Capability and Opportunism:

Evidence from City Officials in China

Tianyang Xi†, Yang Yao†, and Muyang Zhang‡

†National School of Development and CCER, Peking University‡China Public Finance Institute and School of Public Economics and

Administration, Shanghai University of Finance and Economics

This version: March 2017

Abstract

This paper empirically investigates whether public officials’ capability mitigate their

opportunistic behavior in China. Taking advantage of China’s unique institutional

setup and adopting the method developed by the literature on employer-employee

matched data, the paper estimates leaders’ personal capabilities using a large data

set of city officials for the period 1994–2011. Studying officials’ responses to political

turnovers following the cycle of the communist party’s national congress, the paper

finds a significant political business cycle in Chinese cities. However, more capable

leaders are found to generate more modest political business cycles than less capable

ones. This result is robust when political connections are taken into account. Further

explorations find that nonrandom entry and exit, and endogenous shuffling of leaders

does not pose a challenge to our results. The paper enriches the study of government

officials in developing countries with weak institutional environments.

Keywords: Political business cycle, political opportunism, capability versus incentive

JEL: H11, O47, P26

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Highlights

• We study how bureaucratic capability affect political opportunism in China.

• Capability is estimated as city leaders’ personal contribution to economic growth.

• Opportunism is estimated as the increase in growth along with political cycles.

• The paper finds strong opportunism for officials with strong promotion incentive.

• More capable leaders are found to less opportunistic.

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1 Introduction

Politicians are often opportunistic. Moreover, it is found that the pursuit of such

opportunistic policies is more prevalent in developing countries with weak institutional

environments where the constraints on the executive branches are less binding (Brender

and Drazen, 2005; Drazen and Eslava, 2010; Shi and Svensson, 2006; Akhmedov and

Zhuravskaya, 2004). Therefore, strengthening the institutions is often proposed as a key

solution to prevent opportunism. Politicians, on the other hand, may differ enormously

in their inherent capabilities for serving their jobs. And a fundamental purpose of po-

litical selection, in addition to shaping incentives, is to pick up the good types of public

officials who are capable of producing satisfactory performance, including attending to

the society’s long-term interests. However, it has never been empirically tested whether

more capable government officials are less opportunistic.

The unique institutional setup in China provides an opportunity for us to conduct the

test. Based on a unique dataset of city officials from 312 cities for the period 1994-2011,

this paper intends to provide an empirical study on whether the capability of city officials

has a positive effect to reduce their opportunism on local economic growth.

City officials enjoy a high degree of autonomy due to China’s decentralized fiscal

system. Fiscal decentralization is often regarded as one of the most important drivers

for China’s marvelous economic growth in the reform era (Qian and Weingast, 1997; Xu,

2011). Decentralization provides local officials strong incentives to develop local economy.

However, there are also concerns that the strong promotion and fiscal incentives have led

officials to take short-sighted and opportunistic actions, particularly land development

projects (Han and Kung, 2015) and debt-financed investment projects, to achieve quick

results.1 Those actions inevitably distort the economy and cause efficiency losses (Xu,

2011).

It is noteworthy that the performance of fiscal decentralization varies a lot across

countries. Enikolopov and Zhuravskaya (2007) show that strong parties and subordination

of local governments to the central government in developing and transition countries

significantly improve the effects of decentralization. In a comparison between China

1According to the latest data released by the National Audit Office, local governments.commercialdebt liabilities were 11.9 trillion RMB by June 2013 (National Audit Office, 2013). While the centralgovernment has allowed local governments to issue longer-term government bonds to swap their commercialdebts, new commercial debts have emerged again since the central government began another round ofcounter-cyclical policies in the spring of 2016.

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and Russia, Blanchard and Shleifer (2001) attribute China’s better performance to the

strong central government. One of the underlying mechanisms is China’s bureaucratic

promotion system that follows the country’s meritocratic tradition to emphasize a person’s

overall quality when it selects and promotes government officials. More capable officials,

particularly those with better records of economic performance, are rewarded by better

chances of promotion (Li and Zhou, 2005; Lu et al., 2014; Jia et al., 2015; Yao and

Zhang, 2015). It is likely that more capable officials do not need to resort to short-

term actions as much as less capable officials do because the former are able to develop

the economy by more efficient measures (such as attracting more efficient companies,

investing in technologies, and improving the business environment). If the promotion

system consistently promotes high-quality officials, opportunism will be subdued.

In addition to enriching our understanding of the Chinese promotion system, this study

provides meaningful results for developing countries to enhance the accountability of local

officials. Developing countries often have weak institutions, and a holistic improvement

is often beyond the capacity of most of them. A finding of the substitutive role of quality

for opportunism would suggest that strengthening the selection process to select more

capable politicians can be an alternative to improve their performance.

Several models provide theoretical insights for understanding the interaction between

capability and opportunism in the context of political cycles. In the classical career–

concern model developed by Holmstrom (1999), agents do not know their own abilities.

Hence their choices for the level of efforts do not depend on their levels of ability. However,

when agents have private information about their own abilities, their efforts tend to

depend on their levels of ability. In a signaling model, Rogoff (1990) proposes that a

political business cycle (PBC) ensues because the politician uses fiscal spending as a

costly signal to persuade voters that he or she is highly capable. Drazen (2001) extends

Rogoff (1990) to account for both fiscal and monetary policies. The signaling models are

able to generate a PBC in which the incumbent manipulates economic performance near

the election year, and imply that the more capable incumbent manipulates more.

Capability and incentive need not be positively correlated, however, when agents’

political returns depend on their long term reputations, for which the principal may

already have a prior over the course of previous interactions. Taking this concern into

consideration, Martinez (2009) studies the PBC in a reputation model in which the agent

optimally distributes her efforts to build up reputation over time. Similar to Rogoff (1990)

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and Drazen (2001), the level of effort increases as an election approaches, because the

marginal cost of efforts spent in later periods is discounted more than in earlier periods.

But different from Rogoff (1990), an agent would exert less effort in response to the cycle

provided she already had a higher initial reputation and that reputation increases over

time.2 To the extent that the initial reputation is correlated with capability, the results

of Martinez (2009) imply that politicians’ capability moderates political opportunism.

In Martinez (2009), officials know their initial reputations (estimates of their capa-

bilities). In Rogoff (1990), officials are assumed to have perfect knowledge about their

capabilities. The reality may be in between. The CCP’s organizational department does

not know officials’ true abilities, and it is able to update its belief on officials’ capability

upon observing their performance. Moreover, officials themselves may not have perfect

knowledge about their capabilities, but they can infer them from their past experiences.

These two institutional features give rise to an opportunism with the presence of promo-

tion incentives, however they also imply that the focus on officials’ long-term traits by the

Party’s organizational department can alleviate opportunism. This provides the basis for

our empirical testing. To our best knowledge, this study provides the first empirical test

for the question whether capability mitigates or increases officials’ opportunism.

The main methodological challenge lies in how to measure capability. In the literature

on electoral accountability, some use the performance difference between first-term and

reelected politicians to identify the growth effect of capability (Alt et al., 2011). In the

cross-country setting for studying how national leaders affect economic performance, the

sample of random leadership transitions are explored (Besley et al., 2011; Jones and

Olken, 2005). Neither approach is able to obtain a precise estimate for the capability

of individual leaders with a wide coverage. We borrow from the recent labor economics

literature on performance decomposition using employer-employee matched data (Abowd

et al., 1999; Bertrand and Schoar, 2003) and empirically disentangle city leaders’ relative

contributions to economic growth (hereafter, the “leader effects”) from the unobserved

city and year fixed effects.

