Managerial Autonomy, Incentives and Firm Performance:

Evidence from Investment Climate Survey in China

Xiaoyang Li♣

October 2007

Abstract

This paper studies the relationship between firms’ use of incentive compensation and

managerial autonomy, as well as how managerial autonomy affects firm performance. We

develop a simple framework in which the principal employs compensation contract and

delegation of autonomy to balance the tradeoff between delegation benefits and agency

costs. We conduct the empirical analysis using Investment Climate Survey data from China.

Our results show that: (i) firm’s use incentive compensation is negatively associated with

general manager’s investment decision autonomy but positively associated with labor

decision autonomy; (ii) general manager’s investment decision autonomy dampens, while

labor decision autonomy boosts firm performance.

♣ I benefit from discussions with Francine Lafontaine, Uday Rajan and Katherine Terrell. I would like to acknowledge the World Bank Enterprise Survey Unit for allowing me to use the Investment Climate Survey data. All remaining errors are my own.

1

“Any effect on incentives is contingent upon the degree of autonomy that managers enjoy. After all, even if

incentives were given, if these managers found their decision making powers substantially constrained, one

would not expect any incentive scheme to have much effect.” --- Shahid Yusuf et al. (2006)

1. Introduction

Recently, there has been a significant increase in research interest on the organizational

structure of the firm. Despite numerous theoretical frameworks modeling delegation and

incentive, empirical evidence is relatively limited. This gap is primarily due to the lack of the

kind of detailed data or even of the convincing measures for delegation and incentive that

allow comparisons across firms.

The 2003 Investment Climate Survey conducted by the World Bank in China

spearheaded using some survey instruments trying to gain insights into firm’s organizational

structure. We are fortunate to have information on general manager’s degree of decision

autonomy and the incentive compensation structure in the firm. We use this dataset to

investigate the relationship between incentive and autonomy, as well as their effects on firm.

We aim to provide empirical evidence on how incentive and decision autonomy interact with

each other and how this imply for firm performance. As Mookherjee (2006) observed that

“One hopes that both theory and empirical datasets regarding these organizational attributes

can be developed interactively, permitting better understanding of their productivity

implications, and how they respond to changes in market competition or information

technology.”1 We hope to contribute to the literature along this line by providing empirical

evidence on the determinants and effects of managerial autonomy and incentive.

Previous literature on principal-agent model and contract is replete with what is the

best way to design an incentive compatible contract, therefore often ignoring autonomy.

However, Shahid Yusuf, Dwight H. Perkins, and Kaoru Nabeshima remarked that “Any

effect on incentives is contingent upon the degree of autonomy that managers enjoy. After

all, even if incentives were given, if these managers found their decision making powers

1 Mookherjee (2006) pp.388.

2

substantially constrained, one would not expect any incentive scheme to have much effect.”2

In line with this, we view their relationship as: It is necessary to have managers to enjoy

decision autonomy for any incentive contract to exert effect on firm outcome while the

converse is not necessarily true.

It is no doubt that managers’ autonomy of decisions is extremely important. Gerard

Fairtlough, former CEO of Shell and author of “The Three Ways of Getting Things Done:

Hierarchy, Heterarchy and Responsible Autonomy in Organizations” developed a famous

“Triarchy” theory. Namely, all organizations use a mixture of these three ways, but the

proportions can differ widely. At present, hierarchy is usually considered essential for all

organizations. Heterarchy and responsible autonomy are often misunderstood or neglected,

but they are equally important.

We all have personal experiences of how autonomy influences our behaviors in

certain ways. But it is hard to define what autonomy is. Boot and Thakor (2003) noted:

“Managers sometimes refer to it (autonomy) as ‘elbow room’, the independence to be able to

alter operating decision and change strategic direction when circumstances change.” In this

paper, we opt for a loosely popular meaning of autonomy instead of a rigorous definition.

The decision autonomy of manager is related to decentralization or delegation; however, it

has more of personal dimension to it.

The first goal of this paper is to analyze the relationship between autonomy and

incentive, especially to see how delegation of autonomy feeds back on incentive contract.

Secondly, we will examine general manager’s decision autonomy on firm performance.

Economists have long documented the huge differences in firm performance within

narrowly defined sectors, (see, for example, Bartelsman and Dhrymes (1998)) but tend to

neglect the effects of organizational characteristics in trying to explain the causes of the

performance differential. We believe addressing these two questions can shed further lights

on our understanding of determinants and consequences of firm’s organization choices.

The rest of the paper is structured as follows: Section 2 discusses the relevant

literature. We set up a simple model in section 3 to motive our empirical work. We describe

our data and some background information in section 4. Regression analysis is performed in

section 5 and section 6 concludes.

