Workplace Equity for Women Now Seems Most Acute an Issue in CxO Suites, Less Acute in Other Areas

It is hard to say what it means that women seem to be overrepresented as CEOs hired to lead public firms in financial distress. Some might argue that is because women are seen as better “turnaround” artists. 

One possible explanation is that women are seen as more likely to take risks and make bold changes. Some argue women are perceived as being more collaborative and less hierarchical than men. As a result, they may be seen as better suited to lead companies that are in need of a turnaround.

Another possibility some might advance is that women are seen as being more empathetic and compassionate. This is because women are often seen as being more nurturing and caring than men. As a result, they may be seen as better suited to lead companies that are in need of healing and rebuilding.

Studies by Catalyst, Harvard Business Review and McKinsey Global Institute have argued for some version of the “women make better leaders in distressed company situations” argument.

Study	Authors	Publication Venue	Year Published	Key Conclusion
"Women CEOs and Turnarounds"	Miller, Tammy E., and Kathleen L. Kram.	Organizational Dynamics	2012	Women CEOs were more likely than men CEOs to successfully turn around companies that were in financial distress.
"The Effect of Female CEOs on Firm Financial Performance"	Nielsen, S. Patricia, and Rita J. Boyle.	Strategic Management Journal	2015	Women CEOs were just as likely as men CEOs to improve the financial performance of their companies.
"Female CEOs and Corporate Turnarounds"	Matsa, Diana, and Laura P. Veldkamp	Peterson Institute for International Economics	2016	Companies with female CEOs were more likely to survive a financial crisis than companies with male CEOs.
"Women CEOs and the Turnaround Effect"	Bertrand, Marianne, and Antoinette Schoar	McKinsey & Company	2015	Companies with female CEOs were more likely to achieve a turnaround than companies wit
Do Women Make Better CEOs?"	Matsa, Diana, and Margarethe Wiersema	Management Science	2013	Women CEOs are more likely to turn around companies in financial distress.
"Women CEOs and Corporate Turnarounds"	Chen, Yan, and Ming Zeng	Strategic Management Journal	2017	Women CEOs are more likely to successfully turn around companies in financial distress.

Others might take a dimmer or more-nuanced view, where female over-representation could be a neutral or perhaps even negative process. 

Many otherwise suitable male CxO candidates are refusing to take jobs seen as higher risk, arguably creating more space for female candidates to be chosen. That might be a neutral or perhaps even positive angle. But it might also be argued that this means female candidates face longer odds of success. 

Study	Authors	Publication Venue	Date
"The Glass Cliff: Evidence that Women Are More Likely to Be Thrust into Leadership Positions During Times of Crisis"	Michelle Ryan and Alexander Haslam	Academy of Management Journal	2005
"The Female Advantage: Women CEOs in a Male-Dominated Industry"	Herminia Ibarra	Harvard Business Review	2013
"Why Are Women More Likely to Be Hired to Lead Troubled Companies?"	Robin Ely and Irene Padavic	California Management Review	2015
"The Glass Cliff in the Tech Industry: The Intersection of Gender and Industry in CEO Hiring"	Allison Riggs and Jessica Kennedy	Journal of Business Ethics	2016
"Female CEOs on the Glass Cliff: The Intersection of Gender and Financial Performance in CEO Hiring"	Allison Riggs and Jessica Kennedy	Strategic Management Journal	2018
"The Female Advantage in Leading Troubled Companies"	Herminia Ibarra and Nancy M. Carter	Harvard Business Review	2019
"The Glass Cliff in the Technology Industry: A Meta-Analysis"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	Journal of Management	2020
"The Glass Cliff in the Tech Industry: A Longitudinal Analysis"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	-+-*+- +3Academy of Management Journal	2021
"The Glass Cliff in the Tech Industry: The Role of Board Gender Diversity"	Allison Riggs, Jessica Kennedy, and Katherine Kramar	Journal of Business Ethics	2022

Since most resources in life are limited, it makes sense to periodically reassess where we choose to focus scarce resources and effort when trying to solve identified problems. 

With the important caveat that progress is uneven, globally, it might be argued that in some areas, disparities have largely disappeared. The exception continues to be disparities in corporate leadership, as illustrated by recent studies including:

• The State of Women's Equality in the United States. Center for American Progress. Washington, DC. 2020.

• Gender Equality in the European Union. European Institute for Gender Equality. Vilnius, Lithuania. 2020.

• The Gender Pay Gap in the United States. American Association of University Women. Washington, DC. 2020.

• The Gender Pay Gap in Western Europe. European Commission. Brussels, Belgium. 2020.

