20/4/16: Russian Deck Update: April 2016

Updated version of my Russian markets deck

19/4/16: Leverage and Equity Gaps: Italy v Rest of Europe

Relating to our previous discussions in the MBAG 8679A: Risk & Resilience: Applications in Risk Management class, especially to the issue of leverage, recall the empirical evidence on debt distribution and leverage across the European countries corporate sectors.

Antonio De Socio and Paolo Finaldi Russo recently contributed to the subject in a paper, titled “The Debt of Italian Non-Financial Firms: An International Comparison” (February 25, 2016, Bank of Italy Occasional Paper No. 308: http://ssrn.com/abstract=2759873).

Per authors, “In the run-up to the financial crisis Italian firms significantly increased their debt in absolute terms and in relation to equity and GDP.” This is not new to us, as we have covered this evidence before, but here are two neat summaries of that data:

What is of greater interest is more precise (econometrically) and robust estimate of the gap in leverage between Italian firms and other European corporates. “The positive gap in firms’ leverage between Italy and other euro-area countries has widened in recent years, despite the outstanding debt of Italian firms has decreased since 2011.”

Another interesting insight is the source of this gap. “We find that, controlling for several firm-specific characteristics (i.e. age, profitability, asset tangibility, asset liquidity, turnover growth), the leverage of Italian firms is about 10 percentage points higher than in other euro area countries. Differences are systematically larger among micro and small firms, whereas they are small and weakly significant for firms with assets above 300 million euros.”

But equity gap, defined as “the amount of debt to be transformed into equity type funds in order to fill the leverage gap with other countries”, is not uniform over time.

“…in order to reach the same average level as other euro-area countries, Italian firms should transform about 230 billion euros of financial debt into equity type finance, corresponding to 18 per cent of their outstanding debt. The gap is largest, at around 28 per cent of outstanding debt, for small firms and micro firms with over 1 million euros of assets.”

Authors note one influential outlier in the data: “A large part of the estimated corrections is due to the comparison with French firms, which on average have one of the lowest levels of leverage in Europe. Excluding these companies, the equity gap would drop to 180 billion euros.”

Dynamically, “the results indicate that the gap has widened somewhat since 2009, from about 180 to 230 billion euros”.

Given the EU-wide (largely rhetorical) push for increasing capital structure gearing toward equity, “the Italian Government recently put in place some incentives to encourage recourse to equity financing by reducing the debt tax shield: a cap on the amount of interest expense that could be deducted from taxable income and tax deductions linked to increases in equity (according to the Allowance for Corporate Equity scheme). Similarly, other measures have also been aimed at strengthening the supply of risk capital for Italian firms. The results of our analysis suggest that Italian firms still need this kind of incentives to strengthen their financial structure.”

18/4/16: Capital Gains Tax & Investment Distortions: Corporate Data from the U.S.

In our MBAG 8679A: Risk & Resilience:Applications in Risk Management class we have been discussing the links between taxation, optimal corporate capital structuring and investment, including the decisions to pursue M&A as an alternative strategy to disbursing cash to shareholders.

Lars Feld, Martin Ruf, Ulrich Schreiber, Maximilian Todtenhaupt and Johnnes Voget recently published a CESIfo Working paper, titled “Taxing Away M&A: The Effect of Corporate Capital Gains Taxes on Acquisition Activity” (January 26, 2016, CESifo Working Paper Series No. 5738: http://ssrn.com/abstract=2744534). The paper links directly taxation structure to M&A decisions and outcomes.

Per authors, “taxing capital gains is an important obstacle to the efficient allocation of resources because it imposes a transaction cost on the vendor which locks in appreciated assets by raising the vendor’s reservation price in prospective transactions.” Note, this is an argument similar to the effects of limited interest deductions on mortgages and transactions taxes on property in limiting liquidity of real estate.

“For M&As, this effect has been intensively studied with regard to shareholder taxation, whereas empirical evidence on the effect of capital gains taxes paid by corporations is scarce. This paper analyzes how corporate level taxation of capital gains affects inter-corporate M&As.”

