Beyond Antitrust: The Role of Competition Policy in Promoting Inclusive Growth
Jason Furman
Chairman, Council of Economic Advisers
Searle Center Conference on Antitrust Economics and Competition Policy
Chicago, IL
September 16, 2016
This is an expanded version of these remarks as prepared for delivery.
Thank you very much for inviting me to today’s conference. Discussions of competition often
center on issues of antitrust enforcement. Those are important issues, but I will not address them
in my remarks today because they are enforcement questions that are within the purview of the
Antitrust Division of the Justice Department and the Federal Trade Commission (FTC). I will
argue, though, that public policy can play an important role in promoting competition that goes
well beyond traditional antitrust enforcement.
The Administration has focused on competition policy in a wide range of areas, from airport
slots to standards essential patents to spectrum allocation. Most recently, this past April, the
President signed an Executive Order calling on agencies to identify creative actions that they can
take to promote competition. The Executive Order calls on agencies to maintain a focus on
competition policy in the future by submitting proposed actions on a semi-annual basis. The
Administration is currently reviewing the first set of proposals from agencies on how we can use
public policy to promote competition, a number of which will be announced in the coming
months.
The first action undertaken as part of this Executive Order was the Administration filing in
support of the Federal Communication Commission's (FCC) proposed rule to bring increased
competition to the market for cable set-top boxes. We have been pleased to see FCC Chairman
Wheeler actively listen to the many stakeholders involved to improve the proposal, and believe
that he is charting out a responsible way to address their meaningful concerns while being
responsive to Congress's explicit directive to ensure a healthy set-top marketplace.
In conjunction with the Executive Order, the Council of Economic Advisers (CEA) released an
issue brief documenting some of the evidence suggesting a reduction in competition throughout
the economy. Our findings are consistent with recent arguments from academic papers such as
Bennett and Gartenberg (2016), and other observers, including The Economist and the Center for
American Progress (CAP), stating that competition in the U.S. economy has declined in recent
years (The Economist 2016; Jarsulic et al. 2016).
Part of the underlying motivation for the Administration’s efforts is the belief that competition
can play an important and broader role not just in static, allocative efficiency but also in dynamic
efficiency—making the economy more innovative and increasing productivity growth. In
addition, there is also increasing evidence that greater competition or more evenly balanced
power in some areas could also play a role in reducing some of the causes of inequality.
2
In my remarks today, I will start by quickly reviewing some of the evidence for greater
concentration in the economy, then provide some broad macroeconomic motivation, before
discussing a few specific areas that the Administration is working on, with a focus on some of
the difficult questions raised by the rapid evolution of technology in recent years.
What Is the Evidence on the Trends in Concentration?
The CEA issue brief released earlier this year reviewed some of the evidence on increased
concentration in the economy. The majority of industries have seen increases in the revenue
share enjoyed by the 50 largest firms between 1997 and 2012 (Table 1). Along similar lines, The
Economist (2016) found that in 42 percent of the roughly 900 industries examined, the top four
firms controlled more than a third of the market in 2012, up from 28 percent of industries in
1997. Of course, an increase in revenue concentration at the national industry level is neither
necessary nor sufficient to indicate increases in market power: the sectors listed here are much
larger than the relevant markets, whether in terms of sub-sectors or geography, and 50 firms is
likely well above the number that would mark an industry as competitive. Nevertheless, it is one
metric among many that create a snapshot of the current state of competition in today’s
economy.
Table 1
These broad trends are consistent with a number of industry-specific studies tracking
concentration over longer periods of time:
Industry
Revenue Earned
by 50 Large s t
Firms, 2012
(Billions $)
Revenue Share
Earned by 50
La rge s t Fi rms ,
2012
Percentage Point
Change in Revenue
Share Earned by 50
Largest Firms, 1997‐2012
Transportation and Warehousing 307.9 42.1 11.4
Retail Trade 1,555.8 36.9 11.2
Finance and Insurance 1,762.7 48.5 9.9
Wholesale Trade 2,183.1 27.6 7.3
Real Estate Rental and Leasing 121.6 24.9 5.4
Utilities 367.7 69.1 4.6
Educational Services 12.1 22.7 4.2*
Professional, Scientific and Technical Services 278.2 18.8 2.8*
Arts, Entertainment and Recreation 39.5 19.6 2.5*
Administrative/ Support 159.2 23.7 1.6
Health Care and Assistance 350.2 17.2 0.8*
Accommodation and Food Services 149.8 21.2 0.1
Other Services, Non‐Public Admin 46.7 10.9 ‐0.2*
Change in Market Concentration by Sector, 1997-2012
3
• In financial services, a study found that the loan market share of the top ten banks
increased from about 30 percent in 1980 to about 50 percent in 2010 (Corbae and
D’Erasmo 2013).
