Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2258259
Lin Mu, Gonglin Yuan, Tianshan Yang
ABSTRACTThe strand research of regional branching identifies relatedness as a key driver of new specializations in a region. The purpose of the present paper is to extend the regional branching framework by investigating how a regional knowledge base of information and communication technologies (ICTs) and others (non-ICTs) influences the specialization of financial technology (FinTech) in China at the city level. Accordingly, the empirical analysis focuses on the relationship between relatedness and FinTech diversification using panel data spanning the period 2008–2021 covering 247 China’s cities. We obtain that the role of ICTs relatedness is positively relevant in explaining the emergence and development of new FinTech domains. However, non-ICT relatedness is a negative factor in the process of FinTech diversification, which we attribute to the fact that FinTech is less reliant on other knowledge base except ICT sectors. Additionally, the effects of ICTs relatedness are higher in peripheral cities compared to core cities in promoting FinTech diversification.KEYWORDS: FinTechtechnological relatednessICTsregional diversificationJEL: O33R11O31 Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingSupported by the National Social Science Youth Foundation of China [Grant No. 19CXW028] and Innovation Project of Guangxi Graduate Education [Grant No. YCBZ2020020].
{"title":"Does China’s Existing Regional Knowledge Base Stimulate or Hinder Diversification into FinTech: The Role of Local ICTs Base","authors":"Lin Mu, Gonglin Yuan, Tianshan Yang","doi":"10.1080/1540496x.2023.2258259","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2258259","url":null,"abstract":"ABSTRACTThe strand research of regional branching identifies relatedness as a key driver of new specializations in a region. The purpose of the present paper is to extend the regional branching framework by investigating how a regional knowledge base of information and communication technologies (ICTs) and others (non-ICTs) influences the specialization of financial technology (FinTech) in China at the city level. Accordingly, the empirical analysis focuses on the relationship between relatedness and FinTech diversification using panel data spanning the period 2008–2021 covering 247 China’s cities. We obtain that the role of ICTs relatedness is positively relevant in explaining the emergence and development of new FinTech domains. However, non-ICT relatedness is a negative factor in the process of FinTech diversification, which we attribute to the fact that FinTech is less reliant on other knowledge base except ICT sectors. Additionally, the effects of ICTs relatedness are higher in peripheral cities compared to core cities in promoting FinTech diversification.KEYWORDS: FinTechtechnological relatednessICTsregional diversificationJEL: O33R11O31 Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingSupported by the National Social Science Youth Foundation of China [Grant No. 19CXW028] and Innovation Project of Guangxi Graduate Education [Grant No. YCBZ2020020].","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2266111
Shunyu Su, Yezhou Sha
ABSTRACTWe present a closed-form solution connecting the probability of informed trading (PIN) to the overlooked parameter that signaling private information is good or bad. Estimating PIN using illegal insider trading data, we find it sensitive to the certainty of positive private information in addition to the existed explanations, offering a new explanation for PIN‘s limitations in prior literature.KEYWORDS: Probability of informed tradingliquidityinformation asymmetryinsider tradingJEL: G12G14 AcknowledgmentsWe thank the editor Paresh Narayan, a subject editor and two reviewers for their comments. We also thank Tao Bing and Nianling Wang from Capital University of Economics and Business, as well as Cheng Yan from the University of Essex, for helpful discussions. We are grateful to Jaideep Oberoi from SOAS, University of London for providing the code estimating PIN with Bayesian approach. We are also indebted to the FinTech Lab of the School of Finance at Capital University of Economics and Business for providing High-Performance Computing (HPC) resources for large dataset estimation. This research is funded by Capital University of Economics and Business (Grant ID: QNTD202301). All errors are our own responsibility.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Capital University of Economics and Business [QNTD202301].
