Abstract
Purpose
The aim of this study is to investigate the impact of local political uncertainty on the asymmetric cost behavior (i.e. cost stickiness) for listed firms in China.
Design/methodology/approach
In this study, the authors manually collect the turnover data of prefecture-city officials as a measure of exogenous fluctuations in political uncertainty and obtain firm-level financial information from the China Stock Market Accounting Research (CSMAR) database. To perform the analysis, the authors augment the traditional cost stickiness model by including the interaction terms of the prefecture-city official turnover, and firm-level and prefecture-city level control variables.
Findings
The authors find that political turnover leads to a higher degree of cost stickiness, implying that firms retain slack resources when political uncertainty is high. Moreover, the effect of political turnover on cost stickiness is more pronounced for firms residing in regions with weaker institutional environments, and firms that are privately owned and with smaller size. The authors further provide evidence that policy uncertainty and the threat of losing political connection are two underlying channels. Overall, this study documents that the local political process is an important channel that influences corporate operational decisions.
Originality/value
This study provides the first piece of evidence on the relation between political uncertainty and cost stickiness at the local government level. Moreover, the authors propose and demonstrate two underlying channels through which political uncertainty affects firms' asymmetric cost behavior.
Keywords
Citation
Pan, Z., Zhang, G. and Zhang, H. (2022), "Political uncertainty and cost stickiness: evidence from prefecture-city official turnover in China", China Accounting and Finance Review, Vol. 24 No. 2, pp. 142-171. https://doi.org/10.1108/CAFR-02-2022-0007
Publisher
:Emerald Publishing Limited
Copyright © 2022, Zheyao Pan, Guangli Zhang and Huixuan Zhang
License
Published in China Accounting and Finance Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
1. Introduction
In recent years, researchers have amply established that politics influence corporate behaviors and economic outcomes, both in developed (e.g. Julio & Yook, 2012; Gulen & Ion, 2016) and developing countries (e.g. An, Chen, Luo, & Zhang, 2016; Xu, Chen, Xu, & Chan, 2016). A significant amount of this research highlights the role of uncertainty in this influence. While extensive prior studies focus on the impact of political uncertainty on corporate investment and financing activities, the evidence on how political uncertainty affects corporate operational decisions remains scarce. This is an important question because politics has direct effects on corporate operations through the cost of inputs (work and resources) and customer behaviors and can also threaten business continuity. In this study, we contribute to the literature by examining how local political uncertainty affects corporate operational decisions evident in cost stickiness.
The concept of cost stickiness is first introduced by Anderson, Banker, and Janakiraman (2003), referring to the phenomenon that costs response to an increase in sales is greater than costs response to an equivalent decrease in sales. Extant research has identified different drivers of cost stickiness, including deliberate managerial decisions (e.g. Anderson et al., 2003; Chen, Lu, & Sougiannis, 2012) and firm-level characteristics such as asset intensity and employee intensity (e.g. Balakrishnan, Petersen, & Soderstrom, 2004) [1]. However, in focusing on the cross-sectional determinants of cost stickiness, existing research generally ignores the inter-temporal changes in cost stickiness. Prior studies argue that managers respond to uncertainty by delaying downward adjustment of committed resources until they are more certain about the permanence of a decline in demand (e.g. Anderson et al., 2003). This suggests that cost stickiness appears when uncertainty rises and reverses when uncertainty is resolved. Nevertheless, this inter-temporal change in cost stickiness is rarely discussed because prior research focuses on the sources of uncertainty rather than its timing. Typically, political uncertainty arises when a political event occurs and later resolves. Given that cost stickiness is an outcome of inter-temporal shifts in uncertainty, the net effect of political uncertainty on the sticky cost behavior provides a good opportunity to examine the inter-temporal pattern in cost stickiness.
In this study, we examine the impact of political uncertainty on cost stickiness in China. Specifically, we use prefecture-city official turnover in China as a measure of local political uncertainty. In China, political uncertainty is unequivocally associated with the election and turnover of government officials at local levels, for example, prefecture-city party secretaries and mayors. Local governments have significant autonomy in their regions [2] and thus their turnover has wide-ranging implications not only for industry regulation, economic policy and tax distortion but also for the overall economic environment where firms operate (Chen, Chen, Wang, & Zheng, 2018; Bai, Hsieh, & Song, 2020). Consequently, firms often face a significant amount of uncertainty related to the timing and content of local government policy changes, as well as the potential impact that these policies will have on firms' operations. Because the option value of waiting increases when uncertainty is high (Bloom, Bond, & Van Reenen, 2007), firms have strong incentives to delay the deliberate resource commitment decisions in turnover years, which in turn increase the degree of cost stickiness. Therefore, the effect of political uncertainty on cost stickiness is an important empirical question associated with firms' operation and profitability. Moreover, we expect that political uncertainty stemming from local government official turnover strengthens the asymmetric cost behavior.
China provides an ideal setting to explore the relation between political uncertainty and cost stickiness for several reasons. First, due to China's unique socioeconomic system and political bureaucracy, politics and politicians play an explicitly predominant role. The government directly controls resources and allocates wealth, which in turn creates uncertainties for firms (Haveman, Jia, Shi, & Wang, 2017). Thus, managing political uncertainty is a primary task for Chinese firms (Han, He, Pan, & Shi, 2018), and political concerns likely exert a paramount effect on business decisions, such as the decision on resource adjustments in our context. Second, prefecture-city official turnover in China creates a unique experimental setting. The upper-level government exclusively determines appointments of prefecture-city officials in secret deliberations. As such, Chinese firms are subject to frequent but unanticipated changes in their political leaders, with no advance knowledge of the likely timing, who is likely to gain the office, or what their approach to business is likely to be [3]. Moreover, local official turnover also indicates the threat of losing firms' prior political connections (Fang, Li, Xu, & Yan, 2018; Jiang, Jia, Bai, & Bruton, 2021). Hence, prefectural official turnovers provide a better randomized sample of external political shocks than the fixed calendar election cycles used in prior studies (e.g. Białkowski, Gottschalk, & Wisniewski, 2008; Boutchkova, Doshi, Durnev, & Molchanov, 2012; Julio & Yook, 2012, 2016; Lee, Pittman, & Saffar, 2020). Third, regional divergences of institutional environment and firm heterogeneities such as ownership structure are more typical in the Chinese market than in the developed countries that form the backdrop of most existing studies (Allen, Qian, & Qian, 2019). Analyzing how these characteristics interact with local political turnover in a market that particularly values business-government relations can deepen our understanding of the possible asymmetry of the political uncertainty effect.
Our empirical analysis offers several findings. First, we show that political uncertainty stemming from prefecture-city official turnover increases firms' asymmetric cost behavior, controlling for firm-level characteristics and local economic conditions. A 1% (corresponding to 50.12 million RMB) decrease in sales revenue leads to a 1.2 million RMB increase in sticky operating costs in turnover years relative to non-turnover years, a large and statistically significant difference. The results are consistent with the findings in Lee et al. (2020) and suggest a positive relation between political uncertainty and cost stickiness. To address the potential endogeneity concern, we conduct an instrumental variable analysis, a placebo test, and matched sample approach, and reach the same conclusion. In addition, our findings are robust to alternative specifications of local official turnover and cost categories and controlling the anti-corruption campaign and national election effect.
After establishing a positive relation between local political uncertainty and the level of cost stickiness, we turn to focus on the cross-sectional analyses. Markets with weak institutions and marketization create opportunities for political factors to influence business activities (Zhang, Han, Pan, & Huang, 2015; Gu, Tang, & Wu, 2020). If political turnover takes place in a region characterized by a weak institutional environment, the political uncertainty arising from changes in local government officials would be amplified, reinforcing firms' asymmetric cost behavior. Consistent with this prediction, we show that the impact of political uncertainty on cost stickiness is stronger when firms are in regions with a weaker institutional environment.
