American Economic Review 101 (June 2011): 1092–1105
http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.4.1092
Equilibrium in the Labor Market with Search Frictions†
By Christopher A. Pissarides*
Research in the economics of the labor market when there are search frictions
started in the 1960s, with influential contributions from George Stigler (1962), John
McCall (1970), and the papers collected in Edmund Phelps et al. (1970). Of course,
as is common in economics, this was not the first time that economists took seriously the role of search frictions in their research. Insightful discussions of the role
of frictions in labor market equilibrium can be found much earlier, in books by John
Hicks (1932) and William Hutt (1939). But it was not until the late 1960s that formal
mathematical models of individual behavior and labor market equilibrium appeared.
The development of formal mathematical models with search frictions coincided
with the time that I was looking for a topic for my PhD research.1 Two things about
search impressed me most. In the Phelps volume, search was claimed as a microfoundation for the natural rate of unemployment, introduced just a year or two
before by Milton Friedman (1968) and Edmund Phelps (1967), and for the inflationunemployment trade-off (the Phillips curve). It was also claimed, by Axel Leijonhufvud
(1968) in particular, that it could provide a microfoundation for Keynes’s concept of
effective demand: the idea was that the job seeker could make her demand for goods
effective only after she succeeded in locating a mutually acceptable job match.
The articles in the Phelps volume, however, especially those by Phelps (1970) and
Dale Mortensen (1970) which had explicit models of the Phillips curve, required
a wage distribution to obtain the microfoundations of the Phillips curve. As Peter
Diamond (1971) and Michael Rothschild (1973) pointed out, this was not consistent with the other assumptions of the models.2 Leijonhufvud’s claims also required
something more than search. They required either wage rigidity or absence of capital markets. Dealing with these two apparently unconnected issues appeared important items on the research agenda of the macroeconomics of the labor market. Both
the Phillips curve and Keynesian demand management played a key role in both
macroeconomic research and policy, yet they lacked good theoretical foundations.3
†
This article is a revised version of the lecture Christopher A. Pissarides delivered in Stockholm, Sweden, on
December 8, 2010, when he received the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel.
This article is copyright © The Nobel Foundation 2010 and is published here with the permission of the Nobel
Foundation.
* London School of Economics. I have benefited from discussions with my two co-winners, Peter Diamond
and Dale Mortensen, and from comments from Gary Fethke, Yannis Ioannides, Rachel Ngai, and Robert Shimer.
1
This timing coincided with my first meeting with Dale Mortensen, who was a visitor at the University of Essex
when I was finishing my undergraduate and Master’s studies there, but our collaboration had to wait for another
20 years to materialize.
2
Another prominent critic of the papers in the Phelps volume, who influenced the literature that followed, was
James Tobin (1972).
3
The outcome of my early research in these issues was my first book, Pissarides (1976), based on my PhD thesis
at the London School of Economics.
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I. Matching Frictions and Two-Sided Search
One of the appeals of early search theory was that it appeared realistic. The widely
used ILO/OECD definition of unemployment is one of workers not in a job, looking
for one, and available to take one. This was precisely the definition of unemployment used in search theory. In earlier theory, both neoclassical and Keynesian, the
unemployed are not doing anything connected with their state. They might be enjoying temporary bouts of leisure or be idle waiting for a job to open up, but they are
not looking for a job. In the Phelps volume, however, as in the important paper by
McCall (1970), the worker is searching for a wage offer from a fixed distribution of
wages, and if she is unemployed it is because she has failed to locate a high enough
wage offer. This did not seem to be consistent with our intuition of what makes one
unemployed for long periods of time, or with the view of unemployment taken by
applied labor economists and official statistical agencies. It also appeared inconsistent with equilibrium in markets with search frictions, at least back then.
Although there were many attempts to derive an equilibrium wage distribution for
markets with search frictions, I took a different approach to labor market equilibrium, that could be better described by the term “matching.” The idea is that the job
search underlying unemployment in the official definitions is not about looking for
a good wage, but about looking for a good job match. Moreover, it is not only the
worker who is concerned to find a good match, with the firm passively prepared to
hire anyone who accepts its wage offer, but the firm is also as concerned with locating a good match before hiring someone.
The foundation for this idea is that each worker has many distinct features, which
make her suitable for different kinds of jobs. Job requirements vary across firms too,
and employers are not indifferent about the type of worker that they hire, whatever
the wage. The process of matching workers to jobs takes time, irrespective of the
wage offered by each job. A process whereby both workers and firms search for each
other and jointly either accept or reject the match seemed to be closer to reality.