{"title":"Good (Bad) News and the Probability of Informed Trading: Evidence from Illegal Insider Trading","authors":"Shunyu Su, Yezhou Sha","doi":"10.1080/1540496x.2023.2266111","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2266111","url":null,"abstract":"ABSTRACTWe present a closed-form solution connecting the probability of informed trading (PIN) to the overlooked parameter that signaling private information is good or bad. Estimating PIN using illegal insider trading data, we find it sensitive to the certainty of positive private information in addition to the existed explanations, offering a new explanation for PIN‘s limitations in prior literature.KEYWORDS: Probability of informed tradingliquidityinformation asymmetryinsider tradingJEL: G12G14 AcknowledgmentsWe thank the editor Paresh Narayan, a subject editor and two reviewers for their comments. We also thank Tao Bing and Nianling Wang from Capital University of Economics and Business, as well as Cheng Yan from the University of Essex, for helpful discussions. We are grateful to Jaideep Oberoi from SOAS, University of London for providing the code estimating PIN with Bayesian approach. We are also indebted to the FinTech Lab of the School of Finance at Capital University of Economics and Business for providing High-Performance Computing (HPC) resources for large dataset estimation. This research is funded by Capital University of Economics and Business (Grant ID: QNTD202301). All errors are our own responsibility.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Capital University of Economics and Business [QNTD202301].","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"61 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2267739
Rui Chen, Jinye Li
ABSTRACTThis study examines the impact of short-term cross-border capital flows on banks’ risk-taking using panel quarterly data from all 37 A-share listed commercial banks in China. We demonstrate that short-term cross-border capital flows increase both ex ante and ex post risk-taking by banks. The impact of short-term cross-border capital flows upon banks’ ex ante and ex post risk-taking is heterogeneous across different kinds of commercial banks and at different phases of the financial cycle. Besides, short-term cross-border capital flows exhibit a non-linear effect on banks’ ex ante risk-taking and ex post risk-taking with changes in capital account openness.KEYWORDS: Short-term cross-border capital flowsbank risk-takingfinancial cyclecapital account opennessChinese A-share listed commercial banksJEL: F32G01G21 AcknowledgmentsWe would like to thank the Editor-in-Chief and Subject Editor of the journal Emerging Markets Finance and Trade for their suggestions on the article, and the reviewers for the journal Emerging Markets Finance and Trade for their comments.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was funded by the Humanities and Social Science Planning Fund of the Ministry of Education of China [No. 19YJA790040] and the Xinjiang Social Science Foundation Project [No. 19BJL028].
{"title":"How Do Short-Term Cross-Border Capital Flows Affect Bank Risk-Taking? Evidence from China","authors":"Rui Chen, Jinye Li","doi":"10.1080/1540496x.2023.2267739","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2267739","url":null,"abstract":"ABSTRACTThis study examines the impact of short-term cross-border capital flows on banks’ risk-taking using panel quarterly data from all 37 A-share listed commercial banks in China. We demonstrate that short-term cross-border capital flows increase both ex ante and ex post risk-taking by banks. The impact of short-term cross-border capital flows upon banks’ ex ante and ex post risk-taking is heterogeneous across different kinds of commercial banks and at different phases of the financial cycle. Besides, short-term cross-border capital flows exhibit a non-linear effect on banks’ ex ante risk-taking and ex post risk-taking with changes in capital account openness.KEYWORDS: Short-term cross-border capital flowsbank risk-takingfinancial cyclecapital account opennessChinese A-share listed commercial banksJEL: F32G01G21 AcknowledgmentsWe would like to thank the Editor-in-Chief and Subject Editor of the journal Emerging Markets Finance and Trade for their suggestions on the article, and the reviewers for the journal Emerging Markets Finance and Trade for their comments.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was funded by the Humanities and Social Science Planning Fund of the Ministry of Education of China [No. 19YJA790040] and the Xinjiang Social Science Foundation Project [No. 19BJL028].","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2266113
