25/10/15: Grifols and the Ghosts of OECD

An interesting set of contrasts: one company, one move, two reports.

Last week, Irish and Spanish press reported on the Spanish Multinational pharma Grifols moving most of its operations from Spain to Ireland. Here are two examples of reports:
- One from Spain (http://economia.elpais.com/economia/2015/10/24/actualidad/1445711002_780890.html?id_externo_rsoc=TW_CM) focusing on tax optimisation reasons behind the Grifols' move; and
- One from Ireland (http://www.irishtimes.com/business/health-pharma/spanish-healthcare-firm-grifols-to-create-140-jobs-1.2401541) without a single mentioning of tax issues. You can also see this one from the Irish Examiner (http://www.irishexaminer.com/business/grifols-creates-140-jobs-in-dublin-360972.html) which also fails to mention tax issues.

Spanish report quotes Grifols CFO on the issue of tax optimisation. Irish reports say absolutely nada about the topic.

Spanish report references the statement that Grifols will channel all of its non-Spanish and non-US revenues via Ireland (a practice used for tax optimisation by many MNCs based here). But both Irish reports linked above fail to mention this quite material fact.

Remember OECD BEPS ‘reforms’? When someone doesn’t want to know the obvious, one doesn’t have to worry about the obvious…

22/10/15: Ah, those repaired credit flows...

BIS data on 2Q 2015 cross border lending is ugly... on so many levels (see breakdowns here: https://www.bis.org/statistics/rppb1510.pdf). But the ugliest is the aggregate rate of change:

Yep, that's right. In the economy repaired by multiple countries surrenders to the IMF, years of massive QEs, the printing presses perpetual overheating and all other policy shenanigans, 2Q 2015 has seen the sharpest decline in cross border lending by the banks in history of the series (from 1978 on).

Borrowing is down across all intermediaries:

and all currencies save Japanese Yen:

and for every borrowing region, save EMEs (read: China):

and if you think the rot is dominated by the Emerging Markets, think again:

So when some time ago I described the state of play in the global economy as being Japanified, I wasn't kidding. The monetary policy dream of 'repairing' credit flows by making credit dirt cheap has had... well... at best an underwhelming effect. Time to think about actual, real, economic demand, maybe?..

22/10/15: Gig Economy and Human Capital: Evidence from Entrepreneurship and Self-Employment

In a couple of weeks, I will be speaking about the role of human capital in the emergence of the new economy at the CXC Corporate event “Globalization & The Future of Work Summit” in Dublin.

Without preempting what I am going to say, here are some key points of interest.

Human capital-centric growth is overlapping, but distinct from the so-called “Gig Economy”, primarily because of the different definition of what constitutes two respective workforces.

Take, for example, the U.S. data. Based on research by the American Action Forum by Rinehart and Gitis (2015) we can define three types of the broadly-speaking “Gig Economy” workers: “For our most narrow measurement of gig workers (labeled Gig 1) we simply include independent contractors, consultants, and freelancers. Our middle measurement (Gig 2) includes all Gig 1 workers plus temp agency workers and on-call workers. Our broadest measurement (Gig 3) includes all Gig 2 workers plus contract company workers.”

The respective numbers engaged in three categories in 2014 range between 20.5 million and 29.7 million with growth rates over the recent years outpacing economy-wide jobs expansion rates across all categories of the Gig Economy workers.

Still, the key problem with identifying underlying trends in the development of the Gig Economy is the lack of data on specifics of occupational choices of the self-employed individuals and the relationship between these choices and human capital held by the Gig Economy participants relative to the traditional employees.

To see the indicators of links between the Gig Economy and human capital, we have to look at the more established literature concerning transition to entrepreneurship.

One interesting set of studies here comes from the Italian Survey of Household Income and Wealth (SHIW), a large biannual household survey conducted by the Banca d’Italia. A 2007 paper by Federici, Ferrante and Vistocco looked at the links between institutional structures, technological innovation and human capital in determining the propensity to transition from employment to entrepreneurship. Looking at the general literature on the subject, the authors state that “…institutions are more important than technology (i.e., technological specialization and/or industry composition) in fostering or restricting entrepreneurship and that the interactions between institutions and occupational choices may be complex and non linear”. The authors caution against directly linking self-employment rates with entrepreneurship rates, as “countries displaying the same self-employment rates, might be endowed with very different amounts and qualities of entrepreneurial skills devoted to innovation and business ventures (or, on the other hand, they might not)”.

