“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.
The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.
Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.
What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?
Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.
As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.
When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.
In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.
Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.
Abstract rules, not random environmental signals.
If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.
What if it cannot be encoded in any way?
The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.
Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.
If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?
However, let us go back to Artificial Intelligence machines and how they work.
In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.
At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.
From the beginning, expert systems were used in financial trading.
There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.
Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.
A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.
In 2011, deep learning (DL) added to the other “expert” systems.
It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.
As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.
DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.
In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.
Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.
The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.
BlackRock has just organized an AILab.
In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.
China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.
China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.
Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.
China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.
The Chinese intelligence industry zone near Tianjin is already active.
In the end, however, how does Artificial Intelligence change the financial sector?
AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.
Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.
Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.
The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.
In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.
There are other AI mathematical mechanisms that come into play here.
There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.
There is also market sentiment.
The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.
There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.
Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.
Here, in fact, 1,800 days of physical trading are reduced to seven minutes.
However, the algorithms developed from evidence work much better than human operators in predicting the future.
Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.
Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.
In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.
As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.
What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.
Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.
So far we have focused on Artificial Intelligence algorithms.
But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.
It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.
Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.
In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.
An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.
WTO’s ‘Crown Jewel’ Under Existential Crisis: Problem Explained
World Trade Organization (WTO) is an international body that acts as a watchdog keeping an eye on the rules of trade between nations. WTO came into operation in 1995 and was founded as a successor to the General Agreement on Tariffs and Trade (GATT), which was incorporated in 1948. It acts as a forum where WTO members discuss and negotiate trade issues. Moreover, it works in the form of different multilateral as well as plurilateral WTO agreements. These agreements live at the heart of WTO as they deal with different aspects of trade policy. Agreements like General Agreement on Trades and Tariffs; General Agreement on Trade in Services; The Agreement on Trade-Related Aspects of Intellectual Property Rights etc. forms the centerpiece of WTO. Through these agreements, one WTO member enters into obligations and formulates the relation of reciprocity with the other WTO member.
Undeniably, the Dispute Settlement System (DSS) that works under the WTO is considered to be the ‘crown jewel’. No matter how stringent the laws are, unless they couldn’t be enforced, they are of not much worth. DSS functions as an effective mechanism to settle disputes and to enforce obligations in case of violation by any WTO member. The ration d’etre of giving birth to DSS was to ensure settlement of disputes in a timely and structured manner. DSS is committed to impede and further mitigate trade imbalances between stronger and weaker players by having their disputes to be settled on the verge of rules and not power. Since the day it came into force in 1995, 595 disputes have been brought before the DSS and out of which 350+ disputes are settled.
DSS is governed by the Dispute Settlement Body (DSB) through the rules incorporated in Disputes Settlement Understanding (DSU). The DSS works as a two-tier redressal forum and is the most important and busiest international tribunal having a binding authority on the parties to the dispute once they adopt the report of findings. On the first level comes the Consultation as per Article 4 of the DSU rules. Article 4 states that “each WTO member undertakes to accord sympathetic consideration to and afford adequate opportunity for consultation regarding any representations made by another Member concerning measures affecting the operation of any covered agreement taken within the territory of the former.” Therefore, Consultation is mandatory before any dispute is addressed to DSB. Once the consultation is failed, the complaining party can request the DSB under Article 6 for the establishment of a panel body that shall aim to settle the disputes between the parties.
On the top of the hierarchy comes the appellate body which shall hear the appeal from panel cases. Any party to the dispute can formally notify DSB of its decision to appeal. Under Article 17 of the DSU rules, DSB shall establish a standing appellate body. Unlike the Panel body, the appellate body is a permanent body composed of seven persons out of which three shall serve on any one case. These members are appointed for a term of four years. It is the duty of DSB to ensure that the vacancies shall be filled as they arise so as to confirm the smooth and timely functioning of the hierarchical mechanism of dispute redressal. Principally, the decision under DSB is taken through consensus methodology. Article 2.4 of DSU explains this method stating that “the consensus is said to be achieved when no WTO member, present at the meeting, formally opposes to the proposed decision”.