Mohammad Hossein Dehghani, Monireh Ravanbakhsh
ABSTRACTIn Iran’s stock market, this paper examines a new dimension of heterogeneity: ownership type, using an intermediary asset pricing model. When only state-owned intermediaries are considered, the price for exposure to capital ratio shocks is negative; thus, the group is not a marginal investor. Considering only privately-owned intermediaries has greater explanatory power than considering both types, and the price for capital risk is positive in both cases. We propose a new criterion to represent the sector with even greater explanatory power: a group of privately-owned intermediaries with positive capital risk prices when tested individually.KEYWORDS: Intermediary asset pricingheterogeneitycapital riskstate-owned vs. privately-owned intermediariesJEL: G12G23C33 AcknowledgmentsWe would like to thank the reviewers for their time and effort. We appreciate their valuable comments and suggestions that helped us improve the quality of this work.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1. Examples are the capital asset pricing model (Lintner Citation1965; Sharpe Citation1964), the consumption-based capital asset pricing model (Breeden Citation1979; Lucas Citation1978), and the multi-factor asset pricing models (Fama and French Citation1993, Citation2015).2. In the present study we use the data for the period 2011Q2–2021Q3, while in the previous study the last quarter was 2018Q4.3. According to He, Kelly, and Manela (Citation2017), the consumption of the intermediary sector, C, is a constant fraction, α, of its total wealth: Ct=αWtI. Given that the economy’s wealth is equal to the sum of the wealth of intermediaries and households: W=WI+WH, they show that the wealth of intermediaries is a fraction of the total wealth of the economy: WtI=θtWt. According to He, Kelly, and Manela (Citation2017), θ equals the capital ratio of the intermediary sector, η, in equilibrium.4. In fact, we use the growth rate of market portfolio excess return as a proxy for total wealth growth rate, dWtWt. Hence, βWi indicates the market factor’s risk exposure and PRW is its price. For more details, see Section 3.1.5. See Shanken and Zhou (Citation2007) for more details on EIV problem.6. There are N×(K+2) moment conditions and N×(1+K)+K parameters. Here N is the number of asset tests and K is the number of the risk factors.7. MAPE is calculated as 1NΣυiEt[Rtei]8. Specifically, υˆ′Cov(υˆ)−1υˆ∼χN−K2 where Cov(υˆ)=1T(Σisin−β(β′Σisin−1β)−1β′)(1+PR′Σf−1PR), Σisin is the variance-covariance matrix of the time series errors, and Σf is the variance-covariance matrix of risk factors. For more details, see Cochrane (Citation2005).9. Closed-end investment funds are known as investment companies in Iran.10. The list of intermediaries can be found in Appendix Table A2.11. The capital ratios are calculated using data from Mabna’s Rahavard Novin 3 database.12. The real period is 1390Q1–1400Q2 based on the Iranian calendar. It is relabeled acco
摘要本文以伊朗股票市场为研究对象，运用中介资产定价模型，考察了异质性的一个新维度:所有权类型。当只考虑国有中介机构时，资本充足率冲击的代价为负;因此，这个群体不是边际投资者。仅考虑私营中介机构比同时考虑两种中介机构具有更强的解释力，并且在两种情况下资本风险价格均为正。我们提出了一个具有更强解释力的新标准来代表该行业:一组在单独测试时具有正资本风险价格的私营中介机构。关键词:中介资产定价;异质性;资本风险;国有与私营中介;jel: G12G23C33致谢感谢审稿人所花费的时间和精力。我们感谢他们的宝贵意见和建议，这些意见和建议帮助我们提高了工作的质量。披露声明作者未报告潜在的利益冲突。例如资本资产定价模型(Lintner引文1965;Sharpe Citation1964)，基于消费的资本资产定价模型(Breeden Citation1979;2. Lucas Citation1978)和多因素资产定价模型(Fama and French Citation1993, Citation2015)。在本研究中，我们使用了2011Q2-2021Q3期间的数据，而在之前的研究中，最后一个季度是2018Q4.3。根据He、Kelly和Manela (Citation2017)的说法，中介部门的消费C是其总财富的常数分数α: Ct=α wti。假设经济的财富等于中介人和家庭财富的总和:W=WI+WH，他们表明中介人的财富是经济总财富的一小部分:WtI=θtWt。根据He, Kelly, and Manela (Citation2017)， θ等于均衡中中介部门的资本比率η。事实上，我们使用市场投资组合超额收益的增长率作为总财富增长率dWtWt的代表。因此，βWi表示市场因子的风险敞口，PRW为其价格。要了解更多细节，请参见3.1.5节。有关EIV问题的更多细节，请参见Shanken和Zhou (Citation2007)。有nx (K+2)个矩条件和nx (1+K)+K个参数。这里N是资产测试的数量，K是风险因素的数量。MAPE计算为1NΣυiEt[Rtei]8。其中，υ)(1+PR)(1+PR)， Σ是时间序列误差的方差-协方差矩阵，Σf是风险因素的方差-协方差矩阵。要了解更多细节，请参见Cochrane (Citation2005)。在伊朗，封闭式投资基金被称为投资公司。中介人名单载于附录表A2.11。资本比率是使用Mabna的raharvard Novin 3数据库中的数据计算出来的。根据伊朗历法，实际周期为1390Q1-1400Q2。它是根据公历重新标记的。本研究中的时间序列变量均与伊朗日历一致，尽管季度以公历表示和标记。请注意，伊朗年的第一个季节通常比公历年的第二个季节早11天开始。TSE市场的主要指数是TEDPIX指数，它是该市场所有上市股票的加权平均价格。这些数据分别来自TSE市场和伊朗中央银行(CBI)。输入数据包括股票收益以及计算规模和账面市值比所需的数据均来自raharvard Novin 3数据库。确切的日期是伊朗年第二季的结束，大部分时间是在9月22日。2011年第二季度和2021年第三季度美元对印度卢比的日平均汇率分别为11532和258319。这些中介机构要么尚未成立，要么其资产负债表尚未公布。19.有关样本资产负债表数据可得性的更多信息，见附录表A2。例如，在2011年第二季度，我们分别使用来自7家和5家相关中介机构的数据计算了民营和国有资本比率。这些数字在2014年第二季度增加到12个和7个，之后保持不变。除了这些早期的季度，在有限的几个季度中，一些资产负债表没有公布，我们用它的上一季度和下一季度的平均值来插值η。由于我们整个样本的前几个季度(2011Q2-2021Q3)缺少一些中介机构的数据，我们在2014年q2开始的较短样本期间估计模型。它使我们能够比较各中介机构的结果。资本风险价格在12家私营中介机构中有4家为负，在7家国有中介机构中有4家为正。在0.1%水平上均具有统计学显著性。
