Watts' model of cascading network failure

I have written in the past about how social and economic networks are a necessary ingredient for a proper understanding of economic patterns. The rise of social network platforms like Facebook and Twitter has allowed a thorough analysis of empirical regularities seen in networks in the social domain. Stephen Wolfram has a great blog about the regularities observed in Facebook data scraped using WolframAlpha.

One of the more interesting networks models is Duncan Watts' model of cascading network failure. Simply, each node in the network has a specific tolerance to failure, and it the share of adjoining nodes that have failed exceeds this tolerance, that node will also fail. For example, a node could have a tolerance of 0.5, so if more than half of its neighbours have failed, it will also fail, leading to a cascade of failures of its other neighbours.

This simple model is quite versatile. It suggests underlying mechanisms behind how fashion fads arise, or why investors tend to go with the herd, or why some industries produce superstars even though no one can objectively tell the difference in the quality of their skills. 

The methodological individualism so fondly embraced by the economics crowd has at its core the concept of utility, but stops short of answering the far more important question – where does our utility function come from if not our environment and our interactions with others? A model of networks can help explain the source of utility and begin to give a picture of how unique cultures and customs arise. 

In any case, I have generated an animated version of the model that simulates over a random network, with 5 random nodes ‘shocked’ to initiate the model. The histogram shows how many of the 20 different shocks have led to cascades of failure of a particular number of nodes. 

Enjoy. Follow me on Twitter. And please share.

Timing the residential property cycle

One interpretation of recent data is that investors seem happy to jump back into Australian residential property markets. Perhaps due to a search for yield. Perhaps from foreign cash seeking a safe harbour. Or perhaps it’s simply time for the Aussie love affair to be rekindled. Holes are over. It’s houses turn.

With these winds of change in the air maybe it is time to take a step back and look at the long term property cycle itself.

Property industry types talk about the cycle like a mythical being - unless you have witnessed it yourself you won’t know how aggressive the beast can be to your leveraged finances.

Long term regularity of asset price cycles is an intriguing proposition. Is the 18 year cycle really a good rule of thumb? If so, why don’t investors expect the cycle, and remove it through their anticipatory actions?

A simple answer might be that investors would anticipate the cycle if credit markets would allow it. But the banks supplying credit are themselves constrained by previous movements of the market. Thus the interaction of prices and the willingness to supply credit seems to be pretty decent explanation of the peculiar regularity of long term cycles. Thanks Minksy.

One way to think about the nature of the cycle is in terms of returns from yield compared to capital growth. At the bottom of the cycle equities, including property, are seen as risky places to preserve capital. During the boom expectations of capital growth return, and equities become the assets to hold.

If this truly reflects some fundamental emergent dynamic in the economy, a simple rule of thumb is to buy the high yields at the bottom of the cycle, and sell capital gains at the top.

But how do we know when yields are high? We need a relative measure rather than an absolute measure.

In the past I have used the mortgage rate divided by gross yield as a measure of the relative value of residential property. The theoretical picture is the the mortgage rate is a good proxy for the yields, net of capital growth, available in the economic generally. Gains above this rate typically arise from capital gains.

When the gross yield is close to the mortgage rate, theory says that the price is reflecting an expectation of low capital gains. But that would be wrong, given that it is the same theory predicts and equilibrium asset price.

In reality this would be a good time to buy.

The theoretical explanation is that these low growth expectations arise from recent experience of low growth - the same feedback that feeds the cycle upswing when high prices feed into expectation. If markets are myopic, you can forget about finding anything useful about expectations in the prices themselves.

So where’s my evidence for this? The graph below is an update from a previous post. With recent rental growth, price falls, and falling interest rates, this simple measure is showing that now is a good time to buy.

I have also created a second measure - the mortgage payment per dollar of a 30 year loan divided by the yield. The second measure adjusts for the fact that the cost of buying asset, in addition to the cost of interest, is a higher portion of the total cost at lower interest rates.

What is more surprising is the regularity of a head and shoulders-type pattern - similar tops and bottoms, and a similar period to the cycle, in this case 15 years. Not too far from the 18 year rule of thumb. And not too far off the stylised asset price cycle seen so regularly when discussing the latest housing boom

Given this regularity, and the strong buy signal, my internal model of the market suggests two possible future paths.