2 Yusuf et al (2006) pp.175

3

2. Motivation and Literature Review

The idea of manager’s decision autonomy comes directly from trade-off of costs and

benefits of decentralization. Decentralization is pivotal to the success of an organization. As

Hayek (1945) noted “… the ultimate decisions must be left to the people who are familiar

with these circumstances, who know directly of the relevant changes and of the resources

immediately available to meet them.” Modern firms especially need decentralization to

ensure its rapid adaptation to changes in the particular circumstances of time and place.

Hayek advocated decentralization on the ground that everybody has his or her

specialized knowledge and decentralization can make the best use of the knowledge. This

notion resonates with the idea that board of directors (the principal) hires a general manager

(the agent) because the general possesses management expertise.

Aghion and Tirole (1997) suggest two other benefits of decentralization.

Decentralization can increase agent’s initiative or incentive to acquire information, and

facilitate agent’s participation in the contractual relationship. They model the allocation of

real authority in the context of project choices where principal and agent each has his/her

own preferred project. They show that delegation is more likely for decisions that are

relatively unimportant for the principal. They also find that large span of control, urgency of

decision, and multiple principals tend to increase an agent’s real authority.

On the other hand, decentralization involves a costly loss of control for the

principal. This cost is generally referred to as the “agency cost”. In their seminal paper,

Jensen and Meckling (1976) define agency cost as the sum of the monitoring expenditure by

the principal, the bonding expenditure by the agent and the residual loss. 3 Agency conflict

between owner and manager arises usually from the manager’s tendency to appropriate

perquisites out of the firm’s resources for his own consumption.

As a result of this tradeoff, decentralization does not imply a full transfer to decision

rights from the principal (owner) to the agent (manager). In the language of Aghion and

Tirole (1997), “formal authority, need not confer real authority, that is, an effective control

over decisions, on its holder … a principal who has formal authority over a decision (or

activity) can always reverse her subordinate’s decision…”4

3 Please refer to Jensen and Meckling (1976) for a detailed explanation for this characterization. 4 Aghion Philippe and Jean Tirole (1997), pp.2.

4

Using whether different units of the firm are organized into “profit centers” as

measure of decentralization, Acemoglu et al. (2007) show that firms closer to the

technological frontier, firms in more heterogeneous environments and younger firms are

more likely to choose decentralization. These predictions are confirmed by empirical results

using datasets of French and British firms in the 1990s.

Bloom and Van Reenen (2006) conduct an innovative survey tool, collecting various

management practices data including operations, monitoring, targeting and incentives from

four countries the United States, Germany, UK and France. They establish that measures in

better management practices are strongly associated with superior firm performance in terms

of productivity, profitability, Tobin’s Q, sales growth and survival rates.

Using a detailed database of managerial job descriptions, reporting relationships, and

compensation structures in over 300 large U.S. firms, Rajan and Wulf find that CEO’s span

of control is increasing, authority is pushing down the organizations and long term incentive

is spreading in the organization.

As we can see, most empirical literature looks at multidivisional firms where

incentives and decentralization are commonly observed. Therefore, the fact that our dataset

contains many observations of small and medium enterprise can complement these studies.

China is a great case in studying the relationship between governance and

performance. Since the late 1990s, China has deepened its economic reform to “emphasize

the institutional innovation of enterprises”. The strategy is to establish a modern corporation

system featuring “clearly established property rights, well-defined power and responsibility,

separation of enterprise from government, and scientific management”.5 In 1999, the

Decision on Several Important Issues Regarding Reform and Development of State-Owned

Enterprise (SOEs), adopted at the Fourth Plenary Session of the 15th Central Committee of

Communist Party of China (CCCPC) in 1999 emphasized that “corporate governance

structure, which can establish checks and balances between the owner and the manager, is

the core of the corporate system and required all corporatized SOEs to establish effective

corporate governance. Meanwhile, the non-state sector also gained momentum in

development through favorable market environment. Many private firms improved

governance structure by forming partnership or alliance with Foreign Invested Enterprises

(FIEs) or benefiting from the spillovers of the presence of FIEs. The reform has greatly 5 See Wu (2005), pp154.

5

improved the status of corporate governance in China, enabling us to apply principal-agent

model to analyze its costs and benefits.

3. Tradeoff between Costs and Benefits of Autonomy

We outline a principal agent model that introduces a trade-off between benefits of delegating

decision autonomy to a better informed agent with expertise and the costs of having that

agent being able to use autonomy to extract resources from the principal. In this paper, we

view autonomy as the optimal degree of decentralization to balance the benefits and costs of

the agency relationship between firm owner and general manager.

The population is composed of two types of agents, of which s share are virtuous

and 1-s share are egoist6. We assume that virtuous agents always behave, i.e. serve the firm in

the best interest of the principal. Egoist agent has a tendency to extract resources from the

firm.

The principal decides whether to control the decisions or to delegate to the agent. If

the principal controls the decision, he produces less than when fully delegating to the agent.

However, if he controls, he can find out whether the egoist agent is extracting the resources

or not; if he finds that the agent is extracting, he can save these resources and further punish

the agent. The compensation contract is either a fixed wage part or a fixed wage plus an

incentive pay linked to the performance of the firm.

