How Telecom Is Becoming A Cyclical Industry, And
What To Do About It
DRAFT.
Eli M.
Noam[1]
Columbia
University
May
5, 2002
Second
DraftJune 28, 2002
This article analyzes
the long-term lessons of the recent upturn and downturn in the
telecommunications industry. It concludes that suchcyclicality will be an inherent
part of the telecom sector in the future. To deal with thecyclicalsuch instabilities,, the most effective responses by companies and
investors is to seek consolidation and cooperation. Hence, an oligopoly
is likely
to be the
equilibrium market structure. This means that government, if seeking
stabilization,
will need to reassess its basic policy approach that has long been focused on the enabling of based on competition. And this, in turn, means that the structure of future network
industry will look a lot more like the old telecom industry and less like the
new internet..
1. The New Cyclicality
How fast things have changed. Only yesterday, the
sky seemed to be the limit for the telecommunications industry. But Ttoday the
electronics-based new media economy has become an old-style
bust. Legacy is
in. Balance sheets are in. . We need not listen anymore to the
purveyors of hype about how bits play by different business rules than atoms, how
the silicon economy is different from the carbon one, and how a P/E ratio need
not have any E that stands for earnings, as long as it stands instead for
electronics. Moore’s sunny law of
exponential one-way progress has met the dismal science, and the two did not
get along.
[add downturn
numbers on investment, traffic, stock valuations, competitors, etc for 1990,
2000, and 2001/2]
[Insert W]
Telecom stock prices,
using February 1996, the birthdate of the Telecommunications Act of 1996, as the benchmark of 100,
rose from about 70 in January 1995 to 225 in March 2000, and then fell back to
about 80 in March, 2002 (Standard and Poor’s in Alleman, 2002). Among new
entrants, of 28 publicly traded
competitive local exchange companies, 13 went bankrupt in 2001. [add earnings, investments and revenues]
In the first quarter of 2002, the total number of phone lines for Verizon dropped by 2.7%, for SBC by 3.6%, and for Bell South by 1.8%. In contrast, in the recession of the early 1990s the total number of phone lines had increased slightly (Stern, 2002). There are predictions of real telecom industry losses of $500 billion, much greater than for the savings and loan debacle of the late 1980s. As network firms sought survival rather than expansion, the telecom equipment manufacturing sector all but collapsed.
The “new”
telecommunications industry is characterized by new technology, new
applications, and maybe new market structures.
It is also beset by a new set of instabilities and uncertainties that
will become the theme of telecommunications debates in the next years, in the
same way that the topic of competition had dominated the discussions of the
1990s. If the change is not
merely a short-term correction but more fundamental in nature, managerial and policy
assumptions need to be reviewed fundamentally, too.
Quite likely, the present downturn is only temporary and the industry will recover, though no at the hyper level of the bubble years. That, however, is not the real problem for the industry. It is not a one-time recovery from a one-time boom and bust. The main problem is that the telecom industry is entering a chronic pattern of volatility, with boom-bust patterns becoming a common occurrence rather than an aberration. Thus the telecommunications network environment is leaving linearity and entering volatility. A pattern of ups and downs may be emerging, a cycle.
Yet manye. pParticipants and observers of the industry miss
or deny
the emergence of cyclicality, sometimes interpretingviewing the term too
literally and needlessly implying it to mean a regularity of ups
and downs.
patterns Most
observers believe
that the present downturn is merely a one-time accidentevent like a train crash, that things will return to their past stability
because we have
learned from past mistakes, and that we will not repeat them). This view is one of denial. True, we do not have
much experience with volatility in telecom to make long-term predictions. And true, we
are learning from the recent past. But if we if we analyze the drivers of the recent volatility, we
mustthe
sources of expansion and the forces of decline, conclude that they will be with us into the
foreseeable future, and cyclicality with them, just as they are in some
other industries unless, that is, we take steps that are at variance with
telecom strategy and policy of two decades.This is neither likely nor necessary for the
arguments that follow. A
regular cycle would be predictable, and one could therefore deal with it much better.
Business cycles are not new, of
course. The Bible tells us of the seven fat years in Egypt, followed by the seven
lean years, and of Joseph who, engaged in what we would today call what
would be described today as a counter-cyclical
economic policy. Economic historians
have identified more than 30 cycles for the years since 1854. There have been nine
“official” recessions in the US since 1950.There have been nine
“official” recessions in the US since 1950. Expansions have become
gradually longer, contractions shorter, and milder (Zarnowitz, 1992). But they
have not gone away. One reason is that
we know better how to deal with downturns through macro-economic policies, and
how to mitigate their negative effects through social policies. A dampening of the business cycle has been a priority for
all governments.
Within the aggregate economy there
are cycles for various asset classes and industries. The swings of the economy
as a whole is a composite of the moves of its various sectors and firms. Just
as in the case of investment portfolios, the aggregate volatility of the
economy is lower than that of the average of the individual volatilities. These
volatilities are interrelated in various reinforcing and offsetting ways. The swings of the economy
as a whole is a composite of the moves of its various sectors and firms. Just
as in the case of investment portfolios, the aggregate volatility of the overall
economy should
be lower than the
average of the industry volatilities.