Specifically, “studying several substantial tax reforms in a panel of 30 countries for the period of 2002-2013, we identify a significant lock-in effect. Results from estimating a Poisson pseudo-maximumlikelihood (PPML) model suggest that a one percentage point decrease in the corporate capital gains tax rate would raise both the number and the total deal value of acquisitions by about 1.1% per year. We use this result to estimate an efficiency loss resulting from corporate capital gains taxation of 3.06 bn USD per year in the United States.”

I am slightly sceptical about the numerical estimate as the authors do not appear to control for M&A successes. However, since the lock-in mechanism applies to all types of re-investment projects, one can make a similar argument with respect to other forms of capex and investment. One way or the other, this presents evidence of distortionary nature of U.S. capital gains taxation regime.

18/4/16: Taxing 1%?.. Make My Day...

An interesting paper on the dynamics of income inequality from Xavier Gabaix, Jean-Michel Lasry, Pierre-Louis Lions and Benjamin Moll (December 2015, CEPR Discussion Paper No. DP11028: http://ssrn.com/abstract=2714268).

Take in the abstract alone for key conclusion:

“The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the long-run income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are an order of magnitude too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: "scale dependence" that may arise from changes in skill prices, and "type dependence," i.e. the presence of some "high-growth types." These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of "superstar" entrepreneurs or managers.”

So the key to alleviating inequality increases (if the key were to be found in income / wealth tax territory so frequently inhabited by socialstas) is not to tax all high earners, but to tax the very left tail of the high earners’ distribution, or so-called “"superstar" entrepreneurs or managers”. It’s not a 1% tax, nor a tax on wealth (capital), nor a tax on “anyone earning more than EUR100,000” (the latter being commonly bandied around the countries like Ireland), that is a panacea. It is, rather, a tax on Zuckerbergs and Bloombergs, Bezoses and Ellisons et al.

Which, sort of, means taxing exactly those who create own wealth, rather than inherit it from mommy or daddy… Perverse? If it is the “high-growth types” that are the baddies, not the Rothschilds or the Kochs who inherited wealth, at fault, then the entrepreneurs should be taken out and fiscally shot.

And if you do, here’s what you will be fiscally shooting at: innovation (see http://www.nber.org/papers/w21247). The linked paper conclusion: “our findings vindicate the Schumpeterian view whereby the rise in top income shares is partly related to innovation-led growth, where innovation itself fosters social mobility at the top through creative destruction”.

Dust out that ‘tax the 1%’ argument, again… please.

18/4/16: Anti-Discrimination Law’s Unintended Consequence?

The Law of Unintended Consequences in a case of anti-discrimination law? It appears to be so.

A graduate paper from MIT Economics by Alexander Bartik and Scott Nelson, titled “Credit Reports as Résumés: The Incidence of Pre-Employment Credit Screening” (see March 7, 2016, MIT Department of Economics Graduate Student Research Paper 16-01: http://ssrn.com/abstract=2759560) looks at “recent bans on employers' use of credit reports to screen job applicants – a practice that has been popular among employers, but controversial for its perceived disparate impact on racial minorities.” Controlling for geographic, temporal, and job-level variations the authors “analyze these bans' effects in two datasets: the panel dimension of the Current Population Survey (CPS); and data aggregated from state unemployment insurance records.”

Key finding: “the bans reduced job-finding rates for blacks by 7 to 16 log points, and increased subsequent separation rates for black new hires by 3 percentage points, arguably contrary to the bans' intended effects. Results for Hispanics and whites are less conclusive. We interpret these findings in a statistical discrimination model in which credit report data, more so for blacks than for other groups, send a high-precision signal relative to the precision of employers' priors.”

