Academic Reading Articles Practice 8 Test 02

A Central bank digital currencies (CBDCs) are commonly described as “digital cash”: an electronic form of fiat currency issued by a nation’s central bank. Unlike private cryptocurrencies, a CBDC would be a direct claim on the state, similar in legal character to banknotes but designed for modern payment ecosystems. Interest has grown as cash use declines in some economies, as mobile payment platforms become dominant, and as policymakers consider resilience during outages, competition in payments, and the long-run protection of monetary sovereignty in an increasingly digital marketplace.

B CBDCs can be built with contrasting architectures that shape how users hold and transfer value. A token-based design resembles a digital bearer instrument: possession of a cryptographic token can authorise payment, potentially reducing the need for continuous identity checks at the point of transfer. An account-based model, by contrast, relies on verified accounts and identity processes, often tied to know-your-customer (KYC) requirements. Some proposals use distributed ledger technology, while others prefer centrally managed databases; either way, the architectural choice affects privacy, cybersecurity exposure, and the practical division of roles between the central bank and private intermediaries.

C A further distinction concerns who can use the instrument and for what purpose. A retail CBDC would be aimed at households and firms for everyday purchases, while a wholesale CBDC would be restricted to financial institutions for interbank settlement and other back-end transfers. Advocates argue that a retail CBDC could improve payment efficiency and broaden access to sovereign money, especially where bank branches are scarce or fees are high. Yet inclusion is not automatic: if digital money assumes constant connectivity, people without smartphones, stable data plans, or reliable internet may be excluded. For this reason, many designs discuss offline options so that basic payments remain possible during network failures or in remote areas.

D Financial stability is often presented as the most sensitive macroeconomic issue. If households can hold risk-free digital money directly, large shifts out of commercial bank deposits could occur, particularly in periods of stress when depositors seek safety. This disintermediation could raise banks’ funding costs, compress their liquidity buffers, and intensify the speed of bank runs by enabling instant transfers into central bank money. To mitigate this, proposals include tiered remuneration that makes large CBDC balances less attractive, or holding limits and caps that restrict how much can be moved at once, while still allowing a useful payment function.

E Privacy debates are equally consequential, partly because they go beyond engineering into institutional design. Cash provides high anonymity, whereas most electronic payments generate transaction trails that can be analysed for commercial or state purposes. A CBDC could be built with privacy-preserving features, but governments also have legitimate objectives such as preventing money laundering, terrorism financing, and tax evasion. The core question is therefore governance: who can access transaction data, under what legal conditions, and with what oversight, auditability, and due-process safeguards. In policy discussions, the objective is often described as “privacy by design” without creating a blind spot for clearly defined law-enforcement thresholds.

F Implementation decisions are not merely technical; they reflect institutional capacity and risk tolerance. Central banks must decide whether to build core infrastructure in-house or commission private providers, how to set cybersecurity standards against fraud and system failure, and how to integrate a CBDC with existing payment rails so that merchants and consumers can adopt it without high switching costs. Interoperability becomes a key design criterion, especially if multiple domestic systems must connect and if cross-border payments are expected to improve. A further question is operational resilience: a CBDC platform must function during peak demand, recover quickly from outages, and manage software updates without undermining public confidence.

G Some analysts argue that the most immediate benefit of a CBDC may be competitive pressure rather than dramatic new capabilities. If public digital money offers a reliable baseline service, private payment firms may respond by lowering fees, improving uptime, and reducing restrictive “walled garden” practices. In this view, a CBDC can act as a contestability tool in markets where network effects have entrenched dominant platforms. Critics counter that similar outcomes can be achieved through faster payment systems and regulatory reforms without introducing a new form of sovereign money. As a result, several jurisdictions have opted for pilots and limited-scale experiments rather than an abrupt nationwide rollout.

H Finally, cross-border considerations add complexity to the long-term vision. If CBDCs interoperate internationally, they might reduce settlement frictions in remittances and trade, but they could also affect capital flows and the transmission of monetary policy. Ultimately, CBDCs represent a policy choice about the future shape of money and the boundary between public and private provision. Their success would depend on design details, the credibility of privacy governance, safeguards for financial stability, and public trust that the system will be reliable and fairly administered. The central question is not only whether CBDCs are technologically feasible, but whether they are socially and economically desirable given the trade-offs between inclusion, efficiency, and control.

