Cfa Level 1 Notes Kaplan

[Brittany Bhadd]

Kaplan and colleagues (KNS from here on) then published a critique of various methods Hogan used to come up with his estimate. KNS provided estimates using different data and a different method to derive the weights, showing that Philadelphia did not have increased homicides post Krasner being elected. For reference:

Part of the reason I am writing this is if people care enough, you could probably make similar back and forths on every synth paper. There are many researcher degrees of freedom in the process, and in turn you can make reasonable choices that lead to different results.

Social scientists tend to not prefer to just extrapolate prior trends from the same location into the future. There could be widespread changes that occur everywhere that caused the increase in city A. If homicide rates accelerated in every city in the country, even those without a new progressive DA, it is likely something else is causing those increases. So say we compare city A to city B, and city B had a homicide count trend during the same time period 10 15 35. Before the new DA in city A, cities A/B had the same pre-trend (both 10 15). The post time period City B increased to 35 homicides. So if using City B as the counterfactual estimate, we have the progressive DA reduced 5 homicides, again observed - counterfactual = 30 - 35 = -5. So even though city A increased, it increased less than we expected based on the comparison city B.

Note that this is not a hypothetical concern, it is pretty basic one that you should always be concerned about when examining macro level crime data. There has been national level homicide increases over the time period when Krasner has been in office (Yim et al, 2020, and see this blog post for updates. U.S. city homicide rates tend to be very correlated with each other (McDowall & Loftin, 2009).

To make the example simple, imagine we have two potential control cities and homicide trends, city C1 0 30 20, and city C2 20 0 30. Neither looks like a good comparison to city A that has trends 10 15 30. But if we do a weighted average of C1 and C2, with the weights 0.5 for each city, when combined they are a perfect match for the two pre-treatment periods:

This is what the synthetic control estimator does, although instead of giving equal weights it determines the optimal weights to match the pre-treatment time period given many potential donors. In real data for example C0 and C1 may be given weights of 0.2 and 0.8 to give the correct balance based on the prior to treatment time periods.

The number of homicides per year is the dependent variable. The challenge with this synthetic control model is to use variables that both produce parallel trends in the pre-period and are sufficiently robust to power the post-period results. The model that ultimately delivered the best fit for the data has population, cleared homicide cases, and homicide clearance rates as regular predictors. Median household income is passed in as the first special predictor. The categorization of the prosecutors and the number of homicides are used as additional special predictors. For homicides, the raw values are passed into the model. Abadie (2021) notes that the underlying permutation distribution is designed to work with raw data; using log values, rates, or other scaling techniques may invalidate results.

You can see although the counts are increasing, the rate is consistent over the time period. There are times I think counts make more sense than rates (such as cost-benefit analysis), but probably in this scenario the researcher would want to look at rates (as the shifting denominator is a simple explanation causing the increase in the counts).

Final point in this section, careful what you wish for with sparse weights and sum to 1 in the synth estimate. What this means in practice when using counts and matching on pop size, is that you need lines that are above and below Philly on those dimensions. So to get a good match on Pop, it needs to select at least one of NYC/LA/Houston (Chicago was eliminated due to having a progressive prosecutor). To get a good match on homicide counts, it also has to pick at least one city with more homicides per year as well, which limits the options to New York and Detroit (LA/Houston have lower overall homicide counts to Philly).

If you wish for more relevant examples, Philly has obviously more issues with street consumption of opioids than Detroit/NOLA/NYC, which others have shown relationships to homicide and has been getting worse over the time Krasner has been in office (Rosenfeld et al., 2023). (Or more simply social disorganization is the more common way that criminologists think about demographic trends and crime.)

I get that collating data takes a long time, and people want to protect their ability to publish in the future. (Or maybe just limit their exposure to their work being criticized.) It is blatantly antithetical to verifying the scientific integrity of peoples work though.

