This raises a host of questions. What caused the financial market gyrations: was it really a reaction to the ‘mini-budget’ or something wider? Was the Bank right to respond? And what does it mean for the rest of us?

This column tells the story using ten charts running from official data. Most are interactive: you can use the date sliders to zoom in on what’s going on right now or zoom out to see the UK’s long-run history.

The background: prices, policy and debt.

To understand the troubles this week, start with the fact that the UK is currently skewered on a fork with three sharp prongs. The first is prices: the extraordinary run-up of inflation over the past six months. Year-on-year prices rose above 10% and are still close to this (Chart 1).

Figure 1: Inflation (CPI, % change over 12 months)

Source: ONS

The Bank of England, which mandated to hit an inflation target of 2% a year, began to raise the interest rate that it controls, aiming to tamp down inflation. Bank Rate has risen from 0.25% in 2021 to 2.25% (Chart 2). This is a large and rapid monetary tightening, something the UK has not seen for decades.

Figure 2: Bank of England interest rates (bank rate, %, 1975-2022)

Source: Bank of England

The Bank’s task, even before this week, was the most difficult it has faced for years. This is the second prong: tricky monetary policy. While the global financial crisis of 2007-09 and its aftermath were devastating in terms of employment and output, monetary policy decisions in the period from 2010 to 2020 were, in a sense, simple. Both output and inflation tended to be too low, both needed to be pushed up, and stimulative policy – low Bank Rate and quantitative easing (QE) – was a way to do this.

The current mix is different. The UK is like a house where one room – the GDP room (Chart 3) – is too cold and another – the inflation room – is too hot. But the radiators in the rooms have no individual controls, and there is just one thermostat. Do you turn it up or down?

Figure 3: GDP growth rate (quarterly, chained volume measure)

Source: ONS (Series: IHYQ)

The third prong is the state of the country’s public finances. The UK’s debt-to-GDP ratio has risen over the past 30 years. Two events – the global financial crisis and the Covid-19 pandemic – explain big changes. But even in calmer times, debt has tended to creep up (Chart 4).

Figure 4: Public sector net debt (% GDP)

Source: ONS (Series: HS6X)

Chancellor Kwasi Kwarteng’s budget last Friday was supposed to be a ‘mini’ one. In it, he set out a range of tax cuts and national insurance reductions, steps that would reduce the country’s tax revenue. This came on top of increased spending on a package to cap energy prices as a way to help households and firms facing higher costs. With income down and spending up, the implication would be more borrowing (Chart 5).

Figure 5: Public sector net borrowing (£ billion), actual and forecast

Sources: ONS, IFS, OBR. Note: The red bars are forecasted figures based on IFS analysis and OBR data

So, the government was due to sell additional new bonds – the IOUs known as gilts by which it funds public spending that exceeds tax revenues. At the same time, the Bank was set to re-sell bonds it had bought as part of QE. The supply of bonds, in other words, would expand. Prices duly fell. This was then reportedly amplified by pension funds, which became forced sellers. With the price of bonds tumbling, their yields – which move inversely to prices and represent the UK’s borrowing costs – shot up (Chart 6).

Figure 6: UK Government borrowing costs (spot curve, nominal)

Source: Bank of England

The UK’s currency – like its bonds – cheapened. A weaker real economy means less profit for firms and makes dividends less likely: this dulls the demand for UK currency to buy stocks and shares. The bond market turmoil meant lower demand for the sterling needed to buy bonds. Supply and demand were working against UK assets, and the currency duly depreciated fast against the dollar (Chart 7).

Figure 7: Sterling-dollar exchange rate (USD into GDP)

Source: Bank of England (Series: XUDULSS)

Was this an overreaction by international markets or a home-grown problem? The answer is a bit of both. It is certainly the case that bond markets more widely have seen some sharp moves, with the yield on the US ten-year bond – a vital global benchmark – creeping up and seeing some large daily moves (Chart 8). Stepping back (use the slider on the chart), it is clear that bond yields have been extraordinarily low for years.

Figure 8: US-UK ten year bonds

Sources: Bank of England, FRED

But some of it is home-made. One quick and dirty way to assess this is to compare the US and UK government yields. This can be thought of as the ‘UK effect’ – stripping out the global trend. Before this summer, the UK paid less on its ten-year IOUs than a comparable US bond. This reversed in August, with a noticeable spike over the past week. Since then, bond buying by the Bank of England has brought yields back down.

Figure 9: Borrowing costs – the UK effect (spread, UK vs US 10y bonds, premium over Germany)

Sources: Bank of England, FRED, Bundesbank

So, what does this mean for the rest of us? The first thing to realise is that, like it or not, we are all bond market investors. Whether through pensions (either private or employers) insurance funds or our banks, the vast majority of us have a claim on government bonds. Losses on these portfolios have long-term effects on our wealth.

More immediately, the hit will be via interest rates, and in particular mortgages. High street banks’ own funding costs tend to move in line with those of the government, and to be passed on to borrowers. Mortgage rates are likely to rise both because of the Bank Rate hike and due to higher bond yields (Chart 10).

Figure 10: UK household interest rates (%, credit cards/mortgages)

Source: Bank of England (Series: IUMCCTL, IUMTLMV)

This one-two punch could deliver a knockout blow, lowering household disposable incomes that have already been chipped away at by higher prices. While the pound may have bounced back, the economic impact will be a slower burn, felt over the coming years.

Author: Richard Davies, Director

Picture by Glyph Studio on iStock

The post What’s happening to the UK economy? appeared first on Economics Observatory.

The UK is home to a dynamic ecosystem of fintech organisations. These include new challenger banks (such as Monzo and Starling) and scale-ups that make money transfers faster and cheaper (for example Wise) and legal compliance easier (for example Cube). The application of technology in finance is not new, but the current wave of innovation is notable for its scope and scale.

Artificial intelligence and machine learning, cloud computing, open application programming interfaces and blockchain are among the technologies with the greatest impact. They are changing the ways in which financial service providers operate, communicate and engage with consumers and other stakeholders. New fintech applications are mobile-first, customer-centric and disruptive to previously unchallenged parts of the finance sector.

Is fintech benefiting everyone?

A central claim of many financial technology firms is that they provide new ways in which to tackle financial exclusion, that is, the ‘inability, difficulty or reluctance to access mainstream financial services, which, without intervention, can stimulate social exclusion, poverty and inequality’ (House of Lords Liaison Committee, 2021).

Being excluded makes life difficult in today’s highly financialised society. Access to a bank account and other basic banking products is a de facto requirement for most forms of accommodation, quality jobs or receiving welfare. But while consumers increasingly embrace digital alternatives for basic banking services, uptake of solutions that could have a greater impact – particularly among excluded and otherwise vulnerable consumers – has been slow.

Research exploring the use of fintech by financially vulnerable consumers shows that, for fintech to be more socially productive, entrepreneurs and policy-makers must improve both access and trust.

What opportunities does fintech offer to financially vulnerable and excluded individuals?

Traditionally, financial services firms have relied on brick-and-mortar branches and rigid legacy technology systems that are inefficient and costly to operate. These inefficiencies were often amplified by governance processes that require the completion of a series of time-intensive, manual tasks.

Recent fintech innovations have changed this, for example, by creating fully digital banking experiences and by implementing artificial intelligence (AI) to automate searching, matching, comparing, filling forms, reviewing and other rules-based back-office activities (Ashta and Herrmann, 2021). This type of automation leads to cost reductions that have the potential to make financial products and services more affordable to low-income consumers (Philippon, 2019).

In addition to increasing efficiency, open finance and AI can significantly improve the quality of debt advice services by providing a holistic picture of a customers’ financial situation. Machine learning algorithms can analyse large quantities of financial and non-financial data and potentially uncover patterns or early signs of vulnerability that humans might not be able to identify (Azzopardi et al, 2019).

These insights can help advisers to improve the accuracy and timing of their recommendations to customers. Similarly, financial institutions can use insights from open data and machine learning to provide personalised products and services that can improve the financial wellbeing and resilience of customers.

Other AI applications help consumers to identify opportunities to reduce expenditure and maximise their income, for example, by providing income-smoothing options (services that turn unpredictable income streams into regular payments by identifying a customers’ average earnings and balancing spikes or dips).

They can also assist by identifying benefits eligibility or offering automated money guidance. One of the most successful Scottish financial inclusion fintechs, InBest, has developed a platform that integrates these services to help vulnerable consumers to improve their situation and build up financial resilience.

Combining open data with AI and machine learning also enables fintech firms to use new approaches to credit scoring and risk assessment (Bazarbash, 2019). These approaches are potentially more transparent and do not rely solely on credit history. They can therefore provide easier access to credit for people with no or limited credit history (Jagtiani and Lemieux, 2017).

Besides directly addressing excluded or vulnerable consumers, fintech can have indirect effects on financial poverty by increasing productivity and fostering sustainable economic growth (Appiah-Otoo and Song, 2021; Song and Appiah-Otoo, 2022).

For example, financial technology tools for payments, accounting, cash flow management, smart contracts and other business functions can help small and medium-sized enterprises (SMEs) to increase productivity and build up competitive advantages (for example, based on reduced cost of capital, improved operational efficiency or increased liquidity). This creates opportunities for quality employment within and outside the fintech space.

Fintech can also positively contribute to financial inclusion, resilience and wellbeing through government services. Digitising government services can make the distribution of stimulus packages or financial aid much more efficient. In April 2020, roughly 7.4 million consumers in the United States opened PayPal accounts to enable faster receipt and cashing in of their economic impact payments (EIP) that were part of the Covid-19 relief efforts. This is one example of the potential of government-fintech collaboration.

Why have we not yet seen the expected results?

Financial technologies have the potential to help marginalised communities, yet progress has been slow. Our research indicates that there are two main barriers that can limit the ethical and equitable application of fintech for financial inclusion. Policy-makers and entrepreneurs should take these into account as they encourage further activity in this area.

First, there are issues around access. Some of the most vulnerable financially excluded groups in developed countries lack access to even the most basic information communication technologies. Even where individuals own mobile phones or have access to a personal computer with broadband internet, there remain underappreciated hurdles relating to ‘data poverty’ that restrict access to online services.

Data poverty occurs where disadvantaged groups cannot afford to purchase enough data to access online services, thus excluding them from the full range of financial services. This data poverty can be especially pronounced in rural areas, where residents can have less reliable 4G and 5G phone signals.

This research also shows that vulnerable consumers often feel excluded from existing fintech services as they do not have sufficient financial literacy to make sense of new products and services. Attempts to address this issue by ‘educating’ vulnerable consumers are often seen as patronising and can disengage users by putting them into boxes they don’t see themselves in. The complexity of technical jargon and the overuse of buzzwords also act as significant barriers to engaging many vulnerable groups.

