What Drives US Household Debt?

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.

6tl-hhfinHousehold 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 creationCredit 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.

Baby booms and busts: how population growth spurts affect the economy

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 graph tracks the three-year moving average of the annual rate of change in the proportion of young adults in the US. The red vertical lines indicate the beginnings of recessions. Data past 2020 are projections. US Census Bureau

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: Diane J Macunovich, Professor of Economics at University of Redlands

Property Markets and Financial Stability: What We Know So Far

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.[1]

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).

Graph 1

Graph 1: House Price Gradient

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).

Graph 2

Graph 2: Capital City Housing Price Growth

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.[2] 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.[3] 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.[4]

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.[5]

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.

Graph 3

Graph 3: Banks' Lending By Type

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.

Graph 4

Graph 4: Credit and Nominal GDP Growth

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[6] – 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.[7] 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.[8]

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).

Graph 5

Graph 5: Banks' Exposures and Non-performance Assets

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).

Graph 6

Graph 6: Credit Growth by Sector

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.

  1. 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.
  2. This is the ‘debt-deflation’ problem described by Irving Fisher (Fisher 1933).
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. I wish I had come up with the metaphor in this rejoinder, but I didn’t. Thanks to Penny Smith for this one.
  8. 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.

The generation game: how society loads the dice against the young

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?

Nice to see you, to see you nice.

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: Simon Reader, Research associate at Lancaster University

Westpac Seeks To Build, Leverages Digital

In today’s market update, we got a glimpse of the banks intentions under CEO Brian Hartzer. They are looking to add more than 1 million new customers (2015-17), and increase the number of products per customer. There is a strong focus on digital transformation, including the development of a customer service hub which links multiple systems to create a single view of a customer; as well more revamped branches, with 55% of the network changed by 2018. The aim is to drive the expense to income ratio below 40% within 3 years, reducing the groups expense growth run-rate to 2-3% per annum.

They plan to lift investment by $200m to $1.1 billion which is directed to growth, service and efficiency initiatives, focussing on digital, simplification and customer service.

They reaffirmed a target ROE of more than 15%.

Nothing wrong with in intent, but the question will be excellence of execution.

No further disclosure on current banking performance. We think a close eye on the performance of the investment mortgage book is warranted given current market dynamics.


Labor 2.0: why we shouldn’t fear the ‘sharing economy’ and the reinvention of work

From The Conversation.

Uber suffered a legal blow this week when a California judge granted class action status to a lawsuit claiming the car-hailing service treats its drivers like employees, without providing the necessary benefits.

Up to 160,000 Uber chauffeurs are now eligible to join the case of three drivers demanding the company pay for health insurance and expenses such as mileage. Some say a ruling against the company could doom the business model of the on-demand or “sharing” economy that Uber, Upwork and TaskRabbit represent.

Whatever the outcome, it’s unlikely to reverse the most radical reinvention of work since the rise of industrialization – a massive shift toward self-employment typified by on-demand service apps and enabled by technology. That’s because it’s not a trend driven solely by these tech companies.

Workers themselves, especially millennials, are increasingly unwilling to accept traditional roles as cogs in the corporate machinery being told what to do. Today, 34% of the US workforce freelances, a figure that is estimated to reach 50% by 2020. That’s up from the 31% estimated by the Government Accountability Office in a 2006 study.

Many aren’t ready for the on-demand economy that Uber represents, such as these taxi drivers in Brazil. Reuters

Rise of the gig-based economy

In place of the traditional notion of long-term employment and the benefits that came with it, app-based platforms have given birth to the gig-based economy, in which workers create a living through a patchwork of contract jobs.

Uber and Lyft connect drivers to riders. TaskRabbit helps someone who wants to remodel a kitchen or fix a broken pipe find a nearby worker with the right skills. Airbnb turns everyone into hotel proprietors, offering their rooms and flats to strangers from anywhere.

Thus far, the industries where this transformation has occurred have been fairly low-skilled, but that’s changing. Start-ups Medicast, Axiom and Eden McCallum are now targeting doctors, legal workers and consultants for short-term contract-based work.

