Do Political Leader’s Demographic Characteristics

Matter for Infrastructure Investments

Guangnan ZhangSun Yat-sen University

Guangzhou, China 51000sysuzgn@gmail.com

Ran Song†

Sun Yat-sen University &Harvard UniverisityCambridge, MA 02138

songr@nber.orgJiehong Qiu

Washington State UniversityPullman, WA 99164jiehong.qiu@wsu.edu

Danglun LuoSun Yat-sen University

Guangzhou, China 51000luodl@mail.sysu.edu.cn

Abstract: Guided by a multi-objectives theoretical model, this paper empirically investigates the influences of political leaders’ demographic characteristics on infrastructure investments. To gain a valid identification, our empirical model accounts for the strategic interaction among leaders of jurisdictions where leader in one place makes decisions on infrastructure investments caring about the decisions of leaders of neighboring jurisdictions. Using data of 210 Chinese prefectural cities during 2001-2006, we find that leaders’ education backgrounds, personal characteristics (such as gender, birth city) and work experience all have significant effects on infrastructure investments. Political leaders with factory management experience and female officials are more likely to increase infrastructure investments, whereas officials graduated from top universities in China and officials with education backgrounds in economics and management tend to reduce infrastructure investments.

Key words: infrastructure investments, leader’s characteristics, strategic interaction

JEL Classification: E6, H3

April 2017

We would like to thank Deniel Berkowitz and Richard Freeman for their helpful comments and discussions. This paper is financially supported by National Natural Science Foundation of China(Project Number: 71573286)†† Corresponding author, National Bureau of Economic Research, 1050 Massachusetts Avenue 3rd Floor, Cambridge, Massachusetts, 02138

I. Introduction

Infrastructure investments affect not only growth and productivity of economies but also production costs and efficiency of corporates1. However, the determinants of infrastructure investments are still uncertain. Current literature on this issue mainly focuses on economic factors and political institution(Henisz, 2002; Arimah, 2005; Yu et al., 2011; Percoco, 2014). The important role that political leaders play in the decision of investment, however, is generally ignored. For example, 2016 democratic candidate, Hillary Clinton, planned to invest $275 billion in infrastructure over 5 years while the republican candidate, Donald Trump proposed over $500 billion to rebuild the country’s infrastructure2. Therefore, political leaders’ perspectives on the importance of infrastructure directly influence the investment in it. In this paper, we investigate how demographic characteristics of political leader’s affect the infrastructure investments.

A few papers exploit the influence of leaders on economic outcomes, for example, Mikosch and Somogyi (2009), Moessinger (2012), and Hayo and Neumeier(2012). However, one of the main threats to the validity of identification of current literature is the existence of strategic interaction of infrastructure investments among governments, for example, political leaders make decisions on infrastructure investments considering the decisions of leaders of neighboring jurisdictions. In this paper, we identify the effects of political leader’s demographic characteristics by taking strategic interaction into account.

To illustrate the mechanism behind the influence, we develop an illustrative model based on the framework that a local official, as a decision-maker of public policies, weights multiple objectives to maximize his utility. The predictions of the theoretical model are twofold. First, political leaders affect fiscal expenditure by changing the weight placed on different administrative objectives, such as residents’ welfare, economic growth and so forth. Second, fiscal policies among prefectural-level cities are mutually interactive. The predictions guide our empirical analysis by introducing a spatial econometric model which considers both the demographic characteristics of political leaders and the strategic interaction among jurisdictions.

The political institution in China offers us a precious opportunity to empirically investigate the theoretical predictions. There are five levels of administration governments in China3. Each level of administration has two political leaders—Secretary of Communist Party and governor of the government. Secretary is a

1 For example, Nadiri and Manumeas (1994) find that infrastructure improvements can significantly lower the average cost of production for the majority of businesses. In addition, Paul et al. (2004) show that infrastructure input can simultaneously lower both labor and capital inputs.2 Ballotpedia, https://ballotpedia.org/2016_presidential_candidates_on_infrastructure, 20163 At the top of that is the central government. The second level--provincial-level government follows, which contains 23 provinces, five minority autonomous regions(Inner Mongolia, Tibet, Xinjiang, Ningxia, and Guangxi), four municipalities(Beijing, Chongqing, Shanghai, Tianjin) and two special administrative regions(Hong Kong and Macau). The third level contains more than 300 prefectural-level cities. The lowest two levels are county level and township level, sequentially.

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standing member of a branch of Communist Party in an administration and is ranked as the head official. Governor of government is the second figure in an administration. Lower-level government are led and supervised by upper-level one. One of the most important rights of higher-level administration is deciding which political leaders should be promoted1 from lower-level government, based on criteria such as economic performance. The criteria provide enough incentives for local official to spend efforts to stimulate economic development and infrastructure investments are one of the best ways to achieve short-term economic growth. Moreover, political leaders lead the administration and have great power to influence fiscal policies. Hence, their personal preferences have a direct impact on infrastructure spending.

This paper primarily focus on the third level of the administration—prefectural-level2. By political leaders, this paper means the Communist Party Secretary, rather than the mayor, for several reasons. First, Chinese laws empower the secretary as the leader of City Committee of the Chinese Communist Party and the mayor is under the leadership of the Committer (Yao and Zhang, 2015). Second, almost all decisions on social and economic development should be discussed in the Office of the Secretary and the Party’s Standing Committee (both of them are led by the Party’s Secretary) before issuing to the public. Thus, secretary’s opinions have direct influence on public policies. Third, most party secretaries are promoted from mayors or are officials who have rich experience in economic management. Once in position, party secretary still has the strong desire to stimulate economic growth. Therefore, we believe the party’s secretary has the larger influence on economic development than the mayor. It is clear that the economic policy of local governments, to a large extent, reflects the will of the city’s secretary.

To have a valid identification of the influence of political leaders’ demographic characteristics, we need to isolate the potential effects of strategic interaction of infrastructure investments among jurisdictions. The interaction is driven by two forces, the political incentives and spillover effects. Since only limited positions are open in upper-level administration and the decisions of promotion are made by the upper-level government, local officials distinguish themselves out of political competition mainly through economic performance, and infrastructure investment is

1 Other rights of higher-level government includes deciding the responsibility division between upper-level and lower-level government, empowering lower government engage in economic and social development management, annulling improper decision of lower-level government.2 Compared to most of current literature concentrating on provincial-level government that is closest to Chinese central government and bears more political functions (Li and Zhou, 2005; Chen et al., 2005; Xu et al., 2011; Sheng, 2009), decisions of promotion of prefectural leaders base more on economic performance. This is mainly because prefectures are the main subjects of China’s economic performance review system and are less affected by Chinese central government.

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an efficient tool to fulfill this purpose. We call this the political incentives. The spillover effects come from the network effect of infrastructure such as highway and railway.