For much of the late twentieth and early twenty-first centuries, globalisation was associated with rapidly expanding trade, intricate cross-border supply chains, and rising international investment. In recent years, “deglobalisation” has become a popular label for a perceived slowdown in that model. Yet the shift is better understood as a change in the form of integration rather than a full retreat. Trade continues to grow in many categories, but the pace is uneven, and the direction of flows is increasingly shaped by political priorities as much as by market price signals.

A central driver is geopolitical fragmentation. Firms that once optimised almost exclusively for cost now weigh exposure to sanctions, export controls, and abrupt policy shifts that can interrupt sourcing overnight. This has encouraged diversification: companies may add alternative suppliers, build redundancy into procurement, or hold larger buffers that would have been rejected under strict just-in-time efficiency. Another strategy is to friend-shore production to countries regarded as politically aligned, reducing vulnerability to hostile policy environments while often accepting higher operating costs and less flexibility than fully global sourcing once offered.

Resilience concerns have also moved from the margins to the centre of boardroom strategy. The pandemic, major shipping disruptions, and episodes of extreme weather revealed how tightly coupled supply networks can transmit shocks. When a single upstream component is delayed, downstream assembly lines may stop, inventories collapse, and contractual penalties mount. In response, some firms simplify multi-tier supply chains, source closer to final markets through near-shoring, or redesign products to rely on more standardised components that can be substituted when disruptions occur. This emphasis on supply chain resilience treats redundancy as insurance, even though it can reduce efficiency in normal times.

Technology is simultaneously changing the economics of location. Automation and advanced manufacturing can reduce the labour-cost advantage that previously made offshoring attractive for some goods, particularly in sectors where robotics and process control compress the role of low-wage labour. However, this does not imply that the labour-cost motive has disappeared across most industries; rather, it interacts with capital intensity, skills availability, and energy prices in complex ways. Digitalisation also expands trade in services, allowing some activities—design, software, analytics, customer support—to be delivered across borders without physical shipment, even when goods flows face new friction.

Strategic technologies have become a focal point for policy intervention. Semiconductors, batteries, and other critical inputs are now treated as national-security assets, prompting governments to subsidise domestic capacity, impose export restrictions, and screen foreign investment more aggressively. Industrial policy is used to accelerate local production ecosystems, with the stated aim of strategic autonomy: maintaining access to essential technologies even under geopolitical stress. These measures can reduce dependence on concentrated suppliers, but they can also introduce tariff barriers and compliance costs that reshape how firms structure global operations.

The outcomes of these pressures are mixed and often look different depending on the metric chosen. In some sectors, firms are engaging in reshoring or building additional capacity closer to consumers; in others, supply chains are not shrinking so much as reconfiguring geographically. A company may add a second or third hub, producing regional networks rather than relying on a single global chain. This can appear as deglobalisation in one statistic—for example, fewer intermediate imports from a particular region—while still reflecting continued cross-border activity in another, such as rising regional trade within a bloc.

Critics warn that the language of deglobalisation can mislead by implying that integration is simply reversing. Total goods trade as a share of global output may plateau, yet services trade and data flows can continue expanding, and investment may shift rather than collapse. Moreover, decoupling can create duplication: multiple plants producing similar components, parallel compliance regimes, and higher consumer prices. These costs can be disproportionately borne by poorer countries that rely on export-led growth, especially if preferential access to rich markets is replaced by tighter rules and politically conditioned market entry.

Policy choices will strongly influence the direction of change. Governments may adopt local-content requirements, broaden investment screening, and support “national champions,” or they may negotiate trusted trade blocs that coordinate rules among politically aligned partners. Such arrangements can increase security and predictability for members, but they can fragment standards globally and reduce the classic gains from specialisation. In this sense, the hyper-globalised era may be ending not because the world is closing, but because integration is being redesigned around resilience, strategic autonomy, and geopolitical risk management.

