Broadband 1

Outline Motivation and Literature Theoretical Framework Data Empirical Strategy Results Discussion and Conclusion

Broadband Internet: An Information
Superhighway to Sex Crime?

Manudeep Bhuller Tarjei Havnes Edwin Leuven
Magne Mogstad

October 11, 2011

Broadband Internet: An Information Superhighway to Sex Crime?


Table of Contents

Motivation and Literature

Theoretical Framework

Data

Empirical Strategy

Results

Discussion and Conclusion



Motivation

Motivation

Political and public concern about the potential adverse
eﬀects of internet. In particular, does the internet use trigger
sex crime?



Motivation

Motivation

Political and public concern about the potential adverse
eﬀects of internet. In particular, does the internet use trigger
sex crime?
An empirical work which uses plausibly exogenous variation in
the internet use to address the issue: a public program
introduced by the Norwegian government in the late 1990s
provided some spatial and temporal variation in the internet
access



Literature

Literature
Efficiency enhancing effects of internet
• Brown and Goolsbee (2002): whether internet makes markets
more competitive
• Freund and Weinhold (2004), Choi (2003): internet’s effect on
bilateral trade and foreign direct investment
• Choi and Yi (2005), Choi and Yi (2009): how internet affects
inflation and economic growth
• Saint-Paul (2009): search engines and economic growth
• George (2008), Ward (2007): how internet affects the markets
for daily newspapers and local music stores



Literature

Literature
Efficiency enhancing effects of internet
• Brown and Goolsbee (2002): whether internet makes markets
more competitive
• Freund and Weinhold (2004), Choi (2003): internet’s effect on
bilateral trade and foreign direct investment
• Choi and Yi (2005), Choi and Yi (2009): how internet affects
inflation and economic growth
• Saint-Paul (2009): search engines and economic growth
• George (2008), Ward (2007): how internet affects the markets
for daily newspapers and local music stores
Internet and sex crime related literature:
• Kendall (2007): pornography, rape, and internet



Parameters:
• N – the population size
• λ – the per year rate at which people meet
• M = λN – number of matches per year
• p = Pr {crime|match} – the probability that a match leads to
a sex crime
• C = pM – number of committed sex crimes per year (or
c = pλ the sex crime rate in per capita terms)
• q = Pr {report|crime} – the probability of reporting a crime
• c ∗ = qc – the reported sex crime rate
• i – the internet user rate (fraction of the population with the
broadband internet access)
The internet user rate i may inﬂuence p, λ or q



Internet access and crime propensity

The eﬀects of the internet access on the crime propensity




• Probability p depends on the latent risk factor r (∂p/∂r > 0)
which in turn may depend on the consumption of pornography




∂p ∂p ∂r ∂porn
• Then = · ·
∂i ∂r ∂porn ∂i




∂p ∂p ∂r ∂porn
• Then = · ·
∂i ∂r ∂porn ∂i
Internet decreases both pecuniary and non-pecuniary costs of
pornography consumption (⇒ ∂porn/∂i > 0)
• Triple-A eﬀect: accessibility, aﬀordability, anonymity




Pornography may trigger sexual aggression, it degrades
women or children to objects and aﬀects social and individual
norms (⇒ ∂r /∂porn > 0): Zillman (1971), Dworkin (1981),
Mackinnon (1995)




Pornography may trigger sexual aggression, it degrades
women or children to objects and aﬀects social and individual
norms (⇒ ∂r /∂porn > 0): Zillman (1971), Dworkin (1981),
Mackinnon (1995)
There are potential cathartic eﬀects of pornography
consumption which decrease sexual aggression
(⇒ ∂r /∂porn < 0)




In psychology much effort was devoted to laboratory
experiments which study the effect of pornography
consumption on sexual aggression
• Some find no effect or a reduction in aggression: Zillman and
Bryant (1984)
• The majority of studies find that pornography consumption
(especially of violent pornography) increases aggression



Internet access and the reported sex crime rate

The effects of the internet access on the reported sex crime
rate: c ∗ (i, r ) = q(i)c(i, r ) = q(i)p(r )λ(i) and therefore

dc ∗ dq dλ ∂p dr
= c + qp + qλ
di di di ∂r di
reporting matching direct
effect effect effect