The identification strategy we use to estimate capability is normally infeasible for

studying local politicians in most democratic settings as politicians rarely serve more than

one locality for the same positions such as a mayor. The system of political selection in

2This result is obtained from Proposition 2 of Martinez (2009) and the fact that the reputation in thesecond period is positively correlated with the reputation in the first period.

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China provides a unique setting in this regard because city leaders are frequently shuffled

among different jurisdictions (Kou and Tsai, 2014). This feature allows us to construct

samples of cities that are connected to each other through transferred leaders and identify

the leader effects of all city leaders, regardless of whether they were transferred or not, in

the largest of those samples.

We measure opportunism by the political business cycle (PBC). The PBC is defined

by the Chinese Communist Party (CCP)’s National Congress, which is regularly held

every five years and is coupled by significant political turnovers at the local as well as the

national levels. In turn, political opportunism (such as heavier borrowing and investment)

could become stronger when the calendar moves toward the congress year. We estimate

the PBC effect by the time profile of the economic growth rate along the cycle of the

national congress. A positive slope of the time profile means the existence of the PBC.

We find significant evidence for the PBC. The annual growth in per capita GDP increases

by close to 0.5 percentage points when it moves one year closer to the next national party

congress.

Taking the leader effects estimated from the largest connected sample as a measure of

capability, our empirical analyses show that the PBC effects are nuanced by capability.

Officials who are able to produce more robust growth throughout the sample period appear

to rely less on the short term boom right before the party congress. The time profiles of the

PBC for the two least capable quarters are highly significant and very steep whereas the

time profiles for the two most capable quarters are insignificant. We test several factors

that may confound our estimates of the leader effects, including nonrandom entry and

exit, directed moves between officials and cities, and incidental shocks, and have found

none of them is serious to challenge our estimates. Because performance and promotion

may be intertwined with political connections (Jia et al., 2015), we check the robustness

of our main results by considering the influence of political connections.

The organization of this paper proceeds as the following. In the next section we discuss

the institutional background of China’s political selection and its implications for political

opportunism. Section 3 introduces our method to measure the PBC and capability. In

Section 4 we introduce data and present descriptive results. The econometric results and

robust checks are presented in Sections 5 and 6. Section 7 concludes.

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2 Institutional Backgrounds

Two features of political selection in China allow us to form a credible empirical

strategy to identify the PBC and officials’ capabilities at the same time.

2.1 The CCP National Congress and the PBC

The first feature is that the Chinese bureaucratic system has a clear political cycle

following the CCP’s National Congress, which is regularly held every five years. Around

the time when the National Party Congress convenes, the party committee at each level

of government holds its own congress. The average chance of promotion for a city official

at the end year of a political cycle is nearly three times that in the beginning year of

a cycle.3 This timing allows us to identify the PBC. Because promotion is more likely

to happen around the congress, officials would signal their capabilities or improve their

reputations by exerting greater effort (a la Rogoff (1990); Martinez (2009)) when the date

of the next congress draws closer, thus creating a PBC.4

However, the PBC is complicated by the rule of retirement. The reform leading to

mandatory retirement was initiated by Deng Xiaoping and formally introduced at the

12th National Party Congress in 1982. As of today, the officials ranking at the sub-

provincial level (or equivalently, deputy ministers) or below, including all mayors and city

party secretaries, are required to retire at age 60. Meanwhile, for incumbent officials who

are close to the retirement age, the chance of retirement at the end of a political cycle

is four times that at the beginning of a cycle.5 Moreover, city officials are supposed to

serve for a certain length of time, normally three years or more, before being promoted.6

3For all city officials in our sample, the average chance of promotion throughout a political cycle,from the beginning to the end, is 8.77, 9.78, 16.35, 23.68, and 25.14 percent for the first through fifthyear, respectively. Half the turnovers of party secretaries happen immediately after the National PartyCongress.

4In the literature, scholars follow different notions to capture China’s political cycles. Guo (2009)measures the cycle effect by the third year for an official in the current tenure, relying on the empiricalobservation that officials are often transferred or promoted after serving the same jurisdiction for threeto four years. We do not adopt this definition for the political cycle, because the length of tenure maybe endogenous to officials’ performance and capability. In comparison, the National Party Congress isexogenous with regard to individual incentives. Our understanding is that the National Party Congress isa more conventional measure for political cycles in the literature on the political economy of China (Nieet al., 2013; Tao, 2006).

5For all city officials in our sample, the average chances of retirement throughout a political cycle,from the beginning to the end is 2.17, 2.17, 4.41, 5.46, and 8.84 percent, for the first through fifth year,respectively.

6According to the Regulations for the Selection and Appointment of Party Cadres announced by thecentral organizational department of the CCP in 2002 and its revision in 2006, an official being promotedshould have served in a position one level lower than the current position for at least three years. Inpractice, the average term length of city officials for the cycle 2002-06 was 4.07 years, and that for 2007-11

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Although some promotions bypassed this restriction, the rule is normally enforced (Kou

and Tsai, 2014).

This rule for length of service before promotion implies that the chance of promotion

is small for city officials over age 57, because they would serve less than a full political

cycle in the new position if promoted. Sub-provincial leaders reaching age 57 are often

transferred to positions with less de facto power, such as the provincial People’s Congress

or People’s Political Consultative Conference. In our empirical work, we use 57 as the age

cutoff to define another dimension of an official’s incentive. This dimension can have an

independent role in shaping an official’s work incentive as well as its interaction with the

PBC. For officials younger than 57, their opportunistic incentive becomes stronger when

they approach age 57. For officials age 57 or older, both kinds of incentives decline or

stay unchanged as time passes.

As the highest officials in prefecture cities, mayors and party secretaries have controls

over a wide range of policy tools to boost performance indicators and signal their capa-

bilities. Like elected officials, they may strategically increase fiscal spending and direct

resources to items more tangibly related to growth, such as infrastructure and fixed assets

investments. Chen and Kung (2016) explore political turnovers at the county level and

find that windfall revenues from land sales enhance the probability of promotion of local

officials through facilitating the investments on flamboyant public projects. Alternatively,

local officials may adjust policy agendas toward development projects that better signal

their personal competence when there is a larger baseline probability of promotion (Pan,

2016). Local officials also exert larger efforts to assure social stability and public safety

toward the end of a political cycle (Nie et al., 2013; Shi and Xi, 2016). The fluctuations of

performance indicators along political cycles are suggestive of political opportunism that

induces distortional policies and resource misallocation.

Aside from the pursuit of opportunistic policies to boost short-term growth, local

officials may also manipulate economic statistics. Recent studies suggest that official data

in China may be susceptible to various kinds of misreports. Fisman and Wang (2016)

use the provincial data on accidental deaths and show that the self-imposed targets of

safety performance set by provincial governments are couple with a sharp discontinuity

of quarterly deaths in the distribution at the targets. Similarly, the quality of China’s

GDP data is often questioned on the ground of an inconsistency within official data

was 2.95 years.

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(Rawski, 2001; Wallace, 2016). However, a comparison between GDP and other coincident

indicators reports only a miniature degree of discrepancy between the two, which was most

likely to have been due temporal shocks (Mehrotra and Paakkonen, 2011). Various other

studies suggest that the problems are incidental than intentional manipulation (Holz,

2004, 2014; Klein and Ozmucur, 2002). Moreover, the GDP statistics may be lower than

the true values due to ubiquitous tax-evasion in the private sector (Koch-Weser, 2013).

