ERC Chart: Receipts vs Spending

Public Sector Receipts vs Spending
As the general election approaches, these charts provide some information on the current state of taxation receipts and public spending in the UK. The UK government has run a budget deficit as a norm over the last 60 plus years, as expenditure is higher than tax revenue, regardless of which party occupies Downing Street. The budget was last balanced in 2001/2 and has been so in only 8 of the years since 1955. The OBR forecasts debt to be equivalent to 86.6% of national income in 2016/17, equating to around £1,730 billion or £62k per household. They also forecast total tax revenue of £721.1 billion, again falling short of planned expenditure of £772.8 billion.
What do the charts show?
Representing the fiscal year from April 2016 to March 2017, the figures presented in this chart compare projections of public sector of spending with receipts. The stacked bars represent the projected total and the breakdown by category of public sector spending and the sources of public sector tax receipts. Although each component is labelled with the absolute amount it represents, along the horizontal axes, the percentages of the total are shown.
Why are the charts interesting?
The taxes that contribute most to the public purse are income tax including National Insurance Contributions and VAT, which are anticipated to raise a combined £421bn. One sizeable contribution comes from corporation tax which represents 7.4% of the total. Business rates, Council and Capital Taxes and Fuel Duty each currently contribute around 4% of the total. Among the ‘other’ tax revenue is income from interest on assets for example FX reserves or student loans alongside income from corporations that remain in public hands.
Expected public spending per household will total £28k or 39.3% of household income this year. Unsurprisingly, spending on health is the largest in a single sector, an estimated 15% of the total. It is noteworthy that the NHS employs over 1.5 million people, and sits among the US Department of Defence, McDonalds and Walmart in the top five largest global workforces. Similarly, education demands a sizeable government expenditure, thought to account for 7.7% of total spend at £59.6 billion. Other expenditure covers day-to-day running costs of the machinery of the state including public services, grants and administration- around some 40% overall.

ERC Chart: Households by Tenure

Households of 25-34 year-olds by Tenure
(Households by Tenure)
The chart shows that 2011/12 marked the point that home-owning 25-34 year olds were no longer in the majority, surpassed by those that rent privately. In 2005/06, homeowners represented 56% of all households headed by 25-34 year olds, and renters, both private and social accounted for 44%. This picture has changed dramatically in the decade shown, with home ownership in this age bracket falling fairly steadily to 38.2% in 2015/16. Social renting has remained relatively stable in this period, but has reduced from 19.8% in 2005/06, to its lowest in 2015/16 of 15.7%. A large increase in the number of 25-34 year olds who rent privately is evident, almost doubling from 24.2% in 2005/06 to 46.1% in 2015/16. Home ownership began to increase again in 2013/14 rising by 2.4% in 2 years.

What does the chart show?
The graph represents a breakdown of all household where the chief householder is aged between 25-34, with their name on the either the rental contract or the ownership deeds. The blue line shows the percentage of such homes that are privately rented, the pink shows social renters in receipt of housing benefit. The black line represents the percentage of home owners. The data runs from 2005/06 until 2015/16 and originates from the government’s annual English Housing Survey. As such it covers the whole of England, including data from London where there is a disproportionately large number of renters.
Why is the chart interesting?
There has been much coverage of the decline in home ownership across the board, with particular focus on the plight of millennials, who have suffered from a range of economic disadvantages following the financial crash. Indeed record numbers of individuals in this age range remain living in their parents’ homes for far longer than in previous years. A recent study by an insurance company showed that in 2005, 27% of 25 year olds still lived with parents, whereas this figure rose to 33% in 2016. Recent reports indicate that the scale of lending from ‘Bank of Mum and Dad’ or ‘BOMAD’, has increased dramatically with parents projected to finance 26% of all mortgages in 2017, an estimated contribution of £6.7bn towards sales totaling £77bn. This is a growing trend that reflects the different economic circumstances between the generations: wage stagnation, the cost of university, the reduction in defined benefit pensions as well as the dearth of affordable housing (a product of both the promotion of buy-to-let as well as lack of supply).

ERC Chart: US Car Loan Delinquency

US Car Loan Delinquency
The charts show the nominal value of car loans issued in the US, which peaked at $1.157 trillion in 2016, representing a 65% increase since 2004. Between 2004 and 2008, the value of loans remained fairly stable, whilst delinquency grew steadily. However, following the global crash in 2008, one can see a clear drop in the value of loans issued, as well as a spike in delinquency, which reflects the efforts of US financial institutions to clean up their balance sheets. This prudence only lasted until 2010, when the value of car finance loans began to steadily increase once again. The value of delinquent loans has grown from $17 billion in 2004 to $43 billion in 2016, an increase of nearly 155%.