= c + qp + qλ
di di di ∂r di

Reporting eﬀect: eﬀect of the internet use on q





= c + qp + qλ
di di di ∂r di

Matching eﬀect: eﬀect of the internet use on λ





= c + qp + qλ
di di di ∂r di

Matching effect: effect of the internet use on λ
Direct effect: effect of the internet use on r



Reporting eﬀect

Reporting eﬀect

There is no possibility to report crimes online in Norway



Reporting eﬀect

Reporting eﬀect

Internet can decrease the stigma of being a victim of a sex
crime (e.g. by increasing awareness or facilitating contact with
victims or support groups)



Reporting eﬀect

Reporting eﬀect

Internet can decrease the stigma of being a victim of a sex
crime (e.g. by increasing awareness or facilitating contact with
victims or support groups)
The authors found no systematic studies of the subject



Matching eﬀect

Matching eﬀect

Internet may reduce information constraints (in particular,
dating sites provide a lot of information)



Matching eﬀect

Matching eﬀect

Internet may expand the choice set and make those matches
available which were previously unavailable



Matching eﬀect

Matching eﬀect

The anonymity may expand the pool of people looking for a
match



Matching effect

Matching effect

The anonymity may expand the pool of people looking for a
match
Incapacitation and substitution effects: the time spent online
may reduce the amount of matches



Direct effect

Direct effect

This is the effect of interest: how the internet use influences
sexual aggression and affects the number of committed sex
crimes



Crime data

Crime data

Administrative police registers:
• Reported crimes for each municipality over 1993 – 2008
• Individual criminal charges for every resident over 1993 – 2004
Crime categories: overall reported sex crime, reported rape
(25%), reported child sex abuse (45%)



Internet access data


The fraction of households with broadband internet
subscriptions (faster than 256 kbit/s) for each municipality
over 2000 – 2008
The fraction of households covered by one of the broadband
internet providers in each municipality over 2000 – 2008




Broadband internet expansion

The expansion of internet was viewed by many governments
as essential for retaining competitiveness and achieving high
standards of living





standards of living
The National Broadband policy was introduced in Norway in
May 1998:
• Every household and private enterprise should have access to
broadband internet at a reasonable and uniform price
• The public sector should quickly adopt broadband internet





standards of living
The National Broadband policy was introduced in Norway in
May 1998:
• Every household and private enterprise should have access to
broadband internet at a reasonable and uniform price
• The public sector should quickly adopt broadband internet
The staged implementation was caused by limited funding and
geography




31

Figure 2. Broadband internet expansion across Norway



Socio-demographic data

Socio-demographic data

Individual socio-demographic information from 1993 to 2008:
gender, age, immigrant status, country of origin, marital
status, number of children, years of education, income,
employment status, geographic identiﬁers



Empirical strategy
Fixed eﬀects regression:
∗
ckt = δikt + xkt β + αk + τt + εkt

Instrumental variables (ﬁrst step):

ikt = φzk,t−1 + xkt β + γk + θt + ηkt

• ikt is the fraction of households with broadband internet
subscription
• zkt is the fraction of households covered by one of the providers
• xkt is the vector of controls



Empirical strategy

In the equations above k corresponds to municipality, t
corresponds to year
Controls xkt include:
• Socio-demographic characteristics of municipalities
• Additional variables: poverty rate, police oﬃcers per capita,
urban settlement, unemployment rate, crime rate (excluding
sex crimes)



Fixed eﬀects

Fixed eﬀects

Possible upward bias: those who commit sex crimes also tend
to consume more internet pornography and thus use internet
a lot



Fixed effects

Fixed effects

Possible upward bias: those who commit sex crimes also tend
to consume more internet pornography and thus use internet
a lot
Possible downward bias: potential sex offenders may use
internet more intensively in the areas with fewer potential
victims



Instrumental variables


The authors treat the broadband internet coverage as an
instrument for internet use and consider it to be random
relative to sex crimes





The internet user rate ikt is instrumented by the previous year
coverage rate zk,t−1





The internet user rate ikt is instrumented by the previous year
coverage rate zk,t−1
To find some evidence in favor of the validity of this
instrument the authors regress ∆zkt on the set of
socio-demographic features in the base year (2000) and find
no systematic effect of such sex crime risk factors as
immigrant population share, poverty rate and so on