For our purpose, possible misreport and manipulation are not an immediately killer of

the research design as the estimation of personal capabilities already takes into account

unobserved city and time specific unobservable effects due to manipulation. The central

finding of our paper, the substitutive relationship between capability and the PBC effect,

may be undermined by misreport only if less capable officials consistently did a better job

than more capable officials at manipulating economic statistics along the time profiles of

the PBC. But this is a piece of evidence itself supporting our main finding because, to

the extent that exaggeration of GDP growth is a sign of opportunism, it says that more

capable officials are less opportunistic.

2.2 Interjurisdictional Transfers of Officials

The second institutional feature that allows us to identify officials’ capabilities is reg-

ular transfers of officials from one city to another. In democracies, local officials rarely

hold office in different jurisdictions. Voters evaluate the performance of incumbent politi-

cians retrospectively and need not make comparisons across jurisdictions when casting

their votes.7 Indeed, cross-jurisdictional comparison is difficult in practice even if voters

want to do so, because economic performance is confounded by unobservable regional

characteristics and temporal shocks. In the case of China, however, many local officials

are regularly shuffled between localities.8 Tracking those officials’ moves, we are able to

construct “connected samples” of cities in which a city had at least one official ever being

moved to at least one other city in the sample. Fortunately, one of those samples is fairly

large. The analysis uses all the officials that have worked in the cities of this largest

7For notable exceptions, Besley and Case (1995) find that fiscal performances are positively correlatedacross states in the United States. Kayser and Peress (2012) argue that European voters compare therate of economic growth with the international benchmark.

8Although there are many reasons why the CCP shuffles its cadres, the following three are the mostimportant. One is to prevent the formation of local factions that have the potential to challenge the rule ofthe center. Another is to prevent local officials from forming alliances with local businesses. The recentlyrevealed high-profile corruption cases have the commonality of strong business interests behind corruptofficials. The third reason is to allow promising local officials to obtain experience in localities of quitedifferent economic and social conditions.

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connected sample (or simply “the connected sample” hereafter), regardless whether the

officials have been shuffled or not. We apply the econometric technique developed in the

literature on employer–employee matched data to disentangle the relative contributions of

officials to economic growth (the leader effects) from the contribution of local conditions.

The leader effect is taken as a measure of an official’s capability.

A priori, capability may substitute or complement the promotion incentive in driving

performance, and the interaction between capability and opportunism is subject to empir-

ical investigation. The signalling effect is likely to be a driver of opportunism. As several

studies in political science show, subnational officials may strategically distort policies

so as to make their loyalty visible to the principals (Kung and Chen, 2011; Shih, 2008).

Similar strategies can be employed to signal capability for managing economic growth,

as Rogoff (1990) argues. At the same time, there is reason to believe that capability

moderates opportunism. The officials analyzed in this paper are not “new faces” in the

bureaucracy. Most city officials in the sample have already had extensive work expe-

riences in the government or party hierarchies. Most have served as county or district

governor or party secretary, or the head of an administrative bureau in the city. Most

have also served as vice mayor or vice party secretary in a city, and most party secretaries

have served as mayor. As a result, the party’s organizational department that manages

the personnel already has a fairly good amount of knowledge about city officials’ true

capabilities. Moreover, most officials are in their early 50s so their capabilities are likely

to be invariant during their tenure as mayor or party secretary.9 Thus, it is reasonable

to believe that an official’s initial level of reputation, that is, the common expectation of

her ability, is correlated with the level of her long-term capability. In our empirical anal-

ysis, capability is measured by an official’s time-invariant contribution to local economic

growth throughout her entire career as a city official (who is likely to have served more

than one city). Based on the above discussion, capability thus measured is the basis for

an official to develop her initial reputation. Hence, Martinez’s (2009) result applies and

this alludes to a moderating role of capability for opportunism.

It is noteworthy that the estimation of capability may be potentially biased by a

complementarity between officials’ political connections and their incentives to enhance

their performance (Jia et al., 2015). In the empirical section, we measure change in a

9Reputation models often assume that change in an agent’s capability over time is random and haszero mean. Martinez (2009) maintains this assumption.

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city leader’s political connections by the turnover of the provincial party secretary who

appointed the city leader. We provide a test for the robustness of our estimations when

the influence of political connections is taken into consideration.

3 Measurement of the PBC and Capability

3.1 Measuring the PBC and the Effects of Age Limits

We estimate the time profile of the PBC by a linear time trend of the growth rate

within each cycle of the National Party Congress. We define the variable PBC(t,T ) by

• PBC(t,T ) = the current calendar year t − the calendar year of the immediate last

National Party Congress, T

The variable takes values between 1 and 5, with a larger value meaning that the time

moves closer to the next National Party Congress.10 Following the theoretical works on

the PBC (Martinez, 2009; Rogoff, 1990), we expect that the variable PBC has a positive

coefficient in a regression that takes the annual growth rate as the dependent variable.

The PBC effect is then represented by an upward-sloping time profile prior to the year of

the next National Party Congress.

Figure 1: Illustration of the PBC and age effects

Note: in the figure, T0 is the year when an official becomes a city leader; T1 is the yearwhen the first National Party Congress is held in his tenure as a city leader; T2 is the yearwhen the second National Party Congress is held in his tenure; T3 is the year when hereaches age 57; T4 is the year when the third National Party Congress is held in his tenure.

10The National Party Congress is always held in October or November. So the fifth year of a cycle canbe counted as a full year.

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At the same time, the PBC effect is shaped by the retirement rule, which is an ex-

tra incentive for officials. Figure 1 provides an illustration for an official who becomes a

city leader in a year immediately after a the National Party Congress (T0) and experi-

ences three National Party Congresses in his career, but has not been promoted until his

retirement.

In the figure, the horizontal axis represents the calendar year, and the vertical axis

represents the level of effort the official expends to promote economic growth. Other

things being equal, the level of effort can be measured by the growth rate. There are five

critical years in the figure. T0 is the year when the official becomes a city leader; T1 is the

year when the first National Party Congress is held in his tenure as a city leader; T2 is

the year when the second National Party Congress is held; T3 is the year when he reaches

age 57; and T4 is the year when the third National Party Congress is held in his tenure.11

The figure shows the age effects and the PBC effects.

In the first political cycle, the official exerts increasing efforts over time, resulting

in the first PBC that ends at T1, the date of the first National Party Congress. This

political cycle is shown by the first darker and solid upward-sloping line (line 1) in the

figure.12 His effort drops after the first National Party Congress, but will increase again

before the second National Party Congress is held. This time, his effort could reach a

higher point than in the first PBC, because he will be turning 57 before the third National

Party Congress and his chances of promotion will decline substantially. This situation is

represented by line 2 in the figure. In the third political cycle, his effort can be different

before and after he reaches 57. Before he turns 57, he may try again because he still has

some chances. This effect is represented by line 3 in the figure with a modest slope. After

he turns 57, his efforts decline regardless of the third National Party Congress (represented

by line 4). Lines 3 and 4 together consist of the PBC for the period between T2 and T4,

which is weak due to the age limit. In terms of estimation, the PBC effects represented

by different lines (1, 2, and 3-4) can be captured by the slope of PBC(t,T ), which takes a

specific value for each year of the National Party Congress, T0, T1, T2, etc.

To capture the effects of the age limit, we define two dummy variables to indicate,

respectively, the leaders who are younger and older than 57 in each year t. The age

11In Chinese cities, most leading officials start their job in their late 40s or early 50s, so it is possiblefor them to experience three National Party Congresses.