To better pinpoint the link between entrepreneurship, self-employment and the institutional and technological drivers for risk taking, Federici, Ferrante and Vistocco augment the survey data with a set of variables describing the social and institutional environment in which self-employed and traditional workers are operating. Crucially, “in addition to standard indexes of economic and social infrastructure at the local level, [the authors] include a measure of creativity developed by Florida (2004).”

The conclusions are strong: “in Italy, both institutional and technological factors have shaped entrepreneurial opportunities requiring, tacit knowledge embedded in social networks and in the cultural background of families… Hence, well-educated people lacking privileged access to tacit knowledge and, in particular, an appropriate family background, could find themselves up against a considerable barrier to entrepreneurship and occupational mobility.” In simple terms, the Gig Economy-related value added can and should be considered within the context of family and cultural institutions as much as technological enablement environment.

As per traditional metrics of human capital, the study conclusions appear to be contradicting the core literature on entrepreneurship. “The evidence of the highly significant negative role of education in entrepreneurial selection is very strong in comparison with the majority of international studies showing that education has either a positive impact (Blanchflower, 1998) or a statistically non-significant effect on occupational choices”. In other words, formal education seems to be more conducive to employment choices in traditional environments (e.g. full time jobs),w it exception, perhaps, of professional skills-based activities.

The negative links between education and propensity to engage in entrepreneurial activity is, however, in line with other Italian study based on the same data, authored by Sabatini (2006).

However, U.S. data-based studies frequently find existence of a U-shaped relationship between income and propensity to transition to self-employment, with highest propensities concentrated around low income earners and high income earners, while lower propensities occurring for middle income earners. One recent example of this evidence is Moutray (2007). In so far as formal education is an instrument for income, especially for sub-populations excluding very high income earners, this suggests that the negative relationship between self-employment and education found in the case of Italy can be culturally conditioned and does not translate to other economies.

Another interesting aspect of transition to the ‘Gig Economy’ relating to the links between human capital and creativity or cultural institutions was uncovered by a 2011 paper by Mitra and Abubakar who looked at data from the Local Authority Districts of Thames Gateway South Essex (TGSE) in East of England. The study attempted “to explore and identify key determinants of business formation in Knowledge Intensive sectors (which include the creative industries) of regions outside the major metropolitan conurbations, and their possible differences with other Non-Intensive Sectors.”

The authors found that human capital is “positively correlated with new business entry in Knowledge intensive sectors”, but at the same time, it is “negatively correlated with new startups in non-knowledge intensive sectors”. Per authors: “This finding suggests that while entrepreneurship in knowledge based and creative industries requires highly skilled labour, in non knowledge based industries, low skilled labour is the primary determinant of new firm creation. Our findings also appear to suggest the need for higher skills/educated base in order to boost the growth of new businesses” in high knowledge-intensity sectors.

Werner and Moog (2009) use data from the German Socio-Economic Panel (SOEP) to map out significant linkages between entrepreneurial learning (and entrepreneurial human capital) and the probability of transition from traditional employment to self-employment. One interesting aspect of their findings is that learning-by-doing occurring (in their sample) during tenure of working for an SME has positive impact on ability to transition to entrepreneurship, confirming similar findings from other European countries. This also confirms findings that show that working for SMEs results in more frequent exits into self-employment and that such exits more frequently result in transition to full entrepreneurship than for self-employment entered from employment in larger firms.

The learning-by-doing effect of pre-transition experience for starting entrepreneurs and self-employed is also confirmed by the UK study by Panos, Pouliakas and Zangelidis (2011) who looked at the self-employment transition dynamics for individuals with dual job-holding and the links between this transition and human capital and occupational choice between primary and secondary jobs. The study used a wide (1991-2005) sample of UK employees from the British Household Panel Survey (BHPS). The authors investigated, sequentially, “first, the determinants of multiple job-holding, second, the factors affecting the occupational choice of a secondary job, third, the relationship between multiple-job holding and job mobility and, lastly, the spillover effects of multiple job-holding on occupational mobility between primary jobs.” The findings indicate that “dual job-holding may facilitate job transition, as it may act as a stepping-stone towards new primary jobs, particularly self-employment.” An interesting aspect of the study is that whilst the major effects are present in the lower skilled distribution of occupations, there is also a significant and positive effect of dual-jobs holding on transition to self-employment for professional (highly skilled) grade of workers.

