Transition to the information society (Part 1: Disruption of households and work)

Transition to the information society (Part 1: Disruption of households and work)

Phuah Eng Chye (27 May 2023)

I am concluding my series on the information society. The remaining articles will summarise my views on disruptive forces and highlight areas to explore for managing the transition to an information society. Then I will summarise my outlook for this decade. I hope readers have found the articles useful and interesting.

Something happened on the way to the information society

In the early decades of the information revolution, governments were anxious not to accidentally snuff out the flames of innovation. They took a backseat and allowed the private sector to take the lead in shaping the economy and society. The private sector unleashed informationalisation, innovation, globalisation and financialisation to great effect and achieved a state of Goldilocks global prosperity over three decades. But development was lop-sided. There were massive imbalances which left huge gaps in social frameworks.

Since 2016, a severe societal backlash against informationalisation, globalisation and financialisation has materialised in the developed societies. This regression is a reminder that great transitions are often accompanied by periods of resistance, conflict and chaos. From a historical perspective, the first great transition was from an agrarian to an industrial society. Rural communities were uprooted with migration to cities and there was alienation of work caused by machines. For example, Luddites were prominent in protesting against machines. In the industrial society, secular ideologies, backed by scientific thinking, were eventually able to replace religion in governance and with “new” industrial society values – such as the ending of slavery, emancipation of female voters and formation of trade unions.

The second great transition is now taking place with the migration from an industrial to an information society. Unlike the first transition where structural change is driven by humans and machines, the second transition is mainly driven by information (data) and algorithms. As before, there is much resistance against informationalisation (surveillance, AI and narratives), globalisation (sanctions and reshoring) and financialisation (inequality, deleveraging). The industrial society norms are fading. “New” information society norms – such as privacy, ESG and LGBT – seems to have emerged nut these norms seem polarising rather than unifying. It would take time for the new information society values to be shaped. Global governance is giving way to realpolitik with conflicts increasingly settled by brute strength or reciprocity.

One recurring theme I have often highlighted is how information effects[1] such as intangibility, speed, size and transparency combine to disrupt industrial society norms and frameworks and make them irrelevant. Information-driven changes are reaching a tipping point with real-time and mobile information capacities, once the preserve of governments and large corporations, now widely accessible to individuals.

Gilles Deleuze hypothesise technological advances have triggered a shift from disciplinary societies (concept proposed by Michel Foucault) to societies of control. He argues disciplinary societies “reached their height at the onset of the twentieth (century). They initiate the organization of vast spaces of enclosure. The individual never ceases passing from one closed environment to another; each having their own laws: first, the family; then the school (you are no longer in your family); then the factory…we are in a generalised crisis in relation to all the environments of enclosure – prison, hospital, school, factory, family…never cease announcing supposedly necessary reforms…But everyone knows that these institutions are finished…It is only a matter of announcing their last rites and of keeping people employed until the installation of the new forces knocking at the door. These are the societies of control, which are in the process of replacing the disciplinary societies”. “In the disciplinary societies, one was always starting again (from school to the barracks, from the barracks to the factory, while in the societies of control one is never finished with anything – the corporation, the educational system, the armed services being metastable states coexisting in one and the same modulation, like a universal system of deformation”.

The breakdown in industrial society paradigms thus leaves a vacuum as there is no ready-made information society paradigm to replace it. In the transition to the information society, economic logic and value shifts from physical to virtual, from hierarchy to peer-to-peer, from linear to non-linear, from permanence (accumulation) to transience (flows), from relationships to transactions and from production to communication. In the process, the information society conjures a mirage of abundance but underneath the surface tectonic changes are in motion with far-reaching displacement effects from the disruption of traditional organisational structures and relationships among families, businesses and governments. Industrial society-built institutions are thus crumbling under the stresses emanating from an information society. Traditional power elites are imbued with a sense that they are losing control and increasingly feeling helpless.

Society is groping for answers. In the face of structural stress, it is natural to retreat into a shell of familiarity and comfort, Hence, societies generally attempt to reorganise to strengthen defensive resilience both domestically and internationally. But maybe more radical reforms are needed. In my next few articles, I will focus on key areas of disruption and suggest some reform priorities.

Disruption of households

Information effects such as intangibility, speed, size, transparency have disrupted organisational structures and relationships. The most basic organisational structure is the household. Households have evolved from large and extended families in villages in the agriculture society to nuclear families (married couple with one or more children) in cities in the industrial society. In this regard, “population ageing, driven by declining fertility, increasing longevity, and the progression of large-sized cohorts to older ages, is the dominant global demographic trend of the 21st century”[2]. Population contraction[3] is accompanied by drastic changes in household[4] size and composition. Information society households are diverse (combinations ranging from same-sex to polygamous families), transient (divorces and mobility), modular (individualistic) and autonomous (independent). These household trends reinforce demographic aging and are reshaping family and community cultures, relationships and the societal order. Attitudes and roles are changing – overturning industrial society ideals on families and responsibilities (family obligations, attitudes on work). Individuals are detached from families as knowledge transmission is virtual and autonomous (social media) rather than physical (school) and relationship-based (family).

A related source of disruption is the monetisation of household costs. When information is scarce, many activities are relationship-driven (e.g. at the household) and thus unpaid or lowly priced. Rising affluence, information abundance, high-population densities and shrinking household sizes contributed to the monetisation[5] of unpaid activities, especially household chores such as cooking, cleaning and caregiving. While monetisation of household chores facilitated individuals to live outside of their families, it meant that previously “free” household services were monetised by strangers. This increases the costs of maintaining a family and makes income indispensable.

Within this context, the 1980s shift to pro-market policies in favour of privatisation, price deregulation and curtailing welfare support coincided with the changing household structure. The application of “for-profit” business models and demand-driven pricing for healthcare, housing, education and transportation are increasing the costs of dependents. This is reinforced by the effects of Baumol’s Cost Disease[6] and the costs for new essential goods related to connectivity and travel.