Some industries are more cyclical
than others. One study of about 250
industries found that durable goods industries are three times as cyclical as
non-durable goods industries. (Petersen and Strongin, 1996). But interestingly,
unionization was not found to be statistically associated with cyclicality,
neither as a cause nor as an effect, despite the greater downward inflexibility
in cost it is reputed to create.
While business cycles
are not new to many industries, in telecom they are an entirelya new phenomenon.
In the past, the network industry progressed in only one direction: up. Telecom
used to be less volatile than the economy as a whole. It grew steadily, with
long planning horizons hardly ruffled by the business cycle. (The only time the
industry declined in volume was in the Great Depression, when subscribers
dropped temporarily by 16%). One company, AT&T, accounted for 80% of network
activity and equipment manufacturing, and provided stability, planning, and an industry-wide
umbrella. Its stocks with their steady dividends were treated by
investors
like bonds. The equipment industry, being also globally diversified, was almost as
stable as the carriers. But
today, in sharp contrast, it the telecom sector may well have become more
volatile than the economy, more like the construction business, less than water
utilities.
The telecom industry was unprepared
for such a transition. Telephone lines had never declined in number
before. The financial community was
similarly lost, and its investment advice and valuations, assuming only
growth were wide
off the mark. Similarly, no academic literature had prepared us to the
topic as
applied to the telecom sectorvolatility. Being
caught by surprise, aGiven this novelty, a massive
hand-wringing and finger-pointing ensued.
This reaction is not altogether surprising. It is characteristic of industries (orand countries)
that had previously experienced long periods of growth to be especially
unprepared for a downturn and be frightened by it, because it is such a
novel, almost existential experience. The South Korean national economy is
one example. Telecommunications is now another.
2II. Why
Cyclicality?
This question is important, because
if we do not know why something happened, we cannot
predict, prevent,
or encourage
recurrence. [2] when
and how it will go away or re-appear.
There are many competing
explanations for economic cyclicality, from sunspots to the alignment of the
planets, and
to the political election cycle. Over
more than a century and a half, many distinguished economists have contributed
their views[3]. I
will discuss six main theories approaches and relate them to the telecom
industry.
21.1 The
Monetarist View. According to that viewtheory, associated
especially with Friedrich von Hayek (1933, 1950) and Milton Friedman (1982),
cycles are caused by flawed monetary policy that causes instability. For
example, if a central bank changes interest rates incorrectly, consumers and
businesses get wrong signals and their expectations lead to reactions that set
off instabilities. (Fischer 1997).
Whether this theory is correct or not for the aggregate economy, it is probably not applicable to the telecom industry. Whatever telecom’s problems are, interest rates, even after being low and contributing to the bubble, did not rise to levels that would put them high on the list.
2.2
The Keynesian Perspective. Aggregate
demand is affected by the mood swings of market participants which often become
self-fulfilling. (Keynes 1936, Hicks 1950, Tobin 1975). The key trigger is psychological and,
on the demand side. Keynes called it the “animal spirits” of entrepreneurs. Today
it has been termed by More recently Allan Greenspan described it as an “irrational
exuberance” in which the stampeding herd of stock market bulls
eventually turned into a throng of lemmings (Shiller,
1978, 2001).
The demand orientation of the Keynesian approach leads Wall Street analysts to look closely at data for consumer spending as leading indicators. But for telecom and the internet, one cannot really blame a drop in consumer demand on the downturn. True, the growth rate in the internet subscribership are not as torrid as before – it was “only” 30-40% in 2001 – and internet minutes per user and day have dropped a bit, as one would expect as more marginal subscribers are signed up. Internet connections increased in America by about 20 million in 2001. Broadband internet subscribership is up and with it the aggregate bit flow for the internet. Similarly, the usage of long-distance minutes, data communications, and wireless minutes keeps rising. With double-digit growth rates in actual consumption, it is hard to blame insufficient demand on the downturn.
2.3.
Real Business Cycles theory (RBC).
This theory is a supply side
story, going back to Prescott (1983) and others. For RBC advocates, cycles are
caused by random shocks and their impact on total factor productivity. The
internet was a positive shock. September 11 was a negative shock. Random positive shocks lead to higher productivity,
higher output, higher real wages, consumption, etc. For RBC advocates, causality does not run from consumption to
output but the other way around. (Espinosa-Vega and Guo, 2001). It therefore
rejects explanations based on consumer psychology such as “exuberant
irrationality.” RBC proponents believe that there is really nothing that
governments can do about a cycle since it is based on random shocks.
How does this perspective fit
telecommunications? Empirical studies
show that single shocks do rarely trigger downturns. But a shock can topple an already weak structure. In telecommunications, several shocks
occurred in the same period and added their impact cummulatively.
