Today we published the latest DFA Household Finance Confidence index (FCI), which showed a further fall. This video blog discusses some of the findings, and considers some of the issues which explains the results, and what may change them.
Australia’s estimated seasonally adjusted unemployment rate for August 2015 was 6.2 per cent, a decrease of 0.1 percentage points. In trend terms, the unemployment rate was unchanged at 6.2 per cent in August 2015, as announced by the Australian Bureau of Statistics (ABS) today.
The seasonally adjusted labour force participation rate decreased 0.1 percentage points to 65.0 per cent in the August 2015 estimate.
The ABS reported the number of people employed increased by 17,400 to 11,775,800 in August 2015 (seasonally adjusted). The increase in employment was driven by increases in male full-time employment, and female full-time and part-time employment, with the largest increase seen in full-time employment for males (up 10,100).
The ABS seasonally adjusted monthly hours worked in all jobs series decreased in August 2015, down 0.6 million hours to 1,623.8 million hours.
The seasonally adjusted number of people unemployed decreased by 14,400 to 781,100 in August 2015, the ABS reported.
The seasonally adjusted underemployment rate remained steady at 8.4 per cent in August 2015, from a revised May 2015 estimate. Combined with the unemployment rate, the latest seasonally adjusted estimate of total labour force underutilisation was unchanged at 14.3 per cent in August 2015, from a revised May 2015 estimate.
The latest edition of DFA’s Household Finance Confidence Index to end August is released today, and it shows a significant fall. With a score of 87.69 (down from 91.1 in July), it is the lowest since the index started, and is well below its neutral setting, which was crossed in April 2014. Recent stock market volatility, concerns about employment prospects, rising living costs and slowing income growth all combine to drive the index lower.
The results are derived from our household surveys, averaged across Australia. We have 26,000 households in our sample at any one time. We include detailed questions covering various aspects of a household’s financial footprint. The index measures how households are feeling about their financial health.
To calculate the index we ask questions which cover a number of different dimensions. We start by asking households how confident they are feeling about their job security, whether their real income has risen or fallen in the past year, their view on their costs of living over the same period, whether they have increased their loans and other outstanding debts including credit cards and whether they are saving more than last year. Finally we ask about their overall change in net worth over the past 12 months – by net worth we mean net assets less outstanding debts.
Looking at the drivers of the index, this month, 40% of households are concerned about rising costs of living (up 1.8% on last month), whilst 52% said their costs had not changed (down 3.3%). In the surveys, recent council charges and school fees were mentioned specifically.
Turning to real income (after inflation), about 4% of households saw their income rise in real terms (up 0.3% on last month), whilst 39.6% said their incomes had fallen (down 0.5%) and 55% said there had been no change. Income from bank deposits continued to drop, thanks to lower rates, and dividends from some shares were lower than expected. A significant proportion did not expect to see any rise in wages in the next six months.
Looking at debt, 11.4% of households were more comfortable with their current levels of borrowing, (down 1.2% on last month), whilst 59% were about the same (up 1.85%), thanks to the expectation that interest rates were unlikely to rise any time soon. There was a rise of 0.5% in those feeling less comfortable about their level of debt, (27%), this was directly linked with concerns about future employment prospects.
Turning to savings, 13.5% of households were more comfortable with their level of savings (down 0.4% compared with last month), whilst 30.2% were less comfortable (a fall of 1.1%). 54% of households were feeling about the same as last month, up 1%.
Looking at job security, 13.8% of households felt more secure about their job prospects (down by 1.9% on last month), whilst 22% felt less secure (up 2.5%) and 61.5% felt as secure as last month (down 0.5%). There were significant state and industry variations, with those employed in mining and construction more concerned, especially in WA, along with those in manufacturing in VIC and SA, and those in QLD and NSW in resources. Those in the service sectors, such as healthcare and finance were more confident. Younger workers and those aged over 50 years were more concerned, whilst females were less concerned than males.
Finally, looking at net household worth, 60% of households thought their net worth had risen (down 2.2% on last month), thanks mainly to rising home values in the eastern states. 24.6% saw no change (up 1.5%) and 13.6% saw a fall. The main drivers of those with concerns can be traced to the volatility on the stock markets, and falling property values in WA. We also note that the one third of households who are not property active are significantly more represented in the falling category, because they do not benefit from the wealth effect of rising property prices.
Overall then household financial confidence continues to languish, despite record low interest rates. Because of this we believe many households will continue to spend carefully, and be careful not to extend their high personal debt further. We did also note though a strong interest in property as the most secure investment option, and as a result, we expect to see ongoing high demand. We will cover this in more detail in a future post.
The New Zealand Reserve Bank today reduced the Official Cash Rate (OCR) by 25 basis points to 2.75 percent.
Global economic growth remains moderate, but the outlook has been revised down due mainly to weaker activity in the developing economies. Concerns about softer growth, particularly in China and East Asia, have led to elevated volatility in financial markets and renewed falls in commodity prices. The US economy continues to expand. Financial markets remain uncertain as to the timing and impact of an expected tightening in US monetary policy.
Domestically, the economy is adjusting to the sharp decline in export prices, and the consequent fall in the exchange rate. Activity has also slowed due to the plateauing of construction activity in Canterbury, and a weakening in business and consumer confidence. The economy is now growing at an annual rate of around 2 percent.
Several factors continue to support growth, including robust tourism, strong net immigration, the large pipeline of construction activity in Auckland and other regions, and, importantly, the lower interest rates and the depreciation of the New Zealand dollar.
While the lower exchange rate supports the export and import-competing sectors, further depreciation is appropriate, given the sharpness of the decline in New Zealand’s export commodity prices.
House prices in Auckland continue to increase rapidly and are becoming more unsustainable. Residential construction is increasing in Auckland, but it will take some time to correct the imbalances in the housing market.
Headline CPI inflation remains below the 1 to 3 percent target due to the previous strength in the New Zealand dollar and the halving of world oil prices since mid- 2014. Headline inflation is expected to return well within the target range by early 2016, as the earlier petrol price decline drops out of the annual inflation calculation, and as the exchange rate depreciation passes through into higher tradables prices. Considerable uncertainty exists around the timing and magnitude of the exchange rate pass-through.
A reduction in the OCR is warranted by the softening in the economy and the need to keep future average CPI inflation near the 2 percent target midpoint. At this stage, some further easing in the OCR seems likely. This will depend on the emerging flow of economic data.
