SELF-ENFORCING CONTRACTS WITH PERSISTENCE * Martin Dumav † William Fuchs ‡ Jangwoo Lee § July 24, 2019 Abstract The use of self-enforcing contracts in an environment with persistent shocks rationalizes: (i) that com- pensation may appear to “reward luck;” (ii) that adverse shocks are compounded via a “low morale effect” reducing performance even further; (iii) that countries with poorer institutions of contract enforcement experience larger dispersions of firm productivities and higher aggregate TFP and GDP volatilities. We relate the model’s predictions to existing empirical findings and provide new evidence that a country’s improved legal environment decreases its aggregate volatility as well. Keywords: Dynamic moral hazard, Productivity, Relational contracts, Persistence, Limited Commit- ment JEL Classification: C73, D24, D82, D86, E24, L14 * Many thanks to Nicolas Aragon, Gregor Matvos, Andy Skrzypacz, Chad Syverson, Giorgio Zanarone, seminar participants at Universidad Carlos III de Madrid, UT Austin, the 4th Relational Contracts Workshop at University of Chicago, the Society for the Advancement of Economic Theory Conference 2019 at Ischia, and the 4th FTG European Summer Meeting at Madrid, for helpful comments. Dumav gratefully acknowledges the financial support from the Ministerio Economia y Competitividad (Spain) through grants ECO2017-86261-P and MDM 2014-0431. Fuchs gratefully acknowledges support from the ERC Grant # 681575. † Universidad Carlos III de Madrid ‡ Universidad Carlos III de Madrid and University of Texas at Austin § University of Texas at Austin 1
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SELF-ENFORCING CONTRACTS WITH PERSISTENCE∗
Martin Dumav† William Fuchs‡ Jangwoo Lee§
July 24, 2019
Abstract
The use of self-enforcing contracts in an environment with persistent shocks rationalizes: (i) that com-
pensation may appear to “reward luck;” (ii) that adverse shocks are compounded via a “low morale effect”
reducing performance even further; (iii) that countries with poorer institutions of contract enforcement
experience larger dispersions of firm productivities and higher aggregate TFP and GDP volatilities. We
relate the model’s predictions to existing empirical findings and provide new evidence that a country’s
improved legal environment decreases its aggregate volatility as well.
Keywords: Dynamic moral hazard, Productivity, Relational contracts, Persistence, Limited Commit-
ment
JEL Classification: C73, D24, D82, D86, E24, L14
∗Many thanks to Nicolas Aragon, Gregor Matvos, Andy Skrzypacz, Chad Syverson, Giorgio Zanarone, seminar participantsat Universidad Carlos III de Madrid, UT Austin, the 4th Relational Contracts Workshop at University of Chicago, the Societyfor the Advancement of Economic Theory Conference 2019 at Ischia, and the 4th FTG European Summer Meeting at Madrid,for helpful comments. Dumav gratefully acknowledges the financial support from the Ministerio Economia y Competitividad(Spain) through grants ECO2017-86261-P and MDM 2014-0431. Fuchs gratefully acknowledges support from the ERC Grant# 681575.†Universidad Carlos III de Madrid‡Universidad Carlos III de Madrid and University of Texas at Austin§University of Texas at Austin
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Conflict-of-interest Disclosure Statement
Martin Dumav
Hereby I declare that I have no conflicts of interests with regards to our submitted paper: Self-Enforcing
Contracts with Persistence. I gratefully acknowledge support from Ministerio Economia y Competitividad
(Spain) through grants ECO2017-86261-P and MDM 2014-0431.
William Fuchs
I have no conflicts of interests with regards to our submitted paper entitled Self-Enforcing Contracts with
Persistence . I gratefully acknowledge support from the European Research Council Grant 681575.
Jangwoo Lee
I have no conflicts of interests with regards to our submitted paper: Self-Enforcing Contracts with Persistence.
2
1 Introduction
Many, if not most, economic interactions are carried out with very incomplete or no formal contracts at all.
In their absence, to overcome the possible hold-up problems, economic agents often rely on the repeated
nature of their interactions to establish relational or self-enforcing contracts. In this paper we show how
limited external enforceability coupled with persistent productivity shocks can generate several interesting
empirical predictions. We show these predictions are consistent with both micro and macro data, and further
establish a new link between a country’s legal environment and its aggregate-level TFP volatility.
Concretely, we build on the model of relational contracts a la Levin (2003) by allowing for persistence in
productivity and show the following three related results:
(i) agents can appear to be rewarded for luck;
(ii) in periods of distress the firm’s profitability is further worsened by “low morale” effect, the difficulty
in incentivizing workers to work hard and/or not to leave the firm;
(iii) the enforceability constraint, which captures the temptation of the principal to renege on the promised
bonus, generates a multiplier effect amplifying the exogenous productivity shocks. This has two impli-
cations for economic environments where enforceability constraints are more binding:
(a) aggregate shocks result in higher TFP and GDP volatilities
(b) idiosyncratic shocks induce a larger dispersion of measured firm productivity levels within a coun-
try or industry.
We further document multiple pieces of evidence in support of (iii)-(a) and (iii)-(b). In particular, we provide
evidence that a country’s enhanced legal environment decreases its aggregate volatility. To avoid endogeneity
problems we use early European settler’s mortality rates from the seminal work Acemoglu et al. (2001) to
instrument for the quality of legal environment. Our identifying assumption hinges on Acemoglu et al.
(2001)’s argument for exclusion restriction. Namely, that although malaria and yellow fever are accountable
for most European deaths in colonization process, they had limited effect on native populations who had
developed various forms of immunity. The merits and weaknesses of the Acemoglu et al. (2001)’s instrumental
variable have extensively been discussed in later works and refer the interested reader there. 1 For the purpose
our paper, we believe that using this instrumental variable provides a better estimate than a simple OLS
estimate.
One could potentially try to come up with different theories that might coincide with our findings for aggregate
volatility and cross-sectional dispersion where the mechanism connecting rule of law and these measures is
different from the one we propose. We do not try to argue such a thing is impossible, but what we find very
unlikely is that such an alternative mechanism could simultaneously also explain the existence of reward for
luck and the morale effect. In this paper we are not taking a single empirical fact and arguing that our theory
is the only one that can support the fact. Instead, we take what we think is a reasonable model and explore
all of its empirical implications. The value of our paper thus relies on providing a parsimonious model that
can explain many different empirical findings simultaneously.
We show that even when exogenous productivity shocks are perfectly observable and independent of the
1See for instance, Glaeser et al. (2004); Albouy (2012); Acemoglu et al. (2012)
3
agent’s effort, bonuses are dependent on the realization of the shocks beyond the agent’s influence. This stands
in contrast to the “Informativeness Principle” (see Holmstrom (1979)), according to which a performance
measure should only affect compensation if it provides information about the agent’s hidden effort. Instead,
the empirical literature has documented several cases in which contracts seem to be rewarding luck. Bertrand
and Mullainathan (2001) document that in the oil industry of the US the compensation of executives in major
companies positively correlates with the price of oil, an element arguably outside the executive’s control.
Garvey and Milbourn (2006) show also that there is reward for luck and further argue it is not symmetric:
good luck is rewarded while bad luck is not punished. Finally, DeVaro et al. (2018) (which we discuss
further below) show empirically that persistence of the state is important for “reward for luck.” They also
provide evidence against capture of boards by CEOs being the main driver of the observed compensation
patterns.