A lack of trust is the second major barrier limiting the extent to which fintech is addressing financial inclusion. Research highlights that some disadvantaged groups are wary of new fintech services that are designed specifically to help them.

For example, this study found resistance to a new service that used an innovative algorithm to maximise government welfare benefits for claimants. This was viewed with suspicion by many potential users despite appearing to be a beneficial service. In particular, vulnerable groups were concerned that providing more information to government agencies and their intermediaries could result in them losing money or otherwise being reprimanded for the information they disclosed.

Given that many fintech solutions rely on large quantities of user data to function, the withholding of important information could undermine the viability of services for disadvantaged groups, leading to even greater marginalisation.

The study also showed that individuals can conflate the use of legitimate digital financial services with an increased risk of online fraud and exploitation. Many older communities, and other vulnerable user groups, generalised that ‘most online services are a scam’ and therefore all digital services are better avoided.

There is general inertia around moving away from physical currency, as cash is perceived as a lower risk. Conflicting expert advice (share your data to get better products and services versus don’t share any data to avoid being exploited), as well as complex public debates around questionable data practices – for example, the Facebook-Cambridge Analytica scandal – make it even more challenging for non-expert consumers to judge the legitimacy of fintech solutions without any form of trusted guidance.

How can fintech overcome remaining barriers?

Financial technology holds promise for addressing social exclusion, but there are still barriers from a user perspective. Policy-makers have an important role to play in bridging these supply and demand-side issues that are currently holding back progress.

A first step in this direction could be the development of a set of principles guiding how fintech products and services are developed for marginalised, vulnerable and excluded groups. If widely adopted these could give those groups confidence that financial products and services were ‘safe’ to use. They would also ensure accessibility for a wider community.

We identify the following six principles for those developing fintech solutions for financially excluded groups.

1. Explainability: technologically augmented decision-making affecting vulnerable groups should be fully explicable and auditable. There should be quick, easy and independent means available to challenge potentially unfair decisions.

• Bias mitigation: fintech developers should evaluate potential direct, indirect and intersecting biases when building products and services for marginalised user groups. Mitigation measures should be transparent and comprehensible to consumers and supporting third-sector organisations.

• Dignity: where possible, innovations should be created with – not for – users. User-centred and co-creation design tools should be adopted to improve the legitimacy and adoption of new innovations.

• Business model transparency: fintech ventures working with marginalised groups should be transparent about how revenue is generated, particularly where there is monetisation of user data or customer service fees and interest charged.

• Lightweight and non-obsolescent technologies: fintech entrepreneurs should build technological solutions that require minimal data usage and work on older hardware and operating systems.

• Accessibility and navigability: products and services should have only necessary functionality, be accessible for physically and cognitively impaired individuals and should adopt regionally appropriate variations of the internet crystal mark, which denotes clear use of language.

A common standard based on these principles, or some variation of them would be a productive way of addressing the concerns many vulnerable groups have around adopting new financial technology innovations. Indeed, the adoption of these guiding principles could help all fintech firms – not just those that specifically address vulnerable consumers – to become more socially productive.

Where can I find out more?

• Fintech: financial inclusion or exclusion? Report by Yoke Wang Tok and Dyna Heng (International Monetary Fund working paper).
• On fintech and financial inclusion: Report by Thomas Philippon (Bank of International Settlements working paper).
• Data poverty in Scotland and Wales: Nesta report by Patricia J. Lucas, Rosa Robinson and Lizzy Treacy.
• The impact of digital credit in low-income countries: Report by Valentina Brailovskaya, Pascaline Dupas and Jonathan Robinson.
• Video might kill the radio star: digitalisation and the future of banking: Report by Thorsten Beck, Stephen Cecchetti, Magdalena Grothe, Malcolm Kemp, Loriana Pelizzon and Antonio Sánchez Serrano.
• Fostering fintech for financial transformation: Report by Thorsten Beck and Yung Chul Park.

Who are experts on this question?

• Christine Oughton
• Sian Williams
• Karen Elliot
• Thomas Philippon

Authors: Felix Honecker, Dominic Chalmers and Nicola Anderson

Authors’ note: We would like to thank the members and guests of the FinTech Scotland Consumer Panel and the organisations they are affiliated with.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 860364. This article reflects only the author’s view and the agency is not responsible for any use that may be made of the information it contains.

Photo by lucigerma from iStock

The post How can new financial technologies help to tackle social exclusion? appeared first on Economics Observatory.

In economics, this is referred to as ‘consumption smoothing’, whereby households use savings accumulated in earlier periods to finance their day-to-day activities and maintain their living standards during downturns. 

While research shows only a weak link between earnings and wealth, there is no doubt that households with higher earners and a greater number of individuals in work can better withstand an unexpected economic shock (Pfeffer and Waitkus, 2021). 

In the case of Covid-19, blue-collar workers were disproportionately affected, due to the types of occupations and sectors in which they work (OECD, 2021).

Further, evidence shows that ethnic minority groups in the UK were more likely to report using savings and/or borrowing to mitigate earnings losses experienced early in the pandemic (Crossley et al, 2021a). 

Indeed, Covid-19 brought into sharp focus the vast differences in wealth among individuals and households. These have received growing attention in recent years, following the influential work of Angus Deaton, Thomas Piketty and Emmanuel Saez, among others. 

Inequalities in wealth within countries are stratified by certain characteristics, such as race. The UK provides a useful case study in this respect, given the range of different ethnic minority groups in the country, many of which emigrated from Commonwealth countries throughout the second half of the 20^th century. 

Based on observable characteristics, such as health, education, income (including earnings) and wealth and outcomes such as poverty, ethnic minority groups have assimilated and thrived to varying degrees. But typically, people in these groups are relatively poorer and less wealthy than their white counterparts (Modood et al, 1997; Nandi and Platt, 2010; Fisher and Nandi, 2015; Perez-Hernandez et al, 2018).

The pandemic has exacerbated existing wealth inequalities both across and within different ethnic minority groups in the UK. To understand the impact of the crisis on the racial wealth gap, we first need to understand differences across groups before the arrival of the virus.

Differences in wealth inequalities by major ethnic group in Great Britain 

There are five major ethnic minority groups in Great Britain. Figure 1 shows average differences in total net household wealth just prior to the pandemic. The data, taken from the Wealth and Assets Survey conducted by the Office for National Statistics (ONS), are split by ethnicity, compared with the white British group. The ethnicity assigned to each household is that of the head of the household. Total net household wealth is defined as the sum of net wealth held in property, private pensions, financial and physical items. 

Figure 1: Total net household wealth by ethnic group relative to the white British group

Source: ONS, 2022 
Note: Estimated difference in total net household wealth after controlling for age, sex, education level, socio-economic classification of the head of household, housing tenure and household composition; April 2018-March 2020 prices.

After controlling for important demographic and socio-economic factors, the data show that Pakistani, black African, Bangladeshi, Indian and other Asian households all report significantly lower levels of total net wealth compared with white British households. 

Two notable findings emerge from Figure 1. First the magnitude of the differences between groups is large. For example, the average white British household has a total net wealth of £243,700 more than the average Pakistani household. 

To benchmark the wealth gap, we note the average level of total net wealth among all households (which includes all ethnic minority groups) based on the same data is £576,235. 

Second, the data highlight significant variation in total net household wealth within the same ethnic group. As a result, while there is a wealth gap between certain ethnic minority groups and white British households, there are no statistically significant differences in average wealth holdings between Pakistani, black African, Bangladeshi, Indian and other Asian households. 

It is also important to note that wealth is heavily skewed and under-reported (see Advani et al, 2021). This means that the differences shown when using group averages (as in Figure 1) are affected by households with extreme wealth holdings. 

For example, if instead one compares the difference in total net median household wealth holdings between Pakistani and white British households, the gap is £164,200 (in favour of white British households). The difference between Indians and white British is -£19,500 – that is to say, median total net wealth is higher among Indians (ONS, 2022).

What contributes to net wealth within different ethnic groups?

To understand how the pandemic has affected different ethnic groups to varying degrees, it is important to consider how the composition of household wealth portfolios varies across groups. 

Total net wealth can be broken down into four components: property, financial, physical and private pension wealth. Figure 2 splits out total median household net wealth by the contribution made by each of these wealth types. 

Figure 2: Total and sub-components of median wealth household wealth by ethnicity

Source: ONS, 2022 
Note: Estimates of total household net wealth and sub-components (property, pension, physical and financial) by ethnic minority group; March 2018-April 2020 prices.

Figure 2 shows considerable differences in the level of certain types of wealth and the overall composition of household wealth portfolios held by ethnic minority households. While for most households, property and pensions typically account for the majority of total net household wealth, Indians hold a relatively larger share of their total wealth in the form of property, even compared with the white British group (44% compared with 25%). Comparably, black Africans only hold 13% of their total net household wealth in this form. 

This reflects the fact that around 80% of Indians are likely to hold housing wealth compared with only 39% and 29% of black Caribbeans and black Africans, respectively (ONS, 2020). The average level of net property wealth is also higher for this group. 

For example, the median level of net property wealth held by Indian households is £165,000 versus zero held by black Caribbeans and black Africans – precisely because a higher fraction of the latter two groups report not holding property wealth at all (ONS, 2022).

In the case of pension wealth, the median level of household private pension wealth held by Indians (at £57,100) is far lower than white British households (at £84,800). This is still higher than any other ethnic minority group (ONS, 2022).

This is likely to be explained in part by the labour market characteristics of household members in white British households compared with ethnic minority groups. These include whether they are employed or self-employed, the sectors in which they work, differences in earnings and the number of individuals employed (Vlachantoni et al, 2015). 

The trend in financial wealth held by ethnic minority groups follows a similar pattern to that for housing and pension wealth. But for the majority of households, except the most wealthy, financial wealth constitutes a relatively small fraction of total household net wealth. There is also no clear pattern by ethnic minority group in the case of physical wealth, which includes household goods and vehicles. 

What has been the impact of Covid-19 for ethnic minorities and wealth inequalities?

Covid-19 has exacerbated existing wealth inequalities (Leslie and Shah, 2021; Xu et al, 2022). The nature of the pandemic and its implications for hybrid working caused a surge in demand for housing, which has driven up prices, particularly in areas outside major cities like London. This has been further exacerbated by existing shortages of suitable housing stock. 

The pandemic also bought into focus the importance of owning particular asset types, such as housing, prior to the pandemic. Those groups that owned homes saw sharp and significant increases in their housing wealth. Pension and financial wealth were also directly affected due to their close links with financial markets. Following a sharp deterioration, markets rebounded quickly, and in early 2022, they are close to reaching pre-pandemic levels in the UK. 

Separately, the pandemic has accelerated structural changes in the labour market resulting from rapid technological change, including teleworking, automation and artificial intelligence. These changes imply that earnings and income inequality are likely to have diverged further, given already increasing wage polarisation (Georgieff, 2021).