Local competition failed; long distance competition, on the other hand, worked only too well,
lowering prices and profits; the dot-com sector crashed back to Planet Earth;
Wall Street became irrationally depressed when stocks declined from their unrealistic heights;
governments
extracted future expected profits by auctioning access to a vital resource,
spectrum; spectrum auctions squeezed the industry; regulators, often beholden to
incumbents’ well-being, held back competitors; etc. But note that most of these events are not the kind of exogenous,
technology oriented, shocks that affect productivity, as
hypothesized by RBC advocates. They are endogenous financial and institutional
variables of the sector itself. They are thus not truly random but
systemic.
There is, however, one important
factor that can be interpreted as a technologically-based shock, if we take a generous
definition of the term. It is the re-emergence of economies of scale of through network
technologies such as fiber-optic distribution transmission cables
and of wireless distribution
systems. On the supply side, the fixed costs of networks are rising and the
marginal cost are dropping – strengthening the classic attributes of “natural”
monopoly. Scale effects are
compounded by “indivisibilities” or “lumpiness” in investment, which leads to
short-term excess capacity (Darby, 2002). Hence, the advantage of being large are greater than
before. Size matters. As a result, for example, the market share of
mobile wireless telecom providers (i.e., relative size) has been an excellent
predictor of profitability (Waverman, 2002).
And the cost of wireless firms is inversely related to the absolute and
relative size of wireless companies, as exhibited in the graph below (Katz et
al, 2002).
Similar effects have long been identified for the telecom long distance
market (see, for example, Denny, Fuss, and Waverman (1981), Nadiri and
Schankerman (1981), Alleman (1983)), as well as for cable TV (Noam
1983,1985). They generally show cost
elasticities with respect to size of 5-15 %.
In addition, the technological expansion in capacity has not only
increased but accelerated (Noll, 2002).
[Rob: where is the 2nd
graph from Booz Allen repost? I faxed it.]
For a long time, these size
advantages in telecom were submerged under the accumulated inefficiencies of
the incumbents. But having had to shape up under the pressure of real or
threatened competition, they reduced their inefficiencies sufficiently for their scale to
overcome the threat efficiency of small entrants. This is not the
classic RBC story, but it is inspired by it.
2.4. Lag
and accelerator models. These models go back to Samuelson (1939). Small
changes in desired capacity levels lead to large differences in capacity
expansion, which drives investment. Where there is a delivery lag,
unanticipated shifts in desired capacity can generate cycles of investment
spending. The key here is the
adjustment lag. These lags induce
oscillation in the same way that a slowly reacting bathroom shower induces
cycles of hot and cold water. The
famous “cobweb” cycle is a model of such overshooting. Industry examples are cattle,
airline services, and office space , where it takes a long
time to increase capacity – and now telecommunications. Here, investments take a long time to get on
line, and disinvestments may take even longer (it took more than a decade for
the excess supply of 1980s Texas office space to dissipate.) The lumpiness of investments in
telecommunications,
coupled with an even slower regulatory and court system, makes the
feedback loop very slow. On top of this is the “chicken and egg” problem of
applications development depending on networked buildouts and vice versa. Since it is
difficult to synchronize the two, developments often progress in spurts. The build-out of networks for
broadband-internet capability is a recent example.
2.5.
The “Austrian” theory. This
view is associated with Mises (1928xx),
Hayek (1933, 1990), Haberler (1937), Böhm-Bawerk (1895xx),
Wicksell (1936), and Schumpeter (1939). It is focused on overcapacity. Assume Such overcapacity
has been created for some reason—whether due to exuberance, excessive bank
lending, monetary policy, or other factors. After an adjustment lag there is
eventually a downturn. The pattern is one of boom, overcapacity, price war,
bust, shakeout. A young industry tends
to start off with small firms, and once their product fetches a high price it
attracts entry, which expands output and lowers price. This goes on for a
while. Industry growth rate slows below that of individual firms, and a
shakeout occurs. For example, there used to be 275 tire manufacturers in US in
1922. Today, less than a dozen survive, even though the tire production as a
whole is vastly larger. This view is common-sensical, with numerous examples
such as snowmobiles, pocket calculators, bowling alleys, PCs, or movie
theaters.
The Austrian view seems to describe well the telecom industry, in which the various network companies over-optimistically projected long distance market shares that added up to over twice the actual market. Everybody built capacity to overwhelm competitors and gain size. Capital expenditures grew by an annual rate of 29% from 1996-2001. The incremental cost of bandwidth fell by about 54% annually. Overcapacity was assisted by the lumpiness of telecom investments such as oceanic cables and its irreversabilities. It was further assisted by the tendency of Wall Street analysts to value a firm’s progress by physical measures of its infrastructure, such as cell-sites and fiber-miles. As the result of these factors, some carriers had over 90% of their fiber “dark” and prices dropped dramatically.
