## Has the UK’s demand for cars run out of gas?

Simon Kirby, Andre Moreira and Michal Stelmach

New car registrations, a timely indicator of the cyclical position of the economy, fell in 2017 for the first time since 2011. Some have attributed this drop to tax changes which took effect last April. But we think the squeeze on real incomes was a more significant factor. Our analysis also suggests that the rapid growth in new car sales seen from 2013-16 was unlikely to be sustained. Given the expected path of household income we expect new car purchases to remain subdued in 2018, compared to levels seen in recent years.

1 Comment

Filed under Macroeconomics

## Algos all go?

Francis Breedon, Louisa Chen, Angelo Ranaldo and Nicholas Vause

Most academic studies find that algorithmic trading improves the quality of financial markets in normal times by boosting market liquidity (so larger trades can be executed more quickly at lower cost) and enhancing price efficiency (so market prices better reflect all value-relevant information). But what about in times of market stress? In a recent paper looking at the removal of the Swiss franc cap, we find that algorithmic trading provided less liquidity than usual, at worse prices, and that its contribution to efficient pricing dropped to near zero. Market quality benefits from a diversity of participants pursuing different trading strategies, but it seems this was undermined in this episode by commonalities in the way algorithms responded.

1 Comment

Filed under Currency, Financial Markets, Financial Stability

## Bitesize: The past decade’s productivity growth in historical context

John Lewis

How poor has the past decade of productivity growth been by historical standards? Exceptionally.

Filed under Bitesize, Economic History, Macroeconomics

## Is the tail wagging the dog? The impact of VIX exchange traded products on equity volatility

Olga Maizels

Volatility returned to markets in early February, sparked by strong US wage growth data.  After  months of calm, the S&P 500 equity index fell by 4% on 5 February and the VIX – a measure of US equity volatility that is sometimes referred to as Wall Street’s “fear gauge” – experienced its largest one-day move in its 28-year history.  Interestingly, measures of volatility in other markets, including interest rates and currencies, moved by much less.  So what caused the outsized spike in the VIX?  Some of the rise was linked to rebalancing flows associated with VIX exchange-traded products (ETPs), which can amplify moves in the volatility market.  The events have also led to some questions whether developments in VIX ETPs can also affect the S&P 500 itself –whether the ‘tail’ can wag the ‘dog’.

1 Comment

Filed under Financial Markets

Tommaso Aquilante, Enrico Longoni, Patrick Schneider

Countries’ goods exports are normally defined in terms of what has been shipped when and where. Recent literature (e.g. Besedeš and Prusa, 2011 and Besedeš et al, 2016) shows that looking at how long trade relationships have been in place is important as well. Using highly granular data, we show that over 60% of the value of UK nominal goods exports is in very mature trading relationships, by which we mean exports of a particular product between a pair of countries in a given year. This is true even with substantial churn (new relationships starting and old ones ceasing) going on all the while, and for exports in real terms as well.

Highly detailed trade data are increasingly available and one of the best sources is the UN’s Comtrade database, which provides detail of nominal trade in goods between countries at the product level. The dataset shows the value of imports and exports flows along four dimensions: country, partner, product and year. In the case of the UK, we have data for a total of 3,454,756 product-level relationships, comprised of up to 1,200 products exported by the UK to 150 countries over the period 1962-2014.

This is enough detail to make a researcher giddy. The granularity of the product definitions means, for example, we can, if so inclined, compare UK exports to France of different types of iron or steel wire (Chart 1).

Chart 1: UK exports to France of different type of wire

But whether you’re interested in wires or not, the dataset and the detail therein are incredibly valuable. As Chart 2 shows, the UN Comtrade data are a pretty good match with the ONS aggregate. But because the UN Comtrade line is a sum over a bunch of products and trading partners, they allow us to look beneath the surface of aggregate statistics.

Chart 2: UK goods export value

We like to think of exports using this disaggregated data in terms of relationships – exports of a particular product between a pair of countries in a given year. So the UK export of ‘Glass envelopes for electric lamps’ to Germany is one unique relationship, as is the export of ‘Distilled alcoholic beverages’ to Japan.