Jobs in certain sectors of the labour market or those that are low paid – which require repetitive non-cognitive tasks – are more likely to be automated. Evidence suggests that this happened in the textile industry during the 1990s in the UK, disproportionately affecting Indian, Pakistani and Bangladeshi groups (Clark and Shankley, 2020).

What does this imply for ethnic minority wealth inequalities? Data covering the pandemic period are not currently available, but we can infer several likely outcomes given what we know about ethnic minority groups prior to the pandemic and evidence from other studies. 

First, there are effects in terms of differences in homeownership and where ethnic minorities reside. House prices increased by 10.8% between December 2020 and December 2021 alone, and the average house in the UK is now worth £275,000 (ONS, 2022a). 

As a result, in the absence of significant changes in housing tenure over the pandemic period, certain groups, such as Indians and the white majority, are likely to have benefited disproportionately from this price increase. This still applies even if the former group was more likely to reside in London, which saw a relatively smaller increase in property values. 

On the other hand, groups that are more likely to rent in the private sector, such as black Africans and Pakistanis, have experienced rent increases, particularly in the latter part of the pandemic. Average rents are predicted to have risen by 2% between January 2021 and January 2022, with further increases expected (ONS, 2022b). 

The fact that average house prices were 6.7 times average earnings in early 2022 (up from 5.8 in 2019) implies that homeownership opportunities are deteriorating (The Guardian, 2022).

Research also shows that ethnic minority groups were more likely to become unemployed rather than put on furlough. This meant that people in these groups were more likely to draw down savings (reducing their wealth) or borrow (increasing their debt) during the early phase of the pandemic compared with their white counterparts (Crossley et al, 2021a; 2021b).

While the employment gap between ethnic minorities and the white majority had returned to pre-pandemic levels by March 2021 (Crossley et al, 2021b), the initial job displacement meant that a higher proportion of ethnic minorities experienced a period of unemployment before making the transition to a new job.

Evidence also shows that savings rates were much lower, and conversely debt increased, among individuals with the lowest incomes before the pandemic (Crossley et al, 2021b). This is typically more likely to include individuals belonging to an ethnic minority group (Platt, 2011; Perez-Hernandez et al, 2018). 

Financial markets have broadly recovered since the drop at the start of the pandemic and despite the recent disruption due to the war in Ukraine. But the changes to pension wealth over the course of the pandemic will depend on an individual’s employment history, age and the underlying portfolio structure of their pension pot.

The extreme swings in market volatility and the fact that a large proportion of certain ethnic minority groups – such as Pakistanis, Bangladeshis and black Africans (particularly older individuals) – do not have any pension wealth suggest that the distribution of this is likely to remain highly unequal. 

On the other hand, financial wealth is heavily concentrated among the wealthiest in society and, as discussed above, it is more likely to be held by the white majority and certain ethnic minorities. 

ONS analysis of the Wealth and Assets Survey covering the period 2018-20 highlights that 8% of black African households hold negative net financial wealth – in other words, they are in debt. The median financial wealth level held by these households is £200, compared with around £11,700 for Indian households. 

Evidence suggests that even prior to the pandemic, inequality in financial wealth was increasing (ONS, 2019). Going forward, inequality in this type of wealth is likely to have increased further. 

Conclusion 

Wealth plays a central role in determining an individual’s living standards. Along with income, it can help households to cushion unanticipated economic shocks. 

There were significant wealth inequalities prior to Covid-19 across and within ethnic minority groups. Given the composition of ethnic minority household wealth portfolios before the pandemic, it is likely that differences in household wealth have widened. This is largely because of how the crisis has affected labour and financial markets. 

This should be a concern for policy-makers. The government needs to help to support and improve living standards and ensure that all households – particularly the least well off, who are disproportionately more likely to be ethnic minority households – are able to withstand any future shocks. The pandemic has deepened an existing crisis, leaving vulnerable groups increasingly at risk to the next big challenge.

Where can I find out more?

• Wealth and Assets Survey: Data from the ONS.
• Household wealth by ethnicity: Report from the ONS.
• Ethnicity facts and figures: GOV.UK.
• Inequality and poverty: Resolution Foundation.
• Inequality (The Deaton Review): Institute for Fiscal Studies.
• Inequality within ethnic groups: Report from the Joseph Rowntree Foundation.

Who are experts on this question?

• Arun Advani
• Ricky Kanabar
• Lucinda Platt
• Thomas Crossley

Author: Ricky Kanabar

Author's note: The prices in the article using round seven of WAS reflect those reported at the time of the survey interview (between April 2018 and March 2020).
Acknowledgment: The author would like to thank the ONS Wealth and Assets Survey team for providing data relating to round seven of the survey, which was used in this article, the Economics Observatory editorial team and Alita Nandi for reading a preliminary version of this article. 

Photo by Dmytro Varavin from iStock

The post How has Covid-19 affected wealth disparities among ethnic groups in the UK? appeared first on Economics Observatory.

Importantly, research also suggests that wealth is likely to be stratified by a number of factors including membership of an ethnic minority group  (Office for National Statistics, ONS, 2020). For example, the average level of total household net wealth holdings in Pakistani and Bangladeshi households is £243,700 and £201,500 lower than that held by white British households (ONS, 2022).

Given the importance of wealth in determining living standards – and for acting as a cushion against economic shocks such as Covid-19 – having a good understanding of the factors that explain the racial wealth gap is important for the design of policies to improve wealth, social mobility and living standards more generally. 

Factors explaining differences in ethnic minority wealth holdings

Research shows that differences in education, earnings, economic status, immigration history, cultural norms and region explain differences in household wealth holdings (Byrne et al, 2020). These same factors also affect portfolio composition, which is the likelihood of owning certain types of wealth, such as housing or pensions. 

Education

Differences in educational attainment and earnings are also important for understanding wealth differences across ethnic minority groups. Research shows that first generation Indians who emigrated to the UK in the 1960s, typically from East Africa, tended to be relatively well educated and/or business owners before arriving. 

In comparison, groups such as Pakistanis and Bangladeshis, who arrived in the 1960s and 1970s, and black Caribbeans and black Africans, who emigrated during the 1950s and 1970s, were less well educated (Dustmann et al, 2011). 

A bulk of first generation migrants typically entered manual and public sector jobs in urban areas and often settled in the poorest central areas of large cities (Rex and Moore, 1969; Finney and Simpson, 2009). 

While education levels differ across ethnic minority groups, evidence suggests that there is strong intergenerational persistence within certain groups. In other words, within the same ethnic group, there is a high degree of association between the educational attainment of parents and their offspring (Dustmann et al, 2012). 

Education is strongly associated with earnings and the latter is important for access to homeownership. But while educational attainment has increased across generations for most ethnic minority groups, this has not translated into higher earnings to the same extent as for the white majority group. Therefore the strength of the assocation between education, earnings and wealth differs across minority and majority groups (Longhi et al, 2013).

Income and economic status

Although on average the ethnic pay gap has fallen over time to around 2% in 2019, there remain significant differences by ethnic minority group. For example, the median pay gap is between 13% and 16% lower among Pakistanis, white and black Africans, Bangladeshis and black Caribbeans compared with the white British group. In contrast, for Indians and Chinese, it is 16% and 23% higher, respectively (ONS, 2020).

Self-employment is more prevalent among some ethnic minorities compared with the white majority (ONS, 2021). Figures based on the Annual Population Survey show that in 2019, 23.2% of Pakistanis and Bangladeshis reported being self-employed, compared with 14.7% of Indians, 14.6% of Chinese, 11.2% of blacks and 14.9% of the white British group (ONS, 2021).

Economic status and earnings also influence pension wealth and, as highlighted above, this type of wealth – while illiquid (so inaccessible) for working-age households – constitutes an important part of total household net wealth, even though ethnic minorities hold lower levels compared with the white British group. The value of such wealth is heavily related to the performance of financial markets. 

Housing

Homeownership opportunities – which are important for wealth accumulation and reducing wealth inequalities – are likely to be affected by membership of an ethnic minority group. Recent analysis using the Wealth and Assets Survey covering Great Britain shows that homeownership and housing wealth are also increasingly stratified by parental wealth, which is lower among particular ethnic minority groups (Gregg and Kanabar, 2022). 

Further, given the concentration of ethnic minorities in low paying sectors and occupations (Longhi and Brynin, 2015), the likelihood of certain groups getting onto the housing ladder is lower, despite major programmes encouraging homeownership, such as Right to Buy during the 1980s. Education, earnings, employment and region all affect homeownership rates and hence property wealth, which, as shown above, varies significantly by ethnic minority group.

Table 1: Housing tenure by ethnic minority group and white British, 2016-18

Source: Ministry of Housing, Communities and Local Government, 2020; 2021
Note: Figures based on English Housing Survey, 2017 and 2018

Table 1 shows clear differences in housing tenure by ethnic minority group. Among Indians, 74% report owning their home outright or with a mortgage, 11% higher than the average across all groups. The equivalent figure stands at only 20% and 40% among the black African and black Caribbean groups, respectively. 

The opposite patterns holds when considering the proportion of each group that reports living in social housing: 7% among Indians compared with 44% and 40% for black African and black Caribbean groups, respectively. The figures for black groups are more than twice the average across all groups (17%). 

Not only is housing tenure important, but so too is housing adequacy relative to household size. Evidence suggests that overcrowding rates are between six and eight times higher among certain ethnic minority groups compared with white British – 41% among Bangladeshis, 32% among Pakistanis and black versus 5% among white British (Finney and Harries, 2015). 

Figure 1: Ethnicity by region (England and Wales only) in 2011

Source: Office for National Statistics, 2020
Note: Figures based on England and Wales 2011 census

Figure 1 shows that ethnic minority groups are geographically concentrated in certain regions. For example, black groups are more likely to live in London, and Asian groups are more likely to report living in London, the Midlands, and Yorkshire and the Humberside. 

Historical differences in housing values in regions such as London – combined with the initial area of settlement among first generation immigrants and differences in housing tenure – are key factors in explaining the differences in property wealth reported by ethnic minority groups. 

Social norms

Separately, it is important to recognise differences in social norms across ethnic groups in understanding wealth inequalities. The first of these relates to the participation of women in the labour market and household income and wealth. 

For example, data from the Annual Population Survey show that in 2019, only 39% of Bangladeshi and Pakistani women aged between 16 and 64 reported being in employment. This compared with 67% of black women, 69% of Indian women and 74% of white British women. 

Differences in employment rates directly influence the likelihood of homeownership, household earnings and savings, and hence wealth. A related point in this context is the prevalence of multigenerational households and the number of adults in employment. 

It is relatively more common among certain ethnic groups to live in such types of households and for there to be fewer individuals in paid employment due to household composition as well as cultural norms (Perez-Hernandez et al, 2018). 