The Austrian theory has its
detractors. Paul Krugman, the MIT
Princeton
economist and New York Times columnist, has called it a “hangover
theory” and “about as worthy of serious study as the phlogiston theory of fire”
(Krugman 1998), largely because it places the blame for a downturn in the boom
itself, and does not explain why each company systematically over-estimates its
market. Krugman’s critique is focused
on the macro-economy rather than specific industries, where he accepts the
notion of over-expansion. For
telecommunications, then, one would need to understand why so many companies
were so wrong about their prospects, and why none engaged in a meaningful
counter-cyclical strategy.
Paul Samuelson, one of
the world’s most distinguished economists, indirectly addressed
this criticism, though he, too, is a Keynesian. Samuelson
by observinged
that economists have no theory to predict when a bubble in asset prices will burst. It is,
therefore, not “irrationally exuberant” but
rather perfectly economically logical rational
for an individual to participate in a bubble. An individual’s
micro behavior may be efficient even if the system is macro-inefficient.
2.6. Externalities. The RBC theory discussed earlier assumes constant returns to
scale. That is, if one increases the capital and labor inputs of the firms
proportionately, their outputs would grow by the same proportion. But for
network industries this ignores the network effects, also known as positive the
network externalities (Farmer and Guo, 1994) or the Metcalfe
effect. An increase in usage leads to greater utility of the product and to
increased demand. This increases productivity and real wages and enables
further consumption. Growth of other network participants is factored in as
part of the value of the product, and leads to still further growth. At some
point, however, the expectations of further growth decline, for example as
saturation occurs. This leads consumers to reassess the value of the service,
and to adjust their consumption to the new value. This reduces demand or at
least its growth, which creates negative network effects. And thus, the
dynamics that had led the system to go up now take it down faster, too. Network
externalities strengthen the oscillations of market demand rather than dampen it[4].
If the firms could coordinate their action they could jointly reduce this
externality accelerator effects, but in a competitive environment they cannot
easily or
legally do so. This story fits well to the telecom and internet markets
of the ‘90s and beyond.
2.7
Adding up the Theories of
Cyclicality
Like the proverbial six blind
observers of an elephant, each of these theories gets something right in its
views of the drivers of cyclicality. Demand growth has slowed. Investment and regulatory lags prevented
adjustment. Network externalities and lumpiness in investments amplified the
swings. Economies of scale and network
externalities created strong incentives for growth strategies, at the expense
of profitability. Financial markets encouraged this strategy. Managers benefited from it in the short
run. Yet while it expansion made sense for each firm
individually, it created an a major oversupply in the aggregate.. Firms also overestimate the size of the market
and of their share in it.
The following graph illustrates the increase in capacity for the North
Atlantic market over a period of the past 3-4 years. The line rising to the
right is capacity, and its increase is enormous. The declining line is the price. The line near hugging the
bottom is the capacity required for trans-Atlantic voice traffic. . (To this one should
must add
capacity to deal with peak load periods, plus, of course, with data traffic, for which time
series data of actual traffic is difficult to obtain. Data traffic is likely to be larger than voice, but not by the hugelarge multiples that would
move actual usage much
closer to capacity. (It is difficult to obtain the numbers for
actual data traffic).
On the
other hand, the multiplexing of voice reduces it from the assumed 64kbps for this calculation
(Alleman, 2002). Thus Tthis
graph is merely suggestive of the huge capacity overhang relative
to voice traffic. This
picturee increases in capacity and
declines in prices would be even more dramatic if graphed back
into the 1980s, or shown for trans-Pacific transmission, where prices of high-capacity
circuits have dropped
by 87% in two years.
In telecommunications, technological and economic obsolescence will
gradually take capacity out of circulation. Satellites, for example,
eventually leave their orbit or burn up. do not stay up forever, for example. But
disinvestments takes time. For Texas office space, it took over a decade in the
1980s, to dissipate the excess supply. For
railroads, it took many decades. Thus, the capacity overhang in telecom will remain
for a long time if it is reduced merely by obsolescence. The key for a recovery is a substantial
growth in demand, probably from mass-media uses of the internet such as
video. When demand has caught up with supply, prices will
rise, supply
will expand, new entrants will emerge, and every firm will aim at increasing
its market share. A new cycle emerges.
3III.
The Implications of Cyclicality
Given this emerging cyclicality in
the telecom market, what should be the response? I will discuss three types of participants: telecom managerscompanies,
investors, and governments. The
conclusion will be reached
is that for the private sector participants the strongest strategy
is to deal with cyclicality by seeking (or financing) an oligopolistic market
structure. This, in turn, has
implications for government policy.
Managers3.1 Telecom Companies
There is, of course, no shortage of
potential actions for telecom managers companies to
take. They includemust start with an intense :
Engage in self-analysis. Firms, managers and their owners and investors In
the first instance, firms need to disentangle their owntheir firm’s performance
from that of their industry, their customers and suppliers, and the regional,
national, and global economies. This is not easy. But it is necessary in order to judge management’s performanceover
the cycle,from the past. Following such analysis, several strategic
options exist.