Unlike in the past, it is hardly possible to survive[7] without income in a monetised society. Yet, welfare support is becoming a burden that many families and businesses are able to bear. At the same time, the broad trends of longevity, rising dependency, depopulation, expanding coverage and rising operating costs are putting the industrial-era welfare, healthcare and retirement systems under tremendous strain.

We should not under-estimate the social risks from disruption of households. An information society with changing family structures, new generational cultures and rising support costs are worsening social vulnerabilities and unbinding the social glue that held families and communities together and diluting the sense of common purpose. The traditional economic paradigms are unable to map the logic of disruption risks. Governments need to step up with a new policy paradigm that recognise their resource constraints and undertake radical reform in managing household disruption and redesigning the social safety net for the sake of future generations.

Disruption of work

Concerns that technology would lead to higher unemployment[8] has not borne out so far. The general impression is that broad economic conditions seem to have a more substantial influence on the levels of unemployment. Instead, the major damage imparted by technology and data seems to emanate from how as they change the nature of work with career jobs ceding significant ground to flexi-work; thereby affecting labour bargaining power and causing labour share of income to decline.

In this context, disruption changes the nature of work into two ways. First, it alters the relationship between work and humans[9]. Machines and AI are relatively passive and generally possess greater task endurance and reliability relative to humans. The marginal cost of replicating knowledge across machines becomes lower than human training costs over time. In this context, rapid obsolescence is diminishing the incentive for humans to accumulate knowledge. There are also concerns that machines and AI are detaching humans from the pleasures of creativity, problem-solving, decision-making and ownership (of the task) associated with work. The loss of valuable social features such as apprenticeship, career progression, interactions and relationships have social exclusionary effects that could lead to an increase in social problems and crime. Technology also amplifies the surveillance and repressive elements when it is used to control rather than to assist workers.

Second, traditional forms of employment are being “fissured” through outsourcing, contracting and flexi-work arrangements. Fissuring reflects the reorganisation of labour supply by demand-driven techniques, enabled by information, to reduce or relocate redundancies. It is estimated[10] the various forms of flexi-work – gig, contract, freelancers or temps – range from between 10% to 40% of total employment. Flexi-work is significant because businesses, especially platforms, like the agility to match workforce size to real-time fluctuations in demand as this allows them to minimise costs and risks. In tandem with this, the pandemic has catalysed a rise in remote and work from home arrangements which supports flexi-work trends.

John M. Barrios, Yael V. Hochberg, Hanyi Yi points out the “gig economy creates opportunities for would-be entrepreneurs to supplement their income in downside states of the world and provides insurance in the form of an income fallback in the event of failure…The introduction of gig opportunities is associated with an increase of ~5% in the number of new business registrations in the local area, and a correspondingly-sized increase in small business lending to newly registered businesses”. Workers also like the autonomy and flexibility. For them, flexi-work provides a contingency source of income if they are unable to find permanent employment or if they need additional income.

However, “fissuring” allows employers to shift risks and responsibilities onto workers and to evade labour protections and taxes. Research[11] generally show flexi workers generally have lower wages, fewer benefits; and suffer from unreimbursed costs (such as business expenses and wait times) and inferior working conditions. In addition, uncertain future income makes it difficult for flexi workers to gain access to bank loans for housing and cars, and usually also mean that they are vulnerable because they lack insurance and retirement savings.

Hence, the policy challenge posed by technology does not revolve around the quantity of jobs but rather around the quality of jobs[12]. In this context, it is a problem specific to matured economies where their industries have hollowed out and where traditional “good” full-time jobs have been replaced by “bad” jobs such as flexi-work. The replacement of good jobs with bad jobs has been accompanied by a shift in income from wages to profits and decreased the labour share of income.

Income-cost and information challenges

Taken together, changes in household and work structures are increasing tensions between income and costs, and increasing the vulnerability of new generations. The older generations had a well-defined path towards achieving a good life. Education was relatively inexpensive and wages assured. Individuals were able to purchase homes and start a family at relatively young ages. The goalposts have moved. The middle class has shrunk and the ranks of the low-income expanded, reflecting less upside opportunity for many. The middle class are finding the income-cost relationship has changed so drastically that they are no longer able to afford the benefits (e.g. house, education, healthcare) once taken for granted. The new generations begin working life at a disadvantage – in debt to finance their education,  finding it harder to secure a stable career and to accumulate savings due to higher living costs, and debt and rental servicing obligations. They are also more vulnerable to setbacks. In effect, economic hardship has become more widespread despite full employment. Rising job and income uncertainties are also dampening household expectations of life-time or “permanent” income. Hence, a confluence of demographic aging, anaemic household and business formation, rising income uncertainty and industrial maturity are likely to have deflationary consequences.

Rising service costs is reducing public access to services, increasing labour market frictions, lowers participation, deters household formation, and increases welfare costs and the requirement for adequate retirement savings.[13] Baumol’s cost disease postulates that in the transition from manufacturing to services, service wages would rise despite the absence of productivity gains. While the prediction on slower productivity growth has borne out, it should be noted that the benefits from rising service prices have flowed into profits rather than into wages. It is thus not a coincidence that the expansion of the service sector has corresponded with the falling share of wages. Baumol’s cost disease is thus profit-driven rather than wage-driven. It is a distributional double-whammy because the profit-motivated rising service costs end up accounting for a larger portion of household income.

Rising service costs are also often blamed on globalisation for facilitating a hollowing out of high-paying manufacturing jobs and on the profit-maximising behaviour of capitalists. But it should be recognised that as countries become more affluent, the pace of monetisation – including corporate profits, asset price increases and returns – will outstrip the incremental wage growth.

Rising service costs symbolises the end of the Protestant ethic culture. The culture of being frugal and industrious is incompatible with a demand-driven and creative economy.  Savers and pensioners would struggle because parsimonious attitudes would no longer be able to cope with monetisation and modern needs (e.g. smartphones, wifi, travel). This situation also reflects that economic relationships are no longer driven by production but by consumption.

