.207*** 0.00209
(0.0353) (0.0349) (0.00671)
Constant -1,450 -7,366*** -1,263 -7,120*** -184.5 -243.8
(1,058) (1,555) (985.1) (1,450) (173.9) (283.2)
R-squared 0.678 0.715 0.680 0.722 0.491 0.492
Panel B: With state controls
Income 371.2*** 1,104*** 341.7*** 1,056*** 29.44** 48.54*
(102.3) (151.9) (97.76) (147.0) (12.61) (24.66)
Income squared -5.090*** -28.10*** -4.671*** -27.09*** -0.416** -1.015
(1.310) (4.007) (1.214) (3.911) (0.170) (0.663)
Income cubed 0.229*** 0.223*** 0.00596
(0.0364) (0.0361) (0.00645)
Constant -9,035** -16,829*** -7,926* -15,520*** -1,130*** -1,333***
(4,101) (3,995) (4,025) (3,918) (408.4) (452.7)
R-squared 0.703 0.743 0.701 0.745 0.579 0.581
Year FE X X X X X X
State FE X X X X X X
Implied income turning pointa 36.46 19.64 36.58 19.49 35.38 23.91
Notes: The number of observations in all cases is 2,100 (50 states × 42 years). Standard errors clustered at the state level
in parentheses. Controls include state and year fixed effects as well as income inequality, population density, employment
opportunities and the state-level male population for age brackets 0-15, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49,
50-54, 55-59 and 60-64. a Implied income turning point, in thousand of real USD of 2009, computed for the regressions
with state level controls (Panel B). *** is significant at the 1% level, ** is significant at the 5% level, * is significant at
the 10% level.
15
Table 3: Is there a classical Kuznets Curve in our sample?
(1) (2) (3) (4)
Income -0.00197 -0.00329 0.000783 -0.00148
(0.00160) (0.00416) (0.00182) (0.00383)
Income squared 2.47e-05 6.69e-05 5.45e-06 7.63e-05
(2.24e-05) (0.000114) (2.27e-05) (0.000100)
Income cubed -4.28e-07 -7.06e-07
(1.08e-06) (9.48e-07)
Constant 0.484*** 0.496*** 0.308*** 0.331***
(0.0203) (0.0421) (0.0987) (0.0961)
R-squared 0.892 0.892 0.914 0.914
Year FE X X X X
State FE X X X X
Controls X X
Notes: The number of observations in all cases is 2,100 (50 states × 42 years).
Standard errors clustered at the state level in parentheses. Controls include
state and year fixed effects as well as population density, employment opportu-
nities and the state-level male population for age brackets 0-15, 15-19, 20-24,
25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59 and 60-64. *** is significant at
the 1% level, ** is significant at the 5% level, * is significant at the 10% level.
16
Table
4:Breakdown
bycrim
ety
pe
Pro
per
tycr
ime
Vio
lent
crim
e
Burg
lary
Lar
ceny
Car
thef
tM
urd
erA
ssau
ltR
ape
Rob
ber
y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Inco
me
61.9
8**
304.
3***
216.
9***
681.
4***
63.5
8***
72.2
2**
-0.1
03-0
.297
7.52
710
.50
0.23
81.
642
21.7
4***
36.9
5***
(30.
38)
(58.
93)
(61.
29)
(97.
16)
(17.
08)
(28.
77)
(0.2
00)
(0.3
14)
(8.6
94)
(19.
47)
(1.1
48)
(2.0
91)
(6.8
95)
(11.
46)
Inco
me
squar
ed-1
.016
***
-8.6
19**
*-2
.752
***
-17.
33**
*-0
.897
***
-1.1
680.
0017
20.
0078
3-0
.114
-0.2
07-0
.006
93-0
.051
0-0
.297
***
-0.7
74**
(0.3
74)
(1.6
77)
(0.7
42)
(2.6
33)
(0.2
25)
(0.8
80)
(0.0
0242
)(0
.007
89)
(0.0
966)
(0.5
20)
(0.0
122)
(0.0
508)
(0.1
07)
(0.2
90)
Inco
me
cub
ed0.
0757
***
0.14
5***
0.00
270
-6.0
8e-0
50.
0009
280.
0004
390.
0047
5*
(0.0
161)
(0.0
248)
(0.0
0926
)(7
.73e
-05)
(0.0
0477
)(0
.000
468)
(0.0
0281
)
Con
stan
t-1
,631
-4,2
07**
*-6
,589
**-1
1,52
6***
194.
010
2.2
-4.1
69-2
.100
-707
.8**
-739
.4**
7.14
6-7
.783
-414
.8**
-576
.5**
*
(1,4
32)
(1,3
46)
(2,9
85)
(3,0
41)
(748
.2)
(847
.3)
(7.1
96)
(7.5
15)
(302
.6)
(349
.2)
(36.
01)
(38.
60)
(183
.5)
(199
.5)
R-s
quar
ed0.
741
0.77
50.
668
0.71
40.
445
0.44
50.
575
0.57
50.
555
0.55
50.
594
0.59
50.
443
0.45
0
Yea
rF
EX
XX
XX
XX
XX
XX
XX
X
Sta
teF
EX
XX
XX
XX
XX
XX
XX
X
Con
trol
sX
XX
XX
XX
XX
XX
XX
X
Implied
inco
me
turn
ing
poi
nt
30.5
017
.65
39.4
119
.66
35.4
430
.92
––
––
––
36.5
023
.87
Not
es:
The
num
ber
ofob
serv
atio
ns
inal
lca
ses
is2,
100
(50
stat
es×
42ye
ars)
.Sta
ndard
erro
rscl
ust
ered
at
the
state
level
inpare
nth
eses
.C
ontr
ols
incl
ude
state
and
year
fixed
effec
tsas
wel
las
inco
me
ineq
uality
,
pop
ula
tion
den
sity
,em
plo
ym
ent
opp
ortu
nit
ies
and
the
stat
e-le
vel
mal
ep
opula
tion
for
age
bra
cket
s0-
15,
15-
19,
20-2
4,
25-
29,
30-3
4,
35-
39,
40-4
4,
45-
49,
50-5
4,
55-
59
and
60-6
4.
***
issi
gnifi
cant
at
the
1%
leve
l,
**is
sign
ifica
nt
atth
e5%
leve
l,*
issi
gnifi
cant
atth
e10
%le
vel.
