Peasant youth experiences of CEOs,risk aversion and corporate performance

Abstract

This article examines the impact of spiritual tempering on corporate performance by investigating the experiences of CEOs who were part of the “peasant youth” between 1957 and 1976 in China. Using a sample of China’s listed companies and by developing propensity score matching and a difference-in-differences model, we find that CEOs who had profound peasant youth experiences have a stronger awareness of risk prevention and that these experiences lead to an improvement in corporate performance of over 3%. In addition, the positive impact of CEOs’ peasant youth experience on corporate performance is pronounced in either state-owned or non-state-owned enterprises. Overall, this study confirms that spiritual tempering has a significant positive impact on corporate performance.

Keywords

Corporate performance peasant youth experience risk appetite spiritual tempering

“. . .when Heaven is about to confer a great office on any man, it first exercises his mind with suffering. . .”

—Mencius¹

Introduction

Early life experiences can be considered key factors affecting managers’ strategic decision-making. For example, those who have experienced moderate hardship may be more interested in high-risk investments, while those experiencing higher levels of hardship may be more conservative (Bernile et al., 2017). The Great Famine experience, which occurred between 1959 and 1961 in China, has been extensively examined in terms of its impact on management decision-making (Feng and Johansson, 2018). However, experiences of “spiritual tempering,” for example, some special life experiences sculpting the spirits,² which play an important role in the development of human society and the formation of executive cognition, have not been fully examined,³ as identifying spiritually tempering events that have available data and are representative is challenging.

From 1957 to 1976, a program was implemented in China that required educated urban youth to work in the countryside. For the educated urban youth, the peasant life is a typical “spiritual tempering” due to the monotony of rural life. This provides a good background for examining the impact of spiritual temper on corporate executive behavior, as it shaped the peasant youth experiences of many Chinese chief executive officers (CEOs). Thus, all A-share listed companies in China from 2007 to 2015 are selected as an observation sample, and the risk appetites of executives with such peasant youth experiences are examined. From a text investigation on the Baidu Encyclopedia website, we find that 22 executives proclaimed their “peasant youth experiences” publicly on the Internet. These disclosures indicate that their memories of these experiences are profound. Another way is looking through the media reports online through Baidu searching engine, the biggest searching tool in China, through which we confirm another 55 CEOs with peasant educated youth experience. The 85 companies for which the 77 CEOs currently or previously work provide 466 observations from 2007 to 2015, which is thus an ideal background for us to examine the relationship between peasant youth experiences and CEO performance. Based on these treated observations, we then establish propensity score matching (PSM) and difference-in-differences (DID) models to address any endogeneity concerns.

We first aim to identify the effects of the peasant youth experiences of CEOs on their firms’ risk-taking behavior. Bernile et al. (2017) suggested that experiences of torture at an early age can affect the risk-taking preferences of CEOs. Those who experienced high levels of torture in their youth were found to be more risk-averse in their adult lives. Drawing on this research, we explore the effects of CEOs’ peasant youth experiences on risk-taking using three measures of the degree of risk-taking: earning volatility, leverage, and stock return volatility. The results are consistent with our conjectures and show that the experiences do make CEOs more cautious.

We next investigate the effects of CEOs’ peasant youth experiences on firm performance using three indicators to proxy for firm performance: return on assets (ROA), return on equity (ROE), and Tobin’s Q. The results strongly suggest that employing CEOs with peasant youth experiences can significantly enhance corporate performance.

Our study relates to three streams of literature. First, Psychologists and sociologists have acknowledged that previous experiences can have a long and persistent effect on people (Holman and Silver, 1998; Kleim and Ehlers, 2009). Jacoby and Kelley (1987) find that memories of prior experiences may exert an unconscious impact on current perception. Specially, the experiences of exposure to disaster would influence individuals deeply (Elder, 1999).

Second, the internal incentive theory regarding managers has recently become a focus of research. The phenomenon of decision-making alienation has been explained to some extent in both psychological and behavioral terms. Managers are assumed to behave rationally and consistently only as far as the bounded rationality and limitations of their cognition allow. Their age, gender, educational level, work experience, and understanding of their environments and lives lead to different attitudes, personalities, and values, which affect firm behavior and organizational outcomes (Adams and Ferreira, 2009; Finkelstein and Hambrick, 1996; Hambrick and Finkelstein 1987).

Adams et al. (2005) suggest that the CEOs’ characteristics have important influence on corporate performance. Similarly, Kaplan et al. (2012) confirm that CEO’s individual characteristics is important for corporate actions and performance. The phenomenon of decision-making alienation can be due to the subjective responses executives have to these characteristics in complex strategic environments (Finkelstein et al., 2009; Pansiri, 2005; Tyler and Steensma, 1998).

CEOs experiencing disasters may change their perceptions of the risk associated with negative events. For instance, Bernile et al. (2017) find CEOs exposed to fatal disasters without extremely negative consequences make more risk-loving decisions, whereas CEOs witnessing terrible consequences of disasters exhibit risk aversion.

Third, the literature related to firms’ risk-taking similarly suggests that intrinsic incentives can lead to significant differences in risk preferences among managers (e.g. Bertrand and Schoar, 2003; Cain and Mckeon, 2016; Fee et al., 2013) due to their heterogeneity. Demographics such as gender, education, age, and marriage can have effects. A younger managerial team (Farag and Mallin, 2016), a higher proportion of male managers (Adhikari, 2018; Faccio et al., 2016; Skała et al., 2018), a higher education level (Belghitar and Clark, 2012; Farag and Mallin, 2016), a higher proportion of single executives (Roussanov et al., 2014), and longer terms in their positions (Farag and Mallin, 2016) all increase the appetite for risk in a company’s senior management team. These qualities make the management team more flexible in their thinking about decisions and increase their ability to process information and their decision-making confidence. Thus, they will be more willing to take risks when making decisions, such as being more likely to be aggressive in their investment expansion and to invest more in R&D.

The influence of work experience and career development background, such as experiences of institutional distress, may result in financial CEOs not only being more cautious and objective but also to consider risk avoidance more when making decisions (Custódio and Metzger, 2013; Dittmar and Ran, 2016). Overconfident managers often take credit for initial successes, attributing them to their own abilities (Doukas and Petmezas, 2007), and generally have high levels of self-confidence and a willingness to take risks. This appetite for excessive risk is likely to bring higher risks to the enterprise. These “soft” incentives can thus lead to significant differences in the risk perceptions and preferences of managers.

Our study makes three main contributions. First, we add to the psychological evidence for the intrinsic incentives of CEOs by considering spiritually tempering experiences. Second, we demonstrate that CEOs with peasant youth experiences are often conservative in their decision-making and more risk-averse, and can improve the performance of their companies. Third, we indicate that firms can reduce their risk-taking while increase their performance by hiring CEOs with spiritually tempering experiences.

The remainder of this paper is organized as follows: the second part provides a theoretical analysis based on a review of the literature and presents the research hypotheses; the third part introduces the research models; the fourth gives the empirical research results; the fifth presents the robustness tests; and the final part concludes.

Institutional background, conceptual framework, and hypothesis development

Institutional background of the Countryside Movement of Educated Youth and the peasant experiences of CEOs

In the period from 1957 to 1976, China’s government mandated the renowned “Down to the Countryside Movement” of Educated Youth under the instruction of the National Chairman, Mao Zedong. After the movement ended with the so-called Cultural Revolution in 1976, most of the educated young people went back to the cities. Some were able to return to study at university, while most embarked on careers in SOEs.

The “peasant-educated-youth” policy was enacted in the 1950s of China due to a swift collapse of industrialization. The high tide of China’s industrialization in 1950s caused by the great support of former Soviet Union suddenly ebbed in 1957, resulting from the deterioration of the relation between China and former Soviet Union, which raised the undersupply of operating post and food in urban. Under this background, the Chinese government had no choice but moving some populations from urban to countryside, incurring the “down to country movement” for educated youth in urban.

In this sense, the “down to country movement” for educated youth in urban could improve the food supply and decrease the material torture for the involved educated youths. From this view, the educated youths encountered more spiritual tempering and torment while decreasing material torture.

Some of the children of these cadres successfully avoided being sent to the countryside by joining the army or moving to the cities’ suburbs, but most middle school students between 15 and 20 years old in urban China were forced to stop their studies and to continue their working lives in the remote countryside. Thus, if a CEO born in an urban area was 15–20 years old in the 1957–1976 period, there was a high likelihood that her youth included a peasant youth experience.⁴

Most of the villages in the countryside that the educated youth worked in were economically backward. However, only some have strong memories of peasant life, as many were allocated to new farms specially built for them, in which they could obtain fixed income like workers in cities, thus avoiding conflicts with local villagers. Therefore, the young people in these new farms identified more with workers rather than peasants.