A 2013 study estimated that almost half of US jobs are at risk of being replaced by a computer within 15 years, signaling most of us may not have a choice but to accept a more tenuous future.

Robot suit via www.shutterstock.com

The economic term referring to this transformation of how goods and services are produced is “platform capitalism,” in which an app and the engineering behind it bring together customers in neat novel economic ecosystems, cutting out traditional companies.

But is the rise of the gig economy a bad thing, as Democratic front-runner Hillary Clinton suggested in July when she promised to “crack down on bosses misclassifying workers as contractors”?

While some contend this sweeping change augurs a future of job insecurity, impermanence and inequality, others see it as the culmination of a utopia in which machines will do most of the labor and our workweeks will be short, giving us all more time for leisure and creativity.

My recent research into self-organized work practices suggests the truth lies somewhere in between. Traditional hierarchies provide a certain security, but they also curb creativity. A new economy in which we are increasingly masters of our jobs as well as our lives provides opportunities to work for things that matter to us and invent new forms of collaboration with fluid hierarchies.

Sharing into the abyss?

Critics such as essayist Evgeny Morozov or the philosopher Byung-Chul Han highlight the dark side of this “sharing economy.”

Instead of a collaborative commons, they envision the commercialization of intimate life. In this view, the likes of Uber and Airbnb are perverting the initial collaborative nature of their business models – car-sharing and couch-surfing – adding a price and transforming them from shared goods into commercial products. The unspoken assumption is that you have the choice between renting and owning, but “renting” will be the default option for the majority.

Idealists take another tack. Part of the on-demand promise is that technology makes it easier to share not only cultural products but also cars, houses, tools or even renewable energy. Add increasing automation to the picture and it invokes a society in which work is no longer the focus. Instead, people spend more of their time in creative and leisurely activities. Less drudge, more time to think.

The “New Work movement,” formed by philosopher Frithjof Bergmann in the late 1980s, envisioned such a future, while economist and social theorist Jeremy Rifkin imagines consumers and producers becoming one and the same: prosumers.

From self-employment to self-organization

Both of these extremes seem to miss the mark. In my view, the most decisive development underlying this discussion is the need for worker self-organization as the artificial wall between work and life dissolves.

My recent work has involved studying how the relationship between managers and workers has evolved, from traditional structures that are top-down, with employees doing what they’re told, to newer ones that boast self-managing teams with managers counseling them or even the complete abolition of formal hierarchies of rank.

While hierarchy guarantees a certain security and offers a lot of stability, its absence frees us to work more creatively and collaboratively. When we’re our own boss we bear more responsibility, but also more reward.

And as we increasingly self-organize alongside others, people start to experiment in various ways, from peer to peer and open source projects to social entrepreneurship initiatives, bartering circles and new forms of lending.

The toughest tension for workers will be how best to balance private and work-related demands as they are increasingly interwoven.

Avoiding the pitfalls of platform capitalism

Another risk is that we will become walled in by the platform capitalism being built by Uber and TaskRabbit but also Google, Amazon and Apple, in which companies control their respective ecosystems. Thus, our livelihoods remain dependent on them, like in the old model, just without the benefits workers have fought for many decades.

In his recent book “Postcapitalism,” Paul Mason eloquently puts it like this: “the main contradiction today is between the possibility of free, abundant goods and information; and a system of monopolies, banks and governments trying to keep things private, scarce and commercial.”

To avoid this fate, it’s essential to create sharing and on-demand platforms that follow a non-market rationale, such as through open source technologies and nonprofit foundations, to avoid profit overriding all other considerations. The development of the operating system Linux and web browser Firefox are examples of the possibility and merits of these models.

Between hell and heaven

Millennials grew up in the midst of the birth of a new human age, with all the world’s knowledge at their fingertips. As they take over the workforce, the traditional hierarchies that have long dictated work will continue to crumble.

Socialized into the participatory world of the web, millennials prefer to self-organize in a networked way using readily available communication technology, without bosses dictating goals and deadlines.