17
Figure 1: Non parametric estimates for total crime20
0030
0040
0050
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.32, pwidth = 4.98
Alabama
2000
3000
4000
5000
6000
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.97, pwidth = 7.46
Alaska
40005
00060
00700
08000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.96, pwidth = 4.44
Arizona
2000
3000
4000
5000
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.69, pwidth = 4.04
Arkansas
30004
00050
00600
070008
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.6, pwidth = 6.9
California
20003
00040
00500
060007
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.76, pwidth = 5.64
Colorado
2000
3000
4000
5000
6000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.67, pwidth = 7.01
Connecticut
3000
4000
5000
6000
7000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.86, pwidth = 4.29
Delaware
40005
00060
00700
080009
000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.62, pwidth = 5.43
Florida
20003
00040
00500
060007
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.4, pwidth = 5.1
Georgia
3000
4000
5000
6000
7000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.86, pwidth = 5.79
Hawaii
20002
50030
00350
040004
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.79, pwidth = 4.19
Idaho
3000
4000
5000
6000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.12, pwidth = 4.68
Illinois
30003
50040
00450
05000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.95, pwidth = 4.42
Indiana
20002
50030
00350
040004
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.75
Iowa
30003
50040
00450
05000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.73, pwidth = 5.6
Kansas
2000
2500
3000
3500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.81, pwidth = 4.22
Kentucky
3000
4000
5000
6000
7000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.16, pwidth = 4.74
Louisiana
1000
2000
3000
4000
5000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.91, pwidth = 4.37
Maine
3000
4000
5000
6000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.28, pwidth = 6.42
Maryland
30004
00050
00600
07000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.67, pwidth = 5.51
Massachusetts
3000
4000
5000
6000
7000
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.32, pwidth = 4.97
Michigan
25003
00035
00400
04500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.09, pwidth = 6.13
Minnesota
1000
2000
3000
4000
5000
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 3.28, pwidth = 4.92
Mississippi
3500
4000
4500
5000
5500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.11
Missouri
25003
00035
00400
045005
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
Montana
20002
50030
00350
040004
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.59, pwidth = 5.38
Nebraska
40005
00060
00700
08000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.05, pwidth = 7.58
Nevada
1000
2000
3000
4000
5000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.33, pwidth = 6.49
New Hampshire
2000
3000
4000
5000
6000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.32, pwidth = 6.48
New Jersey
4000
5000
6000
7000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.81, pwidth = 5.72
New Mexico
20003
00040
00500
060007
000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.5, pwidth = 5.25
New York
2000
3000
4000
5000
6000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.04, pwidth = 4.56
North Carolina
1000
1500
2000
2500
3000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.3, pwidth = 6.44
North Dakota
30003
50040
00450
05000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.08, pwidth = 4.61
Ohio
2000
3000
4000
5000
6000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.03, pwidth = 7.55
Oklahoma20
00300
040005
00060
00700
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.92
Oregon
2000
2500
3000
3500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.52, pwidth = 5.27
Pennsylvania
2000
3000
4000
5000
6000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.47, pwidth = 5.2
Rhode Island
20003
00040
00500
06000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.62, pwidth = 3.92
South Carolina
1500
2000
2500
3000
3500
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.37, pwidth = 5.06
South Dakota
20003
00040
00500
06000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.79, pwidth = 5.69
Tennessee
30004
00050
00600
07000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.99, pwidth = 5.98
Texas
3000
4000
5000
6000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.3, pwidth = 3.46
Utah
1000
2000
3000
4000
5000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.65, pwidth = 6.97
Vermont
2500
3000
3500
4000
4500
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.87, pwidth = 7.31
Virginia
3000
4000
5000
6000
7000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.36, pwidth = 5.04
Washington
1000
1500
2000
2500
3000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.27, pwidth = 4.9
West Virginia
25003
00035
00400
04500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
Wisconsin
25003
00035
00400
04500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.12, pwidth = 6.18
Wyoming
18
Figure 2: Non parametric estimates for property crime10
0020
0030
0040
0050
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.5
Alabama
20003
00040
00500
06000
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.93, pwidth = 7.39
Alaska
30004
00050
00600
070008
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.93, pwidth = 4.39
Arizona
2000
3000
4000
5000
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.86, pwidth = 4.28
Arkansas
30004
00050
00600
07000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.13, pwidth = 6.2
California
20003
00040
00500
060007
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.65, pwidth = 5.47
Colorado
2000
3000
4000
5000
6000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.67, pwidth = 7.01
Connecticut
3000
4000
5000
6000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.95, pwidth = 4.43
Delaware
30004
00050
00600
070008
000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.6, pwidth = 5.4
Florida
20003
00040
00500
06000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
Georgia
30004
00050
00600
07000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.84, pwidth = 5.75
Hawaii
20002
50030
00350
04000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.2
Idaho
25003
00035
00400
045005
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.12, pwidth = 4.67
Illinois
2500
3000
3500
4000
4500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.03, pwidth = 4.55
Indiana
20002
50030
00350
04000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.1, pwidth = 4.65
Iowa
30003
50040
00450
05000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.35
Kansas
2000
2500
3000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.63, pwidth = 3.95
Kentucky
2000
3000
4000
5000
6000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.36, pwidth = 5.05
Louisiana
15002
00025
00300
035004
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.91, pwidth = 4.37
Maine
30003
50040
00450
050005
500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.19, pwidth = 6.29
Maryland
2000
3000
4000
5000
6000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.7, pwidth = 5.55
Massachusetts
3000
4000
5000
6000
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
Michigan
2500
3000
3500
4000
4500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.99, pwidth = 5.98
Minnesota
1000
2000
3000
4000
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 3.09, pwidth = 4.63
Mississippi
3000
3500
4000
4500
5000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.47, pwidth = 5.21
Missouri
25003
00035
00400
04500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.88, pwidth = 5.82
Montana
20002
50030
00350
04000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.59, pwidth = 5.39
Nebraska
30004
00050
00600
07000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 6.04, pwidth = 9.06
Nevada
1000
2000
3000
4000
5000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.29, pwidth = 6.44
New Hampshire
2000
3000
4000
5000
6000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.3, pwidth = 6.45
New Jersey
3000
4000
5000
6000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.92, pwidth = 5.88
New Mexico
20003
00040
00500
06000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.36
New York
20003
00040
00500
06000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.52
North Carolina
1000
1500
2000
2500
3000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.34, pwidth = 6.51
North Dakota
25003
00035
00400
04500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.05, pwidth = 4.58
Ohio
2000
3000
4000
5000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.31, pwidth = 6.47
Oklahoma20
00300
040005
00060
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.65, pwidth = 3.97
Oregon
1500
2000
2500
3000
3500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
Pennsylvania
2000
3000
4000
5000
6000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.51, pwidth = 5.26
Rhode Island
2000
3000
4000
5000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.65, pwidth = 3.98
South Carolina
1500
2000
2500
3000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.51, pwidth = 5.27
South Dakota
1000
2000
3000
4000
5000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.89, pwidth = 5.83
Tennessee
3000
4000
5000
6000