We use the following notations and make some assumptions:

- Y is the output when the manager has full decision autonomy;

- y is the output when the owner gives no autonomy to the general manager, i.e. the owner

totally controls all decisions.

- Y > y since manager has superior information and knowledge, so Yy

measures the

delegation benefits;

- b is the share of sales revenue given to manager as the incentive pay;

- a is the share of sales revenue extracted by the egoist manager if he gets full autonomy, so a

measures the potential loss of delegation;

6 This setup is closely related to Carlin and Gervais (2007)

6

- w is the basic wage, which will be given to the manager irrespective of whether the

principal gives how much autonomy to the agent and is equal to agent’s reservation wage;

- The principal maximizes expected payoffs.

In the full information case when the principal can distinguish virtuous agents and

egoist agents, he will give a fixed wage contract equal to his reservation wage and delegate

fully to the agent when facing the virtuous agent.

In case the agent is an egoist agent, we can write out the model in the form of

normal form game as follows:

Principal

Delegate (γ ) Control (1- γ )

Behave ( ) Δ w+bY, (1-b)Y – w w+by, (1-b)y - w Egoist Agent

(1-s) Extract(1- ) Δ w+(a+b)Y, (1-a-b)Y - w w, y - w

If we assume that y > (1-a-b)Y, there is no pure strategy Nash Equilibrium for this

game. If the principal expects the agent to be virtuous, he would fully delegate the decisions

to the agent. But if the principal expects the agent to be egoist, he would choose to control

the decisions to save the resources that would otherwise be extracted by the agent.

One way to understand the mixed strategy is that principal cannot commit to how

much autonomy to delegate to the agent ex ante, but the principal always has the privilege to

overrule the agent when he thinks that the agent is not virtuous. In a mixed strategy Nash

Equilibrium, agent chooses be virtuous with probability Δ and the principal chooses to

delegate with probabilityγ .solving for the mixed strategy Nash Equilibrium, we get:

* (1 )y a b YaY by

− − −Δ =

+

1 (1 ) Ya by

Ya by

− − −=

+

* byaY by

γ =+

bYa by

=+

We can perform simple comparative statics on the equilibrium strategies.

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*

0bγ∂

>∂

indicates that more incentive pay is associated with more decision autonomy

of the agent; *

0aγ∂

<∂

indicates that more loss from extraction is associated with less autonomy;

*

0b

∂Δ>

∂indicates that agent is more likely to behave given more incentive;

*

0Yy

∂Δ<

∂and

*

0Yy

γ∂<

∂ indicate that agent is more likely to choose to extract if he can

bring more benefits than the principal himself, however, the autonomy the agent gets is

negatively associated with the delegation benefits.

When the principal cannot differentiate the virtuous agent from an egoist agent, he

will choose between contract 1: ( 1, w)γ = and contract 2: *( ,by w baY by

γ =+

, )

w

Y* w

.

Under contract 1, the expected payoff for the principal is: 1 ( ) (1 )[(1 ) ] (1 )E s Y w s a Y w a sa Yπ = − + − − − = − + −

Under contract 2, the expected payoff is: 2 * * *[ (1 ) (1 ) (1 ) (1 )(1 )(1 ) ]E s b Y s b Y s a bπ γ= − + − Δ − + − −Δ − −

* *(1 )[ (1 ) (1 ) (1 ) (1 )(1 ) ]s b y s b y s yγ+ − − + − Δ − + − −Δ −

Clearly, if s is sufficiently big, 1Eπ > 2Eπ , we should expect to see contract 1

more often, i.e. full autonomy with fixed wage. Here comes our proposition 1:

Proposition 1 If s is big enough, we should expect more autonomy to be

associated with fixed wage without incentives.

Intuitively, if the population is mostly composed of virtuous people, then the

principal should give full autonomy but no incentive contract to the agent.

If s is small, then 1Eπ < 2Eπ we should expect principal to offer contract 2. Then

the principal’s optimization becomes: 2

bMax Eπ * * * * *(1 ) (1 )(1 )Y a b a sa a s y sb b sγ γ= − − + Δ + + Δ + − − −Δ + Δ*b

The first order condition is:

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2 ** * *[ (1 ) (1 )]E Y a b a sa y sb b s

b bπ γ∂ ∂

= − − + Δ + Δ − − −Δ +∂ ∂

*Δ

* * *

* * *( 1 ) (1 ) ( )Y a as y s b s sbb b b

γ γ∂Δ ∂Δ ∂Δ ∂Δ+ − + + + − − −Δ − + Δ +

∂ ∂ ∂ ∂

**

b

We can solve for the optimal b from the above equation, but we are more

concerned with the relationship between b and *γ . We can use the Implicit Function to

see how b varies with *γ 7. This exercise gives us the following prediction:

Proposition 2 If the delegation benefit is sufficiently big (i.e. Yy

is big) and the

cost of extraction is small (i.e. a is small), more decision autonomy is negatively

associated with incentive compensation.