12Assuming that the trend growth rate is well accounted for by factors other than age and the PBC,then by our construction, which we will introduce later, the trend growth rate should be the rate whenthe official exerts no effort.

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groups are labeled Group A (age < 57) and Group B (age ≥ 57) in the subsequent text.

A refined specification for the PBC effect involves using the interaction between the linear

time trends and the dummy variables indicating the two age groups of officials.

In addition, because promotion can occur at any time within a political cycle, officials

who face a binding retirement age limit might exert efforts differently before and after

turning 57, regardless of the political cycle. According to our theoretical discussions,

the linear trend before age 57 should slope upward, and the trend after 57 should slope

downward. These incentive effects are represented by line A and B, respectively. Following

this reasoning, we can account for the age effect by defining the following variable:

• DAGEt = |57− age|

In a regression with the annual growth rate as the dependent variable, DAGEt cap-

tures the two linear trends with respect to age 57. In our empirical analysis, we will allow

the two trends to have different intercepts by interacting DAGEt with the two dummies

for Groups A and B, that is, the age groups younger and older than 57. The interactions

provide estimates for the time profiles corresponding to lines A and B in Figure 1.

3.2 Measuring Capability

We should first explain what we mean by capability in empirical terms. Capability

can mean many things, including an individual’s inherent skills to gather and process

information, make judgments, persuade people to follow, organize large endeavors, man-

age crises, and so on. None of these attributes is directly measurable, but it is possible

to measure them indirectly by observing outcomes. In the Chinese context, the most

salient outcome for evaluating an official’s capability lies in economic growth, which is

best summarized by the growth rate of GDP. Using the growth rate to measure officials’

capability, however, has to face the challenge of disentangling officials’ contribution and

the local conditions of the cities they have served. In addition, two major officials, the

party secretary and the mayor, always work together at the any point in time. Following

the recent empirical work of Yao and Zhang (2015), we adopt the simple assumption that

city officials make independent contributions to economic growth.

To proceed, we study the following specification for the economic performance of an

official i serving city j during year t:

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yi(jt) = Zi(jt)β + θi + ψj + γt + εi(jt), (1)

where yi(jt) is the real growth rate of per capita GDP (in percentage points) of city j in

year t under official i’s tenure; Zi(jt) is a set of controls of personal and city characteristics;

θi is the fixed effect of official i (either a party secretary or a mayor) of city j in year t; ψj

is city j’s fixed effect; γt is the fixed effect of year t; and εi(jt) is the random disturbance for

city j’s growth in year t. Following the labor economics literature on employer-employee

matched data, the personal fixed effect θi can be interpreted as a measure of official i’s

capability, the so-called “leader effect.” The leader effect is the average contribution of an

official during his tenure as a city leader. Therefore, it may include his responses to the

PBC and age limits. However, because the variable PBCt,T is a linear combination of the

year fixed effects, the PBC effects are effectively controlled in equation (1). In addition,

as we will see in the data section, the officials’ ages were quite concentrated between 49

and 53, so interpersonal differences were small. We will momentarily show that we can

only estimate the relative size of θi, which is sufficient for our study. Therefore, as long

as interpersonal differences do not affect the ranking of an official’s leader effect relative

to another’s, the estimate of θi is sufficient to serve our purposes.13

In our empirical estimation, we treat party secretaries and mayors equally in equa-

tion (1). So in effect we are stacking together the data of two separate regressions for

party secretaries and mayors. The main gain of stacking the data is that it substantially

increases the size of the largest connected sample. The size of a connected sample is a

convex function of the number of leaders moving between cities. If we estimate mayors

and party secretaries separately, the number of officials who were moved between cities in

each sample is about half the number of movers in the combined sample, but the size of

each sample is reduced to less than one-half the size of the combined sample. In fact, the

size of the separate samples can be very small, depending on the actual circumstances,

such as in our case.

In most cases around the world, politicians serve in only one locality for one type of

political office, and lateral moves across different regions are quite unusual. This renders

a difficulty in estimating capability θi in equation (1), because the leader effect θi and the

13Another practical reason for us to omit the age effects is that age information for leaders serving inthe 1990s is scant, but we need a long panel to build a large connected sample.

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city fixed effect ψj tend to share the same dimension of the data and cannot be separately

identified. Only their sum ωij = θi + ψj can be estimated. In the case in which officials

are transferred between two cities, however, we can estimate the relative leader effects of

all the officials who have served in the two cities. The following heuristic example helps

explain the intuition of the identification strategy.

Suppose there are two cities, A and B, and three officials, numbered 1, 2, and 3.

Official 1 worked in both cities, Official 2 worked only in City A, and Official 3 worked

only in City B. Net of the year fixed effects γt and other controls, the variations in local

growth during each official’s tenure are observed as

• Official 1: ω1A = θ1 + ψA, ω1B = θ1 + ψB,

• Official 2: ω2A = θ2 + ψA,

• Official 3: ω3B = θ3 + ψB.

Subtracting ω2A from ω1A, we can obtain the differential of capability between Official

1 and Official 2, θ12 = θ1 − θ2. To compare officials across the two cities, 2 and 3, we

can first take the difference between the two parameters estimated for Official 1 to obtain

the difference between the two cities’ fixed effects: ψA − ψB. Substituting it into the

difference between ω2A and ω3B obtains the differential of capability between Official 2

and Official 3, θ23 = θ2 − θ3. Finally, adding up θ23 and θ12, we get the differential of

capability between Official 1 and Official 3, θ13 = θ1 − θ3. Reiterating this process, we

obtain a complete ordering for the capabilities of the three officials that are independent

of the city fixed effects. We are unable to estimate the absolute level of an official’s leader

effect. In the estimation, we set the mean of leader effects to zero. So the capability

we estimate for each official is his or her contribution to economic growth relative to the

mean of the sample.

4 Data and Variables

The data set used here extends the one in Yao and Zhang (2015) by including more

detailed information on the characteristics as well as the career paths of city leaders:

sex, effective highest education,14 number of cities served since the deputy positions at

14“Effective highest education” excludes degrees offered by party schools.

15

the prefecture level, and membership in the provincial party standing committee. Infor-

mation on the party secretaries and mayors was collected from the China Yearbook of

Municipalities, provincial yearbooks, and reports from the media, especially the Internet.

We then match the leaders to annual macroeconomic data collected from provincial

yearbooks by the following rules:

1. Each city–year observation is matched with one secretary and one mayor.

2. If one turnover occurred within a year, we take the leader who stayed for more than

six months in that year.

3. If multiple turnovers occurred in a year and no leader stayed for more than six

months, we take the leader with the longest stay in that year.

We have data that match officials with cities in 312 of the 333 qualified cities for the

period 1994–2011. Subsequently, we call the 312 cities the “sample cities.” For years

before 2003, however, we do not have substantial information for officials (including age,

which is crucial for our study). For that reason, we rely on several samples to conduct

our empirical work.

We first construct connected samples using the long sample of 1994–2011 and find the

largest connected sample among them. The connectedness of cities is critically dependent

on the number of officials who moved between cities. A short sample does not capture

many moves and thus does not provide large connected samples. The largest connected

sample (hereafter, the 1994-2011 connected sample) thus constructed contains 221 cities

and 1,600 officials (party secretaries and mayors that have served in the connected cities,

regardless of whether they themselves were transferred). Figure 2 presents a map of the

312 cities covered by our study (lightly colored) and the 221 cities in the connected sample

(heavily colored).