But this doesn’t mean we’ll all be contractors. Frederic Laloux and Gary Hamel have shown in their impressive research that a surprisingly broad range of companies have already acknowledged these realities. Amazon-owned online shoe retailer Zappos, computer game designer Valve and tomato-processor Morning Star, for example, have all abolished permanent managers and handed their responsibilities over to self-managing teams. Without job titles, team members flexibly adapt their roles as needed.

Mastering this new way of working takes us through different networks and identities and requires the capacity to organize oneself and others as well as to adapt to fluid hierarchies.

As such, it may be the the fulfillment of Peter Drucker’s organizational vision:

… in which every man sees himself as a “manager” and accepts for himself the full burden of what is basically managerial responsibility: responsibility for his own job and work group, for his contribution to the performance and results of the entire organization, and for the social tasks of the work community.

The Author: Bernhard Resch, Researcher in Organizational Politics at University of St.Gallen

In Defence of Payday Loans

From The Conversation.

Payday lenders have been the subject of trenchant criticism since their popularity exploded following the financial crisis. A recent documentary, “Cash in Hand: Payday Loans”, sought to counter this by giving an insider look at the industry. The show went behind-the-scenes at payday lender Uncle Buck, which possesses a 2% market share behind behemoths such as Wonga and QuickQuid, and followed the daily activities of its customer service and collections operation.

The payday lending market has changed significantly since regulation was announced last year – it appears that the industry is making real efforts to clean up its act. This being the case and in an age of alternative lending models such as peer-to-peer lending and crowdfunding, we should be cautious about automatically dismissing the use of payday loans.

With high interest rates, payday loans are short-term loans that are usually repaid on the debtor’s next payment date. The industry grew exponentially in the wake of the financial crisis and now over 1.2m loans are issued in the UK every year. As the industry has flourished, so has the appetite for their abolition by consumer groups and others, including Labour deputy leader hopeful Stella Creasy.

New rules

It is true that the industry has until recently adopted unsavoury practices such as opaque terms and conditions and illegal collection methods. But as these practices became more apparent the industry attracted the gaze of consumer groups and it was not long before regulatory intervention was the order of the day.

The industry was hit with a raft of regulatory changes at the start of 2015 after public outcry about lending and debt collection practices. In a classic case of public pressure leading to regulatory action, the Financial Conduct Authority (FCA) introduced a series of measures to protect consumers including:

  • A daily interest rate and fee cap of 0.8% for every £100 lent.
  • A total cap on the maximum any customer will pay in interest and default fees equivalent to double the amount advanced.
  • A cap on late payment fees of £15.

The new regulations led to many smaller industry players shutting up shop and prompted many of the industry leaders to revise their business model and their approach to customer care and debt collection.

In some US states, payday loans have been abolished, and interest caps introduced in others. This is primarily due to predatory lending practices targeted at ex-military personnel and single parents.

But the consumer profile of the payday loan customer in the UK is significantly different to customers in the US. According to IRN Research, UK payday loan borrowers are most likely to be young adults with below average incomes, using payday loans with more savvy than is popularly depicted.

In the UK, 67% have a household income of below £25,000 compared to the US where it is closer to 75%. Moreover, while payday borrowers in the US tend to be adults without bank accounts and with poor, “sub-prime” credit histories. This is not the case in the UK.

The IRN research also shows that 33% of payday loan customers have a household income exceeding the national average – 6% of users at more than £50,000 per annum. The truth is that payday loans are a money-saving mechanism for some young professionals.

For example, a £100 payday loan, operating at 0.8% daily interest, paid back in 30 days will cost significantly less than going £100 into an unauthorised overdraft. This is something Steve Hunter at Uncle Buck said in the recent show:

If you were to take out a loan for £300 you would pay back about £458 over three months. We are expensive but it’s very, very short-term. It could be a lot more if you went into your overdraft in an unauthorised way.

It is difficult to argue with this logic. An unauthorised overdraft, with Santander for example, can cost anything up to £95-a-month in fees. Choosing a payday loan in these circumstances is a rational buying decision informed by the cost of both options.

