Debt agreements and how to avoid unnecessary debt traps

From The Conversation.

Debt agreements are the fastest growing form of personal insolvency in Australia. They were designed to offer debtors a low-cost way to make arrangements with their creditors, while avoiding bankruptcy and some of its more serious consequences.

When introduced, law reformers intended that debt agreements should be administered by volunteers rather than by commercial administrators who charge fees. However, in practice, debtors often pay substantial fees to debt agreement administrators.

In fact, many debtors pay more than 100% of their original debt, because of the high cost of administration fees. But there are cheaper options available for managing debt.

Debt agreements

Debt agreements are binding contracts made between debtors and their creditors in accordance with personal insolvency law. They are aimed at providing debtors in financial stress with the option of compromising with creditors. Not all debtors can enter into a debt agreement – there are income and debt limits.

In many cases, debtors pay their creditors an agreed reduced amount by instalments over a period of time. A debt agreement administrator assists in the negotiation process and distributes the payments to creditors.

Debt agreements have fewer adverse consequences than bankruptcy. One key advantage is that debtors may be allowed to keep their home.

Nonetheless, the adverse consequences of debt agreements include having a record on the National Personal Insolvency Index, and difficulties obtaining credit. Debtors’ ability to maintain a licence in various professions may be affected and the debt agreement must be disclosed in certain situations.

A growing problem in Australia

In 2016 there were 12,150 new debt agreements, comprising 41.5% of all personal insolvencies in Australia. While the number of debt agreements has increased steadily each year, bankruptcies have decreased since 2010.

Our research examines three sources of data to gauge the impact of debt agreements. These sources include statistics from the Australian Financial Security Authority (AFSA), an online survey of 400 debtors, and interviews with industry stakeholders.

Most debtors pay more under debt agreements than the amount they originally owed. This is due to the fees charged by AFSA and, in particular, for-profit debt agreement administrators.

In 2016, close to 23% of debtors’ payments went towards debt agreement administrators’ fees. The total amount of fees paid by debtors is higher when Australian Financial Security Agency fees and set-up fees paid to debt agreement administrators are included.

Many debt agreements are unsuitable

Debt agreements are useful for some people, such as those who have a home to protect from seizure in bankruptcy. However, consumer advocates find many instances of debt agreements unsuited to the needs of debtors. High administration fees are detrimental particularly for low income debtors.

Some debtors enter into debt agreements which they clearly cannot afford, aggravating their financial stress. If they are unable to make the payments required under a debt agreement and it is terminated, the fees cannot be recovered but the debts to creditors remain, leaving debtors in a worse position.

Debtors who rely primarily on Centrelink benefits are among the clearest examples of people unsuited to debt agreements. Centrelink benefits are meant to provide a basic standard of living, and diverting a portion of income towards debt agreements is likely to cause significant hardship.

People whose incomes comprise a disability or aged pension may in many cases be better off declaring bankruptcy, or seeking other forms of debt relief.

Better options available

There are several fee-free options for managing debt which do not involve the adverse consequences of debt agreements.

Financial hardship schemes commonly allow payment by instalments, or short term extensions of time, for debts owed to utilities or credit providers. Free independent dispute resolution offered by the Financial Ombudsman Service and the Credit and Investments Ombudsman is available to people who have disputes with financial service providers.

People often enter into debt agreements without seeking independent advice or accessing other options for managing debt. In 2016, 92% of debt agreement debtors relied on debt administrators as their primary source of information. Marketing often emphasises the advantages of debt agreements over bankruptcy.

Debtors often lack adequate knowledge of cheaper, better options for managing debt and of the adverse consequences of debt agreements. When the debt agreement system was established, it was not expected that private, profit-making debt administrators would assume a prominent role.

Law reformers noted in the 1996 Bankruptcy Legislation Amendment Bill that ‘if fees were charged, debt agreements would in many cases not be viable either for the debtor, or for his or her creditors’. They further noted that this would defeat the purpose for which debt agreements were introduced.


Reforms to the debt agreement system are currently being considered, but in order to be effective, these reforms should provide better safeguards for debtors. These should include stricter eligibility requirements for debtors entering into debt agreements such as a minimum income or ownership of assets which are protected from seizure in bankruptcy.

We need a more rigorous, legally binding assessment of debtors’ suitability on the part of debt agreement administrators; the provision of clearer information to debtors; and limits on administrators’ fees. Debtors should have access to free dispute resolution services when problems with debt agreement administrators arise.

Such reforms would reduce the risk of debtors being left worse off, financially, as a result of debt agreements that are unsuited to their circumstances.

Authors: Vivien Chen, Lecturer, Monash Business School, Monash University; Ian Ramsay, Professor, Melbourne Law School, University of Melbourne; Lucinda O’Brien, Research Fellow, University of Melbourne

Do computers make better bank managers than humans?

From The Conversation.

Algorithms are increasingly making decisions that affect ordinary people’s lives. One example of this is so-called “algorithmic lending”, with some companies claiming to have reduced the time it takes to approve a home loan to mere minutes.

But can computers become better judges of financial risk than human bank tellers? Some computer scientists and data analysts certainly think so.

How banking is changing

On the face of it, bank lending is rather simple.