In many senses, information abundance is disrupting and decoupling work from income. The production economy was built on the ethic of hard work with income as an incentive. In this regard, society is harnessing self-interest to reach a state of abundance. Once abundance is achieved, this lessens the need for work and, in tandem with this, income as a motivating force. In this context, abundance is a tantalising puzzle and encapsulates a debate about the relationship between work and income distribution. Does abundance mean citizens should get everything they want: a house, a car and medical care without having to work in exchange? The economics of abundance is handicapped by the absence of a specific mechanism to rival wages and profits as a means of tapping self-interest. Incentive structures rely on scarcity rather than abundance. Output may self-destruct if it is freely given away. The failure to harness abundance leaves us trapped within the maze of the scarcity paradigm which requires relentless and unstable expansion.

Macro policy reforms

A variety of policy reforms have been initiated to tackle different aspects of household and work disruption – centered around the income-cost tension – such as Baumol’s cost disease, the hollowing out of the middle-class and manufacturing, income inequality or, more broadly, to ward off macroeconomic stagnation. However, these policy initiatives have not meaningful reversed the structural decline in labour share of income. There are formidable obstacles to bringing back manufacturing jobs, rebuilding the middle class or trade unions, and reversing the labour share of income decline. Instead, the high service costs environment and flexi-work seems well entrenched. What policies do not seem to heed is that society have moved on from an industrial paradigm of large households, youthful population, physical activities and permanent careers towards an information paradigm of modular households, aging population, virtual activities and transient employment. Can matured economies successfully reindustrialise? Can a strong middle class co-exist with flexi work? Has the main economic challenge shifted from output towards income distribution and service costs? Thus, these policy initiatives are not effective because they are not addressing the income-cost and information challenges posed by the disruption of households and work.

At the income side, profits have emerged as the main driver of macroeconomic growth; at the expense of worker income and government revenues. In recent decades, governments have favoured cutting taxes to stimulate investments and create jobs; accompanied by downsizing their workforce and containing public sector wage costs. The “for-profit” approach generally  constrained the use of fiscal redistribution programs to address inequality. But profits are unable to replace the broad role of wages in connecting work with social needs in an economic system. In this regard, over-dependence on profits to drive economic growth is further widening social gaps.

Marc Lavoie and Engelbert Stockhammer explain pro-capital distributional policies have led to a long-run decline in the wage share in national income. “Pro-capital distributional policies usually proclaim to promote labour market flexibility or wage flexibility, rather than increasing capital income. They include measures that weaken collective bargaining institutions (by granting exceptions to bargaining coverage), labour unions (e.g., by changing strike laws) and employment protection legislation, as well as measures or the lack of measures that lead to lower minimum wages. There are also measures that alter the secondary income distribution in favour of profits and the rich, such as exempting capital gains from income taxation, or reducing the corporate income tax. Ultimately, pro-capital policies impose wage moderation”. They believe pro-labour distributional policies – promoting the welfare state, labour market institutions, collective bargaining, and minimum wages – would likely to increase the wage share.

In my view, the pursuit of profit as a goal is rational only to the extent it incentivises output expansion and motivate participation in societal development. Otherwise, there is little to justify pursuing profit as an objective. In a matured environment where output and participation are stagnating, profit-led growth is driven by service cost increases, asset price increases and debt. Attempts to redistribute income from profits into wages needs to consider the trade-offs between wage effects and asset price effects. In a matured economy, profits account for a large share of GNP. Hence, wage effects would be relatively muted but higher wages and taxes imply a fall in corporate profits. On this note, falling profits and asset prices could lead to a rebalancing of capital-labour share of incomes but the financial contagion risks need to be managed.

Income redistribution strategies face political and business resistance. The most direct and populist approach is to impose high tax rates on the super-rich. The experience has been disappointing. First, when income and wealth are highly concentrated, it means that the tax burden falls on a few persons. Even if the tax rates are extremely high (beyond 70%), the revenue it generates is likely to be insufficient to spread around for the rest of the population. This situation will be worsened by avoidance, evasion and capital flight. In my view, it is more effective to avoid antagonising the super-rich as they control capital and consumption power and count amongst their ranks talented and capable individuals and dominant firms. The reality is that a private sector-driven recovery seldom occurs without support from the rich. It is more effective to keep tax rates at reasonable levels (below 50%), simplify tax laws and close loopholes to ensure the super-rich corporations and families pay their fair share of taxes. Governments should focus on managing relationships to increase the participation of rich families and firms in creating opportunities for others.

Since the core problem relates to income and wealth concentration, the implication is that governments should focus instead on broadening the income base as this could strengthen economic multiplier effects to create a virtuous cycle between higher income and higher taxes to address inequality and demand deficiencies. Wage increases would reduce dependence on the rich to finance the rest of society. Reindustrialisation and tightening controls on migrant labour would put pressure on the services sector to raise wages.

But there are limits to broadening the income base. The implementation of consumption taxes such as value-add tax (VAT) has broaden tax revenue collection but in some countries, tax systems have become regressive as VAT rates have been continuously increased. Another option is to rebalance the incentives for savings, investment, consumption and employment; given that the rich are the biggest beneficiaries of incentives for savings and investments and this aggravates inequality.

One possibility is to explore developing a basic income scheme to use of public assets to widen the sources of income.

Non-conventional approaches to realign tax incentives and capital redistribution to address inequality and household vulnerabilities can also be considered. One approach is to use tax policies to re-align profit growth to wage growth. To address size effects, large companies employing relatively few employees and paying low taxes can be deemed as not bearing their fair share of social costs. A two-prong approach can be used to correct this anomaly. One is to increase the effective tax rate for large companies with few employees while offering tax incentives to increase headcount relative to size. Another approach is, as an alternative to penalising high CEO pay, is to offer incentives (e.g. tax rebates) to firms that offer relatively higher wage growth to non-management employees or contract workers earning below a certain income threshold. To address transience risks, incentives can be provided based on the ratio of full-time to part-time or contract employees[14]. Incentives, or conversely penalties, can be provided to firms that employ disabled persons, older or younger persons or minorities. Overall, incentives should be considered to boost permanent employment while levies could be imposed for the use of flexi-workers. Conversely, incentives (tax benefits and deductibles) could be provided to flexi-workers to mitigate income uncertainties. This income re-alignment initiatives can be complemented by business conduct regulation to ensure that risks (e.g. wait times, capital investment, maintenance costs, insurance) are either borne by the parties most able to bear the costs or that workers are proportionately compensated for taking on such risks.