Guided by the theoretical prediction, we construct a spatial autoregressive model with spatial autoregressive disturbances (SARAR) with individual effects based on Kelejian and Prucha (1998) and Kapoor et al. (2007). This empirical setting accounts for the strategic interaction of infrastructure investments across local governments through a spatial lag term and spatial weighting matrix. To carry out the empirical analysis, we collect information of personal characteristics, education background, and work experience of political leaders of 210 cities in China by reviewing their resumes on the official websites of prefectural governments. The empirical model also includes economic variables of 210 Chinese cities during 2001 to 2006.

We find that officials’ personal characteristics, work experience, and education background significantly affect infrastructure investment. Leaders with factory management experience and female leaders tend to increase infrastructure investments, whereas leaders graduated from top universities in China and secretaries with education backgrounds in economics tend to decrease infrastructure investments. The strategic interaction of infrastructure investments among jurisdictions is mainly driven by political interaction, that is, by competition among local officials for promotional tournament, which may partly explain the overinvestment and disorderly competition in infrastructure investments in China (Zhou, 2004).

Our work contributes to literature in several aspects. First, it enriches the studies on political leaders’ influences on public policies. This paper also directly contributes to the stream of literature on determinants of infrastructure investments by considering the potential influences of policy-makers and the strategic interaction of infrastructure investments. Current literature focuses primarily on the effects of economic development, political institution and government management (Henisz,

2002; Arimah，2005 ；Cadot et al., 2006; Lambrinidis et al.，2005; Yu et

al.，2011；Albalate et al., 2012; Solé-Ollé, 2013; Percoco,2014).

Our last contribution closely relates to the literature of political economics in China. Current literature generally works on provincial-level data to analyze the effects of economic performance on political promotions (Li and Zhou, 2005; Chen et al., 2005; Xu et al., 2011; Sheng, 2009) and the effects of local officials on economic policy (Feng et al., 2012). Prefectural leaders have fewer political functions and a more unambiguous mission to develop local economy. Thus, it will be more appropriate to consider prefecture leaders to study the impacts of political leaders on economic policies.

The remainder of this paper is organized as follows: section 2 is the theoretical model. We construct a multi-objectives model and present the numerical analysis.

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Section 3 discusses the empirical method, including the steps necessary to estimate the empirical model and the selection of spatial weights matrices. Section 4 is data description and definition of variables. Section 5 is the empirical results and analysis. Section 6 is the conclusion.

II. Theoretical Model

(1) A Multi-objectives Model of the Decision Making of Political leaders

We assume that a representative family has infinite lifecycle and possesses

perfect foresight. Following Barro (1990), Turnovsky (2000, 2004), and Park and

Philippopoulos (2004), the long-term welfare of a representative agent has the

intertemporal isoelastic utility function:

u (c , g2)=∫0

∞ (c⋅g2κ)

1−θ

1−θ

dt , κ >0 , θ>0 , (1−θ ) (1+κ )<1 (1)

where c represents per capita consumption. 2g denotes per capita public services

(such as education, medical care and social security) provided by government. κ

index the weight of public services on the utility. θ denotes the impact of

consumption and public service on individual’ utility and 1/θ measures the

intertemporal elasticity of substitution. Previous research on public expenditure generally assumes a benevolent

government that legislates fiscal policy to maximize residents’ long-term welfare (Barro, 1990; Turnovsky, 2000; Park and Philippopoulos, 2004; Chen, 2006; Ghosh and Gregoriou, 2008). This framework ignores government officials’ own interests, such as career concerns. As pointed out by Li and Zhou (2005), the promotion likelihood of a local official is closely related to the economic performance of the region where the official is in charge. To enhance the probability of promotion, government officials might favor public policies that generate significant economic outputs in short-term such as infrastructure investments.

This consideration is motivated by two facts: On the one hand, at the end of each year, China’s Central Economic Work Conference sets a national economic growth target for the following year. To fulfill the targeted national growth rate, prefectures also set a growth goal and it becomes one of the most important tasks for local officers. We can denote the growth goal as the “absolute economic growth rate”. On the other hand, the ranking of the economic growth rate of a city within a province--the “relative economic growth rate” --also matters for political leaders, because this relative economic growth rate is one of the most important criteria for upper-level government to evaluate the capacities of political leaders and thus has direct impact

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on likelihood of promotion.Therefore, political leaders, considering the welfare of residents and own

interests, decide the fiscal expenditure on infrastructure and public consumption goods to maximize the utility of the form:

maxφ V =max {U + χγ+η ( γ−γ n )} ， χ≥0， η≥0 (2)

Where χ is the weight of ‘the absolute economic growth rate’ on officials’ utility. Similarly, η denotes the weight of ‘the relative economic growth rate’. A local official functions like a ‘chief executive officers (CEOs)’ of the administrative region. γ is

the economic growth rate of the jurisdiction in the balanced growth path and γn

corresponding the growth rate of its neighboring area. Moreover, as shown in the appendix, the respective economic growth rates of the jurisdiction and its neighboring areas are:

11/ 1 nA

11/ 1n nA

(3)

i.e., they are the function of infrastructure investments ratio and ratio of

neighboring areas ϕn. Fiscal policies are influenced not only by economic environment and

resources endorsement, but also by the CEOs’ personal characteristics, education background, work experience and so forth. Furthermore, the potential impacts of demographic characteristics of a local official are reflected in the weights given to the absolute and relative economic growth rate. Specifically,

χ= χ (Se c , Eco ) , η=η (Sec , Eco ) (4)

where Sec and Eco denote officials’ demographic characteristics and economic context of the administrative region, respectively. On the balanced growth path,

political leaders’ objective value V can be represented as a function of φ , φn , χ and η .

Since direct solution of is inaccessible, we turn to numerical simulation to

evaluate how do the weight of absolute economic growth rate and the weight of relative economic growth rate affect fiscal expenditure, followed by discussion of the strategic interaction of fiscal expenditure among prefectures.

(2) Numerical Simulation Analysis

Parameters used in simulation are presented in Table 1. Detailed information on

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parameters are provided in Appendix. In the first step, we simulate the relation of , the weight of ‘the absolute economic growth rate’ on officials’ utility, and , the

weight of ‘the relative economic growth rate’, and infrastructure investment ratio .

The simulation results presented in Figures 1 and 2 indicating that, in response of the increase of and , political leaders favor fiscal policies with higher infrastructure

expenditure ratio, i.e. higher . Recalling that both and are a function of S ec

and E co , denoting officials’ demographic variables and economic context of the administrative region, respectively. The simulation results thus give our theoretical insight:

Insight 1: Both demographic variables of political leaders and economic environment affect how political leaders weight the absolute economic growth rate, the relative economic growth rate and welfare of local residents, and thus affect political leaders’ decisions on infrastructure investments.

For example, young officials are more likely to assign higher weights on both the absolute and relative economic growth rate because, compared to older officials, young officials have a greater likelihood of being promoted. On the other hand, officials with good education backgrounds weight people’s livelihood more than the economic growth rates. The economic contexts of the administrative region also affect the allocation of weights to the various policy objectives. For example, officials in more economically developed areas will pay closer attention to their economic growth rate rankings, compared to other regions in the same province, and officials in areas with economically less-developed place lower weights on and values. Intuitively, geographic location, the availability of natural resources, policy inclinations and other economic conditions disadvantage officials from less-developed regions in economic growth competition. To signal their capability to upper-level officials, they thus endeavor to areas such as environmental protection or welfare improvement for residents.

