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INCENTIVES, EQUALITY AND CONTRACT

RENEGOTIATIONS: THEORY AND EVIDENCE IN THECHINESE BANKING INDUSTRY

Hongbin Caiw

Hongbin Liz

Li-An Zhou

Renegotiation plays an important role in contract theory, but theempirical study of renegotiation is almost non-existent in the literature.

Using a unique datasetfrom the Chinese bankingindustry, we find that thelarge majority of managerial incentive contracts are renegotiated afterperformances are realized. We develop a model of contract renegotiationwhere supervisors and managers sign incentive contracts and thenrenegotiate them. In the unique equilibrium of the model, incentivecontracts are almost always renegotiated ex post. Even though renegotia-tion is fully anticipated, incentive contracts affect performance. Thepredictions of the model find strong support from our empirical results.

I. INTRODUCTION

INCENTIVE CONTRACTS IN THE REAL WORLD ARE OFTEN RENEGOTIATED, especially

within firms and organizations. Renegotiation also plays a very important

role in contract theory.1 However, as for contract theory in general, where

THE JOURNAL OF INDUSTRIAL ECONOMICS 0022-1821

Volume LVIII March 2010 No. 1

We thank Masahiko Aoki, Chong-En Bai, Douglas Bernheim, Paul Devereux, AntonioRangel, Minggao Shen, David de Meza, the Editor and the anonymous referees for very helpfulcomments. We are indebted to the William Davidson and the Ford Foundation in Beijing forfunding the survey work in 1998. Cai acknowledges support from NSFC grant (Project No.70573008) and appreciates the financial support of the Project of Chinese Private Economy in the

Center for Research of Private Economy, Zhejiang University. All remaining errors are ours.wGuanghua School of Management and IEPR, Peking University, Beijing, 100871, Chinaand Center for Research of Private Economy, Zhejiang University, 388 Yuhangtang Road,Hangzhou, Zhejiang Province, China.e-mail:[email protected]

zSchool of Economics and Management, Tsinghua University, Beijing, 100084, China.e-mail: [email protected]

Guanghua School of Management and IEPR, Peking University, Beijing, 100871, China.e-mail: [email protected]

1 In moral hazard models, Fudenberg and Tirole [1990] argue that after theagent exerts effortbut before the output is realized, the principal and the agent can achieve mutual gain byrenegotiating the incentive contract to fully insure the agent. In multi-period adverse selection

models, Dewatripont [1988, 1989] argues that if the agents type is revealed in the first period,the principal and the low type agent will want to renegotiate their contract to get rid of anyallocation distortions in the optimal static contract In the same environment, Laffont and
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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rapid theoretical advances are in contrast with a long lag of empirical work,

there has not been much empirical study on contract renegotiation.2 We are

fortunate to have access to a dataset collected from an on-site survey in 1998

that contains valuable information about contract renegotiation in the

Chinese banking industry. As with the typical incentive schemes introduced

in the course of the reform of Chinas State Owned Enterprises (SOEs),

since the mid 1980s, managers in local bank branches have signed explicit

incentive contracts with their supervisors at the beginning of the year that

specified bonus payments based on their annual performance. Quite

strikingly, these incentive contracts were almost always renegotiated ex

post. The dataset contains information about ex ante incentive contracts,

realized performance, and ex post contracts, as well as detailed information

about branch and manager characteristics, thus opening a rare window intothe process of how incentive contracts are designed and then renegotiated.

In trying to connect theory with evidence in the dataset, one finds that the

basic assumptions of the standard theories of renegotiation do not fit the

reality of the Chinese banking industry. The moral hazard theory of

renegotiation does not apply here because contract renegotiations took

place after the realizations of performance, and thus could not have been

driven by the desire to improve risk-sharing between managers and their

supervisors. The adverse selection theory of renegotiation does not apply

here either because contracts were renegotiatedfor the current year, not for

the future, and thus contracts were not renegotiated with dynamic

considerations in mind. Our dataset is drawn from the Chinese banks, but

the same phenomena are prevalent also in other SOEs in China. Thus the

following questions naturally arise. Why were those incentive contracts

renegotiated? If it was expected that contracts would be renegotiated, what

were the roles of those contracts? What were the effects ofex ante and ex post

contracts on performance?3

In this paper, we develop a simple model of renegotiation to answer these

questions. The model is intended to capture some of the essentialinstitutional features of incentive contracting in Chinas state sector. In

our model, supervisors have strong equality concerns over managers undertheir supervision. Moreover, managers enjoy rents-on-the-job (perks and

career development) which come largely under their supervisors discretion.

renegotiation destroys ex ante incentives: the principal will design a renegotiation-proofcontract that is less efficient than the optimal contract without the possibility of renegotiation.

2 Notable exceptions are Filson, Switzer and Besocke [2005] and Gil [2004], which we discuss

in detail later.3 By renegotiation-proof principle, theoretical analysis of renegotiation typically focuses onoptimal contracts that preempt renegotiations (renegotiation-proof). With such contracts,

CONTRACT RENEGOTIATIONS IN THE CHINESE BANKING INDUSTRY 157


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Supervisorsequality concerns and managers rents-on-the-job (which are

subject to manipulation by the supervisors) create opportunities for mutual

gains that constitute the basis for contract renegotiation. In the case of

managers with above average performance, their supervisors will want to

renegotiate their bonus payments downward in exchange for more rents. In

the case of those who have below average performance, their supervisors will

want to pay them higher bonuses than their ex ante contracts specified but

reduce their rents. Ex ante incentive contracts serve as a disagreement point

in the renegotiation process, thus affecting ex post payoffs. Given an ex ante

incentive contract, the manager chooses an optimal effort level anticipating

how the contract will be renegotiated ex post. The supervisor designs an

optimal ex ante incentive contract taking into account the managers best

response and theex post

renegotiation process.We suppose that the benchmark on which supervisors equality concerns

are anchored is endogenously determined. We characterize the unique

equilibrium of the game: the optimal ex ante contracts, the managers best

effort response, performance, and how ex ante contracts will be

renegotiated. The model generates theoretical predictions about how the

endogenous variables ex ante contracts, performance, ex post contracts,

and direction and degree of renegotiation are affected by exogenous

manager and supervisor characteristics and technological conditions. For

example, we show thatex ante

contracts will have higher power andmanagerial performance will be better when the supervisors own incentive

intensity is greater, or the managers value more rents. Moreover, the greater

the supervisors own incentive intensity or the managers responsiveness toincentives, the more likely ex ante contracts will be renegotiated upwards

(higher bonus ex post than specified ex ante) and the greater the degree of

renegotiation (i.e., absolute magnitude of renegotiation) will be.

The predictions of the model find strong support from our empirical results

using the dataset from the Chinese banking industry. In particular, we find that

the supervisors own incentive intensity has positive and statistically significanteffects on both the incentive intensity in the ex ante managerial contracts and

branch performance. Younger managers (who are likely to value more careerdevelopment opportunities) tend to have more highly powered contracts, and

perform better than their older peers. Moreover, when the supervisors own

incentive intensity is greater or the manager is younger, it is more likely that the

bonus payments will be adjusted upwards and the adjustment will be larger.