The economic rationale for “rewarding luck” in our model hinges on the fact that the persistent productivity
shock implies that the continuation value of the firm is higher in the good state than in the bad state. As a
result, the principal has a stronger desire to maintain the relationship in the good state and thus can credibly
promise to pay higher bonuses in such a state. The bonus payments are thus higher in the high state than
in the low state. Note that although luck determines the size of the bonus, the bonus is paid only when the
output measure directly related to the agent’s effort is indicative of effort. More specifically, the agent is
being rewarded, conditional upon success, with a state dependent lottery. For incentives what matters is the
expected payoff of that lottery.
Our second result highlights the role of persistence in productivity during bad times. When the current
state is bad, the future value from maintaining the relationship is low and the principal cannot credibly
promise to make large payments. Thus, in bad times the firm cannot motivate the agent to exert high
effort. This “low morale effect” is consistent with the evidence that labor productivity levels are pro-cyclical
(Baily et al., 2001). We can alternatively recast the model as in Fuchs (2015) where every period the agent
receives a stochastic outside option and must decide whether or not to leave the firm and take the outside
option. In such a reformulation, when the future of the firm looks bleak, the agent would be willing to accept
lower outside options. This is consistent with the findings presented by Baghai et al. (2016) which show
that Swedish firms tend to lose their key talents when their financial health deteriorates. Similarly, using
an online search platform, Brown and Matsa (2016) show that job applicants avoid companies with poor
financial conditions.
In his review on the determinants of productivity Syverson (2011) highlights:
“... robust finding in the literature ... is that higher productivity producers are more likely to
survive than their less efficient industry competitors. Productivity is quite literally a matter of
survival for businesses.”
Our predictions are consistent with this finding and further suggest that the causality can go both ways. In
particular, if a firm is more likely to survive, then it will be able to become more productive. With higher
productivity it becomes easier for the firm to maintain the relationship, i.e., the dynamic enforceability
constraint is less likely to be binding when the productivity is higher. Empirically, the survival prospects of
the firm at time t might not be fully observable to the econometrician but influence time t+ 1 productivity.
This would give appearances of a positive correlation between productivity at t and survival to t + 1, and
yet, it is survivability that determines productivity but not the converse.
4
Syverson (2011) empirically shows large productivity differences within a given industry. Particularly related
to the third implication of our model, he highlights that these differences vary dramatically across countries.
While in the US the most productive firm within a given industry can be twice as productive as the least
productive one, for India and China there can be fivefold differences.2
There are many possible reasons for why these cross country differences arise; our explanation is that it is
partly driven by the observation that the US has an effective legal system that facilitates the enforceability
of contracts while these institutions are much weaker in developing countries.3 Thus, a larger fraction of
business relationships in India and China are governed by relational contracts. As a result, when two firms
in the US and two firms in India experience a pair of low and high exogenous productivity shocks, the spread
in measured productivity in India would be larger than in the US. To see why this is the case, suppose
the productivity shock does not affect the optimal level of effort. With complete contracts there are no
differences in worker effort between the two firms. As a result, the measured productivity differences in the
US firms would be just given by the spread of the exogenous shocks. Instead, with imperfect enforceability,
the optimal effort that can be induced from the workers depends on the shock realization. When the shock
is high, the firm can credibly commit to pay high bonuses and, as a result, demand high effort and become
more productive. The opposite effect takes place when the shock is negative. This implies there is an extra
wedge in the measured productivity of Indian firms given by the fact that the equilibrium effort also varies
with the exogenous shock.
If common persistent shocks impact all firms in economies, our model predicts that the volatility of aggregate
TFP and GDP of economies with poorer legal enforcement would be greater (abstracting from general
equilibrium effects). Consistent with our predictions, for a large sample of countries we find a negative
relationship between dispersion of aggregate TFP and GDP growth rates and quality of law enforcement. We
further address potential endogeneity concerns by exploiting exogenous variations in early settler mortality
rates (Acemoglu et al., 2001) to instrument for the quality of law enforcement. This identification strategy
suggests a causal relationship between the legal environment and aggregate volatility yet to appear in the
literature.
Alternatively, if persistent idiosyncratic shocks affect firm-level productivity, our model predicts that spread
in firm-level TFP in economies with poorer legal enforcement would be wider. In line with our predictions,
we document a negative relationship between the spread in firm-level TFP within a given country and the
quality of the legal system in that country. Though similar findings are also documented in Powell (2017),
our analysis corroborates the robustness of his result with an alternative measure of firm-level productivity
dispersion based on Ackerberg et al. (2015)’s estimation techniques.
There is certainly additional heterogeneity and richness that is not captured by our simple model. Yet, we
believe our model provides a very useful lens through which we can start to uncover how seemingly unrelated
findings at the firm, industry and aggregate economy level can actually be manifestations of a natural friction
economic agents must constantly deal with: namely, how to realize the gains from trade without falling prey
to the hold-up problem.
2See references therein in particular Syverson (2004) and Hsieh and Klenow (2009).3See Syverson (2011) and references therein for a rich discussion of other possible factors influencing firm productivity.
Collard-Wexler et al. (2011), for example, argue that these differences arise due adjustment costs and persistence of shocks tofactors of production.
5
Related Theoretical Literature
Our model belongs to the rich literature on relational incentive contracts that builds on Bull (1987), MacLeod
and Malcomson (1989), and Levin (2003). From a technical perspective the most closely related work to ours
within this literature are Kwon (2016) and DeVaro et al. (2018). We complement Kwon (2016) and go beyond
her analysis by characterizing the optimal contracts. Similar to ours, DeVaro et al. (2018) offers a theoretical
rationale for rewarding luck. In addition, as mentioned earlier, they complement their theoretical analysis
with some corroborating evidence on CEO compensation. Although our main argument for rewarding luck
is similar in spirit, our model uses a different timing of shocks vis-a-vis theirs. In our view, our timing, with
the shock revealed after the effort choice, is more convenient when considering non-separable production
functions. Our timing implies that positive (luck) innovations can lead to higher bonuses without affecting
the current effort choice. Instead, with the DeVaro et al. (2018) timing where the shock is revealed before
the effort choice, it would become less clear whether the increase in compensation is instead driven by the
desire to elicit higher effort in the good states. From an econometrician’s perspective this is problematic
since, like the principal, the econometrician is unlikely to observe the agent’s effort. In addition to this, our
main contribution relative to their work is on highlighting the “morale effect” and the related amplification
of shocks that is induced by the lack of enforceability4.
Three other different theoretical explanations have been put forward for the well-documented “reward for
luck” phenomenon. First, Oyer (2004) argues that the shocks identified in some of the empirical studies not
only affect one firm, but rather the entire sector or industry. As a result, in the high state the aggregate
demand for the agent’s services increases. Thus, in order to retain the agent (CEO), the principal must
increase the agent’s wage. His model does not have implications for the agent’s effort, and thus, unlike our
model, does not speak to the “morale effect” or the correlation between enforceability and volatility.
The second explanation was put forward by Hoffmann and Pfeil (2010) and is also present in DeMarzo et al.