• The share of revenues held by the top four firms increased between 1972 and 2002 in
eight of nine agricultural industries tracked in a Congressional Research Service
study (Shields 2010).
• According to Gaynor, Ho, and Town (2015), hospital market concentration increased
from the early 1990s to 2006. The authors found that the average Herfindahl-
Hirschman Index (HHI), a commonly used measure of market concentration,
increased by about 50 percent to about 3,200, the level associated with just three
equal-sized competitors in a market.
1
• Wireless providers saw increased concentration, with the FCC (2015) finding that the
average HHI in the markets they examined increased from under 2,500 in 2004 to
over 3,000 in 2014.
• Railroad market concentration increases between 1985 and 2007 have been
documented by Prater et al. (2012).
While these facts all suggest that concentration has increased, it is also necessary to consider the
causes of that increase in concentration. Our normative evaluation of the policy implications
would differ depending on whether this increase is the result of greater economies of scale, or the
result of artificial barriers to entry. The causes may also vary from sector to sector or across
geographic markets. This is why even though the broader motivation is important, any particular
policy issue area should be evaluated on its own merits—which is what I attempt to provide a
sampling of below.
Seven Broader Macroeconomic Trends and Their Relationship to the Competitive
Landscape
But before getting to these more specific issues, I want to spend a few moments on some broader
macroeconomic trends that are consistent with increased concentration and decreased
competition coming specifically from barriers to entry (and in the case of the labor market,
barriers to mobility), and on some of their macroeconomic consequences. Let me highlight seven
of them:
1
The Herfindahl-Hirschman Index (HHI) is a commonly used measure of market concentration that is created by
summing up the squared shares of firms in a market. Higher values of the HHI indicate higher market concentration;
it can be close to zero when a market is comprised of a large number of firms of small size and reaches a maximum
of 10,000 when a market is controlled by a single firm. Antitrust agencies generally consider markets in which HHI
is between 1,500 and 2,500 to be moderately concentrated, and consider markets in which the HHI is in excess of
2,500 to be highly concentrated (see https://www.justice.gov/atr/herfindahl-hirschman-index for more detail).
4
1. The economy has seen a slowdown in the creation of new businesses, as the average business
is now older and the top firms capture more market share.
Since the 1980s, young firms (those five years old or less) have been declining as a share of the
economy. In 1982, young firms accounted for about half of all firms, and one-fifth of total
employment. However, these figures have fallen to about one-third of firms and one-tenth of
total employment in 2013 (Figure 1).
Figure 1
Much of this decrease is driven by declining firm dynamism—the entry and exit of firms. While
firm exit has been remained relative steady since the late 1970s, the firm entry rate has decreased
significantly since the late 1970s (Figure 2).
Figure 2
Share of total
firms (left axis)
2013
Share of total employment
(right axis)
10
15
20
25
0
10
20
30
40
50
60
1980 1985 1990 1995 2000 2005 2010
Firm exit
rate
2013
Firm entry
rate
6
8
10
12
14
16
1975 1980 1985 1990 1995 2000 2005 2010
Percent
5
A partial explanation for the decline in firm entry rates may be found in increased barriers to
entry. These barriers to entry can come in the form of advantages that have accrued to
incumbents over time. For example, increased economies of scale may mean that incumbents
experience lower costs than new firms, making it harder for entrants to compete. Or demand-side
network effects—when a product or service increases in quality the more people use it––may tip
the scale in favor of a single provider. Incumbent advantages may also come in the form of
successful political lobbying, in which incumbent firms have the resources to lobby for rules that
protect them from new entrants.
2. Labor markets have become less fluid, with workers less likely to move between jobs,
industries, occupations, and locations.
Like firm dynamism, labor market dynamism—also known as “fluidity” or “churn,” measured as
the frequency of changes in who is working for whom in the labor market—has been declining in
recent decades. The causes and consequences of this decline are still not entirely clear: on the
one hand, lower levels of churn may suggest better worker-employer matching, but may also be
a particular cause for concern given that, for many workers, wage increases typically occur at the
point of job-switching (Molloy et al. 2016).