Aggregate UK exports are the sum of the value of all existing relationships over a given period. Thought of in this way, aggregate trade is the product of the depth and the breadth of relationships between trade partners, i.e. the product of two margins:

1. the intensive margin is the average value for a relationship, and
2. the extensive margin is the number of active (positive value) relationships

Any growth in the aggregate is necessarily driven by changes in these margins. The extensive margin is fairly stable over time. Chart 3 shows that the number of active UK export relationships has been between sixty and eighty-thousand since the 1960s, without much obvious growth. This implies that the substantial growth in aggregate trade has mainly driven by increases in the value of the average trade relationship (the intensive margin).

Chart 3: The extensive margin of UK exports over time

These trends mask a number of things – the total count of active relationships misses churn (the birth of new ones and the death of old ones) and, even within a relationship, we can’t observe the entry and exit of the actual firms doing the trade. Although we can’t observe firms in these data, we can investigate churn at the relationship level by grouping these relationships into birth cohorts – the year since which the relationship has had a positive value.

Long-lasting relationships are the bedrock of UK’s aggregate trade

Chart 4 breaks the extensive margin into contributions from each of these birth cohorts. Each line separates one cohort of surviving relationships in a given year from the next.  The size of the cohort diminishes over time as relationships die off, just as the number of people born in a given year decreases every year. Unlike us, however, trade relationships can re-incarnate; and when they do, they count as members of a new cohort.

From the chart, we can see that just over 25% of more than 60,000 relationships that were active in the early sixties are still in place in the recent data (the black area in the chart). In 2014, this cohort still accounted for 20% of extant relationships (over 16,000 of the total 80,000).

Chart 4: Contributions to the extensive margin by birth cohort

Using the same cohorts, we can track their influence on the aggregate level of goods exports over time (Chart 5). The result here is very striking: over 60% of 2014 exports were in product-country relationships that had been in place since the early sixties. This is despite the fact that these only account for 20% of total active relationships.

Chart 5: Contributions to total exports by birth cohort

We have seen that many trading relationships survive, unbroken, since the sixties and these survivors account for the bulk of total exports. These results are striking, but should we be surprised by them?

We can think of trade, and business in general, as a selection process. Some of the forces that can increase the likelihood of survival – e.g. comparative advantage or proximity of trading partner (Albornoz et al 2016; Besedeš et al, 2016) – will increase the value of the trade flow as well. Furthermore, the value of trade flows tend to increase over time, as existing companies deepen existing partnerships and expand their customer networks, and new companies enter to compete with them (Albornoz et al, 2012).

So just as ancient vampires tend to be the more powerful (e.g. here), older trading relationships tend to be the more valuable. But whether we should be surprised by just how much of UK goods trade is accounted for by these long-standing relationships is a question for further investigation.

Conclusion

Disaggregated trade data offer researchers a wealth of opportunities for analysing trade and its dynamics. The empirical trade literature is, accordingly, increasingly making use of this and other rich datasets. Our analysis demonstrates how even simple investigations of these data can lead to striking insights. We showed that aggregate trade appears to be dominated by a few highly valuable and long-lasting relationships.

Tommaso Aquilante, Enrico Longoni and Patrick Schneider work in the Bank’s Monetary Analysis Division.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied.Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Filed under International Economics

## What can regional data tell us about the UK Phillips Curve?

Alex Tuckett

The Phillips Curve (PC) is an old concept in economics, but it is a durable one. The simple idea behind the PC is that the lower the rate of unemployment, the faster wages will grow. If the PC has changed over time, that can have important implications for monetary policymakers. Analysis of regional UK data suggests that the PC has shifted down over time, but has not necessarily become flatter. Higher levels of educational attainment are likely to have contributed to this shift.

Despite its age, the PC remains central to the way that Central Banks attempt to control inflation. In most advanced economies wages are the most important component of domestic costs, so stabilising wage inflation should be sufficient to stabilise price inflation. In turn, the PC says that stabilising wage growth can be achieved by stabilising unemployment. So to meet an inflation target, Central Banks just need to change interest rates to keep the economy growing steadily and prevent unemployment getting too high – or low.