Besides the local institutional environment, we also focus on the impact of ownership identify and firm size on the relation between political uncertainty and cost stickiness. We find that the impact of political uncertainty on cost stickiness is more pronounced for privately owned enterprises (POEs) than for state-owned enterprises (SOEs). This result is consistent with findings by Feng & Johansson, 2017 on innovation and Ni (2019) on cash holdings. SOEs receive various economic benefits from both central and local governments. When the turnover occurs, the state ownership makes the firm more likely to continue receiving support from the new leader when she takes up her new position immediately, leaving such firms less vulnerable to political uncertainty. Due to their larger resource bases and the advantage of economies of scale, larger firms are better equipped than small ones to handle the political uncertainty in a transitional economy like China (Haveman et al., 2017). Consistent with the line of reasoning, we show that the impact of local political turnover concentrates on small firms.
There are two potential channels through which local official turnover can influence cost stickiness: policy uncertainty and the threat of losing political connection. Our above cross-sectional analyses are consistent with the implications from both channels but are hard to differentiate between them. We further test the validity of each channel conditional on the type of official turnover and the type of political connection. First, the degree of economic policy uncertainty driven by local official turnover is arguably dependent on the turnover type. Specifically, economic policy uncertainty is likely to be higher when an official is appointed externally and when the turnover is unexpected. Consistent with this line of reasoning, we show that the impact of local political turnover is more pronounced for external appointments and unexpected turnovers. Therefore, our finding supports the economic policy uncertainty mechanism. Second, following Zhang, Marquis, and Qiao (2016), we identify two types of political connections for POEs: the ascribed bureaucratic connections that represent political background that the individual executive obtained before entering the business, and the other is achieved political connections that result from political appointments to state organs after executives became successful business leaders. Because the latter is easier to lose its current political connection than the former (Zhang et al., 2016; Jiang et al., 2021), the political connection channel predicts that the impact of that firms with executives who have achieved bureaucratic connections are more affected by the political uncertainty arising from changes in local government officials. We confirm this prediction in the empirical analysis. Overall, our findings suggest that these two channels are both valid and jointly shape the impact of local political turnover.
Our study contributes to the literature in the following ways. First, we add to the literature on the economic implications of political uncertainty. Recent studies in corporate finance have documented that political uncertainty has a negative impact on corporate investment and financing activities, such as reduced investments, increased risk premiums and higher cost of capital (e.g. Julio & Yook, 2012; Gulen & Ion, 2016; Çolak, Durnev, & Qian, 2017; Gao, Murphy, & Qi, 2019), but rarely discussed how it affects corporate operational decisions. We extend this strand of research by investigating the relation between political uncertainty and cost stickiness.
Second, our study relates to the work by Lee et al. (2020), which investigates the impact of political uncertainty arising from national elections on cost stickiness in 55 countries. Nonetheless, the authors exclude China in their sample because there is no national election uncertainty in a one-party system. However, local political uncertainty is prevalent in China, where local governments have enormous political power and administrative capacity to influence corporate behaviors (Bai et al., 2020). To fill this gap, we use prefecture-city official turnover as a measure of political uncertainty and provide the first piece of evidence on the relation between political uncertainty and cost stickiness at the local government level. Generally, China has rich prefecture-level city leadership turnover data for analysis. To limit the power controlled by local officials and empower career politicians, political turnover is undergone frequently. By using this large sample data, our study can provide more comprehensive and reliable inferences. Lee et al. (2020) focus on the overall impact of political uncertainty, whereas we take a further step to explore different responses between SOEs and POEs, and more importantly, we propose and identify two underlying channels through which political uncertainty affects firms' asymmetric cost behavior.
Third, this study examines the impact of the regional institutional environment, thereby extending existing cross-country studies to encompass a more microscopic perspective. Calleja, Steliaros, and Thomas (2006), Banker, Byzalov, and Chen (2013) and Lee et al. (2020) argue that differences in legal origins across countries have significant effects on corporate behavior. Based on this logic, we investigate differences in regional institutional environments in China and their impact on the relation between political uncertainty and cost stickiness. In contrast to the finding by Lee et al. (2020) that firms in countries with weak institutions are less sensitive to political uncertainty, we document the opposite at the local government level in China. We, therefore, contribute to the literature by revealing that the local institutional environment plays an important and unique role in the effect of local official turnover on firms' asymmetric cost behavior.
The remainder of the paper is structured as follows. Section 2 presents the literature review and the development of hypotheses. Section 3 discusses data and methodology. Section 4 reports the empirical results. We conclude our paper in Section 5.
2. Literature review and hypothesis development
2.1 The determinants of cost stickiness
The traditional cost behavior model distinguishes costs as fixed and variable and describes a mechanistic relation between economic activity and costs, stating that fixed costs are independent of the level of activity and variable costs are linearly and proportionally related with changes in the level of activity. However, recent studies investigate the complexity between costs and activities and document an asymmetric cost behavior. Noreen and Soderstrom (1994) are the first to examine whether costs change proportionally with changes in the level of activity by using cross-sectional data from hospitals in Washington state. They argue that overhead costs are not proportional to activity levels, although only a few of the differences are statistically significant. Inspired by Noreen and Soderstrom (1994), Anderson et al. (2003) conduct a more comprehensive investigation by examining the relation between selling, general and administrative (SG&A) costs and net sales revenue on US industrial firms from 1979 to 1998. They provide the first piece of strong evidence of the asymmetric cost behavior and characterize this cost behavior as “sticky.” Moreover, they explore the drivers of cost stickiness and argue that sticky costs arise because managers deliberately adjust the resources committed to activities.
Following Anderson et al.'s (2003) seminal work, many studies focus on investigating the determinants of cost stickiness. Among them, one strand of literature aims at understanding how managerial deliberate decisions influence asymmetric cost behavior. For instance, Subramaniam and Weidenmier (2003) explore sticky cost behavior with respect to costs of goods sold (COGS) as well as SG&A and confirm that managers' asymmetric response to demand change drives cost stickiness in both cost categories. Banker, Ciftci, and Mashruwala (2008) shed new insight on the impact of deliberate decisions by examining the relation between managers' future belief of demand and cost stickiness. While Anderson et al. (2003) assume that managerial intervention only affects costs when sales decrease, Banker et al. (2008) further expand this assumption and show that managerial intervention affects costs in both directions, that is, when sales increase as well as when sales decrease.
In another dimension, Chen et al. (2012) focus on the managers' motivation of deliberate resource commitment decisions. They test how empire-building managers affect cost stickiness in response to exogenous demand shocks for firms in the S&P 500 index and show that empire-building managers tend to increase SG&A costs when sales rise but are reluctant to decrease these costs when sales fall, resulting in cost stickiness even in the absence of adjustment costs. Nevertheless, Kama and Weiss (2013) find contrary results and argue that managers' self-interest eliminates the sticky cost behavior. Such tendency is explained by the fact that managers are likely to cut slack resources when facing the pressure to meet earnings targets, even though they believe that the decline of sales is temporary.
Furthermore, prior studies also identify other factors contributing to the asymmetric cost behavior, both at the firm and country levels. For example, Anderson et al. (2003) and Balakrishnan et al. (2004) document that asset intensity, employee intensity and macroeconomic (GDP) growth are positively associated with the degree of cost stickiness. In addition, Venieris, Naoum, and Vlismas (2015) explore the potential relation between intangible investments and cost stickiness. They observe that SG&A costs in firms associated with a high proportion of intangible investments exhibit a sticky cost behavior while SG&A costs in firms associated with a low proportion of intangible investments exhibit an anti-sticky cost behavior.
