As more companies adopt and learn through digital solutions, and as new forms of employment and investment opportunities strengthen the demand recovery, we expect productivity growth to recover, write James Manyika and Myron Scholes in Project Syndicate.
For years, one of the big puzzles in economics has been accounting for declining productivity growth in the United States and other advanced economies. Economists have proposed a wide variety of explanations, ranging from inaccurate measurement to “secular stagnation” to questioning whether recent technological innovations are productive.
But the solution to the puzzle seems to lie in understanding economic interactions, rather than identifying a single culprit. And on that score, we may be getting to the bottom of why productivity growth has slowed.
Examining the decade since the 2008 financial crisis – a period remarkable for the sharp deterioration in productivity growth across many advanced economies – we identify three outstanding features: historically low growth in capital intensity, digitization, and a weak demand recovery. Together these features help explain why annual productivity growth dropped 80%, on average, between 2010 and 2014, to 0.5%, from 2.4% a decade earlier.
Start with historically weak capital-intensity growth, an indication of the access labor has to machinery, tools, and equipment. Growth in this average toolkit for workers has slowed – and has even turned negative in the US.
In the 2000-2004 period, capital intensity in the US grew at a compound annual rate of 3.6%. In the 2010-2014 period, it declined at a compound annual rate of 0.4%, the weakest performance in the postwar period. A breakdown of the components of labor productivity shows that slowing capital-intensity growth contributed about half or more of the decline in productivity growth in many countries, including the US.
Growth in capital intensity has been weakened by a substantial slowdown in investment in equipment and structures. Making matters worse, public investment has also been in decline. For example, the US, Germany, France, and the United Kingdom experienced a long-term decline of 0.5-1 percentage point in public investment between the 1980s and early 2000s, and the figure has been roughly flat or decreasing since then, creating significant infrastructure gaps.
Intangible investment, in areas such as software and research and development, recovered far more quickly from a brief and smaller post-crisis dip in 2009. Continued growth in such investment reflects the wave of digitization – the second outstanding feature of this period of anemic productivity growth – that is now sweeping across industries.
By digitization, we mean digital technology – such as cloud computing, e-commerce, mobile Internet, artificial intelligence, machine learning, and the Internet of Things (IoT) – that is moving beyond process optimization and transforming business models, altering value chains, and blurring lines across industries. What differentiates this latest wave from the 1990s boom in information and communications technology (ICT) is the breadth and diversity of innovations: new products and features (for example, digital books and live location tracking), new ways to deliver them (for example, streaming video), and new business models (for example, Uber and TaskRabbit).
However, there are also similarities, particularly regarding the effect on productivity growth. The ICT revolution was visible everywhere, the economist Robert Solow famously noted, except in the productivity statistics. The Solow Paradox, as it was known (after the economist), was eventually resolved when a few sectors – technology, retail, and wholesale – ignited a productivity boom in the US. Today, we may be in round two of the Solow Paradox: while digital technologies can be seen everywhere, they have yet to fuel productivity growth.
MGI research has shown that sectors that are highly digitized in terms of assets, usage, and worker enablement – such as the tech sector, media, and financial services – have high productivity. But these sectors are relatively small in terms of share of GDP and employment, whereas large sectors such as health care and retail are much less digitized and also tend to have low productivity.
MGI research also suggests that while digitization promises significant productivity-boosting opportunities, the benefits have not yet materialized at scale. In a recent McKinsey survey, global firms reported that less than a third of their core operations, products, and services were automated or digitized.
This may reflect adoption barriers and lag effects, as well as transition costs. For example, in the same survey, companies with digital transformations under way said that 17% of their market share in core products or services was cannibalized by their own digital products or services. Moreover, less than 10% of the information generated and that flows through corporations is digitized and available for analysis. As these data become more readily available through blockchains, cloud computing, or IoT connections, new models and artificial intelligence will enable corporations to innovate and add value through previously unseen investment opportunities.
The last feature that stands out in this period of historically slow productivity growth is weak demand. We know from corporate decision-makers that demand is crucial for investment. For example, an MGI survey conducted last year found that 47% of companies increasing their investment budgets were doing so because of an increase in demand or demand expectations.