Finally, there is a very interesting demographic dimension to transition to self-employment, explored to some extent in the U.S. data by Zhang (2008). The paper focused on the topic of elderly entrepreneurship. The author conjectures that in modern (ageing) demographic setting, “the “knowledge economy” could elevate the value of elderly human capital as the “knowledge economy” is less physically demanding and more human-capital- and knowledge-based.” Zhang (2008) largely finds that professional, skills-based self-employment and entrepreneurship amongst the older generations of workers can act as an important force in reducing adverse impact of ageing on modern economies.


The common thread connecting the above studies and indeed the rest of the vast literature on entrepreneurship, self-employment and transition from traditional employment to more projects-based or client-focused forms of engagement in the labour markets is increasingly shifting toward the first type of the ‘Gig Economy’ engagement. This typology of the ‘Gig Economy’ is becoming more human capital and skills-intensive and is better aligned with the ‘knowledge economy’ and the ‘creative economy’ than ever before. In simple terms, therefore, the ‘Gig Economy’ not only reaches deeper than the traditional view of the shared services (Uber et al) growth trends suggest.

While both increasing in importance and broadening the set of opportunities for economic development, the modern ‘Gig Economy’ is presenting significant challenges to social, cultural and policy norms that require swift addressing. These challenges are broadly linked to the need to Create, Attract, Retain and Enable key human capital necessary to sustain long term development and growth of the ‘Gig Economy’.

With that, tune in to my talk at the CXC Corporate event “Globalization & The Future of Work Summit” (link: http://cxccorporateservices.com/cxc-future-of-work/) in few weeks time for the details as to what should be done to put global ‘Gig Economy’ onto the sustainable development and growth track.


Sources:

Will Rinehart, Ben Gitis, “Independent Contractors and the Emerging Gig Economy” July 29, 2015,

Federici, Daniela and Ferrante, Francesco and Vistocco, Domenico, "On the Sources of Entrepreneurial Talent in Italy: Tacit vs. Codified Knowledge" (July 24, 2007)

Sabatini, Fabio, "Educational Qualification, Work Status and Entrepreneurship in Italy: An Exploratory Analysis" (June 2006). FEEM Working Paper No. 87.2006

Velamuri, S. Ramakrishna and Venkataraman, S., "An Empirical Study of the Transition from Paid Work to Self-Employment". Journal of Entrepreneurial Finance and Business Ventures, Vol. 10, No. 1, pp. 1-16, August 2005

Moutray, Chad M., "Educational Attainment and Other Characteristics of the Self-Employed: An Examination Using Data from the Panel Study of Income Dynamics" (December 11, 2007). Hudson Institute Research Paper No. 07-06.

Mitra, Jay and Abubakar, Yazid, "Entrepreneurial Growth and Labour Market Dynamics: Spatial Factors in the Consideration of Relevant Skills and Firm Growth in the Creative, Knowledge-Based Industries" (August 23, 2011). University of Essex CER Working Paper No. 1.

Werner, Arndt and Moog, Petra M., "Why Do Employees Leave Their Jobs for Self-Employment? – The Impact of Entrepreneurial Working Conditions in Small Firms" (November 1, 2009).

Panos, Georgios A. and Pouliakas, Konstantinos and Zangelidis, Alexandros, "Multiple Job Holding as a Strategy for Skills Diversification and Labour Market Mobility" (August 23, 2011). University of Essex CER Working Paper No. 4.

Zhang, Ting, "Elderly Entrepreneurship in an Aging U.S. Economy: It's Never Too Late" (September 8, 2008). Series on Economic Development and Growth, Vol. 2.

21/10/15: By Current Account Fetish Theory, Russia is Doing Quite Fine...

In charts we live... and in charts we delight.