(2012). Their models, have no enforceability constraints. Instead, the principal needs to finance up-front an
agent protected by limited liability. When productivity is high, the potential cash flows are large. Thus, the
principal has stronger incentives to avoid reducing the size of the firm or triggering termination (which is
the main way of providing incentives within their model). Hence, the principal must give more rents to the
agent, and the “reward for luck” effect arises. However the equilibrium effort in their model is independent
of the productivity shock. Thus, their models do not allow for a “morale effect” nor can they be used to
study the correlation between enforceability and output volatility.
The third explanation, put forward by DeMarzo and Kaniel (2017), is of a very different nature. They
consider agents that not only care about their own compensation but also about their compensation relative
to others. When this second component of the preferences is strong, relative performance-pay may not be
optimal. As a result, they argue, under certain parameter configurations it appears as if the agents are
rewarded for luck.
When considering the “morale effect,” Barron et al. (2018) is closely related. They study the problem of an
entrepreneur which needs both: (i) to take an initial loan to finance its venture and (ii) to hire an agent to
work on it. The contract between the entrepreneur and the worker is not enforceable. Thus the continuation
value of the entrepreneur plays an important role in determining the extent to which the enforceability
4Although in their model the morale effect is implicitly present they do not explicitly discuss it. Furthermore, while in theirsettings these two effects must always co-exist, in our model we could have reward for luck without it necessarily implying thepresence of the morale effect. For more details, see Remark 1 in section 4.2.
6
constraint binds. A highly levered entrepreneur is similar to the principal in our model that experiences a
low productivity shock. In both cases, their equity value is low and thus there is a limit on how large a bonus
they can credibly promise to the agent. Thus, when the firm is in poor financial shape, it cannot induce the
worker to work hard. This is similar to the “morale effect.” Their paper also provides empirical evidence
supporting the mechanism in their model.
Our results about macroeconomic volatility in productivity are related to Ramey and Watson (1997) and
Den Haan et al. (2000). Ramey and Watson (1997) develop a theory of labor contracting in which negative
productivity shocks lead to costly job loss due to limited enforceability. Their model is used in Den Haan
et al. (2000)’s dynamic general equilibrium analysis to illustrate a propagation effect through which cyclical
fluctuations in the job-destruction rate magnify the shocks to output and make them persistent. In contrast,
in our analysis, the firm’s commitment power is driven by the endogenous value of maintaining relationship
where persistence plays an important role. Furthermore, our analysis sheds lights on output volatility as well
as its level, and we provide empirical support for our model’s implications. Our empirical analysis identifies
a negative relationship between the quality of legal environment and aggregate volatility and thus takes
established findings from Aguiar and Gopinath (2007); Koren and Tenreyro (2007) further.
Finally, the idea that imperfect enforceability of contracts can lead to dispersion in firm productivity is present
in the recent work of Board and Meyer-ter Vehn (2014) and Powell (2017). Their mechanisms provide com-
competitive industry equilibrium where heterogeneity in firms’ transitory shocks generate cross-sectional out-
put dispersion. In an efficiency wage model where the future promises in long-term relationships incentivize
the worker’s current effort choice, Board and Meyer-ter Vehn (2014) shows that wage and productivity dis-
persions among ex ante identical firms arise from labor-market competition and on-the-job search. Like us
Powell (2017) also provides empirical evidence of the positive and meaningful correlation between contract
enforceability and productivity. We supplement his analysis with an alternative measure of productivity
dispersion based on Ackerberg et al. (2015)’s estimation method, and document a new causal link between
aggregate volatility and contract enforceability, which is absent in his work.
We see our main contribution relative to the literature, not being the first to point out a particular effect,
but rather providing a unified model capturing multiple relevant phenomena. The existing literature so far
provides distinct explanations for these facts. Instead, we offer a parsimonious rationale for all three: time-
varying limits to contract enforceability. Furthermore, we add a new link between the legal environment and
aggregate volatility to the existing empirical literature.
2 Setup
A principal (“she”) and an agent (“he”) interact repeatedly over time. Time is discrete and indexed via
t ∈ {1, 2, ...}. At the beginning of every period, the principal makes the agent an offer consisting of an
enforceable wage payment wt, a schedule of non-enforceable bonus payments {bt} and recommends the
agent take an action at. If the agent rejects, the game ends and both receive their outside options, which
we normalize to be zero. If the agent accepts, the agent privately chooses his action at ∈ [0, 1] incurring
a cost c (·). We assume c (·) is continuously differentiable, strictly increasing and strictly convex, with
c (0) = c′ (0) = 0 and 1 < c′(1) ≤ ∞.
7
The firm’s publicly observable output is composed of two additively separable components πt = yt + θt. The
first component takes on binary values y ∈ {0, 1} and is partially under the control of the agent. Specifically,
the agent’s unobservable action at determines the probability that yt = 1. On the other hand, the distribution
over publicly observable state θt ∈ {L,H} is independent of the agent’s actions. We assume H > L > 0
and it is worth noting that one can show that the value to the firm is weakly decreasing on the spread
of the shock H − L. Thus, to the extent possible, firms would want to hedge this shock, our underlying
assumption is that hedging cannot be done perfectly5. The process for θt follows a first-order Markov chain
with a symmetric persistence parameter λ := Prob (θt = θt−1) > 1/2 for both θt−1 = H,L. The model could
be extended to richer stochastic processes. Similarly, the additive separability assumption on output can
be easily relaxed. Yet, the additive structure provides a very clear benchmark since it implies the first best
action is independent of the state6.
Figure 1: Timeline
t t+1
the principal offers a
relational contract
(wt, bt)
the agent
accepts
(/rejects)
the principal
pays wage wt
the agent
chooses at
outcome yt and
state θt realizes
the principal
pays bonus bt
See Figure 1 for the order of play. The relationship terminates the first time the parties renege on payments,
or the first-time the agent rejects a contract. Let τ denote the time when the contracting relation terminates.
After termination, both players receive the value of outside option, normalized to 0.
Both players are risk-neutral, have a common discount factor δ, and have deep pockets. The principal maxi-
mizes the present value of the expected discounted output streams minus her payments to the agent:
Πt = (1− δ)Etτ∑s=t
δs−t (πs − ws − bs)
The agent maximizes the expected discounted payments received minus his cost of effort:
vt = (1− δ)Etτ∑s=t
δs−t (ws − c(as) + bs)
Given any public history ht = {θ0, θ1, ...θt−1, y1, ...yt−1, b1, ...bt−1, w1, ...wt−1} ∈ Ht, a contract specifies the
compensation mix the principal offers; whether or not the agent accepts it; and the effort and discretionary
bonus payment decisions. The compensation is given by the functions of the following form: wt : Ht → R,
bt : Ht × (Θ× Y)→ R+.
A contract is self-enforcing if it is a perfect public equilibrium of the repeated game. We are interested
in characterizing the efficient self-enforcing arrangements that would govern this relationship. As a first
step, using an extension of the methods by Spear and Srivastava (1987) and Abreu et al. (1990) we can
5Even if θt is contractible, firms cannot fully hedge θt as long as the court (or any outside third party) cannot verify whetherone party has reneged. Assume that the principal and agent write a long-term contract based on θt. The long-term contractwould always be in effect, regardless of whether a player has reneged or not. Hence, the long-term contract does not affect anyparty’s decision to renege.