For example, according to our estimation, if a branch manager is 5 years

younger than the mean, the chance for an upward renegotiation will be 610

per cent higher. The evidence here suggests that our model, with endogenouslydetermined bonus benchmarks, fits the data pretty well.By incorporating some essential organizational features into the standard

158 HONGBIN CAI, HONGBIN LI, LI-AN ZHOU


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concerns and managers rents. Incentive contracts affect performance, but

they do so through mechanisms which are more complicated than thestandard

agency model suggests. A number of papers, e.g., Groves et al. [1994], Li

[1997], and Jefferson and Rawski [1999] have tried to estimate whether and by

how much the incentive schemes in Chinese SOEs affected firm productivity

and financial performance. Overall, they found positive correlations between

incentive intensity and firm performance. However, in light of our theoretical

results and empirical findings, it may be an over-simplification to interpret the

positive correlations in the way described in the standard agency model. Given

that incentive contracts were typically renegotiated, the incentive schemes

introduced in Chinese SOE reforms helped improve performance, most likely

because they constrained the renegotiation outcomes and therefore affected

the managers payoffs. Thus, the correlation between performance and actualbonus payments may in fact underestimate the actual incentives managers

could look forward to when deciding howhard to work, because exceptionally

good (bad) performance was more likely to be associated with downward

(upward) renegotiation of bonus payments.

To the best of our knowledge, Filson, Switzer and Besocke [2005] and Gil

[2004] are the only papers that investigate empirically contractual renegotia-

tion. Both consider revenue-sharing contracts between movie theaters and

distributors: the former is about the United States and the latter is about

Spain. Both papers argue that renegotiation in the movie industry does not fitthe standard contract theory of moral hazard or adverse selection. Filson,

Switzer and Besocke [2005] suggest that risk aversion and transaction costs

may be the forces driving renegotiation. Using a large dataset, Gil [2004]shows that renegotiation in the Spanish movie industry is quite common

(55%) and is always one-sided. He argues that dynamically adjusting prices

as more information is available can be rationalized in a setting of incomplete

contracts and learning. In the context of the Chinese banking industry, our

paper shows that renegotiation is pervasive and two-sided. More impor-

tantly, renegotiation in our context is about modifying theexpost

contractualterms that were agreed upon at the beginning of the current period, instead of

adjusting future contractual terms. We argue that equality concerns of thesupervisors are the driving force of contractual renegotiations in our context.

A large body of literature has recently emerged about the economic

implications of fairness, inequality aversion, and other social psychological

considerations (see, e.g., Fehr and Schmidt [1999]).4 For example,

Englmaier and Wambach [2006] and Rey Biel [2007] extend the standard

moral hazard model by considering inequality averse agents, and show,

among other things, that incentives tend to be flatter and team-based



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incentives are more likely to be used than in the standard model. These

papers do not consider contractual renegotiation. Unlike these papers, our

purpose is to explain contractual renegotiations observed in our data. For

this purpose, we analyze a situation in which the principal rather than the

agent has equality concerns.

Besides the renegotiation literature, our paper also contributes to the

literature on incentives within firms.5 In particular, the spirit of our paper is

closely related to that of Prendergast [2002a]. Because of our different

objective, our model differs from Prendergast [2002a] in that: (i) whereas in

our model, supervisors care about income equality among their subordi-

nates, Prendergast [2002a] supposes that supervisors will display favoritism

towards individual subordinates; (ii) whereas in our model, supervisors

design incentive schemes for their subordinates, in Prendergast [2002a]bonus schemes are decided by firms; and (iii) a feature that is crucial in our

model but is absent in Prendergast [2002a] is that supervisors in our model

have control over their subordinates rents-on-the-job.

The rest of the paper is structured as follows. The next section outlines the

institutional backgrounds of incentive contracting and renegotiation in the

Chinese banking industry. In Section 3, we present the model and derive its

theoretical predictions. Section 4 then describes the data, and Section 5

presents the empirical results. In Section 6, we discuss possible extensions of

the model, robustness and alternative explanations of our empirical results.Section 7 contains concluding remarks.

II. INSTITUTIONAL BACKGROUNDS IN THE CHINESE BANKING INDUSTRY

In this section, we briefly describe the institutional backgrounds of the rural

financial institutions in China: the Agricultural Bank of China (ABC) and

the Rural Credit Cooperatives (RCCs). The discussion below serves twopurposes. First, it familiarizes readers with the institutional context of our

dataset, which was collected from an on-site survey of these two financial

institutions. Second, it provides motivations and justifications for some key

elements of our model.

The ABC is one of the four specialized state-owned banks in China.6 The

RCCs were formerly under the supervision of ABC, but they attained

independence as financial institutions in 1994. The ABC, with headquarters

in Beijing and branch offices at locations at every administrative level, servesclients both in urban and rural areas.7 The RCCs, which collectively make

5

For surveys on incentive provision within firms, see Prendergast [1999] and Gibbons andWaldman [1999]

6 The other three are the Industrial and Commercial Bank of China, the Construction Bank



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up the Federation of RCCs (xin yong lian she), or the headquarters at the

county level, only provide financial services to rural areas. In general, both

the ABC and the RCCs establish branches in each township, paralleling the

territorial structure of the government system in order to minimize

overlapping within the same institution. The ABC and RCCs currently

dominate the formal financial system in rural China. As of the late 1990s,

they accounted for nearly 80 per cent of total rural deposits and loans (Parket al. [1997]).

In terms of internal management and operation, the ABC and the RCCs

are very much alike, because the RCCs were formerly a part of the ABC and

they operate in similar regulatory, institutional and business environments.

The main difference between these two financial institutions lies in the

ownership structure. The ABC is state-owned, while the RCCs arecollectively owned by member rural households. The collective ownership

of the RCCs and its less hierarchical internal structure (only two

management levels: town branches and the county headquarters, under

the board of trustees) probably lead to a more equitable culture than in the

ABC.8

In the pre-reform era, Chinese state-owned banks, like other SOEs, did

not give any monetary incentives to managers and workers. Their wages

were fixed by certain pre-determined formulae linking wages to worker

characteristics such as age, seniority and position, but not to performance.Since the early 1980s, as part of the enterprise reform programs, China has

sought to reform its banking sector. As a major reform initiative to improve

the performance of state-owned banks, and in particular to increase depositsand reduce non-performing loans, the government introduced a bonus

system into the sector in the mid 1980s (Dipchand et al. [1994]). Typically,

supervisors sign a so-called responsibility contract with each individual

branch manager. The contract, normally signed on an annual basis, specifies

a formula tying the branch managers monetary rewards to branch

performance. For both the ABC and the RCCs, county-level banksupervisors determine incentive contracts for township branch managers.

While the details of the incentive system (such as the kinds of contracts andthe autonomy allowed to supervisors by government regulations) has

evolved over the years of the reform, its basic structure has remained more or

less the same. Moreover, while the economy has grown rapidly with market

forces playing an increasingly important role, the internal organization

structure of the large SOEs and state banks has remained quite stable. In

particular, in the late 1990s, there was still no active outside managerial

market, hence managers careers were largely confined within the enterprise



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or bank that first employed them. It remains difficult to dismiss or lay off

managers and workers.

In order to assess how and how well the incentive system introduced in the

Chinese SOE reforms performs, it is important to understand how the

incentive schemes operate to impact performance in the institutional

environment of the Chinese SOEs and state banks. One puzzling

phenomenon is that actual bonus payments to managers at the end of the

year systematically differ from those agreed upon at the beginning of the

year. In our dataset, contract renegotiation occurred in more than 90 per

cent of the cases. Such contract renegotiation is common in other SOEs.

Typically, good-performing SOE managers get downward revisions of

promised bonuses, while bad-performing ones are not punished finan-

cially, as they are supposed to be according to their original contracts (see,e.g., Shirk [1993]).