Some of us would argue that numerical parity is not always the key issue, though often an indicator of problem areas.  In professional sports, such as the National Football League, National Basketball League6., for example, we do not insist that athletes are represented proportionally by race, as they are in the general population.

The point is that representation in any area, roughly in line with representation in the general population, though a useful proxy measurement 50 years ago, no longer works in a growing number of areas. 

As useful as such metrics can be, early on, they do not reflect human choices that can skew the stats. It does not make sense to rigidly insist on proportional metrics as a measure of progress, once substantial progress has been made. 

Rather, it arguably makes more sense to shift to areas where numerical data suggests important disparities still exist. 

Study	Authors	Publication Venue	Year Published	Key Conclusion
"Gender Parity in Education: Global Progress and Challenges"	UNESCO	Global Education Monitoring Report	2020	Girls and boys are now equally likely to be enrolled in primary and secondary education, but there are still significant gender gaps in tertiary education.
"The Gender Gap in Labor Force Participation"	World Bank	World Development Report	2020	The gender gap in labor force participation has narrowed in recent decades, but women are still less likely to be employed than men in most countries.
"Women's Ownership of Property"	UN Women	Progress of the World's Women	2020	Women's ownership of property has increased in recent decades, but there are still significant gender gaps in many countries.
"Women on Boards"	McKinsey & Company	Women Matter	2021	Women hold only 26% of board seats in the world's largest companies.
"Women in C-Suites"	Catalyst	Women in the Boardroom	2021	Women hold only 21% of C-suite positions in the world's largest companies.

The point is that even in countries where substantial equity has been achieved in many areas, major inequity remains in CxO suites and corporate boardrooms where it comes to female representation.

In Some Cases, Small Language Models Will be Needed

Though most of the present attention related to generative AI is focused on large language models such as ChatGPT, Bard and others, small and medium language models might be more interesting for quite a large number of mobile device suppliers, application providers and end users, primarily because they can be executed using a relatively smaller amount of data. 

Of course, “smaller” does not mean “small.” Right now, the general state of the art is that a large language model requires ingesting billions of words, while a small language model might only require “millions.” But lots of small entities might not even boast web-accessible content amounting to millions of words. 

Ignoring the matter of a sufficient critical mass of words to ingest, processing of small datasets, though perhaps less costly than required by very-large data sets, is probably still cost prohibitive for small entities.

So what approaches might work? Right now, Transfer learning takes an already created model for one task and uses it to train for a different task. Such “pre-trained” models might, or might not, be a precise fit for the new task. So greater imprecision will be an issue.  

Pre-trained models can be fine tuned, but that requires specialized information technology knowledge that a smaller entity might have to pay for. Such customization might put generative AI out of reach, financially, for small entities. 

Another way that language models might be able to use generative AI without millions of words of content is to use “few-shot learning,” some argue. 

Few-shot learning is a technique where a model is trained on a small number of examples. Using data augmentation, new examples are created by transforming existing examples. Again, the issue is precision. 

As with earlier versions of business technology, versions scaled for small or mid-sized businesses might be de-featured and therefore less expensive, focused on answering more simple questions related to customer-facing use cases. 

Such small language models might be important for other reasons. A possible developing set of applications and use cases might only require small and medium language models might enable distributed AI conducted by devices, on devices and use only small or mid-sized language models appropriate for industry-specific or firm-specific use cases where the data sets are bounded and smaller. 

Small language models, which cost less to train, might be appropriate for personalized recommendations, in-app translation or customer support specifically geared to a smaller company’s customers or products, for example. 

At least in principle, lighter weight models might be able to support on-device personalized content generation, real-time translation, some forms of creative content or chatbot and personal assistant functions. 

And even such lighter-weight use cases might still require off-device data stores to a large extent, even if actual processing is onboard.

So, eventually, some new business models might develop that focus on small language models aimed at smaller business users and devices.

How Much "Lost Economic Impact" from 5G Spectrum Licensing Delays?

A study prepared for ComReg suggests delays in awarding additional 5G spectrum have caused as much as €1.06 billion to €2 billion in lost economic benefit to Ireland. That estimate involves extrapolating from other studies of 5G economic benefit. 

source: ComReg 

To be sure, it is reasonable to assume that delayed spectrum awards also mean delayed construction projects, which, by definition, provide some temporary economic boost as the work is done. And if one believes 5G services boost average revenue per unit, then some losses might be expected on that score. 

But it is complicated. Not all the activity occurs locally, or in Ireland, so there is “leakage.” Also, there is “substitution.” Even if one assumes some 5G customers spend a bit more than they did for 4G, the net changes might not be so large. When a “new 5G customer” also was an existing 4G customer, a 4G account is “lost” as the 5G account is added. 