Proposition 2 states that, in case of big delegation benefits but little agency cost, the

principal trust the agent more, but gives less incentive. In other words, the principal runs the

loss extracted by the agent but may reap big delegation benefits.

As for the effect of autonomy on the principal’s expected payoff, we have

Proposition 3 The effect of autonomy on the firm performance is

indetermined: Full autonomy can be associated with either higher outcome (case 1)

or lower outcome (case 2).

In the next section, we will take the above three predictions as well as other

hypotheses identified in the literature to the data.

4. Data and Descriptive Statistics

We are fortunate to have the Investment Climate Survey conducted by the World Bank in

China in 2003. It surveys 2,400 firms in 17 cities from 14 provinces and one municipality

directly under the jurisdiction of central government (Chongqing)8. 12 cities are capital cities

of each province and the remaining cities are also urban centers of its own province.

This survey is a stratified random survey on both manufacturing and service sectors

including “Garments and leather products”, “Electronic equipment”, “Electronic parts”, 7 The result can be obtained upon request. 8 The sample is composed of 100 firms from Benxi, 150 from Changsha, 100 from Dalian, 150 from Harbin, 100 from Jiangmen, 150 from Changchun, 100 from Guiyang, 100 from Hangzhou, 150 from Kunming, 150 from Nanchang, 100 from Shenzhen, 100 from Wenzhou, 150 from Wuhan, 150 from Xi’an, 150 from Lanzhou, 150 from Zhengzhou and 150 from Nanning.

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“Household electronics”, “Auto & auto parts”, “Information technology”, “Accounting-

banking and financial services”, “Advertisement and marketing”, “Business services”, “Food

processing”, “Chemical products and medicine”, “Biotech products and Chinese medicine”,

“Metallurgical products”, and “Transportation equipment”.

It collects information on various kinds of firm information including general

characteristics, innovation and technology, certification of products or services, relations

with clients, suppliers and governments, sales and supplies, labor, infrastructure, trade,

finance and taxes etc. One section of particular interest is “Information about the general

manager and board of directors” provide us with information on organizational

characteristics of the firm. Questions in this section provide us with information on general

managers and board of directors. Information on general manager includes the education,

the tenure, the party position and how the general manager was appointed etc. In addition,

we know the ratio of wage of general manager to that of middle-level manager and whether

manager has any incentive compensation plans linking to firm performance.

We obtain the measure of manager autonomy from three questions of autonomy of

general manager on production decisions (output, quantity, quality, investment, and so on),

autonomy of investment decision and investment on labor flexibility (hiring, firing and

wage). The answers to this question is structured on a percentage basis, specifically, eight

category of 100%, 90-99%, 80-89%, 70-79%, 60-69%, 40-59%, 20-39% and 0-19% with

each corresponding to a score from 8 to 19, thus higher scores imply greater degree of

autonomy. In particular, we focus on the autonomy of investment and labor decisions since

many service firms do not answer the production autonomy the same way service firms

answer this question.

We also have general manager’s autonomy directly measured in different aspects,

which allows us to conduct a breakdown analysis. Previous literature often uses proxies for

manager’s autonomy. For example, Acemoglu et al. (2007) uses “whether your firm is

organized into different profit centers” to measure decentralization as they argue that when a

firm organizes into profit centers a manager is responsible for the profits of the unit she

manages. In general the profit center manager is given considerable autonomy to make

decisions on the purchase of assets, hiring of personnel, setting salary and promotion

schedules and managing inventories. Their measure depends crucially on the scale of the 9 In the original survey, each category corresponds to a score from 1 to 8.

10

firm. Small firms’ manager autonomy will be excluded from their measures. Our measures

do not have this problem, however, this is a very subjective measure of manager’s autonomy

and the category is not very well defined.

Table 1 presents summary statistics of the two measures of autonomy. We can see

that, on average, general managers obtain more autonomy in labor decisions than in

investment decisions.

In general, firms give to its general manager more autonomy on labor than on

investment decisions. It is interesting to compare the means of manager’s decision autonomy

for the group of firms with incentive compensation and the group without it. Table 1c

shows that general manager with an incentive compensation contract have more autonomy

in investment decision but less autonomy in labor decisions.

Table 1 Here

5. Empirical Investigation

5.1 Baseline Specification

5.1.1 Effects of Managerial Autonomy and Incentives

We assume that firm performance is a function of firm level, industry level variables,

augmented by general manager’s incentive and autonomy.

ijc i i j j ijcY X X aut incβ β ϕ η= + + + +ε (1)

Where is the output measure for firm i in industry j at city c. We primarily use

sales revenue but also use sales growth and sales per employee.

ijcY

iX is a vector of firm

characteristics, including labor and capital, jX is a vector of industry characteristics, aut

includes investment autonomy and labor autonomy of the general manager, inc is the

incentive compensation for the general manager.