Calleja et al. (2006) examine how corporate governance and legal system affect the stickiness of operating costs for listed firms in the UK, US, France and Germany and show that the French and Germany firms have stickier operating costs than the UK and US firms, suggesting that rigorous external scrutiny and managerial oversight weaken cost stickiness. Banker et al. (2013) investigate the relation between the degree of cost stickiness and employment protection legislation strictness in 19 OECD countries and identify a positive relation. Their results indicate that employment protection legislations restrict firms' ability to cut slack labor resources when demand declines, which in turn increases the cost stickiness. Lee et al. (2020) provide the first piece of evidence that political uncertainty is an important determinant of cost stickiness, which is the closest work to our study. They find that political uncertainty created by national elections increases cost stickiness in 55 countries. However, they exclude China in their sample because it has a one-party system and therefore there is no national election uncertainty.
2.2 The economic impact of political uncertainty
A growing body of research investigates the importance of political uncertainty to economic activities and financial outcomes. Białkowski et al. (2008) and Boutchkova et al. (2012) examine stock market volatility around national elections and find that volatility is significantly higher in election years, suggesting that political uncertainty has adverse effects. Likewise, Pastor and Veronesi (2012) show that announcements of policy changes trigger a decline in stock prices and an increase in both volatility and risk premia. In addition, Kim, Pantzalis, and Park (2012) document that political geography has a pervasive effect on the cross-section of stock returns.
Besides its influence on the financial markets, political uncertainty also shapes corporate decisions. Julio and Yook (2012) examine the impact of national elections on the investment behavior of firms in 48 countries and find that firms reduce investment expenditures by an average of 4.8% during the election years relative to non-election years. An et al. (2016) reinforce Julio and Yook's (2012) argument by exploring the effect of political uncertainty on corporate investment at the local government level in China. They document that political turnover leads firms to significantly decrease investment expenditures. Instead of focusing on the overall corporate investment, Julio and Yook (2016) study the effect on a specific type of investment, that is, foreign direct investment (FDI). They show that FDI flows from US companies to foreign affiliates drop significantly in election years, that is the FDI rate declines by 14 to 21% on average. In addition, Feng & Johansson, 2017 explore the relation between political uncertainty and investments in activities related to innovation and document a negative relation between them. Unlike most studies, which use national or local elections as a measure of political uncertainty, Gulen and Ion (2016) employ the Economic Policy Uncertainty (EPU) index, which allows researchers to measure the actual level of political uncertainty at every point in time, to estimate the effect of policy uncertainty on corporate investment. Gulen and Ion (2016) find a strong negative relation between the aggregate level of uncertainty associated with future policy and regulatory outcomes and both firm and industry-level capital investments.
Xu et al. (2016) investigate the association between political uncertainty stemming from prefecture-city leadership changes and cash holdings for firms in China and show that firms hold less cash in turnover years relative to non-turnover years. This result is consistent with the “grabbing hand” hypothesis, that is, that politicians intend to extract resources from firms. When political turnover arises, a firm perceives the political uncertainty as just another opportunity for the newly appointed official to extract its assets, and hence firms respond by significantly reducing their cash holdings to minimize such risk. Ni (2019) extends Xu et al.'s (2016) study by documenting that firms significantly decrease cash holdings during the turnover periods and such effect concentrates in POEs.
According to recent studies, we can see that political uncertainty has an adverse effect on economic outcomes and financial activities. However, how political uncertainty affects corporate operational decisions is still not well understood. Our study extends the strand of research investigating the economic implications of political uncertainty to include cost stickiness and provides a clear, cogent explanation of the importance of local political uncertainty on corporate operational decisions.
2.3 Political uncertainty and cost stickiness: hypothesis development
China's political system is a bureaucratic and hierarchical structure, consisting of five levels: central government, provinces, prefecture-level cities, counties and townships (Yao & Zhang, 2015). The Chinese Communist Party (CCP) is the head of this “multidivisional system,” and ultimately determines policies, operations and appointments of central and local government officials (Xu et al., 2016). Under this hierarchical system, the central government determines national economic plans and policies, and the local governments carry out those plans and policies. However, within a decentralized economic system in China, local government officials have a high degree of autonomy in their jurisdiction. For example, they decide land usage, set up business rules, codes and regulations, and provide investment opportunities and financial credits for local firms (Bai et al., 2020). These policies strongly affect firms' daily operations. Thus, the profound political uncertainty touches them deeply and complicates their operational decisions.
Political uncertainty stemming from local government official turnover affects corporate operations in two ways. First, prefecture-city official turnover has a direct impact on managers' resource adjustments decisions. These decisions naturally shape cost behavior, including cost stickiness. In China's governmental hierarchy, local officials' superiors evaluate them based on conformance to tasks and targets, and local economic growth largely dictates local officials' opportunities for career advancement (Li & Zhou, 2005). As such, they normally have strong incentives to set their own goals and strategies to boost local economic growth. When local officials' superiors pursue their political and social objectives, they routinely exercise power to favour potential socio-economic projects that will benefit their careers. Since not all officials agree about which projects will help them realize their career goals, policies and projects may well cease following a turnover. Local firms may therefore delay making decisions until some or all of the policy uncertainty is resolved. In terms of corporate operations, the decisions refer to capacity choices and resource adjustments. The intuition is that managers are not sure whether their goods or service still align with officials' political and social objectives when prefecture-city leadership changes. The new leaders may have very different policy preferences and favored economic outcomes compared with the departing leaders. As Julio and Yook (2012, 2016) note, the value of waiting increases when uncertainty related to changes in government policy is high. As a result, managers are likely to hold slack resources although the demand declines during this high uncertainty period, which in turn increases the degree of cost stickiness.
Second, political uncertainty arising from government official turnover weakens the firm's existing political connections, which affects its operational decisions. Research undertaken in various countries documents those political connections enhance firms' operations and increase their value (e.g. Fisman, 2001; Faccio, 2006; Faccio, Masulis, & McConnell, 2006). Likewise, political connections are crucial for firms to do business in China (e.g. Xu, Yuan, Jiang, & Chan, 2015; Xu et al., 2016; Chen et al., 2018). Due to the bureaucratic structure in China, politics and politicians play an important and profound role in the Chinese economy and business activities. Businesses need to cultivate and maintain good relationships with ruling politicians for policy protection and other economic benefits. The resulting relational economy leads firms to pay special attention to political uncertainty (Xu et al., 2016). Thus, managers are likely to slow business growth and retain the existing resources until they make connections with, or at least discover the policy preferences of, new local government officials. This is likely to increase cost stickiness.
According to the above discussion, we hypothesize:
Cost stickiness is greater in turnover years than in non-turnover years.
If political uncertainty drives the degree of cost stickiness, its impact is likely to vary across different regions. The imbalances of economic development in China's eastern, central and western regions lead to a significant difference in policy, tax distortions and institutional environment (Allen, Qian, & Qian, 2005). On the one hand, Feng & Johansson, 2017 find that political effect on business is more pervasive in weak institutional environments, which amplifies the political uncertainty associated with government official turnover. On the other hand, Lee et al. (2020) document that firms in countries with less democracy and weaker institutions are less sensitive to political uncertainty. They argue that wide government policy swings are relatively scarce in countries with weak institutional environments. Following this logic, the government officials in regions with weak institutions may be unlikely to make significant changes in existing policies unless the central government officials request them to do so, lowering political uncertainty. As it is hard to predict the direction of the effect of institutional environment on the relation between political uncertainty and cost stickiness, we formulate the following hypothesis without a directional prediction:
The relationship between political uncertainty and cost stickiness is associated with the local institutional environment.
Besides the local institutional environment, the ownership structure may also influence the relationship between political uncertainty and cost stickiness. In China, the state maintains its controlling power over SOEs through equity holdings (Sun & Tong, 2003; Jiang & Kim, 2020). This makes SOEs more susceptible to political uncertainty. Because in addition to the usual political influence, their operational and personnel decisions are also under the influence of or even controlled by the local leaders, especially for SOEs that are controlled by local governments (Chen, Tang, Wu, & Yang, 2021; Guo, Pan, & Tian, 2021). As a result, strict government control and restriction continually affect firms' general operations and decision-making, leaving them more sensitive to political risk. As An et al. (2016) and Xu et al. (2016) have argued, political uncertainty has a much stronger effect on SOEs, as they are more politically connected. Thus, it is reasonable to expect that the effect of political uncertainty on stickiness is significantly stronger for SOEs.