It is worth noting limitations to the study, clearly identified by the authors, however. In particular those relating to “Catch-22” scenario: “the question of how [survey data] interacts with household balance sheets: if highly levered households are more likely to become delinquent soon after job loss, employers’ use of PECS will make job finding more difficult for these households, thus exacerbating long-run unemployment for an important subset of the population. Indeed, the “Catch-22” of being unable to repay debts because of unemployment, and being unable to become employed because of unpaid debts, has been another salient policy motivation for [use of credit reports in hiring] bans”.

On the other hand, as noted by authors, other studies largely align with the core findings that the ban has been harmful to the category of applicants its is designed to protect.

“Is it reasonable that restrictions on the use of information like PECS in the hiring process can have such a large impact on job-finding rates? Other evidence from the literature suggests yes. Studying the effect of the usage of credit information in hiring in Sweden, Bos et al. (2015) find that the removal of information on past defaults from credit reports results in a 6.5 percent increase in employment rates for affected individuals in the year after the past default information removal. In related work, Wozniak (2014) finds that laws discouraging or encouraging the use of drug-testing in the hiring process have a 7 to 30 percent effect of black employment levels in affected industries. Both of these papers suggest that regulations of information used in the hiring process can have economically large impacts on employment outcomes.34 However, the large magnitude of our results does suggest the need for caution in their interpretation until these findings can be explored in further research.”

And to illustrate:

Figure 6: Event-Time Analysis of the Effect of PECS on Job-Finding

State-Race Fixed Effects (FE), Time-Race FE, Time-State FE

Note: If anyone seen any worthy responses / comments relating to this paper, its findings and/or methodology, do let me know by commenting below. I am sure we are going to see some serious debates emerging over time about these findings.

18/4/16: Leverage Risk, the Burden of Debt & the Real Economy

Risk of leverage has been a cornerstone of our recent lectures concerning the corporate capital structure decisions in the MBAG 8679A: Risk & Resilience:Applications in Risk Management class at MIIS. However, as noted on a number of occasions in both MBAG 8679A and other courses I teach at MIIS, from macroeconomic point of view, corporate leverage risks are just one component of the overall economic leveraging equation. The other three components are: household debt, government debt, and the set of interactions between the burden of all three debt sources and the financial system at large.

An interesting research paper by Mikael Juselius and Mathias Drehmann, titled “Leverage Dynamics and the Burden of Debt” (2016, Bank of Finland Research Discussion Paper No. 3/2016: http://ssrn.com/abstract=2759779) looks that both leverage risk arising from the U.S. corporate side and household side.

Per authors, “in addition to leverage, the debt service burden of households and firms is an important link between financial and real developments at the aggregate level. Using US data from 1985 to 2013, we find that the debt service burden has sizeable negative effects on expenditure.” This, in turn, translates into lower economy-wide investment and consumption - two key components of the aggregate demand. Debt “interplay with leverage also explains several data puzzles, such as the lack of above-trend output growth during credit booms and the depth and length of ensuing recessions, without appealing to large shocks or non-linearities. Using data up to 2005, our model predicts paths for credit and expenditure that closely match actual developments before and during the Great Recession.”

With slightly more details: the authors found that “the credit-to-GDP ratio is cointegrated with real asset prices, on the one hand, and with lending rates, on the other. This implies that the trend increase in the credit-to-GDP ratio over the last 30 years can be attributed to falling lending rates and rising real asset prices. The latter two variables are, moreover, inversely related in the long-run.”

In addition and “more importantly, we find that the deviations from the two long-run relationships - the leverage gap and the debt service gap henceforth - have sizeable effects on credit and output. …real credit growth increases when the leverage gap is negative, for instance due to high asset prices. And higher credit growth in turn boosts output growth. Going beyond the existing evidence, we find that the debt service gap plays an additional important role at the aggregate level that has generally been overlooked: it has a strong negative impact on consumption and investment. In addition, it negatively affects credit and real asset price growth.”