1. Renewed cycle

A great time to buy in most capital cities was around 1998. This year preceded a boom in Sydney, that cascaded across the country for the next 8 years. My chart shows the cycle at around 15 years, meaning this year is a good time to buy. The 18 year rule of thumb is then 2016 - just three years time.

Given the expected resources shock in the second half of this year and early next, I would not be in a rush. Also it may be wise to get better signals about the direction of international markets, particularly the US before leveraging into Australian housing.

I expect to be on the lookout for well located land in about two years time unless I get strong signals that the second path is playing out.

2. Stagnation

Given the weight of private debt, the already low interest rates across most of the developed economies, and a general reluctance for increased public spending to maintain employment and stimulate private investment, could we be heading to a long credit-constrained stagnation that requires major price adjustments in wages, rents, and currencies.

I have no good reason to believe one way or another. Political outcomes in Europe, China and Japan will be critical, as will our domestic adjustment following the mining investment peak.

My gut says that the fundamentals of continued current account deficits, which reflect inflow of foreign asset demand, and scope for much lower mortgage rates will probably allow for another cycle to ramp up by 2017.

I don’t expect it to be as severe as the last cycle for a few reasons.

• Inflation will be low unless the AUD falls significantly. Thus real gains could be high without such dramatic nominal gains.
• Mortgage rates still have scope to fall to around 4% in the next two years.
• But I expect the memory of the financial crisis and a stricter regulatory environment will mean tighter bank lending
• The demographic shift of baby boomers seeking to get out of negatively geared residential property will dampen capital gains

If most investors are myopic, those who consider the long term will have an advantage in any market. And what we have seen here is that we seem to be in a relatively attractive period for buying residential property assets. Just remember to consider all the other macro and political factors in your own assessment of the market.

Australian age-dependency ratios

Everywhere you turn it seems that higher rates of population growth are seen as a 'solution' to an ageing population.  Here's one recent example.  My general views on this matter are found here.

At the very least there should be a publicly accessible model of population growth to verify the claims being made in this debate.  The productivity commission has modelled population growth for this purpose, although the intricacies of the model are not at all clear or public.

My first step towards this is to actually look at the historical demographic record in Australia.  As you can see from the interactive chart below, the country's age dependency ratio has been steadily increasing since at least the 1970s.

Offsetting this age dependency has been a quite dramatic decline in youth dependency as fertility rates fall. The total dependency, or number of children and retirement age people per working age person is at record lows.

The next step is the add some features to this model to allow a choice of assumptions about immigration rates (and ages) as well as birth and death rates, to see exactly what how immigration policy is affecting demographics and whether there are some circumstances in which the 'immigration solves ageing' slogan may hold.

Enjoy.

Are there supply curves in a theory of return-seeking firms

In the theory of return-seeking firms there is no supply curve as such.  There are simply reactions by firms given their expectations about 1) the persistence of a demand shock, 2) their competitiveness.

Under normal conditions where demand increases in line with expectations, mark-up pricing that is set at a level to discourage competitor entry, can continue to be used.  However, there are many pricing options available to a firm to win market share (a discussion for a later post). 

The below model shows the case of three firms in a market.  The rate of return earned at the starting position is proportional to the market power/competitiveness of the industry.  The theory has nothing to say about whether three firms will result in reduced competition.  Competition, or lack thereof, is an artifact of local monopolies, regulatory frameworks, capital barriers and so forth. In a market with free entry and local competition, three firms can easily be very competitive. 

A shift in the demand curve in this model need not have any special impacts on prices under any period of analysis.  There are no assumptions about the slope of a supply curve.  What exists is an ability to interpret price changes as evidence of market/monopoly power.  For example, if demand for oil tankers increased over a short period, ship builders would have years to increase their mark-ups and returns before a competitor could become established.  However, they may choose not to take all the possible increase in returns to decrease the attractiveness for a new competitor, or to win market share from an existing competitor - no use making high return now, but being forced to accept very low returns in the future when new firms enter the market.  

The price setting during a short term demand shock is not at all the result of costs faced by firms, but of market power. 

To recap, an unexpected sudden shift in demand can provide temporary monopoly power for firms currently in the market (since the shift is beyond the planned capital investments in the market). In markets where new capital takes many years of investment, or there are regulatory barriers to investment, higher prices would be expected.  However in markets where production is highly competitive between established firms vying for market share,  sudden shifts in market demand may lead to falling prices. 