People with excess money deposit it in a bank, expecting to earn interest. People who need cash borrow funds from the bank, promising to pay the amount borrowed plus interest. The bank makes money by charging a higher interest rate to the borrower than it pays the depositor.

Where it gets a bit trickier is in managing risk. If the borrower were to default on payments, not only does the bank not earn the interest income, it also loses the amount loaned (provided there wasn’t collateral attached, such as a house or car).

A borrower who is deemed less creditworthy is charged a higher interest rate, thereby compensating the bank for additional risk.

Consequently, the banks have a delicate balancing act – they always want more borrowers to increase their income, but they need to screen out those who aren’t creditworthy.

Traditionally this role was fulfilled by an experienced credit manager — a judge of human character — who could distinguish between responsible borrowers and those who would be unlikely to meet their repayment schedules.

Are humans any good at judging risk?

When you look at the research, it doesn’t seem that humans are that great at judging financial risk.

Two psychologists conducted an experimental study to assess the kind of information that loan officers rely upon. They found that in addition to “hard” financial data, loan officers rely on “soft” gut instincts. The latter was even regarded as a more valid indicator of creditworthiness than financial data.

Additional studies of loan officers in controlled experiments showed that the longer the bank’s association with the customer, the larger the requested loan, and the more exciting its associated industry, the more likely are loan officers to underrate loan risks.

Other researchers have found that the more applications that loan officers have to process, the greater the likelihood that bank officers will use non-compensatory (irrational) decision strategies. For example, just because a customer has a high income that doesn’t mean they don’t have a bad credit history.

Loan officers have also been found to reach decisions early in the lending process, tending to ignore information that is inconsistent with their early impressions. Lastly, loan officers often fail to properly weigh the credibility of financial information when evaluating commercial loans.

Enter algorithmic lending

Compared with human bank managers, a computer algorithm is like a devoted apprentice who painstakingly observes each person’s credit history over many years.

Banks already have troves of data on historical loan applications paired with outcomes – whether the loan was repaid or defaulted. Armed with this information, an algorithm can screen each new credit application to determine its creditworthiness.

There are various methods, based on the specific data in each applicant’s profile, from which the algorithm identifies the most relevant and unique attributes.

For example, if the application is filled in by hand and scanned into the computer, the algorithm may consider whether the application was written in block capitals or in cursive handwriting.

The algorithm may have detected a pattern that applicants who write in all-caps without punctuation are usually less educated with a lower earning potential, and thereby inherently more risky. Who knew that how you write your name and address could result in denial of a credit application?

On the other hand, a degree from Harvard University could be viewed favorably by algorithms.

On balance, computers come out ahead

A large part of human decision making is based on the first few seconds and how much they like the applicant. A well-dressed, well-groomed young individual has more chance than an unshaven, dishevelled bloke of obtaining a loan from a human credit checker. But an algorithm is unlikely to make the same kind of judgement.

Some critics contend that algorithmic lending will shut disadvantaged people out of the financial system, because of the use of pattern-matching and financial histories. They argue that machines are by definition neutral and thus usual banking rules will not apply. This is a misconception.

The computer program is constrained by the same regulations as the human underwriter. For example, the computer program cannot deny applications from a particular postal code, as those are usually segregated by income levels and racial ethnicity.

Moreover, such overt or covert discrimination can be prevented by requiring lending agencies (and algorithms) to provide reasons why a particular application was denied, as Australia has done.

In conclusion, computers make lending decisions based on objective data and avoid the biases exhibited by people, while complying with regulations that govern fair lending practices.

Author: Saurav Dutta, Head of School at the School of Accounting, Curtin University

How marketers use algorithms to (try to) read your mind

From The Conversation.

Have you ever you looked for a product online and then been recommended the exact thing you need to complement it? Or have you been thinking about a particular purchase, only to receive an email with that product on sale?

All of this may give you a slightly spooky feeling, but what you’re really experiencing is the result of complex algorithms used to predict, and in some cases, even influence your behaviour.

Companies now have access to an unprecedented amount of data on your present and past shopping and browsing preferences. This ranges from transactional data, to website traffic and even social media posts. Predictive algorithms use this data to make inferences about what is likely to happen in the future.

For example, after a few times visiting a coffee shop, the barista might notice that you always order a latte with one sugar. They could then use this “data” to predict that tomorrow you will order the same thing, and have it ready for you before you get there.

Predictive algorithms work the same way, just on a much bigger scale.

How are big data and predictive algorithms used?

My colleagues and I recently conducted a study using online browsing data to show there are five reasons consumers use retail websites, ranging from simply “touching base” to planning a specific purchase.

Using historical data, we were able to see that customers who browse a wide variety of different product categories are less likely to make a purchase than those that are focused on specific products. Meanwhile consumers were more likely to purchase if they reached the website through a search engine, compared to a link in an email.

With information like this websites can be personalised based on the most likely motivation of each visitor. The next time a consumer clicks through from a search engine they can be led straight to checkout, while those wanting to browse can be given time and inspiration.

Somewhat similar to this are the predictive algorithms used to make recommendations on websites like Amazon and Netflix. Analysts estimate that 35% of what people buy on Amazon, and 75% of what they watch on Netflix, is driven by these algorithms.