In sharp contrast, Feng and Johansson (2017) and Ni (2019) document that the detrimental effect of political uncertainty is much stronger for POEs than for SOEs. The latter receive various economic benefits from both central and local governments, such as tax breaks on certain products, lower interest rates on loans, and financial support from the government (Hou, Kuo, & Lee, 2012). SOEs, which are embedded in the bureaucratic hierarchies, have maintained institutional connections to local governments and superior government knowledge to understand and foresee economic policies. Institutional connections are more stable than personal connections that POEs usually develop with local officials (Bai et al., 2020; Hou & Li, 2020). When turnover occurs, POEs may lose their established political connections and face a higher policy risk [4]. In comparison, SOEs are less vulnerable to political uncertainty and more likely to continue their operations as normal. Thus, compared to SOEs, POEs are apt to be more sensitive to local official turnover, resulting in a greater impact of political uncertainty on cost stickiness. Again, there is no consensus on whether the impact of political uncertainty is stronger for SOEs or POEs. We, therefore, state the third hypothesis without a directional prediction:
The relationship between political uncertainty and cost stickiness is related to ownership.
Firms with larger resource bases can take advantage of economies of scale and market power to drive down their costs, enabling them to recover from the costs of making operating mistakes more easily. As a result, large firms are better equipped than small ones to handle political uncertainty (Haveman et al., 2017). Moreover, in a transitional economy like China, large firms have less exposure to regulations, taxes, fees and fines (Tsai, 2002), and have lower risks of government expropriation of assets (Li, Meng, Wang, & Zhou, 2008; Jiang & Kim, 2020). As such, the impact of local political turnover is expected to concentrate on small firms. Therefore, we make the fourth hypothesis with a directional prediction:
The relationship between political uncertainty and cost stickiness is more pronounced in small firms.
3. Research design
3.1 Sample and data
We collect data in two stages. First, prefecture-city official turnover is used as our measure of political uncertainty. The two top leaders in a prefecture-city government are the party secretary and mayor. The administrative head structure in all Chinese localities means that both turnover of city heads and turnover of city mayors may affect corporate operations. For simplicity, we refer to them as the “city head” and “city mayor,” respectively. We manually collect the turnover data of city heads and mayors from various public resources, such as official government websites, press releases, and newspapers [5]. The data contain detailed personnel information regarding each governor's name, age, education, previous working experience, and the timing and nature of the turnover and appointment.
Second, we obtain firm-level financial information from the China Stock Market Accounting Research (CSMAR) database for all non-financial firms listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 1998 to 2018 [6]. In addition, we source the national-level inflation rate, city-level annual real GDP growth rate, and city-level unemployment rate from the CEIC database.
Following Banker et al. (2013), our initial sample meets the following criteria: (1) the firm must have valid sales, assets, and operating costs over the past two years; (2) we exclude firms with extreme operating costs (i.e. operating costs which are under 50% or exceed 200% of sales over the past two years); (3) we exclude firm-years with extreme sales (i.e. when sales rose by more than 50% or fell by more than 33% in the past two years) since these large shifts in sales likely capture unusual events such as mergers or divestitures. We further exclude firms located in Beijing, Shanghai, Tianjin, and Chongqing, which are cities directly controlled by the Chinese central government. We then assign each listed firm to a particular city based on the location of its headquarter. Combining firm-level information with prefecture-city official turnover data for 260 cities (including 1,206 heads and 1,346 mayors), our final sample consists of 20,392 firm-year observations from 2000 to 2018 [7].
3.2 Measure of political uncertainty
The key variable of interest for this study is political uncertainty. Prefecture-city official turnover provides an ideal measure for two main reasons: (1) prefectural-level governments are the lowest hierarchy that has the right to make local policies and rules and thus they have a substantial impact on firms' operation; (2) prefectural-level governments are also the highest hierarchy that has direct impacts on local economic affairs, such as urban infrastructure development and urban land usage and supply. Many of these affairs touch on daily business operations. Furthermore, many prefectural-level government officials serve for very short periods although their nominal term is typically five years (Ni, 2019). Finally, using prefectural-level rather than province-level turnover can help us better control for local economic conditions with a matched sample approach. We, therefore, take advantage of the frequent prefecture-city official turnover in China. We construct a dummy variable
3.3 Empirical model
To test our hypotheses, we augment Anderson et al.'s (2003) model by including the interaction terms of the prefecture-city official turnover,
Under our H1, we expect that the coefficient estimation for
4. Empirical results
4.1 Descriptive statistics
We present the distribution of sample observations by year from 2000 to 2018 in Panel A of Table 1. The sample period encompasses 20,392 firm-year observations, with the most (2,060) in 2018 and the least (457) in 2000. Panel B presents the distribution of prefecture-city official turnover ratio by year. The average turnover ratios of city heads and city mayors are 29.01 and 29.63%, respectively [9]. In column (3), we report the distribution of the key variable
Table 2 presents the summary statistics for key variables used in this study. All the continuous variables are winsorized at the top and bottom 1% of their respective distributions to mitigate the effect of outliers. The mean (median) of
4.2 Main results
Table 3 presents the results of our main analysis. Prior to formally testing the impact of political uncertainty on cost stickiness, we initially estimate Anderson et al.'s (2003) model to provide some preliminary evidence of asymmetric cost behavior as reported by Anderson et al. (2003). The testing result is reported in column (1). Consistent with the sticky cost hypothesis, the coefficient of the interaction term between
In addition to being statistically significant, our test results are also economically material. Recall that the estimated
4.3 Endogeneity and further robustness checks
In this section, we conduct a set of tests to reinforce the consistency and reliability of the baseline results. Our tests involve an instrumental variable approach, placebo tests, matched sample approach, using alternative specifications and measures of explanatory variables and dependent variables, and controlling the anti-corruption campaign and national election effect.
4.3.1 Addressing potential endogeneity concern
Although we have controlled a number of variables in the baseline regression, endogeneity is still possible between political uncertainty and cost stickiness. As Rodrik (1991) notes, it is very difficult to find strong empirical support for uncertainty-driven predictions because political instability and uncertainty are likely endogenous to other factors that affect firms' investment and operating decisions. Estimating the direction of causality between economic outcomes and policy uncertainty requires employing a variable or event that is correlated with policy uncertainty but uncorrelated with the economic conditions that drive cost stickiness. Typically, the tenure of government officials in China is fixed at five years. However, unobservable time-varying local economic factors might still drive both government official turnover and the asymmetric cost behavior. In such a case, the observed relationship between political uncertainty and cost stickiness could be spurious.