6If the output is additively separable, the first-best effort level aFB satisfy: ∂∂at
(E(πt|at, θt−1) − c(at)
)∣∣∣∣∣at=aFB
= 1 −
c′(aFB) = 0.
8
conveniently surmise a given public history of the game ht, into a continuation value for the agent v and the
past state θt−1. The principal is choosing the current actions (a,w, b, v′) based on the public history (v, θt−1)
This allows us to capture the principal’s problem with the following recursive representation:
There is an intuitive explanation for this result that highlights the effect of persistent productivity during
bad times. When the current state is bad, the future value of the relationship is low and the principal can
no longer credibly promise to make large payments. Thus, it can no longer motivate the agent to exert very
high effort.
Our model thus rationalizes the “low morale effect” during firm distress, once the profitability is interpreted
as a driver of financial distress. In our model, firms reduce the expected bonus payments during difficult
times, to which workers respond by exerting less effort. Thus, firms’ productivity is further depressed due to
11
de-moralization of the workforce. This mechanism resonates well with multiple labor concession episodes in
the domestic steel industry during 1980s (DeAngelo and DeAngelo, 1991).
DeAngelo and DeAngelo (1991) illustrates this effect with the labor problems experienced by the US steel
plants in the 1980s. This was a challenging time for US steel producers as they faced stiff competition
from more productive Japanese counterparts coupled with a decline in global demand. Mas (2008) uses
re-sale data for industrial machinery to show how Caterpillar’s problems with its workers had a negative and
economically significant impact on the quality of the machinery it produced.
The paper by Barron et al. (2018) has a similar mechanism at play. Although the shocks in their model are
transient, their firms have different levels of leverage which generates persistence. An increase in leverage in
the previous period in their model is similar to having experienced a negative persistent shock in our model
and leads to a lower labor productivity. They use data on a large sample of European firms to show that
indeed, an increase in leverage in year t− 1 is followed by a decrease in year t productivity. They report that
one standard deviation increase in contemporary leverage is correlated with a decrease in TFP-R equal to
9-15% of the median within firm standard deviation.
Furthermore, we can recast our model as in Fuchs (2015) to rationalize why distressed firms are more likely
to lose, or less likely to attract workers. This is consistent with the empirical findings in Baghai et al. (2016),
and Brown and Matsa (2016). When a negative productivity shock hits the firm, workers with high outside
values would leave the company, because firms cannot promise sufficiently high bonuses to retain them.
Remark 1. Importantly, unlike related papers (Kwon, 2016; DeVaro et al., 2018), the reward-for-luck in our
setup is distinct from the morale effect. In our setup, the morale effect necessarily leads to the reward-for-luck
effect, but not vice versa.
Claim 4.1. a∗ (L) < a∗ (H)→ b∗(θ−1, L) < b∗(θ−1, H) for both θ−1 ∈ Θ, but the converse is not necessarily
true8.
To understand this result it is important to recall that the enforceability constraint will always be more
binding in the when θt = L than when θt = H (see Lemma 1). When the enforceability constraint first
becomes binding it implies that the principal must promise lower bonus when θt = L is realized. Absent any
other adjustments to the compensation, the expected bonus would decrease and thus effort would inefficiently
decrease. To try to avoid this inefficiency, the principal can raise the bonus conditional on θt = H being
realized. If the enforceability constraint does not bind in the high state, that means that the principal can
fully offset the decrease in expected payments and thus keep the agent at the efficient effort level regardless
of the state θt−1. This is a situation in which we have reward for luck but no morale effect. The morale effect
only arises when the enforceability constraint also starts binding for θt = H and, as a result, the expected
bonus is no longer sufficient to induce high effort. Note, that in particular, this would imply lower effort
when last period shock was low. This follows because, due to persistence, the low state is more likely to be
realized again and thus the expected bonuses must be lower.
8Under certain parameters, all optimal bonuses b∗(·) exhibit reward-for-luck, but a∗(θ−1) = aFB for both θ−1 ∈ {L,H}.Hence, even if the agent is risk averse, the agent would still be rewarded for luck. This result is available upon request. Theproof for the first part of the claim is included in the appendix.
12
4.3 Amplification and Contract Enforcement
We have focused our analysis and discussion on a single firm but our analysis at the micro level has impli-
cations from an industry wide and macroeconomic perspective. If we assume that the productivity shocks
are correlated across firms our model generates an amplification effect of these shocks when there is poor
contractual enforceability. This is consistent with the empirical findings that show that emerging economies
have much higher output volatility than developed economies.9 Naturally there could be many explanations
for this difference and indeed there is a rich literature providing a number of different rationales for these
facts. However, our proposed mechanism has an additional advantage that it can rationalize other phenom-
ena as the reward for luck and morale effect. We are unaware of any other alternative mechanisms that are
consistent with these stylized facts.
Our paper contributes to this literature by arguing that the larger reliance on self-enforcing contracts by
firms in emerging economies might provide an amplifier effect on exogenous productivity shocks leading
to larger business cycle volatility. It is well documented that emerging economies tend to have poorer
legal enforcement and a larger underground economy (Porta et al., 1998; Allen et al., 2005). As a result,
enforceability constraints play a larger role in those countries. Therefore, the amplification effect generated
by the enforceability constraints would lead to larger volatility of labor productivity and output in emerging
economies vis a vis developed ones.
To approximate the data more closely we generalize our benchmark model and introduce a parameter α
that measures the strength of contract enforcement institutions. In particular, assume that whenever the
principal reneges on her promised payments, she can walk away with only a fraction (1−α) of the sum. Weak
contract institutions amplify the effects of productivity shocks on the volatility of output, mainly because
weak institutions constrain the value promises the principal credibly makes. With this generalization, the
principal’s contracting problem (1) now takes the following form
Notice that the Proposition 4 has a different implication when coupled with a different assumption. Under the
first assumption, the aggregate volatility measureE(∫ 10yi,tdi|
∫ 10θi,tdi=H)
E(∫ 10yi,tdi|
∫ 10θi,tdi=L)
decreases in α. Under the second as-
sumption, the ratio between more productive firm’s productivity and less productive counterpartE(yi,t|θi,t=H)E(yj,t|θj,t=L)
also decreases in α. Hence, the proposition 4 amplifies the fundamental shock at both levels.
Unfortunately, there are no data available on measures of α at the individual firm level to contrast with
the prediction on productivity volatility in given Proposition 4. Fortunately there are a number of well-
established indicators for the strength of contracting institutions at the country level. Since enforcement
of contracts requires well functioning court systems, we believe these measures are likely to correlate well
with the degree of reliance on self-enforcing contracts. Specifically, we use the ‘Rule of Law’ indicators from
the Worldwide Governance Index (Kaufmann et al., 2011) in the results presented below.The Rule of Law
variable measures the agents’ confidence in contract enforceability, property rights, police, courts and the
likelihood of crime and violence. Using this measure as our proxy for α, we study empirically two implications
of Proposition 4: 1) Aggregate shocks would result in larger aggregate TFP and GDP volatility in countries
14
with poor rule of law measures; 2) Idiosyncratic shocks would result in a larger dispersion of TFP measures in
countries with worse rule of law. In our data appendix, we provide a detailed account of our data construction
procedures and summary statistics in each sample.