The alternative is to contain living costs. There are four approaches. First, governments could provide the services directly. Prior to the 1980s, governments used to consider it their duty to provide public goods at low cost. But government operations suffered from rising operating costs and inefficiencies. Many services were privatised to reduce the fiscal burden and increase efficiencies. However, the experience is that privatised entities have been repricing their services upwards to improve their profitability. The question now arises as to whether it is timely for governments to intervene through more stringent price regulation, issue more licenses or become a direct provider again. There are obviously complex legal issues involved as it could be in breach of contracts or even involve nationalisation. However, governments need to face up to the challenge of whether they are capable of actually lowering operating costs and the impact of their intervention on private sector investors.

Second, governments should review the use of subsidies to make goods and services affordable. Subsidies comes in various forms (subsidies, grants, tax breaks) and are much maligned for their purpose (nullifying market discipline, political), inefficiencies (especially leakages) and their costs. But since the pandemic and US-China tensions, governments have taken the opportunity to gain political mileage by freely handing out subsidies. However, the consequences to uninhibited fiscal largesse are beginning to be felt. Overall, it is difficult to generalise on subsidies due to the complexities involved. Lower prices can lead to shortages, rationing, reduced quality and less investments. Cutting fiscal subsidies (such as on education, healthcare and housing) could reduce corporate profits and trigger a downturn in asset prices. On the other hand, higher prices are critical factors in the international competition for talent, innovation and investments. Thus, there is a balancing act between the beneficial impact of higher prices on growth and their adverse effects on social inequality and fiscal soundness. In theory, governments should aim for subsidies to be temporary with plans put in place to promote competition and supply expansion that would eventually lead to lower costs. Third, subsidies should be accompanied by controls (such as rationing, quotas, prohibitions and taxes) to align demand to supply.

Fourth, matured “service” economies suffer from crowding. Services sector growth tends to be driven by global inflows that bid up the prices of assets (especially houses), goods and services that puts it out of the reach of locals. This is where policy ambivalence creeps in – whether countries should welcome or restrain foreign inflows (residents, capital). These tensions underline the incompatibility between low-price manufacturing and high-cost services. Hence, a strong currency may hurt manufacturing export competitiveness but it would cushion the deleterious effect of rising service costs.

Outside of the cost-income considerations, governments should strengthen their role in creating good jobs, eliminating or transforming bad jobs (that didn’t meet social needs), or providing alternatives to wage income. In this respect, manufacturing jobs are qualitatively better than non-manufacturing jobs because they were subjected to stringent regulation and worker representation. Governments should take the lead by ensuring that civil service jobs are good jobs. In tandem with this, efforts should shift from trying to create good production-related jobs towards creating good information-related jobs.

Another important area of reform is the welfare safety net. With household and work disruption, the ability of households and firms to provide support have diminished significantly. The burden of protecting individuals from health, education, living costs, employment, income and retirement risks thus falls squarely on the shoulders of the government. Governments are themselves boxed in by aging[15], longevity costs and fiscal constraints. Governments have tried to address the welfare pressures by reforming the pension system, extending the retirement age, reducing various age-related benefits and to bring health costs under control.

In my view, governments should adopt a holistic approach to managing the different aspects of household and work disruption. One priority is to reconstruct the welfare safety net to cope with challenges posed by concentration, modularity and transience. Towards this end, there has been a logical shift away from corporate-sponsored and defined-benefit schemes towards government-mandated and defined-contribution schemes; and accompanied by extending the retirement age to ensure retirement schemes are financially sound. What is now needed is a shift from a “single lifetime job and career-defined savings” paradigm towards creating a “multiple jobs and multiple sources of savings and wealth” paradigm. The objective to ensure that the savings and benefits are portable to enable to align welfare and retirement schemes with a flexi-work environment.

The backbone for retirement schemes is well established. The main pillar is a provident fund where contributions (by employers and employees) are mandatory, is conservatively managed and enjoys substantial tax benefits. Good examples would be the provident funds of Malaysia and Singapore. The second pillar comprise privately managed schemes, under government oversight, where contributions are voluntary and eligible for some tax benefits (such as 401K in US, superannuation funds in Australia).

The retirement system can be further enhanced. First, the participation of flexi-workers should be formalised. Platforms, agencies and other companies that “hire” or “manage” flexi-workers should make appropriate[16] contributions to the mandatory fund on their behalf. Second, “it is difficult to reverse inequality trends if a large percentage of the population did not own assets. Hence, it is important to implement schemes to redistribute capital (or returns) or to widen ownership of assets. The ability to receive income streams from a variety of sources would mitigate household income uncertainty. It would also ensure that all individuals benefit from rising asset prices. This includes (1) Schemes such as cooperatives or ESOS plans already exist. Incentives can be given to ESOS plans to skew the distribution of equity in favour of low-income workers. (2) An unusual but highly effective idea is to facilitate the redistribution of capital returns to ensure greater equitability. In this regard, most retirement or savings funds exhibit the same pattern of wealth concentration, with the top 10% accounting for about 80% of the fund’s assets. As a result, low-income account holders are unable to accumulate sufficient savings while the incentives for saving seem to benefit the rich. My suggestion is for funds (that benefit from government incentives and guarantees) to redistribute the returns on capital. For example, for accounts with accumulated funds above a threshold, part of their returns (e.g. 25 basis points) can be redistributed to account holders with low balances. This would substantially assist in accelerating the pace of savings accumulation by low-income account holders”[17].