Unlike the Great Famine event of 1959–1961 in China, which caused extreme hunger, the reallocation movement is associated more with the “spiritual tempering memory” of those involved. First, compared with the short duration (3 years) of the Great Famine period, the period for the educated youth was typically more than 10 years. Accordingly, they had more memories of spiritual tempering than of hunger. Second, the memory of hunger is commonly strong in childhood than in later years. The educated youth were usually 15–20 years old when they experienced peasant life, so they experienced a deeper memory of spiritual tempering than of hunger in their earlier peasant life.

With the economic growth, the foodstuff short-supply had been gradually mitigated. Particularly, when China began to enact the economic opening-up policy in 1978, The peasant-educated-youths would not like to consecutively endure the high spiritual torment in dairy rural lift. In the December of 1978, more than 50 thousand of educated youths in Yunnan province stroked, and petitioned the provincial government, asking for “back to urban.”⁵

Under this situation, the former chairman of Chinese Communist Party CCP), Hu Yaobang, suggested ceasing the “educated-youth down-to-countryside” policy in 8th, May of 1980. In accordance with his suggestion, in 1st, October of 1980, the central committee of the CCP approved that the peasant educated youths could go back their hometown cities.⁶

Numerous literatures demonstrate that most educated urban youth were reluctant to go to the countryside and tried hard to return to cities they lived before (Bernstein, 1977; Chen and Cheng, 1999; Rosen, 1981; Zhou and Hou, 1999).

Educated urban youth did farm work which most of them have never done before. Compared to cities, they have no modern living and medical facilities in the countryside. They needed to endure and overcome various hardships. Poor conditions of rural residence leaded to no voluntary mobility from urban to rural areas after1949 in China (Wu and Treiman, 2004). Meanwhile, educated urban youth took part in political study usually held in the evening. In the early years, educated urban youth were obliged to recite Chairman Mao’s quotations under the oppressive atmosphere of political study. The thoughts, speech, and acts of the educated urban youth are not free (Bonnin and Horko, 2013). In addition, the educated urban youth lost the opportunity to study when they were children, causing a long and lasting influence for most of them. Educated urban youth are called a “lost generation” since they lost their youthful and pure idealism in the political environment (Bonnin, 2016).

However, it is possible that many of the educated urban youth found ways to cope with and survive the hardships resulted from the peasant youth experience. The extreme hardship in the countryside forced educated urban youth deeply understand the hardships of agricultural labor, and more adaptive and resilient in the later period of economic reform (Chen and Cheng, 1999).

Conceptual framework

The impact of traumatic events on the risk appetite of managers is persistent

Evidence from social psychology literatures suggests that people suffered from disasters are more likely to exhibit increased levels of stress after the trauma (e.g. Brodie et al., 2006; Colville and Cream, 2009; Hammarberg and Silver, 1994). Specifically, the cognitive processing of trauma may trigger emotionally memory, causing the confusion between the past trauma and the current circumstances. People experiencing trauma tend to be immersed in the past and result in high levels of psychological stress (Holman and Silver, 1998; Kleim and Ehlers, 2009).

Moreover, experiencing disasters could affect individuals’ risk preferences. For example, Sacco et al. (2003) find that people would make more conservative and less risky decisions after the “9/11” terrorist attack. They subscribe such behavior to a way of compensating for the feelings of insecurity caused by the disaster. In comparison, Eckel et al. (2009) examine the short-term impact of Hurricane Katrina evacuees in the US and find the evacuees exhibit risk-loving behavior. They attribute the risk-loving choices to the heightened stress of the evacuees shortly after the hurricane. Similarly, Li et al. (2011) investigate the effects of disasters on risky decision making through an unprecedented snowstorm and a major earthquake in China. They suggest that people are not inclined to risk averse after a disaster.

Some literatures have investigated the effect of peasant youth experience on educated urban youth. Zhou and Hou (1999) show that all social groups in China were affected by the “peasant youth” policy, while the power class had some capacity to reduce some adverse impact on their children. Chen and Cheng (1999) argue that the miserable experience in the countryside resulted in educated urban youth more adaptive and resilient in the later period of economic reform. Yang (2003) addresses that the hardship experiences in the countryside have benefited the educated urban youth. Xie et al. (2008) find that compared with urban youth, the proportion of the educated urban youth who attained a college education after 1977 is significantly higher.

Like the Great Famine event, which has been extensively researched,⁷ the working experience in the countryside of CEOs also has a far-reaching influence across China. It was an event that brought mental suffering and thus has a continued physical and psychological impact on the individuals concerned. Individual thinking patterns, behavioral choices, and heterogeneity between groups are all affected by this experience, even after many years (Castillo and Carter, 2011). Long-term and multiple secondary catastrophic events increase perceptions of uncertainty and change cognitive preferences and attitudes toward risk (Covello et al., 2001). People who have experienced severe traumatic events at an early age will show greater risk aversion when they grow up (Kim and Lee, 2014). Accordingly, the experience and memory of traumatic events will lead to heterogeneity between individuals. The significant differences in the attitudes toward risk preference between individuals are an important manifestation of this heterogeneity.

Corporate executives who experienced the Great Depression may become more risk-averse and choose to maintain a lower debt ratio (Malmendier et al., 2011). For a long period after the Great Depression, companies still maintained a low debt ratio (Graham and Narasimhan, 2004). Domestic scholars have found that managers who experienced the Great Famine during childhood and adolescence (the specific age group is selected with reference to research into traumatic events, and is usually around 7–15 years old) are more averse to risk, choosing more conservative financial, investment, and cash holding policies than their counterparts (Feng and Johansson, 2018). Although there are major differences in the environment and time of the Great Depression and the Great Famine, these two events both bring serious material poverty and deprivation to the individuals who experienced them. Consequently, the experiences of severe material deprivation in the early stages of development have been found to shape the future risk aversion attitudes of managers, and they prefer conservative decisions in their corporate decision-making.

Degrees of experienced events and managers’ risk appetites

The different degrees of severity of the event experiences also have various dramatic impacts on the risk appetites of managers (Bernile et al., 2017; Castillo and Carter, 2011). The impact of a traumatic experience on individual behavioral decisions depends on the individual’s understanding of the trauma caused by the event; thus, people of different ages have different memories of the same experience. Psychological research shows that some strong memory experiences will lead to strong feelings and form unique perceptions and understandings in the individual. The individual will then use her current knowledge to integrate the information, and the result of this processing determines the formation of risk perception to different degrees in individuals (Shi, 2010).

For individuals experiencing moderate or weak events, these experiences bring trauma but simultaneously the ability to respond to crisis situations. They are likely to attribute their development to their strong coping ability under such experiences of small and medium-level suffering. These individuals are likely to be overconfident when they encounter risk events in the future, as medium and low levels of traumatic event memory lead to higher risk tolerance and appetite for risk. However, for those who have experienced major trials, disasters, or more incidents of hardship, the experiences will not only lead to a more stable attitude toward life but also improve their risk management abilities. Thus, the early years of an experience that has more serious consequences will affect executives’ risk decisions in many ways and will ultimately enhance their abilities and enable them to achieve high levels of corporate performance under a steadier approach to risk management.

Identification of peasant youth experiences

In this study, the identification managers have with their peasant youth experiences is derived from the information disclosures online of chairmen.⁸ We identify the CEOs with “peasant educated youth” experience by precisely looking into the CEO’s information online. That said, we collect the background information of CEOs through two ways. One is looking into CVs of CEOs disclosed by themselves on the Baidu Wikipedia, through which we find 22 CEOs with peasant educated youth experience as documented in the previous draft. Another way is looking through the media reports online through Baidu searching engine, the biggest searching tool in China, through which we confirm another 55 CEOs with peasant educated youth experience. In details, we use three keywords “peasant educated youth”(知青，知识青年in Chinese),“down to the countryside”(上山下乡in Chinese), “educated youth working in the countryside” (下乡插队in Chinese), combined with the names of CEOs, to screen the related online-reports through Baidu searching engine. We then manually scrutinize the searching results one by one, and verify total 77 CEOs with peasant educated youth experience, and the 466 firm-year observations involved with these CEOs.

Hypothesis development

The analysis of the above literature demonstrates that although the main focuses on special event experience, managers’ personal experience, and managers’ endogenous incentives and risk preferences are very clear, there is still a lack of research into the impact the specific spiritual suffering experiences of managers who worked in the countryside from 1957 to 1976 has on their decision-making behavior.