Regulation in action

Of course, the majority of people that use payday loans have household incomes below the national average. The FCA estimates that since it took over regulation of the industry, the number of loans and amount borrowed has reduced by 35%. Up to 70,000 customers have now been denied access to the market. This is a positive step forward.

With new emphasis on affordability checks, it is right that those who cannot afford to repay a short-term loan are denied from taking it out in the first place. But it is vital that those who are denied access do not turn to unregulated money lenders or other unsavoury finance streams. To this effect, efforts must continue to improve people’s financial literacy and consumer support groups need funding to cater for those who find themselves in financial difficulty.

The new regulatory terrain in this industry signals a new dawn for payday lenders. They now have an opportunity to reconstruct their reputation and operate more responsibly. As long as they adhere to the new regulations and abide by the laws of the industry, there is no reason why payday lending cannot be a useful financial tool for many.

Author: Christopher Mallon, PhD Candidate – Financial Regulation at Queen’s University Belfast

ASIC and FSI Outcomes

In a speech given by Greg Medcraft, Chairman, Australian Securities and Investments Commission at the 32nd annual conference of the Banking and Financial Services Law Association (Brisbane), he looked at the Financial System Inquiry from a regulator’s perspective.

Specifically, he sees three FSI recommendations as complementary. Product intervention powers would complement and reinforce the good practices and controls required by product design and distribution obligations. Where product design and distribution obligations were in place, and were effectively being complied with, there would be less need for ASIC to intervene. Adequate penalties provide a deterrent for gatekeepers against engaging in misconduct, and this in turn influences their behaviour. Gatekeepers who already have a solid culture have nothing to fear from these recommendations. For those who fall short, ASIC will continue to use the right nudge to change their behaviour. The introduction of a product intervention power, design and distribution obligation and appropriate penalties will assist ASIC in providing the right nudge.

Today I would like to talk about three particular recommendations of significance to ASIC.

1. for ASIC to have a new ‘product intervention’ power
2. to introduce a new product design and distribution obligation on product issuers, and
3. that penalties should be increased to act as a credible deterrent, and that ASIC should be able to seek disgorgement of profits gained by wrongdoing.

I would like to spend a little time now speaking about each of these recommendations in turn.

Product intervention power

Globally, regulators are looking for a broader toolkit to address market problems, including moving away from purely disclosure-based regulation. For example, the International Organization of Securities Commissions (IOSCO) has recommended that regulators look across the financial product value chain, rather than simply disclosure at the point of sale. In the United Kingdom, the Financial Conduct Authority has a product intervention power in place. A product intervention power would give ASIC a greater capacity to apply regulatory interventions in a timely and responsive way. It would allow ASIC to intervene in a range of ways where there is a risk of significant consumer detriment. ASIC would be able to undertake a range of actions, including simple ‘nudges’, right through to product bans. I know that some commentators have been worried that ASIC would use its powers to ban products – and that this would affect innovation and competition.

We think that such a power would not stifle innovation that has a positive impact on consumers. In fact, banning products would be very rare and would only occur in the most extreme circumstances. Both industry and regulators have a common interest in seeing innovation that fosters investor and financial consumer trust and confidence – innovation that helps investors, but does not harm them. Most interventions would likely fall well short of product banning. For example, we might be able to require amendments to marketing materials, or additional warnings. In more extreme cases, we might be able to require a change in the way a product is distributed or, in rare cases, ban a particular product feature. We agree that the use of intervention powers by ASIC would naturally need to have transparency, clear parameters and accountability mechanisms.

However, let me say that a ‘product intervention’ approach – that is, regulation that is not purely based on disclosure – is not new in the regulation of retail financial markets in Australia. This kind of regulation has improved investor outcomes in a wide range of markets over many years, for example: the Future of Financial Advice (FOFA) reforms, including the restriction on conflicted remuneration, and more broadly, the prohibition on unfair contract terms for financial products.

The FSI’s recommendation would mean that ASIC itself would have greater capacity to apply such non-disclosure based approaches in a timely and responsive way. This would be an alternative to waiting – sometimes many years – for legislation to address the problem.