Third, retirement schemes should also be extended to unemployed citizens. “Multiple funds, but centrally administered, can be established for citizens to meet the needs of individuals on a holistic basis. These funds can be established based on a one-time endowment from proceeds from government assets[18] or based on transfers from specific taxes such as inheritance or property, or be based on charitable donations. These funds can be used for purposes such as education, affordable housing and entrepreneurship. This would provide room for cross-linkages where unused welfare or medical benefits can be transferred as a credit to retirement savings”[19]. In this context, the fund can be used as an anti-poverty vehicle to assist the poor to accumulate assets or as a conduit for philanthropic or crowd-funded income or wealth transfers. In tandem with this, countries may integrate their basic income schemes with the government mandatory retirement savings schemes – where the sums may be drawn down or subsidies provided for specific items or activities (which is possible to manage with CBDCs). In tandem with this, greater use should be made of technology and AI to control costs and leakages. Some of these funds can be specifically earmarked to support welfare, unemployment or debt repayment assistance or even to create business opportunities for the low-income group. As multiple sources of savings and income are established, a central administrator should be set up. In my view, we should treat basic income[20] as a national inheritance to provide greater choice to the poor. Basic income schemes would also match well with flexi-work.

How information is disrupting the labour market

The structural challenges around the changing nature of work, driven by information effects, needs to be more effectively managed. Governments[21] have focused on regulation as a means of neutralising the monopsony power of platforms. This includes reviewing the status of gig workers, enhancing their rights, wages, benefits and working conditions and scrutinizing platform practices for monopolistic and abusive conduct. For example, Monika Grzegorczyk, Laura Nurski, Mario Mariniello, Tom Schraepen suggest “hybrid work arrangements should contain the following agreements on flexibility in terms of the place and time of work: Workspace flexibility, minimum and/or maximum amounts of time spent working remotely; stricter limits on management monitoring of teleworking, preventing the use of spying technologies and requiring transparent information to be given to employees for any performance-measuring technology used; well-defined video-connection rules, such as maximum amounts of daily time spent on video calls and strict virtual commuting times between video calls/meeting;  a right to disconnect, i.e. to prevent workers from engaging in work-related tasks – such as video calls, work interaction on digital platforms and emails – outside working hours; the use of technologies and processes to monitor, anticipate and prevent remote workers’ digital exhaustion”.

In the industrial society setting, the price of labour was generally set by bargaining between trade unions and employers. In a fissured or flexi-work labour environment, the price of labour is continuously fluctuating and tends to be demand-driven in platform-based markets. In such markets, workers seem to operate at a substantial information disadvantage.

Veena Dubal notes technological advances “have ushered in extreme levels of workplace monitoring and surveillance across sectors. These developments have given rise to range of now well-known concerns: limitations on worker privacy and autonomy, the potential for society-level discrimination to seep into machine-learning systems, and a general lack of transparency and consent regarding workplace data collection and retention…for a growing number of low-income and racial minority workers in the United States, on-the-job data collection and algorithmic decision-making systems are…undermining the possibility of economic stability and mobility through work by transforming the basic terms of how workers are paid…Rather than receiving a predictable hourly wage – or a salary – workers…have been earning under a new system in which their constantly fluctuating wages are closely tied to algorithmic labor management. Under these new remuneration schemes, workers are paid different wages – calculated using opaque and ever-changing formulas reflecting individual driver location, behavior, demand, supply, and other factors – for broadly similar work. While companies like Uber use dynamic pricing and incentive structures, companies like Amazon pay workers through algorithmically-determined bonuses and scorecards that influence driver behavior through digitalized surveillance and adjudication…In contrast to more traditional forms of variable pay like commissions, algorithmic wage discrimination arises from (and functions akin to) to the practice of consumer price discrimination, in which individual consumers are charged as much as a firm determines they are willing to pay. As a labor management practice, algorithmic wage discrimination allows firms to personalize and differentiate wages for workers in ways unknown to them, paying them to behave in ways that the firm desires, perhaps for as little as the system determines that they may be willing to accept. Given the information asymmetry between workers and the firm, companies can calculate the exact wage rates necessary to incentivize desired behaviors, while workers can only guess as to why they make what they do. In addition to being rife with mistakes that are difficult or impossible for workers to ascertain and correct, algorithmic wage discrimination creates a labor market in which people who are doing the same work, with the same skill, for the same company, at the same time, may receive different hourly pay. Moreover, this personalized wage is determined through an obscure, complex system that makes it nearly impossible for workers to predict or understand their frequently declining compensation. Across firms, both in the on-demand economy and in some cases, beyond, the opaque practices that constitute algorithmic wage discrimination raise central questions about the changing nature of work and its regulation under informational capitalism. Most centrally, what makes payment for labor today fair? How does algorithmic wage discrimination change and affect the everyday experience of work? And, considering these questions, how should the law intervene in this moment of rupture?”

Veena Dubal notes “certain harms that arise from digitalized variable pay – the constant uncertainty and sense of manipulation – call for additional regulation. Some organized groups of workers and labor advocates have turned their advocacy toward the data and algorithmic control that are invisible to them. Using data privacy laws, workers in both Europe and the U.S. are suing to make transparent the data and algorithms that determine their pay. Others are engaging in data transparency attempts through the counter-collection of data, accomplished through data cooperatives…addressing the extraordinary problems posed by algorithmic wage discrimination must go beyond longstanding transparency, consent, and ownership models…think more expansively not just about the legal parameters of whether data collection is consensual, what happens to data after it is collected, and who owns the data, but also about the legal abolition of digital data extraction at work, or what I have called the data abolition. Digitalized data extraction at work is neither an inevitable, nor, especially when analyzed through the lens of moral economy, a necessary instrument of labor management”.

Marshall Steinbaum argues ride platforms operate “a tacitly collusive equilibrium sustained by vertical restraints preventing drivers from setting prices” and “use a variety of tools to enforce the loyalty of their drivers in ways that impair platform competition”. Drivers are corralled into accepting “the wrong customers…whose destination, current location, or willingness and ability to pay means that there will be a large block of uncompensated deadhead time and/or distance at the other end of the trip, or who will be charged too low a price (at the platform’s discretion) to make the trip worthwhile for the driver. This is where platform deception comes into play. Workers are typically told neither the fare nor the destination of an offered gig in advance. They are only told vague information about the location of the start of the trip (such as the distance from the driver’s current location), which they must accept or reject on that basis alone, with only a few seconds to decide. If they cancel an accepted gig after learning information that indicates it will be unprofitable to undertake, they risk de-activation from the platform entirely. Meanwhile, the platform knows both the origin and the destination of each trip, and of course the platform itself sets the fare based on what it already knows the customer is willing and able to pay. The platform knows that some gigs are less profitable than others, but it promises to serve all its customers and to do so with a minimal wait time. Thus, the platform seeks to induce workers to accept unprofitable gigs…the overall system operates as a non-linear pay structure designed to create dependence on the part of workers (in economic terms, to reduce their residual labor supply elasticity vis-a-vis any one platform), which in turn enables the platform to worsen labor standards and destroy any notion of flexible work, since the worker has no real ability to leave”. In this context, “data increases drivers’ labor supply elasticity; the minimum acceptance rate reduces it”.