Cut cost and contract. A downturn puts more
pressures on the firm and makes cost-cutting more acceptable. Similarly,
a downturn provides an opportunity to change the internal structure and shed
marginal operations. This
strategy It may also include thes
deferraling
of innovation due to its riskiness. This strategy works best if
competitors, too, follow it and slow the rate of their own investment in innovation.
Expand in the downturn. The opposite strategy from contraction may also make sense. The prices to acquire other firms through mergers or to expand by internal investment drop in a downturn, and it is a good time to get ready for the upturn, especially where lag times for investments are long.
However, in many countries the dominant telecom incumbent cannot easily be contrarian, because it may account for 80% or more of the market. This strategy might work better for a small firm—assuming it can raise the capital in a downturn, which is a fundamental dilemma.
One conclusion is that a strategy of expansion of capacity by internal growth makes less sense than the acquisition of a competitor’s capacity. Such a merger does not add to overall industry capacity (which would lower prices and profitability) but instead eliminates a competitor, which may result in higher returns.
Design the firm for downward (and upward)
flexibility. Firms need to operate with built-in adjustments for the cycle.
They need to implement scalable technology and flexible labor costs,
for example, through
profit sharing, commissions, and outsourcing.
However, the labor component is becoming smaller as capital-labor ratios
increase in the economy, and is therefore becoming less of a factor than it was
in more labor intensive days.
Similarly, firms need to engage in financial hedging. And in their capital structure, firms need to substitute corporate
debt with convertible debt or preferred equity because this enables them to
reduce a debt load in a downturn.
In contrast, the capital structure of large telecom firms has become
weighed by debt: it was 325% of market capitalization of France Telecom in
2001/2; 163% of Deutsche Telekom; 60% of BT; 66% of Telefonica.
Diversify in product
markets and geography. Diversification reduces risk in some
ways, but also may
also get a firm to move
outside its core area of competence, which raises risk again.
Expansion into other countries creates exposure to political vagaries. Vertical expansion into related
elements of the value-chain may create synergies (economies of scope) but may tie one part
of the firm to others within the same corporate family, even if they are
costlier and less desirable. It can also lead to a competition with one’s own
customers. And, any expansion into multiple product lines inevitably creates
and requires changes in the firm’s corporate culture, which may entail
significant and mostly hidden costs.
Properly assess the risk of the business in a volatile environment, and
factor it into all decisions.
Avoid a heavy debt load. In the downturn, cash is king. It reduces payment obligations and enables acquisitions. In the recent past, even established telecom firms have loaded up on debt in dramatic ways, and the result is that they are hurting in the downturn. Some of the reasons for such debt load was that the resource requirements of global and product expansion are huge, for example for the move to next generation wireless. Add to that the expense of firms engaging in empire building and the shortening of product cycles of technology, and the debt begins to balloon. Deutsche Telekom paid over $40 billion for the second-tier mobile carrier Voicestream; France Télécom paid $30 billion for Orange; and Vodafone paid $180 billion for Mannesmann.
After the 1996 Telecom Act a huge investment boom took place in the industry, in the order of $1.3 trillion. Between 1999-2001 alone, US telecom firms borrowed over $320 billion from banks. Credit was not difficult to obtain, and banks often accepted a subordinated creditor status without much collateral. But the revenues per investment dollar dropped. In 1996, according to Lehman Brothers, it was $5.08. But in 2001 it had fallen to $2.84 (Darby, 2002). Merril Lynch estimates that return on equity for the telecom industry declined from 13.8 % in 1996 to 5.9 % in 2001. In consequence, investments are expected to decline by an annual average rate of –14%. Verizon alone invested 33.7 billion over the period 1999-2002 with limited impact (Stern, 2002). Qwest’s debt load in 2002 was $25 billion. Around the world, the same story can be told. Deutsche Telekom’s debt is $64 billion, France Télécom’s $68 billion, and Telefonica $20 billion. As a percentage of revenues, France Télécom’s debt is 141%, Deutsche Telekom’s 140%, Telefonica’s 92%, BT’s 75%, and Telecom Italia’s 67%. Even these mountains of debt are being understated by various means. The cumulative debt of the seven largest European telecom firms exceeded $210 billion in 2002, greater the GDP of Belgium. It required some firms, on a daily basis, to commit over $10 million for debt service.
In theory, fAvoid a
heavy debt load. In the
downturn, cash is king. It reduces payment obligations and enables
acquisitions. With the resource
requirements of global and product expansion (e.g., the move to 3G wireless is
hugely expensive) and a bit of empire building, even established telecom firms
have loaded up on debt in dramatic ways, and are hurting in the downturn. But this must be qualified. The conventional
view holds thatfirms without
leverage will do better in downturn. But economy-wide studies also show that
some highly leveraged firms have been helped in downturn by banks which
cancelled payments rather than foreclosed. (Field, 1985, in Mascarenhas and Aaker 1989). and bySimilarly, governments that aremay be helpful in the
downturn,
especially when it affects
several firms
in an essential industry. Given such a safety net, big telecom firms had fewer reasons to be prudent. But they had an incentive to be too big and
essential, to be permitted to fail, and this, encouraged expansion.