We know relatively more about job flows (job creation and destruction) than worker flows (hires
and separations) since series data are available back to the 1980s. Literature based on these data
concludes that job flows have markedly declined over the last 20 to 30 years. For example,
Decker et al. (2014) and Davis and Haltiwanger (2014) document that job creation and job
destruction fell from the late 1980s to just before the 2007 recession, as shown in Figure 3. Hyatt
and Spletzer (2013) find larger declines, of roughly one-quarter to one-third, for both job
creation and destruction between the late 1990s and 2010.
Figure 3
Although data on worker flows are more recent, they too show evidence of reduced fluidity.
There has been a long-run downward trend in job-to-job transitions since at least 2000 (Hyatt
Job Creation
2013
Job Destruction
10
12
14
16
18
20
22
1975 1982 1989 1996 2003 2010
Labor Market Dynamism, 1977-2013
Rate (Percent)
6
and Spletzer 2013). Other measures of worker mobility that extend further back in time also
show evidence a steady decline. Long-distance migration in the United States, which typically
involves a change of employer or labor force status, has also been in a decades-long decline,
falling by as much as 50 percent since the late 1970s (Molloy et al. 2014; Kaplan and
Schulhofer-Wohl 2012). Both intra- and inter-county migration have followed similar patterns,
as shown in Figure 4.
Figure 4
While the causes of decreased labor market dynamism are not well understood, research has
shown that it is related both to changes in firm composition—with employment being
increasingly concentrated in older, larger firms—and to declines in worker movements between
existing jobs (Davis and Haltiwanger 2014; Hyatt and Spletzer 2013). Both market concentration
and frictions that reduce worker mobility can lead to greater monopsony power for employers.
With fewer firms competing for a given type of worker, each firm is more likely to exercise local
monopsony, and their smaller numbers may also facilitate tacit or explicit collusion. If, on top of
that, employees face greater search frictions or costs of moving, then this reduces their ability to
raise their wages by changing jobs and thus also reduces their bargaining power with their
current employer (Manning 2003).
3. The share of income going to capital has risen, and the share of income going to labor has
fallen.
Up until recently, the share of income in the nonfarm business sector accruing to labor was
generally stable, though it varied somewhat from year to year depending on the economy’s
cyclical position. This was considered such a strong empirical regularity that it was enshrined in
the pantheon of Nicholas Kaldor’s stylized facts about growth (Kaldor 1957). But starting around
2000, the distribution of income between labor and capital shifted noticeably away from the
former and towards the latter, as shown in Figure 5. Today, the labor share of income is in the
mid-50s, compared to the mid-60s two decades ago. A large literature has examined this decline
in the labor share of income, and candidate explanations include institutional changes, including
Inter‐county,
same state
(left axis)
Intra‐
county
(right axis)
2013
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
1948 1958 1968 1978 1988 1998 2008
7
the decline of private-sector unions and fall in the real value of the minimum wage; the general
reduction in competition shifting the balance of bargaining power towards employers; and skill-
biased technological change (Elsby, Hobijn, and Şahin 2013; Karabarbounis and Neiman 2013;
Blanchard 1997; Bentolila and Saint-Paul 2003; Azmat, Manning, and van Reenen 2011;
Harrison 2005; Jaumotte and Tytell 2007).
Figure 5
4. The rate of return on capital has risen relative to the safe rate of return.
Since the 1980s, the safe rate of return, as measured by real interest rates on government bonds,
has fallen steadily. However, the rate of return on capital—both all private capital and
nonfinancial corporate capital—has held steady or even increased over the same period,
mirroring, at least in the last decade and a half, the share of income going to capital instead of to
labor (Figure 6).
54
56
58
60
62
64
66
68
1948 1958 1968 1978 1988 1998 2008
Percent
2016:Q2
8
Figure 6
One explanation of the apparent increase in the premium on the return to capital is that it is
another manifestation of the decrease in the labor share of income and associated increase in the
capital share of income. This, in turn, is consistent with the increased prevalence of economic
rents and a broader trend of reduced competition, although other explanations, including the
changing risk characteristics of returns to private capital or government bonds could also be
playing a role (Kozlowski, Veldkamp, and Venkateswaran 2015; Campbell, Pflueger, and
Viceira 2014).