The genesis of the crisis is attributable to the U.S. who through its non-consensus has blocked the selection procedure to fill the vacancies alarming in the Appellate Body. The minimum requirement for Appellate Body to function is at least three persons out of total strength of seven. However, on 11th December 2019, the term of two of the remaining three members came to an end. At present, the Appellate Body has only one member and thus, it is dysfunctional and the resolution mechanism has brought to a grinding halt. The political façade started long back in 2017 when the U.S. cleared its intention of not allowing the selection procedure to taken place in order to fill the vacancies in the Appellate Body. Nonetheless, the Appellate Body continued its function as the compositional requirement was manageable due to the tenure of three of its members remaining but ultimately the crisis knocked the doors of WTO in the last month of 2019.
Although, at present, the composition of the Panel Body has not been interjected and the process of addressing disputes through Panel Body is still in continuance. However, the problem is as per the trends, in 67 percent of the cases, one of the parties to the dispute appeals the finding of the panel body and thus; when the Appellate Body is itself dysfunctional, the order remains non-binding and the whole mechanism of the dispute resolution is disrupted severing the gravity of the political disaster. The reasons for the U.S. to block the normal functioning of the Appellate Body have been shared with other countries as well. Fortunately, no other country has repelled in the way the U.S. is exclaiming to address the loopholes. The dissatisfaction of the U.S. administration with the WTO is not a secret anymore when Mr. Donald Trump labeled the WTO as ‘disaster’ for their nation.
The reason for the U.S. to express dissatisfaction is because of the overreaching power that Appellate Body enjoys. To combat that, on a lighter note, the U.S. has shown a preference of going back to the non-binding dispute settlement system that was prevalent at the time of GATT, 1948. Ironically, it was the U.S. who during the Uruguay round of negotiations (1986-1994) pressured and voted for creating a dispute redressal system that is binding and enforceable, however as the tables have turned now and the Appellate Body has become an irksome affair for the U.S.
The central issue of the U.S. to cordon the appointment revolves around the problem ofjudicial overreach. To elaborate the claim, the U.S. believes that the dispute settlement system interprets the WTO rules in such a way that instead of simplifying, it rather creates new obligations for the WTO members. What the U.S. believes is that the Appellate Body drifts away from its original mandate due to its practice of issuing decisions that either burden the WTO members with new obligations or diminishes the right they enjoyed earlier.
Further, the U.S. has raised the objections against the procedural irregularities by the Appellate Body. Entangling the issues of the procedure, firstly, the U.S.has pointed out the contradiction of the DSU rules adopted by the WTO members and the Appellate Body Working procedure which are drawn up by the Appellate Body itself. As per the Rule 15 of the latter, it allows the Appellate Body members to remain on board and to continue to serve on appeals which are pending during their terms; however, as per Article 17.9 of the former, a member enjoys the position for a fixed four-year term. Thus, the Appellate Body working procedures violate the provisional requirement as laid down in DSU rules.
The second procedural issue raised by the U.S. deals with the violation of completing the report by Appellate Body within the time frame of 90 days as prescribed by the DSU rules. The US has pointed out that the extraordinary delay violates the mandate of a speedy trial and further it negates the right of the complaining party as well as the party brought to dispute due to the hauling of their economies to a hiatus. It is the belief of the U.S. that the prospective incapacitation of the Appellate Body is undoubtedly a menace for the WTO and its members because once the report of panel body is appealed, it cannot be made enforceable unless the appellate body decides and thus, it holds the country for the indefinite timeframe not authorizing the party to retaliate on whose favour the panel body decided the dispute.
It is indisputable that the DSS need to undergo a series of reform in order to gain the lost confidence. Unfortunately, the step taken by the U.S. has been termed as harsh and politically motivated. One move of the U.S. has paralyzed the ability of the ‘crown jewel’ to resolve international trade disputes. Even going against the decision of the U.S. and outcasting the consensus power it holds won’t serve the purpose as the U.S. is an important player of WTO and if the U.S. is not a party to it; the WTO would be synonymous to a toothless tiger.