The second issue around differences in social norms relates to intergenerational transfers, such as inheritances, which have been shown to be correlated with parental wealth and are important for explaining wealth inequalities in the UK (Palomino et al, 2021). 

Despite the lack of research on this issue, given the wealth gap between the majority of ethnic minority groups (except Chinese) and the white majority, even if the likelihood of inheritances or lifetime transfers is equal across groups, holding all else constant – so factors that affect both offspring and parent wealth accumulation – the level differences mean that wealth inequalities are likely to pass down between generations.

Taken together, compared with white British households, ethnic minorities are more likely to live in households with lower levels of total net wealth and income, with fewer people in work. They were also less likely to report homeownership. 

Finally, as has been highlighted, it is important to note the significant differences in household net wealth between and within ethnic minority groups. 

Conclusion 

Wealth plays an important role in influencing living standards throughout people’s lives. Disparities in wealth, which continue to grow over time, are especially influenced by factors such as parental wealth, education and ethnicity (ONS, 2020; Gregg and Kanabar, 2022). 

These same factors also influence the composition of household wealth holdings and whether individuals own their home. Both pension and housing wealth typically account for the bulk of a household’s total net wealth (ONS, 2020).

While white British households hold relatively high levels of housing and pension wealth, for certain ethnic minority groups – such as Indians, Pakistanis and Chinese – housing represents almost half of total net household wealth (ONS, 2022).

On the other hand, for Bangladeshis, black Africans and black Caribbeans, housing only accounts for 13% and 26% of total net household wealth. This is precisely because these groups report much lower levels of homeownership and are concentrated in areas with lower house prices.

Returns to housing are non-trivial: UK house prices have risen by 65% on average in the decade to January 2022. This significant increase highlights a two-sided story in terms of wealth gains – that is, owning versus not owning your home (ONS, 2022).

Tackling wealth inequalities is complex given that individuals accumulate wealth over their lifetimes from a variety of sources, including their parents. Indeed, evidence shows that inequality of opportunity starts early in life (OECD, 2018).

Nonetheless, continued differences in educational achievement and the fact that homeownership is increasingly stratified by parental wealth (Gregg and Kanabar, 2022) – combined with historical differences in housing tenure by ethnic minority group – mean that policy has a crucial role to play. Indeed, the fact that we observe varying levels of wealth and social mobility across and within countries highlights that such outcomes are not inevitable. 

International evidence highlights that policy-makers should focus resources on education, health and family policies, especially early in life to promote social mobility. In terms of dealing with wealth inequalities specifically, policies to limit tax avoidance with respect to wealth, inheritance and gifts have been suggested (OECD, 2018). More recently, there has also been a renewed focus in the UK on tackling regional inequalities.

It is vital to ensure that while policies to promote wealth and social mobility should benefit all individuals, they should especially help those from the least privileged backgrounds and these will inevitably include minority groups. 

Where can I find out more?

• Wealth and Assets Survey: Data from the ONS.
• Inequality within ethnic groups: Report from the Joseph Rowntree Foundation.
• Household wealth by ethnicity: Report from the ONS.

Who are experts on this question?

• Arun Advani
• Ricky Kanabar
• Simonetta Longhi 
• Alita Nandi 
• Lucinda Platt 
• Paul Gregg

Author: Ricky Kanabar

Author's note: The prices in the article using round seven of WAS reflect those reported at the time of the survey interview (between April 2018 and March 2020).
Acknowledgment: The author would like to thank the ONS Wealth and Assets Survey team for providing data relating to round seven of the survey, which was used in this article, the Economics Observatory editorial team and Alita Nandi for reading a preliminary version of this article. 

Photo by VictorHuang from iStock

The post What explains the UK’s racial wealth gap? appeared first on Economics Observatory.

While the MPC has recently started to increase interest rates, they will remain very low by historical standards and substantially below the rate of inflation. This article aims to help us understand what rising inflation will mean in this low interest rate environment.

What are the links between inflation, interest rates and real economy activity?

Economics textbooks usually say that there is a key difference between anticipated inflation (inflation that was expected beforehand) and unanticipated inflation. When inflation is anticipated, transactions can be agreed, with future changes in prices taken into account. In this case, the levels of output, employment and the quantities of goods and services produced remain unaffected by the exact level of inflation.

As inflation rises, so too does the rate of lending/borrowing before considering inflation – what’s known as the ‘nominal interest rate’. This change accommodates the additional growth in prices to maintain the same overall (or real) interest rate. In simple terms, the real rate of interest is simply the nominal rate minus the inflation rate.

With anticipated inflation, if all wages and prices are perfectly flexible (meaning that they can be changed with no additional cost), then they can also be raised together with general inflation. In this case, the relative prices of goods, services and real wages will be unaffected by inflation.

In economics, this idea is known as the ‘classical dichotomy’, and it represents a theoretical benchmark that indicates conditions under which inflation will have no effect on the real economy in terms of things such as output and employment.

If this theory were applicable to the UK economy, we would expect to see the real rate of interest (the nominal rate minus the inflation rate) vary only a little – due to productivity shocks and other factors. In contrast, the nominal interest rate would vary a lot with inflation.

But in fact, at certain times, we observe the exact opposite: the real interest rate varies a lot while the nominal interest rate varies less. This has certainly been the case in the UK since 2009, when the nominal interest rate was first fixed at just above zero (with slight variation) and the real interest rate essentially mirrored this.

In this case, the real interest rate is simply the negative of the inflation rate. For economists, it is this real interest rate that matters for households and firms when they make their savings and investment decisions.

This means that the MPC policy of near zero interest rates will result in notable effects on the real interest rate as inflation varies. Even when fully anticipated, inflation will have real effects on the economy, altering consumption, investment and employment. In the period since 2009, there have been big swings in the real interest rate – from almost -5% in 2011 to just above zero in January 2015-March 2016 (see Figure 1).

Figure 1: Nominal and real interest rates, from 2009 to 2022

Source: Bank of England

This is the longest period of sustained negative real interest rates in UK history, having lasted over a decade. This has had a variety of effects on the economy, but it is the effect on the redistribution of income and wealth that is most important. Since the Second World War, there have only previously been two brief periods of negative real rates: from 1950 to 1953; and from 1974 to 1978.

What role does the central bank play?

The policy rate of interest set by the MPC is essentially a short-run interest rate, captured in the price of short-term government bonds. The other big policy used by central banks since the global financial crisis of 2007-09 has been quantitative easing (QE). This enables the central bank to influence long-term interest rates.

One of the main ways this has affected the economy is via large-scale central bank purchases of longer-term bonds. This has raised the prices of these bonds, which means that the corresponding longer-term interest rates have also fallen.

To illustrate this, Figure 2 compares the yield curves in 2010, 2018 and 2021. The yield curve shows how the interest rate (yield) on government bonds varies with the length of the bond (also called the ‘maturity’). The vertical axis shows the (nominal) return on government bonds and the horizontal axis shows the length of maturity in years.

For all three yield curves, the ‘short end’ at half a year is roughly equal to the policy rate (0.5% in 2010 and 2018, 0.1% in 2021). But the large amounts of QE undertaken between these years has ‘flattened the curve’, bringing down longer-term returns on government bonds. This has led to the real interest rates on longer-term bonds becoming negative as well as at the short end shown in Figure 1.

This means that QE has helped to bring long-term nominal interest rates down closer to zero and below the inflation rate, at least in more recent years. The combination of the MPC interest rate decisions and QE has been to make both short- and long-term real interest rates negative.

Figure 2: UK yield curves in 2010, 2018 and 2021

Source: Bank of England

Note: Since 2010, QE has 'flattened' the yield curve. While the 'short end' is around 0.5 for both 2010 and 2018, the longer end has gone down. 20 years maturity went from 4.5% in 2010 to 2% in 2018, and 1.2% in 2021

One of the major effects of flattening the yield curve has been to boost asset prices, including the stock market and house prices. In general, there is an inverse relationship between the rate of interest and the value of financial assets: lower interest rates mean higher asset prices.

For example, if an asset yields £10 per year, its market value is approximately equal to £10 divided by the interest rate, which gives the amount you would need to invest at that interest rate to give you an income of £10. Using this approximation, if the interest rate was 10%, you would only need to invest £100 to give you an income of £10 per year. If the interest rate is 0.5% – as it has been most of the time – the income of £10 per annum is worth £2,000. Whether the income is from rent, dividends or bonds, the income generated by the asset is worth more when interest rates are lower.

This also has real effects, since the ownership of assets is highly skewed with a small proportion of households owning most of the wealth. Wealth inequality has increased in the UK in recent years.

What are the effects of inflation when there are near zero or fixed interest rates?

So, how does inflation affect the economy in an environment where nominal rates are fixed, as they have been since 2009? There are several effects, even when inflation is anticipated.

First, higher inflation redistributes from lenders to borrowers. With a fixed nominal interest rate, inflation reduces the amount that borrowers have to repay to lenders overall (in real terms). If one person has borrowed £100 from another, and promised to repay £105 in 12 months’ time, the actual value of the eventual £105 will depend on inflation (if prices have gone up, £105 is less valuable).

If inflation over the 12 months is equal to 5%, then the £105 that the borrower has to repay has the same purchasing power as the £100 initially borrowed a year ago. This implies a zero real interest rate. If there had been no inflation, then the real interest rate would have been 5%. If interest rates were operating normally, this effect would be present only if the inflation was not fully anticipated.

Second, the government is the biggest borrower in the UK, with debt equal to almost 100% of GDP. There is therefore a big ‘inflation tax’. The real value of the governments’ liabilities declines, which is in effect a tax levied on the holders of government bonds. If I own bonds that promise to pay me £1,000 next year, the purchasing power of that £1,000 will be less as a result of inflation.

For example, if inflation was 5%, my purchasing power available from the £1,000 will be 5% less (because the items I consume have gone up in price). I will be in exactly the same position as if there had been no inflation and I had been taxed directly on the bond payment.

Due to QE, a large proportion of the UK government debt is held by the Bank of England. But the Bank’s bond purchases are ultimately funded by the creation of reserves at the Bank held by commercial banks. These are liabilities of the public sector to commercial banks, so that inflation erodes their value in the same way as it does bonds held by the private sector and households.

The size of the inflation tax is very large given the high level of debt relative to GDP. The Bank of England expects inflation to reach over 10% in 2022, so the inflation tax will be equivalent to about 10% or more of GDP.

Note that the inflation tax on bonds relies on the fixed interest rate policy. If interest rates were free to adjust, then they would rise with inflation so that the real return on bonds was not affected: the additional interest payments on the bonds would compensate for the extra inflation.