These algorithms also work by analysing both your past behaviour (e.g. what you have bought or watched), as well as the behaviour of others (e.g. what people who bought or watched the same thing also bought or watched). The key to the success of these algorithms is the scope of data available. By analysing the past behaviour of similar consumers, these algorithms are able to make recommendations that are more likely to be accurate, rather than relying on guess work.

For the curious, part of Amazon’s famous recommendation algorithm was recently released as an open source project for others to build upon.

But of course, there are innumerable other data points for algorithms to analyse than just behaviour. US retailer Walmart famously stocked up on strawberry pop-tarts in the lead up to a major storm. This was the result of simple analysis of past weather data and how that influenced demand.

It is also possible to predict how purchase behaviour is likely to evolve in the future. Algorithms can predict whether a consumer is likely to change purchase channel (e.g. from in-store to online), or even if certain customers are likely to stop shopping.

Prior studies that have applied these algorithms have found companies can influence a consumer’s choice of purchase channel and even purchase value by changing the way they communicate with them, and can use promotional campaigns to decrease customer churn.

Should I be concerned?

While these predictive algorithms undoubtedly provide benefits, there are also serious issues about privacy. In the past there have been claims that companies have predicted consumers are pregnant before they know themselves.

These privacy concerns are critical and require careful consideration from both businesses and government.

However, it is important to remember that companies are not truly interested in any one consumer. While many of these algorithms are designed to mimic “personal” recommendations, in fact they are based on behaviour across the whole customer base. Additionally, the recommendations or promotions that are given to each individual are automated from the database, so the chances of any staff actually knowing about an individual customer is extremely low.

Consumers can also benefit from companies using these predictive algorithms. For example, if you search for a product online, chances are you will be targeted with ads for that product over the next few days. Depending on the company, these ads may include discount codes to encourage you to purchase. By waiting a few days after browsing, you may be able to get a discount for a product you were intending to buy anyway.

Alternatively, look for companies who adjust their price based on forecasted demand. By learning when the low-demand periods are, you can pick yourself up a bargain at lower prices. So while companies are turning to predictive analytics to try to read consumers’ minds, some smart shopping behaviours can make it a two-way street.

Author: Jason Pallant, Lecturer of Marketing, Swinburne University of Technology

The spooky mortgage risk signs our bankers are ignoring

From The Conversation.

I’m not normally a fan of parliament hauling private sector executives before them and asking thorny questions. But when the Australian House of Representatives did so this week with the big banks it was both useful and instructive.

And, to be perfectly frank, terrifying.

Let’s start with Westpac CEO Brian Hartzer. First, he confirmed the little-known but startling fact that half of his A$400 billion home loan book consists of interest-only mortgages.

Yep, half. Of A$400 billion. At one bank. Oh, and ANZ, CBA and NAB are all nearly at 40% interest-only.

Hartzer went on to make the banal statement: “we don’t lend to people who can’t pay it back. It doesn’t make sense for us to do so.”

So did it make sense for all those American mortgage lenders to lend to people on adjustable rates, teaser rates, low-doc loans, no-doc loans etc. before the global financial crisis?

Of course not. The point is that banks are not some benevolent, unitary actor taking care of their own money. There are top managers like Harzter acting on behalf of shareholders. Those top managers delegate authority to lower-level managers, who are given incentives to write lots of mortgages. And, as we know, the incentives of those who make the loans are not necessarily aligned with those of the shareholders. Those folks may well want to make loans to people who can’t pay them back as long as they get a big payday in the short term.

ANZ CEO Shayne Elliot repeated Hartzer’s mantra, saying: “It’s not in our interest to lend money to people who can’t afford to repay.” Recall, this is the man who on ABC’s Four Corners said that home loans weren’t risky because they were all uncorrelated risks (the chances that one loan defaults does not affect the chances of others defaulting). That is a comment that is either staggeringly stupid or completely disingenuous.

Messers Harzter and Elliot must take us all for suckers. They have made a huge amount of interest-only loans, at historically low interest rates, to buyers in a frothy housing market, who spend a large chunk of their income on interest payments. This certainly looks troubling. It may not be US sub-prime, but it could be ugly. Very ugly.

To put it in context, there appears to be in the neighbourhood of A$1 trillion of interest-only loans on the books of Australian banks. I say “appears to be” because reporting requirements are so lax it’s hard to know for sure, except when CEOs cough up the ball, like this week.

The big lesson of the US mortgage meltdown is that the risks on these mortgages are all correlated. If a few people aren’t paying back an interest-only loan, that is a fair predictor that others won’t pay back their loans either. Yet it seems Australian banks are a decade behind the learning curve.

The Reserve Bank cautions that one-third of borrowers don’t have a month’s repayment buffer. And where are interest rates going to go from here? Up. It is just a question of when. And when that does happen – or when the interest-only period on loans (typically five years) rolls off and principal payments start having to be made – watch out.

We should all remember that the proximate cause of the US mortgage meltdown was borrowers with five-year adjustable-rate mortgages (ARMs) that had huge step-ups in repayments and needed to be refinanced to be serviceable. When the market couldn’t bear that refinancing, defaults went up. Then the collapse of US investment bank Bear Stearns, then Lehman, then Armageddon.