Vasiliki (Vass) Bednar points out personalized pricing or algorithmically-determined prices distort markets by “setting prices at a different level for each individual consumer, based on an estimation of what they are willing and able to pay”. In some instances, it could be construed as a form of “price gouging based on personal data”. “This opacity raises the question of whether personalized pricing is inherently discriminatory and what recourse people have if they cannot reliably access the lowest possible price”.

Overall, platforms have been criticised for using data and AI to gamify pricing of medicines, rides, rents[22] to manage yields (maximise profits) from customers and other participants[23]. In effect, intermediaries are taking a bigger cut from an existing industry while individuals (households) are losing out on two fronts; once as workers and again as customers.

Policy challenges: Microstructure

Platforms have emerged as the dominant vehicles for pricing and allocating labour, goods and services. Platforms has provided a means to unbundle and re-bundle, and to price labour and consumption of goods and services into smaller parcels over a shorter time span. This has disrupted the traditional pricing, bundling and regulation of labour, goods and services and given rise to microstructure challenges. There are several aspects to consider.

  • Regulation of prices. Traditionally, prices tended to be fixed by regulation. This changed as airlines adopted yield management and adopted dynamic pricing based on demand and supply elasticities to maximise revenues. Taxi fares were heavily regulated but this changed with ride-sharing platforms[24]. Now, taxi fares and the compensation to drivers is determined by dynamic pricing algorithms; albeit regulators now bind fares and compensation within parameters. While customers and drivers accept modest price variance, nonetheless there are expectations of a degree of fairness and constancy (i.e. prices are non-discriminatory and non–exploitative). Hence, there has been much criticism of surge pricing as a form of price gouging.

I think these criticisms of demand-driven pricing needs to be tempered with context. First, with abundant information and the ability to react quickly, demand-driven pricing and “use” models is here to stay. It would not make sense to revert to price regulation given the technical complexities. Second, it could be argued, in general, that dynamic pricing (and platform efficiency) produces lower prices (than the regulated prices) and expands both demand and supply. It is unfair to focus on the high prices while ignoring the benefits of lower prices and expansion in activities. Third, there is a need to differentiate between price volatility (which is acceptable) vs discriminatory and abusive behaviour which should be mitigated through conduct regulation.

  • Business model uncertainties. In the initial phases, investors’ financing enabled platforms to subsidise (cheaper) fares and (higher) driver compensation to capture market share. Once platforms achieve a monopoly position, the premise is they can then raise fares or reduce driver compensation to grow profitability. This is an unproven hypothesis. Henry Grabar point out “Uber has lost an astounding sum since its founding in 2009, including more than $30 billion in the five-odd years…Together with earlier losses and a similar strategy at rival Lyft, this has amounted to an enormous, investor-fueled subsidy of America’s ride-hailing habit…Uber has always said it would reach profitability at scale, thanks to network effects, etc….but what is scale if not a company that operates in 72 countries and more than 10,500 cities, which last year had 118 million active users every month and completed 6.3 billion rides/trips/deliveries? Uber is the definition of scale, yet it is still nowhere near consistent and reliable profitability. How Uber rights the ship is not for me to figure out, but one obvious answer is that rides have been getting – and will continue to get – more expensive. Average Uber prices rose 92 percent between 2018 and 2021…Both Uber and Lyft have added a surcharge for riders that helps drivers account for high gas prices…It’s the end of a decade in which we changed our systems, our habits, even our architecture, around the assumption that we could be driven around for cheap. The cynical assumption was always that Uber was burning all that investor cash in order to corner the market. Once it killed off car service, taxi cartels, and its ride-hail rivals, the company would stop charging riders less than it was paying drivers and prices would have to go up”. There are thus a lot of uncertainties surrounding the viability and stability of platform business models; which has implications for its workers and customers (households).

There are varied approaches to addressing the microstructure challenges arising from dynamic pricing. The main two approaches view dynamic pricing within the context of market competition and/or information asymmetry. In its analysis of potential harms and benefits, Consumers International and Mozilla argue “personalised pricing is not inherently harmful to consumers, but rather is not always being implemented fairly, responsibly, and transparently, or with proper oversight. There must be effective legal, institutional, and social mechanisms in place to ensure that personalised pricing works in the best interest of all consumers”. They encourage businesses to be more transparent in using personalized pricing and to provide “meaningful transparency and access” for consumers to make informed decisions. They recommend “a future review of Canada’s competition law, with a focus on wage fixing, deceptive pricing and anti-consumer practices”.

Another approach is design regulations to enhance data transparency and choice to drivers. Tyler Sonnemaker notes that to prove its drivers were independent, “in January 2020, Uber gave California drivers more control by allowing them to set their own prices for rides and see passengers’ destinations before picking them up”. “Now that Uber no longer needs to convince California authorities that its drivers are independent, the company plans to reclaim control, revoking the price-setting and passenger destination features it gave drivers barely a year ago…Too many drivers took advantage of the control Uber gave them, picking the most profitable rides while declining others, making it harder for customers to get rides and hurting Uber’s business…one-third of drivers turned down 80% of rides”.

Hence, regulators can choose to oversight data and AI that drive platform pricing algorithms as well as impose transparency and choice requirements to neutralise the platforms’ monopsony advantage with a view to ensuring fairness for workers and customers. Rather than directly target data and AI, regulators can instead focus on outcomes. Regulators can establish public good objectives such as labour and environmental protections and/or to reduce traffic congestion and accidents. It can then design the regulations and set KPIs for platforms.  