Managers’ peasant youth experiences and risk preference

Spiritual suffering is a key part of the educated youth who volunteered to participate in the countryside work program from 1957 to 1976. The pressure has left them with indelible scars, but they have realized enormous improvement in their ability to analyze and solve problems as educated youth. The hard-won learning opportunities also enable them to cherish and achieve continuous progress through gaining more knowledge. These unforgettable special event memories naturally provide them with a deeper understanding of the peasant youth experience than their peers. Therefore, we expect that these special experiences will make these managers more affected by this period, and thus belong to the category of “strong event experiences.” When they become senior managers, they are more likely to avoid possible risks, and they thus show a more conservative management style. We therefore propose the following hypothesis.

Hypothesis 1: Executives with peasant youth experiences have a stronger risk-aversion tendency and a more conservative decision-making style, and they are less risk-taking.

Managers’ peasant youth experiences and corporate performance

Corporate performance is a key indicator that investors consider, and it is a comprehensive reflection of the capabilities of an enterprise management team. Managers who have a deep memory of their working experience in the countryside are more cautious in making decisions because of their sensitivity to risks. They pay more attention to maintaining the stable growth of enterprises than to making quick profits. Under this prudent management style, they are more likely to seek better solutions and projects, and they thus play a significant role in improving enterprise performance. Therefore, we propose the following hypothesis.

Hypothesis 2: Executives with peasant youth experiences will significantly improve the performance of their businesses.

Data and empirical models

Data

Our sample includes A-share Chinese listed firms from 2007 to 2015. The company characteristic data are from the CSMAR and RESSET databases.⁹ Each company’s executive lists for each year are from the Wind Database.¹⁰ We obtain managers’ characteristic data from the personal information category in the CSMAR Database and search the missing data by hand. We process the original data and exclude (1) financial listed firms; (2) ST (Special Treatment) and PT (Particular Transfer) firms; (3) observations in which there are no senior managers or changes of senior managers in that year and observations without disclosures of managers’ ages. Additionally, we winsorize the main continuous variables of Earnings Volatility, Leverage, Stock Return Volatility, ROA, ROE, Tobin’s Q, Size, and Growth at the 1st and 99th percentiles in our regressions. We obtain 17,720 observations including characteristic variables of companies and managers.

The data of chairmen’s peasant youth experiences are collected from online information using the Baidu searching engine and web crawler technology, as detailedly described in section 2.2.3.¹¹ We collect total 77 CEOs with peasant educated youth experience, and the 466 firm-year observations involved with these CEOs. Then we use the method of PSM (1:3 nearest-neighbor-algorithm-without-replacement) to obtain 1398 matching observations as our control groups. That said, we totally have 1864 observations in the sample, including 466 observations in the treatment group and 1398 observations in the control group. After samples with omitted variables, we finally include 1359 observations for exploiting regressions.

Propensity score matching method

We use propensity score matching (PSM) to address the selection bias of our sample before investigating the effect of chairmen with peasant youth experiences on risk preference and corporate performance. Thus, we estimate the following logit regression model to calculate the propensity score for matching:

\begin{array}{l} L o g i t P_{{EYexp,}_{i, t}} = α_{0} + α_{1} S i z e_{i, t} + α_{2} G r o w t h_{i, t} + α_{3} T O P_{i, t} + α_{4} S O E_{i, t} \\ + α_{5} A g e_{i, t} + α_{6} Y e a r_{i, t} + α_{7} I n d u s t r y_{i, t} + ε_{i, t} \end{array}

(1)

where P denotes the matching score; Size refers to the size of the firm; Growth measures the growth rate of the operating income; TOP measures ownership concentration; SOE is a dummy variable and denotes the ownership nature of the firm; Age is the age of the chairman,¹² and Year and Industry control the impacts of the year and the industry, respectively.

After obtaining the propensity score as a matching variable, we construct the treatment and control groups. Following Rosenbaum and Rubin (1985), when the absolute values of the standard deviations of the matching variables of the treatment and the control groups are significantly less than 20%, the matching variables and matching methods can be regarded as effective. Thus, we assess the effectiveness of the results of propensity score matching using this method.

Econometric model

First, we examine the effect of the chairmen with peasant youth experiences on risk preferences and corporate performance. We estimate the following regression:

Y_{i, t} = α + β_{1} P Y E x p_{i, t} + λ X_{i, t} + Y e a r + I n d u s t r y + ε_{i, t}

(2)

where i indexes the firm and t indexes time. The dependent variables Y consist of two categories: risk-taking and corporate performance variables. With reference to the literature, we choose the following three indicators to measure the level of enterprise risk-taking.

Earnings volatility: The risk-taking degree of the firm determines whether its future cash inflow is stable. The earnings volatility of the enterprise is a widely used variable for measuring the risk-taking degree of the firm. Following Faccio et al. (2011, 2016), we use ROA volatility to measure the risk-taking degree. The observation period uses a three-year rolling method.

Leverage: MM theory (Modigliani and Miller, 1958) suggests that a reasonable choice of debt level for a company is a major risk decision, and it is used to achieve stable development and to maximize value. Thus, we choose the asset liability ratio to measure the risk preference of the chairmen.

Stock return volatility: Studies have shown that the ex-post performance of corporate stock in the market also depends on the risk decision-making of managers in the early stage, that is, the attitude of senior managers toward risk (Bernile et al., 2017). Accordingly, we also use the stock return volatility as an indicator variable to evaluate managers’ risk preference, which is a popular variable denoting firm’s risk-taking in the prior literature (e.g. Li et al., 2013).

In terms of corporate performance, return on total assets (ROA), return on equity (ROE), and market value (Tobin’s Q) are used as indicators to measure the degree of risk-taking (Baker et al., 2012; Huang et al., 2011). We analyze the effect of chairmen with peasant youth experiences in period t on corporate performance in period t+1.

The independent variable PYExp is a dummy variable that equals 1 if the chairman has peasant youth experience and 0 otherwise. X denotes a vector of control variables. We control corporate characteristic variables including enterprise size (Size), operating income growth (Growth), ownership concentration (TOP), property state (SOE), and managers’ personal characteristic variables, including a CEO dual position indicator (Dual), CEO age (Age), CEO gender (Gender), and CEO education (Education). We also introduce the chairmen’s Great Famine experience GFExp as a control variable (Feng and Johansson, 2018; Malmendier et al., 2011).¹³ GFExp is a dummy variable that equals 1 if the chairman has “Great Famine” experience from 1959 to 1961 and 0 otherwise. In addition, Year and Industry are used to control the impact of time and industry, respectively. Detailed definitions of each variable are given in Table 1.

Table 1.

Variable description.

Category	Variable name	Variable Abbr.	Description
Dependent variables: Risk-taking degree	Earnings volatility	EVol	Annual ROA volatility adjusted by industry average.
	Leverage	Leverage	The ratio of total liabilities to total assets.
	Stock return volatility	StockRVol	Annual volatility of daily return of corporate stocks.
Dependent variables: Corporate performance	Return on assets	ROA	The ratio of net profits to total assets.
	Return on equity	ROE	The ratio of net profits to owner’s equity.
	Tobin’s Q	Tobin’s Q	The ratio of enterprise market value to total asset.
Independent variable	Peasant youth experience	PYExp	A dummy variable that equals 1 if CEO has peasant youth experience, otherwise equals 0.
Control variables	“Great Famine” experience	GFExp	A dummy variable that equals 1 if CEO has “Great Famine” experience in 1959–1961, otherwise equals 0. GFExp-i (i = 1,2,3,4) respectively represent the CEO was in the birth cohort of infants, childhood, youth and adults during “Great Famine.”
	Size	Size	Natural logarithm of total assets.
	Growth	Growth	The ratio of operating income of the current year to that of the previous year.
	Ownership concentration	TOP	The shareholding ratio of the largest shareholder.
	State-ownership dummy	SOE	A dummy variable that equals 1 for state-owned-enterprises (SOEs), otherwise equals 0.
	Dual position	Dual	A dummy variable that equals 1 if CEO has dual position of CEO and general manager simultaneously, otherwise equals 0.
	CEO age	Age	Age of CEO.
	CEO gender	Gender	A dummy variable that equals 1 if CEO is male, otherwise equals 0.
	CEO Education	Education	Educational background of the chairman, which equals to 1–6; and respectively represents the qualification of secondary school and below, college, undergraduate, master’s, doctorate and others.
	Year	Year	A dummy variable, controlling the impact of year.
	Industry	Industry	A dummy variable, controlling the impact of industry.

This table reports summary description of all variables.

Main results

Summary statistics

Panel A of Table 2 summarizes the variables that are matched by PSM through a one-to-many matching method, which can effectively reduce bias in the case of inexact matching.

Table 2.

Summary statistics.

Panel A–Descriptive statistics of variables.

Variable	N	Mean	Median	Std. Dev.
Earnings volatility	1804	0.0420	0.0277	0.0715
Leverage	1804	0.5003	0.5108	0.2053
Stock return volatility	1804	0.0316	0.0303	0.0098
Return on assets	1804	0.0436	0.0363	0.0538
Return on equity	1804	0.0829	0.0821	0.1182
Tobin’s Q	1785	1.5420	1.1030	1.4656
Peasant youth experience	1804	0.2511	0.0000	0.4338
“Great Famine” experience	1804	1.7339	2.0000	0.7654
Size	1804	22.6149	22.5097	1.3851
Growth	1804	1.2140	1.1179	0.6824
Ownership concentration (%)	1804	39.9509	39.4900	16.4654
State-ownership	1804	0.6430	1.0000	0.4792
Dual position	1777	0.1289	0.0000	0.3351
CEO age	1804	57.2855	57.0000	5.5889
CEO gender	1797	0.9510	1.0000	0.2159
CEO education	1397	3.3035	3.0000	0.9136

Panel B–Distribution of different ownership enterprises with peasant-youth-experienced CEOs.

Ownership	Number of enterprises	Obs	Proportion
State-owned enterprises	55	290	62.23%
Non-state-owned enterprises	30	176	37.77%

Panel C–Difference of risk-taking and corporate performance between CEO with and without peasant youth experience.

Variable	Obs	Mean	Obs	Mean	Difference between(2) and (4)
	(1)	(2)	(3)	(4)
	(Experience = 1)	(Experience = 1)	(Experience = 0)	(Experience = 0)
Earnings volatility	453	0.047	1351	0.040	0.007
Leverage	453	0.455	1351	0.516	−0.060***
Stock return volatility	453	0.031	1351	0.032	−0.001**
Return on Assets(ROA)	453	0.059	1351	0.039	0.020***
Return on Equity(ROE)	453	0.105	1351	0.075	0.030***
Tobin’s Q	447	1.824	1338	1.448	0.376***

p ⩽ 0.1; **p ⩽ 0.05; ***p ⩽ 0.01.

Panel B of Table 2 reports the number and observations of different ownership enterprises with peasant-youth-experience CEOs. The 466 observations consist of 55 state-owned enterprises (290 observations) and 30 non-state-owned enterprises (176 observations).¹⁴ Panel C of Table 2 presents the t-test results for the difference in risk-taking and corporate performance between CEOs with and without peasant youth experiences. Companies with peasant-youth-experience CEOs have significantly lower stock return volatility than those without peasant-youth-experience CEOs. The t-test results also indicate that ROA, ROE and Tobin’s Q are significantly higher in enterprises with peasant-youth-experience CEOs. Thus, the results demonstrate that these enterprises have significantly lower firm risk and higher corporate performance, which is consistent with hypotheses 1 and 2.

The impact of CEOs’ peasant youth experiences on enterprise risk-taking

PSM model matching results

We first use the 1:3 propensity score matching (PSM) method to address the problem of sample selection bias. The results of PSM, which are based on equation (1), are displayed in Table 3. The absolute standard deviation of the treatment and control groups are given. The standard deviation values of Growth, TOP, SOE, year, and industry are significantly less than 20% after matching. Although the standard deviation values of Size and Age after matching are more than 20%, their values are greatly reduced, from 66.9 and 100.3 to 23.8 and 26.4, respectively. Therefore, the matching result can be regarded as effective.

Table 3.

Comparison of sample balance test before and after PSM: firm’s risk-taking.

Variable	Sample (before or after PSM)	Mean		Std. Dev.%
Variable	Sample (before or after PSM)	Treatment group	Control group	Std. Dev.%
Size	before	22.6950	21.7800	66.9
Size	after	22.6950	22.3700	23.8
Growth	before	1.2559	1.1980	8.0
Growth	after	1.2559	1.2265	4.1
TOP	before	39.5310	36.2170	21.2
TOP	after	39.5310	38.6990	5.3
SOE	before	0.6203	0.4525	34.1
SOE	after	0.6203	0.6101	2.1
Age	before	57.1460	51.2790	100.3
Age	after	57.1460	55.6000	26.4
Year	before	2,010.8000	2,011.6000	−28.7
Year	after	2,010.8000	2,011.1000	−8.7
Industry	before	4.8786	4.8328	1.2
Industry	after	4.8786	4.9263	−1.2

This table reports the mean value and absolute standard deviation of the treatment group and the control group before and after PSM kernel matching when outcome variables are risk-taking degree variables. Definitions of variables are listed in Table 1.

Regression results

We then examine the impact of peasant-youth-experience CEOs on enterprise risk-taking degree. The OLS estimates of equation (2) are presented in Table 4.

Table 4.

The effect of CEOs’ peasant youth experience on firm’s risk-taking.

Dependent variable:	(1)	(2)	(3)
Dependent variable:	Earnings volatility	Leverage	Stock return volatility
PYExp	0.0084	−0.0647***	−0.0010***
PYExp	(0.0075)	(0.0104)	(0.0003)
GFExp-2	−0.0017	−0.0223	−0.0002
GFExp-2	(0.0056)	(0.0169)	(0.0005)
GFExp-3	0.0001	0.0063	−0.0023**
GFExp-3	(0.0095)	(0.0305)	(0.0009)
GFExp-4	−0.0129	−0.0698	−0.0027
GFExp-4	(0.0200)	(0.0460)	(0.0017)
Size	−0.0054**	0.0577***	−0.0013***
Size	(0.0022)	(0.0043)	(0.0001)
Growth	−0.0045*	0.0092	0.0018***
Growth	(0.0026)	(0.0089)	(0.0006)
TOP	−0.0002*	−0.0010***	−0.0000
TOP	(0.0001)	(0.0003)	(0.0000)
SOE	0.0019	0.0382***	−0.0010***
SOE	(0.0044)	(0.0116)	(0.0004)
Dual	0.0005	−0.0178	−0.0003
Dual	(0.0040)	(0.0143)	(0.0005)
Age	−0.0007	−0.0024	0.0001
Age	(0.0007)	(0.0022)	(0.0001)
Gender	0.0150**	0.0239	−0.0006
Gender	(0.0071)	(0.0188)	(0.0006)
Education	0.0004	0.0076	−0.0002
	(0.0020)	(0.0056)	(0.0002)
Constant	0.2129***	−0.6245***	0.0684***
Constant	(0.0601)	(0.1514)	(0.0044)
Observations	1359	1359	1359
Adjusted R ²	0.2315	0.3596	0.7086
Year	YES	YES	YES
Industry	YES	YES	YES

This table reports OLS regression estimates for the relation between CEOs’ peasant youth experience and enterprise risk-taking degree. Dependent variables given in column titles are Earnings Volatility (measured by annual ROA volatility adjusted by industry average), Leverage (measured by total liabilities/total assets) and Stock Return Volatility (measured by annual data on daily return volatility of corporate stocks) respectively. Independent variable is CEOs’ peasant youth experience indicator (PYExp). GFExp-1 is dropped due to collinearity. Definitions of variables are listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicates significance at 1%, 5%, and 10% respectively.

The coefficients of PYExp in Columns (2) and (3) are significant at the level of 1%. The leverage of the enterprises managed by CEOs with peasant youth experiences is thus significantly lower (0.0647) than that of CEOs without peasant youth experiences. In addition, the stock returns volatility is significantly lower (0.0010) in companies that have managers with peasant youth experiences. The results in Table 4 indicate that the risk-taking level of firms with peasant-youth-experience CEOs is lower than that of firms without, which confirms hypothesis 1: executives with peasant youth experiences have a stronger tendency for risk aversion and more conservative decision-making styles, and they take fewer risks.

The impact of CEOs’ peasant youth experiences on corporate performance

PSM model matching results

We also use 1:3 PSM method to match the samples of the treatment group and the control group through the kernel matching method. The results of PSM based on equation (1) are displayed in Table 5. The absolute standard deviation values of Growth, TOP, SOE, year, and industry are found to be significantly below 20% after matching, while the standard deviations of Size and Age are less than those before matching. Thus, the matching results are valid.

Table 5.

Sample balance test before and after PSM: corporate performance.

Variable	Sample (before or after PSM)	Mean		Std. Dev.%
Variable	Sample (before or after PSM)	Treatment group	Control group	Std. Dev.%
Size	before	22.6940	21.7770	67.1
Size	after	22.6940	22.3700	23.7
Growth	before	1.2556	1.1977	8.0
Growth	after	1.2556	1.2269	4.0
TOP	before	39.5130	36.1970	21.3
TOP	after	39.5130	38.6910	5.3
SOE	before	0.6189	0.4513	34.1
SOE	after	0.6189	0.6103	1.8
Age	before	57.1540	51.2720	100.6
Age	after	57.1540	55.5950	26.7
Year	before	2,010.9000	2,011.6000	−28.9
Year	after	2,010.9000	2,011.1000	−8.6
Industry	before	4.8700	4.8398	0.8
Industry	after	4.8700	4.9197	−1.3

This table reports the mean value and absolute standard deviation of the treatment group and the control group before and after PSM kernel matching when outcome variables are corporate performance variables. Definitions of variables are listed in Table 1.

Regression results

In this subsection, we investigate the effect of CEOs’ peasant youth experiences on corporate performance within the above successful matching samples. The OLS estimates of equation (2) are presented in Table 6.

Table 6.

The effect of CEOs’ peasant youth experience on corporate performance.

Dependent variable:	(1)	(2)	(3)
Dependent variable:	ROA_t+1	ROE_t+1	Tobin’s Q_t+1
PYExp	0.0188***	0.0260***	0.3743***
PYExp	(0.0037)	(0.0078)	(0.0850)
GFExp-2	0.0043	0.0075	0.1643
GFExp-2	(0.0052)	(0.0112)	(0.1281)
GFExp-3	0.0026	0.0011	0.3302
GFExp-3	(0.0088)	(0.0186)	(0.2376)
GFExp-4	0.0054	−0.0079	1.0133*
GFExp-4	(0.0165)	(0.0314)	(0.5861)
Size	0.0040***	0.0196***	−0.4815***
Size	(0.0013)	(0.0030)	(0.0297)
Growth	0.0070***	0.0188***	0.1153***
Growth	(0.0024)	(0.0052)	(0.0409)
TOP	0.0002**	0.0002	0.0096***
TOP	(0.0001)	(0.0002)	(0.0023)
SOE	−0.0173***	−0.0302***	−0.2186***
SOE	(0.0035)	(0.0071)	(0.0833)
Dual	−0.0015	0.0018	0.0387
Dual	(0.0044)	(0.0092)	(0.1087)
Age	0.0007	0.0018	−0.0087
Age	(0.0007)	(0.0015)	(0.0161)
Gender	−0.0160***	−0.0284***	−0.2474
Gender	(0.0056)	(0.0104)	(0.2076)
Education	−0.0008	−0.0038	0.0796*
Education	(0.0016)	(0.0035)	(0.0412)
Constant	−0.0819*	−0.4397***	11.7528***
Constant	(0.0484)	(0.1121)	(1.0873)
Observations	1359	1359	1345
Adjusted R ²	0.1261	0.1073	0.3751
Year	YES	YES	YES
Industry	YES	YES	YES

This table reports OLS regression estimates for the relation between CEOs’ peasant youth experience and corporate performance. Dependent variables given in column titles are ROA (measured by net profit/total assets), ROE (measured by net profit/equity) and Tobin’s Q (measured by enterprise market value/total asset) respectively, with the variable values in the second year. Independent variable is CEOs’ peasant youth experience indicator (PYExp). GFExp-1 is dropped due to collinearity. Definitions of variables are detailedly listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.

In Columns (1–3), the coefficients of PYExp are significantly positive at the 1% level. This indicates that managers’ peasant youth experiences can significantly improve performance. The ROA, ROE, and Tobin’s Q of enterprises with peasant-youth-experience CEOs are significantly higher (0.0188, 0.0260, and 0.3743) than those without. Thus, the results suggest that CEOs’ peasant youth experiences play a significant role in improving corporate performance, which confirms hypothesis 2.

Robustness tests

Peasant-youth-experience CEOs in different property rights enterprises

We divide our sample into state-owned and non-state-owned subgroups to analyze whether the reduced risk and enhanced performance effects of CEOs with peasant youth experiences are pronounced in both state-owned enterprises and non-state-owned enterprises. The grouping helps us to mitigate the disclosure bias concern in firms with different ownerships regarding the CEOs.

We use 1:3 PSM for effective sample matching¹⁵ and then conduct the regressions based on equation (2). The results are shown in Table 7. The coefficients of PYExp are our main focus. When the dependent variable is leverage, the coefficients of PYExp in either SOEs group or non-SOEs group are significantly negative, which indicates that the leverage are significantly lower in firms with peasant-youth-experiences CEOs. When the dependent variable is stock returns volatility, the coefficient of PYExp is only significantly negative in non-SOEs group. That said, it seems that the effects of peasant youth experiences on CEO’s risk-taking are pronounced in both state-owned enterprises and non-state-owned enterprises, while the effects in the latter are mildly stronger than the former.

Table 7.

The effect of CEOs’ peasant youth experience on corporate risk-taking in SOEs versus that in non-SOEs.

Dependent variable:	(1)	(2)	(3)	(4)	(5)	(6)
Dependent variable:	Earnings volatility	Earnings volatility	Leverage	Leverage	Stock return volatility	Stock return volatility
Property rights:	SOEs	Non-SOEs	SOEs	Non-SOEs	SOEs	Non-SOEs
PYExp	0.0057	0.0146	−0.0817***	−0.0356*	−0.0006	−0.0018***
PYExp	(0.0052)	(0.0106)	(0.0126)	(0.0187)	(0.0004)	(0.0006)
GFExp-2	−0.0066	0.0093	−0.0326*	0.0112	−0.0000	−0.0000
GFExp-2	(0.0070)	(0.0103)	(0.0190)	(0.0354)	(0.0006)	(0.0011)
GFExp-3	−0.0034	0.0168	−0.0448	0.0665	−0.0011	−0.0031*
GFExp-3	(0.0137)	(0.0175)	(0.0354)	(0.0595)	(0.0012)	(0.0018)
GFExp-4	−0.0251	0.0240	−0.0998*	−0.0179	−0.0004	−0.0051*
GFExp-4	(0.0184)	(0.0280)	(0.0552)	(0.0839)	(0.0018)	(0.0029)
Size	−0.0039	−0.0115***	0.0467***	0.0849***	−0.0011***	−0.0016***
Size	(0.0029)	(0.0043)	(0.0047)	(0.0085)	(0.0001)	(0.0002)
Growth	−0.0027	−0.0046	−0.0024	0.0085	0.0007**	0.0028***
Growth	(0.0042)	(0.0032)	(0.0144)	(0.0103)	(0.0003)	(0.0008)
TOP	−0.0004**	0.0000	−0.0014***	−0.0000	−0.0000	0.0000
TOP	(0.0002)	(0.0002)	(0.0004)	(0.0005)	(0.0000)	(0.0000)
Dual	−0.0055	0.0044	−0.0396**	0.0133	−0.0003	−0.0007
Dual	(0.0047)	(0.0062)	(0.0179)	(0.0210)	(0.0006)	(0.0007)
Age	0.0001	−0.0024*	−0.0011	−0.0033	0.0000	0.0001
Age	(0.0009)	(0.0013)	(0.0026)	(0.0043)	(0.0001)	(0.0001)
Gender	0.0085**	0.0252	−0.0231	0.0834**	−0.0004	−0.0010
Gender	(0.0042)	(0.0171)	(0.0170)	(0.0397)	(0.0007)	(0.0012)
Education	0.0038	−0.0021	0.0005	0.0135*	−0.0003	−0.0000
Education	(0.0035)	(0.0022)	(0.0076)	(0.0079)	(0.0003)	(0.0003)
Constant	0.1482**	0.4090***	−0.2413	−1.3267***	0.0666***	0.0664***
Constant	(0.0705)	(0.1318)	(0.1746)	(0.3072)	(0.0052)	(0.0084)
Observations	858	515	858	515	858	515
Adjusted R ²	0.3178	0.1815	0.3274	0.4204	0.7561	0.7056
Year	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES

This table reports the effect of CEOs’ peasant youth experience on corporate risk-taking in different ownership nature of enterprises. Dependent variables given in column titles are corporate risk-taking variables (Earnings Volatility, Leverage and Stock Return Volatility) in SOEs and non-SOEs respectively. Independent variable is PYExp (peasant-youth-experienced CEOs indicator). GFExp-1 is dropped due to collinearity. Definitions of variables are detailedly listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicate significance at 1%, 5%, and 10% respectively.

In addition, the results in Table 8 show that the effects of CEO’s peasant youth experiences on the market performance of enterprises is significantly positive in both SOEs and non-SOEs in most of cases.

Table 8.

The effect of CEOs’ peasant youth experience on corporate performance in SOEs versus that in non-SOEs.

Dependent Variable:	(1)	(2)	(3)	(4)	(5)	(6)
Dependent Variable:	ROA_t+1	ROA_t+1	ROE_t+1	ROE_t+1	Tobin’s Q_t+1	Tobin’s Q_t+1
Property rights:	SOEs	Non-SOEs	SOEs	Non-SOEs	SOEs	Non-SOEs
PYExp	0.0172***	0.0180***	0.0260**	0.0239**	0.5727***	−0.0159
PYExp	(0.0048)	(0.0058)	(0.0106)	(0.0120)	(0.1029)	(0.1335)
GFExp-2	0.0074	−0.0125	0.0178	−0.0272	0.1245	0.4853*
GFExp-2	(0.0064)	(0.0090)	(0.0146)	(0.0166)	(0.1322)	(0.2802)
GFExp-3	0.0070	−0.0176	0.0108	−0.0375	0.4249	0.5752
GFExp-3	(0.0109)	(0.0152)	(0.0246)	(0.0265)	(0.2807)	(0.4983)
GFExp-4	−0.0355*	0.0115	−0.0696	0.0031	−0.4028	1.8214**
GFExp-4	(0.0192)	(0.0252)	(0.0430)	(0.0420)	(0.3675)	(0.8879)
Size	0.0022	0.0087***	0.0136***	0.0348***	−0.3797***	−0.6552***
Size	(0.0015)	(0.0027)	(0.0033)	(0.0060)	(0.0315)	(0.0655)
Growth	0.0073*	0.0054*	0.0228**	0.0114**	0.1356**	0.0678
Growth	(0.0041)	(0.0029)	(0.0095)	(0.0054)	(0.0599)	(0.0528)
TOP	0.0001	0.0003**	0.0000	0.0006***	0.0035	0.0128***
TOP	(0.0001)	(0.0001)	(0.0003)	(0.0002)	(0.0027)	(0.0038)
Dual	0.0069	−0.0077	0.0170	−0.0053	0.2380	−0.1451
Dual	(0.0070)	(0.0056)	(0.0147)	(0.0121)	(0.1635)	(0.1446)
Age	0.0009	0.0013	0.0020	0.0029	−0.0054	−0.0345
Age	(0.0008)	(0.0011)	(0.0021)	(0.0020)	(0.0142)	(0.0379)
Gender	0.0007	−0.0427***	−0.0200	−0.0563***	0.3976***	−0.8264**
Gender	(0.0057)	(0.0096)	(0.0125)	(0.0194)	(0.1410)	(0.3672)
Education	0.0040	−0.0038*	0.0033	−0.0074*	0.1075**	0.0600
Education	(0.0027)	(0.0022)	(0.0065)	(0.0039)	(0.0540)	(0.0605)
Constant	−0.1117**	−0.1530*	−0.3962***	−0.7317***	7.6210***	17.6485***
Constant	(0.0566)	(0.0864)	(0.1411)	(0.1750)	(0.9811)	(2.3913)
Observations	858	515	858	515	848	511
Adjusted R ²	0.1026	0.2187	0.0934	0.2259	0.3542	0.4518
Year	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES

This table reports the effect of CEOs’ peasant youth experience on corporate performance in different ownership nature of enterprises. Dependent variables given in column titles are corporate performance variables (ROA, ROE and Tobin’s Q, with values in the second year) in SOEs and non-SOEs respectively. Independent variable is PYExp (peasant-youth-experienced CEOs indicator). GFExp-1 is dropped due to collinearity. Definitions of variables are detailedly listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicate significance at 1%, 5% and 10% respectively.

Combining the results in Tables 7 and 8 together, we confirm the stronger effect of CEOs with peasant youth experiences on firm risk and corporate performance in both SOEs and non-SOEs, which indicates that the ownership effects on the roles of peasant youth experiences are mild.

An alternative measure of CEOs’ peasant youth experiences

We use the Cultural Revolution in place of the Peasant Youth Experience to measure CEOs’ spiritual memory to check the robustness of our research. As the Cultural Revolution also had a profound impact on the spiritual hardship of young individuals, CEOs who experienced the Cultural Revolution in their youth are likely to have a deeper spiritual memory (Lingguo and Ye, 2011).

We categorize the managers who were in their youth (12–18 years old) and born between 1948 and 1958, during the Cultural Revolution, as the youth group. The richer life experiences of these managers should mean they have deeper feelings and richer experiences of the Great Cultural Revolution, reflecting the domain of “strong event experience.” In terms of the impact category, we expect these managers to have risk aversion attitudes, which is consistent with the impact of the managers’ peasant youth experiences in our main results. We also repeat the above empirical process, using PSM for effective sample matching and then regressing the successful matching samples.

The regression results are shown in Table 9, which indicates that the earnings volatility and leverage are significantly lower and that the market performance variables (Tobin’s Q) are significantly higher in companies where the managers are in the youth cohort group when experiencing the Cultural Revolution. The evidence shows that managers in the youth cohort group, who have a deeper experience of the Cultural Revolution, have stronger risk aversion and produce higher market performance in their companies, which is consistent with our results.

Table 9.

Regressions based on an alternative proxy of CEOs’ peasant youth experience.

Dependent variable:	(1)	(2)	(3)	(4)	(5)	(6)
Dependent variable:	Earnings volatility	Leverage	Stock return volatility	ROA_t+1	ROE_t+1	Tobin’s Q_t+1
PYExp	−0.0100***	−0.0236**	0.0003	0.0016	0.0002	0.1320*
PYExp	(0.0038)	(0.0099)	(0.0003)	(0.0027)	(0.0057)	(0.0729)
GFExp-2	0.0011	−0.0708***	0.0007	0.0129***	0.0197*	0.4712***
GFExp-2	(0.0064)	(0.0196)	(0.0006)	(0.0050)	(0.0101)	(0.1356)
GFExp-3	−0.0159*	−0.0758**	−0.0000	0.0240***	0.0371**	0.6989***
GFExp-3	(0.0095)	(0.0301)	(0.0009)	(0.0083)	(0.0164)	(0.2330)
Size	−0.0054***	0.0694***	−0.0011***	−0.0006	0.0087***	−0.6530***
Size	(0.0013)	(0.0031)	(0.0001)	(0.0009)	(0.0019)	(0.0280)
Growth	−0.0004	0.0176***	0.0008***	0.0075***	0.0171***	0.1509***
Growth	(0.0036)	(0.0067)	(0.0003)	(0.0014)	(0.0029)	(0.0557)
TOP	−0.0001	−0.0015***	0.0000**	0.0002***	0.0003**	0.0045**
TOP	(0.0001)	(0.0002)	(0.0000)	(0.0001)	(0.0001)	(0.0019)
SOE	−0.0039	0.0573***	−0.0006**	−0.0161***	−0.0303***	−0.3802***
SOE	(0.0033)	(0.0083)	(0.0003)	(0.0023)	(0.0048)	(0.0633)
Dual	0.0063	0.0010	−0.0002	−0.0026	−0.0066	−0.1473**
Dual	(0.0041)	(0.0093)	(0.0003)	(0.0026)	(0.0058)	(0.0722)
Age	0.0007	0.0057**	−0.0001	−0.0017**	−0.0024*	−0.0586***
Age	(0.0010)	(0.0026)	(0.0001)	(0.0007)	(0.0014)	(0.0187)
Gender	0.0032	0.0548***	−0.0005	−0.0050	−0.0121	−0.0679
Gender	(0.0066)	(0.0131)	(0.0005)	(0.0051)	(0.0112)	(0.0921)
Education	−0.0000	0.0007	0.0001	0.0005	0.0009	0.0956***
Education	(0.0015)	(0.0037)	(0.0001)	(0.0011)	(0.0023)	(0.0314)
Constant	0.1583***	−1.2714***	0.0670***	0.1106***	−0.0359	17.6576***
Constant	(0.0508)	(0.1375)	(0.0044)	(0.0390)	(0.0833)	(1.0648)
Observations	3,251	3,229	3,246	3,229	3,231	3,253
Adjusted R ²	0.1555	0.3340	0.6652	0.0604	0.0536	0.3749
Year	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES

This table reports the effect of CEOs with peasant youth experience replaced by “Great Cultural Revolution” experience on corporate risk-taking and performance. Dependent variables given in column titles are corporate risk-taking variables (Earnings Volatility, Leverage and Stock Return Volatility) and corporate performance variables (ROA, ROE and Tobin’s Q, with values in the second year) respectively. Independent variables are PYExp (measured by CEO experiencing “culture revolution” in the youth cohort). GFExp-1 and GFExp-4 are dropped due to collinearity. Definitions of variables are detailedly listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicate significance at 1%, 5%, and 10% respectively.

Introducing more control variables relative to firm’s characteristics

The firm’s characteristics might greatly impact on firm’s risk-taking, leading the estimating bias in our regressions. For example, the non-SOEs might be more risk-loving due to affording less social responsibility. To rule out this concern, we further present an additional test by incorporating three more control variables into the regressions for capturing more effects of firm’s risk-preference characteristics. These variables include the ratio of cash(Cash), computed as the cash and equivalents scaled by total assets, the Non-debt Tax Shields(NDTS), computed by the depreciation and amortization for fixed assets and intangible assets, plus long-term prepaid expenses, and scaled by total assets, and the Size of firm’s board(Board), calculated by the logarithm of one plus the numbers of directors in board. Since firms with high risk-preference would like to hold less cash, accrue lower NDTS and maintain a smaller size of board, we use these variables to robustly isolate the effect of firm’s risk-preference.

The relative results are displayed in Table 10, which are consistent with the results in Tables 4 and 6, highlighting that our findings are robust.

Table 10.

Regressions involved more variables of firm’s characteristics.

Dependent variable:	(1)	(2)	(3)	(4)	(5)	(6)
Dependent variable:	Earnings volatility	Leverage	Stock return volatility	ROA_t+1	ROE_t+1	Tobin’s Q_t+1
PYExp	0.0115	−0.0512***	−0.0010***	0.0161***	0.0230***	0.3074***
PYExp	(0.0145)	(0.0096)	(0.0003)	(0.0034)	(0.0073)	(0.0820)
GFExp-2	−0.0033	−0.0278*	−0.0001	0.0063	0.0093	0.2145*
GFExp-2	(0.0058)	(0.0155)	(0.0005)	(0.0048)	(0.0107)	(0.1238)
GFExp-3	−0.0041	−0.0055	−0.0022**	0.0094	0.0113	0.4083*
GFExp-3	(0.0109)	(0.0297)	(0.0010)	(0.0082)	(0.0183)	(0.2293)
GFExp-4	−0.0149	−0.0855**	−0.0026	0.0136	0.0028	1.1265*
GFExp-4	(0.0197)	(0.0429)	(0.0017)	(0.0156)	(0.0304)	(0.5796)
Size	−0.0049**	0.0495***	−0.0014***	0.0058***	0.0218***	−0.4245***
Size	(0.0020)	(0.0042)	(0.0001)	(0.0013)	(0.0030)	(0.0292)
Growth	−0.0032	0.0023	0.0018***	0.0074***	0.0185***	0.1398***
Growth	(0.0023)	(0.0074)	(0.0005)	(0.0028)	(0.0059)	(0.0375)
TOP	−0.0002*	−0.0004	0.0000	0.0001	0.0001	0.0066***
TOP	(0.0001)	(0.0003)	(0.0000)	(0.0001)	(0.0002)	(0.0023)
SOE	0.0001	0.0248**	−0.0009***	−0.0134***	−0.0230***	−0.1420*
SOE	(0.0043)	(0.0108)	(0.0004)	(0.0033)	(0.0070)	(0.0832)
Cash	0.0058	−0.5066***	−0.0036***	0.1313***	0.1694***	2.7967***
Cash	(0.0273)	(0.0526)	(0.0012)	(0.0124)	(0.0252)	(0.3617)
NDTS	0.4253	−0.0490	−0.0222***	−0.0354	−0.2277	−0.9853
NDTS	(0.4731)	(0.8373)	(0.0064)	(0.0631)	(0.1732)	(2.0366)
Board	−0.0036	0.0164	−0.0015*	0.0118	0.0136	0.1172
Board	(0.0100)	(0.0253)	(0.0009)	(0.0075)	(0.0150)	(0.2193)
Dual	0.0005	−0.0048	−0.0002	−0.0051	−0.0024	−0.0494
Dual	(0.0038)	(0.0125)	(0.0005)	(0.0040)	(0.0086)	(0.1023)
Age	−0.0006	−0.0008	0.0001	0.0001	0.0010	−0.0164
Age	(0.0007)	(0.0020)	(0.0001)	(0.0006)	(0.0015)	(0.0157)
Gender	0.0181***	0.0295**	−0.0008	−0.0119**	−0.0193**	−0.2399
Gender	(0.0064)	(0.0140)	(0.0006)	(0.0053)	(0.0093)	(0.2082)
Education	0.0005	0.0137***	−0.0001	−0.0028*	−0.0070**	0.0415
Education	(0.0019)	(0.0050)	(0.0002)	(0.0015)	(0.0034)	(0.0409)
Constant	0.1881***	−0.5034***	0.0726***	−0.1369***	−0.5070***	10.2852***
Constant	(0.0592)	(0.1408)	(0.0044)	(0.0454)	(0.1099)	(1.1368)
Observations	1,340	1,340	1,340	1,340	1,340	1,326
Adjusted R ²	0.2498	0.4661	0.7302	0.2280	0.1530	0.4263
Year	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES

This table reports the effect of CEOs with peasant youth experience by introducing three more variables relative to firm’s characteristics. Dependent variables given in column titles are corporate risk-taking variables (Earnings Volatility, Leverage and Stock Return Volatility) and corporate performance variables (ROA, ROE and Tobin’s Q, with values in the second year) respectively. Independent variables are PYExp (peasant-youth-experienced CEOs indicator). Cash is computed as the cash and equivalents scaled by total assets, the Non-debt Tax Shields(NDTS), is computed by the depreciation and amortization for fixed assets and intangible assets, plus long-term prepaid expenses, and scaled by total assets, and the Size of firm’s board(Board), is calculated by the logarithm of one plus the numbers of directors in board. GFExp-1 is dropped due to collinearity. Definitions of other variables are detailedly listed in Table 1. Robust standard errors are reported in parentheses. ***, ** and * indicate significance at 1%, 5%, and 10% respectively.

Conclusion

Traumatic events experienced by executives, particularly the hardships of natural disasters, are considered to be critical factors affecting the strategic decision-making of their companies. However, little research has been conducted on the impact of spiritual hardship. This study focuses on the major spiritual tempering event of working in the countryside, as experienced by educated youth from 1957 to 1976 in China, and who are now managers. This is the first study to examine the risk preferences and enterprise performance value created by managers who have peasant youth experiences.

Our evidence suggests that the peasant youth experiences of CEOs have a significant positive impact on their decision-making, as CEOs with such experiences prefer less risk and achieve better corporate performance. We also examine the impact of mental hardship on the management behavior of executives in different property state enterprises, and we find that the influence of managers’ peasant youth experience factors on enterprise risk decision-making and market performance is mildly affected by firm’s ownership. Our study rigorously confirms that spiritually tempering events have a significant impact on the risk preference of senior managers. In addition, compared with the Great Famine, the peasant experiences of CEOs have a significantly positive impact on their decision-making. The results demonstrate that spiritual tempering in youth is extremely important to the development of successful CEOs.

Footnotes

Acknowledgements

We warmly thank the insightful suggestions from the managing editor, Elissa Wolfson and the two anonymous reviewers.

Author contributions

The authors contributed equally and are listed alphabetically.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Kebin Deng acknowledges the financial support from the Fundamental Research Funds of the Central Universities [grant number XYZD201905]. The other authors have nothing to declare.

ORCID iD

Zhong Ding

Notes

Author biographies

Kebin Deng is a professor of South China University of Technology. His research field includes behavioral economics, economic transition and corporate finance.

Zhong Ding is an associate professor of Guangdong University of Foreign Studies. Her research field includes behavioral economics and corporate finance.

Yalu Wang is a graduate student of South China University of Technology. Her research field includes behavioral economics and corporate finance.

References

Adams

Almeida

Ferreira

(2005) Powerful CEOs and their impact on corporate performance. Review of Financial Studies 18(4): 1403–1432.

Adams

Ferreira

(2009) Women in the boardroom and their impact on governance and performance. Journal of Financial Economics 94(2): 291–309.

Adhikari

(2018) Female executives and corporate cash holdings. Applied Economics Letters 25(13): 958–963.

Almond

Edlund

, et al. (2006) Long-term Effects of the 1959-1961 China Famine: Mainland China and Hong Kong. NBER Working Paper. New York: Columbia University.

Baker

Dutta

Saadi

, et al. (2012) Are good performers bad acquirers? Financial Management 41(1): 95–118.

Belghitar

Clark

(2012) The effect of CEO risk appetite on firm volatility: An empirical analysis of financial firms. International Journal of the Economics of Business 19(2): 195–211.

Bernile

Bhagwat

Rau

(2017) What doesn’t kill you will only make you more risk-loving: Early-life disasters and CEO Behavior. Journal of Finance 72(1): 167–206.

Bernstein

(1977) Up to the Mountains and Down to the Villages: The Transfers of Youth from Urban to Rural China. New Heaven: Yale University Press.

Bertrand

Schoar

(2003) Managing with Style: The effect of managers on firm policies. Quarterly Journal of Economics 118(4): 1169–1208.

10.

Bonnin

Horko

(2013) The Lost Generation: The Rustication of China’s Educated Youth (1968–1980). Hong Kong: The Chinese University of Hong Kong Press.

11.

Bonnin

(2016) Restricted, distorted but alive: The memory of the “lost generation” of Chinese educated youth. The China Quarterly 227: 752–772.

12.

Brodie

Weltzien

Altman

, et al. (2006) Experiences of Hurricane Katrina evacuees in Houston Shelters: Implications for future planning. American Journal of Public Health 96(8): 1402–1408.

13.

Cain

Mckeon

(2016) CEO personal risk-taking and corporate policies. Journal of Financial and Quantitative Analysis 51(1): 139–164.

14.

Castillo

Carter

(2011) Behavioral responses to natural disasters. Working Papers 143(6): 724–732.

15.

Colville

Cream

(2009) Post-traumatic growth in parents after a child’s admission to intensive care: Maybe Nietzsche was right? Intensive Care Medicine 35(5): 919–923.

16.

Covello

Peters

Wojtecki

, et al. (2001) Risk communication, the West Nile virus epidemic, and bioterrorism: Responding to the communication challenges posed by the intentional or unintentional release of a pathogen in an urban setting. Journal of Urban Health 78(2): 382–391.

17.

Chen

Cheng

(1999) Comment on Zhou & Hou: A negative life event with positive consequences? American Sociological Review 64(1): 37–40.

18.

Chen

Zhou

(2007) The long-term health and economic consequences of the 1959–1961 famine in China. Journal of Health Economics 26(4): 659–681.

19.

Custódio

Metzger

(2013) Financial expert CEOs: CEO’s work experience and firm’s financial policies. Journal of Financial Economics 114(1): 125–154.

20.

Dittmar

Ran

(2016) Looking in the rearview mirror: The effect of managers’ professional experience on corporate financial policy. Review of Financial Studies 29(3): 565–602.

21.

Doukas

Petmezas

(2007) Acquisitions, overconfident managers and self-attribution bias. European Financial Management 13(3): 531–577.

22.

Eckel

El-Gambal

Wilson

(2009) Risk loving after the storm: A Bayesian-Network study of Hurricane Katrina evacuees. Journal of Economic Behavior and Organization 69(2): 110–124.

23.

Elder

(1999) Children of the Great Depression: Social Change in Life Experience. Boulder, CO: Westview Press.

24.

Faccio

Marchica

Mura

(2016) CEO gender, corporate risk-taking, and the efficiency of capital allocation. Journal of Corporate Finance 39: 193–209.

25.

Faccio

Mura

Marchica

(2011) Large shareholder diversification and corporate risk-taking. The Review of Financial Studies 11(11): 3601–3641.

26.

Farag

Mallin

(2016) The influence of CEO demographic characteristics on corporate risk-taking: Evidence from Chinese IPOs. The European Journal of Finance 24(16): 1528–1551.

27.

Fee

Hadlock

Pierce

(2013) Managers with and without Style: Evidence using exogenous variation. Review of Financial Studies 26(3): 567–601.

28.

Feng

Johansson

(2018) Living through the Great Chinese Famine: Early-life experiences and managerial decisions. Journal of Corporate Finance 48: 638–657.

29.

Finkelstein

Hambrick

(1996) Strategic Leadership: Top Executives and their Effects on Organizations. Minneapolis, MN: West Publishing Company.

30.

Finkelstein

Hambrick

Cannella

(2009) Strategic Leadership: Theory and Research on Executives, Top Management Teams, and Boards. New York, NY: Oxford University Press.

31.

Gorgens

Meng

Vaithianathan

(2005) Stunting and Selection Effects of Famine: A Case Study of the Great Chinese Famine. Working Paper, Australian National University.

32.

Graham

Narasimhan

(2004) Corporate Survival and Managerial Experiences During the Great Depression. Working Paper, Duke University.

33.

Hambrick

Finkelstein

(1987) Managerial discretion: A bridge between polar views of organizations. In: Cummings

Staw

(eds) Research in Organizational Behavior, vol. 9. Greenwich, CT: JAL Press.

34.

Hammarberg

Silver

(1994) Outcome of treatment for post-traumatic stress disorder in a primary care unit serving Vietnam veterans. Journal of Traumatic Stress 7(2): 195–216.

35.

Holman

Silver

(1998) Getting “stuck” in the past: Temporal orientation and coping with trauma. Journal of Personality and Social Psychology 74(5): 1146–1163.

36.

Huang

Jiang

Liu

, et al. (2011) Agency cost, top executives’ overconfidence, and investment-cash flow sensitivity – Evidence from listed companies in China. Pacific-Basin Finance Journal 19(3): 261–277.

37.

Jacoby

Kelley

(1987) Unconscious influence of memory for a prior event. Personality and Social Psychology Bulletin 13(3): 314–336.

38.

Kaplan

Klebanov

Sorensen

(2012) which CEO characteristics and abilities matter? Journal of Finance 67(3): 973–1007.

39.

Kleim

Ehlers

(2009) Evidence for a curvilinear relationship between posttraumatic growth and post trauma depression and PTSD in assault survivors. Journal of Traumatic Stress 22(1), 45–52.

40.

Kim

Lee

(2014) The long-run impact of a traumatic experience on risk aversion. Journal of Economic Behavior & Organization 108: 174–186.

41.

Wang

, et al. (2011) Are People always more risk averse after a major snow-hit and a major earthquake in China in 2008. Applied Cognitive Psychology 25(1): 104–111.

42.

Griffin

Yue

, et al. (2013) How does culture influence corporate risk-taking? Journal of Corporate Finance 23: 1–22.

43.

Lingguo

(2011) Does famine experience in childhood influence one’s saving decision? —A new explanation of China’s high household saving rate. Economic Research Journal 2011(8): 119–132. (in Chinese)

44.

Malmendier

Tate

Yan

(2011) Overconfidence and early-life experiences: The effect of managerial traits on corporate financial policies. Journal of Finance 66(5): 1687–1733.

45.

Meng

Qian

(2009) The Long Term Consequences of Famine on Survivors: Evidence from a Unique Natural Experiment using China’s Great Famine. NBER Working Paper. Cambridge, MA: National Bureau of Economic Research.

46.

Modigliani

Miller

(1958) The cost of capital, corporation finance and the theory of investment. American Economic Review 48(3): 261–297.

47.

Pansiri

(2005) The influence of managers’ characteristics and perceptions in strategic alliance practice. Management Decision 43(9): 1097–1113.

48.

Rosen

(1981) The role of sent-down youth in the Chinese Cultural Revolution: The case of Guangzhou. Berkeley, CA: The Regents of the University of California.

49.

Rosenbaum

Rubin

(1985) Discussion of “On State Education Statistics”: A difficulty with regression analyses of regional test score averages. Journal of Educational Statistics 10(4): 326–333.

50.

Roussanov

Savor

(2014) Marriage and managers’ attitudes to risk. Management Science 60(10): 2496–2508.

51.

Sacco

Galletto

Blanzieri

(2003) How has the 9/11 terrorist attack influenced decision making? Applied Cognitive Psychology 17(9): 1113–1127.

52.

Shi

(2010) Disaster Psychology. Beijing: Science Press, pp. 35–70. (in Chinese).

53.

Skała

Weill

(2018) Does CEO gender matter for bank risk? Economic Systems 42(1): 64–74.

54.

Tyler

Steensma

(1998) The effects of executives’ experiences and perceptions on their assessment of potential technological alliances. Strategic Management Journal 19(10): 939–965.

55.

Walder

(1994) Collective behavior revisited: Ideology and politics in the Chinese cultural revolution. Rationality and Society 6(3): 400–421.

56.

Treiman

(2004) The household registration system and social stratification in China: 1955–1996. Demography 41(2): 363–384.

57.

Xie

Jiang

Greenman

(2008) Did send-down experience benefit youth? A reevaluation of the social consequences of forced urban–rural migration during China’s Cultural Revolution. Social Science Research 37(2): 687–700.

58.

Zhang

, et al. (2016) Is natural experiment a cure? Re-examining the long-term health effects of China’s 1959–1961 famine. Social Science & Medicine 148: 110–122.

59.

Yang

(2003) China’s Zhiqing generation: Nostalgia, identity, and cultural resistance in the 1990s. Modern China 29(3): 267–296.

60.

Zhou

Hou

(1999) Children of the cultural revolution: The state and the life course in the People’s Republic of China. American Sociological Review 64(1): 12–36.