Australia’s large proportion of five-year interest-only loans – turbocharged by an out-of-control negative-gearing regime – looks spookily similar.

It’s one thing for borrowers to do silly things. When it becomes dangerous is when lenders not only facilitate that stupidity, but encourage it. That seems to be what has happened in Australia.

And APRA’s “crackdown” and the Reserve Bank’s warning may be far too little, way too late.

We might stumble though this. I hope we do. But if so, it will be because of dumb luck, not good institutional and regulatory design. And definitely not because of good corporate governance.

Whatever happens, we should learn those lessons.

Author: Richard Holden, Professor of Economics and PLuS Alliance Fellow, UNSW

The data is mixed but worrying signs from mortgagees

From The Conversation.

Data released this week in Australia and the United States showed continued strength – or at least a lack of weakness – in consumer spending and unemployment.

New car sales in Australia – one measure of consumer sentiment and spending – declined slightly in September from the year before. However, new car sales are still slightly ahead (0.2%) of last year’s numbers, on a yearly basis.

Building approvals also showed some mixed signs. The data shows overall approvals were up 0.4% in August. They had fallen 1.2% (on revised figures) in July. This was driven by apartment approvals, which were up 4.8%, while approvals for houses fell 0.6%. Year-on-year the news is still bleak. Approvals are down 15.5% on that basis – the 12th consecutive month they have fallen.

Perhaps developers don’t like to build into property bubbles, at least not forever.

In the US there was continued strong employment data, this time from payroll processing records. ADP data showed that US private-sector employers added 135,000 jobs in September. This was well above market forecasts that were subdued due to Hurricanes Harvey and Irma. It is now expected that the US unemployment rate will stay at the current very low 4.4% when official data is released.

However, the US Federal Reserve is still concerned about the low level of inflation, which has been persistently below its target range. Recently, Fed Chair Janet Yellen observed that:

My colleagues and I may have misjudged the strength of the labor market, the degree to which longer-run inflation expectations are consistent with our inflation objective, or even the fundamental forces driving inflation.

That was a very candid observation, but perhaps just as importantly, Yellen pointed out one important reason why it matters, saying:

Sustained low inflation such as this is undesirable because, among other things, it generally leads to low settings of the federal funds rate in normal times, thereby providing less scope to ease monetary policy to fight recessions.

Meanwhile, in Australia the RBA left the official cash rate at 1.50% for the 13th (lucky for some?) consecutive meeting, with no rate rise in sight. RBA governor Philip Lowe said this week:

…slow growth in real wages and high levels of household debt are likely to constrain growth in household spending.

This underlines the consistent bind that Lowe finds himself in. If he raises rates then he could put already debt riddled households under further pressure and crater spending – which accounts for roughly three-quarters of GDP – as those households try and adjust their balance sheets in the face of stagnant wages.

It has been a consistent theme of this column that both the Fed and RBA are in something of a bind – if for slightly different reasons. And both Yellen’s and Lowe’s remarks highlight this.

But the truly scary news this week was about Australian mortgages, with a survey from UBS saying that one-third of Australian borrowers don’t know their mortgages are interest only. Or as UBS put it:

We are concerned that it is likely that approximately one-third of borrowers who have taken out an IO mortgage have little understanding of the product or that their repayments will jump by between 30-60 per cent at the end of the IO period.

Now, economists don’t normally put much stock in surveys – we like “revealed preference” data. But this survey I will listen to.

This has US circa 2008 written all over it. Back then, many borrowers with adjustable-rate mortgages didn’t realise that the rates ratcheted up sharply after (typically) five years. When those rates did, and the borrowers couldn’t refinance because of market conditions, there was a meltdown that led to Bear Sterns and Lehman Brothers going to the wall. And then, of course, the whole financial crisis.

If UBS is right, Australia has a similar time-bomb ticking away. Maybe not quite as bad, but I don’t know. Remember ANZ chief executive Shayne Elliot on Four Corners telling us how his mortgage book was super-diversified because they were all uncorrelated individual mortgages? Well, not if his book includes folks who don’t understand their loan contracts.

Whatever APRA is doing right (or wrong), it can’t deal with loans that have already been written. That puts the RBA in a bind – and should make us all cautious about how robust the Australian economy really is.

Author: Richard Holden, Professor of Economics and PLuS Alliance Fellow, UNSW

Australian household electricity prices may be 25% higher than official reports

From The Conversation.

The International Energy Agency (IEA) may be underestimating Australian household energy bills by 25% because of a lack of accurate data from the federal government.

The Paris-based IEA produces official quarterly energy statistics for the 30 member nations of the Organisation for Economic Cooperation and Development (OECD), on which policymakers and researchers rely heavily. But to provide this service, the IEA relies on member countries to provide it with good-quality data.

Last month, the agency published its annual summary report, Key World Statistics, which reported that Australian households have the 11th most expensive electricity prices in the OECD.

But other studies – notably the Thwaites report into Victorian energy prices – have reported that households are typically paying significantly more than the official estimates. In fact, if South Australia were a country it would have the highest energy prices in the OECD, and typical households in New South Wales, Queensland or Victoria would be in the top five.