To address the potential endogeneity issue, we first apply an instrumental variable (IV) analysis approach. Referring to the study by Ni (2019), we use two IVs for local official turnover: local government officials' age (
To further ally the endogeneity concern, we conduct a placebo test to verify whether the results remain the same in the absence of prefecture-city official turnover. Following Xu et al. (2016), for each political uncertainty event due to prefecture-city official turnover, we assume that the event recurs for the same firm in the next three years in the same city. We set up a simulated dummy variable for each year (using the same procedure as for
4.3.2 Controlling for the anti-corruption campaign and national election effect
In November of 2012, China initiated the largest and most pervasive anti-corruption regulation in the history of modern China (Pan & Tian, 2020), creating a significant increase in political uncertainty (Stanfield, Zhang, & Zhang, 2019) [12]. To address the concern about the impact of this campaign, we split our sample into two shorter time periods, 2000–2012 and 2013–2018, and re-estimate the baseline model separately for each subsample. The results are reported in columns (1) and (2) of Panel A of Table 6. The coefficient estimates for
There were four national elections during our sample period (2002, 2007, 2012 and 2017). As shown in Panel B of Table 1, the national election may influence prefecture-city official turnover. To alleviate the concern that firms may adjust their operational decisions in anticipation of these elections and of any resulting political turnover, we remove the observations in 2002, 2007, 2012 and 2017 and re-run the baseline model to assess our main results. Column (3) shows that the coefficient estimate for
4.3.3 A matched sample approach
Following Çolak et al. (2017) and Lee et al. (2020), we also use a matched sample approach. For each firm-year that experiences political uncertainty (treatment firm-year), we match a firm-year that does not experience political turnover, is from the same industry and province, and has similar magnitudes of control variables used in Equation (1) based on the propensity score matching approach [13]. As such, we have two groups of firms with similar fundamentals and economic conditions but only the treatment firms face an increased political uncertainty due to local official turnover. Thus, we can control for other factors that could affect a firm's asymmetric cost behavior. We estimate Equation (1) using the two groups and report the results in column (4) of Panel A of Table 6 [14]. We find that the coefficient on
4.3.4 Alternative specifications of explanatory variables
We also use alternative specifications of political turnover for robustness. In a Chinese municipality, the city head and the mayor could have a different impact on local businesses. In each prefecture government, the party secretary is the highest-ranking official empowered with decision-making rights on key political and economic matters, followed by the administrative head, to ensure the CCP has full control (Chen et al., 2021). Thus, the communist party secretary has more power than the mayor at the prefecture-city level, and the turnover of the city head may have a greater impact than the turnover of mayors. To examine different effects of city heads and mayors, we re-regress the baseline model in terms of different explanatory variables:
4.3.5 Alternative measures of dependent variables
The analysis thus far has been focused on a composite measure of operating costs. To reinforce our baseline results, we then study whether the supportive results can be found in other sub-cost categories, i.e. selling, general, and administrative (
4.4 Cross-sectional variation
So far in the analyses, we document a positive effect of political uncertainty on cost stickiness. However, this effect may vary with different local institutional environments, firms' ownership structure, and firm size. Thus, in this section, we turn to focus on the potential drivers behind the relationship between political uncertainty and cost stickiness and examine H2–H4.
4.4.1 The effect of the local institutional environment
To test H2, we add the interaction terms of the institutional environment into the baseline model. While it is usually difficult for a single country study to identify such impact, China offers a rare opportunity as significant variations in institutional environments exhibit both across regions and over time. We use the National Economic Research Institute Marketization index proposed by Fan, Wang, and Zhu (2011) as a proxy for the local institutional environment. The index measures the levels of institutional development in the 31 provinces of China and the institutional development level is assessed in five fields by a total of 23 indicators [15]. For each year, we rank firms based on the Marketization index of their registration location (provincial level) and assign to the weak (strong) institutional environment group those firms below (above) the median value of the Marketization index distribution. The rankings are performed on an annual basis [16]. We then construct a dummy variable,
The results are presented in Table 7. The estimates on the full sample are reported in column (1) and the estimates on the subsamples split by the institutional environment are reported in columns (2) and (3). As shown in column (1), the coefficient on
4.4.2 The effect of firms' ownership structure
Next, we examine H3 to test whether cross-sectional variation in firms' ownership structure affects the relationship between political uncertainty and cost stickiness. To conduct the analysis, we create a dummy variable,
The above results also help directly address one alternative plausible explanation of our results. It is potentially possible that the incoming local government officials aim at maintaining stability and therefore encourage the listed firms to retain resources. We find such reasoning to be unconvincing because it fails to explain our finding that the impact of local official turnover is concentrated in POEs rather than in SOEs. SOEs in China are naturally connected to the government by ownership ties (Calomiris, Fisman, & Wang, 2010). As a result, it is less costly for the government to intervene in the corporate operations in SOEs (Gu et al., 2020). For instance, Chen et al. (2021) document that the impact of local official turnover on tax avoidance concentrates in SOEs. Therefore, if the underlying mechanism driving our finding is the politician's goal of maintaining stability, we should observe a larger impact for SOEs instead.
4.4.3 The effect of firm size
To test H4, following Haveman et al. (2017), we classify a firm as small (denoted by SMALL) in year t if its total assets are below the sample median in year t, and zero otherwise. We then interact
5. Channel analysis
In this section, we shed light on the channels through which political uncertainty affects firms' asymmetric cost behavior. As discussed in the hypothesis development section, policy uncertainty and the threat of losing political connection are two potential channels through which local official turnover can influence cost stickiness. Our cross-sectional analyses on the local institutional environment, ownership type, and firm size provide evidence consistent with both channels. However, it is difficult to distinguish between these two channels. In this section, we aim at testing the validity of each mechanism conditional on the types of turnover and political connection, respectively.
5.1 Channel one: economic policy uncertainty
We first test the economic policy uncertainty channel conditional on the turnover type. In the Chinese political system, local officials have significant autonomy in their regions and have authority in determining local economic policies (Xu, 2011; Chen et al., 2018; Bai et al., 2020). Therefore, the turnover of local officials generates significant economic policy uncertainty. The degree of this uncertainty for local firms is arguably dependent on the type of turnover [18] Specifically, we adopt two alternative methods to measure the degree of economic policy uncertainty driven by official turnover. First, we follow An et al. (2016) and distinguish local official turnover into two types: local promotion and external appointment. Local promotion, the replacement of a local government official with an official within the same city, creates minimal policy uncertainty. Conversely, external appointments, in which an official from another city replaces the local government official generally result in a more dramatic change in local economic policies (An et al., 2016). The alternative method is to explore whether the official turnover is expected or unexpected (Ru, 2018). The nominal term of government officials in China is normally five years. Nevertheless, some officials may leave their positions earlier than expected because of unforeseen events such as corruption charges, promotion or death [19]. As such, when turnover is unexpected, local firms face a higher degree of policy uncertainty because they do not have enough information or time to strategize their operational decisions.
Based on the above reasoning, if economic policy uncertainty is the underlying channel, we expect that the effect of political uncertainty on cost stickiness will be more pronounced if the new official belongs to the external appointment and turnover is unexpected. To test these two predictions, we construct two dummy variables, denoted as
5.2 Channel two: political connections
To explore whether the negative impact of local official turnover is associated with a change in political connections, we follow Zhang et al. (2016) and identify two types of political connections for private firms [20]. The first type is ascribed political connections that represent the political background the individual executive obtained before entering the business. The other is achieved political connections that result from political appointments to state organs (such as congresses and political councils) after executives became successful business leaders. We expect that firms with executives who have ascribed bureaucratic connections are less likely to be affected by the political uncertainty arising from changes in local government officials compared to firms whose executives do not have such connections. As argued by Zhang et al. (2016), ascribed bureaucratic connections often indicate deep political embeddedness. Previous government working experience endows the executives with unique information about government bureaucracy and operation and allows them immediately building up a relationship with new officials by using their common language. On the other hand, firms with executives who have achieved political connections are more likely to be affected by political uncertainty compared to firms whose executives do not have such connections. Achieved political connections are largely a result of ongoing cooperation and favor exchange between business and the existing local government (Zhang et al., 2016). When the turnover occurs, a firm's political connections established in the previous political regime may disappear and their ongoing projects could be in danger of being discontinued (Jiang et al., 2021). As such, firms with achieved political connections are more sensitive to the changes in local government officials compared with firms that do not rely on government support. To test this prediction, we collect the detailed personal background of all the board members from the CSMAR database and annual reports and then identify the specific type of political connection if any. Firms with at least one board member with a certain type of political connection are classified into that type. Our sample period for this analysis is from 2008 to 2017.
Table 11 presents the estimation results. The estimates on the subsamples split by ascribed bureaucratic connections are reported in Panel A and the estimates on the subsamples split by achieved political connections are reported in Panel B. As shown in column (1) of Panel A, the coefficient on
Overall, we provide some evidence showing that both economic policy uncertainty and political connection are likely to be two underlying channels that jointly shape the influence of local political uncertainty. As it is challenging to generate definitive proof of the underlying channels, the results should be interpreted with caution.
