Wednesday, January 18, 2017

18/1/17: Bitcoin Demand: It's a Chinese Tale

Bitcoin demand by geographic location of trading activity:

H/T for the chart to Dave Lauer @dlauer

It shows exactly what it says: Bitcoin is currently driven by safe haven instrument (and not as a hedge) against capital controls. Which implies massive expected price and volumes volatility in the future, wider cost margins and artificial support for demand in the near term.

17/1/17: Government Debt in the Age of Austerity

The fact that the world is awash with debt is hard to dispute (see data here and here), but it is quite commonly argued that the aggressive re-leveraging happening in the corporate and household sectors runs contrary to the austerity trends in the public debt segment of the total economic debt. The paradox of the austerity arguments is, of course, that whilst debt is rising, public investment is falling and public consumption remains either stagnant of rising slowly. This should see public debt either declining or remaining static. Of course, banks bailouts in a number of advanced economies would have resulted in an uplift in public debt during the early years of the Global Financial Crisis and the Great Recession, but these years behind us, we should have witnessed the austerity translating into moderating debt levels in the global economy when it comes to public debt.

Alas, this is not the case, as illustrated in the chart below:

Here's a tricky bit:

  • In the 5 years 2012-2016 (post-onset of the recovery) Government debt around the world rose 11.4% in level terms (USD), and 14.51 percentage points as a share of GDP per capita. During the crisis years of 2007-2011, Government debt rose 72.7% in dollar terms and was down 4.39 percentage points as a share of GDP.
  • In the advanced economies, Government debt rose 67.6% in dollar terms in 2007-2011 period, up 4.7 percentage points, before rising 5.44% in dollar terms over subsequent 5 years (up 26.65 percentage points in terms of debt to GDP ratio). 
  • In the euro area, Government debt was up 57.4% in dollar terms and up 0.51 percentage points in GDP ratio terms over the period of 2007-2011, before falling 6.9 percent in dollar terms but rising 24.8 percentage points relative to GDP in 2012-2016 period.
  • And so on...
As the above chart shows, globally, total volume of Government debt was estimated to be USD63.2 trillion at the end of 2016, up USD6.46 trillion on the end of 2011. That is almost 84.1% of the world GDP today, as opposed to 78% of GDP at the end of 2011. More than half of this increase (USD3.91 trillion) came from the Emerging and Developing Economies, and USD2.3 trillion came from G7 economies. Meanwhile, euro area Government Debt levels declined USD815 billion, all of which was due solely to changes in the exchange rate and the rollover of some debt into multinational organisations' (e.g. ESM) and quasi-governmental (e.g. promissory notes) debt. Worse, over the said period of time, only one euro area country saw reduction in the levels of debt: Greece (down EUR34.46 billion due to restructuring of debt). In fact, in Euro terms, total euro area government debt rose some EUR1.36 trillion over the span of the 2011-2016 period.

All in, global pile of Government debt is now USD27.84 trillion (or 78.7%) up on where it was at the end of 2007 and the start of the Global Financial Crisis.

So may be, just may be, the real economy woe is that most of the new debt accumulated by the Governments in recent years has flown into waste (supporting banks, financial markets valuations, doling out subsidies to politically favoured sectors etc), instead of going to fund productive public investments, including education, skills training, apprenticeships and so on. Who knows?..

Tuesday, January 17, 2017

17/1/17: Economics of Blockchain

One of the first systemic papers on economic of blockchain, via NBER ( by Christian Catalini and Joshua S. Gans, NBER Working Paper No. 22952 (December 2016).

In basic terms, the authors see blockchain technology and cryptocurrencies influencing the rate and direction of innovation through two channels:

  1. Reducing the cost of verification; and 
  2. Reducing the cost of networking.

Per authors, for any "exchange to be executed, key attributes of a transaction need to be verified by the parties involved at multiple points in time. Blockchain technology, by allowing market participants to perform costless verification, lowers the costs of auditing transaction information, and allows new marketplaces to emerge. Furthermore, when a distributed ledger is combined with a native cryptographic token (as in Bitcoin), marketplaces can be bootstrapped without the need of
traditional trusted intermediaries, lowering the cost of networking. This challenges existing
revenue models and incumbents's market power, and opens opportunities for novel approaches to
regulation, auctions and the provision of public goods, software, identity and reputation systems."