The link between leverage gap and debt service gap:

In summary, “The leverage and debt service gaps hold the key for explaining the divergence of credit and output in recent decades. For instance, in the late 1980s and mid 2000s both gaps were negative boosting credit and asset price growth. This had a positive effect on output, but not one-to-one with credit, which caused the credit-to-GDP ratio to rise. This in turn pushed the debt service gap to positive values, at which point it started to offset the output effects from high credit growth so that output growth returned to trend. Yet, as the leverage gap remained negative, credit growth was still high, ie we observed a “growthless” credit boom. This continued to increase the debt-service gap, which had a growing negative effect on asset prices and expenditure, driving the leverage gap into positive territory. And once both gaps became positive they worked in the same direction, generating a sharp decline in output even without additional
large shocks or crises-related non-linearities. The subsequent downturns were deep and protracted, as the per-period reduction in credit had to be faster than the per-period decline in output in order to lower the credit-to-GDP ratio and thereby close the two gaps. This also implied that the recovery was “creditless”.”

Highly intuitive and yet rather novel results linking leverage risk to debt financing costs.

18/4/16: Rollover Risk, Competitive Pressures & Capital Structure of the Firm

Capital structure of the firm, as we discussed in our MBAG 8679A: Risk & Resilience:Applications in Risk Management class in recent weeks, is about counter-balancing equity (higher cost capital with greater safety cushion for the firm) against debt (lower cost capital with higher risk associated with leverage risk). As we noted in some extensions to traditional models of leverage risk, decision to take on new debt as opposed to issue new equity can also involve considerations of timing and be linked to future expected funding demands by the firm.

An interesting corollary to our discussions is what happens when risk of debt roll-over at maturity enters the decision making tree.

A recent paper by Gianpaolo Parise, titled “Threat of Entry and Debt Maturity: Evidence from Airlines” (April 2016, BIS Working Paper No. 556: http://ssrn.com/abstract=2758708) tries to address this question.

In the presence of low-cost competition airlines, traditional, large airlines tend to alter their debt structure. This effect, according to Parise, is pronounced in the case of legacy airlines forced to defend their strategically important routes from new entrants. Per Parise, “…the main findings suggest that airlines respond to entry threats trading off financial flexibility for lower rollover risk.”

More specifically, Parise found that “…a one standard deviation increase in the threat of entry triggers an increase of 4.5 percentage points in the proportion of long-term debt held by incumbent airlines (a 7.4% increase relative to the baseline of 60%). This effect is particularly strong for airlines whose debt is rated as “speculative” and that are financially constrained, i.e., airlines that have in general a more difficult access to credit.”

On the other hand, “the threat of entry has no significant effect on the leverage ratio.”

Overall, “threatened airlines issue debt instruments with longer maturity and with covenants” and that debt issuance aiming to increase maturity comes via intermediated lending (loans) rather than via bond markets (direct market).

“The results are consistent with models in which firms set their optimal debt structure in the presence of costly rollover failure As Parise notes, “Longer debt maturity allows firms to reduce
rollover (or liquidity) risk, i.e., the risk that lenders are unwilling to refinance when bad news
arrives. Rollover risk enhances credit risk…, magnifies the debt overhang problem…, weakens investment,… and exposes the firm to costly debt restructuring…”

A very interesting study showing dynamic and complex interactions between capital structure of the firm and exogenous pressures from competitive environments, in the presence of systemic roll-over risks in the financial system.

18/4/16: Demographics, Ageing & Inflation

In my Investment Theory & ESG Risk course, a week ago, we were looking at Asset Price Models extensions to incorporate inflation risks. One discussion we had was about the possible correlation between inflation and investor behaviour / choices, linked to behavioural anomalies.

A recent Bank of Finland working paper by Mikael Juselius and Elod Takats, titled “The Age-Structure – Inflation Puzzle” (2016, Bank of Finland Research Discussion Paper No. 4/2016: http://ssrn.com/abstract=2759780) sheds some light on this link via demographic side of investor / economic agent impact on inflationary expectations.

Specifically, the authors uncovered “a puzzling link between low-frequency inflation and the population age-structure”.