Product design and distribution obligation

I will now turn to the recommendation to introduce a product design and distribution obligation for product issuers. For this recommendation, I want to set the context from ASIC’s perspective. There are three cornerstones of the free market-based financial system. These are: investor responsibility, gatekeeper responsibility, and the rule of law.

The ability of the free market-based system to function effectively and efficiently, and to meet investor and financial consumer needs, is greatly influenced by the real behaviour of its participants. Investor responsibility is key in our free market-based financial system. It is important that losses remain an inevitable part of this market system. ASIC will not, and cannot, be expected to prevent all consumer losses. In addition, it is important that gatekeepers take responsibility for their actions. Recently I have talked a lot about the culture of our gatekeepers. The culture of a firm can positively or negatively influence behaviour. Poor culture – such as one that is focused only on short-term gains and profit – often drives poor conduct. Conversely, good culture will drive good conduct. I see a good culture as one that puts the customer’s long term interests first.

So the FSI recommendation – that a broad, principles-based obligation be placed on financial institutions to have regard to the needs of their customers in designing and targeting their products – is a recommendation that puts the interests of the customer at its centre. In my view, the FSI’s recommendation aligns very closely with the theme of culture. Product manufacturers should design and distribute products with the best interests of the investor or financial consumer in mind. This is part of having a customer-focused culture.

In fact, the FSI has noted that the kinds of practices required by a design and distribution obligation would already be in place in many institutions that already invest in customer-focused business practices. Firms that already have a customer-focused culture would not need to significantly change their practices.


Finally I would like to turn to the recommendation on penalties. The FSI recommended that penalties for contravening ASIC legislation should be substantially increased, and that ASIC should be able to see disgorgement of profits obtained as a result of misconduct. Comparatively, the maximum civil penalties available to us in Australia are lower than those available to other regulators internationally. And they are fixed amounts, not multiples of the financial benefit obtained from misconduct.

In order to regulate for the real behaviour of gatekeepers in the system, penalties need to be set at an appropriate level. And we need a range of penalties available, to act as a deterrent to misconduct. Penalties set at an appropriate level are critical in the ‘fear versus greed’ calculation of the potential wrongdoer. Penalties need to give market participants the right incentive to comply with the law. They should aim to deter contraventions and promote greater compliance, resulting in a more resilient financial system.


Middle Income Households Income Is Getting Squeezed

Data from the ABS looking at income and wealth, shows that the average income of high income households rose by 7 per cent between 2011-12 and 2013-14, to $2,037 per week, whist low income households have experienced an increase of around 3 per cent in average weekly household income compared with middle income households which have changed little since 2011-12.

The average income of all Australian households has risen to $998 per week in 2013–14, while average wealth remained relatively stable at $809,900. Similarly, change in average wealth was uneven across different types of households. For example, the average wealth of renting households was approximately $183,000 in 2013-14. Rising house prices contributed to an increase in the average wealth for home owners with a mortgage ($857,900) and without a mortgage (almost $1.4 million).

Most Australian households continue to have debts in 2013-14, with over 70 per cent of households servicing some form of debt, such as mortgages, car loans, student loans or credit cards. For example, the average credit card debt for all households was $2,700.

One quarter of households with debt had a total debt of three or more times their annualised disposable income. Mortgage debt was much higher

These households are considered to be at higher risk of experiencing economic hardship if they were to experience a financial shock, such as a sudden reduction in their income or if interest rates were to rise, increasing their mortgage or loan repayments.

The survey findings also allow comparisons of income and wealth across different types of households.

In 2013–14, couple families with dependent children had an average household income of $1,011 per week, which was similar to the average for all households at $998 per week.

By comparison, after adjusting for household characteristics, one parent families with dependent children had an average household income of $687 per week.

Australia’s economy is slowing: what you need to know

From The Conversation.

Australia’s economy grew by just 0.2% in the June quarter, below expectations of 0.4%, largely as a result of reduced mining and construction activity and a decline in exports of 3% during the quarter.

Nominal Gross Domestic Product grew by 1.8% during the year, which the Australian Bureau of Statistics said was “the weakest growth in nominal GDP since 1961-62”. Despite this, Australia has now recorded 24 straight years of growth.