Third, this inflation tax does not fall on financial intermediaries such as banks, because both their assets and liabilities will generally be defined in nominal terms: inflation will reduce the value of their assets (bonds, reserves at the Bank of England) and their liabilities (deposits of firms and households).

Exceptions to this are defined benefit pension schemes, where the liabilities are defined in real inflation-indexed terms and the bonds held are nominal. Any assets or liabilities that are indexed to inflation will not be subject to the inflation tax.

Ultimately, the bulk of the inflation tax is levied on households indirectly, even if they do not directly own government bonds. This is because the broad measure of money (known as M4) takes the form of household deposits at commercial banks. The value of this money held by households is eroded by inflation.

So, in the fixed interest rate environment that has been in place since 2009, inflation has big effects even if fully anticipated. It redistributes purchasing power from savers to borrowers. Savers are hit as the real return on their savings declines and becomes negative. Households with mortgages will benefit as the real cost of their loans falls. But the biggest beneficiary of all is the government, since the value of its debt will decline in real terms and there is in effect an inflation tax.

What are the other effects of inflation?

There are several more standard costs to inflation, even when interest rates are not fixed. The most important ones in the current situation are the following.

Eroding purchasing power

Inflation erodes the real value of wages and benefit payments. If these are set in nominal terms, the process is obvious. Over time, the fixed nominal income is able to buy less if prices are going up. Now, benefits and the minimum wage may be indexed to inflation, in the sense that each year they are updated as a result of last year’s inflation. An increase in inflation will still have an effect: since the indexation is lagged and not instantaneous, real income will be falling until the minimum wage or benefit is updated.

For example, if I have an income of £100 per week, if inflation is 5%, then each month my income will fall by about 0.42% (5% divided by 12 months). But at the end of the year, if it is indexed it will be updated by 5% to take it back to its original level in terms of purchasing power. But in the intervening months, the real value of that income has been declining.

Wages may in principle rise faster than inflation, leading to a rise in real wages. But if inflation is higher than was expected when the wages were agreed, the unexpected inflation will still reduce real wages below the expected value. Where nominal wages are inflexible downwards, inflation might be the natural method of reducing real wages in response to some negative effect.

Informational costs

Inflation reduces the information conveyed by prices in terms of the relative costs of different items, and so may lead to a misallocation of resources. Prices are going up and down all the time. The Consumer Prices Index (CPI) measures the inflation of a basket of over 700 items covering most household expenditures. When inflation is low, most of the changes reflect real changes in relative prices.

But when inflation is higher, the public may confuse nominal price changes and real (relative) changes. People may be put off buying a good or service when its price goes up because they think it has gone up relative to other goods when it has not.

Further, if inflation leads to more uncertainty about relative prices, it will lead households to devote more time to researching prices – sometimes called the ‘shoe leather’ cost of inflation.

Higher costs of holding money

Inflation introduces a cost of holding money (at least for non-interest bearing deposits). This means that it erodes the ability of money to act as a store of value. When inflation is very high, households may be driven to holding other assets that are more volatile, such as gold or Bitcoin, or substituting foreign currency (for example, the US dollar) for the domestic currency. It should be noted that this flight from money usually only happens in a hyper-inflation.

Indeed, in his theory of the optimal quantity of money, Milton Friedman argued that to encourage people to hold money, the inflation rate should be negative (so that there was a real return to holding money). The role of money as a store of value is very important and encourages savings. This role is undermined by inflation.

Great uncertainty

Inflation creates uncertainty, which discourages investment by firms since the returns to investment become less predictable. This uncertainty also makes households worse off. In essence, higher inflation can lead to households and firms putting a higher probability of a bad outcome.

History tells us that when inflation is prolonged, it becomes entrenched and then it is hard and costly to reduce it. If higher inflation becomes part of firms’ and households’ expectations, then inflation can become hard-wired into wage and price decisions.

The costs of curbing these inflationary expectations will come in the form of low growth and unemployment, as illustrated by the big recessions of the 1980s in the UK and the United States when the ‘Great Inflation’ of the 1970s was reversed.

What are the implications for monetary policy?

Prior to 2009, the MPC set interest rates above the rate of inflation nearly all of the time. Inflation almost always remained in the target range of 1-3% from 1993 onwards, with average inflation at exactly 2%.

This was a period of active inflation targeting, where the nominal interest rate was kept above the inflation rate and varied to keep inflation on target. Inflation expectations settled down at 2% from the mid-1990s as the private sector came to trust that the Bank of England was willing and able to keep inflation at this long-run average.

Since 2009, the priorities of the MPC have shifted away from inflation and more towards supporting the recovery. Inflation has swung around more wildly: from 0% to 5%, but still with an average around 2%.

Until 2020, inflation expectations have remained anchored. But now there is something of a moment of truth for the MPC. Inflation looks set to rise to 10% or possibly more in 2022. If the Bank of England keeps interest rates at these very low levels, then the private sector will lose faith in the Bank’s willingness and ability to control inflation and keep it low.

But the Bank may be a prisoner of circumstances. It is ultimately subordinate to the elected government and the chancellor faces a very high level of government debt. If the Bank were to raise interest rates, this would have a negative effect on government finances. Also, low interest rates have led to high asset prices, not just in bond markets but also in the housing and stock markets.

Raising interest rates might well lead to a decline in the values of these assets, which would be very unpopular with the wealthy and homeowners. This means that the Bank may be unwilling and unable to raise interest rates significantly.

Indeed, although current expectations for inflation are close to or above double digits in Europe and North America, markets expect interest rates to stay at levels well below inflation and below their levels before the global financial crisis of 2007-09.

Such a situation is sometimes referred to as ‘Japanification’, in reference to Japan’s experience since 1990. Given the testing fiscal situation of many economies, perhaps inflation will be the inevitable outcome of an inability of central banks to raise interest rates and governments to raise taxes or cut expenditures.

The war in Ukraine and ensuing sanctions imposed by the West have only made matters worse in terms of policy choices of central banks and governments by making the supply side of the economy worse.

Where can I find out more?

• What may happen when central banks wake up to more persistent inflation: Charles Goodhart and Manoj Pradhan.
• NIESR Monthly Inflation Blog by Huw Dixon.

Who are experts on this question?

• Jagjit Chadha
• Huw Dixon
• Michael McMahon
• Richard Davies

Author: Huw Dixon

Picture by Thinglass on iStock

The post How does inflation affect the economy when interest rates are near zero? appeared first on Economics Observatory.

web3 promoters

For its advocates, web3 marks an important shift towards the next iteration of the internet. Its predecessor, Web 2.0 – the era of large, powerful social media platforms (such as Facebook) – is said to be characterised by asymmetries and injustices.

Dominated by a small number of Big Tech companies whose founders and investors have amassed unprecedented amounts of wealth and power, Web 2.0 has had consequences that are widely seen as damaging to society and democratic institutions. 

This financial success seems to have been built off the backs of Web 2.0’s users. Professional creators of music, imagery and video receive only a small fraction of the revenues that their content generates for platforms like Spotify and YouTube.

Developers of apps have no option but to pay 15-30% of their revenue to the App Store (Apple) and Play Store (Google/Android) in return for distribution. At the same time, ordinary users supply the posts, engagement and behavioural data that are integral to the advertising-based business models of Instagram, Twitter and TikTok. Despite their role as ‘prosumers’ (producing as well as consuming), they receive no financial compensation. 

By contrast, web3 is said to offer a more egalitarian, peer-to-peer vision of the web, giving all users 'skin in the game'. By using blockchain technology to decentralise the web’s technical, legal and payments infrastructure, web3 supposedly promises to sweep away today’s Big Tech companies, which are seen as abusing their market position as gatekeepers to extract economic rents. 

In their place will be new protocols and platforms, constituted as distributed autonomous organisations (DAOs). According to web3 advocates, DAOs will be governed by their communities, transparent in their operations and immune from capture by narrow financial interests thanks to smart contracts (self-enforcing contracts programmed in computer code). 

Transactions will take place in cryptocurrencies, with non-fungible tokens (NFTs) allowing intellectual property rights to be asserted over digital files, with benefits for creators and markets.

In time, these technologies will supposedly form the basis for a thriving economy in the metaverse – the putative 3D online world in which people will be able to work, socialise and play games in virtual reality. For now, the majority of web3 companies are focused on building the underlying ‘rails’, such as payments (for example, Ripple), technical infrastructure (for example, Aligned) and fraud detection (for example, Chainalysis). 

web3 detractors

Critics of web3 bring a very different perspective. Cryptocurrency sceptics – so-called ‘NoCoiners’ – see web3 as a cynical rebranding exercise. In their view, blockchain is a defunct technology and cryptocurrencies are scams that combine elements of Ponzi, pyramid and multi-level marketing schemes. 

In such schemes, a constant supply of new marks is required to provide earlier investors with liquidity – and the inevitable conclusion is collapse. These critics say that web3 should therefore be understood as a story invented to make cryptocurrency investment appear more attractive to digital creators and those who otherwise dislike Big Tech. 

Some within the crypto movement also have deep reservations about web3 – including former Twitter chief executive officer, Jack Dorsey. Here, the objection relates to the influence of venture capital investors. With more funds at their disposal than there are good investment opportunities, investors like Andreesen Horowitz – a venture capital firm based in Silicon Valley – have been highly active in developing the web3 market, through public relations and government outreach (as well as large investments in web3 companies like the NFT marketplace OpenSea). 

Outsized returns for the same group of investors who have profited from the dominance of today’s Big Tech companies are clearly at odds with the libertarian project of radical decentralisation, to which Jack Dorsey and many other crypto enthusiasts subscribe. 

What are some possible implications for the UK economy?

The criticisms levelled by web3’s detractors seem to be good reasons to reserve judgement on the overall vision for web3. It is also useful to break it down into its component parts, specifically cryptocurrency adoption, tokenisation and virtual economic growth.

Cryptocurrency adoption 

‘Cryptocurrency’ is something of a misnomer. There are very few things that Bitcoin, Ether or DogeCoin can actually be spent on – illegal drugs and NFTs notwithstanding. While cryptocurrency exchanges report billions of dollars’ worth of trading, this is overwhelmingly financial speculation and barely touches the real economy. In fact, it is possible that such speculation is channelling capital away from more productive forms of investment.

Cryptocurrency prices are also extremely volatile. According to the Financial Conduct Authority (FCA), 2.3 million UK consumershave already invested in crypto assets, meaning that a market crash might lead to large losses for retail investors. This would inevitably bring adverse consequences for consumer confidence and spending.

The same goes for fraud, which appears to be endemic to the crypto space. Meanwhile, the anonymity afforded by cryptocurrency significantly increases cybercriminals’ economic incentives to mount ransomware attacks. Affecting three-quarters of UK businesses in 2021, these involve hackers encrypting an organisation’s data and demanding a Bitcoin ransom to decrypt it. 

