Do Political Leader’s Demographic Characteristics Matter for Infrastructure Investments
Guangnan ZhangSunYat-sen University
Guangzhou, China 51000
/ Ran Song[†]
SunYat-sen University &
Harvard Univerisity
Cambridge, MA 02138
JiehongQiu
Washington State University
Pullman, WA 99164
/ Danglun Luo
SunYat-sen University
Guangzhou, China 51000
Abstract: Guided by a multi-objectives theoretical model, this paper empirically investigates the influences of politicalleaders’ 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
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I. Introduction
Infrastructure investments affect not only growth and productivity of economies but also production costs and efficiency of corporates[1]. 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 infrastructure[2]. 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), andHayo 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. Thepredictions 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 China[3].Each level of administration has two political leaders—Secretary of Communist Party and governor of the government. Secretary is a 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 promoted[4] 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 investmentsare 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-level[5]. 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 investmentsamong 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 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. 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:
/ (1)where represents per capita consumption. 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.denotes the impact of consumption and public service on individual’ utility and 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 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:
/ (2)Whereis the weight of ‘the absolute economic growth rate’ on officials’ utility. Similarly, denotes the weight of ‘the relative economic growth rate’. A local official functions likea‘chief executive officers (CEOs)’ of the administrative region. is the economic growth rate of the jurisdiction in the balanced growth path and 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:
/ (3)i.e., they are the function of infrastructure investments ratio and ratio of neighboring areas .
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 characteristicsof a local official are reflected in the weights given to the absolute and relative economic growth rate. Specifically,
/ (4)where and denote officials’ demographic characteristics and economic context of the administrative region, respectively. On the balanced growth path, political leaders’ objective value can be represented as a function ofand.
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 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’, andinfrastructure 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 and , 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’,. The simulation results are presented in Figure 3. There is a clear positive relationship between and , which leads to our second insight:
Insight 2: Infrastructure investmentsare spatially interacted among prefectures.
Table 1: Model Norm Parameters
Utility Function /Production Function /
Fiscal Policy /
Figure 1: The Relation between theAbsoluteEconomic Growth Rate ()and Fiscal Expenditure Structure()
Figure 2: The Relation between theRelativeEconomic Growth Rate () and Fiscal Expenditure Structure()
Figure 3: The Relation between Fiscal Expenditure Structure() of Local Area and Fiscal Expenditure Structure of Neighboring Area ()
III. Empirical Method
1. Specification and Estimation
The illustrative theoretical model predicts the influences of both political leaders and strategic interaction oninfrastructure 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 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:
/ (5)where is the ratio of infrastructure investment to budgeted fiscal expenditure in cities at time t, analogous notation for. is the element in spatial weighted matrix defined in the following section. Therefore, captures the influence ofinfrastructure investments of city on city. 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. is a vector of characteristics variables of a local official, is a vector of economic variables, and is the disturbance term.
Denoting Equation (5) in a matrix form, we have:
/ (6)where is the column vector, is the matrixcomprised by , , and , and is the column vector of