The news has some analysts and economists spooked, and politicians blaming each other for the slowdown.

Treasurer Joe Hockey said:

At a time when other commodity based economies like Canada and Brazil are in recession, the Australian economy is continuing to grow at a rate that meets and sometimes beats our most recent budget forecasts.

He also said it was “factually wrong” to say it was the weakest growth since 1961.

The fact is that the economic growth we had in the last quarter was in line with expectations. Of course it bounces around from quarter to quarter, but it was in line with our overarching expectation to have two and a half per cent growth in the last financial year.

Shadow Treasurer Chris Bowen said:

Growth has flat-lined since the Abbott government’s first damaging budget last year and cost of living pressures are continuing to increase. This is the biggest quarterly decline in living standards since the global financial crisis.

This is a very weak set of figures and for the government to cast around for international comparisons to try and make it sound better is a pretty pathetic excuse.

The Treasurer says Australia is still doing better than Canada, Brazil, the US and New Zealand. How should people view these numbers in a global context? To what extent is the slowing rate of growth due to global economic headwinds, and to what extent is it due to domestic factors?

Griffith Business School Professor Fabrizio Carmignani answers:

In the past, the Australian economy has proved to be quite resilient to global economic shocks. Today we are facing what could be potentially a perfect storm.

For one thing, international commodities prices are very volatile and have resulted in a sharp contraction of Australian’s terms of trade. For another, China is going through a complicated economic phase and it is not, at this moment, the same solid anchor for the Australian economy as it might have been previously. So, it is not surprising to see that on a seasonally adjusted basis, quarterly growth in Australia has been oscillating between 0.2% and 0.3% for the last five quarters.

We owe it to some good old Keynesian stimulus on the demand side (read: government consumption and to a lesser extent public gross fixed capital formation) if we are not entering a technical recession.

The comparison with Canada, on surface, is favourable to Australia. Canada has officially entered a recession after recording two consecutive quarters of negative GDP growth in the first half of 2015. This is essentially due to low oil prices. However, according to media reports, Canada is still committed to achieving a target of annual growth of 2.5% this year, which is exactly what the Treasurer has stated for Australia. So, it seems to me that the difference between Australia and Canada here is thinner that what might appear at first sight. A fraction of a percentage point below or above the zero growth line is not really indicative of substantially different structural positions.

Both Australia and Canada are facing similar challenges in terms of diversification. The current “crisis” to me shows that these challenges are still far from being fully addressed in both countries.

Australia has had 24 years of consistent growth. How much of this can we attribute to the mining boom? And given the cyclical nature of the economy, can we expect a downturn?

Griffith University Professor Tony Makin answers:

Australia has performed relatively well compared to other OECD economies over recent decades, though did actually experience a recession during the GFC according to income and production measures of GDP.

Taking population growth into account, Australia’s economic performance since the global financial crisis has been worse than the raw GDP numbers show. On a per capita basis, national income has grown on average below one per cent per annum, less than half the almost two and a half per cent per head per annum average rate in the decade before the GFC.

The extraordinary boost to the terms of trade from the world commodity price hike, especially between 2005 and 2011, substantially raised Australia’s international purchasing power. However, GDP growth during the mining boom was actually less than during the economic reform era from the mid-1980s through to the end of the 1990s when commodity prices were fairly flat.

The main culprit for Australia’s sub-normal economic growth in recent years has not been falling commodity prices, which have undoubtedly played a role, but Australia’s underlying competitiveness problem, combined with a productivity slowdown that began from the turn of the century.

While the recent depreciation of the dollar will go some way to restoring Australia’s competitiveness and help stave off recession, genuine productivity-enhancing reform focusing on the economy’s supply side remains as important as ever for returning GDP and income per head growth to long-term average rates.

One journalist at Wednesday’s press conference said the new data showed “the weakest growth since 1961”, but the Treasurer said that was factually wrong. Who is right?