{"title":"Heterogeneous Intermediary Asset Pricing in Iran’s Stock Market: Privately-Owned vs. State-Owned","authors":"Mohammad Hossein Dehghani, Monireh Ravanbakhsh","doi":"10.1080/1540496x.2023.2266113","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2266113","url":null,"abstract":"ABSTRACTIn Iran’s stock market, this paper examines a new dimension of heterogeneity: ownership type, using an intermediary asset pricing model. When only state-owned intermediaries are considered, the price for exposure to capital ratio shocks is negative; thus, the group is not a marginal investor. Considering only privately-owned intermediaries has greater explanatory power than considering both types, and the price for capital risk is positive in both cases. We propose a new criterion to represent the sector with even greater explanatory power: a group of privately-owned intermediaries with positive capital risk prices when tested individually.KEYWORDS: Intermediary asset pricingheterogeneitycapital riskstate-owned vs. privately-owned intermediariesJEL: G12G23C33 AcknowledgmentsWe would like to thank the reviewers for their time and effort. We appreciate their valuable comments and suggestions that helped us improve the quality of this work.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1. Examples are the capital asset pricing model (Lintner Citation1965; Sharpe Citation1964), the consumption-based capital asset pricing model (Breeden Citation1979; Lucas Citation1978), and the multi-factor asset pricing models (Fama and French Citation1993, Citation2015).2. In the present study we use the data for the period 2011Q2–2021Q3, while in the previous study the last quarter was 2018Q4.3. According to He, Kelly, and Manela (Citation2017), the consumption of the intermediary sector, C, is a constant fraction, α, of its total wealth: Ct=αWtI. Given that the economy’s wealth is equal to the sum of the wealth of intermediaries and households: W=WI+WH, they show that the wealth of intermediaries is a fraction of the total wealth of the economy: WtI=θtWt. According to He, Kelly, and Manela (Citation2017), θ equals the capital ratio of the intermediary sector, η, in equilibrium.4. In fact, we use the growth rate of market portfolio excess return as a proxy for total wealth growth rate, dWtWt. Hence, βWi indicates the market factor’s risk exposure and PRW is its price. For more details, see Section 3.1.5. See Shanken and Zhou (Citation2007) for more details on EIV problem.6. There are N×(K+2) moment conditions and N×(1+K)+K parameters. Here N is the number of asset tests and K is the number of the risk factors.7. MAPE is calculated as 1NΣυiEt[Rtei]8. Specifically, υˆ′Cov(υˆ)−1υˆ∼χN−K2 where Cov(υˆ)=1T(Σisin−β(β′Σisin−1β)−1β′)(1+PR′Σf−1PR), Σisin is the variance-covariance matrix of the time series errors, and Σf is the variance-covariance matrix of risk factors. For more details, see Cochrane (Citation2005).9. Closed-end investment funds are known as investment companies in Iran.10. The list of intermediaries can be found in Appendix Table A2.11. The capital ratios are calculated using data from Mabna’s Rahavard Novin 3 database.12. The real period is 1390Q1–1400Q2 based on the Iranian calendar. It is relabeled acco","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"28 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2266114
Ye Zhou, Juejin Chen
This study examines the effects of macro-prudential policy on bank systemic risk by using a cross-country panel data of 65 economies. We find that: (1) macro-prudential policy mitigates bank systemic risk by decreasing the individual risk of banks and systemic linkage between banks; (2) the effect is stronger for banks with larger sizes, wider geographical regions of operation, and more diversified business services; (3) the effect is stronger in countries with less-developed or less-open financial systems, more concentrated banking industries, and emerging economies; (4) the policy-risk relationship is stronger when credit is more crunched, monetary policy is looser, a financial crisis occurs, or after the subprime crisis.