Reality is rarely that simple. Central Banks often face external cost shocks, such as large movements in commodity prices or exchange rates, which can move inflation and unemployment in opposite directions. In this case, Central Banks face a ‘trade-off’ between stabilising inflation or unemployment. The PC determines the terms of that trade-off.

A number of economists and policymakers have argued that the PC has become flatter or disappeared – in other words, a given change in unemployment has less effect on inflation. There could be a number of reasons for such a ‘flattening’: changes in the labour market, greater integration of the global economy or more credible policy frameworks.

If true, this flattening in the Phillips Curve may be a mixed blessing. On the one hand, a shock to the economy that changes unemployment will not move inflation too far from target. Set against that, trade-offs are more painful when the PC is flat. Suppose inflation is high because the exchange rate has depreciated. With a flat PC, bringing inflation back to target would require a larger increase in unemployment.

Regional data can potentially be a helpful source of information about the stability of the PC, and has been used to analyse this question for the US (as well as other questions about the labour market). It provides more data points, making it easier to look for changes in parameters. And regional variation can be used to explore potential reasons for changes. In this post I investigate what regional data for the UK can tell us about how – and why – the PC may have shifted in the UK over the past 20 years.

Has the Phillips Curve remained stable in the UK?

Phillips Curves are normally estimated on macroeconomic data for wage growth and the unemployment rate at a national level, but the same principal can be applied to regional level data, using panel techniques.  Figure 1 shows the results of fitting a simple Phillips Curve relationship for the UK using either Local Authority (LA) level data (column (1)) or NUTS1 level data (which divides the UK into twelve nations or regions – column (4)), using a fixed effects panel estimator. The growth rate of median hourly wages is negatively affected by the rate of unemployment in the previous year; an increase in the unemployment rate of 1 percentage point results in wage growth that is roughly 0.5 percentage points weaker.

Figure 1: Phillips Curves estimated on regional data

All regressions estimated on annual data, and include geographic fixed effects. Wage growth is median hourly wages for full-time workers, resident basis for LA, workplace basis for NUTS1.  *** Significantly different from zero at 1% probability **5% probability * 10% probability.

Are these parameters stable over the sample? Whether the level of the PC has changed over time can be tested by including a time trend in the regression, as shown in Columns (2) and (5) of Figure 1. The trend is negative, and highly significant. Over the sample, the same level of unemployment has been associated with lower wage growth. An alternative model, which allows a discrete shift in the PC, finds a lower level after the crisis than before.

Columns (3) and (6) look for evidence that the curve has become flatter, instead of (or in addition to) moving downwards, by allowing the coefficient on unemployment to change over time. The results indicate that, if anything, the slope of the Phillips Curve appears to have trended downwards (become steeper) over time, although the coefficient is very close to zero and not significant.

Another way to assess whether the slope of the PC has changed over time is to use rolling regressions – estimating a PC over successively later samples and seeing how the slope coefficient changes, as in Leduc and Wilson (2017). Figure 2 shows the slope parameter of the PC estimated over rolling seven year periods, and how this estimate has changed over time. The curve appears to steepen during the financial crisis; indeed it is difficult to find a downward slope for sample windows prior to the crisis. This probably illustrates the effect of sample selection – it is difficult to estimate the PC over a period with no recession, even with regional data.

Figure 2: rolling estimates of the slope of the Phillips Curve

β coefficient in regression  median hourly pay growth = α + β* lagged unemployment rate + θ*lagged National CPI Inflation rate. Dotted lines show 90% confidence interval around the fixed-effects panel estimate. Regressions estimated over 7-year overlapping windows, for instance 2017 values are estimated on 2001-17 data.

There is some tentative evidence that the PC has become flatter over the past few years. However this could easily be the flip side of the same sample coin – as the Great Recession moves further into the rear-view mirror, it becomes harder to identify cyclical relationships. Overall there is stronger evidence for there being a drop, not a tilt, in the UK wage Phillips Curve.