5. But businesses are investing less.
Contrary to what economic theory would predict, the higher returns to capital have not been
associated with an increase in business investment. In fact, business investment has been
particularly weak in recent years. Some of most recent weakness likely represents temporary
adjustments to transitory factors, like low oil prices, but nonresidential fixed investment as a
share of overall GDP has shown a downward trend since the 1980s, as shown in Figure 7. Again,
multiple explanations are possible. One explanation is that monopoly power has increased—
which is consistent with higher returns and lower output.
2014
‐4
‐2
0
2
4
6
8
10
12
1985 1990
1995 2000 2005 2010
Returns to Capital
Percent
Return to Nonfinancial
Corporate Capital
Return to All Private Capital
One‐Year Real Interest Rate
9
Figure 7
Low levels of business investment are particularly troubling because of their impact on
productivity growth via capital deepening. The largest contributor to recent low productivity
growth has been the decline, for the first time since World War II, in capital services per worker-
hour in the last five years—due to both slower investment growth and a large increase in worker
hours. As a result, a worker today has less capital at his or her disposal than a worker five years
ago.
6. The rate of return across businesses has become increasingly dispersed.
Recent years have also seen dramatic increases in the dispersion of returns to firms, as returns on
invested capital for publicly-traded U.S. nonfinancial firms have become increasingly
concentrated. Figure 8 indicates that the 90
th
percentile firm sees returns on investment in capital
that are more than five times the median. The ratio was closer to two just a quarter of a century
ago.
2016:Q2
8
9
10
11
12
13
14
15
16
1950 1960 1970 1980 1990 2000 2010
Business Fixed Investment as Share of GDP
Percent
10
Figure 8
This concentration of returns among a small number of firms raises the question of whether, and
to what extent, economic rents may be playing a role here, too. The data show that two-thirds of
the non-financial firms enjoying an average return on invested capital of 45 percent or higher
between 2010 and 2014 were in either the health care or information technology sectors,
industries where, as mentioned above, other measures point to a reduction in competition
(Furman and Orszag 2015).
7. Wage inequality has grown substantially between workers at different businesses and
establishments.
As is often noted, income inequality has increased in the United States over the last several
decades. Recent research from Song et al. 2016 suggests that especially among firms with 100 to
1,000 employees (which contain over 70 percent of employees and 99 percent of firms), a large
share of this inequality is driven by increased divergence in the average earnings of workers in
between different firms rather than a divergence of the wages within the same firm. This finding
is consistent with evidence from Barth et al. (2014) on establishments. Figures 9a and 9c show
that while individual wage disparities have clearly risen, so too have disparities among firms.
Figure 9b, which shows the individual wage structure divided by the firm wage structure,
demonstrates that between-firm changes account for much of the increased dispersion in
individual wages. These trends could indicate that a prime driver of inequality is the difference
between the most and least profitable companies, although it also may reflect the sorting of
workers with different abilities across firms—the subject of a long-standing debate over inter-
industry wage differentials (Krueger and Summers 1988; Katz 1992; Abowed et al. 2012).
Median
90th
Percentile
2014
75th
Percentile
25th Percentile
0
20
40
60
80
100
120
1965 1975 1985 1995 2005 2015
Percent
11
Figure 9
Change in Percentiles of Annual Earnings Within and Between Firms Relative to 1981
These seven trends are not concrete proof of decreased competition stemming from barriers to
entry, but that is one explanation consistent with the facts I have presented. Declining firm
dynamism, high returns and low output, and disparities in the rate of return on investment are all
potential consequences of increasing barriers to entry.
Some Pro-Competition Policy Applications
To the extent that these macroeconomic trends are related to decreased competition, then pro-
competitive policies have potential to not only benefit consumers but also improve the state of
the macroeconomy by, for example, increasing productivity and ensuring that the benefits of
growth are widely shared. For these reasons, the Administration has taken several significant
policy actions to promote competition. I will next briefly touch on four examples.
Intellectual Property and Patent Reform
The first area I will discuss is in some senses the intellectually and substantively hardest:
intellectual property and patent reform. In this case, of course, intellectual property protections
25th
Percentile
50th
Percentile
90th
Percentile
99th
Percentile
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1978 1982 1986 1990 1994 1998 2002 2006 2010 2014
(a) Individuals: Change in Wage Structure Since 1981
Change in Log Real Annual Wage
2013
25th Percentile
50th Percentile
90th
Percentile
99th
Percentile
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1978 1982 1986 1990 1994 1998 2002 2006 2010 2014
(b) Individuals/Firms: Change in Wage Structure Since 1981
Change in Log Real Annual Wage
2013
25th
Percentile
50th
Percentile
90th
Percentile
99th
Percentile
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1978 1982 1986 1990 1994 1998 2002 2006 2010 2014
(c) Firms: Change in Wage Structure Since 1981
Change in Log Real Annual Wage
2013
12
are intended to increase innovation by granting temporary monopoly power—increasing the
private rate of return to investments in research that might otherwise have been competed away.