This kind of renegotiation of incentive contracts is puzzling, because it

does not fit in with the standard explanations. One obvious candidate for an

explanation would be that incentive contracts are not carried out as they

were agreed upon because they cannot be enforced by the shaky Chinese

court system. However, this cannot answer the question, why are those

contracts written at all, if they are expected to be non-binding ex post? And

why do they seem to have significant impacts on performance, according to

various empirical studies (e.g., Groveset al

. [1994]; Li [1997]; and Jeffersonand Rawski [1999])? The standard theories of renegotiation based on

adverse selection and moral hazard cannot explain contract renegotiations

in the Chinese banks and SOEs. Renegotiations of incentive contracts in theChinese banks and SOEs take place after performance is realized. This is

inconsistent with renegotiation in moral hazard models, where renegotia-

tion can only happen after the agent exerts effort but before the output is

realized. It is also different from renegotiation in multi-period adverse

selection models in that contracts can be renegotiated from the current

period to future periods, but the contracts of the current period are binding.To understand incentive contracting and renegotiations in the Chinese

banks and SOEs, we need to incorporate important features of theinstitutional environment into the standard agency model. First, supervisors

themselves are employees in state banks and SOEs which are burdened with

a rigid institutional system, and whose objective functions are quite different

from that of the principal in the standard agency model. Specifically, they

themselves come within the same incentive system as the managers under

their supervision, and hence care about the overall performance of branches

they are responsible for. However, they are not mere profit or outputmaximizers. As employees of the state banks and SOEs, supervisors have totake into account the goals and objectives of their organizations The SOEs



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paternalism referred to by Kornai [1980, 1992] when he describes the

typical social relation between superiors and subordinates in SOEs. More

generally, supervisors in large organizations may care about income equality

among their subordinates because they want to minimize costly influence

activities arising from unequal distribution of wages and bonuses (Milgrom

[1988]), or to avoid being accused of favoritism (Prendergast and Topel

[1996]). Supervisors equality concerns can also result from considerations

that unequal distribution among managers may make them less cooperative

towards one another or may directly reduce their utilities if managers have a

desire for fairness, inequality aversion or have other social psychological

considerations such as envy (Fehr and Schmidt [1999]). Moreover,

supervisors in the Chinese state banks and SOEs probably have strong

tendencies toward centrality bias and leniency bias that are welldocumented in the personnel literature for firms in the developed

economies.9 Combining these reasons, supervisors in the Chinese banks

and SOEs can have quite a strong concern for equality to maintain balance

among their managers in terms of wages and bonuses.

Secondly, as is typical in the strictly hierarchical Chinese banks and

SOEs, supervisors have a great deal of discretion over the well-being and

careers of managers under their supervision. Supervisors control evaluation,

recommendation and other decisions contributing positively or negatively

to managers promotion prospects. Supervisors have discretion overmanagerial perks (e.g., company apartments, cars, etc.) and whether and

how far to intervene in the management of local branches (e.g., on personnel

decisions, task assignments) so much as to affect managers rents-on-the-job. A supervisors discretion over managers rents can come in a variety of

forms, many of which are very hard to specify in advance and extremely

difficult for outsiders to verify (e.g., participation in a delegation that will

visit the United States on a business trip). Thus, such rents cannot be

specified ex ante in incentive contracts, and are at the discretion of the

supervisorsex post

.

10

Thirdly, supervisors tend to care more about income equality than rent

equality among their managers for the following reasons. Some rents, suchas opportunities to enhance promotion chances (e.g., slots in training

programs) are intrinsically unequal, so supervisors are not expected (and

will not be able) to act equally with regard to promotion prospects among

9 See, for example, Murphy and Cleveland [1991] and Coens and Jenkins [2000]. Fordiscussions and references on leniencybias and centrality bias, see Prendergast [1999] section

2.2.10 The broad discretion of supervisors over subordinates, such as promotions, taskassignments, and housing allocations, has been thought of as a salient institutional feature



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managers under their supervision. In addition, while managers incomes can

always be compared on a dollar to dollar basis, it can be very difficult to

compare various kinds of perks and favors and career development

opportunities each manager receives. Moreover, it can be very hard to

discern whether supervisors discretionary decisions over managers career

issues are purely favors (which would require justification from the equality

perspective) or purely business decisions. Therefore, relative to income

equalization, supervisors can find it harder to equalize rents on the one hand

and feel much less pressured to do so on the other.

In the next section, we build a simple model to capture these important

features of incentive contracting and renegotiations in the Chinese state

banks and SOEs, and show that they give rise to a renegotiation process

different from those described in the standard models of renegotiation.

III. THE MODEL AND THEORETICAL ANALYSIS

In our model, a supervisor S (county branch manager) supervises a numberof ex ante identical managers (township branch managers). Let M be a

representative manager. M is expected to exert some minimally acceptable

effort, and has a guaranteed employment with a fixed wage w0. The wage w0

is fixed in two senses. First, it is independent of the managers performance(unless in extreme events such as criminal acts, in which case he will be

demoted or fired). Second, the wage formula is fixed by the bank

headquarters in Beijing, so it is out of the control of S. Suppose M can

exert extra effort e to improve the performance of his branch: x5 e y,where x is a performance measure and y is a random variable that is

independent across managers. For simplicity, and to ensure non-negative x,

we assume y is uniformly distributed on [0, Z]. We will call Ms extra effort

beyond the minimal acceptable level simply his effort. Ms private effort

cost is 0.5ge2

, whereg4

0 is a cost parameter.To motivate M to exert effort, at the beginning of the year S offers M a

linear incentive contract: w5w0 ax, where the choice variable a ! 0measures the ex ante incentive intensity of the contract. After signing theincentive contract with S, M chooses an effort level e. The performance

measure x is then observed by both M and S near the end of the year. Our

model departs from the standard moral hazard model in the final contract

execution stage. Instead of enforcing the ex ante contract based on the

observed performance x, at the end of the year, M and S can renegotiate to

reach a different agreement. Note that since performance is realized andknown to both parties, renegotiation at this stage cannot be driven by risk-

sharing concerns (even if M is more risk-averse than S) and thus is not the



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simplicity, and fits well with the reality of the Chinese state banks and firms

since the supervisor has authority over the managers.

During the renegotiation stage in our model, S will choose the amount of

rents that she will give to M, and also adjust the ex ante bonus rate a to a0,

which represents the ex post incentive intensity. As discussed earlier, the

rents S allocates to M during the renegotiation stage represent the

supervisors ex post discretion over Ms conditions of employment, such as

whether and how much to intervene in Ms management of his branch (e.g.,

on personnel decisions), and other tangible and intangible perks and career

opportunities (e.g., training opportunities). These kinds of rents are suitable

for renegotiation instruments, because they are difficult to specify ex ante

and hence cannot be easily included in the ex ante contract.11

LetdA

[0,D

] be the amount of rents S gives M during the renegotiationprocess. We suppose that M is risk neutral, thus his payoff function isu5w0 ax bd 0.5ge

2, where b4 0 is a parameter measuring how much

M values rents.

We suppose that the supervisors payoff function from supervising a

representative manager M is given by

v A 0:5d2 r ax 0:5lax b2

where the interpretation of each term is as follows:

The first term A is a positive constant. The second term 0.5d2 is the cost to S of giving rents dto M. The third term (r a0)x reflects the fact that S benefits from good

performance by M (where the parameter r4 0 measures how much S

values Ms performance) and that a bonus payment to M reduces her

payoff. As discussed in the preceding section, the supervisor herself is an

employee like the managers, except that she is one level above M in thebanks hierarchical structure. Thus S is evaluated and rewarded by her

own superior based on the aggregate performance of all branches under

her supervision. The parameter r depends on how strong the supervisors

own incentive intensity is. The last term 0:5lax b2 represents the supervisors equality

concerns, where b denotes the bonus benchmark which the supervisor

uses to compare Ms bonus payment, and the parameter l measures how

strongly the supervisor cares about income equality. As discussed earlier,supervisors in the Chinese banking sector and SOEs have quite strong

equality concerns regarding their subordinates and thus we expectl tobe

large. How b is determined will be specified later. Roughly speaking, b

can be thought of as the average bonus among branch managers,



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hence ax b measures how far a particular Ms bonus is from theaverage.