Then, we define a shorter sample that covers the period 2003–11. We have data for

leaders’ personal characteristics for this sample. Subsequently, this sample will be called

the “2003–11 sample.”15 It contains all the cities in the 1994–2011 sample, but only

officials who served after 2003.

15The year 2003 was chosen as the starting year because (a) we have complete data for officials sincethat year, and (b) the CCP held its 16th National Party Congress in November 2002, and Hu Jintaobecame the general party secretary. That regime switch marked a new era in China’s political arena, andwe can focus on the political players at the city level throughout the Hu period, so we can avoid potentialconfounding impacts from the regime change at the central level.

16

Figure 2: The 312 sample cities and 221 cities in the connected sample in 1994-2011

Lastly, we define the “2003–11 connected sample,” which includes all the cities in the

1994–2011 connected sample, but only officials in that sample who served after 2003. We

will provide details about how this sample and the previous two are used when we present

our econometric strategies and results.

As shown in Figure 3, most city officials were in their late 40s or early 50s. The age

distribution has a mean of 50.5 and a median of 51, and it is almost symmetric. Except

for only one leader who retired two months after his 60th birthday, all officials retired at

or before age 60, and fewer than 10 percent of the leaders were older than 55. On the

lower end, the youngest leader was 36; those younger than 45 accounted for about 12

percent of the sample.

5 Main Empirical Results

5.1 Estimating the PBC and Age Effects

Following the discussions carried out in Section 3, we run the following regression to

estimate the PBC and the effects created by age limits:

yi(jt) = δ1PBC + δ21DAGE × (age < 57) + δ22DAGE × (age ≥ 57)

Zi(jt)β + ψj + εi(jt) (2)

17

Figure 3: Age distribution

0.0

2.0

4.0

6.0

8F

ract

ion

35 40 45 50 55 60Leader's age

In the equation, Zi(jt) is a set of variables describing a leader’s personal characteris-

tics. Because officials.personal data, including their ages, are only available since 2003,

Equation (2) can only be estimated with the 2003-11 full sample. As a result, the leader

effects cannot be estimated. Their omission will not affect our results about the PBC be-

cause the variable PBC is orthogonal to the leader effects — PBC is defined on calendar

years, and leader effects do not vary over time. Also, because political cycles are collinear

with the year dummies, we do not include year fixed effects in the equation.

The existence of the PBC requires that δ1 be positive. The age effects independent

of the PBC require that δ21 and δ22 both be negative. In our empirical study, we will

also interact PBC with the two ago group dummies, Group A (age < 57) and Group B

(age ≥ 57), to obtain the PBC.s heterogeneous effects created by the age limits. Table

1 reports the estimates of Equation (2). The variables included in Zi(jt) are explained in

the table.

In column 1, we only study the PBC effect. Because the dependent variable is the

growth rate measured in percentage points, the coefficient of PBC is interpreted as the

extra increase in growth rate when moving one year closer to the next party congress.

The coefficient for PBC is 0.51, which translates to 2.55 percentage points throughout

a complete political cycle of five years. The PBC for GDP growth is not usually found

to be significant in democracies (Drazen, 2000). The significant result here probably can

18

Table 1: Testing the PBC: the 2003õ11 full sample

Dependent variable: annual growth of per capita GDP

(1) (2) (3)

PBC 0.510*** 0.501***(0.120) (0.120)

DAGE× Group A (age<57) -0.0634**(0.0278)

DAGE× Group B (age≥57) -0.0428(0.0328)

PBC × Group A (age<57) 0.520***(0.123)

PBC × Group B (age≥57) 0.243(0.283)

Group B (age≥57) 1.043(0.942)

Provincial standing committee member 0.141 0.117 0.125(0.282) (0.282) (0.283)

Number of cities served 5,342 5,311 5,342R-squared 0.193 0.193 0.193

City F.E. (ψj) Y Y YObservations 3,829 3,810 3,829

R-squared 0.791 0.791 0.791

Standard errors clustered at cities are in the parentheses. *** p < 0.01, **p <0.05, *p < 0.1. All estimations are based on the 2003-11 sample. Controlvariables that are included in each regression but are not reported includethe dummy variables for leaders’ education received from community college,college, master, Ph.D. and post-doctorate programs, whether the city leader isfemale, log city GDP per capita at the beginning of an official.s tenure, logcity population, the inflation rate, and the constant term.

19

be explained by the characteristics of China.s political selection. Because the promo-

tion criteria are centered on economic growth, officials do not need to engage in welfare

and redistribution to please citizens. Instead, they may expand fiscal expenditures on

productive activities to boost growth. Hence, fiscal expansion and growth are unified16.

In column 2 we further add DAGE ×GroupA and DAGE ×GroupB to account for

the age effects. While the coefficient of PBC virtually does not change, the coefficient of

DAGE × GroupA(age < 57) is negative and statistically significant, and the coefficient

of DAGE×GroupB(age ≥ 57) is negative, but statistically insignificant, both consistent

with our theoretical expectations. The age effect for officials in Group A is relatively

small; an official of age 57 only produces a growth rate 1.33 percentages higher than the

youngest official, who was 36 in our sample. So the effect of political cycles dominates the

incentive effect of age limits. However, officials from different age cohorts may respond

differently to the political cycles. Column 3 provides a test for this idea. The interaction

terms between the PBC and the two age dummies, PBC × Group A and PBC × Group

B, are intended to capture officials’ heterogeneous responses to the PBC according to

the time horizon they face. We expect the coefficient for the first interaction term to be

positive, and the coefficient for the second interaction term to be insignificant. In addition,

we include Group B as the reference group. As Column 3 shows, the standing-alone age

group dummy Group B is statistically insignificant, indicating that the incentive effect

due to age per se is relatively weak. But the two groups of officials do show different rates

of response to the PBC. For the group of officials below 57, one year closer to the next

party congress increases their growth rate by 0.52 percentage points, almost the same as

the average PBC effect obtained in column 2. However, political cycles do not provide

incentive for officials beyond 57, confirming the prediction that officials passing the age

limit do not respond to political cycles.

In each column of Table 1, we report the results of two control variables (the results of

other control variables are not shown to save space). One is a dummy variable indicating

whether an official was a provincial standing committee member, and the other is a

continuous variable indicating the number of cities the official had served. Being a member

16It is a worthy exercise to examine fiscal spending. However, about half of local government spendingcomes from non-budgetary revenues, particularly those from land development (?), that are not properlyaccounted for in official statistics. While scattered data of extra-budgetary revenues can be found forsome cities, only budgetary revenues and expenditures are well documented. We have tried to analyzebudgetary expenditures, but have not found any meaningful regularities. We attribute that to the lack ofextra-budgetary expenditures.

20

of provincial standing committee usually means that an official has a high chance to get

promoted to the province in the next round of shuffling, so he may not be as opportunistic

as other officials. On the other hand, an official usually also has a larger chance to get

promoted if he has served more than one city. However, serving more than two cities

probably means that an official is losing his chances. Table 1 shows that neither being a

member of the provincial standing committee, nor serving more cities significantly affects

officials’ incentive to respond.

5.2 Heterogeneous Effects of the PBC by Capability

We first estimate Equation (1) based on the 1994-2011 connected sample to obtain

the leader effects (capabilities). Figure 4 shows the kernel density of the estimated leader

effects and compares it with the normal distribution. The kernel density function has a

positive mode and is more compact than the normal distribution.