The ABS data on housing finance today suggests that the momentum in housing is shifting, as the tighter restrictions on investment lending bites; this despite strong market demand and the fact that investor property finance has never been higher at 38.9%. Looking at the monthly flow trend data, lending overall rose 0.52% in the month, by $169 m. Within that, monthly approvals data shows that owner occupied lending rose 0.84% (up $105 m from last months approvals), refinancing up 0.72% ($44 m) and Investment lending up 0.14% ($19 m). In seasonally adjusted terms, the total value of dwelling finance commitments excluding alterations and additions rose 1.5%.
Within these numbers, we see that owner occupied construction fell 1% compared with last month, owner occupied new property purchases rose 2.24%, owner occupied refinance rose 0.72% and owner occupied “other” purchases rose 1%. On the investment side of the equation, investment purchases by individuals fell 0.47%, whilst investment construction rose 4.3% and investment by other entities (including SMSFs) rose 2%. Still momentum, but the investment sectors is shifting. We expect to see ongoing strong demand from the SMSF sector.
Looking at first time buyers, both the original data from the ABS, shows a small fall in the month to 15.4% in July 2015 from 15.8% in June 2015, and the DFA data for investor FTB also fell. The number of first time buyers are still sitting at around 12,000 a month in total, still well below the peaks in 2009. Our surveys indicate strong FTB investor appetite. The changed underwriting requirements however are having an impact.
Looking at the loan stock data, the major banks still have the lion’s share, but we see that on the investment side, credit unions grew their books the largest in percentage terms, with a 1.1% rise in investment loans (compared with a rise of 0.52% by the banks and 0.68% for the building societies). We suspect some investors are switching to smaller banks, credit unions and the non-bank sector when they find the larger players less willing to lend. Overall growth on the owner occupied side was 0.43%.
Finally, looking at the overall stock of loans, we see that investment loans now make up a record 38.9% of the total portfolio, thanks partly to the recent restatement of loan types by some the banks. We think this is too-higher share of housing lending (it is more risky in a down-turn) and the banks 60% total loan portfolio in housing is also too high, sucking finance from business sectors which might contribute to real economic growth.
From The Conversation.
A good rule of thumb in debates on who bears the economic cost of a policy change is to look at the positions taken by vested interests in the matter. If anyone is going to know if they bear the cost, it is those who pay. In the case of infrastructure charges on new property developments, the vocal objections from the property industry are a sure sign that they bear the economic costs.
Infrastructure charges are levied by local governments on developers of new land estates, based on an increased load on essential infrastructure services the council is responsible for.
A research paper reported in The Conversation recently claimed that property developers could pass on these charges in the price of new homes with a mark-up of 400%. The paper also claimed that these charges had the same price effects on existing homes, meaning that new home buyers ultimately bear the cost of infrastructure charges, rather than developers.
But the logic of this should be challenged and is not borne out in the results of other rigorous academic studies of infrastructure charges, which have in fact found the opposite.
The idea that costs of developer charges can be passed on through new home prices sounds intuitive. But it is based on an incorrect notion that prices are determined by costs.
In fact, developers already charge the maximum the market will bear. To not do so would be the equivalent of selling your house for half the market price, just because it only cost you that amount 10 years ago when you bought it. You wouldn’t do it, and nor would a developer.
Using a statistical analysis of a simple regression of home prices with developer charges, along with many hedonic control variables – as this study has – will find a positive correlation simply because charges are set in proportion to housing size. But that isn’t a causal relationship.
As Ian Davidoff and former academic economist (now the ALP’s shadow assistant treasurer) Andrew Leigh succinctly describe in their study on how stamp duty affects the market:
…if one were to simply regress the sale price on the tax payable on that property, the coefficient would capture both the mechanical fact that the tax amount is a function of the price, as well as any behavioural impact of taxes on prices.
It’s true that observations of this mechanical relationship have been widely interpreted as a behavioural effect in the literature on developer charges. But the best analysis does not interpret such results in this way.
A better way to observe behavioural impacts is take advantage of natural experiments, such as when a developer charge is increased in one area but not in a comparable adjacent area, then look at any subsequent price changes compared to the “control group”.
These types of natural experiments can alternatively be attempted with statistical controls, and a recent paper does just that when looking at the house price effects from additional costs imposed to finance infrastructure.
They find that not only are proper statistical controls very difficult to implement, but that prices decrease per dollar of additional infrastructure charge by somewhere in the range of $0.33 to $2.09.
This range captures the standard view that costs cannot be passed on in prices, which in the case of developer charges means that the developer or previous landowner bears the full cost of the charge, and not the home buyer. Davidoff and Leigh’s controlled results support this view on the incidence of stamp duties in Australia.
These more properly controlled results are consistent with the political actions of the property industry who oppose developer charges because they bear the full cost.
Why is all this important? Vested interests benefit from any illusion of unsettled academic debate. In the case of developer charges the property lobby can maintain an intelligent-sounding “Goldilocks” view in public debates that goes something like this: “The research is not settled. But it is likely that we don’t pay the full charge, nor do we pass it on completely in home prices. The cost is probably shared between us and the homebuyer.”
They capitalise on this apparent uncertainty by claiming that their interests are aligned with the home-buying community; a seductive “Goldilocks” view that is hard for politicians to ignore.
Author:, Economist at The University of Queensland
Analysis from the Federal Reserve Bank of St Louis shows that in the US, whilst overall household credit is lower now, this is being driven by reduced credit creation, and not increased credit destruction. We see a very different profile of debt compared with Australia, where household debt has never been higher. However, our analysis shows that core debt is also being held for longer, so the same effect is in play here, although new debt is also accelerating, driven by housing.
Household debt in the United States has been on a roller coaster since early 2004. As the first figure shows, between the first quarter of 2004 and the fourth quarter of 2008, total household debt increased by about 46 percent—an annual rate of about 8.3 percent. A process of household deleveraging started in 2009 and stabilized at a level 13 percent below the previous peak in the first quarter of 2013. During those four years, the household debt level decreased at a yearly rate of 3 percent. Since then, it has moved only modestly back toward its previous levels.
This essay provides a simple decomposition of the changes in debt levels to shed light on the sources of those changes. The analysis is similar to the decomposition of labor market flows performed by Haltiwanger (2012) and the decomposition of changes in business credit performed by Herrera, Kolar, and Minetti (2011). We use the term “credit change” to refer to the change in household debt: the difference between household debt (D) in the current period, t, and debt in the previous period, t –1, divided by debt in the previous period, t –1:
The total household debt is the sum of debt for each household i, so this can also be written as
Equivalently, one can add the changes in debt for each household i:
The key advantage of using household-level data is that one can separate positive changes (credit creation) from negative changes (credit destruction) and compute the change in debt as
Credit change = Credit creation – Credit destruction,
These concepts are interesting because they can be linked to different household financial decisions. Credit creation can be linked to additional credit card debt or a new mortgage and credit destruction can be linked to repaying debt or simply defaulting.