Our second simulation focus on strategic interactions among prefectures. To exploit this, fixing other parameters, we analyze how a city’s infrastructure

investments ratio is affected by that of neighboring areas’, φn . The simulation

results are presented in Figure 3. There is a clear positive relationship between φ and

φn , which leads to our second insight:

Insight 2: Infrastructure investments are spatially interacted among prefectures.

Table 1: Model Norm Parameters

Utility Function 0.04 3 0.3

Production Function 0.57A 0.08v 0.75

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0.05 (0) 20k

Fiscal Policy0.1513 0.365n

Figure 1: The Relation between the Absolute Economic Growth Rate ( χ ) and Fiscal Expenditure

Structure(φ )

Figure 2: The Relation between the Relative Economic Growth Rate (η ) and Fiscal Expenditure Structure(

φ )

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Figure 3: The Relation between Fiscal Expenditure Structure (φ ) of Local Area and Fiscal Expenditure

Structure of Neighboring Area (φn )

III. Empirical Method

1. Specification and Estimation

The illustrative theoretical model predicts the influences of both political leaders

and strategic interaction on infrastructure investments. One conventional way to

capture the strategic interaction among governments is using the spatial

autoregressive (SAR) model by including the lagged dependent variable. However,

this model is invalid in analyzing the spatial spillover effect of fiscal policies because

it fails to account for the cross-sectional correlation in disturbance term caused by the

macro economic circumstances and economic exchanges among all areas. Although

the cross-sectional correlation can be controlled by the seemingly unrelated regression

(SUR) of Zenller (1962), this approach works only under panel data whose time

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dimension(T) is greater than cross-section dimension(N), which, unfortunately, is not

the case in this paper. Ignoring the correlation leads to misleading estimators

(Brueckner, 2003).

Based on Kelejian and Prucha（1998）and Kapoor et al. (2007), we finally use

the model composing of “spatially and temporally autoregressive error components”

and “spatially autoregressive dependent variable” (hereafter SARAR model). The

panel data SARAS model controls both strategic interaction of infrastructure

investments and cross-sectional correlation in disturbance term by introducing a

spatially lagged dependent variable and a spatially auto-correlated disturbance term,

respectively. The model has following form:

, ,1 1inf _ exp inf _ exp sec_ var _ varL M

it ij jt l l it m m it itj l mra W ra eco u

(

5)

where inf _ expitra is the ratio of infrastructure investment to budgeted fiscal

expenditure in cities i at time t, analogous notation for inf _ exp jtra . ijW is the element

in spatial weighted matrix W defined in the following section. Therefore,

inf _ expij jtjW ra captures the influence of infrastructure investments of city j on

cityi . A statistically significant and positive implies that strategic interaction is characterized by mutual imitation, whereas a significant and negative value implies

that the strategic behavior is differentiated. sec_ varit is a vector of characteristics

variables of a local official, _ variteco is a vector of economic variables, and itu is the

disturbance term.Denoting Equation (5) in a matrix form, we have:

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( )TY I W Y Xb u (6)

where Y is the 1TN column vector inf _ expitra , X is the (1 )TN L M matrix

comprised by , sec_ varit , and _ variteco , and u is the 1TN column vector of itu

Based on Kapoor et al. (2007), the disturbance term in model (6) has the following form:

( )Tu I W u (7)

where is the spatial autoregressive parameter of the disturbance term. Besides, to

account for intertemporal correlation in the disturbance term, is set to be:( )T ni I v

(8)

where is the 1N vector and indexes a city’s individual effect i that is

independently distributed with zero mean and variance 2 . v is the 1NT vector

formed by itv ; itv changes with cities and times; the obeying mean value is 0; and the

variance of the iid process is 2v . i and itv are mutually independent and have a

finite fourth moment. Details on estimation of Equation (6) are provided in Appendix.

2. Selection of Spatial Weighted MatrixTo identify whether the strategic interaction of infrastructure investments is

caused mainly by political incentives of political leaders or spillover effects, based on Anselin and Bera (1998) and Fingleton and Gallo (2008), we construct a socio-economic spatial weights matrix and an economic distance spatial weights matrix and apply them in our empirical model. If ρ obtained from a socio-economic spatial weights matrix is greater than the one from an economic distance spatial weights matrix, the strategic interaction is primarily driven by political incentives, otherwise, by spillover effects. (1). Socio-economic Spatial Weights Matrix

In line with Anselin and Bera (1998), the socio-economic spatial weights matrix has the following form:

1| |ij

i j i

Wx x S

, where

1| |i j

i j

Sx x

(9)

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where x is an economic or demographic variable. All diagonal elements are zeros. This matrix weights more on cities having similar demographic characteristic with city i .

In this paper, considering the political competition in China, x is measured by GDP. This is motivated by the fact that political competition is more fierce among

jurisdictions with similar economic performance. Specifically, if two cities, i and j ,

locate in the same province, then the spatial spillover weight between i and j is:

1

| | 1iji j i

Wgdp gdp S

, where

1| | 1i j

i j

Sgdp gdp

(10)

Otherwise, 0ijW . Since promotion competition within province is fiercer, this

matrix gives greater weight to cities in the same province with similar economic scales and ignores the spatial effect of cities from neighboring provinces.

(2). Economic Distance Spatial Weights MatrixFingleton and Gallo (2008) argue that the spillover effect across regions is not

only affected by geographic distance but also by relative economic distance. For example, the correlation of housing prices among large cities is expected to be greater than the correlation between big cities and neighboring small towns or between big cities and rural areas. Therefore, Fingleton and Gallo (2008) define the weight of the economic distance matrix as:

exp( )i j ijij

i

E E dW

S

, where

exp( )i i j ijjS E E d (11)

where iE and jE represent the employment scale in i and j , respectively; ijd

represents the geographic distance between i and j ; and all diagonal elements of w

are zeros. Infrastructure investments have spillover effects1 (Cantos et al., 2005) which

affect the economic development and marginal benefits of infrastructure investments of neighboring areas and therefore their fiscal expenditure. The spillover effect of infrastructure increase with the economic scale of a city and decrease with the distance between cities. Therefore, we use GDP as the index for the economic scales and define the economic distance spatial weights matrix as:

exp( )i j ijij

i

gdp gdp dW

S

, where

exp( )i i j ijjS gdp gdp d

(12)

1 As Cantos et al. (2005) find, transportation infrastructure not only affects the productivity of the region in which it is located but also has a spillover effect on neighboring areas. Consider the post spillover effect on Spain, where the infrastructure elasticity increased from 0.042 to 0146 between 1965 and 1995.