A bit more granularly, per authors,

  • "Because of how it provides incentives for maintaining a ledger in a fully decentralized way, Bitcoin is also the first example of how an open protocol can be used to implement a marketplace without the need of a central actor." In other words, key feature of cryptocurrencies and blockchain is that it removes the need to create a central verification authority / intermediary / regulator or repository of data. The result is more than the cost reduction (focus of the Catalini and Gans paper), but the redistribution of market power away from intermediaries to the agents of supply and demand. In other words, a direct streamlining of the market away from third parties power toward the direct power for economic agents.
  • "Furthermore, as the core protocol is extended (e.g. by adding the ability to store documents through a distributed ledger-storage system), as we will see the market enabled by a cryptocurrency becomes a  flexible, permission-less development platform for novel applications." Agin, while one might focus on reductions in the direct costs of innovation in that context, one cannot ignore the simple fact that blockchain is resulting in reduced non-cost barriers to innovation, further reducing monopolistic market power (especially of intermediaries and regulators) and diffusing that power to innovators.

So what are the implications of this view of economics of blockchain? "Whereas the utopian view has argued that blockchain technology will affect every market by reducing the need for intermediation, we argue that it is more likely to change the scope of intermediation both on the intensive margin of transactions (e.g., by reducing costs and possibly influencing market structure) as well as on the extensive one (e.g., by allowing for new types of marketplaces)." So far, reasonable. Intermediation will not disappear as such - there will always be need for some analytics, pricing, management etc of data, contracts and so on, even with blockchain ledgers in place. However, the authors are missing a major point: blockchain ledgers are opening possibility to fully automated direct data analytics and AI deployment on the transactions ledgers. In other words, traditional forms of intermediation (for example in the context of insurance contract transactions, those involving data collection, data preparation, risk underwriting, contract pricing, contract enforcement, contract payments across premia and payouts, etc) all can be automated and supported by live data-based analytics engine(s) operating on blockchain ledgers. If so, the argument that the utopian view won't materialise is questionable.

The paper is worth reading, for it is one of the early attempts to create some theoretical framework around blockchain systems. Alas, my gut feeling is that the authors are failing to fully understand the depth of the blockchain technology. 

17/1/17: Russian Economic Policy Uncertainty 2016

In the previous post (link here), I covered 2016 full year spike in economic policy uncertainty in Europe on foot of amplification of systemic risks. Here is the analysis of Russian index.

As shown in the chart above, 2016 continued the trend for downward correction in Russian economic policy uncertainty that took the index from its all-time high in 2014 (at 180.4) to 160 in 2015 and 142.5 in 2016. All data is rebased to 1994 - the first year for which Russian data is available. However, at 142.5, the index is still well above its historical average of 94.1 and stands at the fifth highest reading in history.

Much of the reduction in economic policy uncertainty over 2016 came over the fist seven months of the year, with index readings rising into the second half of 2016 and peaking at 251.1 in December.

In simple terms, while the peak of 2014 crisis has now passed, questions about economic policies in Russia remain, in line with concerns about the sustainability of the nascent economic recovery. Moderation in economic policy uncertainty over the course of 2016 appears to be closely aligned with:

  1. Variations in oil prices outlook; and
  2. External geopolitical shocks (including the election of Donald Trump, with raw index data spiking in August and September 2016 and November and December 2016, while falling in October, in line with Mr. Trump's electoral prospect).
In other words, relative moderation in the index appears to reflect mostly exogenous factors, rather than internal structural reforms or policies changes.

Monday, January 16, 2017

15/1/17: 2016 was a year of records-breaking policy uncertainty in Europe

When it comes to economic policy uncertainty, 2016 was a bad year for the Big 4 European states, except for one: Italy.

Consider the above chart showing indices of Economic Policy Uncertainty across Europe's Big Four states, as represented by period averages across four main periods, plus 2016.

German economic policy uncertainty rose from 87.9 average for the period of 2002-2007 to 144.5 for the period of 2008-2011 and 152.1 over 2012-2015. In 2016, the index averaged 230.5. While not in itself indicative of a crisis, the trajectory is consistent with systemic rise in uncertainty, especially since 2016 was not a political outlier year (there were no major elections or external shocks, other than shocks related to German policy itself, such as the refugees crisis). That German index increase took place during one of the strongest years for growth and employment is, in itself, quite revealing.