As this decomposition makes clear, a stable level of debt (a net change of 0) could be the result of a large credit creation offset by an equally large credit destruction. Or it could indicate no creation and no destruction at all. To differentiate between these cases, it is useful to consider “credit activity” (also called reallocation), which is defined as
Credit activity = Credit creation + Credit destruction.
This is a useful measure because it captures credit activity ignored by the change in total debt.
The second figure shows credit creation, destruction, change, and overall activity. Recall that credit change is the difference between credit creation and destruction, while credit activity is the sum of credit creation and destruction. The credit change shown in the second figure traces the increase in debt before the 2008 crisis, the deleveraging that followed, and the relative stability of debt over the past 3 years. Analysis of debt creation and destruction shows that the expansion of debt was due to above-average creation of debt before the crisis—not insufficient credit destruction; credit destruction was actually slightly above average. Thus, credit activity was extensive during that period, with large amounts of both destruction and creation.
The deleveraging involved a decrease in creation (or origination) of debt: Creation started at nearly 10 percent in the expansion period but dropped below 5 percent after the financial crisis. Credit destruction was not the main contributor to the deleveraging: Destruction did not grow during the deleveraging period; it was actually slightly lower than during the expansion period. Thus, the deleveraging period of 2009-11 saw a very low level of credit activity, mainly due to the small amount of new credit issuance.
Finally, the stability of debt from 2011 to 2013 masked the increasing credit activity since both destruction and creation increased but offset each other. In sharp contrast, during the past year, the stability of debt has been due to very low levels of creation and destruction. In fact, credit activity is currently as low as it was in the middle of the financial crisis: about 9 percent of total household debt.
Overall, this analysis of household debt suggests that reduced credit creation, and not increased credit destruction, has been the key driver of the recent evolution of U.S. household debt. A topic for future investigation is that U.S. households are currently engaging in record low levels of financial intermediation, which is not obvious by simply observing the level of household debt.
From The Conversation.
A baby boom is generally considered to be a sustained increase and then decrease in the birth rate. The United States, the UK and other industrialized economies have experienced only one such baby boom since 1900 – the one that occurred after World War II.
In addition, many currently developing economies such as India, Pakistan and Thailand have experienced a baby boom since 1950 as a result of a sustained decline in infant and child mortality rates as a result of improved medicine and sanitation.
So what’s the economic impact of these baby booms? Do demographics play a role in determining when an economy expands and contracts? Do they boost incomes or cause them to fall as more young people enter the workforce? I’ve been studying the impact of baby booms on wages, unemployment, patterns of retirement and gross domestic product (GDP) growth for 20 years and, while there are some questions that haven’t been answered, here’s what we’ve learned so far.
Negative impact on employment
The initial impact of a baby boom is decidedly negative for personal incomes.
Baby booms inevitably lead to changes in the relative size of various age cohorts – that is, a rise in the ratio of younger to older adults – a phenomenon first described by economist Richard Easterlin. (In statistics, a cohort is a group of subjects who have shared a particular event together during a particular time span.)
These effects cause a decline in young males’ income relative to workers in their prime, a higher unemployment rate, a lower labor force participation rate and a lower college wage premium among these younger workers.
This occurs because younger workers are generally poor substitutes for older ones, so the increased supply of youths leads to these negative employment results.
Back in the 1950s, entry-level young males in the US were able to achieve incomes equal to their fathers’ current income. This was because of that age group’s reduced relative size as a result of the low birth rates in the 1930s. But by 1985 – about the time the peak of the baby boom had entered the labor force – that relative income had fallen to 0.3; in other words, entry-level men were earning less than one-third of what their fathers made.
In developing countries, these relative cohort size effects – the reduction in young males’ relative income and increase in their unemployment rate – are multiplied by the impact of increasing modern development, especially the rising level of women’s education.
In addition, the large influx of baby boomers into the labor market in the US forced many older workers, who would otherwise be working in “bridge jobs” prior to retirement, into earlier retirement. This explains how the average age of retirement for men and women went down in the 1980s.
This decline in income relative to their parents and their own material aspirations has a host of repercussions on family life. It leads to reduced or delayed marriage, lower fertility rates and increased female labor force participation rates as young people struggle to respond to their worsened prospects.
From boom to bust … to boom?
The reduction in relative income – which the US experienced in the ‘60s and ’70s – thus results in a subsequent “baby bust” as people delay starting a family.
It was hypothesized that these baby booms might be self-replicating as reduced birth rates on the trailing edge of the boom caused the subsequent cohort to be smaller in size, thus leading to better labor force conditions, increased birth rates and an “echo boom” in the next generation.
This theory was based on what led to the baby boom in the first place, when the favorable labor market conditions experienced in the 1950s emerged as a result of fewer children being born during the 1930s, reducing the young-to-old-adult ratio.
Though the echo boom of the 2000s represented an increase in the absolute number of young adults, it didn’t lift their cohort size relative to their parents because birth rates have remained fairly stable at low rates since the end of the post-WWII baby boom.
That has not, however, translated into significantly better labor conditions, at least not the kind experienced by young adults in the 1950s that led to the baby boom. The reasons for this phenomenon have not yet been explained.
So can changing demographics cause recessions?
Another way of exploring the effects of changes in the proportion of young adults in the population is to look at fluctuations in the relative size of the young adult population over time. These seem to have a significant effect on the economy.
As young adults move out of high school and college and set up their own households, they generate new demands for housing, consumer appliances, cars and all the other goods attendant on starting a new adult life. These new households don’t account for a large share of total expenditures, but they represent a major share of the growth in total consumer expenditures each year.
So what happens if, after a period of growth in this age group, the trend reverses? It is likely that industries counting on further strong growth will be forced to cut back on production, and in turn to cut back on deliveries from suppliers – which will in turn cut back on deliveries from their suppliers, creating a snowball effect throughout the economy.
This picture is supported by the patterns over the past 110 years depicted in the graph shown below.
The curve on the graph represents a three-year moving average of the annual rate of change in the proportion of young adults in the US population, as given by the United States Census Bureau. “Young adults” are defined as those aged 15-19 prior to 1950, and 20-24 in the years after, given changing levels of education over time. This curve is overlaid with vertical lines that mark the start of recessions, as defined by the National Bureau of Economic Research.
