Cigarette Taxes and Cigarette Smuggling by State, 2020

Link: https://taxfoundation.org/cigarette-taxes-cigarette-smuggling-2022/

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Key Findings:

  • Excessive tax rates on cigarettes induce substantial black and gray market movement of tobacco products into high-tax states from low-tax states or foreign sources.
  • New York has the highest inbound smuggling activity, with an estimated 53.5 percent of cigarettes consumed in the state deriving from smuggled sources in 2020. New York is followed by California (44.8 percent), New Mexico (45.5 percent), Washington (41.5 percent), and Minnesota (34.8 percent).
  • New Hampshire has the highest level of net outbound smuggling at 52.4 percent of consumption, likely due to its relatively low tax rates and proximity to high-tax states in the northeastern United States. Following New Hampshire is Indiana (35.6 percent), Virginia (27.6 percent), Idaho (25.8 percent), Wyoming (24.4 percent), and North Dakota (18.6 percent).
  • Illinois and New Mexico significantly increased their cigarette tax rate from 2019 to 2020. Both states saw major increases in cigarette smuggling.
  • Policymakers interested in increasing tax rates should recognize the unintended consequences of high taxation rates. Criminal distribution networks are well-established and illicit trade will grow as tax rates rise.

Author(s): Adam Hoffer

Publication Date: 6 Dec 2022

Publication Site: Tax Foundation

(Updated) New Hong Kong Watch report finds that MSCI investors are at risk of passively funding crimes against humanity in Xinjiang

Link: https://www.hongkongwatch.org/all-posts/2022/12/5/updated-new-hkw-report-finds-that-msci-investors-are-at-risk-of-passively-funding-crimes-against-humanity-in-xinjiang

Report PDF: https://static1.squarespace.com/static/58ecfa82e3df284d3a13dd41/t/638e318e6697c029da8e5c38/1670263209080/EDITED+REPORT+5+DEC.pdf

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A new report by Hong Kong Watch have found that a number of pension funds may be passively invested in at least 13 China based companies where there is credible evidence of involvement in Uyghur forced labour programs and construction of internment camps in Xinjiang.

 As part of the report, Hong Kong Watch found that major asset managers are exposed passively to these companies as a result of their inclusion on Morgan Stanley Capital International’s Emerging Markets Index, China Index and All World Index ex-USA.  

….

Commenting on the release of the report, Johnny Pattersonco-founder and a research fellow at Hong Kong Watch, said:

“13 companies on MSCI’s emerging markets index are either known to have directly used forced labour through China’s forcible transfer of Uyghurs, or been involved in the construction of camps. Given this Index is the most widely tracked Emerging Markets index in the world, it raises serious questions about how seriously international financial institutions take their international human rights obligations or the ‘S’ in ESG.

Our view is that firms known to use modern slavery or known to be complicit in crimes against humanity should be classed alongside tobacco as ‘sin stocks’, or stocks which investors do not touch. Governments have a duty to signal which firms are unacceptable, but international financial institutions must also be doing their full due diligence. It is unacceptable that enormous amounts of the money of ordinary pensioners and retail investors is being passively channelled into firms that are known to use forced labour.” 

Publication Date: 5 Dec 2022

Publication Site: Hong Kong Watch

Hong Kong Watch gives evidence to the Canada-China Relationship Committee on ESG investment & country risk analysis

Link: https://www.hongkongwatch.org/all-posts/2022/12/1/hong-kong-watch-gives-evidence-to-the-canada-china-relationship-committee-on-esg-investment-amp-country-risk-analysis

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On Tuesday, Hong Kong Watch’s co-founder and trustee, Aileen Calverley, and Director of Policy and Advocacy, Sam Goodman, gave evidence to the Special Committee on the Canada–People’s Republic of China Relationship on the exposure of Canadian pension funds to Chinese stocks and bonds.

Hong Kong Watch has previously written extensively on the question of ESG, business, human rights, and Canadian pension funds exposure to Chinese companies linked to gross human rights violations, including the internment camps in Xinjiang.