This approach to search has the advantage that it makes unemployment neither
“voluntary” nor “involuntary,” concepts that caused a lot of confusion and fruitless
debate in the literature. Unemployment is instead the outcome of a decentralized
equilibrium, which may or may not be optimal. It seemed to me that the two-sided
matching view had a better chance of success, both in grounding itself in microeconomic theory and in interpreting the facts about unemployment. It allowed
one to study equilibrium models that could incorporate real-world features like
differences across workers and jobs, and differences in the institutional structure
of labor markets.
The step from a theory of search based on the acceptance of a wage offer to one
based on a good match is small but has far-reaching implications for the modeling
of the labor market. The reason is that in the case of searching for a good match we
can bring in the matching function as a description of the choices available to the
worker. The matching function captures many features of frictions in labor markets
that are not made explicit. It is a black box, as Barbara Petrongolo and I called it
in our 2001 survey, in the same sense that the production function is a black box of
technology. But it captures the key idea of a good match: it takes time to find a good
match, the length of time it takes varies across workers in unpredictable ways, and if
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there were more job vacancies available, on average workers would find a good job
much faster. The same applies to firms looking for workers; the matching function
treats workers and firms symmetrically.
Importantly, because the matching function was similar to other aggregate functions used in economic models, it became possible to write small equilibrium models
of the labor market, with frictions captured by the matching function. It also became
possible to estimate these models with real world data. I estimated the matching
function with British data, and Olivier Blanchard and Peter Diamond estimated it
with US data, with encouraging results (see Pissarides 1986; and Blanchard and
Diamond 1989).
II. Towards an Equilibrium Model
I first used the matching function explicitly in 1979 (Pissarides 1979), making it
the main building block of an economywide model, at about the same time that Peter
Diamond and Eric Maskin (1979) used the similar idea of the “search technology.”
The model of my paper had no wage differentials, but it had different methods of
search. My main interest was to show that with a matching function one could get
an interesting, simple model of equilibrium vacancies and unemployment without a
wage distribution.
But my 1979 paper still had no theory of wages. Soon after, however, it seems
that all three of us independently realized that since frictions imply that the firm
and the worker in a good match enjoy some monopoly power, wages need to share
it between them. It helped that in the early 1980s, independent developments in bargaining theory were working out solutions for the splitting of a “cake.”4 The rewards
from a good match in the Diamond-Mortensen-Pissarides (DMP) model was the
cake that workers and firms had to split.
I attempted the derivation of a wage equation in a search model using ideas in
bargaining theory. The outcomes were some working papers that appeared around
1982, before the electronic era. I was unaware that both Peter Diamond (1982) and
Dale Mortensen (1982) were working on similar issues and were one or two years
ahead of me. The wage equation that I was deriving from the Nash solution to the
wage bargain was virtually identical to the wage equation in Diamond’s (1982)
paper. Seeing their papers on wages and efficiency made me switch to another issue
that needed to be dealt with in an equilibrium model, that of job creation.
III. Job Creation
In both the Diamond (1982) and Mortensen (1982) papers the problem investigated was that of a fixed number of workers interacting with a fixed number of jobs.
Yet, when looking at the workings of real labor markets over time, the most striking
feature that one sees is how employment and job vacancies fluctuate; in other words,
4
Most influential among these was Ariel Rubinstein’s (1982) work. A bigger influence on my research were
informal discussions with my colleagues at LSE, who also pioneered work in this area, in particular Kenneth
Binmore, Avner Shaked and John Sutton. See Binmore, Rubinstein, and Asher Wolinski (1986) and Shaked and
Sutton (1984).
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how the total number of jobs varies over time, depending on economic conditions.
In order to make the theory applicable to business cycles there was a need for a theory of job creation and job destruction. In my empirical research with British data
(Pissarides 1976), I found that the entry into unemployment was a virtually constant
fraction of employment.5 Consequently, I focused on the derivation of a variable exit
from unemployment, though a theory of job creation.