There is a very close correspondence between the vertical lines, and peaks in the curve, as well as points where the curve turns negative. In addition, the deep trough between 1937 and 1958 contained another four recessions, and there were two in the trough between 1910 and 1920 (not marked on the graph). The only recessions over the last 110 years that don’t appear to correspond to features of the curve, are those in 1920, 1926 and 1960.
The pattern of causation – if it is one – cannot run from the economy to demographics, since these are young people born over 15 years before each economic downturn. In addition, there’s a one-year lag in the age groups that has been used to control for possible migration effects of a recession – that is, how many people left the US as a result of worse labor market conditions.
The fact that no “double dip” recession occurred in 2012, even as the share of young people fell that year, might be the result of the economic stimulus applied after the most recent recession.
Food for future thought
Obviously there are many other factors associated with economic downturns, but aspects of the empirical regularity demonstrated here can be seen in many countries over the past 50 years – especially regarding the international financial crises of 1980-82, 1992-94, and 1996-98 and 2007-2008.
This is not to say that demographics were the sole cause of the recessions, but rather that they influenced the timing of such events, given a host of other possible factors. For example, did they play a role in determining when the recent housing bubble burst? That question has yet to be answered, but further study may shine some light.
Author:, Professor of Economics at University of Redlands
Interesting perspectives from Luci Ellis, Head of Financial Stability Department, RBS speaking at the University of New South Wales (UNSW) Real Estate Symposium 2015. She correctly highlights that the property market is not a single amorphous whole, and that a wide range of interconnected drivers are linked. However, one important point which though mentioned, is not really explored sufficiently, is the significantly higher proportion of bank lending on housing in Australia (60%), compared with the US (25%) – see graph 3. This over reliance on housing in Australia surely highlights the potential systemic risks by over exposure to housing, and by the way relatively less lending to productive businesses. This seems to me to be the core issue, as availability of finance is one of the strongest influences of house price momentum.
Financial stability and property markets are inextricably linked. It’s an important topic, and yet there is still so much to learn.
In many respects I am reminded of the early 1990s and monetary policy. Inflation targeting was fairly new. Around the world there was important foundational work on how this new approach to monetary policy should operate. Some of that work took place at the Reserve Bank. We learned a lot along the way – the concept of an output gap, the appropriate definition of the target, goal versus instrument independence (Debelle and Fischer 1994), as well as the appropriate forecast horizon (de Brouwer and Ellis 1998). Of course there were some intellectual dead ends, too – the idea of sticking to a fixed interest rate rule or a monetary conditions index being an example.
I often feel that we are at a similar point now in financial stability policy world as we were back then on monetary policy. We are seeing a flourishing of work – sometimes it is hard to keep up with the flow of new, interesting papers! Like the early 1990s work on monetary policy, it is the central banks and policy institutions leading much of the research. Academia is contributing, but it is not the dominant voice. And as for that earlier work, there will inevitably be some dead ends in all this new research.
Policymakers and academics alike were interested in financial stability issues long before the crisis, but the crisis has certainly ramped up the scale of that interest. And because of the crisis, much of the research work being done has a tendency to leap very quickly to the policy conclusion. That’s a natural temptation when the stakes are so high. But the policy imperatives inspiring the work make it even more important to be scientific in our approach. By scientific, I mean the idea that the celebrated physicist Richard Feynman talked about in a much-cited university commencement address (Feynman 1974).
Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can – if you know anything at all wrong, or possibly wrong – to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it.It’s an argument for nuance, for being rigorous about your approach and for being prepared to admit you might be wrong. But I don’t want to understate the challenges this poses in a policy institution. Putting the necessary caveats on our work comes naturally to a cautious central banker: that’s not the problem! Once published, though, those carefully drafted caveats are either ignored, or treated as the ‘real’ finding. Sometimes the headline on the reporting is the exact opposite of the real conclusion of the paper.
Despite these difficulties, I think it’s fair to say that we already know quite a bit about property markets and financial stability. Some of the things we know are old lessons, while others have been reinforced by recent events. There is still a lot we don’t know; yet sadly, some lessons we already know risk being forgotten. I will touch on each of these categories today. If there is a common thread to all of them, it is the need to respect the physical realities of the subject.
What We Already Know
For property, the physical reality is that it endures for a long time and is fixed in place.
The reality of property as place
Because property endures, price is determined by demand and supply for a stock of property. The flow of new supply is generally small compared with the stock. This is not a new point; I have made it many times before. The first implication of this relevant to financial stability is that property is prone to ‘hog cycles’ and ultimately to overhangs of excess supply.
The second implication is that acquiring a particular property and the housing or commercial accommodation services it provides is a large upfront cost. Accordingly it makes sense to make that acquisition with some leverage. We’ve known since before the crisis that busts in asset prices of themselves need not be problematic for financial stability (Borio and Lowe 2002). It is the leverage against those assets that matters more. And the highest leverage can be obtained when borrowing is secured against property. I shall turn to the question of leverage in more detail in a moment.
Even more important than its endurance, to my mind, is that property is fixed in place. The Deputy Governor recently talked about the general fascination with land. It is true that if you take the price of land as being the difference between the total price of the property less the replacement cost of the building, it is land prices that have risen relative to incomes (Lowe 2015). But land is two things: it is both space and place. Many have observed that Australia has plenty of the former. But I think the lesson of past booms as well as recent times is that it’s place – location – that really matters. If we think back to boom–bust episodes of the past, whether in land for new development, railways or prime office buildings, in every case you can see people trying to get their hands on the best locations, to take advantage of whatever future economic outcomes they expect.
The same holds true for more recent times, and for residential property. Prior work at the Reserve Bank has shown that location explains far more of the variation in individual property prices than block size (Hansen 2006). Yes, some people like a bigger garden, for privacy or to enjoy in other ways. But being in the ‘right’ kind of neighbourhood with the best amenities, close to commercial centres and other services, is more important to most people, if their willingness to pay for it is any guide.
The physical reality is that the supply of good locations is more or less fixed in the short term. So any sizeable boost to demand cannot be fully absorbed by more supply. The newly built property is simply not the same as the existing stock, because it’s somewhere else. We should therefore not be surprised that strong demand for property does not just change the general price level for that asset, but also its distribution. We can see this in the increase in prices of inner-ring properties relative to those further out, especially in Sydney (Graph 1).
We can also see this in the wedge between growth rates of prices of apartments versus detached houses, as the rising share of apartments in new construction serves to make existing detached houses relatively scarcer (Graph 2).