4.3.1 Aggregate Volatility and Rule of Law
Proposition 4 under Assumption 2 implies that aggregate shocks are further amplified under poor legal
environments. To test our model’s implication in the presence of aggregate shocks, we employ ‘Adjusted
Growth Accounting and Total Factor Productivity’ from Total Economy Database (conference-board.org)
to obtain country-level GDP and TFP growth rates in percentage points. After merging with WGI, our
sample covers 123 countries from 1996 to 2016. For each country c, we compute the variable V OLc as the
volatility of the country c’s GDP (TFP) growth rates over the sample period. We calculate the Average Rule
of Law variable AV G RLc as the average of Rule of Law of the country c over the sample period. We include
Continent Dummies to partially control for region-specific effects. For all 123 countries in our sample, we
estimate:
V OLc = α+ βOLSAV G RLc + Continent Dummies + εc (3)
Figures 2a and 2b plot the Average Rule of Law against the GDP and TFP volatility, respectively, after
partialling out regional averages (Continent Dummies). Equations above scatter plots also report estimated
coefficients, robust standard errors in parentheses, and “within R-squared” values after partialling out regional
averages. According to our “within R-squared” values, within-region variations in Average Rule of Law
explains 9.83% (15.39%) of within-region variations in GDP (TFP) growth rates.
As predicted by our model, the more a country relies on formal contracts (i.e., the better the legal environment
is) the lower the TFP and GDP volatility it has experienced over the last 20 years. After partialling out
region-specific effects, one standard deviation increase in Average Rule of Law would decrease the volatility of
GDP growth rates by 0.88 percentage points and TFP growth rates by 0.99 percentage points. Given that in
our sample the volatility of annual GDP growth rates averages around 3.41 percentage points while volatility
of TFP growth rates around 3.16 percentage points, the magnitude of these effects is quite large.
This, of course, is just a correlation. It is well documented in the existing literature (Aguiar and Gopinath,
2007; Koren and Tenreyro, 2007) that developing countries’ outputs are more volatile. Since contracts
are more difficult to enforce in poor countries, even when looking at departures from regional averages, the
approach in Figure 2a and 2b cannot distinguish the effect of the legal environment from other determinants
of TFP and GDP volatility of a country.
In order to address the possible endogeneity, we exploit exogenous variations in early European settler’s (log)
mortality rates Sc from the seminal work by Acemoglu et al. (2001). After merging our original sample
with Acemoglu et al. (2001)’s dataset, our sub-sample covers 50 countries. Except for few exceptions as the
United States, Australia, and New Zealand, most countries in the sample are developing countries. To test
the inclusion restriction condition, we use our sub-sample to estimate the first-stage regression:
AV G RLc = γSc + Asia Dummy + Africa Dummy + “Other Region” Dummy + ηc
15
Figure 2: Rule of Law and Output Volatility (OLS)
(a) Rule of Law and GDP Volatility
(b) Rule of Law and TFP Volatility
Panel (a) plots standard deviations of country-level GDP growth rates against Average Rule of Law measure, afterpartialling out regional fixed effects. Panel (b) plots standard deviations of country-level TFP growth rates againstAverage Rule of Law measure, also after partialling out regional fixed effects. Above each plot is the line of best fitin red. The equation reports estimated coefficients, robust standard errors in parentheses, and “within R-squared”values after partialling out regional fixed effects.
16
Figure 3: Rule of Law and Output Volatility (IV)
(a) Rule of Law and GDP Volatility
(b) Rule of Law and TFP Volatility
Panel (a) plots standard deviations of country-level GDP growth rates against instrumented Average Rule of Lawmeasure, after partialling out regional fixed effects. Panel (b) plots standard deviations of country-level TFP growthrates against Average Rule of Law measure, also after partialling out regional fixed effects. For both panels,Acemoglu et al. (2001)’s early European settler’s (log) mortality rates Sc instruments the Average Rule of Law.Above each plot is the line of best fit in red. The equation reports estimated coefficients, robust standard errors inparentheses, and “within R-squared” values after partialling out regional fixed effects.
17
Our specification is similar to Acemoglu et al. (2001). The t-statistics for our γ estimate is −3.72. Thus,
our Cragg-Donald Wald F-statistic 13.81 exceeds Stock and Yogo (2005)’s suggested threshold value at 10.
Hence, our maximal IV estimator bias is likely to be small. So early settler’s mortality rates Sc are likely to
satisfy the inclusion restriction as well.
For 50 countries in our sub-sample, we use mortality rates Sc as an excluded instrument and estimate:
V olc = βIVAV G RLc + Asia Dummy + Africa Dummy + “Other Region” Dummy + εc (4)
Figure 3a and 3b plot results from instrumental variable regressions, after partialling out regional dummies.
According to our “within R-squared” values, within-region variations in Average Rule of Law explains 8.15%
(21.53%) of within-region variations in GDP (TFP) growth rates.
After partialling out region-specific effects and instrumenting with mortality rates Sc, one standard devia-
tion increase in Average Rule of Law would decrease the volatility of GDP and TFP growth rates by 0.62
percentage points after rounding. Note that for this sub-sample the volatility of annual GDP growth rates
averages 2.76 percentage points while the volatility of TFP growth rates is 2.71 percentage points. Thus,
the magnitudes remain economically significant when using IV to estimate the effects. Hence, in conjunction
with the Proposition 4, our IV estimates suggest that for these countries output may be more volatile due
to their larger reliance on relational contracting.
In untabulated regressions, we also estimate βOLS with our sub-sample for both GDP and TFP growth rates
without any excluded instrument. OLS estimates are very similar to IV estimates. One standard deviation
increase in Average Rule of Law is associated with decrease in annual GDP volatility by .45 percentage
points and annual TFP volatility by .59 percentage points. Indeed, Durbin-Wu-Hausman F-tests fail to
reject the null hypotheses that the instrumental variable estimates are significantly different from the OLS
estimates. As long as mortality rates satisfy exclusion restrictions, the Durbin-Wu-Hausman F-tests suggest
that endogeneity is not as severe.
In the data appendix, we use aggregate variance instead of volatility, and cluster for standard errors at
different levels to check robustness of our analysis. These additional analyses yield qualitatively similar
conclusions.
4.3.2 Dispersion in Firm-Level Productivity and Rule of Law
In the presence of idiosyncratic shocks, the same mechanism has implications for the cross-sectional distribu-
tion for firm-level TFP. Namely, the amplification effect, implies that firms in countries with poor contract
enforcement have a larger dispersion of TFP at any given point in time as well. The comparison between
the findings by Syverson (2004) for the US and Hsieh and Klenow (2009) for India and China are clearly in
line with our predictions. While for the US there is a twofold difference in productivity between the 10th
and 90th percentile firm, in China and India there is a fivefold difference.
To obtain firm-level TFP estimates, we download firm-level financial statements from ORBIS database.