A spokesperson for the federal Department of Environment and Energy, the agency responsible for providing electricity price data to the IEA, told The Conversation:

Household electricity prices data for Australia are sourced from the Australian Energy Market Commission annual Residential electricity price trends report. The national average price is used, with GST added. It is a weighted average based on the number of household connections in each jurisdiction.

The Australian energy statistics are the basis for the Australia data reported by the IEA in their Key world energy statistics. The Department of the Environment and Energy submits the data to the IEA each September. Some adjustments are made to the AES data to conform with IEA reporting requirements.

But it is clear that the electricity price data for Australia published by the IEA is at least occasionally of poor quality.

The Australian household electricity series in the IEA’s authoritative Energy Prices and Taxes quarterly statistical report stopped in 2004, and only resumed again again in 2012.

Between 2012 and 2016, the IEA’s reported residential price series data for Australia showed no change in prices.

Yet the Australian Bureau of Statistics’ electricity price index, which is based on customer surveys, showed a roughly 20% increase in the All Australia electricity price index over this period.

Australia is also the only OECD nation not to report electricity prices paid by industry.

Current prices

This year’s reported household average electricity prices are almost certainly wrong too. The IEA reports that household electricity prices in Australia for the first quarter of 2017 were US20.2c per kWh.

At a market exchange rate of US79c to the Australian dollar, this puts Australian household electricity prices at AU28c per kWh. Adjusted for the purchasing power of each currency, the comparable price is AU29c per kWh.

By contrast, the independent review of the Victorian energy sector chaired by John Thwaites surveyed the real energy prices paid by customers, as evidenced by their bills. In a sample of 686 Victorian households, those with energy consumption close to the median value were paying an average of AU35c per kWh in the first quarter of 2017. This is 25% more than the IEA’s official estimate. At least part of this difference is explained by the AEMC’s assumption that all customers in a competitive retail market are supplied on their retailers’ cheapest offers. But this is not the case in reality.

Surveying real electricity and gas bills drastically reduces the range of assumptions that need to be made to estimate the price paid by a representative customer. Indeed, as long as the sample of bills is representative of the population, a survey based on actual bills produces a reliable estimate of representative prices in retail markets characterised by high levels of price dispersion, as Australia’s retail electricity markets are.

Pointing to a reliable estimate of Victoria’s representative residential price is, of course, not enough to prove that the IEA’s estimate is wrong. It could just as easily mean that Victorians are paying way more than the national average for their electricity.

But the idea that Victorians are paying more than average does not stack up when we look at the state-by-state data, which suggests that Victoria is actually somewhere in the middle. Judging by the prices charged by the three largest retailers in each state and territory, Victorian householders are paying about the same as those in New South Wales and Queensland, less than those in South Australia, and more than those in Tasmania, the Northern Territory, Western Australia and the Australian Capital Territory.

Residential electricity prices. Author provided

The IEA can not reasonably be blamed for the inadequate residential data for Australia that they report, and the nonexistent data on electricity prices paid by Australia’s industrial customers. The IEA does not do its own calculation of prices in each country, but rather it relies on price estimates from official sources in those countries.

An obvious question that arises from this is where Australia really ranks internationally if we used prices that reflect what households are actually paying.

This is contentious, not least because prices in New South Wales, Queensland and South Australia increased – typically around 15% or more – from July this year. We do not know how prices have changed in other OECD member countries since the IEA’s recent publication (which covered prices for the first quarter of 2017). But we do know that prices in Australia have been far more volatile than in any other OECD country.

Assuming that other countries’ prices are roughly the same as they were in the first quarter of 2017, our estimate using the IEA’s data is that the typical household in South Australia is paying more than the typical household in any other OECD country. The typical household in New South Wales, Queensland or Victoria is paying a price that ranks in the top five.

It should also be remembered that these prices are after excise and sale tax. Taxes on electricity supply in Australia are low by OECD standards – so if we use pre-tax prices, Australian households move even higher up the list.

There are serious question marks over Australia’s official electricity price reporting. Policy makers, consumers and the public have a right to expect better.

Author: Bruce Mountain, Director, Carbon and Energy Markets., Victoria University 

Australia needs new insolvency laws to encourage small businesses

From The Conversation.

The Ten Network’s recent experience of voluntary administration and subsequent rescue by CBS demonstrates how insolvency law works for large Australian companies. But 97% of Australian businesses are small or medium size enterprises (SMEs), and they face a system that isn’t designed for them.

60% of small businesses cease trading within the first three years of operating. While not all close due to business failure, those that do tend to face an awkward insolvency regime that fails to meet their needs in the same way it does Network Ten.

The lack of an adequate insolvency regime for SMEs inhibits innovation and growth within our economy. It adds yet more complexity to the already difficult process of structuring a small business. Further, it inceases the cost of funding. Lenders know that recovering their money can be onerous if not impossible, so they impose higher costs of borrowing.

Australia’s insolvency regime

Australian insolvency law is divided into two streams, each governed by a separate piece of legislation.