But UK regulators seem to be more concerned about the risk of financial instability. Most cryptocurrency is held by institutional investors, including hedge funds with leveraged positions. A collapse in crypto asset prices could force investors to sell off other assets to cover losses, reducing liquidity in the financial system and affecting investor sentiment. This could then have potential knock-on consequences for the real economy. 

As such, cryptocurrency markets can be compared to markets for derivatives such as futures and options: they represent a growth opportunity for the financial sector, but a systemic risk to the wider economy. 

But were the Bank of England to launch a central bank digital currency (CBDC), other opportunities might open up. For example, in a future downturn, the government might want to use monetary policy to stimulate economic activity. If it were to issue stimulus payments to individuals and businesses in a CBDC, it could programme in rapid devaluation, creating a strong incentive to spend rather than save, and hence increasing the effectiveness of the policy.

Tokenisation

Rather than issuing shares, web3 organisations issue tokens. These can offer rights of access to the organisation’s products, voting rights on aspects of the organisation’s decision-making, rights over digital property or a combination of all three. 

As tokens are financial assets, they can be traded speculatively in secondary markets. Much commentary has focused on cases where tokens have been instrumentalised in ‘pump-and-dump’ schemes – a form of scam where token-holders hype an asset to drive its price up sharply (pumping), before selling off their holdings (dumping) and precipitating a crash. 

Concerns have also been raised about the tokenisation of loyalty programmes and merchandise by UK football clubs, since it exposes fans to volatile crypto asset markets without obvious benefits over more conventional structures.

But from a purely economic perspective, tokenisation may prove to be an important innovation, in that it provides a new way for organisations to raise capital. The existence of the secondary market means that seed investors in web3 start-ups benefit from much greater liquidity than would be available if they bought equity. 

This can reasonably be expected to increase the pool of capital accessible to early stage tech businesses, with favourable consequences for the development of the tech sector. Given the UK’s strength in financial technology (fintech), decentralised finance (or DeFi) seems like a particular opportunity. 

Similarly, tokenisation could provide small and medium-sized businesses, which are ordinarily subject to banks’ fluctuating appetite for risk, with an alternative source of growth capital. Meanwhile, other types of organisation that typically have limited access to capital markets – including social ventures and community projects – may see issuing tokens as a scalable alternative to grant applications or crowdfunding.

Tokenisation is perhaps most advanced in the creative sector. Before the advent of NFTs, there were few incentives for producing monetisable digital artwork, as files could easily be pirated. By providing more or less immutable records of ownership for digital files, NFTs provide incentives and make it technically possible for artists to receive automatic royalties on re-sales of their work. 

Combined with the ability to sell directly to the public without intermediation by commercial galleries, NFTs seem to be making it easier for creators to develop real businesses (although it is not yet clear whether current levels of demand are sustainable).

In general, if one subscribes to the view that greater supply of capital leads to productive investment, job creation and growth, the potential of tokenisation should be taken seriously. 

Figure 1: UK consumer interest in cryptocurrencies and NFTs during the pandemic period, as measured by internet searches

Source: Google Trends

Virtual economic growth

The idea of a metaverse economy might seem particularly far-fetched, but a substantial virtual economy already exists. Sales of virtual goods inside ‘massively multiplayer online games’ (or MMOs) are estimated – admittedly by gaming industry market intelligence firms – to have amounted to $40-93 billion globally in 2019, and to be growing at a rate of around 15%. 

Many games have native currencies that can be exchanged for skins (virtual goods such as clothes or armour, which alter a player’s in-game appearance). In the video game Elite Dangerous, for example, a currency called ARX can be bought or earned through game-playing and then used to purchase livery for the player’s spacecraft.

Advocates of web3 argue that the development of the wider virtual economy is held back by the absence of property rights. Currently, a dashboard ornament purchased in Elite Dangerous cannot be taken into World of Warcraft: it remains the property of the game’s developer, Frontier Developments, which could, if they wished, confiscate it from a player who had paid for it. 

Replacing native currencies with cryptocurrency and minting skins as NFTs, on the other hand, would provide stronger incentives for third-party developers to create new ranges of virtual goods, and for players to increase their spending on them. This seems at least plausible and much more like real economic activity than cryptocurrency speculation (though it should be noted that many in the gaming community are unconvinced that it would be technically feasible or additive to the game-playing experience). 

What is more certain is the contribution of the UK video gaming industry to the economy: around £1.8 billion towards GDP and around 40,000 jobs in 2018. Larger than any of its European counterparts, it is well-placed to benefit if web3 technologies do indeed drive gaming innovations.

Conclusion

Predicting the economic impact of emerging technologies is notoriously difficult. The biggest benefits from technological change often come from positive spillovers and the biggest risks from unforeseen externalities. Which of the stories about web3 sketched out in this piece will come true is anybody’s guess. 

But increasing amounts of Silicon Valley’s abundance of capital and software engineering talent are being poured into web3 projects. And as the Web 2.0 era has shown, these decisions about where to focus energy will have repercussions for the economy and beyond. 

Where can I find out more?

• Policy Brief: Crypto, web3, and the metaverse: A simple explanation of cryptocurrencies, blockchain, NFTs and the metaverse, together with discussion of web3’s policy implications, by the Bennett Institute for Public Policy.
• web3 policy handbook: US venture capital investors Andreesen Horowitz make a bullish case for web3 and suggest actions that governments should take to encourage its development.
• Line Goes Up – The Problem With NFTs: an entertaining if somewhat polemical video essay that aims to debunk claims that web3 technologies can form the basis of a more equitable internet.
• The Crypto Syllabus: comprehensive reading lists for studying web3 from social, economic and technological perspectives, with short introductory overviews. 

Who are experts on this question?

• Molly White
• Vitalik Buterin
• Edward Ongweso Jr
• Balaji Srinivasan
• Evgeny Morozov

Author: Sam Gilbert

Picture by Antonio Solano

The post What is web3 and what might it mean for the UK economy? appeared first on Economics Observatory.

Figure 1: Inflation rates in the last decade

Source: Office for National Statistics

Most of this recent growth in inflation was driven by goods and services that have bounced back this year compared with when prices fell in 2020. For example, on average the prices of food, transport, clothing, eating out and fuel have all increased. This was offset partially by a fall in the price of goods that rose in cost during the first lockdown, such as toys, games and other products related to hobbies.

Compared to 2020, prices have risen significantly, but compared with pre-pandemic prices they are relatively stable. For example, the price of a litre of petrol has risen by 23p since June 2020 (from £1.07 to £1.30) but only by 1p compared with June 2019 (from £1.29 to £1.30).

The current high levels of inflation are likely to only be ‘transitory’ – as each month of new data sees a month of 2020 data ‘drop out'. For example, these new data add the latest inflation figures since May, but also now exclude the data from over a year ago (i.e. May-June 2020 ‘drops out’). Further, some predict that inflation is likely to rise until early 2022 and then come down again because the unusually low prices of 2020 will have ‘dropped out’.

The largest contributor to inflation came from transport, contributing 0.11% of the 0.5% rise – its highest contribution since November 2011. This was due to falling prices last year, where reduced travel saw petrol prices hit a four-year low. Prices picked up in 2021, with inflation for motor fuels at 20.3% for the year – the highest since 2010. Second-hand cars also contributed to transport prices, with global supply shortages of microchips slowing the production of new cars. This has increased the demand for used cars and thereby their prices.

Other contributors were clothing and footwear (which contributed 0.06%), as well as food and non-alcoholic beverages (0.08%). Clothing and footwear usually see a seasonal fall in prices at this time of year – the summer sales – but the timing of lockdown easing meant people were back shopping regardless of any sales, countering this seasonal trend.

Figure 2: Inflation levels by sector

Source: Office for National Statistics

The Retail Price Index (RPI) – another way of measuring inflation which excludes housing costs – was up from 3.3% in May to 3.9%. This is important as many pensions’ values increase in line with the RPI inflation rate, which is almost always higher than the Consumer Price Index (CPI) rate.

While these figures seem high for the UK, inflation in the United States currently sits at 5.4%. The contributors to US inflation are different to the UK, with the trade-war with China having a direct impact on the prices of some goods through increased tariffs. There is a chance that US inflation may spread to other countries, as many world prices are denominated in US dollars. But this remains to be seen and will depend on dollar exchange rates with other currencies.

What happens next?

At 2.5%, UK inflation is above the country’s 2% target. But it is within one percentage point of the target and does not yet warrant an explanatory letter from the Bank of England to the Chancellor of the Exchequer. Andy Haldane, departing Chief Economist for the Bank of England, predicts that inflation could reach almost 4% this year, while the National Institute Of Economic and Social Research (NIESR) predict there is a 1 in 10 chance that inflation could exceed 5%. For context, inflation has remained below 4% since 2012.

The Monetary Policy Committee have tools to control inflation and have not yet acted on this current change. The committee predict that inflation will temporarily peak at slightly above 3% this year as the economy transitions ‘back to normal’. Interest rates – the cost of borrowing from the central bank – are the main tool to control inflation and currently remain at their 0.1%.

The Monetary Policy Committee next meet on 5 August 2021 to decide the base interest rate . As it stands, inflation is within the target range (1 to 3%) and can be explained by last year’s supply shortages and low prices. UK inflation is not as high as in the United States, and the Monetary Policy Committee have the tools at hand to prevent inflation rising continuously. But it will not be until early-2022 before we have a more stable picture of post-pandemic inflation. Until then, the committee’s base rate decisions can give an insight into future expectations.

Where can I find out more?

• This data release from the ONS provides more detail on the latest trends in inflation.
• Previous data releases can be found here.
• CPI Inflation, June 2021: NIESR blog post from Huw Dixon on inflation.

Who are experts on this question?

• Jagjit Chadha, NIESR
• Richard Davies, Economics Observatory & University of Bristol
• Hande Küçük, NIESR
• Michael McMahon, University of Oxford
• Corrado Macchiarelli, NIESR
• Huw Dixon, Cardiff University

Author: Max Wood

The post UK inflation: What do the latest data tell us? appeared first on Economics Observatory.

Our evidence suggests that a large part of the world’s population have insufficient savings to weather an income shock. Indeed, financing an income shortfall for a period of three months would lead to a complete depletion of liquid financial assets for a third of households in our global sample. To determine effective policy, understanding the resilience of households to severe income shocks remains essential for any future economic disruptions, such as those resulting from the looming climate crisis.

One important policy instrument during such emergencies is the direct financial support to households. But the extent to which countries can provide this type of support varies, not least because of the differences in their capacity to take on debt to finance such a scheme.

While advanced economies usually have the ability to access global capital markets quickly and at a reasonable cost, emerging economies are seldom permitted this luxury and face higher rates on loans. For example, while Germany, Switzerland and the UK can borrow virtually at zero cost, the rates on government debt reach 6% per year for India, 7% for Mexico and 9% for Brazil (Bloomberg, 2021). This ‘credit constraint’ can particularly limit the space for government action in emerging economies (Kose et al, 2017).