In the baseline regression, the dependent variable is for year 2002 and the logarithm

of labor and capital are for year 2000, which is an attempt to prevent the most obvious form

of reverse causality. All omitted factors are captured by the error term, which we assume to

be normally distributed.

5.1.2 The Relationship between Managerial Autonomy and Incentive

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In this part, we will document a number of correlations motivated by related literature and

by the propositions presented in section 3.

ijc i i j j ijcinc aut X Xα φ β β ε= + + + + (2)

5.1.3 The Determinants of Managerial Autonomy

In searching for explanatory variables for managerial autonomy, we include three sets of

covariates, industry level, firm level and personal level.

Consider the following model for general manager’s autonomy determination:

ijc i i j j ijcaut per X Xα φ β β ε= + + + + (3)

Where i denotes firm, j denotes industry and c denotes city. iX is a vector of firm

level variables, include firm size, firm’s capital intensity, firm’s age, and firm’s distance to

frontiers and its competition environment. jX is a vector of industry characteristics

including the heterogeneity of environment measured by the sales growth rate differential in

the industry. Acemoglue et al. (2007) document that younger firms, firms in more

heterogeneous environment and firms far away from the technological frontier use more

decentralization than others. The set of personal characteristics include general manager’s

education, how long the general manager has held this position, the tenure of the general

manager and whether the general manager’s wage is paid annually.

5.2 Empirical Strategy

We aim to estimate equations (1) (2) and (3) together. This of course argues for the use of

some kind of simultaneous equations system estimator. Because the three variables of

interest, manager’s incentive and two measures of manager’s autonomy are potentially

endogenous, we will opt for Three Stage Least Squares (3SLS) estimation. In the 3SLS

estimation, all the personal, firm industry and city level variables will be treated as

instruments. We also use system Ordinary Least Squares (OLS) estimation for comparisons.

All regression control of industry and city fixed effects.

5.3 Regression Results

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Table 2 presents our baseline regression results using log of sales revenue as dependent

variable. The first four columns estimate using system OLS and the last four columns

estimate using 3SLS.

When we look at the performance estimation, we find that that general manager’s

investment autonomy is negatively associated with sales revenue, but labor autonomy is

positively associated with sales revenue. Comparing OLS results with 3SLS results, we find

that the magnitude the coefficients on the two autonomy measures increase significantly. In

terms of economic significance, on average, increasing general manager’s investment

autonomy by 10% is associated with a decrease of firm’s sales revenue by 0.8 standard

deviations; increasing general manager’s labor autonomy by 10% is associated with an

increase of firm’s sales by 1.5 standard deviations. Besides, the coefficient on incentive

switch signs from positive to significantly negative.

The second and sixth columns estimate the effects of managerial autonomy on

incentive compensation. We find that manager’s investment autonomy is negatively

associated with incentive, but manager’s labor autonomy is positively associated with

incentive compensation. This partly confirms our proposition 1 and 2.

Estimation on the determinants of managerial autonomy tells us that general

mangers with a longer tenure, with more managing experiences tend to have more autonomy

in both dimensions. Younger firms generally give more autonomy especially labor autonomy

to their managers. General managers who are hired outside the firm usually get more

autonomy. If the firm is further away from the industry productivity frontier, general

managers are expected to have more autonomy on investment decisions.

Table 2 Here

Table 3 reports 3SLS results using sales per employee and sales growth rate as

dependent variables. In general, these two regressions display similar patterns of results: the

coefficient on incentive is negative, the coefficient on investment autonomy is also negative,

but the coefficient on labor autonomy is positive. All of them are statistically significant at

1% level. In terms of economic significance, on average, increasing general manager’s

investment autonomy by 10% is associated with a decrease of 1500 yuan of sales per

employee (the sample mean is 4300 yuan) and with a decrease of annual sales growth rate by

about 20%; increasing general manager’s labor autonomy by 10% is associated with an

13

increase of sales revenue per employee by 2500 yuan and with an annual sales growth rate by

about 40%.

Table 3 Here

The huge increase in the magnitude of the coefficients on two autonomy measures

calls into question of the selection of instruments: the instruments are potentially correlated

with our dependent variables. The effects of instruments on firm performance are absorbed

by the two autonomy measures. Finding better instrument will be our future work.

5. 4 Robustness Checks

To check the robustness of our results, we first run separate regressions on firms with or

without incentive structure. The estimation results are reported in table 4. For all three

dependent variables, the overall pattern remains the same, however, we find that the

magnitude of the coefficients on the two autonomy measures is bigger for firms without

incentive compensation for the general manager. This result is consistent with the

implication from the previous story: in case of fixed wage contract, if the virtuous agents

bring better outcome to the firm, but egoist agents extract more resources in case of more

autonomy. The two contrary effects magnify the impact of autonomy.