7000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.87, pwidth = 5.8
Texas
3000
4000
5000
6000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.32, pwidth = 3.47
Utah
1000
2000
3000
4000
5000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.69, pwidth = 7.03
Vermont
20002
50030
00350
04000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.11, pwidth = 7.67
Virginia
3000
4000
5000
6000
7000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.3, pwidth = 4.95
Washington
1000
1500
2000
2500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.24, pwidth = 4.86
West Virginia
25003
00035
00400
04500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.33, pwidth = 5
Wisconsin
20002
50030
00350
040004
500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.16, pwidth = 6.25
Wyoming
19
Figure 3: Non parametric estimates for violent crime20
040
060
080
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.91
Alabama
200
400
600
800
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 5.74, pwidth = 8.62
Alaska
3004
0050
0600
700
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.01, pwidth = 4.52
Arizona
2003
0040
0500
600
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.17, pwidth = 3.26
Arkansas
400
600
800
1000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.49, pwidth = 5.23
California
3003
5040
0450
5005
50
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.56, pwidth = 5.34
Colorado
100
200
300
400
500
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.99, pwidth = 7.49
Connecticut
3004
0050
0600
7008
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.56, pwidth = 3.85
Delaware
400
600
8001
0001
200
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.64, pwidth = 5.47
Florida
200
400
600
800
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
Georgia
100
150
200
250
300
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 6.16, pwidth = 9.24
Hawaii
1001
5020
0250
300
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Idaho
400
600
800
1000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.22, pwidth = 4.84
Illinois
2003
0040
0500
600
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.45, pwidth = 3.67
Indiana
010
020
030
040
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.09, pwidth = 4.63
Iowa
100
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.52, pwidth = 5.28
Kansas
2002
5030
0350
4004
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.56, pwidth = 3.85
Kentucky
400
600
800
1000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.81, pwidth = 4.21
Louisiana
5010
0150
2002
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.1, pwidth = 4.65
Maine
5006
0070
0800
900
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.76, pwidth = 8.64
Maryland
200
400
600
800
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.78, pwidth = 5.67
Massachusetts
500
600
700
800
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.08, pwidth = 4.62
Michigan
1502
0025
0300
350
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.34, pwidth = 5.01
Minnesota
100
200
300
400
500
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.2, pwidth = 3.29
Mississippi
3004
0050
0600
7008
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.77, pwidth = 4.16
Missouri
100
200
300
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.36, pwidth = 3.55
Montana
100
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.14, pwidth = 4.71
Nebraska
200
400
600
8001
000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.28, pwidth = 4.92
Nevada
5010
015
020
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.14, pwidth = 6.21
New Hampshire
3004
0050
0600
700
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 5.24, pwidth = 7.86
New Jersey
200
400
600
8001
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.52
New Mexico
2004
0060
08001
00012
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
New York
300
400
500
600
700
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.11, pwidth = 4.67
North Carolina
010
020
030
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.37, pwidth = 6.55
North Dakota
2503
0035
0400
4505
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.2, pwidth = 4.8
Ohio
2003
0040
0500
600
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.84, pwidth = 4.26
Oklahoma20
030
040
050
060
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.39, pwidth = 3.58
Oregon
2002
5030
0350
4004
50
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.62, pwidth = 5.44
Pennsylvania
2002
5030
0350
400
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.17
Rhode Island
2004
0060
0800
1000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.6, pwidth = 3.9
South Carolina
010
020
030
040
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.51
South Dakota
200
400
600
800
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.34, pwidth = 5
Tennessee
3004
0050
0600
7008
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.15
Texas
1001
5020
0250
3003
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.4, pwidth = 3.6
Utah
5010
015
020
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.15, pwidth = 7.73
Vermont
200
250
300
350
400
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.67, pwidth = 5.51
Virginia
200
300
400
500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.79, pwidth = 5.68
Washington
1001
5020
0250
3003
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.63, pwidth = 3.95
West Virginia
5010
0150
2002
5030
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.58, pwidth = 5.37
Wisconsin
010
020
030
040
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.24, pwidth = 6.36
Wyoming
20
Figure 4: Inequality and Income.4
5.5
.55
.6.6
5
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.76, pwidth = 4.14
Alabama
.3.4
.5.6
.7
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.83, pwidth = 5.74
Alaska
.4.4
5.5
.55
.6.6
5
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.2, pwidth = 3.3
Arizona
.45
.5.5
5.6
.65
.7
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.73, pwidth = 4.09
Arkansas
.45
.5.5
5.6
.65
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.81, pwidth = 4.21
California
.45
.5.5
5.6
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.1, pwidth = 4.65
Colorado
.45
.5.5
5.6
.65
.7
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.57, pwidth = 6.85
Connecticut
.45
.5.5
5.6
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.37, pwidth = 5.06
Delaware
.45
.5.5
5.6
.65
.7
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.56, pwidth = 3.84
Florida
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.87, pwidth = 5.8
Georgia
.45
.5.5
5.6
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.56, pwidth = 5.35
Hawaii
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.87, pwidth = 4.3
Idaho
.45
.5.5
5.6
.65
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.19
Illinois
.4.4
5.5
.55
.6.6
5
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.99, pwidth = 4.49
Indiana
.4.4
5.5
.55
.6
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.72, pwidth = 5.57
Iowa
.45
.5.5
5.6
.65
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.01, pwidth = 4.52
Kansas
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.39, pwidth = 3.58
Kentucky
.45
.5.5
5.6
.65
.7
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.96, pwidth = 5.94
Louisiana
.4.4
5.5
.55
.6
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.88, pwidth = 4.32
Maine
.4.4
5.5
.55
.620 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.85, pwidth = 5.78
Maryland
.45
.5.5
5.6
.65
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.61, pwidth = 6.91
Massachusetts
.4.4
5.5
.55
.6
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.83, pwidth = 5.74
Michigan
.45
.5.5
5.6
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.56, pwidth = 5.34
Minnesota
.45
.5.5
5.6
.65
.7
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.88
Mississippi
.45
.5.5
5.6
.65
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.45, pwidth = 3.68
Missouri
.4.5
.6.7
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.85, pwidth = 4.28
Montana
.45
.5.5
5.6
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.58, pwidth = 5.37
Nebraska
.4.4
5.5
.55
.6.6
5
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.52, pwidth = 5.28
Nevada
.4.4
5.5
.55
.6
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.85, pwidth = 5.78
New Hampshire
.45
.5.5
5.6
.65
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 3.84, pwidth = 5.76
New Jersey
.45
.5.5
5.6
.65
.7
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.88, pwidth = 4.33
New Mexico
.45
.5.5
5.6
.65
.7
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.65
New York
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
North Carolina
.4.6
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.1, pwidth = 6.15
North Dakota
.4.4
5.5
.55
.6
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.64, pwidth = 3.96
Ohio
.4.5
.6.7
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.72, pwidth = 5.58
Oklahoma.4
5.5
.55
.6.6
5
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.64, pwidth = 3.95
Oregon
.4.4
5.5
.55
.6
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.71, pwidth = 4.06
Pennsylvania
.45
.5.5
5.6
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.82, pwidth = 4.23
Rhode Island
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.88, pwidth = 4.32
South Carolina
.4.4
5.5
.55
.6.6
5
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.97, pwidth = 5.95
South Dakota