The below interactive graph the demand shock slider shifts the demand curve.  The market power slider sets the starting market power and shows that higher mark-ups / returns will be acheived with greater market power. The checkbox allows market power to be related to demand shocks to demonstrate the case that even in apparently competitive markets unexpected demand shocks might themselves create temporary market power. 

How economists think of themselves

Tyler Cowen has an article in the New York Times about the egalitarian tradition of economics.  It appears to be a genuine effort to promote economic analysis and rationale as THE tool for social analysis, since it is the only value-free objective way to look at society.  My experience in the profession has given me strong reasons not to be easily convinced.  The very fact that most economists I know have linked to the article with ringing endorsements sheds some light on how economists perceive their role.  

In fact economics has a very distinct moral alignment, and even the basic notion of utility merely reflects an individual’s interpretation of contemporary morals. 

Cowen builds this backdrop of value-free objectivity as a foundation for his pro-immigration arguments. 

Let’s take it one point at a time.  

Economic analysis is itself value-free, but in practice it encourages a cosmopolitan interest in natural equality

What is “natural equality”? No seriously.  What is it?

Many economic models, of course, assume that all individuals are motivated by rational self-interest or some variant thereof; even the so-called behavioral theories tweak only the fringes of a basically common, rational understanding of people. The crucial implication is this: If you treat all individuals as fundamentally the same in your theoretical constructs, it would be odd to insist that the law should suddenly start treating them differently.

Behavioural theories that economists themselves accept could be considered minor tweaks - by definition the discipline won’t accept a rewrite of their fundamental belief structure. A genuinely objective, or value-free, observer of the behavioural tradition would reject the notion of rational self-interest, especially rationality as defined by consumer theory. 

Further, treating all individuals the same in theory is not what economists have set out to do, but what they are required to do to gain mathematical tractability of their core model.  It is equivalent to assuming a single person is the average of all.  Economic models can and do treat different people and groups differently.  For example the overlapping generations model.

 And the classical economists Jeremy Bentham and John Stuart Mill promoted equal legal and institutional rights for women long before such views were fashionable. Their utilitarian moral theories placed individuals on a par in the social calculus by asking about the greatest good for the greatest number.

Bentham and Mill didn’t support personal liberty in every instance — Mill was a proud imperialist when it came to India, and Bentham’s idea for a Panopticon prison was a model of state-sponsored surveillance. But they prepared the way for dissecting the prevailing defenses of hierarchy and injustice.

So basically if you look hard you can find instances where economists historically appeared to be value-free or egalitarian, but if you look even harder you realise that this is chance, and you are equally likely to find the opposite. 

Gary Becker, the Nobel laureate who is one of the founders of this approach, used the economic method to lay bare the selfish motives behind racial and ethnic discrimination. 

In my view Thomas Schelling was perhaps more influential in this area, but of course doesn’t identify as an economist, so his ideas can be dismissed.  Also, Becker’s model “often includes a variable of taste for discrimination in explaining behavior”.  So if people have a taste for it, they derive utility, the economic answer is that discrimination is good.

At this point Cowen turns to immigration an makes the point that we should include the benefits to immigrants in the cost-benefit analysis of immigration, rather than just the current citizens. 

The obvious but too-often-underemphasized reality is that immigration is a significant gain for most people who move to a new country.

In fact the key point of the whole article is in the following two paragraphs

In any case, there is an overriding moral issue. Imagine that it is your professional duty to report a cost-benefit analysis of liberalizing immigration policy. You wouldn’t dream of producing a study that counted “men only” or “whites only,” at least not without specific, clearly stated reasons for dividing the data.

So why report cost-benefit results only for United States citizens or residents, as is sometimes done in analyses of both international trade and migration? The nation-state is a good practical institution, but it does not provide the final moral delineation of which people count and which do not. So commentators on trade and immigration should stress the cosmopolitan perspective, knowing that the practical imperatives of the nation-state will not be underrepresented in the ensuing debate.

I can tell you a good answer of why to report costs and benefits only for the US when considering new laws, particularly with respect to trade and immigration.  Imagine you have the without immigration case.  You don’t draw the line at national borders so you need to include potential immigrants staying in their home country to understand the current situation.  But you don’t know who they are. So you have to take the whole origin country, and since you don’t know which countries they will all come from, you have to take all countries.  If you can’t draw the line as Cowen argues, you have to conduct a global analysis of every decision. 