Similarly, [ADD]for its
capital structure, firms may substitute corporate debt with convertible debt or
preferred equity
because this enables them to reduce a debt load in a downturnDeclare Bankruptcy. This step (e.g., a Chapter 11 reorganization)
may wipe out debt, but will also make investors and lenders wary of future
participation with the firm or its industry.
Engage in price cutting. This strategy has drawbacks when price cuts are matched by competitors. Hence, there are incentives to oligopolistic cooperation entailing the mutual reduction of capacity rather than engaging in price wars.
Diversify in product
markets and geography. This reduces risk in some ways, but also may
get a firm to move outside its core area of competence, which increases risk
again. Expansion into other countries poses a problem of timing and exposure to
political vagaries. Expansion into related elements of the value-chain create
synergies but may tie one part of the firm to others within the same corporate
family, even if they are costlier and less desirable. It can also lead to a
competition with one’s own customers. And, any expansion into multiple product
lines inevitably creates and requires changes in the firm’s corporate culture.
To conclude: many of the strategies in a downturn have common elements to make them successful: expansion and consolidation of surviving firms within the industry, and collaboration, to the extent possible, with one’s competitors.
3.2 Investors
In the past, the underlying
assumption for government policy in telecommunications policy had been that if
one gets a competitive structure in place, investment will follow efficiently
and plentifully (Darby, 2002). The present downturn challenges negates this
assumption in the short run (which is tolerable) and possibly in the long-run
(which is much less tolerable).
For better orf
for worse, there have been increasingly close linkages between financial
markets and the real economy, with the links forged through such instruments as
securitization, derivatives, and leveraged investments. Financial markets are also becoming
increasingly volatile, as risk taking becomes easier but risk assessment
harder. These trends, taken together, mean that financial turbulence makes
industries more vulnerable to financial shocks.
Wall Street has thus been part of
the problem, though it has also paid dearly for it, in money as well as
credibility. Share prices seem to move in ways that seem unrelated to the
underlying discounted value of cash flows.
In fairness, it is hard to value companies in volatile industries, both
conceptually and institutionally. “Consensus” earnings forecasts have been
useless for investors in cyclical industries, especially for startup firms in high-growth, high-risk industries. Forecasts tend to be rosy and asymetrical. . According to a McKinsey study,
for volatile industries the forecasts are generally upbeat, and “the forecasts
don’t acknowledge even the existence of a cycle” (de Heer and Koller, 2000).
Why this positive bias? Maybe it is the pressure of the investment banking part
of the firms to avoid unfavorable evaluations of companies which would lose
them as clients. Maybe it is the opaque accounting practices, and
proliferating derivativeng
securities and
stock options that make assessments difficult. The authors sum up: “In light of these
worries, it is reasonable to conclude that analysts as a group are unable or
unwilling to predict the business cycle for these companies.” [p.65,66].
According to Joseph Stiglitz
(2002), deregulations are often always associated with periods of frenzied
activity that often tend to go wrong. And indeed, after the 1996 Telecommunications Act, financial
markets gave the wrong signals to firms by raising stock values enormously. They evaluated
unprofitable startup firms by proxies such as fiber miles and cellsites. This
led telecom firms’ to over-invest in such physical
elements,tment
and to an over-investment in such firms.
The bubble was further fed by the ability to borrow against the rising
value of the stock; later,
when of course as stock values dropped, they this necessitated further
sell-offs and led to further price declines, etc. Traditional theory has it that investors look at
total risk-market risk plus financial risk. If they are additive, then high
market risk in this sector should have been offset by investor unwillingness to
support highly leveraged capital structures. But, it seems just the opposite
happened. Eventually,
What triggers the
downturn are rapidrRapid
changes in market expectations that lead
to rapid reassessment of asset prices. This causeds massive
reduction in asset-holder’s wealth, which lowereds
expectations further. Clearly, returns were
retreating broadly, and investments with them.
As that happened, zooming share prices ceased to be a
motivation for investments, and the new entrants rapidly fell out of favor,
since they could not offer any earnings or service. Nor could they source their debt. Confidence was further
shaken by questionable
accounting practices.
Studies show that the inability of lenders like banks to discover the relevant characteristics of borrowers projects, e.g., when information is asymmetrical, leads to poor projects driving out good projects. This is Akerlof’s “lemon” principle (1970). In the new media and telecom sectors, lenders’ and investors’ ability to evaluate projects has declined, and with it the quality of loans. When this became obvious even to the investment bankers, they raised the threshold, cutting off in the process projects they could evaluate before.
For a firm’s stability,
in a volatile environment, stock is better than debt. Since it does not require interest payment in a downturn
situation. The increasingly high debt load of telecom firms used to finance
various ventures thus subject these firms to more stress. Now the real prospect of
default sends shudders through the financial community and leads it to support
incumbents’ and their profitability against new-style entrants.