In his remarks, Sam Goodman, discussed why China should be considered an ESG investment risk, recommending that:

  • Lawmakers consider sensible regulations to define ESG, label China as an ESG risk, and introduce a blacklist like the USA to restrict investment in Chinese firms with questionable human rights, environmental, and governance credentials.

In her remarks, Aileen Calverley discussed the risk of pension fund investments in China in the event of sanctions, recommending that the Government:

  • Include a China Country Risk Analysis in the Indo-Pacific Strategy.
  • Encourage publicly controlled pension funds to avoid exposure in China.

The full committee hearing can be watched here.

Publication Date: 1 Dec 2022

Publication Site: Hong Kong Watch

Defining Discrimination in Insurance

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Defining_Discrimination_In_Insurance.pdf

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Unfair Discrimination without Disproportionate Impact. As previously defined, unfair discrimination occurs when rating variables that have no relationship to expected loss are used. A hypothetical example could be if an insurer decided to use rating factors that charged those with red cars higher rates, even if the data did not show this. In this case, there would be no disproportionate impact, assuming protected classes do not own a large majority of red cars.
Disparate Treatment. Disparate treatment and unfair discrimination are not directly related if we use the Fair Trade Act definition of unfair discrimination. However, in states where rating on protected class is defined to be unfair discrimination, disparate treatment would be a subset of unfair discrimination. In such cases, an insurer would explicitly use protected class to charge higher rates, with the intention of prejudicing against that class.
Intentional Proxy Discrimination. If proxy discrimination is defined to require intent, it would be a subset of disparate treatment, whereby an insurer would deliberately substitute a facially neutral variable for protected class for the purpose of discrimination. Redlining is an example of this type of discrimination, given the use of location characteristics as proxies for race and social class.
Disproportionate Impact. Disproportionate impact focuses on effect on protected class, even if there is a relationship to expected loss. An example of this is the one mentioned in the AAA study, whereby a rating plan that uses age could disproportionately impact a minority group if those in that minority group tend to have higher risk ages. This disproportionate impact is not necessarily the same as proxy discrimination, since it is likely that even after controlling for minority status, age would have a relationship to
expected costs.

Unintentional Proxy Discrimination. If proxy discrimination is defined to be unintentional, the focus is more on disproportionate outcomes and the variables used to substitute for protected class. Several variables are being investigated by regulators to potentially be proxy discrimination and include criminal history for auto insurance rating. In order to prove proxy discrimination, an analysis would have to be performed to understand the extent to which criminal history proxies for minority status, and whether its predictive power would decrease when controlling for protected class. It is important to note once
again that terms like “unintentional proxy discrimination” may be subsumed by “disparate impact,” but they are included in this paper to show how various stakeholders use the term differently.
Disparate Impact. Disparate impact is unintentional discrimination, where there is disproportionate impact, but also other legal requirements, such as the existence of alternatives. To date, no disparate impact lawsuits against insurance companies have been won. An example of potential disparate impact (although it was not litigated as a lawsuit) is from health care. Optum used an algorithm to identify and allocate additional care to patients with complex healthcare needs. The algorithm was designed to create a risk score for each patient during the enrollment period. Patients above the 97th percentile were automatically enrolled in the program and thus allocated additional care. Upon an independent peer review of the model, researchers found that the model was in fact allocating artificially lower scores to Black patients, even though the model did not use race. The reason behind this was the model’s use of prior healthcare costs as an input. Black patients typically spend less than white patients on health care, which artificially allocated better health to Black patients.18
Unfair Discrimination and Disproportionate Impact. In this case, an insurer would use a variable that both has no relationship to expected loss, but also has an outsized effect on protected classes. An example of this could be the same red car case above, but where protected classes also owned almost all the red cars. In this case, higher rates would create a disproportionate effect on protected classes, while also having no relationship to expected loss.

Author(s): Kudakwashe F. Chibanda, FCAS

Publication Date: 2022

Publication Site: Casualty Actuarial Society