Our model has symmetric and complete information, thus we use

backward induction to solve for the symmetric subgame perfect equilibrium(henceforth equilibrium) of the game in which ex ante all managers are

treated equally.

III (i). Renegotiation Stage: Ex Post Incentives

In the renegotiation stage, given the ex ante incentive scheme a and the

realized performance measure x, S chooses dand a new bonus scheme a0 to

maxd;a0

v A 0:5d2 r a0x 0:5la0x b2

subject to Ms participation constraint:

w0 bd a0x 0:5ge2 ! w0 ax 0:5ge

2

which can be simplified as a0x bd ! ax.Solving for the supervisors optimization problem, we have (all technical

proofs are in the Appendix)

Lemma 1. When ax ! B, S will lower a to a0 a 11lb2 Bx lb2

1lb2, and in

exchange offers M rents d lbaxB1lb2

. When axoB, S will increase a to

a 05B/x, and gives Mno rents.

Lemma 1 is easy to understand. The variable B b 1=l can be thoughtof as an adjusted bonus benchmark. When the bonus payment to M in

accordance with the ex ante contract is larger than the benchmark B,Swould

like to renegotiate it down in exchange for more rents to M. In this case,

Lemma (1) says that the ex post bonus paymenta0x is a linear combination ofthe ex ante bonus payment ax and the benchmark B. Consequently, the ex

post incentive intensitya0 is increasing in the ex ante incentive intensitya and

decreasing in the realized performance measure x. On the other hand, when

the bonus payment to M according to the ex ante contract is smaller than the

benchmark B, S does not need to provide M with any rent (d5 0), but her

equality concern leads her to offer a higher bonus to M.

III (ii). Managerial Optimal Effort Choice

Given an ex ante incentive intensitya, if M exerts effort e, his expected payoffcan be calculated as follows If ax5 a(e y) ! B or if y ! ye B=a e



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w0 a0x 0.5ge25w0 B 0.5ge

2. Therefore, Ms expected payoff is

1

Eu w0 0:5ge2 B

y

Z

ZZy

ae y

Zdy

w0 0:5ge2 B

y

Z

a

ZeZ ey 0:5Z2 0:5 y

2

Using dy=de 1, solving from the FOC for Ms optimal effort gives

2 e Za B

Zg a

For the second order condition to hold, it must be that aoZg. For e to be

non-negative, it must be that a ! B/Z. Otherwise, ifao

B/Z, then e5

0.Intuitively, since M expects to receive the bonus benchmark B no matter how

low his performance is, exerting a small amount of effort does not increasehis actual income when aZoB (note that M receives B ex post as long as

a(e y) a(e Z)oB). But effort is costly. Thus M will exert effort only ifaZ ! B. When Equation (2) gives Ms optimal effort, it must be thatZ2g4 aZ ! B. It follows that

3de

da

Z2g B

Zg a

2>0

Lemma 2. If the ex ante incentive intensity a ! B/Z, then M will chooseeffort in accordance with Equation (2); ifaoB/Z, Mwill choose e5 0. Ms

optimal effort is increasing in a.

Lemma 2 shows that the ex ante contract can provide incentives for

managerial effort even if it is anticipated that it will be renegotiated. The

reason is that the bonus and rents M will receive ex post is determined by his

ex ante contract when y !

y, thus, greater ex ante incentive intensity inducesgreater managerial effort. Moreover, higher managerial effort reduces y and

hence increases the likelihood that M gets a higher payoff, which provides an

additional incentive for M to exert more effort.

III (iii). Optimal Ex Ante Incentive Intensity

Ex ante, S is to choose an incentive intensity a to maximize her expected

payoff in the whole game. Once a is chosen, S can correctly anticipate Ms

optimal effort choice given by Lemma 2. Since managers are ex anteidentical, S will choose the same a for all managers; and managers willchoose the same effort level under the same contract (at least in the



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and when performance is bad, i.e., y ! y 0:5Z 1=al, a0 is

7 a0 a

1 0:5Z 1=al y

x

Proposition 1 characterizes the equilibrium of the whole game. In deriving

the results in Proposition 1, since the exact solution is not easy to obtain, for

simplicity we used an approximation by assuming thatl2 is sufficiently large

relative to other parameters of the model (see the proof in the appendix for

details). This approximation does not affect the qualitative properties of the

solution and has no effect on the comparative statics results we derive below.The institutional environment in the Chinese state banks also fits well with

the assumption of large l, where supervisors are paternalistic towards their

subordinates (Kornai [1980], [1992]).

III(iv). Theoretical Predictions of the Model

From Proposition 1, it is easy to derive the following result regarding the

comparative statics of our model.13

Proposition 2. The optimal ex ante incentive intensity (a) and the

managerial performance (x) are increasing in the supervisors own incentive

intensity (r) and the managers preferences for rents (b), and decreasing inthe effort cost parameter (g) and the riskiness of the environment (Z).

While the effects ofr and g can appear from the standard principal-agent

models, the effects of b and Z arise because of the new features of

renegotiation in our model. The intuition is as follows. When the manager

has stronger preferences for rents (greater b), the supervisor can use rents

more effectively to reduce his ex post bonus payments if he achieves above

average performance. Since expected ex post bonus payments will be lower

and hence the cost of providing incentives is lower, the supervisor can usehigher a to motivate the manager to work harder, leading to greater

expected performance. When the environment is more risky (greater Z), the

manager exerts less effort for a given incentive intensity a, because it is more

likely that his performance falls below the bonus benchmark, and hence he

gets paid the benchmark bonus independent of his effort (Equation 4). This

increases the cost of providing incentives, reducing the optimal incentive

intensity and performance.14

13 For Z, since l is assumed to be relatively large, the term 2/(Zl) in the denominatorof Equation (5) will be relatively small.



15/35

We next derive implications on contract renegotiation. Let Da a0 a

be the difference between ex post and ex ante incentive intensity. Then

Da4 0 if y


16/35

For largel, the above expression is increasing ina because the denominator

is decreasing in a, and the numerator is not much affected by a. Thus, the

degree of renegotiation Y is also increasing in r and b for downward

renegotiation. As above, the comparative statics with respect to g and Zare

ambiguous. Summarizing, we have

Proposition 4. The degree of renegotiation, namely, the absolute value of the

difference between ex post and ex ante incentive intensities, Y5 |Da|, is

increasing in r and b.

Proposition 4 states that the degree of renegotiation will be greater when

the supervisor faces stronger incentives herself (larger r), or when the

manager has stronger preferences for rents (largerb

). The reason for theseresults is as follows. For larger r or larger b, the optimal ex ante incentive

intensitya and the managerial effort e will be greater. As a result, the bonus

benchmark will be higher. If a managers performance falls below this

benchmark, his bonus payment will be adjusted upwards to the benchmark

during the ex post renegotiation stage. In this case, the higher the

benchmark, the greater the degree of renegotiation. If a managers

performance exceeds the bonus benchmark, his bonus payment will be

reduced to a linear combination of the ex ante bonus payment and the bonus

benchmark during theex post

renegotiation stage. In this case, the degree ofrenegotiation, namely the amount of adjustment, will be greater if the ex ante

bonus payment and the benchmark are both larger. Therefore, for both

downward and upward renegotiations, we obtain the same comparativestatics results about the amount of bonus adjustment with respect to r andb.