But even if governments can access capital quickly and at scale, without a clear understanding of households’ financial circumstances and resources, direct support mechanisms are blunt instruments, often unable to target with accuracy those most in need.

What does the evidence tell us?

Since early 2020, a number of studies have explored households’ ability to cope with the economic costs of the pandemic. Typically, these use high-frequency data for a non-representative sample of households within specific countries to learn about how spending patterns have changed and how income opportunities have dwindled in different sectors of the economy.

For example, research shows that in the UK, roughly a third of households have trouble keeping up with bill payments, 42% have cut other expenditure to prioritise housing costs and over half have dipped into their savings (Brewer and Handscomb, 2020). Other work uses high-frequency commercial data from financial technology (‘fintech’) companies to document rising inequality during the pandemic, with the most economically vulnerable groups experiencing the largest declines in spending and the slowest recovery (Baker et al, 2020; Hacioglu et al, 2020).

In terms of suggested solutions, studies indicate that traditional macroeconomic tools used to stimulate demand across the whole economy have little power to restore employment when consumer spending is limited due to lockdowns (Chetty et al, 2020). For example, encouraging spending has little effect when shops, restaurants and entertainment venues are closed. Instead, these studies suggest that direct government support for households is more effective for reducing economic hardship.

Measures designed to support households helped to mitigate income inequality in the early stage of the crisis (O’Donoghue et al, 2020). This is chiefly because of their effects on vulnerable sections of the population (such as those who are self-employed and cannot rely on social networks). Households are prepared to sacrifice, on average, up to 10% of their regular income to be guaranteed similar support during future shocks.

It is also important to have a clear understanding of household expenditure across the income and wealth distributions. Without such information, the identification of financially vulnerable households can end up being imperfect. For example, one study shows that 60% of postponements to mortgage or loan payments in the United States was provided to individuals with above the median income, since higher income individuals tend to have greater debt balances (Cherry et al, 2021).

In emerging economies, policy targeting issues are an additional problem to contend with, as support schemes need to be implemented with little existing infrastructure in place, and the social protection systems that financially vulnerable households can access, such as unemployment benefits and insurance, are generally weak (Gerard et al, 2020).

What do we mean by financial vulnerability?

Two important features of a household’s financial position determine its vulnerability. The first is the minimum level of spending required for food, essential utilities, housing or rental expenses and any monthly debt repayments.

The second is the level of liquid financial assets at the household’s disposal at the point of a shock (such as cash savings, deposit accounts or other liquid financial assets). Without state support, households will need to draw immediately on their liquid financial assets and/or reduce consumption to weather the storm.

Using data comparing 12 developed countries and 12 emerging/middle-income countries, Figure 1 shows the fraction of households in each category that hold different types of asset classes, debt and consumption.

Most households around the world own positive amounts of liquid financial assets (mainly in the form of cash or deposit accounts), as well as physical assets such as cars and property. A lower fraction of households in all countries hold illiquid financial assets, such as pensions and retirement savings accounts. Overall, roughly 50% of households in the developed world have illiquid financial assets, while less than 25% do so in emerging economies.

Figure 1: Participation rates in assets and debt markets

Source: Authors’ calculations based on international household survey data in the euro area, South Africa, Thailand, India, China, the UK and the United States.

In terms of debt, one in two households in developed countries have outstanding debt, but less than a quarter of households in emerging economies do so. Secured debt (debt that is collateralised by assets of some form) is found mainly in developed economy households, while the fractions of households holding unsecured debt are similar in developed and emerging economies.

In terms of monthly consumption expenditures, households in developed countries are more likely to be renters and to repay debt. But they are also more likely to have excess savings – that is, enough excess income to engage in non-essential purchases and to accumulate savings.

Figure 2 takes a ‘balance sheet’ view of household assets and liabilities, adding up all households into a single ‘representative household’ and showing the shares of different assets and liabilities for this grouped entity. Across all countries, physical assets account for the largest share of total assets, and households hold only relatively modest levels of liquid financial assets.

Figure 2: Balance sheet of the representative household

Source: Authors’ calculations

On the liabilities side of the household balance sheet, unsecured borrowing accounts for a very small share of outstanding debt. In emerging markets especially, this reflects the limited ability of households to access credit products, as well as the often-prohibitive costs of both formal and informal borrowing. This may also reflect unrecorded informal debt obligations, or obligations to family and friends.

Finally, the representative household in emerging economies spends a larger share of income on food, utilities and rent than their counterpart in a developed economy, an almost negligible amount is allocated to debt repayment, and a significantly lower fraction of income remains to cover non-essential consumption and savings – around 40% compared with 63% for a typical household in a developed economy.

How can we quantify and compare financial vulnerability?

Liquid financial wealth on household balance sheets is an essential buffer in the event of an income shock. A household’s resilience or vulnerability can be computed as its total level of liquid financial wealth divided by its total monthly consumption expenditure.

This measure of resilience tells us how many months a given household can maintain its level of consumption after an income shock by solely relying on its financial wealth. In a given country’s population, the level of financial wealth across households and the patterns of consumption across this distribution jointly determine the level of resilience of the country’s households.

Figure 3 reports the fraction of households in each country in the sample (of 12 developed and 12 emerging economies) that have a resilience level below three months, six months and one year.

There is significant variation in resilience across these 24 countries. While the fraction of households that cannotself-sustain using financial wealth for three months is higher on average for emerging markets (around 50%) than developed markets (around 40%), it is noteworthy that by the one-year mark, the vulnerable fraction of the population is not very different across emerging and developed economies, at around 70%.

Figure 3: Financial vulnerability in the face of income shocks

Source: Authors’ calculations.
Note: A small number of households in each country consume amounts that exceed their incomes – for example, because they receive emergency relief or other forms of short-term income support that are not reported in the surveys. Such households are excluded from the computation of net measure of financial vulnerability.

How can households cope following an income shock? Figure 4 displays the impact of different coping mechanisms available to households, including the effect of the introduction of policy measures such as debt relief.

The left panel of the figure shows the cumulative share of vulnerable households for various months after the initial income shock. The lower the curve on the vertical axis, the lower the fraction of vulnerable households; conversely, the higher the curve, the higher the fraction of resilient households.

The figure also shows that if all household debt repayments were completely stopped through policy, the vulnerability/resilience curve shifts only very marginally for developed markets, and it hardly moves at all for emerging markets. This illustrates the relatively low power of temporary debt relief programmes, especially in emerging economies.

Figure 4: Margins of adjustment and coping mechanisms

Panel A

Panel B

Source: Authors’ calculations.

Instead, if households were to liquidate their illiquidfinancial assets fully, there is a much more substantial positive effect on resilience. But roughly 40% of developed market households continue to be vulnerable six months after the initial shock. In contrast, in emerging markets, illiquid financial assets shift the curve down, but only marginally, reflecting relatively low levels of savings in the form of retirement savings or pensions held by most households in such economies.

These alternative coping strategies highlight the important role of wealth inequality in amplifying vulnerability, and demonstrate the ineffectiveness of particular policy options during crises such as the pandemic. For example, debt relief as a policy tool is likely to be ineffective as those it benefits are already resilient to an income shock, and those who would benefit most from accessing illiquid savings during difficult circumstances have not been able to accumulate such assets, given their relatively low disposable income in good times.

In both emerging and developed markets, a policy instrument that dramatically changes the shape of the vulnerability curve is income support. A 50% income support policy brings the total fraction of vulnerable households to near zero for the first six months following the shock in both sets of economies.

But such policies are enormously expensive to implement and pose major administrative challenges for governments. This is especially true in emerging markets where fiscal imprudence is unsustainable. Finally, efficiently implementing direct transfers is often infeasible for sheer lack of information about the population and limited infrastructure to administer payments.

Conclusions

Faced with enormous income uncertainty in the spring and summer of 2020, households around the world were faced with two main options if the worst materialised: either to cut consumption deeply and defer non-essential purchases; or to draw down accumulated savings.

Unfortunately, for a large part of the world’s population, the second option is just not viable. Financing an income shortfall for a period of three months – not an unusual circumstance for self-employed entrepreneurs and wage earners in many industries – results in complete depletion of liquid financial assets for roughly every third household in the sample of countries that we have analysed.

In the absence of substantial government support, over a longer horizon of a year, income shortfalls given these low levels of observed financial resilience can push two-thirds of households into significant hardship. This generates enormous reliance on government support, but most emerging markets possess little wiggle room to provide support at such a massive scale. What’s more, when shocks are long lasting, neither advanced nor emerging economies can sustain such interventions for prolonged periods of time.

Where can I find out more?

• Cushioning the poor from the COVID-19 shock: The Abdul Latif Jameel Poverty Action Lab.
• The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data: Opportunity Insights.
• COVID-19 Government Response Tracker: Blavatnik School of Government.
• Policy Responses to COVID19: International Monetary Fund.
• Heterogeneous effects of Covid-19 on households' financial situation and consumption: VOX, CEPR Policy Portal.
• Inequality: the Institute for Fiscal Studies Deaton Review.
• COVID-19 Household Survey: UChicago Urban Labs.

Who are experts on this question?

• Cristian Badarinza, National University of Singapore
• Vimal Balasubramaniam, Queen Mary University of London
• Louiza Bartzoka, Imperial College London
• Marianne Bertrand, University of Chicago
• Richard Blundell, University College London
• Karen Dynan, Harvard University
• Rema Hanna, Harvard University
• Jonathan Parker, MIT
• Tarun Ramadorai, Imperial College London
• Silvana Tenreyro, LSE and Bank of England

Authors: Cristian Badarinza, Vimal Balasubramaniam, Louiza Bartzoka, and Tarun Ramadorai

Photo of volunteers in Cape Town packing food parcels for households in need during the pandemic lockdown in South Africa, from Wikimedia Commons.

The post How has the pandemic affected household finances in developing economies? appeared first on Economics Observatory.

This week, the prime minister confirmed that the next easing of lockdown restrictions in England will go ahead on Monday. With the number of vaccine doses administered climbing steadily and case rates as low as they were last summer, there is some optimism in the air – at least here in the UK.

But as the public health crisis slowly subsides, the economic impacts left behind are becoming clear. According to the latest UK data, 4.9 million people are still on furlough, with a further 1.7 million unemployed. And this week, the Office for National Statistics announced that the economy shrank by 1.5% in the first quarter of 2021, and GDP is now 8.7% below where it was before the pandemic.

Faced with these contrasting trends, economists can help to clarify what is happening. Will the economy bounce back, as people who have had little opportunity to spend return to shops, pubs and restaurants? Or will long-lasting harm brought about by Covid-19 forever alter how we live?