The Corporations Act deals with the insolvency of incorporated organisations, and the Bankruptcy Act addresses the insolvency of people and unincorporated bodies (such as sole traders and partnerships).

Both schemes are aimed at providing an equal, fair and orderly process for the resolution of financial affairs. But a large part of the Corporations Act procedure has been developed with the complexity of a large corporation in mind. For example, there are extensive provisions that allow the resolution of disputes between creditors that are only likely to arise in well-resourced commercial entities.

The Bankruptcy Act, by contrast, takes account of the social and community dimensions of personal bankruptcy. This legislation seeks to supervise the activities of the bankrupted person for an extended period of time to encourage their rehabilitation.

SME’s awkwardly straddle the gap between these parallel pieces of legislation. Some SMEs are incorporated, and so fall under the Corporations Act. SMEs that are not incorporated are treated under the Bankruptcy Act as one aspect of the personal bankruptcy of the business owner. But of course, SMEs are neither people nor large corporations.

How insolvency works

Legislation governing corporate insolvency is founded on the assumption that there will be significant assets to be divided among many creditors. Broadly speaking, creditors are ranked and there are sophisticated and detailed provisions for their treatment. If Ten would have proceeded to liquidation, creditors would have been broadly grouped into three tiers and paid amounts well into the tens of millions.

One type of creditor is a “secured creditor”. Banks, for example, will often require that loans for the purchase of business equipment are secured against that equipment. In the event of default, the bank takes ownership of the equipment in place of the debt, if they can’t be paid out.

Unsecured creditors, on the other hand, do not have an “interest” over anything. If a company goes into liquidation, an unsecured creditor will only be paid if there are sufficient funds left after the secured creditors have been paid, and the cost of the process has been covered. There is no guarantee that unsecured creditors will be paid. Most often, they are only paid a portion of what they are owed.

The unique challenges of SME insolvency

When it comes to SMEs, there is little or no value available to lower-ranking, unsecured creditors in an SME insolvency estate. At the same time, higher-ranking, secured creditors tend to have effective methods of enforcing their interest outside the insolvency process. For instance they could individually sue the debtor to recover money owed. As a consequence, creditors are rarely interested in overseeing or pursing an SME insolvency process. This means the system is not often used and creditors with smaller claims go unpaid.

Even if creditors do want to use the insolvency process, it is likely the SME’s assets are insufficient to cover the cost of employing an insolvency practitioner and the required judicial oversight.

This problem is made worse because SMEs often wait too long to file for insolvency, owing to their lack of commercial experience or the social stigma of a failing business. Instead, debts continue to grow well beyond the point of insolvency, and responsibility falls on creditors to deal with the issue.

There are further difficulties depending on whether the SME is incorporated. Incorporated SMEs are frequently financed by a combination of corporate debt, taken on by the SME, and the personal debt of the business owner. This may result in complex and tedious dual insolvency proceedings: one for the bankruptcy of the owner and the other for the business.

Unincorporated SMEs, in turn, suffer from two stumbling blocks. First, the personal bankruptcy scheme has not been created to preserve the SME or encourage its turnaround. Second, personal bankruptcy proceedings require specific evidence that the person has committed an “act of bankruptcy”, such as not complying with the terms of a bankruptcy notice in the previous six months.

This hurdle makes the process far more time-consuming than the corporate scheme. It is also more difficult for creditors to succeed in recovering their investment and, by extension, prevents them from efficiently reallocating it. There is a real danger that this will deter creditors and raise the cost of capital at first instance.

What can we do about it?

The best way to meet the needs of SMEs would be to create a tailored scheme that sits between the corporate and personal regimes, as has been done in Japan and Korea. These regimes focus on speeding up the proceedings, moving the process out of court where possible and reducing the costs involved.

However, as the legislation in these two countries notes, there can be marked differences between small and medium-sized businesses that all fall under the SME banner. Therefore, what is needed is a flexible system made up of a core process, together with a large array of additional tools that may be invoked.

Designing such a scheme remains no easy feat. However, at its core, such a scheme would ideally allow business owners to commence the insolvency process and remain in control throughout. The process would sift through businesses to identify those that remain viable, and produce cost-effective means for their preservation.

Non-viable businesses would be swiftly disposed of, using pre-designed liquidation plans where possible and relying on court processes and professionals only where absolutely necessary. Creditors would therefore receive the highest return possible, and importantly, honest and cooperative business owners would be quickly freed from their failed business and able to return to economic life.

Authors: Kevin B Sobel-Read, Lecturer in Law and Anthropologist, University of Newcastle; Madeleine MacKenzie, Research assistant, Newcastle Law School, University of Newcastle

Don’t count your economic chickens before they hatch

From The Conversation.


After their customary two-day meeting, the Fed announced that they were holding interest rates at their current level, but would begin unwinding the massive bond-buying program they instituted in the wake of the financial crisis.

The Fed’s statement said:

In October, the Committee will initiate the balance sheet normalization program described in the June 2017 Addendum to the Committee’s Policy Normalization Principles and Plans.

This was met with whoops and hollers – from most quarters – and interpreted as a sign that the US economy is back on track.