These predictions cannot be easily anticipated if one is considering a model

with renegotiation using an exogenously fixed bonus benchmark.

IV. DATA

The data were collected by one of the authors and colleagues from rural China

during the summer of 1998. The survey randomly sampled 59 townships in 15

counties in Jiangsu and Zhejiang, two of Chinas most developed coastalprovinces, one north and the other south of Shanghai. As discussed earlier, the

hierarchical structure of Chinas banking system resembles the governments

administrative system; thus, each township surveyed has one ABC branch and

one RCC branch. In only three instances was a bank branch missing from the

survey, and in total, 57 ABC and 58 RCC branches were sampled.

The bank survey had two main components. The first component,conducted through interviews with township branch managers, acquired

detailed information about branch managers incentive contracts actual



17/35

of the bank branches provided the second part of the data by filling out a set

of financial tables from their branchs financial accounting records. For

various reasons outside our control, there are a certain number of obser-

vations with missing values on performance and contract data.17 Since our

study requires information on performance and contractual terms, we end

up with between 79 and 115 observations. To maximize the sample size for

the analysis, we allow the number of observations to vary for different

regressions. Despite the small sample size, this dataset is still quite valuable,

given the rich information about contracts and renegotiations it contains.

For both ABC and RCC branches in our dataset, a typical incentive

contract for branch managers is constructed as follows. First, the contract

specifies a number of performance measures: deposit growth, loan

performance (i.e., percentage of performing loans), bank safety, and otheradministrative and party duties. Each performance measure is scaled to

points which typically range from zero to 100. The better the performance,

the higher the points obtained. If a branch manager performs very poorly, he

might get zero points. In any case, the points cannot be negative. To get a

measure of overall performance, the contract uses a weighting system,

assigning relative weight to each performance measure. For example, if

deposit growth, loan performance and all other measures have weights of

25%, 25%, and 50%, and a branch manager receives 90, 80, and 100 points

on the three measures, then his overall performance amounts to a weighedsum of three performance points, which equals 92.5 points. Finally, the

contract specifies the amount of bonus or reward each performance point is

worth. In the previous example, if each performance point is worth 50yuan,

then the branch manager should get 4,625 yuan, according to the contract.

Supervisors at county banks determine the weighting system and the

incentive intensity (i.e., how much each performance point is worth) for each

branch manager under her supervision. The weights assigned for each

branch in a county are not exactly the same, but very similar. The variations

in weights mainly come from the cross-county differences.In our empirical study, we focus on deposit growth and loan performance

and ignore other performance measures. Deposit growth and loanperformance are two key measures of bank performance. To achieve a high

growth of deposit, a branch manager must try very hard to develop ties with

township and village enterprises and convince them to deposit their funds in

his branch. Achieving high loan performance is obviously not an easy matter

in rural China. Just around the time when our survey was underway, Chinese

state banks accumulated so many bad loans that Chinas policy makers were

afraid of a potential financial crisis. To improve loan quality, Chinese banks



18/35

introduced strong incentive mechanisms in China, and some even let bank

managers hold lifetime responsibility for bad loans. Understandably, with

strong emphasis on loan performance, bank managers became fairly

conservative or risk-averse in their lending.

The importance of deposit growth and loan performance is reflected in

their relatively high weights among all performance measures in the

weighting system, each with an average weight of 26% (Table I). In some

cases, the weight is as high as 75% for deposit growth, and in others, loan

performance accounts for an even greater weight. Other measures,

accounting for less than 50% on average in the weighting system, are

mainly non-business oriented (e.g., bank safety, administrative and party

duties) and hard for outside observers to interpret. Profit does enter into the

contracts of some branch managers, but its weight is generally low (less than10% on average), and nearly half of the branches have zero weight for profit.

This is understandable because it is hard to measure, and bank branches

have little control over many of the variables (e.g., interest rates, wages, and

numbers employed) that affect profits.

Table I

SumaryStatistics

VariablesNumber of

Observations MeanStandardDeviation Min Max

Branch performance measureDepoist growth (a) 82 0.24 0.29 0.41 1.45Performing loans/all loans (b) 93 0.83 0.24 0.36 1.00Weight on deposit growth (c) (%) 80 25.60 15.07 0 75.00Weight on loan performance (d) (%) 80 25.73 12.48 0 78.79Weighted bank performance measure(ac bd)

80 0.59 0.24 0.36 1.37

Wage and bonus reward schemesFixed wage (A)(1,000 yuan) 111 9.48 2.73 3.48 18.00Ex ante reward per point (e) (yuan/point) 115 35.17 36.87 4.50216.00Ex post reward per point (f) (yuan/point) 109 48.51 44.36 2.94259.26Actual total bonus reward (1,000 yuan) 109 4.37 3.20 0.20 18.00

Branch incentive measureEx ante incetive intensity (B5 ce de) 80 19.89 22.24 0 118.8Normalized ex ante incentive intensity (B/A) 80 2.22 2.27 0 9.93Ex post incentive intensity (C5 cf df) 79 22.56 21.56 0.38108.79Normalized ex post incentive intensity (C/A) 79 2.56 2.58 0.08 13.89

Contract renegotiation measuresProportion of contracts renegotiated 79 0.94Upward renegotiation (I5upward,05otherwise)

79 0.32

County banks performance incentives(bonus/wage ratio)

101 0.77 0.36 0.18 1.67

Branch mangers age (years) 115 39.90 7.60 28 60Branch mangers education (years) 113 12.63 1.66 9 19Branch mangers years of residence in thetownship

115 17.92 19.67 0 60

Range of industry growth rate during 19947 105 0 55 0 37 0 1 61



19/35

Focusing on deposit growth and loan performance, we construct a

weighted branch performance measure that equals the weighted average of

deposit growth and percentage of performing loans, where the weights are

their respective incentive weights. This will be used as the empirical measure

of the overall branch performance x in our theoretical model.18 Table I

reports the summary statistics of the performance measures and other

variables. In our sample, bank deposits on average grew 24 per cent, similar

to the macro-level deposit growth rates in both Jiangsu and Zhejiang

provinces in the same year (1997). Percentage of performing loans was on

average 83 per cent of all the loans in the sample branches, somewhat higher

than the national average.19 Both performance measures, as well as the

weights, exhibit a great deal of variation across bank branches in the sample.

The weighted branch performance measure we construct has a mean of 0.59and a standard deviation of 0.24, with a minimum of 0.36 and a maximumof 1.37.

Information on contract terms is also reported in Table I. On average,

branch managers in our sample get paid a fixed wage of 9,480 yuan, and a

year-end bonus of 4,370 yuan, or about 46 per cent of the fixed wage. This

indicates that incentive schemes are important for branch managers since

bonuses are quite a significant portion of their total income. It can also be

seen that both the fixed wage and the bonus have quite large variations in the

sample. In theex ante

contracts, the bonus reward for each performancepoint has a mean of 35.17 yuan and a standard deviation of 36.87, with a

minimum of only 4.5 yuan and a maximum of a hefty 216 yuan. Since we

focus on deposit growth and loan performance, we define the ex anteincentive intensity corresponding to a in the model as the bonus reward per

performance point specified in the ex ante contracts weighted by deposit

growth and loan performance. Specifically, the ex ante incentive intensitya is

defined as the total weights on deposit growth and loan performance

multiplied by the bonus reward per performance point and then divided by

the fixed wage (in thousand yuan). We use the fixed wage as a normalization,because fixed wage variations largely reflect the differences in standard of

living across locations. Our empirical results are qualitatively unchanged if

18 Here we do not consider issues of effort allocations related to multi-tasks (Holmstrom andMilgrom [1991]). Multitasking is an important topic that deserves thorough investigation, butis beyond the scope of this paper. Moreover, it appears that the relative weights of differentperformance measures were very close within each county, and thus probably reflected mostlycounty supervisors preferences. The composite measure of performance is sufficient for ourpurposes for two reasons. First, contract renegotiations were about incentive intensities (i.e.,

bonus payment per performance point) but not the weighting system. Second, we get similarregression results by using deposit growth and loan performance separately as managersperformance measures.