Investors now favored incumbent
telecom firms, which looked much better than before when they had been derided
as “dinosaurs” by the financial community. In contrast, the financing of new entrants
largely dried up. If anything, further
stability was desired. The incumbents’
traditional
shareholders
seek a utility-style stock with predictably steady dividends. When the industry
becomes volatile, such investors leave. Market power, on the
other hand, lowers risk, and raises prices
and cash flows. One can see how market power benefits market
valuations. In America, rural LECs, facing
little prospect of competition, maintained their value much better than other telecom
firms. In other countries, Telmex did
better than most large incumbents, mostly because of its hold over the Mexican
market. Its stock
rose 28% in the first four months of 2002.
One factor for the mispricing may have been that the value of “real
options’ was not properly accounted for.
The opening of a market to competition, for example, creates an option
to enter. These options are not
exercised in a smooth pattern, and can sit idle for a long time and then take off in
a burst (Grenadier, 1996).
The valuation of these options
is important because under existing practices, they are not accounted for, and
hence are missing in the financial evaluations of firms and industry (Alleman
and Noam, 2000).Traditional theory has it that investors look at
total risk-market risk plus financial risk. If they are additive, then high market risk in this sector should have been offset
by investor unwillingness to support highly leveraged capital structures. But, it seems just the opposite happened.
The downturn has also cast doubts on the Efficient market
hypothesis Efficient Market Theory. According to that hypothesis, which has been a cornerstone of finance theory, (see, e.g., Fama 1970) financial prices reflect
all available public information and hence are always correctly priced. Market
trading should eliminate
mispricing. Yet it is not easy to reconcile this view convincingly with the
extreme swings in stock prices in the telecom market. Obviously, new
information has become available. But much of it – on excess capacity,
commodification, economies of scale – had been widely available before.[ADD]
It was not information that had been missing, but its proper analysis.
3.3 Government
The wisest thing for government would be to ride out the cycle, but a hands-off policy might not be easy to maintain. Cyclicality is undesirable to government. When it comes to Schumperterian dynamics of “creative destruction”, governments dislike destruction even more than they fear creativity. Clearly, too much stability is also undesirable. See the example of Japan. What is the proper level of instability? Too little of it reduces innovation, but too much stability does the same. An optimum instability might exist, but it is difficult to agree on that level. Entire political philosophies hinge on the different views on the acceptable societal risk.
To the extent that volatility raises uncertainty it also raises the cost of producing telecom services, which is an essential and universal input. This has also some distributional implications such as fluctuations in employment. And through network effects, everybody is negatively affected. The losses from cyclicality can be substantial. Estimates for the economic losses from the oversupply in US of office space in the 1980s are $130 billion, in lost rents only, without counting the secondary effects of reduced tax receipts and negative multiplier impacts on the rest of the economy.
Imagine the impact of a telecom downturn on smaller countries where a telecom firm has a large presence and is affected by shocks originating from the outside. In Finland, Nokia accounts for 35% of exports and 14% of GNP. Similarly, the telecom sectors of less developed countries have become more volatile as they have opened up to the rest of the world and became engulfed in external instabilities over which they have no control.
All of this and more are reasons for government to fear cyclicality and to engage in counter-vailing policies, even though government policy may also be a contributing cause to cyclicality, for example through regulatory delay. If such involvement is likely, for better or for worse, what are the potential tools of government for dampening the cycles of the telecommunications industry? Some such tools are discussed in the following and they should be read, as a list of potential actions rather than as recommendations.
Flexibility
in taxes and other payments..
Such a policy tends
The US telecom industry is subject, to telecom
taxes beyond the the regular business taxes. Those
taxes could be automatically
adjusted through the cycle if the tax rates was levied on earnings rather
than on revenues. Similarly, spectrum
license auctions could be structured in a way that would collect payments over
the life of the license, with annual collections based on earnings, and would thus become thus an
automatic stabilizer.
Flexibility of retail price regulation.
Retail prices, if regulated, could be adjusted through the cycle, again by
automatic adjustments such as the inclusion of a growth rate factor in the
price-cap formula.
Flexibility of wholesale prices.
If one can pick a single variable that is potentially most effective in
influencing telecom prices and the relation of incumbent and competitors, it is
the wholesale price of interconnection.
That price, charged by carriers to
each other, is regulated, and is usually set by some result-oriented economic
methodology such as
TELRIC or ECPR (Noam 2001). Such
a price could be made variable and dependent on the state of the telecom
market. If the entrants falter,
as a group, the interconnection prices could be lowered. If incumbents weaken as a group, on the
other hand, one
would raise interconnection prices.
Some formula might be established in advance to deal with those
situations. Or, the regulatory agency
could vary the price to affect the sector in the same way that a central bank
uses the discount rate. This article is not the place to elaborate on this
and other proposals, but it aims to encourage thinking in that direction.