What does the chart show?
The blue bars in the first chart display the total, nominal value of all loans issued against cars on the road in the US in trillions of US dollars. The data runs from 2004 to year-end 2016 and is measured against the left hand axis. The orange line represents the percentage of these loans which are considered ‘delinquent’, i.e. four missed payments or unpaid 90 days after the due date. This is measured against the right hand axis. The second chart displays the real nominal value of these delinquent loans over the same period, in billions of US dollars.
Why is the chart interesting?
The increase in delinquency reflects the growing use of in-house financing by US car manufacturers; which, due to the incentive of growing their sales, are increasingly being issued to clients of poorer credit quality. The Federal Reserve Bank of New York estimates that manufacturer-underwritten loans account for 75% of all loan delinquency and contrasts this to loans issued by banks and credit unions, who have experienced reductions in bad loans since the financial crash. The ready availability of new car finance has encouraged consumers to not only opt for new cars over used ones, but also to choose more expensive models, with longer financing periods, as the difference in monthly payments can seem insignificant. Loans are based on the resale value of the used model, however many automotive experts predict the second-hand car market to be in decline, largely due to the increase in new-car incentives offered by manufacturers and the resultant influx of younger used cars onto the market. Although delinquency is rising on car loans it still lags behind delinquency on both student and credit card debt in the US.

ERC Chart: UK Voter Sentiment

UK Voter Sentiment on Key Issues
The chart shows that the Conservative Party enjoys the majority of support across 7 of 9 issues, with Labour support exceeding the Tories’ only on NHS and Housing. Echoing this pattern, when questioned on their preference Theresa May was chosen by 50% of the sample as the best Prime Minister, and Jeremy Corbyn only 14%. Those that opted for ‘Other’, ‘None’ and ‘Don’t Know’ consistently make up around 40% of votes; they are represented by the various grey sections. This uncertainty was lowest on the topic of immigration and asylum, where the conclusive party preferences made up 67% of the total. Unsurprisingly, UKIP secures its highest support (15% of votes) on Immigration and Asylum, coming in second only to the Conservatives at 29%. Education and schools draw up a close run between Conservative and Labour at 26% and 23% respectively, suggesting perhaps that the electorate isn’t decided on the issue of grammar school expansion. UKIP trails in the area of education with its lowest share of 2%. Interestingly, housing is where the largest proportion of those polled are undecided, a significantly greater number than those favouring the leading party in this area, Labour. The chart shows economic wellbeing to be Conservative’s strongest area, which may reflect both the ability to unify the party on messaging in this area and the relative calm so far following the turbulence anticipated in the wake of Brexit. On handling Britain’s exit from the EU, UKIP support exceeds Labour by 1%, but trust in the Conservatives is nearly four times as high. In the area of law & order, a traditionally Tory strong suit, almost triple the votes are in favour of the Conservatives compared to Labour. Unemployment is one of the Liberal Democrat’s weakest areas with only 5% support, one sixth of the Conservative support. Although 34% of people polled believed that leaving the EU will be bad for UK jobs, this did not translate to greater support in this area for the Lib Dems, despite their overt preference for a softer Brexit. The category of taxation generally shows a similar pattern as unemployment, but Lib Dems manage to secure slightly more trust at the expense of Labour.

What does the chart show?
The chart shows the results of an opinion poll on 12th/13th April. A representative sample of 2,069 respondents (who voted Leave and Remain proportionally), drawn and weighted across the UK, were asked which of the parties they would favour on a number of key issues. Each bar is split into sections represented by each Party’s traditional colours; Conservative in blue, Labour in red, Lib Dems in yellow, UKIP in purple. The data displayed is from a YouGov opinion poll. Following the erroneous polling results during the previous general election and in the US 2016 election, caution must be exercised with this type of data.
Why is the chart interesting?
The high proportion of undecided voters may encourage tactical voting efforts, such as the formal campaign launched by Gina Miller which is focussed on encouraging the tactical support of europhile MPs regardless of party affiliation. Following the announcement of the snap-election, there was initial mention of the prospect of what Mrs May terms a ‘coalition of chaos’; a potential alliance between Labour, Lib Dem and SNP. Despite the rejection of this idea by both Labour and the Lib Dems, the chart shows that even when the sum of all Lib Dem, Labour and ‘other’ support is compared to the Conservatives, this coalition would secure overall support in 4 out of the 9 categories, and not necessarily the categories which voters may choose to prioritise.