If, as Cowen suggests, there are massive benefits from immigration at destination countries, then there may very well be massive costs to emigration from origin countries. Yet Cowen expressly ignores these in his supposedly value-free analysis, even though they may very well be higher in welfare terms because of wealth disparities - small benefits are valued more highly the lower your wealth.  

So much for value-free analysis then.  How about we actually consider these important issues like immigration from a moral standpoint instead - at least then we are debating our shared group values rather than using economic analysis to disguise one particular moral judgement. 

Network map of Australian lobbyists

The image below is a network image of Australian lobbyists and their clients.  Click for full size image.

An interactive growth model

Earlier this year I wrote a Mathematica model to demonstrate some of the fallacies of neo-classical models of economic growth.  For some reason the fact that the basic models did not result in growth when time equals infinity, was cause for alarm.  New models that grew for infinite time must be found.

I argued that perhaps we don't yet have the evidence to dismiss models that show diminishing growth over time.  At least it doesn't feel like I am at the end of time yet.

So I wanted to have a closer look at what sort of rates of growth we could expect, and for how long they persist, with reasonable parameters for models with diminishing returns.

This post is simply to test whether I can embed the interactive results of one of my early models into a blog (and after 9 attempts, the answer is yes).

The basic gist of the model is that it growth is dependent on capital, but with diminishing returns (alpha is less than 1).  Economies with higher investment ratios will have higher growth in the long run, but less consumption in the short run.

The market/institution multiplier is just a way of adjusting the resulting output from a given level of capital.  It is designed to represent the governance and institutions that allow more efficient use of capital.

And the technology parameter is meant to represent new methods of production.

For now, I am just glad I finally have it working on the blog.

Land boom ruins productivity measure

Article first appeared at MacroBusiness

Even though more words have been written about Australia’s productivity performance than most other economic issues, I have learnt very little about what our productivity trends really mean.

Recently, the RBA tried to unravel the mystery. My wise colleagues at MacroBusiness have often penned their interpretation of events.

To throw a little more confusion into the mix, the RBA’s D’Arcy and Gustafsson notes that

...there is considerable measurement error in the estimates of productivity growth making it difficult to be precise about the timing of changes in the underlying trend; and productivity growth is the result of the interaction of many fundamental and proximate factors.
Technological, structural and regulatory changes, as well as cyclical variation in factor utilisation, can all affect measured productivity, making it very difficult to identify and disentangle the various effects.

But we are given a hint at the important conceptual basis of the thing we refer to as productivity. 

Conceptually, economists often view technology as determining the productivity ‘frontier’; that is, the maximum amount that could be produced with given inputs.

Factors affecting how production is organised, including policies affecting how efficiently labour, capital and fixed resources are allocated and employed within the economy, determine how close the economy is to the frontier. Trend productivity growth is then determined by the rate at which new technologies become available—how fast the frontier is expanding and the rate of improvement in efficiency—as well as how fast we are moving to the frontier.

It all sounds very theoretical, but the reality is simple. The chart below shows the two key measures of productivity since the 1970s, and the declining multifactor productivity (MFP) that has attracted so much attention. Labour productivity growth has remained positive, if a little lower than the historical average.

There are two questions I will answer in this article: 

1. Why is labour productivity growth historically low? 
2. Why has MFP growth been negative for the past decade? 

To answer the first question we need some perspective about whether Australia’s performance is abnormal compared to other nations. If not, then I suggest there is little that can, or should, be done.

The Productivity Commission has some good figures on our performance against other comparable nations. It seems that our productivity performance is... wait for it... actually pretty good, and fairly stable in relation to the US and EU. Comparing GDP per hour worked, the fundamental measure of labour productivity, Australia has made gains on the EU15 during the 2000s, and has lost just a little ground against the US up to 2007. The chart from the RBA below clearly shows that we were middle of the road of productivity growth in global terms.

So why then would labour productivity be historically low across the world? Mostly, it has to do with significant structural declines in unemployment. Typically the least productive people, those with few skills to utilise capital effectively—to ‘leverage’ their work with the help of machines, computers, tools and so on—are the last to be employed during periods of strong growth, and the first to lose work during economic contractions. Thus the expected outcome is that during economic boom periods of declining unemployment, labour productivity will be biased down by these new workers, compared to if unemployment was flat. We should also expect that during periods of increasing unemployment that labour productivity surges again. When the least useful one percent of the workforce is laid off, production usually declines just a fraction of that one percent. 