Here is a neat summary of global current account balances (horizontal axis) against the rate of change in the current account over 12 months through 2Q 2015.

Source: @NickatFP 

6th largest surplus: Russia. Fastest growth in surplus: Russia.  So that petrodollar economy, then... like, say Saudi Arabia?..

Now, think of the favourite theory of 'sustainability' advanced by the likes of the Euro-centric Bruegel and its followers. The said theory rests on sustainability being equivalent to medium-term or long-term external balances... either that theory (underpinning most of the official EU economic mantra) off the rocker or... take a look at the chart again.

No comment beyond.

20/10/15: New Governor of the Central Bank of Ireland

Congratulations to my former colleague at TCD Economics, and my thesis supervisor from years back, Professor Philip Lane on his appointment. Here is a good summary of my view why the Irish Government has made a good choice, via Central Banking

18/10/15: Is Ireland a Euro Periphery Outlier? Some Historical Data

How unique is Ireland within the club of euro peripheral countries? Well, historically, rather unique. Alas, sometimes for the reasons not entirely in our favour.

The following are excerpts from the recent ECB paper titled “Fiscal policy adjustments in the
euro area stressed countries: new evidence from non-linear models with state-varying thresholds”.

Quote: “Fiscal policy authorities of Greece, Ireland, Portugal and Spain are shown to have, on average, historically followed a "spend-and-tax" model of fiscal adjustment, where government spending is decided by the political process, and the burden of correcting fiscal disequilibria is entirely left to the tax instrument.”

Of course, ‘historically’ here means over the period 1960-2013 for all countries, with exception of Spain (1970-2013).

But before then, what were the pre-conditions (thresholds) for taking action? “During the 1960-2013 period for Greece, Ireland and Portugal and during the 1970-2013 period for Spain, we find that the threshold estimate for the budget deficit-to-GDP ratio, which led to different fiscal correction regimes, was on average 4.90% for Greece, 5.10% for Ireland, 3.22% for Portugal and 3.12% for Spain.”

In other words, Ireland had the greatest tolerance - over the entire period - for deficits, opting to wait until average deficit as % of GDP would hit above 5.1%, well above Greece (4.9%) and the rest of the peripheral states.

So now, let’s tackle the more recent period, from the start of the Euro: “When considering the period after 1999, this overall picture worsens for Greece and Portugal and improves for Ireland. Moreover, the results for Ireland and Spain are driven by the financial crisis period. In particular for Ireland, the decoupling dynamics of the government spending reflects the support to the financial sector. In fact, when considering the pre-crisis EMU period between 1999 and 2007, the threshold for fiscal adjustment in Ireland and Spain are estimated to be positive; namely, the regime change took place when the budget balance was in surplus. Conversely, the fiscal deficit-to-GDP thresholds estimated at 5.32% for Greece and 4.08% for Portugal remained rather high.”

The above basically boils down to the following: since 1999, growing economies of Ireland and Spain allowed two countries to substantially reverse pro-cyclicality of deficits and significantly reduce thresholds for budgetary actions. This did not happen in Greece and Portugal. While Ireland gets a pat on the back for pre 2007 period, it is hardly unique in this achievement.

But despite the gains of the 1999-2007, things did not change all that much within the structure of deficits and adjustments. So per ECB paper “Looking at the effects of the economic cycle, we find that fiscal deficit-to-GDP ratio was not reduced in Greece, Ireland and Portugal with the improvement in economic activity. Consequently, during the contractionary times, fiscal corrections became more costly, as tax adjustments became a priority in an attempt to restore fiscal discipline.”

In other words, we were not unique in the way we handled the underlying structure of public spending imbalances, despite having substantially reduced the fiscal action thresholds.

But may be during the peak of the crisis we widened up? Indeed, ECB offers some positive evidence in this direction, but it also argues that the same took place in Spain and Portugal. “The results also suggest that during a financial crisis the fiscal deficit-to-GDP threshold was relaxed in Ireland and Spain, while it was reduced in Portugal. By relaxing the fiscal deficit-to-GDP threshold (in an attempt to stave off deep recessionary pressures) Ireland and Spain relied on business cycle improvements to raise tax revenues. Given the tendency by Portuguese authorities to improve the fiscal imbalances during a financial crisis, these figures make sustainability concerns for Ireland, Portugal and Spain less of an issue compared to Greece, in an historical perspective.”