UNSW Australia Professor Richard Holden answers:

The statement that it is the slowest growth since 1961 seems, to me, to be false. We have had recessions in the 1990s and 1980s, which is two successive quarters of negative growth. And yesterday we had positive growth, so it was a slowdown but not the worst we have seen since 1961. I think the journalist’s statement doesn’t seem correct to me, on the face of it. I think the Treasurer is right.

It is possible the journalist was referring to the Australian Bureau of Statistics comment yesterday that:

GDP growth for 2014-15 was 2.4%. Nominal GDP growth was 1.8% for the 2014-15 financial year. This is the weakest growth in nominal GDP since 1961-62.

Nominal growth and growth are not quite the same thing. Nominal growth means GDP growth that is not adjusted for inflation.

But yes, yesterday’s numbers are still below projected growth. It is below market expectations. I think the Treasurer saying we have projected 2.5% annual growth this year and this is basically on target is a bit disingenuous. This is slow growth, it’s actually very troubling.

I understand the Treasurer can’t talk down the economy so his comments are understandable and he is in a difficult position. But the low rate of growth is genuine cause for concern.

I have written before about the concept of secular stagnation, which is the idea that growth of advanced economies looks like it has slowed down dramatically. The figures yesterday are further evidence of that theory.

Victoria University Senior Research Fellow Janine Dixon answers:

While it is factually correct that real GDP – the volume of production in the economy – has grown, the low growth in nominal GDP points to an underlying weakness in the economy. This is our exposure to the very large fall in commodity prices. When we translate real GDP into real income, we take into account that fact that the prices of the things we produce for export have fallen relative to the prices of the things we consume, some of which are imported. This has been a very important determinant of real incomes in the last few years.

Real net national disposable income is a better measure of our living standards than GDP. As well as adjusting for prices, we take into account the fact that some of the income generated domestically actually accrues to the rest of the world if the factors of production are foreign owned. We also deduct the value of capital that is “used up” or depreciated during the year.

Real net national disposable income per person has now fallen for 14 quarters in a row. This represents the most sustained fall in standards of living in the last 50 years.

What’s especially interesting about this period is that falling incomes have not been associated with falling output or particularly high unemployment. In the 1990-91 recession (the one we had to have) or the early 1980’s, incomes fell, but the solution to the problem was fairly clear. More than 10% of the workforce was unemployed. Fixing unemployment would boost production, incomes and living standards.

This time around, incomes are falling because commodity prices are falling. Commodity prices, set on world markets, are largely out of our hands. The labour market is much more flexible these days, and unemployment is 6%, not 10%. We are left with just one way to turn things around. In the words of Nobel laureate Paul Krugman, “Productivity isn’t everything, but in the long run it is almost everything”.

Is GDP really in line with expectations, both of the government and the market?

Griffith University Professor Ross Guest answers:

These GDP expectations are continuously being revised down as new information comes to hand.

The projected growth is lower than nearly everybody expected and everybody is having to revise downward their expectation.

What will the slowing annual growth mean for the federal budget, which had forecast growth for 2015-16 of 2.75%?

Ross Guest answers:

If growth were to remain at its current level of 2%, the budget deficit would be A$15 billion larger, in ball park terms, than the government projected. To put that in perspective, the total amount we spend on unemployment benefits is A$10 billion.

Australia living standards and the Australian government budget are being hit by a perfect storm of lower commodity prices and lower productivity growth.

Victoria University Senior Research Fellow Janine Dixon answers:

The GDP growth forecast for 2015-16 is fairly subdued at 2.75% and the budget not overly ambitious – a deficit of 2% of GDP. The trouble lies in 2016/17 and beyond, when annual GDP growth is forecast to be above 3%.

Over the next five years a couple of downside risks exist that will make it unlikely that GDP will grow this strongly, and consequently the budget’s return to surplus will be more difficult to achieve.

If the terms of trade fall further than allowed for in the budget forecasts, and if productivity growth remains weak, as it has been in recent years, real national income could be 3% lower than forecast by 2020. Roughly, this means the tax base for the government will be 3% smaller than expected. Rather than having a balanced budget by 2020, we would still be running a deficit, of around 0.75% of GDP or $12 billion in today’s terms.