In addition, much of the mainstream productivity discussion is dominated by the influence of mining and infrastructure, the two industries with the largest declines in productivity. The basic arguments are as follows.

1. As widely noted, we have a ‘wall of wire’ problem in much of our basic infrastructure. This simply means that the honeymoon period of relatively new electrical, phone, water, and waste infrastructure is over, and major maintenance and capital expenditure is becoming more frequently required to deliver the same service. 
2. In mining, a sector showing substantial productivity declines in recent years, we have the situation where “rising minerals prices meant low-productive mines were profitable, and thus the extraction of minerals from those mines actually assisted in lowering the sector's and the economy's productivity” 

These arguments are both true and apply to those sectors in terms of both labour productivity and MFP. Another major factor is unpredictable seasonal changes in the agricultural sector output. 

So what of our MFP performance?

If we have been tracking fine in terms of labour productivity, the actual only meaningful and useful productivity measure that reflects the benefits from economic growth, why the dismal pattern of MFP, the measure most economists prefer to fuss over? And why do they prefer it anyway, when labour productivity is the only one that matters?

As noted in the RBA report, economists believe that Total Factor Productivity (TFP), or Multi-factor Productivity (MFP), measures changes in technology and market structure that enables the ‘production frontier’ to shift outwards. But when the idea of MFP was originally put forward, it was known as the Solow Residual, because it is “the part of growth that cannot be explained through capital accumulation or the accumulation of other traditional factors, such as land or labor”. Essentially, it is the bit left over after we measure all the inputs and outputs of the economy. Economists thought they might call it ‘technology’ or ‘productivity’ because it appears to measure our ability to get something for nothing.

But in reality, it is capital accumulation that almost exclusively improves labour productivity and the scale of our per capita productive capacity. Having more, and better, machinery, buildings, infrastructure networks and other capital equipment is what enables each person to be more productive. Using better machines, for example, can improve how many meters of road can be laid by a small team of workers in one day, and the quality and durability of the resulting surface. As the economy accumulates capital, all parts of production require less labour per output. It is one of the main reasons the agricultural sector requires such a small workforce. If I haven’t repeated myself enough already, it is capital accumulation that explains almost all the improvement in labour productivity (for example, see here).

To recap, labour productivity is simply a measure of output, usually GDP, divided by labour input, either in employed persons, working hours, or population. MFP is a measure of output divided by the sum of inputs of labour and capital, including land. I use the term productivity to mean MFP, or will explicitly state labour productivity when referring to it.

To answer the question of why we have experience declining MFP, we have to think about what can cause a divergence between the two productivity measures. MFP is the result of dividing output, measured by GDP, by the sum of labour and capital inputs. So either we are using our capital less efficiently, requiring more new capital for each improvement in output (diminishing returns to capital), or we have some kind of measurement anomaly in the estimation of the balance of capital assets. Indeed there may be some diminishing returns to capital effect, but after investigating this anomaly I found that falling MFP is substantially the result of estimates of land prices in the measure of the capital stock.

The culprit is hidden deep in the ABS release 5204.0 System of National Accounts. Back in 1999 the methodology for estimating MFP changed. One critical change was the inclusion of non-agricultural land in the capital stock.

...the scope of capital inputs has been changed to include the capital services of livestock, intangibles and non-agricultural land and to exclude ownership transfer costs.

The ABS believes that the exclusion of non-agricultural land biased the measure of MFP downwards in the past. But this only applies to the situation where the value of land assets grows with inflation. When land values significantly exceed inflation, which has especially been the case since 2001, the capital stock component in the denominator of the MFP calculation increases, for no particular reason. Theoretically, the inclusion of land is very odd, since it is always fixed in any case.

The ABS explains that they take the balance sheet value of land from the national accounts to include as the land component of capital stock. We can observe in the chart below the rise in the value of the land balance sheet value against the estimate of MFP, and indeed against an estimate of the land balance if land values simply tracked inflation. Quite clearly, from about 2002 onwards the abnormal increase in the value of land lead to a flattening and falling estimate of MFP. More telling is that fall in all land asset values in 2009 lead immediately to an increase in the MFP measure, only for the next wave of land price escalation, especially FHOB stimulated residential land, to cause a deterioration in MFP during 2011.