The key feature of the matching model that I made use of was that employment
was derived as the sum of distinct units called jobs, and not as an aggregate that
could be chosen as a single unit. A job is an asset owned by the firm: if it is vacant it
has some value because it can expect to recruit a worker and yield some profit in the
future; if it is filled it is producing for profit. Vacant jobs are like nascent investment
projects that have not started yielding a return yet. If their net value is positive, the
firm can create them for profit; if it is negative, it is losing money from them, so it
makes sense to close them down. It follows that an equilibrium number of jobs could
be derived from the condition that the value of a new job vacancy must be zero.6
When the zero-profit condition for new vacancies is combined with the Nash
wage equation it gives an equilibrium wage rate and job creation rate that depend on
the frictions as summarized in the matching function, and on all the other variables
that influence labor market outcomes in standard models, such as productivity and
taxes. From this condition I can get the equilibrium ratio of vacancies to unemployment, called tightness and usually denoted by the Greek letter θ. See Figure 1: the
zero-profit condition slopes down because at a lower wage rate jobs are more profitable, and more vacancies are created; the wage equation slopes up because at higher
tightness workers are more likely to locate an alternative job offer, and firms are less
likely to locate an alternative worker if the wage bargain fails, so the worker’s hand
in the wage bargain strengthens.
IV. Beveridge Curve Equilibrium
The transition from a model that yields an equilibrium ratio of vacancies to
unemployment to one that yields an equilibrium unemployment is made through
the Beveridge curve. The Beveridge curve shows combinations of vacancies and
unemployment that are consistent with equality between the entry into unemployment and the exit from it. This implies that once on the Beveridge curve, unemployment is not changing, unless one of the flows (in or out) changes because of
an exogenous shock. When the entry into unemployment is a constant fraction of
employment, as we are still assuming, the shape and properties of the Beveridge
curve are essentially given by the aggregate matching function. At a higher vacancy
rate there are more matches between a given number of unemployed workers and
job vacancies. Unemployment falls because of the increased exit from it, and as
employment rises, the entry into unemployment also rises. A new point is reached
with lower unemployment rate, more job matches taking place, and more workers
entering unemployment.
5
6
This feature of the data changed dramatically in the years that followed. See Petrongolo and Pissarides (2008).
See Pissarides (1984, 1985) for the first applications of the “zero-profit condition” to close the model.
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Wages
Wage curve
Job creation
Vacancies/unemployment (θ)
Figure 1. Equilibrium Wages and Market Tightness
The Beveridge curve is shown in Figure 2. The convex shape is due to the constant
returns satisfied by the aggregate matching function. To find equilibrium unemployment I need to know at what point on the curve the economy will settle. But since I
already have an equilibrium value for the ratio of vacancies to unemployment from
the job creation condition, I can find that point immediately. I draw a line through
the origin with slope the equilibrium θ, and the intersection of the Beveridge curve
with this line is the overall equilibrium of this economy.
At the core of this economy are the frictions that characterize the labor market.
These frictions are the forces that keep the Beveridge curve away from the origin.
The frictions could be due to a number of factors, such as mismatch between the skill
requirements of jobs and the skill mix of the unemployed, differences in location, the
institutional structure of an economy with regard to the transmission of information
about jobs, and others. An economy characterized by more frictions has a Beveridge
curve further away from the origin than an economy with fewer frictions.
Because of frictions, jobs that compete for the same workers could have different
productivities and yet survive in equilibrium. In frictionless markets only the most
productive of these jobs survive, as competition drives the wages in all jobs to the
wage offered by the most productive. Workers search for the best job that they can
find. In models that allow for different productivities, the position of the Beveridge
curve is also affected by the incentives that workers have to search for and accept
jobs. The matching rate in these models depends on two factors, making a contact
with a firm looking for workers and finding the firm’s offer acceptable.
In an economy where workers do not have strong incentives to accept an offer
quickly, for example because they are generously compensated without preconditions by the unemployment insurance system, the Beveridge curve lies further away
from the origin. An income support policy that does not impose preconditions is
called a passive policy. But policies that support the unemployed during search and
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Vacancies
Job creation
Beveridge
curve
θ
Unemployment
Figure 2. The Beveridge Curve and Equilibrium Vacancies and Unemployment
provide incentives for more intensive job search can shift the Beveridge towards
the origin and improve the performance of the labor market in matching workers to
jobs. In this case policies are called “active.”
A leader in the implementation of active labor market policies is Sweden, which
spends far more than other advanced countries on bringing unemployed workers to
jobs. In contrast, up to the 1980s most countries supported the unemployed through
passive policies, with poor outcomes in the recession of the 1980s. Most countries
have switched from passive to active policies in the course of the 1990s, following
the poor performance of their labor markets in the 1980s.