.45
.5.5
5.6
.65
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.92, pwidth = 4.37
Tennessee
.45
.5.5
5.6
.65
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.91, pwidth = 4.37
Texas
.45
.5.5
5.6
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.25, pwidth = 3.38
Utah
.45
.5.5
5.6
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.11, pwidth = 4.66
Vermont
.45
.5.5
5.6
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.69, pwidth = 5.54
Virginia
.45
.5.5
5.6
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.74, pwidth = 5.62
Washington
.4.4
5.5
.55
.6.6
5
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.4, pwidth = 3.59
West Virginia
.4.4
5.5
.55
.6
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.2
Wisconsin
.4.4
5.5
.55
.6.6
5
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.76, pwidth = 7.13
Wyoming
21
Figure 5: Non parametric estimates for burglary40
0600
80010
00120
01400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 4.54, pwidth = 6.82
Alabama
050
010
0015
00
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.72, pwidth = 7.09
Alaska
5001
0001
5002
0002
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
Arizona
600
800
1000
1200
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.51, pwidth = 3.77
Arkansas
5001
0001
5002
0002
500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.35, pwidth = 5.03
California
500
1000
1500
2000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
Colorado
500
1000
1500
2000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.75, pwidth = 7.12
Connecticut
6008
00100
012001
40016
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.07, pwidth = 4.61
Delaware
5001
0001
5002
0002
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.29
Florida
6008
00100
012001
40016
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.49
Georgia
500
1000
1500
2000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.6, pwidth = 5.4
Hawaii
400
600
8001
0001
200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.54, pwidth = 3.81
Idaho
6008
0010
00120
01400
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.39, pwidth = 5.09
Illinois
600
8001
0001
2001
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
Indiana
400
600
800
1000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.91, pwidth = 4.36
Iowa
600
8001
0001
2001
400
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.17, pwidth = 7.75
Kansas
6007
0080
0900
1000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.75, pwidth = 4.12
Kentucky
8001
0001
2001
4001
600
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.71, pwidth = 5.57
Louisiana
4006
0080
010001
20014
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.86
Maine
6008
00100
012001
40016
0020 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.78, pwidth = 7.17
Maryland
500
1000
1500
2000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.63, pwidth = 5.45
Massachusetts
500
1000
1500
2000
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.65
Michigan
400
600
8001
0001
200
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.32, pwidth = 6.48
Minnesota
050
010
0015
00
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 4.03, pwidth = 6.05
Mississippi
6008
00100
012001
40016
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.36
Missouri
200
400
600
8001
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.32, pwidth = 4.98
Montana
4005
0060
0700
800
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.67, pwidth = 5.51
Nebraska
5001
00015
00200
02500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.62, pwidth = 6.93
Nevada
2004
0060
08001
00012
00
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.76, pwidth = 7.13
New Hampshire
500
1000
1500
2000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.14, pwidth = 6.22
New Jersey
500
1000
1500
2000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.33, pwidth = 4.99
New Mexico
050
0100
0150
0200
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.51, pwidth = 5.26
New York
6008
00100
012001
40016
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.4, pwidth = 5.1
North Carolina
200
300
400
500
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.97, pwidth = 7.46
North Dakota
600
8001
0001
2001
400
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.04, pwidth = 4.56
Ohio
6008
00100
012001
40016
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.42, pwidth = 5.13
Oklahoma50
010
0015
0020
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.73, pwidth = 4.09
Oregon
400
600
800
1000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.7, pwidth = 5.56
Pennsylvania
500
1000
1500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.47, pwidth = 5.2
Rhode Island
8001
00012
00140
01600
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.11, pwidth = 4.67
South Carolina
3004
0050
0600
700
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.14
South Dakota
6008
0010
00120
01400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.68, pwidth = 5.51
Tennessee
500
1000
1500
2000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.27, pwidth = 4.91
Texas
4006
0080
0100
01200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.34, pwidth = 3.52
Utah
4006
0080
010001
20014
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.94, pwidth = 5.91
Vermont
4006
0080
0100
01200
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.6, pwidth = 8.4
Virginia
80010
00120
014001
60018
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.21, pwidth = 4.82
Washington
300
400
500
600
700
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.51
West Virginia
400
600
800
1000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.76
Wisconsin
3004
0050
0600
7008
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.58, pwidth = 6.87
Wyoming
22
Figure 6: Non parametric estimates for larceny10
00150
020002
50030
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.77, pwidth = 4.15
Alabama
15002
00025
00300
03500
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 5.51, pwidth = 8.26
Alaska
2000
3000
4000
5000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.95, pwidth = 4.42
Arizona
1000
1500
2000
2500
3000
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.14
Arkansas
15002
00025
00300
035004
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.42, pwidth = 5.13
California
1000
2000
3000
4000
5000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.58, pwidth = 5.36
Colorado
15002
00025
00300
03500
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 5, pwidth = 7.5
Connecticut
15002
00025
00300
035004
000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.79, pwidth = 4.18
Delaware
2000
3000
4000
5000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.69, pwidth = 5.53
Florida
1000
2000
3000
4000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.5
Georgia
20002
50030
00350
040004
500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.39, pwidth = 6.58
Hawaii
1000
1500
2000
2500
3000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.93, pwidth = 4.39
Idaho
1500
2000
2500
3000
3500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.07, pwidth = 4.61
Illinois
1500
2000
2500
3000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.99, pwidth = 4.48
Indiana
1500
2000
2500
3000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.75
Iowa
1000
2000
3000
4000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.45, pwidth = 6.67
Kansas
10001
20014
00160
018002
000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4.01
Kentucky
15002
00025
00300
035004
000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.34, pwidth = 5
Louisiana
1000
1500
2000
2500
3000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.42, pwidth = 5.13
Maine
2000
2500
3000
3500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.28, pwidth = 6.42
Maryland
1000
1500
2000
2500
3000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.66
Massachusetts
2000
2500
3000
3500
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.13, pwidth = 4.69
Michigan
1500
2000
2500
3000
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.7, pwidth = 5.55
Minnesota
5001
00015
00200
02500
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.72, pwidth = 4.08
Mississippi
1500
2000
2500
3000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.33, pwidth = 4.99
Missouri
15002
00025
00300
03500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.74, pwidth = 5.61
Montana
10001
50020
00250
03000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.29
Nebraska
1000
2000
3000
4000
5000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 6.81, pwidth = 10.22
Nevada
50010
00150
020002
50030
00
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.21, pwidth = 6.32
New Hampshire
10001
50020
00250
03000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.41, pwidth = 6.61
New Jersey
2000
2500
3000
3500
4000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.07, pwidth = 4.6
New Mexico
15002
00025
00300
03500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.89, pwidth = 5.84
New York
10001
50020
00250
030003
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.74, pwidth = 4.12
North Carolina
5001
0001
5002
0002
500
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.32, pwidth = 6.49
North Dakota
1500
2000
2500
3000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.99, pwidth = 4.48
Ohio
10001
50020
00250
03000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.76, pwidth = 7.14
Oklahoma10
0020
0030
0040
0050