Why has the Phillips Curve changed?

What could have caused such a drop?  Slower productivity growth is probably part of the story. However, if aggregate productivity growth is added to the panel equations there remains a significant downward trend.

Phillips Curve models often include a measure of the ‘unemployment gap’, instead of simply unemployment. The idea is that there is a ‘natural rate’ of unemployment – often termed the NAIRU, or U* – at which wage inflation should be consistent with the inflation target. U* is not directly observable; it can only be estimated. The evidence presented above – that the constant term in the PC has fallen – can be interpreted as a fall in U*.

Why has U* fallen? As outlined in Saunders (2017), there are a number of possible reasons: changes to the benefits system, the rise of the ‘gig economy’, demographics (Tuzeman (2017)), inward migration, and the increase in educational attainment. Educational attainment is a particularly compelling candidate. Workers with more education have higher participation rates and lower unemployment rates on average. This could be for a number of reasons. Higher levels of formal education may make workers more effective at finding and applying for jobs; make it easier to make the transition into occupations or industries where employment is growing; or shift the mix of jobs in the economy towards occupations which are more secure.

Regional data is well suited to testing the idea that higher educational attainment may have lowered U*. In Figure 3 I show what happens when a measure of educational attainment (the share of the population with degree-level education) is introduced into the Phillips Curve equations.  Education seems to shift the PC downwards (see columns (1) and (3)). However, educational attainment increases in almost all of the Local Authorities and regions over the last 12 years, so the result may spuriously reflect a more general downward trend in wage growth. Columns (2) and (4) test this by including a time trend in the equations; although smaller, the effect of education remains statistically significant.  Educational attainment also remains significant if productivity growth is included instead of a time-trend.

Figure 3: has educational attainment shifted the Phillips Curve?

All regressions estimated on annual data, and include geographic fixed effects. Wage growth is median hourly wages for full-time workers, resident basis for LA, workplace basis for NUTS1.  *** Significantly different from zero at 1% probability **5% probability * 10% probability.

This is a conditional result, which does not mean that education lowers wages. A more intuitive way to put it is that rising educational attainment has reduced U*, allowing for unemployment to be lower without causing wage growth that is too high for the inflation target. The net effect on wages could easily be positive.

Based on the LA level results, rising levels of educational attainment can explain a fall in U* of around 1ppt over the last 12 years. The estimated effects are even larger with mean wages, or using NUTS1 data.

Will U* continue to fall?

Average levels of educational attainment in the workforce are likely to continue to increase over the next two decades.  As older workers retire from the workforce, the average level of educational attainment for the workforce as a whole will increase, even without any further increase in the proportion of school leaves going to university. Figure 4 shows a projection for how the share of graduates in the workforce could progress, under a few other simple assumptions. If levels of education continue to rise as projected in Figure 4, then this should reduce U* by around ½ ppt over the next 15 years.

Figure 4: projections for share of graduates in 16-64 population

Assumes that number of new graduations remains a constant share of the 21-24 year old population. Baseline projection assumes net migration is neutral for educational attainment. Migration variant assumes migration continues at levels in year to June 2017, and rates of higher education amongst graduates are as estimated in ONS analysis. Both projections assume mortality rates are equal for graduates and non-graduates.

To summarise, analysis of regional data suggests that the Phillips Curve has shifted down in the UK – that is, U* has fallen. Higher levels of educational attainment can partly explain this downward shift. There is little evidence that the Phillips Curve has become flatter.

Alex Tuckett works in the Bank’s External MPC unit.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied.Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Comments Off on What can regional data tell us about the UK Phillips Curve?