0.0

1.0

2.0

3.0

4D

ensi

ty

-50 0 50 100Leader effect

Kernel density estimateNormal density

kernel = epanechnikov, bandwidth = 1.6699

Figure 4: The kernel density of estimated leader effects (θi)

Leader effects are estimated according to equation (1), based on the 1994-2011connected sample. Control variables include log initial GDP per capita at citylevel, log city population, the inflation rate, the city and year fixed effects.

With the estimated leader effects, we proceed to explore the heterogeneous responses

to the PBC among officials with different capabilities. This time, we have to use the

2003-11 connected sample because both leader effects and officials’ personal information,

particularly age, are needed. We classify officials in the 2003-11 connected sample into

21

four equal-sized groups by their leader effects, and Equation (2) are estimated separately

for the four groups to obtain the PBC effects and age effects for officials in the four

quarters.17 Table 2 presents the results.18

Columns 1 - 4 in Table 2 correspond to the specification of Column 1 in Table 1, which

focuses only on the PBC. Columns 5 to 8 replicate Column 2 in Table 1, which takes into

consideration the effects caused by the age limits. Lastly, Columns 9 to 12 correspond

to Column 3 in Table 1, which takes care of PBC’s heterogeneous effects by age groups.

Columns 1 - 4 show that the PBC exists for city leaders in all four quarters of capability,

but its magnitude declines uniformly from lower quarters to higher quarters. In addition,

it is only significant for leaders in the two lowest quarters. The results of PBC provided

by Columns 5 õ8 are broadly similar to those of the first four columns. Column 5 shows

that for the lowest quarter of leaders, the age effects are similar to those found by Column

2 in Table 1. However, Columns 6-8 do not find any significant age effects for the other

three quarters of higher levels of capability. Columns 9 - 12 show that as in the previous

results, age alone does not have a direct association with growth, but age limits have a

significant effect in structuring the PBC effect. Officials who are over 57 do not respond

to the PBC, but for officials younger than 57, the PBC effect declines uniformly from the

least capable quarter to the most capable quarter, just like what we have found for the

whole sample. A slightly different result is that while the time profiles of the two least

capable quarters remain statistically significant, that of the most capable quarter also

becomes marginally significant.

The results in Table 2 clearly show that capability mitigates political opportunism for

officials who face promotion incentives. This conclusion is a piece of evidence favoring

the reputation models (such as Martinez (2009)) and against the signaling models (such

as Rogoff (1990)). One of the key differences between the reputation models and the

signaling models is that the former assume that the principal knows something about the

agent through his initial reputation whereas the latter assume that the agent has perfect

information about his ability, but the principal knows nothing about the agent throughout

the game. In the Chinese case, the organizational department in the province probably

17Because tenure varies among leaders, the number of observations in the four groups does not equal.18There may be a concern that because the estimated leader effects are positively correlated with

economic growth, any regularity found for the effect of PBC among the four quarters is a mechanicalresult of the grouping. It is indeed a mechanical result we find that officials in higher-order quarters tendto produce higher growth rates. However, what we are interested in is not the differences of level, but thedifferences of the slope of the PBC’s time profile. There is no mechanical reason to believe that officialsin higher-order quarters have larger or smaller slopes than their counterparts in lower-order quarters.

22

Tab

le2:

Het

erog

eneo

us

resp

onse

sto

the

PB

Cby

cap

abil

ity

Dep

enden

tva

riable

:ζ i

(jt)

=yi(jt)−θ i

1st

2nd

3rd

4th

1st

2n

d3rd

4th

1st

2n

d3rd

4th

qu

arte

rqu

arte

rqu

arte

rqu

art

erqu

art

erqu

art

erqu

art

erqu

art

erqu

art

erqu

art

erqu

art

erqu

art

er

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

PB

C0.8

43***

0.5

36***

0.4

00

0.2

73

0.7

80***

0.5

40***

0.3

85

0.2

82

(0.2

58)

(0.1

63)

(0.2

71)

(0.1

69)

(0.2

55)

(0.1

65)

(0.2

77)

(0.1

70)

DA

GE×

Gro

up

A(a

ge<

57)

-0.2

13**

0.0

355

-0.0

573

-0.0

185

(0.0

865)

(0.0

687)

(0.0

852)

(0.0

639)

DA

GE×

Gro

up

B(a

ge≥

57)

-0.3

37

-0.3

09

-0.0

118

0.7

59

(0.4

11)

(0.8

48)

(0.6

07)

(0.7

83)

PBC

×G

roup

A(a

ge<

57)

0.8

51***

0.5

40***

0.4

10

0.3

00*

(0.2

64)

(0.1

64)

(0.2

82)

(0.1

69)

PBC

×G

roup

B(a

ge≥

57)

0.7

02

0.4

10

0.0

726

-0.6

39

(0.7

14)

(0.8

32)

(0.4

63)

(1.1

22)

Gro

up

B(a

ge≥

57)

0.3

71

0.0

474

1.8

49

4.4

18

(1.9

99)

(2.1

37)

(2.1

07)

(3.4

21)

Cit

yF

.E.

(ψj)

YY

YY

YY

YY

YY

YY

Obse

rvati

ons

929

879

958

1,0

63

921

876

954

1,0

59

929

879

958

1,0

63

R-s

quare

d0.2

05

0.4

56

0.2

91

0.3

71

0.2

14

0.4

56

0.2

90

0.3

73

0.2

05

0.4

56

0.2

92

0.3

74

Not

e:S

tan

dar

der

rors

clu

ster

edat

citi

esar

ein

the

pare

nth

eses

.***p<

0.01,

**p

<0.

05,

*p<

0.1

.E

stim

ati

on

sare

con

du

cted

onth

e20

03-1

1co

nn

ecte

dsa

mp

le.

Con

trol

vari

ab

les

that

are

incl

ud

edin

each

regre

ssio

nb

ut

are

not

rep

ort

edin

clu

de

the

du

mm

yva

riab

les

for

lead

ers’

edu

cati

onre

ceiv

edfr

omco

mm

unit

yco

lleg

e,co

lleg

e,m

ast

er,

Ph

.D.

an

dp

ost

-doct

ora

tep

rogra

ms,

wh

eth

erth

eci

tyle

ader

isfe

mal

e,lo

gci

tyG

DP

per

cap

ita

at

the

beg

inn

ing

of

an

offi

cial.

ste

nu

re,

log

city

pop

ula

tion

,th

ein

flati

on

rate

,an

dth

eco

nst

ant

term

.

23

already has a good knowledge about city officials’ capabilities before they are considered

for promotion. Their competition reveals more information about their capabilities and

enables the provincial organizational department to have a better judgment. This is

probably why our results support the reputation models. It awaits further studies to

assess the situation in other countries.

5.3 Political Connections

A concern may arise that political connections help an official’s economic performance.

Given the previous findings in the literature about the importance of political connec-

tions in determining the promotion of provincial leaders (Jia et al., 2015; Jiang and Zhang,

2015; Shih et al., 2012), it is a legitimate question how the omission of political connec-

tions can bias our results. Because we measure both capability and the PBC in terms of

GDP growth, the estimate for a particular official can be biased upward if (1) his political

connections help him to obtain more fiscal resources and policy favoritism from upper-

level governments; and (2) more political connections induce him to exert more efforts

in response to the PBC. However, those two factors may not create serious biases in our

estimates, for two reasons. First, the primary interest of our study is not the levels of

officials’ capability (θi) per se, but their ranks, which are unaffected by political connec-

tions as long as the growth benefits brought by connections are not negatively correlated

with capability in a systemic way. Second, if connections and responses to the PBC are

positively correlated, we will observe a complementarity between capability and the PBC

effect. But this is not supported by our data, as the results in Table 2 show.