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.51, pwidth = 3.77
Oregon
500
1000
1500
2000
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.12, pwidth = 4.68
Pennsylvania
1500
2000
2500
3000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.71, pwidth = 5.57
Rhode Island
10001
50020
00250
030003
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.44, pwidth = 3.66
South Carolina
1000
1500
2000
2500
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.64, pwidth = 5.47
South Dakota
50010
00150
020002
50030
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.68, pwidth = 5.52
Tennessee
15002
00025
00300
035004
000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.37, pwidth = 6.55
Texas
20002
50030
00350
040004
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.47, pwidth = 3.7
Utah
50010
00150
020002
50030
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.46, pwidth = 6.68
Vermont
1500
2000
2500
3000
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.18, pwidth = 7.78
Virginia
20002
50030
00350
040004
500
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.31, pwidth = 4.97
Washington
500
1000
1500
2000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.15
West Virginia
1500
2000
2500
3000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Wisconsin
1500
2000
2500
3000
3500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.02, pwidth = 6.03
Wyoming
23
Figure 7: Non parametric estimates for car theft15
0200
2503
0035
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.71, pwidth = 5.56
Alabama
020
040
060
080
0
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.49, pwidth = 6.74
Alaska
4006
0080
0100
01200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.82, pwidth = 4.23
Arizona
1001
5020
0250
3003
50
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.69, pwidth = 4.04
Arkansas
400
600
800
1000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
California
020
040
060
080
0
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.81, pwidth = 5.72
Colorado
2003
0040
0500
6007
00
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.32, pwidth = 6.48
Connecticut
2003
0040
0500
600
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4.01
Delaware
200
400
600
8001
000
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.28, pwidth = 4.92
Florida
2003
0040
0500
6007
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.07
Georgia
2003
0040
0500
6007
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.3
Hawaii
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.2, pwidth = 4.81
Idaho
2003
0040
0500
6007
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.19, pwidth = 4.78
Illinois
200
300
400
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.15, pwidth = 4.73
Indiana
100
150
200
250
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.71, pwidth = 5.56
Iowa
200
250
300
350
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.74, pwidth = 5.61
Kansas
100
200
300
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.59, pwidth = 3.89
Kentucky
2003
0040
0500
6007
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.24, pwidth = 4.87
Louisiana
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Maine
3004
0050
0600
700
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.59, pwidth = 5.38
Maryland
050
010
0015
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.95, pwidth = 5.93
Massachusetts
3004
0050
0600
7008
00
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.51, pwidth = 5.26
Michigan
100
200
300
400
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.34, pwidth = 5
Minnesota
010
020
030
040
0
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 3.19, pwidth = 4.79
Mississippi
200
300
400
500
600
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.29
Missouri
150
200
250
300
350
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.08, pwidth = 4.62
Montana
1502
0025
0300
3504
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.5, pwidth = 5.25
Nebraska
400
600
8001
0001
200
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.65, pwidth = 6.98
Nevada
5010
0150
2002
5030
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.36
New Hampshire
2004
0060
0800
1000
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 5.25, pwidth = 7.88
New Jersey
200
300
400
500
600
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.54, pwidth = 5.32
New Mexico
020
0400
6008
00100
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.38, pwidth = 5.06
New York
1502
0025
0300
3504
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.49, pwidth = 5.24
North Carolina
5010
015
020
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.04, pwidth = 6.06
North Dakota
2003
0040
0500
600
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.05, pwidth = 6.08
Ohio
200
300
400
500
600
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.59, pwidth = 5.39
Oklahoma0
200
400
600
800
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.03, pwidth = 4.55
Oregon
100
200
300
400
500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.11
Pennsylvania
200
400
600
8001
000
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.11
Rhode Island
2002
5030
0350
4004
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.62, pwidth = 3.94
South Carolina
5010
015
020
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.52, pwidth = 5.29
South Dakota
2003
0040
0500
6007
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 4.13, pwidth = 6.2
Tennessee
200
400
600
800
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.07, pwidth = 6.11
Texas
2002
5030
0350
4004
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.37, pwidth = 3.56
Utah
5010
0150
2002
5030
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.12, pwidth = 6.19
Vermont
1001
5020
0250
3003
50
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.84, pwidth = 5.77
Virginia
2003
0040
0500
6007
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.51, pwidth = 6.77
Washington
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.6, pwidth = 3.9
West Virginia
1502
0025
0300
3504
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.72, pwidth = 4.09
Wisconsin
5010
0150
2002
5030
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.18, pwidth = 7.77
Wyoming
24
Figure 8: Non parametric estimates for murder6
810
1214
16
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.92
Alabama
05
1015
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.68, pwidth = 7.02
Alaska
67
89
1011
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.86
Arizona
46
810
12
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.38, pwidth = 3.57
Arkansas
46
810
1214
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.87, pwidth = 5.8
California
24
68
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 5, pwidth = 7.5
Colorado
23
45
6
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.42, pwidth = 6.63
Connecticut
24
68
10
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.9, pwidth = 4.35
Delaware
510
15
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.43, pwidth = 8.15
Florida
510
1520
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.03, pwidth = 4.54
Georgia
02
46
8
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.98, pwidth = 5.97
Hawaii
12
34
56
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.18, pwidth = 4.77
Idaho
46
810
12
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.18, pwidth = 4.77
Illinois
34
56
78
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.94, pwidth = 4.41
Indiana
11.
52
2.5
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.46, pwidth = 6.68
Iowa
23
45
67
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.91, pwidth = 5.87
Kansas
46
810
12
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.75, pwidth = 5.62
Kentucky
510
1520
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.96, pwidth = 4.44
Louisiana
11.
52
2.5
33.
5
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.36
Maine
68
1012
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
Maryland
12
34
5
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.66, pwidth = 5.49
Massachusetts
68
1012
14
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.91, pwidth = 4.37
Michigan
12
34
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.23, pwidth = 4.85
Minnesota
68
1012
1416
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.57, pwidth = 3.85
Mississippi
67
89
1011
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.51, pwidth = 5.26
Missouri
23
45
6
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.84, pwidth = 5.77
Montana
12
34
5
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.66
Nebraska
68
1012
1416
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.47, pwidth = 6.71
Nevada
11.
52
2.5
33.
5
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.67, pwidth = 5.5
New Hampshire
34
56
7
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.5, pwidth = 6.75
New Jersey
46
810
12
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.11
New Mexico
46
810
1214
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.05, pwidth = 4.57
New York
68
1012
14
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.2
North Carolina
01
23
4
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.38, pwidth = 6.57
North Dakota
45
67
89
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.72, pwidth = 4.09
Ohio
46
810
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.66
Oklahoma2
34
56
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.31, pwidth = 4.96
Oregon
55.