We closely follow Gopinath et al. (2017)’s data construction procedure and use Ackerberg et al. (2015)’s
methodology to estimate firm-level TFP. We refer the interested readers to our data appendix for a more
detailed account. The final sample contains 4, 786, 856 firm-years observations from 27 countries. We merge
18
this sample with WGI data. For each country c, we compute TFP9010c as the TFP difference between the
firm with the top 10% productivity and the bottom 10% firm in a country c in the logarithmic scale. Also,
AV G RLc denotes the average rule of law of the country c over the corresponding sample period. Since
all countries in the sample belong either to Asia or Europe, we include a separate dummy variable Euro
indicating whether the country belongs to Europe or not. We estimate the following regression:
TFP9010c = α+ βOLSAV G RLc + Euro + εc (5)
Figure 4 shows that the relationship implied by Proposition 4 is indeed consistent with what can be ob-
served more broadly. Within R-squared value suggests that within-region variations in Average Rule of Law
would explain 78.5% of within-region variations in country-level dispersion in firm productivity. In data
appendix, we check the robustness of our finding with alternative measures of country-level dispersion in
firm productivity and different econometric specifications. We find qualitatively similar results.
The countries with better contract enforcement have, on average, lower dispersion of TFP than those with
weaker contract enforcement. One standard deviation increase in Average Rule of Law would decrease the
productivity gap between the top 10% most productive firm and the bottom 10% firm by 0.409 in logarithm
scale. This translates into reduction in productivity gap by 33.6%.
Since sample countries in the ORBIS universe do not overlap with those in Acemoglu et al. (2001)’s dataset,
we cannot conduct an instrumental variable regression based on Acemoglu et al. (2001)’s European settler
mortality rates. We are hopeful that future research can explore this possibility with new datasets.
Figure 4: Rule of Law and Firm-Level TFP Dispersion
Figure 4 plots the TFP difference between the firm with the top 10% productivity and the bottom 10% firm in acountry c in the logarithmic scale against the Average Rule of Law measure. Above the plot is the line of best fit inred. The equation reports estimated coefficients, robust standard errors in parentheses, and “within R-squared”values after partialling out regional fixed effects.
19
5 Conclusion
We have shown that a very natural and parsimonious economic environment that features time-varying yet
persistent prospects in value creation and limits to enforceability of future promises delivers predictions that
are consistent with a wide range of empirical findings. Although many these empirical facts had mostly
been documented before and individual theories had been put forward for many of them, our paper pro-
vides a unique and simple framework that sheds light on how all these facts might be connected. Finally,
we contribute to empirical literature by identifying a new link between legal environment and aggregate
volatility.
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22
Data Appendix
Sources and Sample Construction
A. Rule of Law Measure
We obtain this measure directly from the Worldwide Governance Indicators (WGI) project website (Kauf-
mann et al., 2011). The database covers 216 countries from 1996 to 2016.
B. Country-Level GDP / TFP Growth Rate
We use country-level GDP/TFP growth rates in the Adjusted Growth Accounting and Total Factor Pro-
ductivity, 1950-2016 from the Conference Board (TCB) Total Economy Database website. After we merge
this database with WGI database, our sample covers 123 countries over 1996-2016. For each country c, the
variable V OLc denotes the volatility of the country c’s GDP (TFP) growth rates from 1996 to 2016. The
Average Rule of Law AV G RLc denotes the average of Rule of Law of the country c from 1996 to 2016.
C. Early European Settler’s Mortality Rates
We obtain early European settler’s mortality rates used in Acemoglu et al. (2001) from Daron Acemoglu’s
website. After merging with TCB Total Economy Database and WGI, our sub-sample covers 50 countries.
D. Firm-Level TFP Dispersion
We closely follow data construction procedures in Gopinath et al. (2017). We download all unconsolidated
financial statements of all manufacturing firms (2-digit NACE code from C.10-C.33) with from the entire
ORBIS universe. Then, we drop entire history of a firm if the firm has any basic accounting errors in any year
(e.g. negative total assets, employment, sales). After removing observations that indicate basic accounting
errors, we focus on ones that has non-missing data on total assets, tangible fixed assets, sales, operating
revenues, employment, and country information. We also keep a firm-year only if value added, defined as
the difference between the operating revenue and material costs, is positive. For informativeness, we drop
countries with less than one hundred observations (firm-years). After these procedures, we have 4,786,856
firm-years with 27 countries. Nominal values are deflated via Producer Price Indices (PPI) obtained from
the Datastream database.
We adopt a standard methodology for TFP estimation in the literature. For each country c, we use the
methodology from Ackerberg et al. (2015) to separately estimate parameters βl,c and βk,c:
log(yi,c,t) denotes the nominal value added deflated by PPI for the domestic manufacturing firms, log(li,c,t)
the nominal wage bill deflated by the same PPI, and log(ki,c,t) the value of the sum of tangible and intangible
fixed assets deflated by PPI for the investment good.10 We use intermediate goods as a control variable.
For each country c, LN TFP9010c denotes the TFP difference between the firm with the top 10% produc-
tivity and the bottom 10% firm in a country c in the logarithmic scale. As samples above, AV G RLc denotes
10We deflate all variables by economy-wide PPI’s for following countries, because other PPI’s were are not available: Bosniaand Herzegovina, Montenegro, the Republic of Macedonia, Romania, Serbia, and Ukraine.
23
the average rule of law of the country c over the corresponding sample period. After merging the ORBIS
sample with WGI data, our final sample contains 27 countries.
Summary Statistics
Table 1: Sample After Merging WGI and TCB Total Economy Database
Variable Obs Mean Median Std. Dev.Average Rule of Law 123 .097 -.105 1.028Volatility of GDP Growth Rates 123 3.411 2.812 2.137Volatility of TFP Growth Rates 123 3.159 2.748 2.026
Table 2: Sub-Sample of Table 1 After Merging with Acemoglu et al. (2001)
Variable Obs Mean Median Std. Dev.Average Rule of Law 50 -.187 -.456 .928Volatility of GDP Growth Rates 50 2.763 2.425 1.578Volatility of TFP Growth Rates 50 2.71 2.513 1.289Log(Settler Mortality) 50 4.495 4.266 1.275
Table 3: Sample After Merging WGI and ORBIS
Variable Obs Mean Median Std. Dev.Average Rule of Law 27 .843 .944 .81490/10 Difference in Firm-Level TFP 27 1.246 1.093 .45
24
Robustness Checks for Empirical Analyses
In this section, we check robustness of our empirical analyses. In Table 4, we use aggregate variance itead of
aggregate volatility to check the robustness of equation 3. In Table 5, we check the robustness of equation
4 by clustering at different levels and alternative econometric specifications. Furthermore, we use aggregate
variance instead of aggregate volatility in Table 6. In Table 7, we again use aggregate variance instead of
aggregate volatility to check the robustness of equation 5. While most results involving TFP growth rates
are statistically significant, results for GDP growth rates are less often statistically significant.
Moreover, in untabulated regressions, to address the finite sample bias associated with robust standard errors,
we also estimate standard errors without adjusting for heteroskedasticity. Most of our results involving TFP
growth rates remain statistically significant even under non-robust standard errors, but the result involving
GDP growth rates is more mixed11. We believe that TFP results are more relevant for our theoretical results,
because other determinants of GDP growth rates (e.g. population growths) may potentially confound our
regressions results involving aggregate GDP growth rates.