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Most studies assign a fixed value to (Fingleton and Gallo, 2008) according to

the particular needs of a study. To ensure that each prefecture-level city in the sample

has a “neighboring” city (Blanc-Brude et al., 2014), this paper sets 1 and the

element on the diagonal as 0.

IV. DataOur dataset is prefectural-level and we treat the Communist Party Secretary as

the local official of a city. By reviewing the resumes of political leaders on the official websites of local governments, we collect demographic variables of political leaders, which include personal characteristics, education background, and work experience during 2001-20091. Personal characteristics include age, gender, ethnic group, and whether the city where he/she holds a position is his/her hometown; education background includes whether the secretary graduated from a Project 985 university2, whether he/she has education background in economics, and whether he/she has a postgraduate education. Work experience refers to the secretary’s tenure in office as a local official, whether he/she was promoted from the city where he/she used to working, whether he/she has business management experience, and whether he/she has worked in upper-level government.

Existing literature on government public expenditure generally use per capita expenditure as the dependent variable for empirical analysis (Arimah, 2005; Baicker, 2007; Yu et al., 2011; Albalate et al., 2012). Considering per capita expenditure may not reflect political leaders’ preference in public spending, we thus use the ratio of infrastructure investment to budgeted fiscal expenditure as the dependent variable. Data on infrastructure investments and budgeted fiscal expenditure are obtained from “Statistical Materials of City and County Public Finances”.

Data of economic variables are obtained from three sources: the China City Statistical Yearbook, statistical yearbooks of different provinces, and the Statistical Materials of City and County Public Finances. Specifically, the data on real per capita GDP, urbanization level, population density, and openness are collected from the China City Statistical Yearbook and statistical yearbooks of provinces. The Financial Statistics of Cities and Counties provides data on fiscal decentralization and the proportion of populations supported by public finance. The fiscal expenditure classification used in the statistical yearbooks was modified in 2007; therefore, to ensure the unity and comparability, we use economic data and municipal party secretaries data of 210 cities during 2001-2006.The definitions and statistical descriptions of the variables are shown in Table 2, and detailed descriptions of relevant explanatory variables are provided below.

1 Pre-2001 data regarding the characteristics of municipal party secretaries in prefecture-level cities are harder to obtain systematically and completely.2 The Project 985 universities in China is like the Ivy League in the U.S.. The Project includes the best 39 universities in China.

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1. Officials’ Demographic Characteristics VariablesAge: In the 1980s, China emphasized the importance of cadres’ being

“revolutionary, youthful, knowledgeable, and professional”. To increase the “youthfulness” of cadres, many cities implemented maximum age limits for a local official to hold the position (Kou and Tsai, 2014). Accordingly, age has become an important factor in deciding promotion (Yao and Zhang, 2015). Relatively young political leaders may have greater incentives to promote economic growth by increasing infrastructure investments.

Gender: The gender of a leader may affect economic policies. Chattopadhyay and Duflo (2004) find that more female political participation in India increases public investment in projects like clean water. Clots-Figueras (2012), and Ferreira and Gyourko (2014) conclude that female mayors in the United States have greater political capabilities than male mayors. There are also significant gender-based differences in Chinese officials’ likelihood of promotion. Specifically, Shih et al. (2012) find that after controlling for factors including factional relations, princeling status, education level, the economic performance of the administrative region, and fiscal revenues, the political position of female officials is clearly lower than that of male officials. To account for the potential effects of gender on fiscal expenditure, we include gender dummy in empirical model, which equals to 1 if the party secretary is female and 0 otherwise.

Ethnic group: Most ethnic minorities officials hold positions in autonomous regions. The emphasis placed by the central government on social stability and financial support for ethnic minority areas may cause differences in fiscal expenditure preference between secretaries who are ethnic minority and secretaries who are not. Accordingly, we add a dummy variable, for an ethnic group, which equals to 1 if the municipal party secretary is an ethnic minority and 0 otherwise.

Birth city of the party secretary (Birth Place): Hayo and Neumeier (2012) find that personal decision-making preferences are largely linked to the environment in which a person grew up. Pande (2003)’s work on India finds that officials who were members of disadvantaged classes and tribes increase the transfer of payments to the classes and tribes. Similarly, the fiscal expenditure may be influenced by whether the secretary is serving his/her hometown. We include a dummy to capture this potential influence. If the municipal party secretary holds the office of his/her hometown, then the dummy is 1 and 0 otherwise.

Whether the municipal party secretary is a graduate of a Project 985 university (University985) and whether the municipal party secretary has a graduate degree (Postgraduate): Officials’ education level affects their decision making and their education background affects their probability of being promoted1. Besley et al. (2005) argue that education can significantly reduce the possibility that politicians will use power opportunistically. Additionally, universities in China dramatically differ in their quality. The Project 985 universities in China, like the Ivy League in the U.S., include the best 39 institutes. Whether one graduated from this 1 Shih et al. (2012) include the entire membership and alternate members of the 12th to 16th Chinese Communist Party Central Committees in their samples and find that educational background significantly influences the positions of officials at the 12th, 13th, 15th, and 16th National Party Congresses.

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group of university affects one’s personal capabilities and thereby one’s fiscal policy tendencies. Moreover, the alumni networks of prestigious schools within officials affect the availability of fiscal resources to political leaders. Therefore, we add two dummy variables, University985 and Postgraduate, to capture the impacts of officials’ graduated university and officials’ education level, respectively. If the municipal party secretary was an undergraduate from a Project 985 university, then University985 is 1 and 0 otherwise. Analogous notation applies for officials’ education level.

Whether the municipal party secretary has education background in economics or management (Background): The professional background of officials affects their policy tendencies. For example, Moessinger (2012) concludes that finance ministers with education background in economics are more likely to increase the national debt. Mikosch and Somogyi (2009) find that professional and political experience of political leaders have a significant impact on the public deficit. To account for this potential influence, the empirical model includes a dummy variable, Background, which equals to one if the municipal party secretary has education background in economics or management, and 0 otherwise.

The secretary’s tenure in office (Tenure): Roubini and Sachs (1989) argue that politicians with longer tenures are more likely to reduce fiscal budgets and thereby decrease deficits. In China, the relevant regulations stipulate that “each Party and Government leadership position has a term of five years”1 and that, without special circumstances, “a full term should be served” (Kou and Tsai, 2014). However, the term of the secretary is typically less than five years due to the nomenklatura system that allow an exchange of officials to other cities and exceptional promotions. Uncertainty in tenure may produce ambiguous influence on fiscal expenditure.

Whether the municipal party secretary is promoted from the same city (Promoted): Compared to a secretary once serving in other city, a secretary, promoted from the same city, has higher efficient in policy-making and policy-implementation for his/her familiarity with the circumstance. We capture this effect by adding a dummy variable into the empirical model, which equals to 1 if the municipal party secretary is promoted from the same city and 0 otherwise

Whether the municipal party secretary has business management experience (Business): Officials’ decision making may be related to their professional background. For example, Dreher et al. (2009) find that officials with business backgrounds are more likely to support market-based reforms during their terms. We add a dummy variable equaling to 1 if the municipal party secretary has business management experience and 0 otherwise.