Nevertheless, we deal with city leaders’ connections by controlling the turnover of

provincial party secretaries. In the empirical literature on political connections, a com-

monly used identification strategy relies on the exogenous turnovers occurring in the

upper levels of government. The most important connection a city official relies on is

the one with the provincial party secretary, who has a large say on the promotion and

appointment of city officials. Thus, it is natural to believe that a city official should be

connected to the provincial party secretary who was an incumbent at the time when that

city official was first appointed to this position. When the provincial secretary leaves the

office, being either retired or moved to another position outside the province, such connec-

tions tend to vanish. Thus, we construct a dummy variable CPijt to capture the growth

effect of political connections. The dummy variable takes value 1 for the years when the

24

provincial party secretary who appointed leader i was in office, and 0 if otherwise.19 Next

we conduct robustness checks by controlling CPijt in equation (1). Because CPijt is not

a complete measure of political connections, the robustness results should be treated as

indicative.

Figure 5: Kernel density of estimated leader effects (θi), with political connections

0.0

1.0

2.0

3.0

4D

ensi

ty

-40 -20 0 20 40Leader capability

Kernel density estimateNormal density

kernel = epanechnikov, bandwidth = 1.8852

Note: Leader effects are estimated according to equation (1), based on the 1994-2011 connected sample. Control variables include CPijt, log initial GDP per capitaat the city level, log city population, the inflation rate, city and year fixed effects.

We first estimate equation (1) again by adding CPijt as an extra control variable.

The distribution of the new leader effects is presented in Figure 5. Compared with the

distribution in Figure 4, the new distribution looks more disbursed, indicating that polit-

ical connections do affect officials’ performance. Then we repeat the regressions of Tables

1 and 2. The new results are mostly qualitatively identical to the old results. To save

space, we do not present them here. Instead, we only present the heterogeneous PBC

effects of the four quarters of officials in Figure 6. The pattern shown by this figure is

largely in line with the pattern shown by Table 2. The results give us confidence that the

consideration of political connections does not affect our main conclusion that capability

moderates officials’ opportunistic incentives.

19In the literature (e.g., Jia et al. (2015)), a commonly used measure for political connections is builton the common hometown, alumi, and colleagueship shared by lower-level officials and their superiors.Although this definition has its merits, it is currently infeasible for city officials because of the lack ofdata, particularly for a large data set that stretches back to the 1990s.

25

Figure 6: PBC by capability quarters, with political connections

cycle

Lead

er r

ank

by c

apab

ility

-.5 0 .5 1 1.5

1st Quartile 2nd Quartile3rd Quartile 4th Quartile

Note: The data are drawn from the regressions reported in columns 1–4 in Table 2,with capability estimated when CPijt is controlled.

5.4 A Nested Estimation

To further check the robustness of our main findings, we present the results of a

nested estimation in this subsection. We use the 1994-2011 connected sample to estimate

the leader effects and officials’ heterogeneous responses to the PBC together. In the

meantime, we also control officials’ political connections. Because each official stayed in

the sample for several years, it is possible to estimate a time profile of the PBC for each

official. However, the estimate may contain a lot of noise because the average tenure of

the officials is short. In addition, we are unable to take care of the age effects, because

there are not enough data for age before 2003.20 However, because we do not find that

age has a significant and independent effect on growth, our omission of age may not be

very problematic.

Our nested estimation takes up a revised version of equation (1):

yi(jt) = Zi(jt)β + (θi + φiPBC) + τCPijt + ψj + γt + εi(jt). (3)

In this equation, we allow leaders to have their own rates of response to political

cycles, which are measured by the φi’s. The θi’s are now the leader effects net of leaders’

20We could use the 2003-11 connected sample to conduct the nested estimation. The problem is thatthis sample is small and does not allow us to produce robust estimates for the leader effects.

26

Figure 7: Capability and the rate of response to political cycles

Note: The estimation is based on the connected sample for the period between 1994and 2011. Control variables include log initial GDP per capita at the city level, logcity population, and the inflation rate.

responses to political cycles, so they are closer to leaders’ true capability. The correlation

between φi and θi informs us about whether capability mitigates the PBC. The estimate

of τ is 0.681, and the p-value is 0.151. That is, political connections do somewhat help

an official’s performance, but the effect is not statistically significant.

We plot in Figure 7 the estimates of φi and θi obtained from equation (3) to validate the

negative correlation between capability and the PBC. The figure reinforces the previous

result that capability mitigates opportunism.21

6 Issues Concerning the Estimates of Leader Effects

Our empirical work critically depends on the accuracy of our estimates of the leader

effects. Those estimates may be compromised by three issues. First, entry and attrition

might not be random; second, moves of leaders might be directed in the sense that certain

types of leaders are deliberately selected to move to certain types of cities; and third, city-

specific transitory shocks may contaminate our estimates. In this section, we will provide

tests to see if those issues seriously impede our estimates.

21The estimates of φi are quite dispersed and some of them have quite large magnitudes. This is createdby the short tenure that officials had. As a result, the findings of this subsection are more indicative thandefinitive.

27

Nonrandom entry and attrition have been studied in the literature on employer-

employee matched data. In Abowd et al. (1999), labor force mobility is assumed as

exogenous. It was not until recently that researchers started to address the issue in this

strand of the literature. Although Abowd et al. (2010) propose two new tests for the

validity of the assumption of exogenous mobility, the actual magnitude of the bias is not

yet known, and the correction algorithm is still under development.

On the one hand, entry into our sample is nonrandom because city officials are selected

from lower levels of the government by the officials’ qualifications. On the other hand,

attrition involves three types of exit: retirement, moving to other jurisdictions or cities

that are not covered by our sample, and promotion, which are unlikely to require the

same level of capability. To see whether entry and attrition affect our sample composi-

tion, we test whether the distributions of the estimated leader effects of newly appointed

officials and leaving officials are significantly different from the distribution of the leader

effects of the other officials in the sample. Because our analysis has mostly relied on the

2003–2011 connected sample, our test also focuses on this sample. Figure 8 compares the

distributions of three types of officials, newly appointed (dashed line), leaving (dotted

line), and others (solid line) in three years, 2004, 2007, and 2010. It is clear that the three

distributions are quite similar to each other and all are close to the normal distribution.

We then conduct pair-wise t-tests between the means and standard errors of the three

distributions for each year, and do not find any significant difference in any case. There-

fore, it is safe to conclude that entry and attribution do not affect the composition of our

sample.

Figure 8: Distribution of capabilities: Entries, exits, and the rest of the sample

0.0

1.0

2.0

3.0

4.0

5D

ensi

ty

-40 -20 0 20 40 60x

Entry in 2004 Exit in 2004Other leaders in 2004

0.0

1.0

2.0

3.0

4.0

5D

ensi

ty

-40 -20 0 20 40 60x

Entry in 2007 Exit in 2007Other leaders in 2007

0.0

1.0

2.0

3.0

4D

ensi

ty

-40 -20 0 20 40 60x

Entry in 2010 Exit in 2010Other leaders in 2010

Note: The figure shows the kernel density estimation of capabilities for all officials who newly entered thesample, permanently exited from the sample, and others in a given year. The three panels respectively plotthe distributions for 2004, 2007, and 2010.