This link is pretty simple: due to asymmetric relationship between consumption, savings and investment across the life cycle, “the young and old (dependents) are inflationary whereas the working age population is disinflationary”.

In other words, risks of higher inflation are demographically tilted against markets / economies with either high young age dependencies, old age dependencies or both.

According to authors, “the relationship is not spurious and holds for different specifications and controls in data from 22 advanced economies from 1955 to 2014.”

And effects are large: “The age-structure effect is economically sizable, accounting e.g. for about 6.5 percentage points of U.S. disinflation from 1975 to today’s low inflation environment. It also accounts for much of inflation persistence, which challenges traditional narratives of trend inflation.”

Crucially, “the age-structure effect is forecastable” in so far as we can see pretty accurately long term demographic trends, “and will increase inflationary pressures over the coming decades”. In other words, deflationary environment today is expected to become inflationary environment tomorrow:

Hence, the rising demand for real assets and structural support for new levels of gold prices.

It’s all in the long run game.

17/4/16: Start Ups, Manufacturing Jobs and Structural Changes in the U.S. Economy

In the forthcoming issue of the Cayman Financial Review I am focusing on the topic of the declining labour productivity in the advanced economies - a worrying trend that has been established since just prior to the onset of the Global Financial Crisis. Another trend, not highlighted by me previously in any detail, but related to the productivity slowdown is the ongoing secular relocation of employment from manufacturing to services. However, the plight of this shift in the U.S. workforce has been centre stage in the U.S. Presidential debates recently (see http://fivethirtyeight.com/features/manufacturing-jobs-are-never-coming-back/).

An interesting recent paper on the topic, titled “The Role of Start-Ups in Structural Transformation” by Robert C. Dent, Fatih Karahan, Benjamin Pugsley, and Ayşegül Şahin (Federal Reserve Bank of New York Staff Reports, no. 762, January 2016) sheds some light on the ongoing employment shift.

Per authors, “The U.S. economy has been going through a striking structural transformation—the secular reallocation of employment across sectors—over the past several decades. Most notably, the employment share of manufacturing has declined substantially, matched by an increase in the share of services. Despite a large literature studying the causes and consequences of structural transformation, little is known about the dynamics of reallocation of labor from one sector to the other.”

“There are several margins through which a sector could grow and shrink relative to the rest of the economy”:

1. “…Differences in growth and survival rates of firms across sectors could cause sectoral reallocation of employment”
2. “…differences in sectors' firm age distribution could affect reallocation since firm age is an important determinant of growth or survival behavior” 
3. “…the allocation of employment at the entry stage which we refer to as the entry margin could contribute to the gradual shift of employment from one sector to the other.”
4. “…because the speed at which differences in entry patterns are reflected in employment shares depends on the aggregate entry rate, changes in the latter could affect the extent of structural transformation.”

Factors (1) and (2) above are referenced as “life cycle margins”.

The study “dynamically decomposed the joint evolution of employment across firm age and sector”, focusing on three sectors: manufacturing, retail trade, and services.

Based on data from the Longitudinal Business Database (LBD) and Business Dynamic Statistics (BDS), the authors found that “…at least 50 percent of employment reallocation since 1987 has occurred along the entry margin.” In other words, most of changes in manufacturing jobs ratio to total jobs ratio in the U.S. economy can be accounted for by new firms creation being concentrated outside manufacturing sectors.

Furthermore, “85 percent of the decline in manufacturing employment share is predictable from the average life cycle dynamics and the early 1980s distribution of startup employment across sectors. Further changes over time in the distribution of startup employment away from manufacturing, while having a relatively small effect on manufacturing where entry is less important, explain almost one-third of the increase in the services employment share.”

Again, changed nature of entrepreneurship, as well as in the survival rate of new firms created in the services sector, act as the main determinants of the jobs re-allocation across sectors.