V. Comparing Economies over Space and Time
We can use the model to compare two economies, one with more frictions and
passive policies with one with fewer frictions and active policies. The first economy
has a Beveridge curve further away from the origin. It also has fewer job vacancies
for each unemployed worker, because firms expect to take longer to fill a vacancy.
Figure 3 compares these two economies. The economy with more frictions is shown
with the broken lines. An important conclusion is that the economy with more frictions has more unemployment than the economy with fewer frictions, but the two
economies may have a similar level of vacancies.
This conclusion can be contrasted with the comparison of two economies at different levels of aggregate economic activity, demand or supply. A lower level of
aggregate activity implies lower profitability from new jobs. Job creation falls, and
this rotates the job creation line clockwise, but the Beveridge curve does not move.
Equilibrium unemployment increases and vacancies fall in response to this shock.
The different response of vacancies to more frictions and lower level of aggregate activity was used by a number of authors to identify the reasons for the rise
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Beveridge curves
Vacancies
Job creation lines
Economy
with more
frictions
v
θ
u
Unemployment
Economy
with lower
level of
aggregate
activity
Figure 3. Comparing Economies
in unemployment in different countries. Richard Jackman, Richard Layard, and
Pissarides (1989) first used it to argue that the rise in unemployment in Britain in the
1980s, after the initial big surge associated with Prime Minister Thatcher’s restrictive monetary and fiscal policies, took place at more or less constant vacancies. This
is shown in Figure 4. The underlying reasons must have been related to increased
frictions in the labor market. These could be associated with increased mismatch,
as the transformation of the economy from an industrial to a service one intensified,
and to more generous income support for the unemployed.
It was also very likely due to the build-up of long term unemployment, which
disillusions the unemployed and damages the incentives they have to look for work.
Long-term unemployment, meaning unemployment that lasts for a year or more,
is a serious consequence of recession that disenfranchises workers from the labor
force and prolongs the impact that recession has on the quality of the work force. It
can explain why the unemployment rate in Britain was not falling in the 1980s when
the rest of the economy was booming. Governments realized the negative impact
of long-term unemployment since then, and they have tried to contain it with active
labor market policies.7 For this reason, more recent recessions do not exhibit the big
shifts in the Beveridge curve and the long persistence of the negative shocks on the
labor market.
We can see this contrast for Britain when we compare the economy’s responses to
the recession of 2008, in Figure 5. Although other reforms took place in Britain following the recession of the early 1980s, active policy also played an important role
7
Several countries adopted such policies, led by the Nordic countries. See OECD (2007) for an update.
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1.20
1.00
Vacancies
79
0.80
78
76
0.60
84
83
77
75
80
82
81
0.40
0.20
0.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Unemployment
Figure 4. The British Beveridge Curve, 1975–1984
2.9
2.7
2008M1
Vacancies
2.5
2.3
2.1
1.9
2010M10
1.7
2009M4
1.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
Unemployment
Figure 5. The British Beveridge Curve, 2008–2010
in containing long-term unemployment. The path of the economy in the Beveridge
diagram in the recent recession is a typical example of the response of an economy
to a negative aggregate shock.
The British experience contrasts sharply with the experience of the United States.
Katharine Abraham and Lawrence Katz (1986) used unemployment and vacancy
data for the United States8 to argue that the business cycles of the 1970s and 1980s
were due to aggregate shocks and not sectoral shocks, as argued by David Lillien
(1982) and others. Sectoral shocks would have similar implications to mismatch
shocks. But the economy in the late 1970s and early 1980s was tracing a Beveridge
curve in the south-eastern direction, as implied by an aggregate shock (see Figure 6).
8
The vacancy data were derived from the Conference Board Help-Wanted Index. See their article for more
details.
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5.5
79
5
78
80
4.5
81
Vacancies
77
84
4
76
75
3.5
82
83
3
2.5
2
1.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
Unemployment
Figure 6. The US Beveridge Curve, 1975–1984
In the 2008 recession, the US economy started off on a downward south-eastern
direction, but after the initial shock it traced an increase in unemployment at more
or less constant vacancies. It is still too soon to conclude that this is a shift of the
Beveridge curve to the right, but if it was it would be a feature of intensifying frictions
in the United States labor market, which have not yet been identified (Figure 7). The
rise in vacancies and recovery in output at virtually constant unemployment rate
explain why the fragile recovery has been described as “jobless.”