Because of this, you can leave-a-year-out in the pre-treatment time period, run your synth algorithm, and then predict that left out year. A good synth estimator will be close to the observed value for the out of sample estimates in the pre-treated time period (and as a side bonus, you can use that variance estimate to estimate the error in the post-trend years).

If Crim and Public Policy still did response pieces maybe I would go through that trouble of doing the cross validation and making a different estimator (although I would unlikely be an invited commenter). But wanted to at least do this write up, as like I said at the start I think you could do this type of critique with the majority of synth papers in criminology being published at the moment.

For many realistic situations though, I think criminologists need to go beyond just point and clicking in software, especially for this overdetermined system of equations synthetic control scenario. I did a prior blog post on how I think many state level synth designs are effectively underpowered (and suggested using lasso estimates with conformal intervals). I think that is a better default in this scenario as well compared to the typical synth estimators, although you have plenty of choices.

Again I had initially written this as trying to two side the argument, and not being for or against either set of researchers. But sitting down and really reading all the sources and arguments, KNS are correct in their critique. Hogan is essentially hiding behind not releasing data and code, and in that scenario can make an endless set of (ultimately trivial) responses of anyone who publishes a replication/critique.

A Superior Court judge correctly determined that a long-term commercial lease did not contain an implied covenant that the tenant would remain in business at the site, where there was no evidence that the parties intended to provide for such an undertaking by the tenant and the lack of such a provision in the lease was evidence there was no such understanding. [502-503]

A commercial landlord did not unreasonably withhold his consent to a sublease of the premises by the tenant, where percentage rent payments were contemplated over the long term of the lease, where the landlord could reasonably insist on a subtenancy that would be likely to generate at least a reasonable amount of percentage rent, and where the tenant gave no information to the landlord to indicate that the proposed sublease of half the space would produce any percentage rent or how the remaining space would be used. [503-506]

The judge in a civil action was warranted in finding that a commercial tenant had violated G. L. c. 93A, in its dealings with its landlord, and he also correctly found that the violation had caused the landlord no substantial harm. [506]

FINE, J. This appeal concerns the obligations of parties to a long-term commercial lease for a retail store, percentage rent being an important feature, when the tenant, for business reasons, seeks to relocate during the course of the lease term.

The tenant, Worcester-Tatnuck Square CVS, Inc. (CVS), brought an action seeking a declaration that the landlord, Lewis Kaplan, acted unreasonably in withholding his consent to a sublet. Kaplan counterclaimed seeking damages and injunctive relief based upon CVS's alleged breach of the lease in failing to remain in business at the site. Kaplan also sought G. L. c. 93A damages because of certain allegedly deceptive conduct on CVS's part.

After a jury-waived trial, a Superior Court judge found that CVS's failure to remain in business at the site was not a breach of the lease and that Kaplan had not acted unreasonably in refusing to consent to the sublease. He also found no c. 93A violation. Both parties appealed. We affirm.

We summarize the judge's findings insofar as they are material to the issues raised on appeal. Until 1976, Kaplan operated a food market in the premises which he owned in Worcester. On August 23, 1976, he and CVS signed a lease of the premises after negotiations during which both parties were represented by counsel. The lease provided for an initial seven-year term, with options for CVS to extend for three additional five-year periods. Initially, the base rent was $30,000 annually, and it was to increase by a fixed amount each time CVS exercised its option to extend. The base rent at the time of the trial in 1990 was $38,500. In addition, CVS was to pay a percentage rent based upon gross sales over a specified amount. There was no express requirement in the lease that CVS continue to operate its business at the site throughout the term of the lease.

The lease provided that CVS had the right to sublet all or part of the premises so long as Kaplan consented in writing. Kaplan, however, could not unreasonably withhold his consent. Compare 21 Merchants Row Corp. v. Merchants Row, Inc., 412 Mass. 204 (1992). In the event of a sublet, CVS would remain liable for all of the "tenant's covenants," and the subtenant would be required to report its gross sales so that the percentage rent due, if any, could be calculated.

c80f0f1006