Across industries, the slow recovery in demand following the financial crisis was a key factor holding back investment. The crisis increased uncertainty about the future direction in consumer and investment demand. The decision to invest and boost productivity was correctly deferred. When demand started to recover, many industries had excess capacity and room to expand and hire without needing to invest in new equipment or structures. That led to historically low capital-intensity growth – the single biggest factor behind anemic productivity growth – in the 2010-2014 period.
But, as more companies adopt and learn through digital solutions, and as new forms of employment and investment opportunities strengthen the demand recovery, we expect productivity growth to recover. Myriad factors contribute to productivity gains, but it is the twenty-first century’s steam engine – digitization, data, and its analysis – that will power and transform economic activity, add value, and enable income-boosting and welfare-enhancing productivity gains.
Source: Project Syndicate
INDUSTRY GROUPS SUE TO STOP CALIFORNIA NET NEUTRALITY LAW
The broadband industry’s four main lobbying groups are joining forces to stop California’s state net neutrality law from taking effect.
On Wednesday, mobile-industry lobbyist the CTIA; USTelecom, which lobbies for the telecommunications industry; and the two lobbying organizations for the cable industry, NCTA and the American Cable Association, jointly filed a lawsuit in federal court against the state of California to block its new law.
Collectively, the groups represent almost every broadband provider in the country, including mobile operators like T-Mobile and Sprint as well as cable and telecom companies such as AT&T, Comcast, Charter, Verizon, Frontier and CenturyLink.
The suit, filed in the US District Court for the Eastern District of California, asserts that California’s net neutrality protections are illegal because they’re pre-empted by the Federal Communications Commission, which rolled back federal net neutrality rules earlier this year.
The trade groups’ suit calls California’s law a “classic example of unconstitutional state regulation,” and it’s asking the court to block the rules from taking effect on Jan. 1.
This is the second lawsuit filed against California since Gov. Jerry Brown. The Trump administration’s Department of Justice is also suing California and seeking a preliminary injunction to stop the law from going into effect.
At issue is whether California and other states have the right to pass net neutrality laws, which they claim are necessary to protect their citizens. As part of its roll-back of federal net neutrality rules in June, the FCC included a provision in its order that pre-empted states from creating their own regulations. The DOJ and the broadband industry argue it would be too complicated for internet service providers to follow different net neutrality rules in 50 states.
US Attorney General Jeff Sessions said California’s law violated the Commerce Clause of the US Constitution.
“Under the Constitution, states do not regulate interstate commerce — the federal government does,” he said in a statement.
But net neutrality supporters argue that since the FCC has refused to regulate these services and because the agency actually abdicated its authority for such regulation to the Federal Trade Commission, states can impose their own rules for services delivered in their states.
California’s law, which prohibits internet service providers from slowing down or blocking access to websites or charging companies like Netflix extra to deliver their services faster, is based on Obama-era net neutrality protections that the Republican-led FCC rolled back earlier this year. But California’s law goes further, also outlawing some zero-rating offers, such as AT&T’s offer, which exempts its own streaming services from its wireless customers’ data caps. The law also applies the net neutrality rules to so-called “interconnection” deals between network operators, something the FCC’s 2015 rules didn’t explicitly do.
The legislation has been opposed by the broadband industry, which considers it too restrictive.
California is just one of several states looking to enact its own rules governing an open internet, after the FCC, under Pai, rolled back the Obama-era net neutrality rules in June. States such as Washington have pushed through a net neutrality law, while others are considering doing so.
Meanwhile, attorneys general of 22 states and the District of Columbia have already filed a brief to a US Appeals Court to reverse the FCC’s move. Companies like Firefox publisher Mozilla and trade groups also filed arguments.
Net neutrality, the principle that all internet traffic is treated fairly, has been one of the hottest topics of debate over the past several years. Consumers, tech companies and Democrats have pushed for stricter regulations prohibiting the prioritization of traffic, which resulted in the rules put in place by the previous FCC. But the Trump-era FCC has agreed with ISPs and Republicans who fear the regulations are onerous and hurt capital investment.
Taking It to Extremes: Mix insane situations — erupting volcanoes, nuclear meltdowns, 30-foot waves — with everyday tech. Here’s what happens.
WITH WATSON, TECHNICIANS ARE EMPOWERED TO MAKE THE RIGHT REPAIRS. THE FIRST TIME. ANYWHERE.