Over the much longer term, the set of good locations does change. Improving transport infrastructure can certainly help here; the process of gentrification is probably even more important. To give a few examples, in the space of a few decades, suburbs like Paddington, Newtown and Balmain in Sydney or Fitzroy and Northcote in Melbourne went from ‘scary’, to edgy, to trendy, to pricey. The housing stock was also renovated in this process, but most of the price action can probably be explained by the rising relative price of those locations.
Taking all these physical elements together, we have a set of related assets – land for development, existing housing and the various segments of commercial property – that will inherently experience strong, but perhaps temporary, price increases in the face of increases in demand. Irrational exuberance and speculative bubbles aren’t even necessary to get that result, though it’s fair to say that they’d exacerbate it. Simultaneous boom–bust episodes in both prices and rents have been endemic to commercial property markets, and evident in every mining town during every mining boom known to history. Some fundamentals themselves have a boom–bust shape; the inherently sluggish supply of location strengthens this dynamic.
The importance of leverage
I’d like to turn back now to the question of leverage. Like property, the physical reality of debt cannot be ignored. Three aspects are particularly relevant to financial stability and its connections with (leveraged) property.
The first aspect is that debt is almost always a nominal contract. The rate of price inflation in the economy matters enormously for the incentives to take on debt. Negative price inflation – deflation – has long been known to be problematic for borrowers, including otherwise sound ones. More generally, different average rates of inflation involve different average nominal interest rates and different rates of decline in the burden of a fixed-repayment mortgage. The housing literature sometimes calls this ‘mortgage tilt’. Different nominal interest rates also translate the same repayment into a different allowable loan size. The Bank has explained on many occasions that this fact implies that a permanent disinflation has macro implications for debt, asset values, and the distribution of both of them (Ellis and Andrews (2001), RBA (2003), RBA (2014)).
The second aspect is that there is only imperfect information on borrowers’ ability and willingness to pay. Even the borrowers themselves do not know for sure, because they do not know what will happen to their capacity to pay in the future. So some borrowers end up defaulting on their debts, and lenders cannot perfectly predict who will default or even the probability of default. There is of course an enormous literature on credit risk that tries to get a better read on those probabilities. The main point to bear in mind for our purposes, though, is that credit constraints are pervasive and take a range of forms. In financial stability analysis and policy, we often talk about the importance of maintaining lending standards. All we really mean by that is that the credit constraints that exist should be designed well and for the right purpose – to manage credit risk. It will never be possible or desirable to eliminate credit constraints entirely.
The third aspect is that legal definitions of liability differ, and those differences matter to the interplay between debt, property and financial stability. The most relevant difference is that companies have limited liability and individuals do not. This in turn affects the recovery lenders might expect from a borrower who defaults, and therefore the credit risk posed by different kinds of borrowers. This need have nothing to do with the borrower’s intent. It is simply recognising that a bankrupt individual can continue to earn income afterwards, while a company that defaults, goes bankrupt and is wound up ceases to exist. The kinds of property owned by companies might therefore pose different credit risks to those owned by individuals. This is not the only difference between commercial real estate and owner-occupied housing relevant to financial stability, but it is a fundamental one.
The nature of the liability and the claim also helps explain why, as I mentioned earlier, property is permitted to be leveraged more than other assets such as equities. I am not aware of any literature that sets this out clearly. The leveraged asset is not directly the means by which the borrower pays the loan down. Rather, there is an income stream servicing the debt, which might be the rental income on the property, or the labour and other income of a homeowner. The property is the security, the collateral that can be claimed if the borrower does default. Contrast this with an equity claim on a company, such as collateralises a margin loan. The market value of that claim is generally more volatile in the short term than the price of property, which is one reason why a lender might want to limit leverage more. More importantly, the residual claim is against the assets of the business and their ability to produce future income. But business assets – the equipment and other realisable assets of the business – depreciate more quickly than property. That is partly because their rates of wear and tear differ, but it is mostly because the land component of property – the location value – does not physically depreciate.
What this means for systemic risk
So we know that sluggish supply can create boom–bust dynamics in a property market. And we know that these asset classes are particularly amenable to leverage. Is this enough to create systemic risk to financial stability? To answer that, we can turn to a simple framework that the Bank uses to think about what might pose systemic risk (see RBA (2014), Chapter 4). The features we see as posing systemic risk are: size, interconnection, correlation and procyclicality.
The size aspect is obvious. Something can pose systemic risk even if it is not that risky in and of itself, because its impact on the system is large. That is certainly the case for the housing market. In most countries, existing residential housing is not that risky, and neither is the mortgage book. But the housing market is large: housing is a large fraction of household wealth; the housing services provided by the housing stock represent more than 20 per cent of household consumption, much of it implicit in home ownership; and mortgage debt is in many countries a large fraction of the assets of banks and other financial intermediaries. A large enough downturn in housing prices would harm output through its effect on household spending, even if it did not spark a financial crisis through loan losses. This effect was surely at play in the United Kingdom in the early 1990s and the Netherlands in the early 2000s. Consumption weakened, but the increase in non-performing mortgage loans didn’t push the banks into distress. Major losses on home mortgage portfolios are rare, and usually driven by high unemployment. That is to say, they are more often the consequence of a downturn than its cause. The US meltdown was an exception, enabled by gaps in the regulatory system, such that it could not prevent an extreme easing in lending standards (Ellis 2010). But if the mortgage book is large enough relative to the rest of the financial system, even moderate losses would exacerbate an initial downturn that started somewhere else.
Commercial real estate is usually a smaller part of the total stock of property than housing. Yet it is an important part of the capital stock. For example, it is around one-quarter of fixed assets in the United States, that is, excluding the land values The figure for Australia is not quite comparable, but our best estimate is that it is even higher than that. The importance of property to business should be no surprise. Businesses need buildings: offices to work in; retail space to sell from; factories and workshops to make products in; and warehouses to store them.
The sheer size of these asset classes helps explain their interconnection with the financial system, another aspect of their systemic risk. Property is not just a large part of household and business balance sheets. Property-related exposures of various kinds are often large parts of bank balance sheets (Graph 3). In some countries, pension funds are also heavily exposed. Some recent literature has suggested that connections on their own aren’t the real issue – it is the pattern of those connections that matters (Acemoglu et al 2012). And since the financial sector touches every other in some way, the sectors that matter to the financial sector will have disproportionate ultimate effects on the rest of the economy.