But it has also long been understood that a balance needs to be struck between the dynamic
incentives conferred by intellectual property and the static costs of the monopoly power, a
balance that is manifested in the finite lives and limited scope of patents, trademarks and
copyrights. Moreover, it is increasingly understood that overly stringent intellectual property
practices can impede innovation itself—including by reducing the follow-on innovation that so
often can be important, especially in areas like technology.
These considerations have played a role in the Administration’s approach to patent policy. For
example, many of the interconnected services available today require different firms to use the
same standard technology. The Administration recognized that if that technology was patented,
the patent holder could exercise excessive power and “hold up” the ecosystem over a “standards
essential patent” that was necessary for increasingly interconnected devices to work together. In
response to this, the U.S. Patent and Trade Office and DOJ came together to provide guidance to
the International Trade Commission (ITC) and suggest ways to prevent that hold up—guidance
that was the basis for the President’s decision to block the ITC’s exclusion order on certain
smartphones based on a claim that they had infringed a standards essential patent.
A second example of the Administration’s patent policy is its work to boost patent quality and
limit the ability of overly aggressive patent assertion entities to quell innovation. In 2011 the
America Invents Act put in place new mechanisms for post-grant review of patents and other
reforms to boost patent quality. Further, to hasten the patent litigation process, accused infringers
have the opportunity to challenge the patentability of a claim through an inter partes review,
which is handled by the Patent Trial and Appeal Board rather than a Federal court (which
handles the appeals process). This process for challenging the validity of a patent provides a
quick, inexpensive alternative to district court litigation, and should help improve patent quality
and ultimately reduce frivolous litigation.
Increasing the Bargaining Power of Workers
Generally, it is a goal of economic policy to increase competition and then let the market
discover prices. In some markets, however, some monopoly or monopsony power is inevitable.
In the case of monopsony, the labor market is one leading and important example because search
costs and other labor market frictions make it hard for employees to shop around for another
employer any time they experience changes in their wages or job conditions. Considerations like
commuting costs, which tie employees to their current employers, give those employers some
power to set the parameters of pay negotiations or even pay lower wages.
There is no reason to think incentives to exercise market power are any less powerful in the labor
market than they are in the product market. Even as far back as Adam Smith (1776) economists
have noted that:
What are the common wages of labor, depends everywhere upon the contract usually made
between [employers and employees], whose interests are by no means the same. The
workmen desire to get as much, the masters to give as little as possible. The former are
disposed to combine in order to raise, the latter in order to lower the wages of labor. It is not,
13
however, difficult to foresee which of the two parties must, upon all ordinary occasions,
have the advantage in the dispute, and force the other into a compliance with their terms. The
masters, being fewer in number, can combine much more easily…
As this quote suggests, employers can more easily dictate the level of wages and other terms of
employment when they are few in numbers—and this is a potential concern with the rising
concentration of the U.S. markets. (However, since explicit and illegal wage collusion is a matter
under the purview of enforcement agencies, I will not discuss such issues further.)
However, employers can also shift the balance of power in their favor by means that are legal in
many States, including through the increasingly widespread practice of non-compete agreements.
By reducing workers’ job options, non-compete agreements force workers to accept lower wages
in their current jobs, and may sometimes induce workers to leave their occupations entirely,
foregoing accumulated human capital (U.S. Treasury 2015). By one estimate, 18 percent of those
in the U.S. labor force, or roughly 28 million people, are currently covered by non-compete
agreements (Star, Bishara, and Prescott 2016). While such agreements can sometimes promote
innovation through the protection of trade secrets, they are common among workers who are less
likely to possess such secrets, especially lower-skilled workers (U.S. Treasury 2015).
Other frictions that reduce worker mobility and increase monopsony power can occur naturally.
These include the costs of moving, commuting, and searching for another job. And labor market
frictions can also be created by restrictions such as occupational licensing laws and overly
stringent land-use policies that drive up housing costs. Regardless of the source, such frictions
effectively reduce competition among firms in the market for labor. With fewer competitors,
employers are able to pay lower wages, and they have an incentive to do so—even if this means
reducing employment and forgoing some productive employment relationships.