6. Conclusion
Using hand-collected data on prefecture-city official turnover for 260 cities in China, we examine the effect of political uncertainty on operational decisions evident in cost stickiness. This analysis contributes to the literature on the economic implications of political uncertainty by testing the relation between political uncertainty and cost stickiness at the local government level.
According to the baseline results, we document that cost stickiness is greater in turnover years than in non-turnover years, suggesting that managers retain slack resources when political uncertainty is high. Our results are robust to the instrumental variable analysis, placebo tests, matched sample approach, alternative specifications of local official turnover, and using other cost categories as well as controlling the anti-corruption campaign and national election effect. Moreover, we conduct a set of cross-sectional analyses and show that the relation between political uncertainty and cost stickiness is more pronounced for firms located in regions with a weak institutional environment, firms that are privately owned, and firms with a smaller size. Our channel analyses provide consistent evidence that local political turnover affects firms' sticky cost behavior through both the policy uncertainty and the threat of losing political connection mechanisms. Overall, these findings imply that political uncertainty stemming from prefecture-city official turnover is an important channel through which local political process influences firms' cost behavior, and firms do take political uncertainty into account when setting their resources adjustment policies.
Sample description
| Panel A: Sample distribution by year | |||
|---|---|---|---|
| Year | No. of observations | No. of cities | No. of provinces |
| 2000 | 457 | 152 | 27 |
| 2001 | 521 | 166 | 28 |
| 2002 | 588 | 168 | 28 |
| 2003 | 649 | 179 | 28 |
| 2004 | 661 | 182 | 28 |
| 2005 | 739 | 197 | 28 |
| 2006 | 813 | 203 | 28 |
| 2007 | 770 | 196 | 28 |
| 2008 | 853 | 196 | 28 |
| 2009 | 926 | 205 | 28 |
| 2010 | 939 | 214 | 28 |
| 2011 | 1,092 | 212 | 28 |
| 2012 | 1,364 | 227 | 28 |
| 2013 | 1,539 | 229 | 28 |
| 2014 | 1,616 | 234 | 28 |
| 2015 | 1,562 | 229 | 28 |
| 2016 | 1,599 | 235 | 28 |
| 2017 | 1,644 | 227 | 28 |
| 2018 | 2,060 | 244 | 28 |
| Total | 20,392 | 260 | 28 |
| Panel B: Distribution of prefecture-city official turnover by year | ||||
|---|---|---|---|---|
| Year | Head turnover (%) | Mayor turnover (%) | Head or mayor turnover (%) | Head and mayor turnover (%) |
| 2000 | 22.98 | 28.45 | 40.92 | 10.50 |
| 2001 | 28.98 | 32.25 | 40.50 | 20.73 |
| 2002 | 34.86 | 29.76 | 51.19 | 13.44 |
| 2003 | 37.29 | 49.31 | 61.94 | 24.65 |
| 2004 | 14.98 | 13.16 | 23.15 | 4.99 |
| 2005 | 30.72 | 26.12 | 40.19 | 16.64 |
| 2006 | 15.62 | 20.79 | 26.69 | 9.72 |
| 2007 | 29.74 | 38.83 | 49.48 | 19.09 |
| 2008 | 36.34 | 26.14 | 45.60 | 16.88 |
| 2009 | 9.07 | 16.09 | 20.95 | 4.21 |
| 2010 | 23.75 | 22.36 | 34.29 | 11.82 |
| 2011 | 28.02 | 33.06 | 43.59 | 17.49 |
| 2012 | 34.24 | 35.48 | 46.48 | 23.24 |
| 2013 | 28.91 | 32.75 | 44.64 | 17.02 |
| 2014 | 13.99 | 14.73 | 23.76 | 4.95 |
| 2015 | 36.94 | 24.14 | 52.62 | 8.45 |
| 2016 | 32.40 | 29.39 | 45.53 | 16.26 |
| 2017 | 53.71 | 43.19 | 72.26 | 24.64 |
| 2018 | 38.70 | 46.96 | 64.31 | 21.35 |
| Average | 29.01 | 29.63 | 43.58 | 15.06 |
Note(s): This table presents the sample distribution by year (Panel A) and the distribution of prefecture-city official turnover by year (Panel B) over the period 2000–2018
Summary statistics
| N | Mean | SD | 25% | Median | 75% | |
|---|---|---|---|---|---|---|
| 20,392 | 0.092 | 0.180 | −0.012 | 0.102 | 0.214 | |
| 20,392 | 0.085 | 0.170 | −0.018 | 0.098 | 0.207 | |
| 20,392 | 0.280 | 0.449 | 0 | 0 | 1 | |
| 20,392 | 0.449 | 0.498 | 0 | 0 | 1 | |
| 20,392 | 0.109 | 0.036 | 0.081 | 0.104 | 0.135 | |
| 20,392 | 0.030 | 0.034 | 0.023 | 0.029 | 0.035 | |
| 20,392 | 0.619 | 0.672 | 0.205 | 0.597 | 1.005 | |
| 20,392 | 0.276 | 0.988 | −0.261 | 0.356 | 0.897 | |
| 20,392 | 0.251 | 0.433 | 0 | 0 | 1 |
Note(s): This table reports summary statistics for the major variables used in the empirical analysis. The subscripts
Political uncertainty and asymmetry of operating costs
| Exp. sign | (1) | (2) | |
|---|---|---|---|
| 0.995*** (144.039) | 0.987*** (76.564) | ||
| − | −0.042*** (−5.178) | −0.102*** (−4.285) | |
| −0.007 (−1.531) | |||
| − | −0.028*** (−2.761) | ||
| 0.136* (1.815) | |||
| −0.063 (−0.740) | |||
| 0.005 (1.261) | |||
| 0.001 (0.476) | |||
| − | −0.551** (−2.380) | ||
| 0.938* (1.716) | |||
| − | −0.025*** (−3.379) | ||
| − | −0.038*** (−7.275) | ||
| 0.044*** (4.808) | |||
| 0.004*** (5.368) | 0.004*** (5.493) | ||
| Year fixed effect | Yes | Yes | |
| Firm fixed effect | Yes | Yes | |
| Observations | 20,392 | 20,392 | |
| Adjusted | 0.857 | 0.778 |
Note(s): This table presents the results of testing the impact of political uncertainty on the asymmetric behavior of operating costs. The dependent variable is
Political uncertainty and asymmetry of operating costs: instrumental variable approach
| Variable | First stage | Second stage | |
|---|---|---|---|
(1) | (2) | (3) | |
| Panel A: Turnover of city heads | |||
| −0.979*** (−17.283) | |||
| 0.278*** (22.007) | |||
| −1.299*** (−22.749) | |||
| 0.204*** (15.846) | |||
| 4.274*** (18.819) | −0.004 (−0.521) | 0.969*** (44.108) | |
| −0.015 (−0.365) | 5.579*** (24.336) | −0.077*** (−3.509) | |
| −0.025 (−0.725) | |||
| −0.188*** (−3.522) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 20,392 | 20,392 |
| Adjusted | 0.311 | 0.315 | 0.735 |
| Panel B: Turnover of city mayors | |||
| −0.979*** (−18.895) | |||
| 0.162*** (12.606) | |||
| −1.197*** (−22.012) | |||
| 0.097*** (7.391) | |||
| 4.283*** (20.729) | 0.000 (0.043) | 0.978*** (62.703) | |
| −0.025 (−0.585) | 5.156*** (23.642) | −0.093*** (−2.652) | |
| 0.011 (0.411) | |||
| −0.106** (−2.336) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 20,392 | 20,392 |
| Adjusted | 0.296 | 0.311 | 0.680 |
Note(s): This table presents the results of 2SLS regressions. We use local officials' age (
Political uncertainty and asymmetry of operating costs: a placebo test
| (1) | (2) | (3) | |
|---|---|---|---|
| 0.962*** (69.915) | 0.964*** (70.168) | 0.965*** (70.154) | |
| −0.145*** (−3.063) | −0.143*** (−3.013) | −0.153*** (−3.223) | |
| 0.017* (1.801) | |||
| 0.009 (0.443) | |||
| 0.006 (0.373) | |||
| −0.025 (−1.169) | |||
| 0.008 (0.704) | |||
| −0.037 (−1.063) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 20,392 | 20,392 |
| Adjusted | 0.777 | 0.777 | 0.777 |
Note(s): This table presents the results of a placebo test. For each political uncertainty event, we assume that the event happens again in the next three years and set up a simulated dummy variable for each year. The three dummy variables are denoted as
Robustness checks
| Panel A: Alternative subsamples | ||||
|---|---|---|---|---|
| Pre anti-corruption campaign | Post anti-corruption campaign | Excluding election years | Matched sample analysis | |
| (1) | (2) | (3) | (4) | |
| 1.005*** (59.382) | 1.040*** (46.898) | 0.991*** (68.681) | 0.979*** (76.840) | |
| −0.032*** (−2.773) | −0.208*** (−4.698) | −0.094*** (−3.397) | −0.120*** (−4.211) | |
| −0.018*** (−2.782) | 0.005 (0.777) | −0.005 (−0.940) | −0.004 (−0.736) | |
| −0.020** (−2.148) | −0.037*** (−2.563) | −0.024** (−2.049) | −0.027** (−2.372) | |
| Control | Yes | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes |
| Observations | 10,372 | 10,020 | 16,026 | 11,140 |
| Adjusted | 0.775 | 0.777 | 0.775 | 0.675 |
| Panel B: Alternative explanatory variables | |||
|---|---|---|---|
| (1) | (2) | (3) | |
| 0.984*** (76.922) | 0.988*** (77.469) | 0.986*** (79.655) | |
| −0.104*** (−4.437) | −0.116*** (−4.980) | −0.117*** (−5.103) | |
| −0.006 (−1.214) | |||
| −0.034*** (−3.078) | |||
| −0.013** (−2.508) | |||
| −0.019* (−1.741) | |||
| −0.018*** (−2.695) | |||
| −0.035** (−2.474) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 20,392 | 20,392 |
| Adjusted | 0.777 | 0.778 | 0.778 |
| Panel C: Alternative dependent variables | ||||
|---|---|---|---|---|
| Dependent variable | ||||
| (1) | (2) | (3) | (4) | |
| 0.987*** (76.564) | 0.677*** (21.901) | 0.620*** (23.987) | 0.999*** (63.775) | |
| −0.102*** (−4.285) | −0.440*** (−5.646) | −0.115* (−1.722) | −0.024*** (−2.912) | |
| −0.007 (−1.531) | 0.003 (0.228) | −0.003 (−0.205) | −0.011** (−2.090) | |
| −0.028*** (−2.761) | −0.105*** (−4.468) | −0.038** (−2.176) | −0.015* (−1.830) | |
| Control | Yes | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes |
| Observations | 20,392 | 20,392 | 20,392 | 20,392 |
| Adjusted | 0.778 | 0.235 | 0.032 | 0.766 |
Note(s): This table presents the results of several robustness checks on the impact of political uncertainty on the asymmetric behavior of operating costs. In Panel A, we conduct a set of subsample analyses, including anti-corruption campaign effect, national election effect, and matched sample analysis. In Panel B, we test the impact of political uncertainty on asymmetric behavior of operating costs in terms of alternative specifications of political turnover. In Panel C, we test the baseline results in terms of alternative dependent variables. The subscript
Institutional environment: weak institutions vs strong institutions
| Full sample | By institutional environment | ||
|---|---|---|---|
| Weak institutions | Strong institutions | ||
| (1) | (2) | (3) | |
| 0.992*** (70.173) | 0.960*** (69.004) | 1.009*** (71.171) | |
| −0.106*** (−4.288) | 0.013 (0.337) | −0.198*** (−6.040) | |
| −0.013** (−2.146) | 0.002 (0.232) | −0.015** (−2.523) | |
| −0.023** (−2.068) | −0.032** (−2.115) | −0.014** (−2.016) | |
| −0.012** (−1.982) | |||
| −0.021* (−1.661) | |||
| 0.014 (1.511) | |||
| −0.013** (−2.161) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 9,098 | 11,294 |
| Adjusted | 0.678 | 0.671 | 0.683 |
Note(s): This table presents the results of testing the impact of the institutional environment on the relation between political uncertainty and asymmetric behavior of operating costs. The subscript
Ownership structure: SOE vs POEs
| Full sample | By ownership | ||
|---|---|---|---|
| SOEs | POEs | ||
| (1) | (2) | (3) | |
| 0.984*** (70.628) | 0.980*** (68.533) | 0.992*** (62.695) | |
| −0.085*** (−3.473) | −0.067* (−1.876) | −0.117*** (−3.434) | |
| −0.000 (−0.725) | −0.014** (−2.057) | −0.002 (−0.245) | |
| −0.050*** (−3.555) | −0.004** (−2.272) | −0.039*** (−2.718) | |
| 0.009 (1.394) | |||
| −0.030** (−2.208) | |||
| −0.015 (−1.615) | |||
| 0.046** (2.275) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 9,971 | 10,421 |
| Adjusted | 0.728 | 0.688 | 0.683 |
Note(s): This table presents the results of testing the impact of firm ownership on the relation between political uncertainty and asymmetric behavior of operating costs. The subscript
Firm size: small firms vs large firms
| Full sample | By firm size | ||
|---|---|---|---|
| Small firms | Large firms | ||
| (1) | (2) | (3) | |
| 0.980*** (75.908) | 0.986*** (65.688) | 0.971*** (68.301) | |
| −0.133*** (−5.258) | −0.120*** (−3.512) | −0.128*** (−3.656) | |
| −0.014** (−2.184) | −0.001 (−0.080) | −0.013** (−2.113) | |
| 0.013 (0.892) | −0.059*** (−3.987) | 0.010 (0.699) | |
| 0.009 (1.364) | |||
| 0.051*** (3.647) | |||
| 0.015* (1.643) | |||
| −0.077*** (−3.846) | |||
| Control | Yes | Yes | Yes |
| Year fixed effect | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes |
| Observations | 20,392 | 10,202 | 10,190 |
| Adjusted | 0.677 | 0.671 | 0.684 |
Note(s): This table presents the results of testing the impact of firm size on the relation between political uncertainty and asymmetric behavior of operating costs. The subscript
Channel test one: turnover types
| (1) | (2) | |
|---|---|---|
| 0.986*** (73.158) | 0.987*** (73.495) | |
| −0.101*** (−4.196) | −0.103*** (−4.310) | |
| −0.016* (−1.631) | −0.010 (−1.309) | |
| −0.016*** (−3.304) | −0.034** (−1.982) | |
| 0.011 (1.030) | ||
| −0.025*** (−3.163) | ||
| 0.004 (1.505) | ||
| −0.021*** (−3.023) | ||
| Control | Yes | Yes |
| Year fixed effect | Yes | Yes |
| Firm fixed effect | Yes | Yes |
| Observations | 20,392 | 20,392 |
| Adjusted | 0.754 | 0.777 |
Note(s): This table presents the results of comparing the effect of the change of government officials according to different types of turnover. Column (1) reports the estimation by distinguishing official turnover into the external appointment and local promotion. Column (2) reports the estimation by distinguishing official turnover into expected and unexpected. The subscript
Channel test two: political connection
| Panel A: Ascribed political connections | ||
|---|---|---|
| Ascribed political connection | No-ascribed political Connection | |
| (1) | (2) | |
| 0.826*** (6.507) | 0.984*** (46.941) | |
| −0.878** (−2.153) | 0.055 (1.136) | |
| 0.031 (0.659) | −0.005 (−0.640) | |
| 0.169 (1.243) | −0.042** (−2.322) | |
| Control | Yes | Yes |
| Year fixed effect | Yes | Yes |
| Firm fixed effect | Yes | Yes |
| Observations | 255 | 8,227 |
| Adjusted | 0.701 | 0.683 |
| Panel B: Achieved political connections | ||
|---|---|---|
| Achieved political connections | Non-achieved political connections | |
| (1) | (2) | |
| 0.927*** (21.176) | 0.991*** (41.949) | |
| 0.401*** (3.438) | 0.010 (0.180) | |
| 0.018 (1.160) | −0.009 (−1.082) | |
| −0.113*** (−2.843) | −0.036* (−1.772) | |
| Control | Yes | Yes |
| Year fixed effect | Yes | Yes |
| Firm fixed effect | Yes | Yes |
| Observations | 1,675 | 6,807 |
| Adjusted | 0.694 | 0.683 |
Note(s): This table presents the results of testing the impact of political connections on the relation between political uncertainty and asymmetric behavior of operating costs. We distinguish political connections into two types: ascribed bureaucratic connections (Panel A) and achieved political connections (Panel B). The subscript
Variable definitions
| Variable | Definition |
|---|---|
| City-level variables | |
| A dummy variable that takes a value of one if there is a change of CCP head or mayor in year | |
| A dummy variable that takes a value of one if there is a change of CCP head in year | |
| A dummy variable that takes a value of one if there is a change of mayor in year | |
| A dummy variable that takes a value of one if there is a change of CCP head and mayor in year | |
| Natural log of prefecture-city officials' age prior to the turnover year | |
| Mean value of prefecture-city official turnover of other cities in the same province in year | |
| A city's real GDP growth rate | |
| A city's unemployment rate | |
| A dummy variable that takes a value of one for the head or mayor is replaced with an external appointment and zero otherwise | |
| A dummy variable that takes a value of one for the head or mayor severs the position for less than five years and zero otherwise | |