We can dig a little deeper into the ‘land balance sheet’ in the system of national accounts, and look closely at the type of increases in land value estimated. The chart below shows in blue the neutral holding gains - that is, the change in the value of land expected if prices tracked inflation. In red we see the real holding gains, which are market-based increases in land values. As the ABS notes “Holding gains and losses accrue to the owners of assets and liabilities purely as a result of holding the assets or liabilities over time, without transforming them in any way”. In economic terms, they are pure rents.

When red is greater than blue, we find a significant downward bias in the MFP estimate. It is really that simple. And we are not alone in this either. Spain’s land price boom resulted in a similar pattern of declining MFP during their land price boom in the early 2000s. 

Let us wrap up by summarising the key points from this analysis. 

1. Australia is not performing abnormally low by international standards in productivity growth. 
2. Labour productivity is the most important productivity measure and improves almost entirely through capital accumulation. 
3. Labour productivity is usually biased by changes in unemployment. Reduction in unemployment results in a downwards bias as new labour is employed before capital can be produced to help the expanded workforce produce more effectively. 
4. Multifactor productivity is the bit left over after adding up all the economy’s outputs and subtracting all the inputs. It typically captures compositional changes in goods produced. 
5. Multifactor productivity has fallen mostly because the denominator of the productivity equation has been so heavily influenced by inflated land prices across all sectors since 2002. I expect if the slow melt in land prices continues we will see a 'surprising' recovery of the multifactor productivity measure in the coming years.

Ridiculous debates on funding health care in Australia

There is a detailed academic debate surrounding health care funding and provision in Australia (as there is globally). 

But the debate is clouded by observer bias – by the relatively wealthy senior academics, government officials, and consultants, who provide the analysis in the policy-making environment.  If everyone involved feels burdened by their own choice to send their children to private school, or irrationally choose private health insurance, because of a culture of social class bias, what type of policy recommendations can you expect?

Consider the King-Gans argument, based on a theoretical micro-economic model of health insurance developed in this paper.  The model assumes individuals with perfect knowledge of future health care needs, which leads to a massive adverse selection problem for the private health insurance market.  After a few pages of intellectual mathematical masturbation, the conclusion follows that –

…those in society who are most likely to be ill will ‘opt out’ of public insurance and purchase private insurance. The public health insurance will only be used by those in society who are healthiest (i.e. least likely to become ill). The high-risk individuals are made worse off by the public insurance because they are required to cross-subsidise the public insurance of the low-risk individuals through the tax system (my emphasis).

This conclusion completely contradicts the current reality in Australian health care, whereby the public system typically deals with the most serious cases of trauma and chronic illness.  While the model conclusions are laughable, the policy implications that follow from this model outcome are being taken seriously. It's a worry.

As far as the details of the model are concerned, we all know the best measure of a model’s usefulness is how well it predicts outcomes.  The King-Gans model requires the following assumptions to come to the conclusion that a mix of private and public insurance will result in higher cost to the most ill people, because only the most ill will choose private health insurance –

1.     Adverse selection of private health insurance by a set of people who happen to know in advance their future health care needs and probability of illness

2.     People value actuarial fair insurance.  That is to say that they value insurance only as a tool for spreading health costs over their lifetime.

3.     They assume a perfectly competitive private insurance market.  While the market is good, I would be hesitant to got that far, but it is probably okay.

4.     They assume there are not ‘too few’ people with a high risk of being ill who know their risk in advance.

5.     They assume identical income for all individuals at a level that justifies PHI for those individuals who know their risk of illness, and subsequently their expected future health costs, in advance.

6.     The ignore all externalities associated with health care.

For me, apart from the above-list of possibly unrealistic assumptions, the analysis ignores the critical fact that risk averse wealthy people will both choose private health insurance (PHI) AND preventative health care through healthy living choices.  PHI is a complement to better voluntary health choices, so we should expect that private patients are typically the healthier members of society, and the publicly funded public hospitals will be treating the most ill patients.

This paper, by Buchmueller et al., finds empirical evidence that contradicts the result of the King-Gans theoretical model.  They find that there is beneficial selection of PHI in Australia, meaning that the healthiest people tend to be privately insured.  May I suggest this conflict between empirical ‘reality’ and theoretical abstraction is the result of the above ignore concept of complementarity of PHI and healthy lifestyle choices.