Nevertheless, arbitration under Article 25 of the DSU rules can act as an alternative to the hierarchal redressal system, as well as, solving disputes through bilateral agreements can be another alternative during the time of this existential crisis. The proposed idea of forming a Multi-party Interim Appellate arrangement will not succumb for long because the U.S. will not be its part and as it is certain, U.S. forms a considerable part of international trade, thus, there will again be a situation of deadlock. Moreover, choosing such interim mechanisms for the long run can raise a threat to the uniformity of rulings that WTO embraces. All in all, WTO is currently under jeopardy and it can be the beginning of the end if a solution to the crisis is not found in a timely manner. As of now, the Supreme Court of the international Trade ceases to exist and is in a life or death moment.
How Local Governments in China can Utilize New Infrastructure Policy to Promote Development
Authors: Chan Kung and Wei Hongxu*
In an effort to promote economic recovery, the central government, local governments, and enterprises have placed high expectations on the investment of new infrastructure, hoping it would promote the development of the digital economy, so as to enhance the internal driving force of economic development. Especially when the scale of local special bonds is expected to be increased and again issued ahead of schedule, many local governments hope to seize the opportunity of digital economy development and increase investment in new infrastructure areas to drive regional economic development. Unlike the conventional economy and conventional infrastructure investment, the new infrastructure is not a simple way to boost investment, but rather to help the conventional industries realize digital and intelligent transformation as soon as possible, and to create new consumption, new manufacturing, and new services. While the new infrastructure investment brings a new economic model, it is different from the past in terms of content, mode, and financing channels. It requires local governments to make corresponding changes with market-oriented thinking.
New infrastructure investment is not only the demand side of local users, but also the supply side of technology investment. From the perspective of the scope of new infrastructure, new infrastructure projects include 5G base stations, ultra-high voltage (UHV) electricity, industrial Internet, intercity high-speed railway, intercity rail transit, new energy vehicle charging piles, artificial intelligence, and Big Data centers. At present, rail transit and new energy infrastructure are not much different from conventional infrastructure investment. The degree of local participation of UHV electricity is limited, while the investment in other aspects, such as 5G base stations and Big Data centers, is relatively mature in technology and has good market supply capacity. In other aspects, it is more necessary to start from the aspects of technology research and industrial cultivation, and to invest in projects that encourage innovation and industrial park construction. Therefore, this requires not only clear investment objectives on the demand side, but also needs to expand the supply side such as technology research and application at the same time, which undoubtedly increases the complexity of new infrastructure investment.
At the same time, the sources and financing channels of new infrastructure investment still need to be explored. Recently, local governments in China have begun planning to finance new infrastructure projects through issuing special bonds, and many local governments have put new infrastructure projects on their agenda. Some market analysts believe that at present, 5G is still mainly invested in base stations. Generally, telecommunications companies such as China Unicom and Mobile Communications can invest on their own without issuing special bonds, thereby the special bonds can be invested in projects related to data centers. However, such projects are only available in first-tier cities, and there are not many such projects in second-tier, third-tier, fourth-tier, and fifth-tier cities. New infrastructure projects should be more market-driven and local governments should avoid excessive involvement via direct investment in industrial projects. Local governments also need to promote the public-private partnership (PPP) model and introduce more social capital to improve efficiency and broaden financing sources.
Even for new infrastructure projects funded by special bonds, attention should be paid to the financing capacity of the projects to avoid adding to the financial burden. There are two main ideas for the new infrastructure special bond declaration projects in many provinces. One is to build a digital information application platform at the county and district level based on the resources of the provincial and municipal cloud platforms. The second is to promote the optimization and upgrading of conventional infrastructure projects with the theme of digital and wisdom. Some local finance people worry that many of these projects are packaged around the concept of “new infrastructure” and are mostly non-yielding or low-yielding projects that may require the government to cover future bond payments. Therefore, the special bond for new infrastructure construction should be invested in public welfare projects that can generate income, rather than public welfare projects that do not.