Interestingly, the authors found “…little role for the year-to-year variation in incumbent behavior conditional on firm age in explaining long-term sectoral reallocation.” So legacy firms have little impact on decline in manufacturing sector jobs share, which is not consistent with the commonly advanced thesis that outsourcing of American jobs abroad is the main cause of losses of manufacturing sector jobs share in the economy.

Lastly, the study found that “…a 30-year decline in overall entry (which we refer to as the \startup deficit) has a small but growing effect of dampening sectoral reallocation through the entry margin.”

These are pretty striking results.

The idea that the U.S. manufacturing (in terms of the sector importance in the economy and employment) is either in a decline or on a rebound is not as straight forward as some political debates in the U.S. suggest.

Reality is: in order to reverse or at least arrest the decades-long decline of manufacturing jobs fortunes in America, the U.S. needs to boost dramatically capex in the sector, as well as shift the sector toward greater reliance on human capital-complementary technologies. It is a process that combines automation with more design- and specialist/on-specification manufacturing-centric trends, a process that is likely to see accelerated decline in lower skills manufacturing jobs before establishing (hopefully) a rising trend for highly skilled manufacturing jobs.

17/4/16: Human Capital, Management & Value-Added

The value of management to a given firm rests not only in more efficient use of physical resources and financial capital, as well as corporate / business strategy, but also in the ability of the firm to identify, hire, retain and enable high quality human capital. This is a rather common sense conclusion that might be drawn by any analyst of management systems and any business student.

However, the question always remains as to how much of the firm value-added arises from managerial inputs, as opposed to actual human capital inputs.

Stefan Bender, Nicholas Bloom, David Card, John Van Reenen, and Stephanie Wolter decided to attempt to quantify these differences. In their paper “Management Practices, Workforce Selection and Productivity” (March 2016, NBER Working Paper No. w22101: http://ssrn.com/abstract=2752306) they note that “recent research suggests that much of the cross-firm variation in measured productivity is due to differences in use of advanced management practices.”

“Many of these practices – including monitoring, goal setting, and the use of incentives – are mediated through employee decision-making and effort. To the extent that these practices are complementary with workers’ skills, better-managed firms will tend to recruit higher-ability workers and adopt pay practices to retain these employees.”

The authors then use a survey data on the management practices of German manufacturing firms, as well as data on earnings records for their employees “to study the relationship between productivity, management, worker ability, and pay”.

Per authors: “As documented by Bloom and Van Reenen (2007) there is a strong partial correlation between management practice scores and firm-level productivity in Germany. In our preferred TFP [total factor productivity] estimates only a small fraction of this correlation is explained by the higher human capital of the average employee at better-managed firms. A larger share (about 13%) is attributable to the human capital of the highest-paid workers, a group we interpret as representing the managers of the firm. And a similar amount is mediated through the pay premiums offered by better-managed firms.”

Human capital value-added is neither uniform across types of employees, nor is it independent of the management systems, which means that increasing the value of human capital in the economy requires more emphasis on the structure of the overall utilisation of talent, not just acquisition of talent. This is exactly consistent with the C.A.R.E. framework for human capital-centric economy that I outlined some years ago, here http://trueeconomics.blogspot.com/2013/11/14112013-human-capital-age-of-change.html, the framework of Creating, Attracting, Retaining and Enabling human capital.

The study also confirms that “looking at employee inflows and outflows, … better-managed firms systematically recruit and retain workers with higher average human capital.”

Overall, the authors concluded that “workforce selection and positive pay premiums explain just under 30% of the measured impact of management practices on productivity in German manufacturing.”

These results should add to questions about the ability of the Gig-economy firms, e.g. online platforms providers for labour utilisation, such as Uber, to significantly improve productivity in the economy. The reason for this is simple: contingent workforce talent pool is at least one step further removed from management than in the case of traditional employees. As the result, it is quite possible that contingent workforce productivity does not benefit directly from management quality. If so, that sizeable, ‘just under 30% of the measured impact’ in terms of improved productivity, arising from better management practices, workforce selection and pay premiums can be out of reach for Gig-economy firms and their contingent workers.