Which, once more, does not really identify Ireland as a ‘unique’ case amidst the imprudent (but learning) peripherals.

“The results …suggest that during a financial crisis the fiscal deficit-to-GDP threshold was relaxed from 5.10% to 6.99% in Ireland and from 3.12% to 4.00% in Spain, while it was reduced from 3.22% to 1.92% in Portugal.” In other words, Irish Government thresholds actually worsened in the financial crisis, albeit most of that worsening is attributable to the Government decision to rescue Irish banks.

Overall, as table below illustrates, Ireland has managed to perform best during 1999-2007 period in fiscal adjustment thresholds terms, while Spain was the overall best performer in 1999-2013 period and over the entire sample.

My handy addition to the chart are red boxes (highlighting worst performers) and green boxes (best performers) when it comes to budgetary adjustment thresholds.

This completes the arguments about Ireland’s alleged uniqueness as an outlier to the group of peripheral states: with exception of the period during which our property and financial sectors bubbles were inflating to unprecedented proportions, Ireland was pretty much a ‘normal’ peripheral state when it comes to fiscal management. Celtic Tiger et al…

So let's hope the latest Budget 2016 does not return us back to the historical record track...

17/10/15: Let’s talk about the Law of Small Numbers

Wonkishly awesome, folks…

Let’s start with a set up

You decide to will flip a coin 4 times in a row and record the outcome of each flip. After you done flipping, you look at every flip that “immediately followed an outcome of heads, and compute the relative frequency of heads on those flips”.

“Because the coin is fair, [you] of course expect this empirical probability of heads to be equal to the true probability of flipping a heads: 0.5.”

You will be wrong. If you “were to sample one million fair coins and flip each coin 4 times, observing the conditional relative frequency for each coin, on average the relative frequency would be approximately 0.4.”

Two researchers, Joshua Miller and Adam Sanjurjo “demonstrate that in a finite sequence generated by i.i.d. [independent, identically distributed] Bernoulli trials with probability of success p, the relative frequency of success on those trials that immediately follow a streak of one, or more, consecutive successes is expected to be strictly less than p, i.e. the empirical probability of success on such trials is a biased estimator of the true conditional probability of success.”

Which implies

So far, pretty innocuous from the average punter perspective. But wait. “While, in general, the bias does decrease as the sequence gets longer, for a range of sequence (and streak) lengths often used in empirical work it remains substantial, and increases in streak length.” In other words, while empirical probability does approach closer and closer to true conditional probability, it does so in trials so large (so many coins flips) that such convergence does not make much of the difference in our, human, decision making.

And that is pretty pesky for the way we look at probabilistic outcomes and make decisions based on our expectations, whenever our decisions are sequential.

Impact on decision making

“This result has considerable implications for the study of decision making in any environment that involves sequential data”. These implication are:

1. This provides “a structural explanation for the persistence of one of the most well-documented, and robust, systematic errors in beliefs regarding sequential data—that people have an alternation bias (also known as negative recency bias)… — by which they believe, for example, that when observing multiple flips of a fair coin, an outcome of heads is more likely to be followed by a tails than by another heads;
2. It also helps resolve “…the closely related gambler’s fallacy…, in which this alternation bias increases with the length of the streak of heads.”
3. “Further, the result shows that data in the hot hand fallacy literature …has been systematically misinterpreted by researchers; for those trials that immediately follow a streak of successes, observing that the relative frequency of success is equal to the overall base rate of success, is in fact evidence in favor of the hot hand, rather than evidence against it.”

And tangible applications are

So the realisation that “the empirical probability of success on such trials is a biased estimator of the true conditional probability of success” helps explain why “…the inability of the gambler to detect the fallacy of his belief in alternation has an exact parallel with the researcher’s inability to detect his mistake when concluding that experts’ belief in the hot hand is a fallacy.”

But there is more. Per authors, “the result may have implications for evaluation and compensation systems. That a coin is expected to exhibit an alternation “bias” in finite sequences implies that the outcome of a flip can be successfully “predicted” in finite sequences at a rate better than that of chance (if one is free to choose when to predict).”