Var GDP Var GDP Var GDP Var GDP Var TFP Var TFP Var TFP Var TFPAVG RL -8.036 -8.036 -11.306 -11.306 -8.539 -8.539 -11.088 -11.088
(2.341) (3.374) (3.880) (7.232) (2.201) (2.743) (3.611) (5.735)Region FE No No Yes Yes No No Yes YesClustering No Yes No Yes No Yes No Yes
(Continent)
The table reports ordinary least squares (OLS) regression results of a country’s variance of aggregate growth rateson its average rule of law. Var GDP (Var TFP) is calculated as the variance of aggregate GDP (TFP) growth ratesover 1996-2016 from TCB Total Economy Database. AVG RL is calculated as the average of rule of law measureover the from World Governance Index. Region FE denotes whether continent dummies are included in the righthand side. Parentheses reports standard errors. Clustering denotes whether standard errors are clustered atcontinent levels. All standard errors are robust to heteroskedasticity.
Table 5: V olc = α+ βIVAV G RLc(+Region Dummies) + εc
Vol GDP Vol GDP Vol GDP Vol TFP Vol TFP Vol TFPAVG RL -.517 -.676 -.676 -.664 -.669 -.669
(.250) (.157) (.403) (.225) (.148) (.296)Region FE No Yes Yes No Yes YesClustering No Yellow Fever Region No Yellow Fever Region
The table reports the second-stage regression results of a country’s volatility of aggregate growth rates on itsaverage rule of law using from the instrumental variable (IV) method. The only excluded instrumental variable isAcemoglu et al. (2001)’s logarithm of early European settler’s mortality. Vol GDP (Vol TFP) is calculated as thevolatility of aggregate GDP (TFP) growth rates over 1996-2016 from TCB Total Economy Database. AVG RL iscalculated as the average of rule of law measure over the from World Governance Index. Region FE denotes whetherregion dummies (Asia, Africa, and Others) are included in the right hand side. Yellow Fever denotes whether thevector yellow fever is present today. Parentheses reports standard errors. Clustering denotes the level at whichstandard errors are clustered. All standard errors are robust to heteroskedasticity.
11These results are available upon request.
25
Table 6: V arc = α+ βIVAV G RLc(+Region Dummies) + εc
Var GDP Var GDP Var GDP Var GDP Var TFP Var TFP Var TFP Var TFPAVG RL -3.108 -3.108 -4.771 -4.771 -3.589 -3.589 -3.680 -3.680
(1.593) (.851) (2.092) (2.202) (1.445) (1.133) (1.552) (1.683)Region FE No No Yes Yes No No Yes YesClustering No Yes No Yes No Yes No Yes(Region)
The table reports the second-stage regression results of a country’s variance of aggregate growth rates on its averagerule of law using from the instrumental variable (IV) method. The only excluded instrumental variable is Acemogluet al. (2001)’s early European settler’s (log) mortality rates Sc. Var GDP (Var TFP) is calculated as the volatilityof aggregate GDP (TFP) growth rates over 1996-2016 from TCB Total Economy Database. AVG RL is calculatedas the average of rule of law measure over the from World Governance Index. Region FE denotes whether regiondummies (Asia, Africa, and Others) are included in the right hand side. Parentheses reports standard errors.Clustering denotes the level at which standard errors are clustered. All standard errors are robust toheteroskedasticity.
(.0588) (.0376) (.018) (.0249) (.0094) (.0254) (.004)Euro Dummy No No Yes No No Yes Yes
Clustering No Yes Yes No Yes No Yes(Region)
The table reports the ordinary least squares (OLS) regression results of a country’s firm-level productivitydispersion measures on its average rule of law. TFP9010 is calculated as the difference in logged firm-level totalfactor productivity (TFP) between top 10% and bottom 10% for a country over the sample period. TFP9010 iscalculated as the volatility of logged firm-level total factor productivity (TFP) within a country over the sampleperiod. AVG RL is calculated as the average of rule of law measure over the from World Governance Index. RegionFE denotes whether region dummies (Asia, Africa, and Others) are included in the right hand side. Parenthesesreports standard errors. Clustering denotes the level at which standard errors are clustered. All standard errors arerobust to heteroskedasticity.
26
Appendix for Proofs
Proof of Proposition 1. Suppose that the optimal dynamic contract generates surplus s∗(θ) starting from
the initial state θ and that it specifies after the public history ht−1 and state θt−1 the fixed wage payments
wt(ht−1, θt−1) and contingent payments bt(h
t−1, θt−1, yt, θt) which induce from the agent effort at(ht−1, θt−1)
and yields him continuation expected value vt(ht−1, θt−1, yt, θt). Denote the value of the principal’s outside
option as π and the agent’s outside option as u.
Fix θ and let v∗(θ) ∈ [u, s∗(θ) − π] be the agent’s value from the optimal contract. We now construct from
the optimal contract a Markovian relational contract that transfers any variation in the continuation value
into the bonus payments that implements et(ht−1, θt−1) and yields the agent value v∗(θt−1).
Define the history-dependent bonus payments:
bt(ht−1, θt−1, yt, θt) = bt(h
t−1, θt−1, yt, θt) +δ
1− δ
(vt(h
t−1, θt−1, yt, θt)− v∗(θt−1))
and the fixed payments:
wt(ht−1, θt) = v∗(θ) + c
(at(h
t−1, θt−1))− Eyt,θt
[bt(h
t−1, θt−1, yt, θt)|at(ht−1, θt−1), θt−1
]
Notice that the contract(wt(h
t−1, θt), bt(ht−1, θt, yt, θt+1)
)induces the same effort level at(h
t−1, θt−1) as in
the optimal contract and yields the agent his outside value v∗(θt−1).
Now define the Markovian bonus payments b∗′(θt−1, yt, θt) as the bonus payments b that maximize the
expected profit, which is equal to the expected per-period joint surplus, for each state:
b∗′(θ−1, y, θ) = argmaxbt(ht−1,θ−1,·;·),ht−1,t
Eθ[θ|θ−1] + at(ht−1, θ−1)︸ ︷︷ ︸
=Ey[y|at(ht−1,θ−1)
]−c(at(h
t−1, θ−1))
s.t. at(ht−1, θ−1) = argmax
a,θ′Ey,θ′
[bt(h
t−1, θ, y, θ′)|a, θ−1
]− c(a)
Let a′(θ) be the solution
a′(θ−1) = argmaxa
Ey,θ[b∗′(θ−1, y, θ)|a, θ−1
]− c(a)
and define the state-dependent wages:
w∗′(θ−1) = v∗(θ−1) + c(a′(θ−1))− Ey,θ′[b∗′(θ−1, y, θ)|a′(θ−1), θ−1
](6)
By construction the Markovian contract (w∗′, b∗′) is self-enforcing, yields to the agent value v∗(θt−1) and
to the principal s∗(θt−1) − v∗(θt−1). Finally, because this Markovian contract repeats in each following
period, the continuation contract is self-enforcing. Thus the constructed contract is self-enforcing and gives
a per-period expected surplus s∗(θt−1).
Notice that taking v∗ ∈ [u,min{s∗(H)− π, s∗(L)− π}], a state-independent value for the agent, and setting
27
w∗′(θ−1) = v∗ − Ey,θ[b∗′(θ−1, y, θ)|a′(θ−1), θ−1
]+ c(a′(θ−1)) for all θ−1 redistributes the surplus from the
optimal Markovian relational contract and establishes that the property (R-1) holds in any optimal Markovian
contract.