For instance, the New York Times ran a headline “Confident Fed Sets Stage for December Rate Hike” and noted:

…[the Fed] would begin to withdraw some of the trillions of dollars that it invested in the American economy after the 2008 financial crisis. The widely expected announcement reflected the Fed’s confidence in continued economic growth. The current expansion is now in its ninth year, one of the longest periods of growth in American history.

Now, none of those words are wrong, but there’s more to this story.

The Fed is planning to shrink its US$4.5 trillion balance sheet by US$10 billion a month. That’s hardly a massive vote of confidence in the economy. In their statement, the Fed also expressed continued concern about stubbornly low inflation.

On top of all of that, we might be in the ninth year of economic expansion, but at what rate? Answer: well below historical levels.

So, as they say in showbusiness, hold the balloons.

Meanwhile in Australia, RBA assistant governor Luci Ellis issued an upbeat assessment of the economy in a speech to the Australian Business Economists forum. Her positive outlook is based on a textual reading of the IMF World Economic Outlook.

In 2016 about 6% of the “important” words were deemed positive and 10% were negative. This year 13% are positive and only 5% negative.

IMF word cloud. Reserve Bank of Australia

Let me add a few negative words to the count. First, it’s not as if the IMF is never wrong. Second, if one tries this kind of word-counting exercise in an academic seminar then people either giggle, cry, or start throwing things.

Economists (like those at the RBA) typically put little weight on chatter and a lot of weight on “revealed preference” – the decisions actually being made. For instance, the RBA continues to keep the cash rate at 1.5%. One can only surmise that they are concerned about what would happen if they did raise rates.

And with good reason. As the RBA themselves have pointed out, Australian households are deeply indebted. Moreover, the big four banks are extremely exposed to the housing market, and at least some of their chief executives seem rather naive about the risks they have taken on.

For instance, ANZ chief executive Shayne Elliot made the astonishing claim on ABC’s Four Corners recently that the bank’s risks are diversified because each mortgage was its own individual risk. This is exactly the kind of thinking that led to the financial crisis in 2008.

The RBA is in a bind, and they know it. So until they start raising rates it is wise to believe that the economy is more fragile than they often say it is.

Indeed, the one positive thing the US and Australia and Australia have in common is a relatively low unemployment rate. Yet wage growth is also very low.

All in all, the positive responses to the Fed’s announcement, and the happy sentiments in Luci Ellis’s speech, seem premature at best. The world economy may be picking up a little bit, but there is a long way to go before we can say that we are in a stable recovery.

Author: Richard Holden, Professor of Economics and PLuS Alliance Fellow, UNSW

What we can do once the banks give us back our data

From The Conversation.

Macquarie Bank has started a trial, giving customers access to the data the bank has collected on them. These might include the number and types of account held, average balances, regular payments and income and credit score information. This information helps to determine both the need for products and the risk of a customer.

This idea is called open banking and will see customers use their data in a whole range of ways – to ensure they are getting a good deal on their credit cards or mortgages, to see how they are faring financially against people in similar situations, and even to make paying taxes easier. Until recently our banks have had exclusive access to all of this data. The banks used it for marketing and product design. That is, your data was used to increase their profits.

The absence of sharing meant the data was a hurdle to customer switching. But the Productivity Commission has said consumers should be given a “comprehensive right” to their data.

In fact, you can already see some of use cases for your data in services the banks themselves provide. For example, Ubank has a tool that allows customers to work out a budget, and compare themselves to others of similar ages, household types etc. And many banks and credit card companies allow you to dive into your spending habits, to see where your money is going.

Treasury is currently examining how open banking should work in practice, and the Productivity Commission is looking at competition in the financial services sector. So this Macquarie Bank trial is just the beginning of open banking in Australia.

Is it safe?

You might be worried about how these other services will access you data. You don’t have to share your passwords or bank login, rather the data is shared using a standardised application programming interface or API.

An API creates a standard for connecting to a service, similar to how there is a standard for writing down your home address. To mail a letter you write down a street number, street name, suburb, state, postcode. If you write down the latitude and longitude of the person’s house then the letter won’t get there, because it doesn’t abide by the standard.

API’s have security standards as well, with two elements. One is authentication – making sure that the machine seeking access is the machine it says it is – and the other is authorisation – making sure that the machine is permitted to access the API. In practice, the authentication component could be done by a trusted third party, such as Facebook or Google.

An open banking API would need to allow enough information about a customer to be accessed to allow for service comparisons. However, the data must not contain enough information to identify an individual. This is essential under Australian privacy law and proposed standards would also need to comply with the European General Data Protection Regulation (GDPR).

What will I use the data for?

The fact that all this data has largely been held by the banks until now means there aren’t a lot of services for us to connect to immediately.

The most immediate example is to use your data to make sure you are getting the best deal you can on your loans. This is one of the reasons the British Competition and Markets Authority decided that open banking was necessary.

Under this scheme, if you want to compare service providers, you can download your anonymised data in a standard form and then upload it to a bank, a price comparison website or an app. In the case of the app, it would present to you your best options, given your current banking profile. This would include staying with your current bank or changing one or more accounts to a different institution.

This data could also be used to get approval for a new loan. Your anonymous data, in combination with identity information, includes enough material for a lender to decide whether to give you a loan for a specific purpose.