They offer the following example of this: “suppose that each day a stock index goes either up or down, according to a random walk in which the probability of going up is, say, 0.6. A financial analyst who can predict the next day’s performance on the days she chooses to, and whose predictions are evaluated in terms of how her success rate on predictions in a given month compares to that of chance, can expect to outperform this benchmark… For instance, she can simply predict “up” immediately following down days, or increase her expected relative performance even further by predicting “up” only immediately following longer streaks of consecutive down days.”

Going back to the first example with coin flipping, the law of large numbers implies that as your sampling size (number of coin flips) rises, “…the average empirical probability of heads would approach the true probability. The key to why this is not the case, and to why the bias remains, is that it is not the flip that is treated as the unit of analysis, but rather the sequence of flips from each coin. In particular, if [you] were willing to assume that each sequence had been generated by the same coin, and [you] were to compute the empirical probability by instead pooling together all of those flips that immediately follow a heads, regardless of which coin produced them, then the bias would converge to zero as the number of coins approaches infinity.”

What this means is that “…in treating the sequence as the unit of analysis, the average empirical probability across coins amounts to an unweighted average that does not account for the number of flips that immediately follow a heads in each sequence, and thus leads the data to appear consistent with the gambler’s fallacy.”

Per authors, “the implications for learning are stark: to the extent that decision makers update their beliefs regarding sequential dependence with the (unweighted) empirical probabilities that they observe in finite length sequences, they can never unlearn a belief in the gambler’s fallacy…”

Overall, we have

To sum this up, the authors found “a subtle but substantial bias in a standard measure of the conditional dependence of present outcomes on streaks of past outcomes… The mechanism is a form of selection bias, which leads the empirical probability …to underestimate the true probability of a given outcome, when conditioning on prior outcomes of the same kind. The biased measure has been used prominently in the literature that investigates incorrect beliefs in sequential decision making --- most notably the Gambler's Fallacy and the Hot Hand Fallacy.”

The two fallacies are defined as follows:

• “…People believe outcomes alternate more than they actually do, e.g. for a fair coin, after observing a flip of a tails, people believe that the next flip is more likely to produce a heads than a tails. Further, as a streak of identical outcomes increases in length, people also tend to think that the alternation rate on the outcome that follows becomes even larger, which is known as the gambler’s fallacy”.
• “The hot hand fallacy typically refers to the mistaken belief that success tends to follow success (hot hand), when in fact observed successes are consistent with the typical fluctuations of a chance process.”

After correcting for the bias, the authors show that “the conclusions of some prominent studies in the literature are reversed.” Awesomely wonkish...


Full paper: Miller, Joshua Benjamin and Sanjurjo, Adam, Surprised by the Gambler's and Hot Hand Fallacies? A Truth in the Law of Small Numbers (September 15, 2015). IGIER Working Paper #552. http://ssrn.com/abstract=2627354

17/10/15: ‘Dream Wedding’ Fairy Tales

Working through some papers, I just came across this now nearly classic, but still recent study that I wanted to share with you ages ago, but somehow forgot. So here it is - a brilliant example of empirical economics at its most interesting edge:

The paper evaluates “the association between wedding spending and marriage duration using data from a survey of over 3,000 ever-married persons in the United States.”

Stats informing this study objective are frightening: “In 2014, wedding industry revenues are projected to exceed $50 billion in the United States. According to a national survey conducted annually by the top wedding website TheKnot.com, the average wedding cost was $29,858 in 2013… In 1959, Bride’s recommended that couples set aside 2 months to prepare for their wedding and published a checklist with 22 tasks for them to complete. By the 1990s, the magazine recommended 12 months of wedding preparation and published a checklist with 44 tasks to complete.” Presumably, humanity lost count of months and tasks required to execute a fairytale wedding since then. But, “in 2012, total expenditures on diamond rings were roughly $7 billion in the United States.”

But is any of this ‘happiness industry’ working? “Overall, we find little evidence that expensive weddings and the duration of marriages are positively related. On the contrary, in multivariate analysis, we find evidence that relatively high spending on the engagement ring is inversely associated with marriage duration among male respondents. Relatively high spending on the wedding is inversely associated with marriage duration among female respondents, and relatively low spending on the wedding is positively associated with duration among male and female respondents. Additionally, we find that having high wedding attendance and having a honeymoon (regardless of how much it cost) are generally positively associated with marriage duration.”

