Quote (thesnipa @ Jan 19 2018 02:45pm)
its not an easy field to study in a black-white fashion. its hard to quantify a lot of the items so perhaps some of those studies tried and undervalued them, or they didnt try at all in search of an end result. its very common in criminology studies to do both.
some examples, how does a researcher quantify the flight risk factors for children? their age? their number? the presence or lacktherof a significant other? the presence or lacktherof a alternate parents?
then add in tertiary factors in such as employment level, employment duration, salary, home ownership, etc. some of the factors are additionally controlled by other factors, how the methodology deals with this can spell out massively different end results.
if you find any studies post them, i'll read them for sure. it used to be my bread and butter
I just wanted to update this, I spent some time reading through Starr's research paper on this, and a lot of the things you're talking about are actually referenced and even tested in the data. I'll share some notes I made while reading through on differences between this study and others as well as some interesting conclusions. I think you should actually read it for yourself, as it's actually pretty interesting and covers a lot of potential theories.
https://poseidon01.ssrn.com/delivery.php?ID=718089001090031000083009087101103014099041034067091025005102125018117010106092066081011061119126051016016071023114114075084002010025086075035126003114096104113005095055063106066121016065119103119084077120098068086103097024022000127124001090103104031&EXT=pdf* A large part of the reason for the gap in sentencing is due to prosecutors overcharging men in the first place, which is something a lot of studies done on the sentencing gap overlook.
* This study treats non-prison sentences (eg, fines / community service) as effectively 0 years sentenced. Other studies often attempt to calculate an equivalent sentencing time, but obviously it's completely arbitrary as to the conversion ratios used.
* It includes a ton of data and encompasses a bunch of the factors like the ones you were referencing.
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The data include rich offense and offender information, including arrest offense
(which USMS identifies with 430 codes),
3 gender, race, age, marital status, district,
citizenship, a string field describing the offense, criminal history, number of dependents,
education, Hispanic ethnicity, counsel type, co-defendant information, and county. AOUSC
also lists the initial and final charges ; these statutory sections then had to be coded on a
numeric charge severity scale. I constructed three such scales based on combined severity of
all charges: the statutory maximum, the statutory minimum, and a Guidelines-based measure.
If the statute prescribed varying sentences depending on case facts, I used default
assumptions grounded in legal research. For further details, see the Data Appendix.
* Parental responsibility is probably somewhat responsible for the gap, but even considering this, there is a huge gap left. Interestingly, it also suggests that women are being handed an advantage in this area mostly invisible ways rather than through the formal legal mechanism for this, which is probably a separate issue that should be looked at.
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The childcare theory suggests that one would expect to see the largest gender
disparities among single parents, and the smallest among defendants with no children. That
expectation is borne out by the data: compare Table 5, Columns 6-8. The TUT estimate is
still over 50% among childless defendants, however, so the childcare theory appears not to
fully explain the gender gap, but it probably explains part of it.28
* While premeditation isn't explicitly referenced, the idea that some such factor that biases sentences to be harsher against men is considered and was found to be an unlikely explanation:
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One obvious question is whether the crimes differ in ways not captured by the arrest
offense codes. The arrest offense is not a perfect proxy for underlying criminal conduct, and
if it overstates the severity of female conduct relative to that of men, that might explain some
of the observed disparity. In particular, one might wonder whether the disparities introduced
at sentencing fact-finding merely represent the process’s proper accounting for nuance
differences in facts within offense categories, which is, after all, fact-finding’s purpose.
Unobserved differences naturally cannot be ruled out, but there are good reasons to
doubt that they explain much of the observed disparity. First, the observable covariates are
detailed, capturing considerable nuance. They include not just the 430 arrest codes and the
multi-defendant flag (a proxy for group criminality, an important severity criterion), but also
additional flags based on the written offense description (see Table 4, Rows 15-16). Second,
the disparities are similar across all case types (and across arresting agencies), suggesting it is
not a matter of a few crimes being “worse” when men commit them. Such differences would
have to be prevalent across a variety of crimes and agencies to explain the result.