Table 4 Here

Previous related literature mostly conducts empirical analysis based on firm from

manufacturing sector. In our sample, about one fourth of firms are from service sector, thus

enabling us to check whether the results differ across sectors. Table 5 presents the

breakdown estimation results. Compared with our baseline results, we lose significance levels

on investment autonomy coefficients on service sector regressions with the dependent

variables being sales growth rate and sales per employee and on labor autonomy coefficients

on service sector with the dependent variable being sales growth rate. The results from

regressions on manufacturing sectors are robust throughout.

Table 5 Here

On average, general managers in SOE receive less autonomy than managers in other

types of firms10 as they are to some extent constrained by the government. Thus it is

interesting to see if autonomy for managers in SOE has different effects from managers in

10 The sample average for investment autonomy is 5.7 and the sample average for SOE manager is 5.2; The sample average for labor autonomy is 6.5 and the sample average for SOE manager is 5.8.

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other types of firms. We can see from table 6, consistent with our conjecture that autonomy

measures indeed have a bigger impact on firm performance.

Table 6 Here

Overall, as we can see, our results therefore are fairly robust to different sectors and

different ownership forms, although the baseline results are a little bit more driven by firms

from manufacturing sectors and Non-SOE firms.

6 Making Sense of the Regression Results

The previous analysis seems to tell a consistent story that general manager’s investment

autonomy does harm to the firm, but labor autonomy benefits the firm. In this section, we

try to provide some intuition for this result.

It is now cliché to say that managers tend to over invest. As Dessein (2002) note “It

is well accepted, for instance, that managers have a propensity to cause their department,

division or firm to grow beyond the optimal size, i.e. they are empire builders and undertake

too many investments. They further seldom take externalities on future managers into

account and, hence, are excessively oriented towards short-term profitability and results.”

Hennessey and Levy (2002) develop a unified model to test various hypotheses of manager

investment distortions. They find strong empirical evidence in favor of empire building

hypothesis, with investment being highly correlated with CEO entrenchment. Shleifer and

Vishny (1989) describe entrenchment in terms of manager-specific investments, where

manager entrenches himself by investing excessively in assets that are complementary to

manager’s skills thus reducing the probability of being replaced. The two papers do not link

manager’s investment decisions to firm performance; however, it should not be surprising

that if left unchecked, manager’s tendency to over invest can jeopardize the firm

significantly.

Bodmer (2002) studies the relationship between the manager’s autonomy and

productivity, using a survey of 769 SOEs during 1980–94 in China. He uses four dummy

variables: manager’s performance contract, the share of contract workers, manager’s output

autonomy, and manager’s autonomy in hiring/firing workers. The empirical results show

that the first three variables have significant and positive effects on productivity, although

employment reform yields a negative but insignificant outcome, suggesting that labor

decision has little effect on productivity.

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Compared with investment decisions, general managers do not have too much

incentive to distort firing/hiring decisions as these do not bring as big personal benefits as

investment decisions.

7 Concluding Remarks

This paper tries to achieve two goals: First is to establish the relationship between firm’s use

of incentive compensation contract and its general manager’s decision autonomy and the

second is to examine the effects of managerial autonomy on firm performance.

We develop a simple principal agent model where the principal makes use of

incentive compensation and delegation of autonomy to maximize the benefits of delegation

and minimize the agency costs. We show that, firms may use less incentive compensation

combined with more decision autonomy to best exploit the agent.

We use Investment Climate Survey data from China to conduct the empirical

analysis. This survey contains direct measures of general manager’s decision autonomy and

has information on general manager’s compensation contract. We estimate a system of

equations in which we allow manager’s decision autonomy and firm’s use of incentive

contract to be endogenous. Our results show that firm’s use of incentive compensation is

negatively associated with general manager’s investment autonomy but positively associated

with labor autonomy. General manager’s personal characteristics can explain much variation

in his decision autonomy. We also find that general manager’s investment autonomy hurts

while labor autonomy benefits the firm across different performance measures. This result is

much driven by firms in manufacturing sectors and Non-SOE firms.

In the future, I plan to better tie the theoretical framework with empirical analysis,

especially how to incorporate both dimensions of decision autonomy into the principal agent

model. Besides, more carefully work needs to be done in finding exogenous instruments for

managerial autonomy.

16

References

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Aghion, Phillippe and Jean Tirole (1997) “Formal and Real Authority in Organizations”,

Journal of Political Economy, vol. 105, no. 1 pp. 1-29.