{"title":"Macro-Prudential Policy and Bank Systemic Risk: Cross-Country Evidence Based on Emerging and Advanced Economies","authors":"Ye Zhou, Juejin Chen","doi":"10.1080/1540496x.2023.2266114","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2266114","url":null,"abstract":"This study examines the effects of macro-prudential policy on bank systemic risk by using a cross-country panel data of 65 economies. We find that: (1) macro-prudential policy mitigates bank systemic risk by decreasing the individual risk of banks and systemic linkage between banks; (2) the effect is stronger for banks with larger sizes, wider geographical regions of operation, and more diversified business services; (3) the effect is stronger in countries with less-developed or less-open financial systems, more concentrated banking industries, and emerging economies; (4) the policy-risk relationship is stronger when credit is more crunched, monetary policy is looser, a financial crisis occurs, or after the subprime crisis.","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1080/1540496x.2023.2266109
Ping Zhang, Na Cao, Jieying Gao
ABSTRACTMergers and acquisitions (M&As) and innovation as essential business development strategies have attracted much interest in the capital market. A difference-in-differences model employing Chinese listed firms indicates that M&As significantly enhance corporate innovation. The potential mechanisms lie in the fact that M&As generate synergy in finance, corporate governance, and information. Complementary evidence shows that the M&A effect on innovation is heterogeneous across payment methods and goodwill. We conclude that synergies obtained from combining and restructuring are important drivers of innovation capabilities.KEYWORDS: Mergers and acquisitionscorporate innovationsynergygoodwillJEL: G30G34O32 Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Beijing Social Science Fund [No. 22JCC066].
{"title":"Mergers and Acquisitions, Synergy, and Corporate Innovation: Evidence from China","authors":"Ping Zhang, Na Cao, Jieying Gao","doi":"10.1080/1540496x.2023.2266109","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2266109","url":null,"abstract":"ABSTRACTMergers and acquisitions (M&As) and innovation as essential business development strategies have attracted much interest in the capital market. A difference-in-differences model employing Chinese listed firms indicates that M&As significantly enhance corporate innovation. The potential mechanisms lie in the fact that M&As generate synergy in finance, corporate governance, and information. Complementary evidence shows that the M&A effect on innovation is heterogeneous across payment methods and goodwill. We conclude that synergies obtained from combining and restructuring are important drivers of innovation capabilities.KEYWORDS: Mergers and acquisitionscorporate innovationsynergygoodwillJEL: G30G34O32 Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Beijing Social Science Fund [No. 22JCC066].","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"29 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/1540496x.2023.2266116
Huan Zhou, Ying Ji
ABSTRACTHousehold entrepreneurship can not only promote the sustainable development of the national economy, but also reduce unemployment. However, the urban-rural dual pattern leads to systematic differences in household entrepreneurship. This heterogeneity complicates the relationship between personality traits and household entrepreneurship. In order to study this complex relationship, this study empirically analyzed the influence of personality traits on entrepreneurial decisions and entrepreneurial returns of urban and rural households. Specifically, this study adopts the well-known “Big Five” personality classification criteria in the literature and constructs Probit, Tobit, and Heckman models based on the data of Chinese Family Panel Studies (CFPS) in 2016 and 2018. The results determine those factors beneficial for entrepreneurship, and the return of entrepreneurship depends upon the optimism, rigorousness, and preciseness of the householder. Additionally, the unrestricted intention of the household’s head is not conducive to entrepreneurship. The relevant departments should thoroughly acknowledge the influence of personality traits on the effectiveness of policies while making relevant policies involving household entrepreneurship.KEYWORDS: Personality traitsentrepreneurial decisionsentrepreneurial returnsJEL: G41D91D13 Disclosure statementThe authors declare no conflict of interest.Additional informationFundingThis work was supported by the [National Natural Science Foundation of China] under Grant [number 72171149, 72171123]; [Natural Science Research of the Academic School of Anhui Province] under Grant [number KJ2020A0712]; and [Annual Planning Project of Philosophy and Social Sciences of Henan Province] under Grant [number No.222102210117].
{"title":"Personality Traits and Household Entrepreneurship: Evidence from China","authors":"Huan Zhou, Ying Ji","doi":"10.1080/1540496x.2023.2266116","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2266116","url":null,"abstract":"ABSTRACTHousehold entrepreneurship can not only promote the sustainable development of the national economy, but also reduce unemployment. However, the urban-rural dual pattern leads to systematic differences in household entrepreneurship. This heterogeneity complicates the relationship between personality traits and household entrepreneurship. In order to study this complex relationship, this study empirically analyzed the influence of personality traits on entrepreneurial decisions and entrepreneurial returns of urban and rural households. Specifically, this study adopts the well-known “Big Five” personality classification criteria in the literature and constructs Probit, Tobit, and Heckman models based on the data of Chinese Family Panel Studies (CFPS) in 2016 and 2018. The results determine those factors beneficial for entrepreneurship, and the return of entrepreneurship depends upon the optimism, rigorousness, and preciseness of the householder. Additionally, the unrestricted intention of the household’s head is not conducive to entrepreneurship. The relevant departments should thoroughly acknowledge the influence of personality traits on the effectiveness of policies while making relevant policies involving household entrepreneurship.KEYWORDS: Personality traitsentrepreneurial decisionsentrepreneurial returnsJEL: G41D91D13 Disclosure statementThe authors declare no conflict of interest.Additional informationFundingThis work was supported by the [National Natural Science Foundation of China] under Grant [number 72171149, 72171123]; [Natural Science Research of the Academic School of Anhui Province] under Grant [number KJ2020A0712]; and [Annual Planning Project of Philosophy and Social Sciences of Henan Province] under Grant [number No.222102210117].","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/1540496x.2023.2260544
Shuhai Niu, Kexin Zhang, Juan Zhang, Yanchao Feng
ABSTRACTThe upgrade of industrial structure is one type of momentum for improving urban ecological efficiency, and exploring the nexus between them can help implement the concept of green development. Using data from 284 cities in China between 2004 and 2019, this research employs a series of econometric models to examine how improving industrial structure contributes to the growth of urban ecological efficiency. The research results reveal that urban ecological efficiency can be significantly improved by industrial structure upgrade, while ecological efficiency is inhibited by low-quality GDP and financial development models and insignificantly decreased by industrial structure rationalization. In addition, industrial structure upgrading has failed to play a mediating role in promoting eco-efficiency through technological innovation and the reduction of pollutant emission intensity. Moreover, officials’ turnover plays a negative moderating influence on the role of industrial upgrading in increasing ecological efficiency, while environmental regulation plays the opposite role. This study further conducts triple heterogeneity tests including city size, resource endowment, and regional heterogeneity, which provide a feasible way for further implementation of relevant policies.KEYWORDS: Industrial upgradingurban ecological efficiencymoderating mechanismmediating mechanism AcknowledgementsWe would like to thank the editor professor Chun-Ping Chang and the other three anonymous reviewers for their constructive comments and suggestions.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1. In the test of city size, the division of city size in this paper is based on the population size of the city. According to whether the total urban population is more than 1 million, the city size is divided into two categories: large and small.2. In the resource endowment test, cities are divided into resource-based cities and non-resource-based cities according to the differences in resource endowments of each city. The division is based on the Notice of the State Council of China on the “National Sustainable Development Plan for Resource-Based Cities (2013–2020)” issued on November 12, 2013.3. In the geographical location test, this paper classifies cities according to four regions: east, middle, west and northeast. The prefecture-level cities under Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan belong to the eastern region The prefecture-level cities under Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan belong to the central region; the prefecture-level cities under Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang belong to the western region; The prefecture-level cities under Liaoning, Jilin and Heilongjiang belong to the Northeast region.Additional informationFundingThe work was supported by the National Social Science Fun
{"title":"How Does Industrial Upgrading Affect Urban Ecological Efficiency? New Evidence from China","authors":"Shuhai Niu, Kexin Zhang, Juan Zhang, Yanchao Feng","doi":"10.1080/1540496x.2023.2260544","DOIUrl":"https://doi.org/10.1080/1540496x.2023.2260544","url":null,"abstract":"ABSTRACTThe upgrade of industrial structure is one type of momentum for improving urban ecological efficiency, and exploring the nexus between them can help implement the concept of green development. Using data from 284 cities in China between 2004 and 2019, this research employs a series of econometric models to examine how improving industrial structure contributes to the growth of urban ecological efficiency. The research results reveal that urban ecological efficiency can be significantly improved by industrial structure upgrade, while ecological efficiency is inhibited by low-quality GDP and financial development models and insignificantly decreased by industrial structure rationalization. In addition, industrial structure upgrading has failed to play a mediating role in promoting eco-efficiency through technological innovation and the reduction of pollutant emission intensity. Moreover, officials’ turnover plays a negative moderating influence on the role of industrial upgrading in increasing ecological efficiency, while environmental regulation plays the opposite role. This study further conducts triple heterogeneity tests including city size, resource endowment, and regional heterogeneity, which provide a feasible way for further implementation of relevant policies.KEYWORDS: Industrial upgradingurban ecological efficiencymoderating mechanismmediating mechanism AcknowledgementsWe would like to thank the editor professor Chun-Ping Chang and the other three anonymous reviewers for their constructive comments and suggestions.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1. In the test of city size, the division of city size in this paper is based on the population size of the city. According to whether the total urban population is more than 1 million, the city size is divided into two categories: large and small.2. In the resource endowment test, cities are divided into resource-based cities and non-resource-based cities according to the differences in resource endowments of each city. The division is based on the Notice of the State Council of China on the “National Sustainable Development Plan for Resource-Based Cities (2013–2020)” issued on November 12, 2013.3. In the geographical location test, this paper classifies cities according to four regions: east, middle, west and northeast. The prefecture-level cities under Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan belong to the eastern region The prefecture-level cities under Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan belong to the central region; the prefecture-level cities under Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang belong to the western region; The prefecture-level cities under Liaoning, Jilin and Heilongjiang belong to the Northeast region.Additional informationFundingThe work was supported by the National Social Science Fun","PeriodicalId":11693,"journal":{"name":"Emerging Markets Finance and Trade","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/1540496x.2023.2266115
Gaowen Kong, Lihua Liu, Dongmin Kong, Jian Zhang
ABSTRACTThe intrafirm pay gap plays a significant role in firm outcomes and prior research focuses on examining its determinants based on firm characteristics and institutional factors, with limited understanding of how labor mobility, driven by improvements in transportation, affects the pay gap. This study utilizes the opening of high-speed rails (HSRs) as an exogenous shock of the improvement of transportation infrastructure and finds that HSRs substantially increase the within-firm pay gap, especially for firms located in small cities. Mechanism tests reveal that the widening pay gap can be attributed to the decreased employee’s wages, driven by the increased supply of low skilled labors. The effect is more pronounced in labor-intensive firms, and those with weaker employee bargaining power. Overall, we provide implications for regulators and management who are concerned about pay inequity.KEYWORDS: Within-firm pay gaphigh-speed railslabor mobilityChinaJEL: M41J31O18 AcknowledgmentsWe appreciate the constructive suggestions from Yezhou Sha. We also thank the seminar participants at the 16th Bulletin of Monetary Economics and Banking International Conference and Call for Papers.Disclosure StatementWe declare that we have no financial and personal relationship with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature of kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.Notes1. Source from: https://www.ilo.org/global/lang–en/index.htm.2. According to an AFL-CIO’s Executive Paywatch news, the average CEO pay at an S&P 500 Index company surged to an of 361 times more than American average rank-and-file workers in 2017.3. We have conducted extensive research and reading on the literature concerning high-speed rails and pay gap. Existing discussions on the impact of high-speed rails on income disparities mainly focus on its effects on the urban-rural income disparity (Yu and Pan Citation2019), regional income inequalities (Jiang and Kim Citation2016), and the wage gap between rural-urban migrants (Kong, Liu, and Yang Citation2021). However, we have found that there is currently no research exploring the effect of high-speed rails on within firm pay gap (pay inequity between the managers and the rank-and-file employees).4. Since 2004, the Ministry of Railways has strengthened its cooperation with local governments, however, by the end of 2008, local government and social investment only accounted for about 20% of the total investment scale of the contracted projects. Since the Ministry of Railways has promulgated route planning policies prior to local government financing, the layout of high-speed rail lines is less influenced by local governments, i.e., the effect of HSRs connection on the within firm pay gap is more exogenous. In addition, this study finds that
【摘要】企业内部薪酬差距在企业成果中发挥着重要作用，以往的研究主要集中在企业特征和制度因素的基础上考察其决定因素，而对交通运输改善驱动的劳动力流动如何影响薪酬差距的理解有限。本研究利用高速铁路(HSRs)的开通作为交通基础设施改善的外生冲击，发现高铁大幅增加了企业内部薪酬差距，特别是对于位于小城市的企业。机制检验表明，工资差距扩大可归因于低技能劳动力供应增加导致雇员工资下降。这种效应在劳动密集型企业和员工议价能力较弱的企业中更为明显。总的来说，我们为关注薪酬不平等的监管机构和管理层提供了启示。关键词:企业内部薪酬差距，高铁，劳动力流动性，中国[j] [j]感谢沙叶洲提出的建设性意见。我们也感谢参加第16届货币经济与银行国际会议和论文征集的与会者。我们声明，我们与其他个人或组织没有任何财务和个人关系，这些关系可能会不恰当地影响我们的工作，在任何产品、服务和/或公司中都没有任何性质的专业或其他个人利益，这些利益可能会被解释为影响文章中所呈现的立场或对文章的审查。来源:https://www.ilo.org/global/lang -en /index. html。根据劳联-产联的高管薪酬观察新闻，2017年，标准普尔500指数公司首席执行官的平均薪酬飙升至美国普通工人平均工资的361倍。我们对高铁与薪酬差距的相关文献进行了广泛的研究和阅读。现有关于高铁对收入差距影响的讨论主要集中在高铁对城乡收入差距(Yu and Pan Citation2019)、区域收入差距(Jiang and Kim Citation2016)和城乡流动人口工资差距(Kong, Liu, and Yang Citation2021)的影响上。然而，我们发现目前还没有研究探讨高铁对企业内部薪酬差距(管理人员与普通员工之间的薪酬不平等)的影响。自2004年以来，铁道部加强了与地方政府的合作，但截至2008年底，地方政府和社会投资仅占承包工程总投资规模的20%左右。由于铁道部出台的路线规划政策先于地方政府融资，高铁线路布局受地方政府的影响较小，即高铁对接对企业内部薪酬差距的影响更具外生性。此外，本研究发现，高铁开放对薪酬差距的影响主要集中在小城市的企业中，大城市的影响较弱。考虑到小城市政府对高铁布局的影响更小，在研究高铁开放对企业内部薪酬差距的影响时，高铁的开放是一个更外生的事件。DID方法能够在一段时间内对实验组和对照组进行比较，有效地隔离了治疗(即高速铁路的开通)对结果(即公司内部薪酬差距)的因果影响。通过考虑时不变因素并使用处理前后的时间，DID方法可以有效地减轻与内生性和选择偏差相关的问题。之所以剔除金融业样本，是因为金融业适用的会计准则与其他行业不同，导致信息披露存在显著差异。因此，在变量结构上存在差异。删除资产负债率大于1的样本，主要是因为净资产为负的公司与正常运营的公司相比，可能在财务行为和工资决策方面表现出差异，从而可能影响所得结果。基于此，我们遵循现有文献(Liu and Shu Citation2022)，排除金融行业和资产负债率大于1.7的样本。由于我们样本的高铁开通日期大多在下半年，因此我们将高铁开通年份定义为高铁初始开通日期的下一年。我们感谢中国国家社会科学基金的资金支持[批准号:)。21 zda010;22 vrc145)和中国国家自然科学基金(批准号71991473;71772178)。
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