The most promising area is to create competition to the platforms by promoting competitors as platform cooperatives[25]. Megan Carnegie notes “social enterprises, which aim to improve their local social economy and include platform co-ops in their remit, already represent more than 8 percent of Europe’s GDP and provide 13.6 million jobs. When workers own and govern their own firms, they don’t have to be subject to problematic algorithms, arbitrary dismissal, or a lack of customer service, running the firm – or voting on its running – in a way that meets their needs…The benefits for workers are both on the remuneration and labor process side and they can do more to control malfeasant customers, which the platform companies have largely been unwilling to do…To compete with well-funded private platforms, active government and municipal intervention is vital for platform cooperatives. This could be through procurement policies that give platform cooperatives preferential treatment over privately-owned companies, conducting research into how laws must adapt around shifts in digital technology and designating public spaces to be used as platform cooperative hubs…With no surge pricing and low cancellation rates, it’s popular with passengers too. Even with appropriate funding, finding team members with the expertise to build the cooperative models and develop the digital tools isn’t easy”.

Ursula Huws “believe the principles of platform technology could be used by governments to transform the way public services are delivered, taking advantage of the way that they efficiently connect users with the services that they want. Under municipal control, or through public-private partnerships, platforms could transform service delivery to citizens…But what if platform technologies could be used to…build new kinds of digitally-managed services in a 21st century version of the welfare state that has become so battered in recent years? Could they be the key to developing new models that promote better work-life balance and improve working conditions for gig workers while also contributing to the reduction of waste and addressing the climate emergency?…For example, the kinds of algorithms that connect workers with customers at short notice could be used to supply social care in response to the real needs of clients rather than the existing rushed and rigid predetermined 15-minute care slots. Or they could be used to provide transport to get patients to hospital appointments or children to school and give people fresh nutritious meals at home, combining free services to those who are eligible with paid-for ones, using voucher systems…Democratically controlled and responsive to local residents, platforms could provide a way to give workers decent jobs and citizens the services they actually want, when they want them”.

Governments could also choose to develop a central platform to coordinate the diverse range of public and private sector schemes covering employment, social protections, healthcare, pensions and others. A platform ensures there is a single true source of data with up-to-date profiles. This would reduce administrative and verification costs, improve matching, cross-referencing, reduce fraud and facilitate self-organising. The consolidation of information would provide a coherent overview of overlaps and gaps. It would facilitate targeting specific objectives such as unemployment by location or groups; or activities such as environmental sustainability, care-taking, welfare services and community building. It is possible to coordinate welfare and participation schemes to feed into each other to strengthen the community ecosystem and economic renewal process. Employment schemes could be designed to assist debt repayment and promote business start-ups; the concept of retirement homes can include work programs; welfare or retirement payments can be linked to payment for caring services by family or relatives. Industry apprenticeship or skill-building schemes can be improved and tied in with the educational system or with other job-matching platforms. In addition, such platforms can provide notification of relevant job openings and may reduce the search frictions workers face in transitioning between jobs in search of higher pay and a better overall fit. There are also prospects of finding synergy from job matching schemes with team-building and community-based enterprise creation to bridge the gap between schooling and work.


Governments are only now beginning to come to terms with managing household and work disruption. At the moment, it is a patchwork of policies. We are missing a macroeconomic model with insights on managing growth when the industrial engine is replaced by digital and financial engines; and on mitigating the social externalities generated by information disruption. In my view, the response to disruption cannot be incremental. It requires a radical reimagination of how society, with more information, should be organised. This would include building an integrated baseline for the most vulnerable, promoting greater equitability to redress imbalances and using platforms to strengthen “modular” communities and to achieve public goals.

In the process, we should relinquish our fears that humans would be replaced by AI or machines. I think is it great if robots and AI do as much “work” as possible. In this context, the bigger challenge is to reorganise or to reinvent of work. Rather than delay creative destruction, governments should focus on promoting the creation of economic activities – whether it is repackaging of physical work or activities in the metaverse – with a view to generating new jobs, income streams and social interactions. In tandem with this, policies should aim to empower individuals by putting more technology tools – like ChatGPT – in their hands and building more platforms that maximise their participation.


André Dua, Kweilin Ellingrud, Bryan Hancock, Ryan Luby, Anu Madgavkar, Sarah Pemberton (23 August 2022) “Freelance, side hustles, and gigs: Many more Americans have become independent workers”. McKinsey & Company.

Angela Chen (13 May 2019) “How Silicon Valley’s successes are fueled by an underclass of ghost workers”. The Verge.

Ben Zipperer, Celine McNicholas, Margaret Poydock, Daniel Schneider, Kristen Harknett (1 June 2022) “National survey of gig workers paints a picture of poor working conditions, low pay”. Economic Policy Institute (EPI).

Charles I. Jones (January 2020) “The end of economic growth? Unintended consequences of a declining population”. National Bureau of Economic Research (NBER).

Conor Gallagher (31 March 2023) “Senators alarmed by use of rent-setting algorithm, say it’s helping drive inflation”. Naked Capitalism.

Consumers International, Mozilla (8 February 2022) “A consumer investigation into personalised pricing”.

Dani Rodrik, Charles Sabel (April 2019) “Building a good jobs economy”. Draft.

David Bloom (14 October 2019) “Live long and prosper? The economics of ageing populations”. Voxeu.

David Coady, Delphine Prady (31 July 2018) “Universal basic income in developing countries: Issues, options, and illustration for India”. International Monetary Fund IMF).

Eric Helland, Alexander Tabarrok (22 May 2019) “Why are the prices so damn high?” Mercatus.

Erik Baker (May 2023) “The age of the crisis of work”. Harpers.

Gilles Deleuze (Winter 1992) “Postscript on the societies of control”. October, Vol. 59.

Gwynn Guilford (21 November 2019) “The great American labor paradox: Plentiful jobs, most of them bad”. Quartz.

Henry Grabar (18 May 2022) “The decade of cheap rides is over”. Slate.

