Ещё одна статья Лотта, в которой он объясняет, почему так трудно собирать статистику по преступлениям. И, кстати, почему нет смысла опровергать его в стиле ттт... (ссылка идёт с
http://www.guncite.com/gun_control_gcdgcon.html ).
While this discussion is well understood, the net effect of ‘‘shall issue’’
right-to-carry concealed handguns is ambiguous and remains to be tested
when other factors influencing the returns to crime are controlled for. The
first difficulty involves the availability of detailed county-level data on a
variety of crimes over 3,054 counties during the period from 1977 to 1992.
Unfortunately, for the time period we study, the Federal Bureau of Investigation’s
(FBI) Uniform Crime Report includes only arrest rate data rather
than conviction rates or prison sentences. While we make use of the arrest
rate information, we will also use county-level dummies, which admittedly
constitute a rather imperfect way to control for cross-county differences
such as differences in expected penalties. Fortunately, however, alternative
variables are available to help us proxy for changes in legal regimes that
affect the crime rate. One such method is to use another crime category as
an exogenous variable that is correlated with the crimes that we are studying
but at the same time is unrelated to the changes in right-to-carry firearm
laws. Finally, after telephoning law enforcement officials in all 50 states,
we were able to collect time-series county-level conviction rates and mean
prison sentence lengths for three states (Arizona, Oregon, and Washington).
The FBI crime reports include seven categories of crime: murder, rape,
aggravated assault, robbery, auto theft, burglary, and larceny.25 Two addi-
tional summary categories were included: violent crimes (including murder,
rape, aggravated assault, and robbery) and property crimes (including auto
theft, burglary, and larceny). Despite being widely reported measures in the
press, these broader categories are somewhat problematic in that all crimes
are given the same weight (for example, one murder equals one aggravated
assault). Even the narrower categories are somewhat broad for our purposes.
For example, robbery includes not only street robberies, which seem
the most likely to be affected by ‘‘shall issue’’ laws, but also bank robberies,
where, because of the presence of armed guards, the additional return
to having armed citizens would appear to be small.26 Likewise, larceny involves
crimes of ‘‘stealth,’’ but these range from pickpockets, where ‘‘shall
issue’’ laws could be important, to coin machine theft.27
This aggregation of crime categories makes it difficult to separate out
which crimes might be deterred from increased handgun ownership and
which crimes might be increased as a result of a substitution effect. Generally,
we expect that the crimes most likely to be deterred by concealed
handgun laws are those involving direct contact between the victim and the
criminal, especially those occurring in a place where victims otherwise
would not be allowed to carry firearms. For example, aggravated assault,
murder, robbery, and rape seem most likely to fit both conditions, though
obviously some of all these crimes can occur in places like residences
where the victims could already possess firearms to protect themselves.
ttt, какую статистику будете приводить теперь для опровержения выводов Лотта?
И ещё:
Previous concealed handgun studies that rely on state-level data suffer
from an important potential problem: they ignore the heterogeneity within
states.28 Our telephone conversations with many law enforcement officials
have made it very clear that there was a large variation across counties
within a state in terms of how freely gun permits were granted to residents
prior to the adoption of ‘‘shall issue’’ right-to-carry laws.29 All those we
talked to strongly indicated that the most populous counties had previously
adopted by far the most restrictive practices on issuing permits. The implication
for existing studies is that simply using state-level data rather than
county data will bias the results against finding any effect from passing
right-to-carry provisions. Those counties that were unaffected by the law
must be separated out from those counties where the change could be quite
dramatic. Even cross-sectional city data30 will not solve this problem, because
without time-series data it is impossible to know what effect a change
in the law had for a particular city.
З.Ы. после получения пистона от модератора: использование "средней температуры по больнице" а ля ттт неправомерно для анализа влияния законов о скрытом ношении на преступность по многим причинам. Среди них:
- ФБР даёт статистику о числе арестов, а не вынесенных приговоров
- Способ разбивки на преступления маскирует эффект из=за слишком широких категорий (ограбления включают в себя уличные, где можно ожидать, что наличие оружия у жертвы повлияет на исход дела и банковские, где уже есть вооружённые охранники и наличие оружия у толпы клиентов особо ни на что не влияет)
- нет статистики о месте совершения преступления (ограбление на улице - влияние есть, в доме - нет, ибо у гражданина и так, и так может быть оружие в доме)
- нельзя усреднять по штатам, ибо сами правоохранительные органы признают, что с практической т.з. местные акты могут настолько затруднить доступ к оружию, что эффект от принятия закона на уровне штата может быть сведён к нулю для данного графства. Причём чем больше населения в графстве, тем чаще это происходит, искажая общештатовскую статистику.