The conflicting result is also a product of ignoring the relationship between income, health, and PHI.  Higher income people both typically value health more highly (since the can, even though it may not be as high as a proportion of their income of many not-so-wealthy individuals), and are incentivised to be covered by PHI through tax rebates.

The second major shortcoming of the King-Gans model, and the analysis of health care funding and insurance more generally, is that it ignores the fact that end of life health costs are unavoidable.  Death is usually a costly process.  Thus, there is no way to adversely select health insurance for these ‘death costs’ in advance.

Third, many PHI covers are incomplete covers.  That is, that when a health service is claimed against the insurance cover, the reimbursement is a fraction of the total cost, and there are still out-of-pocket costs for the patient.  Thus, for anyone expecting to require a lot of non-elected medical care, public health provision may be the rational choice, given the risk-adjusted premiums for the most comprehensive PHI cover.

Fourth, PHI is incomplete in terms of scope of medical coverage.  Emergency departments, for example, are typically the domain of public hospitals, treating public and privately insured patients at public cost. 

In all, these overlooked considerations render the theoretical outcomes of the King-Gans model inaccurate, and the policy implications that follow from it to be counterproductive.  The reality of beneficial selection of PHI means that wealthy sick individuals are provided a premium health insurance service, because their insurance funds are pooled with a selection of healthy people, to offer an attractive way of making elective health services more affordable.

While King and Gans acknowledge some of these shortcomings, they stand by the conclusions of their model, which feed into their policy recommendations.  Given their academic reputations in the policy-making community, this is dangerous territory.

Finally, my personal gripe with the paper, and the policy agenda being pushed of a result of this (and similar) analysis, is that it ignores the social agreement that has evolved to the current provision of a pooled national health insurance program.  When PHI coverage was over 70% in the 1970s, reforms were aimed at generating a more equitable and broader system of public health coverage.  The very nature of a mostly private health care system is that the unhealthy poor have both the highest need, and the least ability to pay for health services.

That basic philosophy of public health care is that national insurance, funded in a progressive manner through the tax system rather than through actuarial risk-reflective premiums, is provided by publicly run enterprises to fulfill equity considerations (for example, geographically equitable access to health care) and provide broad external community benefits by having a healthier population.

It is also easier to evolve a publicly run system of hospitals and health services to become a platform for implementing auxiliary health policy goals, such as vaccination programs, education programs, doctor training programs, and research.  The current system provides, on the whole, quite a reasonable combination of the benefits of both a private and public system, even if it does perpetuate notions of social class in Australia.

All things considered, the legislation currently being proposed to remove the income tax rebate for individuals covered by PHI may actually promote a more effective market for PHI to offer simple top-up coverage at reduced cost, and generate a welfare shift from the wealthy insured to the poor uninsured. Back in 2004, Dawkins et al. noted that the implementation of the 30% rebate for PHI in 1999 (following more subtle incentives in 1997 and the implementation of ‘lifetime’ health cover) made those on high incomes better off –

There is strong evidence that not only a larger number of households of higher income and socio-economic standings responded to the policy changes, but also they were more likely to have PHI even without the policy changes. These latter households enjoyed “deadweight benefits,” in the sense they needed no such benefits to purchase PHI to begin with. Given that households who took up PHI ought, by their revealed preference, to be better off, we can reasonably conclude that households with high income and socio-economic standings are the main beneficiaries of the policy changes.

As we have seen above, the idea that the ill rich are paying twice for health care is surely not at all representative of the present situation in the Australian health care system, and furthermore, policy advice derived from a bizarre theoretical model whose results oppose the empirical evidence should be carefully scrutinized with a dose of old-fashioned commonsense.

US gas glut may dampen energy markets

Article first appeared at MacroBusiness

The US economy has shown some signs of stabilisation over the past few months.  For example, retail spending appears to be revisiting a growth path.  According to some commentators, the US economic green shoots appear robust and healthy, while I remain cautious about projecting anything more than muddling though, with a drawn-out grinding improvement in employment as the consumer debt burden is reduced by inflation, repayment and default.

The US ‘recovery’ is the result of many factors, including the relatively cheap US dollar (the US TWI is back at levels last seen in the mid 1990s), but importantly, and often overlooked in financial discussions, relatively cheap domestic energy fuel prices.  WTI crude is hovering around $100/barrel, still 25% down on the pre-crisis boom period.