Again, as I noted repeatedly, including in my recent presentation at the CXC Global “Future of Work” Summit (see here: http://trueeconomics.blogspot.com/2016/04/7416-globalization-and-future-of-work.html), the key to developing a productive and sustainable Gig-economy will be in our ability to develop institutional, regulatory and strategic frameworks for improving management of human capital held by contingent workforce.

17/4/16: Peer Effects in Classroom: The Value of Better Discipline?

In workplace, as well as in education, peer-pressure and competition, peer-linked cooperation and collaboration and other peer-linked effects of have been important contributors to work- and learning-related outcomes. Ditto for entrepreneurship, as collaborated by the effects of clusters, such as the Silicon Valley. However, we tend to think of peer effects as arising from more mature, adult-level interactions in either third level education, or career-linked workplaces.

As noted by Scott Carrell, Mark Hoekstra and Elira Kuka in their paper “The Long-Run Effects of Disruptive Peers” (February 2016 as NBER WP No. w22042: http://ssrn.com/abstract=2739567), “there is relatively little evidence on the long-run educational and labor market consequences of childhood peers.”

So the authors examined “administrative data on elementary school students” and students’ “subsequent test scores, college attendance and completion, and earnings” to identify any potential effects of childhood peers on educational outcomes.

“To distinguish the effect of peers from confounding factors, we exploit the population variation in the proportion of children from families linked to domestic violence, who were shown by Carrell and Hoekstra (2010, 2012) to disrupt contemporaneous behavior and learning.”

The end results show that “exposure to a disruptive peer in classes of 25 during elementary school reduces earnings at age 26 by 3 to 4 percent. We estimate that differential exposure to children linked to domestic violence explains 5 to 6 percent of the rich-poor earnings gap in our data, and that removing one disruptive peer from a classroom for one year would raise the present discounted value of classmates' future earnings by $100,000.”

These are striking numbers. Accumulated over life-cycle of employment, gains from reducing classroom-level disruptive behaviour of peers can lead to a significant uplift in both, economic welfare and individual financial wellbeing. It can also, potentially, help closing the income inequality gaps. The question, however, is how does one achieve such an outcome in the real world, where even disruptive students have a right to education.

15/4/16: Tech Sector Finance: Gravity of Gravy

Previously, having posted on disconnection between S&P500 market valuations and basic corporate finance (earnings and distributions) - see that post here: http://trueeconomics.blogspot.com/2016/04/15416-corporate-finance-s-and-bubble.html - it is time to remind us all what a popping bubble looks like.

Earlier this month, I was in San Francisco, the epicentre of the corporate finance-free world of tech. Not surprisingly, few smoke breaks and few chats over a glass of wine with some tech folks revealed a very interesting insight: every single one tech CEO/CFO/COO (but not CTO) I spoke to was concerned with evaporating funding in the markets for non-public equity financing around the Silicon Valley.

Need confirmation? Here is a chart through 1Q 2016 on Tech IPOs trends:

Source: https://www.theatlas.com/charts/Nkk4jHLCe

And a note from the WSJ: http://www.wsj.com/articles/startup-investors-hit-the-brakes-1460676478 on same with a handy graph:

And the numbers of deals? Why, off the cliff too:

Source: https://www.theatlas.com/charts/Vk8_bYUAl

There is not panic, yet, but there is panic already in works: techies are retrenching on new hiring and there are rumours of some layoffs in younger companies. Meanwhile, states-sponsored agencies seeking to lock start ups and existent players into relocating to their countries or landing in the countries with regional HQs are still shopping around, as if money will always be there to rent plush offices and the case-styled furniture for those whiz kids who make up apps with little cash flow behind them...

It all might be temporary. Or it might be the beginning of the real thing. But one thing is certain: on a long enough timeline, one can defy gravity of basic corporate finance only as long as the interest rates defy gravity of risk pricing.