Now, to establish the property (R-2) we start by noticing that by (R-1) the optimal Markovian relational
contract reduces to solving the following equations recursively (we illustrate for the lagged state is H and
the arguments follow analogously for the state L):
Notice that by (R-1) without loss we set u∗(θ) = u and hence ensuring the participation constraints can
safely be dropped and that since u = 0 the dynamic enforcement constraints [DEPH] and [DEPL] reduce to
respectively, bH(0, H) ≥ 0 and bH(0, L) ≥ 0. Furthermore, using 6 we have wH = u+c(a)−a(λbH(1, H)+(1−λ)bH(1, L))− (1− a)(λbH(0, H) + (1− λ)bH(0, L)) and the principal’s contracting problem further simplifies
To establish (E-2), denote the principal’s surpluses induced from b as Π, and the new recommended effort
levels a. From similar representation as 7, Π(θ) > Π∗(θ) ∀θ, because a(θ′) − c(a(θ′)) > a∗(θ′) − c(a∗(θ′))from a∗(θ′) ∈ (a∗(θ′), aFB) and a(θ) ≥ a∗(θ) ∀θ. Now, we will arrive at a contradiction if the new contract
{b, a, Π} satisfies all the dynamic enforceability conditions, because the new contract strictly does better than
Since the marginal cost of effort is strictly increasing, this implies that a∗(L) > a∗(H). But this contradicts
the first part of Lemma 1 that a∗(L) ≤ a∗(H) ≤ aFB .
Proof of Proposition 4. Outline of the Proof
The proof is quite long, so it is helpful to sketch the gist of proof before we start the proof. It would be useful
to revisit this outline after each step to keep track of what is going on. The figure 5 is a graphical illustration
of the proof. In the step 1, we can divide [0, 1] into two distinct segments: I1 = {α ∈ [0, 1] : a∗(H,α) < aFB},and I2 = {α ∈ [0, 1] : a∗(H,α) = aFB}. Next, we show that I1 = [0, α0) and I2 = [α0, 1] for some α0. In step
2, we show that Y (H,α)Y (L,α) is decreasing in α in the closed interval I2. According to one of our auxillary claims
presented below, a∗(θ, α) is weakly increasing in α. Hence, the logic is that Y (H,α) = a∗(H,α) +E(θ|θ−1 =
H) = aFB + E(θ|θ−1 = H) has no more room for further increase because it already reached the maximal
possible level, whereas the denominator Y (L,α) can potentially increase when α increases. The step 3 is a
preliminary step before implicit differentiation in step 4, to check whether we can indeed implicitly differenti-
32
Figure 5: Graphical Illustration
ate Y (H,α)Y (L,α) w.r.t. α’s in I1. It turns out that we can implicitly differentiate everywhere on I1, except at most
one α ∈ I1. We shall denote such point as α0, the region on the left side of α0 as IL1 = [0, α0), the region on
the right side of α0 as IR1 = (α0, α0). So even when α0 does exist, we can still use implicit differentiation to
analyze Y (H,α)Y (L,α) in IL1 ∪ IR1 . In step 4, we implicitly differentiate the ratio Y (H,α)
Y (L,α) in the region IL1 ∪ IR1 (or
I1 if α0 does not exist), and show that the ratio ∂∂α
Y (H,α)Y (L,α) ≤ 0 whenever implicit differentiation is possible.
After we are done with the step 4, we know that Y (H,α)Y (L,α) is decreasing in each of region IL1 , IR1 (or I1 if α does
not exist) and I2, but we still do not know what might happen around α0 and α due to jumps. The last step
in step 5 will analyze local behaviors around α0 and α0 with “pasting” argument. Thanks to the continuity
of Y (H,α)Y (L,α) , irregular jumps are ruled out. Hence, we conclude by showing that Y (H,α)
Y (L,α) is decreasing in all of
[0, 1] and finish the proof.
Auxiliary Claims
33
Though often referenced throughout the main proof for the amplification effect, these auxillary claims are
not directly related to the proof of the amplification effect and can be verified independently of the main
proof. Hence, their proofs will be presented after the main proof. 12
Lemma 3 (Monotonicity). In any optimal Markovian relational contract the constraint optimal effort and
In other words, this bonus system {b(·, ·, α2)} will solicit maxα1,α2
a∗(θ, α) for each θ. Furthermore, observe
that for all θ′ ∈ Θ, a∗(θ′, α1) < a∗(θ′, α2) ≤ aFB by monotonicity (M’-1), so a∗(θ′, α1) − c(a∗(θ′, α1)) <
a∗(θ′, α2)− c(a∗(θ′, α2)).
Now, by the system of equations 5, for any stationary contract,
[Π∗(H,α)
Π∗(L,α)
]=
[γ 1− γ
1− γ γ
][a∗(H,α)− c(a∗(H,α)) + H
a∗(L,α)− c(a∗(L,α)) + L
]
42
We can use a similar representation for the new continuation values under the new bonus system {b(·, ·, α2)}for Π(·, α2), as well. Since the new bonus system solicits higher effort level max
α1,α2
a∗(θ, α), the system of
equations above shows that Π(θ, α2) ≥ max{Π∗(θ, α2),Π∗(θ, α1)}. Now, for all θ−1 ∈ Θ,
Given our range for ε, c′(a∗(H,α)) = c′(a∗(H,α)) + ε < 1. Since a∗(H,α) < a∗(H,α) < aFB , a∗(H,α) −c(a∗(H,α)) < a∗(H,α) − c(a∗(H,α)). From the system of equations in Lemma 5, new continuation values
{Π(·, α)} are also pinned down. Also, it must be that Π(H,α) > Π(H,α) and Π(L,α) > Π(L,α). Our next
task is show that the new bonus system {b(·, ·, α)} satisfies the dynamic enforceability condition.
For convenience, denote the increment in the new recommended effort level a∗(H,α) relative to the old effort
level a∗(H,α) by η(ε) := a∗(H,α)− a∗(H,α). Observe that:
The equality above follows from Lemma 5. Since x − c(x) is a strictly increasing function on [0, (c′)−1(1)],
a∗(L,α)− c(a∗(L,α)) ≤ a∗(H,α)− c(a∗(H,α)) holds from the assumption that a∗(H,α) < aFB = (c′)−1(1)
and the monotonicity in effort a∗(L,α) ≤ a∗(H,α). Hence, it must be that a∗(H,α) − c(a∗(H,α)) + H ≥a∗(L,α)− c(a∗(L,α)) + L, which shows the inequality above.
Now, since a∗(H,α)+H
a∗(L,α)+Llies between a∗(H,α)−c(a∗(H,α))+H
a∗(L,α)−c(a∗(L,α))+Land c(a∗(H,α))
c(a∗(L,α))15, it follows that:
c′(a∗(H,α))
c′(a∗(L,α))≤ min{a
∗(H,α)− c(a∗(H,α)) + H
a∗(L,α)− c(a∗(L,α)) + L,c(a∗(H,α))
c(a∗(L,α))} ≤ a∗(H,α) +H
a∗(L,α) + L
This completes the proof.
15This follows from the following fact: assume a1, a2, b1, b2 > 0 and a1a2≤ b1