Like Germany, France also experienced increases in uncertainty index over the recent years, with index rising from 109.7 in 2002-2007 period to 189.2 average over the period of 2008-2011 and to 235.6 over the years 2012-2015. In 2016, the index averaged 309.6. Once again, as in the Germany's case, there were no external or political catalysts to this, other than the dynamics of internal / domestic policies. And, as in the German case, economic cycles cannot explain this rise either. Thus, it is quite reasonable to conclude that systemic uncertainty is rising within the French society at large.

Perhaps surprisingly - given the outrun of the Italian Constitutional Referendum and the dire state of the Italian economy - Italy's Economic Policy Uncertainty Index has managed to eek out a small (statistically insignificant) reduction in 2016, falling to 129.3 in 2016 from 2012-2015 average of 130.9. However, December 2016 referendum is not fully factored in the 2016 average, yet (there are lags in Index adjustments and revisions that are yet to show up in the data), and both 2016 average and 2012-2015 average are well above 2008-2011 average of 113.7 and 2002-2007 average of 94.3.

Perhaps the only European country where index readings in 2016 can be clearly linked to internal structural shocks is the UK, where 2016 average index reading reached 528.8, compared to 2012-2015 average of 228.5. Chart below clearly shows that the increase in uncertainty started around the date of the Brexit referendum.

Overall, taken over longer term horizon, and smoothing out some occasionally impressive volatility, index averages across all four European economies shows structural increases in uncertainty relating to economic policy since the start of the Global Financial Crisis. These structural increases are not abating since the onset of economic recoveries and, as the result, suggest that the improvement in the European economies sustained since 2011 onward is not seen as being well anchored (or structurally sustainable) on the ground and amongst the newsmakers.

Friday, January 13, 2017

13/1/17: AID:Tech in Global Top 10 at IBM SmartCamp 2016

Another brilliant win for the AID:Tech team placing into Global Top 10 Startups for IBM SmartCamp

12/1/17: Betrayal Aversion, Populism and Donald Trump Election

In their 2003 paper, Koehler and Gershoff provide a definition of a specific behavioural phenomenon, known as betrayal aversion. Specifically, the authors state that “A form of betrayal occurs when agents of protection cause the very harm that they are entrusted to guard against. Examples include the military leader who commits treason and the exploding automobile air bag.” The duo showed - across five studies - that people respond differently “to criminal betrayals, safety product betrayals, and the risk of future betrayal by safety products” depending on who acts as an agent of betrayal. Specifically, the authors “found that people reacted more strongly (in terms of punishment assigned and negative emotions felt) to acts of betrayal than to identical bad acts that do not violate a duty or promise to protect. We also found that, when faced with a choice among pairs of safety devices (air
bags, smoke alarms, and vaccines), most people preferred inferior options (in terms of risk exposure) to options that included a slim (0.01%) risk of betrayal. However, when the betrayal risk was replaced by an equivalent non-betrayal risk, the choice pattern was reversed. Apparently, people are willing to incur greater risks of the very harm they seek protection from to avoid the mere possibility of betrayal.”

Put into different context, we opt for suboptimal degree of protection against harm in order to avoid being betrayed.

Now, consider the case of political betrayal. Suppose voters vest their trust in a candidate for office on the basis of the candidate’s claims (call these policy platform, for example) to deliver protection of the voters’ interests. One, the relationship between the voters and the candidate is emotionally-framed (this is important). Two, the relationship of trust induces the acute feeling of betrayal if the candidate does not deliver on his/her promises. Three, past experience of betrayal, quite rationally, induces betrayal aversion: in the next round of voting, voters will prefer a candidate who offers less in terms of his/her platform feasibility (aka: the candidate less equipped or qualified to run the office).

In other words, betrayal aversion will drive voters to prefer a poorer quality candidate.

Sounds plausible? Ok. Sounds like something we’ve seen recently? You bet. Let’s go over the above steps in the context of the recent U.S. presidential contest.

One: emotional basis for selection (vesting trust). The U.S. voters had eight years of ‘hope’ from President Obama. Hope based on emotional context of his campaigns, not on hard delivery of his policies. In fact, the entire U.S. electoral space has become nothing more than a battlefield of carefully orchestrated emotional contests.