Since 2014 Apple and IBM have been working with clients to usher in a new era of smart enterprise. The latest collaboration offers companies interested in artificial intelligence (AI) and machine learning (ML) a chance to be a part of the next big shift in enterprise mobile intelligence — by bringing the power of IBM’s Watson AI services and Apple’s machine learning framework, Core ML, to native iOS apps. IBM Watson Services for Core ML delivers native iOS apps that give developers access to vast amounts of data, both on their device and through the cloud. This means that users can access information and deep insights directly on their iPhone or iPad, even when it’s not connected to a network.
The Coca-Cola Company is always innovating across their technology landscape, and AI is a key focus area. When presented with the opportunity to explore the value of IBM Watson services and machine learning, they quickly engaged. With the Coca-Cola emphasis on quality, they are currently partnering with IBM, working on prototypes for how IBM Watson Services for Core ML may transform in-field capabilities. Initial functionalities being analyzed are visual recognition problem identification, cognitive diagnosis and augmented repair. Early exploration is promising, and Coca-Cola and IBM continue to determine next steps.
Challenges in the field
Field technicians are deployed to service and repair beverage dispensing machines at restaurants and venues around the world. Once on site, the tech must be able to diagnose and correct an enormous array of problems, relying ultimately on their personal expertise and experience. If the system is not one the technician is familiar with – an uncommon water filter, for example – then routine repairs can become frustrating and time-consuming. Adding to the challenges, many sites are in remote or rural locations with no data connectivity, meaning no access to support, and therefore limited ability to make repairs. In these cases, the tech would need to spend time searching through informational databases, product manuals, and might even need to call in or consult with a colleague or specialist – resulting in lost productivity and prolonged system downtime.
Enter Watson Services for Core ML
With the AI capabilities of IBM Watson and Core ML, relevant information is put directly into a tech’s hands the moment she needs it, allowing her to resolve the issue quickly. Coca-Cola used Watson Services for Core ML to build an app that leverages visual recognition and augmented reality to identify equipment issues, diagnose problems, and troubleshoot repairs.
Through the app, the tech can use their iPhone or iPad camera to diagnose system problems via a virtual overlay and guided instructions pulled from the cloud, with zero latency, and even in areas with no network connectivity. Watson Visual Recognition on the device helps the technician identify older or poorly differentiated systems, or unfamiliar parts, and pinpoint the problem right away. Then, Watson Discovery Service helps identify possible solutions for the specific systems and type of malfunction.
Using ARKit, an iOS framework with resources to help create augmented reality experiences for the iPhone and iPad, developers are able to integrate apps with augmented reality models that help the technician solve complex problems on less-familiar systems.
As the technician is working on the job, data is captured. That data is then sent to the cloud once the device is back on the network, so Watson can learn from the interaction and make the learning available to other technicians in near real-time. Using the guided repair system, the technician is empowered to solve the problem the first time, increasing productivity, and elevating customer service – all without needing to call for assistance or reschedule the repair.
E. & J. GALLO WINERY IS WORKING WITH WATSON TO DEVELOP AN INTELLIGENT IRRIGATION SYSTEM THAT INCREASES THE QUALITY OF ITS GRAPES.
Overview: The problem
Having farmed in California for more than 80 years, E. & J. Gallo Winery knows that no resource is more important than water, which is why water management has been a top priority for the company for decades.
E. & J. Gallo Winery and Watson are now using weather reports and remote sensor data to deliver precise amounts of water to each vine, optimizing growth. The secret is located above the clouds, in a satellite looking down on the vineyard.
This allows the exact needed amount of water – based on highly targeted irrigation requirements – to be dispensed to each grapevine. As the weather changes, the irrigation methods react to ensure vines only receive water when needed.
Powered by IBM Cloud
IBM Cloud is built to handle cognitive workloads, such as the immense amount of data from satellites and IoT sensors. It also uses The Weather Company data from 2.2 Billion locations, which is combined with E. & J. Gallo Winery’s other data sources to help perfect individualized irrigation plans.
A competitive advantage
Because of this tailored watering, E. & J. Gallo Winery reduced its water use by 25%, while also improving the quality of its wine.
Wineries, hospitals, businesses, educators and governments are now working with Watson. In 45 countries and 20 industries, Watson is helping people make sense of data so they can make better decisions while uncovering new ideas.