Fixed eﬀects

FE estimates
0.466∗∗ 0.549∗∗ 0.546∗∗ 0.570∗∗
All sex crimes
(0.197) (0.212) (0.212) (0.210)
0.141∗∗ 0.127∗ 0.127∗ 0.130∗
Rape
(0.071) (0.071) (0.071) (0.071)
0.158 0.180 0.180 0.193
Child sex abuse
(0.134) (0.147) (0.147) (0.145)
Demographic
controls
Police density
Other crimes
Note: Crime rates are calculated per 100,000 persons. Eﬀects are reported per
percentage point increase in the internet user rate. Standard errors are
heteroskedasticity robust and clustered at the municipality level.
* p < 0.10, ** p < 0.05, *** p < 0.01




IV estimates
1.152∗∗ 1.167∗∗ 1.166∗∗ 1.217∗∗
All sex crimes
(0.518) (0.534) (0.534) (0.533)
0.378∗∗ 0.391∗∗ 0.390∗∗ 0.401∗∗
Rape
(0.163) (0.164) (0.164) (0.164)
0.616∗ 0.421 0.421 0.443
Child sex abuse
(0.355) (0.370) (0.370) (0.370)
Demographic
controls
Police density
Other crimes
Note: Crime rates are calculated per 100,000 persons. Eﬀects are reported per
percentage point increase in the internet user rate. Standard errors are
heteroskedasticity robust and clustered at the municipality level.
* p < 0.10, ** p < 0.05, *** p < 0.01




Economic signiﬁcance

Counterfactual sex crime rates
• The estimates suggest that about 3.5% of the total number of
sex crimes which occured between 2000 and 2008 would have
been avoided




Economic signiﬁcance

Counterfactual sex crime rates
• The estimates suggest that about 3.5% of the total number of
sex crimes which occured between 2000 and 2008 would have
been avoided
• When the actual trends are compared with the counterfactual
ones the diﬀerence peaks in 2006 when according to the
estimates roughly 3 out of 50 sex crimes per 100,000
inhabitants would have been avoided



Robustness checks

Different specifications

Municipality-specific trends are estimated using the prior data
and then included in the regression
Linear time trend interacted with baseline variables (as of year
2000)
The same for time dummies



Robustness checks

Placebo tests

The internet expansion is placed in the period before the
actual roll-out (more precisely in 1993 – 2000)
Future internet use is added to check whether it aﬀects the
current sex crime rate



Robustness checks

More tests

Outliers excluded
Large cities excluded (to control for changes between rural
and urban areas which coincide with the expansion)
Sex crimes in neighboring municipalities are included
Sorting is checked (the potential oﬀenders could move to the
areas with expanding broadband internet)



Discussion

Discussion

The study by Kendell (2007) regresses the amount of rape
incidences on the fraction of households with internet access
and ﬁnds a negative association



Discussion

Discussion

The new estimates are potentially less biased



Discussion

Discussion

• Norway has much more homogeneous population than the
United States



Discussion

Discussion

• Norway has much more homogeneous population than the
United States
• Pornography was banned in Norway and broadband internet
made it easily available



Discussion

Mechanisms

Reporting eﬀect:
• Marginal reports are expected to be weaker and the ratio of
charges to reports is expected to decrease. No evidence



Discussion

Mechanisms

Reporting eﬀect:
• Other changes in reporting behavior. The timing of reports
could change. No evidence



Discussion

Mechanisms

Reporting effect:
Matching effect:
• If the time spent online crowds out other activities there
should be effect on crimes other than sex crimes. No evidence



Discussion

Mechanisms

Reporting effect:
Matching effect:
• If the time spent online crowds out other activities there
should be effect on crimes other than sex crimes. No evidence
Direct effect:
• Prior availability of pornography should lower the internet
access effect. The effect is weaker in border areas



Conclusion

Conclusion

The estimates suggest that broadband internet use is
associated with an increase in the amount of reported rapes
and other sex crimes



Conclusion

Conclusion

The ﬁnding can be important for the ongoing debate
regarding internet regulation



Conclusion

Conclusion

The ﬁnding can be important for the ongoing debate
regarding internet regulation
If the matching eﬀect is important than the policy response
may be to provide more information about the risks associated
with the internet use


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