56
6.5
7
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.2
Pennsylvania
12
34
5
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.24, pwidth = 6.35
Rhode Island
510
1520
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.41, pwidth = 3.61
South Carolina
12
34
5
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.73, pwidth = 7.09
South Dakota
68
1012
14
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.66, pwidth = 4
Tennessee
510
15
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.63, pwidth = 5.44
Texas
12
34
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.87, pwidth = 4.3
Utah
01
23
4
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.31, pwidth = 6.46
Vermont
46
810
12
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.08, pwidth = 4.63
Virginia
23
45
6
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.3, pwidth = 6.45
Washington
34
56
78
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.76, pwidth = 4.13
West Virginia
23
45
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.69, pwidth = 4.03
Wisconsin
02
46
8
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 10.04, pwidth = 15.06
Wyoming
25
Figure 9: Non parametric estimates for assault20
030
040
050
060
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.65, pwidth = 3.98
Alabama
1002
0030
0400
5006
00
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 5.53, pwidth = 8.29
Alaska
100
200
300
400
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4
Arizona
100
200
300
400
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.16, pwidth = 3.24
Arkansas
2003
0040
0500
600
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.75
California
1502
0025
0300
3504
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.19, pwidth = 4.78
Colorado
5010
0150
2002
50
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.74, pwidth = 7.11
Connecticut
100
200
300
400
500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.57, pwidth = 3.86
Delaware
200
400
600
800
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.39, pwidth = 5.08
Florida
100
200
300
400
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.86, pwidth = 4.28
Georgia
5010
015
020
0
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.13, pwidth = 4.7
Hawaii
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.95, pwidth = 4.43
Idaho
1002
0030
0400
5006
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.77, pwidth = 4.15
Illinois
100
200
300
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.44, pwidth = 3.66
Indiana
5010
0150
2002
50
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.18, pwidth = 4.77
Iowa
1001
5020
0250
3003
50
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.35
Kansas
1001
5020
0250
3003
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.87
Kentucky
2003
0040
0500
6007
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.88, pwidth = 4.32
Louisiana
050
100
150
200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.11
Maine
200
300
400
500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.16, pwidth = 6.24
Maryland
1002
0030
0400
5006
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.05, pwidth = 6.07
Massachusetts
100
200
300
400
500
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
Michigan
5010
015
020
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.14, pwidth = 4.72
Minnesota
100
150
200
250
300
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.26, pwidth = 3.39
Mississippi
100
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.83, pwidth = 4.24
Missouri
5010
0150
2002
5030
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.28, pwidth = 3.41
Montana
1001
5020
0250
3003
50
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.31, pwidth = 4.97
Nebraska
1002
0030
0400
500
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.04, pwidth = 4.55
Nevada
2040
6080
1001
20
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.42, pwidth = 6.63
New Hampshire
1001
5020
0250
300
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.29, pwidth = 6.43
New Jersey
020
040
060
080
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.18, pwidth = 4.78
New Mexico
200
300
400
500
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.18, pwidth = 4.78
New York
2503
0035
0400
450
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.15, pwidth = 4.72
North Carolina
050
1001
5020
0250
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.69, pwidth = 7.03
North Dakota
1001
5020
0250
300
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.92
Ohio
100
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.83, pwidth = 4.24
Oklahoma10
0150
2002
5030
0350
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.52, pwidth = 3.78
Oregon
5010
015
020
025
0
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.31, pwidth = 6.47
Pennsylvania
100
150
200
250
300
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.07, pwidth = 4.61
Rhode Island
200
400
600
800
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.59, pwidth = 3.89
South Carolina
050
1001
5020
0250
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.04, pwidth = 4.55
South Dakota
1002
0030
0400
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.97, pwidth = 4.45
Tennessee
100
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.15, pwidth = 4.72
Texas
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.33, pwidth = 3.49
Utah
2040
6080
1001
20
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 6.56, pwidth = 9.84
Vermont
1001
2014
0160
1802
00
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.54, pwidth = 5.31
Virginia
1001
5020
0250
300
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.73, pwidth = 5.6
Washington
5010
0150
2002
5030
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.53, pwidth = 3.79
West Virginia
050
100
150
200
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.24, pwidth = 4.86
Wisconsin
5010
0150
2002
50
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.18, pwidth = 6.28
Wyoming
26
Figure 10: Non parametric estimates for rape15
2025
3035
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 4.11, pwidth = 6.17
Alabama
2040
6080
100
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 8.5, pwidth = 12.74
Alaska
2025
3035
4045
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4
Arizona
1020
3040
50
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.42, pwidth = 3.63
Arkansas
2030
4050
60
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.81, pwidth = 4.21
California
(file graphs/lpoly_rape_r.gph saved)> F format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_murder_r.pdf written in PD(file graphs/lpoly_murder_r.gph saved)> PDF format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_property_r.pdf written in (file graphs/lpoly_property_r.gph saved)> DF format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_violent_r.pdf written in P(file graphs/lpoly_violent_r.gph saved) 5. } 4. graph export ${graphs}`y'.pdf, replace. graph save ${graphs}`y', replace 3. > */ ${graphs}`y'_46.gph ${graphs}`y'_47.gph ${graphs}`y'_48.gph ${graphs}`y'_49.gph ${graphs}`y'_50.gph> */ ${graphs}`y'_41.gph ${graphs}`y'_42.gph ${graphs}`y'_43.gph ${graphs}`y'_44.gph ${graphs}`y'_45.gph /*> */ ${graphs}`y'_36.gph ${graphs}`y'_37.gph ${graphs}`y'_38.gph ${graphs}`y'_39.gph ${graphs}`y'_40.gph /*> */ ${graphs}`y'_31.gph ${graphs}`y'_32.gph ${graphs}`y'_33.gph ${graphs}`y'_34.gph ${graphs}`y'_35.gph /*> */ ${graphs}`y'_26.gph ${graphs}`y'_27.gph ${graphs}`y'_28.gph ${graphs}`y'_29.gph ${graphs}`y'_30.gph /*> */ ${graphs}`y'_21.gph ${graphs}`y'_22.gph ${graphs}`y'_23.gph ${graphs}`y'_24.gph ${graphs}`y'_25.gph /*> */ ${graphs}`y'_16.gph ${graphs}`y'_17.gph ${graphs}`y'_18.gph ${graphs}`y'_19.gph ${graphs}`y'_20.gph /*> */ ${graphs}`y'_11.gph ${graphs}`y'_12.gph ${graphs}`y'_13.gph ${graphs}`y'_14.gph ${graphs}`y'_15.gph /*> */ ${graphs}`y'_6.gph ${graphs}`y'_7.gph ${graphs}`y'_8.gph ${graphs}`y'_9.gph ${graphs}`y'_10.gph /*> /* 2. gr combine ${graphs}`y'_1.gph ${graphs}`y'_2.gph ${graphs}`y'_3.gph ${graphs}`y'_4.gph ${graphs}`y'_5.gph> oly_burglary_r lpoly_larceny_r lpoly_car_theft_r lpoly_index_r {. foreach y in lpoly_violent_r lpoly_property_r lpoly_murder_r lpoly_rape_r lpoly_robbery_r lpoly_assault_r lp. .