ERC Chart: Trends in Take-Home Pay

Trends in Take-Home Pay 
The chart shows that take home wages for the 2nd and 9th decile of earners have been strongly correlated in the years since the financial crisis. Earners in the 20th percentile have seen their wages shrink compared to the preceding year in four out of past 9 years, whereas wages of those in the top bracket decreased in six. Following the crash, wages for the lowest earners initially fell more sharply than those at the top, but recovered more quickly comparatively.

What does the chart show?
The chart displays how the earners in the top and bottom income brackets have experienced changes in their take-home pay since 2008, adjusted for inflation. The chart shows the percentage annual change in take-home pay from the years 2008-2016 in real terms, as well as projections from 2017 through to 2020, shown by the dotted lines. The orange line shows the change for the 2nd decile of earners; the blue line for the 5thdecile (the median), and the green line represents earners in the 9th decile. The 1st decile was excluded because the lowest earning 10% of the population will generally be the national minimum wage earners, whose wages are not as exposed to changes in the market and whose income doesn’t fluctuate as much due to changes in fiscal policy.
Why is the chart interesting?
There is a marked jump in take-home pay between 2014 and 2015, which is likely attributable to the raising of the personal tax allowance, allowing workers at the lower end of the income distribution to take home a greater proportion of their wage packet. In 7 out of the 9 years from 2008-2016, top earners were below the median take-home wage growth rate, but are forecast to see increases that exceed the median growth rate in the coming 4 years. For the lowest 20% of earners, 5 out of the last 9 years’ wage growth exceeded the median rate, and indeed it is forecast to remain well above the median and also above the top 10% of earners between now and 2020.
According to Mark Carney in February, wage growth will be a key driver in the Bank of England’s willingness to tolerate post-Brexit inflation in excess of its 2% target- it remains to be seen whether the Bank will alter interest rates in the near future.

ERC Chart: Risk of Automation

Workers at Risk of Automation vs Employment Share by Sector 
This analysis suggest that up to 30% of UK jobs are considered to be at high risk of automation by the early 2030s. This places fewer UK workers at risk than in the US and Germany where the figures are 38% and 35% respectively, but the UK fares worse than Japan (21%). The sector with the highest risk is manufacturing where the report estimates that 46.4% of workers could be at risk. When considering the share of total employment within manufacturing, this translates to 1.22 million jobs. Although wholesale and retail trade enjoys a lower percentage of workers at risk, as it represents nearly 15% of total employment, the number of workers at risk is nearly double those in manufacturing, at 2.25million. In the health, care and education sectors, there is a markedly lower risk to workers, and is projected to affect less than 1 million workers.

What does the chart show?
The blue bars represent the percentage of workers from each sector whose jobs are at high risk of automation by the early 2030s, measured against the left hand axis. This data has been drawn from a report by a consultancy who used previous studies and data from the OECD’s  Programme for the International Assessment of Adult Competencies (PIAAC) to estimate whether or not jobs were automatable. It is worth considering that other studies in this area have found quite divergent results; notably Frey and Osborne in 2013, whose estimates are slightly higher as well as a 2016 study by the OECD whose estimates are far lower. This study, unlike the two prior, attempts to take into account potential job creation through automation as well as accounting for limits of job automation that are separate to what technology permits. The orange dots show the percentage of total employment in the UK in each sector. It is measured against the right hand axis and uses ONS data for 2016.
Why is the chart interesting?
There has been much talk of automation as a solution to labour market shortages. One example came in February 2017, when Secretary of State for Environment, Food and Rural Affairs, Andrea Leadsom, addressed the National Farmers Union annual conference, stating that the solution to concerns about the potential exodus of EU workers lay in new technology and automation that would ‘complement the workforce’. However the data on automation displayed here suggests that only 18.7% of jobs in the category of agriculture, forestry and fishing have a high probability of becoming automated (which translates to 130,000 actual workers). EU workers make up some 90% of all seasonal agricultural workers according to the Association of Labour Providers, which also reported that there had been a dramatic dip in applicants for these seasonal positions in 2016. They attributed this to several factors; the post-referendum devaluation of the pound, which negated the introduction of National Living Wage for migrant workers, and the reported rise in anti-immigrant sentiment, both of which make other countries increasingly attractive to low-wage migrant workers.
Education is a significant differentiator in the risk to workers from computerisation. Those working in jobs that require only GCSE level qualification are at almost four times higher probability of losing out to automation compared to university graduates.
Two important caveats to this data exist: though possible, not every job that can be replaced through computerisation will be as other regulatory and legal factors may come into play; and should some level of automation occur, there would likely be wealth-generating productivity gains which could support job expansion in less automatable areas.

