In order to plan for economic and social change, it is useful to know what is happening in an economy. Various economic measures indicate current developments, GDP being a widely used overall measure of how national economies are changing. Employment levels and the skill structure of the economy, and those employed or unemployed are other statistics reported quarterly and annually. Others have proposed a measure of happiness.
The remainder of this posting deals with two topics, 1. The adequacy of GDP accounting to assess the state of an economy, and 2. How skill requirements are changing as a result of computers and communications technology, and what this may mean for those providing and receiving education.
GDP was never designed as a measure of overall social welfare although, perhaps out of convenience or laziness, it is often used as a proxy for welfare. Its shortcomings are well known, recently discussed by Diane Coyle in GDP: A Brief but Affectionate History, (Princeton, 2014). To paraphrase Coyle’s preliminary comments on the limitations of GDP (p.35):
- It measures paid for goods and service, excluding many unpaid services such as parents’ care of children, cooking at home and housework.
- It includes “bads” such as the environmental costs of pollution.
- It ignores improvements in the quality of new goods, especially when technology changes (for example from manual to electric typewriter to word processor).
- It excludes many indicators of progress such as health, education, infant mortality and life expectancy.
- The simple reporting of GDP per capita does not show the distribution of GDP between rich and poor.
Coyle surveys other indicators such as the Human Development Index, Gross National Happiness, and the output of a working group lead by Nobel winning economists Amartya Sen and Joseph Stiglitz examining the Measurement of Economic Performance and Social Progress.
In sum, there is ongoing research both to improve the measurement of GDP and to develop indicators which incorporate other aspects of social, political and economic welfare. Economic activities associated with the second machine age create some urgency for this work, as many information related activities generate free but valuable goods and especially services, and therefore underestimate a country’s GDP.
Downturns, such as followed the recent recession, may not be as bad in aggregate terms as reported. By April 2014, ninety-three percent of the labour forces in Canada and the USA were employed. But the downside is that at the same time the internet and communications have altered the skill structure of the labour force leading to un- and underemployment. We look at this in the next section.
- Skill requirements for employment
Andrew McAfee, coauthor of Race Against Machines and The Second Machine Age predicts that rapid advancements in automation are eliminating more middle class jobs. The skill profile of the workforce will change from looking like a bowl, with lower skills at one end moving bowl-like to higher skills at the other, to a Tuna can with almost entirely low skilled jobs at one end and high skilled jobs at the other, and very little need for medium skilled (perhaps middle class) jobs. The hamburger flippers are at one end and computer scientists at the other. These skill changing forces are reflected in the rhetoric of politicians who try to win votes by pledging to save the middle class. which is adversely affected by the changes. Probably they cannot deliver.
These trends will likely accelerate. While Canada decries the loss of so-called good jobs in manufacturing to low wage countries, the same loss is happening in China. While initially the jobs moved from high to low wage countries, low cost automation is now replacing low wages.
John Carroll, co-author of The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups, states:
- “Technology has improved so much, and will keep improving for the foreseeable future. Sensors are so cheap that you can build them into anything for almost no cost. Add a motor and you have a robot. Computing power costs essentially nothing, and everything can be controlled wirelessly these days, so it isn’t hard to imagine interesting things that the robots can do.”
If robots are going to substitute for people, then schools and post secondary institutions will have to adjust their course offerings and their means of delivery with more of it online. Students who want a liberal arts education will still be able to find one, but it may not lead to the desired type and level of paid employment. At the same time they will have the opportunity for lifelong learning, due to the availability of various combinations of online and in-class learning with some of the best instructors from around the world. Indicative of this trend is the appointment of the former President of Princeton University to become the CEO of Coursera, one of the main commercial firms offering online courses.
Three of the current remarkable examples of computer robots are Google’s driverless car, the computer which beat a chess champion, and the one which won at Jeopardy by answering questions.
Following are some further references to the probable changing skill structure of the workforce, from the Conversible Economist posting for April 9, 2014. (http://conversableeconomist.blogspot.ca/).
It reads as follows:
The current discussion is about robots that are mobile, able to receive a variety of commands, and with the capability to carry them out. For example, the March 29 issue of the Economist has a lengthy cover story on the “Rise of the Robots.” But I’ll focus here on Stuart W. Elliott’s article, “Anticipating a Luddite Revival,” which discusses how robots will affect the future of human work. It appears in the Spring 2014 edition of Issues in Science and Technology. Elliott did a literature review of the robot capabilities that are cutting edge and now becoming feasible as discussed in AI Magazine and IEEE Robotics & Automation Magazine from 2003 to 2012. Here, I’ll refer to his discussion of the more recent capabilities of robots in four areas: language capabilities, reasoning capabilities, vision capabilities, and movement capabilities.
Language capabilities. “[T]he tasks included screening medical articles for inclusion in a systematic research review, solving crossword puzzles with Web searches, answering Jeopardy questions with trick language cues across a large range of topics, answering questions from museum visitors, talking with people about directions and the weather, answering written questions with Web searches, following speech commands to locate and retrieve drinks and laundry in a room, and using Web site searches to find information to carry out a novel task.”
Reasoning capabilities. “[T]he tasks included screening medical articles for inclusion in a systematic research review, processing government forms related to immigration and marriage, solving crossword puzzles, playing Jeopardy, answering questions from museum visitors, analyzing geological landform data to determine age, talking with people about directions and the weather, answering questions with Web searches, driving a vehicle in traffic and on roads with unexpected obstacles, solving problems with directions that contain missing or erroneous information, and using Web sites to find information for carrying out novel tasks. One of the striking aspects of the reasoning systems was their ability to produce high levels of performance. For example, the systems were able to make insurance underwriting decisions about easy cases and provide guidance to underwriters about more difficult ones, produce novel hypotheses about growing crystals that were sufficiently promising to merit further investigation, substantially improved the ability of call center representatives to diagnose appliance problems, achieved scores on a chemistry exam comparable to the mean score of advanced high-school students, produced initial atomic models for proteins that substantially reduced the time needed for experts to develop refined models, substituted for medical researchers in screening articles for inclusion in a systematic research review, solved crossword puzzles at an expert level, played Jeopardy at an expert level, and analyzed geological landform data at an expert level.”
Vision capabilities. “[T]he tasks included recognizing chess pieces by location, rapidly identifying types of fish, recognizing the presence of nearby people, identifying the movements of other vehicles for an autonomous car, locating and grasping objects in a cluttered environment, moving around a cluttered environment without collisions, learning to play ball-and-cup, playing a game that involved building towers of blocks, navigating public streets and avoiding obstacles to collect trash, identifying people and locating drinks and laundry in an apartment, and using Web sites to find visual information for carrying out novel tasks such as making pancakes from a package mix.”
Movement capabilities. “[T]he tasks included moving chess pieces, driving a car in traffic, grasping objects in a cluttered environment, moving around a cluttered environment without collisions, learning to play ball-and-cup, playing a game that involved building towers of blocks, navigating public streets and avoiding obstacles to collect trash, retrieving and delivering drinks and laundry in an apartment, and using the Web to figure out how to make pancakes from a package mix.”