28

In the Chinese system, moving an official from one place to another is usually a signal

that this official is hopeful for promotion. Therefore, moves in our sample are likely to

be nonrandom. One of the purposes to move officials laterally is to train their abilities to

deal with different problems. This kind of directed moves may lead to biased estimates

for the leader effects. For example, a mediocre leader in an already prosperous city may

only show average ability, but when he is moved to a less prosperous city, he may register

a remarkable performance record. The same leader may have a specific talent that, when

matched with the right city, may lead to high growth rates. In both cases, his leader effect

will be overestimated. On the other hand, a capable leader may perform badly if he is

moved to a city that does not match some of his talents (e.g., he is capable of managing

a well-functioning city, but does not have the ability to deal with a chaotic city full of

political strife). In this case, his leader effect will be underestimated.

To get a sense of the moves in our sample, we created Table 3 in which we divide the

pre-move cities and the post-move cities respectively into four equal quarters according

to per-capita GDP and show the number of moves from each pre-move quarter to each

post-move quarter. It is clear that except those in the 3rd pre-move quarter, movers

had the largest probability to get transferred to a city of the same level of income. This

was particularly true for movers in the 4th pre-move quarter where 20 out of 22 moves

happened in the same income quarter. The number of movers moving to higher income

quarters was 24, almost the same as the number of movers moving to lower income

quarters, which was 23. Therefore, as far as income is concerned, no systematic direction

is found for the moves.

Table 3: Moves in the sample

Post-move city quarters

Pre-move city quarters 1 2 3 4 Total1 11 9 4 0 242 9 12 6 2 293 6 6 7 3 224 1 1 0 20 22

Total 27 28 17 25 97

Of course, moves might be directed by other city and leader attributes. While we

can provide more descriptions about the pattern of match between cities and leaders,

we realize that what matters for us is whether moves, directed or not, “increase” or

“decrease” leaders’ capabilities. This boils down to a test on whether moves affect the

29

estimates of leader effects. Thus, we estimate a variant of equation (1) as follows:

yijt = Zi(jt)β + θi + αiMoveit + ψj + γt + εijt (4)

where Moveit is a dummy variable indicating whether official i served beyond his first city

in year t. That is, Moveit = 1 is the subset of a mover’s personal dummy excluding his

tenure in his first city. As a result, θi is the personal effect of leader i in the first city

and αi is the added value of his moves.22 Note that we can only estimate αi for movers.

An F-test of the joint significance of αi provides a decisive conclusion on whether moves,

directed or not, affect our measure of individual leader effects. Using the 2003-11 sample,

we obtain F (35, 3, 981) = 1.00, and p = 0.47. If we limit the analysis to the connected

sample during the period, we obtain F (28, 2814) = 0.91 and p = 0.60. Therefore, we

conclude that directed moves do not affect our estimates of individual leader effects.

Another more stringent test is to test whether the estimated leader effects θi’s are

correlated with the estimated city fixed effects ψj ’s. To do that, we first get the average

leader effects for leaders who worked in specific cities and then check their correlation

with ψj ’s. The correlation coefficient is -0.293, which is fairly small in magnitude. That

is, the estimated leader effects are almost orthogonal to the city fixed effects. As far as

the leader effects are concerned, directed moves are not an issue for our estimation.

Lastly, because in essence we use the city growth records during leaders’ tenures

to estimate their leader effects, our estimates may only pick up the transitory shocks

happening during leaders’ tenures in specific cities. Yao and Zhang (2015) conduct several

tests to show that is not the case. The tests include applying Bertrand and Schoar

(2003)’s method to study the residuals of Equation (1) without the leader effects, a placebo

test that compares the estimates based on the observed leader tenures with estimates

created by random perturbations of leaders’ tenures, and an introduction of city-specific

autoregressive processes into Equation (1). We do not repeat those tests in this paper.

7 Conclusion

A glaring feature of China’s political system is its hierarchical control over local officials

without holding them accountable to the electorate. In the Chinese bureaucracy, all

22Less than 2 percent of the leaders in our sample served more than two cities. We treat those leadersas having had one move.

30

officials are evaluated, appointed, and promoted, first by their superiors, and ultimately by

the central authority. Yet for the very reason that officials face the same yardstick criteria

in a centralized bureaucracy, it is meaningful to compare their personal effects on local

economic growth and study their reaction to the structure of political incentives. Studying

local economic growth rates, we find that officials do respond strategically to political

cycles, a finding suggestive of opportunism. But at the same time, the opportunism is

nuanced by capability. More capable officials are less responsive to political cycles. Age

also matters, but only to the extent that it manifests the officials’ cumulative responses

to political cycles. These results are consistent with the literature on political selection

and indicate that the reputation model is closer to the Chinese reality than the signaling

model in explaining the PBC.

Our empirical findings on the interaction between capability and political opportunism

shed light on the institutional design of political selection in developing countries. In

a canonical paper on political accountability, Barro (1973) studies the mechanism of

electoral control and assumes reelection as the only mechanism for providing incentive

for politicians. In turn, the immediate implication of his model on the design of electoral

terms is to implement very short terms without term limits. However, that conclusion is

challenged by the empirical fact that legislators become more entrepreneurial when they

are granted longer terms (Dal Bo and Rossi, 2011). Our study reinforces that challenge by

showing that capability substitutes for short-term incentives. To the extent that reelection

incentives often lead to opportunistic behavior, as suggested by the theoretical models

of the PBC, selection probably should supersede reelection incentives in the design of

political selection.

Finally, our findings provide clues for scholars to understand why some non-democracies

achieve better economic performance than others. In the literature, leaders are found

to have significant impacts on the records of economic performance in non-democracies

(Besley et al., 2011; Glaeser et al., 2004; Jones and Olken, 2005); however, less attention

has been paid to disentangling officials’ capability from their incentives in driving their

performance. Our paper exploits the unique institutional features offered by China’s po-

litical system to identify how local leaders’ capability and incentives play a role in local

economic growth.

The conclusion of this paper applies narrowly to economic growth, and thus should

not be read as reacting to the normative debate about political meritocracy in China

31

(Bell, 2015; Elliott, 2012). Our focus is the relationship between leaders’ capability and

incentives regarding economic performance per se, leaving aside the normative question

of whether economic growth should be the main goal of the government, let alone the

integrity of the whole bureaucracy. Indeed, our finding of a strong PBC shows that

selection based on capability to promote economic growth could be simply detrimental

for other social goods. Our paper fulfills a positive objective to study how the mechanism

of political selection supplements fiscal decentralization in China.

Acknowledgements

We thank Lixing Li, Xiaobo Lu, Hao Ma, Adam Przeworski, Kjetil Storesletten, Fabrizio Zilibotti, Xiaobo

Zhang, and the participants at the “Quantitative Studies of the Chinese Elite” workshop at the University

of California, San Diego, the 2015 meeting of Young Economists at Xiamen University, the Second Biennial

Conference of China Development Studies at Shanghai Jiaotong University, the 4th Young Scholars Fo-

rum on China’s Development at HSBC Business School, Peking Univerisity (Shenzhen), the 3rd Summer

Institute of Applied Microeconometrics at Shanghai University of International Business and Economics,

the 2015 Workshop for Chinese Public Administration and Politics, and the 2015 meeting of the Ameri-

can Political Science Association; and seminar participants at the Chinese University of Hong Kong, the

University of Hong Kong, University of WisconsinõMadison, the Shanghai University of Finance and

Economics, and the Shanghai Academy of Social Sciences for their helpful comments and suggestions.

Muyang Zhang gratefully acknowledges the support from the research grant from the Shanghai Philo-

sophical and Social Science Program (2014EJB004). We also thank Junyan Jiang for sharing data with

us.

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