Filed under Macroeconomics, Monetary Policy

## What did the CBPS do to corporate bond yields?

Calebe de Roure, Ben Morley and Lena Boneva

In August 2016 the MPC announced a package of easing measures, including the Corporate Bond Purchase Scheme (CBPS). In a recent staff working paper, we explore the announcement impact of the CBPS, using the so called “difference in differences” (or “DID”) approach. Overall – to deliver the punchline to eager readers – this analytical technique suggests that the announcement caused spreads on CBPS eligible bonds to tighten by 13bps, compared with comparable euro or dollar denominated bonds (Charts 1b, 2). Continue reading

Comments Off on What did the CBPS do to corporate bond yields?

Filed under Financial Markets, Monetary Policy

## Bitesize: UK real interest rates over the past three centuries

John Lewis

How low are UK real interest rates by historical standards? Using the Bank’s Millennium of Macroeconomic Data, I compute real bank rate, mortgage rates, and 10-year government bond yields over time.

Comments Off on Bitesize: UK real interest rates over the past three centuries

Filed under Bitesize, Economic History, Financial Markets, Macroeconomics

## The UK’s productivity puzzle is in the top tail of the distribution

Patrick Schneider

UK productivity growth has been puzzlingly slow since the crisis. After averaging 2% every year in the pre-crisis decade, growth in labour productivity (output per hour worked) has slowed to an average of only 0.5%. Extensive research and commentary on the productivity puzzles has suggested myriad causes for the malaise – including ‘zombie’ firms hoarding resources, sluggish investment in the face of uncertainty, mismeasurement and more – and have dismissed others that no longer seem plausible – including temporary labour hoarding. Using firm-level data, I show that slower aggregate growth is entirely driven by the more productive firms in the economy.

In apparent contrast to my results, recent arguments have focused on the role that the weakest firms play in keeping down aggregate productivity. For example, Andy Haldane highlighted the ‘long tail of low-productivity companies’, which drags on the aggregate, in a speech last year and the OECD has published several papers (e.g. here [pdf] and here) analysing the divergence of the top end of the productivity distribution (‘frontier’ firms) from the rest (‘laggards’).

These ideas have been very influential. But unproductive firms are not responsible for all of the UK’s issues. Using a new method that links aggregate productivity to its distribution across workers, I found that the slowdown in productivity growth is isolated in the top tail of the distribution of productivity across workers. The most productive firms are failing to improve on each other at the same rate as their predecessors did.

You can see this in Chart 1 – the two lines track the average, annual change in productivity at different parts of the distribution across workers, before and after the crisis. The post-crisis line is well below the pre-crisis one, but only toward the right, the top tail of the distribution. Surprisingly, the bottom end of the distribution appears to have been growing faster in recent years than it was leading up to the crisis.

Chart 1: Av. annual change in productivity, by centile of the productivity level distribution

The beauty of this chart is that the average height of each line is about equal to the change in the aggregate – so we can see which part of the distribution is moving (or not) to cause the aggregate to move. And, again, it’s the top end that’s doing the work. This fact doesn’t explain the puzzle. As with any statistical decomposition, we’re brought no closer to the why of the issue, but the where is a little clearer.

That’s about all I have to say. The rest of this post provides the working behind Chart 1. In the next section, I sketch the method I used, and then I take it to UK data, giving a bit more flavour to the result, ending up right back at Chart 1.

One thing this extra detail does is to confirm the headline results in Andy’s speech and the OECD’s papers. You can see the long tail of low productivity firms in Chart 4, and the divergence of the top end of the distribution from the rest in Chart 1. But in the data we have, these features are always there. So they can’t be to blame for the slowdown in growth. Indeed, if anything, the increasing dispersion is where aggregate growth usually comes from. In this light, the UK growth puzzle is there because the increasing dispersion has slowed down since the crisis.

The method (for the interested reader)

So how did I get to Chart 1?

Aggregate labour-productivity (value-added per worker) is usually measured by taking a labour-weighted average of firm-level productivity.  But it can also be approximated by the average of a number (Q) of equally spaced quantiles (as I outline in a forthcoming working paper). So, for example, we could measure aggregate productivity by taking the average of the 1st through 99th centiles of the distribution. More explicitly

$\Pi = \frac{VA}{L}=\sum_{i} \frac{l_i}{L} \pi_i \approx \frac{1}{Q} \sum_{j} q^{\pi}(j)$

where $i$ and $j$ represent firms and quantiles, $\pi$ is productivity, $va$ is value-added and $l$ is labour. $q^{\pi}(j)$ is the quantile function of productivity across workers, it picks out the productivity of the worker who is more productive than exactly $j/Q$ of the others.