John M. Barrios, Yael V. Hochberg, Hanyi Yi (May 2020) “Launching with a parachute: The gig economy and new business formation”. National Bureau of Economic Research (NBER).

Karen Hao (31 May 2019) “The AI gig economy is coming for you”. MIT Technology Review.

Marc Lavoie, Engelbert Stockhammer (2012) “Wage-led growth: Concept, theories and policies”.  International Labour Office.

Marshall Steinbaum (4 July 2022) “The antitrust case against gig economy labor platforms”. LPE Project.

Matt Bruenig (2018) “Social wealth fund for America”. People’s Policy Project.

Megan Carnegie (30 June 2022) “Worker-owned apps are redefining the sharing economy”. Wired.

Michael Karpman, Stephen Zuckerman, Dulce Gonzalez (August 2018) “Material hardship among nonelderly adults and their families in 2017: Implications for the safety net”. Urban Institute.

Monika Grzegorczyk, Laura Nurski, Mario Mariniello, Tom Schraepen (9 June 2021) “Blending the physical and virtual: a hybrid model for the future of work”. Bruegel.

OECD (2018) “Working better with age: Japan, ageing and employment policies”. OECD Publishing.

OECD (2019) “Policy responses to new forms of work”.

Phuah Eng Chye (2015) Policy paradigms for the anorexic and financialised economy: Managing the transition to an information society.

Phuah Eng Chye (26 August 2017) “The services economy: Revisiting Baumol’s cost disease”.

Phuah Eng Chye (3 February 2018) “The sharing economy: A futuristic taxi landscape (Part 4: The coming of AVs)”.

Phuah Eng Chye (10 February 2018) “The sharing economy: Sharing infrastructure and beyond”.

Phuah Eng Chye (3 March 2018) “Organisation of households: Changing household structures and dependency”.

Phuah Eng Chye (10 March 2018) “Organisation of households: Shrinking households, labour market frictions and societal cultures”.

Phuah Eng Chye (24 November 2018) “Future of work: Redefining work (Part 3: Bad jobs, good jobs and what governments could do about it)”.

Phuah Eng Chye (2 March 2019) “Future of work: Transition to the information society”.

Phuah Eng Chye (16 March 2019) “Future of work: Strategy roadmap for labour”.

Phuah Eng Chye (15 August 2020) “Economics of data (Part 3: Relationship between data and value and the monetisation framework)”.

Ryan Hayes (19 November 2019) “Worker-owned apps are trying to fix the gig economy’s exploitation.” Tech by VICE.

Tyler Sonnemaker (5 April 2021) “Uber gave drivers more control to prove they’re independent. Now the company is taking back control because drivers actually used it”. Business Insider.

Ursula Huws (15 September 2020) “Lessons from the gig economy for transforming public services”. The Conversation.

Vasiliki (Vass) Bednar (24 February 2022) “Personalized pricing of dating apps is deceiving: Competition law may be the cure”. Centre for International Governance Innovation (CIGI).

Vass Bednar, Ana Qarri, Robin Shaban (January 2022) “Study of competition issues in data-driven markets in Canada”. Vivic Research.

Veena Dubal (19 January 2023) “On algorithmic wage discrimination”. UC Hastings Law.

Veena Dubal (23 January 2023) “The house always wins: The algorithmic gamblification of work”. LPE Project.

[1] See Policy paradigms for the anorexic and financialised economy: Managing the transition to an information society.

[2] David Bloom.

[3] Charles I. Jones “The end of economic growth? Unintended consequences of a declining population”.

[4] “Organisation of households: Changing household structures and dependency”; “Organisation of households: Shrinking households, labour market frictions and societal cultures”.

[5] “Economics of data (Part 3: Relationship between data and value and the monetisation framework)”.

[6] “The services economy: Revisiting Baumol’s cost disease”; See Eric Helland and Alexander Tabarrok.

[7] See Michael Karpman, Stephen Zuckerman and Dulce Gonzalez on the hardship of families and the implications for the safety net.

[8] Though concerns have resurfaced that recent AI advances may result in the loss of many jobs. See also Karen Hao “The AI gig economy is coming for you”.

[9] See Erik Baker “The age of the crisis of work”.

[10] See André Dua, Kweilin Ellingrud, Bryan Hancock, Ryan Luby, Anu Madgavkar and Sarah Pemberton.

[11] Georgios Petropoulos, J. Scott Marcus, Nicolas Moës and Enrico Bergamini point out “EU policymakers must find answers to pressing questions: If technology has a negative impact on labour income, how will the welfare state be funded? How can workers’ welfare rights be adequately secured?” See Ben Zipperer, Celine McNicholas, Margaret Poydock, Daniel Schneider and Kristen Harknett; See Angela Chen.

[12] See “Future of work: Redefining work (Part 3: Bad jobs, good jobs and what governments could do about it)”; Dani Rodrik and Charles Sabel “Building a good jobs economy”; Gwynn Guilford “The great American labor paradox: Plentiful jobs, most of them bad”.

[13] Phuah Eng Chye “The services economy: Revisiting Baumol’s cost disease”.

[14] See “Future of work: Strategy roadmap for labour”

[15] See OECD “Working better with age: Japan, ageing and employment policies”.

[16] Platform contributions can be benchmarked against revenues and compared with the proportion made by traditional employers.

[17] See “Future of work: Strategy roadmap for labour”

[18] Matt Bruenig “Social wealth fund for America”.

[19] See “Future of work: Strategy roadmap for labour”

[20] David Coady, Delphine Prady “Universal basic income in developing countries: Issues, options, and illustration for India”.

[21] OECD (2019) “Policy responses to new forms of work”; See “Future of work: Strategy roadmap for labour”

[22] See Conor Gallagher on how algorithms are used to help landlords maximise rents.

[23] See Vass Bednar, Ana Qarri and Robin Shaban for their study on competition issues in data-driven markets in Canada.

[24] See “The sharing economy: A futuristic taxi landscape (Part 4: The coming of AVs)”; “The sharing economy: Sharing infrastructure and beyond”.

[25] See Ryan Hayes.