A
Automation is often portrayed as a single wave that “replaces jobs,” but its labour-market effects are usually more granular and uneven. Most technologies substitute for specific tasks, complement other activities, and then trigger organisational redesign as firms reconfigure workflows. As a result, the same tool can reduce employment in one occupation while increasing demand in another, depending on how quickly processes are reorganised and how rapidly workers can adjust skills. The central question is therefore not whether machines arrive, but how adoption interacts with training, management choices, and the pace of change in local labour markets.

B
Historical experience suggests that technology can create as well as destroy employment, even when displacement is severe in the short run. Mechanisation dramatically reduced agricultural labour, and automation later altered manufacturing headcounts, yet new sectors expanded and absorbed workers over time. However, the benefits were not automatic, and transition costs were often painful. Workers leaving routine roles did not always move smoothly into emerging occupations, and places dependent on a single industry could experience long spells of frictional unemployment and declining local demand. This history is frequently used to argue that the most important issue is not the long-run arithmetic of “jobs lost versus jobs gained,” but the social and geographic unevenness of adjustment.

C
A key analytical concept is task composition. Most jobs are bundles of activities, ranging from routine and predictable work to judgement-intensive, social, or physically adaptable tasks that are harder to codify. Automation tends to target routine tasks first, especially where data are structured and outcomes can be measured. When routine elements are removed, what remains may become more complex and may require reskilling, because workers are left with exceptions, interpretation, and coordination rather than repetition. This is one reason economists discuss labor market polarization: demand can rise at the high end for cognitive non-routine tasks and at the low end for in-person services, while middle-skill routine roles face pressure.

D
New work arrangements are emerging alongside automation, particularly through platforms and remote collaboration tools. Digital labour markets can match freelancers to short projects, widening access to opportunities for some people and allowing firms to source skills across regions. Yet flexibility can come with weaker security: work may be intermittent, benefits may be non-portable, and workers may bear more risk. In addition, algorithmic management can monitor performance through ratings, GPS traces, and time-stamped activity logs, shifting control from human supervisors to software dashboards. These arrangements can blur the boundary between work and rest, especially when tasks arrive continuously and workers feel compelled to remain “available” to protect their visibility in ranking systems.

E
Recent advances in machine learning extend automation beyond physical labour into cognitive domains, including drafting text, classifying images, and detecting patterns in medical scans. These systems can be valuable, but they are often brittle in novel conditions, particularly when inputs differ from training data or when rare edge cases occur. Models can also amplify algorithmic bias embedded in historical datasets, producing uneven error rates across groups and raising questions of accountability. For many organisations, the first challenge is not simply deploying models but maintaining human-in-the-loop oversight, documenting limitations, and deciding who is responsible when automated recommendations contribute to harm.

F
Productivity gains from automation do not automatically translate into shared prosperity. When technology raises output per worker, the distribution of that gain depends on institutions such as unions, labour regulation, corporate governance, and tax systems. If automation mainly increases profits for owners while wages stagnate, inequality can rise even when average productivity improves. Some analysts therefore emphasise bargaining power as a key variable in determining who gains, arguing that the labour market is shaped by rules and negotiation, not just engineering. The same productivity shock can look like broad wage growth in one setting and rising concentration in another, especially if displaced workers face weak re-employment prospects.

G
Policy responses typically focus on skills and safety nets, but governments do not converge on a single model. Many programmes subsidise training, expand apprenticeships, or fund lifelong learning accounts to help workers move into new roles before displacement becomes crisis. Other proposals include portable benefits, strengthened unemployment support, or wage insurance that cushions earnings losses when workers shift into lower-paid jobs. Debates also include broader ideas, such as universal basic income, though views differ on feasibility and incentives. Across these approaches, a recurring argument is that support should be proactive and targeted, reducing the length and severity of transition rather than waiting for unemployment to become entrenched.

H
Firm strategy and market demand can change the net employment effect of automation. Some companies use automation primarily to cut labour costs; others use it to raise quality, expand output, or enter new markets. When demand grows, automation can coincide with hiring because new tasks appear in maintenance, compliance, data preparation, customer support, and supervision. The future of work is therefore not predetermined by machines: it will be shaped by adoption choices, the speed of skills adjustment, and the institutional capacity to manage transition fairly. Automation can raise productivity and create new roles, but without credible safeguards for security and distribution, it can also deepen inequality and insecurity.