While enforcement can and does play a role in promoting competition in labor markets, some
market power is inevitable and policy should concern itself with how this power is balanced.
Traditionally, monopsony power in labor markets was countered in the United States by two
institutions—unions and minimum wage laws. An important benefit was distributional: both
unions and minimum wages helped bolster the wages of lower- and middle-wage workers and, in
turn, helped reduced inequality. But to the extent that they helped to counter monopsony power,
they also helped to limit inefficiently low employment that results when firms pay sub-
competitive wages.
But union membership has declined consistently since the 1970s, as shown in Figure 10.
Approximately a quarter of all U.S. workers belonged to a union in 1955 but, by 2015, union
membership had dropped to just below 10 percent of total employment, roughly the same level
as the mid-1930s. In some states, just 3 percent of workers belong to unions (CEA 2015).
14
Figure 10
At the same time, the real value of the minimum wage has declined 24 percent since its peak of
$9.55 in 1968 (Figure 11), eroding its ability to protect those workers with the fewest options and
the least bargaining power.
Figure 11
Reforming Occupational Licensing
One example of policies that create inefficient and inequitable rents is the requirement of a
government-issued license to be employed in certain professions (“occupational licensing”). The
share of the U.S. workforce covered by State licensing laws grew five-fold in the second half of
the 20th century, from less than 5 percent in the early 1950s to 25 percent by 2008, as shown in
Figure 12 (Kleiner and Krueger 2013). While licensing can play an important role in protecting
Troy and
Sheflin (1985)
CPS:
Membership
0
10
20
30
40
50
60
70
80
1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 2015
Union Membership as a Share of Total Employment and
Bottom 90 Percent Income Share, 1915-2015
Percent
Bottom 90 Percent Share of Income
2015
2015
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
1960 1970 1980 1990 2000 2010
Real Value of the Federal Minimum Wage, 1960–2015
2015 Dollars
15
consumer health and safety, there is evidence that some licensing requirements create economic
rents for licensed practitioners at the expense of excluded workers and consumers—increasing
inefficiency and potentially also increasing inequality (Furman 2015).
Figure 12
Not only have licensing laws proliferated in recent years, they also vary dramatically across
States. The patchwork of State regulations and the lack of reciprocity agreements has raised the
cost of moving across State lines for workers in licensed occupations, and may be one factor
contributing to the decline in geographic mobility (Department of the Treasury, Office of
Economic Policy, Council of Economic Advisers, and Department of Labor 2015).
In 2015, the Administration released a series of best practices to help State and local
governments better tailor their occupational licensing laws to meet consumer health and safety
needs without acting as undue barriers to entries into particular occupations. And this summer,
the Department of Labor invested $7.5 million to support States’ efforts to increase the
portability of licenses across State lines and to lower barriers to enter the labor market through
reforming licensure. Since the release of the best practices and recommendations last year,
legislators in at least 11 States have proposed 15 reforms in line with these recommendations,
and four State bills have passed so far.
Reforming Land-Use Regulation
Competition policy also has applications beyond traditional product or labor markets. One such
area is in the housing and land sectors. Nationwide, real house prices have grown substantially
faster than real construction costs since at least the mid-1980s, implying that returns to
scarcity—i.e., “rents” in the economic sense—have played an important role in house prices,
reducing the stock of affordable housing (Gyourko and Molloy 2015).
2008
0
4
8
12
16
20
24
28
1950s 1960s 1970s 1980s 1990s 2000 2008
Share of Workers with a State Occupational License
Percent
16
Figure 13
Numerous studies, including Glaeser and Gyourko (2003) and Gyourko and Molloy (2015) have
argued that land-use regulations are what explain these occurrences of prices that substantially
exceed construction costs. As with occupational licensing, well-designed land-use restrictions
can play an important role in promoting social welfare. Environmental reasons may make it
appropriate to limit high-density or multi-use development in some localities. Similarly, health
and safety concerns—such as an area’s air traffic patterns, viability of its water supply, or its
geologic stability—may merit height and lot size restrictions.
But in a number of cases, overly burdensome land-use restrictions—like minimum lot sizes, off-
street parking requirements, height limits, prohibitions on multifamily housing, or lengthy
permitting processes—can instead artificially reduce competition by acting as supply constraints.
In doing so, such policies both allow a small number of landowners to capture economic rents
and reduce the stock of available affordable housing. These constrains can also limit productivity
growth and labor mobility by making it more difficult for workers to move to higher-productivity
cities (Furman 2015).