Whether the secretary once held a position in upper-level government departments (Upper-level): Officials’ political capital affects the availability of fiscal resources for them. A party secretaries who once worked in national- or provincial-level government departments may have a stronger political connection and thus more likely to obtain fiscal support from the upper-level government, which can affect their

1 The “Party and Government Leading Cadre Appointment Duration Temporary Regulations” printed and issued by the General Office of the Chinese Communist Party Central Committee

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fiscal expenditure. Accordingly, we add a dummy variable, Upper-level, to account for this influence. If the municipal party secretary once held a position in provincial-level or central government departments, Upper-level is 1 and 0 otherwise.

2. Economic VariablesReal per capita GDP (GDP): Cities in different developing levels also differ in

fiscal revenue and expenditure. Therefore, in consistent with Borck et al. (2005), Hayo and Neumeier (2012) and Wu and Lin (2012), we use real per capita GDP to measure the effect of economic development on fiscal expenditure.

Population density (Population): Cities with higher population density may require more investment in infrastructure (Borck et al., 2005; Baicker, 2006; Yu et al., 2011), this paper uses average population per square kilometer to account the effect of population density on infrastructure investment.

Urbanization: Rural areas and cities differ in demands of public goods such as infrastructure. Areas in low urbanization level are often in greater need of infrastructure investments. On the other hand, highly urbanized cities may also require massive investment in infrastructure for their larger scale. Therefore, based on Yu et al. (2011), we use “the ratio of non-agricultural population in total population” to weigh the effect of urban-rural structure on infrastructure investments.

Openness: Cities with a high degree of openness often attract foreign investment by improving the business environment through increasing infrastructure investments. Our empirical model consider this potential effect by adding the variable of openness, measured by “the ratio of FDI to GDP” .

Fiscal revenue decentralization (Decentralization): Cities with higher local fiscal revenue relative to provincial-level fiscal revenue may have different fiscal expenditure preference over other cities. Based on the approach of Jin and Zou (2002) and Wu and Lin (2012), we measure fiscal revenue decentralization by “the ratio of city’s budgeted fiscal revenue to the provincial-level budgeted fiscal revenue,”.

The Proportion of government staff supported by public funds1 (Government Staff): Salary of staff and infrastructure investments are the two most important components of local fiscal expenditure in China. The higher the proportion of staff supported by public funds, the less fiscal resources are available for infrastructure investments (Yin and Xu, 2011). We consider the influence of the proportion of government staff, measured by “the ratio public funding government staffs to total population”.

1 The population supported by public funds comprises the sum of the administrative staff, institutional staff, state workers, collective workers, retired personnel, the number of people on long-term leave at the end of the year in the fiscal budget appropriation and fiscal subsidy expenditure, and other staff supported by public funds (Yin Heng and Xu Yanchao, 2011). The data are obtained from the “Financial Statistics of Cities and Counties.”

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Table 2: Explanations and Statistical Descriptions of the Variables

Variable Definition Observations Mean Standard Deviation Minimum Maximum

Infrastructure

investments

Ratio of infrastructure investments to public expenditure 1260 6.700 5.992 0.002 61.615

Independent Variables: Officials Variables

Age Secretary’s age 1260 50.986 3.997 39 67

Gender Secretary’s gender 1260 0.031 0.173 0 1

Ethnic group Whether Secretary is from an ethnic minority 1260 0.060 0.240 0 1

Birth Place Whether the secretary holds the office of his/her hometown 1260 0.074 0.262 0 1

University985 Whether the secretary is a graduate of a 985 school 1260 0 .156 0.363 0 1

Postgraduate Whether the secretary has a postgraduate degree 1260 0.633 0.482 0 1

Background Whether the secretary has an education background in economics or management 1260 0.183 0.386 0 1

Tenure Secretary’s length in office 1260 2.763 1.677 1 11

Promoted Secretary is promoted within the city 1260 0.465 0.499 0 1

Business Whether the secretary has business management experience 1260 0.373 0.484 0 1

Upper-level Whether the secretary has worked in upper-level government 1260 0.666 0.472 0 1

Independent Variables: Economic Variables

GDP Per capita GDP (10,000 RMB, around $1200 during 2001-2006) 1260 2.017 1.436 0.124 15.210

Population Average population per square kilometer 1260 1152.15

2

1206.245 13.000 14052.410

Urbanization Non-agricultural population divided by total population (%) 1260 62.794 22.482 13.052 100

Openness Ratio of FDI to GDP (%) 1260 3.540 5.766 0 134.592

Decentralization Ratio of prefectural fiscal revenue to provincial fiscal revenue (%) 1260 8.200 6.336 0 .945 33.315

Government Staff Ratio of government staffs to total population (%) 1260 14.313 9.789 0.803 56.912

17

V. Empirical Results and Analysis

Using data of prefectural party secretaries and economic variables of 210 cities during 2001-2006, we estimate equation (6)—the infrastructure investment determination equation. Firstly, we estimate the equation under fixed effect and random effect, respectively. The empirical results are shown in Models (1) and (2) in Table 3. The F-tests and Breusch and Pagan Lagrange Multiplier (LM) tests on the two models indicate the existence of individual effects1. Moreover, based on the socio-economic spatial weights matrix, Moran’s I statistic is significant at 1% level, indicating that there is spatial correlation in the disturbance term of the empirical model. Therefore, we re-estimate the equation (15) under an individual effects SARAR framework, controlling the strategic interaction of infrastructure investments. Results are found in Model (3) and Model (4). Model (3) assumes that empirical equation is a SARAR-FE model with fixed effects while Model (3) assumes the equation is a SARAR-RE model with random effects.

We use the socio-economic spatial weights matrix to identify strategic interaction among governments. As shown in Models (3) and (4), the coefficients of spatial lag of infrastructure investments are statistically significant and positive, implying that, due to political incentive, local leaders imitates each other on making infrastructure investment decision. Because infrastructure investments can effectively boost economic growth in the short-term, political leaders generally support infrastructure investments to enhance their political achievements and hence the likelihood of promotion. Additionally, all spatial autoregressive coefficients on the disturbance terms are significant and the Sargan test does not significantly indicate that the SARAR model is appropriate. Furthermore, the spatial Hausman test shows no statistically significant correlation between individual effects and explanatory variables, which implies the correct specification of the SARAR-RE model. Therefore, Model (4) gives consistent and efficient estimators.