Such variability of prices may seem
to create uncertainty. But certainty
does not mean a fixed stability. It can also mean a dynamic regulation that adjusts
predictably over the cycle. In practical terms, it means that
The
problem with government policy is that it may be ineffective; or counter-acted
by firms’ counter-strategies (Barro 1980); or simply too slow in being
implemented. So to avoid that lag, counter-cyclical
government policies need to be as either automatic
as possible, and
indexed to defined variables. Even
then, such measures increase the complexity and extent of government
involvement.
Industrial Policy. Government could support the creation of demand in order to increase utilization of networks, or support new entry. Examples might be the creation or distribution of content such as distance education or health delivery, and the release of spectrum to new service providers.
Competition Policy. Perhaps most important is the government’s policy in permitting or preventing market power. The process of consolidation is far from over. Where competition exists, especially in long-distance and wireless, the number of carriers is likely to decline. The challenge is therefore to deal with an environment of potential oligopoly. On the one hand, oligopolies help generate greater profitability and lower volatility. The downside, of course, is that the users pay for this greater profitability in higher prices, potentially lower service quality, and slower innovation. Several of these problems might be dealt with onside of the market structure, by regulation such as price caps or minimum service level requirements, triggered by high levels of market concentration. Similarly, rules that keep the market open to potential entrants might establish a “contestability” that can be an effective dampener on oligopolistic behavior.
.
Conclusion
OBut over-expansion, by itself, is
a hallmark of health, not weakness. At one time or another, tEarly
railroads were vastly overbuilt in the US. here were hundreds of
companies making automobiles, motorcycles, airplanes, tires, and
microcomputers. One of the functions of
slowdown is consolidation. That is, to reduce competition, t. To
reduce the commodification that lowers profitability and future investments.
This must be a telecom firm’s overriding strategy (together with designing the
firm for downward flexibility and avoiding excessive debt, to deal with the
next cycle if they survive the present one). The present contraction will
therefore inevitably raise industry concentration, slow innovation, reduce
capacity expansion, and raise prices. (This strategy will, of course, be
publicly denied by the survivors). Regrettably but realistically, wWhat
will turn the telecom industry around will not be more competition but more of
an oligopolisticy
market structure, coupled with increasing demand. (A
positive technology shock might also do the job, but one cannot base the future
of essential infrastructure industries on unexpected events.)
We have analyzed the
strategic options of telecom firms and of their investors, and concluded that
the most likely scenario is one of consolidation. Once recovery is on its way,
additional competitive entry is likely again. In time, such competition will
generate overcapacity, plummeting prices, and the cycle will turn downwards
again, with marginal firms failing.
For public policy, this suggests,
as
oneseveral
alternatives. The first alternative is
to let nature take its course through the business cycle, relying on natural
contractions and expansion cycles. This approach recognizes realistically
the difficulty in
identifying problems and creating timely and workable solutions to them. However, this policy is less likely to be
chosen by politically sensitive regulators when the downturn persists, when
essential service providers falter, and when service quality deteriorates.
The second option is for government to take an activist, almost macro-economic, approach to the sector and try to raise it from recession. This would involve significant and ongoing intervention. A related but less intrusive strategy would be to automatically adjust existing rules and requirements over the business cycle, as discussed in the section above.
The third option is not to focus on
the downturn itself but on the responses by the telecom industry to it. This would mean, a benign
neglect of oligopoly in the downturn. It would mean that the vigor of
pro-competition policies itself should be cyclical, and take into account the
macro state of the industry. This would require a fairly radical departure from
the regulatory philosophy of the past 20 years. For a generation now, liberalization, deregulation, and
competition have been the keystones of telecom policy and strategy. Now, one business cycle later, and facing
future volatility, we
may have to get used to the idea of living with oligopoly in telecom rather than the
hoped-for
competition.
The volatility of the telecom
sector thus points to a scenario of market power and regulation, not of
competition and of the withering away of governmental intervention. Government policy, investor behavior, and telecom
management will all have to become responsive to periodic volatility and vary
across it. Thus, the The cyclicality
of the industry will thus lead to a cyclicality of behavior within and towards
the industry. And the effect of such
volatility on the telecom market structure will lead to a new model of
regulatory policy. The second alternative
is to let nature take its course through the business cycle, relying on natural
contractions and expansion cycles. That policy
is less likely to be chosen by politically sensitive regulators when the
downturn persists, when essential service providers falter, and when
service quality deteriorates. A hands-off policy assures that government’s
regulatory process does not add the kind of lags that create or accentuate
oscillations in response. .
[Insert W]
Telecom stock prices, using February 1996 as the
benchmark of 100, rose from about 70 in January 1995 to 225 in March 2000, and
then fell back to about 80 in March, 2002 (Standard and Poor’s in Alleman, 2002). Of 28 publicly traded competitive local
exchange companies, 13 went bankrupt in 2001. [add earnings, investments and
reven
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