| A dummy variable that takes a value of one if a firm is located in a region with a below-median marketization index, and zero otherwise. The marketization index measures the levels of institution development, with a lower value indicating a weak institutional environment | |
| Firm-level variables | |
| The change in the natural logarithm of operating costs, calculated by costs of goods sold plus other operating expenses (i.e. selling, general, administrative costs, and research and development expenses) | |
| The change in the natural logarithm of selling, general and administrative costs | |
| The change in the natural logarithm of research and development expenses | |
| The change in the natural logarithm of cost of goods sold | |
| The change in the natural logarithm of sales | |
| The natural logarithm of total assets to sales | |
| The natural logarithm of the number of employees to sales | |
| A dummy variable that takes a value of one if sales decreased in year t, and zero otherwise | |
| A dummy variable that takes a value of one if sales decreased in year t − 1, and zero otherwise | |
| A dummy variable that takes a value of one for a state-owned enterprise, and zero otherwise | |
| SMALL | A dummy variable that takes a value of one for a firm with total assets below the sample median in year t, and zero otherwise |
Notes
See Banker and Byzalov (2014) and Banker, Byzalov, Fang, and Liang (2018) for a comprehensive review. Section 2 of this paper also provides a brief literature review on the determinants of cost stickiness.
Xu (2011) describes China as “regionally decentralized authoritarianism.”
Local political leaders may also differ in their abilities to promote local economic growth (Yao & Zhang, 2015).
For instance, Hou and Li (2020) show that new provincial governors distribute significantly fewer subsidies to private enterprises after taking their offices. Fang et al. (2018) find that compared to SOEs, private firms seek to use more perks to rebuild the political connections during local political turnovers.
Our data are mainly collected from the following websites: the Party and Government Leaders Database provided by the News of the Communist Party of China (http://cpc.people.com.cn/), Baidu Wikipedia (http://baike.baidu.com) and Zecheng Web (http://www.hotelaah.com). If the personal information provided by these websites is inconsistent, we then search for public news on the appointment of city heads and mayors through http://www.baidu.com/ to make revisions.
In the CSMAR database, the earliest year with data available is 1990. However, beginning the sample period in 1990 would result in a highly unbalanced sample because there are only a few observations at the beginning of the sample, which may undermine the reliability of test results. Moreover, the disclosure of cash flows has been mandatory in China since 1998.
Variables in the study (e.g. sales growth) require data lagged for 2 years, as a result, our sample period begins in 2000.
Although the log-linear specification of asymmetric cost behavior model is the most used model in the extant literature (e.g. Banker et al., 2013; Cheng, Jiang, & Zeng, 2018; Lee et al., 2020), Balakrishnan, Labro, and Soderstrom (2014) note that this does not explicitly control for a firm's cost structure. The estimated coefficients are influenced by the magnitude of fixed or non-controllable costs in the function, leading to a non-constant elasticity in the response. Considering the possible bias toward finding cost asymmetry due to fixed or non-controllable costs, we also adopt Balakrishnan et al.'s (2014) model and estimate the linear specification of the baseline model. In untabulated results, we find our conclusions are robust to this alternative specification. Our findings are also consistent with Cheng et al. (2018) that Chinese firms exhibit sticky cost behavior with Balakrishnan et al. (2014)'s specification. In addition, Dierynck, Landsman, and Renders (2012) argue that including independent variables, such as asset intensity, employee intensity and GDP growth, can effectively control for fixed costs and economic conditions. Because we include sufficient control variables in the baseline model, to a great extent our log-linear model mitigates the effect of fixed or non-controllable costs.
In our sample period, the median (mean) tenure of city head is 3.92 (4.17) years, while the median (mean) of city major is 4 (4.23) years.
There is a concern that these two variables might be related with local economic conditions and therefore can affect local firms' operational decisions. According to the tests in Ni (2019), there is no significant association between these two IVs and the local economic conditions. It largely attenuates this concern. Furthermore, we have controlled for local economic conditions in our analysis, which further mitigates the concern of violating the exclusion condition.
In untabulated results, we test the relation between IVs (
The anti-corruption campaign was initiated shortly after the conclusion of the 18th National Congress of the Communist Party of China on November 14th, 2012. We, therefore, use 2012 as the cut-off year to define pre- and post-anti-corruption campaign.
We thank an anonymous reviewer for this suggestion.
In an unreported analysis, we find that these two groups have similar asymmetric cost behaviors in the absent of political turnover, further confirming the causal impact of political turnover on cost stickiness.
The five fields of the marketization index are: (1) relation between government and market; (2) the market development of the non-state enterprise sectors; (3) product marketization; (4) factor market development; (5) the development of market intermediaries and the legal framework. This index has been widely used across economics, finance and business (e.g. Feng, Johansson, & Zhang, 2014; Zhang et al., 2015; Feng & Johansson, 2017; Jia, Huang, & Zhang, 2019; Zhang et al., 2015).
The median value is set according to the distribution of the actual NERI Marketization index of the regions that the firms are located in and thus generates an unequal number of observations being assigned to each of the groups. This approach is to ensure that we do not assign firms located in regions with a low Marketization index to the strong institutional environment group and that firms located in regions with same index are always assigned to the same group.
To address the concern that some SOEs located in the capital city of provinces might be subject to the ruling of province-level government officials rather than city-level government officials, we remove SOEs located in the capital cities and re-estimate the regressions. The results are similar to the main results in Table 8. We thank an anonymous reviewer for this suggestion.
We thank an anonymous reviewer for the suggestion on testing this channel.
In our sample, 68.8% (68.4%) of city heads (mayors) sever their position for less than five years.
We focus on private firms because all the SOEs are politically connected by nature. Our prior finding that the impact of political turnover is less pronounced in SOEs is also consistent with the implication of the political connection mechanism, though we cannot rule out the economic policy uncertainty mechanism.
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Acknowledgements
The authors thank Dan Li (the editor), two anonymous reviewers, Di Bu, Jianlei Han, Tom Smith, and seminar participants at Macquarie University for their helpful comments and suggestions. All errors remain their own.