Moreover, inappropriate land-use policies can also reduce equity by allowing a small number of
individuals to enjoy the benefits of living in a community while excluding many others, limiting
diversity and economic mobility. This is of particular concern given recent research by Chetty et
al. (2014) showing that economic mobility varies greatly across cities. Moreover, moving from a
low to a high mobility area confers lifelong socio-economic benefits on the children whose
families move (Chetty at al. 2015).
While most land-use regulations are appropriately made at the State and, especially, the local
level, the Federal government can also play a role in encouraging land-use regulations that help,
and do not hinder, mobility and economic growth. This month, the Administration will release a
new toolkit that highlights actions that States and local jurisdictions are taking to promote
Real House Prices
2013
Real Construction Costs
70
110
150
190
230
1980 1984 1988 1992 1996 2000 2004 2008 2012
Index
(1980 = 100)
17
affordable, high-opportunity housing markets. These best practices—including streamlining
permitting processes, eliminating off-street parking requirements, reducing minimum lot sizes,
and enacting high-density and multifamily zoning policies—provide a starting point for other
local efforts to reduce overly burdensome land-use policies.
The Future of Competition in the Digital Age
One topic we have been grappling with in a range of economic issue areas, including competition
policy, is the ever-increasing role that digitization plays in our economy. The digital age has the
potential to increase competition in many ways, but at the same time, changing technology will
bring new challenges to policymakers, challenges that will come increasingly to the fore as the
digital economy expands.
So far, internet markets have tended to favor digital giants that hold high market shares, a
characteristic that is traditionally associated with low competition in brick-and-mortar markets.
However, understanding the competitive implications of these new markets requires a closer
analysis. The markets of the digital economy are in many ways different from “old economy”
markets. Some of those differences are differences of degree—the internet lowers many costs for
small businesses, increasing their ability to rapidly and inexpensively scale up, collect
information on potential consumers, and create new products and ideas. These differences do not
transform the structure of the market; instead, they merely lower the cost of doing business.
Other differences, however, are differences of type: business models may be dramatically
different due to digitization. These differences of type warrant closer consideration.
One type of business model that has flourished with digitization is the “platform” model, which
relies heavily on network effects to grow because the primary product is access to other
customers. Examples include payment platforms like PayPal, sales platforms like eBay, and
social networks like Facebook. Switching costs for customers are particularly high in these
markets—no one wants to be the first and only user of a platform—and these network effects can
act as a barrier to entry.
However, it is not as clear whether these “quasi-monopolies” pose the same harm to consumers
as traditional monopolies. In these markets, highly concentrated market share might not be as
detrimental to customers as in traditional markets because the services provided by these
businesses are more valuable to consumers as their consumer base grows. This means that
determining the optimal level of competition in these new markets is a dramatically different and
harder task.
Even the task of measuring competition is complicated in digital markets. Usually, economists
use prices as indicators of the level of competition, but we cannot necessarily do that here
because many markets are two-sided and there are different types of consumer harm. Businesses
on the internet are often complementary, so companies may subsidize one side of the market by
profiting from the other side of the market. For example, social media sites often offer free
services to users and charge for ads. However, the lack of high prices for consumers does not
mean that consumer harms or other risks could not occur. Industry watchers have raised concerns
18
about whether the large companies that dominate search and social networking may be able to
acquire inefficient power in ads or control people’s access to news. Another concern is that
instead of raising prices or reducing quantity, these companies may reduce innovation. Firms
holding quasi-monopolies may lose the incentive to keep improving the quality of their products.
Switching costs are traditionally an indicator of competition, and many may assume that
switching costs in internet markets are virtually zero because competition is just a click away.
This may have been true in the early ages of the internet, but to automatically assume zero
switching costs now would be to miss a large part of what is happening. For example, the
original search engines were merely directories of websites, and their quality didn’t depend on
how many users they had. However, search engines today collect data on the behavior of their
users and use it to improve their services and tailor those services to individual users. Thus, in
order for other firms to be competitive, they need a large user base and the data that comes with
it. Furthermore, for each individual user looking to switch services, the incumbent, with its
existing knowledge of that user, has a significant advantage over a competitor that does not yet
know the user and therefore cannot tailor services to him or her.