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Table 1.a Summary statistics

Investment autonomy Labor autonomy Incentive

Industry N Mean Standard deviation Mean

Standard deviation Mean

Standard deviation

Garment & leather 353 6.1841 2.5568 6.8017 2.0395 0.2436 0.4299

Electronic equipment 185 5.0703 2.7644 6.8378 1.8403 0.3135 0.4652

Electronic parts 276 5.5435 2.6591 6.4493 2.2758 0.2826 0.4511 Household

electrics 63 5.9365 2.5895 7.1429 1.6545 0.1746 0.3827 Auto & auto

parts 358 5.5670 2.6966 6.6006 2.1103 0.2374 0.4261 Information technology 203 5.8325 2.5797 6.7586 2.0064 0.3645 0.4825

Food processing 71 5.3380 2.5351 6.2817 2.2815 0.2958 0.4596 Chemical product 66 5.8636 2.7112 6.4545 2.3415 0.3182 0.4693 Biotech products 36 6.0556 2.2032 6.6667 2.1909 0.4444 0.5040

Metallurgical products 158 6.4937 2.1936 6.6456 2.1358 0.1266 0.3336

Transportation equipment 50 6.2200 2.1313 6.4600 2.0525 0.2400 0.4314 Accounting

&non-banking services 157 5.9108 2.4557 6.4013 2.2385 0.2293 0.4217

Advertisement & marketing 154 5.6429 2.6437 6.3117 2.3529 0.2727 0.4468

Business service 270 4.9259 2.8288 5.9259 2.4575 0.3815 0.4867 Total 2400 5.6875 2.6400 6.5388 2.1748 0.2763 0.4472

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Table 1.b Correlation of variables

Investment autonomy

Labor autonomy Incentive LnL2000 LnK2000 LnY2000

Investment autonomy 1

Labor autonomy 0.4474 1 Incentive -0.1338 0.0122 1 LnL2000 -0.1452 -0.0075 0.0805 1 LnK2000 -0.2046 -0.0377 0.0976 0.7872 1 LnY2000 -0.1815 0.0414 0.1237 0.6981 0.6987 1

Table 1.c Mean comparisons of managerial autonomy for firms with and without incentive

Variable

Incentive? Observations Mean Std. Dev.

Investment autonomy

NO 1737 5.9159 2.5811

Investment autonomy

YES 663 5.0889 2.6999

Labor autonomy

NO 1737 6.5204 2.2298

Labor autonomy

YES 663 6.5867 2.0243

Table 2 Baseline Regression using log of sales revenue as dependent variable

LnY02 IncentiveInvestment Autonomy

Labor Autonomy LnY02 Incentive

Investment Autonomy

Labor Autonomy

System OLS 3SLS LnL00 0.614*** 0.017** -0.043 0.053 0.551*** -0.002 -0.021 0.000

(0.040) (0.008) (0.053) (0.043) (0.093) (0.014) (0.052) (0.040) LnK00 0.281*** 0.191***

(0.024) (0.028) Incentive 0.375*** -6.132***

(0.080) (0.583) Investment Autonomy -0.046*** -0.021*** -2.189*** -0.163***

(0.016) (0.005) (0.254) (0.034) Labor

Autonomy 0.072*** 0.021*** 4.179*** 0.315*** (0.019) (0.006) (0.426) (0.057)

GM tenure 0.079*** 0.039*** 0.083*** 0.028*** (0.016) (0.013) (0.016) (0.010)

GM experience 0.121*** 0.048*** 0.111*** 0.076*** (0.017) (0.014) (0.016) (0.011)

GM education -0.165 0.021 -0.170* 0.043 (0.105) (0.086) (0.102) (0.057)

GM annual pay 0.015 0.266** -0.093 0.625*** (0.153) (0.125) (0.149) (0.096)

GM inside -0.294** -0.366*** -0.435*** 0.015 (0.129) (0.105) (0.125) (0.079)

Firm age -0.133 -0.333*** -0.139 -0.321***

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(0.097) (0.079) (0.094) (0.062) Distance to

frontier 0.071** -0.021 0.102*** -0.084*** (0.033) (0.027) (0.032) (0.019)

Industry heterogeneity 0.040 0.009 0.034 0.024

(0.059) (0.048) (0.057) (0.031) Observations 1730 1730 1730 1730 1730 1730 1730 1730

R-squared 0.62 0.07 0.15 0.07 Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

Table 3 3SLS Regression using sales per employee and sales growth rate as dependent variable

Sales per employee Sales growth rate Incentive

Investment autonomy

Labor autonomy

LnY00 -0.165***

(0.021) LnL00 -0.346*** 0.084*** -0.006 -0.041 0.056

(0.074) (0.032) (0.014) (0.052) (0.040) LnK00 0.183*** -0.002

(0.023) (0.016) Incentive -4.864*** -0.609***

(0.462) (0.233) Investment Autonomy -1.743*** -0.435*** -0.189***

(0.200) (0.085) (0.033)

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Labor Autonomy 3.260*** 0.868*** 0.348***

(0.336) (0.147) (0.055) GM tenure 0.082*** 0.032***

(0.016) (0.010) GM

experience 0.115*** 0.066*** (0.016) (0.011)

GM education -0.174* 0.050

(0.103) (0.061) GM annual

pay -0.109 0.634*** (0.149) (0.097)

GM inside -0.376*** -0.087 (0.125) (0.081)

Firm age -0.128 -0.373*** (0.095) (0.063)