These tools will foster more competition between banks as customers will find it easier to compare services and switch, but it will also mean customers can make sure they are getting the best product available at the bank they are currently at.

But beyond comparison and switching, there are a number of interesting examples of how you can benefit from the data in your bank.

A budgeting app connected to your bank account, for example, can use your anonymous data to help you plan your finances. Using both your banking and “tap and go” payment history, it can help you analyse your spending and set goals. These services can even tap into outside data, such as interest rates, to help you determine what to do if rates go up. It’s that spooky moment when your phone becomes your conscience.

Online accounting software such as Xero or MYOB allows daily reconciliation of business accounts. These software systems already use APIs provided by the major banks to reconcile current accounts, loan accounts and credit card services. One variant on the open banking API could let customers “mark” transactions that are employment related expenses or health related expenses to simplify tax returns.

Going beyond fintech

But beyond these examples there are any number of possibilities for what we can do with this data. For instance, we could see an app that helps you make shopping decisions to increase the amount of loyalty points you earn. That is, using data on prices, goals and financial history to benefit consumers and not just sellers.

There are already limited examples of such schemes. The Coles “Fly Buys” scheme is connected to Virgin Velocity points. Both Coles and Velocity prompt members to earn points. Adding an overlay of which credit card to use at the checkout is currently up to you. However, it would be perfectly feasible for an app in your phone to choose which credit card the phone uses to pay at the supermarket to give you maximum points.

There’s also an opportunity here to connect your stream of financial data to what might seem like unrelated data. For example, what if your smart watch prompted you to walk home if you’ve spent more on eating out than your budget allowed? That is, open banking might actually improve your fitness, or at least make you feel guilty about overspending.

Author: Rob Nicholls, Senior lecturer in Business Law, UNSW

Who holds more than one job to make ends meet?

From The Conversation.

Women who work in the arts or services industries, and who are young, are the ones most likely to be working more than one job in Australia.

HILDA Survey data show that, in recent years, approximately 7% to 8% of employed people hold more than one job. And while this hasn’t been growing, the proportion of people using multiple jobs as a way of achieving full-time employment has been rising. This is when a worker combines two or more part-time jobs that add up to 35 or more hours per week.

Overall, the HILDA Survey data suggests there are two broad groups of multiple job holders. The first group is made up of those who supplement their full-time employment with a relatively small number of additional hours of employment, perhaps doing the same kind of work as their main job – such as private tutoring done by teachers and informal child care provided by child care workers.

The second group comprises those working part-time in their main job and using multiple jobs as a means to getting enough hours of work. For these people, it may be more likely that their second job is a different type of work to their main job.

This second group has grown in size since the global financial crisis, rising from approximately 54% of multiple job holders in 2008 to approximately 62% in 2015. Associated with this has been growth in people using multiple jobs as a route to full-time employment. In 2014 and 2015, approximately one in four multiple job holders were part-time in each of their jobs, but full-time in all jobs combined. This was up from approximately one in six multiple job holders in the mid-2000s.

This growth is likely to be strongly connected to the rise in underemployment – part-time employed people who want more hours of work – that has occurred since the global financial crisis.

When an increasing number of people can’t find a full-time job (or a part-time job with sufficient hours), it’s unsurprising that there is a rise in part-time employed people taking second jobs, as a solution to insufficient hours.

Women holding more than one job

It’s women who are more likely to hold more than one job. This is likely to be connected to the higher proportion of women than men who are employed part-time, since multiple job-holding is more common among part-time workers.

There are also substantial differences by age group. Employed people aged 15-24 are the most likely to hold multiple jobs, and employed people aged 65 and over are the least likely to hold multiple jobs. Women aged 45-54 are also relatively likely to have multiple jobs.

The differences by age group in part reflect the prevalence of part-time employment in each age group. People aged 15-24 are particularly likely to be employed part-time.

However, other factors are also likely to play a role. For example, a significant proportion of women aged 45-54 could be seeking to increase their hours of work as their children get older, and for some this will involve taking on a second job.

The types of work where more than one job is common

There may be some truth to the stereotype of the underemployed actor working as a waiter. Approximately 15% of employed people whose main job is in arts or recreation services industries have more than one job. People employed in education and training and health care and social assistance industries also have quite high rates of multiple job holding.

In these industries in particular, there are more opportunities for extra work in the same industry. For example, teachers may be able to privately tutor outside of school hours, and child care workers (who are in the health care and social assistance industry) can provide informal child care outside of child care centre operating hours.

Community and personal service workers, followed by professionals, have relatively high rates of multiple job holding. Managers, machinery operators and drivers and technicians and trades workers have relatively low rates of multiple job holding. These differences also reflect both rates of part-time employment and opportunities for supplemental work outside the main job.

The HILDA data further show that multiple job holding is typically not a long-term arrangement. On average, over 50% of multiple job holders in one year no longer hold more than one job in the following year. Whether this will continue to be the case if current high levels of underemployment persist remains to be seen.

Author: Roger Wilkins, Professorial Research Fellow and Deputy Director (Research), HILDA Survey, Melbourne Institute of Applied Economic and Social Research, University of Melbourne