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Autonomous Vehicles Industry Dynamics | Key Considerations and Implications for Winners & Losers

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Automotive
  • AI
  • Emerging Technologies
  • Electric Vehicles


Publication | Update: Sep 2020

According to Protivity research, widespread use of autonomous vehicles will affect a number of established sectors and industries. Below, we outline key sectors and the changes they are likely to experience.

Automobile Manufacturing & Technology

·       Automated vehicles will cause an initial surge in new and used car sales, estimated at $ 600 billion a year globally, but sales could drop significantly once it becomes possible for unmanned cars to be summoned via an app and shared by multiple people. Parallel with this, there will be a market for technology designed to retrofit vehicles with self-driving abilities. A startup company called Otto is developing a self-driving kit for trucks, which sells for ,000.

·       Security is always a risk with newly introduced technology. Car manufacturers will need to ensure cybersecurity vulnerabilities in the technology used to build out autonomous vehicles is properly addressed and assessed once adoption becomes widespread. As more and more cars connect to the internet, the attack surface for hackers will increase, providing them with a greater incentive to invest in car-hacking skills and with a greater return on their efforts.

·       Currently, automakers are limited in testing their vehicles in real-life conditions, due to a legal proposition which states that a human must be “in control” of a vehicle. U.S. regulators are making some progress toward guidelines for testing self-driving vehicles on roads shared with human drivers, such as allowing automakers to apply for exemptions to the rules in order to advance progress. Google recently received guidance clarifying that its software used to control the self-driving vehicle can be considered a “driver.” However, progress remains slow, partly because states make their own road laws.

Insurance

·       As cars become automated, accidents are expected to decrease, and car owners are expected to incur less insurance costs, leading to less coverage over time. A study by the Eno Centre for Transportation estimates that if 90 per cent of the cars on American roads were autonomous, the number of accidents would fall from 5.5 million a year to 1.3 million, and road deaths from 32,400 to 11,300. Customer premiums could drop as much as 60 per cent in 15 years as adoption increases.

·       The auto insurance industry will not disappear altogether, as cars will still face risks such as flooding, damage or theft; however, the underwriting process will change. The traditional underwriting criteria, such as miles one expects to drive, will still apply, but the model, make and style of the car will assume greater importance. In the short term, insurer premiums will remain the same until insurers actually see declines in accident frequency. Over the long run, insurance companies will need to adjust their business strategies to reflect the reality of fewer accidents. Those that can’t will likely exit the market.

Law Enforcement

·       Autonomous vehicles have the potential to cut police forces in half. According to the Bureau of Justice Statistics most recent survey, more than 85 per cent of the 31 million people who were involuntarily stopped by the police in 2011 were stopped for traffic-related reasons. The need for these activities could decrease significantly with the adoption of autonomous vehicles since they will be programmed to obey all traffic rules.

·       Reducing the number of officers can have a negative impact on safety and crime, however. About 4 per cent of all drivers stopped for traffic violations each day are also searched by the police, often resulting in the discovery of more serious crimes. This crime-fighting opportunity may be reduced with driverless cars.

Government

·       Once self-driving vehicles become available, ordinary cars will gradually be banned, starting with city centres, business parks and campuses. Car-sharing services will increase, causing the number of cars on the road to drop. Initially, government revenues may decrease due to the elimination of licensing fees, taxes and tolls, and a reduction in fines from traffic violations.

·       With fewer cars on the road, the existing roadway infrastructure would be used more efficiently and the need for new roadways may decrease. Even though road repair will still be necessary, the federal and state governments may be able to reallocate a good portion of the roughly billion spent annually on new roads and highways.

·       For local governments, active police forces comprise 5 per cent of their spending. A reduction in law enforce- ment staff, as explained above, would mean more money in local and state budgets. The potential savings that autonomous vehicles present is the main reason the government has proposed almost billion for automated vehicle research over the next decade, even with the initial decline of revenue.

Winner qualities include having strong AV technology and a clear AV business plan that considers all of the important network and product aspects: servicing, fleet management, cybersecurity etc. For AV Subscriptions, advantages will extend to having a full line of vehicles for swapping, a liquid dealer network, and robust connectivity/security.

For auto suppliers, relating to the AV technology suite itself to the vehicle electrical architecture, cockpit electronics, and other experience-related content (seats, displays).

According to Itay Michaeli, Citi’s Auto and Auto Parts Analyst, the shift in industry dynamics and profit pools is also bound to affect stakeholder industries to automotive: 

·       How will the traditional rental car industry respond to potential competition from sourcing cars from AV subscribers)?

·       How will the auto aftermarket respond if AV Networks attempt to take back aftermarket/maintenance profit pools that today sit outside of their ecosystem?

·       If personal AVs can do last-mile delivery to your home, how does that affect freight and retail companies?

·       The impact on insurance companies and repair shops if accidents decline?

·       Who benefits from unlocking time spent in cars previously dedicated to driving?

·       Real estate in cities if to the extent fewer parking spots are eventually needed?

·       Real estate in general if living near a city becomes less of an advantage thanks to reduced traffic or AV commuter cars being able to travel faster

 

 

 

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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