In terms of officials’ demographic characteristics variables, gender has a significantly positive effect on infrastructure investments. The ratio of infrastructure investment to fiscal expenditure in cities administered by female officials is 1.6% higher than that in cities administered by male ones. This finding is different from Ferreira and Gyourko (2014)’s conclusion that the gender of U.S. mayors does not matter in the composition of municipal fiscal expenditure. Ferreira and Gyourko (2014) argue that the political and economic environment in the U.S. provide little room for female mayors to change redistributive policies, and local politicians may be more responsive to the median voter. Contrarily, under the system of political centralization and economic decentralization in China, political leaders are empowered to change local fiscal expenditure in their favors (Guo, 2009). In an male-dominated elite political circle, a female official may need to achieve better economic

1 This paper did not use the Hausman test to identify the fixed- and random-effect models because the Hausman test must be performed under the assumption of iid disturbance term. Thy null hypothesis of Hausman test is individual effects are uncorrelated with independent variables and thus random effect estimation gives consistent estimators. Since Moran’s I statistics are significant and positive in this paper, indicating that the disturbance term does not satisfy the iid assumption, which makes the Hausman test unnecessary.

18

performance than their male peers to win the same promotion opportunities, which leads to higher expenditure in infrastructure.

Whether the secretary is serving his/her hometown city has a significant and negative effect on infrastructure investments, implying that, compared to the secretary from other cities, a local secretary has deeper connection to residences and invests more in public services. This result is consistent with Hayo and Neumeier (2012)’s findings that officials in Germantend to increase fiscal expenditure on areas that can benefit the social classes that they have social connections with. For example, officials from the bottom of the society are more inclined to increase fiscal expenditure on social security, education, health, and public safety. Additionally, neither the age nor the ethnic group of municipal party secretary has a significant effect on infrastructure investments.

Regarding officials’ education backgrounds, both graduating from a Project 985 university and education background in economics (or management) have a significant and negative effect on infrastructure spending. Specifically, the ratio of infrastructure investments in cities administered by secretaries with education backgrounds in economics or management is 0.61% lower than that in cities managed by officials with other education backgrounds. One explanation is officials with backgrounds in economics may be more familiar with macroeconomic principles and may attach greater importance to long-term growth and the sustainable development of a city and thus are less likely to rely on short-term economic stimulus such as infrastructure investments. This conclusion is in line with existing research claiming that trained economists formulate economic policy based on ration, rather than emotion (Frey and Meier,2003; Rubinstein, 2006) and politicians with economics background, tend to promote market-based reforms (Dreher et al., 2009). In addition, the ratio of infrastructure investments in cities administered by secretaries graduated from Project 985 universities is 0.92% lower than the ratio in other cities This implies that the “Ivy League” education in China may mitigate leaders’ political opportunism. Therefore, these leaders may pay closer attention to residents’ welfare and to public consumption services. This finding is similar to that of Besley et al. (2005) who uses household survey data from India and finds that education can significantly reduce the possibility of the abuse of political power. Whether a local official gains a graduate degree does not matter for infrastructure investments. This is partly because most officials gain the graduate degree through on-the-job continue education and thus the graduate degree does not truly reflect the education level of political leaders.

Whether the secretary has business management experience has a significant and positive effect on infrastructure investments. Specifically, the ratio of infrastructure investment in cities administered by officials who have worked in business is 0.46% higher than that in other prefecture-level cities. This may imply that officials with business experience have a better understanding of enterprises’ demand on infrastructure and thus tend to increase infrastructure investments to improve the business operating environment. The related literature also evident that a business management background affects officials’ policy preferences (Dreher et al. ,2009; Roubini and Sachs ,1989; Besley and Case ,1995; Johnson and Crain, 2004). The

19

coefficient of the tenure of secretary is statistically insignificant. One possibility is

although a local leader is supposed to hold the position for 5 years（ Kou and

Tsai，2014）, secretaries in prefecture-level cities usually serve less than five years.

This uncertainty in tenure, to some extent, affects an official’s policy consistency. We do not find the significant effects of “whether the secretary is promoted from within the city where he/she holds the post” and “whether the secretary has worked in upper-level government” .

The coefficient of real per capita GDP is significant and positive, indicating that more economically developed cities in China invest more in infrastructure. The effect of fiscal revenue decentralization also has significant and positive effect on infrastructure investments. The larger the ratios of local fiscal revenue to provincial-level fiscal revenue, the higher the fiscal strength a city has. Those cities have more fiscal resources to boost economic growth by investing in infrastructure. As expected, the proportion of staff maintained by public finance negatively affects infrastructure spending. Money spent to pay government staff’s salary reduce available fiscal support for infrastructure. Population density, urbanization level, and openness have no significant influence on infrastructure investments.

We identify strategic interaction of infrastructure driven by economic force by using the economic distance spatial weights matrix. Results are found in Table 4. The Hausman test still supports the SARAR-RE model as in Table 3. In Model (2) of Table 4, the coefficient of spillover effect is significant and positive, indicating that the infrastructure investments in neighboring areas can benefits infrastructure investments in a city. However, the magnitude of the coefficient in Table 4 is smaller than that in Table 3, which means that, although the economic force matters, strategic interaction in infrastructure investments is mainly driven by political force.

20

Table 3 Infrastructure investment DecisionsBased on the Socio-economic Spatial Weights Matrix

Model (1) Model (2) Model (3) Model (4)

Variables & Tests FE RE SARAR-FE SARAR-RE

Spatial lag term 0.829*** 0.749***

Off

icia

ls’ D

emog

raph

ic C

hara

cter

istic

s

Age 0.017 0.014 0.002 -0.003

Gender 1.236* 1.657** 0.991* 1.574***

Ethnic group 0.592 0.923 -0.025 0.280

Birth Place -0.927* -0.774 -0.788** -0.634*

Background -0.938*** -0.787** -0.723*** -0.614**

University985 -0.796** -0.995*** -0.748*** -0.919***

Postgraduate 0.268 0.062 -0.035 -0.164

Tenure 0.044 0.028 -0.021 -0.010

Promoted 0.153 0.028 -0.023 -0.043

Business 0.675** 0.687*** 0.463** 0.460**

Upper-level -0.158 -0.013 -0.088 -0.003

Econ

omic

Var

iabl

es

GDP 0.367** 0.293** 0.174 0.234**

Population 0.000 -0.000 -0.000 -0.000

Urbanization -0.017 -0.002 -0.008 -0.003

Openness 0.021 0.024 0.010 0.019

Decentralization -0.318*** 0.034 -0.035 0.047**

Government Staff -0.119*** -0.147*** -0.072** -0.069***

Test

s

-0.444*** -0.444***

F-test16.130***

Breusch and Pagan LM

test

1487.100***

Moran’s I test 25.556*** 25.556***

Hausman test 3.337 3.337

Anderson canon. corr. LM 256.343*** 103.581***

Sargan test 9.332 14.443

Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.