(Aside: we need to be careful to work with the correct distribution. The key is that aggregate productivity is the mean of the distribution across workers. Because we usually measure productivity at the firm level, we need to use labour weights to adjust the calculation to the right distribution.)

With this approximation, we can measure changes in the aggregate by averaging over changes in the centiles as well. Using the formula below, we can see which part of the distribution is moving (or not) to cause the aggregate to move.

$\Delta \Pi \approx \frac{1}{Q} \sum_{j} \Delta q^{\pi}(j)$

Note that we’re tracking growth in the distribution here – not growth in firms, nor the distribution of firm growth, so it could be that parts of the distribution move around or stay still, but the firms that are located there are shifting around a lot.

The data (for the still interested reader)

So now that we have the method in hand, let’s apply it to UK data.

I’m using ONS firm-level microdata (combining ARDx with ABS 2015), and measuring productivity as real value-added (using 2-digit sector deflators) per employee.

This dataset doesn’t cover the whole economy – the surveys only try to cover the non-financial business economy (which is a shame given the importance of finance in the growth puzzle) and some other sectors only pop up from 2009, so I had to cut these from the whole dataset for consistency. Because of these survey limitations, the ‘aggregate’ in the below results is a subset of the overall UK aggregate economy.

OK so let’s first check how close the approximation is. The charts below show actual productivity and its growth as measured from the micro-data, compared with the approximation. As you can see in Chart 2, the approximation has a consistent negative bias, because cutting out the top 1% drops some very large outliers, but the growth path is about right (Chart 3).

Chart 2: Aggregate productivity and its centile approximation (level)

Chart 3: Aggregate productivity and its centile approximation (growth)

Now that we know the approximation matches the aggregate patterns pretty well, we can look at the distributions underlying these aggregate figures. And we find:

1. The productivity distribution is highly skewed (chart 4), so the top tail has a very strong influence on the aggregate.

The distribution has a long tail of workers in unproductive firms at the bottom, and workers in a collection of the ‘happy few’ extremely productive firms at the top. This is a well-known feature of the productivity distribution, regardless of whether we weight by labour or not. An implication of this shape is that the top tail has a very strong influence on the level of aggregate productivity in any given year, just as large outliers will push up on any average.

Chart 4: Most of the distribution is stable over time

2. The top tail has an even greater influence on changes in aggregate productivity from year to year.

Over 70% of the growth in aggregate productivity between 2003 and 2015 was driven by the top two deciles.  This is because the rest of the distribution doesn’t move around much; at least not in magnitudes that compare to movements in the upper tail (chart 4). Incidentally, this is the same thing as the OECD’s observation that the top tail is diverging from the rest.

3. The productivity puzzle (slower aggregate growth after the crisis than before) is located in the top tail of the distribution.

We can locate the growth puzzle by comparing changes in the pre- and post-crisis periods. Chart 5 is a reproduction of chart 1 with an extra line; it shows the average annual change of each centile over three distinct periods – the pre-crisis years (2004-07), the crisis (2008-09) and the post-crisis period (2010-15).

To read the chart, pick a centile and the three lines show how that part of the distribution changed, on average, over these different periods. For example, the median grew at about the same rate pre- and post-crisis, and had quite a drop during the crisis.

Chart 5: Average annual change in productivity by centile

The growth puzzle is the gap between the pre- and post-crisis lines. The chart shows that lower sections of the distribution have actually grown faster post-crisis than they did before it (pink line above the navy) and so cannot be driving the puzzle. By contrast, the top two deciles grew far slower (pink line below the navy) and therefore this is where the growth puzzle is located.

This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.

Patrick Schneider works in the Bank’s Structural Economic Analysis Division.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied.Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.