But the key energy consideration for the US recovery, and future US political–economic trade policy, is the current domestic natural gas boom which has meant the US has shifted from gas net importer to net exporter.

Below we can see the recent rise in natural gas production, mostly as a result of the emergence of economical shale-gas extraction, for the past three years or so.  With natural gas comprising a quarter of domestic US energy needs, the scale of this boost in energy supply is significant.  Some have gone so far as to suggest that the natural gas boom, as a result of fracking and coal seam technology, is leading to a ‘new world energy order”.

We can also see that the US domestic spot price for natural gas has remained quite subdued in this period due to a combined of demand destruction, particularly during the financial crisis period between 2008 and 2010, and increased domestic supply.

The US government closely regulates natural gas exports, and any import or export of natural gas requires approval of the Department of Energy, as per Section 3 of the Natural Gas Ast 1938. (approvals in progress are here).

Debate is now brewing over the direction of US energy policy in the treatment of export approvals for the natural gas glut, given the significant profits to be made from liquefaction and export to Asian markets.

As the Wall Street Journal notes:

The U.S. already exports some natural gas, mostly via pipeline to Canada and Mexico. A recent wave of export proposals by energy firms seeks to liquefy gas and ship it overseas in tankers.
U.S. natural-gas prices have fallen below $3 per million British thermal units, pushed down by swelling production that became possible with the advent of new drilling technologies. With prices so low, U.S. producers are eager to reach customers in other parts of the world, such as Japan, that pay three to four times as much as U.S. users.
Collectively, they want to ship out about 14 billion cubic feet of natural gas a day, roughly 20% of current U.S. production.
But some lawmakers on Capitol Hill are opposed to increased exports and are urging the Department of Energy not to issue the required permits. The department will use the findings released Thursday in making its decision.
The administration is reviewing the export proposals “to ensure they are in the best interests of American taxpayers,” Energy Department spokeswoman Jen Stutsman said.

One argument is that maintaining a tight limit on exports will keep the domestic price low, and domestic energy intensive industry more globally competitive:

 The estimate by the Energy Information Administration, which compared gas-price projections in given years with and without higher exports, appeared to bolster assertions by U.S. manufacturers that they could face stiffer prices for natural gas and lose a competitive edge over companies abroad.
“Higher levels of exports would certainly impact the manufacturing recovery that has been revitalized in the U.S.,” said George Biltz, Dow Chemical Co.’s vice president for energy and climate change. “Exporting too much natural gas simply exports well-paying U.S. jobs.”

US Energy Information Administration (EIA) analysis of price impacts from increased exports shows that, under various scenarios, of high and low growth paths for shale-gas production, and rapid and slow scale up to exports, domestic price increases could be in the order of 15-35%, depending on the speed of development of export capacity.  See their chart of model results below.

As secondary concern has been whether exposing US domestic natural gas production to the global market will import price volatility due to global events.  If export capacity is high enough, this may be the case.  But if export capacity is limited by physical capital – the number and size of pipelines, and finite capacity of LNG conversion and loading facilities which would require a decade or so lead time to expand – then domestic prices will not capture global volatility, as high prices cannot be passed through to the domestic market.

The big question for US energy policy, is how best to share the benefits of this new energy supply.  The way I see it, maintaining tight control on export markets keeps prices for domestic gas users at a globally low level, making a diverse range of US industries more globally competitive.  However, this benefit comes at a cost to the gas industry that is being denied access and profits from global markets, such as Japan, Korea, China and India, where gas prices are significantly higher.

Indeed, allowing exports would in a way provide economic benefits to the destination countries, as the global price of LNG will be reduced as the increased supply comes on board.  For Australia, where the infant Coal Seam Gas (CSG) industry is being relied upon as a driver of economic growth, increased global competition in the gas market from US exporters may be cause to pull pack some of their price forecasts in the short term. Last week, Fitch placed the entire sector on negative watch for this reason among a growing list of other negatives.

The current US debate is interesting from the lens of an Australian resource State, where almost all energy resources are developed for export markets.  It is surely inconceivable that Australian authorities would consider such regulation to ensure competitiveness among our own industry. (Although, as mentioned earlier, the ability for export markets to compete with domestic energy markets is dependent on the interconnection of the supply chain.)

It is worth keeping a close eye on how US energy policy plays out in the treatment of the shale-gas boom, and the potential implications for LNG markets globally.

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