Forward-Looking Safe Harbor Statement This press release contains certain forward-looking statements, as that term is defined in the Private Securities Litigation Reform Act of 1995, including those relating to the products and services described herein and to fiscal 2020 business performance and beyond and the Company's plans for growth and improvement in profitability and cash flow. You can identify these statements by the use of the words "may," "will," "could," "should," "would," "plans," "expects," "anticipates," "continue," "estimate," "project," "intend," "likely," "forecast," "probable," "potential," and similar expressions. These forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those projected or anticipated. Such risks and uncertainties include, but are not limited to, continued funding of defense programs, the timing and amounts of such funding, general economic and business conditions, including unforeseen weakness in the Company's markets, effects of any U.S. Federal government shutdown or extended continuing resolution, effects of continued geopolitical unrest and regional conflicts, competition, changes in technology and methods of marketing, delays in completing engineering and manufacturing programs, changes in customer order patterns, changes in product mix, continued success in technological advances and delivering technological innovations, changes in, or in the U.S. Government's interpretation of, federal export control or procurement rules and regulations, market acceptance of the Company's products, shortages in components, production delays or unanticipated expenses due to performance quality issues with outsourced components, inability to fully realize the expected benefits from acquisitions and restructurings, or delays in realizing such benefits, challenges in integrating acquired businesses and achieving anticipated synergies, increases in interest rates, changes to cyber-security regulations and requirements, changes in tax rates or tax regulations, changes to interest rate swaps or other cash flow hedging arrangements, changes to generally accepted accounting principles, difficulties in retaining key employees and customers, unanticipated costs under fixed-price service and system integration engagements, and various other factors beyond our control. These risks and uncertainties also include such additional risk factors as are discussed in the Company's filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended June 30, 2019.
A common statistical problem in econometrics is to estimate the impact of a treatment on a treated unit given a control sample with untreated outcomes. Here we develop a generative learning approach to this problem, learning the probability distribution of the data, which can be used for downstream tasks such as post-treatment counterfactual prediction and hypothesis testing. We use control samples to transform the data to a Gaussian and homoschedastic form and then perform Gaussian process analysis in Fourier space, evaluating the optimal Gaussian kernel via non-parametric power spectrum estimation. We combine this Gaussian prior with the data likelihood given by the pre-treatment data of the single unit, to obtain the synthetic prediction of the unit post-treatment, which minimizes the error variance of synthetic prediction. Given the generative model the minimum variance counterfactual is unique, and comes with an associated error covariance matrix. We extend this basic formalism to include correlations of primary variable with other covariates of interest. Given the probabilistic description of generative model we can compare synthetic data prediction with real data to address the question of whether the treatment had a statistically significant impact. For this purpose we develop a hypothesis testing approach and evaluate the Bayes factor. We apply the method to the well studied example of California (CA) tobacco sales tax of 1988. We also perform a placebo analysis using control states to validate our methodology. Our hypothesis testing method suggests 5.8:1 odds in favor of CA tobacco sales tax having an impact on the tobacco sales, a value that is at least three times higher than any of the 38 control states.
The U.S. healthcare industry is looking less like a special case, a large segment of the U.S. economy with its own unique quirks, and is beginning to behave like other industries, according to "Top health industry issues of 2019: The New Health Economy comes of age," the 13th annual healthcare report from consulting giant PwC. So for PwC Health Research Institute's latest report, rather than focusing on issues only U.S. health organizations face, it for the first time is examining how healthcare is adapting to factors common to all industries: deals, business and tax strategy, risk and regulatory issues, workforce trends and digital transformation. The details may be specific to healthcare, but the business issues are shared with many other parts of the economy. In 2019, new entrants and biopharmaceutical and medical device companies will bring to market new digital therapies and connected health services that can help patients make behavioral changes, give providers real-time therapeutic insights, and give insurers and employers new tools to more effectively manage beneficiaries' health, the PwC report said. "The arrival of digital therapeutics – an emerging health discipline that uses technology to augment or even replace active drugs in disease treatment – is reshaping the landscape for new medicines, product reimbursement and regulatory oversight," PwC said. "This means that new data sharing processes and payment models will be established to integrate these products into the broader treatment arsenal and regulatory structure for drug and device approvals." As digital therapeutics and connected devices have transitioned from concept to reality, investors have poured $12.5 billion into digital health ventures in 2017 and 2018, PwC reported.
Donald Trump's $1.5 trillion tax cut has increased incentives to replace workers with robots, contradicting his campaign promise to restore well-paying manufacturing jobs in the nation's heartland. The Trump tax bill permits "U.S. corporations to expense their capital investment, through 2022. So, if a U.S. corporation buys a robot for $100 thousand, it can deduct the $100 thousand immediately to calculate its U.S. taxable income, rather than recover the $100 thousand over the life of the robot, as under prior law," Steven M. Rosenthal, a senior fellow at the Urban Institute and a specialist in tax policy, wrote me by email. I have addressed the impact of robotics on Trump voters in previous columns, but today I want to explore these developments in greater detail as tools to gather and analyze information have improved. One of the most striking developments in recent decades is the ongoing decline in work force participation among men, from 88.7 percent in July, 1947 to 68.7 percent in September, 2010, according to the Federal Reserve.
An NBER conference on Economics of Artificial Intelligence took place in Toronto on September 13-14, 2018. Research Associates Ajay K. Agrawal, Joshua S. Gans and Avi Goldfarb of University of Toronto and Catherine Tucker of MIT organized the meeting, sponsored by the Alfred P. Sloan Foundation, CIFAR, and the Creative Destruction Lab. These researchers' papers were presented and discussed: Emilio Calvano, Vencenzo Denicolò, and Sergio Pastorello, University of Bologna, and Giacomo Calzolari, European University Institute Q-Learning to Cooperate AI algorithms are increasingly replacing human decision making in real marketplaces. To inform the debate on potential consequences, Calvano, Calzolari, Denicolò, and Pastorello run experiments with AI agents powered by reinforcement learning in controlled environments (computer simulations). In particular, the researchers study multi-agent interaction in the context of a workhorse oligopoly model: price competition with Logit demand and constant marginal costs.
It's been just over 30 years since the last major overall of the U.S. tax code. In that time the world has been transformed - the Soviet Union collapsed, the Berlin Wall fell, dot coms boomed and busted, terrorism struck and launched the U.S. into the longest war in its history. A financial crisis shook the country and the world to its knees, and the rise of big data, artificial intelligence, genomics, new materials, cloud computing, blockchain, the sharing and gig economies, and many other new, advanced technologies and business models signaled the start of the Fourth Industrial Revolution. In that time, tax law and accounting practices have struggled to keep pace with innovations, sometimes leading to a wild West free for all for businesses and consumers, some of who managed to profit while others lost or were swindled of their life savings. Even today tax laws still fail to address the issues that the Age of Computer and the Internet brought, such as the internet sales tax, which may be heading to the US Supreme Court in the next year or so.
Republicans argue that the lower taxes for corporations and wealthy individuals promised in the tax bill currently before Congress will result in new investment in businesses and more jobs. But in the age of artificial intelligence and automation, trickle-down economics won't create employment. What corporations and the US economy at large need most in this emerging era is not more free cash, but a new approach to machine-assisted human productivity and purpose.