At the same time, there are new requirements for investment entities in new infrastructure investment. Some financial institutions said that after the issuance of new infrastructure special bonds, most of them will eventually be invested in local urban projects. However, local urban projects were good at conventional infrastructure construction, unfamiliar with new infrastructure construction, and lacks experience in new infrastructure project operation. If we speed up the construction of new infrastructure projects without considering the actual situation, it will easily lead to the mismatch between the capacity and the project requirements, and drag on the development of local governments and enterprises. In particular, unlike conventional investment in forming fixed assets, a considerable part of new infrastructure investment in research, personnel training, and other forms of intangible assets will be formed. The conventional urban investment model does not have the ability to use and dispose of these assets. At the same time, the large amount of hardware equipment invested in the new infrastructure is different from the conventional “iron and steel foundation”. Its wear and tear, operation, and upgrading all require continuous follow-up investment, which cannot be “invested all at once.” These are also not available in some conventional urban investment enterprises. If the local government cultivates and supports relevant enterprises by means of industrial investment, it needs more consideration in terms of income distribution and asset management. Such investment cannot be simply measured by the unit of land and capital, but more in the form of equity investment such as industrial funds and venture capital. In this respect, the local government needs to have the investment entities and relevant personnel with the ability to invest in relevant industries.
Different from the past, local governments need to play their roles in market construction and maintenance, investment entities, and end-users in promoting new infrastructure investment and the development of the digital economy. In the cultivation of the digital market, market demand, and the maintenance of the market order, local governments should play the role as a supervisor, take the development of the market as the guide, and develop the local digital market. In terms of investment, it is necessary to start with basic research and development and personnel training, promote market-oriented investment and technological innovation to enhance the competitiveness of the digital industry. In terms of end-users, it is necessary to integrate their own digital resources, establish a public digital space, and expand digital demand with the digital transformation of public services and government affairs as the direction. These three new roles are the basic problems to be solved in the process of promoting new infrastructure.
While much attention has been paid to new infrastructure, the reality is that, in terms of overall size, it needs to be recognized that infrastructure investment is still dominated by conventional infrastructure projects, with new infrastructure as defined by the market accounting for less than 15%. ANBOUND is not a proponent of separating infrastructure from the old and the new, so one cannot fully “bet” on new infrastructure to revive the post-pandemic economy. From the perspective of economic development trends and current reality, the role of new infrastructure is to promote the coordinated and integrated development of digital technology to industry and regional economy. Therefore, local governments need to make good use of fiscal expansion policies and financing tools to build new infrastructure, rather than investing for investment’s sake, they need to pay attention to the trend of economic digitization and promote the market efficiency and the expansion of market space.
Final analysis conclusion:
Promoting economic recovery and the development of the digital economy with new infrastructure are the keys to current macro policies. In this regard, local governments need to pay attention to the differences between the new infrastructure and the conventional infrastructure model, and they need to make corresponding adjustments in the investment model and development thinking so as to give full play to the efficiency of the digital economy.
*Wei Hongxu, graduated from the School of Mathematics of Peking University with a Ph.D. in Economics from the University of Birmingham, UK in 2010 and is a researcher at Anbound Consulting, an independent think tank with headquarters in Beijing. Established in 1993, Anbound
‘Business as unusual’: How COVID-19 could change the future of work
Millions of people around the world have been working remotely due to the coronavirus pandemic and now experts are asking whether this “business as unusual” could be the future of work, at least for those people whose job doesn’t require them to be tied to a particular location.
UN News spoke to Susan Hayter, a Senior Technical Adviser on the Future of Work at the Geneva-based International Labour Organization, about how COVID-19 could change our working lives.
What are the longer-term effects of the pandemic on the workplace in developed countries, once the immediate crisis is over?
Before the pandemic, there was already a lot of discussion on the implications of technology for the future of work. The message was clear: the future of work is not pre-determined, it is up to us to shape it.
However, that future has arrived sooner than anticipated as many countries, companies and workers shifted to remote working in order to contain the transmission of COVID-19, dramatically changing how we work. Remote virtual meetings are now commonplace and economic activity has increased on a range of digital platforms.
As the restrictions are lifted, a question that is on everybody’s mind is whether this ‘business as unusual’ will become the ‘new normal’. A few large companies in developed economies have already said that what has been a large and unplanned pilot – remote teleworking – will become the standard way of organizing work. Employees need not commute to work again, unless they choose to do so.
Is this a good thing?