20/35

we drop this normalization. As can be seen from Table 1, before

normalization, the ex ante incentive intensity has a mean of 19.89 yuan

per weighted performance point and a standard deviation of 22.24. After

normalization, it has a mean of 2.22 and a standard deviation of 2.27.

However, managers usually did not get paid the bonus rewards exactly as

specified in the ex ante contracts. Renegotiations of incentive contracts were

prevalent in our dataset. On the whole, 94 per cent of incentive contracts

were ex post renegotiated to varying degrees, of which 25 per cent were

renegotiated upward, and the rest renegotiated downward. Contract

renegotiations took the form of adjusting the reward per performance

point. From Table I, the ex post reward per point has a mean of 48.51 yuan, a

large increase from the ex ante reward per point of 35.17 yuan. As with the ex

anteincentive intensity, we define the

ex post incentive intensitycorrespond-ing to a0 in the model as the bonus reward per performance point actually

paid to the managers weighted by deposit growth and loan performance.

The ex post incentive intensity has a mean of 22.56 yuan per weighted

performance point and a standard deviation of 21.56. After being

normalized by the fixed wage, it has a mean of 2.56 and a standard deviation

of 2.58.20

Figure 1 presents a scatter plot of ex ante and ex post incentives, both

normalized by wage.21 Consistent with Lemma 1, there is a strong positive

correlation betweenex ante

andex post

incentives. More importantly, theslope is clearly less than 45 degrees. Without renegotiation, all the

observation points should be on the 45 degree line. The fact that the slope

is below the 45 degree line implies that managers with good performance arelikely to receive bonuses less than their ex ante contacts specified.

One unique prediction of our theory, that there is a positive relationship

between the supervisors incentives and the renegotiation adjustment (the

absolute value of the difference between ex post incentive intensity andex ante incentive intensity), is confirmed by the data. Figure 2 shows that the

renegotiation adjustment (with normalization) indeed increases withthe county bank incentive intensity, i.e., the supervisors bonus/wage ratio.

We will test this relationship more rigorously in Section V.Table I also reports the summary statistics of other variables used in the

empirical analysis. We use the ratio of bonus to fixed wage of county bank

employees, termed the county banks performance incentive, to measure

the supervisors incentive intensity, r, in the model. This ratio is on average

0.77, significantly higher than that of township branch managers. Note that

20 In the subsequent analysis in Section 5, we mainly use the normalized incentive measuresfor branch managers. However, our results remain qualitatively unchanged if we only use



21/35

each county corresponds to one supervisor of either the ABC or the RCCs.

We use age to proxy for the managers preference for rents, b. Younger

managers have longer career horizons and value more career development

opportunities (e.g., slots in training programs), and thus have higher b than

older managers. In our sample, branch managers are about 40 years old onaverage, with the youngest at 28 and the oldest at 60. Education and years of

residence in the township are used as proxies for g, because managers withmore education and living in the locality longer (i.e., with more local

experience and connections) have a lower effort cost (smaller g). Finally, the

riskiness of the environment, Zin the model, is proxied by the range of the

annual growth rates of per capita real industrial GDP in the townships

during the period 19941997.22

Expostincentives

Ex ante incentives

Ex post incentives Fitted values

0 9.93104

.079657

13.8961

Figure 1

A Scatter Plot ofEx ante and Ex post Incentives

The ex ante incentives are defined as the total weights on deposit growth and loan performance

multiplied by the bonus reward per performance point specified in the ex ante contracts and then

divided by the fixed wage. The ex post incentives are defined in the same way as the ex ante

incentives except that the bonus reward per performance point is the actual one.

22 For example, if the annual growth rates of per capita real industrial GDP in a township



22/35

V. EMPIRICAL TESTS

In this section we present the results testing the theoretical predictions of the

model regarding the ex ante incentive intensity and branch performance

(Proposition 2) and renegotiations (Propositions 3 and 4).

V(i). The Ex Ante Incentive Intensity and Bank Performance

Proposition 2 states that the ex ante incentive intensity (a) increases with the

county supervisors performance incentives (r), the township managers

preference for rents (b), and responsiveness to incentives ( g), but decreaseswith the riskiness of the environment in the township (Z). The linear

regression for testing Proposition 2 can be expressed as follows:

10 a a0 a1 r a2 age a3 education a4 years a5 Z e

where r is the county bank managers bonus wage ratio to measure the

supervisors incentive intensity age education and years (of residence in

Renegotiationadjustment

County bank incentive intensity

Renegotiation adjustment Fitted values

.176471 1.66667

0

3.45238

Figure 2

A Scatter Plot of Renegotiation Adjustment against County Bank Incentive Intensity

Renegotiation adjustments refers to the absolute value of the difference between ex post incentive

intensity and ex ante incentive intensity, and country bank incentive intensity refers to the ratio of

bonus to fixed wage of county bank employees.



23/35

potential difference between ABC and RCCs, we also include a bank type

indicator (ABC5 1, RCCs5 0) in some specifications. Proposition 2

predicts that a14 0, a2o 0, a34 0, a44 0 and a5o 0.

In Column 1 of Table II, we report the regression result testing the main

predictions of Proposition 2 regarding the determinants of the ex ante

incentive intensity. The major findings are the following. First, consistent

with the theoretical prediction, the county banks performance incentives

have a positive effect on the township managers ex ante incentive intensity.

It is significant at the five per cent level. The estimate of the coefficient is

1.959, which means that an increase in the county bank performance

incentive (bonus wage ratio) by one standard deviation (0.36) will increase

the township branchs ex ante incentive intensity by 32 per cent of its mean.23

Second, consistent with the prediction that theex ante

incentive intensity isdecreasing in the responsiveness to incentives, managers with more

education and local experience have ex ante contracts of greater incentive

intensities, although only the coefficient of education is significant. Third,

consistent with the prediction that the ex ante incentive intensity increases

in b, the branch managers age has a negative effect on the ex ante in-

centive intensity, although it is not statistically significant at the conven-

tional level.

Finally, the estimated effect of the range of township industrial growth

has the expected sign, though not significant. The insignificance of thisvariable as well as others is probably a consequence of the relatively small

number of observations in our sample. In addition, this can also be caused by

other counterweighing forces such as those summarized by Prendergast[2002b], i.e., incentives and risk are positively correlated. Prendergast [2000,

2002a, b] provides several theoretical explanations as to why, contrary to the

prediction of the standard moral hazard model, incentives can be positively

correlated with risks. These explanations include (i) supervisor favoritism;

(ii) costly investigations; and (iii) firms trade-off between direct monitoring

and delegation. These effects may operate in our settings to cause thepositive correlation between incentives and risks (proxied by the range of

township industrial growth).Proposition 2 also states that bank performance increases with the county

supervisors performance incentives and the township managers human

capital, but decreases with the riskiness of the environment in the township.

We test this part of Proposition 2 by using the weighted performance

measure as the dependent variable with the same regression specification as

above except that the dependent variable is now replaced by the weighted

performance measure.