Per authors summary: “The wedding industry has consistently sought to link wedding spending with long-lasting marriages. This paper is the first to examine this relationship statistically. We find that marriage duration is either not associated or inversely associated with spending on the engagement ring and wedding ceremony.” Or in other words, you might be able to buy your way into marriage, but you are unlikely to buy your way into a happy union.


Full paper: Francis, Andrew M. and Mialon, Hugo M., ‘A Diamond is Forever’ and Other Fairy Tales: The Relationship between Wedding Expenses and Marriage Duration (September 15, 2014): http://ssrn.com/abstract=2501480. Note, the paper has been since published in the Economic Inquiry (Volume 53, Issue 4, pages 1919–1930, October 2015).

16/10/15: Euro Area Inflation, via Pictet

An interesting chart highlighting the poor prospects for inflationary expectations in both Euro area and the U.S. via Pictet:

5yr/5yr swaps are basically a measure of market expectation for 5 year average inflation starting from 5 years from today, forward (so years 6-10 from today). This is a common referencing point for the ECB technical view of inflation expectations, and as the above clearly shows, we are heading for testing January 2015 lows.

Here’s Picket analysis (comments and emphasis are mine): “In September, headline inflation in the euro area dipped back into negative territory (-0.1% y-o-y) for the first time in six months.

"This weakness must be put into context though as it is primarily due to the steep slide in energy prices. If volatile components (food and energy) are stripped out, core inflation was steady at +0.9% y-o-y. Furthermore, prices of services, which better reflect domestic conditions, rose.

"Nonetheless, falling commodity prices, coupled with the rise in the euro’s trade-weighted value, caused the inflation outlook to worsen. Long-run inflationary expectations, as measured by the break-even swap rate, have been softening steadily since early July and have now reached their lowest level (1.56%) since February this year.

…In parallel, findings from economic and business surveys (PMIs, European Commission surveys) for the third quarter showed decent resilience despite the worries about the Chinese economy. They point to GDP growth of around 0.4% q-o-q in Q3 and Q4.”

Picket projects growth of 1.5% y/y for 2015, “led by domestic demand” that is expected to “continue to benefit from normalisation of the jobs market, subdued inflation, the gradual revival in consumer confidence and an upturn in lending to the private sector.”

In short, sensible view of inflation - low inflation, per Pictet is helping, not hurting the euro economy.

16/10/15: IG Conference: Markets Outlook

My speaking points on the topic of the Markets Outlook for yesterday's IG conference:

Short themes:

Theme 1: Markets pricing in advanced economies: 

- EV/EBITDA ratios signal overvaluation; 

- EBITDA/Interest Expenses ratio is at below 2010 levels (below 14%) despite extremely cheap debt.

A handy Bloomberg chart:

- Global debt cycle has turned – sovereigns are not leveraging as fast or deleveraging, but corporates leveraged up. 

- Much of pricing today reflects migration from equity to debt

- In this environment – long only allocations are problematic.

Theme 2: Emerging markets, especially BRICS

- Idea of 3rd Wave – Goldman’s thesis – is based on two drivers: duration of the crisis (‘this can’t be going for so long…’) and firewalls (‘this can’t spill into the developed economies…’) both of which are 

- There are no fundamentals to support robust recovery view

- Again, allocations are highly problematic.

So short-term summary is poor when it comes to hard numbers:

- World economic growth for 2015-2017 forecast is down from 14.1% in 2012 to 10.9% today

- Euro area economy forecasts are flat: 5% in 2012 and 4.9% today, holding relatively steady compared to the rest of the world solely because Europe is now Japanified

- Advanced economies down from 8.1% to 6.6% - another miserably Japanese-styled performance compared to past averages

- Emerging and developing economies from 19.55 in 2012 to 14.0%.

On inflationary targets and rates: the only way we are going to get to the inflationary expectations consistent with monetary policy normalization, is by literally superficially jacking up prices through Government controlled sectors and/or via regulatory policies. Which is to say that any inflation above, say 1% or so in the Advanced Economies, today, will be consistent with stripping income out of the economy to prime up financials (in the short run) and public purse.