Two: an acute feeling of betrayal is clearly afoot in the case of the U.S. electorate. Whether or not the voters today blame Mr. Obama for their feeling of betrayal, or they blame the proverbial Washington ’swamp’ that includes the entire lot of elected politicians (including Mrs. Clinton and others) is immaterial. What is material is that many voters do feel betrayed by the elites (both the Burn effect and the Trump campaign were based on capturing this sentiment).

Three: of the two candidates that did capture the minds of swing voters and marginalised voters (the types of voters who matter in election outrun in the end) were both campaigning on razor-thin policies proposals and more on general sentiment basis. Whether you consider these platforms feasible or not, they were not articulated with the same degree of precision and competency as, say, Mrs Clinton’s highly elaborate platform.

Which means the election of Mr Trump fits (from pre-conditions through to outcome) the pattern of betrayal aversion phenomena: fleeing the chance of being betrayed by the agent they trust, American voters opted for a populist, less competent (in traditional Washington’s sense) choice.

Now, enter two brainiacs from Harvard. Rafael Di Tella and Julio Rotemberg were quick on their feet recognising the above emergence of betrayal avoidance or aversion in voting decisions. In their December 2016 NBER paper, linked below, the authors argue that voters preference for populism is the form of “rejection of “disloyal” leaders.” To do this, the authors add an “assumption that people are worse off when they experience low income as a result of leader betrayal”, than when such a loss of income “is the result of bad luck”. In other words, they explicitly assume betrayal aversion in their model of a simple voter choice. The end result is that their model “yields a [voter] preference for incompetent leaders. These deliver worse material outcomes in general, but they reduce the feelings of betrayal during bad times.”

More to the point, just as I narrated the logical empirical hypothesis (steps one through three) above, Di Tella and Rotemberg “find some evidence consistent with our model in a survey carried out on the eve of the recent U.S. presidential election. Priming survey participants with questions about the importance of competence in policymaking usually reduced their support for the candidate who was perceived as less competent; this effect was reversed for rural, and less educated white, survey participants.”

Here you have it: classical behavioural bias of betrayal aversion explains why Mrs Clinton simply could not connect with the swing or marginalised voters. It wasn’t hope that they sought, but avoidance of putting hope/trust in someone like her. Done. Not ‘deplorables’ but those betrayed in the past have swung the vote in favour of a populist, not because he emotionally won their trust, but because he was the less competent of the two standing candidates.

Jonathan J. Koehler, and Andrew D. Gershof, “Betrayal aversion: When agents of protection become agents of harm”, Organizational Behavior and Human Decision Processes 90 (2003) 244–261:

Di Tella, Rafael and Rotemberg, Julio J., Populism and the Return of the 'Paranoid Style': Some Evidence and a Simple Model of Demand for Incompetence as Insurance Against Elite Betrayal (December 2016). NBER Working Paper No. w22975:

Thursday, January 12, 2017

12/1/17: Breaking EU Rules? Often and Freely

EU's Fiscal Discipline in one table: here is a summary of the EU member states' performance when it comes to 3% deficit ceiling set out as a core fiscal criteria:

Yes, even after a large scale fiscal 'retrenching' of 2016, on average, EU member states have been outside satisfying fiscal deficit ceiling criteria 41 percent of the time, with EA12 average being worse - at 43 percent.

Six EU states are more than just serial violators of the rule, with their respective frequencies of falling outside the rule constraints being in excess of 2/3rds. It is worth noting that in this group, all states are violating rules predominantly during the years of economic expansion.

Another 11 states are frequent violators, breaking the rule more than 1/3rd of the time but less than 2/3rds. Here too, with exception of Cyprus and Slovenia, more violations took place during the times of expanding economies than during the periods of recessions. All in, 17 states of the EU are breaking the EU fiscal rule on deficit ceilings more than 1/3rd of the time. Only 7 states break the rule less than 25 percent of the time and only 5 break the rule less than 10 percent of the time.

Surely, nothing to worry about.

12/1/17: NIRP: Central Banks Monetary Easing Fireworks

Major central banks of the advanced economies have ended 2016 on another bang of fireworks of NIRP (Negative Interest Rates Policies).