(file graphs/lpoly_index_r_50.gph saved)(file graphs/lpoly_index_r_49.gph saved)(file graphs/lpoly_index_r_48.gph saved)(file graphs/lpoly_index_r_47.gph saved)
3035
4045
50
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.86, pwidth = 5.79
Colorado
510
1520
2530
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 6.48, pwidth = 9.72
Connecticut
2040
6080
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.93, pwidth = 5.89
Delaware
1020
3040
5060
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.78, pwidth = 4.18
Florida
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 4.06, pwidth = 6.08
Georgia
1020
3040
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.99, pwidth = 7.49
Hawaii
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.92
Idaho
1520
2530
3540
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Illinois
1520
2530
3540
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.95, pwidth = 4.43
Indiana
510
1520
2530
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Iowa
1020
3040
50
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.55, pwidth = 5.33
Kansas
1520
2530
35
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.75, pwidth = 4.12
Kentucky
2025
3035
4045
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.46, pwidth = 5.19
Louisiana
010
2030
40
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.11, pwidth = 4.66
Maine
2025
3035
4045
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.27, pwidth = 6.41
Maryland
1015
2025
3035
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.76, pwidth = 5.64
Massachusetts
2040
6080
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.93, pwidth = 4.4
Michigan
1020
3040
5060
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.06, pwidth = 6.09
Minnesota
1020
3040
50
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.46, pwidth = 3.69
Mississippi
2025
3035
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3, pwidth = 4.5
Missouri
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 4.06, pwidth = 6.09
Montana
010
2030
40
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.15, pwidth = 4.72
Nebraska
020
4060
80
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.72, pwidth = 7.09
Nevada
010
2030
40
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.65, pwidth = 6.98
New Hampshire
1015
2025
3035
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.54, pwidth = 6.81
New Jersey
2030
4050
60
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.52, pwidth = 5.28
New Mexico
1015
2025
3035
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.15
New York
1015
2025
3035
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.15
North Carolina
010
2030
4050
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.66
North Dakota
1020
3040
50
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.48, pwidth = 3.72
Ohio
1020
3040
50
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.16, pwidth = 4.74
Oklahoma10
2030
4050
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.89, pwidth = 5.83
Oregon
1015
2025
30
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.67, pwidth = 5.5
Pennsylvania
010
2030
40
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.59
Rhode Island
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.84, pwidth = 5.76
South Carolina
020
4060
80
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.74, pwidth = 5.61
South Dakota
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.93, pwidth = 4.4
Tennessee
2030
4050
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.77, pwidth = 5.65
Texas
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.48, pwidth = 3.71
Utah
010
2030
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.95, pwidth = 7.42
Vermont
1520
2530
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.32, pwidth = 6.48
Virginia
1020
3040
5060
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.47, pwidth = 6.71
Washington
05
1015
2025
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.59, pwidth = 3.89
West Virginia
510
1520
25
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.64, pwidth = 3.96
Wisconsin
1020
3040
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 5.04, pwidth = 7.57
Wyoming
27
Figure 11: Non parametric estimates for robbery0
5010
015
020
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.06, pwidth = 4.6
Alabama
4060
8010
0120
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 5.34, pwidth = 8.01
Alaska
5010
015
020
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.88
Arizona
4060
8010
0120
140
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.36, pwidth = 3.54
Arkansas
1502
0025
0300
3504
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.14, pwidth = 6.22
California
(file graphs/lpoly_robbery_r.gph saved)> format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_rape_r.pdf written in PDF (file graphs/lpoly_rape_r.gph saved)> F format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_murder_r.pdf written in PD(file graphs/lpoly_murder_r.gph saved)> PDF format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_property_r.pdf written in (file graphs/lpoly_property_r.gph saved)> DF format)(file /Users/paolinhoz/Dropbox/research/progetti_jfv/kuznets/estimates/graphs/lpoly_violent_r.pdf written in P(file graphs/lpoly_violent_r.gph saved) 5. } 4. graph export ${graphs}`y'.pdf, replace. graph save ${graphs}`y', replace 3. > */ ${graphs}`y'_46.gph ${graphs}`y'_47.gph ${graphs}`y'_48.gph ${graphs}`y'_49.gph ${graphs}`y'_50.gph> */ ${graphs}`y'_41.gph ${graphs}`y'_42.gph ${graphs}`y'_43.gph ${graphs}`y'_44.gph ${graphs}`y'_45.gph /*> */ ${graphs}`y'_36.gph ${graphs}`y'_37.gph ${graphs}`y'_38.gph ${graphs}`y'_39.gph ${graphs}`y'_40.gph /*> */ ${graphs}`y'_31.gph ${graphs}`y'_32.gph ${graphs}`y'_33.gph ${graphs}`y'_34.gph ${graphs}`y'_35.gph /*> */ ${graphs}`y'_26.gph ${graphs}`y'_27.gph ${graphs}`y'_28.gph ${graphs}`y'_29.gph ${graphs}`y'_30.gph /*> */ ${graphs}`y'_21.gph ${graphs}`y'_22.gph ${graphs}`y'_23.gph ${graphs}`y'_24.gph ${graphs}`y'_25.gph /*> */ ${graphs}`y'_16.gph ${graphs}`y'_17.gph ${graphs}`y'_18.gph ${graphs}`y'_19.gph ${graphs}`y'_20.gph /*> */ ${graphs}`y'_11.gph ${graphs}`y'_12.gph ${graphs}`y'_13.gph ${graphs}`y'_14.gph ${graphs}`y'_15.gph /*> */ ${graphs}`y'_6.gph ${graphs}`y'_7.gph ${graphs}`y'_8.gph ${graphs}`y'_9.gph ${graphs}`y'_10.gph /*> /* 2. gr combine ${graphs}`y'_1.gph ${graphs}`y'_2.gph ${graphs}`y'_3.gph ${graphs}`y'_4.gph ${graphs}`y'_5.gph> oly_burglary_r lpoly_larceny_r lpoly_car_theft_r lpoly_index_r {. foreach y in lpoly_violent_r lpoly_property_r lpoly_murder_r lpoly_rape_r lpoly_robbery_r lpoly_assault_r lp. .