21

Table 4 Infrastructure investment Decisions

Based on the Economic Distance Spatial Weights Matrix

Model (3) Model (4)

Variables & Tests SARAR-FE SARAR-RE

Spatial lag term 0.514*** 0.343***

Off

icia

ls’ D

emog

raph

ic C

hara

cter

istic

sAge 0.035 0.024

Gender 1.293** 1.717**

Ethnic group -0.140 0.737

Birth Place -0.783* -0.685

Background -0.660** -0.547*

University985 -0.672** -0.919***

Postgraduate 0.122 -0.053

Tenure -0.018 -0.002

Promoted 0.132 0.090

Business 0.521** 0.602**

Upper-level -0.075 0.002

Econ

omic

Var

iabl

es

GDP 0.225 0.219*

Population 0.000 -0.000

Urbanization -0.016 -0.001

Openness 0.008 0.017

Decentralization -0.177** 0.052*

Government Staff -0.088** -0.120***

Test

s

-0.165** -0.165**

Moran’s I test 10.197*** 10.197***

Hausman test 9.579 9.579

Anderson canon. corr.

LM

46.810*** 99.311***

Sargan test 11.263 19.224

Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.

VI. Conclusion

Previous literature on infrastructure determination generally ignores the role that political leaders play. Additionally, empirical studies in China overlook the uniqueness of China’s infrastructure investment determination. On the one hand, the special institute arrangement in China empowers political leaders to have giant influence on decision and implementation of infrastructure investments. On the other hand, the objectives of political leaders are not only the improvement of welfare of residence, but also the ranking of economic growth within a province. The latter object triggers strategic interaction among leaders of jurisdictions where leader in one place makes decisions on infrastructure investments caring about the decisions of

22

leaders of neighboring jurisdictions. To address those issues, we construct a multi-objectives model of political

leaders, considering both the long-term welfare of local residents and the ranking of economic growth within a province . Furthermore, in this multi-objectives model, the weight placed on each objective relies on an official’s demographic variables. Guided by the theoretical model, we test the predictions in an individual effects SARAR model. Using both a socio-economic spatial weights matrix and an economic distance spatial weights matrix, we also identify the potential influence of political and economic forces on infrastructure investments. A prefectural party secretary is considered to be the primary decision maker of a local government. We estimate the empirical model using data containing both demographic variables of political leaders and economic variables of 210 cities during 2001 to 2006.

We find that officials’ education background, personal characteristics, and work experience all have a significant effect on public expenditure. Political leaders with factory management experience and female officials are more likely to increase infrastructure investments, whereas officials graduated from Project 985 universities or with education backgrounds in economics tend to decrease infrastructure investments. The result also show significant influence of fiscal decentralization and real per capita GDP. The proportion of public funded government staff has negative relationship with infrastructure investments. The results provide little evidence of the influence of population density, the levels of urbanization and openness. Strategic interaction among officials is mainly driven by political forces. One potential corresponding consequence is disorder competition and overinvestment in infrastructure.

23

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27

AppendixI. Theoretical Model -- A Multi-Objectives Model of the Decision Making of Political leaders

Our model considers both the public’ long-term welfare and political leaders’ own interests. Previous research on public expenditure generally assumes a benevolent government that legislates fiscal policy to maximize residents’ long-term welfare (Barro, 1990; Turnovsky, 2000; Park and Philippopoulos, 2004; Chen, 2006; Ghosh and Gregoriou, 2008). This framework ignores government officials’ own interests, such as career concerns. As pointed out by Li and Zhou (2005), the promotion likelihood of a local official is closely related to the economic performance of the region where the official is in charge. To enhance the probability of promotion, government officials might favor public policies that generate economic outputs in short-term such as infrastructure investments.

We assume that a representative family has infinite lifecycle and possesses perfect foresight. Following Barro (1990), Turnovsky (2000, 2004), and Park and Philippopoulos (2004), we define the long-term welfare of a representative agent as the intertemporal isoelastic utility function:

u (c , g2)=∫0

∞ (c⋅g2κ)

1−θ

1−θ

dt , κ >0 , θ>0 , (1−θ ) (1+κ )<1 (A1)

where c represents per capita consumption. 2g denotes per capita public services

(such as education, medical care and social security) provided by government.

index the weight of public services on the utility. θ denotes the impact of consumption

and public service on individual’ utility and 1/θ measures the intertemporal elasticity of substitution. The representative agent maximizes the lifetime utility in equation (A1) subject to the capital accumulation equation:

dk rk w cdt

(A2)

where k and ω is capital per worker and wage, respectively. Transversality condition in equation (A3) provides sufficient condition for infinite horizon optimal control.

0lim exp 0

t

tk t r s ds

∫ (A3)

One of the characteristics of public services are congestion effects ( Barro and Sala-i-Martin, 1992). To account for this, we assumes that the public services available to an individual are equal to the ratio of government’s expenditure on

28

economic construction in the region, 1G , to the aggregate of private capital, K .

Moreover, given the strategic interaction of public services of neighboring areas,

denoted by G1 n/ Kn , we construct the production function of a jurisdiction and it’s

neighboring city as

11 n

n

GGy AkK K

,

1 1nn n

n

G Gy AkK K

(A4)

where y and yn denotes per capita output of a city and the neighboring city,

respectively. These parameters satisfy 1a , 1 0a , and 0 1 1.

Following Barro and Sala-i-Martin (1992) we assume a constant population L . We

thus have 1 1G Lg and 1 1nn nL gG ,where 1g and 1ng are per capita public

expenditure of a city and that of neighboring cities, respectively. Additionally, this model prohibits cross-regional flow of capital (Figui`eres et al., 2013).

Government budget is constructed according to Devarajan et al. (1996). All tax

revenues are spent on economic development 1G , and public services 2G :

1 2Y G G (A5)

where Y Ly represents total output and indexes a flat-rate income tax. As in

Devarajan et al. (1996) and Park and Philippopoulos (2004), we assume:

1G Y , 2 1G Y (A6)

where 0 1 denotes the ratio of infrastructure investments to government public

expenditure and 1 represents the ratio of government-provided consumption good

expenditure to government public expenditure. The production function can be

1 If the neighboring area is a central city and has a greater economic spillover effects on the subject area, then

a situation in which α≤β may exist. However, the analysis of the strategic interaction of fiscal policies across local governments in this paper excludes the possibility that the subject area is a suburb and the neighboring area is a central city because the analysis focuses on economic performance that is driven by competition between officials in the subject area and officials in the neighboring area. This type of competition only exists among areas within a province that have similar economic scales. Competition for promotions based on economic performance essentially does not exist between officials from cities with large economies and officials from small towns that surround these cities.

29

derived from Equations (A4) and (A6) as:

1, , n ny f k A k (A7)

Where

2 2

2 2

1 2 211 1 2 2 1

,

2 2

2 2

21 2 2

, and

2 2

2 2

2 111 1 2 2 1

a

.

This is the endogenous growth AK model. Since the central government decides the tax rate , the productivity of private capital is thus determined by the fiscal expenditure of both the jurisdiction and its neighboring areas. Profit maximization and zero profit conditions imply that wage equals to the after-tax marginal product of labor and that the rental of capital equals to the after-tax marginal product of capital. Therefore, the rent of capital is:

11 nr A (A8)

with r the interest rate and the depreciation rate.