24/35

Tabl

eII

RegressionResults D

ependentvariable

Exanteincentiveintensity

(normalized)

Weightedbranch

per

formance

Upward

renegotiation

Upward

renegotiation

Upward

renegotiation

Degreeof

re

negotiation

Fullsample

Fu

llsample

Fullsample

RCCs

ABC

F

ullsample

OLS

OLS

Probit

Probit

Probit

OLS

(1)

(2)

(3)

(4)

(5)

(6)

banks

performanceincentives

wagera

tio,r)

1.959

0.150

0.853

0.566

1.812

0.904

(2.58)

(2.43)

(3.93)

(1.87)

(3.23)

(1.74)

anagersage(

b)

0.073

0.006

0.028

0.041

0.002

0.087

(1.65)

(1.28)

(2.61)

(3.15)

(0.13)

(1.23)

anagerseducation(

g)

0.483

0.011

0.072

0.054

0.165

0.187

(2.27)

(0.76)

(1.69)

(1.02)

(2.53)

(2.55)

anagersyearsofresidenceinthe

p(

g)

0.026

0.003

0.010

0.007

0.010

0.031

(1.64)

(2.21)

(2.36)

(1.25)

(1.54)

(1.56)

ofindus

trygrowthrateduring1994

0.162

0.145

0.146

0.240

0.263

1.671

(0.24)

(2.81)

(0.78)

(1.05)

(0.94)

(1.68)

pe(RC

C5

0,ABC5

1)

0.448

0.014

0.279

0.982

(0.92)

(0.27)

(20.9)

(1.82)

ations

78

66

79

41

38

67

ed

0.22

0.16

0.23

heexant

eincentiveintensityisdefinedasthetotalweightsondepositasthegrowth

andloanperformancemultipliedby

thebonusrewardperperformancepointspecifiedin

ntecontractsandthendividedbythefixedwa

ge.Upwardrenegotiationisadumm

yvariablewhichequalsoneiftheex

postincentiveintensityexceedstheex

anteincentive

.Thedegreeofrenegotiationisdefinedasth

eabsolutevalueofthedifferencebetweenex

postandex

anteincentivein

tensities(normalizedbyfixedwage).Countrybank

eintensityreferstotheratioofbonustofixedwageofcountybankemployees.Num

bersinparenthesesaret-ratios.Forcolumns(3)-(5),marginaleffects(dF/

dx)ratherthan

ficientsarereported.Significancelevel0.1,0.05and0.01arenotedby,

,and,

.



25/35


26/35

opportunity to test this prediction. It is likely that the county supervisors of

RCCs have a larger equality concern for two reasons. First, RCCs are

collectives, while ABC is a state-owned bank, and thus RCCs may care

more about equality. Second, the headquarters of the RCCs are at the

county level, and thus the supervisors as the top managers are likely to care

about the equality of the whole organization. In contrast, the ABC county

branches are only a bottom level of the hierarchical structure of a large state

bank with the headquarters in Beijing, which may not care as much about the

equality of its managers as is the case with the RCCs. Because RCCs have

stronger equality concerns, we expect that the impact of the county

supervisors incentives on the probability of upward renegotiation will be

smaller.

Regression results reported in Columns 45 of Table II indeed show thatthe impact of the county supervisors incentives on the probability of

upward renegotiation is smaller for RCCs than ABC. The impact for the

ABC is 1.812, which more than triples that for RCCs (0.566). This result

provides strong support for our model, which predicts that the impact of the

county supervisors incentives on upward renegotiation will be smaller when

they have stronger equality concerns.

Finally, we test Proposition 4, which predicts that the degree of

renegotiation increases with the county supervisors incentive intensity

and the branch managers preferences for rents. The dependent variable, thedegree of renegotiation, is defined as the absolute value of the difference

between ex post and ex ante incentive intensities (normalized by fixed wage),

and independent variables are the same as before. The regression result(Column 6, Table II) indeed shows that the county supervisors incentive

intensity has a positive and significant effect on the degree of renegotiation,

which is consistent with the predication of Proposition 4. The bank

managers age has a negative effect on the degree of renegotiation, as

predicted by the model, though it is insignificant.

To summarize, the empirical test results generally support the theoreticalpredictions of our model. The key variables have the expected effects

just as the model predicts, and are both statistically and economicallysignificant in most cases. Although the empirical results on Proposition 2 are

just standard outcomes from agency models, the evidence in support of

Propositions 3 and 4 is unique for the test of our theoretical model, which

does not follow from simple intuitive arguments based on conventional

agency models or renegotiation models. These predictions are mainly driven

by the trade-off between the supervisors equality concerns and the

managers preference for rents. Incentive contracts affect performance,but they do so through mechanisms more complicated than the standardagency model suggests Institutional environment and organizational



27/35

VI. DISCUSSIONS ON ALTERNATIVE EXPLANATIONS

VI(i). Manager Heterogeneity and Relative Performance

One concern about our analysis is how robust our results are if we take into

account differences in managerial ability. In our model, we assume that allbranch managers in a county are ex ante identical, but our data indicates

clearly that managers differ in their age, education, experience, etc.

Obviously, our symmetry assumption is made to greatly simplify the

analysis by reducing the problem of multiple agents into that of a single

agent. However, we believe this modeling device is for simplicity and is not

the driving force of the main results. When managers are observably

different (e.g., age, education, experience), the supervisors equality

concerns will most likely allow for a certain amount of income differences

among managers. For instance, if everyone in an organization feelsthat more senior or experienced managers should be paid more, then

equalizing income among managers will be considered as unequal. If

the heterogeneity of managerial ability can be fully taken into account

in the supervisors equality concerns, then our analysis does not need to

change much because the residual income inequality after taking into

account the explicit differences will still be symmetric among managers.

If the supervisors equality concerns do not account for the observable

differences of managers, then the supervisor, in the renegotiation stage,

will allocate rents and re-adjust ex post incentive intensities exactly asin the current model. However, managers with different abilities will have

different best responses to the same incentive contract and the supervisor

will need to design different incentive contracts for different managers.

While the analysis will unavoidably become very cumbersome, the

main points of the current model should still be valid. Empirically,

managers observable personal characteristics are all controlled for in ouranalysis.

A further extension of our analysis is to consider the situation in which

managers abilities may not be observable to supervisors, that is, thesituation with adverse selection. With adverse selection, there could either be

a separating equilibrium (though the state bank many not be allowed to offer

a menu of contracts) or a pooling equilibrium in which all managers choose

a same incentive contract. In a separating equilibrium, the managers

abilities are revealed through choosing different contracts, then the rest ofthe game is played as a complete information subgame just as in our model.

In a pooling equilibrium, managers with the same incentive contract and

different abilities will choose different effort levels, but the supervisor will

allocate rents and adjust bonuses based on the observable performances ofthe mangers. In either case, the qualitative results of our model regarding



28/35

Finally, since the situation we analyze has multiple agents, another

question naturally arises: are relative performance contracts used?

Theoretically, relative performance contracts are useful when there is some

common shock in the agents performance measures. However, they are not

useful in our model as y is assumed independent across branches.

Empirically, our survey of the managers compensations does not show

any evidence of relative performance evaluations.

VI(ii). Subjective Performance and Relational Contracts

One may wonder whether contractual renegotiations observed in our data

can be explained by relational contracting. In the literature on relational

contracting (e.g., MacLeod and Malcomson [1989]; Baker, Gibbons andMurphy [1994]; and Levin [2003]), firms use discretionary and non-binding

bonus (based on subjective performance measures) promises to motivate

managers to exert efforts, while the bonus promises are made credible

through repeated interactions (relational capital). Take the model of Baker,

Gibbons and Murphy [1994] as an example. They show that under certain

conditions, bonus payments based on both subjective and objective

performance measures can be optimally combined to provide proper

incentives. Since subjective performance measures are not observable to

outsiders, theex post

bonus payment will be different from the bonuspayment according to the ex ante contract based on objective performance

measures.