ERC Chart: Eurozone Companies

Eurozone Companies’ Reaction to Political Risk
While 2017 will see a number of different political risks coming to the fore, what is clear is that uncertainty about both Britain’s new relationship with Europe and the future policies of the Trump administration are perceived to pose the biggest risk to Eurozone businesses (27% and 28% of respondents respectively).

What does the chart show?
The chart reflects research undertaken between 10th January and 13th February, in which 600 Eurozone-based firms were polled on their attitudes to 2017 political risks. 74% of companies polled had UK investments and the companies were all based in either Germany, France, Italy or Spain. The respondents (from 150 firms per country listed) all had decision-making responsibility or understanding of their companies’ investment strategy. The data shows the percentage of respondents against the right hand axis citing the political risks detailed. The report authors noted that firms were likely to rate risks in their own countries more highly than abroad.
Why is the chart interesting?
Ahead of the triggering of Article 50 on 29th March, the poll results suggest that businesses are exercising caution where UK domestic exposure is concerned. Only 14% of those surveyed cited France’s election as a significant concern, despite staunch Eurosceptic Marine le Pen’s vows to renegotiate the country’s relationship with the EU. Whether this fearfulness about Brexit among Eurozone businesses will have any substantive effect is unclear, as 54% of respondents stated that Brexit was unlikely to affect their investment strategy.

ERC Chart: Oil Price v Scottish Nationalism

Oil Price v Scottish Nationalism
While the decline in price of a barrel of Brent Crude oil has been newsworthy for economists all over the world, it has been scrutinised particularly closely by Scottish Nationalists, as it would prove an important source of income were Scotland to secede. The chart shows an initial loose correlation between oil price and nationalist sentiment in 2014 and H1 2015 but this ends in H2 2015 when nationalist sentiment remains above 40% as oil price plummets. This demonstrates that the desire for Scottish independence is fuelled by other factors. Just after the first independence referendum in September 2014, Brent crude was priced at $87 per barrel but within 3 months, this dropped to under $50. As oil price dropped, opinion polls showed that approximately 10% fewer individuals supported independence. However despite recovering slightly and remaining above $50 for 8 months of 2015, by January 2016 oil price had halved to $26.  Nationalist sentiment on the other hand showed a modest rise to 50%.

What does the chart show?
The orange line shows the dollar price of a barrel of Brent Crude oil at the close of trading each day, measured against the left hand axis. The data is from 16th October 2014 until 6th March 2017. The blue line shows the percentage of individuals polled who would vote ‘yes’ to the question ‘Should Scotland be an independent nation?’ and is measured against the right hand axis. The polls were conducted on behalf of a range of parties from newspapers to think tanks. Although the question is the consistent across all polls shown here, around one fifth of the polls include opinions from those aged 16+, with the remainder polling only those aged 18+. The chart only shows those who are sure they would vote for independence and does not include those who responded ‘don’t know’.
Why is the chart interesting?
Since the low point in January 2016, shortage in global supply has pushed oil price back up to over $50.  However, as prices rise once again, suppliers may be coaxed back into business (particularly shale producers), which could depress prices once more. What is clear is that this has little bearing on support for Scottish independence which spiked following the referendum on Britain leaving the EU in June 2016. In the Brexit vote the Scottish electorate voted to remain with a convincing majority of 62%. Another spike can be seen in September 2016, perhaps linked to the extensive coverage of UK government’s desire to trigger Article 50 without a parliamentary vote which may have galvanized the perception among the Scottish of a democratic deficit in Westminster. In January 2017, Teresa May confirmed that Britain would leave the single market which may have been a driver in the rise in support for Scottish independence that can be seen in Q1 2017. Although tax revenues will undoubtedly suffer from low oil prices, this could be mitigated by the benefit of low energy costs for other Scottish industries. Ultimately it is difficult for voters and economists alike to speculate on how an independent Scottish economy might look not least due to the volatility of oil price, but also as so much would hinge on the outcome of negotiations, including what responsibility Scotland might take for costs such as UK government debt.