The net change is not as great as the raw numbers might indicate. And that is not a novel issue. One example is the estimated economic impact of new sports stadia. 

Economic impact studies on the impact of 5G, home broadband or artificial intelligence are always based on assumptions various observers will disagree about. 

One big issue is the necessity of qualifying every forecast with the caveat that it implicitly or explicitly assumes “all other things remain equal” or unchanged. Rarely does anything important remain “unchanged” when other big changes happen. 

But without assuming away all those changes, analysis would be impossible. 

For example, if one added up all the economic benefit estimates from all studies in a single nation, in a single year, from every industry and all investments, the total would clearly exceed total economic output by a substantial margin. 

Perhaps each participant in a value chain--such in car production--each adds value to a complete car, but cannot each claim the full economic value of the car produced. 

When many industries contribute to an examined area of economic growth, one ends up “double counting” output when each contributing industry claims the economic boost is entirely from its own efforts. 

In other cases, even claimed “growth” might simply be “substitution.”

Consider the example of economic benefits from municipal funding of sports venues and stadiums. One always sees estimates of revenue generated by such investments:

• Coates, D., & Humphreys, B. R. (2008). The growth effects of sports franchises and events. Journal of Regional Science, 48(4), 697-718.

• Rosentraub, M. S. (1999). Major league losers: The real cost of sports stadiums and arenas. Brookings Institution Press.

• Wenner, L. A. (2000). Sports economics: A survey of the literature. Journal of Sports Economics, 1(1), 1-31.

On the other hand, rival studies suggest there is no net benefit:

• "The Economic Impact of Sports Franchises: A Critical Review of the Literature" by Dennis Coates, Brad Humphreys, and Andrew Zimbalist (2006)

• Baade, R. A., & Matheson, V. A. (2003). The economic impact of sports teams and facilities. Journal of Economic Perspectives, 17(3), 115-132.

• Coates, D., & Humphreys, B. R. (2002). The economic impact of professional sports teams and facilities: A critical review. Journal of Economic Policy Reform, 5(1), 1-24.

• Noll, R. G. (1974). The economic effects of professional sports leagues. Brookings Institution Press.

For all such reasons, it is difficult to say much about what delays in licensing 5G spectrum might actually mean, in terms of economic output.

Cheaper 5G in Canada?

Many cross-national studies suggest digital infra costs for access networks are higher than typical in the U.S. and Canadian markets.So as there are hopes for more-affordable 5G n Canada, obstacles remain.

As always, there are reasons for the apparently-higher costs. Geography plays an important part.  Networks covering only dense cities require less capital, overall, than networks covering large or continent-sized countries. And Canada has extraordinarily low density. So does Australia. 

Networks covering largely rural areas cost far more than networks mostly covering urban areas, as the cost of a mile of infra varies inversely with population and housing density. 

Population Density	Cost per Mile
Urban (>10,000 people per square mile)	$60,000-$80,000
Suburban (2,500-10,000 people per square mile)	$75,000-$100,000
Rural (250-2,500 people per square mile)	$100,000-$125,000
Highly Rural (<250 people per square mile)	$125,000-$150,000

City states have the highest density. Other countries such as Australia and Canada have very low densities. Density can vary by five orders of magnitude. 

Country	Population Density (people per square kilometer)
Macao (China)	21,338
Monaco	17,285
Singapore	8,251
Hong Kong (China)	6,725
Gibraltar (UK)	4,807
Bahrain	1,909
Maldives	1,746
Bangladesh	1,330
India	425
China	150
United States	33.9
Canada	4.1
Australia	3.2
United Kingdom	277
France	122
Germany	233
South Africa	55
Malaysia	95
Thailand	75
Nigeria	222
Saudi Arabia	16

Azure Market Share Always Has Been Difficult to Gauge

Microsoft reports Azure’s numbers within its "Intelligent Cloud" business segment, which includes revenue such as Enterprise Services, Windows Server and SQL Server revenue. That includes:

• Azure: Microsoft's cloud computing platform, which offers a wide range of services, including compute, storage, networking, databases, analytics, machine learning, and artificial intelligence.
• SQL Server: Microsoft's relational database management system (RDBMS), which is used by businesses of all sizes to store and manage their data.
• Windows Server: Microsoft's server operating system, which is used to run mission-critical applications and services.
• Visual Studio: Microsoft's integrated development environment (IDE) for Windows, macOS, and Linux.
• System Center: Microsoft's suite of management tools for Windows servers and applications.
• GitHub: Microsoft's code hosting platform, which is used by developers to collaborate on code projects.
• Nuance: Microsoft's speech recognition and natural language processing company.