VI. Wage Stickiness
The response of unemployment to shocks is bigger when wages are sticky. Do
markets with frictions have anything new to say about wage stickiness? I am talking
here about real wage stickiness, although similar arguments should apply to nominal
wage stickiness.
In frictionless markets there are no compelling reasons for wage stickiness. In
contrast, in markets with frictions and Nash wage bargains there is a built-in reason
for some wage stickiness. It is that the wage rate depends on the worker’s nonmarket returns, which include unemployment insurance income, the value of home
activities like home decoration or child care, and the value of extra leisure, like more
sleep. The payoffs from these activities are not cyclical. When the market payoffs go
down because of recession, the home payoffs remain high, and this stops the Nash
wage rate short of falling by as much as the market payoffs.
I explored this wage stickiness in my 1985 paper to derive cyclical fluctuations in
unemployment in the model with frictions. But as Robert Shimer (2005) has shown,
it is not enough to explain all the amplification of the shocks required to match
the data. Subsequent work has shown that it can be enough, but only if the firm’s
profit share from the match is very small, either because workers take most of the
surplus or because firms have large labor hiring costs (Marcus Hagedorn and Iourii
Manovskii 2008; Pissarides 2009).
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3.1
2.9
2008M1
Vacancies
2.7
2.5
2.3
2010M10
2.1
1.9
1.7
2009M4
1.5
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
Unemployment
Figure 7. The US Beveridge Curve, 2008–2010
But markets with frictions can justify another form of wage stickiness, much more
substantial than the one implied by the Nash wage equation. This was first explored
by Robert Hall (2005), who argued that since the monopoly power implied by frictions implies that there are no conventional supply and demand functions to tie
down the wage rate, the Nash wage is only one possible outcome consistent with
equilibrium. Another is the wage that depends on the historical median hiring wage,
and shocks to demand do not necessarily change it by much. Hall showed that the
wage stickiness consistent with this argument is enough to give an equilibrium that
delivers all the amplification of shocks on job creation that we see in the data.
This analysis reopens the issue of wage determination and puts it into center stage
as a topic for future research. Given the many possibilities allowed by the bilateral
monopoly nature of a good match, research needs to be driven by empirical studies
of wage determination for new hires at different phases of the cycle (see Christian
Haefke, Marcus Sonntag, and Thijs van Rens 2007; and Pissarides 2007).
VII. Job Destruction
New establishment data by Steven Davis and John Haltiwanger (1990) and others working in Industrial Organization, published in the late 1980s and early 1990s,
showed that job destruction rates varied a lot across business cycles. The main
cause of job destruction and the biggest number were connected with idiosyncratic
establishment-specific shocks, but there was enough variation over the business
cycle to render untenable the assumption that the rate at which workers flow into
unemployment is constant. Although the rate of job destruction determined the job
flow, and not the worker flow into unemployment, the two are closely connected.
Anyway, subsequent work showed that flows into unemployment were cyclical too,
although not as cyclical as the flow out of unemployment (see Shigeru Fujita and
Garey Ramey 2009).
Following a criticism by Mortensen (1992) of the assumption of a constant flow
rate of workers into unemployment in the first edition of my book (Pissarides 1990),
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we came together to work on extending the model to a variable job destruction rate.
The outcome was our 1994 paper (Mortensen and Pissarides 1994) and a series of
papers that applied the model to several issues (Mortensen and Pissarides 1999).
In our analysis of job destruction we assumed that once jobs are created, they cannot easily adapt to new technologies. In the simplest version of the model they do not
adapt at all, so the firm keeps the job going for as long as it is still profitable. When
shocks hit that make a job unprofitable, it is destroyed, the worker is made unemployed, and some new job is established elsewhere to take its place.
We have shown that like the job acceptance decision in the first generation of
search models, the job destruction decision was governed by a reservation productivity. The firm and the worker agreed which jobs to destroy on the basis of their
joint real return. Under our rules, most job destruction in the steady state is due to
unidentified idiosyncratic shocks, as in the data. But over the cycle, job destruction
goes up in recession, usually with a sudden upsurge of terminations when the news
first breaks out, and goes down in the recovery phase. This introduces cyclicality in
both the job creation and job destruction rate, predicting well the time series data
for the United States.