Across the six major advanced economies (G6), namely the U.S., the UK, Euro area, Japan, Canada and Australia, average policy rates ended 2016 at 0.46 percent, just 0.04 percentage points up on November 2016 and 0.13 basis points down on December 2015. For G3 economies (U.S., Euro area and Japan, December 2016 average policy rate was at 0.18 percent, identical to 0.18 percent reading for December 2015.

For ECB, current rates environment is historically unprecedented. Based on the data from January 1999, current episode of low interest rates is now into 100th month in duration (measured as the number of months the rates have deviated from their historical mean) and the scale of downward deviation from the historical ‘norms’ is now at 4.29 percentage points, up on 4.24 percentage points in December 2015.

Since January 2016, the euribor rate for 12 month lending contracts in the euro interbank markets has been running below the ECB rate, the longest period of negative spread between interbank rates and policy rates on record.

Currently, mean-reversion (to pre-2008 crisis mean rates) for the euro area implies an uplift in policy rates of some 3.1 percentage points, implying a euribor rate at around 3.6-3.7 percent. Which would imply euro area average corporate borrowing rates at around 4.8-5.1 percent compared to current average rates of around 1.4 percent.

11/1/17: Mr. Trump's Plan for Addressing Conflicts of Interest is a Fig Leaf of Corporate Governance

Why PEOTUS Donal Trump’s plan to donate hotels profits earned from foreign government payments to the U.S. Treasury is a fig leaf of corporate governance measures?

Photo credit: GettyImages

There are several reasons why a commitment to donate profits arising from foreign governments' payments to his hotels will not reduce, nor even alleviate, business incentives for potential conflict of interest that may arise in the future.

Firstly, donating profits from such activities requires that profits are declared on these activities in the first place. Since profits are declared across the entire business, not on the basis of individual transactions, Mr. Trump can use full extent of tax laws and accounting procedures, including cumulated losses deductions and tax shields on investment, to effectively reduce such denotable profits to nil over the next 4-8 years. 

Secondly, profits are not the most important financial line on which Mr. Trump operates. Mr. Trump operates on the basis of business (net) worth (value of his business) which reflects not so much the declared profits, but rather the earnings generated by his businesses (cash flow basis, e.g. EBITDA) and also reflects earnings over the longer term time horizon (timing factor). 

Now, consider the following hypothetical scenario: suppose Mr. Trump’s hotels receive USD1 million in foreign government’s bookings in 2017. Suppose he earns 10 percent profit margin on these earnings (so we neglect the issue raised in the first argument above). The profit is declared and Mr. Trump donates USD100K to the U.S. Treasury in 2017. The problem is that the 10% profit margin is across the entire group of hotels, not across the individual rooms and services supplied in exchange for the USD1 million foreign Governments' payments. As the result, 10% margin reflects costs and investments undertaken by the whole group. Foreign earnings, therefore, can be used to fund internal investment activities, ammortization and capital replacement costs, hiring costs, new services deployments etc. All of which will increase the value of Mr. Trump's hotels, including hotels that did not collect foreign payments.

In the mean time, Mr. Trump's business earnings did increase in 2017 by USD1 million as the result of the assumed foreign governments' payments. If this increase is viewed as organic or permanent, rather than a one-off windfall, his business value will increase as the result of these 2017 earnings even independent of the aforementioned investment. Why? Because companies are valued on the basis of their cash flow. Not on the basis of declared profits.

Furthermore, foreign governments' paid earnings will increase Mr. Triump's businesses capacity to borrow and raise equity. These increased borrowings and equity raises can further be used to invest in new business capital. This too will enhance business valuations for Mr. Trump.

In simple terms, even after donating his profits, Mr. Trump will be able to still gain substantially from increased revenues paid for by foreign governments. 

Thirdly, there is a host of other implications relating to Mr. Trump’s plan. 
  1. It will be hard to account for all payments by ‘foreign governments’ because many such payments can come via private foreign and even domestic companies, foreign organisations and foreign individuals, or for that matter, via domestic agents and agencies acting on behalf of these foreign governments. 
  2. How will the donations to Treasury be treated under the U.S. tax laws is material as well. If these are treated as charitable donations, they can be tax deductible, creating a tax shield for Mr. Trump. This tax shield can be extremely valuable, especially if his businesses use foreign-funded earnings to borrow for investment (effectively transferring these payments into future interest-related tax benefits). 
  3. Mr. Trump announced today that his companies will not be permitted to make any new foreign deals during his presidency tenure. However, domestic deals will be allowed. The problem is that this does not preclude use of foreign governments’ payments/earnings for the purpose of reinvestment in the U.S. Which cycles us back to the argument that these payments can still be used to enhance Mr. Trump’s business valuations.