This may indeed be cause to celebrate, for people and the planet. But the idea of an end to “The Office” is certainly overblown. The ILO estimates that in high-income countries 27 per cent of workers could work remotely from home. This does not mean that they will continue to work remotely. The question is how we can adapt work practices and reap the benefits of this experience with remote working – for employers and workers – while not losing the social and economic value of work as a place.
In celebrating the innovations in work organization that have supported business continuity during the health crisis, we cannot forget that many will have lost their jobs or gone out of business as the pandemic has brought some industries to a standstill. For those returning to their place of work, the quality of work will be a key issue, in particular safe and healthy workplaces.
What needs to happen next?
The degree of workers’ trust in the measures taken by employers to make workplaces safe, will no doubt have an impact on the return to work. Engagement with trade union representatives, where these exist, is a must.
Everything from protocols for social distancing, monitoring and testing, and the availability of personal protective equipment (PPE) need to be discussed to make this work.
For workers in the gig economy, such as food delivery and ride-hailing workers, work is not a place, but an activity performed for an income. The pandemic has revealed the false choice between flexibility and income security. These workers may have no or inadequate access to sick leave and unemployment-insurance benefits. We need to tap into the brave new world to ensure that their work is performed under conditions that are safe.
How different do you expect the workplace in developing countries to look?
The ILO estimates a 60 per cent decline in the earnings of the almost 1.6 billion workers in the informal economy in the first month of the crisis. These workers are simply not able to work remotely and face the impossible choice of risking life or livelihood. Some countries have adopted measures to shore up this essential income while also ensuring adequate hygiene and PPE for employees and customers, informal enterprises and workers.
As companies begin to evaluate the effectiveness of the shift to remote work and their ability to tackle data security concerns, new opportunities may open up in services for developing countries with the necessary infrastructure.
However, these off-shoring opportunities in activities such as software development and engineering to financial services, may be accompanied by the reshoring in of other jobs as companies seek to improve inventory management and the predictability of supply chains.
This will have longer-term effects on employment in developing and emerging economies. The challenge is that while it will take time for new service sectors to mature, the negative impact of rising unemployment will be felt immediately. Inequalities in digital readiness may further inhibit countries from seizing these opportunities.
What are the benefits and drawbacks of remote work?
The shift to remote work has enabled many companies to continue to operate and ensure the health and safety of their employees. Those able to make the transition to remote work during the health crisis have had the opportunity to share meals with their families. Work has become human-centred to accommodate homeschooling and child and elder care.
Yet, the lines between working time and private time have become blurred for these individuals, causing an increase in stress and exposure to mental health risks.
In the face of a dramatic economic downturn caused by the pandemic and surging unemployment figures, there are opportunities to leverage these changes in work organization to design new job-sharing schemes that allow for flexibility and save jobs. This may mean shorter work weeks or work-sharing arrangements to avoid furloughs in lean times, while reshaping working time arrangements to achieve better work-life balance in the longer-term.
The digital transformation of work and possibility to engage in remote work has also been accompanied by other benefits. It has presented possibilities for older, more experienced workers to prolong their working life on their terms and provided work opportunities for those in rural communities. However, for many others, it has compounded a sense of isolation and a loss of identity and purpose. The social value of work and the dignity and belonging we derive from it cannot be replaced by virtual rooms, no matter how casual our attire while we occupy them.
To what extent will the pandemic entrench rising inequality?
While the pandemic may represent a tipping point for the digital transformation of the workplace, it has also revealed deep fault lines. It is those in the upper income brackets who are the most likely to choose to work remotely, whereas those in the lowest have no choice; they will have to commute and are more likely to be time-poor as a result.
Looking to the future, as digital and online work becomes the new normal, the demand for skilled workers is likely to rise along with their wages. The contributions of care-workers and other workers (e.g. teachers and staff in grocery stores) will be more highly valued than before. Yet, many low-paid workers whose wages have been stagnating in the face of declining union power and a shifting employment relationship are likely to see their incomes eroded even further as the ranks of the unemployed increase.
Historically, economic shocks, pandemics and wars have exacerbated inequality. The remaining question is whether this one will be a tectonic shift with rising political and social instability, or a shock that leads us to reinforce the foundations of just societies and the principles of solidarity and democratic decision-making that move societies, labour markets and workplaces in the direction of equality.
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