ERC Chart: Self Employment and Costs

Self Employment v.s. Cost of Starting a Business
The chart shows that a correlation exists between the number of people taking up self-employment and the cost of starting a business in this country. The cost of starting a business has fallen significantly, with a tenfold reduction between 2004 and 2016. Self-employment has risen modestly since 1994, however in recent years far fewer people leave self-employment. While the growth in self-employment may have been accelerated by the recession in 2008, the trend predates this. A relatively large jump in number of self-employed people can be seen in 2013. This could be related to the 2013 extension of the ‘New Enterprise Allowance’ widening access to the scheme, which provided a weekly allowance plus a loan of up to £2k alongside business mentoring. Additionally people may have found an incentive to either declare themselves, or remain as self-employed on low income, in favour of becoming unemployed, in order to claim tax credits following toughening of Jobseeker’s Allowance sanctions in 2012.

What does the chart show?
The orange bars show the cost of starting a new business as a percentage of income per capita according to the World Bank’s Doing Business report. It is yearly from 2004 until 2016 and measured against the right hand axis. The blue line, measured on the left hand axis shows number of self-employed individuals. The data is quarterly, in millions, and runs from Q1 2004 to Q1 2016.
Why is the chart interesting?
The overall trends of rising self-employment and the reduction in associated cost, is likely related to the development and accessibility of technology; including platforms such as Ebay, Deliveroo and Uber. These platforms cheaply and easily match the self-employed with their customer base. Demographic changes, particularly rising life expectancy have also encouraged older workers into freelance employment beyond traditional retirement age.Some are critical of the tax structure for the self-employed, which allows them to pay tax only on net income. Self-employed individuals earn less on average than those employed. Rising VAT and excise duties also create a greater appetite for ‘grey economy’ transactions, which involve legal goods and services but are conducted illegally, beyond the reach of the taxman. All of this, has implications for tax revenue and the welfare bill, making the self-employed a target for the chancellor. It remains to be seen whether Philip Hammond’s latest tax hike will make self-employment less attractive or indeed if it will be a measure that alienates traditional conservative voters, those upholding the Thatcherite ideals of enterprise and self-reliance.
Self Employment and Costs

ERC Chart: Bitcoin v Internet Users

Bitcoin Price v.s. Internet Users by Region
Summary: The chart shows that Bitcoin is a niche market which bears no correlation to the number or distribution of individuals across the globe who have internet access. The most prominent feature on the graph is the extraordinary rise in Bitcoin value during 2013 when the crypto-currency reached 5 years of age. Bitcoin has been on the rise again since mid-2015 sustaining a price over $1000 for the longest period to date. Last night’s closing price was at its highest since the peak 2013, within $30 of the all-time high. With the exception of 2014, Bitcoin has outperformed every other currency each year.

What does the chart show? The black line shows the Coindesk daily Bitcoin Price Index in USD from 18th July 2010 until 22nd February 2017. The other lines show the number of internet users per region, as compiled by UN agency, ITU, using a variety of sources. The internet user data is annual and given in users per 100 inhabitants.
Why is the chart interesting? Some of the initial interest in Bitcoin during early 2013 can be attributed to the Cypriot banking crisis earlier that year but it was a flood of interest from largely middle class Chinese investors in H2 2013 that fuelled its astonishing rise. It dominated news in November 2013 when it reached its peak price (a couple of dollars above that day’s spot gold price). This represented a 10,250% rise from the value at the start of the year. A dramatic drop followed, and investors saw a fall of 28% in a fortnight. In 2015/16 Bitcoin appears to be rising in a more stable fashion than before- during 2013 it had daily swings of up to 40% whereas this volatility has calmed to around 10% per day.
The recent rise is surprising following actions by the China’s central bank to restrict Bitcoin exchanges in a bid to stem capital flight, which prompted withdrawal freezes by a few of them. On March the 11th the US Securities and Exchange Commission is set to issue a decision on approval for the first Bitcoin exchange-traded fund, a venture of the Winklevoss brothers and no doubt a source of some investors’ optimism.

Source: The Economic Research Council