In simple terms, Mr. Trump’s plan to prevent conflicts of interest arising does not add up to reducing incentives for conflict of interest. It is a fig leaf of corporate governance.

Wednesday, January 11, 2017

10/1/17: For Love or Money: Gender Gap in Online Labor Markets

The issue of a gender gap in the workplace, relating to gender differences in terms of occupations, is a highly contentious, politically charged and, despite a wealth of research on the subject, not fully explained to-date. One thing that economists generally agree on is that it is not one caused by a single factor or even a confluence of factors stemming from a single origin (e.g. access to education, time taken for maternity leave or concerted discrimination against women in the workplace, or any other set of closely linked factors). Instead, a range of exogenous, endogenous, personal, institutional, social etc factors determine the size of the gap, its existence and its evolution over time.

Hence, any new research identifying new factors is both - confusing (especially to those of us, who would stress the social equality dimension of the labour market outcomes) and important (especially to those of us, who prefer evidence-based policy and institutional responses to the issue). Note: the two sets of ‘us’ identified above are not mutually exclusive. In fact, I would suggest that majority of us - researchers, policymakers, analysts, and generally-speaking people, belong to both groupings, being concerned simultaneously with the social justice dimension of the labor market gender gaps and the need for well-designed policy responses to the problem.

With this preamble, here is a new piece of research on the subject. In their paper, titled “For Love or Money? Gender Differences in How One Approaches Getting a Job”, UC Berkeley researchers, Ng, Weiyi and Leung, Ming D (March 22, 2015: that current theories of the labor markets “conclude that women and men apply to different jobs”. However, these theories fail “to explain differences in how [men and women] may behave when applying to the same job.”

The authors “correct this discrepancy by considering gendered approaches to the hiring process. We propose that applicants can emphasize either the relational or the transactional aspects of the job and that this affects getting hired.”

What do these two approaches mean?

  • “Relational job seekers focus on developing a social connection with their employer.”
  • “Transactional job seekers focus on quantitative and pecuniary aspects of the job.”

The authors “hypothesize that the approach women take in applying for a job will differ from men. In particular, we believe that women, enacting their gender will focus on the relational aspects of the exchange: they emphasize the social, emotional aspects of the employment relationship and focus on mutually beneficial interests. On the other hand, men will be more transactional in nature: they focus more on the task at hand, their own qualifications and achievements, and highlight the quantifiable, observable and tangible aspects of the job.”

The evidence in support of these hypotheses is presented in the paper (for example, see Chart below).

Crucially, the authors note that “while both these approaches have their merits, this difference should result in variation in a person’s likelihood of being hired.”

The study then applies this theoretical hypothesis to see if it can account for “the hiring gap
between male and female job applicants we observe [in the actual data], net of controls for underlying ability, in an online market for contract labor,”

The reason the authors chose the online labor market data is that

  1. “The online setting provides a richness and granularity of data which allows us to further unpack the nuances in the strategies employed by job seekers. The transparency of the setting provides a glimpse into the black box of the hiring process. For example, the data provides insight and access to the details of every job posting, the applicant pool, background work histories of each applicant, their photographs, how much they were willing to work for, the text of their job proposal, and the eventual winner of the job.”
  2. Secondly, there is an “increasing trend towards self-employment whereby labor market participants eschew the long-term role as a corporate employee and instead participate on a contract basis, moving from job to job and working for different employers” which further validates the use of online labor market data. 

Based on the data and a barrage of econometric tests, the authors concluded that “women are more likely to be hired than men by about 5.2% [in the type of the labor market]. Quantitative linguistic analysis on the unstructured text of job proposals reveals that women (men) adopt more relational (transactional) language in their applications. These different approaches affect a job seeker’s likelihood of being hired and attenuate the gender gap we identified.”

Besides own interesting insights and conclusions, the paper is well-worth reading for the quality of discussion it presents relating to existent social and economic literature on the subject of gender gaps. If anything, this discussion itself is worth paying close attention to, for it highlights the wealth of our knowledge on the subject as well as posits some serious questions about the future of gender gap research.