Longer themes:

As much as I love the good story of innovation and technological revolutions, I am afraid to say my fear is that we are heading for the twin secular stagnation scenario:

Supply side stagnation: 

- Technological returns (productivity growth and new value added) are tapering out

- Substitutability of labour is rising and with it, risks to economic systems

- Regionalisation of trade and production are gaining ground and markets fragmentation is going to play a disruptive havoc with our traditional market valuations

- So expect more volatility on flatter trend.

Demand side stagnation: 

- Demographics 

- Savings/investment imbalances, 

- Debt overhang – across both advanced economies and, increasingly also, emerging markets, so we have a Myth of Post Financial Crisis Deleveraging (via BAML)

Global Debt to GDP

2010-2015: 220 to 240%

2000-2010: 190 to 200%

1990-2010: 170 to 190%

Or a handy chart

- Wealth and income inequalities, including intergenerational effects

- Rebalancing of economic growth drivers (human capital focus pushes incomes gap wider and deeper, but also clashes with current taxation and political systems)

Key forward is to expect:

- Flatter growth trend and more volatility around that trend 

- Higher volatility / instability in higher moments 

- Financial imbalances accelerating and amplifying

- Financial imbalances / cycles leading real cycles (Excess Financial Elasticity hypothesis)

- Economic volatility spilling, increasingly, into political volatility (political economy). 

Key strategy points:

- Focus on lower debt levels on companies balance sheets

- Focus on companies actually paying attention to core basics, e.g. earnings, sales, profit margins, as opposed to subscription bases, user counts etc

- Focus on companies with strong regional reach (not only in product markets, but in logistics and production bases)

- Focus on companies with revenues linked to multi-annual contracts

- Go defensive, stay defensive in core allocation

- Go speculative with low leverage only and on a small share of total wealth

- Go speculative trades on uncertainty and long 5-10 percentile under-performers

16/101/5: Millennials: A Power Poverty Gap?

Having discussed the plight of the Millennials' Generation in global context on numerous occasions, I am too familiar with the problems faced by the current 'younger' middle of demographic pyramid. Hence, not surprisingly, I found this article http://www.independent.ie/opinion/ireland-forces-young-people-to-delay-lifes-milestones-31599496.html to be quite a reasonable summation of the modern reality in which the current younger generations can no longer expect to have better quality of life (measured by more traditional metrics) than their predecessors.

I will ignore the set of prescriptive policies at the end of the article - some make sense, others largely represent well-intentioned economic sentimentality. But the key issue is an important one.

And here is a counter-part piece on the Millennials trends in the U.S.: http://www.nielsen.com/us/en/insights/reports/2015/millennials-in-2015-financial-deep-dive.html.

16/10/15: Gold and Bitcoin: Adjacency and Hedging Properties

This week, I spoke at a joint Markets Technicians Association and CAIA seminar hosted by Bloomberg, covering two recent research projects I was involved with on the role of Gold and Bitcoin as safe havens and hedges for other assets.

Here are my slides (omitting section division slides):

The first section was based on the following paper: http://www.sciencedirect.com/science/article/pii/S1057521912001226

A caveat to the above, we are seeing increasing evidence that Gold's hedging properties may be changing over time, especially due to increased financialisation of the asset. In this context, it is worth referencing a recent working paper by Brian M. Lucey et al linked here that I also cited at the seminar.

The Bitcoin section is based on a work-in-progress paper with Cormac Ennis: "Is Bitcoin like Gold? Hedging and Safe Haven Properties of the Virtual Currency". The results of presented below should be treated with serious caution as they are extremely preliminary.

Note: we are extending data set to cover longer period, although even with this extension data coverage for Bitcoin is still suboptimal in both duration and quality. Many thanks to the seminar participant for pointing out two key caveats to the overall data coverage:

1. The 'lumpy' nature of demand around Cypriot banking crisis; and
2. Potential effects on data quality reported for Bitcoin from a small number of high profile pricing events, such as technical glitches and supply/demand shifts linked to large exchanges-linked events (e.g. MtGox).

 Summarising the two papers findings: