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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychol.</journal-id>
<journal-title>Frontiers in Psychology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychol.</abbrev-journal-title>
<issn pub-type="epub">1664-1078</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyg.2022.1041630</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>CEOs&#x00027; early-life disaster experience and corporate earnings quality: Focusing on the Great Chinese Famine</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Zhao</surname> <given-names>Yang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1632027/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Hu</surname> <given-names>Jun</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1536066/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Lang</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2103020/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>School of Management, Jinan University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Management School, Hainan University</institution>, <addr-line>Haikou</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Development and Planning Department, Jinan University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Rui Zheng, Institute of Psychology (CAS), China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Guanhua Huang, Agricultural Development Bank of China, China; Katarina Valaskova, University of &#x0017D;ilina, Slovakia; Ping Zhou, Guangdong University of Foreign Studies, China; Feng Cao, Hunan University, China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Jun Hu <email>jnuhujun&#x00040;163.com</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology</p></fn></author-notes>
<pub-date pub-type="epub">
<day>25</day>
<month>11</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>13</volume>
<elocation-id>1041630</elocation-id>
<history>
<date date-type="received">
<day>11</day>
<month>09</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>11</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2022 Zhao, Hu and Liu.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Zhao, Hu and Liu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license></permissions>
<abstract>
<p>This paper aims to examine the impact of CEOs&#x00027; early-life disaster experiences on corporate earnings quality. We proxy the disaster experience with whether CEOs lived through the Great Chinese Famine and the famine intensity they experienced. The results indicate that CEOs&#x00027; early-life famine experience is significantly positively associated with corporate earnings quality, and the famine effects are more obvious for CEOs who experienced the famine at adolescent ages. Further tests show that the famine experience effects are more pronounced in companies with high investor protection and cross-listing and with CEOs who have a relatively high level of education or background in economic management. The findings suggest CEOs would bear the imprint of an adverse early-life experience, which has risk aversion and learning effects on their decision making in corporate earnings information disclosure.</p></abstract>
<kwd-group>
<kwd>the Great Chinese Famine</kwd>
<kwd>risk prevention</kwd>
<kwd>learning effect</kwd>
<kwd>earnings quality</kwd>
<kwd>early-life disaster experience</kwd>
</kwd-group>
<contract-num rid="cn001">72101249</contract-num>
<contract-num rid="cn002">2021GZQN05</contract-num>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></contract-sponsor>
<contract-sponsor id="cn002">Guangzhou Office of Philosophy and Social Science<named-content content-type="fundref-id">10.13039/100018976</named-content></contract-sponsor>
<counts>
<fig-count count="2"/>
<table-count count="14"/>
<equation-count count="5"/>
<ref-count count="67"/>
<page-count count="22"/>
<word-count count="15336"/>
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</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Imprinting theory holds that the process through which a focal entity develops characteristics that reflect prominent features of the environment, and these characteristics persist despite significant environmental changes (Kriauciunas and Kale, <xref ref-type="bibr" rid="B44">2006</xref>; Marquis and Tilcsik, <xref ref-type="bibr" rid="B53">2013</xref>). Based on this theory, a growing body of literature has shown that the early-life imprint on CEOs affects their management style (Bukalska, <xref ref-type="bibr" rid="B5">2020</xref>; G&#x000F3;rska and Mazurek, <xref ref-type="bibr" rid="B30">2021</xref>), such as their professional background (Hitt and Tyler, <xref ref-type="bibr" rid="B34">1991</xref>; Waller et al., <xref ref-type="bibr" rid="B61">1995</xref>; Ding et al., <xref ref-type="bibr" rid="B19">2021</xref>), military experience (Malmendier et al., <xref ref-type="bibr" rid="B51">2011</xref>; Benmelech and Frydman, <xref ref-type="bibr" rid="B2">2015</xref>), and financial experience (Frank and Goyal, <xref ref-type="bibr" rid="B27">2003</xref>; Graham et al., <xref ref-type="bibr" rid="B31">2013</xref>; Jiang et al., <xref ref-type="bibr" rid="B38">2016</xref>). In this study, we regard CEOs&#x00027; experience of the Great Chinese Famine as an imprint and investigate the impact of CEOs&#x00027; early-life disaster experiences on corporate earnings quality.</p>
<p>Exploring the influencing factors of corporate earnings quality is one of the fundamental topics in accounting research. Corporate earnings information is the cornerstone of the efficient operation of the capital market, on which investors and information intermediaries&#x00027; decision making are heavily dependent (Mao et al., <xref ref-type="bibr" rid="B52">2013</xref>). The information of earnings quality directly affects the information asymmetry between the inside and outside of an enterprise, and to a large extent, it also affects the efficiency of the capital market (Kim and Verrecchia, <xref ref-type="bibr" rid="B40">1994</xref>; Cheng et al., <xref ref-type="bibr" rid="B10">2014</xref>; Jiang et al., <xref ref-type="bibr" rid="B38">2016</xref>; Durana et al., <xref ref-type="bibr" rid="B20">2021</xref>; Valaskova et al., <xref ref-type="bibr" rid="B60">2021</xref>). Prior studies have shown that companies with high-quality earnings information can reduce external financing costs (Francis et al., <xref ref-type="bibr" rid="B26">2005</xref>), alleviate financing constraints (Biddle and Hilary, <xref ref-type="bibr" rid="B4">2006</xref>), and reduce litigation risk (Palmrose and Scholz, <xref ref-type="bibr" rid="B57">2004</xref>) as well as bid-ask spreads (Handa et al., <xref ref-type="bibr" rid="B32">2003</xref>). Therefore, it is a common concern and practice in academia in exploring the factors affecting corporate earnings quality.</p>
<p>Based on the imprinting theory, researchers have documented that CEOs&#x00027; experiences of adversity can have a profound impact on their future managerial style. For example, Malmendier et al. (<xref ref-type="bibr" rid="B51">2011</xref>) found that CEOs who experienced the Great Depression are more cautious in debt financing and exhibit more biases toward internal financing. Bernile et al. (<xref ref-type="bibr" rid="B3">2017</xref>) found that CEOs who have experienced major disasters are more risk-averse, which results in companies with lower financial leverage, more cash holdings, and less mergers and acquisition (M&#x00026;A) activities. Based on the Great Famine of 1959&#x02013;1961 in China, studies found that the painful psychological imprint of this event has led people to have a higher desire for savings (Cheng and Zhang, <xref ref-type="bibr" rid="B11">2011</xref>), pursue more conservative financial policies (Zhang, <xref ref-type="bibr" rid="B66">2017</xref>; Feng and Johansson, <xref ref-type="bibr" rid="B24">2018</xref>), and feel a greater sense of social responsibility (Xu and Ma, <xref ref-type="bibr" rid="B63">2022</xref>). Although more research studies have been carried out on the long-term effects of CEOs&#x00027; early-life experiences, there have been relatively few studies that focus on corporate earnings quality.</p>
<p>In addition, it is worth pointing out that although previous studies have found that CEOs&#x00027; different early-life experiences have an important impact on corporate behavior, such as military experience (Malmendier et al., <xref ref-type="bibr" rid="B51">2011</xref>; Benmelech and Frydman, <xref ref-type="bibr" rid="B2">2015</xref>) and financial experience (Frank and Goyal, <xref ref-type="bibr" rid="B27">2003</xref>; Graham et al., <xref ref-type="bibr" rid="B31">2013</xref>), the results of these studies may induce selection bias problems. For example, an individual&#x00027;s personality is likely to play a role in their decision to join the army, which would confound the results regarding the effects of military experience. However, investigating differences in decision-making behaviors due to involuntary life experiences may effectively alleviate this problem. The Great Chinese Famine is a rare opportunity for us to explore the impact of CEOs&#x00027; early-life experiences on their future corporate behaviors. First, it is difficult for most citizens to anticipate such a tragedy; however, it may be reasonable to assume the Great Chinese Famine to be a clear exogenous shock for CEOs living during that age. Moreover, although the famine affected almost all regions of China, people now over the age of 59 years have all experienced it (Chen and Yang, <xref ref-type="bibr" rid="B8">2015</xref>); however, due to differences in natural resources, culture, and economic conditions in different regions, the impact of the famine was not the same everywhere in China (Cao, <xref ref-type="bibr" rid="B7">2005</xref>). By identifying the intensity of the impact of the Great Famine in different places using the difference in differences (DID) method, we could alleviate the potential endogenous problems in estimating the influence of CEOs&#x00027; early-life experience on corporate earnings quality.</p>
<p>To test our proposition, we manually collected CEOs&#x00027; personal information (birthplace and age) to match it with the occurrence of the Great Famine and the severity of the famine&#x00027;s impact on different regions. We proxy the CEOs&#x00027; famine experience with whether a CEO lived through the years of the Great Chinese Famine (1959&#x02013;1961) and the famine&#x00027;s intensity in the CEOs&#x00027; birthplace. Following prior studies (e.g., Lennox et al., <xref ref-type="bibr" rid="B47">2016</xref>, <xref ref-type="bibr" rid="B46">2018</xref>), we used the modified Jones model proposed by Dechow et al. (<xref ref-type="bibr" rid="B17">1995</xref>), which is widely used in the literature, to measure corporate earnings quality. Using the DID method and taking 2000&#x02013;2015 Chinese listed companies as the sample, we found that CEOs&#x00027; early-life famine experience is significantly positively associated with corporate earnings quality. Furthermore, by identifying the age at which CEOs experienced the famine, we showed that the effects of famine experience on corporate earnings quality are more pronounced for CEOs who had this experience at the adolescent stage. These results are consistent with the imprint theory and suggest that CEOs would bear an imprint of their adverse early-life experiences, which would influence their decision making in corporate earnings information disclosure.</p>
<p>We also examined two potential channels, namely, the risk aversion effect and the famine learning effect, to explain the relationship between CEOs&#x00027; famine experience and corporate earnings quality. On the one hand, the risk aversion effect holds that the early-life famine experience makes CEOs more risk-averse toward earnings information disclosure as low-quality earnings may aggravate the risk of litigation. On the other hand, the famine learning effect holds that the epochal characteristics of and reasons for the occurrence of the Great Chinese Famine increase the knowledge of famine-experienced CEOs, which results in less earnings manipulation and high-quality earnings information. By dividing our sample into different groups, we found that the relationship between CEOs&#x00027; Great Famine experience and earnings quality is more pronounced for firms with high investor protection and cross-listing and for CEOs with a relatively high level of education or a background in economic management. These results are consistent with the theories of risk aversion and famine learning effects, which suggest that the famine experience may influence CEOs&#x00027; decision making through the two channels.</p>
<p>Our paper contributes to the literature in several ways. First, our paper deepens the understanding of the influences of corporate earnings quality. High-quality financial information is the cornerstone of the efficient operation of capital markets and has a significant impact on the efficient allocation of market resources and long-term enterprise development (Dechow et al., <xref ref-type="bibr" rid="B15">2010</xref>). Prior studies have discussed the factors that may influence corporate earnings quality in relation to corporate governance structures (Dhaliwal et al., <xref ref-type="bibr" rid="B18">2006</xref>), company growth (Collins and Kothari, <xref ref-type="bibr" rid="B14">1989</xref>), equity concentration (Fan and Wong, <xref ref-type="bibr" rid="B23">2002</xref>), information media (Bushee et al., <xref ref-type="bibr" rid="B6">2010</xref>; Cheng et al., <xref ref-type="bibr" rid="B10">2014</xref>), and policies and regulations (Cohen et al., <xref ref-type="bibr" rid="B13">2008</xref>; Koh et al., <xref ref-type="bibr" rid="B42">2008</xref>). However, taking CEOs&#x00027; heterogeneity into account, this paper provides empirical evidence that the CEOs&#x00027; early-life famine experience is significantly positively associated with corporate earnings quality, thereby further enriching and supplementing the relevant areas of literature on corporate earnings quality.</p>
<p>Second, our paper contributes to the literature related to CEOs&#x00027; managerial styles using the imprint theory. A growing literature has explored the potential impacts of CEOs&#x00027; managerial styles and their consequences, such as professional background (Waller et al., <xref ref-type="bibr" rid="B61">1995</xref>), military experience (Malmendier et al., <xref ref-type="bibr" rid="B51">2011</xref>; Benmelech and Frydman, <xref ref-type="bibr" rid="B2">2015</xref>), and financial experience (Frank and Goyal, <xref ref-type="bibr" rid="B27">2003</xref>; Graham et al., <xref ref-type="bibr" rid="B31">2013</xref>). Our research indicates the significance of the impact of CEOs&#x00027; experience of the Great Chinese Famine on corporate earnings quality using the imprint theory and provides two potential channels for explaining this relationship, enriching the literature on CEOs&#x00027; managerial style. Further, due to the exogenous and unpredictable nature of the Great Famine, our paper also provides a more robust estimate of the impact of CEOs&#x00027; early-life experiences on corporate decision making, thereby addressing the selection bias in the existing literature that considered financial experience, overseas experience, and military experience of CEOs.</p>
<p>Third, concerning corporate earnings quality, our paper also enriches the literature related to the long-term economic consequences of the Great Chinese Famine. Prior studies have documented several impacts and outcomes of the Great Famine, but most focused on social psychology (Chen and Zhou, <xref ref-type="bibr" rid="B9">2007</xref>; Meng and Qian, <xref ref-type="bibr" rid="B55">2009</xref>), macroeconomics (Harbaugh, <xref ref-type="bibr" rid="B33">2004</xref>), and corporate finance (Zhang, <xref ref-type="bibr" rid="B66">2017</xref>; Feng and Johansson, <xref ref-type="bibr" rid="B24">2018</xref>; Hu et al., <xref ref-type="bibr" rid="B35">2019</xref>, <xref ref-type="bibr" rid="B36">2020</xref>). Complementing the existing literature, this paper examined the event&#x00027;s impact on corporate earnings quality and found that CEOs&#x00027; early-life famine experience significantly positively affected the quality of corporate earnings. It was also found that this effect was more pronounced for CEOs who experienced the famine at the adolescent age; this finding enriches the literature on the long-term economic consequences of the Great Chinese Famine.</p>
<p>The remainder of this paper is organized in the following. The second section presents the literature review and research hypotheses. Materials and methods section describes the research design. The fourth section reports the empirical results. The fifth section presents the robustness tests. The sixth section discusses the results of further analyses. The seventh section provides the discussion of this study. The final section provides the limitation and future research.</p>
</sec>
<sec id="s2">
<title>Literature review and research hypotheses</title>
<sec>
<title>The 1959&#x02013;1961 Great Famine in China</title>
<p>China is one of the most famine-affected countries in the world and has even been referred to as &#x0201C;a land of famine&#x0201D; by scholars (Mallory, <xref ref-type="bibr" rid="B50">1926</xref>). Historically, the Great Chinese Famine of 1959&#x02013;1961 was the most recent famine in China. It had a significant impact on the population, economy, and social development at that time. <xref ref-type="fig" rid="F1">Figure 1</xref> presents the average mortality rates in every province over the 3 years before, during, and after the famine. From the figure, we can infer the following: first, this famine had a wide impact. During the famine, the average mortality rate of all provinces in China showed a significant upward trend, indicating that almost all provinces and regions were affected (Chen and Yang, <xref ref-type="bibr" rid="B8">2015</xref>). Second, the impact was different in different regions. For example, during the famine, the average mortality rate in Anhui, Guizhou, and Sichuan provinces exceeded 3%, which was significantly higher than that in other provinces. Third, the impact faded rapidly, as the average mortality rate of 3 years after the famine returned to the rate of 3 years before it.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Distribution of the severity of the famine by province. This figure shows the distribution of the severity of the famine by province. The sample is from 1956 to 1964. The X-axis is 28 provinces in China. Tibet is not listed because of lacking data. The Y-axis is the average mortality rate for each province. We calculate the average mortality rate in three ways: 3 years before the <italic>Great Famine</italic>, 3 years during the <italic>Great Famine</italic>, and 3 years after the <italic>Great Famine</italic>.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1041630-g0001.tif"/>
</fig>
</sec>
<sec>
<title>Consequences of the Great Chinese Famine</title>
<p>The Great Chinese Famine of 1959&#x02013;1961 was one of several extraordinary natural disasters in China for which more data are available. It had a profound impact on both the social and economic development of the region. Related topics have been discussed by researchers in the fields of sociology, economics, and finance. Sociological researchers focused on the long-term effects of the famine on personal growth and found that the starvation and abnormal deaths during the Great Chinese Famine had an irreversible impact on the future of survivors, resulting in smaller populations, lower body weights, and reduced labor force supply (Chen and Zhou, <xref ref-type="bibr" rid="B9">2007</xref>; Meng and Qian, <xref ref-type="bibr" rid="B55">2009</xref>). Further, the health status of the generation who experienced the famine was also relatively poor (Ma, <xref ref-type="bibr" rid="B49">2011</xref>).</p>
<p>Studies on the long-term effects of the Great Chinese Famine on macroeconomic and individual behaviors have accumulated in the literature, providing rich evidence. For example, Gooch (<xref ref-type="bibr" rid="B29">2017</xref>) found that although the state of the country returned to how it was 3 years before the famine within 3 years after it, this event had a significant negative impact on the economic growth of the country in the long run. Moreover, due to the memory of the famine, individuals who experienced the early-life famine tend to pay closer attention to food storage (Cao, <xref ref-type="bibr" rid="B7">2005</xref>), exhibiting a greater tendency to save (Harbaugh, <xref ref-type="bibr" rid="B33">2004</xref>). Wang et al. (<xref ref-type="bibr" rid="B62">2015</xref>) found that experiencing the famine early in life had a major impact on individuals&#x00027; self-employment choices: it made such individuals more risk-averse, leading them to avoid starting up a business.</p>
<p>Finance scholars have also examined the impact of the Great Famine experience on corporate behavior. O&#x00027;Sullivan et al. (<xref ref-type="bibr" rid="B56">2021</xref>) found that an experience of trauma early in a CEO&#x00027;s life is positively associated with corporate social performance. Xu and Ma (<xref ref-type="bibr" rid="B63">2022</xref>) found that firms with CEOs who experienced early-life poverty are associated with more socially responsible activities and fewer socially irresponsible activities. Zhang (<xref ref-type="bibr" rid="B66">2017</xref>) showed that CEOs who experienced the famine had a higher risk aversion tendency, which manifested itself in lower debt levels, more cash holdings, and lower probability of engaging in M&#x00026;A activities. Zhou et al. (<xref ref-type="bibr" rid="B67">2022</xref>) demonstrated that CEOs&#x00027; early-life adversity experiences have a significantly positive effect on firm internationalization. Concerning accounting and finance policies, Feng and Johansson (<xref ref-type="bibr" rid="B24">2018</xref>) found that companies with CEOs having lived through the famine are associated with more conservative financial, investment, and cash-holding policies, a lower likelihood of unethical behavior, and better firm performance during economic downturns. Hu et al. (<xref ref-type="bibr" rid="B35">2019</xref>) found that CEOs who experienced the Great Famine (1959&#x02013;1961) hold higher amounts of cash and increased cash sensitivity. Hu et al. (<xref ref-type="bibr" rid="B36">2020</xref>) found that accounting conservatism was significantly higher for companies whose CEOs have experienced the Great Chinese Famine. Yao et al. (<xref ref-type="bibr" rid="B64">2020</xref>) found firms led by top executives who experienced the Great Famine in early life are less likely to conduct fraudulent financial reporting. Long et al. (<xref ref-type="bibr" rid="B48">2020</xref>) found that companies whose CEOs experienced the &#x0201C;Great Chinese Famine&#x0201D; in early life have lower stock price crash risk than those with CEOs who did not experience the famine. Using the Korean War as an exogenous shock, Choi and Jung (<xref ref-type="bibr" rid="B12">2021</xref>) found that firms managed by war-exposed CEOs have greater information transparency than firms managed by CEOs who are not war-exposed.</p>
</sec>
<sec>
<title>Hypotheses development</title>
<p>From existing studies, we know that the Great Chinese Famine left an indelible imprint on the psychological growth of individuals who experienced the disaster, which would affect individuals&#x00027; future behavioral decisions (Zhang, <xref ref-type="bibr" rid="B66">2017</xref>; Hu et al., <xref ref-type="bibr" rid="B35">2019</xref>). Drawing on the related studies, we predict that CEOs&#x00027; famine experience has the following two effects on corporate earnings quality.</p>
<p>On the one hand, the painful memory of the Great Chinese Famine makes a CEO more risk-averse, which could improve corporate earnings quality. This is the &#x0201C;risk aversion effect.&#x0201D; The existing literature found that early disaster experience has a long-term physical and psychological impact on CEOs, leading to a higher degree of risk aversion. This could affect corporate earnings quality in various ways. Corporate earnings information is the cornerstone of the efficient operation of capital markets, on which investors and information intermediaries&#x00027; decision making are heavily dependent (Mao et al., <xref ref-type="bibr" rid="B52">2013</xref>). For corporate CEOs, although low-quality earnings information disclosure can increase investor information asymmetry, which may be beneficial for managers&#x00027; opportunistic behavior and obtaining excess returns, it may also lead to a higher uncertainty and litigation risk (Field et al., <xref ref-type="bibr" rid="B25">2005</xref>). According to the imprint theory, individuals are imprinted by adverse early-life experiences (Marquis and Tilcsik, <xref ref-type="bibr" rid="B53">2013</xref>), famine-experienced CEOs are more risk-averse and precautionary about uncertainties (Zhang, <xref ref-type="bibr" rid="B66">2017</xref>; Hu et al., <xref ref-type="bibr" rid="B35">2019</xref>), and thus, they are more likely to overstate the possible risk and understate the benefit of earnings management, which would reduce the incentive to earnings management and result in a high quality of earnings disclosure.</p>
<p>On the other hand, the background and causes of the famine may increase CEOs&#x00027; knowledge and cognition, and the famine learning effect may also improve corporate earnings quality. Previous literature has found that the <italic>catch-up strategy</italic> employed during the Great Leap Forward and the associated over-purchase of food were important causes of abnormal deaths during the Great Famine (Kung and Lin, <xref ref-type="bibr" rid="B45">2003</xref>). Psychological research has shown that individual decision making is rooted in the memory of past experiences and conditioned reflexive learning, which form a sort of knowledge guide for current actions (Freud, <xref ref-type="bibr" rid="B28">1915</xref>; Schlag, <xref ref-type="bibr" rid="B58">1998</xref>). The famine learning effect may be recognized in CEOs&#x00027; reflection on the negative consequences of the exaggerated behavior (e.g., catch-up strategy) during the Great Leap Forward, which ultimately affects their motivation to manipulate corporate earnings. Feng and Johansson (<xref ref-type="bibr" rid="B24">2018</xref>) found that CEOs&#x00027; early-life famine experience increased corporate conservatism, and such CEOs rarely cheated. CEOs who are influenced by the famine learning effect can be expected to pursue less earnings manipulation and have relatively higher requirements for the quality of their corporate information disclosures, which may improve corporate earnings quality.</p>
<p>Therefore, the risk prevention effect and the famine learning effect produced by the famine experience may lead the CEO to have higher requirements for corporate earnings quality. Accordingly, we formulate our first hypothesis, which is presented as follows:</p>
<list list-type="simple">
<list-item><p><italic>H1: Controlling for other factors, CEOs&#x00027; famine experience is positively associated with corporate earnings quality</italic>.</p></list-item>
</list>
<p>Differences in cognitive ability can be found between different age groups. Psychological research indicates that adolescence is the most crucial phase in the formation of individual thinking and values. During this period, experiences with clothing, food, cold, and warmth play a key role in the formation of individual qualities, psychological tendencies, and personality structure (Elder Jr et al., <xref ref-type="bibr" rid="B21">1991</xref>; Becker, <xref ref-type="bibr" rid="B1">1992</xref>). Since adolescence is the key stage in the formation of cognitive abilities and personality traits, the imprint left by the Great Chinese Famine at this stage may have a larger and more profound impact on personal behavior. By distinguishing between the birth cohorts of family heads, Cheng and Zhang (<xref ref-type="bibr" rid="B11">2011</xref>) found that famine experience during adolescence has a greater impact on an individual&#x00027;s propensity to save. Feng and Johansson (<xref ref-type="bibr" rid="B24">2018</xref>) showed that famine experience during CEOs&#x00027; childhood or adolescence has a more pronounced impact on their corporate decision making than famine experience in other life stages. Based on the above studies, we expect that CEOs who experienced the Great Chinese Famine during adolescence would have a more pronounced effect on corporate earnings quality than those who experienced it in other ages. We propose the second hypothesis as follows:</p>
<list list-type="simple">
<list-item><p><italic>H2: The experience of the Great Famine during adolescence has a more significant positive effect on corporate earnings quality than that at another life stage</italic>.</p></list-item>
</list>
</sec>
</sec>
<sec sec-type="materials and methods" id="s3">
<title>Materials and methods</title>
<sec>
<title>Data and sample</title>
<p>Our original sample includes all companies listed on the Chinese A-share market, and we obtained financial data from the China Stock Market and Accounting Research (CSMAR) database. The CSMAR database contains financial and corporate governance information from 1999 to date. However, our sample was limited to 2000 to 2015 because observations for some test variables in 1999 are missing from the database. Our sample ending range is 2015 because the CEOs&#x00027; background information that we manually collected has been limited to 2015.</p>
<p>We applied the following exclusion criteria: samples from the financial and insurance industry; ST and <sup>&#x0002A;</sup>ST listed companies; samples whose CEOs were of a non-Chinese nationality or born in Taiwan, Hong Kong, Macau, or Tibet; and samples with missing financial information or missing place of CEO&#x00027;s birth. To control the influence of outliers, all continuous variables were winsorized at the 1 and 99% quantiles. Ultimately, we obtained 23,974 enterprise-year samples.</p>
</sec>
<sec>
<title>Variables</title>
<sec>
<title>Earnings quality</title>
<p>We used the modified Jones model proposed by Dechow et al. (<xref ref-type="bibr" rid="B17">1995</xref>) to measure earnings quality. The following calculation method was used. First, we used a cross-section regression estimation model (1) to obtain the parameters k<sub>1</sub>, k<sub>2</sub>, and k<sub>3</sub>.</p>
<disp-formula id="E1"><label>(1)</label><mml:math id="M1"><mml:mtable class="eqnarray" columnalign="left"><mml:mtr><mml:mtd><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mtext>&#x00394;</mml:mtext><mml:mi>S</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mtext>&#x00394;</mml:mtext><mml:mi>P</mml:mi><mml:mi>P</mml:mi><mml:msub><mml:mrow><mml:mi>E</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B5;</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>In model (1), <italic>TACC</italic><sub><italic>t</italic></sub> represents the total accrued profits in year t, calculated by <italic>NI</italic><sub><italic>t</italic></sub>-<italic>CFO</italic><sub><italic>t</italic></sub>, where <italic>NI</italic><sub><italic>t</italic></sub> represents the net profit in year t and <italic>CFO</italic><sub><italic>t</italic></sub> represents the net cash flow from operating activities in year t; <italic>TA</italic><sub><italic>t</italic>&#x02212;1</sub> is the total assets in year t&#x02212;1; &#x00394;<italic>Sales</italic><sub><italic>t</italic></sub> represents the changes in corporate operating income calculated by the difference between corporate operating income in years t and t&#x02212;1; and <italic>PPE</italic><sub><italic>t</italic></sub> represents the net amount of fixed assets in year t.</p>
<p>Second, we calculated the controllable accruals of the sample companies based on model (2), as follows:</p>
<disp-formula id="E2"><label>(2)</label><mml:math id="M2"><mml:mtable columnalign='left'><mml:mtr><mml:mtd><mml:mi>D</mml:mi><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>&#x02212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x02212;</mml:mo><mml:mo stretchy='false'>(</mml:mo><mml:msub><mml:mover accent='true'><mml:mi>k</mml:mi><mml:mo>&#x0005E;</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>&#x02212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mover accent='true'><mml:mi>k</mml:mi><mml:mo>&#x0005E;</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msub><mml:mfrac><mml:mrow><mml:mtext>&#x00394;</mml:mtext><mml:mi>S</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:msub><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>&#x02212;</mml:mo><mml:mtext>&#x00394;</mml:mtext><mml:mi>A</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>&#x02212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>&#x0002B;</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:msub><mml:mover accent='true'><mml:mi>k</mml:mi><mml:mo>&#x0005E;</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:msub><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mi>P</mml:mi><mml:msub><mml:mi>E</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>&#x02212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo stretchy='false'>)</mml:mo><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p><italic>DACC</italic><sub><italic>t</italic></sub> represents discretionary accruals in year t; &#x00394;<italic>AR</italic><sub><italic>t</italic></sub> is the change in corporate receivables; and the estimated values for parameters k<sub>1</sub>, k<sub>2</sub>, and k<sub>3</sub> are estimated from model (1). Specifically, <italic>DACC</italic><sub><italic>t</italic></sub> is the degree to which actual discretionary accruals of an enterprise deviate from ideal discretionary accruals. The greater the deviation, the more severe the earnings management. We take the absolute value of <italic>DACC</italic><sub><italic>t</italic></sub> obtained by model (2) and expressed it as <italic>EQ</italic><sub><italic>t</italic></sub>. The larger the <italic>EQ</italic><sub><italic>t</italic></sub>, the worse the earnings quality, and the smaller the <italic>EQ</italic><sub><italic>t</italic></sub>, the better the earnings quality.</p>
</sec>
<sec>
<title>CEO famine experience</title>
<p>The Great Chinese Famine had widespread and far-reaching effects, but as shown in <xref ref-type="fig" rid="F1">Figure 1</xref>, its severity was different in different regions. Referring to Chen and Zhou (<xref ref-type="bibr" rid="B9">2007</xref>), Cheng and Zhang (<xref ref-type="bibr" rid="B11">2011</xref>), and Wang et al. (<xref ref-type="bibr" rid="B62">2015</xref>), we used CEOs&#x00027; birth year and birthplace to identify who had experienced the Great Famine. First, we relied on CEOs&#x00027; birth year to define whether a CEO was living through the Great Famine. If a CEO was born in 1961 or before, we defined such CEOs as famine-experienced CEOs and encoded <italic>famine experience</italic> to 1 and otherwise encoded to 0. Second, we performed the following steps to measure famine intensity: calculate the difference in average death rates between 1959&#x02013;1961 and 1956&#x02013;1958 for each province and the difference in average death rates between 1959&#x02013;1961 and 1962&#x02013;1964 for each province; estimate the mean average excess death rate (<italic>EDR</italic>) using these two differences to obtain the <italic>EDR</italic> for each province during 1959&#x02013;1961. Further, based on the <italic>EDR</italic>, we set a dummy variable <italic>EDD</italic>, which was encoded to 1 if the province experienced a median-above <italic>EDR</italic> during the three famine years and otherwise 0. Finally, following Hu et al. (<xref ref-type="bibr" rid="B36">2020</xref>) and Long et al. (<xref ref-type="bibr" rid="B48">2020</xref>) we constructed two interaction variables to capture CEOs&#x00027; famine experience: <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDRandFamine</italic><sup>&#x0002A;</sup><italic>EDD</italic>. It can exclude the impact of CEOs&#x00027; age on the conclusion.</p>
</sec>
<sec>
<title>Control variables</title>
<p>Following Lennox et al. (<xref ref-type="bibr" rid="B47">2016</xref>), we selected control variables associated with corporate earnings quality. <italic>Size</italic> is the natural logarithm of the company&#x00027;s total assets at the end of the year t. <italic>Lev</italic> is the company&#x00027;s total liabilities scaled by total assets at the end of the year t. <italic>ROA</italic> is the return on assets, calculated by the net profit divided by total assets at the end of the year t. <italic>Grow</italic> is sales revenue growth, defined by the annual sales revenue minus the previous year&#x00027;s sales revenue and then scaled by the previous year&#x00027;s sales revenue. <italic>SOE</italic> is a dummy variable, defined as 1 if a company is state-owned and otherwise 0. <italic>Audit</italic> is a dummy variable that is encoded to 1 if the company is audited by a Big 4 or 5 audit firm and otherwise 0. <italic>Dual</italic> is 1 if the CEO is also the chairman of the company and otherwise 0. <italic>OCF</italic> is the free cash flow, which is the net cash flow of the company scaled by total assets at the end the year t. First is the ratio of shares held by the largest shareholder of the company. <italic>IO</italic> is the share ratio for institutional investors. Regarding managerial characteristics, <italic>Age</italic> is the natural logarithm of CEOs&#x00027; age; <italic>Gender</italic> is a dummy variable that is encoded to 1 if the CEO is female and 0 otherwise; <italic>Degree</italic> is the natural logarithm of 1 plus CEOs&#x00027; degree, which is assigned the value of 1, 2, 3, 4 or 5 if the CEO has a middle school, high school, college, master, or doctoral degree, respectively. Detailed definitions of all these variables are given in <xref ref-type="table" rid="T14">Appendix 1</xref>.</p>
</sec>
</sec>
<sec>
<title>Model</title>
<p>It should be noted that age is an important factor for managers&#x00027; risk preference and corporate earnings quality. For example, older managers are more inclined to avoid risk than younger ones (Yim, <xref ref-type="bibr" rid="B65">2013</xref>; Serfling, <xref ref-type="bibr" rid="B59">2014</xref>), and as such, the reported earnings quality of their corporations tends to be higher (Huang et al., <xref ref-type="bibr" rid="B37">2012</xref>). Since CEOs who experienced the Great Chinese Famine tend to be older, age may be an alternative explanation for our hypothesis. To address this issue, we set up a treatment group and a control group, which were distinguished by whether the CEO experienced the famine and the degree of famine intensity in the region where the CEO was born. We used a DID method to test the effect of CEOs&#x00027; famine experience on corporate earnings quality. The model is as follows:</p>
<disp-formula id="E3"><label>(3)</label><mml:math id="M3"><mml:mtable columnalign='left'><mml:mtr><mml:mtd><mml:mi>E</mml:mi><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mi>F</mml:mi><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>e</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mtext>2</mml:mtext></mml:msub><mml:mi>F</mml:mi><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>e</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mtext>&#x0002A;</mml:mtext><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>(</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>)</mml:mo><mml:mo>&#x0002B;</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mtext>3</mml:mtext></mml:msub><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>(</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>)</mml:mo><mml:mtext>&#x000A0;</mml:mtext></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:mo>&#x0002B;</mml:mo><mml:mi>C</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>l</mml:mi><mml:mi>s</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>Y</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>I</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>P</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>B</mml:mi><mml:mi>P</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>B</mml:mi><mml:mi>Y</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B5;</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>In model (3), <italic>EQ</italic> indicates corporate earnings quality; famine experience is the independent variable that is proxied by <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>EDD</italic>); <italic>Ctrls</italic> are the control variables as described above. Further, due to the economic cycle, industry environment, regional factors, and CEO growth environment may affect the validity of the regression estimation. Thus, in order to control the different fixed effects, we added five dummy variables to our model: year (Y), industry (I), province (P), CEO birthplace (BP), and CEO birth year (BY).</p>
<p>It is worth noting that although the famine experience is an interaction term for <italic>Famine</italic> and <italic>EDR</italic> (<italic>EDD</italic>), these two variables do not change with time and province. While we controlled the fixed effects of BY and BP, we did not add the variables <italic>Famine</italic> and <italic>EDR</italic> (<italic>EDD</italic>) to the model again. In model (3), our variable of interest is <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR (EDD)</italic>. If our hypothesis is true, we can expect that the coefficient of &#x003B2;<sub>2</sub> will be negative and significant even for CEOs who experienced the famine during the same age as different intensities of famine in different regions may lead to differences in earnings quality.</p>
</sec>
</sec>
<sec id="s4">
<title>Empirical results</title>
<sec>
<title>Descriptive statistics</title>
<p><xref ref-type="table" rid="T1">Table 1</xref> reports the descriptive statistics of the main variables in this paper. The average earnings quality (<italic>EQ</italic>) is 0.047, and the median is 0.036, but the standard deviation is 0.051, indicating a large difference in earnings quality in the sample. The average value of <italic>Famine</italic> is 0.419, indicating that about 42% of the CEOs in the sample experienced the Great Chinese Famine. The average <italic>EDR</italic> is 0.591%, which indicates that the average excess mortality rate during the 3-year famine is close to 0.6%. Given China&#x00027;s large population, this number indicates a painful and profound impact. The standard deviation of the <italic>EDR</italic> is 0.534, which is close to the average, indicating that the impact of the famine differed greatly in different provinces. The descriptive statistics of other control variables are mostly consistent with prior studies and have not been described in detail in this paper.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Descriptive statistics of the main variables.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variable</bold></th>
<th valign="top" align="center"><bold><italic>N</italic></bold></th>
<th valign="top" align="center"><bold>Mean</bold></th>
<th valign="top" align="center"><bold>Std dev</bold>.</th>
<th valign="top" align="center"><bold>Q1</bold></th>
<th valign="top" align="center"><bold>Median</bold></th>
<th valign="top" align="center"><bold>Q3</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">EQ</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">0.016</td>
<td valign="top" align="center">0.036</td>
<td valign="top" align="center">0.055</td>
</tr>
<tr>
<td valign="top" align="left">Famine</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.419</td>
<td valign="top" align="center">0.493</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">1.000</td>
</tr>
<tr>
<td valign="top" align="left">EDR (%)</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.591</td>
<td valign="top" align="center">0.534</td>
<td valign="top" align="center">0.190</td>
<td valign="top" align="center">0.368</td>
<td valign="top" align="center">0.785</td>
</tr>
<tr>
<td valign="top" align="left">EDD</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.498</td>
<td valign="top" align="center">0.500</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">1.000</td>
</tr>
<tr>
<td valign="top" align="left">Size</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">21.650</td>
<td valign="top" align="center">1.231</td>
<td valign="top" align="center">20.805</td>
<td valign="top" align="center">21.503</td>
<td valign="top" align="center">22.313</td>
</tr>
<tr>
<td valign="top" align="left">Lev</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.474</td>
<td valign="top" align="center">0.230</td>
<td valign="top" align="center">0.310</td>
<td valign="top" align="center">0.472</td>
<td valign="top" align="center">0.623</td>
</tr>
<tr>
<td valign="top" align="left">ROA</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.032</td>
<td valign="top" align="center">0.065</td>
<td valign="top" align="center">0.012</td>
<td valign="top" align="center">0.033</td>
<td valign="top" align="center">0.061</td>
</tr>
<tr>
<td valign="top" align="left">Grow</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.163</td>
<td valign="top" align="center">0.348</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.093</td>
<td valign="top" align="center">0.224</td>
</tr>
<tr>
<td valign="top" align="left">SOE</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.575</td>
<td valign="top" align="center">0.494</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">1.000</td>
<td valign="top" align="center">1.000</td>
</tr>
<tr>
<td valign="top" align="left">Audit</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.069</td>
<td valign="top" align="center">0.254</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">Dual</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.176</td>
<td valign="top" align="center">0.380</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">OCF</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">0.078</td>
<td valign="top" align="center">0.003</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">0.089</td>
</tr>
<tr>
<td valign="top" align="left">First</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.377</td>
<td valign="top" align="center">0.157</td>
<td valign="top" align="center">0.252</td>
<td valign="top" align="center">0.362</td>
<td valign="top" align="center">0.495</td>
</tr>
<tr>
<td valign="top" align="left">IO</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.168</td>
<td valign="top" align="center">0.200</td>
<td valign="top" align="center">0.012</td>
<td valign="top" align="center">0.085</td>
<td valign="top" align="center">0.258</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">3.849</td>
<td valign="top" align="center">0.141</td>
<td valign="top" align="center">3.761</td>
<td valign="top" align="center">3.850</td>
<td valign="top" align="center">3.951</td>
</tr>
<tr>
<td valign="top" align="left">Gender</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">0.052</td>
<td valign="top" align="center">0.221</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">Degree</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">1.431</td>
<td valign="top" align="center">0.214</td>
<td valign="top" align="center">1.386</td>
<td valign="top" align="center">1.386</td>
<td valign="top" align="center">1.609</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table presents the sample descriptive statistics. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is the excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable, which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Size is the natural logarithm of the total assets of the company at the end of the year. Lev is the total liabilities of the company scaled by total assets at the end of the year. ROA is the return on assets, defined by the net profit divided by total assets at the end of the year. Grow is sales revenue growth, defined by the annual sales revenue minus the previous year&#x00027;s sales revenue and then scaled by the previous year&#x00027;s sales revenue. SOE is a dummy variable, defined as 1 if a company is state-owned and otherwise defined as 0. Audit is a dummy variable that if the company audited by a Big 4 or 5 audit firm is encoded to 1 and 0 otherwise. Dual is 1 if the CEO is also the company&#x00027;s chair and 0 otherwise. OCF is the free cash flow, using the company&#x00027;s net cash flow scaled by total assets at the end the year. First is the shares ratio held by the largest shareholder. IO is the share ratio for institutional investors. Age is the natural logarithm of CEO age. Gender is a dummy variable which is encoded to 1 if the CEO is a female and 0 otherwise. Degree is the natural logarithm of 1 plus CEOs&#x00027; degree, which is assigned 1, 2, 3, 4, or 5 if CEO has the middle school, high school, college, master&#x00027;s, or doctoral degree, respectively. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all variables.</p>
</table-wrap-foot>
</table-wrap>
<p>Based on the intensity of the famine in the region where the CEO was born, we further described the association between CEOs&#x00027; famine experience and the distribution of the earnings quality. The results are given in <xref ref-type="table" rid="T2">Table 2</xref>. In the table, the first column shows the distribution of CEO birth areas in our sample, and the second column shows the population <italic>EDR</italic> in each region from 1959 to 1961. As shown in the table, <italic>EQ</italic> is relatively higher among CEOs who were born in provinces with higher mortality, indicating the average earnings quality is slightly lower for CEO who did not experience the famine.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Descriptive statistics of CEO birthplace distribution, excess death rate (EDR) for each province during the famine period and corporate earnings quality.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>CEO birthplace distribution</bold></th>
<th valign="top" align="center"><bold>Excess death rate (EDR)</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Famine CEOs, EQ</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Non-Famine CEOs, EQ</bold></th>
</tr>
<tr>
<th/>
<th/>
<th valign="top" align="center"><bold>CEOs</bold></th>
<th valign="top" align="center"><bold>Mean EQ</bold></th>
<th valign="top" align="center"><bold>CEOs</bold></th>
<th valign="top" align="center"><bold>Mean EQ</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Anhui</td>
<td valign="top" align="center">2.102</td>
<td valign="top" align="center">351</td>
<td valign="top" align="center">0.037</td>
<td valign="top" align="center">573</td>
<td valign="top" align="center">0.043</td>
</tr>
<tr>
<td valign="top" align="left">Beijing</td>
<td valign="top" align="center">0.169</td>
<td valign="top" align="center">420</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">508</td>
<td valign="top" align="center">0.053</td>
</tr>
<tr>
<td valign="top" align="left">Chongqing</td>
<td valign="top" align="center">1.796</td>
<td valign="top" align="center">116</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">218</td>
<td valign="top" align="center">0.049</td>
</tr>
<tr>
<td valign="top" align="left">Fujian</td>
<td valign="top" align="center">0.368</td>
<td valign="top" align="center">303</td>
<td valign="top" align="center">0.046</td>
<td valign="top" align="center">497</td>
<td valign="top" align="center">0.046</td>
</tr>
<tr>
<td valign="top" align="left">Gansu</td>
<td valign="top" align="center">1.049</td>
<td valign="top" align="center">117</td>
<td valign="top" align="center">0.032</td>
<td valign="top" align="center">215</td>
<td valign="top" align="center">0.050</td>
</tr>
<tr>
<td valign="top" align="left">Guangdong</td>
<td valign="top" align="center">0.350</td>
<td valign="top" align="center">880</td>
<td valign="top" align="center">0.050</td>
<td valign="top" align="center">1,253</td>
<td valign="top" align="center">0.051</td>
</tr>
<tr>
<td valign="top" align="left">Guangxi</td>
<td valign="top" align="center">1.098</td>
<td valign="top" align="center">122</td>
<td valign="top" align="center">0.035</td>
<td valign="top" align="center">137</td>
<td valign="top" align="center">0.047</td>
</tr>
<tr>
<td valign="top" align="left">Guizhou</td>
<td valign="top" align="center">1.695</td>
<td valign="top" align="center">90</td>
<td valign="top" align="center">0.035</td>
<td valign="top" align="center">111</td>
<td valign="top" align="center">0.049</td>
</tr>
<tr>
<td valign="top" align="left">Hainan</td>
<td valign="top" align="center">0.350</td>
<td valign="top" align="center">129</td>
<td valign="top" align="center">0.061</td>
<td valign="top" align="center">65</td>
<td valign="top" align="center">0.052</td>
</tr>
<tr>
<td valign="top" align="left">Hebei</td>
<td valign="top" align="center">0.197</td>
<td valign="top" align="center">385</td>
<td valign="top" align="center">0.053</td>
<td valign="top" align="center">371</td>
<td valign="top" align="center">0.046</td>
</tr>
<tr>
<td valign="top" align="left">Henan</td>
<td valign="top" align="center">1.019</td>
<td valign="top" align="center">405</td>
<td valign="top" align="center">0.037</td>
<td valign="top" align="center">627</td>
<td valign="top" align="center">0.046</td>
</tr>
<tr>
<td valign="top" align="left">Heilongjiang</td>
<td valign="top" align="center">0.174</td>
<td valign="top" align="center">212</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">248</td>
<td valign="top" align="center">0.051</td>
</tr>
<tr>
<td valign="top" align="left">Hubei</td>
<td valign="top" align="center">0.500</td>
<td valign="top" align="center">465</td>
<td valign="top" align="center">0.041</td>
<td valign="top" align="center">755</td>
<td valign="top" align="center">0.054</td>
</tr>
<tr>
<td valign="top" align="left">Hunan</td>
<td valign="top" align="center">0.875</td>
<td valign="top" align="center">539</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">1,051</td>
<td valign="top" align="center">0.046</td>
</tr>
<tr>
<td valign="top" align="left">Jilin</td>
<td valign="top" align="center">0.225</td>
<td valign="top" align="center">279</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">287</td>
<td valign="top" align="center">0.049</td>
</tr>
<tr>
<td valign="top" align="left">Jiangsu</td>
<td valign="top" align="center">0.515</td>
<td valign="top" align="center">863</td>
<td valign="top" align="center">0.038</td>
<td valign="top" align="center">1,165</td>
<td valign="top" align="center">0.048</td>
</tr>
<tr>
<td valign="top" align="left">Jiangxi</td>
<td valign="top" align="center">0.238</td>
<td valign="top" align="center">262</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">402</td>
<td valign="top" align="center">0.045</td>
</tr>
<tr>
<td valign="top" align="left">Liaoning</td>
<td valign="top" align="center">0.517</td>
<td valign="top" align="center">463</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">471</td>
<td valign="top" align="center">0.052</td>
</tr>
<tr>
<td valign="top" align="left">Inner Mongolia</td>
<td valign="top" align="center">0.082</td>
<td valign="top" align="center">104</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">150</td>
<td valign="top" align="center">0.045</td>
</tr>
<tr>
<td valign="top" align="left">Ningxia</td>
<td valign="top" align="center">0.106</td>
<td valign="top" align="center">40</td>
<td valign="top" align="center">0.043</td>
<td valign="top" align="center">60</td>
<td valign="top" align="center">0.043</td>
</tr>
<tr>
<td valign="top" align="left">Qinghai</td>
<td valign="top" align="center">1.263</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">0.043</td>
<td valign="top" align="center">39</td>
<td valign="top" align="center">0.039</td>
</tr>
<tr>
<td valign="top" align="left">Shandong</td>
<td valign="top" align="center">0.785</td>
<td valign="top" align="center">780</td>
<td valign="top" align="center">0.038</td>
<td valign="top" align="center">1,127</td>
<td valign="top" align="center">0.050</td>
</tr>
<tr>
<td valign="top" align="left">Shanxi</td>
<td valign="top" align="center">0.096</td>
<td valign="top" align="center">244</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">264</td>
<td valign="top" align="center">0.051</td>
</tr>
<tr>
<td valign="top" align="left">Shaanxi</td>
<td valign="top" align="center">0.015</td>
<td valign="top" align="center">230</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">456</td>
<td valign="top" align="center">0.056</td>
</tr>
<tr>
<td valign="top" align="left">Shanghai</td>
<td valign="top" align="center">0.093</td>
<td valign="top" align="center">679</td>
<td valign="top" align="center">0.049</td>
<td valign="top" align="center">457</td>
<td valign="top" align="center">0.060</td>
</tr>
<tr>
<td valign="top" align="left">Sichuan</td>
<td valign="top" align="center">1.796</td>
<td valign="top" align="center">440</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">627</td>
<td valign="top" align="center">0.050</td>
</tr>
<tr>
<td valign="top" align="left">Tianjin</td>
<td valign="top" align="center">0.137</td>
<td valign="top" align="center">137</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">198</td>
<td valign="top" align="center">0.049</td>
</tr>
<tr>
<td valign="top" align="left">Xinjiang</td>
<td valign="top" align="center">0.263</td>
<td valign="top" align="center">106</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">101</td>
<td valign="top" align="center">0.040</td>
</tr>
<tr>
<td valign="top" align="left">Yunnan</td>
<td valign="top" align="center">0.313</td>
<td valign="top" align="center">83</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">211</td>
<td valign="top" align="center">0.042</td>
</tr>
<tr>
<td valign="top" align="left">Zhejiang</td>
<td valign="top" align="center">0.190</td>
<td valign="top" align="center">766</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">1,290</td>
<td valign="top" align="center">0.048</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table presents the descriptive statistics of CEO birthplace distribution, excess death rate (EDR) for each province during the famine period, and corporate earnings quality. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine CEOs was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is the excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables.</p>
</table-wrap-foot>
</table-wrap>
<p>To intuitively describe the relationship between famine experience and earnings quality, we plotted the excess mortality rate in a CEO&#x00027;s birth place during the famine and the company&#x00027;s earnings quality based on descriptive statistical analyses. As shown in <xref ref-type="fig" rid="F2">Figure 2</xref>, the straight line represented by <italic>Famine</italic> = 1 is lower than the straight line of <italic>Famine</italic> = 0, indicating that famine experience improves corporate earnings quality. Regardless of <italic>Famine</italic> = 1 or 0, corporate earnings quality increases with an increase in the impact of the famine (the trend line slopes downward to the right). Compared with <italic>Famine</italic> = 0 companies, the downward trend represented by <italic>Famine</italic> = 1 companies is more obvious. These results suggest that when a CEO experiences Great Famine, their company&#x00027;s earnings quality improved significantly, and the more severe the famine, if experienced, the higher the earnings quality.</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Trend graph of the mean average excess death rate of the <italic>Great Famine</italic> (EDR) and the average corporate earnings quality. This figure shows the mean average excess death rate of the <italic>Great Famine</italic> (EDR) and the average corporate earnings quality. The sample is from <xref ref-type="table" rid="T2">Table 2</xref>. The X-axis is the mean average excess death rate (EDR). We use the following methods to calculate the EDR: calculating the difference in the average death rate between 1959&#x02013;1961 and 1956&#x02013;1958 for each province, and the difference in average death rates between 1959&#x02013;1961 and 1962&#x02013;1964 for each provinces; and then calculating the mean average excess death rate (EDR) using the above two differences to obtain the EDR for each province during 1959&#x02013;1961. The Y-axis is the average earnings quality for each province. We draw two lines based on whether the CEO experienced the <italic>Great Famine</italic>.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1041630-g0002.tif"/>
</fig>
</sec>
<sec>
<title>Univariate test</title>
<p>Before regression analyses, we performed a univariate test on earnings quality to check whether CEOs experienced the famine and the extent of famine intensity in CEOs&#x00027; birthplace (<italic>EDR</italic>). The results are given in <xref ref-type="table" rid="T3">Table 3</xref>. For CEOs who did not experience the Great Famine (<italic>Famine</italic> = 0), no significant difference was seen in the mean and median of <italic>EQ</italic> between the <italic>EDD</italic> = 0 and <italic>EDD</italic> = 1 groups. However, for CEOs who experienced the famine (<italic>Famine</italic> = 1), the average <italic>EQ</italic> and median <italic>EQ</italic> in the <italic>EDD</italic> = 1 group were 0.038 and 0.036, respectively, which were significantly lower than those in the <italic>EDD</italic> = 0 group. This indicates that CEOs&#x00027; famine experience significantly improved earnings quality. We also tested the DID results for <italic>EQ</italic> in the famine and <italic>EDD</italic> groups. The difference of the mean and median <italic>EQ</italic> was &#x02212;0.008 and &#x02212;0.002, respectively, both of which are significant at the 10% level at least. These results indicate that whether a CEO experienced the famine and the extent to which he experienced it have a significant impact on earnings quality.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Univariate analyses, grouped by whether the CEO experienced famine and the severity of the famine.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variable = EQ</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Famine</bold> = <bold>0</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Famine</bold> = <bold>1</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Differences</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="left"><bold>Mean</bold></th>
<th valign="top" align="left"><bold>Median</bold></th>
<th valign="top" align="left"><bold>Mean</bold></th>
<th valign="top" align="left"><bold>Median</bold></th>
<th valign="top" align="left"><bold>Mean</bold></th>
<th valign="top" align="left"><bold>Median</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>EDD</italic> = 0</td>
<td valign="top" align="left">0.050</td>
<td valign="top" align="left">0.035</td>
<td valign="top" align="left">0.048</td>
<td valign="top" align="left">0.039</td>
<td valign="top" align="left">&#x02212;0.002<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="left">0.004<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left"><italic>EDD</italic> = 1</td>
<td valign="top" align="left">0.049</td>
<td valign="top" align="left">0.034</td>
<td valign="top" align="left">0.038</td>
<td valign="top" align="left">0.036</td>
<td valign="top" align="left">&#x02212;0.010<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="left">0.002<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">Differences</td>
<td valign="top" align="left">&#x02212;0.001</td>
<td valign="top" align="left">&#x02212;0.001</td>
<td valign="top" align="left">&#x02212;0.009<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="left">&#x02212;0.003<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="left">&#x02212;0.008<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="left">&#x02212;0.002<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table presents univariate analyses on earnings quality based on whether the CEO experienced the famine and the extent of the famine suffered in the CEO&#x00027;s birth region (EDD). EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0.</p> 
<fn id="TN1">
<p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively, based on p-values from t-tests. See <xref ref-type="table" rid="T1">Table 1</xref> for variable definitions.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Baseline regression</title>
<p><xref ref-type="table" rid="T4">Table 4</xref> reports the baseline regression results for the impact of CEOs&#x00027; famine experience on corporate earnings quality. In columns (1) and (3), we did not include the control variables but controlled the fixed effect of industry, year, province, CEO birthplace, and CEO birth year. The coefficient of the interaction term <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> is &#x02212;0.352 and that of the interaction term <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> is &#x02212;0.007; both are significant at least at the 5% level. In columns (2) and (4), all firm-level and CEO-level control variables were controlled in the regresses. The coefficient of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> was significantly negative at the 5% level in column (2), and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> in column (4) was significantly negative at the 1% level. It should be noted that since the fixed effect of the birthplace and birth year of the CEO was considered, the influence of famine experience expressed by <italic>Famine</italic> and the intensity of famine expressed by <italic>EDR</italic> (<italic>EDD</italic>) were absorbed by the fixed effect of birth year and birthplace, and their coefficients were omitted due to collinearity (similar to subsequent regressions). Overall, the regression results given in <xref ref-type="table" rid="T4">Table 4</xref> show that CEOs&#x00027; famine experience significantly improved earnings quality of the company, which is in line with our basic expectations and supports H1.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Regression results of CEOs&#x00027; famine experience and corporate earnings quality.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Famine<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref>EDR</td>
<td valign="top" align="center">&#x02212;0.352<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.272<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.51)</td>
<td valign="top" align="center">(&#x02212;2.00)</td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Famine<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref>EDD</td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.007<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;4.17)</td>
<td valign="top" align="center">(&#x02212;3.66)</td>
</tr>
<tr>
<td valign="top" align="left">Size</td>
<td/>
<td valign="top" align="center">&#x02212;0.005<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.005<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;7.29)</td>
<td/>
<td valign="top" align="center">(&#x02212;7.25)</td>
</tr>
<tr>
<td valign="top" align="left">Lev</td>
<td/>
<td valign="top" align="center">0.022<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">0.022<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(6.54)</td>
<td/>
<td valign="top" align="center">(6.52)</td>
</tr>
<tr>
<td valign="top" align="left">ROA</td>
<td/>
<td valign="top" align="center">&#x02212;0.016<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.016<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.76)</td>
<td/>
<td valign="top" align="center">(&#x02212;1.76)</td>
</tr>
<tr>
<td valign="top" align="left">Grow</td>
<td/>
<td valign="top" align="center">0.016<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">0.016<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(9.47)</td>
<td/>
<td valign="top" align="center">(9.46)</td>
</tr>
<tr>
<td valign="top" align="left">SOE</td>
<td/>
<td valign="top" align="center">&#x02212;0.002<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.002<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.58)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.60)</td>
</tr>
<tr>
<td valign="top" align="left">Audit</td>
<td/>
<td valign="top" align="center">&#x02212;0.003<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.003<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.83)</td>
<td/>
<td valign="top" align="center">(&#x02212;1.75)</td>
</tr>
<tr>
<td valign="top" align="left">Dual</td>
<td/>
<td valign="top" align="center">&#x02212;0.000</td>
<td/>
<td valign="top" align="center">&#x02212;0.000</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;0.11)</td>
<td/>
<td valign="top" align="center">(&#x02212;0.04)</td>
</tr>
<tr>
<td valign="top" align="left">OCF</td>
<td/>
<td valign="top" align="center">0.008</td>
<td/>
<td valign="top" align="center">0.008</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(1.44)</td>
<td/>
<td valign="top" align="center">(1.45)</td>
</tr>
<tr>
<td valign="top" align="left">First</td>
<td/>
<td valign="top" align="center">0.007<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">0.007<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(2.82)</td>
<td/>
<td valign="top" align="center">(2.86)</td>
</tr>
<tr>
<td valign="top" align="left">IO</td>
<td/>
<td valign="top" align="center">0.002</td>
<td/>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(1.09)</td>
<td/>
<td valign="top" align="center">(1.11)</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td/>
<td valign="top" align="center">0.058<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">0.058<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(2.03)</td>
<td/>
<td valign="top" align="center">(2.03)</td>
</tr>
<tr>
<td valign="top" align="left">Gender</td>
<td/>
<td valign="top" align="center">0.002</td>
<td/>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(1.24)</td>
<td/>
<td valign="top" align="center">(1.28)</td>
</tr>
<tr>
<td valign="top" align="left">Degree</td>
<td/>
<td valign="top" align="center">&#x02212;0.007<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.88)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.82)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.090</td>
<td valign="top" align="center">0.113</td>
<td valign="top" align="center">0.091</td>
<td valign="top" align="center">0.114</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results for famine experience on earnings quality. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree are control variables. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN2">
<p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>The control variables for the size of the company (<italic>Size</italic>), the return on assets (<italic>ROA</italic>), the nature of property rights (<italic>SOE</italic>), audited by one of the Big 4 or 5 audit firms (<italic>Audit</italic>), and CEOs&#x00027; education degree (<italic>Degree</italic>) were significantly negatively correlated with <italic>EQ</italic>, indicating that when an enterprise is larger, its return on assets is higher, it is state-owned, it is audited by a major accounting firm, or the CEO has a higher degree, the quality of corporate earnings is relatively higher. Financial leverage (<italic>Lev</italic>), company growth (<italic>Grow</italic>), the largest shareholder&#x00027;s ratio of shares (<italic>First</italic>), and the age of a CEO are significantly positively related to <italic>EQ</italic>, indicating that when companies have a higher debt ratio, they experience a higher speed of revenue growth and have more shareholding by the largest shareholder; likewise, when the CEO is relatively old, the earnings quality is worse. The results for the control variables are basically consistent with the findings of previous research (Fan and Wong, <xref ref-type="bibr" rid="B23">2002</xref>; Lennox et al., <xref ref-type="bibr" rid="B47">2016</xref>).</p>
</sec>
<sec>
<title>CEO birth cohort effect</title>
<p>According to H2, the period of adolescence is a key stage for individuals&#x00027; formation of cognitive abilities and personality traits, so the influence of famine on a CEO at this stage is more profound, and as such, the impact on earnings quality is more obvious. To test this hypothesis, we referred to Cheng and Zhang (<xref ref-type="bibr" rid="B11">2011</xref>) and further divided the age at which famine was experienced into infancy (&#x0003C;3 years), childhood (3&#x02013;6 years), adolescence (7&#x02013;17 years), and adult years (18 years and older) and named as <italic>Cohort</italic><sub>1</sub>, <italic>Cohort</italic><sub>2</sub>, <italic>Cohort</italic><sub>3</sub>, and <italic>Cohort</italic><sub>4</sub>, respectively. The regression results given in <xref ref-type="table" rid="T5">Table 5</xref> show that the famine experience of CEOs of the difference cohorts has different effects on corporate earnings quality.</p>
<table-wrap position="float" id="T5">
<label>Table 5</label>
<caption><p>Regression results for CEO age during the famine and the corporate earnings quality.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M4"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.028</td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;0.15)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M5"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.212</td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;1.55)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M6"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.590<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.90)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M7"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</italic></td>
<td valign="top" align="center">0.417</td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(1.43)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M8"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.002</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.11)</td>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M9"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.004<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.03)</td>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M10"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.009<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;4.37)</td>
</tr>
<tr>
<td valign="top" align="left"><italic><inline-formula><mml:math id="M11"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</italic></td>
<td/>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(0.59)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.114</td>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M12"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD) = <inline-formula><mml:math id="M13"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD)</td>
<td valign="top" align="center"><italic>F</italic> = 5.47<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.019</td>
<td valign="top" align="center"><italic>F</italic> = 3.54<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.063</td>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M14"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD) = <inline-formula><mml:math id="M15"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD)</td>
<td valign="top" align="center"><italic>F</italic> = 3.46<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.063</td>
<td valign="top" align="center"><italic>F</italic> = 3.24<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.072</td>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M16"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD) = <inline-formula><mml:math id="M17"><mml:msubsup><mml:mrow><mml:mtext>Cohort</mml:mtext></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR(EDD)</td>
<td valign="top" align="center"><italic>F</italic> = 8.88<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.002</td>
<td valign="top" align="center"><italic>F</italic> = 10.55<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref>, <italic>P</italic> &#x0003E; <italic>F</italic> = 0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results for CEO age during the famine and corporate earnings quality. EQ is the corporate earnings quality, calculated by the modified Jones model. Cohort<sub>1</sub>, Cohort<sub>2</sub>, Cohort<sub>3</sub>, and Cohort<sub>4</sub> were encoded to 1 if the CEOs experienced the famine in their infancy (&#x0003C;3 years old), childhood (3&#x02013;6 years), adolescence (7&#x02013;17 years), and adulthood (18 years and older), respectively and 0 otherwise. EDR is the excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN3">
<p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>As shown in column (1) of <xref ref-type="table" rid="T5">Table 5</xref>, the coefficients of the interaction terms <inline-formula><mml:math id="M18"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic> were significantly negatively correlated with <italic>EQ</italic> at the 1% level, but the coefficients of <inline-formula><mml:math id="M19"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR, Cohort</italic><inline-formula><mml:math id="M20"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic>, and <inline-formula><mml:math id="M21"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic> did not have statistical significance. The estimated coefficients of <italic>Cohort</italic><sup>&#x0002A;</sup><italic>EDD</italic> (column 2) for each interactive variable in infancy, childhood, and adolescence are similar to those in the first column.</p>
<p>To compare whether the above differences in coefficients are statistically significant, we performed <italic>t</italic>-tests on the estimated coefficient value after regression. The statistical results are reported in the last three rows of each column in <xref ref-type="table" rid="T5">Table 5</xref>. We found that the coefficients of<inline-formula><mml:math id="M22"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic> were significantly bigger than those for other three interactions. This shows that the impact of CEOs&#x00027; famine experience in adolescence is significantly greater than those for experience in infancy, childhood, and adulthood. In summation, the results of <xref ref-type="table" rid="T5">Table 5</xref> indicate that CEOs&#x00027; famine experience in adolescence had a significantly greater effect on corporate earnings quality, supporting H2.</p>
</sec>
</sec>
<sec id="s5">
<title>Robustness testing</title>
<p>Although the regression results obtained with the DID method in previous studies support the theoretical expectations of this article, the results may be distorted by endogenous problems, such as missing variables, the assumption of an equilibrium trend for the DID method, and unobservable factors at the company or macro-level. We adopted the following methods to address possible endogeneities and tested the robustness of our results.</p>
<sec>
<title>Common trends test</title>
<p>We mainly used DID to test our hypotheses. However, there is a very important prerequisite for using this method, the equilibrium trend hypothesis. Without this, the DID method cannot effectively alleviate potential endogeneity problems. Accordingly, we referred to the practices in the previous studies and designed the following model to test the equilibrium:</p>
<disp-formula id="E4"><label>(4)</label><mml:math id="M23"><mml:mtable columnalign='left'><mml:mtr><mml:mtd><mml:mi>E</mml:mi><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>&#x0002B;</mml:mo><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>h</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mo>&#x02212;</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002A;</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>(</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>)</mml:mo><mml:mo>&#x0002B;</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mtext>2</mml:mtext></mml:msub><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>h</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mo>&#x02212;</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002A;</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>(</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>)</mml:mo><mml:mo>&#x0002B;</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:msub><mml:mi>&#x003B2;</mml:mi><mml:mtext>3</mml:mtext></mml:msub><mml:mi>F</mml:mi><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:msub><mml:mi>e</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>&#x0002A;</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>(</mml:mo><mml:mi>E</mml:mi><mml:mi>D</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy='false'>)</mml:mo><mml:mo>&#x0002B;</mml:mo><mml:mi>C</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>l</mml:mi><mml:mi>s</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:mo>&#x0002B;</mml:mo><mml:mi>Y</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>I</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>P</mml:mi><mml:mtext>&#x0002B;</mml:mtext><mml:mi>B</mml:mi><mml:mi>P</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>B</mml:mi><mml:mi>Y</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mi>&#x003B5;</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>where <italic>Cohort</italic><sub>&#x02212;1</sub> denotes CEOs born 3 years after the famine, 1962&#x02013;1964; <italic>Cohort</italic><sub>&#x02212;2</sub> denotes CEOs born during the fourth to sixth years after the famine, 1965&#x02013;1967. In model (4), we used CEOs born after 1967 as the benchmark for the equilibrium trend test. The coefficient for <italic>Cohort</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>EDD</italic>) represents CEOs who were born during these two periods after the famine. Assuming equilibrium trends, we expected that the coefficients of <italic>Cohort</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>EDD</italic>) should not be significantly different from 0.</p>
<p><xref ref-type="table" rid="T6">Table 6</xref> reports the results of regression tests using model (4). Columns (1) and (2) used <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> as variables of interest, respectively. The results in these columns indicate that the coefficients of <inline-formula><mml:math id="M24"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic> (<inline-formula><mml:math id="M25"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDD</italic>) and <inline-formula><mml:math id="M26"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDR</italic> (<inline-formula><mml:math id="M27"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula><italic>EDD</italic>) were not significant, suggesting that CEOs born after the famine have no significant differences in terms of influence on corporate earnings quality. These results are in line with the assumption about common trends in DID estimation methods. More importantly, for CEOs born before the Great Famine, the coefficients of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic>) are all significantly negative at the 5% level, indicating that CEOs&#x00027; famine experience are positively associated with corporate earnings quality.</p>
<table-wrap position="float" id="T6">
<label>Table 6</label>
<caption><p>CEO famine experience and corporate earnings quality tested with common trends.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Famine<xref ref-type="table-fn" rid="TN4"><sup>&#x0002A;</sup></xref>EDR</td>
<td valign="top" align="center">&#x02212;0.647<xref ref-type="table-fn" rid="TN4"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.12)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M28"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</td>
<td valign="top" align="center">&#x02212;0.300</td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;1.36)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M29"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDR</td>
<td valign="top" align="center">&#x02212;0.303</td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;1.27)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Famine<xref ref-type="table-fn" rid="TN4"><sup>&#x0002A;</sup></xref>EDD</td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN4"><sup>&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.51)</td>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M30"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</td>
<td/>
<td valign="top" align="center">&#x02212;0.004</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.27)</td>
</tr>
<tr>
<td valign="top" align="left"><inline-formula><mml:math id="M31"><mml:msubsup><mml:mrow><mml:mi>Cohort</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x0002A;</mml:mo></mml:mrow></mml:msubsup></mml:math></inline-formula>EDD</td>
<td/>
<td valign="top" align="center">&#x02212;0.002</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;0.69)</td>
</tr>
<tr>
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.114</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of common trends. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. Cohort<sub>&#x02212;2</sub> was encoded to 1 if the CEO was born 4&#x02013;6 years after the famine, that is, during 1965&#x02013;1967, and Cohort<sub>&#x02212;1</sub> was encoded to 1 if the CEO was born during 1962&#x02013;1964, 3 years after the famine, respectively, and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN4"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Firm fixed effects and regional omission variables</title>
<p>Fixed factors of a company that do not change over time, such as honesty in the corporate culture, may also cause differences in corporate earnings quality. To control for the impact of factors that may be omitted in the regression results, we further test the main hypothesis by controlling firm fixed effects. The results are reported in the first two columns of <xref ref-type="table" rid="T7">Table 7</xref>. After controlling for these fixed effects, the coefficient for the interaction of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) remained significant and negative at the 5% level, which is consistent with earlier findings, indicating that the results were not sensitive to the firm-level factors that do not change over time.</p>
<table-wrap position="float" id="T7">
<label>Table 7</label>
<caption><p>CEO famine experience and corporate earnings quality&#x02014;the effects of firm fixed effects and regional omissions.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" colspan="2"><bold>Firm fixed effect</bold></th>
<th valign="top" align="center" colspan="2"><bold>Using neighbor province as control</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.444<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;1.215<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.26)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.61)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;</sup></xref>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.005<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.007<xref ref-type="table-fn" rid="TN5"><sup>&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.45)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.38)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">6,612</td>
<td valign="top" align="center">6,612</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.178</td>
<td valign="top" align="center">0.178</td>
<td valign="top" align="center">0.122</td>
<td valign="top" align="center">0.122</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of firm fixed effects and regional omissions. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is the excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN5"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Other factors that do not change over time may impact the conclusions of this study, such as cultural differences between different regions. To resolve this issue, we matched the provinces with higher-level <italic>EDR</italic> due to the famine with neighboring provinces with lower-level <italic>EDR</italic>. Specifically, we selected three pairs of matched samples from Shandong and Shanxi, Henan and Hebei, and Guangxi and Guangdong. These regions differ little in geography and culture, so we could use it to control some unidentified macro-level factors. The results based on the samples from the six provinces are shown in the last two columns of <xref ref-type="table" rid="T7">Table 7</xref>. After excluding the samples from other provinces, <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) remained significantly negative at least at the 5% level, which is consistent with the previous findings.</p>
</sec>
<sec>
<title>Propensity score matching</title>
<p>Differences in company characteristics, the CEO, and the region where the company is located may also cause bias. We use propensity score matching (PSM) to test the basic hypothesis. The procedures are as follows: the financial characteristics and CEO characteristics of our sample were assessed for whether the CEO experienced a famine (<italic>Famine</italic> = 1) and whether famine was severe in the region where the CEO was born (<italic>EDD</italic> = 1). A one-to-one closest-match procedure was performed to obtain two sets of highly similar samples and to eliminate differences in the earnings quality due to differences in characteristics. Subsequently, the samples were regressed. If the CEO&#x00027;s famine experience continues to have a positive effect on corporate earnings quality, the aforementioned indicators can be ruled out as causing differences in the corporate earnings quality.</p>
<p>Panel A in <xref ref-type="table" rid="T8">Table 8</xref> reports the differences in characteristic variables between the corporate samples before and after PSM. Before PSM, the mean values of most feature variables are significantly different at the 10% level. However, after the one-to-one match, except for CEO age (<italic>Age</italic>), the mean differences in other characteristic variables were no longer significant, indicating that there are no differences between the treatment group and the control group. Panel B in <xref ref-type="table" rid="T8">Table 8</xref> reports the regression results using the PSM samples. The coefficient of the CEO&#x00027;s famine experience variable (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) was still significantly negative, which is consistent with the earlier research conclusions. The matching results based on PSM showed that the promotion effect for the CEO&#x00027;s famine experience on the quality of corporate earnings was not due to differences in characteristic variables.</p>
<table-wrap position="float" id="T8">
<label>Table 8</label>
<caption><p>Panel A: Propensity score matching parallelism hypothesis test.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;"><bold>Before PSM procedure</bold></th>
<th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;"><bold>After PSM procedure</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold><italic>Famine</italic> = 1</bold></th>
<th valign="top" align="center"><bold><italic>Famine</italic> = 0</bold></th>
<th valign="top" align="center"><bold>Difference</bold></th>
<th valign="top" align="center"><bold><italic>Famine</italic> = 1</bold></th>
<th valign="top" align="center"><bold><italic>Famine</italic> = 0</bold></th>
<th valign="top" align="center"><bold>Difference</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Size</td>
<td valign="top" align="center">21.704</td>
<td valign="top" align="center">21.663</td>
<td valign="top" align="center">0.042<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">21.702</td>
<td valign="top" align="center">21.692</td>
<td valign="top" align="center">0.010</td>
</tr>
<tr>
<td valign="top" align="left">Lev</td>
<td valign="top" align="center">0.485</td>
<td valign="top" align="center">0.472</td>
<td valign="top" align="center">0.014<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.486</td>
<td valign="top" align="center">0.487</td>
<td valign="top" align="center">&#x02212;0.001</td>
</tr>
<tr>
<td valign="top" align="left">ROA</td>
<td valign="top" align="center">0.030</td>
<td valign="top" align="center">0.032</td>
<td valign="top" align="center">&#x02212;0.002<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.030</td>
<td valign="top" align="center">0.029</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left">Grow</td>
<td valign="top" align="center">0.125</td>
<td valign="top" align="center">0.158</td>
<td valign="top" align="center">&#x02212;0.032<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.126</td>
<td valign="top" align="center">0.125</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left">SOE</td>
<td valign="top" align="center">0.656</td>
<td valign="top" align="center">0.505</td>
<td valign="top" align="center">0.152<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.652</td>
<td valign="top" align="center">0.650</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">Audit</td>
<td valign="top" align="center">0.088</td>
<td valign="top" align="center">0.057</td>
<td valign="top" align="center">0.031<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.087</td>
<td valign="top" align="center">0.081</td>
<td valign="top" align="center">0.006</td>
</tr>
<tr>
<td valign="top" align="left">Dual</td>
<td valign="top" align="center">0.189</td>
<td valign="top" align="center">0.167</td>
<td valign="top" align="center">0.022<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.186</td>
<td valign="top" align="center">0.181</td>
<td valign="top" align="center">0.005</td>
</tr>
<tr>
<td valign="top" align="left">OCF</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">0.041</td>
<td valign="top" align="center">0.010<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.050</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">First</td>
<td valign="top" align="center">0.388</td>
<td valign="top" align="center">0.366</td>
<td valign="top" align="center">0.022<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.387</td>
<td valign="top" align="center">0.385</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">IO</td>
<td valign="top" align="center">0.159</td>
<td valign="top" align="center">0.187</td>
<td valign="top" align="center">&#x02212;0.028<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.160</td>
<td valign="top" align="center">0.160</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">3.958</td>
<td valign="top" align="center">3.779</td>
<td valign="top" align="center">0.178<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">3.958</td>
<td valign="top" align="center">3.744</td>
<td valign="top" align="center">0.214<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td valign="top" align="left">Gender</td>
<td valign="top" align="center">0.059</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">0.011<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">0.057</td>
<td valign="top" align="center">0.054</td>
<td valign="top" align="center">0.003</td>
</tr>
<tr>
<td valign="top" align="left">Degree</td>
<td valign="top" align="center">1.424</td>
<td valign="top" align="center">1.454</td>
<td valign="top" align="center">&#x02212;0.030<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">1.433</td>
<td valign="top" align="center">1.435</td>
<td valign="top" align="center">0.002</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left" colspan="7"><bold>Panel B: CEO&#x00027;s famine experience and corporate earnings quality&#x02014;regression results after propensity score matching</bold>.</td>
</tr>
 <tr>
<td/>
<td valign="top" align="left"><bold>(1)</bold></td>
<td valign="top" align="left"><bold>(2)</bold></td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><bold>EQ</bold></td>
<td valign="top" align="left"><bold>EQ</bold></td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="left">&#x02212;0.406<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">(&#x02212;3.95)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;</sup></xref>EDD</italic></td>
<td/>
<td valign="top" align="left">&#x02212;0.026<xref ref-type="table-fn" rid="TN6"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">(&#x02212;2.82)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="left">9,582</td>
<td valign="top" align="left">9,582</td>
</tr>
<tr>
<td valign="top" align="left">Adj. R<sup>2</sup></td>
<td valign="top" align="left">0.132</td>
<td valign="top" align="left">0.132</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table presents the variables descriptive statistics of PSM variables and the regression results for famine experience on earnings quality based on PSM samples. Panel A shows the variables descriptive statistics before and after PSM, and panel B shows the regress results. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN6"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Other robustness tests</title>
<sec>
<title>CEOs&#x00027; distribution</title>
<p>As given in <xref ref-type="table" rid="T2">Table 2</xref>, the number of samples from each region is different, and too many and too few CEO samples from the same region may also affect our regression results. Hence, we deleted the three largest and three smallest provinces and performed a regression on the remaining. The results are given in <xref ref-type="table" rid="T9">Table 9</xref>. Columns (1) and (2) exclude Guangdong, Jiangsu, and Zhejiang, the three provinces with the largest numbers of CEO births. Columns (3) and (4) exclude Qinghai, Ningxia, and Hainan, the three provinces with smallest numbers of CEO births. Columns (5) and (6) exclude both the sets. In these estimates, <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) remained significantly negative at least at the 1% level, which is consistent with the previous conclusions, indicating that the impact of the famine experience on the quality of corporate earnings was not due to the region of birth.</p>
<table-wrap position="float" id="T9">
<label>Table 9</label>
<caption><p>CEOs&#x00027; famine experience and the quality of corporate earnings&#x02014;the impact of excluding CEO samples.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
<th valign="top" align="center"><bold>(5)</bold></th>
<th valign="top" align="center"><bold>(6)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" colspan="2"><bold>Delete upper three provinces</bold></th>
<th valign="top" align="center" colspan="2"><bold>Delete bottom three provinces</bold></th>
<th valign="top" align="center" colspan="2"><bold>Delete both sets</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.296<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.271<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.290<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.05)</td>
<td/>
<td valign="top" align="center">(&#x02212;1.98)</td>
<td/>
<td valign="top" align="center">(&#x02212;1.99)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;</sup></xref>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN7"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;3.63)</td>
<td/>
<td valign="top" align="center">(&#x02212;3.62)</td>
<td/>
<td valign="top" align="center">(&#x02212;3.58)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">17,795</td>
<td valign="top" align="center">17,795</td>
<td valign="top" align="center">23,610</td>
<td valign="top" align="center">23,610</td>
<td valign="top" align="center">17,431</td>
<td valign="top" align="center">17,431</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.115</td>
<td valign="top" align="center">0.115</td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.115</td>
<td valign="top" align="center">0.116</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of excluding some special CEO samples. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years or famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN7"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Sensitivity of earnings quality indicators</title>
<p>In the previous test, we used the modified Jones model as a proxy for earnings quality. To test whether the results are sensitive to the measurements of earnings quality, we retested the basic assumptions with different measures. First, the method of Dechow and Dichev (<xref ref-type="bibr" rid="B16">2002</xref>) was used to consider the impact of current, past, and future cash flows to measure earnings quality. Second, following Kothari et al. (<xref ref-type="bibr" rid="B43">2005</xref>), we used the Jones model modified by performance to measure earnings quality. Third, referring to McNichols (<xref ref-type="bibr" rid="B54">2002</xref>), we used a comprehensive DD model (Dechow and Dichev, <xref ref-type="bibr" rid="B16">2002</xref>) and a modified Jones model to measure earnings quality.</p>
<p><xref ref-type="table" rid="T10">Table 10</xref> reports the regression results for these three indicators to measure corporate earnings quality. For all six columns, the coefficients of the interaction term <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) are all significantly negative at the 5% level, which is consistent with the previous results, indicating that CEOs&#x00027; famine experience improves corporate earnings quality. The results given in <xref ref-type="table" rid="T10">Table 10</xref> also indicate that changing the measurement of the earnings quality did not affect our basic conclusions.</p>
<table-wrap position="float" id="T10">
<label>Table 10</label>
<caption><p>CEOs&#x00027; famine experience and the quality of corporate earnings&#x02014;sensitivity test for the proxies of earnings quality.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
<th valign="top" align="center"><bold>(5)</bold></th>
<th valign="top" align="center"><bold>(6)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>1</sub></bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>1</sub></bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>2</sub></bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>2</sub></bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>3</sub></bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ<sub>3</sub></bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" colspan="2"><bold>DD model</bold></th>
<th valign="top" align="center" colspan="2"><bold>ROA adjust Jones Model</bold></th>
<th valign="top" align="center" colspan="2"><bold>DD and Jones Model</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.294<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.306<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.373<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;2.21)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.15)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.63)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;</sup></xref>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.005<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.005<xref ref-type="table-fn" rid="TN8"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;3.84)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.47)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.58)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">22,473</td>
<td valign="top" align="center">22,473</td>
<td valign="top" align="center">22,473</td>
<td valign="top" align="center">22,473</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.114</td>
<td valign="top" align="center">0.191</td>
<td valign="top" align="center">0.191</td>
<td valign="top" align="center">0.123</td>
<td valign="top" align="center">0.123</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of the sensitivity test for the proxies of earnings quality. EQ is the corporate earnings quality, which is calculated by the DD model in the first two columns, ROA adjust Jones model in the middle two columns, and DD and Jones model in the last two columns. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is the excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN8"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Considering impact of the new accounting standards and some omitted factors</title>
<p>The period of our sample begins in 2000, which may ignore the huge potential impact of the new accounting standards for enterprises in 2007 on earnings quality. In our previous model, we have controlled the annual fixed effect to control these factors. We conducted a new robustness test using the sub-sample after 2007 in this part. The results are shown in columns (1) and (2) of <xref ref-type="table" rid="T11">Table 11</xref>. We found that the coefficients of <italic>Famine</italic> <sup>&#x0002A;</sup> <italic>EDR</italic> and <italic>Famine</italic> <sup>&#x0002A;</sup> <italic>EDD</italic> are &#x02212;0.632 and &#x02212;0.010, respectively. They were significant at the 1% level, which still supports our hypothesis.</p>
<table-wrap position="float" id="T11">
<label>Table 11</label>
<caption><p>CEOs&#x00027; famine experience and the quality of corporate earnings&#x02014;using sub-sample after 2007 and adding more fixed effects.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" colspan="2"><bold>Using samples after 2007</bold></th>
<th valign="top" align="center" colspan="2"><bold>Control more fixed effect</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.632<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.315<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;3.32)</td>
<td/>
<td valign="top" align="center">(&#x02212;2.35)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;</sup></xref>EDD</italic></td>
<td/>
<td valign="top" align="center">&#x02212;0.010<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td valign="top" align="center">&#x02212;0.006<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;4.85)</td>
<td/>
<td valign="top" align="center">(&#x02212;4.22)</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;</sup></xref>Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year<xref ref-type="table-fn" rid="TN9"><sup>&#x0002A;</sup></xref>Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">15,310</td>
<td valign="top" align="center">15,310</td>
<td valign="top" align="center">23,974</td>
<td valign="top" align="center">23,974</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.145</td>
<td valign="top" align="center">0.146</td>
<td valign="top" align="center">0.125</td>
<td valign="top" align="center">0.126</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of the sensitivity test using the sub-sample after 2007 and adding more fixed effects. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable that is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN9"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Addition, some factors that have a strong influence on the motivation of earnings management are not included in the model, such as the motivation to avoid losses. We added more fixed effects to the model to weaken the possible impact of these missing variables. In columns (3) and (4), we conducted a robustness test to control the fixed effects of <italic>Year</italic> <sup>&#x0002A;</sup> <italic>Industry</italic> and <italic>Year</italic> <sup>&#x0002A;</sup> <italic>Province</italic>. The results show that the coefficients of <italic>Famine</italic> <sup>&#x0002A;</sup> <italic>EDR</italic> and <italic>Famine</italic> <sup>&#x0002A;</sup> <italic>EDD</italic> are significantly negative at least at the 5% level. The results given in <xref ref-type="table" rid="T11">Table 11</xref> also indicate that our conclusions are relatively robust.</p>
</sec>
</sec>
</sec>
<sec id="s6">
<title>Further analyses</title>
<p>The aforementioned regression results showed that CEOs&#x00027; famine experience was significantly positively associated with corporate earnings quality. As detailed in hypothesis 1, we considered two potential mechanisms: the risk prevention effect and the famine learning effect. Next, to reveal the internal mechanism of CEOs&#x00027; famine experience affecting the corporate earnings quality, we further tested the potential mechanisms.</p>
<sec>
<title>Risk prevention effects</title>
<p>Risk prevention effects assume that the early-life famine experience of CEOs makes them more risk-sensitive and risk-averse. Low-quality earnings information disclosure may lead to a higher risk of litigation. Therefore, CEO&#x00027;s manipulation of earnings information is more cautious, and the quality of corresponding earnings information disclosure is thus relatively high. Based on the above analysis, if the CEO&#x00027;s early famine experience affected the corporate earnings quality mainly through risk prevention effects, it can be expected that when a company&#x00027;s low-quality earnings information disclosure incurs relatively higher risks, the CEO&#x00027;s risk prevention motivation would be relatively stronger, and the association between the CEO&#x00027;s famine experience and corporate earnings quality should be more obvious.</p>
<p>We measured risk intensity in terms of investor protection and whether the company was cross-listed. Regarding investor protection, the existing literature shows that in regions with high levels of investor protection, laws and regulations are more easily met, so the degree of coordination between laws and market mechanisms is relatively high, and investors&#x00027; awareness of protections is more visible and intense (Klapper and Love, <xref ref-type="bibr" rid="B41">2004</xref>). In this context, the potential risk intensity for low-quality information disclosure is higher. For cross-listed companies, the regulatory environment of cross-listed companies is more stringent, and the awareness of investor protections is also higher (Ke et al., <xref ref-type="bibr" rid="B39">2015</xref>). This means that cross-listed companies face more risk in relation to low-quality earnings information disclosure.</p>
<p>Based on the above considerations, we used the investor protection index (IPI) from the China Marketization Index Report prepared by Fan et al. (<xref ref-type="bibr" rid="B22">2011</xref>) to measure the degree of investor protection. Meanwhile, we used whether the company is cross-listed in both A and H shares (AH) to measure cross-listing. We divided our sample into two groups to test the risk prevention effects of CEOs&#x00027; experience of the Great Famine. <xref ref-type="table" rid="T12">Table 12</xref> reports the regression results. In the first four columns, the coefficients of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> are negatively associated with <italic>EQ</italic> in two low-IPI groups (where IPI is less than the value of the median), while in the high-IPI group, the <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> is not significant but <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> is significant at the 10% level. In the last four columns, the results show that <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> are all negative at least at the 10% level in the AH list group and only A list group. We further tested the coefficient differences between these divided groups using the chi-square method. The results indicated that the absolute value of the coefficient of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> in the high-IPI (AH list) group is significantly larger than that for the low-IPI (A list only) group. These results suggested that when the risk intensity for low-quality information disclosure is higher, CEOs&#x00027; famine experience has a more significant effect on the corporate earnings quality, which is consistent with our expectations regarding the risk prevention effect.</p>
<table-wrap position="float" id="T12">
<label>Table 12</label>
<caption><p>CEO famine experience and quality of corporate earnings&#x02014;a test of risk aversion effects.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
<th valign="top" align="center"><bold>(5)</bold></th>
<th valign="top" align="center"><bold>(6)</bold></th>
<th valign="top" align="center"><bold>(7)</bold></th>
<th valign="top" align="center"><bold>(8)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
<th valign="top" align="center" style="border-bottom: thin solid #000000;"><bold>EQ</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>Low IPI</bold></th>
<th valign="top" align="center"><bold>High IPI</bold></th>
<th valign="top" align="center"><bold>Low IPI</bold></th>
<th valign="top" align="center"><bold>High IPI</bold></th>
<th valign="top" align="center"><bold>A list only</bold></th>
<th valign="top" align="center"><bold>AH list</bold></th>
<th valign="top" align="center"><bold>A list only</bold></th>
<th valign="top" align="center"><bold>AH list</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="center">&#x02212;0.007</td>
<td valign="top" align="center">&#x02212;0.711<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.134<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.979<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="center">(&#x02212;0.04)</td>
<td valign="top" align="center">(&#x02212;3.22)</td>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.66)</td>
<td valign="top" align="center">(&#x02212;2.46)</td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref> EDD</italic></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.004<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.010<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.004<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.014<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;1.90)</td>
<td valign="top" align="center">(&#x02212;4.33)</td>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.46)</td>
<td valign="top" align="center">(&#x02212;2.62)</td>
</tr>
<tr>
<td valign="top" align="left"><italic>Differences</italic></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 6.95<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 4.43<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 3.11<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 3.17<xref ref-type="table-fn" rid="TN10"><sup>&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> = 0.008)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> = 0.036)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> = 0.078)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> = 0.075)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth place</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="center">11,787</td>
<td valign="top" align="center">12,187</td>
<td valign="top" align="center">11,787</td>
<td valign="top" align="center">12,187</td>
<td valign="top" align="center">22,930</td>
<td valign="top" align="center">1,044</td>
<td valign="top" align="center">22,930</td>
<td valign="top" align="center">1,044</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="center">0.109</td>
<td valign="top" align="center">0.127</td>
<td valign="top" align="center">0.109</td>
<td valign="top" align="center">0.127</td>
<td valign="top" align="center">0.113</td>
<td valign="top" align="center">0.238</td>
<td valign="top" align="center">0.113</td>
<td valign="top" align="center">0.242</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of risk aversion effects. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier, and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. IPI measures the degree of investor protection index prepared by Fan et al. (<xref ref-type="bibr" rid="B22">2011</xref>), high IPI means the province in 1 year got a median-above IPI compared to others. A List only means firms only listed A share market in China, while AH List means cross-list with A and H share. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN10"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Famine learning effect</title>
<p>The famine learning effect holds that the epochal characteristics and reasons behind the occurrence of the Great Famine increase the knowledge of famine-experienced CEOs. Under the influence of the learning effect, the probability of the CEO to implement earnings manipulation and disclose untrue earnings information is lower, resulting in higher quality earnings information disclosure. Based on the above analysis, if CEOs&#x00027; early famine experience affected the quality of corporate earnings, mainly through the famine learning effect, it can be expected that when the corporate CEOs&#x00027; learning ability and knowledge accumulation of the famine event are relatively higher, the association between CEOs&#x00027; famine experience and corporate earnings quality should be more obvious.</p>
<p>We used CEO education and professional background to measure CEOs&#x00027; learning ability and knowledge accumulation related to the early famine event. Jiang et al. (<xref ref-type="bibr" rid="B38">2016</xref>) found that when the CEO has more education, the secretary&#x00027;s financial experience plays a larger role in information disclosure, and earnings information of the company is of higher quality. Following their research, we expected that when the CEO&#x00027;s education is higher, the learning effect of the famine event should be more obvious. When the professional background of the CEO is in economics or management, the CEO can better understand the reasons for the occurrence of the Great Famine and could learn more from the disaster.</p>
<p>Based on CEO education and professional background data compiled in the CSMAR database, we manually collected samples to supplement the missing data as much as possible. To measure CEO education (<italic>Degree</italic>), we followed Jiang et al. (<xref ref-type="bibr" rid="B38">2016</xref>) again and divided academic education into five levels: high school, junior college, undergraduate degree, masters, and doctorate levels; then, we defined CEOs with a master&#x00027;s or doctorate degree as the high-degree group, and the remaining as the low-degree group. Meanwhile, when the CEOs&#x00027; major was in economics or management, we assigned these CEOs to the E&#x00026;M group; otherwise, they were assigned to the non-E&#x00026;M group. <xref ref-type="table" rid="T13">Table 13</xref> reports the results of the CEOs&#x00027; famine learning effect. In the first four columns, the coefficient of the interaction term <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> (<italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic>) is &#x02212;0.832 (&#x02212;0.104) in the high-degree group and is significant at the 1% level, while in the low-degree group, the coefficient is not significant. In the remaining four columns, the coefficients of <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> in the E&#x00026;M group are negatively significant at the 5 and 1% levels, respectively, while in the non-E&#x00026;M group, the <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> is not significant, but <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> is significant at the 5% level. Using the chi-square method, we further test the differences of coefficients between these two groups. We found that the absolute value of the coefficient for <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDR</italic> and <italic>Famine</italic><sup>&#x0002A;</sup><italic>EDD</italic> in the high-degree (E&#x00026;M) group is significantly larger than that for the low-degree (non-E&#x00026;M) group. In summation, these results indicated that when the CEO&#x00027;s learning ability and knowledge accumulation are relatively higher, the famine experience has a more significant effect on corporate earnings quality, which hold against the expectations of the famine learning effect.</p>
<table-wrap position="float" id="T13">
<label>Table 13</label>
<caption><p>CEO famine experience and the quality of corporate earnings&#x02014;a test of famine learning effects.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>(1)</bold></th>
<th valign="top" align="center"><bold>(2)</bold></th>
<th valign="top" align="center"><bold>(3)</bold></th>
<th valign="top" align="center"><bold>(4)</bold></th>
<th valign="top" align="center"><bold>(5)</bold></th>
<th valign="top" align="center"><bold>(6)</bold></th>
<th valign="top" align="center"><bold>(7)</bold></th>
<th valign="top" align="center"><bold>(8)</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
<th valign="top" align="center"><bold>EQ</bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>Low degree</bold></th>
<th valign="top" align="center"><bold>High degree</bold></th>
<th valign="top" align="center"><bold>Low degree</bold></th>
<th valign="top" align="center"><bold>High degree</bold></th>
<th valign="top" align="center"><bold>Non EandM</bold></th>
<th valign="top" align="center"><bold>EandM</bold></th>
<th valign="top" align="center"><bold>Non EandM</bold></th>
<th valign="top" align="center"><bold>EandM</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;</sup></xref>EDR</italic></td>
<td valign="top" align="left">&#x02212;0.008</td>
<td valign="top" align="center">&#x02212;0.832<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.147</td>
<td valign="top" align="center">&#x02212;1.037<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
</tr>
<tr>
<td/>
<td valign="top" align="left">(&#x02212;0.03)</td>
<td valign="top" align="center">(&#x02212;3.60)</td>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;0.85)</td>
<td valign="top" align="center">(&#x02212;3.16)</td>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><italic>Famine<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;</sup></xref> EDD</italic></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.001</td>
<td valign="top" align="center">&#x02212;0.014<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;0.004<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center">&#x02212;0.017<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;0.73)</td>
<td valign="top" align="center">(&#x02212;5.48)</td>
<td/>
<td/>
<td valign="top" align="center">(&#x02212;2.38)</td>
<td valign="top" align="center">(&#x02212;4.79)</td>
</tr>
<tr>
<td valign="top" align="left"><italic>Differences</italic></td>
<td valign="top" align="left" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 8.66<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 15.55<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 6.25<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;</sup></xref></td>
<td valign="top" align="center" colspan="2" style="border-top: thin solid #000000;">Chi<sup>2</sup> = 11.46<xref ref-type="table-fn" rid="TN11"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref></td>
</tr>
<tr>
<td/>
<td valign="top" align="left" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> = 0.003)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> &#x0003C; 0.001)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E;Chi<sup>2</sup> = 0.012)</td>
<td valign="top" align="center" colspan="2">(<italic>P</italic> &#x0003E; Chi<sup>2</sup> &#x0003C; 0.001)</td>
</tr> <tr style="border-top: thin solid #000000;">
<td valign="top" align="left">Controls</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Year</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Industry</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Province</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birthplace</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Birth year</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left"><italic>N</italic></td>
<td valign="top" align="left">16,066</td>
<td valign="top" align="center">7,908</td>
<td valign="top" align="center">16,066</td>
<td valign="top" align="center">7,908</td>
<td valign="top" align="center">9,173</td>
<td valign="top" align="center">4,439</td>
<td valign="top" align="center">9,173</td>
<td valign="top" align="center">4,439</td>
</tr>
<tr>
<td valign="top" align="left">Adj-R<sup>2</sup></td>
<td valign="top" align="left">0.111</td>
<td valign="top" align="center">0.131</td>
<td valign="top" align="center">0.111</td>
<td valign="top" align="center">0.134</td>
<td valign="top" align="center">0.121</td>
<td valign="top" align="center">0.238</td>
<td valign="top" align="center">0.122</td>
<td valign="top" align="center">0.204</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>This table reports the regression results of famine learning effects. EQ is the corporate earnings quality, calculated by the modified Jones model. Famine was encoded to 1 if the CEO was born in 1961 or earlier and 0 otherwise. EDR is excess death ratio, valued as the mean difference of mortality in the given province during 3 years of famine subtracted from the average mortality before and after 3 years of the Great Famine. EDD is a dummy variable which is encoded to 1 if the provinces have a median-above EDR during 3 years of famine and otherwise encoded to 0. Degree means the degree of CEOs. High Degree means the CEOs got a master&#x00027;s and doctorate degrees. EandM means the CEOs&#x00027; major was in economics or management and otherwise assigned to non-EandM group. Controls include Size, Lev, ROA, Grow, SOE, Audit, Dual, OCF, First, IO, Age, Gender, and Degree. <xref ref-type="table" rid="T14">Appendix 1</xref> provides a detailed description of all the variables. The t-value is given within parentheses based on robust standard errors clustered by the firm level.</p> 
<fn id="TN11"><p><sup>&#x0002A;&#x0002A;&#x0002A;</sup>, <sup>&#x0002A;&#x0002A;</sup>, and <sup>&#x0002A;</sup> represent statistical significance at the 1%, 5%, and 10% levels, respectively.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="s7">
<title>Discussion</title>
<sec>
<title>Main findings</title>
<p>Using the exogenous event of the Great Chinese Famine as the starting point, this research examined the impact of CEOs&#x00027; experience of this famine on corporate earnings quality. This research found that CEOs who experienced this event led to higher earnings quality for their companies. We also showed that this effect is more pronounced for CEOs who experienced this famine during their childhood or adolescence. On the one hand, CEOs who experienced the famine showed a higher risk aversion behavior. When the degree of investor protection is high and the company is cross-listed, the positive effects of the famine experience on earnings quality are more obvious. On the other hand, CEOs show a certain learning effect related to the disaster. More education or professional background in economics or management led to significantly better corporate earnings quality for companies with famine-experienced CEOs. The above findings prove that the CEOs&#x00027; famine experiences affect the earnings quality through the risk aversion effect and famine learning effect. These conclusions were validated through a series of robustness tests.</p>
</sec>
<sec>
<title>Theoretical implications</title>
<p>The results of the current study are consistent with those of previous research, and this study makes several contributions to the literature. First, taking CEOs&#x00027; heterogeneity into account, our paper provides empirical evidence that CEOs&#x00027; early-life famine experience is significantly positively associated with corporate earnings quality, thereby further enriching and supplementing the relevant areas of the literature on corporate earnings quality. Second, due to the exogenous and unpredictable nature of the Great Famine, our paper also provides a more robust estimate of the impact of CEOs&#x00027; early-life experiences on corporate decision making, thereby addressing the selection bias in the existing literature that considered financial experience, overseas experience, and military experience of CEOs. Third, we explored the mechanism of CEOs&#x00027; famine experiences affecting the earnings quality, and we found that the risk aversion effect and famine learning effect are the two mechanisms of CEOs&#x00027; famine experiences affecting earnings quality. These findings enrich the literature on the long-term economic consequences of the Great Chinese Famine.</p>
</sec>
<sec>
<title>Practical implications</title>
<p>We can draw some practical implications from our research results. The impact of informal institutions on corporate governance is attracting increased attention. Our paper indicated that CEOs&#x00027; famine experience operates in a similar manner as an implicit contract, according to which managers seek to produce high-quality outputs in their decision-making process. When an enterprise operates in an environment of high uncertainty, this implicit contract improves governance.</p>
</sec>
</sec>
<sec id="s8">
<title>Limitation and future research</title>
<p>This study has several limitations. First, the family background is an important factor influencing CEO&#x00027;s perception of the Great Chinese Famine. If the manager came from a relatively well-off family in comparison to the average financial situation at the time, the famine experience may have little impact on them. On the contrary, CEOs who came from a poor family may have a deep impression on the Great Chinese Famine. However, it is difficult for us to obtain the CEO&#x00027;s family data, such as income and their parents&#x00027; work. Second, it is not just only a CEO who can decide corporate&#x00027;s behavior, and the corporate&#x00027;s behavior is also influenced by other factors, such as the board of directors. In practice, other managers and independent directors also can affect the corporate&#x00027;s economic decision making. Therefore, it may not be enough to just take CEO as the research object. If we conduct research based on the management team, it is difficult to construct the indicators of the management team&#x00027;s famine experience at the team level.</p>
<p>In future research, it is expected that more information about CEOs and their family background can be obtained through big data or field research. It is also expected to explore some new famine experience indicators at the management team level. We are looking forward to providing more causal evidence on the impact of famine experience on corporate behavior.</p>
</sec>
<sec sec-type="data-availability" id="s9">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="s10">
<title>Author contributions</title>
<p>YZ contributed significantly to analysis and manuscript preparation. JH performed the data analyses and wrote the manuscript. LL helped perform the analysis with constructive discussions. All authors contributed to the conception of the study and approved the submitted version.</p>
</sec>
<sec sec-type="funding-information" id="s11">
<title>Funding</title>
<p>We acknowledge the support from the National Nature Science Foundation of China (Grant Number: 72102149) and the 2021 project of Guangzhou Planning of Philosophy and Social Science (Grant Number: 2021GZQN05).</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s12">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack><p>We gratefully acknowledge the support from the Research Center of Low Carbon Economy for Guangzhou Region. We also would like to thank Editage (<ext-link ext-link-type="uri" xlink:href="https://www.editage.com">www.editage.com</ext-link>) for English language editing.</p>
</ack>
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<title>Appendix 1</title>
<table-wrap position="float" id="T14">
<caption><p>Definitions and methods of calculation for the main variables.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Symbol</bold></th>
<th valign="top" align="left"><bold>Name</bold></th>
<th valign="top" align="left"><bold>Definition and method of calculation</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">EQ</td>
<td valign="top" align="left">Earnings quality</td>
<td valign="top" align="left">Modified Jones model; the method of calculation is described in the introduction</td>
</tr>
<tr>
<td valign="top" align="left">Famine</td>
<td valign="top" align="left">Famine experience</td>
<td valign="top" align="left">1 if the CEO was born in 1961 or earlier, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Cohort<sub>1</sub></td>
<td valign="top" align="left">Infancy</td>
<td valign="top" align="left">1 if the CEO was born between 1959 and 1961, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Cohort<sub>2</sub></td>
<td valign="top" align="left">Childhood</td>
<td valign="top" align="left">1 if the CEO was born between 1955 and 1958, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Cohort<sub>3</sub></td>
<td valign="top" align="left">Teenager</td>
<td valign="top" align="left">1 if the CEO was born between 1944 and 1954, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Cohort<sub>4</sub></td>
<td valign="top" align="left">Adult</td>
<td valign="top" align="left">1 if the CEO was born before 1944, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">EDR</td>
<td valign="top" align="left">Excess mortality</td>
<td valign="top" align="left">Excess mortality, valued as the difference in mortality in the given province for the 3 years of the famine and the average for the previous 3 years overall</td>
</tr>
<tr>
<td valign="top" align="left">EDD</td>
<td valign="top" align="left">Excess mortality</td>
<td valign="top" align="left">1 if EDR<sub>1</sub> is higher than the median, 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Size</td>
<td valign="top" align="left">Company size</td>
<td valign="top" align="left">Natural logarithm of company&#x00027;s total assets at the end of the year</td>
</tr>
<tr>
<td valign="top" align="left">Lev</td>
<td valign="top" align="left">Financial leverage</td>
<td valign="top" align="left">Gearing ratio, the company&#x00027;s total liabilities at the end of the year divided by total assets at the end of the year</td>
</tr>
<tr>
<td valign="top" align="left">Roa</td>
<td valign="top" align="left">Profitability</td>
<td valign="top" align="left">Return on assets, calculated as net profit divided by total assets at the end of the year</td>
</tr>
<tr>
<td valign="top" align="left">Grow</td>
<td valign="top" align="left">Company growth</td>
<td valign="top" align="left">Sales revenue growth rate, the annual sales revenue minus the previous year&#x00027;s sales revenue divided by the previous year&#x00027;s sales revenue</td>
</tr>
<tr>
<td valign="top" align="left">Soe</td>
<td valign="top" align="left">Property right</td>
<td valign="top" align="left">1 if the company is a state-owned enterprise and 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Big</td>
<td valign="top" align="left">Big Four</td>
<td valign="top" align="left">1 if the corporate auditor is in the Big Four, otherwise Big is assigned a value of 0</td>
</tr>
<tr>
<td valign="top" align="left">Dual</td>
<td valign="top" align="left">Two jobs in one</td>
<td valign="top" align="left">1 for CEOs who are also chairs and 0 otherwise</td>
</tr>
<tr>
<td valign="top" align="left">Ocf</td>
<td valign="top" align="left">Free cash flow</td>
<td valign="top" align="left">Net cash flow generated by the company at the end of the year divided by total assets at the end of the period</td>
</tr>
<tr>
<td valign="top" align="left">First</td>
<td valign="top" align="left">Major shareholder holdings</td>
<td valign="top" align="left">Number of shares held by the company&#x00027;s largest shareholders divided by total number of shares</td>
</tr>
<tr>
<td valign="top" align="left">Inst</td>
<td valign="top" align="left">corporate investor</td>
<td valign="top" align="left">Number of shares held by institutional investors divided by total shares</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="left">CEO age</td>
<td valign="top" align="left">Natural logarithm of CEO age</td>
</tr>
<tr>
<td valign="top" align="left">Gender</td>
<td valign="top" align="left">CEO gender</td>
<td valign="top" align="left">1 if CEO is female and 0 if male</td>
</tr>
<tr>
<td valign="top" align="left">Degree</td>
<td valign="top" align="left">CEO degree</td>
<td valign="top" align="left">Natural logarithm of population in the province where the business is located</td>
</tr>
</tbody>
</table>
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