The variable job destruction rate has implications for the dynamics of unemployment and the Beveridge curve, but it does not affect the dynamics of the job creation
rate that I discussed earlier in this lecture. It also implies that there is now an incentive to search on the job and move from one job to another without experiencing
unemployment. The reason is that workers have an incentive to leave the jobs that
are becoming obsolete or have low profitability for other reasons. The full model is
set out in the second edition of my book (Pissarides 2000). Rather than discuss the
full model here, I will discuss the implications of employment protection legislation for unemployment and job flows. This is one policy whose study needs the
extended model with variable job destruction rates, since its objective is to make job
destruction more difficult for the firm, with the objective of securing a longer-lasting
employment spell for the worker.
VIII. Employment Protection Legislation
Although all advanced countries exercise some kind of employment protection,
there are large variations in the type of restrictions implemented, and there are also
big differences in their severity across countries (see Per Skedinger 2010 for a full
analysis). At the risk of oversimplifying, we know from the OECD that the southern European countries have much stricter employment protection legislation than
the northern countries, and especially than the United States and United Kingdom
(OECD 1999). I will discuss here the restrictions on dismissals that take the form
of administrative procedures that cost the firm time and money. These can be represented in our models as a pure tax paid by the firm at the time of dismissal.9
9
In the simple model there is no obvious benefit to the worker from these administrative restrictions on dismissal, other than saving some jobs from destruction. But in more general models with risk aversion these restrictions also can act as insurance against sudden income fluctuations, and these can be beneficial to the worker. The
welfare implications of employment protection legislation would then be different in these models. See Pissarides
(2010) for one such model.
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A tax on dismissals reduces job separations. Some low-productivity jobs that would
have been destroyed before the imposition of the tax will now not be destroyed, as
the firm reduces its reservation productivity to reduce the chances that it might have
to pay the tax. So one implication of employment protection legislation is that the
size of the flow into unemployment is lower than otherwise; but average labor productivity is also lower and wages should also be lower to compensate the firm for
the tax and the lower productivity.
Another important impact of employment protection legislation is on job creation.
When the firm is creating a job it expects to have to pay the tax at some future date
if it has to dismiss the worker. Job creation falls as a result, so just like the flow into
unemployment, the flow out of unemployment at given unemployment also falls.
The net impact on unemployment depends on which flow falls more. If the flow
into unemployment falls more than the flow out of unemployment, unemployment falls to compensate, and vice versa. Empirical work shows that the impact
of employment protection legislation on unemployment is small and can go either
way; but the size of the flows falls, there is less labor and job turnover, lower average labor productivity, and longer durations of both unemployment and employment
(OECD 1999).
In extended versions of our models, with training, the longer durations of employment might encourage more training, as workers are more secure in their jobs and
are more willing to undertake training that is specific to the needs of their firm. And
in yet other extended versions with different kinds of workers, employment protection legislation tends to benefit primary workers, usually male workers over 25 years
old, but hurts other workers, like women and youths, who go in and out of the labor
force at more frequent intervals than prime-age males.
IX. Concluding Remarks: Where Do We Go Next?
Search and matching theory has come a long way since the early 1980s, when the
first equilibrium matching models appeared in the literature. A recent book by Brian
and John McCall that surveys the economics of search is 550 pages long, and there
was a second volume planned for the things left out (McCall and McCall 2008).
But there is still a lot to do. We have discovered, just as Sir John Hicks did in
1932, that the theory of wages is key to understanding the functioning of labor
markets. Sir John described in detail how frictions in labor markets, mobility costs,
and trade unions and other institutions influence wages, and how wages influence
employment, very much along the lines that modern theory describes. But modern
theory has still to explore more fully the role of institutions in its formal models, and
this is an area of research that should attract a lot of attention in the future.
I have also argued that wage stickiness is as important an issue as it has ever been
in macroeconomics. Markets with frictions open up many more possibilities for
wage stickiness, and future research needs to explore these.
The financial crisis of 2008 has thrown open the question of the interaction
between capital and labor markets. Equilibrium matching models are built on the
assumption of perfect capital markets. The implied arbitrage equations under perfect foresight and unlimited borrowing and lending are used to calculate a value
for jobs and workers. These are good starting assumptions, and they have yielded
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important results. But future work needs to explore other assumptions about capital
markets and integrate the financial sector with the labor market. This might suggest
another amplification mechanism for shocks, independent from wage stickiness or
fixed costs.
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