At this point we must distinguish between loans financing the purchase of property and loans financing the construction and development of property. At least some existing property is owned outright, not leveraged at all. Financial institutions are not exposed to these properties. Development projects, by contrast, almost always seem to involve at least some debt, usually intermediated debt from banks and similar institutions. This means that banks’ exposures to construction and development of property are usually out of proportion to the flow of new construction relative to the stock of existing property. Given the relative risk profile of the two types of exposure, this strengthens the interconnection between construction activity and the financial sector. This is especially so for the United States, where commercial real estate exposure is not that much smaller than housing exposures. The same would be true in any country where the government intervenes, as it has for many years in the United States, to boost securitisation markets and make it easier for banks to get (low-risk) residential mortgages off their balance sheets.
Direct interconnections are one channel of contagion that creates systemic risk. Correlation, without direct connections, is another. Every property is different in at least some respects. Features, layout, internal fittings and location: all differ across individual properties. So you might think that property is not particularly correlated within the asset class. And you might expect that market participants’ decisions to buy or sell would not be that correlated – that is, that they would not act as a herd. Unlike financial markets, a lot of property is owner-occupied, held for the services it provides. Unlike financial returns, those services do not suddenly deteriorate just because the price of the asset has fallen. So unless the owner is distressed, they have no particular reason to sell just because prices have fallen. They do not have short-term return benchmarks to meet on their property holdings, unlike many fund managers investing in financial assets. And if property prices have fallen, they have generally fallen relative to rents. So selling an owner-occupied property and renting instead actually becomes less attractive.
And yet property markets are thoroughly correlated. Sure, every property is different. So the level of prices differs across individual properties. And yes, there is some idiosyncratic noise in returns, especially if someone falls in love with a property, pays too much and later discovers that the rest of the market does not share their valuation. Still, much of what drives the change in property prices is common to all – interest rates, incomes, lending standards, supply responses. The relative values of particular property features vary rather less over short periods than these macro drivers do.
But if there is one aspect of systemic risk that makes property markets especially important for financial stability, it is procyclicality. The physical realities of property I described earlier, and the fact that it can be leveraged to such an extent drive that procyclicality.
In saying that, I think it’s important to be clear about what we mean by procyclicality. Something could be regarded as procyclical because the amplitude of its cycle is bigger than that in output. This is certainly true of asset prices and credit (Graph 4), as well as many other variables such as investment and corporate profits. But it is not the relevant definition from the perspective of financial stability.
For something to be procyclical in a way that matters to financial stability, its dynamics should be causal for the overall dynamics of economic output and wellbeing. Some variable might well be correlated with the cycle, even predictive of future distress, but if it is not actually causal, leaning on it will not produce the desired outcome of promoting financial stability. I shall have more to say about this point in a moment.
What many people implicitly have in mind when they talk about procyclicality is something even more specific: positive feedback. This is when a movement of a variable in one direction fosters further moves in the same direction, often until a new equilibrium is reached. Such self-reinforcing dynamics and ‘tipping points’ are seen in many complex systems – for example they are well known in certain ecological contexts – so it seems reasonable to believe that they can also occur in economic-financial systems, including in property. An example would be if investors sell an asset after its price falls, inducing further sales and falls in the price. It probably hardly needs pointing out that positive feedback involving plants and rain, or algae and plankton, doesn’t need speculative motives or irrationality, just the right kind of nonlinearity. It might well be that certain kinds of expectations produce that nonlinearity in an economic system, but perhaps we should not assume that is the only way to get it.
What We Do Not Yet Know
So we know a lot: that property booms and busts, partly because of its physical realities; and that it can be highly leveraged, which can sometimes be dangerous for economic and financial stability. There is certainly a lot of evidence, or at least some strong indications, that property has something to do with the boom-bust episodes that so often engender financial instability and crisis. What we don’t yet know in all this is what the mechanism behind these connections really is. This comes back to the point I made just before about needing a causal link if something is to warrant a policy response.
We do know that there are strong correlations between strong upswings in credit, measured in a variety of ways, strong growth in property prices, and subsequent bad events. What isn’t yet settled is whether the credit causes the prices, the property markets drive the credit, or whether either of these is the decisive factor in generating economic downturns or financial distress. There is some interesting recent literature that tries to tease out these relationships (e.g. Geanokoplos and Fostel (2008) and Geanokoplos (2009)) but I don’t think the profession has reached a consensus on this as yet.
I’ve heard it said that paying attention to these correlations is still worthwhile, because you don’t have to know what causes a typhoon to know that it is dangerous. But in that situation, there is nothing to stop you from believing that the typhoons are a punishment from the weather gods and that the appropriate policy is a program of sacrifices to placate them. You don’t need to know the causes of a crisis – or a typhoon – to encourage a bit more resilience to their effects. More capital and faster debt amortisation are two good examples of increasing financial resilience. As soon as you start to talk about preventative policy, though, you should at least have a good theory about the mechanism, and some evidence to back it up. Otherwise, how can you distinguish what is really causal, from what is merely a correlation?
Another issue that I do not consider to be settled is whether we should regard these boom-bust dynamics as a cycle, and if so, whether it represents a credit cycle that is somehow independent of the business cycle. Certainly there have been many papers asserting the existence of a credit or financial cycle that has a longer frequency than the conventional business cycle frequency, which is usually assumed to be much less than a decade.
I would be wary of assuming too readily that property finance really is the driver of the cycle in the way some literature has claimed. It might well be, but some recently released empirical analysis suggests that, for the United States at least, it is unsecured corporate borrowing that drove the cyclicality in business credit in recent decades, not (commercial) mortgages and other secured credit, which seems more or less acyclical (Azariadis, Kaas and Wen 2015). Much of the work that claims to find mortgage-driven credit cycles rest heavily on pre-war data (Jordá, Schularick and Taylor 2014). I do not wish to take away from the achievement of the compilation of these data sets. Rather, I simply want to inject a note of caution against jumping to strong policy conclusions on the basis of data that might not be the most relevant.
In calling for that caution, I am if anything harking back to even longer-run evidence on the causes and effects of numerous boom-bust episodes. Kindleberger noted in his magisterial analysis of these episodes that every mania started with a ‘displacement’ (Kindleberger and Aliber 2000). That is, something real happened, something that would endure even after the panic and crash. His and other historical analyses of these episodes point to a range of one-offs as triggers for the booms: new products, political change, financial deregulation all being mentioned in many cases. If that’s right, perhaps we should not speak of a cycle, but rather, simply a parade of stuff happening.