Distance to frontier 0.061* 0.017

(0.032) (0.020) Industry

heterogeneity 0.038 0.020 (0.058) (0.033)

Observations 1730 1730 1730 1730 1730 Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

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Table 4 3SLS regression results on firms separately with and without incentives

Log of sales Sales growth rate Sales per employee w/o incentive w/ incentive w/o incentive w/ incentive w/o incentive w/ incentive

LnL00 0.584*** 0.574*** 0.058 0.152*** -0.310*** -0.355*** (0.119) (0.130) (0.048) (0.051) (0.093) (0.107)

LnK00 0.177*** 0.190*** -0.002 0.004 0.164*** 0.194*** (0.050) (0.037) (0.023) (0.024) (0.040) (0.033)

Investment Autonomy -1.524*** -0.803*** -0.379** -0.214** -1.203*** -0.690***

(0.400) (0.251) (0.149) (0.086) (0.310) (0.205) Labor

Autonomy 2.702*** 1.865*** 0.714*** 0.500*** 2.071*** 1.500*** (0.603) (0.472) (0.233) (0.158) (0.467) (0.385)

LnY00 -0.147*** -0.209*** (0.034) (0.031)

Observations 1192 538 1192 538 1192 538 Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

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Table 5 3SLS regression results on manufacturing and service firms

Log of sales Sales growth rate Sales per employee Manufacturing Service Manufacturing Service Manufacturing Service

LnL00 0.583*** 0.659*** 0.053 0.214*** -0.298*** -0.315*** (0.090) (0.094) (0.039) (0.049) (0.072) (0.079)

LnK00 0.214*** 0.199*** 0.037* -0.090*** 0.195*** 0.202*** (0.030) (0.054) (0.019) (0.029) (0.025) (0.046)

Incentive -3.073*** -1.746** -0.270 1.122*** -2.563*** -1.303* (0.638) (0.785) (0.257) (0.393) (0.503) (0.670)

Investment Autonomy -1.521*** -0.498* -0.354*** 0.125 -1.229*** -0.387

(0.211) (0.285) (0.081) (0.131) (0.167) (0.239) Labor

Autonomy 2.919*** 1.137*** 0.750*** -0.181 2.292*** 0.917*** (0.356) (0.410) (0.142) (0.191) (0.282) (0.344)

LnY00 -0.193*** -0.093*** (0.026) (0.035)

Observations 1341 389 1341 389 1341 389 Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

25

Table 6 3SLS regression results on SOE and Non-SOE firms

Log of sales Sales growth rate Sales per employee SOE Non-SOE SOE Non-SOE SOE Non-SOE

LnL00 0.659*** 0.216* 0.214*** 0.130*** -0.315*** -0.594*** (0.094) (0.123) (0.049) (0.039) (0.079) (0.099)

LnK00 0.199*** 0.225*** -0.090*** 0.007 0.202*** 0.208*** (0.054) (0.036) (0.029) (0.020) (0.046) (0.030)

Incentive -1.746** -0.278 1.122*** 0.636** -1.303* -0.553 (0.785) (0.901) (0.393) (0.258) (0.670) (0.719)

Investment Autonomy -0.498* -1.918*** 0.125 -0.188** -0.387 -1.557***

(0.285) (0.256) (0.131) (0.076) (0.239) (0.205) Labor

Autonomy 1.137*** 6.414*** -0.181 0.523*** 0.917*** 5.025*** (0.410) (0.498) (0.191) (0.165) (0.344) (0.402)

LnY00 -0.093*** -0.219*** (0.035) (0.026)

Observations 389 1281 389 1281 389 1281 Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

26

Appendix A: Definition of variables

LnL00 Logarithm of average number of worker in 2000 LnK00 Logarithm of total fixed asset value for year 2000 LnY02 Logarithm of value of total sales Sales growth rate

Ln(Y2002/Y2000)

Investment autonomy

Score 1 to 8 correspond to 0-19%; 20-39%; 40-59%; 60-69%; 70-79%; 80-89%; 90-99% and100%.

Labor autonomy

Labor flexibility (hiring, firing and wage). Score 1 to 8 correspond to 0-19%; 20-39%; 40-59%; 60-69%; 70-79%; 80-89%; 90-99% and100%.

Incentive Dummy variable to question “Does the General Manager has any incentive plans linking his/her income to firm performance, Yes/No”

GM tenure What is the tenure for the general manager? (Measured in years) GM inside Dummy variables whether the General Manager is from the firm or hired from outside GM Annual Pay

Dummy variable to question “Is the General Manager’s wage paid annually? Yes/No”

GM experience

“How many years had the General Manager held this position?”

GM education

Scores 1 to 6 corresponding to “no education; primary school education; secondary education; high-school education; undergraduate education and postgraduate education

Firm age Logarithm of firm’s age Distance to

frontier Firm’s solow residual to the solow residual of the best firm in its industry

Industry heterogeneity

The range of sales growth rate between 2000 and 2002

27