This week at the Economics Observatory, several articles have explored these concerns. At a time when finding answers has been deeply challenging, we believe that asking the right questions is a good place to start. You can ask us one here.

Testing the connection

In a New York Times column back in April 2020, economics Nobel laureate Paul Krugman lamented that ‘the stock market is not the economy’. At the time, US share prices were surging even as the economy was in disarray. This week, we revisited this phenomenon: why is it that the ups and downs of the stock market often don’t reflect the wider economy?

Gareth Campbell (Queen’s University Belfast) highlights how share prices often fluctuate far more than is justified by what is happening to the company in question. For many, this heightened volatility is a reason not to trust the stock market as an accurate predictor of the real economy.

And yet, when looking at historical data, Gareth points out that previous asset price changes have been a fairly good indicator of economic growth. Before 1913 and in the period up to 1945, asset price returns explained an average of about 22% of GDP growth, rising to 32% by 1976 and up to 38% thereafter.

But Gareth rounds off his piece with some words of caution, reminding us that the predictive power of the stock market remains far from perfect. Over the past year in particular, there has been considerable price volatility, suggesting uncertainty about the future. As the standard investment warning says, ‘past performance is no guarantee of future results’.

Better pay

For many people, the pandemic has brought worries about their personal finances. The risk of being laid off – or even dropping down to 80% furlough wages – is a serious threat to the ability to stay afloat, particularly for those already paid the minimum wage.

On Tuesday, we posted a piece by Andre Couture (Bristol) on the potential effects of the recent increase in UK minimum wages on jobs, work and pay. Andre points out that the pay rise will be positive news for people currently working in minimum wage jobs. This is particularly promising for 23-24-year-olds, whose wages could increase by up to 9%. And given that young people have consistently borne the brunt of the pandemic, this news should be welcome.

But again, the reality is slightly more complex. As Andre highlights, sectors that employ a larger share of low-wage workers – such as hospitality, retail, and cleaning and maintenance – have been hit hard during lockdown. While minimum wage rises are encouraging, if there are fewer of these jobs out there, young people will continue to struggle.

Tightening the commuter belt

Another way in which people’s working lives may change relates to how often they travel to work. Prior to the pandemic, over 70% of workers were commuting to their jobs on most days. As highlighted in a new piece from Paul Mizen (Nottingham), Nick Bloom (Stanford) and Shivani Taneja (Nottingham), the pattern is changing.

They explore the future of commuting, using survey data to evaluate people’s travel preferences. As of April 2021, around half the UK labour force was working from home. Looking forward, the data suggest that most employees expect to be away from the office two or three times a week, even after lockdown is relaxed.

'How often would you like to commute to work after Covid-19?'

Notes: Data are from two surveys of 5,000 UK residents carried out by Prolific in March and April 2021 on behalf of the University of Nottingham and Stanford University. The authors reweighted the sample of respondents to match the Labour Force Survey figures by age, gender and education.

For all the conversations about getting back to normal, it appears that some things may never be quite the same again – at least in terms of the commuting habits of UK workers. And as Paul, Nick and Shivani stress, these changes in attitude will have wider economic impacts, affecting the transport industry, workplaces and the future of cities ­– a topic we have explored in depth before.

Market forces

Any chance of permanently emerging from lockdown has rested on the development of effective vaccines. When the news broke late last year that several jabs had passed clinical trials, it felt like there was a collective sigh of relief. Hope, however dim at first, flickered at the prospect of being able to beat the virus.

Yet the market for vaccines is a wrinkle in this story. Vaccines have become a fiercely contested resource, and the rise of a new phenomenon – vaccine nationalism – is a concern

On Thursday, Flavio Toxvaerd (Cambridge) and Anthony McDonnell (Center for Global Development) highlighted the complexity of the vaccine market. They explain that the production of vaccine technology rests on an intricate market structure, combining both the public and private sectors. Crucially, the market is highly concentrated on both sides: not only are there few firms that produce vaccines, there are also relatively few buyers.

This means that despite representing only 16% of the world’s population (and under 6% of global deaths from infectious diseases), high-income countries account for 82% of the global vaccine market. Flavio and Anthony warn that this uneven structure has skewed the market towards serving people in parts of the world where the need for jabs is lowest. As the horror in India continues to unfold, it is questionable whether vaccines are really helping us to beat coronavirus at all.

One response to the rising case numbers in South Asia has been a call for patent restrictions on the Covid-19 vaccines to be waived so that low-income countries can produce doses for themselves. In our final piece this week, Flavio has teamed up with Michele Boldrin (Washington University in St Louis) and David Levine (European University Institute) to explore the likely impact.

They conclude that patent waivers are likely to have little effect for good or ill. It’s not intellectual property protection that’s holding back vaccine production and distribution to poorer countries. The big problems are a lack of scientific know-how, manufacturing capacity and the political will across the international community.

Observatory news

• Next week, we will be publishing another series of themed articles. Put together by Economics Observatory founder and lead editor Rachel Griffith, these will focus on increasing food insecurity and obesity, particularly among children. This trend raises concerns for their futures, in terms of health, education and long-term prospects.
• We are publishing the first edition of our magazine later this month. If you haven’t done so already, sign up to receive a free copy here.

Author: Charlie Meyrick

Photo by Ketut Subiyanto on Pexels

The post Reality check appeared first on Economics Observatory.

For example, in 2020, US GDP fell by 3.5% – the largest contraction since the end of the Second World War (Furman and Powell, 2021). But the S&P 500 – a weighted index used to measure the US stock market – increased substantially during this period, more than recovering any temporary losses and ending the year 15% higher than its pre-pandemic level.

How do we explain this apparent disconnect?

Economic news and the stock market

The market value of a company should reflect how much cash investors believe the firm will make in the future. If changes in the broader economy are likely to affect company performance, then this should lead to changes in share prices. But it is important to emphasise that investors will consider not only what is happening now, but also what is likely to occur in the future.

Research has shown that a considerable proportion of the variation in share prices can be explained by key economic variables such as industrial production, inflation and interest rates, as well as changes in the dividends that companies pay to shareholders (Cutler et al, 1988). The evidence suggests that expectations about future changes in the economy play an important role in current pricing, although there may be some feedback effects involved, as changes in the stock market may actually cause changes in the wider economy.

But it has been has argued that share prices fluctuate more than they should – they exhibit ‘excess volatility’ (Shiller, 1981). What this means is that asset prices fluctuate more than is justified by changes to the fundamental characteristics of the underlying companies, which suggests that share prices may not always predict accurately what will happen in the future.

Prices are also affected by changes in the interest rate that is used to ‘discount’ future cash flows (Cochrane, 2011). Again, this means that prices may not necessarily reflect only predictions about company growth.

Historical experiences

An analysis of historical returns on stock market investments and GDP growth suggests that there is not always a close contemporary relationship. Table 1 shows the ten largest declines and increases in asset markets worldwide since 1870, using data compiled by Jorda et al (2017, 2019).

Major stock market crashes have typically been associated with extreme events such as defeat in the Second World War (Japan, Italy), monetary concerns (Germany in 1924 and 1948) or financial crisis (Belgium, Norway and Finland). Some of these episodes also saw large declines in GDP, but in other cases there was actually positive growth in output that year.

Many of the largest stock market increases seem to reflect recoveries from previous declines (Germany in the 1920s and 1949, Italy in 1946 and the UK in 1975), or unique factors such as Nokia’s dominance of the Finnish market during the ‘dot-com’ boom of 1999. During these periods of strong price increases, GDP growth has tended to be positive, but the scale of the changes in the stock market were typically much greater than those in GDP.

Table 1: Largest annual changes in equity returns versus GDP growth

Notes: Calculated from Jorda et al (2017, 2019). Equity returns and GDP growth are both expressed in real terms after controlling for inflation.

Expectations about the future

The stock market and the real economy may not move together at the same time if investors think that something might change in the future. One way to explore the role of expectations is to analyse whether changes in share prices can predict what might happen next.

Table 2 shows the correlation between GDP growth and returns on investments. The percentages (R² values) listed in the table illustrate the extent to which share prices can explain subsequent growth in the real economy, with an R² of 100% suggesting that changes in share prices perfectly explain GDP changes, while an R² of 0% indicating no explanatory power. The results are shown for different countries and different time periods, using data from Jorda et al (2017, 2019).

Table 2: How much of GDP growth is explained by the current and past three years of equity returns?

Notes: Calculated using data from Jorda et al (2017, 2019). Uses R² from regression explaining GDP growth in terms of current and past three years of equity returns.

There is considerable variation across countries for different periods. But on average, previous equity market returns have been a fairly good predictor of future economic growth. In the period before 1913 and in the period up to 1945, returns explained an average of about 22% of GDP growth, rising to 32% in the period to 1976 and 38% thereafter. This suggests that for all periods, particularly recently, there is evidence that stock market movements at least partially predict future changes in economic output.

What are the implications?

Figure 1 illustrates the returns on various stock market measures from before the Covid-19 pandemic began to the end of April 2021. The historical relationship between equity returns and future GDP suggests that these changes in the stock market may be a useful indicator of what might happen in the real economy.

Figure 1: Equity returns (31 December 2019 to 30 April 2021)

Notes: Data from Bloomberg and MSCI country stock market indices. Returns expressed in terms of local currencies and include dividends.

The strong increases that have been observed in share prices over the past year in the United States and China suggest that economic output is likely to grow faster in these countries than would have been the case pre-pandemic – possibly due to fiscal and monetary stimulus from emergency policies. Additional growth is likely to be more modest in Europe, but still positive.

Despite the success of the vaccine rollout, the stock market in the UK remains below its pre-pandemic level, possibly reflecting concerns about how long it will take to make up for the losses associated with the particularly severe nature of the crisis in this country.

But the predictive power of the stock market is far from perfect and there has been considerable volatility in prices over the past year, suggesting uncertainty about the future. Historical patterns suggest that the stock market can be a useful indicator of the real economy, but we should be cautious in making bold predictions given the heightened uncertainty that has arisen due to Covid-19.

Where can I find out more?

• How do changes in asset prices affect the real economy? This post discusses how share prices may not only reflect expectations about the economy, but also have an impact on it.

• Coronavirus: Impact on Stock Prices and Growth Expectations This research paper uses data from the stock market and dividend futures to estimate investor expectations about economic growth during the pandemic.

• China is wrong to think the US faces inevitable decline: This article notes that the stock market value of a company should be a relatively impartial assessment of its prospects, and uses this information to consider the innovativeness of different companies and regions.

Who are the experts on this question?

• John Turner
• Elroy Dimson
• Gareth Campbell

Author: Gareth Campbell, Queen's University Belfast

Photo by lo-lo on Unsplash

The post Does the stock market reflect the economy? appeared first on Economics Observatory.