Since many of these boom-bust episodes were common across countries, we should also remember that many financial institutions reach across borders, and that many institutional and regulatory changes do as well. There has probably not been enough recognition of the role of international institutions and peer effects amongst policymakers in creating correlated institutional change across countries. One example is the wave of financial deregulation in the 1970s and 1980s that culminated in financial crises in Japan, the Nordic countries and (almost) Australia in the late 1980s and early 1990s.
The relatively better performance of these countries in the subsequent global financial crisis has sometimes been attributed to a kind of scarring effect – or scaring effect, if you like. According to this narrative, the people who went through the early 1990s crises or near-crises were still in charge in the lead-up to the more recent crisis, and their earlier experience made them more cautious. There is probably something to this story and, if so, it raises the question of how to pass that realistic approach to risk down to future generations of bankers and policymakers. But there is an alternative interpretation of events, which is simply that the financial sector can only be deregulated once from its post-war restrictions. The resulting over-exuberance, borne of inexperience, could only re-occur if something else came along that resulted in a similar transition period of fast credit growth, at the same time as we somehow forgot everything we have learned since about credit risk management.
The Things We Risk Forgetting
I don’t want to sound flippant about this, because history does tell us that it is possible to forget good credit risk management. One of the things we risk forgetting about property markets and financial stability, and about risk more generally, is that it is possible to forget. As we get further away from the peak of the crisis, increasingly we will hear points of view questioning what the fuss was all about. If there is indeed a trade-off between growth and financial stability – and that’s by no means settled – policymakers must balance both considerations. In doing so, they must not forget the full costs of financial instability and the distress it can cause.
In particular, it is possible to forget how to do good credit risk management. The body of knowledge about best practice in this area has certainly expanded over the past quarter century, but that doesn’t mean it is always practiced. It is all too tempting to ease standards over time. It is like one of those humorous verb conjugations: ‘I am just responding to strong competition; you have relaxed your standards; he is being imprudent’.
We saw a kind of forgetting about credit risk management in the US mortgage market, because often it was new (non-bank) firms doing the lending. Without an existing corporate culture about risk, often without a prudential supervisor to enforce those standards and practices, without ‘skin in the game’ in the form of their own balance sheet absorbing that risk, the new wave of US mortgage lenders slid inexorably into a stance of utter imprudence.
Another thing we risk forgetting is that property markets are not just about households’ mortgages. Property development, including for residential property, and commercial lending related to property more generally, should also receive sufficient attention from risk managers, policymakers and academic researchers. It is these segments of lending that tend to grow in importance in the late stages of a boom, and to account for a disproportionate share of loan losses in a bust (Graph 5).
And if we are looking for surges in credit growth as precursors to painful downturns, we should bear in mind that, historically, these surges have been evident in business credit far more than in housing credit. That is certainly what we see in the Australian data (Graph 6).
We don’t only risk forgetting that property is not just about home mortgages. We also risk forgetting that these different market segments are not all the same as each other, or across countries. Institutional settings and public policies affect credit risk greatly, sometimes in ways that are not obvious. There are clear connections between financial stability outcomes and the mandate, powers and culture of the prudential supervisor, or the form and coverage of consumer protection regulation around credit. But it is perhaps less obvious that labour market institutions, for example, or the way health care is paid for, can affect the idiosyncratic risks households face, and thus the credit risk they pose to lenders.
Though the profession has clearly learned that leverage matters, we risk forgetting that credit is not an amorphous blob. It embeds an agreed flow of payments, certainly, but also a complex set of contract terms. These contract terms touch on the resulting credit risk at many points: not just the collateral posted and how it is valued, but the assumptions about serviceability, the length and flexibility of the loan term, the rate of amortisation required or allowed and so on. In other words, lending standards are multidimensional. Excessive focus on one dimension to the exclusion of others could in some cases be counterproductive.
One final thing I do not want us to forget: that while policy institutions such as central banks will do much of the running on policy-relevant research, we need sound contributions from academia to keep us honest and keep us smart. Good academic work such as the ones I have cited today can provide us with both tools and insights that we might not have come up with ourselves. Researchers at policy institutions generally try very hard to follow the evidence where it leads, even if it isn’t consistent with the previously stated positions of the institution; parallel contributions from academia are valuable information to test whether we are doing well enough in that regard. And the scientific project of explaining something new, the core academic value of working out the implications of your assumptions or your theory and testing those implications, remains the standard we all aspire to. Richard Feynman put it well in the same address that I quoted earlier.
* Thanks to Kerry Hudson for assistance in preparing this speech, and to Penny Smith, Fiona Price and participants at a workshop on the same topic at the Banco Central de Chile on 25 April 2014 for helpful comments and discussion.
- Fisher and Kent (1999) discusses in some detail the land boom of the 1880s, which ended in Australia’s first (and last really severe) banking crisis. A similar jostling for ‘positions’ of market dominance might also have driven episodes of speculation involving new technologies, such as railways in the 19th century, electricity in the early 20th century and IT and Internet-related products in the 1990s.
- This is the ‘debt-deflation’ problem described by Irving Fisher (Fisher 1933).
- Of course, this distinction narrows when individuals can take out non-recourse mortgages, but that practice is more or less exclusive to the United States and even there, only available in a few states.
- These incentives are reflected in regulatory incentives, whereby loans with property collateral generally involve lower capital requirements than loans collateralised against business equipment, and lower still than loans against unsecured lending, even if the borrower is the same entity. But even lenders that are not prudentially regulated and investors in capital markets tend to allow greatest leverage for loans collateralised against property than other assets, so there seems to be something more fundamental about the nature of the security going on.
- This is not quite the same issue as systemic impact in the event of failure, which is the test used by the Basel Committee on Banking Supervision to determine which banks should be deemed to be globally systemically important. That test also includes an institution’s complexity and the substitutability of the services it provides. Both factors affect the consequences of failure more than its probability.
- A simple ecological example is that vegetation absorbs more heat than barren land, which promotes more evaporation and local rainfall, which promotes more vegetation. For a survey of these issues that is reasonably accessible to the somewhat mathematically inclined layperson, see Scheffer (2009).
- I wish I had come up with the metaphor in this rejoinder, but I didn’t. Thanks to Penny Smith for this one.
- Even authors writing about the financial cycle concede that it is probably not literally a cycle (Borio 2012, p 6).
Acemoglu D, VM Carvalho, A Ozdaglar and A Tahbaz-Salehi (2012), ‘The Network Origins of Aggregate Fluctuations’, Econometrica, 80(5), pp 1977–2016.
Azariadis C, L Kaas and Y Wen (2015), ‘Self-Fulfilling Credit Cycles’, Federal Reserve Bank of St Louis Working Paper 2015-005A.
