High-Tax States Scheming to Offset New Tax Law Impact

Tax and Financial News April 2018

High-Tax States Scheming to Offset New Tax Law Impact

The new GOP tax law places high income and property tax states at a distinct disadvantage. Before the new tax bill, any and all state and local taxes (commonly referred to as SALT) could be taken as itemized deductions without limitations. SALT includes both income and property taxes, so states where both are high such as New York, New Jersey and California are the most impacted. The new law caps SALT itemized deductions at $10,000, potentially creating a substantially increased tax burden for higher-income residents.

Many pundits predict any effort to offset the SALT changes at the state level will fail because the changes aim to help only the state’s wealthier residents. In the past, lower-income residents were less likely to itemize deductions. The expansion of the standard deduction to $24,000 (married filing jointly) will now mean even fewer people eligible to itemize regardless of a cap on the SALT component.

It’s important to remember that itemizing benefits a taxpayer only to the extent that his itemized deductions exceed his standard deduction. This is an either/or game; you get to take one or the other, but not both.

As a result, most lower- and middle-income taxpayers will benefit or at least break even from the doubling of the standard deduction, even in high-tax states. The highest earning taxpayers pay the most income and property taxes, so they stand to lose the most from the changes relative to tax brackets. Politicians in these states are looking at ways to offset these changes; the two most notable examples are California Gov. Jerry Brown and New York Gov. Andrew Cuomo.

California Gov. Jerry Brown is proposing the California Excellence Fund. The idea is to let taxpayers fulfill their state level tax liabilities by contributing to a state-sponsored charity in lieu of direct income tax payments to the state. This work around would allow amounts paid a full deduction as a charitable donation, side-stepping the new SALT limits. The fund’s feasibility is suspect and would certainly be challenged by the IRS as it violates the spirit of the new federal tax laws.

New York Gov. Andrew Cuomo is proposing a more complex work around. The central idea is to shift the personal income tax away from the individual and implement it as a business payroll tax. This concept is less likely to the resisted by the IRS; however, it could easily trigger other unintended consequences. For example, unless the state structures a progressive payroll tax system, all businesses (and therefore employees indirectly) will subsidize the wealthiest residents. Even if the structure was well-thought out and avoided such issues, it could easily create business flight or scare away commuters who account for a considerable portion of New York’s tax revenue.

These are just two examples. As time passes and more states consider their own versions of tax changes to offset the federal tax law changes to SALT deductions, perhaps a workable plan will emerge that can pass muster with the IRS. Until then, most plans currently contemplated are overly complex and likely to create many unintended consequences. Given the need to respond to their constituents, however, we should see more schemes by high-tax states in an attempt to counter the impact on their residents.




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What’s New in Technology April 2018

Deep Learning for Deeper Understanding

We all learn in different ways. Some people are book smart, meaning they glean knowledge from reading books. Others learn better through classroom or one-on-one instruction. Still others learn by doing – maybe jumping into an assembly project without reading the instructions.

And then there are those that are more visual – they can better comprehend information when they see examples of it through pictures, videos and other types of images. This is the genesis of what is called deep learning. Deep learning is a subcategory in the study of artificial intelligence (AI), which is simply the practice of machines – typically computers – learning to mimic the thought processes of humans.

Deep learning is focused on learning through visuals, and it has a near-infinite capacity for both learning and applications. In fact, it is based on downloading vast stores of imaged data. The machine can then scan through this colossal amount of information and identify solutions. In this way, it actually mimics the human brain’s ability to identify collected knowledge and memories and evaluate what is relevant and useful for the current query. The difference is that the human brain has only so much capacity to upload and process information; a lone computer has near infinite capacity.

This concept of deep learning is best conveyed with examples, and there are plenty of potential applications. Let’s start with healthcare. A patient presents with multiple symptoms, which could point to any number of medical conditions. His physician could rely on a variety of screens to make a diagnosis, including lab tests, X-rays, MRIs, CT scans, ultrasound, physical exam, his formal education, his personal experience with previous patients, and consultation with any number of other physicians, radiologists and specialists. The sum total of this knowledge base then comes up with a diagnosis, but it might not be accurate. As we’ve observed on television shows such as House, it often becomes a process of trial and error to make an accurate diagnosis.

Deep learning, though, can exponentially improve both the speed and accuracy of this process. Imagine that every physician across the globe uploads his patient files, images, observations, etc. into a centralized database. When a doctor needs to make a diagnosis, he can enter specific personal information and text results about his patient. The machine then scans its vast universe of data to identify the most relevant cases, information and images that match this individual patient’s symptoms. In short, because a machine with deep learning capabilities can store, assess and identify a massive number of variables, it might be able to diagnose patient conditions quickly and more accurately – saving crucial treatment time, money and the discomfort of ineffective trial and error treatments.

Deep learning basically follows the human process of assimilating information to learn by example, only it has the capacity to sort through so many more real-world examples than any one human brain can compile, let alone assess.

The following instances are just the tip of the iceberg of the many ways that deep learning can be applied to help various professions become vastly more efficient.

  • Driverless Cars – automatically detect objects such as stop signs, traffic lights, and even pedestrians to help make driving safer and decrease accidents.
  • New smart technology machines such as Alexa and Siri are used by companies to help customers access information or decide what to purchase or watch on television.
  • Farmers are able to take photos of ailing crops via smartphone and scan the visuals to a deep learning machine that can pinpoint the disease.
  • In the construction industry, project managers can track the most egregious potential malfunctions based on plan specifications, phase timing and severity to help keep projects on time, on budget and prevent safety hazards.
  • In the retail industry, companies can upload scores of data regarding customer buying habits, enabling frontline retail clerks to make immediate recommendations based on what customers who bought the same or similar items purchased in the past.
  • In the aerospace and defense industry, deep learning can identify objects in satellite images to help identify safe or unsafe zones for troops.