Relational contracts, however, do not seem to be able to explain the

pattern of renegotiations in our data. As shown before, the ex post bonus

adjustments in our context are negatively correlated with the objective

performances. That is, managers with good performances are more likely to

see their actual bonuses reduced relative to the ex ante contracts. To be

consistent with Baker, Gibbons and Murphy [1994], one must assume that

the subjective performance measures are negatively correlated with theobjective performance measures. This is highly unlikely in our context.

VI(iii). An Alternative Explanation Based on Profit-Sharing

One possible explanation of the positive correlation between the county

supervisors incentive intensity and the likelihood of upward renegotiation is

profit-sharing within a firm. That is, if the firm does well, everyone in the firm

gets a greater bonus that year, including the county supervisor as well as

branch managers. In this case, the county supervisors incentive intensity, asmeasured by the actual incentive pay of the county supervisor, may have

picked up the impact of profit sharing



29/35

that the degree of upward renegotiation (i.e., ex post incentives minus ex ante

incentives) should be positively correlated with the county supervisors

incentive intensity. However, both our theory (Proposition 4) and empirical

tests (Column 6 of Table II) show that the county supervisors incentive

intensity is positively correlated with the degree of renegotiation (i.e., the

absolute value of the difference between ex post incentives and ex ante

incentives).25 Secondly, the organizational difference between the ABC and

RCCs is also inconsistent with the profit-sharing hypothesis. Profit sharing

would suggest a stronger correlation between the county supervisors bonus

income and township managers bonus income for the RCCs than for the

ABC because RCCs are cooperatives and hence more likely to share profits.

But our results in Columns 4 and 5 in Table II show the opposite.

VII. CONCLUSIONS

Motivated by the observations coming from the Chinese banking industry,

we build a model of incentive contracting and renegotiation thatincorporates some of the essential organizational features of the Chinese

state banks into the standard agency framework. In our model, incentive

contracts are almost always renegotiated in equilibrium, yet they still affect

performance even though renegotiation is fully anticipated. Contract

renegotiations result from the trade-off between supervisors equalityconcerns and managers rents-on-the-job that come under their supervisors

discretion. We then use a dataset from the Chinese banking industry to test

the predictions of the model. Overall, our theory of renegotiation fits theevidence quite well.

Our theory can be applied to contract renegotiations in other contexts.

For example, consider the so-called fiscal contracting system adopted in

19801993 in China (Shirk [1993]). Researchers have long noticed the high

and pervasive levels of complaints among provincial officials about the

central governments discretion in the enforcement of intergovernmentalfiscal contracts. For instance, the central government created numerous

means to level up revenue-submission ratios for those provinces exhibitinggood fiscal performance, including forced borrowing or reclaiming central

ownership of lucrative local firms (Wong et al. [1995]; Ma [1997]). However,

there is also evidence that despite these enforcement problems, provincial

officials still responded to fiscal contract incentives (Jin et al. [2005]). These

observations can be easily explained by our theory. Our model also suggests

that it may be an over-simplification to draw conclusions about the effects of

25 We run an OLS regression where the degree of upward renegotiation is the dependent



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the incentive system on provincial fiscal performance simply by regressing

performance on the incentive intensity of the agreements.

As a final note, we would like to point out two limitations of our study.

One is the relatively small sample size, which causes some of the parameter

estimations to be statistically insignificant. Another limitation is that our

theoretical model is static, and correspondingly our empirical analysis is

cross-sectional due to data limitations. In future research, it would be

interesting to study the dynamics of contracting and renegotiating when the

principal has equality concerns.

APPENDIX

Proof of Lemma 1: If Ms participation constraint is binding, then substituting

a0x5ax bd into the objective function (and ignoring constant terms) gives0:5d2 bd 0:5lax bd b2. Solving for the first order condition, we get

11 d lb1=l b ax

1 lb2

lbax B

1 lb2

where we define B b 1=l. We will assume that l is sufficiently large so that B4 0.d ! 0 requires that ax ! B b 1=l. When axoB, then d50. The second ordercondition is clearly satisfied.

Then the optimal ex post bonus scheme is

12 a0 a bd=x a 11 lb2

Bx

lb2

1 lb2

In this case, it can be calculated that Ss payoff is n5A n2, where

13 v2 rx B 0:5

l

0:5lax B2

1 lb2

If Ms participation constraint is not binding, then S will set d50. Solving for a0

from the first order condition, we get

14 a0

b 1=lx

Bx

The second order condition is clearly satisfied. In this case, it is easy to show that Ss

payoff is n5A n1, where

15 v1 rx B 0:5=l

So the lemma is proven. Q.E.D.

Proof of Proposition 1:

Since y B=a e 0:5Z 1=al, Ss expected payoff can be written as

Ev A

Zyv1=Zdy

ZZv2=Zdy



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Rewriting Equation 15 gives

v1 re ry ae 0:5Z 0:5=l r ae 0:5Za ry 0:5=l

Since ax b ae y ae 0:5Z ay 0:5Z, by Equation 13, we have

v2 v1 0:5lax B2

1 lb2 v1

0:5lay 0:5Z 1=l2

1 lb2

Plugging in the values ofn1 and n2 into En gives

Ev A r ae 0:5Za 0:5rZ 0:5=l 0:5l

Z1 lb2

ZZy

ay 0:5Z 1=l2dy

A 0:5rZ 1=l r ae 0:5Za l

6aZ1 lb20:5Za 1=l3

C r ae 0:5Za l6Z1 lb2

0:5Z3a2 30:5Z2a=l 1=al3

where Cis a constant.

The FOC with respect to a is

r ade

da e 0:5Z

l

6Z1 lb220:5Z3a 30:5Z2=l 1=a2l3

0:5r a

g

1

Zgl 0:5Z

1

6Z1 lb2lZ3a=4 3Z2=4 1=al2

0

Simplifying it, we have

3rZ1 lb2

g

61 lb2

gl 3Z2lb2 1:25

1

al2

alZ3=4 6Z1 lb2

g

It is easy to check that Ss objective function is globally concave in a, and thus the first

order condition gives the optimal effort choice.The exact solution to this equation is not easy to obtain. However, the solution is

easy to visualize. The right hand side is linear in a, and can be thought of as the

marginal cost ofa. The left hand side can be thought of as the marginal benefit ofa.

It consists of several constant terms plus 1/(al2), and is hence strictly decreasing in a.

From the shapes of the marginal cost and benefit of a, clearly there is a unique

solution to the first order condition. As an approximation, we suppose that l2 is

sufficiently large relative to the other parameters in the model and relative to the

feasible range ofa, so that the term 1/(al2) on the left hand side is sufficiently small and

can thus be omitted. This approximation makes the marginal benefit curve flat and

shifts it down, resulting in an underestimation of a. However, the properties of thesolution, in particular the comparative statics with regard to other parameters, are not

affected by this approximation That S cares strongly about wage equality (large l) is



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With this approximation, we can solve for a from the first order condition:

16 a ffi3rZ 6

l 3gZ21 1

41lb2

lgZ3

41lb2

6Z

By Equations 4 and 5, we get Ms effort choice as

17 e 0:5a=g 1=Zgl ffi3rZ 6l 3gZ

21 141lb2

lg2 Z3

21lb2 12gZ

1

Zlg

Ms performance is simply x5e y.

By Equations 12, 14 and 5, we obtain the ex post incentive intensity as follows. When

y ! y, or ax ! B, the ex post incentive intensity is given by

18

a0 a1

1 lb2

ae y a0:5Z y 1=l

x

lb2

1 lb2

a1 y 0:5Z 1=al

x

lb2

1 lb2

When y


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