Borio CEV and PW Lowe (2002), ‘Asset Prices, Financial and Monetary Stability: Exploring the Nexus’, BIS Working Paper 114.
Borio CEV (2012), ‘The Financial Cycle and Macroeconomics: What Have We Learnt?’, BIS Working Paper 395.
de Brouwer G and L Ellis (1998), ‘Forward-looking Behaviour and Credibility: Some Evidence and Implications for Policy’, RBA Research Discussion Paper No 9803.
Debelle G and S Fischer (1994), ‘How Independent Should a Central Bank Be?’, in J Fuhrer (ed), Goals, Guidelines, and Constraints Facing Monetary Policymakers, Federal Reserve Bank of Boston, Boston.
Ellis L and D Andrews (2001), ‘City Sizes, Housing Costs, and Wealth’, RBA Research Discussion Paper No 2001-08.
Ellis L (2010), ‘The Housing Meltdown: Why did it Happen in the United States?’, International Real Estate Review, 13(3), pp 351–394.
Feynman R (1974), ‘Cargo Cult Science’, Commencement Address, Caltech University, Pasadena, CA. Available at <http://neurotheory.columbia.edu/~ken/cargo_cult.html>.
Fisher C and C Kent (1999), ‘Two Depressions, One Banking Collapse’, RBA Research Discussion Paper No 1999-06.
Fisher I (1933), ‘The Debt-Deflation Theory of Great Depressions’, Econometrica, 1(4), pp 337–357.
Geanokoplos J and A Fostel (2008), ‘Leverage Cycles and the Anxious Economy’, American Economic Review, 98(4), pp 1211–1244.
Geanokoplos J (2009), ‘The Leverage Cycle’, in D Acemoglu, K Rogoff and M Woodford (eds), NBER Macroeconomics Annual, Volume 24, University of Chicago Press, Chicago, pp 1–65.
Hansen J (2006), ‘Australian House Prices: A Comparison of Hedonic and Repeat-sales Measures’, RBA Research Discussion Paper No 2006-03.
Jordá Ò, M Schularick and AM Taylor (2014), ‘The Great Mortgaging: Housing Finance, Crises, and Business Cycles’, NBER Working Paper 20501.
Kindleberger CP and RZ Aliber (2000), Manias, Panics and Crashes: A History of Financial Crises, 5th Edition, Palgrave Macmillan, Basingstoke.
Lowe PW (2015), ‘National Wealth, Land Values and Monetary Policy’, Address to the 54th Shann Memorial Lecture, Perth, 12 August.
RBA (2014), ‘Financial System Inquiry’, Submission to the Financial System Inquiry, 31 March.
RBA (2003), ‘Household Debt: What the Data Show’, Reserve Bank Bulletin, March, pp 1–11.
Scheffer M (2009), Critical Transitions in Nature and Society, Princeton Studies in Complexity, Princeton University Press, Princeton.
From The Conversation.
The UK’s July budget, regarded by some as an outright attack on the young, prompted some timely discussion on the question of intergenerational justice. Among other things, George Osborne, the chancellor of the exchequer, has abolished housing benefit for under-21s, scrapped maintenance grants for the poorest students, and locked under-25s out of a new living wage.
This final measure was greeted memorably in the House of Commons by the fist-pumping of Iain Duncan Smith, the work and pensions secretary.
At the same time, the latest findings of the Intergenerational Foundation highlighted a starkly widening gap in its fairness index between those under 30 and those over 60. In just the last five years, they report a 10% deterioration in the prospects of younger generations relative to older generations across a range of measures including education, income, housing and health.
Responding to the report, former World Bank economist Lawrence Kotlikoff called intergenerational inequity the moral issue of the day, and accused the UK of engaging in “fiscal, educational, health and environmental child abuse”.
In June, the Centre for Policy Studies issued a report detailing a bleak outlook for Generation Y (those born between around 1980 and 2000), who will have to pick up the tab for apocalyptic levels of national debt incurred by baby-boomer overspending. The report’s author, Michael Johnson, said:
Baby-boomers have become masters at perpetrating intergenerational injustice, by making vast unfunded promises to themselves, notably in respect of pensions. Indeed, such is their scale that if the UK were accounted for as a public company, it would be bust.
The injustice and the urgency of the issue seems obvious, but the want of political will to address this suggests that we still don’t know how to think well about the generation game.
The problem of generations
In 1923, the Hungarian-born sociologist Karl Mannheim wrote an essay called The problem of generations, which points us helpfully to some of the structural and sociological features of the relationships between young and old.
Mannheim carefully observed the tension involved in the continuous process of transitioning from generation to generation, a phenomenon based ultimately on the biological rhythm of birth and death. While former participants in what Mannheim calls the “cultural process” are constantly disappearing in death, new ones are constantly emerging through birth into their own time of life.
This phenomenon creates the responsibility to continually transmit the accumulated cultural heritage to new generations. However, tensions arise as young people appropriate that heritage, but want to interpret the world afresh and shape it differently. Mannheim observes that younger generations tend to be “more dramatically aware of a process of destabilisation and take sides in it” while “the older generation cling to the reorientation that had been the drama of their youth.”
It seems older generations have become much better at clinging on. Only recently, for instance, has 87-year-old Bruce Forsyth retired from his regular prime-time slot on Saturday night television. If there is a generation game, didn’t he do well?
Fixing the future against the young
There are powerful establishment narratives that discourage the destabilising political agency of the young, not least a creeping broad-brush rhetoric around “extremist” views and so-called British values. But an especially effective modern mechanism of holding new generations in thrall to the old is to make the young pay a fare for their futures.
The chancellor’s recent policy announcements only advance on the norms of a society that has quickly built the accumulation of enormous personal debt into securing the advantages attained so cheaply by previous generations, such as housing and education. You can have your cultural heritage, only now you’re going to have to pay for it. When older generations can impoverish or indebt young people swiftly and heavily enough for the advantages they are schooled to covet, their behaviours can be better disciplined to preserve the stability of a prevailing culture and pacify the threat of the new.
But Mannheim also shows us that the great virtue of the young is that they make fresh starts possible. Being open to the destabilising effect of new generations “facilitates re-evaluation of our inventory and teaches us both to forget that which is no longer useful and to covet that which has yet to be won.”
The stability that is so prized and clung to by older generations cannot last forever, and our social future requires the kind of radical re-evaluation that only the young can effect. But while figures like the young Scottish National Party MP Mhairi Black may offer a glimmer of hope, too many young people are being offered little more to covet than a living wage and the payment of their debts.
Author:, Research associate at Lancaster University