(file graphs/lpoly_index_r_50.gph saved)
5010
015
020
0
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.36, pwidth = 6.54
Colorado
5010
015
020
025
0
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 5.76, pwidth = 8.64
Connecticut
100
150
200
250
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.54
Delaware
100
200
300
400
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.86, pwidth = 5.78
Florida
5010
0150
2002
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.09, pwidth = 4.64
Georgia
050
100
150
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
Hawaii
1020
3040
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.78, pwidth = 4.18
Idaho
1502
0025
0300
3504
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.43, pwidth = 5.14
Illinois
8010
012
014
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.61, pwidth = 3.91
Indiana
2030
4050
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.23, pwidth = 4.85
Iowa
4060
8010
012
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.48, pwidth = 5.22
Kansas
6070
8090
100
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.97, pwidth = 4.46
Kentucky
100
150
200
250
300
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.87, pwidth = 4.3
Louisiana
010
2030
40
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.63, pwidth = 3.94
Maine
1502
0025
0300
3504
0020 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.34, pwidth = 6.51
Maryland
100
150
200
250
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.8, pwidth = 5.71
Massachusetts
100
200
300
400
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.3
Michigan
6080
100
120
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.7, pwidth = 5.56
Minnesota
050
100
150
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.71, pwidth = 4.07
Mississippi
100
150
200
250
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.74, pwidth = 4.12
Missouri
1020
3040
50
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.07, pwidth = 4.6
Montana
2040
6080
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.45, pwidth = 5.18
Nebraska
010
020
030
040
0
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.09, pwidth = 6.14
Nevada
010
2030
40
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.24, pwidth = 6.36
New Hampshire
100
150
200
250
300
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 6.31, pwidth = 9.46
New Jersey
6080
1001
2014
0160
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.8, pwidth = 4.21
New Mexico
1002
0030
0400
5006
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.55, pwidth = 5.32
New York
050
100
150
200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.12, pwidth = 4.68
North Carolina
05
1015
20
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.84, pwidth = 7.26
North Dakota
1201
4016
0180
2002
20
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.29, pwidth = 4.94
Ohio
4060
8010
0120
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.41, pwidth = 5.12
Oklahoma50
100
150
200
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.78, pwidth = 4.17
Oregon
1001
2014
0160
1802
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.57, pwidth = 5.35
Pennsylvania
6080
100
120
140
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.21, pwidth = 6.32
Rhode Island
050
100
150
200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4.01
South Carolina
1015
2025
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.4, pwidth = 5.11
South Dakota
5010
015
020
025
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Tennessee
100
150
200
250
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.9, pwidth = 5.85
Texas
4050
6070
80
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.4, pwidth = 3.59
Utah
010
2030
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.27, pwidth = 6.4
Vermont
6080
100
120
140
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.04, pwidth = 6.06
Virginia
8010
012
014
0
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 4.2, pwidth = 6.3
Washington
2030
4050
60
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.99, pwidth = 5.98
West Virginia
2040
6080
1001
20
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.72, pwidth = 4.09
Wisconsin
010
2030
40
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 6.22, pwidth = 9.33
Wyoming
28
Figure 12: Non parametric estimates for robbery20
0300
4005
0060
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.65, pwidth = 3.98
Alabama
200
400
600
800
25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 6.1, pwidth = 9.16
Alaska
200
300
400
500
600
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.67, pwidth = 4.01
Arizona
100
200
300
400
500
10 15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.16, pwidth = 3.23
Arkansas
2003
0040
0500
6007
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.28, pwidth = 4.92
California
2002
5030
0350
4004
50
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.31, pwidth = 4.96
Colorado
1001
5020
0250
300
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.74, pwidth = 7.11
Connecticut
1002
0030
0400
5006
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.6, pwidth = 3.9
Delaware
200
400
600
800
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.34, pwidth = 5.01
Florida
200
300
400
500
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.94, pwidth = 4.4
Georgia
5010
015
020
0
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.76
Hawaii
5010
0150
2002
5030
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.17, pwidth = 4.76
Idaho
2003
0040
0500
6007
00
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 2.76, pwidth = 4.13
Illinois
100
200
300
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.5, pwidth = 3.75
Indiana
5010
0150
2002
5030
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.15, pwidth = 4.72
Iowa
100
200
300
400
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.55, pwidth = 5.33
Kansas
100
200
300
400
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.57, pwidth = 3.85
Kentucky
2003
0040
0500
6007
00
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.88, pwidth = 4.32
Louisiana
050
100
150
200
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.28, pwidth = 4.92
Maine
200
300
400
500
600
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.42, pwidth = 6.63
Maryland
1002
0030
0400
5006
00
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.02, pwidth = 6.03
Massachusetts
2003
0040
0500
600
20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.99, pwidth = 4.49
Michigan
5010
015
020
025
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.24, pwidth = 4.85
Minnesota
1001
5020
0250
3003
50
10 15 20 25 30kernel = epanechnikov, degree = 3, bandwidth = 2.18, pwidth = 3.28
Mississippi
200
300
400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.83, pwidth = 4.25
Missouri
010
020
030
040
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.3, pwidth = 3.44
Montana
100
200
300
400
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.23, pwidth = 4.85
Nebraska
1002
0030
0400
5006
00
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.08, pwidth = 4.62
Nevada
4060
8010
0120
140
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.27, pwidth = 6.4
New Hampshire
1001
5020
0250
3003
50
20 30 40 50 60kernel = epanechnikov, degree = 3, bandwidth = 4.43, pwidth = 6.65
New Jersey
200
400
600
800
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.16, pwidth = 4.74
New Mexico
2003
0040
0500
600
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.44, pwidth = 5.16
New York
2503
0035
0400
4505
00
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.16, pwidth = 4.74
North Carolina
010
020
030
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.37, pwidth = 6.55
North Dakota
1001
5020
0250
3003
50
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.84, pwidth = 4.26
Ohio
1002
0030
0400
500
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.83, pwidth = 4.24
Oklahoma10
020
030
040
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 2.54, pwidth = 3.81
Oregon
100
150
200
250
300
20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 4.19, pwidth = 6.29
Pennsylvania
1001
5020
0250
300
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.2, pwidth = 4.8
Rhode Island
2004
0060
0800
1000
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.63, pwidth = 3.95
South Carolina
010
020
030
0
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.03, pwidth = 4.54
South Dakota
2003
0040
0500
600
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 3.02, pwidth = 4.53
Tennessee
200
300
400
500
600
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.21, pwidth = 4.81
Texas
5010
0150
2002
5030
0
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.47, pwidth = 3.71
Utah
4060
8010
0120
140
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 5.55, pwidth = 8.32
Vermont
100
150
200
250
10 20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 3.53, pwidth = 5.3
Virginia
100
200
300
400
20 25 30 35 40 45kernel = epanechnikov, degree = 3, bandwidth = 3.78, pwidth = 5.67
Washington
1001
5020
0250
300
15 20 25 30 35kernel = epanechnikov, degree = 3, bandwidth = 2.58, pwidth = 3.87
West Virginia
5010
015
020
0
15 20 25 30 35 40kernel = epanechnikov, degree = 3, bandwidth = 3.46, pwidth = 5.19
Wisconsin
5010
0150
2002
5030
0
20 30 40 50kernel = epanechnikov, degree = 3, bandwidth = 4.18, pwidth = 6.27
Wyoming
29