Lastly, digitization could bring a new level of opacity to businesses. Traditionally, price fixing
and collusion could be detected in the communications between businesses. The task of detecting
undesirable price behavior becomes more difficult with the use of increasingly complex
algorithms for setting prices. This type of algorithmic price setting can lead to undesirable price
behavior, sometimes even unintentionally. The use of advanced machine learning algorithms to
set prices and adapt product functionality would further increase opacity.
Competition policy in the digital age brings with it new challenges for policymakers. It will be
imperative that agencies continue the great work and creative solutions that came out of the
President’s Executive Order to promote competition and inclusive growth in the digital age.
Conclusion
Recent trends in concentration in a range of industries suggest decreasing levels of competition,
and many concerning macroeconomic trends seem to suggest that this decrease not just due to
increases in economies of scale, but rather that increases in barriers to entry are playing a role.
For the sake of both consumers and the macroeconomy as a whole, the Administration has used
and will continue to use public policy to address these concerns. Increasing competition has the
potential to drive faster productivity and output growth, faster real wage growth, and increased
equity. We have moved forward in areas such as intellectual property and patent reform,
increasing worker bargaining power, and reforming occupational licensing and land use
regulations. While these are examples of positive changes, our work in promoting competition
does not end here. The President’s Executive Order will continue to encourage agencies to
develop creative solutions for increasing competition by soliciting new ideas on a regular basis.
In considering the future of competition policy, we must also keep in mind the way in which
changes in the economy, such as digitization, will affect how we evaluate competition
effectively.
19
Notes to Tables and Figures
Table 1
Note: Concentration ratio data is displayed for all North American Industry Classification
System (NAICS) sectors for which data are available from 1997 to 2012. * indicates that the
percentage point change is calculated using only taxable firms in that industry, as its 1997
revenue share data are only available for the 50 largest taxable firms and the 50 largest tax-
exempt firms as separate categories, rather than for all firms combined. Performing this same
calculation using data for only tax-exempt firms results in two additional industries showing a
decline in concentration (Arts, Entertainment and Recreation, and Educational Services), while
one shows a slight uptick (Other Services).
Source: Census Bureau, Economic Census (1997 and 2012).
Figure 1
Note: Young firms are of age 5 years or less.
Source: Longitudinal Business Database 1977-2013.
Figure 2
Source: Longitudinal Business Database 1977-2013.
Figure 3
Source: Longitudinal Business Database 1977-2013.
Figure 4
Source: Molloy, Smith, and Wozniak (2014).
Figure 5
Notes: Shading denotes recession.
Source: Bureau of Labor Statistics, Productivity and Costs.
Figure 6
Notes: Shading denotes recession.
Source: Bureau of Economic Analysis; Federal Reserve; Bureau of Labor Statistics.
Figure 7
Notes: Shading denotes recession.
Source: Bureau of Economic Analysis.
Figure 8
Note: The return on invested capital definition is based on Koller et al (2015), and the data
presented here are updated and augmented versions of the figures presented in Chapter 6 of that
volume. The McKinsey data includes McKinsey analysis of Standard & Poor’s data and exclude
financial firms from the analysis because of the practical complexities of computing returns on
invested capital for such firms.
Source: Koller et al. (2015); McKinsey & Company; Furman and Orszag (2015).
20
Figure 9
Notes: Only firms and individuals in firms with at least 20 employees are included. Only full-
time individuals aged 20 to 60 are included in all statistics, where full-time is defined as earning
the equivalent of minimum wage for 40 hours per week in 13 weeks. Individuals and firms in
public administration or educational services are not included. Firm statistics are based on the
average of mean log earnings at the firms for individuals in that percentile of earnings in each
year. Data on individuals/their firms are based on individual log earnings minus firm mean log
earnings for individuals in that percentile of earnings in each year. All values are adjusted for
inflation using the PCE price index.
Source: Song et al. (2016).
Figure 10
Note: Total employment from 1901 to 1947 is derived from estimates in Weir (1992). For 1948
to 2014, employment data are annual averages from the monthly Current Population Survey.
Source: Troy and Sheflin (1985); Bureau of Labor Statistics, Current Population Survey; Weir
(1992); World Wealth and Income Database; CEA calculations.
Figure 11
Note: Adjusted for inflation using the CPI-U-RS.
Source: Department of Labor; Bureau of Labor Statistics, Consumer Prices; CEA calculations.
Figure 12
Source: Council of State Governments (1952); Greene (1969); Kleiner (1990); Kleiner (2006);
and Kleiner and Krueger (2013), Westat data; CEA calculations.
Figure 13
Source: Gyourko and Molloy (2015).
21
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