On the balanced growth path, the growth rates of per capita consumption, per capita capital, and per capita output equal to the same constant. The respective economic growth rates of the jurisdiction and its neighboring areas are:

11/ 1 nA

11/ 1n nA (A9)

As demonstrated in Equation (A9), given the total government expenditure, the larger the ratio of infrastructure investments ϕ is, the higher the economic growth rate is. Moreover, both per capita consumption denoted as c and per capita welfare

expenditure indexed by 2g grow at a constant rate . The utility of representative

agent has a closed form. Denoting 0 tc t c e and 2 0 tg t g e , Equation (A1)

becomes:

1

20 01 11 1

c gU

(A10)

where (0)c and 2 (0)g represents initial per capita consumption and initial per capita

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public consumption expenditure, respectively. If (0)k represents initial per capita

capital, then the production function of Equation (A7) implies that:

10 1 0nc A k

12 0 1 0ng A k

(A11)

Existing studies (such as Barro, 1990; Turnovsky ,2000; Park and Philippopoulos ,2004), assume that a benevolent local government decides fiscal expenditure policy to maximize the long-term welfare of residents denoted by equation (10). Our model also considers the interest of political leaders because they may legislate fiscal policies in favor of their promotion. This consideration is motivated by two facts: On the one hand, at the end of each year, China’s Central Economic Work Conference sets a national economic growth target for the following year. To fulfill the targeted national growth rate, prefectures also set a growth goal and it becomes one of the most important tasks for local officers. We can denote the growth goal as the “absolute economic growth rate.” On the other hand, the ranking of the economic growth rate of a city within a province--the “relative economic growth rate” --also matters for political leaders, because this relative economic growth rate is one of the most important criteria for upper-level government to evaluate the capacities of political leaders and thus has direct impact on likelihood of promotion.

Therefore, political leaders, considering the welfare of residents and own interests, decide the fiscal expenditure on infrastructure and public consumption goods to maximize the utility of the form:

max max nV U

(A12)

where 0 is the weight of “the absolute economic growth rate” on officials’

utility. Similarly, 0 denotes the weight of “the relative economic growth rate”.

Political leaders function as the chief executive officers (CEOs) of administrative regions. Fiscal policies are thus influenced not only by economic environment and resources endorsement, but also by the CEOs’ personal characteristics, education background, work experience and so forth. Furthermore, the potential impacts of demographic variables are reflected in the weights given to the absolute and relative economic growth rate. Specifically,

χ= χ (Se c , Eco ) , η=η (Sec , Eco ) (A13)

where Sec and Eco denote officials’ characteristics and economic context of the

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administrative region, respectively. Substituting Equations (A9), (A10), and (A11) into Equation (A12), the policy objectives becomes

1 11 1 1 1

1

1 1

1 1 / / 101 1 1 /

1 / 1 /

/ / 1

n n

n

n n

A AkV

A

A A

(A14)

Since direct solution of is inaccessible, we turn to numerical simulation to

evaluate how do the weight of absolute economic growth rate and the weight of relative economic growth rate affect fiscal expenditure, followed by discussion of the strategic interaction of fiscal expenditure among prefectures.

II. Numerical Simulation AnalysisFollowing Turnovsky (2000, 2004) and Eicher and Turnovsky (2000),we set rate

of time preference 0.04 . Based on Turnovsky (2000), we set 3 . Based on

Turnovsky (2000, 2004), the effect of government-provided consumption good on

residents’ utility K is 0.3. 0.3 . During 1990-2006, the average ratio of

government fiscal expenditure to GDP is 0.1513 and the average ratio of

infrastructure investments to government fiscal expenditure is 0.365. Therefore, we

set 0.1513 and 0.365n . Based on Turnovsky (2000) and Chen and Lee

(2007), we assign 0.08v . Because of 1a and 1 0a , we have

1 0.5a . Taking the average, we set 0.75a . We set 0.05 as the capital

depreciation rate. This paper sets the private capital depreciation rate as 0.05 in

according to Wang (2000) and Zhang and Song (2013) From equation (A9) we have

1/ 11/ 1 / 1 nA

. Plugging 10.2%, the average economic

growth rate of China’s during 1990-2006, into the formula, we have , which

is slightly smaller than the value (A=0.6) used by Chatterjee and Turnovsky (2007).

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Finally, following Ghosh and Nolan (2007), initial per capita capital k(0) is set to 20.

III. Empirical EstimationBased on Kapoor et al. (2007), the disturbance term in model (6) has the

following form:( )Tu I W u

(A15)

where is the spatial autoregressive parameter of the disturbance term. Besides,, to

account for intertemporal correlation in the disturbance term, is set to be:( )T ni I v

(A16)

where is the 1N vector and indexes a city’s individual effect i that is

independent and identically distribution with zero mean and variance 2 .v is the

1NT vector formed by itv ; itv changes with cities and times; the obeying mean

value is 0; and the variance of the IID process is 2v . i and itv are mutually

independent and have a finite fourth moment. We take the following four steps to estimate Equation (6).

First, we solve the endogeneity issue of the spatial lag. Rearranging Equation (6)

to 1 1[ ( ) ] [ ( ) ]T N T NY I I W Xb I I W u , it can be seen from this equation

that ordinary least squares (OLS) estimators are inconsistent due to the correlation of

dependent variable Y with error component u . We turn to instrumental variables to

obtain consistent estimators for parameter b and residual u . The valid instrumental

variables are WX .

The second step is to estimate the spatial autoregressive parameter . Based on the generalized moments method (GMM) proposed by Kapoor et al. (2007), let

T T NP i i T I , T T T NQ I i i T I , TI W , and 2 2 21 T , we

obtain six moment conditions:

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2

2

1

1

1 0

E Q N T

E Q N T tr W W N

E Q N T

21

21

0

E P N

E P N tr W W N

E P N

(A17)

Given the estimator of error component u from the first step, applying nonlinear least squares (NLS) we construct the sample moments conditions to consistently

estimate , 2 , and

2 .

The third step address the spatial correlation of error components The consistent

estimators for , 2 , and

2 in the second step are , ˆv , and ˆ , respectively. We

implement Cochrane-Orcutt procedures on all variables (denoted as Z ) in Equation

(6) to get* [ ( )]T NZ I I W Z .

Fourth, we implement IV estimation on the transformed data in a fixed or

random effects panel data framework, respectively. Let ** *

, .ˆ

it re it tZ Z Z and

2 2 2ˆ ˆ ˆ ˆ1 T . Using post-transformation data

**,it reZ to conduct IV

estimation, we get the random effect estimators for Equation (6). Denote** *

, .it fe it tZ Z Z . We use **

,it feZ to conduct IV estimation on Equation (6) to get the fixed effect estimators. After implementing the non-identifiability and overfitting tests on SARAR fixed-effects (SARAR-FE) model and SARAR-FE1 model, we apply spatial Hausman test (Mutl and Pfaffermayr, 2011) to examine whether the empirical model is fixed or random effect.

1 Here, SARAR-FE refers to a SARAR model with fixed effects and SARAR-RE refers to a SARAR model with random effects. This abbreviation works through the paper.

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